the integration of corporate social - RMIT Research Repository

THE INTEGRATION OF CORPORATE SOCIAL

RESPONSIBILITY INTO BUSINESS STRATEGY AND ITS

IMPACT ON COMPANY PERFORMANCE:

AN INVESTIGATION OF THE INDONESIAN

MANUFACTURING INDUSTRY

A thesis submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Esti Dwi Rinawiyanti

Master of Business Administration, University of Nuertingen-Geislingen,

Nuertingen, Germany

Bachelor of Industrial Engineering, Institute of Technology Sepuluh Nopember,

Surabaya, Indonesia

School of Management

College of Business and Law

RMIT University

June 2021

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DECLARATION

I certify that except where due acknowledgement has been made, the work is that of the author

alone; the work has not been submitted previously, in whole or in part, to qualify for any other

academic award; the content of the thesis is the result of work which has been carried out since

the official commencement date of the approved research program; any editorial work, paid or

unpaid, carried out by a third party is acknowledged; and, ethics procedures and guidelines

have been followed.


2 June 2021

ii

DEDICATION

This thesis is dedicated to:

My beloved parents,

Erudhito Sudjono (deceased)

Astiti Wardojo

AND

My loving husband,

Sahrum Beru

AND

My lovely children,

Syafero Neumarzello Beru

Rieny Gracierra Beru

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ACKNOWLEDGEMENTS

Praise God for all His blessings that have made my endeavor fruitful. I thank Him for giving

me patience, strength, and endurance throughout my PhD journey. The journey of working on

this thesis has been one of the most challenging but rewarding experiences of my life. By His

grace, I can finish this thesis well.

This thesis would not have the soul it has without valuable academic and moral support and

trust in me as a researcher, given by scholars, family, and friends. I am wholeheartedly thankful

to my main supervisor, Dr Xueli (Charlie) Huang, for his patience, supportive feedback,

valuable guidance, and inspiration. My heartfelt thanks go to Prof Sharif As-Saber, my

associate supervisor, for his concern, insightful comments, and encouragement. I regard both

of them not only as research supervisors, but also as mentors in the development of academic

skills needed to be a successful scholar and in improving my publication writing skills. They

comforted and strengthened me to keep on getting up and moving again when I fell and gave

up hope. I feel incredibly lucky and proud to be a student of them.

My sincere appreciation goes to Indonesia Endowment Fund for Education (LPDP) in

collaboration with Directorate General of Higher Education (DIKTI), who gave me the

opportunity to undertake my study with full financial support. I am so grateful that I was chosen

as one of the awardees of the Indonesian Lecturer Excellence Scholarships (BUDI).

I also thank the University of Surabaya for allowing me to further study and providing

support for my studies. Special thanks to the academic and administrative staff at the School

of Management, RMIT University and at the Study Program of Industrial Engineering as well

as at the Faculty of Engineering, University of Surabaya, for being supportive, helpful, and

pleasant. This thesis is also dedicated to my friends and PhD colleagues in RMIT University

whose names I cannot mention one by one. I would like to thank them for our friendship and

unforgettable wonderful times that we cherished together.

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Besides, I am grateful for my Indonesian friends and community in Melbourne. I thank them

for their help, gathering and prayer that made my life here colourful and joyful. Because of

them, I felt at home during my stay in Melbourne.

I am very grateful for the support provided by Sahrum, my husband. His immense love, care

and patience during this journey have enabled me to complete this thesis. I thank him for always

being with me physically and emotionally to share all the ups and downs. My deepest thanks

go to my lovely children, Syafero and Riri, who always comforted me and excited me.

Last but not least, to my dear parents, thank you for allowing me to go far for such a long

time to pursue my dreams. I am also deeply indebted to my brothers and sisters, for their

support, assistance, and understanding while I was away.

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TABLE OF CONTENTS

DECLARATION....................................................................................................................... i

DEDICATION.......................................................................................................................... ii

ACKNOWLEDGEMENTS .................................................................................................. iii

TABLE OF CONTENTS ........................................................................................................ v

LIST OF TABLES .................................................................................................................. xi

LIST OF FIGURES ................................................................................................................ vi

LIST OF APPENDICES ....................................................................................................... vii

LIST OF ACRONYMS .......................................................................................................... xi

ABSTRACT .............................................................................................................................. 1

CHAPTER 1: INTRODUCTION ........................................................................................... 4

1.1 Research Background ...................................................................................................... 4

1.2 An Overview of CSR Practices in Indonesia .................................................................. 8

1.3 Research Questions and Research Objectives ............................................................... 10

1.4 Research Contributions and Implications ..................................................................... 11

1.5 Overview of Research Methodology ............................................................................ 13

1.6 Structure of Thesis ........................................................................................................ 15

1.7 Summary of Chapter 1 .................................................................................................. 17

CHAPTER 2: LITERATURE REVIEW ............................................................................ 19

2.1 Non-Market Strategies .................................................................................................. 19

2.2 Corporate Social Responsibility .................................................................................... 22

2.2.1 CSR Definitions .................................................................................................. 23

2.2.2 CSR Dimensions ................................................................................................. 26

2.2.3 CSR Strategies .................................................................................................... 27

2.2.4 Different Theories in CSR Studies ...................................................................... 30

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2.3. Business Strategy ......................................................................................................... 33

2.3.1 Porter’s Generic Strategy .................................................................................... 34

2.3.2 Business Strategy and Company Performance ................................................... 35

2.4. The Integration of CSR into Business Strategy ........................................................... 40

2.4.1 Findings from Prior Studies ................................................................................ 41

2.4.2 Dimensions of the Integration of CSR and Business Strategy ............................ 52

2.5 Company Performance .................................................................................................. 61

2.5.1 Financial Performance ........................................................................................ 61

2.5.2 Non-Financial Performance ................................................................................ 63

2.6 Research Gaps ............................................................................................................... 67

2.7 Indonesia as a Context for the Research ....................................................................... 69

2.7.1 An Overview of Indonesia .................................................................................. 69

2.7.2 The Indonesian Manufacturing Industry ............................................................. 72

2.7.3 CSR Implementation in Indonesia ...................................................................... 75

2.7.4 CSR Studies in the Indonesian Manufacturing Industry ..................................... 81

2.8 Summary of Chapter 2 .................................................................................................. 82

CHAPTER 3: RESEARCH FRAMEWORK ...................................................................... 83

3.1 Theoretical Framework ................................................................................................. 83

3.1.1 Strategy and Company Performance ................................................................... 83

3.1.2 Theoretical Underpinnings .................................................................................. 84

3.2 The Theoretical Framework of the Integration of CSR into Business strategy ............ 86

3.2.1 CSR Dimensions and Strategies.......................................................................... 87

3.2.2 Business Strategy Applied in This Thesis ........................................................... 88

3.2.3 The Integration of CSR into Business Strategy .................................................. 88

3.2.4 Essential Aspects of Company Performance ...................................................... 95

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3.3 Research Hypotheses .................................................................................................... 99

3.3.1 The Relationship between CSR Integration and Company Performance ........... 99

3.3.2 Mediating Effects in the Relationship between CSR Integration and Financial

Performance ............................................................................................................... 101

3.3.3 Moderating Effects in the Relationship between CSR Integration and Company

Performance ............................................................................................................... 104

3.4 Summary of Chapter 3 ............................................................................................... 108

CHAPTER 4: RESEARCH METHODOLOGY .............................................................. 109

4.1 Research Approach ..................................................................................................... 109

4.2 Research Paradigm ...................................................................................................... 110

4.3 Research Design .......................................................................................................... 111

4.4 Questionnaire Development ........................................................................................ 113

4.4.1 The Development of the Measurements and Their Scales ................................ 117

4.4.2 Pilot Study ......................................................................................................... 127

4.5 Sampling Process ........................................................................................................ 128

4.5.1 Population and Sample ...................................................................................... 128

4.5.2 Sampling Frame ................................................................................................ 129

4.5.3 Sample Size ....................................................................................................... 130

4.5.4 Sampling Technique.......................................................................................... 131

4.6 Data Collection ............................................................................................................ 131

4.6.1 Data Collection Methods .................................................................................. 131

4.6.2 Results of Data Collection ................................................................................ 135

4.7 Data Analysis Procedures ........................................................................................... 137

4.7.1 Data Coding ...................................................................................................... 137

4.7.2 Data Screening .................................................................................................. 138

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4.8 The Respondents’ Profiles and Characteristics ........................................................... 144

4.9 Partial Least Square with Structural Equation Modelling ........................................... 149

4.9.1 Model Specification .......................................................................................... 151

4.9.2 Reflective and Formative Constructs Specification .......................................... 152

4.9.3 Hierarchical Component Model ........................................................................ 153

4.9.4 Model Assessment ............................................................................................ 157

4.9.5 Mediating Effect................................................................................................ 167

4.9.6 Moderating Effect ............................................................................................. 170

4.10 Ethical Considerations .............................................................................................. 174

4.11 Summary of Chapter 4 .............................................................................................. 175

CHAPTER 5: STRATEGIC INTEGRATION-FINDINGS AND DISCUSSION ......... 176

5.1 Descriptive Statistical Analysis in Strategic Integration ............................................. 176

5.2 Model Specification of Strategic Integration .............................................................. 178

5.3 Model Assessment for Strategic Integration ............................................................... 179

5.3.1 Assessment of the Measurement Model in Strategic Integration ..................... 179

5.3.2 Assessment of the Structural Model in Strategic Integration............................ 185

5.4 Discussion of Strategic Integration ............................................................................. 197

5.4.1 Discussion of Strategic Integration and Company Performance ...................... 197

5.4.2 Discussion of Mediating Effect in Strategic Integration ................................... 202

5.5 The Moderating Effect in Strategic Integration .......................................................... 207

5.5.1 MGA Business Strategy in Strategic Integration .............................................. 208

5.5.2 MGA CSR Strategy in Strategic Integration ..................................................... 219

5.5.3 MGA Company Size in Strategic Integration ................................................... 233

5.5.4 MGA Industry Type in Strategic Integration .................................................... 237

5.5.5 Discussion of Moderating Effect in Strategic Integration................................. 243

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5.5.6 Results of all Tested Hypotheses in Strategic Integration ................................ 252

5.6 Summary of Chapter 5 ................................................................................................ 253

CHAPTER 6: FUNCTIONAL INTEGRATION AND COMBINED CSR

INTEGRATION-FINDINGS AND DISCUSSION ........................................................... 255

I. Functional Integration .................................................................................................... 255

6.1 Desriptive Statistical Analysis in Functional Integration ............................................ 255

6.2 Model Specification of Functional Integration ........................................................... 256

6.3 Model Assessment of Functional Integration ............................................................. 257

6.3.1 Assessment of the Outer Measurement Model in Functional Integration......... 257

6.3.2 Assessment of the Structural Model in Functional Integration ......................... 263

6.4 Discussion of Functional Integration .......................................................................... 270

6.4.1 Discussion of Functional Integration and Company Performance ................... 270

6.4.2 Discussion of Mediating Effects in Functional Integration .............................. 275

6.5 Multi-group Analysis in Functional Integration ......................................................... 279

6.5.1 MGA Business Strategy in Functional Integration ........................................... 279

6.5.2 MGA CSR Strategy in Functional Integration .................................................. 283

6.5.3 MGA Company Size in Functional Integration ................................................ 289

6.5.4 MGA Industry Type in Functional Integration ................................................. 292

6.5.5 Discussion of Multi-Group Analysis in Functional Integration ........................ 295

6.6 Results for all Tested Hypotheses in Functional integration ............................... 303

6.7 Conclusion of Functional Integration .......................................................................... 305

II. The Combined CSR Integration .................................................................................... 306

6.8 Model Assessment in The Combined CSR Integration .............................................. 306

6.8.1 Assessment of the Measurement Model in The Combined CSR Integration ... 307

6.8.2 Assessment of the Structural Model in The Combined CSR Integration ......... 312

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6.9 Model Comparison ..................................................................................................... 316

6.10 Discussion of The Combined CSR Integration ......................................................... 319

6.11 Conclusion of Combined CSR Integration ............................................................... 320

6.12 Summary of Chapter 6 ............................................................................................... 321

CHAPTER 7: CONCLUSIONS AND IMPLICATIONS ................................................ 323

7.1 Introduction ................................................................................................................. 323

7.2 Thesis Summary .......................................................................................................... 325

7.3 Conclusions ................................................................................................................. 331

7.4 Theoretical Contributions ............................................................................................ 337

7.5 Methodological Contributions .................................................................................... 338

7.6 Managerial Implications .............................................................................................. 340

7.7 Research Limitations and Directions for Future Research ......................................... 344

7.8 Summary of Chapter 7 ................................................................................................ 345

REFERENCES ..................................................................................................................... 347

APPENDICES ...................................................................................................................... 385

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LIST OF TABLES

Table 2.1: Results of Previous Studies of Porter’s Generic Strategies .................................... 40

Table 2.2: Prior Studies of CSR Integration ............................................................................ 48

Table 2.3: CSR Impacts on Company Performance ................................................................ 67

Table 2.4: Indonesian Manufacturing Snapshot ...................................................................... 73

Table 4.1: Business Strategy Measurement Items ................................................................. 119

Table 4.2: CSR Strategy Measurement Items ........................................................................ 120

Table 4.3: Strategic Integration Measurement Items ............................................................. 121

Table 4.4: Functional Integration Measurement Items .......................................................... 122

Table 4.5: Company Performance Measurement Items ......................................................... 125

Table 4.6: Period of Data Collection ..................................................................................... 135

Table 4.7: Results of Data Collection .................................................................................... 137

Table 4.8: Skewness and Kurtosis ......................................................................................... 141

Table 4.9: Respondents’ Profiles ........................................................................................... 144

Table 4.10: Profiles of the Respondents’ Companies ............................................................ 147

Table 4.11: Construct Operationalisation in Strategic and Functional Integration ............... 155

Table 5.1: Reflective Construct Assessments of Model 1 ..................................................... 182

Table 5.2: Fornell-Larcker Testing of Model 1 ..................................................................... 183

Table 5.3: HTMT Values of Model 1 .................................................................................... 184

Table 5.4: Collinearity Test of Formative Measures of Model 1 .......................................... 185

Table 5.5: Indicator Validity of Formative Measurements in Model 1 ................................. 185

Table 5.6: Outer VIF Values of Model 1 ............................................................................... 186

Table 5.7: Inner VIF Values of Model 1................................................................................ 186

Table 5.8: Direct Effects of Model 1 ..................................................................................... 188

Table 5.9: Specific Indirect Effects of Model 1 ..................................................................... 189

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Table 5.10: Direct Effects, Indirect Effects and Total Effects of Model 1 ............................ 190

Table 5.11: VAF of Mediation Effect of Model 1 ................................................................. 192

Table 5.12: R2 and Q2 values of Model 1 .............................................................................. 193

Table 5.13: f2 Values of Model 1 ........................................................................................... 195

Table 5.14: q2 Values of Model 1 .......................................................................................... 196

Table 5.15: Factor and Items of Business Strategy ................................................................ 210

Table 5.16: K-means Business Strategy with Three Clusters ................................................ 212

Table 5.17: Respondents Characteristics in Each Cluster of Business Strategy.................... 214

Table 5.18: Step 2 MICOM of Business Strategy in Strategic Integration ........................... 216

Table 5.19: Step 3 MICOM of Business Strategy in Strategic Integration ........................... 216

Table 5.20: Permutation Test of Business Strategy in Strategic Integration ......................... 217

Table 5.21: PLS-MGA Results of Business Strategy in Strategic Integration ...................... 218

Table 5.22: Multi-group Results of Business Strategy in Strategic Integration .................... 218

Table 5.23: Parametric and Welch-Satterthwaite Tests of Business Strategy in Strategic

Integration .............................................................................................................................. 219

Table 5.24: PLS Multi-group Results of Business Strategy in Strategic Integration ............ 219

Table 5.25: Factor and Items of CSR Strategy ...................................................................... 221

Table 5.26: K-means CSR Strategy with Three Clusters ...................................................... 223

Table 5.27: Respondents Characteristics in Each Cluster of CSR Strategy .......................... 225

Table 5.28: PLS-MGA Proactive and Reactive in Strategic Integration ............................... 228

Table 5.29: PLS Multi-group Results of Proactive and Reactive in Strategic Integration .... 229

Table 5.30: MGA Proactive and Accommodative in Strategic Integration ........................... 230

Table 5.31: PLS-MGA Accommodative and Reactive in Strategic Integration .................... 232

Table 5.32: PLS Multi-group Results of Proactive and Reactive in Strategic Integration .... 232

Table 5.33: MGA Results of CSR Strategy in Strategic Integration ..................................... 233

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Table 5.34: Comparison of CSR Strategy in Strategic Integration ........................................ 234

Table 5.35: PLS-MGA Company Size in Strategic Integration ............................................ 236

Table 5.36: MGA Results of Company Size in Strategic Integration .................................... 236

Table 5.37: PLS Multi-group Results of Company Size in Strategic Integration ................. 237

Table 5.38: Industry Type Categories .................................................................................... 239

Table 5.39: Respondents Characteristics in Each Category of Industry Type ...................... 240

Table 5.40: PLS-MGA Industry Type in Strategic Integration ............................................. 242

Table 5.41: MGA Results of Industry Type in Strategic Integration .................................... 242

Table 5.42: PLS Multi-group Results of Industry Type in Strategic Integration .................. 243

Table 5.43: Final Results of Hypothesis in Strategic Integration .......................................... 253

Table 6.1: Reflective Construct Assessments of Model 2 ..................................................... 259

Table 6.2: Fornell-Larcker Testing of Model 2 ..................................................................... 261

Table 6.3: HTMT Values of Model 2 .................................................................................... 262

Table 6.4: Collinearity Test of Formative Measures of Model 2 .......................................... 262

Table 6.5: Indicator Validity of Formative Measurements of Model 2 ................................. 263

Table 6.6: Outer VIF values of Model 2 ................................................................................ 264

Table 6.7: Inner VIF values of Model 2 ................................................................................ 264

Table 6.8: Direct Effects of Model 2 ..................................................................................... 265

Table 6.9: Specific Indirect Effects of Model 2 ..................................................................... 266

Table 6.10: Direct Effects, Indirect Effects and Total Effects of Model 2 ............................ 267

Table 6.11: VAF for Mediation Effect of Model 2 ................................................................ 268




Table 6.15: PLS-MGA Business Strategy in Functional Integration .................................... 281

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Table 6.16: Multi-group Results Business Strategy in Functional Integration ...................... 282

Table 6.17: PLS Business Strategy in Functional Integration across Methods ..................... 282

Table 6.18: PLS-MGA Proactive and Reactive in Functional Integration ............................ 284

Table 6.19: PLS Proactive and Reactive in Functional Integration across Methods ............. 285

Table 6.20: PLS-MGA Proactive and Accommodative in Functional Integration ................ 286

Table 6.21: PLS Proactive and Accommodative in Functional Integration across Methods 287

Table 6.22: PLS-MGA Accommodative and Reactive in Functional Integration ................. 288

Table 6.23: MGA Results CSR Strategy in Functional Integration....................................... 289

Table 6.24: Comparison of CSR Strategy in Functional Integration ..................................... 289

Table 6.25: PLS-MGA Company Size in Functional Integration.......................................... 291

Table 6.26: MGA Results Company Size in Functional Integration ..................................... 291

Table 6.27: PLS Company Size in Functional Integration across Methods .......................... 292

Table 6.28: PLS-MGA Industry Type in Functional Integration .......................................... 294

Table 6.29: MGA Results Industry Type in Functional Integration ...................................... 294

Table 6.30: PLS Industry Type in Functional Integration across Methods ........................... 295

Table 6.31: Final Results of Hypothesis in Functional integration ....................................... 304

Table 6.32: Reflective construct assessments of Model 3 ..................................................... 308

Table 6.33: Fornell-Larcker Testing of Model 3 ................................................................... 311

Table 6.34: HTMT Values of Model 3 .................................................................................. 312

Table 6.35: Direct Effects of Model 3 ................................................................................... 314




Table 6.39: Direct Effects and Total Effect Comparison across Three Models .................... 317

Table 6.40: Specific Indirect Effects Comparison across Three Models ............................... 318

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Table 6.41: MGA Results Model 1 and Model 2................................................................... 319

Table 7.1: Final Results of Hypothesis of CSR Integrtation ................................................. 329

Table 7.2: Summary of Hypotheses Verification .................................................................. 331

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LIST OF FIGURES

Figure 1.1 Thesis Outline ......................................................................................................... 16

Figure 2.1 The Biggest Economies in the World ..................................................................... 70

Figure 2.2 Indonesia’s Vision 2025 ......................................................................................... 71

Figure 3.1: The Conceptual Framework for the Integration of CSR into Business Strategy and

Its Impact on Company Performance ...................................................................................... 86

Figure 3.2: The Theoretical Framework for the Integration of CSR into Business Strategy with

the Hypotheses ....................................................................................................................... 108

Figure 4.1: Research Approach of This Thesis ...................................................................... 113

Figure 4.3: Main Products of Respondents’ Companies ....................................................... 148

Figure 4.4: The Four Types of Hierarchical Component Models .......................................... 154

Figure 4.5: A Conceptual Model of CSR Integration into Business Strategy ....................... 157

Figure 4.6: Repeated-Indicator Approach for Type II HCM ................................................. 159

Figure 4.7: (A) Illustration of a direct effect. X affects Y; (B) Illustration of a mediation design.

X is hypothesized to exert an indirect effect on Y through M. .............................................. 168

Figure 5.1: Model of Strategic Integration and Company Performance (Model 1) ............... 179

Figure 5.2: Results of PLS Algorithm of Model 1................................................................. 180

Figure 6.1: Model of Functional Integration and Company Performance (Model 2) ............ 257


Figure 6.3: Model of CSR Integration and Company Performance (Model 3) ..................... 307


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LIST OF APPENDICES

Appendix A.1: Concept Definition and Concept Measurement Used in This Thesis ........... 385

Appendix A.2: Questionnaire - English Version ................................................................... 390

Appendix A.3: Questionnaire – Indonesia Version ............................................................... 395

Appendix A.4: Mann-Whitney Test of Business Strategy..................................................... 400

Appendix A.5: Kruskal-Wallis Test of Strategic Integration ................................................ 401

Appendix A.6: Normality Test of Business Strategy ............................................................. 402

Appendix A.7: Skewness and Kurtosis for All Items ............................................................ 403

Appendix A.8: Kruskall-Wallis Test of Company Performance ........................................... 404

Appendix A.9: Mann-Whitney Test of Strategic Integration ................................................ 405

Appendix A.10: Mann-Whitney Test of Company Performance .......................................... 406

Appendix A.11: Ethics Approval ........................................................................................... 407

Appendix A.12: PCA Constructs of Strategic Integration ..................................................... 408

Appendix A.13: PCA Constructs of Functional Integration .................................................. 409

Appendix A.14: PCA Constructs of Company Performance ................................................ 410

Appendix B.1: Descriptive Analysis of Business Strategy .................................................... 411

Appendix B.2: Descriptive Analysis of CSR Strategy .......................................................... 412

Appendix B.3: Descriptive Analysis of Strategic Integration ............................................... 413

Appendix B.4: Descriptive Analysis of Company Performance ........................................... 414

Appendix B.5: Outer Model Loadings and Cross Loadings of Model 1 ............................... 415

Appendix B.6: Varimax-Rotated Common Factor Matrix of Business Strategy .................. 416

Appendix B.7: Collinearity of Business Strategy .................................................................. 417

Appendix B.8: Notification from SmartPLS about Small Sample Size ................................ 418

Appendix B.9: Varimax-Rotated Common Factor Matrix of CSR Strategy ......................... 419

Appendix B.10: Collinearity of CSR Strategy ....................................................................... 420

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Appendix B.11: Step 2 MICOM Proactive and Reactive of Strategic Integration ................ 421

Appendix B.12: Step 3 MICOM Proactive and Reactive of Strategic Integration ................ 422

Appendix B.13: Permutation Test Proactive versus Reactive of Strategic Integration ......... 423

Appendix B.14: Parametric and Welch-Satterthwaite Tests Proactive and Reactive of Strategic

Integration .............................................................................................................................. 424

Appendix B.15: Step 2 MICOM Proactive and Accommodative of Strategic Integration ... 425

Appendix B.16: Step 3 MICOM Proactive and Accommodative of Strategic Integration ... 426

Appendix B.17: Permutation Test Proactive and Accommodative of Strategic Integration . 427

Appendix B.18: Parametric and Welch-Satterthwaite Tests Proactive and Accommodative of

Strategic Integration ............................................................................................................... 428

Appendix B.19: Step 2 MICOM Accommodative and Reactive of Strategic Integration..... 429

Appendix B.20: Step 3 MICOM Accommodative and Reactive of Strategic Integration..... 430

Appendix B.21: Permutation Test Accommodative and Reactive of Strategic Integration .. 431

Appendix B.22: Parametric and Welch-Satterthwaite Tests Accommodative and Reactive of

Strategic Integration ............................................................................................................... 432

Appendix B.23: Step 2 MICOM Company Size of Strategic Integration ............................. 433

Appendix B.24: Step 3 MICOM Company Size of Strategic Integration ............................. 434

Appendix B.25: Permutation Test Company Size of Strategic Integration ........................... 435

Appendix B.26: Parametric and Welch-Satterthwaite Tests Company Size of Strategic

Integration .............................................................................................................................. 436

Appendix B.27: Step 2 MICOM Industry Type of Strategic Integration .............................. 437

Appendix B.28: Step 3 MICOM Industry Type of Strategic Integration .............................. 438

Appendix B.29: Permutation Test Industry Type of Strategic Integration ............................ 439

Appendix B.30: Parametric and Welch-Satterthwaite Tests Industry Type of Strategic

Integration .............................................................................................................................. 440

ix

Appendix C.1: Descriptive Analysis of Functional Integration............................................. 441

Appendix C.2: Outer Model Loadings and Cross Loadings of Model 2 ............................... 442

Appendix C.3: Step 2 MICOM Business Strategy of Functional Integration ....................... 443

Appendix C.4: Step 3 MICOM Business Strategy of Functional Integration ....................... 444

Appendix C.5: Permutation Test Business Strategy of Functional Integration ..................... 445

Appendix C.6: Parametric and Welch-Satterthwaite Tests Business Strategy of Functional

Integration .............................................................................................................................. 446

Appendix C.7: Step 2 MICOM Proactive and Reactive of Functional Integration ............... 447

Appendix C.8: Step 3 MICOM Proactive and Reactive of Functional Integration ............... 448

Appendix C.9: Permutation Test Proactive and Reactive of Functional Integration ............. 449

Appendix C.10: Parametric and Welch-Satterthwaite Tests Proactive and Reactive of

Functional Integration ............................................................................................................ 450

Appendix C.11: Step 2 MICOM Proactive and Accommodative of Functional Integration. 451

Appendix C.12: Step 3 MICOM Proactive and Accommodative of Functional Integration. 452

Appendix C.13: Permutation Test Proactive and Accommodative of Functional Integration

................................................................................................................................................ 453

Appendix C.14: Parametric and Welch-Satterthwaite Tests Proactive and Accommodative of


Appendix C.15: Step 2 MICOM Accommodative and Reactive of Functional Integration .. 455

Appendix C.16: Step 3 MICOM Accommodative and Reactive of Functional Integration .. 456

Appendix C.17: Permutation Test Accommodative and Reactive of Functional Integration 457

Appendix C.18: Parametric and Welch-Satterthwaite Tests Accommodative and Reactive of


Appendix C.19: Step 2 MICOM Company Size of Functional Integration .......................... 459

Appendix C.20: Step 3 MICOM Company Size of Functional Integration .......................... 460

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Appendix C.21: Permutation Test Company Size of Functional Integration ........................ 461

Appendix C.22: Parametric and Welch-Satterthwaite Tests Company Size of Functional

Integration .............................................................................................................................. 462

Appendix C.23: Step 2 MICOM Industry Type of Functional Integration ........................... 463

Appendix C.24: Step 3 MICOM Industry Type of Functional Integration ........................... 464

Appendix C.25: Permutation Test Industry Type of Functional Integration ......................... 465

Appendix C.26: Parametric and Welch-Satterthwaite Tests Industry Type of Functional

Integration .............................................................................................................................. 466

Appendix D: Thesis Related Publications…………………………………………………..467

xi

LIST OF ACRONYMS

AVE Average Variance Extracted

BPS Biro Pusat Statistik (Central Bureau of Statistics)

CCP Company Customer Performance

CEO Chief Executive Officer

CEP Company Employee Performance

CFA Confirmatory Factor Analysis

CFP Company Financial Performance

CIVETS Colombia, Indonesia, Vietnam, Egypt, Turkey and South Africa

COP Company Operational Performance

CP Company Performance

CR Composite Reliability

CSP Company Social Performance

CSR Corporate Social Responsibility

EFA Exploratory Factor Analysis

ESI Environmentally Sensitive Industries

GDP Gross Domestic Product

GRI Global Reporting Initiative

HCM Hierarchical Component Model

HOC Higher-Order Component

HTMT Heterotrait-Monotrait Ratio of Correlations

IDR Indonesian Rupiah

IDX Indonesian Stock Exchange

ISIC Indonesian Standard Industrial Classification

ISO International Organisation for Standardisation

xii

ISRA Indonesian Sustainability Reporting Award

JIEP Jakarta Industrial Estate Pulogadung

LOC Lower-Order Component

KIW Kawasan Industri Wijayakusuma

KMO Kaiser-Meyer-Olkin Measure

MCAR Missing Completely At Random

MGA Multi-Group Analysis

MICOM Measurement Invariance of Composite Models

MP3EI Master Plan Acceleration and Expansion of Indonesia Economic Growth

NCF Net Cash Flow

NGO Non-Governmental Organisation

NMS Non-Market Strategy

PCA Principal Component Analysis

PKM Persuasion Knowledge Model

PLS Partial Least Squares

PLS-SEM Partial Least Squares-Structural Equation Modelling

R2 Coefficient of Determination

RBV Resource Based View

RDT Resource Dependency Theory

ROA Return On Assets

ROE Return On Equity

ROI Return On Investment

ROS Return On Sales

SDG Sustainable Development Goals

SEM Structural Equation Modelling

xiii

SME Small Medium Enterprises

SOE State-Owned Enterprise

SPSS Statistical Package for the Social Sciences

SRA Sustainability Reporting Awards

SRMR Standardized Root Mean Square Residual Value

TOL Tolerance

UNIDO United Nations Industrial Development Organisation

USD United States Dollar

UU Undang-Undang

VAF Variance Accounted For

VIF Variance of Inflation Factor

1

ABSTRACT

Although the extant literature pays considerable attention to whether corporate social

responsibility (CSR) should be integrated with business strategies to gain benefits, research on

how CSR and business strategies could be successfully integrated is scant. This thesis aims to

investigate how CSR and business strategies are integrated, strategically and functionally, and

to examine the impact of such integration on company performance. In addition to direct effect

analyses, this thesis also assesses the mediating effect of company social performance (CSP)

on the relationship between CSR and company financial performance (CFP) and analyses the

moderating effect of business and CSR strategies, company size, and industry type in this

relationship.

A theoretical framework for integration is developed based on a literature review and is

embedded in contingency and stakeholder theories. With a sample of 435 usable responses

from a survey of 1,055 manufacturing companies in Indonesia, this thesis employs Partial Least

Squares with Structural Equation Modelling (PLS-SEM) to analyse the data. Exploratory and

confirmatory factor analyses are conducted, and the hypotheses are verified by examining

direct, indirect, and total effects. The evaluation of measurement models is satisfactorily

achieved. Reflective and formative measures suggest that all constructs are reliable and valid

in the context of this thesis. The assessment of structural models reveals that both strategic and

functional integrations have a significant positive impact on company performance, including

customer, employee, operational and financial performance. More specifically, the empirical

results confirm that strategic and functional integration have an essential effect on customer,

employee, and operational performance, and that these performances can mediate the

relationship between the integrations and financial performance.

The findings also show a positive relationship between strategic and functional integrations.

Integration of CSR at the strategic level has an impact on integration of CSR at the next level,

2

such as at the functional level. Furthermore, the findings presented in this thesis indicate that

business and CSR strategies have a moderating effect on CSR integration. Notably, regarding

business strategy, the results suggest that differentiation strategy has a larger total effect on

customer and financial performance than cost leadership strategy in strategic CSR integration.

However, in functional CSR integration, the findings suggest that differentiation strategy has

a larger total effect than cost leadership strategy, on customer performance only.

With respect to CSR strategy, findings in this thesis highlight that CSR strategy has a

moderating effect on the relationship between CSR integration and company performance.

Proactive and accommodative strategies have a greater total effect on customer and financial

performance than reactive strategy in strategic CSR integration. Conversely, in functional CSR

integration, empirical evidence indicates that reactive strategy has a larger total effect on

employee and financial performance than proactive strategy. In functional integration,

accommodative strategy has a greater total effect on operational performance than proactive

strategy.

In terms of company size, the findings of strategic CSR integration show that large

companies have a greater total effect on customer, employee, operational and financial

performances than small and medium enterprises (SMEs). On the other hand, large companies

have better operational performance than SMEs. These results confirm that company size has

a moderating effect on the relationship between CSR integration and company performance.

Moreover, the results suggest that industry type moderates the relationship between CSR

integration and company performance. In particular, non-environmentally sensitive industries

(non-ESI) have a bigger total effect than ESI in the relationship between strategic CSR

integration and financial performance. In relation to functional CSR integration, findings

indicate that non-ESI groups have a higher total effect in customer and financial performances

than ESI groups.

3

From a theoretical perspective, this research contributes to the body of knowledge by

focusing on CSR integration to understand its impact on company performance. This thesis

contributes to the literature by developing a framework that enables researchers to identify the

different types of benefits provided by CSR integration, and the effect that these benefits have

on the stakeholder-company relationship. This research presents empirical evidence that, in

evaluating the mechanism between CSR integration and company performance, stakeholder

and contingency theories should be taken into account.

From a practical point of view, the findings of this thesis can encourage companies,

particularly Indonesian manufacturing companies, to implement CSR within their company.

Companies should treat primary stakeholders—those who interact with business operations

directly and daily including employees, suppliers, and customers—well, and act on their needs

related to CSR practices. These results could, especially, motivate manufacturing companies

to increase their level of CSR practice. In doing so, they will not conduct CSR merely as a

charity or philanthropy or only to comply with regulations (as many currently practice);

instead, they will strategically and effectively carry out CSR through a range of activities to

achieve superior performance, both socially and financially.

Keywords: CSR integration, company performance, mediating effect, manufacturing sector,

developing countries.

4

CHAPTER 1: INTRODUCTION

This chapter serves as the introduction to the thesis. It begins with a description of the

research background and research questions, and then moves on to the research contributions

and implications. It also contains an overview of research methodology and an explanation of

the thesis' structure as well. There is a summary at the end of this chapter.

1.1 Research Background

The research background for this thesis is discussed in this section. It explains corporate

social responsibility (CSR), CSR integration, and company performance briefly. It also

indicates the research gaps that this thesis will fill.

European Commission (2011) defines CSR as when companies integrate social and

environmental concerns on a voluntary basis into their business practices and relationships with

their stakeholders. This definition implies companies should embrace social, environmental,

ethical human rights and consumer concerns into their business activities and core strategies

when collaborating with their stakeholders. CSR signifies ‘the integration of an enterprise’s

social, environmental, ethical, and philanthropic responsibilities towards society into its

operations, processes and core business strategy in cooperation with relevant stakeholders’

(Rasche, Morsing & Moon 2017, p. 6). These two definitions emphasise the integration of CSR

into business activities and involvement of stakeholders. Because CSR is an organisational

activity that relies on several connections within an organisation’s management and operation

(Valdez-Juárez, Gallardo-Vázquez & Ramos-Escobar 2018), the development of CSR

practices depends on how they are integrated into current business practices (Marín, Rubio &

de Maya 2012; Marques-Mendes & Santos 2016).

Over the past two decades, the concept of CSR has become increasingly popular (Malik

2015). There is growing awareness that business activities create short- and long-term impacts

on society and the environment, as well as economics (Carroll & Shabana 2010). Therefore,

5

CSR has become a concern for all businesses (Martinuzzi & Krumay 2013), as they attempt to

comply and gain advantages from it (Razafindrambinina & Sabran 2014). Managers look for

ways to position their companies to take advantage of the business opportunities created by

CSR, while also trying to find solutions to address environmental and societal problems (Rosen

2001).

Although CSR is a common issue on the agenda of companies, it is not yet a strategic

priority. One reason for this is the lack of understanding of the benefits of CSR for

competitiveness (Porter & Kramer 2006). The relationship between companies’ activities and

their impact on competitive advantage has been extensively examined in recent research (Porter

& Siggelkow 2008), but it remains unclear whether and how CSR can impact company

competitiveness (Carroll & Shabana 2010).

To address this question, previous studies have demonstrated a positive correlation between

CSR and company performance, which leads to improved competitive advantage (Carroll &

Shabana 2010; Orlitzky, Schmidt & Rynes 2003). For example, some research has indicated

that CSR has a positive impact on financial performance (Ameer & Othman 2012; Beck, Frost

& Jones 2018; Sindhu & Arif 2017). Other studies also found that CSR significantly affects

employee performance (Bauman & Skitka 2012; Sprinkle & Maines 2010), customer

performance (García-Madariaga & Rodríguez-Rivera 2017; Park, Kim & Kwon 2017), and

operational performance (Sánchez & Benito-Hernández 2015; Sun & Yu 2015). Many studies

examine the relationship between CSR and company performance and confirm that CSR

provides several benefits to a company’s social and financial performance (Bauman & Skitka

2012; Beck, Frost & Jones 2018; García-Madariaga & Rodríguez-Rivera 2017; Kim, Kim &

Qian 2018; Malik 2015). However, it remains unclear under what mechanism this relationship

can occur.

6

Despite the fact that prior studies have demonstrated that CSR has a positive impact on

company performance, several authors have argued that CSR must be connected and integrated

into business strategy to benefit companies (Dey & Sircar 2012; Ganescu 2012b; Husted &

David 2011). Researchers have emphasised the importance of integration of CSR into business

strategy to enhance companies’ social and financial performance (Carroll & Shabana 2010;

Galbreath 2006; Ganescu 2012b; Hasan et al. 2018; Marín, Rubio & de Maya 2012; Porter &

Kramer 2011). Integration between CSR activities and core business functions is critical to

value creation by CSR and vital to the success of companies (Carroll & Shabana 2010; García-

Madariaga & Rodríguez-Rivera 2017). A common problem with the planning and

implementation of CSR strategies and practices, however, appears to be the lack of alignment

with business strategy (Rangan, Chase & Karim 2012). Many managers agree on a strategic

interest in CSR, but few fully incorporate CSR aspects into their business practices. Integrating

CSR into a business strategy decision is one of the most challenging tasks facing managers

(Carroll & Shabana 2010).

Because it is critical to have a better understanding of the relationship between CSR

integration and business strategy (Insight 2016), several studies on the integration of CSR into

business strategy have been carried out, either conceptually or empirically. For example, Maon,

Lindgreen and Swaen (2009) integrated top-down and bottom-up processes throughout the

integration process on the basis of the stakeholder theory. They also explained the roles of the

primary and secondary stakeholders and provided specific case studies to support their

interpretation. Notably, they highlighted the importance of integrating CSR into the objectives

and strategies of a company, identifying key stakeholders and their critical problems,

developing an integrated CSR, and engaging employees in implementation, monitoring and

evaluation. Asif et al. (2013) built the framework for integrating CSR into business processes

across all stages of business processes, suggesting top-down and bottom-up approaches to

7

integration. This study implied that CSR integration can be carried out through many activities,

including vertical and horizontal integration. Furthermore, Lindgreen et al. (2011) highlighted

that CSR integration involves cultural change led by top management and other change agents,

who drive CSR values throughout the company. Marques-Mendes and Santos (2016)

developed a more comprehensive framework and explained each stage in detail by providing

indicators and related concepts. Their studies demonstrated how integration can take place

thoroughly, beginning with strategic integration (ideological models), implementation

(procedural models), and then the effect of the integration (consequentialist models).

Nevertheless, there is scant empirical research on the integration of CSR into business

strategy and the resulting impact on organisational performance (Vitolla, Rubino & Garzoni

2017). It remains unclear how CSR and business strategies can be integrated, as well as how

such integration can affect company performance. Furthermore, the integration of CSR is an

important topic, but it is seldom considered in strategic management (Engert, Rauter &

Baumgartner 2016; Kiron et al. 2013). Since most of the relevant literature are prescriptive and

deal with theoretical frameworks and concepts, empirical studies on the integration of corporate

sustainability into strategic management and models that show how CSR can be integrated into

business strategy are needed (Engert, Rauter & Baumgartner 2016; Guadamillas-Gómez,

Donate-Manzanares & Škerlavaj 2010).

This thesis seeks to fill current research gaps by developing and testing a model that

describes CSR integration at the strategic and functional levels. It investigates how CSR and

business strategy are integrated, examines the impact of this integration on organisational

performance and analyses the mechanisms through which CSR integration affects company

performance.

8

1.2 An Overview of CSR Practices in Indonesia

This section describes currect CSR practices and regulations in Indonesia. It also explains

an overview of Indonesia and the impact of CSR rules on the manufacturing industry.

CSR practices are implemented at relatively higher and more intense levels in developed

countries than in developing countries (Bhattacharyya 2010). Most studies on CSR are

concerned with investigating the practices adopted by companies in developed countries (Zhu,

Liu & Lai 2016). CSR is emerging as a distinct area of management studies in developing

countries (Jamali & Karam 2018), and it is essential to identify significant information on the

contribution of CSR and the main factors affecting CSR performance (Blowfield 2007; Branco

& Rodrigues 2006; Crifo, Diaye & Pekovic 2016), including in Indonesia.

Indonesia is not only the world’s fourth most populous nation with more than 267 million

people, but also has the largest economy in Southeast Asia (Worldbank 2020). CSR has become

a major concern in Indonesia over the past two decades (Maris 2014). In particular, the

Indonesian government has released Law No. 40 2007 on Limited Liability Companies (or

Undang-Undang (UU) No. 40 Tahun 2007 tentang Perseroan Terbatas). With this law,

Indonesia is the world's first country to mandate that companies, especially those related to

natural resources, must implement CSR, and report their CSR activities (Maris 2014; Ridho

2018; Rosser & Edwin 2010; Sheehy & Damayanti 2019; Waagstein 2011). In practice,

however, this is problematic as it needs not only a specific definition of CSR, but also an

effective implementation process and tools to evaluate its impact (Waagstein 2011). Hence, the

government needs to develop policy regulation, clearly defining CSR and considering

stakeholders’ perceptions that companies should respond to and implement an appropriate

business practice for their survival. As CSR is a matter of good business practice, companies

should care for their stakeholders across a variety of interactions (Maris 2014).

9

Although there are laws regulating CSR, many Indonesian companies misinterpret it. CSR

is assumed to be a corporate social activity for the community and environment through profit

distribution to philanthropic programs (Radyati 2014). Prior studies show that most Indonesian

companies embrace CSR through charitable practices, corporate philanthropy, and community

development (Ambadar 2017; Joseph et al. 2016; Razafindrambinina & Sabran 2014; Widjaja

2011). Few activities refer to social and environmental concerns (Waagstein 2011), and many

companies enforce CSR through simple and instant activities, such as donations and

fundraising (Maulamin 2017; Ridho 2017).

Because manufacturing companies carry out activities related to natural resources, they have

been affected by this law, while also dealing with a growing number of environmental, labour

and human rights laws at both national and international levels (Bernal-Conesa, de Nieves-

Nieto & Briones-Peñalver 2017). Several studies have investigated CSR implementation in the

Indonesian manufacturing industry. For instance, based on a sample of 53 manufacturing

companies, Hasanudin and Budianto (2013) found that both employee CSR and corporate

reputation have a positive impact on company performance, but environmental CSR has a

negative impact. Purbowati and Mutiarni (2017) assessed the effect of corporate characteristics

on CSR disclosure, using a sample of 50 listed manufacturing companies. They claimed that

company size has a significant impact on CSR disclosure while the company's profile, the size

of the commissioner board, and ownership concentration have no significant impact on the

disclosure of CSR. Furthermore, with survey data from 173 manufacturing companies,

Handayani, Wahyudi and Suharnomo (2017) highlighted the significant influence of CSR on

company performance and emphasised the importance of integrating social and environmental

aspects in CSR implementation.

Even though several studies have examined CSR practices in the context of the Indonesian

manufacturing industry, limited research has comprehensively analysed the relationship

10

between CSR integration and company performance. In particular, the extent to which

manufacturing companies, after 13 years of CSR regulation in Indonesia, improve and enforce

their CSR practices needs to be investigated further.

1.3 Research Questions and Research Objectives

This section begins by determining research questions. It then goes on to research objectives

that this thesis will achieve.

The integration between CSR and business strategy has been discussed in the literature, but

substantial identifiable gaps remain, which lead to this thesis’s key research question (RQ):

what is the extent to which CSR and business strategy are integrated? This main research

question can be divided into several supplementary research questions:

RQ1: To what extent does CSR integrate into business at the strategic and functional levels?

RQ2: To what extent does CSR integration affect company performance?

RQ3: To what extent does social performance mediate the relationship between CSR

integration and financial performance?

RQ4: To what extent do business and CSR strategies moderate the impact of CSR

integration on company performance?

RQ5: To what extent do company size and industry type moderate impact of CSR


To address these research questions, this thesis investigates how CSR can be integrated into

business at strategic and functional levels. This thesis also aims to examine the impact of

strategic and functional CSR integrations on company performance, particularly in the

Indonesian manufacturing industry. As a result, six specific objectives were established:

▪ Investigate the essential dimensions of CSR integration at the strategic level;

▪ Examine the crucial dimensions of CSR integration at the functional level;

11

▪ Identify the relationship between CSR integration (strategic and functional integrations)

and company performance;

▪ Uncover the mediating effect of customer, employee, and operational performance in the

CSR integration-CFP relationship;

▪ Discover the moderating effect of business and CSR strategies in the CSR integration-

company performance relationship; and

▪ Identify the moderating effect of company size and industry type in the CSR integration-

company performance relationship.

1.4 Research Contributions and Implications

In this section, the contributions of this thesis to current knowledge about CSR are

presented. It also identifies the research implications of this thesis, not only for top managers

but also for policymakers.

From a theoretical perspective, this thesis is one of the first studies to propose a model that

comprehensively examines the relationship between CSR integration and company

performance. Thus, this thesis makes an essential contribution to the existing literature on CSR

integration and the impact of integration on company performance.

First, this thesis develops a conceptual framework by combining CSR integration and its

effect on company performance based on the strategy-context-performance relationship at

strategic and functional levels. Second, this thesis clarifies the relationship between the

proposed variables in the conceptual framework for strategic and functional integration to

demonstrate that the integration of CSR and business strategies has an impact on company

performance. Third, this thesis presents new empirical evidence on the relative impact of

various integration mechanisms on integration by suggesting that social performance can

mediate the impact of CSR integration on financial performance. By incorporating stakeholder

theory, the findings underline that the stakeholder relationship is a key mechanism through

12

which companies can benefit financially from the integration of CSR into their business

strategies. Specifically, this thesis provides new empirical evidence that the stakeholder

relationship should be considered when evaluating the relationship between CSR integration

and financial performance as such a relationship can be mediated by customer, employee and

operational performance. Fourth, this thesis offers new insights into the impact of CSR on

company performance from a contingency perspective. The results of the moderation analysis

show that the impact of CSR integration on company performance is contingent and dependent

on business and CSR strategies, company size and industry type. Finally, this thesis is one of

the first to empirically investigate the performance implications of integrating CSR into

business strategy and reveals new findings on how such integration can substantially improve

company performance.

From a practical perspective, the findings of this thesis have important implications for

executives and managers, particularly those working in manufacturing companies in

developing countries. First, executives and managers can be better motivated to integrate CSR

into their company strategies more strategically and to conduct business operations consistent

with CSR practices. In doing so, CSR should be incorporated from upstream to downstream or

along the value chain, covering all functions within the company and connecting to internal

activities of the company. Second, this thesis provides key findings that instead of undertaking

CSR as mere charity, philanthropy or compliance activities (as many currently practice),

companies integrating CSR with their business strategy can benefit from considerable, positive

impacts on their financial and non-financial performance, which in turn could enhance their

competitive advantage.

In addition, this thesis makes recommendations to decision makers, such as the Indonesian

government. First, they should not only make it mandatory for companies to engage in CSR,

but they should also establish clearer and more extensive guidelines. Companies could then

13

understand to what extent they can implement CSR and the scope of CSR that they can carry

out. Second, the government can communicate regulations and guidelines more frequently and

effectively on their official websites, at association conferences and annual gatherings, and

through awards. In this way, large companies and small and medium enterprises (SMEs) can

recognise the regulations and understand how to comply with them.

1.5 Overview of Research Methodology

This section presents an overview of research methodology applied in this thesis. It starts

with a research paradigm and a research framewok, followed by a research method, a survey,

as well as a data collection. Then, it describes a data analysis that will be conducted in this

thesis and research findings briefly.

This thesis adopts a positivist research paradigm and applies a deductive research design as

it seeks to develop and validate a theoretical model. The theoretical framework is based on

stakeholder and contingency theories. The framework describes the relationship between

strategic and functional integration and company performance, including customer, employee,

operational and financial performance. The theoretical framework contains 47 hypotheses. This

thesis examines several mediating and moderating effects in the relationship between CSR

integration and company performance.

This thesis conducts an explanatory study and uses quantitative research to address the

research questions proposed. It considers three essential dimensions of strategic CSR

integration: (1) aligning CSR with the company’s strategy, (2) gaining support from top

management, and (3) developing effective communication. Functional CSR integration

comprises six critical dimensions: (1) Cost, (2) Innovation, (3) Quality, (4) Supplier, (5)

Customer, and (6) Employee. To comprehensively analyse the impact of CSR integration,

company performance is assessed across four criteria: customer, employee, operational and

financial. The items for integration and company performance are adopted from previous

14

research on strategic management, CSR, and manufacturing. To examine the mediating effect

of the CSR integration-company performance relationship, this thesis uses business and CSR

strategies. Business strategy follows the most widely-used typology from Porter (1980a). The

measurement of CSR is borrowed from Maignan and Ferrell (2000, 2001), based on Carroll’s

four dimensions (Carroll 1979, 1991). In addition, this thesis uses company size and industry

type. Company size uses the number of employees to define small, medium, and large

companies. The criteria for company size follow the classification from Statistic Indonesia or

Badan Pusat Statistik (BPS 2017a). Industry type includes environmentally sensitive industries

(ESI) and non-ESI, and the criteria for industry type were based on the literature review and

prior studies.

To facilitate respondent understanding of the survey, the questionnaire developed was

translated from English to Indonesian using a back-translation method. Pre-tests were

conducted to ensure the survey questions were clear, concise, and specific to the context. Prior

to data collection, the questionnaire was submitted and approved by RMIT University’s Ethics

Committee. Before conducting the survey, a pilot test was undertaken with 30 respondents.

After the pilot test, the main survey with a non-probability purposive sampling method was

officially carried out across five regions in Java, Indonesia: East Java, Central Java,

Yogyakarta, West Java, and Jakarta.

From June to October 2018, the self-completion questionnaires were distributed through

post, e-mail, and personal delivery survey. In total, surveys were sent to 1,055 manufacturing

companies, and 514 surveys were returned. After data screening, 435 usable responses

remained, with a final response rate of 41.23%, which were used for data analysis.

To provide a plausible answer for each research question and research objective (Section

1.3), Statistical Product and Service Solutions (SPSS) 26 and partial least square structural

equation modelling (PLS-SEM) were employed for a series of comprehensive data analyses.

15

Specifically, SmartPLS 3, a professional software of PLS-SEM, was used to determine a model

specification that contained constructs and indicators, and to develop a model. Then, the

evaluation of the model was conducted in two steps: (1) the measurement model and (2) the

structural model. This thesis examined the direct effects, indirect effects, and total effects to

explore direct relationships and mediating effects. In addition, multi-group analysis (MGA)

was performed to assess the mediating effects.

The findings of this thesis demonstrate that structural and functional integrations have an

essential role in achieving better financial and non-financial performance. More specifically,

the findings show that customer, employee, and operational performance can significantly

mediate the impact of CSR integration on financial performance. Notably, the findings suggest

that business and CSR strategies can substantially moderate the effect of CSR integration on

company performance. Also, company size and industry type can significantly affect this

integration’s impact on company performance.

1.6 Structure of Thesis

This section describes the structure of the thesis, which is divided into seven chapters as

seen in Figure 1.1:

1. The first chapter has introduced the background of the research and the justification for this

thesis. This chapter briefly outlined the research questions and outlined the potential

contributions and implications of the research.

2. Chapter 2 presents the literature relevant to this thesis. This chapter provides a critical

review of existing literature about the integration of market and non-market strategies,

followed by CSR-related concepts, business strategy, CSR integration, and company

performance. This chapter also discusses the findings of prior studies regarding CSR

integration. Based on an extensive and critical literature review, this chapter explains the

16

research gaps that the thesis aims to address. Besides, this chapter offers an overview of the

Indonesian context around CSR implementation and the manufacturing industry.

Figure 1.1 Thesis Outline

3. Chapter 3 presents the research framework by explaining the theories used to underpin this

thesis. As a result, this chapter provides the theoretical framework that describes the extent

to which CSR integration has an impact on company performance in the context of the

Indonesian manufacturing industry. This theoretical framework is used as a guide to propose

47 hypotheses and analyse the findings of current empirical research on CSR integration.

4. Chapter 4 explains the research approach, the research paradigm and the research design

employed by this thesis. This chapter also presents the methodology that includes the

questionnaire development, sampling process and data collection. The data analysis

procedure is then explained, followed by a preliminary analysis of collected data which

included testing for nonresponse and common method bias, data screening, and identifying

17

respondents’ characteristics. Information of PLS-SEM is presented as a multivariate

analysis technique employed to analyse the data. Ethical considerations are also explored.

5. Chapter 5 presents the results related to strategic CSR integration. The conceptual model

proposed in Chapter 4 is tested and validated through rigorous steps of multivariate analysis.

The reflective and formative measurement models are explained, followed by the structural

model assessments. The mediation analysis is presented to examine the mediating effect of

business and CSR strategies. The following chapter provides MGA to investigate the

moderating effects of company size and industry type. The last section of this chapter

provides the results of the hypotheses testing.

6. Chapter 6 explains the results related to functional CSR integration. The descriptive

statistical analysis, model specification, model assessments, mediation and moderation

analyses of functional CSR integration are presented. To explain the results, a discussion is

provided, followed by the verification of hypotheses. The last section provides the results,

discussion, and hypotheses testing for the combined CSR integration.

7. Chapter 7 provides an overview of the entire thesis and highlights key findings in response

to the research questions. The conclusion of this thesis is then described in this chapter. The

final research model is presented to incorporate the research findings. Also, methodological

and theoretical contributions are explained, followed by practical implications for

executives and top managers as well as policymakers. This chapter finishes with limitations

of the study and recommendations for future research.

1.7 Summary of Chapter 1

This section provides a summary of this chapter. This introductory chapter presents an

overview of the thesis. It provides a discussion of the research background and explained the

research questions and objectives. This chapter also describes the theoretical and practical

18

research contributions of this thesis. An overview of research methodology is briefly discussed

and finally, the chapter outlines the structure of the remaining thesis.

19

CHAPTER 2: LITERATURE REVIEW

This chapter reviews the available literature by focusing on the theoretical background and

findings from previous research. The existing literatures are divided into the following sections:

(1) non-market strategies (NMS), (2) CSR, (3) business strategy, (4) the integration of CSR

and business strategy, and (5) company performance. Following these five sections are two

more subsections that (6) identify research gaps and (7) provide a brief description of Indonesia

as the context for this thesis.

2.1 Non-Market Strategies

This section explains NMS that underpin CSR. It identifies the distinction between NMS

and market strategies, followed by integrating NMS and market strategies.

The business environment consists of two inter-related components: market and the non-

market environments. The market environment includes activities governed by the market or

private agreements. In addition to the market environment, businesses are embedded in the

non-market environment, characterised by the social, political, and legal arrangements that

structure interactions beyond, but in conjunction with, the market and private agreements and

that restrict or facilitate the activities of companies (Baron 1995a, 1997; Yoo 2015). Efficient

management of the non-market environment shapes the operating climate in which businesses

compete but also enhances and promotes their competitive advantages (Baron 1995a).

The non-market environment differs from the market environment in several important

respects. First, the market environment includes suppliers, customers, and competitors. On the

contrary, non-market environments mainly consist of government, regulators, citizens, media,

activists, and non-governmental organisations (NGOs) (Bach & Allen 2010; Mellahi et al.

2015). Second, in the market environment, companies usually compete for resources, revenues,

and profits, while the non-market environment considers ethical behaviour, policy achievement

and social responsibility (Doh, Lawton & Rajwani 2012).

20

The inter-relationship between the market and non-market environments requires executives

to be responsible for performance in both environments. To achieve good performance,

companies operate and take strategic actions in both the market and non-market environment

(Rudy & Johnson 2013). They use a market strategy that consists of the suppliers, customers,

and competitors to handle the market environment (Doh, Lawton & Rajwani 2012) and focus

on creating value for customers, shareholders, and society.

As non-market forces are crucial, and the ‘rules of the game’ of market competition are

often influenced by various non-market players, including regulators and activist groups, in

addition to market strategy, companies also need NMS (Baron 1995b; Xie, Li & Xie 2014).

NMS is generally defined as a company’s efforts to arrange the institutional or social context

of economic competition to improve its performance (Baron 1995a). Similar to market strategy,

the goal of NMS is to achieve superior performance by creating and maintaining competitive

opportunities and advantage, creating a favourable climate for improving the performance of

companies and the industry in which they operate, and effectively resolving specific non-

market issues (Baron 1997; Xie, Li & Xie 2014). There is a need for long-term strategic

commitment to NMS and the variety of mechanisms across different levels through which

strategies are operationalised (i.e., individual companies, national alliances, and global

coalitions) (Lawton, Doh & Rajwani 2014).

NMS has two broad functions, namely buffering and bridging (Meznar & Nigh 1995).

Buffering techniques are used to protect an organisation from the external environment by

attempting to prevent the environment from interfering with internal activities and trying to

influence the external environment (Meznar & Nigh 1995). In contrast, bridging techniques are

used to meet and fulfil regulatory requirements by fostering organisational compliance with

changing social standards and focusing on integrating non-market issues with market strategy

(Meznar & Nigh 1995; Yoo 2015). Through bridging techniques, a company may actively

21

adapt to its external environment (environmental protection, public policy, social culture) in

the process of making strategic choices so as to meet and exceed stakeholder expectations (Yoo

2015). Either buffering or bridging may be used to enhance overall company performance,

rather than impact only economic performance (Yoo 2015).

CSR and corporate political activities (CPA) are two critical approaches that companies use

to strategically manage their non-market environment (Du, Bai & Chen 2019; Lawton, Doh &

Rajwani 2014; Mellahi et al. 2015). Increasingly, CSR is considered an essential element of

any NMS (Baron & Diermeier 2007) that needs to be effectively integrated with a company’s

market strategy (Yoo 2015).

Baron (1995a) used the term ‘integrating market and non-market strategies’ for the first

time. In this respect, integration means simply a combination (i.e., simultaneous use) of market

and non-market strategies. Baron (1997) affirms that synergies exist between market and non-

market strategies that can be achieved when these two strategies are integrated. Moreover, for

an effective business strategy, these two strategies should be tailored to a firm’s market and

non-market environment, as well as to a firm’s competencies (Baron 1997). In competitive

arenas, integration of those strategies are a primary determinant of advantage (Lawton, Doh &

Rajwani 2014).

Furthermore, Baron (1997) suggested three levels of integration. The first level is that of the

overall business structure (e.g., the defining of the company’s business lines), the company’s

boundaries (what it does and what others do with it), internal company governance and

compensation systems, and standards of conduct based on ethical principles and responsibility

concepts. At the second level, a company’s strategy explicitly competes against other

companies’ strategies. The second level is where the integration of market and NMS is most

productively considered. Last, integration at the third level involves coordination of several

functions, such as finance, production, and marketing.

22

As an emerging topic in the literature (Mellahi et al. 2015), the integration of market and

NMS is very important (Xie, Li & Xie 2014). With the progress of NMS research, increasing

attention has been given to empirical analysis of the relationship between integrated strategies

and company performance (Xie, Li & Xie 2014). Surveying journal articles across a 15-year

period from 2000 to 2014, Mellahi et al. (2015) provided a review on the link between NMS

and organisational performance and identified the mechanisms through which NMS influences

organisational performance. The logical response to the question why companies should

integrate market and NMS is that there is advantage from this alignment (Xie, Li & Xie 2014).

The benefit of integrating non-market and market strategies lies in the synergy between the

two. Only when the right content is incorporated and smoothly coordinated does high synergy

from the integration of non-market and market strategies result (Xie, Li & Xie 2014).

In conclusion, business should deal with its market and non-market environments and, as a

result, implement market and non-market strategies. Synergy can be achieved by integrating

both strategies. According to prior research, there are a number of benefits from such

integration.

2.2 Corporate Social Responsibility

Following on from the prior section, this section explains more about CSR, a critical element

of NMS. This section is divided into four subsections, each of which explains (i) CSR

definitions, (ii) CSR dimensions, (iii) CSR strategies, and (iv) different theories in CSR studies.

A broad variety of NMS includes CSR (Baron & Diermeier 2007), which has received much

attention in the managerial and organisational literature (Tuzzolino & Armandi 1981) and has

been the focus of intense debate and concern over the past three decades (Jamali 2008).

Business and society interdepend intensely and dynamically (Afrin 2013), and CSR is the most

frequently used term to suggest the correlation between the two (Branco & Rodrigues 2006)

for various literature and business practices (Carroll & Shabana 2010). CSR clarifies that

23

companies have an obligation to bear their social and environmental responsibilities, beyond

legislative compliance and individual liability demand (Blowfield & Frynas 2005).

Consequently, all companies, regardless of their size, sector, or location, must pay attention to

CSR (Martinuzzi & Krumay 2013). As CSR has become more popular (Campbell 2007; Carroll

& Shabana 2010; Malik 2015) and important to business organisations (Zatwarnicka-Madura

et al. 2019) over the past two decades, companies have attempted to comply with legislation

and seek advantages from it (Razafindrambinina & Sabran 2014).

2.2.1 CSR Definitions

In this subsection, a number of CSR definitions that have been used in previous studies are

provided. Specifically, the definitions are relevant to the topic of this thesis.

The extensive literature provides various definitions of CSR (Ağan et al. 2016). Bowen

(1953) described CSR as a manager's obligation to implement those policies, make certain

decisions or follow the lines of action that are appropriate in terms of our society's goals and

values. The World Business Council for Sustainable Development involved economic, ethical

and social considerations and declared CSR to be a continuous business agreement to have

ethical behaviour and to benefit from sustainable economic development; while at the same

time enhancing the quality of life of employees, their families, the local community and wider

society (Moir 2001). Kotler and Lee (2005) defined CSR as a commitment to improve

community well-being through discretionary business practices and the contributions of

corporate resources. Dahlsrud (2008) identified 37 definitions of CSR used in 27 studies from

1980 to 2003. Findings from this study suggested the five most frequently used dimensions of

CSR: economic, social, environmental, stakeholder and voluntary. Moreover, Turker (2009)

expressed CSR as the behaviours of a company that aim to have a positive impact on social

and non-social stakeholders and go beyond their economic interests.

24

The European Commission declared CSR to be ‘a concept where-by companies integrate

social and environmental concerns in their business operations and in their interaction with

their stakeholders on a voluntary basis’ (European Commission 2011, p. 3). From this

definition, compliance of a voluntary nature and to an extent beyond that required as the

minimum, are essential components. In 2011, this definition was expanded by declaring that

companies should embrace social, environmental, ethical human rights and consumer concerns

into their business operations and core strategies when collaborating with their stakeholders.

This definition underlines two objectives of CSR: (1) maximising the creation of value for

shareholders and the community, through a long-term strategic approach to CSR and the

development of products, services and innovative business models; and (2) identifying,

preventing and mitigating its possible negative impacts (European Commission 2011). CSR is

generally regarded as an approach that can make business processes more open and socially

accountable (Asif et al. 2013) and can be considered as the companies’ ability to be socially

responsible for the development and growth of the societies where they run their businesses

(Adeneye & Ahmed 2015). Furthermore, using a quantitative analysis of 110 definitions from

1953 to 2014, Sarkar and Searcy (2016) suggested six core dimensions underpinning the CSR

concept, namely economic, social, ethical, stakeholders, sustainability and voluntary.

Specifically regarding integration, Rasche, Morsing and Moon (2017, p. 6) emphasised that

CSR indicates ‘the integration of an enterprise’s social, environmental, ethical, and

philanthropic responsibilities towards society into its operations, processes and core business

strategy in cooperation with relevant stakeholders’. CSR is considered to be the way in which

a company transparently and accountably integrates social, environmental and economic issues

into their beliefs, culture, decision-making, strategy and operations, thereby creating best

practices within the company, generating wealth and improving society (Afrin 2013).

25

Indeed CSR can be defined using many terms, all of which express common sense with

regards to the activities, relationships and responsibilities of companies to and with society

(Branco & Rodrigues 2006). Based on the basic concept of CSR, business and society are not

separated into different entities; it connects them (Wood 1991). Nonetheless, the definition of

CSR depends in particular on the local context and understanding of the relevant stakeholders

(Welford, Chan & Man 2008). van Marrewijk (2003) suggested that effective CSR strategy

needs a specific context for each individual organisation, such as which CSR issues should be

monitored and how to embrace stakeholders. Similarly, Aguinis and Glavas (2012) highlighted

that institution-based policies and practices can affect how CSR is defined, because parties at

all levels of analysis (institutional, organisational and individual) have an effect on different

stakeholders.

The many attempts at defining CSR cannot provide a definitive description of CSR. Several

studies consider various terms as being interchangeable with CSR, such as corporate

citizenship (Dahlsrud 2008; Glavas & Piderit 2009; Matten & Crane 2005; Vidaver-Cohen &

BrØNn 2008), corporate social accountability (Aqueveque & Encina 2010; Laufer 2003) and

corporate sustainability (Eccles, Ioannou & Serafeim 2014; Grewatsch & Kleindienst 2015;

Lai, Lin & Wang 2015; Lourenço & Branco 2013; Salzmann, Ionescu-somers & Steger 2005;

Windolph, Harms & Schaltegger 2014).

Nonetheless, several definitions of CSR describe CSR as a multidimensional concept that

covers a number of areas, such as the community, employees, customers, the environment, and

human rights. Some of these areas refer to stakeholder needs, for instance, the needs of

employees and customers, whereas other areas relate to benefits to the community, such as the

environment and human rights (Yuan et al. 2020).

Overall, this subsection covers a variety of CSR definitions that are pertinent to this thesis’s

topic. The majority of CSR definitions describe the interaction between business and society

26

while considering the stakeholders' interests. Several definitions, in particular, emphasise the

need of integrating companies’ responsibilities into their business strategy and operations. This

subsection also mentions various terms that can be used interchangeably with CSR.

2.2.2 CSR Dimensions

This subsection explains the dimensions of CSR. Carroll (1979, 1991) proposed that CSR

could be viewed from four distinct and separate perspectives:

1. Economic responsibilities. Business has traditionally been conceptualised as an economic

entity with a responsibility to produce and provide goods and services as efficiently as

possible, while making an acceptable profit (Carroll 1979, 1991; Jamali 2006) and fulfilling

the consumer needs of society (Aupperle, Carroll & Hatfield 1985).

2. Legal responsibilities. Business is expected to comply with the laws and regulations

promulgated by federal, state, and local governments as the ground rules under which it

must operate (Carroll 1991).

3. Ethical responsibilities. Ethical responsibilities embody the standards, norms, or

expectations that reflect a concern for what customers, employees, shareholders and the

community regard as fair, just, or in keeping with the respect or protection of the

stakeholders' moral rights (Carroll 1991).

4. Philanthropic responsibilities or discretionary responsibilities (Carroll 1979). Philanthropy

encompasses corporate actions undertaken in response to society's expectations that

businesses be good corporate citizens. Philanthropy is discretionary or voluntary despite

societal expectations that businesses (Carroll 1979, 1991) give back to society with nothing

to expect in return (Galbreath 2006).

Carroll’s definition of CSR addresses the range of obligations that businesses have to society

(Garriga & Melé 2004). This definition has been one of the most accepted and widely used

27

definitions of CSR in academic research (Dhanesh 2014) and is recognised as comprehensive

and integrative by numerous theorists and empirical researchers (Wang & Berens 2015).

Overall, CSR contains four essential dimensions that describe the companies’ responsibility.

These four dimensions are seen to be comprehensive and integrative and have been employed

extensively in previous research.

2.2.3 CSR Strategies

This subsection opens with an explanation of why having a CSR strategy is crucial. The

four CSR strategies used in this thesis are then presented. This subsection also discusses the

link between CSR strategies and company performance.

A CSR strategy can be defined as ‘plans, investments and actions put into practice by a

company within the scope of attaining sustained competitive advantages and, simultaneously,

better social and economic performances’ (Husted & Allen 2001, p.3, cited in Marques-Mendes

& Santos 2016). A common, critical problem with CSR practice is that companies often

implement various CSR programs but do not align them with strategy (Rangan, Chase & Karim

2012). Consequently, it is important to have a CSR strategy that unifies a diverse range of the

company’s philanthropic giving, supply chain, and system-level initiatives all under one

umbrella (Rangan, Chase & Karim 2012). By selecting an appropriate CSR strategy, companies

benefit, such as through reducing their costs and risk, increasing their profits and competitive

advantage, improving their reputations and legitimacy, and creating synergistic values (Kurucz

et al., cited in Ganescu 2012b).

Galbreath (2006) highlighted that how companies respond to their responsibilities will have

an impact on how they practice shared value creation, which can be described by four types of

CSR strategy (Wood 2010):

1. Reactive. Companies implement reactive strategy if they apply CSR at the basic level

required to meet their regulatory compliance (Torugsa, O'Donohue & Hecker 2013).

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Companies that apply reactive strategy reject social responsibility and do less than required

by societal standards (Maignan et al. 1999). They passively meet regulations, or react to

protect their corporate image after unethical behaviour has been reported (Wagner, Lutz &

Weitz 2009).

2. Defensive. Companies that apply a defensive strategy reject their ethical responsibilities and

protect their interests within the legal framework (Ganescu 2012b).

3. Accommodative. Companies adopt the accommodative strategy by supporting certain

ethical responsibilities, particularly those of their stakeholders, without initiating voluntary

actions for the common good (Ganescu 2012b).

4. Proactive. Companies that apply the proactive strategy aim at transforming their operational

activities to achieve eco-efficiency and develop environmental-friendly products/services

(Bos-Brouwers 2010). They can actively manage the sustainability of economic, social and

environment efforts, and support their CSR activities (Wagner, Lutz & Weitz 2009) beyond

the compliance (Torugsa, O'Donohue & Hecker 2013). They engage in CSR to avoid any

negative reports that could be received by their external stakeholders (i.e., customers) (Du,

Bhattacharya & Sen 2007).

These four types of CSR strategy describe that organisations can be seen to function across

a spectrum of CSR from reactive to proactive (Carroll 1979; Wartick & Cochran 1985).

However, most studies use only two of these strategies: reactive and proactive (Bocquet et al.

2013; Chang 2015; Goran & Greg 2004; Groza, Pronschinske & Walker 2011; Torugsa,

O'Donohue & Hecker 2013).

Several prior studies examined the relationship between CSR strategy and company

performance. For instance, Groza, Pronschinske and Walker (2011) conducted two

experiments to reveal consumer preferences for communicated CSR information using the

theory of attribution and persuasion knowledge model (PKM) as a theoretical basis. Study 1

29

analysed the mediating role of the attributions in explaining the relationship between CSR

strategy (i.e., proactive vs. reactive) and attitudes toward the company and purchasing

intentions. Study 2 examined how the source of knowledge (i.e., internally vs. externally

published) and the position of the CSR initiative (i.e., local vs. non-local) influenced the

attributed motivations of customers. Taken together, the findings indicated that the attributions

helped clarify how customers respond to the CSR initiatives and (as expected by the PKM)

these presumed motivations could be influenced by specific message characteristics.

Companies should take enhanced steps to disclose proactive CSR information from internal

sources to maximise the positive returns from CSR investments. Alternatively, companies

should rely on information from external sources to transmit any divisive information to reduce

any potential negative effects.

Torugsa, O'Donohue and Hecker (2012) empirically explored the relationship between three

specified capabilities (shared vision, stakeholder management and strategic proactivity),

proactive CSR and financial performance. Using quantitative data collected from a sample of

171 SMEs in the Australian machinery and equipment sectors, they found all specified

capabilities are positively correlated with SMEs' adoption of proactive CSR, and that proactive

CSR, in effect, is correlated with improving company financial performance (CFP). In addition,

Torugsa, O'Donohue and Hecker (2013) highlighted the economic dimension of proactive

CSR. Selectively focusing on proactive social and environmental aspects of CSR that drive and

maintain economic performance is crucial to sustainable, long-term financial SME

performance.

With a sample of 155 French SMEs, Vo, Delchet-Cochet and Akeb (2015) compared the

roles of economic, social, and environmental motives in driving SMEs to make CSR an integral

part of their strategic planning and daily operational performance. The results showed that the

economic motive had the highest effect in driving French SMEs to integrate CSR into their

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business strategy. The social motive comes second, while there was no relation to the reactive

environmental motive. Specifically, regarding environmental measures, only proactive

environmental motives, which indicate the degree to which the organisation transforms its

business practices and operations to protect the environment, play a role. Reactive companies

could not gain competitive advantage from CSR because they only enforced CSR to comply

with regulations.

Moreover, Chang (2015) developed a framework to examine whether CSR plays a

mediation role between green organisational culture and green product innovation

performance. After dividing CSR into proactive CSR and reactive CSR, the empirical results

verify that green organisational culture positively impacts the performance of proactive CSR

and green product innovation. Proactive CSR mediates the positive relationship between green

organisational culture and green product innovation performance, but reactive CSR does not.

Green organisational culture is a driving force for proactive CSR and green product innovation

performance.

In summary, there are four CSR strategies explained in this subsection: reactive,

accommodative, defensive, and proactive. These four strategies represent how companies carry

out their responsibilities. Previous research examined the relationship between CSR strategies

and company performance. Most of them highlighted that adopting a proactive strategy

provides various benefits for companies.

2.2.4 Different Theories in CSR Studies

This section goes through various theories that have been employed in CSR studies. The

stakeholder theory, which is used in this thesis, is explained in further detail.

Previous studies on CSR apply different theories that refer to the links between company

and society, including stakeholder, institutional, legitimacy, resource-based view (RBV),

resource dependency theory (RDT), and agency theories. In particular, stakeholder,

31

institutional, legitimacy and RDT stress the role of external actors in terms of managerial

practices of a company and focus on nature correlation between the company and society as

well as the environment (Frynas & Yamahaki 2016). On the other hand, for studies focused on

companies’ internal processes, RBV and agency theories are more appropriate (Frynas &

Yamahaki 2016).

Institutional and stakeholder theories can be implemented to understand the essential

interconnections between business and society (Chen, Feldmann & Tang 2015). Frynas and

Yamahaki (2016) suggested that institutional or stakeholder theories can serve multilevel

analysis that includes macro-level (between the company with outside stakeholders), meso-

level (among company’s member), and micro-level (individual) factors. Stakeholder and social

exchange theories are the major theories used to explain the correlation between CSR and

company performance. Social exchange theory explains interconnections between business and

society. Because members of society buy a company’s products, the company gives back to

society through CSR activities (Adeneye & Ahmed 2015).

Stakeholders are groups and individuals who can affect, or are affected by, the achievement

of a company’s mission (Freeman 2010). As any business operation directly or indirectly

affects stakeholders (Shital 2014), stakeholder theory argues that companies have obligations

not only to shareholders but also to stakeholders (Freeman 2010). Internal stakeholders consist

of the owners, managers and employees of a company who stay within the company’s borders,

whereas external stakeholders include the suppliers, customers, communities and government

(Freeman 2010).

Furthermore, companies need to focus on primary and secondary stakeholders (Hult, cited

in Tore 2012). Primary stakeholders are essential for the company's continued life (Ihlen 2008)

and include investors, employees, customers, suppliers and public stakeholder groups, such as

governments and communities that include services and markets, whose laws and regulations

32

must be followed, and to whom taxes and other liabilities may be due (Clarkson 1995).

Secondary stakeholders are directly or indirectly affected by the decisions of a company (Ihlen

2008). However, they do not participate in transactions with them and are not necessary for

their survival. Examples of secondary stakeholders include the media and special interest

groups (Clarkson 1995).

CSR relates to how to manage internal and external stakeholders of the company ethically,

socially and responsibly to improve living standards while conserving the company

profitability for internal and external stakeholders (Hopkins 2005). Subsequently, CSR

practices can be complex and risky from a managerial perspective because they have to

consider how to respond to a variety of internal and external stakeholder pressures and how to

evaluate whether CSR practices will fit with existing business operations (Yuan, Bao &

Verbeke 2011). Accordingly, stakeholder theory can aid in understanding the CSR practices of

companies to address stakeholder interest. Company behaviours reacting to stakeholder

expectations of being a socially responsible corporate citizen in society can be demonstrated

by how they identify and implement CSR activities (Zhu, Liu & Lai 2016). Moreover,

stakeholder theory is capable of explaining part of the relationship between CSR and financial

performance. Most studies focused on the stakeholder theory's instrumental approach indicate

that adherence to values and practises in collaboration with stakeholders helps the company

meet its goals as well as or better than their rivals (Chtourou & Triki 2017).

As mentioned above, CSR studies employ a variety of theories. They can use stakeholder,

institutional, and legitimacy theories, as well as RDT, to manage the connection between the

company and society or the company and the environment. RBV and agency theories, on the

other hand, might be used when focusing on a company's internal processes. In light of the

research objectives determined in Chapter 1, stakeholder theory is considered to be appropriate

for use in this thesis.

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2.3. Business Strategy

Following an explanation of CSR as a critical component of NMS, this section discusses

business strategy. There are two subsections in this section. First, the generic strategy of Porter

is explored. Second, it elucidates the link between business strategy and company performance

and provides several previous studies focusing on this topic.

Strategy is a collection of plans and policies through which an organisation tries to obtain

benefits over its rivals (Skinner 1969, p. 139). Usually a strategy involves product plans and

the marketing of goods to a particular group of customers (Skinner 1969). Business policy

theoretical literature has increasingly stressed the distinction between three levels of

organisational strategy: (1) corporate-level strategy, which deals with questions about what

business to compete in, (2) business-level strategy, concerned with questions about how to

succeed within a given business (Beard & Dess 1981) and (3) functional level-strategy (Hax &

Majluf 1984; Venkatraman 1989). Corporate-level strategy deals with domain selection, such

as the vertical, horizontal and market reach, relationship, and level of cooperation between the

different companies (Bourgeois 1980). Corporate strategy explains what an organisation does

and how the organisation interacts to achieve success (Weir et al. 2000). Business-level strategy

involves domain navigation, that is, how the organisation competes successfully in an industry

(Beard & Dess 1981; Weir et al. 2000). Business strategy defines the long-term plan of action

a company may pursue to achieve its goals (Zahra & Covin 1993). On the other hand,

functional-level strategy describes how functions will contribute towards a business strategy

(Weir et al. 2000) by optimising the productivity of resources within each particular function,

and is typically derived from the business strategy (Schendel & Hofer, 1979, cited in

Nandakumar, Ghobadian & O'Regan 2011). Traditionally, these three levels of strategy form

a hierarchy that implies a top-down approach to formulating a strategy (Weir et al. 2000).

34

Compared to other the two strategies, business strategy offers a focus for a vast majority of

the strategy research studies (Nandakumar, Ghobadian & O'Regan 2011). More specifically,

business or market strategy describes how to position companies in their competitive

environment and/or develop their strategic capabilities in a way that allows them to gain an

advantage against their competitors (Porter 1987). The concept of aligning companies’

resources with environmental challenges and opportunities is embedded in business strategy

(Lawton, Doh & Rajwani 2014).

2.3.1 Porter’s Generic Strategy

This subsection discusses Porter's generic strategy, which is one of the most extensively

used business strategy in management studies. Porter (1985) developed a framework that

outlines how firms might choose a generic strategy to compete effectively, namely either a cost

leadership strategy or a differentiation strategy. In the literature of business policy, this

framework is a dominant paradigm (Hill 1988) that tends to be most effective because it builds

on previous findings and is specific and unambiguous (Hambrick 1983).

The purpose of cost leadership strategy is to make the company’s products at the lowest cost

possible in the company’s industry. Reduced costs in manufacturing operations are critical for

the implementation of a cost leadership strategy (Porter 1985). As cost leadership emphasises

producing standardised products at a low per-unit cost for price-sensitive consumers (David &

David 2014; Ramachandran 2011), companies that adopt this strategy need to tightly control

their costs, refrain from incurring too many expenses from innovation or marketing and cut

their prices when selling the products (Porter 1985), as they see cost management as their

highest priority (Liu, Li & Li 2019). Nonetheless, lower cost, while not neglecting quality,

service and other areas, emphasises a company's ability to design, manufacture and sell a

standardised product or service more efficiently than its competitors, with a focus on cost

benefits from all sources (O'Farrell, Hitchens & Moffat 1992). In addition, cost leadership

35

means offering customers value at a lower cost equivalent to that provided by rivals (Spanos,

Zaralis & Lioukas 2004).

On the other hand, differentiation strategy requires the development of goods or unique

services that are unmatched, by relying on the customer’s loyalty to the brand. Companies can

provide a product or service that is of superior value to customers (O'Farrell, Hitchens & Moffat

1992; Ramachandran 2011), for instance, with higher quality, better performance or unique

features so that they can justify higher prices (Galbreath 2009; Hax & Majluf 1984; O'Farrell,

Hitchens & Moffat 1992; Porter 1985; Valipour, Birjandi & Honarbakhsh 2012).

To sum up, Porter developed two major types of business strategy. The cost leadership

strategy focuses on cost, whereas the differentiation strategy emphasises the distinctivenesss

of a company's products or services. In this thesis, these two strategies are used to show how

companies employ them to run their businesses.

2.3.2 Business Strategy and Company Performance

Following on from the preceding subsections, this one explains how business strategy

affects company performance. It begins by presenting several previous studies on this topic.

Then, it summarises their main points in a table.

Porter's framework also contains a theoretical proposition about the business strategy's

effect on business performance. Porter (1980a) contends that cost leadership and differentiation

strategies can lead to success. However, as each requires different resources and organisational

configurations, they are incompatible, and only those companies that can focus on one will

achieve excellent performance (Dess & Davis 1984; González-Benito & Suárez-González

2010).

Using the business strategy typology of Porter, several studies have investigated the

relationship between business strategy and company performance. For example, Dess and

Davis (1984) established a construct validity of Porter's typology and concluded that companies

36

following generic strategies performed very well. By introducing a game-theoretical model of

oligopoly competition, Karnani (1984) offered analytical support for differentiation strategies

and cost leadership. He concluded that a low cost or differentiated position would lead to

greater market share, which in effect would lead to higher profitability.

White (1986) analysed how cost leadership and differentiation strategies impact the business

unit performance. With organisational data obtained from 69 business units, the findings

showed how performance differences, such as sales growth and return on investment (ROI) for

business units, are related to organisational differences through Porter's generic strategies of

overall cost leadership and differentiation. Business units with pure cost strategies actually

achieve higher ROI when they have low autonomy.

Robinson and Pearce (1988) then examined the strategy-performance relationship in 97

manufacturing firms representing 60 different industries. They found significant performance

differences across selected groups. Strategic orientations emphasising product innovation or

those combining strategic behaviour patterns of 'efficiency' and 'differentiation' were correlated

with significantly higher levels of performance than other groups.

With a sample of service companies in Scotland and England, O'Farrell, Hitchens and

Moffat (1992) investigated whether companies pursuing a clear-cut strategy (focus

differentiation, differentiation or low cost leadership) achieved a superior performance. They

found that commitment to at least one of these three strategies would lead to better performance

than if companies failed to develop a generic strategy (that is, stuck-in-the-middle, in terms of

Porter’s model). Thus, their empirical evidence supports the idea of a strategy-performance

relationship.

Moreover, Kotha and Nair (1995) analysed the effect of environment and strategy on

performance with a sample of 25 Japanese machine tool companies, using longitudinal data

over the period 1979 to 1992. Their results showed cost leadership and differentiation strategies

37

and the environment play a significant role in influencing profitability and growth. More

specifically, whereas both strategy and environmental variables are significantly related to firm

profitability, only environmental variables are associated with firm growth.

Sharma (2002) examined the pattern of adoption of Porter's generic strategies in the

Australian Manufacturing Industry through contextual factors such as an industry category, a

type of goods, a product life cycle stage, a company size, a market type and a business unit's

growth trend relative to industry. The findings suggested that there is a statistically significant

relationship between the choice of generic strategy and firm size, product life cycle stage, and

business unit's growth trend relative to industry. The findings revealed that companies adopting

cost leadership strategy had the highest performance of labour productivity in terms of sales

per employee; substantially higher than those using focus strategy or those following

differentiation and focus strategy combined. Companies pursuing the differentiation strategy

were the largest in company size, both in terms of total annual sales and number of employees.

In domestic markets, the differentiators had the highest sales growth.

Furthermore, Spanos, Zaralis and Lioukas (2004) analysed the impact of strategy and

industry factors on profitability, using data on Greek manufacturing industries. They found that

the more generic strategy dimensions included in the strategy mix, the more profitable the

strategy, provided that one of the key components is low cost. Pure strategies generally appear

to produce below average results. Companies pursuing pure differentiation strategies are less

profitable compared with those having no clear strategy. Finally, stuck-in-the-middle,

conceptualised as a particular underdeveloped form of a hybrid strategy, appears to be more

profitable than hypothesised, yielding above average performance.

Then, González-Benito and Suárez-González (2010) integrated strategies, capabilities and

performance in a single model and proposed that both manufacturing competitive priorities and

capabilities, articulated in terms of cost and flexibility, are essential for explaining the link

38

between generic business strategies and business commercial and financial performance. Using

a sample of 148 Spanish manufacturers, they noted the significant and positive indirect paths

from generic business strategies to business performance variables. Their findings suggested

that business strategy based on cost leadership must be associated with manufacturing strategy

and capabilities focused on cost reduction to be effective. In contrast, manufacturing strategy

and capabilities focused on flexibility are necessary for an effective business strategy based on

differentiation.

Moreover, Nandakumar, Ghobadian and O'Regan (2011) examined the applicability of

Porter's generic strategies in describing discrepancies in company performance through

focusing on electrical and mechanical engineering companies in the United Kingdom. The

findings of this study suggested that companies that follow one of the strategies, namely cost-

leadership or differentiation, perform better than stuck-in-the-middle companies that do not

have a dominant strategic orientation. This study identified some of the gaps in the literature

through a systematic literature review.

Banker (2017) explored the relationship between the strategic positioning of companies and

sustainability of company performance. With 12,849 firm-year data from 1989 to 2003, this

study provided empirical evidence that in general, differentiation is a source of sustainable

performance, while cost leadership is not. They found that companies adopting differentiation

strategy can sustain their current performance in the future to a greater extent than those

following cost leadership.

In terms of the Indonesian context, Omsa (2017) conducted a survey on the owners of 305

small and medium-sized wooden furniture companies in East Java, Indonesia, to investigate

how generic strategies affect company performance in terms of profit and sales volume. The

findings of this study revealed that both differentiation strategy and focus strategy have a

significant impact on company performance, while cost leadership strategy has no significant

39

impact on company performance. In addition, Ridjal and Muhammadin (2018) examined the

influence of generic strategies on bank performance. With a sample of 101 banks in Indonesia,

they discovered that low cost and focus strategies have a significant influence on companies’

financial and organisational performance.

Table 2.1 summarises the main findings of previous studies examining the relationship

between Porter’s generic strategic and company performance. In conclusion, several studies

have investigated that business strategy has a significant impact on company performance. Despite

their distinctive findings, the majority of them argued that differentiation strategy benefits the

company more.

40

Table 2.1: Results of Previous Studies of Porter’s Generic Strategies

Author (s) Findings

Dess and Davis (1984) Organisations adopting one of the strategies perform better than stuck-in-the-

middle companies.

Karnani (1984) Organisations adopting either a cost-leadership or differentiation strategy were able

to increase their market share and profitability.

White (1986) Companies following a cost-leadership strategy performed well when they had low

autonomy and differentiators performed well in conditions of high autonomy.

Robinson and Pearce

(1988)

Strategic orientations emphasising product innovation or those combining strategic

behaviour patterns of 'efficiency' and 'differentiation' were correlated with

significantly higher levels of performance than other groups.

O'Farrell, Hitchens and

Moffat (1992)

Among service companies, those adopting a differentiation strategy performed

better than the ones which are stuck in the middle.

Kotha and Nair (1995) Strategy and environment significantly influence firm profitability.

Sharma (2002) Companies that followed the cost leadership strategy had the highest labour

productivity performance in terms of sales per employee, significantly higher than

those that used focus strategy or those that used the combined strategy of

differentiation and focus. Companies that adopted the differentiation strategy were

largest in company size, both in terms of total annual sales and number of

employees. In domestic market, the differentiators also had the highest sales

growth.

Kim, Nam and Stimpert

(2004)

Cost-leaders performed at the lowest level, and companies combining cost-

leadership and differentiation strategies performed at the highest level.

González-Benito and

Suárez-González (2010)

A business strategy based on cost leadership must be associated with manufacturing

strategy and capabilities focused on cost reduction to be effective. In contrast,

manufacturing strategy and capabilities focused on flexibility are necessary for an

effective business strategy based on differentiation.

Nandakumar,

Ghobadian and O'Regan

(2011)

Cost-leaders and differentiators performed better than stuck-in-the-middle

companies in terms of both the subjective and objective performance measures.

Banker (2014) The differentiation strategy helps a company to retain its current profitability to a

greater degree than the cost leadership strategy.

Omsa (2017) Both differentiation strategy and focus strategy affect company performance, while

cost leadership strategy does not significantly affect company performance.

Ridjal and Muhammadin

(2018)

Low-cost strategy and focus strategy have a significant influence on the financial

and organisational performance.

Source: Adopted and modified from Nandakumar, Ghobadian and O'Regan (2011).

2.4. The Integration of CSR into Business Strategy

This section explores literatures discussing the integration of CSR into business strategy. It

begins by explaining findings from previous studies that are relevant to this topic, followed by

41

summarising the key points in a table. Then, it delves into the dimensions of CSR integration

at strategic and functional levels.

NMS activity should be given a major role in a company’s operations and become an

integral part of strategic activity. Resources should be allocated and several employees should

be given responsibility for non-market activity (Xie, Li & Xie 2014). Consequently, companies

should give adequate consideration to their social responsibility and managers should not think

of CSR as an optional action, but an action that must be integrated with business strategy

(Kapoor & Sandhu 2010). Social responsibility and business strategy have been examined in

different ways, relating to the company’s social and economic priorities, respectively (Izzo

2014).

To survive in modern society, a company tends to pay attention to social responsibility as

an integral part of its strategy (Galbreath 2006). In recent decades, businesses have shown a

growing interest in incorporating social elements into their business strategies with the goal of

achieving sustainable development (Huang 2010). But, most companies that understand the

importance of CSR also carry out CSR practices irrelevant to their business strategy and

obviously beyond their core competencies (Dey & Sircar 2012).

2.4.1 Findings from Prior Studies

In this subsection, previous studies that investigated the integration of CSR into business

strategy are presented. There are both qualitative and quantitative studies available. A table is

created at the end of this subsection to list their critical findings.

Several studies have been conducted on the integration of CSR into business strategy, either

conceptually or empirically. Most of them propose a process of integration that may consist of

several stages or levels and may adopt different approaches. For example, Valente (2009)

developed four approaches (alter, absorb, align, and avoid) with respect to how NMS intersect

with market strategies in the strategic management process. His typology can give an

42

understanding of how market and non-market strategies can be integrated and how companies

implement this integration differently.

Maon, Lindgreen and Swaen (2009) introduced an integrative framework of CSR design

and implementation. They suggested four stages, namely sensitising, unfreezing, moving, and

refreezing, with a nine-step process. Using stakeholder theory, they combined top-down and

bottom-up processes in the integration process and describe the roles of each stakeholder, both

the primary and secondary stakeholders, throughout the integration phase as well as presenting

specific case studies to support their explanation. In their framework, they highlighted the

importance of aligning CSR with a company’s overall goals and strategies, identifying key

stakeholders and critical stakeholders’ issues, development of an integrated CSR, and engaging

employees in implementation, monitoring and evaluation. They also introduced critical success

factors in the CSR process at the corporate, organisation, and managerial levels. Some are used

in this study as indicators in the construct of strategic and functional integration, such as

connecting CSR vision and initiatives with an organisation’s core values and competencies,

gaining key people’s commitment (directors, owners, senior managers), and training

employees in CSR-related issues.

Specifically, Guadamillas-Gómez, Donate-Manzanares and Škerlavaj (2010) recommended

three stages for the integration of CSR into a company’s strategy: the introduction,

implementation, and generalisation of CSR. Their study described how organisational

functions are involved in integration and how each stage of integration interacts with each

other. Based on an exploratory case study within a Spanish technology-intensive firm, they

demonstrated how this company has developed and performed an explicit plan for the

integration of ethical values and CSR initiatives into its corporate and business strategies.

Dey and Sircar (2012) described how some Indian companies implemented the integration

between CSR initiatives and companies’ business strategies and proposed five stages of the

43

integration conceptually. They also explained the benefits of integration for society as well as

for the company. Their arguments supported findings from prior studies that integration is

possible if there is a clear purpose shown by managers by placing CSR at the heart of their

businesses. Nevertheless, this study did not mention specific indicators or defined activities for

each stage.

Martinuzzi and Krumay (2013) then presented a referential stage model to understand the

different stages of integrating CSR into existing business operations, leading to a competitive

advantage. They suggested four stages which should occur sequentially with the impacts on

social, environmental, and competitiveness following the sequence: project-oriented CSR,

quality-oriented CSR, strategic CSR, and transformational CSR. Based on the model, they

classified the CSR practiced by companies depending on their correlation to the business

operations. They provided a new insight that the quality of management can be assessed by its

CSR implementation.

Furthermore, Asif et al. (2013) developed the framework for incorporating CSR into all

levels of business processes. Using the Plan-Do-Check-Act (PDCA) cycle, they explained that

CSR integration is an iterative mechanism requiring continuous improvement. Extending the

prior study by Maon, Lindgreen and Swaen (2009), they considered two approaches of how to

integrate CSR into business processes, namely top-down and bottom-up approaches. A top-

down approach focuses on defining stakeholder expectations and combining CSR with internal

management systems. The 'top-down integration' strategy also involves the creation of internal

measures, such as those relating to the health and safety of employees, environmental effects

and equity concerns. A top-down approach implements the necessary processes at all levels of

the organisation, determines CSR roles, and allows more systematic communication and

information flow. Thus, this approach offers a structured framework for transforming strategic

CSR priorities into business processes and incorporating various stakeholder requirements. On

44

the other hand, a bottom-up approach focuses on discussion with stakeholders in the

community and the development of indicators linked to community needs, such as the

corporation’s role in providing an appropriate quality of life. More specifically, the bottom-up

approach offers a systematic framework where companies can engage with the community to

better understand how the community affects business activities, how the company can

contribute to improving their living conditions, and what types of measures can be used to

assess quality of life improvements. Although this study offered a conceptual framework with

no evidence as to whether the framework is applicable, it suggested that CSR integration can

be conducted with many activities including vertical and horizontal integration.

In addition, Lindgreen et al. (2011) analysed the change process undertaken by Dutch

organisations, with a specific emphasis on the role of high potentials, or those individuals who

are chosen to fast track into senior management. They suggested nine stages of how to

implement CSR to achieve integration. They stated, however, that an organisation may not pass

through every stage, and the stages may progress sequentially or simultaneously, and be

repeated. This study provided an essential insight that the integration of CSR requires cultural

change driven by top management and other agents of change who push CSR principles

throughout the organisation.

Similar to prior studies, Gazzola and Colombo (2014) emphasised the role of stakeholders

in integration. They developed a model that shows that socially responsible actions should be

designed into a corporate strategy because they could contribute significantly to the generation

of wealth through intangible assets, which are the foundation of the competitive advantage of

modern enterprises.

Vitolla, Rubino and Garzoni (2016) enhanced the findings of prior studies by identifying

the driving factors and means of CSR integration. By combining two distinct methods, semi-

structured interviews and content analysis of documents and websites, they identified that CSR

45

integration depends on three factors: the macro-environment, the competitive context and the

management philosophy. Management philosophy is the internal variable that depends on the

form of strategic or operational integration. There is complete integration of CSR into the

strategic management of the business in the presence of a socially-focused management

philosophy. This applies to governance, organisational structure, strategic management

processes, stakeholder relationships and systems of human resources management. In

comparison, partial integration requires an economically-driven management aspect. As a

result, external variables influence the expectation of CSR and its integration into the

company's management, but they are not essential for strategic CSR implementation. External

environment conditions clearly support the use of formalised and standardised methods and

processes for CSR management.

Moreover, Tonysheva and Chumlyakova (2016) recommended the integration of social

responsibility into the strategic management of organisations across eight steps, which include

management tools to identify strategic priorities for CSR development. The developed model

presented the main indicators connecting strategic objectives, quantitative indicators of their

accomplishments and tactical social responsibility actions.

A more comprehensive framework was proposed by Marques-Mendes and Santos (2016),

consisting of three models: (1) ideological models, (2) procedural models, and (3)

consequentialist models. Based on the literature review regarding the integration of society and

business, they explained each stage by providing some indicators and related concepts. Their

study expanded theoretical frameworks from previous studies by analysing how integration can

comprehensively occur, starting with its strategic integration (ideological models), its

implementation (procedural models), and impact of the integration (consequentialist models).

Although those proposed models developed the framework of how CSR is integrated into

business strategy, there is a lack of empirical studies to prove their claims. One of these

46

empirical studies was conducted by Ganescu (2012b) in the European automobile industry to

highlight the impact of CSR strategies on sustainable businesses. The findings suggested the

selection and implementation of appropriate CSR strategies are important to achieve added

value through the creation and strengthening of a sustainable business. These findings were

extended by Wei et al. (2016), who examined a simultaneous consideration of ‘non-market’

and ‘market’ factors. By adopting resource-based view theory, they conducted an empirical

analysis of the relationships between resources, strategies, and performance, focusing

specifically on Chinese enterprises. Results revealed that a non-market strategy correlates

positively with a market strategy, whereas a non-market strategy has a significant indirect

impact on market performance through non-market performance.

Furthermore, Ooi, Amran and Yeap (2017) defined and conceptualised strategic CSR based

on an extensive review of the literature. They proposed strategic CSR as a formative construct.

It was then demonstrated, based on research among Malaysian companies, that the combination

of three indicators, (1) CSR values embedded in corporate vision and missions, (2) presence of

the CSR committee and (3) collaboration with NGOs, explains the meaning of strategic CSR

from an integrated perspective. The findings of this study indicated how to determine and

measure strategic CSR. Moreover, in this research, two former measurement items were

included in the proposed model as two critical components for strategic-level CSR integration:

embedding CSR into the vision and mission of the organisation (as one indicator of aligning

CSR with the company’s strategy) and establishing CSR committee (as one indicator of

obtaining support from top management).

Cazeri et al. (2018) evaluated the integration between CSR practices and management

systems in companies in Brazil and identified the best and least integrated practices. Using the

integration model proposed by Asif et al. (2013), the results revealed that all CSR practices

evaluated by the respondents had means greater than three and less than five. This result

47

indicated that CSR practices are applied superficially by companies in Brazil and ample

opportunities exist for improvement. In a comparative analysis, two practices stood out in

relation to the others: reporting of CSR results to stakeholders and evaluation of the

performance of CSR activities using pre-established indicators. Practices associated with

planning of CSR activities are the most superficially implemented, adversely affecting CSR

performance.

Table 2.2 presents an overview of the major themes of research on CSR-business strategy

integration. It is clear that most studies were conceptual research, and only a few were

quantitative studies.

As previously stated, a number of studies examined CSR integration, including both

quantitative and qualitative investigations. They built conceptual frameworks for CSR

integration that could consist of numerous stages with distinctive approaches and theories. The

important findings from prior studies helped to shape the theoretical framework for this thesis.

48

Table 2.2: Prior Studies of CSR Integration

Stages of CSR Integration Orientation Findings and Limitations

Maon, Lindgreen and Swaen (2009)

1. Raising CSR awareness inside the organisation

2. Assessing corporate purpose in a societal context

3. Establishing a working definition and vision for CSR

4. Assessing current CSR status

5. Developing an integrated CSR strategic plan

6. Implementing the CSR integrated strategic plan

7. Maintaining internal and external communication

8. Evaluating CSR-related strategies and communication

9. Institutionalizing CSR policy

Empirical qualitative

using multiple case

studies

Using stakeholder theory, the authors involved top-

down and bottom-up process and explain a defined

action for each step by presenting a good example

(case study). However, the impacts of the integration

on company performance are not defined clearly.

Valente (2009)

1. Align: the companies unite market and non-market strategies yet employ separate

organisational mechanisms for implementation.

2. Absorb: the formulation of the NMS is disconnected from the market strategy, yet

companies draws on similar organisational mechanisms to implement both of them.

3. Alter: companies change existing market strategies to coincide with NMSs and use similar

organisational structures and processes for implementation

4. Avoid: the formulation of the NMS is disconnected from the market strategy and

companies employs very different organisational mechanisms in their implementation.


using case studies of 30

companies

A typology is based on the degree to which market and

non-market strategies intersect in the strategy

formulation and strategy implementation processes;

although this study presented some case studies that

describe the four approaches in more detail, but it is

difficult to understand the indicators for each approach

and how to measure it.

Škerlavaj, Donate-Manzanares and Guadamillas-Gómez (2010)

1. Introduction: the transmission of ethical principles and their integration into the culture

which is shared by the organisational members.

2. Implementation: knowledge management systems and organisational culture are essential

aspects in this stage to adapt the organisational structure to the new situation.

3. Generalisation: it implies a global change for the company, since CSR will be

incorporated into firm’s culture, mission and values. This third stage is completed with

reports in order to measure advances in CSR and benefits for the stakeholders.


using case study of one

company

Using stakeholder theory, the authors showed how the

company can integrate ethical values and CSR

initiatives into its corporate and business strategies.

However, the relationships between CSR actions and

company performance are difficult to found and to get

the generalization regarding results obtained.

49

Table 2.2 (continued)

Lindgreen et al. (2011)

1. Conduct zero-assessment

2. Develop CSR goals in organisation’s mission, vision, and strategy

3. Gain top management support

4. Gain employee support to ensure they own CSR as part of their work life activities

5. Gain support from external stakeholders

6. Prioritise change effort and focus on achieving it



companies

The authors identified the integration stages with

focusing on the involvement of leaders (high

potentials) and their impacts on the CSR

implementation.

Dey and Sircar (2012)

1. Incorporating corporate citizenship into the company’s values and aspirations.

2. Stakeholder analysis integrating CSR initiatives with the company’s core competencies.

3. Forming strategic alliances with non-governmental organisations (NGOs).

4. Integrating CSR into the company’s supply chain.


using case studies of

four companies

The authors suggested some ways of integrating CSR

initiatives with a company’s business strategy based

on its core competencies that benefit the company as

well as the society.

Ganescu (2012)

1. Rejection

2. Ignorance

3. Compliance

4. Efficiency

5. Proactive strategies

6. Corporate sustainability

Empirical quantitative


companies

The author showed that corporate sustainability

strategies can be significantly influenced by social

responsibility strategies.

Martinuzzi and Krumay (2013)

1. Project-oriented CSR: Many companies initiate a social or environmental project, and

thus aim at ‘doing good’.

2. Quality-oriented CSR: Most companies applied a quality-oriented approach to protect

their image, brand, and license to operate.

3. Strategic CSR: Companies include environment and society in strategic decisions and to

open up an innovation potential.

4. Transformational CSR: Organisations foster the abilities, which form the basis of these

advantages: the ability of an organisation to develop its capabilities for reacting in a

flexible way on social, ecological, and economical requirements and to continue with

progress.

Conceptual The authors created different stages of integrating

CSR into existing business operation that led to

competitive advantage. The authors described the

different organisational changes along this path, from

being slightly unchanged (project oriented CSR) to a

stage where recurring changes and learning are the

basis for success (transformational CSR). However,

there is no explanation whether each stage gives an

impact on the company performance in a different way

and how its relationship may occur.

50


Asif et al. (2013)

Two approaches of how to integrate CSR into business processes have been proposed, namely

the top-down and bottom up approach.

Conceptual CSR integration can be conducted through vertical and

horizontal integration.

Gazzola and Colombo (2014)

1. Informal and defensive CSR: the size of the enterprise and the nature of its operations

influence the complexity of the integration of the CSR in the strategy.

2. Charitable CSR: various social and environmental causes through donations and

sponsorships, for community groups or civil society organisations.

3. Systemic CSR: CSR is focused on the micro level, supporting social or environmental

issues that happen to align with its strategy, but without changing that strategy.

4. Innovative CSR: changing CSR strategy to optimize the outcomes for this larger human

and ecological system.

5. Dominant CSR: involving cycles of CSR policy development, goal and target setting,

programmed implementation, auditing and reporting.

Conceptual Using Greiner’s model the authors explained the CSR

development in each step based on the theoretical

framework by focusing on the development of CSR

determined by company culture; it is a conceptual

study, and there is only a short explanation of each

stage with a little of an applicable example.

Izzo (2014)

1. Decisions: determining the firm’s main CSR area of interest through the analysis of the

company value proposition, its environment, and stakeholders’ demand.

2. Design: defining the firm CSR priorities, shifting the focus toward planning,

implementation, communication, and control activities to integrate the social strategy into

the business activity.

3. Action: the implementation of CSR activities will produce a series of benefits and costs.

4. Results: if the benefits directly and indirectly created by CSR exceed the costs, the firm

will benefit from a value creation point of view.

Conceptual Defining a potential value creation path that a

responsible firm can take, assuming that it integrates a

social agenda into its competitive strategy and

considering that the market appreciates real and

effective social efforts of companies (conceptual

framework).

Tonysheva and Chumlyakova (2016)

1. Analysis of current state and dynamics of socially responsible business.

2. Defining social responsibility of business development level.

3. Establish the importance and social responsibility of the company by focus groups

priorities of the strategic partnership for the territory of the company's presence and its

charitable activities.

4. Determining the degree of involvement of stakeholders in the implementation of strategic

objectives.

Conceptual Although they suggested the specific activities in each

steps, there is no empirical evidence whether they are

applicable

51


5. Establish the degree of integration of social responsibility of business in strategic

management and the determination of its trajectory.

6. Adjusting the strategic goals of social responsibility, taking into account the interests of

stakeholders.

7. Develop key performance indicators in relation to CSR profile of strategic business value

to stakeholders.

8. Justification of strategic actions in the field of CSR.

Marques-Mendes and Santos (2016)

1. Ideological models: the analysis of the set of values, ideological profiles and cultural traits

underlying corporate decision-making which have a determinant role in the relationship

between companies and societies.

2. Procedural models: the types of processes, structures, and practices implemented by

companies, either regarding the responses to external stakeholders’ pressures, the

structure of the management practices in place or the exercise of corporate citizenship.

3. Consequentialist models: the identification of the types of benefits and impacts of actions

taken by organisations, aimed at creating or appropriating socially added value.

Conceptual The authors developed a conceptual framework

through summarising an extensive literature review,

characterizing the levels of CSR maturity, and

identifying the driving forces of CSR integration in a

firm.

Filippo, Michele and Antonello (2017)

1. The formulation of the intended strategy, which defines the modes of change indicated

by the top management.

2. The execution which identifies the managerial implementation of change based on the

intended strategy.

3. The bottom-up innovations, which determine a change of direction and/or the modes of

change compared to the strategic planned approach of the top management.


using case studies of six

companies

By connecting management and CSR in a dynamic

perspective strategically, three circles of change can

describe the integration of CSR into strategic

management. The authors developed some research

propositions that need to be investigated further.

Ooi, Amran and Yeap (2017)

1. CSR values embedded in corporate vision and missions.

2. Presence of the CSR committee.

3. Collaboration with NGOs, explains the meaning of strategic CSR from an integrated

perspective.

Quantitative research

with a sample of 100

Malaysian companies

A strategic CSR is a formative construct, involving

these three indicators.

Cazeri et al. (2018)

All the CSR practices evaluated are implemented superficially by companies in Brazil, and

there are ample opportunities for improvement.

Quantitative research

involving 48 experts

Using the integration model developed by (Asif et al.

2013).

52

2.4.2 Dimensions of the Integration of CSR and Business Strategy

Following on from the preceding subsection, this subsection describes the dimensions of

CSR integration. It is separated into two subsections, each of them presents (i) strategic CSR

integration and (ii) functional CSR integration.

CSR practices are increasingly being implemented and legitimised in business and impact

the strategic and operational levels in various areas. The integration of these criteria and

practices into strategic management includes several aspects, and human resource management

is a crucial aspect for the accomplishment of such initiatives (Talita & Maria Laura Ferranty

2016). To have a significant effect, CSR should be effectively integrated into every level of a

company and in different directions, regarded as an imperative of organisation (Asif et al. 2013;

Ooi, Amran & Yeap 2017; Zatwarnicka-Madura et al. 2019). Thus, regarding the dimensions

of integration, two have several essential activities to be considered in the integration of CSR

into business strategy.

2.4.2.1 Strategic Integration of CSR

The strategic integration of CSR is discussed in this subsection. By presenting several prior

studies in this topic, this subsection shows what aspects can be covered when CSR and business

strategy are integrated at the strategic level.

A strategic process describes the managerial activity inherent in shaping expectations and

goals and facilitating the work of an organisation in achieving these goals (Simons 1990). CSR

reaches a strategic level in organisations as it contributes to the achievement of its strategic

objectives (Talita & Maria Laura Ferranty 2016), which describes how far the company’s

business strategy embraces CSR at its heart (Sousa Filho et al. 2010).

A growing number of companies develop their CSR in response to a variety of social,

environmental and economic pressures (Gazzola & Colombo 2014). At the moment, the scope

of CSR not only gives priority to the employee, the customer and the local community, but also

53

respects human rights and conducts business with integrity. Since alignment between CSR

programs and core business is essential to value creation by CSR and vital to the success of

companies (Carroll & Shabana 2010; García-Madariaga & Rodríguez-Rivera 2017), CSR

should be managed strategically in the context of what a company is trying to obtain (Jeremy

2009) and is started at a company’s business strategy level (Busaya, Kalayanee & Gary 2009).

Moreover, CSR represents a company's 'philosophy' that affects all of its major business

activities and is integral to corporate strategy (Zatwarnicka-Madura et al. 2019).

The strategic level of CSR includes the adaptation of strategic planning, which can be

described as a technique used to direct the activities of the organisation with a focus on its long-

term objectives, vision and mission and deployment (Chiarini 2015). Companies should match

their CSR priorities and decision-making with their overall objectives and strategies and need

to set new CSR criteria and principles (Maon, Lindgreen & Swaen 2009). Specifically, strategic

CSR integration signifies the existing level of interconnectedness between CSR and business

strategy. The existence of a CSR strategy that matches business strategy indicates its capability

to achieve expected outcomes for the company as well as for society (Marques-Mendes &

Santos 2016). CSR activities need to be aligned with the overall business objectives and

strategies, to create and capture the value intended (McWilliams & Siegel 2011). Usually

companies state their strategy in their mission statement, which should be derived and aligned

with the company vision (Wibisono 2011). The mission statement is a statement of justification

for an organisation (David & David 2014).

Some authors propose that the first important step in integrating CSR into a business strategy

is to include the idea of social responsibility in the company’s vision and mission

(Bhattacharyya 2010; Dey & Sircar 2012; Ganescu 2012b; Guadamillas-Gómez, Donate-

Manzanares & Škerlavaj 2010; Maon, Lindgreen & Swaen 2009). Companies should develop

CSR objectives within their mission and vision (Lindgreen et al. 2011). Organisations with

54

missions and values embedded with CSR are more likely to create a stakeholder-oriented

environment that will lead to sustainable competitive advantage (Ooi, Amran & Yeap 2017).

Accordingly, CSR activities should become an integral part of the vision and mission of

companies (Shital 2014) that reflects the actual importance of CSR to the company’s mission

(Burke & Logsdon 1996).

Furthermore, aligning the organisation’s strategic thrust with the principles of CSR needs

the support and encouragement of senior managers (Isabelle, Ferrell & Linda 2005; Lindgreen

et al. 2011; Mahmoud, Blankson & Hinson 2017). Top management are key stakeholders who

have a critical influence on CSR initiatives. Top management support ensures the success of

CSR programs (Mahmoud, Blankson & Hinson 2017). With more support from top

management, more focus would be paid to CSR programs compared to lower managerial

support (Aguinis & Glavas 2012). Despite this support, CSR programs can be difficult to devise

and enforce (Mahmoud, Blankson & Hinson 2017), which can act as critical barriers to CSR

implementation (Werre 2003).

To support CSR programs, top management can create a CSR committee, which ensures

CSR activities are part of the company’s strategic direction and can be translated into

substantial actions based on available experience, skills, and knowledge (Ooi, Amran & Yeap

2017). The CSR committee also serves as an instrument of governance to monitor CSR

performance. CSR committee members should promote the planning, implementation, and

reporting of CSR activities to ensure the organisation's mission and vision remain relevant

priorities. For strategic CSR, therefore, organisational structure that includes a CSR committee

is important (Ooi, Amran & Yeap 2017).

Another essential aspect of strategic integration is an effective communication plan to

promote CSR activities and generate a clear perception that CSR is an aspect of strategic

importance to the company (Dobele et al. 2014; Guadamillas-Gómez, Donate-Manzanares &

55

Škerlavaj 2010; Lindgreen et al. 2011; Putra 2015). There should be transparent

communication, both internally and externally, which contributes positively to the fulfilment

of the company’s objectives and is an essential part in the integration process of corporate

sustainability (Engert, Rauter & Baumgartner 2016). Effective communication of corporate

responsibility relies on a clear strategy for assessing both opportunities and risks for the

company, making it suitable for delivering messages to various stakeholder groups (Dawkins

2005).

Because CSR relates to the concerns of stakeholders, it is crucial that companies keep

internal and external stakeholders informed of the initiatives undertaken to address CSR issues

(Maignan & Ralston 2002; Maon, Lindgreen & Swaen 2009). Communication with

stakeholders is a major way to include them in a business's operations through collaboration,

cooperation and dialogue, (Maráková 2019). Employees, as key primary stakeholders, are

crucial to potential communication of companies’ CSR practices, as they have a wide reach to

other stakeholder groups and are considered as especially credible sources of information

(Dawkins 2005). For employees to understand CSR strategy and implementation, objectives

and measures must be communicated well, clearly and transparently (Engert, Rauter &

Baumgartner 2016; Maon, Lindgreen & Swaen 2009). Moreover, continuous internal contact

increases awareness of CSR, such as through newsletters, annual reports, meetings and training

(Maon, Lindgreen & Swaen 2009). Other internal communication channels (e.g., intranet,

email, seminars, presentations, folders) are also essential for the implementation of corporate

sustainability strategies (Engert, Rauter & Baumgartner 2016; Maráková 2019).

In addition to internal communications, companies should communicate externally with

their stakeholders about what they have learned, outcomes of their CSR activities, and what

they still expect to achieve (Asif et al. 2013; Maon, Lindgreen & Swaen 2009). Along with

increasing media coverage of CSR issues, companies themselves are also taking direct and

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visible steps to communicate their CSR initiatives to various stakeholders, including customers,

who are among the most significant stakeholders and crucial for companies’ long-term survival

(Clarkson 1995; Luo & Bhattacharya 2006). Good and transparent communication can occur

through official documents, such as annual reports, corporate brochures, company websites and

online postings (Asif et al. 2013; Galbreath 2006; Huang 2010; Maignan & Ralston 2002;

Maon, Lindgreen & Swaen 2009; Maráková 2019; Widjaja 2011).

Previous studies examined the strategic integration of CSR and suggested what should be

included in this integration. Because the integration is carried out at the strategic level, they

discovered that aligning CSR with the company vision and mission is crucial. CSR should be

communicated to internal and external stakeholders, in addition to being incorporated into the

company's vision and mission. Furthermore, top management support is also strongly required

for strategic CSR integration. As previously explained, strategic CSR integration include a

variety of stakeholders, such as top managers, employees, and customers. What explained in

this subsection can be a guidance to develop a theoretical framework of strategic CSR

integration.

2.4.2.2 Functional Integration of CSR

Several prior studies that relate to functional CSR integration are presented in this

subsection. Because alignment between CSR initiatives and core business is a key element for

creating value through CSR (Luo & Bhattacharya 2006), companies must pursue CSR activities

relating to their core business (García-Madariaga & Rodríguez-Rivera 2017). By integrating

and strategically managing CSR into the overall operations of the companies, several

companies have expanded their use of CSR operational management approaches and strategies

(Vitolla, Rubino & Garzoni 2016).

CSR is an organisational activity that relies on several connections within its management

and operation (Valdez-Juárez, Gallardo-Vázquez & Ramos-Escobar 2018). Most CSR

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practices in companies are handled by managers who are in positions below the executive

committee level (Rangan, Chase & Karim 2012), as they know their products and markets best

(Bach & Allen 2010). Practicing CSR activities can be complex and risky from a managerial

perspective because they have to consider how to respond to a variety of internal and external

stakeholder pressures and how to evaluate whether CSR practices actually fit within the

existing business operations (Yuan, Bao & Verbeke 2011). The development of CSR practices

depends on how they are integrated into the current business practices (Marín, Rubio & de

Maya 2012; Marques-Mendes & Santos 2016) so that they not only achieve significant social

benefits but also bring significant business-related benefits to the organisation (Bhattacharyya

2010).

Porter and Kramer (2006) argue that the mutual dependence between companies and society

must follow the principle of shared values with choices benefiting both sides. From a shared

value viewpoint, companies must integrate a social perspective into the core framework that

they use to understand competition and develop business strategies. The idea of shared value

can be seen from two perspectives. On society’s side, shared value relates to better quality of

the natural environment, nutrition, access to water and housing, health, education, and income.

On the organisation’s side, it relates to the profits it delivers (including increased sales, savings,

productivity), access to resources (including raw materials, employees), and improved

competitive position (Dembek, Singh & Bhakoo 2016). CSR can be considered a key factor

for success and a chance to generate shared value by combining social and environmental issues

and redefining companies’ strategies according to four questions: what, where, how, and for

whom are companies’ products (Martinuzzi & Krumay 2013).

With respect to business operations, CSR can be implemented through ‘built-in’ and ‘bolt-

on’ approaches. The former is strategic: incorporating socially responsible behaviours into

companies’ operations, processes, and decision-making. The latter relates more to potential:

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embracing social activities that extend beyond current business operations (Insight 2016; Porter

& Kramer 2006). The former should involve mainstream functions (e.g., production, logistics,

and quality control) (Busaya, Kalayanee & Gary 2009). The production/operations function of

a business involves all activities that transform inputs into goods and services. It represents the

largest part of an organisation’s human and capital assets that consist of five functions: process,

capacity, inventory, workforce, and quality (David & David 2014). Concerning manufacturing

companies, prior studies note four competitive priorities: low cost, quality, delivery

performance (speed and reliability), and manufacturing flexibility (Chi 2015; Ward et al. 1995;

Weir et al. 2000).

Bhattacharyya (2010) suggests that CSR implementation improves the quality of products

and their production. Therefore, quality must be considered by integrating corporate

sustainability into strategic management (Engert, Rauter & Baumgartner 2016; Martinuzzi &

Krumay 2013). Another important dimension for manufacturing companies, is innovation

(Theodorou & Florou 2008), which is one of the main drivers in the strategic orientation of a

company (Paraschiv et al. 2012) and necessary for corporate sustainability as part of strategic

management (Baumgartner 2014).

Some authors highlight the necessity of incorporating CSR actions into the core activity of

the value chain (Rangan, Chase & Karim 2012; Vitolla, Rubino & Garzoni 2017; Witek-

Hajduk & Zaborek 2016). Companies should consider all social issues linked to the company’s

core activities, both the primary and supporting activities that are part of the value chain, so

that they can evaluate which need to be enhanced to broaden the social agreement (Vitolla,

Rubino & Garzoni 2017). The value chain dimension consists of its suppliers, customers and

specific tools (Witek-Hajduk & Zaborek 2016). The activities in the value chain improve

operational effectiveness (Rangan, Chase & Karim 2012) and enhance the social,

environmental, and economic capabilities of supply chain members (Crane et al. 2014). Hence,

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the match between the CSR strategy and companies’ value chains is crucial for the survival

and success of companies, particularly when they understand the contingent existence of CSR

in a competitive context (Hasan et al. 2018).

Supply chain management encompasses the entire network of organising people, activities,

information, and resources engaged in moving a product/service from the supplier to end users.

The maintenance of an ethical supply chain would be beneficial to a company, as the reputation

of a company’s supply chain members has the potential to affect its own reputation. Monitoring

suppliers and influencing them to meet the required environmental and societal standards

specified in a company’s code of conduct might incorporate CSR strategy (Dey & Sircar 2012).

Thus, CSR will ensure that responsible and sustainable business practices are being

implemented across the supply chain (Sprinkle & Maines 2010). Socially responsible

companies broaden their activities of CSR to include managing their partners in the supply

chain (Quarshie, Salmi & Leuschner 2016), such as suppliers and customers. A key aspect of

many CSR programs is assuming responsibility for suppliers, as companies face many

obstacles in terms of their supply chain (Öberseder, Schlegelmilch & Murphy 2013).

In addition, Guadamillas-Gómez, Donate-Manzanares and Škerlavaj (2010) argue that

human resource management is another important component of the integration process. It

facilitates CSR implementation by providing employees with the willingness, training, and

motivation necessary to apply CSR actions and initiatives, which in turn allows companies to

attain their strategic goals and objectives (Waring & Lewer 2004). Engaging employees in CSR

implementation requires focusing on their awareness and ensuring they understand the context

and background of the organisation’s CSR approach, including its motivation, the reasons for

adopting a specific approach, its relevance to the organisation, how it fits with existing

organisational objectives, any changes made to the current approaches, and other implications

(Maon, Lindgreen & Swaen 2009).

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Specifically, employees are key stakeholders, who are not only the beneficiaries of CSR

policies but also the primary instruments for enforcing the policies of a company (Dey & Sircar

2012). Employee involvement in CSR is crucial to effectively demonstrating to the public that

a company is committed to treating them equitably in the greater interest of the community. To

accomplish this, it is important that all employees are treated with respect and that there is a

comfortable work atmosphere free from all types of violence, whether physical, verbal or

psychological. Full support and active employee engagement is essential for the effective

implementation of a company's CSR agenda (Dey & Sircar 2012). Thus, CSR that focuses on

handling current business operations in a more responsible manner can be carried out through

paying fair salaries, safeguarding health and safety of employee (Halme & Laurila 2009). The

balance among social, environmental and economic values can only be achieved by a company

through its workforce involvement; hence, human resource departments are often part of CSR

strategy (Talita & Maria Laura Ferranty 2016).

As the functional CSR integration is conducted at the functional level, the crucial elements

of this integration relate to business operations. In terms of the manufacturing industry, they

connect to production (cost, innovation, and quality), suppy chain (suppliers and customers),

and employees. Like strategic CSR integration, most of key elements of CSR integration at the

functional level involve several stakeholders, namely suppliers and customers as external

stakeholders as well as employees as an internal stakeholder.

Because the functional CSR integration is conducted at the functional level, the most

important aspects of this integration are business operations. In terms of the manufacturing

industry, they are linked to production, the supply chain, and employees. CSR integration at

the functional level, like strategic CSR integration, comprise several stakeholders, including

external stakeholders such as suppliers and consumers, as well as internal stakeholders such as

employees.

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2.5 Company Performance

Previous studies of company performance, particularly in relation to CSR, are presented in

this section. It starts with an explanation of financial performance. It then moves on to non-

financial performance, which includes employee, customer, and operational performances.

An increasing body of scholarly literature has grown around the relationship between CSR

and company performance (Beck, Frost & Jones 2018; Boesso, Favotto & Michelon 2015;

Chen, Feldmann & Tang 2015; Ridho 2018; Saeidi et al. 2015; Zhu, Liu & Lai 2016). Recent

research over the past two decades has shown different results about the relationship between

CSR and company performance, both for financial and non-financial performance, and has

suggested a positive or a negative relationship, or no relationship at all (Moczadlo 2015). A

research by Mellahi et al. (2015) identified that most empirical journal papers (102 out of 163)

reported a positive correlation between NMS (e.g., CSR) and company performance. However,

43 empirical studies (26%) showed mixed relationships, 12 studies (7%) showed negligible

correlations and 6 studies (4%) reported negative relationships between NMS and company

performance. Hence, due to a lack of agreement on measures for respective performance,

differences in determining responsibility, and measurement errors, findings remain contentious

(Linnenluecke & Griffiths 2010; Orlitzky, Schmidt & Rynes 2003).

2.5.1 Financial Performance

Previous research regarding the impact of CSR on financial performance are presented in

this subsection. Financial benefits are the primary driving force for profit-oriented

organisations (Zbuchea & Pînzaru 2017). Companies are concerned about the financial impact

of CSR (Bhardwaj et al. 2018) and it is difficult for managers to decide to engage in responsible

activities unless they see the possibility of enhancing financial performance (Chtourou & Triki

2017). A key and divisive debate in literature has long been about the relationship between

CSR and CFP. Although some studies argue that CSR has positive effects on CFP (Carroll &

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Shabana 2010; Orlitzky, Schmidt & Rynes 2003), prior empirical research draws inconsistent

conclusions (Bhardwaj et al. 2018; Wang, Dou & Jia 2016). There are contradictory results in

the extant literature about the effect of CSR on the CFP.

For example, Kapoor and Sandhu (2010) conducted a content analysis of annual reports

among 93 Indian companies to examine the impact of CSR on CFP. The results indicated a

significant positive impact of CSR on company profitability measured in terms of return on

sales (ROS), return on assets (ROA), and return on equity (ROE). Using different measures,

Ameer and Othman (2012) affirmed that companies that engage with CSR show better financial

performance compared to other companies that have no commitment to CSR as measured by

higher sales growth, earnings before tax, ROA, and cash flows from operations.

Moreover, Tang, Hull and Rothenberg (2012) contended that companies can achieve better

profit from CSR practices if they engage in it consistently. Based on a sample of US companies

over a nine-year period, Boesso, Favotto and Michelon (2015) comprehensively analysed the

relationship between seven areas of CSR (environment, community, corporate governance,

diversity, employee relations, human rights, and product quality) and CFP in terms of short-

term accounting-based measures, long-term accounting-based measures, and market-based

measures. The results revealed that companies that prioritise CSR activities have superior

financial performance.

Similarly, with a sample of 116 large public companies, Beck, Frost and Jones (2018) found

a positive correlation between CSR and CFP after controlling for CSR results, industry-level

effects, country-level effects, company size, financial risk, type of insurer and investment

returns. In addition, using data from 113 U.S. software industry publicly-listed companies

between 2000 and 2005, Kim, Kim and Qian (2018) found that socially responsible activities

(positive CSR) boost CFP when the company's level of competitive action is high, while

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socially irresponsible activities (negative CSR) actually improve company financial

performance when the level of competitive action is low.

In the Indonesian context, Sayekti (2015) conducted a content analysis on the annual reports

of 136 Indonesian-listed companies between 2005 and 2008. This study showed that strategic

CSR positively affects financial performance measured by ROA. Similarly, through a content

analysis on CSR reports and information on company websites of top 200 Indonesian-listed

companies between 2014 and 2015, Ridho (2018) found that CSR implementation has a

positive and significant influence on the financial performance of companies, measured by

ROE and ROA. On the other hand, using the annual reports of publicly-listed companies in the

Indonesian Stock Exchange (IDX) in 2008–2010, Razafindrambinina and Sabran (2014)

investigated that CSR has no significant effect on CFP, measured by ROA.

As mentioned above, many studies have examined the impact of CSR on financial

performance. Even though their findings are contradictory, they provide input to this thesis on

which indicator should be considered when measuring financial performance.

2.5.2 Non-Financial Performance

This subsection continues the explanation of company performance by discussing non-

financial performance. It is made up of three aspects: employee performance, customer

performance, and operational performance.

CSR includes assessing the economic, social and environmental impact of the company,

taking measures to enhance it in accordance with stakeholder needs and reporting on relevant

measurements (Katsoulakos & Katsoulacos 2007). A study by Aguinis and Glavas (2012)

reveals that CSR creates positive non-financial impacts at the institutional, organisational, and

individual levels. In addition, CSR can benefit stakeholders in various forms (Bhattacharya,

Korschun & Sen 2009), such as adding strategic advantage to a company and creating

synergistic value for stakeholders (Chang & Yeh 2016). As a result, along with financial

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performance, several studies highlight a significant impact of CSR on non-financial

performance.

2.5.2.1 Employee Performance

This subsection outlines previous studies that discover how CSR can improve employee

performance. Employees are key stakeholders who directly contribute to company

performance, so that understanding the effects of CSR on employees can answer questions

about whether and how CSR impacts companies (Bauman & Skitka 2012). The current

empirical literature provides a small but growing body of evidence demonstrating that CSR

does indeed affect the perceptions of employees and prospective employees regarding

companies and behaviour in the workplace (Aguinis & Glavas 2012; Bauman & Skitka 2012).

For instance, Dawkins (2005) contended that CSR programs have the ability to increase

employee motivation and boost employer perception of their companies. Branco and Rodrigues

(2006) pointed out that CSR activities, such as fair salaries, a clean and safe working

atmosphere, training opportunities, health and education benefits for employees and their

families, childcare services, flexible working hours and job sharing, can bring direct benefits

to a company by increasing morale and productivity while minimising absenteeism and

employee turnover and saving on recruiting and training expenses for new employees. Sprinkle

and Maines (2010) claimed that CSR practices can provide companies with many benefits,

such as enhanced employee motivation and turnover reduction. Bauman and Skitka (2012)

identified four distinct paths through which CSR may affect employees’ relationship with their

companies that correspond to four universal psychological needs: security, self-esteem,

belongingness, and a meaningful existence. They indicated that by providing individuals with

opportunities to share their ideals, contribute to the community and society at large, and

potentially create or at least be part of a legacy, CSR can have a similar impact. Employees can

achieve greater life-satisfaction and enhanced emotional well-being by fulfilling their need for

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a meaningful existence. Dey and Sircar (2012) also highlighted that employees who are

satisfied with their company's commitment to CSR tend to be more positive, more loyal, and

more productive than those who work for less committed employers.

In conclusion, prior studies indicated that CSR could improve employee performance. They

claimed that if the companies engage in CSR, their employees will perform better. As a result,

employee performance can be increased, including employee motivation and satisfaction.

2.5.2.2 Customer Performance

This subsection lists prior studies that address the effect of CSR on customer performance.

For example, using a survey of Spanish hotel consumers, Martínez and Rodríguez del Bosque

(2013) found that customers are more likely to trust responsible companies that operate

honestly and reflect the interests of all parties when making decisions. In addition, customers

are more willing to engage with companies that carry out socially responsible initiatives. Their

results indicate that customers are likely to support and reward the companies that spend most

in socially responsible programs by showing the greatest loyalty to them. Exploring the

automobile industry as a single industry spanning eight years, García-Madariaga and

Rodríguez-Rivera (2017) investigated the relationship between CSR and customer

performance, particularly customer satisfaction measured through the American Customer

Satisfaction Index (ACSI) which ranges from 0 to 100. Their findings show that CSR has a

positive impact on customer satisfaction. Moreover, Park, Kim and Kwon (2017) identified the

fit between consumer values and the objectives of CSR activities and corporate ethical

standards as two main determinants of CSR quality and commitment. Working with collected

data from 931 retail consumers in South Korea, the results reveal that higher ethical standards

direct consumers to perceive that the company is committed to its CSR activities. A company's

CSR commitment encourages greater satisfaction and trust in the company and its services,

which in turn encourages consumers to remain loyal.

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In summary, several studies found that CSR can boost customer performance, such as

customer satisfaction and loyalty. Their findings support this thesis by recommending which

indicators should be considered when evaluating customer performance.

2.5.2.3 Operational performance

Completing the discussion of non-financial performance, operational performance is

explained in this subsection. Although very little is known about whether or how CSR affects

operational efficiency, prior studies show that CSR has a positive impact on operating

performance. For instance, Sun and Yu (2015) used Kinder, Lydenberg and Domini (KLD)

data to identify a positive relationship between CSR and operational performance, suggesting

that employees in socially responsible companies generate better operational performance than

their peers in less socially responsible companies. Consequently, employees work more

productively in socially responsible companies in terms of sales per employee and net income

per employee. Sánchez and Benito-Hernández (2015) analysed the relationship between CSR

and operational performance measured by labour productivity. The results of a sample of 929

Spanish micro and small manufacturing companies revealed that CSR policies have a positive

relationship with labour productivity. Particularly, CSR actions related to internal aspects of

the company, such as a commitment to quality in internal operational processes, promotion of

innovation and employee care, contribute to a short-term increase in labour productivity.

Table 2.3 summarises the impacts of CSR on company performance. It can be seen that CSR

provides benefits not only on financial performance, but also non-financial performance, such

as employee, customer, and operational performance.

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Table 2.3: CSR Impacts on Company Performance

Company

Performance Impacts of CSR

Financial performance company profitability, cost reduction, better stock market and accounting

performance, minimising financial problem in the long-term, increased ROA,

improved ROS and ROE.

Employee performance strengthening human resources, greater employee loyalty and retention, employee

engagement, high-quality connections, and creative involvement, enhanced

employee satisfaction.

Customer performance increased customer satisfaction, improved consumers response(Sen &

Bhattacharya 2001), customer loyalty and product evaluation.

Operational

performance

enhanced employee productivity, more efficient operation, improved management

operations, product quality, and process efficiencies.

Overall, prior studies have argued that CSR benefits companies both financially and non-

financially. To assess the impacts of CSR comprehensively, then four essential aspects should

be included: financial, employee, customer, and operational performances. The indicators

listed in Table 2.3 can be used to evaluate a company's financial and non-financial

performance.

2.6 Research Gaps

This section discusses several research gaps that can be identified based on the findings

from prior studies. Many CSR studies have focused on organisational responses to external

stakeholder’s demands, but less research has explored how companies attempt to integrate CSR

activities into their business (Yuan, Bao & Verbeke 2011). In the context of a company's

business strategy, shortcomings remain around particular concepts, mechanisms for integration

activities, and specific objectives in the field of social responsibility (Tonysheva &

Chumlyakova 2016).

Notably, most of the proposed models for integration are conceptual, and there is little

empirical evidence to test theoretical frameworks; research has not yet specified how such

integration can be implemented, nor what the likely impacts would be (Yuan, Bao & Verbeke

2011). Other studies are exploratory and descriptive so that it is difficult to generalise results.

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In this present study, the integration of CSR into company strategy will be developed and tested

quantitatively, putting forward a preliminary model through which companies could make this

integration possible.

CSR becomes part of the business and adds long-term value for the company and society

(Rochlin et al. 2005, cited in Ganescu 2012b). Accordingly, business strategy and CSR should

be seen together as maximising both economic and social results (Husted & Allen 2001, cited

in Bhattacharyya 2010). When developing a framework for CSR integration into business

strategy, it should facilitate analysis of the integration’s impacts on company performance.

Despite the fact that current literature suggests that integrating market strategy and NMS is

critical, the empirical evidence available on the impact of the integration on company

performance is very limited (Mellahi et al. 2015). Because the strategic alignment of CSR and

business strategies has rarely been studied together, their causal relationship in promoting

company performance remains theoretically underdeveloped. In other words, the

understanding of how the integration of CSR and business strategy influences that company’s

performance is still lacking. Furthermore, there is a shortcoming of meaningful contributions

that investigate the systematic strategic integration of CSR, referred to as a socially oriented

management philosophy, recognising both economic and social aspects of the company

(Vitolla, Rubino & Garzoni 2017).

To sum up, several research gaps have been discovered in prior studies in relation to the

integration of CSR and business strategy. There is a scarcity of quantitative research in this

field, as well as an assessment of how this integration affects company performance. As, this

th a result, this thesis seeks to fulfil these research gaps by empirically investigating causal-

inferential links between CSR integration and company performance comprehensively.

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2.7 Indonesia as a Context for the Research

NMS is largely dependent on the institutional context (Doh, Lawton & Rajwani 2012).

Although the alignment of a NMS with a competitive market strategy is essential in strategic

management, such a process remains opaque to practitioners and researchers, especially in

emerging countries (Yoo 2015).

This section provides background information about Indonesia as a context for this thesis.

It contains four subsections, each of which covers (i) an overview of Indonesia, (ii) the

Indonesian manufacturing industry, (iii) an overview of CSR implementation in Indonesia, and

(iv) CSR studies in the Indonesian manufacturing industry.

2.7.1 An Overview of Indonesia

An overview of Indonesia is presented in this subsection. As the world’s fourth most

populous nation (Worldbank 2020), with a population of more than 267 million, Indonesia is a

diverse archipelago nation of more than 300 ethnic groups and rich in all types of natural

resources as well as cultural diversity (OECD 2020; Worldbank 2020). Indonesia is a market

economy in which state-owned enterprises (SOEs) and large private business groups

(conglomerates) play a major role. Hundreds of diversified privately-owned company groups

in Indonesia (a small fraction of the total number of companies operating in Indonesia)

dominate the domestic economy along with SOEs (Investment 2020).

The Indonesian economy is the largest in Southeast Asian and one of the world's emerging

market economies (Worldbank 2020). Since overcoming the Asian financial crisis of the late

1990s, in 1997–1998, Indonesia has charted remarkable economic growth (Worldbank 2020).

Over the past two decades, GDP per capita has increased by 70% and it is now the 16th largest

economy in the world by nominal GDP. In fact, Indonesia was the 7th largest GDP in the world

in 2018 as displayed in Figure 2.1 (OECD 2020; Worldbank 2020). As a result, Indonesia has

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a number of features that place it in a great position for newly advanced economic growth

(Investment 2020).

Figure 2.1 The Biggest Economies in the World

Source: International Monetary Fund, cited in https://www.statista.com/chart/19489/biggest-

economies-in-the-world/, viewed 7 April 2020.

The Indonesian government has established a long-term master plan, known as the Master

Plan Acceleration and Expansion of Indonesia Economic Growth 2011–2025 (MP3EI), to

accelerate the implementation of its national strategy to make Indonesia a self-sustaining,

advance, sound and wealthy country (Indonesia 2011). The plan includes guidance, framework,

steps, strategic initiatives, and the fundamental principles of intended economic growth.

Following this master plan, Indonesia aims to gain its position as one of the world's developed

countries by 2025 with projected per capita income of US Dollar (USD) 14,250–15,500 with a

total GDP of USD 4.0–4.5 trillion with the decline in the inflation rate to 3.0% in 2025, as

illustrated in Figure 2.2. The combined rates of growth and inflation reflect developed world

characteristics (Indonesia 2011).

https://www.statista.com/chart/19489/biggest-economies-in-the-world/

https://www.statista.com/chart/19489/biggest-economies-in-the-world/

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Figure 2.2 Indonesia’s Vision 2025

Source: Master Plan Acceleration and Expansion of Indonesia Economic Development 2011–

2025 (Indonesia 2011).

Indonesia is also the only country representing Southeast Asia in the G20 (Salikha 2018)

and has often been listed as an appropriate candidate to enter the BRIC countries (Brazil,

Russia, India and China). Indonesia is a member of emerging economies-grouped under the

acronym CIVETS (Colombia, Indonesia, Vietnam, Egypt, Turkey and South Africa), which

have gained prominence because their members have relatively sophisticated financial

structures and fast-growing populations (Investment 2020).

As mentioned previously, Indonesia is a big country that has many advantages, such as a

good location, abundant natural resources, and a lot of workforces. Being one of the largest

developing countries, Indonesia has many opportunities to accelerate and expand its economy.

Therefore, the government has established a long-term strategic planning to support this.

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2.7.2 The Indonesian Manufacturing Industry

This subsection provides information about the Indonesian manufacturing industry. The

manufacturing or processing industry is an economic activity that carries out the activity of

changing a basic item mechanically, chemically or by hand so that it becomes finished/semi-

finished goods with higher value (Statistik 2020). As part of the strategic concept that relates

the strengths and resources of a business to market opportunities (Skinner 1969), the aim of

manufacturing is to serve the business—to fulfil its survival, profit and growth needs.

Indonesia's manufacturing industry is extremely diverse and represents a vast array of

natural resources available to the region (GlobalBusinessGuide 2011). Previously, economic

development in Indonesia has been focused on agricultural and natural commodities, due to the

abundance of renewable and non-renewable resources, such as oil and other mining resources

(Republic of Indonesia, Master Plan 2011). As one of the long-term development goals is to

fundamentally change the structure of the Indonesian economy, industry outside of agriculture

becomes the backbone of the economy (Bappenas n.d.). Thus, after more than six decades of

Indonesian independence or in 2005, the government announced plans to change the economy

from an agricultural economy to a manufacturing- and service-oriented economy to boost the

country's economic growth (Indonesia 2011).

The revitalisation of the Indonesia manufacturing industry began in 2014. Despite the

economic challenges, the manufacturing industry shows no sign of slowing down and has been

critical in speeding up Indonesia's potential GDP growth and, at least, in sustaining long-term

stability (Gorbiano 2019) for the following reasons. First, manufacturing companies are a

significant contributor to the national economy (Stasiuk-Piekarska 2019). In Indonesia,

manufacturing continues to be the most successful sector in the economy

(GlobalBusinessGuide 2011). As shown in Table 2.4, the manufacturing industry contributes

20.5% of the Indonesia's GDP. The contribution is greater than the manufacturing sector's

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global average GDP (Cekindo 2020). At present, there are only five countries whose

manufacturing industries contribute more than 20% to their GDP: China (28.8%), South Korea

(27%), Japan (21%), Germany (20.6%) and Indonesia (Cekindo 2020).

Second, as presented in Table 2.4, the Indonesian manufacturing sector employs 18.93

million people (14.8%) of the workforce, according to August 2018 data from BPS (Gorbiano

2019). Indonesia has the most attractive labour costs when compared with other Asian

countries (Cekindo 2020). Because of its cost-effective and abundant labour, Indonesia is a

desirable attractive destination for foreign investment in manufacturing (AsiaLinkBusiness

n.d.). In the next 15 years, the manufacturing sector is expected to absorb 30 million workers

(Kemenperin 2018). With its increasingly rising middle class and productive workforce, more

foreign investors benefit from Indonesia's strong manufacturing sector (AsiaLinkBusiness

n.d.).

Table 2.4: Indonesian Manufacturing Snapshot

Number of companies 4.41 million (2017)

Size (number of employees) 0.96% with more than 100 employees (large companies), 99.04% with 5-99

employees (SMEs)

Contribution to GDP 20.5% (2019)

Sector growth 3.8% (2019)

Number employed in the sector 18.93 million (2019)

Export turnover USD 126.57 billion or 75.5% of Indonesian total export (2019)

Main export products Food and beverages, metal products, chemicals, textile and garments, paper

products

Main export markets United States of America (13.64%), China (13.48%), Japan (8.7%),

Singapore (6.94%) and India (5.17%)

Third, the manufacturing industry consistently provides the largest contribution to national

exports. In January to December 2019, exports of processing industry products accounted for

USD 126.57 billion or 75.5% of Indonesia's total exports, which reached USD 167.53 billion

over the past year (2019) (Kemenperin 2020a) (see Table 2.4).

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Fourth, Indonesia has been listed by the United Nations Industrial Development

Organisation (UNIDO) as one of the top 10 manufacturers worldwide (Indonesia 2016)

(Amindoni 2016). Indonesia's manufacturing sector accounts for approximately one quarter of

the country's GDP, according to UNIDO's 2016 International Yearbook of Industrial Statistics

(Indonesia 2016). Also, Indonesia’s manufacturing sector is now larger than the manufacturing

sectors of the United Kingdom, Russian and Mexico (AsiaLinkBusiness n.d.). Indonesia is

therefore poised to become a future global leader in manufacturing with a strong economic

backdrop and is expected to be one of the top 15 manufacturing hubs in the world by 2023

(Cekindo 2020; IndustryToday 2016).

For those reasons, the Indonesian government expects the manufacturing sector to be

Indonesia's next engine of economic growth, and will, therefore, focus on its development over

the next five years (Gorbiano 2019; Kemenperin 2020b). Automotive, textiles and clothing,

food and beverage, chemicals and electronics are the five main sectors chosen as the priority

for addressing industry revolution 4.0, as mentioned in the road map of Making Indonesia 4.0

(Indonesia 2019). Certain sectors are expected to expand rapidly including machinery, leather

goods and footwear as well as the metal and computer industries (Indonesia 2019; Kemenperin

2018). Specifically, automotive, chemicals and electronics industries are expected to contribute

to 25% of the Indonesian economy by 2025 (Salna 2019).

Last, manufacturing companies have more CSR practices, but are also responsible for

harming communities in which they operate (Adeneye & Ahmed 2015). They lead to pollution

of the environment and other social costs. For example, the industrial sector, which includes

mining, manufacturing, and construction, produced 21% of global carbon dioxide emissions in

2014. This is because the use and burning of fossil fuels are important for various steps of the

manufacturing and industrial processes (Cunanan 2018). Specialised manufacturing equipment

that uses a lot of drivers and motors covers 70% of that industry’s power consumption, and the

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other 30% comes from heating and cooling equipment (Renewable Energy 2014). Industry and

production contribute to 22% of greenhouse emissions, referring to the fossil fuels burned to

convert raw materials into finished products (Etee 2019).

To conclude, the manufacturing industry clearly plays a significant role in the Indonesian

economy. As a result, the Indonesian government strongly supports the manufacturing sector,

which is predicted to grow substantially. Nonetheless, the manufacturing industry has a

potential to harm society and the environment in which it operates.

2.7.3 CSR Implementation in Indonesia

This subsection describes CSR implementation in Indonesia. It includes a list of several

CSR regulations that have been adopted in Indonesia and provides an overview of CSR

practices in Indonesia.

2.7.3.1 CSR Regulations in Indonesia

An effort to recognise CSR in Indonesia began over 18 years ago (Ridho 2018). On 28

September 1999, the Ministry of SOEs in Indonesia issued Ministry Decree No. Kep-216/M-

PBUMN/1999, followed by the enactment of Law No. 19 2003 regarding SOEs and the

issuance of SOE Ministry Regulation No. Per-05/MBU/2007 that considers cooperation

between SOEs and small businesses for the environmental protection program (Ridho 2018).

The Public Interest and Research Advocacy group in 2001 showed that the fund from CSR in

Indonesia was approximately $US 11.5 million from 180 organisations and supported 279 CSR

practices. Fund averages for each organisation was 640 million or approximately 413 million

rupiahs for each activity (PIRAC 2002). That study found that companies in Indonesia embrace

CSR in four ways: direct involvement, through a company foundation or social organisation,

partnering with others, and joining a consortium. The study argued that a consequence of late

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adoption of CSR in Indonesia is the lack of a comprehensive legal instrument that regulates CSR

and fosters CSR actions.

This issue has been addressed by the Indonesian government releasing Law 40 2007 on

Limited Liability Company (UU No. 40 Tahun 2007 tentang Perseroan Terbatas), and Law

No. 25 2007 on Investment (UU No. 25 Tahun 2007 tentang Penanaman Modal di Indonesia),

which makes CSR in Indonesia compulsory (Maris 2014). These laws are supported by

Government Regulation 47 2012 concerning social and environmental responsibility of a

limited liability company (Radyati 2015; Supriyanto 2014).

Most CSR definitions are focused on the context of developed countries, especially with

regard to the relationship between CSR and company performance (Amran & Haniffa 2011).

Regulatory frameworks and perceptions about CSR also vary across developing countries

(Moisescu 2018). Therefore, this thesis deals with a definition capturing the uniqueness of such

a relationship from a developing country context. Notably, Law No. 40 2007 uses a term of

tanggung jawab sosial dan lingkungan (TJSL) or social and environmental responsibility and

defines CSR as the company's commitment to engage in sustainable economic development to

improve quality of life and the environment that is beneficial to the company itself, the local

community, and society in general (Erawaty 2019).

More specifically, four critical criteria were stated in Article 74 of Law No. 40 2007:

(Maulamin 2017; OJK 2016) as follows:

1. Companies that conduct their business activities in the field and/or related fields in the

natural resource sector shall be obliged to take on social and environmental responsibilities.

2. Social and environmental responsibilities referred to in paragraph (1) are compulsory for

companies and included in the cost of companies with due regard to propriety and fairness.

3. There will be penalties for companies that fail to fulfil these obligations under applicable

laws.

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4. Further information on social and environmental responsibility regulations will be stipulated

in a government decree.

Such laws have therefore made Indonesia the first nation in the world that mandates

companies to practise CSR and to disclose their CSR activities (Maris 2014; Ridho 2018;

Rosser & Edwin 2010; Sustainablesquare 2017; Waagstein 2011), in particular those related to

natural resources, which often cause serious environmental degradation and negative social

impacts (Achda 2006). According to these laws, the mandatory nature of CSR is legitimate and

thus encouraged (Maris 2014; Waagstein 2011). In general, Indonesia has several rules

regulating CSR, in addition to the two laws discussed above (KlikLegal 2017).

2.7.3.2 Global Standards of CSR Practises in Indonesia

There are several global standards as guidance how to conduct CSR. This subsection

explains three of them that are widely implemented in Indonesia as follows:

▪ International Organisation for Standardisation (ISO) 26000

ISO 26000 is a standard for businesses and organisations committed to operating in a

socially responsible manner. It offers guidance to those who understand that societal and

environmental respect is a crucial success factor. ISO 26000 is increasingly seen as a

measure of assessing an organisation's commitment to sustainability and overall

performance, in addition to being the "right thing" to do (ISO n.d.). With ISO 26000,

organisations will add value to currently developing social responsibility activities by: 1)

developing a consensus on the meaning of social responsibility and its issues; 2) providing

guidance on translating principles into effective activities; and 3) selecting best practices

that have been developed and disseminated for the good of the society and international

community (Mahendra n.d.). There are seven core subjects in ISO 26000. Six of them,

namely human rights, labour practices, fair business practices, consumer, and environmental

issues, relate to major activities of business. Meanwhile, one other subject, community

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involvement and development, puts more emphasis on CSR activities that are carried out

outside the main business activities (Radyati 2015).

▪ Global Reporting Initiative (GRI)

The GRI is an independent, multinational organisation that assists businesses and other

organisations in taking responsibility for their impacts by establishing a global standard

language through which they may communicate such impacts. The GRI Standards are the

world's most extensively used sustainability reporting standards (Globalreporting n.d.a).

The GRI Standards assist organisations in understanding their external impacts, such as on

the economy, environment, and society. This improves accountability and transparency in

terms of their contribution to sustainable development. Organisations can employ the GRI

Standards to generate a sustainability report that complies with the Standards. Alternatively,

they can use certain Standards, or parts of them, to disclose information to specific users or

purposes, such as reporting their climate change impacts to investors and customers

(Globalreporting n.d.b).

In 2017, there are 73 Indonesian companies whose Global Reporting Initiative (GRI) reports

are available in the GRI database, and 20 of them are manufacturing companies (GRI 2019).

They are medium and large companies and also listed in Manufacturing Directory 2017

(BPS 2017).

▪ Sustainable Development Goals (SDGs)

United Nations (UN) Member States endorsed the 2030 Agenda for Sustainable

Development in 2015, which provides a shared roadmap for peace and prosperity for people

and the planet today and in the future. The 17 SDGs are at the heart of it, and they represent

an urgent call to action for all countries, developed and developing, to work together in a

global partnership to achieve them (SDG n.d.). These 17 SDGs are integrated—that is, they

recognise that actions taken in one area have an impact on outcomes in others, and that

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development must strike a balance between social, economic, and environmental

sustainability (UNDP n.d.). Notably, the achievement of the SDGs is the ultimate goal of

sustainability, while the process or activity is CSR itself. CSR thus helps to the

accomplishment of the SDGs by 2030 (Beritasatu 2020).

More specifically, the minister of SOEs has ratified PER-05/MBU/04/2021 concerning CSR

regulation for SOEs. The term of Program Kemitraan and Bina Lingkungan (PKBL) or

Partnership and Community Development Program) has been replaced with TJSL (CSR) in

this new regulation (Radyati 2021). Specifically, this presidential regulation aimed at

fulfilling the Indonesia's commitment to the UN Sustainable Development Goals (SDGs),

which is closely matched with its national development goal. The commitment of Indonesia

to meet the SDGs while also pursuing its own development strategy will benefit both

Indonesians and the global world (Sheany 2017). As a result, PER-05/MBU/04/2021 can

make Indonesian SOEs officially global citizens in order to achieve the SDGs through CSR,

integrate CSR into business strategies, and adhere to international standards such as ISO

26000 for social responsibility and ISO 31000 for risk management (Radyati 2021).

2.7.3.3 CSR Practises in Indonesia

This subsection provides an overview of CSR practises in Indonesia. As a result of several

CSR regulations, CSR has been increasingly a major concern in Indonesia and become a trend

over the last two decades (Achda 2006; Maris 2014). Growing recognition of CSR has yielded

progress in several sectors regarding programs and collaboration (Sustainablesquare 2017).

Consequently, appreciation is given to companies that perform CSR well. For example, the

Indonesia Sustainability Reporting Award (ISRA) was introduced in 2005 to honour companies

that released sustainability reports based on the GRI (NCSR 2020; Ridho 2018). The Indonesia

CSR Awards (ICA), renamed Sustainability Reporting Awards (SRA), has been held since

2006 to provide appreciation for organisations that carry out social responsibility well based

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on Standar Nasional Indonesia (SNI) or ISO 26000:2013 (Guidelines for Social

Responsibility) (BSN 2019). The Centre for Entrepreneurship, Change and Third Sector

(CECT) Sustainability Awards (CSA) is granted to corporates and social entrepreneurs that

have holistic sustainability and good CSR performance, based on Sustainable Development

Goals and ISO 26000 (CECT). There are other similar awards, such as CSR Indonesia Award

(CSR Indonesia 2018), TOP CSR Award (Tempo 2018) and Indonesia's Best Corporate Social

Initiatives (Barlian 2018).

Interestingly, prior studies show that the majority of companies in Indonesia still embrace

CSR at the level of charitable practice (Joseph et al. 2016). Corporate philanthropy is the CSR

activity most frequently carried out by companies (Maulamin 2017; Radyati 2015;

Razafindrambinina & Sabran 2014) through community initiatives and involvement,

education, and donation (Ambadar 2017; Widjaja 2011). Sayekti (2015) identified that the

average level of strategic CSR among 397 listed companies in Indonesia from 2005 to 2008

remained very small. These findings strengthen an argument that CSR is practiced merely to

comply with regulations and meet society's demands, without linking it to the strategy of

companies as a whole (Porter & Kramer 2006). Some companies do not consider CSR an

obligation, and only enforce CSR through simple and instant activities, such as society

donations and fundraising (Maulamin 2017). Surprisingly, the perception of most CSR

managers in listed Indonesian companies with respect to CSR remains limited to donations and

community development activities (Ridho 2017). This phenomenon indicates the need to

further investigate how companies in Indonesia implement CSR, or more precisely, how they

integrate CSR into their business practices.

Overall, there are several regulations related to CSR in Indonesia. To comply with this rules,

prior studies found that most companies still conduct philanthropic activities, such as

donations, charity, and community developments. However, there is an increasing intention to

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standardise CSR implementation using several global standards, such as ISO 26000, GRI, and

SDGs.

2.7.4 CSR Studies in the Indonesian Manufacturing Industry

This subsection presents a number of CSR studies with the Indonesia manufacturing study

as a context study. Using stakeholder theory and taking a sample study of three large

companies, Widjaja (2011) investigated that most companies have been active in a variety of

CSR programs focusing on three areas: helping natural disaster victims, education and

supporting SMEs. Then, based on a sample of 53 manufacturing companies, Hasanudin and

Budianto (2013) examined whether environmental and employee CSR have an impact on

corporate reputation or contribute to company performance. Results showed that employee

CSR and corporate reputation have positive impacts on company performance, but

environmental CSR has negative impacts. Also, the relationship between environmental CSR

and employee CSR and company efficiency is partially mediated by corporate reputation.

Presenting a case study of a large Indonesian company in the pulp and paper industry,

Mursitama, Fakhrudin and Hasan (2014) found that the company effectively transformed not

only its institution but also its substantial CSR value. Moreover, Patrisia and Dastgir (2017)

explored the relationship between business diversification and company social performance

(CSP) with a sample of 107 listed manufacturing companies in IDX. Their finding revealed

that diversification at the industry level does not impact CSP.

With a sample of 50 manufacturing companies listed in IDX in 2015, Purbowati and

Mutiarni (2017) evaluated the effect of corporate characteristics on CSR disclosure. They

found that company size had a significant impact on CSR disclosure, while the company's

profile, the size of the commissioner board and ownership concentration had no significant

impact on the disclosure of CSR. Nonetheless, the combined impact of these four variables is

important for the disclosure of CSR, with company size holding the dominant influence.

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Furthermore, Maulamin (2017) distributed 25 questionnaires to factory managers to gather

opinions and understanding about CSR from plant managers. Results showed that 72% of

respondents reported CSR was compulsory, while the remaining 28% said CSR was a

voluntary initiative to support the society around them. This study indicated that CSR was

implemented using four approaches: coordinating their own social events; through their own

foundations; working with different partners such as social institutions, NGOs and universities;

and joining consortiums to undertake CSR practices.

Using a sample of 173 large manufacturing companies in Central Java, Indonesia,

Handayani, Wahyudi and Suharnomo (2017) examined the mediating role of green innovation

and community engagement in CSR implementation. They emphasised the importance of

involving social and environmental aspects in CSR ethical program implementation.

In summary, several studies have examined CSR practices in the Indonesian manufacturing

industry. However, little study has been done on the link between CSR integration and

company performance. There is a need, therefore, to explore further how manufacturing

companies in Indonesia integrate CSR into their business strategy and operations.


This chapter presents an overview of existing literature on the topic of this thesis. It includes

NMS, CSR, and business strategy. It also covers the integration of CSR into business strategy,

as well as company performance as a result of that integration. Several research gaps are

presented in this chapter, as well as Indonesia as a context for this thesis.

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CHAPTER 3: RESEARCH FRAMEWORK

Based on the literature review and theoretical underpinnings presented in previous chapter,

this chapter discusses the research framework developed in this thesis. It consist of three

sections: (1) theoretical framework, (2) the conceptual framework of the integration of CSR

into business strategy, and (3) research hypotheses. Each section is explained as follows.

3.1 Theoretical Framework

This section explains how the theoretical foundation is taken to build a theoretical

framework of CSR integration.

3.1.1 Strategy and Company Performance

The connection of strategy and company performance is presented in this subsection.

Strategic management is a company’s decision-making standard that ultimately determines the

objectives, policies, and plans to achieve its goals. Companies should decide on which strategic

choice to use to pursue their goals (Porter 1985). These strategies define which businesses the

companies run, the economic and non-economic nature of their actions and contributions, and

the relationship between shareholders, employees, clients and the community (Andrews 1987).

Strategic management is a way to ensure a sustainable competitive advantage by investing the

resources needed to develop critical capabilities that can lead to long-term superior

performance (Lin, Tsai & Wu 2014).

The relationship between strategies and company performance has been well documented

in the literature on strategic management. Many studies have examined the strategy-

performance relationship, both empirically and theoretically (Andrews, Boyne & Walker 2006;

Anwar, Shah & Hasnu 2016; Kotha & Nair 1995; White 1986). While the relationship between

strategy and company performance is widely recognised in the literature, a plethora of

mechanisms have been identified. In the area of CSR, how CSR integration into an

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organisation’s strategy can contribute to company performance remains unclear, although an

increasing number of studies have been undertaken.

3.1.2 Theoretical Underpinnings

This subsection explains theoretical underpinnings of this thesis. Currently, companies are

under great pressure from stakeholders to incorporate social and environmental issues into their

business decisions and strategies (Latif et al. 2020). Stakeholder theory lays the groundwork

for a strategic view of the issue of corporate responsibility (Katsoulakos & Katsoulacos 2007).

A significant contribution of stakeholder theory is to evaluate a company's behaviour towards

its established stakeholders and its associated activities (e.g., CSR practices) to maintain its

relationship with them. CSR practices can therefore be a viable means for companies to direct

organisational resources with the aim of producing benefits for their stakeholders (Zhu, Liu &

Lai 2016). Hence, in addition to the importance of stakeholders in achieving organisations’

goals, stakeholders are a crucial motivating factor for CSR adoption by organisations (Dobele

et al. 2014; Lane & Devin 2018; Zhu, Liu & Lai 2016). Some authors emphasise that it is

essential to include the concerns and objectives of the stakeholders when taking a long-term

perspective (Bhattacharyya 2010; Gazzola & Colombo 2014; Guadamillas-Gómez, Donate-

Manzanares & Škerlavaj 2010; Maon, Lindgreen & Swaen 2009).

The essential dimensions and CSR definitions examined in Chapter 2 highlight that CSR is

a multidimensional concept involving different aspects relevant to a variety of organisational

stakeholders (Kleine & von Hauff 2009). In particular, CSR focuses on how to manage internal

and external stakeholders ethically, socially and responsibly to enhance the living conditions

of internal and external stakeholders, while conserving the organisation’s profitability

(Hopkins 2005). Managers have to consider how to respond to a variety of internal and external

stakeholder pressures and how to evaluate whether CSR practices will fit with the existing

business operations (Yuan, Bao & Verbeke 2011). Subsequently, in understanding the

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relationship between CSR integration and company performance, this thesis involves

shareholders, managers, and employees (as a primary internal stakeholders), and also suppliers

and customers (as primary external stakeholders).

Contingency theory suggests that ‘organizational effectiveness results from fitting

characteristics of the organization, such as its structure, to contingencies that reflect the

situation of the organization’ (Donaldson 2001, p. 1). A contingency is ‘any variable that

moderates the effect of an organizational characteristic on organizational performance’

(Donaldson 2001, p. 7). A contingency variable is significant to the extent that companies that

differ on that variable also have significant differences in how performance is associated with

strategic attributes or actions (Hambrick & Lei 1985).

One example of a contingency variable is strategy (Donaldson 2001; Nicholas & Abby

2006). Contingency theory is distinguished by a particular approach to the strategic fit of

organisation (Parisi 2013). Every company has its specific goals, which are different from those

of other companies and develops a distinct strategy to achieve them. Thus, company

performance, as a result of its strategy implementation, is also different. Baron (2000)

emphasises that a company’s strategy affects its performance. Contingency theories note the

appropriateness of different strategies depends on the competitive business market settings.

These views vary from the universal view, which stress that strategies ‘depend on everything’

(Hambrick & Lei 1985).

This thesis focuses on the integration of CSR into business strategy as a crucial mechanism

that links an organisation’s strategy to its performance. Such integration is increasingly

essential for business competitiveness (Ooi, Amran & Yeap 2017). Strategic integration can be

regarded as being rooted in contingency theory, which argues that the concept of strategy is

universal, but should be fit into its context to improve company performance. Such a strategic

fit is fundamental to the enhancement of companies’ competitive strategies.

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This conceptual framework is briefly outlined in Figure 3.1. In the following subsection, the

key concepts and relationships within the framework are discussed.

Figure 3.1: The Conceptual Framework for the Integration of CSR into Business Strategy

and Its Impact on Company Performance

3.2 The Theoretical Framework of the Integration of CSR into Business strategy

This section discusses the theoretical framework of CSR integration and business strategy

developed in this thesis. As discussed above, this thesis combines both strategic and functional

integration and performance aspects based on stakeholder and contingency theories to

empirically examine whether company performance depends on business and CSR strategies.

Companies implement CSR differently, and each adopts a specific business strategy. By

engaging in CSR, their company performance could be contingent on the CSR and business

strategy applied by companies, which is shaped by the mechanism of the integration between

CSR and its business strategy.

This thesis follows Baron’s statement (1997) that defines integration to be a synergy

between competitive strategies that seek superior performance in the marketplace and NMS

that shape the competitive environment. In this thesis, business strategy represents competitive

(market) strategy, and CSR refers to NMS. Many studies have examined whether either

business strategy or CSR strategy provides some benefits to a company; however, the impacts

will be greater if both strategies are integrated rather than being conducted separately

(Guadamillas-Gómez, Donate-Manzanares & Škerlavaj 2010; Porter & Kramer 2006;

Vilanova, Lozano & Arenas 2009). The broad theoretical framework will be strategy-context-

Business strategy

CSR strategy

Integration Company

performance

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performance and thus cover the following significant concepts: CSR strategy, business

strategy, integration between CSR and the business strategy and company performance.

3.2.1 CSR Dimensions and Strategies

This subsection describes CSR dimensions and strategies used in this thesis’s theoretical

framework. Specifically, CSR is defined to be a company’s ability to be socially responsible

for the development and growth of the society where it runs its business (Adeneye & Ahmed

2015). The World Business Council for Sustainable Development (Moir 2001) supports this

definition by declaring that CSR is a continuous business agreement with ethical behaviour to

benefit sustainable economic development and at the same time to enhance the quality of life

of the employees, their families, and the local community as well as wider society. Moreover,

Rasche, Morsing and Moon (2017, p. 6) highlighted that CSR signifies ‘the integration of an

enterprise’s social, environmental, ethical, and philanthropic responsibilities towards society

into its operations, processes and core business strategy in cooperation with relevant

stakeholders’. These definitions are in accordance with the adopted definition of integration

(as above), that companies run their businesses not only economically, but also ethically so that

they benefit the company and society.

CSR practices adopt the CSR dimensions developed by Carroll (1979, 1991), including

economic, legal, ethical, and philanthropic responsibilities. The measurements for those

dimensions are borrowed from Maignan and Ferrell (2000, 2001) who assessed corporate

citizenship in the United States and France by involving three groups of primary stakeholders:

customers, employees, and public stakeholders. Accordingly, their framework can be a

reference to analyse the influence of CSR on company performance (Marín, Rubio & de Maya

2012). From these measurements, the CSR strategies applied by companies can be identified

and categorised into reactive and proactive strategies as the most commonly used terms in the

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strategic CSR literature (Bocquet et al. 2013; Chang 2015; Goran & Greg 2004; Groza,

Pronschinske & Walker 2011; Torugsa, O'Donohue & Hecker 2013).

3.2.2 Business Strategy Applied in This Thesis

This subsection explains business strategy applied in this thesis. Business strategy refers to

how a company competes and positions itself successfully in the market (Bowman & Helfat

2001). In this thesis, business strategy follows the most widely used typology from (Porter

1985), namely cost leadership and the differentiation strategies (González-Benito & Suárez-

González 2010; O'Farrell, Hitchens & Moffat 1992). Porter's generic strategies tend to be

robust (Kotha & Vadlamani 1995), consistent (O'Farrell, Hitchens & Moffat 1992) and are

commonly used strategy dimensions in the literature (Dess & Davis 1984; Kotha & Vadlamani

1995). Moreover, Porter’s typology has driven the most theoretical refinement and empirical

studies (Dess et al. 1995, p. 375), and gained considerable empirical support over time

(Campbell-Hunt 2000; Danny & Peter 1986; Robinson & Pearce 1988).

3.2.3 The Integration of CSR into Business Strategy

The integration of CSR into business strategy is discussed in this subsection. Because CSR

implementation is very complex, choosing the right combination of activities is one of the key

challenges of implementation of CSR practices (Vidal, Kozak & Hansen 2015). Currently, CSR

issues are being integrated into all elements of business operations and clear commitment to

CSR is made in the visions, missions, and value statements of a growing number of companies

around the world. It should be relevant to the company’s goals and core competencies for

appropriate implementation (Ofori 2007). Based on the findings from the literature review, this

thesis considers that two dimensions for the integration of CSR into the business strategy are

critical to organisations’ performance. They are explored below.

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3.2.3.1 Strategic CSR Integration

This subsection describes strategic CSR integration with its critical dimensions proposed in

this thesis. Because strategic CSR becomes tremendously relevant to companies, it is crucial

for companies to be strategically involved in CSR (Ooi, Amran & Yeap 2017). CSR is

integrated by triggering, maintaining, and sharing a set of principal core values (Marques-

Mendes & Santos 2016). Strategic integration is carried out through the inclusion of social

responsibility objectives in the business strategy (Ganescu 2012b). This integration also

reflects how companies engage CSR in their management systems in practice (Engert, Rauter

& Baumgartner 2016; Werre 2003) and how they manage CSR implementation in their

organisation. Furthermore, this integration identifies the critical factors to drive the integration

internally, including the tools and communication processes used (Engert, Rauter &

Baumgartner 2016). Most activities in this integration are carried out at the strategic level and

conducted by managers (top management); even though they also involve employees and

customers.

This thesis considers three crucial dimensions of integrating CSR into the company’s

strategy at the strategic level highlighted in the literature, as follows:

1. Aligning CSR with the company’s strategy. The integration is only possible by putting CSR

at the core of the business (Dey & Sircar 2012) and aligning CSR with the overall goals and

strategies of the company to create and capture value (McWilliams & Siegel 2011). In this

dimension, the company includes CSR on a mission statement and creates a CSR-shared

vision (Arjaliès & Mundy 2013; Laguir, Laguir & Tchemeni 2019). Mission statements

describe selected criteria and make use of socially desirable approaches designed to attract

and retain shareholders, employees and customers (Chenhall 2003). Some authors highlight

that embedding CSR in the company’s vision and mission is a critical step in integrating

CSR into the business strategy (Dey & Sircar 2012; Guadamillas-Gómez, Donate-

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Manzanares & Škerlavaj 2010). This step can reflect CSR’s actual importance to the

company’s mission (Burke & Logsdon 1996) and will guide all subsequent decision making

in the implementation process (Guadamillas-Gómez, Donate-Manzanares & Škerlavaj

2010; Ooi, Amran & Yeap 2017). In addition, strategic integration can be carried out by

establishing CSR as one of long-term goals of the company (Werre 2003), defining

objectives on social and environmental issues, providing a proper mechanism to evaluate

the results of the objectives and applying this mechanism throughout all areas of business

(Bernal-Conesa, de Nieves-Nieto & Briones-Peñalver 2017).

2. Gaining support and encouragement from top management. Aligning the business strategy

with CSR principles requires the support and encouragement of senior managers (Lindgreen

et al. 2011). Without support from top management, CSR programs would be difficult to

devise and enforce (Mahmoud, Blankson & Hinson 2017), potentially becoming critical

barriers to CSR implementation (Werre 2003). Besides, top management’s support is

essential to determine strategic and organisational boundaries under which employees are

permitted to participate in CSR activities in an effort to ensure employee behaviour is

consistent with the goals of the organisation (Arjaliès & Mundy 2013). For example, a

company can provide interaction between top management and subordinates in an effort to

promote organisational learning and implementation of new strategic initiatives (Gond et al.

2012). A company can also organise CSR efforts and establish a CSR steering committee,

which has formal and periodic meetings with top management to discuss CSR as a key topic

(Guadamillas-Gómez, Donate-Manzanares & Škerlavaj 2010; Laguir, Laguir & Tchemeni

2019; Martinez-Conesa, Soto-Acosta & Palacios-Manzano 2017; Werre 2003). These

meetings allow for regular discussions with operational departments about CSR strategy,

providing senior executives with ideas for a CSR strategy from other areas of the business

to check the validity of the assumptions that underlie the strategic plans (Arjaliès & Mundy

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2013). Moreover, the CSR committee can formulate the direction the company needs to

follow to address stakeholder needs related to social and environmental issues. This ensures

that CSR is institutionalised within the core business vision, mission, priorities and

objectives of the company. Mentoring and coaching are provided to develop decision-

making capabilities that incorporate CSR criteria in the assessment of alternatives to

facilitate CSR institutionalisation (Werre 2003). These activities can represent the purpose

of the company to carry out its activities responsibly (Ooi, Amran & Yeap 2017) and ensure

that the company remains responsive to CSR issues (Bernal-Conesa, de Nieves-Nieto &

Briones-Peñalver 2017).

3. Developing effective communication. The company should develop effective

communication to generate a clear perception that CSR is an aspect of its strategic

importance (Guadamillas-Gómez, Donate-Manzanares & Škerlavaj 2010). CSR

communication can help companies convey the alignment of the CSR strategy with external

objectives to their employees, thereby creating a cohesive program for their CSR strategy

(Arjaliès & Mundy 2013; Guadamillas-Gómez, Donate-Manzanares & Škerlavaj 2010;

Laguir, Laguir & Tchemeni 2019). In addition to generating awareness of CSR initiatives,

CSR communication can be a crucial bond between the company and its stakeholders. CSR

communication can be characterised as the company’s communication to internal and

external stakeholders about its contributions to social, environmental and economic

development of society (Rasche, Morsing & Moon 2017). The company cannot reap the

benefits from CSR if it does not communicate CSR to relevant stakeholders (Maignan &

Ferrell 2004). Internal and external communication, therefore, is one of the critical aspects

of CSR implementation (Dobele et al. 2014). Internal communication can be conducted

through newsletters, intranet, e-mail, seminars, presentations and folders (Engert, Rauter &

Baumgartner 2016; Maon, Lindgreen & Swaen 2009). In contrast, communication with

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external stakeholders can be carried out using official documents, such as annual reports

and corporate brochures, and intensifying the company’s presence on the internet and social

networks to communicate about CSR approaches through the company website and online

postings (Arjaliès & Mundy 2013; Asif et al. 2013; Galbreath 2006; Guadamillas-Gómez,

Donate-Manzanares & Škerlavaj 2010; Huang 2010; Maon, Lindgreen & Swaen 2009;

Reverte, Gómez-Melero & Cegarra-Navarro 2016).

3.2.3.2 Functional Integration

Following the strategic CSR integration presented in the previous subsection, this subsection

discusses the functional CSR integration included in this thesis’s theoretical framework. As

suggested by Asif et al. (2013), vertical integration of CSR is conducted by transforming

organisational objectives into operational and tactical imperatives. Thus, after integrating CSR

at the strategic level, the next dimension is integrating the companies’ social concerns into their

business activities and operations (Guadamillas-Gómez, Donate-Manzanares & Škerlavaj

2010; Quairel-Lanoizelée 2011). Functional integration can be defined as the extent to which

a company makes use of interactions with other intra-organisational units to make its program

objectives and practices consistent with its internal and external requirements (Swink,

Narasimhan & Kim 2005). In this integration, the company identifies its position regarding

responsible business practices and establishes the effectiveness of its activities (Tonysheva &

Chumlyakova 2016).

Similar to strategic integration, stakeholders' interests and objectives must be addressed with

a view to integrating CSR into the company's business operations and activities (Guadamillas-

Gómez, Donate-Manzanares & Škerlavaj 2010). While strategic integration mainly involves

top management and employees, the functional integration of CSR reflects how they manage

employees, customers, and suppliers categorised as primary stakeholders (Famiyeh 2017;

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Maignan & Ferrell 2004). Hence, this thesis proposes six significant dimensions to be involved

in functional integration as follows:

▪ Production. Regarding the literature on manufacturing companies (Chi 2015; Theodorou &

Florou 2008; Ward et al. 1995), this thesis selects two of the manufacturing competitive

priorities, namely low cost and quality, because these two priorities match the business

strategy and are applicable in CSR studies (Boubakary & Moskolaï 2016; Marín, Rubio &

de Maya 2012; Yuan, Bao & Verbeke 2011). Companies are concerned about quality for

the purpose of gaining an advantage by stabilising the quality of the product at a

predetermined level according to the competition. Product quality can be improved through

several activities, such as conducting the statistical control of supplies and production,

creating quality circles and adopting the formalisation and standardisation of processes

(quality manuals). (Indonesia 2011; Theodorou & Florou 2008). In addition to cost and

quality, (3) innovation is also included as an important aspect of manufacturing strategies

and CSR integration (Baumgartner 2014; Theodorou & Florou 2008) through product or

process innovation, such as developing environmentally friendly products and innovations

and improvements in production processes, logistics or distribution (Reverte, Gómez-

Melero & Cegarra-Navarro 2016).

▪ Productivity in the value chain. Activities in the value chain can improve operational

effectiveness (Rangan, Chase & Karim 2012) and enhance the social, environmental, and

economic capabilities of supply chain members (Crane et al. 2014). In addition, a company’s

decisions should be appropriate not only to its internal stakeholders but also to all external

parties, such as suppliers and customers (Huang 2010). Thus, this thesis focuses on suppliers

and customers in the value chain (Witek-Hajduk & Zaborek 2016) as the primary external

stakeholders for CSR practices in the functional integration.

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▪ Strengthening human resources. This thesis includes employees as primary internal

stakeholders. In addition to representing a small society inside the company, employees can

reflect social intentions (European Commision, cited in Vo, Delchet-Cochet & Akeb 2015)

and are involved directly in conducting productive activities (Maignan & Ferrell 2004). CSR

activities aimed at employees (Witek-Hajduk & Zaborek 2016) expose how a company

treats its employees during its operations (Ganescu 2012b). The company can direct social

activities to employees related to internal social responsibility (Tonysheva & Chumlyakova

2016), such as treating employees fairly and respectfully, incorporating their interests in

business decisions, offering appropriate compensation, creating a safe and healthy working

environment, and providing training and advancement programs for employees (Bauman &

Skitka 2012; Bernal-Conesa, de Nieves-Nieto & Briones-Peñalver 2016; Bhattacharya,

Korschun & Sen 2009; Lindgreen et al. 2009; Reverte, Gómez-Melero & Cegarra-Navarro

2016; Werre 2003).

To sum up, strategic CSR integration into business strategies consists of three dimensions:

aligning CSR with the company’s strategy, gaining support from top management, and

developing CSR communication. Functional integration contains six dimensions: cost, quality,

innovation, suppliers, customers, and employees. It can be regarded as a horizontal integration

of CSR since it is applied across divisions, functions, and roles and throughout the supply chain

(Asif et al. 2013). Both strategic and functional integration reflect how companies implement

CSR by integrating social, environmental, ethical, human rights and consumer concerns in their

core businesses in close cooperation with their stakeholders (Raquel, María Victoria & Antonio

2018). Therefore, this thesis simplifies prior frameworks by proposing only two levels of CSR

integration with several significant dimensions.

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3.2.4 Essential Aspects of Company Performance

This subsection explains four essential aspects of company performance employed in this

thesis. It covers financial, employee, customer, and operational performances. It opens by

presenting financial performance. Then, it goes on to social performance, including customer

and customer performances. Operational performance is presented at the end of this subsection.

Measuring the performance of a company in the current competitive environment is highly

complicated. Doing so does not only assess a company's performance but also reflects its

organisational culture and philosophy and explains how well the company performs in terms

of financial and non-financial indicators (Wibisono 2011). Combining both the financial and

nonfinancial aspects of performance is considered to be a multidimensional approach to

performance and presents a more accurate way to measure company performance (Chong 2008,

cited in Stoian & Gilman 2017). Particularly in manufacturing companies, there are five levels

proposed to manage performance: financial perspective, customer perspective, manufacturing

competitive priorities, internal process, and resource availability (Wibisono 2011).

Speficically, top management, situated at the strategic level, needs financial measures for

managerial-level decisions, while lower management, situated at the tactical and operational

levels, and workers need operational measures for daily business activities (Hwang et al. 2014).

From a CSR perspective, evaluations of businesses’ impacts on society have to consider

both the financial and social value created by companies (Marques-Mendes & Santos 2016).

Several studies have found that CSR has positive effects on financial performance (Carroll &

Shabana 2010; Orlitzky, Schmidt & Rynes 2003; Zhu, Liu & Lai 2016). Nonetheless, non-

financial effects of CSR (i.e., corporate reputation, improved employee motivation and

customer satisfaction) have been overlooked (Reverte, Gómez-Melero & Cegarra-Navarro

2016). With respect to CSR integration, it is wise and sensible for business decision makers to

initiate or carry out CSR activities and practices that address not only social and environmental

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concerns, but also economic concerns for their organisations (Ooi, Amran & Yeap 2017). In

this thesis, company performance represents the result of integration between CSR and the

business strategy, and as such, will be measured across four aspects: financial performance at

the strategic level, operational performance, and social performance including customer and

employee performance at the tactical and operational level.

3.2.4.1 Financial Performance

Several indicators of financial performance used in this thesis is listed in this subsection.

The economic dimension is related to the effect of a company on its economic condition and

on the economic system in which it is embedded (Ferraz, António & Gallardo-Vázquez 2016).

In this thesis, financial performance reflects the economic dimension that indicates the impact

of CSR integration measured by monetary terms as follows:

1. Profit. Profit is a widely used measurement to identify the relationship between CSR and

company performance (Chi & Gursoy 2009; Marín, Rubio & de Maya 2012; Martinez-

Conesa, Soto-Acosta & Palacios-Manzano 2017; Torugsa, O'Donohue & Hecker 2012;

Ward et al. 1995).

2. Cash flow. Cash flow (CF) can be defined as an increase or decrease in the amount of money

a business, organisation, or individual has. This term refers to the amount of cash (currency)

that is generated or consumed in a given time period (CFI 2018). Net CF (NCF) refers to

the difference between a company’s cash inflows and outflows in a specific period, and

measures a company’s cash balance in terms of financial accounting (Brealey et al. 2012).

Managers usually use NCF to identify the changes in a company’s cash balance and to

ensure that the company is profitable and has enough capital on hand for its business

operation. A healthier financial condition is presented by a higher NCF (Kun, Nasrin &

Weiquan 2019).

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3. Sales growth. Sales growth is an appropriate reflection of the value that companies create

and share with society that enables cross-company comparisons and implies an increase in

economic growth, as well as an increase in the value of the goods and services delivered to

consumers (Stoian & Gilman 2017). Sales growth reflects an increase in sales over a specific

time period (Chen, Feldmann & Tang 2015). It also captures both the financial and

nonfinancial aspects of performance since it indicates that companies have achieved

profitability in the past and have also delivered customer satisfaction (Chong 2008, cited in

Stoian & Gilman 2017). Sales growth is also used as financial performance indicator in CSR

studies (Reverte, Gómez-Melero & Cegarra-Navarro 2016)

4. Return on investment (ROI). ROI is the most used indicator to assess business success

(Ansoff 1965, cited in Dess & Robinson 1984) and is the most common measure in strategic

management and social responsibility research (Beard & Dess 1981; Chi & Gursoy 2009;

Hambrick & Lei 1985; Maignan & Ferrell 2001; Schniederjans & Cao 2009; Venkatraman

& Vasudevan 1987). ROI is likely to be more controllable by managers (Valipour, Birjandi

& Honarbakhsh 2012) and proposed for managing the performance of a manufacturing

company (Wibisono 2011).

3.2.4.2 Social Performance

This subsection describes social performance that covers customer, employee, and

operational performances. The social dimension refers to the impacts generated by a company

on the social systems within which it works (GRI 2013). In this thesis, social performance

indicates how CSR integration influences social values from the perspective of customers and

employees. Customers and employees are major stakeholders who directly affect and relate to

a company’s operations. Companies have a strong interest in protecting the interests of their

major stakeholders, which influences their own outcomes directly (Dupire & M’Zali 2018).

Subsequently, while several stakeholders are involved in strategic and functional integration,

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only consumer and employee performances are measured in terms of social performance along

with their relationship to financial performance.

In the literature, the impact of CSR integration on customer performance is measured using

customer complaints, customer satisfaction, customer loyalty and increasing number of

consumers (Gallardo-Vázquez & Sanchez-Hernandez 2014; Reverte, Gómez-Melero &

Cegarra-Navarro 2016; Ridho 2018; Santos & Brito 2012; Wibisono 2011). On the other hand,

an understanding of the relationship between CSR and employee performance is crucial

because the success of a company depends very much on its employees (Sun & Yu 2015). CSR

also has the potential to improve employee motivation and boost their opinion of their employer

(Dawkins 2005). In this thesis, employee performance will be measured by employee training,

employee motivation, career opportunities and employee turnover (Bernal-Conesa, de Nieves-

Nieto & Briones-Peñalver 2016; Gallardo-Vázquez & Sanchez-Hernandez 2014; Reverte,

Gómez-Melero & Cegarra-Navarro 2016; Santos & Brito 2012).

3.2.4.3 Operational Performance

Following the financial and social performance subsections, this subsection explains

operational performance and the indicator used in this thesis. At the operational management

and the shop-floor level, non-financial performance measures are more relevant than financial

measures (Wibisono 2011). In addition to financial and social performances, this thesis also

measures operational performance in terms of timelines of customer service and productivity

(Ping-Ju Wu, Straub & Liang 2015; Reverte, Gómez-Melero & Cegarra-Navarro 2016),

delivery (Chi 2015; Ward et al. 1995), operating efficiency (Reimann, Schilke & Thomas 2010)

and on time delivery (Wibisono 2011).

At the heart of stakeholder theory is the belief that a company's long-term sustainability

relies on the support of several stakeholders (Donaldson & Preston 1995). Stakeholders are

critical to the success of a company because they provide the company with resources (e.g.,

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customers, investors and employees), form an industrial structure (e.g., supply chain partners

and strategic alliances) and form a socio-political arena (e.g., communities and governments)

(Post, Preston & Sachs 2002). Accordingly, in general, this thesis involves several primary

stakeholders, such as managers, employees, and customers, in strategic integration. Functional

integration includes suppliers and customers as supply chain partners, in addition to managers

and employees. In terms of performance, this thesis covers employees, customers, managers

(operational performance) and shareholders (financial performance).

3.3 Research Hypotheses

Based on the theoretical framework, this section presents several hypotheses proposed in

this thesis as follows.

3.3.1 The Relationship between CSR Integration and Company Performance

The link between CSR integration and company performance is explained in this section. It

begins by reviewing previous studies on this topic before proposing hypothesis accordingly.

Several authors argue that CSR integration into business strategy enhances company

performance both on social (non-financial) and financial aspects (Galbreath 2006;

Guadamillas-Gómez, Donate-Manzanares & Škerlavaj 2010; Porter & Kramer 2011).

Michelon, Boesso and Kumar (2013) claimed that companies have superior financial

performance if they prioritise CSR activities based on strategic concerns. Besides, CSR is a

vital strategic tool, given its essential role in building customer satisfaction and loyalty (Pérez

& Rodríguez del Bosque 2015). CSR practices offer benefits to customers, such as reduced

customer complaints, improvements in customer service, improvements in the relationship

with customers, and increased customer loyalty (Chi 2015). Implementing appropriate CSR

plans and activities contribute to higher customer satisfaction with the company (Park, Kim &

Kwon 2017).

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In addition to the impact on customers, one way of measuring the effect of social

responsibility strategies is evaluating their impact on employees; that is, whether it is sincere

or mere window dressing (Insight 2016). Employees who are satisfied with their company’s

commitment to society are expected to be more positive, loyal and productive than those

working for less committed employers (Dey & Sircar 2012). Prior studies argue that employees

work more productively in socially responsible companies (Sun & Yu 2015). Subsequently, I

suggest:

Hypothesis 1. Strategic CSR integration has a positive impact on (1a) customer

performance, (1b) employee performance, (1c) operational performance and (1d) financial

performance.

Functional integration reflects how the company integrates CSR with its current business

practices at the functional level (i.e., production, suppliers, customers, and employees).

Through integrating CSR into the management processes and core business activities,

economic and social objectives become easier to achieve, leading to an increase in the

company's social and financial performances (Galbreath 2006; Kapoor & Sandhu 2010). Prior

studies argue that socially responsible practices can increase employee commitment to their

company, morale (Porter & Kramer 2006) and productivity, and reduce absenteeism and

turnover (Chtourou & Triki 2017). If companies actively engage in CSR activities, they can

improve employee motivation to work effectively (Sun & Yu 2015). Moreover, CSR can

provide opportunities to reduce present and future costs to the business, thereby increasing

operational efficiency (Brine, Brown & Hackett 2007) and improving operational capabilities

and overall performance (Famiyeh 2017). Therefore, I suggest:

Hypothesis 2. Functional CSR integration has a positive impact on (2a) customer

performance, (2b) employee performance, (2c) operational performance and (2d) financial

performance.

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Strategic integration describes how the company integrates CSR into its business strategy at

the strategic level. This integration is implemented for the long term and guides activities at

the functional level. From multiple case studies, Lindgreen et al. (2011) found that companies

started CSR integration by incorporating their mission and vision with CSR. This then

influences decisions on the development of CSR practices (Guadamillas-Gómez, Donate-

Manzanares & Škerlavaj 2010). The stages of CSR integration occur sequentially, and a further

stage can be conducted if the previous stage has been completed (Bhattacharyya 2010;

Guadamillas-Gómez, Donate-Manzanares & Škerlavaj 2010; Tonysheva & Chumlyakova

2016). Additionally, functional-level strategic planning should fit with and support business-

level strategies (Schniederjans & Cao 2009). The proposed model in this thesis first puts in

strategic integration and then functional integration. Therefore, I suggest:

Hypothesis 3. Strategic CSR integration has a positive relationship with functional CSR

integration.

3.3.2 Mediating Effects in the Relationship between CSR Integration and Financial

Performance

In the relationship between CSR integration and financial performance, this subsection

discusses mediating effects. It starts by presenting previous studies that have looked at this

topic, and then it presents several hypotheses about it.

The direct relationship between CSR and company performance does not ensure total

reliability, as this relationship can be affected by many factors (Khan et al. 2018; Saeidi et al.

2015), including mediators that help to understand this relationship and improve the reliability

of results (Ali, Danish & Asrar-ul-Haq 2020). With regards to the integration of CSR into the

business strategies and activities of companies, researchers highlight the necessity of

considering the concerns and objectives of stakeholders from a long-term perspective

(Bhattacharyya 2010; Gazzola & Colombo 2014; Guadamillas-Gómez, Donate-Manzanares &

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Škerlavaj 2010; Maon, Lindgreen & Swaen 2009). Companies explicitly emphasise

responsibility, and in particular economic responsibility, towards their shareholders

(Öberseder, Schlegelmilch & Murphy 2013). However, shareholders’ needs cannot be fulfilled

and a company's financial performance cannot be maximised without meeting other

stakeholders’ requirements, such as customers, employees, suppliers and the public to some

extent (Sen & Cowley 2013). Therefore, there is increasing consensus that stakeholders should

be satisfied with a company's CSR implementation before any financial performance

improvements can be achieved (Galbreath & Shum 2012; Sen & Cowley 2013). By satisfying

stakeholder needs, companies encourage their support, which in turn leads to better levels of

performance (Clarkson 1995).

The current literature has examined mediators from the stakeholders’ perspective (Galbreath

& Shum 2012). For instance, previous studies demonstrate that the relationship between CSR

and financial performance can be mediated by customer performance. Using secondary data,

Luo and Bhattacharya (2006) investigated that customer satisfaction plays a significant role

and partially mediates the relationship between CSR and financial performance. García-

Madariaga and Rodríguez-Rivera (2017) confirmed that customer satisfaction mediates the

relationship between CSR and financial performance measured by market to book ratio.

Similarly, Saeidi et al. (2015) examined whether the relationship between CSR and company

performance is fully mediated using sample data from 205 Iranian manufacturing companies.

They used customer satisfaction as a mediator, which includes three dimensions: customer

satisfaction with product or service quality, customer satisfaction with value for price, and

meeting customer expectations. Company performance is measured financially through market

share growth, sales growth, ROE, ROS, ROA, ROI, and net profit margin of the company.

Findings show that customer satisfaction mediates the relationship between CSR and financial

performance.

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Another study by Xie et al. (2017), with a sample of 238 companies in China and Vietnam,

revealed that CSR activities can help companies improve their financial performance by

improving customer satisfaction. Moreover, customer satisfaction will increase their loyalty,

so they are more likely to repeat purchases, which lead to increased demand, bigger sales

volume, and in turn, improved performance levels. Hence, greater customer loyalty will lead

to greater financial performance (Maignan et al. 1999).

Most studies aiming to determine the relationship between corporate social performance and

financial performance reveal a positive correlation between social and financial performance

(Bernal-Conesa, de Nieves-Nieto & Briones-Peñalver 2017; Ganescu 2012a; Lee 2008).

Chtourou and Triki (2017) argued that social performance can affect financial performance.

By responding to the expectations of various stakeholders, a company can enhance its

reputation, which positively influences its financial performance (Famiyeh 2017). Tang, Hull

and Rothenberg (2012) also identified that companies increase profits if they implement CSR

strategy consistently, including related dimensions of CSR, and start with those more internal

to the companies.

Subsequently, this thesis examines a mediating effect among company performances: that

the satisfaction of different stakeholders (including customers and employees) and operational

performance are vital for a company’s financial performance. Thus, I propose:

Hypothesis 4. The relationship between strategic CSR integration and financial

performance is mediated by (4a) customer performance, (4b) employee performance, and

(4c) operational performance.

Hypothesis 5. The relationship between functional CSR integration and financial

performance is mediated by (5a) customer performance, (5b) employee performance, and

(5c) operational performance.

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3.3.3 Moderating Effects in the Relationship between CSR Integration and Company

Performance

The moderating effects in the relationship between CSR integration and financial

performance are explained in this section. It begins by presenting prior research that have

investigated this topic. Then, based on their findings, it proposes hypotheses.

CSR implementation in Indonesia is very complicated, due to related regulations and other

interests (see section 2.8.3 for detail). Because complex phenomena are usually subject to

contingencies, it is essential to consider moderating effects. The word contingency implies that

something is only true under specified conditions (Chenhall 2003). For this reason, this thesis

explores whether CSR integration’s impact on company performance is moderated by business

and CSR strategies adopted by the company and is dependent on company size and industry

type.

Several prior studies investigate the relationship between business strategy and company

performance (Banker, Mashruwala & Tripathy 2014; Dess & Davis 1984; Reverte, Gómez-

Melero & Cegarra-Navarro 2016; Sun & Pan 2011; Valipour, Birjandi & Honarbakhsh 2012).

Most of these studies found that companies adopting one of the business strategies have better

performance than companies implementing another strategy (Banker, Mashruwala & Tripathy

2014; Dess & Davis 1984; González-Benito & Suárez-González 2010; Sun & Pan 2011). Thus,

I suggest:

Hypothesis 6. Business strategy moderates the impact of strategic CSR integration on (6a)

customer performance, (6b) employee performance, (6c) operational performance and (6d)


Hypothesis 7. Business strategy moderates the impact of functional CSR integration on (7a)



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The interactions among activities in companies, particularly those related to CSR, and the

extent to which these interactions can help create and sustain a competitive advantage require

thought. Companies have to decide which CSR strategies they adopt, because CSR strategy

impacts company performance based on how the company behaves and implements CSR

(Galbreath 2006). Some studies argue that different CSR strategies adopted by companies

influence company performance in different ways. The practices of proactive companies can

potentially lead to a competitive advantage (Orsato 2006), but those of reactive companies

cannot. By applying a proactive strategy, a company may reduce its costs and risk, increasing

its profits and competitive advantage, improving its reputation and legitimacy, and creating a

synergistic value (Kurucz et al., cited in Ganescu 2012b). Reactive companies, in merely

implementing CSR to fulfil regulations, cannot obtain a competitive advantage (Vo, Delchet-

Cochet & Akeb 2015). Therefore, I propose:

Hypothesis 8. CSR strategy moderates the impact of strategic CSR integration on (8a)



Hypothesis 9. CSR strategy moderates the impact of functional CSR integration on (9a)



A different contingency is size, which determines the structure that is required.

Organisational size reflects how many people are working in the organisation and is also an

internal organisational characteristic (Donaldson 2001). Traditionally, company size is related

to performance (Reverte, Gómez-Melero & Cegarra-Navarro 2016) and is likely to affect a

company’s non-market and market performance because larger companies have greater

incentives or capabilities to pursue non-market and market strategies (Chen 1996; Schuler et

al. 2002, cited in Wei et al. 2016). Previous studies reveal that company size is frequently

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applied as a control variable that has an impact on how companies engage in CSR (Lorenz,

Gentile & Wehner 2013; Marín, Rubio & de Maya 2012; Vo, Delchet-Cochet & Akeb 2015).

In addition to being one of the strongest correlates of company profit performance (Beard &

Dess 1981), company size is also used in several studies of CSR-company performance

relationships (Chen & Wang 2010; Chtourou & Triki 2017; Hasan et al. 2018; Kim, Kim &

Qian 2018; Michelon, Boesso & Kumar 2013; Torugsa, O'Donohue & Hecker 2013; Wang &

Berens 2015) and manufacturing (Lao, Hong & Rao 2010; Swink, Narasimhan & Kim 2005;

Ward et al. 1995).

Some authors argue that larger companies are more likely to have more resources to employ

in their CSR activities (Hull & Rothenberg 2008; Marcel 2009; Waddock & Graves 1997, cited

in Jones 1999; Kiessling, Isaksson & Yasar 2016; Zbuchea & Pînzaru 2017), and this may

affect their financial performance (Chen & Wang 2010; Wang & Berens 2015). In particular,

company size is associated with the integration of CSR into business strategy (Vo, Delchet-

Cochet & Akeb 2015). Hence, I suggest:

Hypothesis 10. Company size moderates the impact of strategic CSR integration on (10a)

customer performance, (10b) employee performance, (10c) operational performance and

(10d) financial performance.

Hypothesis 11. Company size moderates the impact of functional CSR integration on (11a)



Since regulatory bodies enact legislation on CSR issues such as environmental

sustainability, corporate governance and human rights, such legislation is likely to create

variations in the social performance of companies in the industries affected over time (Short et

al. 2016). The industry in which a company operates can also affect the pressures it faces from

different stakeholder groups (Michelon, Boesso & Kumar 2013), and different types of

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industries show varying tendencies to implement CSR (Kolk 2003). For example, consumer

product companies see their largest exposure and greatest pressure from customer groups

(Michelon, Boesso & Kumar 2013). On the other hand, companies in industries such as

utilities, oil and natural gas are under extensive scrutiny from a wide range of stakeholders (i.e.,

they face significant business exposure and the greatest pressure from stakeholders concerned

about environmental impacts), so they are more likely to engage in certain CSR activities than

companies in other industries. Companies in industrial sectors with relatively high impact on

the environment led other companies in CSR disclosure (Michelon, Boesso & Kumar 2013).

In his study, Kolk (2003) found that the industrial sector scores above average in sustainability

reporting; the chemical and pharmaceutical industries are leaders in sustainability reporting. In

contrast, most sectors that remain below average are non-industrial. Product differentiability

can be considered another significant contingency variable (Hambrick & Lei 1985). It has been

shown that CSR performance differences are obvious between industries (Waddock & Graves

1997). Accordingly, I propose:

Hypothesis 12. Industry type moderates the impact of strategic CSR integration on (12a)



Hypothesis 13. Industry type moderates the impact of functional CSR integration on (13a)



Figure 3.2 displays the theoretical framework for this thesis with its hypotheses. The

framework depicts how CSR can be integrated into business strategy at both strategic and

functional levels. Strategic integration covers three essential dimensions, and functional

integration contains six crucial dimensions.

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Figure 3.2: The Theoretical Framework for the Integration of CSR into Business Strategy

with the Hypotheses

The relationship between integration and company performance will be measured across

four aspects of company performance (i.e., customer, employee, operating and financial

performance). In addition, a mediating effect will be investigated in the interaction among four

aspects of company performance. Moderating effects will also be evaluated based on business

strategy and CSR strategy, whereas company size and industry type will be used as control

variables in the relationship between integration and company performance.


This chapter explores the conceptual framework of strategy and company performance. The

theoretical framework is presented in this chapter, which includes various key elements of the

theoretical framework, such as CSR strategy, business strategy, and CSR integration into

business strategy, as well as company performance. The relationship between CSR integration

and company performance are explained, followed by the mediating and moderating effects in

this relationship. This chapter also covers the development of research hypotheses.

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CHAPTER 4: RESEARCH METHODOLOGY

This chapter presents the methodology used in this thesis. A research approach is explained

first, followed by the research paradigm and research design. Then, the questionnaire

development is discussed, and the sampling process is provided. The next section describes

data analysis procedures and identifies the respondents’ characteristics. Then, PLS-SEM for

data analysis conducted in this thesis is explained, as well as the ethical considerations for this

thesis.

4.1 Research Approach

Research approach employed in this thesis is explained in this section. An explanatory study

emphasises a situation or a problem to explain the relationships between variables (Saunders

2009). As stated in previous chapters, a key research gap is that most previous studies have

established a framework for investigating the integration of CSR into business strategies based

on conceptual or qualitative methods. Although they have contributed new insights to and

understanding of the integration of CSR, these studies have provided little empirical evidence to

explain the relationship between the indicated variables or assess the reliability and relevance of

the framework. Furthermore, empirical measurement of the impact of the integration on company

performance remains limited (Mellahi et al. 2015; Yuan, Bao & Verbeke 2011). Therefore, this

thesis conducts an explanatory study to address this research gap by investigating the relationship

between the integration of CSR into business strategies and company performance (Hair et al.

2011), especially among manufacturing companies in Indonesia. As a result, this thesis examines

‘how’, or the extent to which companies integrate CSR into their business strategy, and ‘what’,

or the impact of CSR integration on company performance.

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4.2 Research Paradigm

This section describes a research paradigm used in this thesis. It includes ontology,

epistemology, and a deductive approach.

A research paradigm represents a set of beliefs, values, perceptions, and aesthetics that define

how a phenomenon of interest is investigated (Lincoln 2011). A research paradigm aims to

determine how the world operates, how information should be gained, which types of questions

should be asked, how data collected should be interpreted, and which criteria support sufficient

answers to research questions (Perri & Bellamy 2012).

With respect to ontology, which relates to the nature of reality (Saunders 2009), this thesis

applies an aspect of objectivism that depicts ‘the position that social entities exist in reality

external to social actors concerned with their existence’ (Saunders 2009, p. 110). Epistemology

connects to ‘what constitutes acceptable knowledge in a field of study’ (Saunders 2009, p. 112)

and refers to how to obtain the knowledge (Hirschheim 1992, cited in Myers 2009). In terms of

epistemology, this thesis implements positivism to build a hypothesis based on the theory

(Saunders 2009).

More specifically, to generate reasonable answers to research questions and elucidate causal

relationships between variables, this thesis employs deductive research by developing

measurements and testing hypotheses using current theories and analytical methods (Perri &

Bellamy 2012; Saunders 2009). To do so, two steps have been taken, namely conceptualisation

and operationalisation (Creswell 2014). Conceptualisation is achieved by formulating and

restricting the definition of the CSR concept through the study of applicable literature and

documents, while operationalisation is carried out by elaborating the definition into indicators and

constructs, later to be developed as instruments of analysis. Appendix A.1 presents the concept

definition and the concept measurement used in this thesis. The operationalisation of indicators

and constructs employed in this thesis is provided in the next section (see Table 4.10 for detail).

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4.3 Research Design

This section outlines a research design conducted in this thesis. It covers quantitative research

and survey. It also provides the supporting and logical reasons for why this thesis chose them. At

the end of this section, there is a figure illustrating the research approach and design of this thesis.

A research design provides the researcher with a road map of the whole research project

(Myers 2009). A quantitative research design examines theories and concepts by assessing the

relationships between particular specified constructs (Creswell & Clark 2018; Perri & Bellamy

2012). In addition, quantitative research can summarise many characteristics (Hair et al. 2011),

and is characterised by deduction, confirmation, theory/hypothesis testing, explanation,

prediction, standardised data collection, and statistical analysis (Hair et al. 2011; Johnson &

Onwuegbuzie 2004; Saunders 2009). Because this thesis investigates CSR integration, which

covers many attributes such as CSR, business strategy and company performance, quantitative

research is considered appropriate to collect, analyse, and integrate research data to address the

research questions (Johnson & Onwuegbuzie 2004). This is consistent with the explanatory,

deductive approach taken in this thesis. Moreover, quantitative research embraces complex

structural equation models that combine causal paths and the identification of many variables

(Creswell 2014).

In this thesis, samples are used to replicate the characteristics of the population: the

Indonesian manufacturing industry. Results of hypothesis testing can serve to provide a deeper

understanding of CSR integration and company performance. The researcher is then able to

conclude the extent to which current theories and knowledge can reasonably explain the

relationship between CSR integration and company performance in the context of the

Indonesian manufacturing industry.

Approximately 568 companies are listed on the IDX; however, not all listed companies

make sustainability reports (Rofelawaty 2014). From 90 companies that participated in the

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Indonesian Sustainability Report Award from 2008 to 2016, 27% or only 24 participants were

manufacturing companies. Besides, there are few websites owned by manufacturing

companies. As a result, this thesis could not obtain a description of CSR activities, business

strategy or company performance through secondary data. A quantitative analysis using a

survey was therefore chosen as the most appropriate method for collecting primary data.

A survey is a research instrument collecting important information by posing questions

(Ruane 2005), which produces ‘a quantitative description of trend, attitudes, or opinions of a

population by studying a sample of that population’ (Creswell 2014, p. 13). Hence, the survey

method chosen for data collection is useful for the researcher in gathering attitudes and

opinions of individuals concerning the phenomenon of interest, based on the intention of

finding empirical evidence (Bryman 2011). As results from the survey, ‘quantitative data refers

to measurements in which numbers are used directly to represent the characteristics of

something’ (Hair et al. 2011, p. 145). Surveys involve the collection of data from samples of a

large population with the purposes of generalising from a sample to a population (Fowler 2008,

cited in Creswell 2014) for explorative and explanatory research (Creswell, 2014) as well as

descriptive research (Saunders 2009). Surveys are consistent with a deductive approach

(Saunders 2009). In addition, surveys are a highly effective social and behavioural science

assessment tool (Ruel, Wagner & Gillespie 2016) because of their flexibility, considering the

many instruments and data collection options available (Ruel, Wagner & Gillespie 2016), and

are therefore useful in hypothesis testing (Huber & Power 1985).

Surveys are suitable for obtaining large amounts of data (Hair et al. 2011), which is useful

given the number of manufacturing companies and their widespread geographical dispersal

across Indonesia, especially in Java. The data gathered from the survey could be used to suggest

possible explanations for different relationships between variables explored in this thesis and

to establish a proposed theoretical model based on these relationships (Saunders 2009).

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Furthermore, the purpose of providing an overview of the implementation of CSR among

Indonesian manufacturing companies at a certain time triggered the selection of a questionnaire

survey. In doing so, the researcher can have a clear snapshot of how companies manage CSR

and its integration into business strategy, as well as its impact on company performance, a

position that has been especially lacking in the preceding literature.

Figure 4.1 illustrates the complete research approach of this thesis.

Figure 4.1: Research Approach of This Thesis

4.4 Questionnaire Development

This section explains how a questionnaire in this thesis is developed that includes several

steps. Then, it is divided into two subsections: (i) the development of the measurements and

their scales and (ii) pilot study.

A questionnaire is a prepared set of questions (or measures) used by respondents to record

data (Hair et al. 2011; Ruel, Wagner & Gillespie 2016). A good questionnaire will ‘stand alone’

and allow a researcher to collect data without having to make any personal interactions with

the respondent, thus overcoming most time and space barriers (Ruane 2005). The survey in this

Philosophies

Positivism

Approaches

Deductive

Methodologies

Quantitative

Method/Strategy

Survey

Approach

Questionnaire

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thesis therefore used the questionnaire for data collection (Fowler 2008, cited in Creswell 2014)

since it is appropriate for explanatory research (Saunders 2009) and used extensively in surveys

(Lewis-Beck, Bryman & Liao 2004).

The questionnaire was developed sequentially in several steps (Churchill 1979). First, a draft

of the questionnaire was designed based on the theoretical framework developed in the

previous chapter by borrowing several variables from the literature. Second, in the process of

developing the questionnaire, a brief discussion was held with 20 executives who have

adequate knowledge and working experience in the Indonesia manufacturing industry. They

were asked to choose five variables for business strategy, CSR strategy and strategic integration

and functional integration, which were the most commonly applicable and relevant to their

company’s situation and condition. In relation to company performance, they were also asked

to select four indicators for each performance (i.e., customer, employee, operating, and

financial performances), which were most frequently used to measure their company’s

performance. On obtaining input from the discussion, an initial version of the questionnaire

was developed. Third, three pre-test procedures were implemented to check the questionnaire

was understood by individuals (Hilton 2017; Ruel, Wagner & Gillespie 2016). Pre-test is a

critical way of minimising measurement error, decreasing respondent workload, and increasing

the response rate (Caspar 2016; Hilton 2017; Ruel, Wagner & Gillespie 2016). Results from

the pre-test determine how well each questionnaire item actually signifies the construct to be

measured; hence, suggest improvements for the instrument design (Ruel, Wagner & Gillespie

2016).

In this thesis, pre-tests were conducted in three stages. First, both supervisors of this thesis

initially systematically evaluated the pool of items for relevance to this thesis (Fowler 2014).

They then reviewed the resulting questionnaire several times, making some improvements to

the design of the instrument, specifically wording and spacing but not the meaning of the

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question. Second, the draft of questionnaire was given to two Indonesian academics with CSR

expertise for expert-driven pre-testing, which is essential for evaluating the face and construct

validity of a measurement (Nunnaly 1994; Ruel, Wagner & Gillespie 2016). These academics

were asked to provide whether the questions were relevant to CSR in an Indonesian context.

Last, a respondent-driven pre-test (Ruel, Wagner & Gillespie 2016) was conducted with 10

respondents. The selected respondents were senior managers with adequate manufacturing

industry experience and had a high managerial position in their companies; for example,

directors or managers in functional areas, such as plant, production and logistics managers.

They evaluated whether the questions were relevant to the Indonesian manufacturing industry.

In these three pre-tests, the experts and respondents helped to verify if all survey questions

were appropriate and necessary and to determine whether the survey moved smoothly from

one question to the next (Ruel, Wagner & Gillespie 2016). Overall, 14 individuals participated

in the pre-tests, consistent with suggestions that surveys should be pre-tested with 12 to 50

people (Ruel, Wagner & Gillespie 2016). To avoid bias, different individuals were involved in

the initial discussion (before pre-tests) and pre-tests.

Two key suggestions emerged from the pre-tests. First, respondents noted that the

questionnaire needed to be translated into the Indonesian language to save time for reading and

to avoid misunderstanding. As a result, one bilingual and native Indonesian translator translated

the questionnaire into Indonesian (original translation), which was then back-translated into

English by one bilingual and native English translator to ensure accuracy (Brislin 1970;

Saunders 2009). This process took extensive refinements until the translated instruments had

conceptual and practical equivalence (Cavusgil & Das 1997). The few discrepancies observed

between the original questionnaire and its back-translated version was only minor and easily

addressed by the translators. Both the Indonesian and English translators also confirmed that

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in each sentence, the two versions of the questionnaire (Indonesian and English) had

appropriate meaning corresponding to the content of the message.

Second, respondents suggested that questions should be put into shorter sentences. Thus,

the sentences in the questionnaire were simplified, particularly because they were translated

into Indonesian. Each statement was checked to ensure that its wording was as correct as

possible (Churchill 1979). This suggestion resulted in small adjustments to the content.

However, none of the items were removed in response to suggestions from the pre-test; all

items were used as they fulfilled the requirements for the item analysis.

Appendices A.2 and A.3 display the full questionnaire in English and Indonesian,

respectively. In total, the questionnaire consisted of 105 questions, covering the following

areas: 10 questions related to business strategy, 20 questions related to CSR strategy, 15

questions related to strategic integration, 30 questions related to functional integration, and 19

questions related to company performance. The remaining 11 questions related to

demographics. The questionnaire was designed so that the dependent variables (company

performance) followed the independent variables (business strategy, CSR strategy, strategic

integration and functional integration) to minimise the impact of consistency motif (Salancik

& Pfeffer 1977).

The questionnaire is an efficient way to collect responses from a large sample as each

respondent is required to answer the same set of questions (Saunders 2009). Because of cost

savings, restricted resources and the vast sample area, the survey questionnaire was self-

administered (Ruane 2005; Ruel, Wagner & Gillespie 2016), which provides the best access

and answer rates in anonymous surveys (Ruel, Wagner & Gillespie 2016). More specifically,

with self-report questionnaires, the degree to which individual items reflect the structure being

evaluated and cover the entire spectrum of the structure (content validity) can be determined

(Field 2009). In the questionnaire, closed-ended questions were provided as predefined

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multiple choices available for each question, so respondents could simply click or circle the

appropriate answer (Fowler 2014). Since closed-ended questions indicate individual answers,

they are likely to be more consistent over time and offer greater reliability (Ruel, Wagner &

Gillespie 2016).

4.4.1 The Development of the Measurements and Their Scales

This section shows how the measurements and scales for each variable in this thesis are

developed. The questionnaire contains dependent and independent variables. The independent

variables (predictors) include business strategy, CSR strategy, strategic integration and

functional integration, while the dependent variables (outcomes) are company’s performances.

This section deals with the measurement items for each part (variable) of the questionnaire and

their scales. Scales include a collection of answers that represent ordered points on a continuum

of possible responses (Ruel, Wagner & Gillespie 2016). A measurement scale can be defined

as an instrument with a predefined number of closed-ended answers that can be used in

response to a question (Hair et al. 2017).

Objective performance assessment is generally difficult to predict, particularly how much

strategic planning affects this performance (Greenley & Foxall 1997). There is a large degree

of convergence between objective and subjective financial performance measures

(Venkatraman & Vasudevan 1987). The strategic management literature also supports the use

of subjective performance measurement (Dess & Robinson 1984; Kabadayi, Eyuboglu &

Thomas 2007; Lee & Miller 1996; Nandakumar, Ghobadian & O'Regan 2011; Pearce, Robbins

& Robinson 1987; Richard, Abdul & Andrew 1995; Wai-Kwong, Priem & Cycyota 2001).

In this thesis, the measurement of independent and dependent variables uses self-reported

data as subjective measures in the context of Indonesian manufacturing companies for three

reasons. First, measuring CSR objectively is not possible; thus, research in this field can focus

on perceptions of CSR (Cochran & Wood 1984). Second, due to limited company annual

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reports which can be accessed publicly, and limited company websites, this thesis could access

descriptions of company performance through secondary data (i.e., objective measurements).

The latest data from GRI and IDX revealed that 109 sustainability reports were issued out of a

total of 629 listed companies (Kencana 2019), and only 59 organisations have employed GRI

as a way to report their CSR practices (GRI 2017). Third, Indonesian manufacturing companies

are mostly reluctant to reveal information concerning their performance, mainly financial

performance, as it is confidential. Consequently, poor accessibility served as a barrier to

objective performance data (Shields, Deng & Kato 2000). As a result, subjective measures,

which rely on respondents’ perceptions, were used as a substitute and viable alternative (Kim,

Nam & Stimpert 2004; Venkatraman & Ramanujam 1986).

4.4.1.1 Measurements of Business Strategy

This subsection explains the measurement of business strategy used in this thesis. As

previously stated, this thesis follows the most widely-used typology from Porter (1980a, 1985):

cost leadership and differentiation strategy. The measurement of these strategies is adopted

from prior studies, mainly Dess and Davis (1984). Other items of cost leadership were taken

from Luo and Zhao (2004), whereas other questions of differentiation were borrowed from

Robinson and Pearce (1988). The cost leadership and differentiation strategies are considered

as two strategic positioning dimensions along which companies may score high or low

(O'Farrell, Hitchens & Moffat 1992) rather than two opposite ends of a continuum. In this

thesis, therefore, the extent to which a company's strategy conforms to a particular strategic

type was assessed using a five-point scale designed to measure each strategic type, ranging

from 1=‘least important’ to 5=‘most important’ (Dess & Davis 1984). This approach allows

hybrid or mixed strategies to be considered. The details of measurement items for business

strategy and their codes are summarised in Table 4.1.

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Table 4.1: Business Strategy Measurement Items

Strategy Supporting Literature

Cost leadership

BS01 Pursuing operating efficiency Dess and Davis (1984)

BS02 Controlling the product quality

BS03 Developing/refining existing products

BS04 Innovation in manufacturing processes

BS05 Emphasis on the efficiency of securing raw materials or components Luo and Zhao (2004)

Differentiation

BS06 Developing brand identification Dess and Davis (1984)

BS07 Having cooperative and supportive channels of distribution

BS08 Creating new product development

BS09 Providing customer service capabilities Robinson and Pearce (1988)

BS10 Innovation in marketing techniques and methods Dess and Davis (1984)

4.4.1.2 The Measurement of CSR Strategy

This subsection identifies the measurement of CSR strategy as other independent variable

in this thesis. The measurement of CSR was borrowed from Maignan and Ferrell (2000, 2001).

They developed an instrument to measure CSR practices based on Carroll’s four dimensions

(Carroll 1991) involving three primary stakeholders (i.e., customers, employees, and public).

Their instrument has been one of the most widely used scales in CSR research (Dhanesh 2014)

and adopted in several studies that examine the impact of CSR on company performance, for

example, Ali et al. (2020), Chen, Hong and Occa (2019), Kunda, Ataman and Behram (2019)

and Latif et al. (2020).

Using a five-point scale ranging from 1=‘strongly disagree’ to 5=‘strongly agree’, the

respondents were asked to indicate how they agree or disagree with each of CSR practice stated

in the questionnaire (Dhanesh 2014; Maignan & Ferrell 2000, 2001). Table 4.2 presents the

measurement items of the CSR strategy with their codes.

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Table 4.2: CSR Strategy Measurement Items

CSR Dimension Supporting Literature

Economic responsibility

CS01 We strive to lower our operating costs. Maignan and Ferrell (2000)

CS02 We closely monitor employees’ productivity.

CS03 Top management establishes long-term strategies.

CS04 We have a procedure in place to respond to every customer complaint. Maignan and Ferrell (2001)

CS05 We continually improve the quality of our products.

Legal responsibility

CS06 Internal policies prevent discrimination in employee’s compensation

and promotion.

Maignan and Ferrell (2001)

CS07 We seek to comply with all laws regulating hiring and employee benefits. Maignan and Ferrell (2000)

CS08 All our products meet legal standards. Maignan and Ferrell (2001)

CS09 Our contractual obligations are always honoured.

CS10 Managers are informed about relevant environmental laws.

Ethical responsibility

CS11 We have a comprehensive code of conduct. Maignan and Ferrell (2000)

CS12 We are recognised as a trustworthy company.

CS13 Fairness toward co-workers and business partners is an integral part of

the employee evaluation process.

CS14 We have a proper procedure for employees to report any misconduct at

work.

CS15 Members of our company follow professional standards. Maignan and Ferrell (2001)

Discretionary/philanthropy responsibility

CS16 We give adequate contributions to charities. Maignan and Ferrell (2000)

CS17 We encourage partnerships with local businesses and schools.

CS18 We give a donation for sport and/or cultural activities. Maignan and Ferrell (2001)

CS19 A program is in place to reduce the amount of energy and materials

wasted in our business.


CS20 We encourage employees to join civic organisations that support our

community.


4.4.1.3 Measurement of Strategic Integration

This subsection lists the measurement of CSR integration at the strategic level. As stated

before, strategic integration is an independent variable, with three dimensions: (1) aligning

CSR with the company’s strategy, (2) gaining support from top management, and (3)

developing effective communication. Table 4.3 displays the operationalisation of these three

dimensions (constructs). Several items were adopted from empirical quantitative studies

(Bernal-Conesa, de Nieves-Nieto & Briones-Peñalver 2016; Maignan & Ferrell 2001; Reverte,

Gómez-Melero & Cegarra-Navarro 2016; Swink, Narasimhan & Kim 2005). Other items were

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taken from either explorative, qualitative, or case studies (Busaya, Kalayanee & Gary 2009;

Dey & Sircar 2012; Guadamillas-Gómez, Donate-Manzanares & Škerlavaj 2010; Maon,

Lindgreen & Swaen 2009; Werre 2003). All items used a five-point scale with higher values

showing more positive responses to the item scaled (1=‘strongly disagree’ to 5=‘strongly

agree’).

Table 4.3: Strategic Integration Measurement Items

Dimension of CSR Strategic Integration Supporting Literature

Aligning CSR with the company’s strategy

SI01 We establish CSR as one of the main long-term goals

of our company.

Werre (2003)

SI02 Objectives have been established relating to social and

environmental aspects.

Bernal-Conesa, de Nieves-Nieto and

Briones-Peñalver (2017)

SI03 Mechanisms are available for evaluating the results of

the objectives.

SI04 CSR strategy is well aligned with corporate vision and

mission.

Dey and Sircar (2012); Guadamillas-

Gómez, Donate-Manzanares and Škerlavaj

(2010)

SI05 Continuous improvement and/or preventive actions

are made in the area of CSR.

Asif et al. (2013); Bernal-Conesa, de

Nieves-Nieto and Briones-Peñalver (2017)

Gaining support from top management

SI06 Top management formulates and shares a clear vision

and core corporate values with regards to CSR.

Dey and Sircar (2012); Guadamillas-

Gómez, Donate-Manzanares and Škerlavaj

(2010)

SI07 Top management remains responsive to the issues

related to CSR.



SI08 Top management provides mentoring and coaching to

managers to develop decision-making skills that

integrate CSR criteria in evaluating options.

Guadamillas-Gómez, Donate-Manzanares

and Škerlavaj (2010); Werre (2003)

SI09 We systematically organize CSR efforts.

SI10 We conduct team meeting regularly with top-

management with CSR as a fundamental topic.

Develop effective communication

SI11 CSR strategies and goals are clearly communicated to

all employees.

Maon, Lindgreen and Swaen (2009);

Swink, Narasimhan and Kim (2005)

SI12 We use IT by intensifying the company's presence on

the Internet and social networks to communicate CSR.

Reverte, Gómez-Melero and Cegarra-

Navarro (2016)

SI13 We communicate CSR activities within the company

through multiple channels, such as face-to-face

meetings, formal communications from senior

managers, and a company newsletter.

Maon, Lindgreen and Swaen (2009);

Werre (2003)

SI14 We create CSR report with detailed CSR activities

information.

Massimo et al. (2014)

Busaya, Kalayanee and Gary (2009);

Guadamillas-Gómez, Donate-Manzanares

and Škerlavaj (2010)

SI15 We provide CSR information on the company’s web.

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4.4.1.4 The Measurement of Functional Integration

The measurement of functional integration as another independent variable in this thesis is

presented in this subsection. As explaind in Chapter 3, functional integration contains six

dimensions: (1) Cost, (2) Innovation, (3) Quality, (4) Supplier, (5) Customer and (6) Employee.

Table 4.4 presents the measurement items for these six dimensions, adopted from previous

studies on strategic management, CSR, and manufacturing. Using a five-point scale with higher

values implying more positive responses to the item scaled (1=’strongly disagree’ to

5=’strongly agree’), the respondents were required to show to what extent they agree or

disagree with each statement.

Table 4.4: Functional Integration Measurement Items

Dimensions of CSR Functional Integration Supporting Literature

Cost

FI01 We achieve/maintain the lowest production cost. Chi (2015)

FI02 We reduce material costs. Ward et al. (1995)

FI03 We reduce overhead costs.

FI04 We increase labour productivity. Chi (2015)

FI05 We increase capacity utilization.

Innovation

FI06 We actively conduct product innovation to improve the product

and service.

Reverte, Gómez-Melero and

Cegarra-Navarro (2016); Xie et al.

(2017); Kun, Nasrin and Weiquan

(2019)

FI07 We develop environmentally friendly products.

FI08 We use eco-friendly technologies and materials in our

processes, products, and packaging.

Witek-Hajduk and Zaborek (2016)

FI09 We produce high-quality products which use raw materials up

to standard and do not use hazardous materials.

Xie et al. (2017)

FI10 We have introduced innovations and improvements in

production processes, logistics or distribution.


Cegarra-Navarro (2016)

Quality

FI11 We reduce defective rates. Ward et al. (1995)

FI12 We improve product performance and reliability.

FI13 We implement quality control circles. Ward et al. (1995)

FI14 Our products and/or services satisfy national and/or

international quality standards.

Gallardo-Vázquez and Sanchez-

Hernandez (2014); Reverte, Gómez-

Melero and Cegarra-Navarro (2016)

FI15 We enforce strict product quality control procedures. Kotha and Vadlamani (1995);

Robinson and Pearce (1988)

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Suppliers

FI16 We treat suppliers, regardless of their size and location, fairly

and respectfully.


FI17 We seek to provide training to our suppliers and partners in

business activities.


Hernandez (2014)

FI18 We incorporate the interests of our suppliers in our business

decisions.

Lindgreen et al. (2009); Martinez-

Conesa, Soto-Acosta and Palacios-

Manzano (2017)

FI19 We inform our suppliers about company changes affecting our

purchasing decisions.

FI20 We strive to enhance stable relationships of collaboration with

our suppliers.


Hernandez (2014)

Customers

FI21 We provide our customers with accurate and complete

information about our products and/or services.


Hernandez (2014)

FI22 We provide a prompt response to the complaints of our

customers about products and/or services.

Martinez-Conesa, Soto-Acosta and

Palacios-Manzano (2017)

FI23 We incorporate the interests of our customers in our business

decisions.


FI24 We provide responsive and fair after-sales service. Stoian and Gilman (2017)

FI25 Our company is honest with the customers in the sale or

promotion of products and services.

Xie et al. (2017)

Employees

FI26 We provide procedures to help to insure the health and safety

of our employees.



Lindgreen et al. (2009), Torugsa,

O'Donohue and Hecker (2013)


Cegarra-Navarro (2016)

FI27 We treat our employees fairly and respectfully, regardless of

gender or ethnic background.

FI28 We incorporate the interests of our employees in our business

decisions.

FI29 We provide our employees with salaries that properly and fairly

reward them for their work.

FI30 We update the social and environmental knowledge through

the training of our employees.


Briones-Peñalver (2016); Torugsa,

O'Donohue and Hecker (2013);

Werre (2003)

4.4.1.5 Measurement of Company Performance

The measurement of company performance as a dependent variable is listed in this

subsection. Subjective measures were employed to obtain financial and non-financial

performance measurement through questionnaires that simultaneously elicit information on

practices (Wall et al. 2004). In this thesis, customers, employees, operating, and financial

performances are measured to comprehensively assess CP (CP). As presented in Table 4.5, the

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measurement items were adopted from the strategic management, manufacturing, and CSR

literature. Respondents were asked to rate their company’s performance relative to their

competitors (Lindgreen et al. 2009; Wall et al. 2004; Wang, Dou & Jia 2016) over the most

recent three-year period (Bu¨ yu¨ kbalcı 2012; Effendi & Kusmantini 2015; Maignan & Ferrell

2001; Michelon, Boesso & Kumar 2013; Richard & Marilyn 2006).

Details on measurement scales of CP are shown in Table 4.5. A five-point-scale was used

to rate responses on 19 items, from 1=‘much longer/much worse/much lower’ to 5=‘much

shorter/ much better/much higher’. In addition, the respondents were requested to give their

opinion about overall CP with the scales ranging from 1=‘very unsatisfied’ to 5=‘very

satisfied’.

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Table 4.5: Company Performance Measurement Items

Company Performance Supporting Literature

Operational performance

CP01 Compared to our competitors, our timeline of customer

service is

Ping-Ju Wu, Straub and Liang (2015)

CP02 Compared to our competitors, our delivery time is Chi (2015); Ward et al. (1995)

CP06 Compared to our competitors, our productivity is Ping-Ju Wu, Straub and Liang (2015);

Reverte, Gómez-Melero and Cegarra-

Navarro (2016)

CP07 Compared to our competitors, our operational efficiency is Reimann, Schilke and Thomas (2010);

Zahra and Covin (1993)

Financial performance

CP03 Compared to our competitors, our cash flow report is Wright, Proimos and Lau (2008)

CP08 Compared to our competitors, our profit is Bernal-Conesa, de Nieves-Nieto and

Briones-Peñalver (2016); Chi (2015);


Hernandez (2014); Santos and Brito

(2012); Schniederjans and Cao (2009);

Venkatraman and Vasudevan (1987)

CP09 Compared to our competitors, our return on investment

(ROI) is

CP10 Compared to our competitors, our sales growth is

Customer performance

CP11 Compared to our competitors, our customer complaints are Gallardo-Vázquez and Sanchez-

Hernandez (2014); Santos and Brito

(2012)

CP12 Compared to our competitors, our customer satisfaction is

CP13 Compared to our competitors, our customer loyalty is

CP14 Compared to our competitors, our increasing number of

consumers is

Ferraz, António and Gallardo-Vázquez

(2016)

Employee performance

CP04 Compared to our competitors, our training of employee is Bernal-Conesa, de Nieves-Nieto and

Briones-Peñalver (2016); Gallardo-

Vázquez and Sanchez-Hernandez

(2014); Santos and Brito (2012),

Torugsa, O'Donohue and Hecker (2013)

CP05 Compared to our competitors, career opportunities in our

company are

CP15 Compared to our competitors, our employee motivation is Massimo et al. (2014)


Briones-Peñalver (2016); Reverte,

Gómez-Melero and Cegarra-Navarro

(2016); Santos and Brito (2012)

CP16 Compared to our competitors, our employee turnover is

Overall performance

CP17 Overall, with the operational excellence, we are Dess and Robinson (1984); Robinson

and Pearce (1988); Richard, Abdul and

Andrew (1995); Kim, Nam and

Stimpert (2004)

CP18 Overall, with the financial performance, we are

CP19 Overall, with the social performance, we are

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4.4.1.6 Demographic Information

This subsection describes demographic information collected by the questionnaire.

Regarding the respondents and their organisations, the information relates to their position in

their organisations, their age, their educational level, and their working experience.

Control variables are a specific type of independent variable that potentially affect the

dependent variable (Creswell 2014). In this thesis, demographic variables (i.e., company size

and type of industry) are used as control variables to describe the variations between

companies. Two control variables were chosen because of their possible effect on CSR

practices and CP:

1. Company size. In this thesis, company size is measured by the number of employees (Ağan

et al. 2016; Chtourou & Triki 2017; Effendi & Kusmantini 2015; Lao, Hong & Rao 2010;

Marín, Rubio & de Maya 2012; Swink, Narasimhan & Kim 2005; Wei et al. 2016; Young

& Makhija 2014). Companies are classified as small (5–19 employees), medium (20–99

employees), and large companies (more than 100 employees) (BPS 2017a). Another

criterion of company size is the turnover: small companies with 300 million rupiahs up to

2.5 billion rupiahs, medium companies with 2.5 billion rupiahs up to 50 billion rupiahs, and

large companies with more than 50 billion rupiahs. Although both criteria were mentioned

in the questionnaire, several respondents gave a ‘not applicable’ answer for the latter

question. Therefore, the former criterion (number of employees) was used in the data

analysis to measure company size.

2. Type of industry. Industry type follows the 33 groups of manufacturers following the

Indonesian Standard Industrial Classification (ISIC) 2009 with two digits (BPS 2017b). The

standard code of business field of an industrial company (ISIC code) is determined based

on its main production, which is the type of commodity produced with the greatest value. If

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an industrial company produces two or more types of commodities with the same value, the

main production is the commodity produced with the largest quantity (Statistik 2020).

4.4.2 Pilot Study

This subsection explains a pilot study conducted in this thesis. Prior to real data collection

and before questionnaires were launched, a pilot study was performed to assess the adequacy

of the research instrument regarding the content, wording, relevance of the questions, format,

and scales (Creswell 2014; Hair et al. 2010; Hulland, Baumgartner & Smith 2018). Through

the pilot study, the researcher could ensure that respondents could understand the questionnaire

appropriately (Gallardo-Vázquez & Sanchez-Hernandez 2014) and have no difficulty

answering the questions (Saunders 2009). In this thesis, the pilot study involved 30 respondents

(Ruel, Wagner & Gillespie 2016) selected based on the sample criteria. They reported that the

questionnaire could be understood obviously as the instructions and questions were brief and

unambiguous (Ruel, Wagner & Gillespie 2016), simple and easy to answer (Ruane 2005). They

also stated that the layout of the questionnaire was clear and attractive (Ruane 2005). Because

it was an anonymous survey, they could give natural and spontaneous answers and complete it

in approximately 15 minutes. The results of this pilot test indicated that most questions had a

wide range of responses, and in general, were related to the current situation and condition of

the manufacturing industry in Indonesia.

The pilot study is often known as a rehearsal of the entire study from start to finish, which

increases the possibility of the main study’s success. The raw data resulting from the pilot study

can be used to evaluate data entry and data processing procedures, including preliminary

coding and analysis (Caspar 2016; Ruel, Wagner & Gillespie 2016). After obtaining raw data

from the pilot study, the researcher developed data coding by creating tables for inputting data

and conducting preliminary analysis, such as descriptive analysis, validity and reliability tests.

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As a result, the researcher could check whether initial data input and coding was correctly,

efficiently and accurately performed in a timely manner (Ruel, Wagner & Gillespie 2016).

4.5 Sampling Process

The sampling process used in this thesis is described in this section. It starts with a

discussion of the population and sample, then moves on to the sampling frame. The sample

size employed in this thesis is then presented.

4.5.1 Population and Sample

This subsection explains the population and sample used in this thesis. As explained in

Chapter 2, there are several reasons why this thesis was conducted in the manufacturing

industry, particularly in Indonesia. In addition to these reasons, the manufacturing industry is

more likely to provide variation in the variables of interest, such as product type and company

size. It is also easier to identify and measure some variables (such as cost, innovation, quality,

productivity, and operating efficiency), particularly at the functional level, and operational

performance among manufacturing companies than companies in service or financial sectors.

Therefore, the manufacturing industry provides an interesting case study that inspires the

researcher to explore something fresh in a specific field (Myers 2009).

The target population is large enough across a large geographic region, so a sample could

be taken that reflects the population as a whole (Field 2009) and describes the propensities of

the population to provide the researcher with a plausible explanation of research questions

(Saunders 2009). A unit of analysis is the entity whose data will be collected (Ruane 2005).

This thesis took a sample of manufacturing companies in Java with organisations as the unit

of analysis for two reasons. First, around 64.29% of 4.41 million Indonesian manufacturing

companies are located in Java (Agustinus 2017), and most the industrial estates are in Java

(IndustryToday 2016). Second, Java is the most significant contributor to the Indonesian

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manufacturing industry, representing over 70% of its national GDP (BPS 2017a), and 47.62%

of national exports (Aisyah 2019). To be able to generalise, the sample study covered varying

company sizes, as explained in the previous subsection.

4.5.2 Sampling Frame

Following the description of population and sample, this subsection presents a sampling

frame used in this thesis. A sampling frame is ‘a comprehensive list of the elements from which

the sample is drawn’ (Hair et al. 2011, p. 166). The sampling frame of a study can be defined

as the set of people that has a possibility to be chosen as participants (Fowler 2014; Ruel,

Wagner & Gillespie 2016). The main sampling frame used in this thesis was the Manufacturing

Industrial Directory 2017 (BPS 2017b), which includes the list of manufacturing companies in

Indonesia. According to this sample frame, the sample study covered 33 manufacturing sector

of ISIC 2009 and encompassed five regions in Java, namely East Java, Central Java,

Yogyakarta, West Java, and Jakarta.

To facilitate data collection, this thesis selected several cities according to two criteria: (1)

high manufacturing industry density and (2) with industrial estates. East Java included

Surabaya, Sidoarjo, Gresik, Pasuruan and Mojokerto. Centre Java was represented by

Semarang, and Yogyakarta included the special region of Yogyakarta. West Java covered

Tangerang, Bekasi, and Bandung, while Jakarta represented the Jakarta metropolitan region.

In addition to industrial estates in selected cities, five economic centres in Java are Jakarta,

Bandung, Semarang, Yogyakarta and Surabaya (Indonesia 2011).

Since the Manufacturing Industrial Directory 2017 contains information about the

manufacturing industry throughout Indonesia, the information for the company (i.e., the

company name, address, product type, company size, and email address (if any)) were grouped

into cities in which the survey would be conducted. This grouping was supplemented by

information from additional sampling frames as follows:

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1. The company list of the bonded zone in Pasuruan (53 companies).

2. The investor list of Kawasan Industri Wijayakusuma (KIW) Semarang (51 companies).

3. The tenant list of Jakarta Industrial Estate Pulogadung (JIEP) (253 companies).

4. The company list of internship and alumni from the Industrial Engineering Department,

University of Surabaya (300 companies).

To sum up, this thesis used main and supplementary sample frames to collect data as

expected. The survey would cover five regions in Java based on these sampling frames.

4.5.3 Sample Size

Sample size is described in this subsection. Given the large target population and the need

to obtain the estimation of its characteristics efficiently, an appropriate sample size is required.

As suggested by Cohen (1992), the minimum sample size needed for a medium size effect

(r=0.3) and a power of 0.8 with the standard alpha-level of 0.05 is 85 (Field 2009). Furthermore,

Hair et al. (2017, p. 24) recommend that the minimum sample size required for analysing data

using PLS-SEM is ‘ten times the largest number of structural paths directed at a particular

construct in the structural model’. As shown in the conceptual framework (see Figure 4.7 for

details), six is the largest number of structural paths directed at a particular construct in the

structural model, which reflects the connection from six exogenous constructs to functional

integration. Consequently, the minimum sample size required is 10 x 6 = 60. The largest

number was used (i.e., 85) as the required sample size in this thesis.

Every attempt was made to select organisations that had the means to maintain awareness

of CSR. There were three criteria to select appropriate samples: (1) companies must operate in

the manufacturing sector, (2) companies must be in Java, and (3) companies must have more

than five employees.

In summary, this section describes how to determine the sample size using specific formulas.

Three criteria are also established in order to identify the proper sample size for this thesis.

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4.5.4 Sampling Technique

A sampling technique that would be conducted in this thesis is explained in this subsection.

Because there is a large number of samples spread over several geographical areas, it is difficult

to determine the probability that each case will be included in the sample, and it is costly, in

terms of time and resources, to access the units of analysis (Huber & Power 1985). Purposive

sampling techniques may be defined as unit selection (e.g., individuals, groups of individuals,

institutions) based on specific purposes related to answering the questions of a research study

(Teddlie & Yu 2007). Thus, purposive sampling (non-random sampling) is considered

appropriate to select units of the sample on the basis of subjective personal judgement and

convenience that enables the researcher to answer research questions (Saunders 2009; Zikmund

et al. 2017), such as conformity with the sample criteria.

4.6 Data Collection

This section discusses the data collection carried out in this thesis. It is divided into two

subsections: (i) data collection methods and (ii) results of data collection.

4.6.1 Data Collection Methods

Data collection methods used in this thesis are explained in this subsection. It contains

online, mail, and personal delivery surveys.

Methods of data collection refer to a procedure for capturing what is relevant from the data

generated to address the research question (Perri & Bellamy 2012). As explained in the

previous subsection, this thesis employed a survey to obtain large quantities of data. The survey

was cross-sectional as it was conducted at a single point in time (Ruane 2005; Ruel, Wagner &

Gillespie 2016; Saunders 2009). In addition to mail and electronical (e-mail and online)

distribution (Creswell 2014), the survey was also administered by delivering the questionnaire

by hand to the potential respondents and collecting later (Saunders 2009). This thesis employed

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these three methods as self-completion methods (Hair et al. 2011) with the aim of minimising

survey nonresponse and reaching respondents who were inaccessible via a single mode alone

(Fowler 2014). Conducting three different types of survey methods also enabled the effective

use of a survey by adapting various data collection methods to the target sample (Ruel, Wagner

& Gillespie 2016).

Following the recommendation of Huber and Power (1985), a single informant was included

in the sample for each company. The questionnaire was then delivered to executives or

managers at the strategic or upper level of the company, including CEOs, directors, managers,

and team leaders to obtain their perceptions concerning each question (indicator). These

individuals are more likely to have a comprehensive view of their company strategy, activities

and implementation of CSR and would therefore be able to fill in the questionnaire (Huber &

Power 1985). Hence, they were considered key informants to minimise bias (Miller & Roth

1994).

4.6.1.1 Online Survey

Online surveys, delivered via e-mail and the internet, have now become a key medium for

the delivery of questionnaires (Ruel, Wagner & Gillespie 2016). Online surveys are popular,

attractive, and inexpensive, and provide quick responses and high-quality data (Hair et al. 2011;

Michaelidou & Dibb 2006). Advantages of online surveys include cost effectiveness, fast

transmission and response turnaround (Fowler 2014; Michaelidou & Dibb 2006; Ruel, Wagner

& Gillespie 2016), the distribution to a target population across a large geographic region and

greater reach of potential respondents (Schmidt 1997; Van Selm & Jankowski 2006).

The questionnaire was designed using SurveyMonkey, whereby the researcher was able to

quickly create a survey using custom templates and send it to respondents (Creswell 2014). To

encourage potential respondents, the layout of the questionnaire was carefully designed.

Closed-ended questions were provided using check boxes for response tags. To draw the

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attention of potential respondents and make the questionnaire friendly and as appealing as

possible, the colour and font of the questionnaire were carefully chosen (Michaelidou & Dibb

2006; Ruel, Wagner & Gillespie 2016). Several tests were undertaken to ensure that responses

could be smoothly transmitted to the sender (the researcher). Once the questionnaire was

completed (in no more than 15 minutes) the answers were transmitted anonymously to the

sender.

After compiling information from the sampling frames, it was identified that approximately

600 companies had an e-mail address. Subsequently, a covering letter was e-mailed to these

companies explaining the purpose of the study and inviting them to access an complete the

questionnaire via a URL (Fowler 2014; Ruel, Wagner & Gillespie 2016; Saunders 2009; Van

Selm & Jankowski 2006). However, approximately 50 messages were not delivered because

the address could not be found or was unable to receive e-mail. Therefore, 550 messages could

be delivered to the potential respondents. An intensive follow-up process was undertaken via

e-mail (which is more efficient then telephone calls), as well as to increase the number of

questionnaires received (Ruane 2005).

4.6.1.2 Mail Survey

Not only being distributed online, but the questionnaires were also mailed to companies that

did not have an e-mail address. Mail is appropriate to use with a geographically dispersed

population (Ruel, Wagner & Gillespie 2016). To make this method effective, companies were

selected based on two requirements: (1) preferably large with more than 100 employees, and

(2) full address and telephone number accessible. Both regular mail and overnight delivery

(Hair et al. 2011) were used to deliver a survey package to 254 selected addresses. A cover

letter representing a formal invitation to participate in a research project (Ruel, Wagner &

Gillespie 2016) was attached to each questionnaire, which explained the research objectives

and assured respondents of the confidentiality of their responses and the voluntary nature of

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participation (Hair et al. 2011; Ruel, Wagner & Gillespie 2016; Saunders 2009). A self-

addressed, postage-paid return envelope was also included in the survey package to increase

the likelihood of a response (Ruel, Wagner & Gillespie 2016).

Although the company address was carefully checked before sending the questionnaires,

eight questionnaires were returned due to wrong address or the company moving to another

location. Thus, only 246 questionnaires could reach potential respondents. A rigorous follow-

up via telephone call was undertaken to check whether they received the survey package and

to encourage them to complete the questionnaire and return it. If they said that they did not

receive the survey, the researcher asked their e-mail address and then e-mailed an invitation to

them with a link to the survey online.

4.6.1.3 Personal Delivery Survey

In addition to online and mail surveys, the questionnaires were also administered through

personal delivery and collection. This approach is cost-effective and has a higher response rate

compared to mail deliveries (Lovelock 1976). Using this method, 290 questionnaires were

distributed directly to the potential respondents, including small, medium, and large companies.

Nonetheless, only 259 questionnaires were distributed because 31 addresses could not be

identified. Similar to the mail survey, a self-addressed, postage-paid return envelope was

supplied, and an intensive follow-up via telephone call was conducted to remind the potential

respondents to fill and return it.

Across all methods, anonymity was guaranteed for all respondents as mentioned in the

covering letter, which enabled the respondents to give their answers openly and honestly

without worrying that others might know their identities. Self-identifiers were omitted during

data collection to ensure confidentiality (Ruane 2005). The respondents were assured that

analysis would be conducted at the aggregate level and that neither company nor personal

information could be identified.

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4.6.2 Results of Data Collection

This subsection shows the results of data collection conducted from June to October 2018.

Approximately 1,055 questionnaires were distributed to manufacturing companies in several

selected cities in Java. Table 4.6 shows the period of data collection, indicating that most data

was collected in August 2018 (38.4%), followed by July 2018 (33.8%), then September–

October 2018 (14.5%), and last, June 2018 (13.3%). Since the survey covered a vast area and

employed three data collection methods, the responses could be collected at the same time.

Because the time of the data collection varied by mode, it should be noted that mail surveys

generally took two months to complete, and the online survey reduced collection time (Fowler

2014). The fastest data collection time was through personal surveys as the responses could be

collected immediately.

Table 4.6: Period of Data Collection

Time Frequency Percentage (%)

June 58 13.3

July 147 33.8

August 167 38.4

September–October 63 14.5

To check potential nonresponse bias in the data, the respondents were divided into

categories of early and late respondents (Armstrong & Overton 1977; Fowler 2014; Ghobadian,

O'Regan & Nandakumar 2010). The assumption behind this method is that respondents who

filled the survey in the last period of data collection have reacted because of the increased

stimuli and are supposed to be similar to non-respondents (Armstrong & Overton 1977). Data

collected in June and July (205 cases) were classified as early respondents, while late

respondents were data gathered in August, September and October (230 cases). Because the

data was not normal (see section 4.7.2), the Mann-Whitney test was then conducted as the non-

parametric test (Field 2009; Pallant 2005) to determine whether significant differences existed

across two groups in the means of business strategy, CSR strategy, strategic integration,

functional integration and CP. For example, Appendix A.4 shows the results of the Mann-

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Whitney test of business strategy between early and late respondents. Eight of 10 items had p-

values of above 0.05, indicating no significant difference between the means of responses

received from early and late respondents at a confidence level of 95% (Field 2009; Pallant

2005). Thus, nonresponse bias was not a serious problem in the data set.

Table 4.7 presents the detail of questionnaire distribution for each sampling method. While

1,055 questionnaires were sent, they were not all returned. Several potential respondents

informed the researcher that they were unable to participate because either they were too busy,

or they could not provide the necessary data. Overall, 514 questionnaires were returned,

yielding a response rate of 48.72%. Since many social research studies use surveys and other

voluntary participation methods, the response rate is generally well below 100%. This response

rate of 48.72% is higher than the average response rate of 15 to 20% for surveys involving

senior managers (Menon, Bharadwaj & Howell 1996).

After data screening (explained in the next section), 435 responses remained in the sample

with a final response rate of 41.23%. The methods sampling to collect data (e.g., mail, online,

telephone) can influence the extent to which potential respondents are both able and willing to

engage in a study (Hulland, Baumgartner & Smith 2018). As displayed in Table 4.7, the result

demonstrates that the highest response rate was achieved through the personal survey

(64.86%), followed by the online survey (47.64%), while the mail survey got the lowest

response rate (34.15%). Nonetheless, the response rate of the mail survey was above 20%,

indicating a good result for the mail survey in Indonesia (Jogiyanto, cited in Ghozali &

Sulistyani 2016).

Having employed three survey methods: personal, mail, and online, the data from each

method should be compared to see the differences among them. The Kruskall-Wallis test was

employed as the non-parametric test to assess differences among several independent groups

(Field 2009; Pallant 2005). For instance, Appendix A.5 displays the results of Kruskal-Wallis

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in 15 items of strategic integration. As 14 items had p-values of above 0.05, no statistically

significant differences across three survey methods were found (Pallant 2005).

Table 4.7: Results of Data Collection

Method Sent Returned Response Rate (%)

Personal 259 168 64.86

Mail 246 84 34.15

Online 550 262 47.64

Total 1,055 514 48.72

Given that the number of data collected was 435 responses (the final data set), the minimum

sample size required to conduct PLS-SEM (i.e., 60 responses) was fulfilled (see 4.5.3 for

details). Besides, 435 responses were more than required, mainly to obtain the medium effect

size suggested by Cohen (1992).

4.7 Data Analysis Procedures

Data analysis procedures employed in this thesis is provided in this section. It is broken

down into two subsections, each of which describes (i) data coding and (ii) data screening.

Data analysis methods are procedures for manipulating data so that the research question

can be answered, usually by identifying important patterns (Perri & Bellamy 2012). Data

analysis involves activities to assess, clean, transform and model data collected to obtain

research answers (Creswell & Clark 2018). In this thesis, data analysis comprised six steps:

data coding, data screening, identifying the characteristics of respondents, assessment of the

measurement model, assessment of the structural model, and MGA. Three former steps are

explained in the following subsections, while other three steps will be discussed in the next

chapters.

4.7.1 Data Coding

This section describes how data in this thesis is coded. Before entering data for statistical

analysis, data have been recorded using numerical codes which allows the researcher to input

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the data quickly and to minimise errors (Saunders 2009). Coding is assigning numbers to

categories in a manner that simplifies measurement (Hair et al. 2017). A code, a set of rules

that translates answers into numbers, is essential for reliable data coding and proper data

interpretation (Fowler 2014). Codes or coding categories are a way of sorting or grouping

responses so that information on a given topic can be physically distinguished from other data

(Ruel, Wagner & Gillespie 2016). Thus, well-defined rules for numbering should be provided

for each and every answer (Fowler 2014).

This thesis followed several recommendations from literatures, such as the consistency in

assigning a number, building codes fit numbers when possible, and defining one code for each

answer. To have a clear understanding of each variable, a variable name and label have been

developed which make sense and which relate it to the questionnaire from which it originated

(Ruel, Wagner & Gillespie 2016). As presented in the previous subsection, each question (or

statement) has been considered as a variable which had a specific code (number), indicating its

name and label. Because of the five-point scale, every answer of measured items has a code

from 1 to 5, which indicates the response level from respondents. With regard to demographic

questions, there was a limited range of specified categories which could place the data into

(Saunders 2009). Hence, in order to know its modes and frequency, each category had a code

to simplify the data groupings.

4.7.2 Data Screening

This subsection discusses data screening which consist of several steps. The most essential

check after data coding and entry is to ensure the data files are complete and organised (Fowler

2014). The data screening process is a vital phase to eliminate observations that contain missing

data and outliers (Hair et al. 2010) and also includes the identification of common method bias

and normality. There are six steps undertaken in this thesis regarding the process of data

screening following the recommendations from Hair et al. (2010), Field (2009), and Hair et al.

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(2017). In doing so, this thesis employed statistical technique software, SPSS 26. First, data

screening tested the conformity with the sample criteria. 16 responses from outside Java and

13 responses from nonmanufacturing industry were eliminated because they did not match the

sample criteria as explained in the previous subsection (see 4.5.1).

Second, data screening checked the presence of missing data. Missing data refers to item

values in the questionnaire that did not have any answers from the respondents who participated

in the surveys. Missing data can impact in two major ways: reduction of sample size or

inconclusive results of analysis due to biased outcomes (Hair et al. 2010). Especially on

quantitative research, the effect of missing data can be serious, resulting in skewed parameter

estimates, information loss, decreased statistical capacity, increased standard errors and

weakened generalisability of findings (Dong & Peng 2013). Missing data in this thesis was

identified as an item nonresponse element refers to incomplete information gathered from a

respondent because the respondent may skip one or two questions on a survey, but the rest have

been answered (Dong & Peng 2013). As recommended by Hair et al. (2017), primarily the

observation was eliminated from the data file if the amount of missing data on a questionnaire

exceeds 15%. Subsequently, incomplete 46 responses were removed from the data set to avoid

biases results emerging from the statistical analysis as they were derived from the measurement

section of the questionnaire and related to dependent variables (Hair et al. 2010). After deletion,

there were 23 cases with missing data for each variable (Hair et al. 2010) or per indicator (Hair

et al. 2017). Because they were less than 5%, they have been detected as missing completely

at random (MCAR) (Hair et al. 2010). When missing data follows the MCAR assumption, they

can be interpreted as a random sample of the entire data (Dong & Peng 2013). As responses

with 5% or less missing data in a random pattern were a less critical problem, they remained

in the dataset (Tabachnick & Fidell 2006). Mean substitution, a popular way to estimate

missing values (Hair et al. 2010; Tabachnick & Fidell 2006) is considered appropriate to apply

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as mean replacement usually gives a little difference in PLS-SEM estimates (Hair et al. 2017).

Specifically, this method was employed by replacing the missing values of an indicator variable

with the mean of valid values of that indicator (Hair et al. 2017). Besides, there were four

responses which contained no data related to the demographic queries. Because the

demographic data, especially company size and industry type, were the control variables and

moderators in MGA, these four responses were eliminated to avoid bias in the statistical results.

Third, data were checked for suspicious response patterns. Since the survey was conducted

using questionnaires in this thesis, it frequently asked the same questions with a little

difference, mainly for several items using reflective measures (Hair et al. 2017). There were

three responses identified with straight lining (Hair et al. 2017), indicating the same answer for

all items measured with a five-point scale. Nevertheless, the results of PLS-SEM algorithm as

well as bootstrapping showed that there was a little impact in PLS estimations (Hair et al.

2017); therefore, they remained in the dataset.

Fourth, data screening involved outlier identification. Outliers refer to data with extreme

values that appear different from other data values (Hair et al. 2010). After identifying outliers,

the researcher must decide whether or not to include them in subsequent analyses (Leys et al.

2018). Two outliers were found due to a procedural error that may result from inaccuracies,

such as an error in data entry or an error in coding (Aguinis, Gottfredson & Joo 2013; Hair et

al. 2010; Ruel, Wagner & Gillespie 2016); thus, they were quickly corrected (Hair et al. 2017).

Additionally, 43 outliers were measured using the Mahalanobis distance (D2) as the most

frequently recommended outlier detection approach (Finch 2012). However, there was no

apparent explanation for those outliers which implied extraordinary observations (Hair et al.

2010). As all variables, both dependent and independent variables, were measured using a five-

point scale, those outliers contained observations which fall within the range of values on each

of the variables. Consequently, those outliers were not particularly high or low on the variables,

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but were unique in their combination of values across the variables (Hair et al. 2010). To

examine the impacts of these outliers in the analysis, the results of PLS-SEM algorithm and

bootstrapping were evaluated between with and without outliers. The results showed that these

outliers did not influence the results substantially. Hence, these outliers were retained (Hair et

al. 2010; Hair et al. 2017) as the larger sample size always produce higher power for statistical

testing (Hair et al. 2010). Furthermore, this thesis uses PLS-SEM as a nonparametric

methodology which does not presume that the data are distributed in any particular way. This

flexibility allows researchers to obtain results which are reliable when outliers are present

(Aguinis, Gottfredson & Joo 2013).

Fifth, data were screened for normality by computing the kurtosis and skewness values. As

PLS-SEM is a nonparametric statistical method, it does not need the normal data distribution

(Hair et al. 2017). However, extremely abnormal data may cause problematic in the assessment

of the parameter’s significance; for instance, standard errors (Hair et al. 2017). This thesis

identified the kurtosis and skewness to check properly the distribution of data set. Kurtosis

refers to whether the distribution is more peaked than the normal distribution, while skewness

is to identify whether the distribution is centred or shifted to one side. Generally, values

between -1 and +1 are considered to be acceptable (Hair et al. 2017). Table 4.8 summarises the

skewness and kurtosis values of the dataset per group/construct, and skewness and kurtosis of

all items are presented in Appendix A.7. Overall, almost all data met the threshold value

indicating normal distribution, except the business strategy which its skewness was exceeding

-1, and its kurtosis was above 1. Hence, the data were considered non-normal (Hair et al. 2017).

Table 4.8: Skewness and Kurtosis

Part Skewness Kurtosis

Business strategy (10 items) -1.184 2.431

CSR strategy (20 items) -0.751 0.669

Strategic integration (15 items) -0.478 -0.067

Functional integration (30 items) -0.793 0.831

Company performance (19 items) -0.268 -0.033

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Moreover, the Kolmogorov–Smirnov test was also employed to check the normality.

Appendix A.6 presents results of normality test of business strategy. Significant values both

Kolmogorov-Smirnov and Shapiro-Wilk test of 10 items of business strategy for three survey

methods were less than 0.05, suggesting a violation of the assumption of normality (Field 2009;

Pallant 2005). It should be noted that abnormal data distribution is one of the underlying

considerations of why this thesis conducted PLS-SEM for data analysis. The skewness and

kurtosis for all items can be seen in Appendix A.7.

Finally, an assessment of common method bias was undertaken. Because this thesis used

self-reported instruments for data collection, responses for all constructs, including the

predictor and criterion variable measurement were collected from a single respondent or a same

person (Hulland, Baumgartner & Smith 2018; Podsakoff et al. 2003). In typical survey studies,

data are likely to be susceptible to common method bias in which the same respondent answers

the items in a single questionnaire at the same time (Lindell & Whitney 2001). In this thesis,

common method bias has been diminished through methodological design and post-hoc

analysis suggested by Podsakoff et al. (2003). As explained in the previous subsections (see

4.4), the survey was developed for methodological design through pre-tests including expert-

and respondent-driven pre-tests to ensure that the survey questions were clear, concise, and

specific to the manufacturing industry in the Indonesian context. Moreover, by preserving the

respondent privacy, common method bias can be minimised (Podsakoff et al. 2003). As

explained in the previous section, the cover letter that accompanied the questionnaires

specifically stated that all answers would be handled in the strictest confidence and that no

names or identities of individual companies would be revealed or released to third parties. The

cover letter also explained the purpose of this thesis, which could help to minimise the potential

for common method bias (Hulland, Baumgartner & Smith 2018).

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For a post-hoc (statistical) analysis, three methods were employed. First, as discussed on the

previous subsection, the means of variables between early respondents and late respondents

were compared using the Mann-Whitney test. The result indicated no significant difference

among two groups. Second, this thesis applied Harman’s one factor (or single-factor) test as

one of the most commonly used method by researchers to tackle the problem of common

method variance (Podsakoff et al. 2003). All the 94 measured variables included in the model

were inputted in an exploratory factor analysis (EFA) (Kock 2015) by conducting un-rotated

principal component analysis (PCA) in IBM SPSS 26. The eigenvalue of un-rotated EFA

solution produced the presence of 17 different factors rather than a single factor. These 17

factors together accounted for 66.45% of the total variance, and the highest portion of the

variance explained by a single factor was 31.94%, less than 50%. This result indicated that

common method bias was not present, and single-source bias was not a major concern for this

thesis. Third, this thesis checked the common method bias in the context of PLS-SEM.

Following the guidelines from Kock (2015), variance accounted for (VIF) was evaluated for all

eight constructs. The result showed that their inner VIF values were below 3.3, indicating that

the model could be considered free of common method bias (Kock 2015; Kock & Lynn 2012).

Overall, the results of three statistical analyses revealed that common method bias was not a

critical issue in this thesis.

In conclusion, this thesis employed six steps for data screening. It included checking sample

criteria conformity, indicating missing data, detecting suspicious response patterns, identifying

outlier, and evaluating data normality. Then, common method bias was assessed using

methodological design and statistical analysis. It was expected that data would be clean and

suitable for further data analysis.

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4.8 The Respondents’ Profiles and Characteristics

This section shows the details on the characteristics of respondents as summarised in Table

4.9. Specifically, Figure 4.2 displays the breakdown of respondents’ position. Because most of

the questions in the questionnaire demanded information at the strategic and functional level,

regarding the business strategy, CSR practices, and company performance, the respondents’

position was considered to be an essential issue in this thesis. Largely fulfilling this

requirement, most respondents had a high position in their companies: 23.4% were in the top

management position (owner, CEO, and director) and 57.8% occupied the managerial position

(senior, middle and assistant manager).

Table 4.9: Respondents’ Profiles

Variable Indicator Frequency (n = 435) Percentage (per cent)

Position owner 73 16.8

CEO 8 1.8

director 21 4.8

senior manager 113 26.0

middle manager 76 17.5

assistant manager 62 14.3

team leader 52 12.0

others 30 6.9

Working

experience (years)

< 5 165 37.9

6-10 102 23.4

11-20 104 23.9

> 20 64 14.7

Age (years) < 25 47 10.8

25 - 30 66 15.2

31 - 40 98 22.5

41 - 50 140 32.2

51 - 60 77 17.7

> 60 7 1.6

Education high school 35 8.0

diploma 30 6.9

bachelor 279 64.1

postgraduate 91 20.9

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Figure 4.2: Respondents’ Position

As presented in Table 4.9, more than one-third respondents had been working more than ten

years of (38.6%). Because there are a substantial proportion of the respondents (37.9%) with

less than five years of working experience in their current companies, a further statistical

analysis was employed by conducting t-tests on 14 items of CP between this group and others

in the sample (see Appendix A.8 for details). The results show that a majority of respondents

(61%) have held a managerial position and there is no statistical difference in the key variables

between this group of the respondents and others. As such, these respondents can answer

questions due to their managerial positions in the company.

A half of the respondents’ age was more than 40 years (51.5%). With regards to education,

most respondents were well educated and had a bachelor’s degree (64.1%) and post-graduate

(20.9%), whereas the remaining (14.9%) have diplomas (6.9%) or are high school graduates

(8%). It should be noted that the quality of human resources poses a problem for Indonesia. A

half of total workforce or 52.4 million workers have primary school education, and only 8% or

12.6 million of workers attain a formal diploma (Indonesia 2011; Kemenperin 2020b).

With reference to respondents with diplomas and high school certificates, further analyses

showed that a total of 88.9% are owners of small and medium-sized enterprises (SMEs) or hold

managerial positions in their company. Moreover, an overwhelming majority (82.7%) of the

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respondents in this group have more than five-years’ experience in their organisation. A series

of t-tests were also conducted in several key variables between this group of respondents and

other groups (see Appendices A.9 and A.10 for the results of Mann-Whitney test for strategic

integration and CP). No statistical significance was identified. Thus, lack of knowledge

capacity in answering the questions is less a concern due to the managerial position held and

working experience.

The organisational characteristics are provided in Table 4.10. Most respondents’ companies

(68.3%) had more than 100 employees, consistent with the criterion of large companies. Nearly

half of respondents’ companies (49.2%) had been in operation between 21 and 50 years. In

terms of the ownership, they were mostly private companies (79.1%). Companies were more

frequently located in an industrial estate (54.3%) than outside an industrial estate (45.7%).

Three-quarters (74.5%) of respondents’ companies were in East Java. Most questionnaires

were distributed in East Java, for two reasons. First, this province has the most industrial zones,

spread across five cities (e.g., Surabaya, Sidoarjo, Gresik, Mojokerto and Pasuruan). Second,

the researcher lived in Surabaya because proximity helped with questionnaire distribution.

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Table 4.10: Profiles of the Respondents’ Companies

Variable Indicator Frequency (n=435) Percentage (%)

Main product

food and beverage 115 26.4

tobacco 11 2.5

textile 29 6.7

leather and footwear 8 1.8

goods from wood, handicraft 5 1.1

paper 25 5.7

coke and refined petroleum products 4 0.9

chemicals and chemical products 48 11.0

pharmaceuticals and medicinal chemical 11 2.5

rubber and plastic products 35 8.0

non-metallic mineral products 21 4.8

basic metals 2 0.5

fabricated metal products, except machinery

and equipment

42 9.7

computers, electronic and optical products 10 2.3

machinery and electrical equipment 20 4.6

automotive 24 5.5

furniture 17 3.9

other manufacturing 6 1.4

repair and installation of machinery and

equipment

2 0.5

Number of

employees

small 44 10.1

medium 94 21.6

large 297 68.3

Turnover in

2017 (rupiahs)

< 2.5 billion 44 10.1

2.5 - 50 billion 86 19.8

> 50 billion 117 26.9

prefer not to answer 188 43.2

Company’s age

(years)

< 5 30 6.9

5-10 53 12.2

11-20 86 19.8

21-50 214 49.2

> 50 52 12.0

Company’s

ownership

state-ownership 13 3.0

private 344 79.1

multinational company 78 17.9

Company’s

location

East Java 324 74.5

Centre Java & Yogyakarta 29 6.7

West Java & Jakarta 82 18.8

In industrial

estate

yes 236 54.3

no 199 45.7

More specifically, Figure 4.3 provides a distribution of the main product of respondents’

companies. Accordingly, samples were selected from a variety of manufacturing companies

and indicated a good spread of representation across industries. The 19 main products can

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represent the 33 classifications (ISIC code). Generally, the five most products of respondents’

companies are food and beverage (26.4%), chemicals and chemical products (11%), fabricated

metal products (9.7%), rubber and plastic products (8%) and textile (6.7%). These products can

represent the features of manufacturing industry as three of them (food and beverage, chemicals

and chemical products, and textile) are superior goods of the manufacturing industry (Indonesia

2019) and contributed the most to the achievement of the export value of the manufacturing

industry in 2019 (Kemenperin 2020a). Besides, the largest processing industry in Java is the

food and beverage manufacturers (33.57%) and the textile and apparel industry (19.81%)

(Agustinus 2017). These two industries, together with transportation equipment industry

(including automotive), are the main focus of the Java economic corridor development

(Indonesia 2011).

Figure 4.2: Main Products of Respondents’ Companies

2

2

4

5

6

8

10

11

11

17

20

21

24

25

29

35

42

48

115

0 20 40 60 80 100 120

basic metals

repair and installation of machinery and…

coke and refined petroleum products

goods from wood, handicraft

other manufacturing

leather and footwear

computers, electronic and optical products

tobacco

pharmaceuticals and medicinal chemical

furniture

machinery and electrical equipment

non-metallic mineral products

automotive

paper

textile

rubber and plastic products

fabricated metal products, except machinery…

chemicals and chemical products

food and beverage

Main Product

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4.9 Partial Least Square with Structural Equation Modelling

This section explains PLS-SEM employed in this thesis for data analysis. It starts by

determining model specification. Then, it goes on to construct specification that is both

reflective and formative. Next, hierarchical component model is presented, followed by a

model assessment. This section also covers how to analyse mediating and moderating effects.

Structural equation modelling (SEM) is a statistical procedure for measuring the functional,

predictive and causal hypothesis, which is essential multivariate statistical tool for

understanding several research elements and performing basic or applied research in

behavioural sciences, management, health and social sciences (Bagozzi & Yi 2012). SEM uses

different types of models to describe relationships between variables observed, with the same

basic purpose to provide a quantitative evaluation of a theoretical model hypothesised by a

researcher. Practically speaking, different theoretical models can be evaluated in SEM which

hypothesises how sets of variables define constructs and how they interrelate (Schumacker &

Lomax 2004). SEM uses the empirical method of hypothesis testing to test theoretical models

to offer a deeper understanding of the complex relationships between constructs (Schumacker

& Lomax 2004).

Partial Least Square (PLS) is one of the methods in the variance-based SEM (Marin-Garcia

& Alfalla-Luque 2019). PLS with SEM approach (PLS-SEM) is now widely applied in many

social science disciplines, including organisational management, operations management, and

strategic management (Hair, Risher, et al. 2018; Sarstedt, Ringle, et al. 2019). PLS-SEM is

useful for testing complex models with many variables (Marin-Garcia & Alfalla-Luque 2019;

Sarstedt, Ringle & Hair 2017) and involving latent variables (Hair et al. 2019; Martinez-

Conesa, Soto-Acosta & Palacios-Manzano 2017). PLS-SEM is appropriate to predict and

explain measured constructs (Hair et al. 2017; Schwaiger & Festge 2007).

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More specifically, this thesis analysed data using PLS-SEM with five reasons. First, as

mentioned in the previous section, this thesis was intended to explore the extent of CSR

integration and also to predict the impacts of this integration on CP, so that it involved complex

models with many relationships among the variables measured with multi-item measures

(Sarstedt, Ringle & Hair 2017). Consequently, the characteristics of PLS-SEM are appropriate

with these objectives (Hair 2017; Matthews, Hair & Matthews 2018) to predict and explain the

primary target construct and to identify its relevant antecedent constructs (Marin-Garcia &

Alfalla-Luque 2019; Sarstedt, Ringle & Hair 2017). In this thesis, CP is an endogenous

construct and a fundamental target construct, while strategic and functional integration are

antecedent constructs. PLS-SEM concentrates on measuring the variance in endogenous

constructs, thus emphasising on prediction (Hair, Sarstedt, Pieper, et al. 2012; Z. Jannoo 2014).

Second, because a priori knowledge of structural model relationships in the proposed

framework and the measurement characteristics of the constructs (latent variables) were

limited, this thesis concentrated on exploration and prediction rather than confirmation. In this

case, PLS-SEM was excellent to be employed (Hair 2017; Matthews, Hair & Matthews 2018)

due to its suitability for a predictive research purpose (Chin 2010; Hair et al. 2014; Sarstedt,

Ringle & Hair 2017).

Third, PLS-SEM incorporates both reflective and formative measures (Benitez et al. 2020;

Chin 2010; Hair, Sarstedt, Pieper, et al. 2012; Helm, Eggert & Garnefeld 2010). As explained

in the next chapter (see 5.2 and 6.2), the developed model used reflective and formative items.

Hence, PLS-SEM was appropriate to verify the model.

Fourth, PLS-SEM provides versatility in the simulation of molar and molecular higher order

structures and has a high complexity scale (Chin 2010). As can be seen in Figure 5.1, Figure

6.1 and Figure 6.2, this thesis developed models using a second order molar model of strategic

and functional integration.

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Fifth, the last reason is the data requirement flexibility (Sarstedt, Ringle & Hair 2017). As

explained in 4.7.2, not all data gathered from the survey was normal (see Table 4.8 and

Appendix A.7 for details). It means that the abnormality of the distribution data was detected

in the data set. PLS-SEM could accommodate this issue because PLS-SEM makes no

distribution assumption (Chin 2010; Hair et al. 2017; Matthews, Hair & Matthews 2018).

This thesis employed PLS-SEM to define the explained variance in the endogenous

constructs by assessing the path relationships in the model; and to estimate the model’s

predictive power in defining the model’s goodness of fit (Hair et al. 2017; Hair, Sarstedt,

Pieper, et al. 2012; Hair, Ringle & Sarstedt 2013). With support from SmartPLS software

version 3 Professional to reinforce the statistical analysis and achieve these objectives, several

steps should be taken as explained in the following subsection and discussed in the next chapter.

4.9.1 Model Specification

This subsection describes how model specification is determined in this thesis. The model

specification manages the arrangement of the inner and outer models as two components of

PLS-SEM (Hair et al. 2014; Jörg et al. 2014). The initial step in employing PLS-SEM is

creating a path model which show connections among variables and constructs based on the

theory and logic (Hair et al. 2014). The PLS path model is a diagram which exhibits the

hypotheses and the variable relationship to be measured in SEM analysis (Bollen 2002, cited

in Hair et al. 2017; Sarstedt, Ringle & Hair 2017).

Constructs, also known as latent variables, are elements of statistical models, representing

conceptual variables identified by researchers in their theoretical models (Sarstedt, Ringle &

Hair 2017). Because latent variables are not directly observable or measured, they are thus

inferred from a set of variables measured using tests, surveys, etc. (Schumacker & Lomax

2004). In path models, constructs are represented as circles or ovals, connected via single-

headed arrows representing predictive relationships (Sarstedt, Ringle & Hair 2017). Latent

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variables (constructs) which only predict other latent variables are defined as exogenous

variables (as independent variables) (Götz, Liehr-Gobbers & Krafft 2010), whereas an

endogenous variable is a dependent latent variable in at least one causal relationship (Hair et

al. 2017; Hair et al. 2014). Similarly with the location of the constructs, the relationships

between them should be clearly defined (Hair et al. 2014).

The indicators, often also called manifest variables or items, are directly measured or

observed variables representing the raw data (e.g., the answer to a questionnaire from the

respondents). They are used to define and operationalise or infer each construct (latent variable)

(Schumacker & Lomax 2004) in order to test the hypotheses (Yuen et al. 2018). In path models,

they are symbolised as rectangles and are connected by arrows to their corresponding

constructs (Sarstedt, Ringle & Hair 2017).

After defining constructs and indicators, the next step is specifying the outer (measurement)

model which requires the decision to use either reflective or formative model. This step is

important because the relationships assumed in the inner model are as valid and reliable as the

outer models (Hair et al. 2014). The outer model which is known as the measurement model

refers to the relationships between the latent variables (constructs) and their manifest variables

(measurable items) (i.e., their indicators). In contrast, the inner (structural) model shows the

hypothesized relationships (paths) between the latent variables (Hair et al. 2010; Hair et al.

2017; Hair, Ringle & Sarstedt 2011).

4.9.2 Reflective and Formative Constructs Specification

This section presents how to specify constructs, including reflective and formative

constructs. Reflective indicators are regarded as all indicator items that reflect the

characteristics of a construct. They represent all potential items that conceptually describe the

propensities of the construct (Hair et al. 2010). Indicators are known as latent variable functions

whereby changes in the latent variable are reflected (i.e., manifested) in the observed indicator

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changes (Diamantopoulos & Siguaw 2006). Two criteria should be fulfilled regarding indicator

items: (1) there should be high correlations among the indicator items that measure the same

construct and (2) indicator items are interchangeable in the sense that they do not change the

meaning of the construct in case some items are dropped (Hair, Ringle & Sarstedt 2011;

Henseler & Fassott 2010; Jarvis et al. 2003). The single-headed arrows display the relationships

between the construct and the indicator items and suggest that any changes in the construct

attributes would automatically cause changes in all indicator items (Hair, Ringle & Sarstedt

2011). Reflective measurement models are based on the presumption that a latent variable

equals the common factor that underlies a set of observed variables (indicators) (Henseler,

Ringle & Sarstedt 2016).

Conversely, formative measurement models reverse the causality path as the indicators form

or represent the latent variable (Götz, Liehr-Gobbers & Krafft 2010), suggesting that the

causality is from the indicators to the construct (Jarvis et al. 2003). Formative measurements

assume that all indicator items cause or develop the construct (Hair et al. 2010; Hair, Ringle &

Sarstedt 2011).

4.9.3 Hierarchical Component Model

This subsection explains hierarchical component model developed in this thesis. A model

with multi-dimensional constructs is classified as hierarchical latent variable or a hierarchical

component model (HCM) (Becker, Klein & Wetzels 2012) which comprises two layered

structures of constructs (Hair, Sarstedt, et al. 2018). HCM provide researchers with a

framework for modelling a construct on a more abstract dimension (called a higher-order

component) and its more concrete sub-dimensions (called lower-order components)

(Matthews, Hair & Matthews 2018). As such, they expand conceptualizations of standard

constructs, which usually depend on a single layer of abstraction (Sarstedt, Hair, et al. 2019).

Figure 4.4 presents four types of HCM.

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Figure 4.3: The Four Types of Hierarchical Component Models

Source: Becker, Klein and Wetzels (2012).

Higher-order modelling includes summarising the lower-order components (LOCs) into a

single multidimensional higher-order component (HOC) (Hair et al. 2017). HCM can increase

the model parsimony of the model and make it easier to understand it by reducing the number

of relationships in the structural model (Hair, Sarstedt, et al. 2018; Matthews, Hair & Matthews

2018). In addition, the use of HCM can minimise bias due to collinearity problems and

eliminate potential discriminant validity problems (Hair et al. 2017). Higher latent variables

are also useful if a researcher decides to model an abstraction level higher than those first order

constructs used in a simple model of PLS (Chin 2010).

Table 4.11 displays the operationalisation of constructs in the model of strategic and

functional integration. There are three exogenous constructs of strategic integration, namely

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aligning CSR with company’s strategy (Aligning), support from top management (SuppTM),

and developing effective communication (EffCom). Functional integration contains of six

exogenous constructs: Cost, Innovation, Quality, Supplier, Customer and Employee. As this

thesis purposed to evaluate CP comprehensively, CP as an outcome from strategic integration

is measured in four endogenous constructs, i.e., company customer performance (CCP),

company employee performance (CEP), company operational performance (COP) and CFP.

Observed variables (indicators) were used to operationalise three constructs of strategic

integration, six constructs of functional integration, and four constructs of CP. Both exogenous

and endogenous constructs were measured with multiple indicators.

Table 4.11: Construct Operationalisation in Strategic and Functional Integration

Construct Type of

Constructs Code for Constructs Code for Indicators

Strategic integration Formative Strategic Integration

Aligning with company’s

strategy

Reflective Aligning SI01, SI02, SI03, SI04, SI05

Support from top management Reflective SuppTM SI06, SI07, SI08, SI09, SI10

Developing effective

communication

Reflective EffCom SI11, SI12, SI13, SI14, SI15

Functional integration Formative Functional Integration

Cost Reflective Cost FI01, FI02, FI03, FI04, FI05

Innovation Reflective Innovation FI06, FI07, FI08, FI09, FI10

Quality Reflective Quality FI11, FI12, FI13, FI14, FI15

Supplier Reflective Supplier FI16, FI17, FI18, FI19, FI20

Customer Reflective Customer FI21, FI22, FI23, FI24, FI25

Employee Reflective Employee FI26, FI27, FI28, FI29, FI30

Company performance

Operational performance Reflective COP CP01, CP02, CP06, CP07, CP17

Financial performance Reflective CFP CP03, CP08, CP09, CP10, CP18

Customer performance Reflective CCP CP04, CP05, CP15, CP16

Employee performance Reflective CEP CP11, CP12, CP13, CP14, CP19

Accordingly, there are 12 paths from strategic integration to CP, 24 paths from functional

integration to CP, and three paths of mediation from CCP, CEP, and COP to CFP. Totally,

there are 39 paths which cause quite complicated modelling and somewhat difficult to analyse.

In order to reduce the complexity and increase the model parsimony, this thesis applied a top-

down approach to HCM by reducing the number of relationships in the structural model (Hair,

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Sarstedt, et al. 2018; Matthews, Hair & Matthews 2018). This approach allows the effect of

various components embedded in a specific construct to be analysed (Hair et al. 2018).

Because of the characteristics of the construct specification, this thesis adopted a Type II

HCM reflective-formative to understand to what extent the integration of CSR and business

strategy of Indonesian manufacturing industry. This form of HCM comes with reflectively

LOCs (Matthews, Hair & Matthews 2018), which do not reveal identical characteristics, yet

they still form the overall understanding that might impact or mediate the impact on an

endogenous variable (Chin 1998). HOC Strategic Integration and HOC Functional Integration

are formative second-order constructs that contain two-layered structures of constructs and

represent a more general construct of their reflective LOCs (Hair, Sarstedt, et al. 2018; Jarvis

et al. 2003). Neither construct of these two HOCs is identical. Omitting one of the constructs

could impede the conceptual meaning of SI as a second-order construct (Hair et al. 2017).

Because of the reflective-formative HCM, HOC Strategic Integration and HOC Functional

Integration may mediate its LOCs and the four constructs of CP (Hair, Sarstedt, et al. 2018).

Figure 4.5 illustrates the conceptual framework of CSR integration into business strategy using

Type II HCM.

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Figure 4.4: A Conceptual Model of CSR Integration into Business Strategy

4.9.4 Model Assessment

After determining model spefications to develop a model, the following step is a model

assessment that is discussed in this subsection. A path model involves two components: (1) the

measurement model and (2) the structural model (Hair et al. 2017; Sarstedt, Ringle & Hair

2017). The empirical measurements allow the researcher to explain theoretically established

measurement and structural models in reality, which is depicted by the sample data (Hair et al.

2017). In PLS-SEM, measurement and structural models are implied as outer and inner models

(Sarstedt, Ringle & Hair 2017). The assessment of the model involves two phases as follows.

4.9.4.1 The Assessment of Measurement Model

The evalution of measurement model as the first phase of model assessment is presented in

this subsection. The measurement model involves the unidirectional predictive relationships

between each latent underlying construct and its related observed indicators (Götz, Liehr-

Gobbers & Krafft 2010; Hair et al. 2017; Hair, Ringle & Sarstedt 2011). Through the

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measurement model, the researcher are able to assess the reliability and validity of the construct

measures (Chin 2010; Hair et al. 2017). Reliability means that a questionnaire represents the

construct that is assessed consistently (Field 2009). Reliability is a signal of the degree to which

measures are free from random error, and thus yield reliable results (Venkatraman 1989).

Validity determines that an instrument measures what it intends to measure (Field 2009). More

specifically, measurement validity refers to the extent that a survey item (or group of items)

gives its target concept an accurate description (DeVellis 2012). Measurement validity defines

the confidence of researchers that a measure operates the way they expect it to. Internal validity

refers to arguments by researchers that the data they analysed shows a casual method at work.

External validity indicates the representativeness or generalisability of research findings, which

relate the trends and relationships found in a restricted sample to a wider social environment

(Ruel, Wagner & Gillespie 2016).

There are three types of measurement validity as follows:

1. Face validity simply means that the measure appears to be a fair way to evaluate the targeted

construct which looks good at face value (Nunnaly 1994; Ruane 2005).

2. Content validity refers to the comprehensiveness, relevance, and representativeness of the

measurement (Ruel, Wagner & Gillespie 2016). Content validity is an important factor when

a researcher deals with complex, multidimensional concepts (Ruane 2005). Face and content

validity are the most common validity evaluation in survey research techniques, in part

because they are the simplest to build (Ruel, Wagner & Gillespie 2016).

3. Construct validity is the most precious measure of validity. The construct validity is shown

when the instrument measures the construct it was intended to measure, not any other

construct. To determine if the measure has construct validity, it is used to hypothesise the

various forms in which the measure should be correlated with other variables (DeVellis

2012). If, as the theory implies, the new measure is connected with these other variables the

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measure has construct validity. A measurement must have convergent validity and

discriminant validity to establish construct validity (Ruel, Wagner & Gillespie 2016).

Convergent validity is an evaluation of the accuracy of measures through several

operationalization, while discriminant validity is established when a measure does not so

much correlate with another measure from which it would differ (Venkatraman 1989).

Measurement of face validity has been undertaken through pre-tests during questionnaire

development (see 4.4 for details). The result reveals that face validity was achieved

satisfactorily. Content and construct validity would be evaluated in the next chapter through

PLS-SEM assessment.

Because this thesis employed the reflective-formative Type II HCM, the repeated indicator

approach is used to measure the estimate parameters of formative constructs (see Figure 4.6)

(Becker, Klein & Wetzels 2012). As a result, two measurement models are conducted: (1) the

measurement model of the LOCs (reflective measures) and (2) the measurement model of the

HOC as a whole (formative measures), reflecting the relationship between the HOC and its

LOCs (Sarstedt, Hair, et al. 2019).

Figure 4.5: Repeated-Indicator Approach for Type II HCM

Source: Becker, Klein and Wetzels (2012).

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As recommended by Götz, Liehr-Gobbers and Krafft (2010), Hair et al. (2017) and Sarstedt,

Ringle and Hair (2017), the assessment of the measurement models of reflective measures

includes five steps as the following:

1. Assessment of content validity

PCA is a suitable way of examining the underlying factor structure of the indicators (Vinzi,

cited in Götz, Liehr-Gobbers & Krafft 2010). In addition to content validity which has been

established before the data collection through the pre-tests (see 4.4 for detail), this thesis

employed PCA after data screening to evaluate the content validity. With respect to the

reflective latent variable, EFA was employed to expose the number of factors and variables

that are included as specific factors (Fabrigar et al. 1999; Sarstedt & Mooi 2014). To do so,

PCA with Varimax rotation (Hair et al. 2010) was conducted for each construct of strategic

integration, functional integration, and CP.

Appendix A.12 shows PCA results of strategic integration. Three constructs have a KMO

value of 0.87, 0.89 and 0.88, respectively, above the threshold value of 0.6 (Tabachnick &

Fidell 2006). One factor-solution resulted by PCA confirms the uni-dimensionality of each

construct of strategic integration and indicates a high content validity (Götz, Liehr-Gobbers

& Krafft 2010). The coefficient alpha as the first indicator of instrument quality assessment

(Churchill 1979) is more than 0.7.

PCA was also conducted to assess the content validity of functional integration using EFA

with Varimax rotation (Hair et al. 2010; Sarstedt & Mooi 2014). As shown in Appendix

A.13, KMO-value of six constructs are meritorious in the range of 0.80 and 0.89 (Sarstedt

& Mooi 2014), exceeding the threshold value of 0.6 (Tabachnick & Fidell 2006). PCA

revealed a one factor-solution, indicating the uni-dimensionality of each construct of

functional integration, which reflects a high content validity (Götz, Liehr-Gobbers & Krafft

2010).

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PCA results of CP are displayed in Appendix A.14. Four constructs have KMO values above

0.6: 0.71 (CCP), 0.80 (CEP), 0.83 (CFP) and 0.76 (COP), respectively. PCA also yielded a

single factor for each construct with the coefficient alpha above 0.7. Based on the results,

content validity has been established for constructs of strategic integration and CP.

After the uni-dimensionality of construct’s indicator has been achieved, further evaluation

regarding reliability and validity is needed (Götz, Liehr-Gobbers & Krafft 2010). Following

the results from EFA, a confirmatory factor analysis (CFA) was conducted to assess the uni-

dimensionality of the measurement items of the constructs (Hair et al. 2010). A measure is

said to be uni-dimensional if it measures a single latent trait or construct by its items

(Tavakol & Dennick 2011). The uni-dimensionality evaluation ensures that all the items

assess the underlying theoretical construct of interest (Venkatraman 1989). Results of CFA

can inform how well the actual data fits the pre-specified structure (Sarstedt & Mooi 2014).

The results can also confirm if measurement items have sufficient variance to be claimed as

reliable measures of an intended single factor (Bagozzi & Yi 2012). Since CFA can provide

a strong check for the theoretically defined dimensionality (Venkatraman 1989), CFA

should be conducted before further data analysis (Schniederjans & Cao 2009) and performed

using PLS-SEM in the following steps.

2. Assessment of indicator reliability

Indicator reliability determines which part of the variance of an indicator can be described

by the latent variable that underlies it. A common threshold criterion is that the latent

construct should explain more than 50% of the variance of an indicator (Götz, Liehr-

Gobbers & Krafft 2010; Hair, Risher, et al. 2018; Sarstedt, Ringle & Hair 2017). This means

that values greater than 0.70 are appropriate for loadings of the latent constructs on an

indicator variable (Götz, Liehr-Gobbers & Krafft 2010; Hair et al. 2017; Hair, Ringle &

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Sarstedt 2013). Consequently, loadings of all reflective indicators should analysed to

determine the indicator reliability (Bagozzi & Yi 2012).

3. Assessment of the internal consistency (construct) reliability

Internal consistency refers to the interrelationship of a sample of test items (Venkatraman

1989). Internal consistency reliability is used to check to what extent the indicators are

assessing the constructs (Gallardo-Vázquez & Sanchez-Hernandez 2014). The most widely

used criterion for internal consistency reliability is Cronbach’s alpha which estimates

reliability based on the inter-correlations of the observed indicator variable (Hair et al. 2010;

Hair et al. 2017; Tavakol & Dennick 2011). However, Cronbach's alpha results in lower

reliability estimates compared to CR (Hair et al. 2017). CR describes to what extent the

indicator items are reliable for measuring the latent construct (Götz, Liehr-Gobbers & Krafft

2010; Hair, Sarstedt, Pieper, et al. 2012). As measured from factor loadings, CR generates

more precise reliability estimates than those given by alpha (Geldhof, Preacher & Zyphur

2014). Thus, CR (ρc) was also used in this thesis to check internal consistency (Chin 2010;

Hair et al. 2017; Hair, Ringle & Sarstedt 2011; Sarstedt, Ringle & Hair 2017). Additionally,

the reliability coefficient ρA is recommended (Benitez et al. 2020), which typically returns

a value between Cronbach's alpha and the CR ρc (Sarstedt, Ringle & Hair 2017).

4. Assessment of the convergent validity

Convergent validity is defined when two or more measures designed to explain the same

complex structure are actually associated with one another in the same study (Ruel, Wagner

& Gillespie 2016). Convergent validity describes the extent to which a measure correlates

positively with alternative measures of the same construct (Hair et al. 2017). Convergent

validity is an evaluation of whether the various items designed to measure a construct

actually do measure it. If different items actually measure same constructs, their fit will be

significant, and they will be highly correlated with each other (Churchill 1979; Gallardo-

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Vázquez & Sanchez-Hernandez 2014; Ruel, Wagner & Gillespie 2016). As it assesses how

high each of the loadings are and whether they are more or less similar (Chin 2010), the

outer loadings of the indicators should be measured to evaluate the convergent validity of

reflective constructs, which should be 0.708 or higher (Chin 2010; Hair et al. 2017).

Another common measure to assess convergent validity is Average Variance Extracted

(AVE) (Götz, Liehr-Gobbers & Krafft 2010). This criterion is defined as ‘the grand mean

value of the squared loadings of the indicators associated with the construct’ (Hair et al.

2017, p. 114). AVE contains the variance of the measures recorded by the construct

compared to the total sum of variance, including the variance due to measurement error

(Götz, Liehr-Gobbers & Krafft 2010). According to Fornell and Larcker (1981), convergent

validity occurs when AVE values are greater than 0.5. indicating that a construct explains

more than 50% of the variance of its indicators (Hair, Ringle & Sarstedt 2011).

5. Assessment of the discriminant validity

Discriminants reflect the degree to which construct indicators assess what they are supposed

to measure and focuses on both the degree of agreement of hypothesised indicators for

measuring a construct and the distinction between such indicators and indicators of distinct

constructs (Bagozzi & Yi 2012). Discriminant validity shows the extent to which the

measure is indeed novel and not merely a representation of any other variable (Churchill

1979). There are several conditions for a construct to have discriminatory validity: (1) a

construct should be different from the other constructs of a model by empirical standards

(Götz, Liehr-Gobbers & Krafft 2010; Hair et al. 2017; Hair et al. 2014), (2) a construct

matches the correct and estimated patterns of relationship with other constructs (Ruel,

Wagner & Gillespie 2016), (3) a construct should have weak correlations with other

constructs, implying each construct measures a different phenomenon (DeVellis 2012;

Gallardo-Vázquez & Sanchez-Hernandez 2014) and (4) a construct is more strongly related

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its own measures than with any other construct by examining the overlap in variance (Chin

2010).

Specifically, assessing the discriminant validity of HOC requires further scrutiny in three

aspects. First, the measurement model of HOC is determined by the relation between the

HOC and its LOCs, not its repeated indicators (Sarstedt, Hair, et al. 2019). Second, HOCs

as a whole should demonstrate discriminant validity of all other constructs in the model,

except for the HOC of which they are a part (Sarstedt, Hair, et al. 2019). Last, the standard

structural model assessment criteria apply for the relationships of the HOC to constructs

other than its LOCs in the model. To put it another way, the LOCs are not considered part

of the structural model and only HOC should be assessed as part of the structural model

(Sarstedt, Hair, et al. 2019).

The cross-loadings are usually the first approach to test discriminant validity (Hair et al.

2017). The indicator’s outer loadings on the related construct must be higher than the cross-

loading with other constructs (Hair et al. 2017; Hair, Ringle & Sarstedt 2011).

The most widely used conservative approach to assess discriminant validity (Hamid 2017;

Wong 2016) is the Fornell-Larcker criterion (Fornell & Larcker 1981; Hair et al. 2017). The

Fornell-Larcker criterion assumes that ‘the square root of each construct’s AVE should be

greater than its highest correlation with any other construct’ (Hair et al. 2017, p. 116).

Discriminant validity can be established if the variance shared between each construct and

its indicator items is greater than the variance shared among other constructs (Fornell &

Larcker 1981; Götz, Liehr-Gobbers & Krafft 2010; Hair et al. 2017).

A new method for assessing discriminant validity in PLS modelling is the heterotrait-

monotrait ratio of correlations (HTMT) (Henseler, Ringle & Sarstedt 2015; SmartPLS

2014). HTMT can be defined as ‘the mean of all correlations of indicator across constructs

measuring different construct’ (Hair et al. 2017, p. 118). HTMT approach is an estimate of

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what the true correlation between constructs would be, if they were perfectly measured (Hair

et al. 2017). HTMT can be measured in two ways: (1) by comparing it to a threshold value

and (2) by defining a confidence interval to test whether HTMT is significantly lower than

a given threshold value (Benitez et al. 2020). Discriminant validity problems occur when

HTMT value is high (Hair, Risher, et al. 2018; Sarstedt, Ringle & Hair 2017). The threshold

value for HTMT is 0.85 if the path model constructs are conceptually more different and

0.90 if the constructs are very similar in concept (Henseler, Ringle & Sarstedt 2015). The

confidence interval of HTMT can be obtained through bootstrapping procedure (Hair et al.

2017).

Next, because of the reflective-formative HCM, the measurement model of HOC should be

evaluated (Hair, Sarstedt, et al. 2018; Sarstedt, Hair, et al. 2019). Since the characteristics of

formative measures are considered independent, not interrelated, and error-free, the parameters

used to assess reflective measurements are not suitable for evaluating the reliability and validity

of formative measurement models (Diamantopoulos & Siguaw 2006). It is not recommended

to check the internal accuracy of the indicators, convergent and discriminant validity as the

weights of formative indicators are involved in the analysis (Chin 1998; Hair et al. 2017).

Subsequently, as suggested by Hair et al. (2017) and Sarstedt, Ringle and Hair (2017), there

are three steps to assess the formative measurements as the following:

1. Assessment of convergent validity

Convergent validity can be defined as ‘the extent to which a measure correlates positively

with other (e.g., reflective) measures of the same construct using different indicators’ (Hair

et al. 2017, p. 140). Because Model 1 HOC Strategic Integration does not include reflective

indicators, this step could be skipped.

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2. Assessment of the collinearity

Two or more variables are said to be collinear when evaluating an object's same attribute,

which is known as a construct (Kock & Lynn 2012). Collinearity is generally evaluated as

a potential predictor-predictor consistency phenomenon in models with multiple variables

(Kock & Lynn 2012). Multicollinearity is not desirable and tends to be problematic when

evaluating formative constructs (Hairet al. 2017) because it can affect the bootstrapping

results and thus allow type II errors (i.e., the probability of arguing that there is no

meaningful relationship between constructs when there is one) to occur (Cenfetelli &

Bassellier 2009).

A related measure of collinearity is the variance inflation factor (VIF), known as ‘the

reciprocal tolerance’ (Hair et al. 2017, p. 143). VIF should be calculated to detect the

presence of collinearity (Götz, Liehr-Gobbers & Krafft 2010). Commonly suggested values

of VIF are 10, 5, and 3.3. If a VIF is equal to or greater than the threshold value, there is

collinearity between variables (i.e., multicollinearity) (Kock & Lynn 2012). Another

criterion of collinearity is the tolerance (TOL), which defines the extent of a formative

indicator's variance which is not affected by the other indicators measuring the same

construct. TOL can be computed as 1/VIF (Hair et al. 2017, p. 143).

3. Assessment of indicator validity

Indicator validity of formative constructs was assessed by checking the significance level,

signs and the magnitude of their relationship to their formative indicators (Hair et al. 2017).

To have a significant correspondence between the formative construct and its indicators, a

threshold value of exceeding 0.1 must be obtained together with a consistent sign which

meets the requirements determined by the underlying theory (Chin 1998). With regard to

HOC using repeated indicators, weights and loadings are defined by path coefficients

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between HOC and its LOCs and not by manifest indicators repeated at the construct level

(Becker, Klein & Wetzels 2012).

4.9.4.1 The Assessment of Structural Model

This subsection explains the second phase of model assessment, that is the evaluation of

structural (inner) model. The structural model deals with the relationships between theoretical

concepts and their hypothesized relationships (Benitez et al. 2020; Götz, Liehr-Gobbers &

Krafft 2010). The structural model, the inner model in the PLS-SEM context, describes the

fundamental structural theories of the path model (Hair, Ringle & Sarstedt 2011) and the

relationships between the latent variables (Hair et al. 2017). Because PLS-SEM does not

establish a goodness-of-fit measure (Sarstedt, Ringle & Hair 2017), instead of testing the

overall goodness of the model fit, PLS-SEM evaluates the structural model based on the

heuristic criteria which are determined by the model’s predictive capabilities (Hair et al. 2017).

Assessing a structural model includes the determining of the model’s predictive capabilities

and the relationships between the constructs (Hair et al. 2017; Hair, Ringle & Sarstedt 2011).

In other words, the evaluation was aimed at determining the value of R2 for each endogenous

construct, the size and significance of path coefficients present in the model (Chin 2010, cited

in Götz, Liehr-Gobbers & Krafft 2010; Vinzi et al. 2010). Because PLS focuses on optimising

the prediction of endogenous constructs (Matthews, Hair & Matthews 2018), the quality of the

model is assessed in PLS-SEM from its ability to predict endogenous constructs which involve

four criteria: path coefficient, coefficient of determination (R2), cross-validated redundancy

(Q2) and effect size (f2) (Chin 2010; Hair et al. 2014).

4.9.5 Mediating Effect

This subsection discusses the mediation analysis that would be examined in this thesis. As

the relationships among constructs in PLS-SEM can be complex and not always

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straightforward (Wong 2016), a mediating effect might occur when a third variable of

constructs intercedes between two other related constructs (Hair et al. 2017). The essence of

mediation analysis is that it presumes the sequence of relationships in which the antecedent

variable influences the mediators, which then affects the dependent variable (Nitzl, Roldan &

Cepeda 2016). A mediation analysis essentially aims to examine the extent to which the

intervening variables (M) mediate the relationship between the independent variables (X) and

a dependent variable (Y) (see Figure 4.7 B) (Preacher & Hayes 2008). Although the purpose

of mediation is to understand the progress of the process (Henseler, Ray & Hubona 2016),

mediation analysis may have an essential role in prediction (Shmueli et al. 2016).

Figure 4.6: (A) Illustration of a direct effect. X affects Y; (B) Illustration of a mediation

design. X is hypothesized to exert an indirect effect on Y through M.

Source: Preacher and Hayes (2008).

According to Baron and Kenny (1986), examining the mediating effect involves three types

of variables: independent variable (X), dependent variable (Y), and mediator (M). The impact

of the independent variable on the intervening variables/mediators (X → M) is symbolised by

a, while the effect of the intervening variable on the dependent variable (M → Y) is noted by

b (see Figure 4.7 B). When paths a and b are controlled, a previously significant relation

between the independent and dependent variables (X → Y), symbolised as c is no longer

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significant (see Figure 4.7 A) (Baron & Kenny 1986). Total effect of X on Y (c') are estimates

of two partial effects; the first is the effect of the mediator on the outcome and the second is

the effect of the independent variable on the outcome (Judd 2010).

It is possible that exogenous constructs intervene in the relationships through more than one

mediating variables called multiple mediation (Hair et al. 2017). The multiple mediation

generates a specific indirect effect that can be assumed as the indirect effect of exogenous

constructs on endogenous constructs through a given mediator, including all other included

mediators (Hair et al. 2017). A more complete picture of the mechanism by which an

exogenous construction affects an endogenous structure can be obtained by simultaneously

considering all the mediators in one model (Hair et al. 2017).

This thesis employed PLS-SEM to assess the measurement model and the structural model

through the PLS algorithm and the bootstrapping procedure. Before running the PLS algorithm,

the algorithmic options and parameter settings must be set-up. The setting involves selecting

the structural model path weighting method, the data metric, initial values to start the PLS-

SEM algorithm, the stop criterion and the maximum number of iterations (Hair et al. 2017).

This thesis used the factor weighting scheme as strongly recommended for parameter

estimations in the reflective-formative HCM (Hair, Sarstedt, et al. 2018), maximum iterations

of 500 and stop criterion of seven (Hair et al. 2017).

Bootstrapping is a non-parametric technique of forming a resample from the original sample

by random replacement to obtain a statistical inference and to estimate the accuracy of the PLS

prediction (Chin 2010; Henseler, Ringle & Sinkovics 2009; Preacher, Rucker & Hayes 2007).

When conducting inferential tests, no assumptions are made about the shape of the statistical

sampling distribution using bootstrapping (Preacher, Rucker & Hayes 2007). Because the

bootstrapping technique treats the observed sample as a population, the word replacement

refers to drawing each observation randomly with returning it to the sampling population for

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generating the next observation (Hair et al. 2017). Consequently, estimates of the expected

value and statistical variability can be calculated as taken from an empirical distribution of the

sampling (Aguinis, Gottfredson & Joo 2013). At least, the number of bootstrap samples must

be equal to the number of valid observations in the data set (Hair et al. 2017). Accordingly, this

thesis used 5,000 bootstrap sample and selected the complete bootstrapping option, bias-

corrected and accelerated (BCa) bootstrap for confidence interval method, and two-tailed

testing (Hair et al. 2017; Hair, Sarstedt, et al. 2018; Streukens & Leroi-Werelds 2016). A

significance (alpha) level of 0.05 was applied as it is appropriate for most research (Barlett,

Kotrlik & Higgins 2001).

4.9.6 Moderating Effect

How to examine the moderating effect is provided in this subsection. Moderating effects are

generated by variables whose variation affects the strength or direction of a relation between

an exogenous and an endogenous variable. The triggers of the moderating effects are called

"moderator factors" or simply "moderators” (Henseler & Fassott 2010). A moderator is a

‘…variable that affects the direction and/or strength of the connection between an independent

or predictor variable and a dependent or criterion variable’ (Baron & Kenny 1986, p. 1174; Bu¨

yu¨ kbalcı 2012). Moderation defines a condition where the strength of relationship between

two variables (constructs) is not constant but dependent on the values of a moderator (Hair et

al. 2017; Preacher, Rucker & Hayes 2007).

Moderator variables are of great relevance since complex relationships are usually subject

to contingencies. Moderators may be either metric or categorical variables. Group comparisons

can be considered as a special case of moderating effects (i.e., comparisons of model estimates

for different groups of observations). The grouping variable is nothing more than a categorical

moderator variable (Henseler & Fassott 2010). Group differences in the structural model should

be accounted for, using the group variable as a moderator (Henseler, Ringle & Sarstedt 2016).

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Because of the complexity of the social and behavioral occurrences, heterogeneity probably

occurs in samples which are used to develop, examine, and refine models (Becker et al. 2013).

It is essential to identify, test, and treat heterogeneity in the data (Hair, Sarstedt, et al. 2018)

since heterogeneity can affect the structural model, the measurement model, or both (Becker

et al. 2013). Moderation can be used as a way of defining heterogeneity in the data (Hair et al.

2017). There are two types of heterogeneity in data: observed and unobserved. If differences

among groups refer to observable characteristics, it is observed heterogeneity (Hair et al. 2016).

Unobserved heterogeneity occurs when data subgroups involve considerably different

estimates of the model (Sarstedt, Ringle, et al. 2019).

MGA or between-group analysis makes it possible to assess differences between the same

models tested for different groups of respondents (Hair et al. 2017; Matthews, Hair & Matthews

2018). MGA can be conducted to check the moderating effect (moderation) based on the

observed and unobserved data. With respect to the observed data, MGA was appropriate to be

employed to examine the differences of the results among groups based on company size and

industry type as discrete moderator variables (Eberl 2010; Hair, Sarstedt, et al. 2018). The path

coefficients across the groups are compared, which provides an interpretation of the differences

in effects between groups (Eberl 2010). MGA allows testing whether differences between

group-specific path coefficients are statistically significant (Hair, Sarstedt, et al. 2018;

Matthews, Hair & Matthews 2018). Thus, MGA via PLS-SEM is an efficient method of

evaluating moderation across multiple relationships (Hair, Sarstedt, Ringle, et al. 2012).

Differences in the model parameters between the distinct data groups are perceived as

moderating effects (Henseler & Fassott 2010). To test the moderating effect, the influence of

the exogenous variable on the endogenous variable, the direct effect of the moderating

variables on the endogenous variable and the influence of the interaction variable on the

endogenous variable are estimated (Helm, Eggert & Garnefeld 2010).

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As guided by Matthews (2017), there are several steps of MGA as follows:

1. Generating data groups

Before conducting MGA, data groups should be generated based on the categorical variable

of interest (Matthews 2017).

2. Analysing the measurement invariance of composite models (MICOM)

Before comparing group-specific parameter estimates for significant differences using

MGA, as recommended by Hair, Sarstedt, et al. (2018), measurement invariance was

conducted to provide confidence that group differences in model estimates are not caused

by the different content and/or meanings of the latent variables across groups. If there is no

measurement variance, it can decrease the statistical power, affect the estimator precision,

and give misleading results (Hair, Sarstedt, et al. 2018). Consequently, measurement

invariance is an essential consideration to be addressed in MGA (Henseler, Ringle &

Sarstedt 2016) to ensure the accuracy of results and conclusions (Hair, Sarstedt, et al. 2018).

As recommended by Henseler, Ringle and Sarstedt (2016), the MICOM consists of three

inter-related hierarchical steps as the following.

▪ Step 1. Configural Invariance. Step 1 addresses the establishment of configural

invariance to ensure that each latent variable in the PLS path model has been specified

equally across all the groups (Henseler, Ringle & Sarstedt 2016). Accordingly, three

procedures were carried out as follows:

a. Identical indicators per measurement model: each measuring model must use the same

indicators across the different groups (Henseler, Ringle & Sarstedt 2016). When

different languages were used in surveys, translation and back-translation is the key

factor in ascertaining the indicators’ equivalence (Hair, Sarstedt, et al. 2018; Henseler,

Ringle & Sarstedt 2016). As explained in the previous chapter (see 4.4 for details), the

questionnaire was translated into the Indonesian language and back-translated into

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English (Brislin 1970). Indonesian and native English translators assured that both

versions of the questionnaire (Indonesian and English) had appropriate corresponding

meanings to the content of each sentence’s message.

b. Identical data treatment. The indicators’ data treatment is similar across all the groups,

including the coding and the data handling, such as reverse coding, missing value

treatment and outlier identification (Henseler, Ringle & Sarstedt 2016). This step has

been conducted through data screening and explained in previous chapter (see 4.7.2).

c. Identical algorithm settings or optimization criteria. Because the same model was

running separately in MGA, the algorithm settings have been set similarly to ensure

that differences in the group-specific model estimations do not result from dissimilar

algorithm settings (Henseler, Ringle & Sarstedt 2016). The permutation test was also

conducted using the similar setting of PLS algorithm and bootstrapping (see 5.3 for

details).

When all three procedures have been met, configural invariance as Step 1 in MICOM

can be established.

▪ Step 2. Compositional invariance. Compositional invariance ensures that the prescription

for composite condensation of the indicator variables is the same for all groups (Henseler,

Ringle & Sarstedt 2016). In other words, the composite score does not vary significantly

between groups (Hair et al. 2017). To check compositional invariance, the permutation

test as a non-parametric test (Henseler, Ringle & Sarstedt 2016) should be conducted by

setting a number of 1,000 permutations and a significance level of 0.05 (Hair, Sarstedt,

et al. 2018).

▪ Step 3. Equality of composite mean values and variances. The configural and

compositional invariance are preconditions for evaluating the equality of the composite

mean values and the variance. Evaluation of the equality of the composite mean values

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and the variance is meaningless if there is no compositional invariance (Henseler, Ringle

& Sarstedt 2016). Because the compositional invariance has been established, Step 3 has

been carried out to evaluate the composite’s equality of mean values and variances across

groups (Henseler, Ringle & Sarstedt 2016). This final step of the MICOM method will

analyse how the mean values and variances between the first group’s composite scores

and second group’s composite score vary with respect to their means and variances (Hair,

Sarstedt, et al. 2018).

3. Analysing and Interpreting Permutation Results

Path coefficients generated from different samples are almost always numerically different,

but the question is whether the differences are statistically significant. MGA helps in

addressing this question by testing one-sided hypothesis (Hair, Sarstedt, et al. 2018).

4.10 Ethical Considerations

The final section in this chapter provides ethical considerations for this thesis. A prerequisite

of PhD candidature is that all research involving human participants receives ethical clearance

before data is collected. Ethical considerations relevant to this thesis have been acknowledged

and officially approved by the board of College Human Ethics Advisory Network (CHEAN),

RMIT University (Ethics approval number: 21372). The risk level was graded as ‘Low Risk’

for subjects involved in this thesis. A copy of the ethics approval is attached in Appendix A.11.

Data collection was carried out after receiving CHEAN approval. The cover letter indicated

that since participation was voluntary, potential participants could withdraw at any time. The

cover letter also stated that neither company nor personal information could be individually

identified to avoid any risks arising from providing information on behalf of the company. The

cover letter also indicated that since it was an anonymous survey, the respondents gave their

consent when they completed and returned the questionnaire to the researcher. As a result,

informed consent was obtained from all respondents included in the study. Like the

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questionnaire, the cover letter was translated into the Indonesian language to make it easier for

the respondents to understand.


In summary, this chapter discusses several important aspects of the research methodology

used in this thesis. This thesis employed a positivist research paradigm and a deductive research

approach. This thesis also conducted an explanatory study and quantitative research with a

survey to answer the research questions. Several items from strategic management, CSR, and

manufacturing studies have been incorporated into the questionnaire. Prior to data collection,

a pilot research was done.

The manufacturing industry in Indonesia is the population in this thesis, and manufacturing

companies from Java is the sample study. This thesis employed purposive sampling in data

collection, which took place in five regions of Java from June to October 2018. The main and

supplementary frames were used to collect data.

This thesis employs SPSS and SmartPLS 3 for data analysis to evaluate not only the

relationship between CSR integration and business strategy, but also the mediating and

moderating impacts on that relationship. Data screening was performed prior to data analysis

to ensure data validity and reliability. PLS-SEM for data analysis is also explained in this

chapter. Ethical considerations is presented, indicating that data collection took place only after

ethical approval.

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CHAPTER 5: STRATEGIC INTEGRATION-FINDINGS AND DISCUSSION

This chapter presents the procedures and results for the statistical analysis, particularly in

relation to strategic integration. This chapter begins by explaining the descriptive statistical

analyses, followed by describing the model specification. Then, the model assessment and the

mediation analysis are presented. The next section explains the MGA in relation to strategic

integration. Last, a summary of this chapter is provided.

5.1 Descriptive Statistical Analysis in Strategic Integration

This section presents descriptive statistical analysis performed in this thesis, with a focus on

strategic integration. It contains a descriptive statistical analysis for each of the model's

constructs.

Descriptive statistics characterise the measured variables, including independent and

dependent variables, such as the mean, standard deviation, and correlation. The mean is

regarded as a statistical model of the data, while standard deviation is used to determine the fit

of the mean, and correlation is calculated to determine the relationship between variables (Field

2009). A correlation coefficient should be between -1 and +1. The coefficient of +1 means a

perfect positive relationship, while -1 indicates a perfect negative relationship. A coefficient of

zero suggests no linear relationship at all, when variables are completely independent (Ruel,

Wagner & Gillespie 2016; Saunders 2009). Because the correlation coefficient reflects the size

of an effect, values of ±0.1 represents a small effect, ±0.3 indicates a medium effect, and ±0.5

reflects a large effect (Field 2009).

In this thesis, SPSS 26 was employed for all variables (constructs) used in the model

measured with a five-point scale, including independent variables, namely business strategy,

CSR strategy, strategic integration and functional integration, and dependent variables—that

is, CP variables. Specifically, this thesis uses Spearman’s correlation coefficient to measure the

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relationship among variables because data are non-parametric regarding non-normally

distributed data.

Appendix B.1 shows means, standard deviations, and correlations among the 10 items of

business strategy. Mean scores range from four to five. Items with the highest mean are:

‘Controlling the product quality’ (SI02, 4.68) and ‘Providing customer service capabilities’

(SI09, 4.54). This suggests that most manufacturing companies in Indonesia prioritise both

strategies: cost leadership and differentiation strategy. Standard deviations for all items are less

than one, representing a reasonable spread of opinions about business strategy across the

sample. As shown in Appendix B.1, 10 items of business strategy correlate to each other with

correlation coefficients between 0.12 and 0.43, implying small to medium effects.

Descriptive analysis of CSR strategy is presented in Appendix B.2, showing that 18 out of

20 items have a high mean score between 3 and 5. Three items with the greatest mean are: ‘We

continually improve the quality of our products’ (CS05, 4.52), ‘All our products meet legal

standards’ (CS08, 4.45) and ‘We are recognised as a trustworthy company’ (CS12, 4.45). The

results indicate most manufacturing companies emphasise the economic, the legal and ethical

responsibilities of CSR. Because four items related to the philanthropic responsibility have a

mean of less than four, the result suggests that respondents have a low perception of this

responsibility. Standard deviations for all items were below one, indicating a reasonable spread

of opinions about CSR strategy across the sample. The correlation coefficients of variables of

CSR strategy are significant and range from 0.09 to 0.59, implying that they correlate to each

other. In general, the results indicate that all surveyed companies have implemented CSR

strategy.

Appendix B.3 exhibits means, standard deviations, and correlations among 15 items of

strategic integration. High mean scores of these items show that respondents are aware of

strategic integration. Two items with the greatest mean are: ‘Mechanisms are available for

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evaluating the results of the objectives’ (SI03, 4.06) and ‘CSR strategy is well aligned with

corporate vision and mission’ (SI04, 3.95). Additionally, 15 items correlate to each other with

significant medium to strong correlation coefficients between 0.36 and 0.68.

Appendix B.4 presents the descriptive analysis of CP. Almost 19 items of CP have high

mean scores. Two items with the highest mean are: ‘Customer satisfaction’ (CP12, 4.00) and

‘Customer loyalty’ (CP13, 3.99). However, not all the correlation coefficients are significant.

Seventeen items correlate to each other, with coefficient correlations ranging from 0.27 to 0.69.

Conversely, two items with the smallest means, CP11 (2.42) and CP16 (2.75), have

insignificant coefficients of correlation. In these two items, reverse coding was used to insert a

positive wording item into a negative wording item (Ruel, Wagner & Gillespie 2016), and their

mean score was already reversed from its original meaning (see Table 4.5). Respondents did

not know that these items were reverse coded (Podsakoff et al. 2003).

5.2 Model Specification of Strategic Integration

Model specification of strategic integration is determined in this section. As shown in Figure

5.1, Model 1 illustrates strategic integration and CP. HOC Strategic Integration consists of

three LOCs: Aligning, SuppTM, and EffCom. Each LOCs has five indicators, indicating a

similar number of indicators used for these LOCs (Matthews, Hair & Matthews 2018). There

are four constructs of CP: CCP with three indicators, CEP with four indicators, CFP, and COP

with five indicators, respectively. This model type refers to a molar model because it has the

arrow from its LOCs to the higher second order model (Chin 2010). This model also implies a

collect model, indicating formative relationships going from the LOCs to forming the HOC

with equal numbers across the LOCs (Hair, Sarstedt, et al. 2018) as suggested when the

repeated indicator approach was employed (Becker, Klein & Wetzels 2012). Similar to four

endogenous constructs of CP, three LOCs of HOC Strategic Integration represent Mode A

because they are reflectively measured constructs. The relationship from three LOCs to HOC

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Strategic Integration refers to Mode B since this HOC is a formatively measured construct

(Hair, Sarstedt, et al. 2018; Sarstedt, Hair, et al. 2019; Sarstedt, Ringle & Hair 2017).

Figure 5.1: Model of Strategic Integration and Company Performance (Model 1)

5.3 Model Assessment for Strategic Integration

This section discusses model assessment, which is particularly employed in strategic

integration. It starts with an explanation of the measurement model evaluation. After that, it

describes the structural model evaluation.

5.3.1 Assessment of the Measurement Model in Strategic Integration

Because Model 1 used reflective-formative HCM, the measurement model assessment of

Model 1 includes the evaluation of reflective and formative measures. The following are the

explanations for each of them.

5.3.1.1 Assessment of Reflective Measurement Model in Strategic Integration

This subsection presents several procedures undertaken to assess the reflective measurement

model in strategic integration (Model 1). Since CFA can provide a strong check for the

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theoretically defined dimensionality (Venkatraman 1989), CFA should be conducted before

further data analysis (Schniederjans & Cao 2009). As EFA for content validity assessment was

conducted in Chapter 4, the next section presents CFA performed using PLS-SEM.


Figure 5.2 presents the result of PLS algorithm of Model 1, and Table 5.1 summarises the

resulting assessment of the structural model of Model 1. The results suggest that indicator

reliability for all indicators, both of strategic integration and CP, are above the threshold

value of 0.50 and significant (Götz, Liehr-Gobbers & Krafft 2010; Hair, Risher, et al. 2018;

Sarstedt, Ringle & Hair 2017).

Figure 5.2: Results of PLS Algorithm of Model 1

2. Assessment of internal consistency (construct) reliability

This thesis uses these three criterion to assess internal consistency and reliability, namely

Cronbach’s alpha, ρA, and ρc, and higher values imply a higher level of reliability (Hair,

Risher, et al. 2018). As summarised in Table 5.1, all reflective measures of Model 1 have

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Cronbach’s alphas between 0.817 and 0.911, indicating that they are well above the

threshold value of 0.70 and satisfactory (Bagozzi & Yi 2012; Hair et al. 2017; Hair, Ringle

& Sarstedt 2011). The CR for all constructs is between 0.88 and 0.94, and the reliability

coefficients ρA for all constructs are above 0.7 and do not exceed the 0.95 level (Bagozzi &

Yi 2012; Hair et al. 2017). These results show that the construct measures of all LOCs

exhibit high levels of internal consistency and reliability.


Two indicators of CP have factor loadings less than 0.7 (CP11 and CP16). These indicators

also have low communalities and negative loadings in EFA (see Appendix A.14 for detail).

These two indicators were eliminated from their related constructs because their removal led

to an increase in the CR and AVE (Hair et al. 2017). After this elimination, the PLS

algorithm was re-run. Results in Table 5.1 show that indicators of strategic integration and

CP have outer loadings above the threshold value of 0.708 (Chin 2010; Hair et al. 2017).

Table 5.1 shows AVE values of above 0.50 for three LOCs of HOC Strategic Integration:

0.67 (Aligning), 0.74 (SuppTM), and 0.75 (EffCom). The four constructs of CP also have

AVE values greater than 0.50: 0.73 (CCP), 0.65 (CEP), 0.62 (CFP), and 0.59 (COP).

Subsequently, the results indicate that all reflective measures demonstrate convergent

validity because the indicators load strongly onto their corresponding constructs (Kock

2015).

4. Assessment of discriminant validity

In terms of cross loadings, the results in Appendix B.7 reveal that each indicator loads more

highly on their own construct than on others (Chin 2010).

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Table 5.1: Reflective Construct Assessments of Model 1

Construct SD t-

value

p-

value

Indicator

reliability

Convergent

validity Internal consistency reliability

Loading AVE Cronbach’s

alpha

Composite

reliability ρA

Aligning CSR with company’s strategy (Aligning) 0.67 0.88 0.91 0.88

SI01 <- Aligning 0.02 33.88 0.00 0.65 0.80

SI02 <- Aligning 0.02 44.50 0.00 0.68 0.82

SI03 <- Aligning 0.03 25.03 0.00 0.53 0.73

SI04 <- Aligning 0.01 58.83 0.00 0.76 0.87

SI05 <- Aligning 0.02 51.20 0.00 0.72 0.85

Support from top management (SuppTM) 0.74 0.91 0.93 0.91

SI06 <- SuppTM 0.02 52.59 0.00 0.73 0.85

SI07 <- SuppTM 0.01 67.71 0.00 0.78 0.88

SI08 <- SuppTM 0.02 45.44 0.00 0.71 0.84

SI09 <- SuppTM 0.01 71.59 0.00 0.77 0.88

SI10 <- SuppTM 0.02 42.96 0.00 0.70 0.83

Developing effective communication (EffCom) 0.75 0.91 0.94 0.91

SI11 <- EffCom 0.02 40.60 0.00 0.70 0.83

SI12 <- EffCom 0.02 53.39 0.00 0.72 0.85

SI13 <- EffCom 0.01 59.38 0.00 0.77 0.88

SI14 <- EffCom 0.01 64.26 0.00 0.77 0.88

SI15 <- EffCom 0.01 64.00 0.00 0.76 0.87

Company customer performance (CCP) 0.73 0.82 0.89 0.81

CP12 <- CCP 0.01 60.99 0.00 0.76 0.87

CP13 <- CCP 0.02 51.20 0.00 0.72 0.85

CP14 <- CCP 0.02 53.72 0.00 0.71 0.84

Company employee performance (CEP) 0.65 0.82 0.88 0.82

CP04 <- CEP 0.02 41.46 0.00 0.66 0.81

CP05 <- CEP 0.02 45.06 0.00 0.71 0.84

CP15 <- CEP 0.02 40.66 0.00 0.66 0.81

CP19 <- CEP 0.03 29.86 0.00 0.58 0.76

Company financial performance (CFP) 0.62 0.84 0.89 0.84

CP03 <- CFP 0.02 32.37 0.00 0.58 0.76

CP08 <- CFP 0.02 39.43 0.00 0.69 0.83

CP09 <- CFP 0.02 47.35 0.00 0.71 0.84

CP10 <- CFP 0.03 26.79 0.00 0.58 0.76

CP18 <- CFP 0.03 25.66 0.00 0.53 0.73

Company operational performance (COP) 0.59 0.83 0.88 0.83

CP01 <- COP 0.03 20.63 0.00 0.52 0.72

CP02 <- COP 0.03 24.73 0.00 0.53 0.73

CP06 <- COP 0.02 36.41 0.00 0.66 0.81

CP07 <- COP 0.02 50.76 0.00 0.67 0.82

CP17 <- COP 0.02 30.11 0.00 0.56 0.75

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In terms of Fornell-Larcker criterion, Table 5.2 displays the square root of AVE as the

diagonal elements and the correlations among constructs in off-diagonal rows and columns.

The results show that the square root of AVE (in a diagonal line in bold) is higher than the

cross-loadings with other constructs. However, for the Aligning-SuppTM, there is little

difference. Because the difference is so small (0.01), it can be ignored (Rahim & Magner 1995).

The result of Fornell-Larcker testing suggests that the discriminant validity has been

established.

Table 5.2: Fornell-Larcker Testing of Model 1

Code Construct AVE 1 2 3 4 5 6 7 8

1 Aligning 0.67 0.82

2 CCP 0.73 0.28 0.85

3 CEP 0.65 0.59 0.63 0.80

4 CFP 0.61 0.40 0.68 0.72 0.78

5 COP 0.59 0.43 0.66 0.72 0.74 0.77

6 EffCom 0.74 0.75 0.27 0.56 0.39 0.38 0.86

7 Strategic Integration 0.62 0.92 0.30 0.63 0.43 0.43 0.92 0.79

8 SuppTM 0.74 0.83 0.28 0.60 0.41 0.41 0.83 0.95 0.86

Note: The square root of AVE (on blue remark) is shown in diagonal while the correlations are off-diagonal.

Table 5.3 displays the HTMT values for all constructs. The results show discriminant

validity between EffCom and Aligning with the HTMT value of 0.83, below the conservative

threshold value of 0.85. Although the HTMT value between SuppTM and Aligning (0.92) and

between SuppTM and EffCom (0.91) are above 0.90, they are significantly different from one

(Sarstedt, Ringle & Hair 2017). The HTMT value between LOCs of HOC Strategic Integration

and other reflectively measured constructs (i.e., CCP, CEP, CFP and COP) range between 0.33

and 0.87. These results show that LOCs have discriminant validity between themselves and all

other constructs in Model 1 (Sarstedt, Hair, et al. 2019). The results also reveal that the

discriminant validity between the HOC Strategic Integration and other constructs in Model 1

have been achieved in the range of 0.34 to 0.71, less than 0.85. In addition, the results from the

bootstrapping procedure show that HTMT values are significantly lower than 1 (Hair, Sarstedt,

et al. 2018; Sarstedt, Ringle & Hair 2017) and below 0.9. Nevertheless, discriminant validity

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cannot be established between HOC Strategic Integration and its LOCs (i.e., Aligning, EffCom,

and SuppTM) because the measurement model of this HOC repeats indicators of its LOCs

(Hair, Sarstedt, et al. 2018; Sarstedt, Hair, et al. 2019). Overall, the results of three criteria of

discriminant validity indicate that discriminant validity has been established for the reflective

measurement model of Model 1.

After having checked for discriminant validity, the reflective measurement model’s

validation process has been completed (Götz, Liehr-Gobbers & Krafft 2010). To conclude, the

reflective evaluation of the Model 1 measurement models has been achieved satisfactorily.

Table 5.3: HTMT Values of Model 1

Code Construct 1 2 3 4 5 6 7 8

1 Aligning

2 CCP 0.33

[0.22, 0.44]

3 CEP 0.69

[0.62, 0.76]

0.77

[0.69, 0.85]

4 CFP 0.46

[0.37. 0.55]

0.81

[0.74, 0.88]

0.86

[0.79, 0.93]

5 COP 0.5

[0.40, 0.59]

0.80

[0.73, 0.86]

0.86

[0.73, 0.92]

0.87

[0.81, 0.92]

6 EffCom 0.83

[0.77, 0.87]

0.31

[0.20, 0.41]

0.65

[0.57, 0.72]

0.44

[0.35, 0.53]

0.42

[0.31, 0.53]

7 Strategic

Integration 1.00

[0.98, 1.02]

0.34

[0.23, 0.43

0.71

[0.64, 0.77]

0.48

[0.39, 0.55]

0.48

[0.38, 0.57]

0.99

[0.97, 1.00]

8 SuppTM 0.92

[0.89, 0.95]

0.33

[0.22, 0.43]

0.70

[0.63, 0.76]

0.46

[0.37, 0.55]

0.46

[0.36, 0.56]

0.91

[0.86, 0.94]

1.02

[1.01, 1.03] 0.00

Note: The values in brackets represent the 95% bias-corrected and accelerated confidence interval of the HTMT

values obtained by running the bootstrapping routine with 5,000 samples in SmartPLS (Hair, Sarstedt, et al. 2018).

5.3.1.2 Assessment of Formative Measurement Model in Strategic Integration

Several steps to evaluate the formative measurement model in strategic integration is

presented in this subsection. Since Model 1 adopts reflective-formative HCM, the collinearity

and the significance and importance of the relations between the LOCs and their HOC should

be evaluated (Hair, Sarstedt, et al. 2018). Table 5.4 presents VIF and TOL values. VIF values

are less than five, and TOL values are above 0.2. Both values show that collinearity is not a

critical issue in Model 1 (Hair et al. 2017; Hair, Risher, et al. 2018).

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Table 5.4: Collinearity Test of Formative Measures of Model 1

Exogenous Construct Endogenous Construct VIF value TOL value

Aligning Strategic Integration 3.28 0.30

SuppTM 4.65 0.22

EffCom 3.31 0.30

Table 5.5 presents results of bootstrapping procedure for Model 1. Three paths from LOC

to HOC get p-values above 0.1 and t-values exceeding 1.96. Because the confidence interval

do not include zero, they are significant (Hair, Risher, et al. 2018). Three LOCs have highly

similar effects on Strategic Integration, showing their slightly equivalent weight for developing

the HOC (Hair, Sarstedt, et al. 2018). SuppTM contribute most substantially to HOC Strategic

Integration (β=0.38), followed by EffCom (β=0.36), and Aligning (β=0.33).

To sum up, the assessment of the measurement model, including both reflective and

formative measures suggested that eight constructs used in Model 1 (seven LOCs and one HOC

Strategic Integration) were reliable and valid in the context of this thesis. Then, the model can

be used for further analysis.

Table 5.5: Indicator Validity of Formative Measurements in Model 1

Second-Order

Constructs Paths Path Coefficient SD t-value p-value*

Strategic

Integration

Aligning → Strategic Integration 0.33 [0.32; 0.35] 0.01 34.03 0.00

SuppTM → Strategic Integration 0.38 [0.36; 0.40] 0.01 45.30 0.00

EffCom → Strategic Integration 0.36 [0.34; 0.38] 0.01 35.73 0.00

Notes: *p < 0.05 (two-tailed t-test for significance testing above 1.96). The values in brackets represent the 95%

bias-corrected and accelerated confidence interval of the path coefficients obtained by running the bootstrapping

routine with 5,000 samples in SmartPLS.

5.3.2 Assessment of the Structural Model in Strategic Integration

Following the evaluation of the measurement model, the structural model of strategic

integration is assessed. This subsection shows how each step of the evaluation was conducted.

In this thesis, the purpose of the structural model assessment is to estimate the variance in

endogenous constructs (four constructs of CP) explained by exogenous constructs (i.e., HOC

Strategic Integration). As suggested by Hair et al. (2017), six steps are conducted.

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5.3.2.1 Assessment of Collinearity in Strategic Integration

This subsection presents how collinearity in strategic integration is assessed. The results in

Tables 5.6 and 5.7 show that outer and inner VIF values are lower than five, indicating no

significant levels of collinearity were detected among the indicators and the constructs (Hair et

al. 2017).

Table 5.6: Outer VIF Values of Model 1

Indicator VIF Indicator VIF Indicator VIF Indicator VIF

SI01 1.91 SI09 2.79 CP02 2.01 CP10 1.59

SI02 2.07 SI10 2.22 CP03 1.61 CP12 1.95

SI03 1.61 SI11 2.21 CP04 1.77 CP13 1.94

SI04 2.68 SI12 2.49 CP05 2.01 CP14 1.62

SI05 2.37 SI13 2.91 CP06 2.09 CP15 1.74

SI06 2.53 SI14 2.98 CP07 2.17 CP17 1.48

SI07 2.99 SI15 2.93 CP08 2.43 CP18 1.47

SI08 2.36 CP01 1.96 CP09 2.57 CP19 1.46

Table 5.7: Inner VIF Values of Model 1

Construct 1 2 3 4 5 6 7 8

1 Aligning 3.28

2 CCP 2.02

3 CEP 3.14

4 CFP

5 COP 2.42

6 EffCom 3.31

7 Strategic Integration 1.00 1.00 1.70 1.00

8 SuppTM 4.65

5.3.2.2 Assessment of the Structural Model Relationships in Strategic Integration

This subsection discusses on how structural model relationship is evaluated in strategic

integration. Path coefficients reflect the strength of the relationships between latent variables

(Sarstedt, Ringle & Hair 2017). The strength and significance of the path coefficients are

assessed in relation to the hypothesised relationships (structural paths between the constructs)

(Sarstedt, Ringle & Hair 2017), which can be measured using a bootstrapping procedure (Hair

et al. 2017; Wong 2016). PLS path modelling employs a nonparametric bootstrap (Davison &

Hinkley 2003; Efron & Tibshirani 1993, cited in Henseler, Ringle & Sinkovics 2009) to

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calculate t and p values of the path coefficient (Sarstedt, Ringle & Hair 2017) and present

confidence intervals for all parameter estimates, building the basis for statistical inference

(Henseler, Ringle & Sinkovics 2009). Critical t-values for a two-tailed test are 1.96 with a

significance level of 5% (Hair, Ringle & Sarstedt 2011). Thus, a path coefficient is significant

at the 5% probability of error level if zero does not fall within the 95% confidence interval

(bias-corrected and accelerated) (Götz, Liehr-Gobbers & Krafft 2010; Sarstedt, Ringle & Hair

2017). With regards to relevance, path coefficients generally range from -1 to +1. Coefficients

closer to +1 indicate strong positive relationships, and those closer to -1 suggest strong negative

relationships (Sarstedt, Ringle & Hair 2017).

To analyse the relationship between strategic integration and CP, direct effect and

moderating effect are tested as follows.

1. Direct Effect in Strategic Integration

A direct effects shows the relationship between two constructs with a single arrow (Hair et

al. 2017). To be considered meaningful, standarised paths are necessary to be at least 0.20

and ideally should be higher than 0.30 (Chin 1998). Table 5.8 presents seven positive direct

effects resulting from the bootstrapping procedure (see also Figure 5.2). Six expose

meaningful and significant relationships with path coefficients above 0.3, t-values

exceeding 1.96, and no zero in their confidence interval (Hair et al. 2017). Only one path

has a weak and insignificant direct effect with a path coefficient of less than 0.2 and t-value

below 1.96: Strategic Integration → CFP. The first three paths indicate that three LOCs of

Strategic Integration have a positive and significant direct effect on their HOC, and among

them, the path coefficient is almost identical. Three paths in the relationshp between HOC

Strategic Integration and endogenous constructs have positive and significant direct effects.

Specifically, the most substantial effect is on Strategic Integration → CEP (β=0.63,

t=21.43), then Strategic Integration → COP (β=0.43, t=10.12), and finally, Strategic

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Integration → CCP (β=0.30, t=6.45). In constrast, Strategic Integration → CFP has the

weakest effect and is also insignificant (β=0.01, t=0.26).

Table 5.8: Direct Effects of Model 1

Path Direct Effect SD t-value p-value* Significant?

Strategic Integration → CCP 0.30 [0.21, 0.39] 0.05 6.45 0.00 Yes

Strategic Integration → CEP 0.63 [0.57, 0.69] 0.03 21.43 0.00 Yes

Strategic Integration → CFP 0.01 [-0.06, 0.08] 0.04 0.26 0.79 No

Strategic Integration → COP 0.43 [0.35, 0.52] 0.04 10.12 0.00 Yes

CCP → CFP 0.25 [0.15, 0.35] 0.05 4.85 0.00 Yes

CEP → CFP 0.30 [0.17, 0.44] 0.07 4.38 0.00 Yes

COP → CFP 0.35 [0.24, 0.46] 0.06 6.31 0.00 Yes

Note: *p < 0.05 (two-tailed t-test for significance testing above 1.96). The values in brackets represent the 95%

bias-corrected and accelerated confidence interval of the path coefficients obtained by running the bootstrapping

routine with 5,000 samples in SmartPLS.

2. Mediating Effect in Strategic Integration

To gain a comprehensive understanding of the role of stakeholders in CSR-CFP

relationships, there are new relationships among CP, which mediates the direct relationship

from Strategic Integration to CFP (see Figure 5.1). The effect of the independent variable,

Strategic Integration, on the dependent variable (CFP), is mediated by three mediators, CCP,

CEP, and COP (Nitzl, Roldan & Cepeda 2016).

The mediating effect testing covers two steps: (a) testing the strength of the indirect effect

and (b) determining the type of effect and/or of mediation (Nitzl, Roldan & Cepeda 2016).

A discussion of each step is presented below.

▪ Step a. Testing the strength of the indirect effect. In PLS-SEM analysis, particularly using

SmartPLS 3, bootstrapping can provide the indirect (mediating) and total effects based

on the bootstrap estimates for the direct relationships (Hair et al. 2017; Streukens &

Leroi-Werelds 2016). The benefits of SmartPLS for mediation are that bootstrapping

requires no assumptions about the distribution of the variables or sampling distribution,

and all mediated relationships are evaluated simultaneously rather than separately,

reducing bias (Hair et al. 2014).

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As shown in Table 5.9, HOC Strategic Integration has a positive and significant direct

effect on three mediators (X → M or a), indicating that a differed from zero (Baron &

Kenny 1986). The result also shows that the three mediators have a positive and

significant direct effect on the dependent variable (i.e., CFP) (M → Y or b) (Baron &

Kenny 1986; Judd 2010). The most substantial path coefficient belongs to COP → CFP

(β=0.35, t=6.31), followed by CEP → CFP (β=0.30, t=4.38), and last, CCP → CFP

(β=0.25, t=4.85). The result indicates positive and significant b (Baron & Kenny 1986),

and both paths (a and b) have an indirect effect. For example, the indirect effect of CEP

(ab) was significant at 0.63*0.30 = 0.19 (t=4.38, p<0.05). Table 5.9 shows three specific

indirect effects. They satisfy the main criterion for determining mediation because they

are significantly different from zero (Baron & Kenny 1986; Nitzl, Roldan & Cepeda

2016) and their confidence interval does not include zero (Hair et al. 2017).

Table 5.9: Specific Indirect Effects of Model 1

Path Specific

indirect effect* SD t-value p-value Significant?

Strategic Integration → CCP → CFP 0.08 [0.04; 0.12] 0.02 3.94 0.00 Yes

Strategic Integration → CEP → CFP 0.19 [0.11; 0.28] 0.04 4.38 0.00 Yes

Strategic Integration → COP → CFP 0.15 [0.10; 0.20] 0.03 5.82 0.00 Yes

*Note: The values in brackets represent the 95% bias-corrected and accelerated confidence interval of the path

coefficients obtained by running the bootstrapping routine with 5,000 samples in SmartPLS.

Moreover, since Model 1 has multiple mediation, the total indirect effect may comprise

several specific indirect effects (Hair et al. 2017). The result reveals the total indirect

effects from Strategic Integration to CFP as the sum of three specific indirect effects

(β=0.42, t=10.62). The total effects refer to the sum of direct and indirect effects

(symbolised by c’) between constructs in the path model (Sarstedt, Ringle & Hair 2017).

The estimation of total effects offers a more comprehensive view of the structural model

relationships (Nitzl, Roldan & Cepeda 2016). The total effect of X on Y is equal to the

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sum of the indirect and direct effects (Hair et al. 2017). Consequently, total effects for

the relationship between Strategic Integration and CFP are calculated as follows:

Direct effect of Strategic Integration → CFP (c) = 0.01

Total indirect effect of Strategic Integration → CFP (ab) = 0.42

Total effect (c’) = direct effet (c) + indirect effects (ab)

Thus, total effect of Strategic Integration → CFP = 0.01 + 0.42 = 0.43

Table 5.10 displays the direct, indirect and total effects of Model 1. The total effect is

different from the direct effect. More specifically, the direct effect from Strategic

Integration to CFP is small and insignificant (β=0.01, t=0.26). But, its total effect is

greater, positive and significant (β=0.43, t=10.12). The result reveals that three mediators

(CCP, CEP and COP) can affect the dependent variable (CFP) controlling independent

variables (HOC Strategic Integration) for direct effect (Baron & Kenny 1986).

Table 5.10: Direct Effects, Indirect Effects and Total Effects of Model 1

Path Direct Effect

(a)

Direct Effect

(b)

Indirect Effect

(ab)

Total Effect

(c’)

t-

value

p-

value

Strategic Integration → CCP → CFP 0.08

[0.04; 0.12] 3.94 0.00

Strategic Integration → CCP 0.30

[0.21, 0.39] 6.45 0.00

CCP → CFP 0.25

[0.15, 0.35] 4.85 0.00

Strategic Integration → CEP → CFP 0.19

[0.11; 0.28] 4.38 0.00

Strategic Integration → CEP 0.63

[0.57; 0.69] 21.17 0.00

CEP → CFP 0.30

[0.17, 0.44] 4.38 0.00

Strategic Integration → COP → CFP 0.15

[0.10; 0.20] 5.82 0.00

Strategic Integration → COP 0.43

[0.35; 0.52] 10.12 0.00

COP → CFP 0.35

[0.24, 0.46] 6.31 0.00

Strategic Integration → CFP 0.01

[-0.06, 0.08] 0.26 0.79


[0.38; 0.53] 10.62 0.00


[0.35; 0.50] 11.38 0.00

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▪ Step b. Determining the type of effect and/or of mediation. The role of the mediator is to

expose the true relationship between exogenous and endogenous constructs (Hair et al.

2017). The result shows that mediators can make significant changes to the insignificant

relationship between Strategic Integration and CFP. Since the indirect effect is significant

but not the direct effect is not, it represents an indirect-only (full) mediation type (Hair

et al. 2017). In other words, the effect of Strategic Integration to CFP is completely

transmitted with help of the mediators (Nitzl, Roldan & Cepeda 2016). Strategic

Integration releases its influence only under certain conditions of CCP, CEP and COP,

on CFP. When CCP, CEP and COP do not mediate the relationship between two

constructs, Strategic Integration has no significant effect on CFP.

After calculating the mediating effects, the strengths of specific mediating effects should

be tested (Nitzl, Roldan & Cepeda 2016) by measuring variance accounted for (VAF)

(Nitzl, Roldan & Cepeda 2016; Wong 2016). VAF determines the extent to which the

mediation process explains the dependent variable’s variance (Nitzl, Roldan & Cepeda

2016) calculated by dividing indirect effect (axb) with the total effect (axbxc’). When

VAF is above the threshold level of 0.2, partial mediation is demonstrated, and full

mediation is expressed when it excedes 0.8 (Hair et al. 2017).

Table 5.11 presents the VAF values in the range from 0.19 to 0.44. As CCP has a VAF

value of 18.60%, the result implies that a part of the total effect of strategic integration

on CFP is explained by the indirect effect of CCP. CEP’s VAF value is 44.19%,

indicating that the indirect effect of CEP explains almost half of the total effect of

Strategic Integration on CFP. Similarly, COP has a VAF value of 34.88%, suggesting

that a part of the total effect of Strategic Integration on CFP is explained by COP’s

indirect effect. Specifically, these are complementary partial mediations, because the

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total effect (c’) and indirect effect a×b point in the same (positive) direction (Baron &

Kenny 1986).

Table 5.11: VAF of Mediation Effect of Model 1

Path Direct Effect

(a)

Direct Effect

(b)

Indirect Effect

(a x b)

Total

Effect

VAF

(%)

Strategic Integration → CCP → CFP 0.30 0.25 0.08 0.43 18.60

Strategic Integration → CEP → CFP 0.63 0.30 0.19 0.43 44.19

Strategic Integration → COP → CFP 0.43 0.35 0.15 0.43 34.88

If a variable has a function as a partial mediation of the relationship between variables, there

is yet another mediator that can be investigated in the relationship (Baron & Kenny 1986).

The results confirm this statement, as there are three partial mediations in Model 1 (i.e., the

multiple mediations). They show that part of the effect of HOC Strategic Integration on CFP

is mediated through CCP (or CEP or COP), because this HOC explains part of CFP, which

is independent of CCP (and CEP as well as COP) (Nitzl, Roldan & Cepeda 2016). A total

VAF value of 97.67% indicates that almost all of the total effect of strategic integration onto

CFP is explained by the indirect effects from CCP, CEP and COP. Because of the multiple

mediation, these three mediators have an indirect-only and full mediation on the relationship

between HOC Strategic Integration and CFP.

To sum up, the results from the structural model assessment of Model 1 show that Strategic

Integration has a positive and significant impact on CP. More specifically, Strategic Integration

has a substantial impact on CCP, CEP and COP, while its impact on CFP is significantly

mediated by CCP, CEP and COP. Notably, among the three mediators, CEP has the greatest

mediating effect on the strategic CSR integration-CFP relationship, followed by COP and CCP.

3.2.3 Assessment of the Coefficient of Determination (R2) in Strategic Integration

This section explains how to check the coefficient of determination in strategic integration.

In addition to determining the significance of all path predictions, an important focus in the

PLS-SEM analysis is explained variance. As PLS-SEM attempts to find the greatest variance

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explained by the dependent variables, the predictive power of the structural model is evaluated

by assessing the significance and magnitude of the coefficient of determination (R2) of the

endogenous constructs (Chin 2010; Hair et al. 2014; Peng & Lai 2012; Streukens & Leroi-

Werelds 2016). As the primary criterion structural (inner) model assessment (Henseler, Ringle

& Sinkovics 2009), the R2 is defined as ‘a measure of the model’s predictive power and is

calculated as the squared correlation between a specific endogenous construct’s actual and

predicted values’ (Hair et al. 2017, p. 198). The R2 can also be used to determine the goodness

of fit of a model (Gallardo-Vázquez & Sanchez-Hernandez 2014).

The range of R2 value is between zero and one, with higher values suggesting higher levels

of predictive accuracy (Hair et al. 2017) and indicating the greater the percentage of variance

explained (Götz, Liehr-Gobbers & Krafft 2010). The desirable values should be 0.1 or greater

(Falk & Miller 1992, cited in Bernal-Conesa, de Nieves-Nieto & Briones-Peñalver 2017). R2

values of 0.75, 0.50, or 0.25 are substantial, moderate or weak levels, respectively (Hair, Ringle

& Sarstedt 2011; Henseler, Ringle & Sinkovics 2009) or small, medium, or large effects,

respectively (Chin 2010) in relation to predictive accuracy.

Table 5.12 presents R2 values between 0.09 and 0.65, showing a predictive capability to

varying degrees. CFP is the largest predictor of the structural model with an R2 value of 0.65.

In other words, HOC Strategic Integration, CCP, CEP, and COP can jointly explain 65% of the

variance of the endogenous construct CFP. Therefore, CFP’s impact can be explained by HOC

Strategic Integration but also from three mediators (see Figure 5.1).

Table 5.12: R2 and Q2 values of Model 1

Endogenous Construct R2 value Q2 value

CCP 0.09 0.06

CEP 0.40 0.24

CFP 0.65 0.37

COP 0.19 0.10

Strategic Integration 1.00 0.58

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Given the predictor specification forms the basis for PLS modelling (Chin 2010), and the

greater the number of predictor constructs, the higher the R2 (Hair, Risher, et al. 2018), this

construct has the highest R2 value. The same model estimation also reveals the R2 value for

another latent construct; Strategic Integration explains 40% of CEP, 19% of COP and nine%

of CCP. Hence, the R2 value for CEP can be considered moderate, whereas R2 values of COP

and CCP are rather weak. Notably, as LOCs Aligning, SuppTM, and EffCom form HOC

Strategic Integration, they explain 100% the variance of their HOC with R2 values of one

(Becker, Klein & Wetzels 2012; Hair et al. 2017; Hair, Sarstedt, et al. 2018).

5.3.2.4 Assessment of the Effect Size (f2) in Strategic Integration

This subsection presents the next step of structural model assessment in strategic integration,

checking the effect size. The effect size (f2) is measured to quantify significant effects

(Henseler, Ray & Hubona 2016). An f2 value of 0.02, 0.15, and 0.35 represent small, medium,

and large effects of the exogenous latent variable, respectively (Chin 2010; Cohen 1992), while

an f2 value of less than 0.02 indicates no effect (Hair et al. 2017).

Table 5.13 shows the range of f2 values from 0.00 to 0.65. The strongest effect size is

Strategic Integration → CEP (0.65). A medium-to-large effect is seen for Strategic Integration

→ COP (0.23), while a medium effect size occurs for COP → CFP (0.14). A weak effect size

relies on Strategic Integration → CCP, CCP → CFP, and CEP → CFP with an f2 value of 0.10,

0.09, and 0.08, respectively. Strategic Integration has no effect size on CFP as its f2 value is

less than 0.02 (Sarstedt, Ringle & Hair 2017). The rank order of the size of the path coefficients

and the f2 effect sizes is often the same (Hair, Risher, et al. 2018). As shown in Tables 5.10 and

5.13, the sequence of both parameters is Strategic Integration → CEP (β=0.63, f2=0.65),

followed by Strategic Integration → COP (β=0.43, f2=0.23), and Strategic Integration → CCP

(β=0.30, f2=0.10).

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Table 5.13: f2 Values of Model 1

Path f2 value

Strategic Integration → CCP 0.10

Strategic Integration → CEP 0.65


Strategic Integration → COP 0.23

CCP → CFP 0.09

CEP → CFP 0.08

5.3.2.5 Assessment of the Predictive Relevance (Q2) in Strategic Integration

This subsection describes how predictive relevance is tested in strategic integration. The

structural model’s predictability can be further assessed using the cross-validated redundancy

measure Q2 (Stone 1974, Geisser 1974, cited in Henseler, Ringle & Sinkovics 2009; Shmueli

et al. 2016). A Q2 value is obtained by employing the blindfolding procedure applicable to

endogenous constructs, which have a reflective measurement (Hair et al. 2017; Henseler,

Ringle & Sinkovics 2009). A Q2 value above zero signifies the model has predictive relevance

for a particular endogenous construct (Hair et al. 2017), while a Q2 value below zero implies a

lack of predictive relevance (Chin 2010). As a rule of thumb, Q2 values of 0, 0.25 and 0.50

illustrate the small, medium and large predictive relevance of the PLS-path model (Hair,

Risher, et al. 2018).

Using an omission distance D=7 (Hair et al. 2017), the blindfolding procedure reveals the

Q2 values in Table 5.12. The resulting cross-validated redundancy positive Q2 values range

from 0.06 and 0.58. As the Q2 value above 0.5 is generally indicative of a predictive model

(Chin 2010), the result suggests that the exogenous construct (HOC Strategic Integration) has

excellent predictive relevance for all four endogenous constructs of CP.

5.3.2.6 Assessment of Effect Size (q2) in Strategic Integration

The final step of the structural model assessment, effect size evaluation, is explained in this

subsection. Another criterion of the structural model assessment is the effect size, q2, which

enables measurement of an exogenous construct’s contribution to an endogenous latent

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variable’s Q2 value. A small, medium, or large predictive relevance for an exogenous construct

for a certain endogenous construct is reflected by q2 values of 0.02, 0.05, and 0.35, respectively

(Hair et al. 2017). As suggested by Hair et al. (2017) , q2 is determined manually by using the

Q2 values included and excluded after extracting a particular predecessor of the latent

endogenous variable estimated by the blindfolding procedure, with this formula:

q2 = [Q2 (included) – Q2 (excluded)] / 1 – Q2 (included) (Hair et al. 2017, p. 207)

Table 5.14 presents results for the q2 calculation, where the endogenous constructs are in

the first row, and the predictor constructs appear in the first column. Because Model 1 applies

HCM, and HOC Strategic Integration is the only predecessor for CP, q2 cannot be identified

between predictor constructs and the four endogenous constructs. These three LOCs have q2

values of 0.05, 0.06 and 0.05, respectively, indicating a small-to-medium predictive relevance

from LOCs to their HOC.

Table 5.14: q2 Values of Model 1

Endogenous Construct Aligning SuppTM EffCom

CCP 0.00 0.00 0.00

CEP 0.00 0.00 0.00

CFP 0.00 0.00 0.00

COP 0.00 0.00 0.00

Strategic Integration 0.05 0.06 0.05

Finally, the standardized root mean square residual value (SRMR) for the structural model

is less than 0.08 (0.07 for Model 1), suggesting a good model fit (Benitez et al. 2020; Hair et

al. 2017; Sarstedt, Ringle & Hair 2017). Therefore, in general, this result indicates that the

proposed Model 1 is well suited to exploring the integration of CSR into business strategy at

the strategic level and predicting the impact of the integration on CP among manufacturing

companies, particularly in Indonesia.

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5.4 Discussion of Strategic Integration

The results of the strategic integration analysis are discussed in this section. The discussion

is based on theoretical framework and hypotheses explained in Chapter 3, as well as current

literature, prior research findings, and PLS-SEM data analysis. This section is divided into two

subsections, each of which describes (i) strategic integration and company performance

discussion and (ii) the mediating effect in strategic integration.

5.4.1 Discussion of Strategic Integration and Company Performance

This subsection explains the results of strategic integration and company performance

analysis. The empirical evidence provides support for the conceptualisation of strategic CSR

integration as a HOC consisting of the three correlated LOCs: aligning with the company’s

strategy, gaining support from top management, and developing effective communication. As

these three LOCs have almost equal weight for developing of the HOC Strategic Integration,

they should be implemented in the integration of CSR into business strategy at the strategic

level. The results of reflective and formative measurement model assessments show that the

alignment of these LOCs reflects a complete description of strategic CSR integration as each

LOC encompasses different aspects of such integration. The assessments of structural models

have been satisfactorily achieved and confirm the model’s good fit. Results show that the

proposed model is appropriate and applicable for exploring the integration of CSR at the

strategic level and predicting its impact on CP, especially in the Indonesian manufacturing

industry.

The relationship between strategic integration and CP was examined with some specific

hypotheses. The direction and strength of each hypothesised relationship is determined based

on the path coefficients and their corresponding t-values resulting from PLS-SEM data

analysis. If paths are significant and show the hypothesised direction, they empirically support

a prior hypothesis. In comparison, if the paths are insignificant and do not follow the

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hypothesised direction, they do not support the relationship proposed (Götz, Liehr-Gobbers &

Krafft 2010).

▪ Hypothesis 1a (H1a): The strategic integration of CSR and business strategy has a

positive impact on customer performance.

The finding reveals that strategic integration has a significant impact on customer

performance (β=0.30, t=6.45, p<0.05), supporting H1a. Customer performance in Model 1

consists of customer satisfaction, customer loyalty, and increasing number of consumers. The

finding suggests that CSR integration into business strategy at the strategic level has a positive

and significant impact on customer performance by increasing customer satisfaction,

enhancing customer loyalty, and generating more consumers.

The finding is supported by previous studies, which indicates that CSR would have an

immense effect on the perceptions and attitudes of customers towards companies and the goods

they manufacture (Nguyen P-M 2020). CSR has a positive effect on customer satisfaction, as

such CSR activities help to boost customer satisfaction (García-Madariaga & Rodríguez-

Rivera 2017) and offer benefits to customers, such as decreased customer complaints, enhanced

customer service, strengthened customer relationships and improved customer loyalty (Chi

2015).

▪ Hypothesis 1b (H1b): The strategic integration of CSR and business strategy has a

positive impact on employee performance.

Findings reveal that strategic integration has a significant effect on employee performance

(β=0.63, t=21.43, p<0.05), supporting H1b. The result also shows that the effect of strategic

integration is primarily on employee performance. There are four indicators for measuring

employee performance: employee training, career opportunity, employee motivation and

overall social performance. By integrating CSR into business strategy at the strategic level,

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companies can provide appropriate training for their employees, offering them good career

opportunities, and thus, increase employee motivation and improve overall social performance.

In Model 1, aligning CSR with a company’s strategy is a critical dimension of strategic

integration. Companies should incorporate their ethical CSR initiatives, such as commitment

to fair treatment of employees and environmentally friendly manufacturing procedures, into

their corporate mission, vision, and values (Chen, Hong & Occa 2019). Moreover, developing

effective communication is a critical dimension of strategic integration. Because appropriate

communication can boost employees’ understanding of CSR (Hadj 2020), a company’s

mission, vision and values should be effectively communicated to employees using various

corporate communications channels as well as through activities and initiatives during which

employees can contribute ideas and participate. Otherwise, CSR efforts could result in

employees’ scepticism (Chen, Hong & Occa 2019).

Previous studies argue that in addition to customers, employees also show their perception

to CSR practices by their companies. Employees are satisfied with and enjoy working for

companies with a high commitment to CSR. These employees tend to be more optimistic, loyal

and productive than those who work for employers with lower commitment to CSR (Dey &

Sircar 2012). CSR practices also have the ability to increase employee motivation and improve

employee perceptions of their companies (Dawkins 2005) and boost employee loyalty, which,

in turn, helps to maintain high quality employees (Shen, Au & Li 2019). Additionally, CSR

helps companies to promote their stakeholders (e.g., employees who give the company a 'good'

image) (Arjaliès & Mundy 2013).

Nonetheless, the findings are not supported by a prior study of Ridho (2018), which found

that CSR implementation has no significant effect on customer performance in terms of growth

in sales and employee performance, as measured by growth in the number of employees.

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▪ Hypothesis 1c (H1c): The strategic integration of CSR and business strategy has a positive

impact on operational performance.

Strategic CSR integration is found to have a positive and significant impact on operational

performance (β=0.43, t=10.12, p<0.05), supporting H1c. Although the activities included in

the three dimensions of strategic CSR integration are not directly related to business operational

activities, they have an impact on operational performance, such as shortened timeline of

customer service (from order to receipt), faster delivery time, increased productivity and

improved operational efficiency.

There is a plausible explanation for this finding. As several related CSR activities have been

defined at the strategic level, they become a guide for implementation at the lower levels,

including the tactical level. In particular, they are incorporated with the vision and mission of

the company, have support from top management and are communicated throughout the

organisation. Moreover, several activities (indicators) in strategic CSR integration are related

to employees, such as ‘CSR strategies and goals are clearly communicated to all employees’

and ‘We communicate CSR activities within the company through multiple channels, such as

face-to-face meetings, formal communications from senior managers, and a company

newsletter’. These will indirectly impact operational performance because employees in

socially responsible companies can produce better operational performance than their peers in

less socially responsible companies (Sun & Yu 2015). Therefore, through integrating CSR into

business strategy at the strategic level, companies can improve productivity, increase

operational efficiency, and shorten timelines of customer service and delivery time.

Consequently, the overall operational performance should increase.

However, this finding is different from other studies. For example, in their survey of 197

CSR-practicing companies in Saudi Arabia, Al-Shuaibi (2016) found no significant

relationship between CSR and productivity.

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▪ Hypothesis 1d (H1d): The strategic integration of CSR and business strategy has a

positive impact on financial performance.

Findings reveals that strategic integration has a significant impact on financial performance

(β=0.43, t=11.38, p<0.05), supporting H1d. In this thesis, financial performance was measured

through cash flow report, profit, sales growth, ROI and overall financial performance. In other

words, companies can have better cash flow reports, higher profits, increased sales growth and

ROI if they integrate CSR into business strategy at the strategic level. It should be noted that

strategic CSR integration has a positive effect on financial performance mediated by other

dimensions of CP, which will be explained in the next subsection (see 5.4.2).

Overall, this finding provides empirical evidence that strategic integration of CSR, non-

financially and financially, benefits companies. When CSR is highly integrated with

management operations, economic and social targets are easier to achieve thereby increasing a

company's social and financial performance (in terms of profitability) (Kapoor & Sandhu

2010). Companies have superior financial performance and sustain financial competitiveness

if they prioritise CSR activities based on strategic concerns and integrate them into business

strategy (Michelon, Boesso & Kumar 2013; Torugsa, O'Donohue & Hecker 2013).

In the Indonesian context, Sayekti (2015) investigated strategic CSR in terms of inside-out

linkages and outside-in linkages among 136 Indonesian-listed companies. Conducting a

content analysis on annual reports between 2005 and 2008, this study found that strategic CSR

positively affect financial performance, as measured by ROA. When CSR practices integrate

with companies operations and strategy, a positive impact on CFP occurs.

Nonetheless, the finding is in contrast with Razafindrambinina and Sabran (2014), who

found that companies in Indonesia do not believe that engaging in CSR will result in long-term

benefit or higher profit. They might be still deeply focused on financial gains rather than moral

obligations, which impede their own awareness of their impact on environment and society. In

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addition, they may not believe engaging in strategic CSR will boost employee welfare,

strengthen ethics, or improve regulatory decisions.

Although the testing of the first four hypotheses gave rise to results similar to previous

studies, this research offers a comprehensive understanding that strategic CSR integration has

a positive impact on whole of CP. Rather than evaluating the effect of strategic CSR integration

on one performance, this thesis provides clear empirical evidence that this integration has a

simultaneous positive impact on all four aspects of CP (i.e., customer, employee, operating and

financial performance). Thus, companies will gain sustainable competitive advantage and

obtain long-term profitability if they practice CSR strategically (Ooi, Amran & Yeap 2017).

5.4.2 Discussion of Mediating Effect in Strategic Integration

In this section, the results of a mediating effect analysis in strategic integration are discussed.

There are three hypotheses of the mediation analysis in terms of strategic integration as follows.

▪ Hypothesis 4a (H4a): The relationship between strategic CSR integration and financial

performance is mediated by customer performance.

Findings reveal that customer performance has a mediation effect on the relationship

between strategic integration and financial performance (β=0.08, t=3.94, p<0.05), supporting

H4a. In other words, strategic CSR integration can improve financial performance through

customer performance.

There is a plausible explanation for this result. Several studies highlighted that customer

satisfaction (as one indicator of the customer performance in this thesis) can lead to higher

levels of financial performance. For example, Luo and Bhattacharya (2006) found CSR affects

financial performance partially through the mediator of customer satisfaction. Using secondary

data, they confirmed that customer satisfaction plays a significant role and partially mediates

the relationship between CSR and financial performance. With a sample of 205 Iranian

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manufacturing and consumer product companies, Saeidi et al. (2015) examined whether the

link between CSR and company performance is a fully mediated relationship. They used

customer satisfaction as a mediator with three indicators: customer satisfaction with product or

service quality, customer satisfaction with value for price, and meeting customer expectations.

Company performance was measured financially through market share growth, sales growth,

ROE, ROS, ROA, ROI, and net profit margin of the company. Their findings show that

customer satisfaction mediates the relationship between CSR and financial performance.

Similarly, García-Madariaga and Rodríguez-Rivera (2017) emphasised that customer

satisfaction mediates the relationship between CSR and financial performance measured by

market to book ratio (MB). Taking 238 companies in China and Vietnam as a sample, Xie et

al. (2017) identified that customer satisfaction has a positive impact on financial performance.

Their findings suggest that customer satisfaction fully mediates the relationship between CSR

and financial performance. They found that CSR activities can help companies improve their

financial performance by improving customer satisfaction. Thus, companies can improve the

effect of CSR efforts on companies' financial performance by using the indirect role of

customer satisfaction.

Customer loyalty can also affect financial performance. Customer satisfaction will increase

customer loyalty, and they are more likely to repeat purchases that lead to higher demand,

greater sales volume, and in turn, improved performance levels (Maignan et al. 1999).

Repeating buying behaviour towards the products and services of a company can increase the

company’s profitability and sustainability performance (Aninkan & Oyewole 2014). As a

result, greater customer loyalty will lead to greater financial performance.

Nevertheless, this thesis extends the results of previous studies because this thesis used

multiple indicators of customer performance. In doing so, this thesis provides a deeper

understanding that customer performance can fully mediate the relationship between strategic

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integration and financial performance through improving customer satisfaction, enhancing

customer loyalty, and increasing number of consumers.

Previous studies highlight that CSR affects financial performance through the mediator of

customer satisfaction (García-Madariaga & Rodríguez-Rivera 2017; Luo & Bhattacharya

2006; Saeidi et al. 2015; Xie et al. 2017). These studies found that CSR activities can help

companies boost their financial performance by enhancing customer satisfaction. Thus,

companies can improve the effect of CSR efforts on companies' financial performance by using

the indirect role of customer satisfaction. A company's CSR commitment encourages greater

satisfaction and trust in the company and its services, which in turn encourages consumers to

remain loyal (Park, Kim & Kwon 2017).

Besides, customer satisfaction can improve customer loyalty. Greater customer loyalty can

lead to greater financial performance as loyal customers tend to repeat buying, leading to

positive word-of-mouth, higher demand, and increased sales volume and in turn, improved

performance levels (Chi & Gursoy 2009; Maignan et al. 1999). Customer loyalty can also

influence customer buying decisions, such as an improving customers’ buying intention or

increasing customers’ willingness to pay higher prices for the companies’ products and services

(Bhardwaj et al. 2018; Goli ´nski 2019).

▪ Hypothesis 4b (H4b): The relationship between strategic CSR integration and financial

performance is mediated by employee performance.

The current finding indicates that the relationship between strategic integration and financial

performance can be mediated by employee performance (β=0.19, t=4.38, p<0.05), supporting

H4b. Among three mediators, employee performance has the largest mediating effect between

strategic CSR integration and financial performance. Accordingly, better financial

performance can be achieved by enhancing employee performance.

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A high employee performance may indicate that employees have access to appropriate

training, a good career path is available in their companies, and they are motivated strongly in

their workplaces (Maignan et al. 1999). These benefits are useful to increase efficiencies and

productivity and thus in turn may improve performance levels. Hence, this finding strengthens

a claim that there is the positive relationship between social and economic performance (Lee

2008) as confirmed by Bernal-Conesa, de Nieves-Nieto and Briones-Peñalver (2017).

▪ Hypothesis 4c (H4c): The relationship between strategic CSR integration and financial

performance is mediated by operational performance.

Operational performance has a significant mediating effect on the relationship between

strategic operations and financial performance (β=0.15, t=5.82, p<0.05), supporting H4c. This

finding suggests that companies can achieve better financial performance through operational

performance as a result of integrating CSR into business strategy at the strategy level.

Investigation of the CRS impact in relation to social performance is relevant when analysing

the mechanism by which CSR affects financial performance. The findings of this thesis thus

enhance the CSR literature by identifying a new contingency, which explains the complex

relationship between CSR integration and financial performance. The satisfaction of various

stakeholder groups (including customers and employees) is critical to an organisation's

financial performance (Orlitzky, Schmidt & Rynes 2003). This thesis expands previous studies

on the mechanisms for the CSR-CFP relationship by integrating the mediating effect of

customer, employee and operational performance along the path from strategic CSR integration

to financial performance. Strategic CSR integration affects customer performance, and in turn

customer performance leads to financial performance. Similarly, strategic CSR integration has

a significant effect on employee performance, and then employee performance influences

financial performance. In addition, strategic CSR integration contributes to operational

performance, which in turn impacts financial performance.

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Notably, strategic CSR integration can represent the commitment of companies to CSR by

aligning CSR with the strategy of the organisation, receiving support from top management

and maintaining effective communication. Many companies in Indonesia adopt CSR in

charitable activities and corporate philanthropy (Joseph et al. 2016; Maulamin 2017;

Razafindrambinina & Sabran 2014; Widjaja 2011), and the current findings show that they

implement CSR beyond these practices. They reflect that the integration of CSR into business

strategy should occur to achieve efficient corporate social strategies (Sousa Filho et al. 2010).

In addition, there is an increasing number of companies that display their CSR activities in

their official corporate websites or via mainstream media news, such as television and

newspapers (Widjaja 2011). They use these communication methods to inform their

stakeholders how they do CSR, which can inspire other companies to perform CSR. Because

of effective communication, the concept of CSR therefore becomes familiar, and companies’

awareness of CSR grows. In addition, CSR awards are becoming a trend that also motivates

companies to implement CSR more seriously and transparently.

The finding aligns with stakeholder theory, which suggests that stakeholders should be

satisfied with a company’s CSR before positive financial performance can be realised. The

findings also confirm that stakeholder theory supports positive CSR-CFP relationships (Wang,

Dou & Jia 2016). Maintaining good relationships with key stakeholders tends to lead to

increased financial returns for these stakeholders, because they help in the development of

valuable intangible assets in companies that can be a source of competitive advantage

(Chtourou & Triki 2017). When executives and managers use a company's CSR resources in a

strategic manner to pursue social goals favoured by the stakeholders of the company, CP

(evaluated in terms of profitability) is likely to improve (Michelon, Boesso & Kumar 2013).

Consequently, CSR activities incorporated as part of a company’s strategy will increase that

company’s financial performance, and at the same time, fulfil the company’s social

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responsibility to its stakeholders (Sayekti 2015). Furthermore, effective management of

stakeholders not only helps a company achieve superior financial performance, but also helps

maintain it over time (Choi & Wang 2009; Michelon, Boesso & Kumar 2013). The company’s

ability to create sustainable shareholder value (financial performance) over the long term

depends on its interaction with its various stakeholders (Post, Preston & Sachs 2002).

The findings of this study are in line with a prior study by Yuen et al. (2018), which argued

that stakeholders (e.g., customers and employees) need to be satisfied with a company’s CSR

activities before positive financial performance can be achieved.

In general, the results provide empirical evidence for Baron's argument that a non-market

strategy (i.e., CSR) does not produce direct benefits but derives its return from the improvement

created by business strategy. While the return to a non-market strategy benefits from its effect

on a company's performance in the markets where it operates, the interrelationship between

market and non-market strategies is a key point in integrating the two components of the

strategy (Baron 1997).

5.5 The Moderating Effect in Strategic Integration

This section presents how to assess moderating effect in strategic integration. Specifically,

it explains the moderating effect analysis for business strategy, CSR strategy, company size,

and industry type.

After evaluating the direct and mediating effects, this thesis used MGA to examine a

moderating effect to gain a more complete picture of the relationship between strategic CSR

integration and CP. The information for segmenting variables was obtained from answers from

demographic questions in the questionnaire. In addition to the observed data, such as company

size and industry type as control variables, this thesis also used unobserved data to investigate

whether there are different effects on strategic integration. In doing so, this thesis used business

strategy and CSR strategy measured using a five-point scale. Because MGA of the unobserved

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data (i.e., business and CSR strategy) is more complex and involves several steps, they are

explained first. Then, MGA of the observed data using company size and industry type is

presented.

5.5.1 MGA Business Strategy in Strategic Integration

How to conduct MGA for business strategy in strategic integration is described in this

subsection. It comprises factor analysis, cluster analysis, and MGA assessment of business

strategy. Business strategy is measured following the typology from Porter (1985), which

consists of cost leadership and differentiation. Business strategy can be considered a continuous

moderator measured with multiple items (Hair et al. 2017); thus, it should first be converted

into a categorical variable by performing factor analysis and cluster analysis.

5.5.1.1 Factor Analysis of Business Strategy

This subsection explains how to conduct a factor analysis for business strategy and how to

interpret the results. To ensure which items can be implemented in this thesis, particularly in

the context of the Indonesian manufacturing industry, factor analysis is employed for variables

of business strategy to determine if any underlying dimensions should be used in the following

analysis (Churchill 1979). Using a ratio 10:1 or 20:1 (Hair et al. 2010; Sarstedt & Mooi 2014),

there should be 100 or 200 observations for 10 variables. Accordingly, the 435 responses

collected in this thesis are appropriate for conducting factor analysis; 300 is typically viewed

as a large enough sample size for this purpose (Comrey & Lee 1992, cited in Field 2009;

Tabachnick & Fidell 2006).

Because 10 items of business strategy use the same scale, factor analysis is employed using

the original data without standardising across items (Hair et al. 2010). The commonly accepted

‘eigenvalues greater than one’ rule and comparing the scree plot are employed to determine the

number of factors to retain (Fabrigar et al. 1999; Field 2009; Hair et al. 2010; Sarstedt & Mooi

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2014). The first run of PCA generates two factors with a total variance of 54.59% (see

Appendix B.6 for details). To enhance the interpretability of the results (Sarstedt & Mooi 2014)

as well as to clarify and reveal the presence of simple structure, Varimax rotation is conducted.

In addition to being the most common choice (Anna & Jason 2005), Varimax is generally

considered the best orthogonal rotation (Fabrigar et al. 1999).

Loading above 0.6 is usually considered high, and those below 0.4 are low (Kakkar & Narag

2007, cited in Moonsamy & Singh 2014). Ten items of business strategy have loading more

than 0.4. However, two cross-loading items, which load at 0.32 or higher on two factors

(Tabachnick & Fidell 2006), are eliminated (Hair et al. 2010): BS03 and BS09. The PCA is

rerun and reveals KMO of 0.84 and Bartlett’s Test of Sphericity of 883.35, significant at the

0.00 level. With eight items remaining, PCA results in two factors, yielding a cumulative total

variance of 56.72%. Table 5.15 exhibits the rotated factor loadings, communalities, and

percentage of variance explained by each factor. The communalities are between 0.47 and 0.72,

suggesting that the two factors explain an acceptable amount of the variance in each of the

items; and 56.72% of the total variance suggests there is little more information to be explained.

Although one item (BS04) has communality of 0.47, slightly less than 0.50, that item is retained

as its loading is above 0.50 (Hair et al. 2010).

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Table 5.15: Factor and Items of Business Strategy

Code Item Factor loadinga

Communalities Factor 1 Factor 2

BS04 Innovation in manufacturing process 0.59 0.47

BS06 Developing brand identification 0.73 0.56

BS07 Having cooperative and supportive channels of distribution 0.61 0.53

BS08 Creating new product development 0.74 0.55

BS10 Innovation in marketing techniques and methods (e.g.,

public relations, sales promotion, direct marketing)

0.73 0.55

BS01 Pursuing operating efficiency 0.84 0.72

BS02 Controlling the product quality 0.76 0.62

BS05 Emphasis on the efficiency of securing raw materials or

components (e.g., bargaining down the purchase price)

0.70 0.53

Mean 4.18 4.51

Standard deviation 0.77 0.67

Cronbach’s alpha 0.76 0.70

Total

Sum of squared loadings (eigenvalue) 3.41 1.13 4.54

% of variance 42.59 14.13 56.72

Note: aFactor loadings less than 0.40 were excluded to improve readability.

Next, reliability of the items scale is checked to validate the questionnaire. Reliability

indicates that a questionnaire consistently reflects the construct that is being measured (Field

2009). The alpha coefficients from the summated scales suggested by the two factors are 0.76

(Factor 1) and 0.70 (Factor 2), which are above the recommended threshold of 0.70 (Hair et al.

2010), indicating that both factors are sufficiently reliable for further analysis. These two

factors have item loadings above 0.60, greater than 0.50, as considered necessary for practical

significance (Hair et al. 2010). However, one item (BS04) has a loading of 0.59, slightly below

0.6. There is no cross-loadings item, and no factors with fewer than three items, suggesting the

best fit to the data (Anna & Jason 2005). Accordingly, the two-factor solution is accepted. The

first factor is composed of items that refer to differentiation strategy, and the second factor

consists of items that relate to cost leadership strategy. Moreover, the two factors are

appropriate according to typology from Porter (1985).

As Varimax (orthogonal) rotation was used and factor scores represent all items loading on

the factor (Hair et al. 2010; Sarstedt & Mooi 2014), it is appropriate to use the scores obtained

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for each respondent calculated against the two factors identified above. These factor scores are

used as a basis for the subsequent cluster analysis (Field 2009; Sarstedt & Mooi 2014).

5.5.1.2 Cluster Analysis of Business Strategy

How to conduct a cluster analysis for business strategy and how to interpret the results are

presented in this subsection. Clusters analysis is a convenient way to classify homogeneous

object groups known as clusters (Sarstedt & Mooi 2014), and it addresses the similarities or

differences among the examined object (Majerova 2017). Through cluster analysis,

respondents are classified into clusters so that respondents in the same cluster are more similar

to each other than respondents in other clusters (Hair et al. 2010).

Based on the factor scores obtained, the sample of respondents is clustered into different

meaningful groups, which were used as clustering variables. Prior to doing so, the different

variables used for clustering are confirmed not to have substantial collinearity, which would

bias the analysis (Hair et al. 2010). As displayed in Appendix B.7, the tolerance value of seven

items is above 0.10, and their VIF values are below 3.0, significantly less than the

recommended cut-off value of 10 (Hair et al. 2010). Thus, these results indicate the absence of

collinearity.

To gain the benefits of hierarchical and non-hierarchical clustering methods, this thesis

employs both methods sequentially (Hair et al. 2010). In the first step, hierarchical clustering

is used to determine the possible cluster solution and the appropriate number of clusters. An

agglomerative hierarchical clustering method with Ward’s algorithm and a squared Euclidean

distance measure is employed to generate the initial cluster subtypes. A commonly used

method in hierarchical clustering (Sarstedt & Mooi 2014), Ward’s method is considered

suitable because it relies on minimisation of heterogeneity to find the greatest similarity

(Majerova 2017) and is more likely to generate clusters with an approximately equal number

of responses (Hair et al. 2010; Sarstedt & Mooi 2014). The agglomeration coefficient was used

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to determine the cluster number by examining incremental changes in the coefficient (Ketchen

& Shook 1996). The larger distances between elements in the data matrix should be associated

with points in different clusters (Milligan & Mahajan 1980). After checking the agglomeration

schedule, a greater coefficient difference (Hair et al. 2010) is found from two to three cluster

(49.82%) than from one to two clusters (37.87%). This suggestion for defining cluster numbers

is then used as initial seed points in the next step, non-hierarchical algorithms (K-means).

In the second step, the resulting outcomes from the hierarchical procedure are input into a

non-hierarchical algorithm (K-means). Table 5.16 displays the results from K-means using

three clusters. Two-factor scores have significant differences among three clusters. Cluster one

consists of 267 respondents with a slightly below-average score for cost leadership (Factor 2)

and an above-average score for the differentiation (factor 1). This implies the tendency for

differentiation strategy. Cluster two involves 154 respondents with an above-average score for

cost leadership, but well-below-average score for the differentiation; thus, it refers to cost

leadership strategy. Cluster three with 14 respondents has a below-average score for

differentiation and well-below-average score for cost leadership. Hence, it can be presumed to

be stuck-in-the-middle companies; that is, companies that do not rely on cost leadership or

differentiation strategies (Nandakumar, Ghobadian & O'Regan 2011).

Table 5.16: K-means Business Strategy with Three Clusters

Cluster Number of cases Regression Score

Factor 1 Factor 2

1 267 0.62 -0.02

2 154 -1.03 0.36

3 14 -0.53 -3.55

ANOVA F 353.75 178.69

5.5.1.3 Demography Characteristics of Business Strategy Clusters

Table 5.17 presents the characteristics of respondents in each cluster of business strategy.

Most respondents are in cluster one (differentiation), which dominate in most main products.

However, in several products, the amount in cluster one and cluster two is almost the same,

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particularly in paper, coke and refined petroleum products, fabricated metal products,

automotive, and furniture. Because most involve mass production, the companies are more

likely to apply cost leadership strategy since the purpose of this strategy is low-cost products

(Valipour, Birjandi & Honarbakhsh 2012). Companies adopting cost leadership strategy

generally require a high relative market share and serve all major customer groups (Sun & Pan

2011). Interestingly, some of respondents’ companies implement differentiation strategy,

implying that they make some effort to fulfil customers’ needs by producing different or high-

quality products (Galbreath 2009; Porter 1985; Valipour, Birjandi & Honarbakhsh 2012). For

instance, an automotive company offers several types of cars to suit customer requests, such as

convertible, coupe, hatchback, minivan, SUV, and MPV.

As shown in Table 5.17, most respondents in clusters one and two are large manufacturing

companies. This indicates the number of large companies implementing cost leadership is

equivalent to those adopting differentiation. This result is different from other studies that

showed manufacturing companies favour combined strategies (Sharma 2002), and most large

companies use both strategies concurrently, rather than one at a time (Baroto 2012).

The majority of respondents have been operating between 21 and 50 years: 132 in cluster

one and 80 in cluster two, respectively. Private companies dominate the respondents in cluster

one (207 companies) and cluster two (125 companies). Most of respondents’ companies are in

East Java in cluster one (190 companies) and cluster two (122 companies). In cluster one, more

than half of respondents (146) are located inside the industrial area, and the rest (121) are

outside the industrial estate. In cluster two, most respondents (86) are located within the

industrial estate, and 68 respondents are outside the industrial area.

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Table 5.17: Respondents Characteristics in Each Cluster of Business Strategy

Variable Cluster 1, n=267

(Differentiation)

Cluster 2, n=154

(Cost Leadership)

Cluster 3, n=14

(No Strategy) Total

Main product

food and beverage 71 38 6 115

tobacco 9 2 0 11

textile 17 11 1 29

leather and footwear 5 2 1 8

goods from wood, handicraft 4 1 0 5

paper 12 13 0 25

coke and refined petroleum products 2 2 0 4

chemicals and chemical products 32 15 1 48

pharmaceuticals and medicinal chemical 9 1 1 11

rubber and plastic products 20 15 0 35

non-metallic mineral products 14 7 0 21

basic metals 2 0 0 2

fabricated metal products, excepts machinery

and equipment

22 20 0 42

computers, electronic and optical products 10 0 0 10

machinery and electrical equipment 13 5 2 20

automotive 12 11 1 24

furniture 8 8 1 17

other manufacturing 5 1 0 6


equipment

0 2 0 2

Number of employees

small 29 11 4 44

medium 51 37 6 94

large 187 106 4 297

Company’s age (years)

< 5 16 10 4 30

5-10 37 15 1 53

11-20 45 36 5 86

21-50 132 80 2 214

> 50 37 13 2 52

Company’s ownership

state-ownership 4 7 2 13

private 207 125 12 344

multinational company 56 22 0 78

Company’s location

East Java 190 122 12 324

Centre Java & Yogyakarta 17 11 1 29

West Java & Jakarta 60 21 1 82

In industrial estate

yes 146 86 4 236

no 121 68 10 199

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5.5.1.4 MGA Assessment of Business Strategy in Strategic Integration

This subsection describes the MGA evaluation of business strategy. MGA is carried out

following the recommendations from Matthews (2017). First, the three clusters of business

strategy presented in the previous subsection are described as categorical variables in the

model. Accordingly, there are three pairwise comparisons: differentiation versus cost

leadership, differentiation versus no strategy, and cost leadership versus no strategy. However,

permutation and MGA cannot be conducted between the differentiation and no strategy group

or the cost leadership and no strategy group. This is because the three groups do not have the

equal (or comparable to) sample sizes (Matthews 2017). Besides, the result of SmartPLS 3

indicated that sample size for cluster three was too small (see Appendix B.8 for details). There

should be at least 16 cases or observations for estimating PLS path models regarding the

number of constructs and relationships in the model. Therefore, MGA is conducted for

comparison between the differentiation and cost leadership group.

Second, this thesis employs the MICOM procedure recommended by Henseler, Ringle and

Sarstedt (2016). Since all three procedures of Step 1 are met, configural invariance has been

established (see 4.9.6 for details). Because companies with differentiation strategy are assumed

to have better strategic integration impact on CP than companies adopting cost leadership

strategy, the permutation is run to compare the differentiation and cost leadership group using

one-tailed testing. Table 5.18 displays the results for Step 2 (compositional invariance).

Comparing the correlation between the composite score of the first and second group (original

correlations), the original correlations are equal to 5% quantile correlations (Henseler, Ringle

& Sarstedt 2016). This result is also supported by p-values exceeding 0.05, indicating they are

non-significantly different from 1 (Hair, Sarstedt, et al. 2018). Hence, compositional invariance

is established for all eight constructs.

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Table 5.18: Step 2 MICOM of Business Strategy in Strategic Integration

Composite Original

Correlation

Correlation

Permutation

Mean

5.0% Permutation

p-Values

Compositional

invariance

established?

Aligning 1.00 1.00 1.00 0.30 Yes

EffCom 1.00 1.00 1.00 0.63 Yes

SuppTM 1.00 1.00 1.00 0.85 Yes

Strategic Integration 1.00 1.00 1.00 0.98 Yes

CCP 1.00 1.00 1.00 0.27 Yes

CEP 1.00 1.00 1.00 1.00 Yes

CFP 1.00 1.00 1.00 0.09 Yes

COP 1.00 1.00 1.00 0.67 Yes

Table 5.19 displays results for Step 3 MICOM of business strategy (equality of composite

mean values and variances). The confidence interval of differences in mean between the

construct score of the first group (differentiation) and second group (cost leadership) does not

include the original difference in mean values. This is supported by permutation p-values for

the mean, which are below 0.05, indicating that there are significant differences in the mean

values of latent variables across the two groups (Hair, Sarstedt, et al. 2018). In contrast, every

confidence interval includes the original difference in variance values. Permutation p-values

for variance for all constructs are also larger than 0.05, indicating that the composite variances

are equal. Accordingly, the results suggest a partial invariance (Matthews 2017) and show

significant differences in the mean values of latent variables across the two groups (Hair,


Table 5.19: Step 3 MICOM of Business Strategy in Strategic Integration

Due to the mediating effect, the total effect is used in the comparison of path coefficients as

presented in Table 5.20. The first two columns show the total effects in group 1

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(Differentiation) and group 2 (Cost) followed by their differences in the original data set and

permutation testing. Seven paths have different total effects between two groups. Two paths

have a significant difference in total effect: Strategic Integration → CFP and COP → CFP with

p-values of 0.02 and 0.04, respectively. With the confidence level of 10% commonly used in

an exploratory study (Hair et al. 2017), two other paths also indicate the total effect difference:

Strategic Integration → CCP (p=0.08) and CCP → CFP (p=0.10). These results suggest

substantial differences between the two groups, and that the structural model relationship varies

between the two groups (Hair, Sarstedt, et al. 2018).

Table 5.20: Permutation Test of Business Strategy in Strategic Integration

To further analyse group-specific effects, PLS-MGA is run on the data (Hair, Sarstedt, et al.

2018; Henseler, Ringle & Sarstedt 2016) using 5,000 bootstrap samples and the ‘no sign

change’ option (Hair, Sarstedt, et al. 2018). Table 5.21 presents the results for MGA business

strategy, showing several differences of total effects across differentiation and cost leadership

groups. The biggest significant differences rely on Strategic Integration → CFP (total effect

difference=0.18, p=0.02) and COP → CFP (total effect difference=0.21, p=0.03). At the 10%

confidence level, Strategic Integration → CCP (total effect difference=0.14) and CCP → CFP

(total effect difference=0.13) have a significant difference in total effects with p-values of 0.08

and 0.09, respectively.

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Table 5.21: PLS-MGA Results of Business Strategy in Strategic Integration

Totally, Table 5.22 presents PLS path estimations for the complete sample and subsamples

of business strategy. The significant difference is indicated by the large difference of the total

effect across all samples and subsamples (shown using bold marks in Table 5.22).

Table 5.22: Multi-group Results of Business Strategy in Strategic Integration

Path

All Samples Group 1

(Differentiation)

Group 2

(Cost) Group 1

vs Group 2 N = 435 N = 267 N = 154

β CI β CI β CI p-value

Strategic Integration -> CCP 0.30 0.21-0.39 0.32 0.22-0.41 0.19 0.04-0.31 0.08

Strategic Integration -> CEP 0.63 0.57-0.69 0.62 0.55-0.68 0.57 0.46-0.65 0.21

Strategic Integration -> CFP 0.43 0.35-0.50 0.47 0.39-0.54 0.29 0.15-0.40 0.02

Strategic Integration -> COP 0.43 0.35-0.52 0.44 0.34-0.52 0.34 0.20-0.46 0.16

CCP -> CFP 0.25 0.15-0.35 0.33 0.23-0.42 0.19 0.06-0.32 0.09

CEP -> CFP 0.30 0.17-0.44 0.27 0.15-0.38 0.25 0.09-0.39 0.40

COP -> CFP 0.35 0.24-0.46 0.27 0.17-0.37 0.48 0.32-0.62 0.03

Note: β = path coefficient; CI = 95% confidence intervals.

Following recommendations from Hair, Sarstedt, et al. (2018), parametric and Welch-

Satterthwaite tests should be performed to give greater confidence in the final results obtained.

Table 5.23 presents the results of both tests. Similar to MGA, the results show significant

differences of total effect between the differentiation and cost leadership groups. More

specifically, at the 5% level, Strategic Integration → CFP and COP → CFP has a more

substantial total effect in the differentiation group than in the cost leadership group. At the 10%

confidence level, Strategic Integration → CCP and CCP → CFP has a bigger total effect in the

differentiation group than in the cost leadership group.

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Table 5.23: Parametric and Welch-Satterthwaite Tests of Business Strategy in Strategic

Integration

To sum up, Table 5.24 displays the PLS multi-group results that indicate no different results

across methods. Permutation and PLS-MGA as non-parametric tests and parametric and

Welch-Satterthwaite tests show similar results.

Table 5.24: PLS Multi-group Results of Business Strategy in Strategic Integration

Path Coefficient Permutation

Test

PLS-MGA

Test

Parametric

Test

Welch-Satterthwaite

Test

Strategic Integration -> CCP X X X X

Strategic Integration -> CEP

Strategic Integration -> CFP X X X X

Strategic Integration -> COP

CCP -> CFP X X X X

CEP -> CFP

COP -> CFP X X X X

5.5.2 MGA CSR Strategy in Strategic Integration

How to conduct MGA for CSR strategy is discussed in this subsection. Similar to business

strategy, it consists of factor and cluster analysis as well as MGA assessment for CSR strategy.

CSR strategy is measured by 20 items adopted from previous studies (Maignan & Ferrell 2000,

2001) (see Table 4.2 for details). Like business strategy, CSR strategy is a continuous

moderator, in that it should be converted to a categorical variable by conducting factor and

cluster analyses.

5.5.2.1 Factor Analysis of CSR Strategy

This subsection shows how to perform a factor analysis of CSR strategy. Factor analysis is

employed to find any underlying dimensions to be used in the following analysis. With 435

responses collected, this sample size is acceptable for conducting factor analysis and there is

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no need to standardise as the 20 items of CSR strategy are measured with the same scale (Hair

et al. 2010). Using SPSS 26, the first run of PCA generates a KMO value of 0.94, a Barlett’s

Test of Sphericity value of 3.880,25 and is significant at 0.00 (see Appendix B.9 for details).

The result indicates high correlations between items (Field 2009). A measure of sampling

adequacy (MSA) for 20 items is in the range of 0.88 and 0.96, exceeding the minimum

acceptable MSA level of 0.50 (Hair et al. 2010). Item communalities for all items of CSR

strategy are 0.396 to 0.741, considered low to moderate (Velicer & Fava 1998, cited in Anna

& Jason 2005). Although 20 items of CSR strategy have loading of more than 0.40 (Kakkar &

Narag 2007, cited in Moonsamy & Singh 2014), there are three cross-loading items

(Tabachnick & Fidell 2006): CS03, CS05, and CS19. Thus, they are deleted (Hair et al. 2010).

The PCA is rerun to produce a KMO of 0.93 and Bartlett’s Test of Sphericity of 3,163.26

with a significance of 0.00. One item, CS04, has loading less than 0.4 in three factors (cross-

loading); thus, this item is removed. With 16 items remaining, the PCA is rerun and reveals a

KMO of 0.92 and Bartlett’s Test of Sphericity of 2,973.83 with a significance of 0.00. As

shown in Table 5.25, three factors are produced with the cumulative of the total variance of

58.89%. Although the results are different from the previous arrangement that CSR strategy

consists of four dimensions (see 4.4.1.2), ethical and legal responsibilities may be grouped

under a common and specific dimension (Maignan & Ferrell 2000). The communalities range

from 0.40 to 0.79, suggesting that the three factors explain an acceptable amount of the variance

in each of the items, while the percentage of variance explained (58.89%) suggests that there

is little more information to be explained. Although one item (CS06) has a communality of

0.40, less than 0.50, that item remains as it is not a cross-loading item and has a loading above

0.50. All items in those three factors have loadings above 0.50 as considered necessary for

practical significance (Hair et al. 2010).

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Table 5.25: Factor and Items of CSR Strategy

Code Item

Factor loadinga

Communalities Factor

1

Factor

2

Factor

3

CS06 Internal policies prevent discrimination in

employee’s compensation and promotion.

0.57 0.40

CS07 We seek to comply with all laws regulating hiring

and employee benefits.

0.74 0.59

CS08 All our products meet legal standards. 0.75 0.62

CS09 Our contractual obligations are always honoured. 0.71 0.57

CS10 Managers are informed about relevant

environmental laws.

0.70 0.52

CS11 We have a comprehensive code of conduct. 0.74 0.61

CS12 We are recognised as a trustworthy company. 0.70 0.52

CS13 Fairness toward co-workers and business partners

is an integral part of the employee evaluation

process.

0.66 0.50

CS14 We have a proper procedure for employees to

report any misconduct at work.

0.71 0.63

CS15 Members of our company follow professional

standards.

0.57 0.52

CS16 We give adequate contributions to charities. 0.67 0.56

CS17 We encourage partnerships with local businesses

and schools.

0.75 0.62

CS18 We give a donation for sport and/or cultural

activities.

0.85 0.74

CS20 We encourage employees to join civic

organisations that support our community.

0.77 0.62

CS01 We strive to lower our operating costs. 0.88 0.79

CS02 We closely monitor employees’ productivity. 0.68 0.61

Mean 4.23 3.68 4.24

Standard deviation 0.73 0.94 0.78

Cronbach;s alpha 0.90 0.81 0.56

Total

Sum of squared loadings (eigenvalue) 6.60 1.77 1.05 9.42

% of variance 41.26 11.06 6.57 58.89


Reliability of the items scale is tested to validate the questionnaire. Reliability measures the

degree of consistency between multiple measurements of a variable (Hair et al. 2010). The aim

is to ascertain that the responses are almost the same across all time periods and that

measurements used at any point in time are reliable (Hair et al. 2010). The alpha coefficients

from the summated scales suggested by the three factors are 0.90, 0.81, and 0.56, respectively,

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indicating that all factors are sufficiently reliable for further analysis as they exceed the

threshold of 0.70 (Hair et al. 2010). The first factor relates to legal-ethical responsibility, the

second factor refers to philanthropic responsibility, and the third factor refers to economic

responsibility.

Table 5.25 shows that the mean score of economic responsibility is similar to legal ethical

responsibility, assuming that respondents are more aware of those two responsibilities than

philanthropic responsibility, which has the lowest mean score. Three dimensions of CSR

strategy emerging from the factor analysis reveal a multidimensional view of CSR. As a further

step, the scores obtained for each respondent are calculated against the three factors identified

above. These factor scores are used as a basis for the subsequent cluster analysis (Field 2009).

5.5.2.2 Cluster Analysis of CSR Strategy

Following an explanation of CSR strategy factor analysis, this subsection shows how to

conduct a cluster analysis of CSR strategy. Prior to cluster analysis, the different variables used

for clustering are confirmed not to have substantial collinearity to avoid biasing the analysis

(Hair et al. 2010). Appendix B.10 shows the tolerance value of 16 items between 0.44 and 0.81,

above 0.10. Also, their VIF values are between 1.24 and 2.30, below 3.0. Accordingly, the

result indicates the absence of collinearity (Hair et al. 2010).

Similar to business strategy, cluster analysis of CSR strategy is employed using hierarchical

and non-hierarchical clustering methods sequentially (Hair et al. 2010) through the same

procedure. Results of the hierarchical cluster from SPSS identified a larger jump from two to

three clusters (24.53%) than from one to two clusters (22.17%). Then, K-means clustering is

conducted using three clusters (Table 5.26). Cluster one consists of respondents with a below-

average score for legal responsibility (factor 1) philanthropy responsibility (factor 2) and an

above-average score for economic responsibility (factor 3). Cluster two involves a group of

respondents with an above-average score for all responsibilities (three factors), while the

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largest score lies with philanthropic responsibility (factor 2). Cluster three is a group of

respondents with a below-average score for philanthropic responsibility (factor 2) and well-

below-average score for economic responsibility (factor 3). Cluster three has an above-average

score for legal ethical responsibility (factor 1).

Table 5.26: K-means CSR Strategy with Three Clusters

Cluster Number of cases Regression Score

Factor 1 Factor 2 Factor 3

1 145 -0.49 -0.86 0.35

2 180 0.18 0.76 0.47

3 110 0.35 -0.10 -1.23

ANOVA F 31.25 204.93 232.75

Sig 0.00 0.00 0.00

According to the results, cluster one, with 145 respondents, can be regarded as a ‘reactive’

group. Companies adopting a reactive strategy will fulfil their economic responsibilities but

ignore their legal, ethical and philanthropic responsibilities and reject any form of social or

ethical responsibility that falls outside their economic interest (Lee 2011). On the other hand,

cluster two consists of 180 respondents assumed to be the ‘proactive’ group, which has the

highest factor score for philanthropic responsibility, followed by economic responsibility, and

then legal ethical responsibility. Companies applying proactive strategy carry out their

philanthropic responsibilities actively, and also conduct some activities related to economic

responsibilities and do not ignore their legal ethical responsibilities. Companies with proactive

strategy fully recognise their social responsibilities and actively strive to meet stakeholder

needs and reduce negative effects of companies (Ganescu 2012b). At the same time, these

companies sustain economic, social and environmental development exceeding levels required

to comply with government regulations (Torugsa, O'Donohue & Hecker 2013; Wagner, Lutz

& Weitz 2009).

Cluster three involves 110 respondents whose businesses comply with legal and ethical

requirements, as this cluster has an above-average score for factor one. However, they do not

practice a great deal of philanthropic responsibility and have the lowest score for economic

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responsibility. Hence, cluster three is the ‘accommodative’ group. Companies applying

accommodative strategy conduct specific ethical responsibilities, particularly those related to

stakeholders, and comply with legal requirements. However, most conduct passive and modest

approaches to stakeholder requests and rarely take voluntary initiatives (Ganescu 2012b; Lee

2011).

Both F value and ANOVA results indicate that all factor scores are significant for three

clusters. In sum, three clusters are considered appropriate to reflect which CSR strategy is

applied by respondents, i.e., defensive, proactive, and reactive.

5.5.2.3 Demography Characteristics of CSR Strategy Clusters

This subsection presents the characteristics of respondents in each cluster of CSR strategy.

For most products, the amount in cluster two (proactive) is more than other clusters, such as

food and beverage, chemicals and chemical products, rubber and plastic products, automotive,

non-metallic mineral products, and tobacco (see Table 5.27). In four other products, cluster one

(reactive) has the most respondents (e.g., fabricated metal products, paper, furniture, and goods

from wood). For textile products, most respondents are in cluster three (accommodative).

In terms of the company size, Table 5.27 shows a similar number of respondents in the three

clusters. A slight difference lies in large companies as most have proactive strategy (cluster

two), followed by reactive strategy (cluster one); and cluster three has the fewest number of

respondents. This result is similar to other evidence that the proactive CSR strategies of large

companies are substantially higher than those among SMEs (Chang 2015).

Regarding company age, most respondents who have been running their business for five to

20 years are in cluster one, while cluster two dominates in terms of respondents operating over

20 years. There is slight equal number for respondents in each cluster who have been

conducting their business for less than five years. This data reflects the ability of organisations

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to function in a range of CSR from reactive to proactive (Carroll 1979; Wartick & Cochran

1985).

Table 5.27: Respondents Characteristics in Each Cluster of CSR Strategy

Variable Cluster 1, n=145

(Reactive)

Cluster 2, n=180

(Proactive)

Cluster 3, n=110

(Accommodative) Total

Main product

food and beverage 39 42 34 115

tobacco 1 6 4 11

textile 8 10 11 29

leather and footwear 2 4 2 8

goods from wood, handicraft 4 0 1 5

paper 14 8 3 25

coke and refined petroleum products 1 2 1 4

chemicals and chemical products 12 26 10 48

pharmaceuticals and medicinal chemical 4 3 4 11

rubber and plastic products 11 15 9 35

non-metallic mineral products 6 12 3 21

basic metals 0 2 0 2

fabricated metal products, excepts

machinery and equipment

17 15 10 42

computers, electronic and optical products 3 5 2 10

machinery and electrical equipment 6 8 6 20

automotive 7 13 4 24

furniture 8 5 4 17

other manufacturing 1 4 1 6


equipment

1 0 1 2

Number of employees

small 15 13 16 44

medium 36 25 33 94

large 94 142 61 297


< 5 11 11 8 30

5-10 20 15 18 53

11-20 36 32 18 86

21-50 68 91 55 214

> 50 10 31 11 52


state-ownership 2 8 3 13

private 125 129 90 344

multinational company 18 43 17 78


East Java 122 120 82 324

Centre Java & Yogyakarta 6 17 6 29

West Java & Jakarta 17 43 22 82


yes 78 113 45 236

no 67 67 65 199

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With respect to ownership, most respondents are in cluster two, regardless of whether they

are state-owned, private, or multinational companies. Similarly, related to region, most

respondents in Central Java and Yogyakarta, together with West Java and Jakarta, are in cluster

two. In East Java, there are equal numbers in clusters one and two. In terms of location, there

is almost the same number of respondents located outside the industrial estate in all three

clusters. However, there is a difference among respondents inside the industrial estate, since

most of them adopt proactive strategy (cluster two), followed by reactive strategy (cluster one),

and accommodative strategy (cluster three).

Overall, based on the results, it can be assumed that most manufacturing companies in

Indonesia apply proactive strategy rather than the other two strategies, reactive and

accommodative.

5.5.2.4. MGA Assessment of CSR Strategy in Strategic Integration

This subsection demonstrates how to evaluate MGA of CSR strategy. Like business

strategy, MGA of CSR strategy is conducted according to Matthews (2017). The three clusters

of CSR strategy are used as categorical variables for data grouping (Matthews 2017). With

three clusters, there are six comparisons of testing (Hair, Sarstedt, et al. 2018): proactive versus

reactive, proactive versus accommodative, and reactive versus accommodative. MGA CSR

strategy is carried out by first employing MICOM (Henseler, Ringle & Sarstedt 2016) with

three steps. As Step 1 of MICOM (configural invariance) has been established, Steps 2 and 3

of MICOM are conducted for each comparison.

1. Proactive versus Reactive in Strategic Integration

▪ Step 2. Compositional invariance. Because the proactive group is considered to have a

bigger impact of strategic integration on CP than the reactive group, one-tailed testing is

employed. Appendix B.11 presents results of Step 2 MICOM in comparing the proactive

and reactive groups. The results show that the value of the 5% quantile is equal to the value

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of the correlation permutation for all constructs (Henseler, Ringle & Sarstedt 2016),

confirmed by most p-values exceeding 0.05. However, Aligning and CCP have p-values

below 0.05, indicating that compositional invariance has not been established.

▪ Step 3. Equality of composite mean values and variances. Appendix B.12 presents the

results of Step 3 of MICOM proactive versus reactive. The mean-original differences are

substantially above the upper level of the confidence interval. Because the confidence

interval of differences in mean between the construct scores of the proactive and reactive

groups does not include the original difference in mean values, there are significant

differences in the mean values of latent variables across the two groups (Hair, Sarstedt, et

al. 2018).

Appendix B.12 also shows that the confidence interval of differences in variance does not

include the original difference in variance values, particularly in six constructs: Aligning,

EffCom, SuppTM, Strategic Integration, CEP and CFP. The original differences in variance

values for those six construct are less than the lower level of the confidence interval, and

permutation p-values for variance of those five constructs are less than 0.05. These results

indicate significant differences in the variance values of latent variables across the two

groups (Hair, Sarstedt, et al. 2018). CCP and COP have p-values greater than 0.05 (0.16 and

0.15, respectively), and their original differences in variance values fall within the

confidence interval.

Because these results imply significant differences in the mean and variance values across

two groups, the comparison of the standardized path coefficient across the groups using

MGA can be conducted (Hair, Sarstedt, et al. 2018; Henseler, Ringle & Sarstedt 2016).

Appendix B.13 displays the total effects of MICOM in the proactive group versus the

reactive group. Significant differences are shown by Strategic Integration → CCP (p=0.03)

and Strategic Integration → CFP (p=0.00).

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PLS-MGA was run with the results presented in Table 5.28. In most paths, the proactive

group has larger total effects than the reactive group. The largest differences rely on Strategic

Integration → CCP (total effect difference=0.20, p=0.04) and Strategic Integration → CFP

(total effect difference=0.22, p=0.01), indicating that total effects from Strategic Integration to

CCP and CFP are larger in the proactive group than in the reactive group.

Table 5.28: PLS-MGA Proactive and Reactive in Strategic Integration

The parametric and Welch-Satterthwaite tests are conducted to give additional confidence

in the final results obtained (Hair, Sarstedt, et al. 2018) with results in Appendix B.14. The

results reveal significant differences in the total effect between the proactive and cost reactive

groups. More specifically, Strategic Integration → CFP and COP → CFP have a more

substantial total effect in the proactive group than in the reactive group. At the 10% level,

Strategic Integration → CCP and CCP → CFP have bigger total effects in the proactive group

than in the reactive group.

Table 5.29 summarises the PLS multi-group results, showing the similar results across four

methods. Results from the permutation test and PLS-MGA are similar, while results from the

parametric test are similar to those from the Welch-Satterthwaite test. In total, there are similar

results among four tests in two paths, Strategic Integration → CCP and Strategic Integration

→ CFP.

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Table 5.29: PLS Multi-group Results of Proactive and Reactive in Strategic Integration

Path Coefficient Permutatio

n Test

PLS-MGA

Test

Parametric

Test

Welch-Satterthwaite

Test





CCP -> CFP

CEP -> CFP

COP -> CFP X X

2. Proactive versus Accommodative in Strategic Integration

▪ Step 2. Compositional invariance. One-tailed testing was employed as the proactive group

is assumed to have larger impacts than the accommodative group. Appendix B.15 shows the

results for the permutation test in relation to comparing these two groups. The value of the

5% quantile is smaller than (or equal to) the value of the correlation permutation for all

constructs (Henseler, Ringle & Sarstedt 2016). P-values for most constructs exceed 0.05,

except for Strategic Integration and the CFP construct, which have p-values below 0.05.

Accordingly, compositional invariance has not been established for eight constructs.


results of Step 3 of MICOM, checking the composite’s equality of mean values and

variances across groups (Henseler, Ringle & Sarstedt 2016). The confidence interval for the

differences in means between the construct score of proactive and accommodative groups

does not include the original difference in mean values. The mean-original differences are

much higher than the upper level of the confidence interval, implying significant differences

in the mean values of latent variables across the two groups (Hair, Sarstedt, et al. 2018).

In contrast, the original difference in variance values fall within the confidence interval for

the differences in variance, and permutation p-values for variance are above 0.05.

Theoriginal differences in variance values for these two constructs are less than the lower

level of the confidence interval, and their p-values are less than 0.05, (apart from Aligning,

CFP and COP with p-values of 0.03, 0.04 and 0.05, respectively). These results indicate

230

significant differences in the variance values of latent variables across the two groups (Hair,


As these results imply substantial differences in the mean and variance values across the

two groups, they support running PLS-MGA (Hair, Sarstedt, et al. 2018). Appendix B.17 shows

the MGA results for the proactive and accommodative groups. Seven paths have permutation

p-values exceeding 0.05, indicating insignificant differences. Therefore, these results suggest

that most structural model relationships between the two groups are not distinct (Hair, Sarstedt,

et al. 2018). To confirm this result, PLS-MGA is run with results presented in Table 5.30. In

most paths, the proactive group has slightly larger total effects than the accommodative group.

However, none of the differences are significant since they have p-values over 0.05.

Table 5.30: MGA Proactive and Accommodative in Strategic Integration

Appendix B.18 shows that the results of parametric and Welch-Satterthwaite tests are

consistent with the MGA results, indicating no significant differences across the two groups.

In total, the permutation test, PLS-MGA, and parametric and Welch-Satterthwaite tests reveal

similar results; that is, no significant differences between the proactive and accommodative

groups in the strategic integration-CP relationship.

3. Accommodative versus Reactive in Strategic Integration

▪ Step 2. Compositional invariance. Appendix B.19 presents the results of the permutation

run comparing the accommodative and reactive groups. The result shows that the value of

the 5% quantile is smaller than (or equal to) the value of the correlation permutation for all

constructs (Henseler, Ringle & Sarstedt 2016), supported by most p-values being above

231

0.05, apart from Strategic Integration and CCP (0.00 and 0.02, respectively). Subsequently,

compositional invariance has not been established.


results for Step 3 MICOM (Henseler, Ringle & Sarstedt 2016). The confidence interval of

differences in mean between the construct score of the accommodative and reactive groups

does not include the original difference in mean values. However, the original difference in

mean values of CCP and COP falls within their confidence interval, implying significant

differences in the mean values across the two groups.

Likewise, the original difference in variance values do not fall within the confidence interval

for differences in variance, particularly in three constructs (EffCom, SuppTM and Strategic

Integration). These constructs have permutation p-values below 0.05, indicating no

significant differences in the variance values across the two groups (Hair, Sarstedt, et al.

2018). Nonetheless, five other constructs have original differences in variance values less

than the lower level of their confidence interval. As their p-values are less than 0.05, this

result implies substantial differences in variance values across the two groups.

Accordingly, these results support the need to compare the standardized path coefficient

across the groups using PLS-MGA (Hair, Sarstedt, et al. 2018; Henseler, Ringle & Sarstedt

2016). Appendix B.21 presents the total effects as results from the mediation effect. All 10

paths have different total effects between the two groups. Only one path has a permutation p-

value less than 0.05: Strategic Integration → CFP (p=0.04). At the confidence level of 10%,

the difference in path Strategic Integration → CCP is also significant (p=0.07). Thus, the results

signify differences in those two constructs across the two groups. To ensure this result, PLS-

MGA was run with the results (Table 5.31).

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Table 5.31: PLS-MGA Accommodative and Reactive in Strategic Integration

Although there are differences of total effect across the two groups, not all differences are

significant (Hair, Sarstedt, et al. 2018). Two paths have significant differences: Strategic

Integration → CFP (total effect difference=0.20, p=0.03) and Strategic Integration → CCP

(total effect difference=0.19, p=0.07). These results indicate a larger total effect in the

accommodative group than in the reactive group.

To complete the MGA, parametric and Welch-Satterthwaite tests are conducted with results

in Appendix B.22. The results show two significant differences: Strategic Integration → CFP

(p=0.07) and Strategic Integration → CFP (p=0.06 and p=0.05, respectively). These results

indicate the accommodative group has a larger total effect than the reactive group.

Table 5.32 summarises the PLS multi-group results, showing similar results across the four

methods. The permutation, PLS-MGA, and parametric and Welch-Satterthwaite tests reveal

the same results; that is, there is a substantial difference in total effects between the two groups,

whereby the proactive group has a greater total effect than the reactive group.

Table 5.32: PLS Multi-group Results of Proactive and Reactive in Strategic Integration


Test

PLS-MGA

Test

Parametric

Test

Welch-Satterthwaite

Test





CCP -> CFP

CEP -> CFP

COP -> CFP

In total, the results for the MGA of CSR strategy in strategic integration are presented in

Tables 5.33 and 5.34. Permutation and PLS-MGA, as a non-parametric test, as well as

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parametric and Welch-Satterthwaite tests reveal the same results. In most of the seven paths,

the proactive group dominates, with the largest total effect. Specifically, the proactive group

has a significant bigger total effect in Strategic Integration → CCP and Strategic Integration

→ CFP. Consequently, the results suggest that CSR strategy moderates the impacts of strategic

integration on CP.

Table 5.33: MGA Results of CSR Strategy in Strategic Integration

Path

All Samples Group 1

(Reactive)

Group 2

(Proactive)

Group 3

(Accommodative)

N = 435 N = 145 N = 180 N = 110

β CI β CI β CI β CI

Strategic Integration -> CCP 0.30 0.21-0.39 0.10 -0.05-0.24 0.30 0.16-0.42 0.29 0.12-0.43

Strategic Integration -> CEP 0.63 0.57-0.69 0.52 0.40-0.61 0.53 0.44-0.61 0.48 0.33-0.59

Strategic Integration -> CFP 0.43 0.35-0.50 0.18 -0.24-0.00 0.40 0.28-0.50 0.38 -0.07-0.15

Strategic Integration -> COP 0.43 0.35-0.52 0.28 0.13-0.42 0.38 0.26-0.48 0.38 0.23-0.51

CCP -> CFP 0.25 0.15-0.35 0.22 0.06-0.38 0.33 0.21-0.45 0.24 0.08-0.40

CEP -> CFP 0.30 0.17-0.44 0.34 0.12-0.54 0.20 0.07-0.32 0.28 0.10-0.44

COP -> CFP 0.35 0.24-0.46 0.37 0.20-0.52 0.37 0.25-0.48 0.34 0.16-0.53


5.5.3 MGA Company Size in Strategic Integration

This subsection explains how to conduct MGA for the company size. MGA company size

in strategic integration is undertaken as recommended by Matthews (2017). Having considered

observed heterogeneity and categorical variables, company size can be used without further

refinement (Henseler & Fassott 2010). As shown in Table 4.10 and Figure 4.4, 297

respondents’ companies have more than 100 employees; 44 respondents’ companies have

fewer than 20 employees (small companies) and 94 respondents’ companies have 20 to 99

employees (medium companies). To simplify the comparisons, data groups are generated

(Matthews 2017) by splitting the data set into two groups: large companies (297 respondents)

and SMEs (138 respondents). Then, the models are estimated separately for each group of data

(Hair et al. 2017).

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Table 5.34: Comparison of CSR Strategy in Strategic Integration

Path

Proactive versus

Reactive

Proactive versus

Accommodative

Accommodative versus

Reactive

Proactive Reactive Proactive Accommodative Accommodative Reactive

Strategic Integration -> CCP 0.30 0.10 0.30 0.29 0.29 0.10

Strategic Integration -> CEP 0.53 0.52 0.53 0.48 0.48 0.52

Strategic Integration -> CFP 0.40 0.18 0.40 0.38 0.38 0.18

Strategic Integration -> COP 0.38 0.28 0.38 0.38 0.38 0.28

CCP -> CFP 0.33 0.22 0.33 0.24 0.24 0.22

CEP -> CFP 0.20 0.34 0.20 0.28 0.28 0.34

COP -> CFP 0.37 0.37 0.37 0.34 0.34 0.37

Note: the bold means the significant difference in each comparison.

Configural invariance has been established as three procedures of Step 1 of MICOM

(analysing the measurement invariance of composite models) have been achieved. Thus, the

following subsection presents Steps 2 and 3 of MICOM.

▪ Step 2. Compositional invariance. Because large companies are assumed to have better

strategic integration impact on CP than SMEs, one-tailed testing is undertaken (Appendix

B.23). The value of the 5% quantile is equal to the value of the correlation permutation for

all constructs (Henseler, Ringle & Sarstedt 2016), supported by p-values of above 0.05.

Effcom and CCP have p-values below 0.05, indicating the correlation is not significantly

below 1. Thus, compositional invariance has not been established.

▪ Step 3. Equality of composite mean values and variances. Appendix B.24 presents results

from Step 3 MICOM, suggesting different results among constructs. Most confidence

intervals of the eight constructs do not include their mean original value with p-values less

than 0.05. The mean value is beyond the upper level of the confidence interval, particularly

in Aligning, EffCom, SuppTM, Strategic Integration and CEP. Three other constructs, CCP,

CFP and COP, have the mean original value inside their confidence interval, with p-values

above 0.05.

By comparison, three other constructs, CCP, CFP and COP, have the original difference in

mean values inside the confidence interval and p-values over 0.05. These results imply no

235

significant differences in these three constructs across two groups. However, for one

construct (CEP), the variance original value falls outside the confidence interval, and its p-

value exceeds 0.05. Consequently, the full measurement variance has not been established,

and the standardized path coefficient across the groups using MGA can be compared with

confidence (Hair, Sarstedt, et al. 2018; Henseler, Ringle & Sarstedt 2016).

Because of the mediating effect, the total effect is used to compare path coefficients (see

Appendix B.25). The first two columns show the total effects for group 1 (large companies)

and group 2 (SMEs) followed by their differences in the original data set and the permutation

testing. Five paths have significant total effect differences with permutation p-values less

than 0.05: Strategic Integration → CCP (p=002), Strategic Integration → CEP (p=0.03),

Strategic Integration → CFP (p=0.00), Strategic Integration → COP (p=0.00) and CCP →

CFP (p=0.04). These results indicate significant differences across two groups in which

large groups have greater total effects than SME groups.

Based on these results, it can be assumed that there is a categorical moderator variable

(company size) that affects the relationships in the PLS path model (Hair, Sarstedt, et al. 2018).

Accordingly, to further analyse group-specific effects, PLS-MGA is run to disclose the effect

of this categorical moderator variable. Because PLS-MGA represents a one-tailed test, p-values

in Table 5.35 indicate the path coefficient is significantly larger in the large companies group

than in the SMEs group (Hair, Sarstedt, et al. 2018). Specifically, four paths have significant

differences with p-values below 0.05: Strategic Integration → CCP (total effect

difference=0.21, p=0.02), Strategic Integration → CFP (total effect difference=0.25, p=0.00)

and Strategic Integration → COP (total effect difference=0.32, p=0.00). Another path, CCP →

CFP, has a significant difference of total effects across two groups (total effect difference=0.19,

p=0.03). At the 10% confidence level, total effect from strategic CSR Integration to CEP is

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significantly different across two groups (total effect difference=0.12, p=0.06), indicating a

stronger total effect in large companies than in SMEs.

Table 5.35: PLS-MGA Company Size in Strategic Integration

Path

Total Effects

Original

(Large)

Total Effect

Original

(SME)

Total Effects-

diff

(Large-SME)

p-Value original

1-tailed

(Large vs SME)

p-Value new

(Large vs SME)

Strategic Integration -> CCP 0.37 0.16 0.21 0.02 0.02

Strategic Integration -> CEP 0.66 0.54 0.12 0.06 0.06

Strategic Integration -> CFP 0.50 0.25 0.25 0.00 0.00

Strategic Integration -> COP 0.54 0.22 0.32 0.00 0.00

CCP -> CFP 0.34 0.15 0.19 0.03 0.03

CEP -> CFP 0.24 0.38 -0.14 0.85 0.15

COP -> CFP 0.30 0.41 -0.10 0.83 0.17

Table 5.36 presents PLS path estimations for the complete sample and subsamples of

company size. Total effects of all samples (pooled data) are different from total effects in

subsamples. The results confirm the assessment of total effects should be conducted across two

groups as they give more detailed analysis.

Table 5.36: MGA Results of Company Size in Strategic Integration

Path

All Samples Group 1 (Large) Group 2 (SME) Group 1 vs

Group 2 N = 435 N = 297 N = 138




Strategic Integration -> CFP 0.43 0.35-0.50 0.50 0.43-0.56 0.25 0.11-0.37 0.00


CCP -> CFP 0.25 0.15-0.35 0.34 0.24-0.45 0.15 0.00-0.28 0.03

CEP -> CFP 0.30 0.17-0.44 0.24 0.11-0.37 0.38 0.20-0.55 0.15

COP -> CFP 0.35 0.24-0.46 0.30 0.19-0.41 0.41 0.25-0.45 0.17

Note: β = path coefficient; CI = 95% confidence intervals; bold marks = the significant difference in group

comparison

To support the MGA results, parametric and Welch-Satterthwaite tests are carried out

(Appendix B.26). The results support permutation test and MGA results, which indicate

significant differences in total effects in the relationship between strategic integration and CP

237

among two groups, at the 5% level (i.e., Strategic Integration → CCP, Strategic Integration →

CFP, Strategic Integration → COP) and the 10% level (Strategic Integration → CEP).

Table 5.37 summarises the PLS multi-group results, showing similar results across four

methods. It can be concluded that regarding company size, the total effect between large

companies and SMEs differs significantly in the relationship between strategic integration and

CP. Hence, the results from this multi-methods approach can provide confidence in the final

results obtained (Hair, Sarstedt, et al. 2018).

Table 5.37: PLS Multi-group Results of Company Size in Strategic Integration


Test

PLS-MGA

Test

Parametric

Test

Welch-Satterthwaite

Test


Strategic Integration -> CEP X X X X


Strategic Integration -> COP X X X X

CCP -> CFP X X X X

CEP -> CFP

COP -> CFP

5.5.4 MGA Industry Type in Strategic Integration

This subsection presents how to carry out MGA for the last moderator, that is the industry

type. As previously shown, the sample in this thesis represents a wide range of manufacturing

companies, with 19 different industry types (see Table 4.10 and Figure 4.3 for details). Previous

studies suggested two categories of manufacturing companies: high impact and other

manufacturing. The former represents the group of the most environmentally harmful

companies, such as mining, chemical, oil and gas, metal, paper and pulp, as well as energy

production /utilities. The latter are less environmentally harmful companies, such as food

processing, automobile industries, plastic, printing, heavy engineering and consumer goods

(Bowen 2000; Halme & Huse 1997). Boesso, Favotto and Michelon (2015) grouped companies

that operate primarily in environmentally sensitive industries (ESI) and those that do not (non-

ESI). Oil exploration, paper, chemical and allied products, petroleum refining, metals, mining

or utilities are categorised as ESI companies that could have a greater environment effect than

238

non-ESI companies. Other empirical literature considers metals, resources, paper and pulp,

power generation, water and chemicals industries as having high environmental impacts

(Bowen 2000; Hoffman 1999). In comparison, other industries, especially the newer

manufacturing and service industries, have significantly smaller environmental impacts and

fewer environmental problems (Reverte, Gómez-Melero & Cegarra-Navarro 2016).

Based on the extant literature, respondents’ companies would be simply categorized into

two industry types: ESI and non-ESI to examine their impact in the strategic integration as

follows:

1. Mining and ore processing. A number of industries rely on the mining and ore processing

industry for the supply of minerals, metals and gems. These products occur in nature in the

form of ore in rock and must be mined and concentrated before use. Those processes

generate large volumes of waste production that is often filled with pollutants, such as

mercury, lead, and cadmium (Nag 2018). Thus, mining and ore processing is a major

pollution source (Etee 2019).

2. Fashion. The fashion industry is considered as the second most polluting industry

(FabricoftheWorld n.d.; Hinsliff 2019), after the oil industry (Qutab 2016).

3. Chemical manufacturing. Chemical manufacturing includes the production of various

chemicals such as plastics, paints, explosives, dyes, pharmaceuticals and petrochemicals.

They have some advantages, but also produce significant volumes of poisonous waste and

by-products during their production (Nag 2018).

4. Tanneries. Tannery is a process of producing leather from raw animal skins. This involves

using various chemicals to remove meat, oil glands, and hair from raw skin. A large amount

of waste is generated in the process. Irresponsible industrial practices often cause

environmental contamination with hazardous chemicals used in tanning, such as chromium,

alum and tannins. (Nag 2018).

239

5. Automotive. Environmental management is perhaps the biggest challenge for the

automotive industry. Problems such as global warming or pollution are a critical issue and

are directly related to car use, production and disposal (García-Madariaga & Rodríguez-

Rivera 2017).

Table 5.38 presents the two groups of respondents’ companies. Group one consists of 10

industry types with 228 ESI-categorised companies, and group two comprises nine other types

of industry with 207 non-ESI-categorised companies.

Table 5.38: Industry Type Categories

Category Industry Type Number Percentage (per cent)

Environmentally

sensitive

industries

(ESI)

chemicals and chemical products 48 11.00

fabricated metal products, excepts machinery and

equipment

42 9.70

rubber and plastic products 35 8.00

textile 29 6.70

paper 25 5.70

automotive 24 5.50

pharmaceuticals and medicinal chemical 11 2.50

leather and footwear 8 1.80

coke and refined petroleum products 4 0.90

basic metals 2 0.50

Total of ESI 228 52.40

Environmentally

non-sensitive

industries

(non-ESI)

food and beverage 115 26.40

non-metallic mineral products 21 4.80

machinery and electrical equipment 20 4.60

furniture 17 3.90

tobacco 11 2.50

computers, electronic and optical products 10 2.30

other manufacturing 6 1.40

goods from wood, handicraft 5 1.10

repair and installation of machinery and equipment 2 0.50

Total of non-ESI 207 47.60

Table 5.39 presents the characteristics of the companies from each group. With respect to

size, category one (ESI) is dominated by medium and large companies. Conversely, most small

businesses are in group two (non-ESI). Most companies in category one have been working for

five to 50 years. Most companies in category two have been running for fewer than five years

or more than 50 years. In terms of ownership, there is almost equal numbers of state-owned,

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private and multinational companies in the two groups. Most companies in East Java are in

group one, while most companies in Centre Java and Yogyakarta as well as in West Java and

Jakarta are in group two. Most companies in group one are inside industrial estates, and most

companies in group two are outside industrial estate.

Table 5.39: Respondents Characteristics in Each Category of Industry Type

Variable Category 1, n=228

(ESI)

Category 2, n=207

(non-ESI) Total

Number of employees

small 16 28 44

medium 51 43 94

large 161 136 297


< 5 10 20 30

5-10 28 25 53

11-20 53 33 86

21-50 119 95 214

> 50 18 34 52


state-ownership 7 6 13

private 182 162 344

multinational company 39 39 78


East Java 178 146 324

Centre Java & Yogyakarta 11 18 29

West Java & Jakarta 39 43 82


yes 144 92 236

no 84 115 199

Next, these two groups of industry type, ESI and non-ESI, are used as categorical variables

in the model to generate data groups (Matthews 2017). Because configural invariance has been

established since the three procedures in Step 1 have been met, the following subsection

presents Steps 2 and 3 of MICOM of the industry type.

▪ Step 2. Compositional invariance. Given the non-ESI group is supposed to have less

strategic integration impact on CP than the ESI group, this thesis employes permutation one-

tailed testing. As displayed in Appendix B.27, the 5% quantile value is equal to the

correlation permutation value for eight constructs (Henseler, Ringle & Sarstedt 2016),

241

confirmed by the p-values exceeding 0.05. Thus, the compositional invariance is achieved

for all eight constructs.


results for Step 3 (Henseler, Ringle & Sarstedt 2016). Only one construct (SuppTM) has the

mean original values within the confidence interval with a p-value less than 0.05, implying

significant differences across the two groups. The confidence intervals of the other seven

constructs do not include their mean original values, and their p-values are above 0.05.

These results indicate insignificant differences across the two groups. Similarly, most

original differences in the variance values fall inside the confidence interval, with p-values

over 0.05. Only two constructs have p-values below 0.05 (CCP and CFP), showing

significant differences across the two groups. Hence, these results imply significant

differences in the mean and variance values across two groups and support the comparison

of the standardized path coefficient across the groups (Hair, Sarstedt, et al. 2018; Henseler,

Ringle & Sarstedt 2016).

Considering the results, the effects of MGA are evaluated by comparing the total effect from

permutation type based on industry type. As shown in Appendix B.29, at the 10% confidence

level, there is a significant total effect difference on Strategic Integration → CFP (-0.12,

p=0.07). To further analyse group-specific effects, PLS-MGA is run on the data (Hair, Sarstedt,

et al. 2018; Henseler, Ringle & Sarstedt 2016). Table 5.40 presents the results of PLS-MGA

industry type. Total effects in the non-ESI group are bigger than in the ESI group, particularly

in Strategic Integration → CFP (total effect difference=0.12, p=0.05). At the confidence level

of 10%, the ESI group has a larger total effect in COP → CFP (total effect difference=0.08,

p=0.08) than the non-ESI group.

242

Table 5.40: PLS-MGA Industry Type in Strategic Integration

Table 5.41 summarises PLS path estimations for the complete sample and subsamples of

industry type. Total effects in all samples (pooled data) are different from total effects in

subsamples of industry type.

Table 5.41: MGA Results of Industry Type in Strategic Integration

Path

All Samples Group 1

(ESI)

Group 2

(Non-ESI) Group 1 vs

Group 2 N = 435 N = 228 N = 107




Strategic Integration -> CFP 0.43 0.35-0.50 0.37 -0.12-0.08 0.49 -0.03-0.12 0.05


CCP -> CFP 0.25 0.15-0.35 0.24 0.09-0.36 0.25 0.16-0.35 0.45

CEP -> CFP 0.30 0.17-0.44 0.23 0.05-0.42 0.39 0.28-0.51 0.11

COP -> CFP 0.35 0.24-0.46 0.41 0.28-0.53 0.27 0.17-0.38 0.08


comparison

Appendix B.30 presents the result of parametric and Welch-Satterthwaite tests. The results

show that COP → CFP has significantly different total effects across the two groups (p=0.08).

The PLS multi-group results are presented in Table 5.42. The results of the permutation test

and PLS-MGA are similar, showing a significant difference across the two groups, particularly

in Strategic Integration → CFP. In contrast, the result of parametric and Welch-Satterthwaite

tests reveal a significant difference in COP → CFP, which also was identified in PLS-MGA.

However, the PLS-MGA results will be used for further analysis while it is a non-parametric

test that is more appropriate in PLS-SEM analysis.

243

Table 5.42: PLS Multi-group Results of Industry Type in Strategic Integration


Test

PLS-MGA

Test

Parametric

Test

Welch-Satterthwaite

Test

Strategic Integration -> CCP


Strategic Integration -> CFP X X


CCP -> CFP

CEP -> CFP

COP -> CFP X X X

5.5.5 Discussion of Moderating Effect in Strategic Integration

This subsection discusses the results of moderating effect analysis in strategic integration.

The objectives are to answer the research questions and to verify the hypotheses related to the

moderating effect. The discussion is based on the conceptual framework and hypotheses

proposed in Chapter 3, the results from PLS-SEM analysis, existing literature, and research

findings from prior studies.

5.5.5.1 Moderating Effect of Business Strategy in Strategic Integration

This subsection explains the moderating effect analysis of business strategy in strategic

integration. Some of the hypotheses relate to whether business strategy has a moderating effect

on the relationship between strategic integration and CP:

▪ Hypothesis 6a (H6a): Business strategy moderates the impact of strategic CSR integration

on customer performance.

The evidence reveals that the relationship between strategic integration and customer

performance is significantly moderated by business strategy (total effect difference=0.14,

p=0.08), supporting H6a. The differentiation group has a larger total effect of strategic

integration on customer performance than the cost leadership group.

▪ Hypothesis 6b (H6b): Business strategy moderates the impact of strategic CSR integration

on employee performance.

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The result suggests the impacts of strategic integration on employee performance is not

moderated by business strategy (total effect difference=0.06, p=0.21), not supporting H6b. In

other words, business strategy does not moderate the impact of strategic integration on

employee performance.

▪ Hypothesis 6c (H6c): Business strategy moderates the impact of strategic CSR integration

on operational performance.

The result indicates that there is no difference in the impact of strategic integration on

operational performance caused by business strategy (total effect difference=0.10, p=0.16), not

supporting H6c. Thus, business strategy does not moderate the impact of strategic integration


▪ Hypothesis 6d (H6d): Business strategy moderates the impact of strategic CSR integration

on financial performance.

The finding confirms that the relationship between strategic integration and financial

performance differs by business strategy (total effect difference=0.18, p=0.02), supporting

H6d. This means that business strategy can moderate the impact of strategic integration on

financial performance. The result shows that the total effect of strategic CSR integration on

financial performance is more significant in the differentiation group than the cost leadership

group. This may be because the differentiation strategy has a bigger effect on CCP → CFP.

While CCP can partly mediate the relationship between strategic integration and CFP, the total

effect on Strategic Integration → CFP is higher in the differentiation group.

Generally, the findings reveal the differentiation group has a greater impact of strategic CSR

integration on customer and financial performance than the cost leadership group. There is a

plausible explanation for this finding. Differentiation strategy creates the development of goods

or unique services by offering higher quality, better performance or unique features (Galbreath

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2009; Porter 1985; Valipour, Birjandi & Honarbakhsh 2012). Customers are more likely to

repeat purchase and pay a premium price when such unique products satisfy them (Tsiotsou

2006). Additionally, companies pursuing a differentiation strategy put emphasis on customer

need recognition and value adding (Sun & Pan 2011), which in turn increases customer

satisfaction and loyalty. Customer satisfaction has a positive impact on financial performance

(Xie et al. 2017), and higher levels of customer satisfaction lead to better financial performance

(Chi & Gursoy 2009). Thus, a differentiation strategy can increase customer satisfaction and

loyalty, which ultimately increases financial performance. As a result, companies adopting

differentiation strategy can have more sustainable financial performance than those following

the cost leadership strategy (Banker, Mashruwala & Tripathy 2014).

Conversely, another result implies that the total effect from operational performance on

financial performance is more substantial in the cost leadership group than the differentiation

group. Cost leadership strategy tends to be oriented more towards competitors than customers

(Frambach, Prabhu & Verhallen 2003), so that companies adopting this strategy aim to make

their products at the lowest cost by tightly controlling their costs, reducing expenses from

innovation or marketing, and cutting their prices when selling products (Liu, Li & Li 2019;

Porter 1985). By producing standardised products at a low per-unit cost for price-sensitive

consumers (David & David 2014; Liu, Li & Li 2019), companies will raise their profit margin

through increased internal efficiency (Liu, Li & Li 2019; Valipour, Birjandi & Honarbakhsh

2012). In addition, cost leadership strategy concentrates on cost reduction and efficiency by

seeking to optimise economies of scale, maintain operational efficiency, apply cost-cutting

techniques, and prioritise overhead and administrative cost reductions. As a result, companies

adopting this strategy can use their cost advantage to charge lower prices or enjoy greater profit

margins (Bu¨ yu¨ kbalcı 2012).

246

These findings offer a deeper understanding that business strategy typologies are an

effective tool for explaining performance heterogeneity in organisations (Nandakumar,

Ghobadian & O'Regan 2011). In terms of contingency theory, the findings prove there is a

distinctive CP based on the business strategy adopted.

5.5.5.2 Moderating Effect of CSR Strategy in Strategic Integration

The moderating effect analysis of CSR strategy in strategic integration is presented in this

subsection. In addition to business strategy, hypotheses also examine if the impact of strategic

CSR integration on CP is moderated by CSR strategy.

▪ Hypothesis 8a (H8a): CSR strategy moderates the impact of strategic CSR integration on


The findings reveal that the impact of strategic CSR integration on customer performance

is significantly different between the proactive and reactive groups (total effect

difference=0.20, p<0.05) as the proactive group has a bigger total effect. There is also a

substantial difference in the impact of strategic integration on customer performance between

the accommodative and reactive groups (total effect difference=0.19, p<0.10) where the

accommodative group has a greater total effect. In comparison, there is no total effect

difference between the proactive and accommodative groups (total effect difference=0.01,

p>0.05). These results suggest that proactive and accommodative groups have a greater total

effect than the reactive group. Therefore, the impact of strategic integration on customer

performance is moderated by the CSR strategy, supporting H8a.

Proactive companies handle customers with due regard. They are likely to closely monitor

customer satisfaction, respond to each customer complaint individually, abide by strict product

safety requirements and provide detailed information about their products. Costumers will then

247

show their trust in the companies and support their efforts by continuing to purchase their

products (Maignan et al. 1999).

▪ Hypothesis 8b (H8b): CSR strategy moderates the impact of strategic CSR integration on


The empirical evidence shows no significant differences in the total effect of strategic

integration on employee performance across the three groups of CSR strategy. Hence, the

impact of strategic CSR integration on employee performance is not likely to be influenced by

CSR strategy, not supporting H8b. Put another way, CSR strategy does not moderate the impact

of strategic integration on employee performance.

▪ Hypothesis 8c (H8c): CSR strategy moderates the impact of strategic CSR integration on

operational performance.

The result indicates that total effect differences in the relationship between strategic CSR

integration and operational performance are not significant across the three groups of CSR

strategy. The impact of strategic integration on operational performance is not moderated by

the CSR strategy; therefore, results do not support H8c.

▪ Hypothesis 8d (H8d): CSR strategy moderates the impact of strategic CSR integration on


The results show the proactive group has a significant and bigger total effect of strategic

CSR integration on financial performance than the reactive group (total effect difference=0.22,

p<0.05). Other results show the accommodative group has a stronger total effect than the

reactive group (total effect difference=0.20, p<0.05). However, there is no significant total

effect difference across the proactive and accommodative groups. Therefore, the results suggest

that CSR strategy moderates the impact of strategic CSR integration on financial performance,

supporting H8d.

248

Importantly, the findings reveal that CSR strategy moderates the relationship between

strategic CSR integration and CP, particularly customer and financial performance, whereby

the proactive group has the biggest total effect among the three groups of CSR strategy.

Reactive companies reject the obligations assigned by their stakeholder groups and do less than

is required by society’s standards (Maignan & Ferrell 2001; Maignan et al. 1999). Companies

following a proactive strategy, on the other hand, completely understand their social

obligations and actively fulfil and participate in meeting stakeholder needs and reducing

adverse effects (Ganescu 2012b; Maignan & Ferrell 2001). Companies that connect their CSR

initiatives to stakeholder expectations will therefore optimise their CSR efforts to enhance their

companies’ performance (Michelon, Boesso & Kumar 2013). In turn, proactive companies can

generate a better attitude response from customers (Groza, Pronschinske & Walker 2011) and

are associated with improved levels of employee commitment, customer loyalty and business

performance (Maignan et al. 1999). Because improving efficiency is a critical factor for

companies in determining whether to implement proactive management practices (Zhu, Liu &

Lai 2016), the findings provide empirical evidence that by adopting proactive strategy,

companies can gain better financial performance.

5.5.5.3 Moderating Effect of Company Size in Strategic Integration

This subsection provides the moderating effect analysis of company size in strategic

integration. Several proposed hypotheses are related to whether company size has a moderating

effect on the relationship between strategic CSR integration and CP.

▪ Hypothesis 10a (H10a): Company size affects the impact of strategic CSR integration on


Findings reveals that the impact of the strategic integration on customer performance differs

according to company size (total effect difference=0.21, p<0.05), supporting H10a. In other

249

words, company size moderates the impact of strategic CSR integration on customer

performance.

▪ Hypothesis 10b (H10b): Company size affects the impact of strategic CSR integration on


The relationship between strategic integration and employee performance is significantly

different between large companies and SMEs (total effect difference=0.12, p<0.10), supporting

H10b. Put another way, company size moderates the impact of strategic CSR integration on


▪ Hypothesis 10c (H10c): Company size affects the impact of strategic CSR integration on


This thesis finds that the relationship between strategic integration and operational

performance is significantly affected by company size (total effect difference=0.32, p<0.05),

supporting H10c. The result reveals that company size can moderate the relationship between

strategic CSR integration and operational performance.

▪ Hypothesis 10d (H10d): Company size affects the impact of strategic CSR integration on


Results confirm the effect of strategic integration on financial performance differs by

company size (total effect difference=0.25, p=0.00), supporting H10d. This result suggests that

company size can moderate the impact of strategic CSR integration on financial performance.

In general, company size can be a moderator and has a moderating effect on the relationship

between strategic integration and CP across four target constructs. The findings suggest that

large companies have better CP resulting from strategic CSR integration than do SMEs. The

findings are supported by previous studies that report a significant relationship between

company size and CSR (Aras, Aybars & Kutlu 2010). As companies grow larger, they become

250

motivated by social responsibilities (Ağan et al. 2016) and are more able to handle complicated

and fast CSR implementation strategies better, because they are more familiar with diverse

operations (Tang, Hull & Rothenberg 2012).

Nevertheless, this finding differs from a prior study that found company size, as a control

variable, does not affect the integration of CSR into business strategy (Vo, Delchet-Cochet &

Akeb 2015). This study reported that companies are homogeneous in terms of CSR behaviour

within the SMEs sector.

5.5.5.4 Moderating Effect of Industry Type in Strategic Integration

This thesis also examined whether industry type influences the relationship between

strategic CSR integration and CP. The moderating effect analysis of industry type is provided

in this subsection.

▪ Hypothesis 12a (H12a): Industry type affects the impact of strategic CSR integration on


Findings reveal that the relationship between strategic integration and customer

performance is not significantly moderated by the industry type (total effect difference=0.00,

p=0.49). Thus, H12a is not supported.

▪ Hypothesis 12b (H12b): Industry type affects the impact of strategic CSR integration on


The findings suggest that the relationship between strategic integration and employee

performance does not differ by industry type (total effect difference=0.03, p>0.05), thereby not

supporting H12b. Industry type does not moderate the impact of strategic CSR integration on


251

▪ Hypothesis 12c (H12c): Industry type affects the impact of strategic CSR integration on


The relationship between strategic integration and operational performance is not

significantly different based on industry type (total effect difference=0.04, p>0.05), thereby not

supporting H12c. In other words, industry type does not moderate the impact of strategic CSR

integration on operational performance.

▪ Hypothesis 12d (H12d): Industry type affects the impact of strategic CSR integration on


Results show a different impact of strategic integration on financial performance moderated

by industry type (total effect difference=0.12, p=0.05), supporting H12d. This result indicates

that the non-ESI group has a bigger total effect than the ESI group in the relationship between

strategic CSR integration and financial performance.

Overall, the finding suggests that industry type can moderate the impact of strategic CSR

integration on CP. Specifically, the finding reveals that the non-ESI group has a greater total

effect on financial performance than the ESI group. This complements the argument that heavy

industry and chemical companies (ESI group) tend to be motivated by regulation rather than

CSR, because they are far from the final supply chain customers (less visible) (Ağan et al.

2016).

When a moderator variable interrelates with a mediator variable, moderated mediation

appears so that the value of the indirect effect varies depending on the value of the moderator

variable (Hair et al. 2017). This situation is referred to as a conditional indirect effect because

the value of the indirect effect is conditional on the value of the moderator variable (Hair et al.

2017). In other words, mediation relationships depend on the moderator level (Preacher,

Rucker & Hayes 2007), which implies that if the mechanism connecting an exogenous

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construct with an endogenous construct through a mediator is a function of another variable,

that variable can be said to be moderate (Hair et al. 2017).

Model 1 involves COP, CCP, and CEP as mediators. The mediation analysis result reveals

that these mediators significantly mediate the strategic CSR integration-CFP relationship.

MGA result also demonstrates that the impact of Strategic Integration → CFP differs by

business strategy. In other words, the total effect from strategic CSR integration to CFP as a

result of mediating effects from CCP, CEP, and COP is moderated by the business strategy.

Similarly, the overall results of MGA show that the strategic CSR integration-CFP relationship

can be mediated by CCP, CEP, and COP, and, at the same time, be moderated by CSR strategy,

company size and industry type.

In sum, this thesis provides new empirical evidence identifying new contingencies in the

CSR integration-CFP relationship. The mechanism of this relationship is not only mediated by

customer, employee, and operational performance, but also by business strategy, CSR strategy,

company size, and industry type.

5.5.6 Results of all Tested Hypotheses in Strategic Integration

Based on evidence derived from the findings, 15 hypotheses are significant and hence

supported, while the remaining eight hypotheses are unsupported. Table 5.43 summarises the

results of all hypothesised relationships. The hypotheses of the impact of strategic CSR

integration on CP as well as the mediating effects on strategic CSR integration-financial

performance relationship are supported. Several hypotheses of the moderating effects on this

relationship are also supported, while others are not.

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Table 5.43: Final Results of Hypothesis in Strategic Integration

Hypothesis Propositions Results

H1a The strategic integration of CSR and business strategy has a positive impact on customer

performance.

Supported

H1b The strategic integration of CSR and business strategy has a positive impact on employee

performance.

Supported

H1c The strategic integration of CSR and business strategy has a positive impact on


Supported

H1d The strategic integration of CSR and business strategy has a positive impact on financial

performance.

Supported

H4a The relationship between strategic CSR integration and financial performance is

mediated by customer performance.

Supported

H4b The relationship between strategic CSR integration and financial performance is

mediated by employee performance.

Supported

H4c The relationship between strategic CSR integration and financial performance is

mediated by operational performance.

Supported

H6a Business strategy moderates the impact of strategic CSR integration on customer

performance.

Supported

H6b Business strategy moderates the impact of strategic CSR integration on employee

performance.

Not

supported

H6c Business strategy moderates the impact of strategic CSR integration on operational

performance.

Not

supported

H6d Business strategy moderates the impact of strategic CSR integration on financial

performance.

Supported

H8a CSR strategy moderates the impact of strategic CSR integration on customer

performance.

Supported

H8b CSR strategy moderates the impact of strategic CSR integration on employee

performance.

Not

supported

H8c CSR strategy moderates the impact of strategic CSR integration on operational

performance.

Not

supported

H8d CSR strategy moderates the impact of strategic CSR integration on financial

performance.

Supported

H10a Company size affects the impact of strategic CSR integration on customer performance. Supported

H10b Company size affects the impact of strategic CSR integration on employee performance. Supported

H10c Company size affects the impact of strategic CSR integration on operational

performance.

Supported

H10d Company size affects the impact of strategic CSR integration on financial performance. Supported

H12a Industry type affects the impact of strategic CSR integration on customer performance. Not

supported

H12b Industry type affects the impact of strategic CSR integration on employee performance. Not

supported

H12c Industry type affects the impact of strategic CSR integration on operational performance. Not

supported

H12d Industry type affects the impact of strategic CSR integration on financial performance. Supported


This chapter provides results related to strategic integration, based on analysis undertaken

using SPSS 26 and SmartPLS 3. HCM, using a repeated-indicator approach, was employed,

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producing reliable and robust results. Following the completion of the reflective and formative

measurement model assessments, the reliability and validity of structural models were tested.

The results showed that the measurement and structural models’ assessments of strategic

integration achieve satisfactory reliability and validity. Accordingly, the hypothesised model

of strategic integration of CSR into business strategy (Model 1) was confirmed. Then, the

mediating effect of Model 1 was evaluated. The results revealed that the significance and

relevance of direct and mediating effects were empirically established. MGA was also

conducted to examine the moderating effect of business strategy, CSR strategy, company size,

and industry type. The findings show that 16 of the 23 hypotheses were supported, with the

remaining seven hypotheses being rejected.

To conclude, the findings reveal that there are contingencies in the relationship between

strategic CSR integration and CP. The findings suggest that this relationship is mediated by

stakeholder relationships (i.e., customer, employee, and operational performances). In addition

to mediating effect, this relationship is also moderated by four moderators: business strategy,

CSR strategy, company size, and industry type.

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CHAPTER 6: FUNCTIONAL INTEGRATION AND COMBINED CSR

INTEGRATION-FINDINGS AND DISCUSSION

This chapter presents the procedures and results for functional integration and combined

CSR integration. Like strategic integration, SPSS 26 and SmartPLS 3 were used for statistical

analysis. This chapter consists of two parts: functional integration and combined CSR

integration. At the end of this chapter, a summary is provided.

I. Functional Integration

This first part of this chapter is divided into eight sections. Several descriptive analyses of

functional integration are described first, followed by the validation of measurement and

structural models. The findings of functional integration are then discussed. Next, MGA results

are explained. A conclusion of functional integration is presented at the end of this part.

6.1 Desriptive Statistical Analysis in Functional Integration

This section provides descriptival statistical analysis regarding functional integration. Since

the previous chapter already presented the descriptive statistical analysis for business strategy,

CSR strategy, strategic integration and CP, this chapter provides descriptive statistical analysis

for functional integration. Appendix C.1 displays the mean standard deviation and correlation

for variables of functional integration. All 30 variables have high mean values with the value

for ‘Our products and/or services satisfy national and/or international quality standards’ and

‘We provide a prompt response to the complaints of our customers about products and/or

services’ (FI14 and FI22, 4.40). The correlation coefficients of functional integration variables

are significant and range from 0.11 to 0.69, indicating that their relationships have small to

large effects.

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6.2 Model Specification of Functional Integration

This section explains how model specification of functional integration is determined. Like

strategic integration, functional integration was conceptualised as a second-order construct.

This construct consisted of six different components with related indicators (Table 4.10).

Functional Integration has six exogenous constructs (i.e., Cost, Innovation, Quality, Suppliers,

Customers, and Employees). CP, as an endogenous construct, involves four dimensions,

namely CCP, CEP, COP, and CFP. Subsequently, there are 24 paths from functional integration

to endogenous constructs, and three paths from CCP, CEP, and COP, to CFP. These 27 paths

make the model quite complex and rather difficult to examine. Thus, HCM is established to

minimise the complexity. Six dimensions formed an overall abstraction of functional

integration, which suggests that omitting one of the dimensions might hamper the conceptual

meaning of HOC Functional Integration as a second-order construct (Hair et al. 2017).

Figure 6.1 presents the model of functional integration and CP (Model 2). HOC Functional

Integration is a formative second-order construct that contains two-layered structures of

constructs and represent a more general construct of their lower-order constructs (LOC)

reflectively measured (Hair, Sarstedt, et al. 2018). It consists of six LOCs: Cost, Innovation,

Quality, Suppliers, Customers, and Employees, with five indicators on each construct. To

examine the impact of functional integration on CP, four LOCs are reflectively measured: CCP

with three indicators, CEP with four indicators, CFP, and COP with five indicators. Model 2

refers to a second-order molar model as it has an arrow from its LOCs to the HOC (Chin 2010)

and suggests a collective model since LOCs have similar numbers to shape the HOC (Becker,

Klein & Wetzels 2012; Hair, Sarstedt, et al. 2018). LOCs of HOC Functional Integration and

four constructs of CP belong to Mode A because these constructs are reflectively measured.

The relationship from LOCs to their HOC, Functional Integration, describes Mode B since this

HOC is a formatively measured construct (Hair, Sarstedt, et al. 2018).

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6.3 Model Assessment of Functional Integration

This section discusses model assessment of functional integration employed in this thesis.

Similar to Model 1, there are two evaluations of Model 2 using the PLS-SEM algorithm and

bootstrapping: the measurement model assessment and the structural model evaluation.

Figure 6.1: Model of Functional Integration and Company Performance (Model 2)

6.3.1 Assessment of the Outer Measurement Model in Functional Integration

This subsection presents the assessment of the outer measurement model. Because Model 2

adopts reflective-formative HCM, this subsection contains reflective and formative measures

evaluation.

6.3.1.1 Assessment of Reflective Measurement Model in Functional Integration

The reflective measurement model assessment in functional integration is discussed in this

subsection. Because all LOCs in Model 2 comprise reflective indicators, the assessment of the

measurement models was conducted as follows:

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Figure 6.2 shows the results for the PLS algorithm of Model 2, while the resulting evaluation

of the Model 2 structural model is summarised in Table 6.1. The results show that indicator

reliability for all indicators of functional integration and CP are above the threshold value

of 0.50 and significant (Götz, Liehr-Gobbers & Krafft 2010; Hair, Risher, et al. 2018;

Sarstedt, Ringle & Hair 2017).


2. Assessment of the internal consistency (construct) reliability

Table 6.1 shows that all reflective measures of Model 2 have Cronbach’s alphas above 0.8,

above the threshold value of 0.7 and, therefore, satisfactory (Bagozzi & Yi 2012; Hair et al.

2017; Hair, Ringle & Sarstedt 2011). CR for all constructs in Model 2 fall between 0.88 to

0.91, signifying that all measures are between the required 0.70 and 0.95 level (Bagozzi &

Yi 2012; Hair et al. 2017). Additionally, all constructs achieve a reliability coefficient ρA in

259

the range of 0.82 to 0.87 (Benitez et al. 2020; Sarstedt, Ringle & Hair 2017). Thus, these

results support the establishment of internal consistency reliability in Model 2.

Table 6.1: Reflective Construct Assessments of Model 2

Construct SD t-

value

p-

value

Indicator

reliability

Convergent



alpha

Composite

reliability

ρA

Cost 0.59 0.83 0.88 0.84

FI01 <- Cost 0.03 23.52 0.00 0.53 0.73

FI02 <- Cost 0.03 20.15 0.00 0.49 0.70

FI03 <- Cost 0.02 33.59 0.00 0.66 0.81

FI04 <- Cost 0.02 46.07 0.00 0.67 0.82

FI05 <- Cost 0.02 37.98 0.00 0.62 0.79

Innovation 0.60 0.83 0.88 0.84

FI06 <- Innovation 0.03 25.36 0.00 0.53 0.73

FI07 <- Innovation 0.03 26.26 0.00 0.62 0.79

FI08 <- Innovation 0.02 40.16 0.00 0.67 0.82

FI09 <- Innovation 0.03 29.96 0.00 0.59 0.77

FI10 <- Innovation 0.02 35.28 0.00 0.59 0.77

Quality 0.66 0.87 0.91 0.87

FI11 <- Quality 0.03 25.04 0.00 0.61 0.78

FI12 <- Quality 0.02 33.62 0.00 0.66 0.81

FI13 <- Quality 0.02 39.70 0.00 0.71 0.84

FI14 <- Quality 0.02 34.29 0.00 0.66 0.81

FI15 <- Quality 0.02 43.18 0.00 0.67 0.82

Supplier 0.61 0.84 0.89 0.84

FI16 <- Supplier 0.03 28.97 0.00 0.56 0.75

FI17 <- Supplier 0.03 27.24 0.00 0.55 0.74

FI18 <- Supplier 0.02 44.06 0.00 0.67 0.82

FI19 <- Supplier 0.03 27.99 0.00 0.62 0.79

FI20 <- Supplier 0.02 41.16 0.00 0.66 0.81

Customer 0.62 0.85 0.89 0.85

FI21 <- Customer 0.02 31.42 0.00 0.61 0.78

FI22 <- Customer 0.02 34.32 0.00 0.66 0.81

FI23 <- Customer 0.02 38.28 0.00 0.67 0.82

FI24 <- Customer 0.03 21.06 0.00 0.52 0.72

FI25 <- Customer 0.03 30.68 0.00 0.64 0.80

Employee 0.66 0.87 0.91 0.87

FI26 <- Employee 0.02 36.91 0.00 0.64 0.80

FI27 <- Employee 0.02 50.41 0.00 0.72 0.85

FI28 <- Employee 0.03 25.67 0.00 0.58 0.76

FI29 <- Employee 0.02 48.79 0.00 0.74 0.86

FI30 <- Employee 0.02 33.94 0.00 0.61 0.78

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CCP 0.73 0.81 0.89 0.82

CP12 <- CCP 0.01 65.34 0.00 0.76 0.87

CP13 <- CCP 0.02 54.07 0.00 0.74 0.86

CP14 <- CCP 0.02 48.45 0.00 0.69 0.83

CEP 0.65 0.82 0.88 0.82

CP04 <- CEP 0.02 42.15 0.00 0.64 0.80

CP05 <- CEP 0.02 44.89 0.00 0.69 0.83

CP15 <- CEP 0.02 43.66 0.00 0.66 0.81

CP19 <- CEP 0.03 28.12 0.00 0.58 0.76

CFP 0.61 0.84 0.89 0.84

CP03 <- CFP 0.02 33.10 0.00 0.58 0.76

CP08 <- CFP 0.02 38.81 0.00 0.69 0.83

CP09 <- CFP 0.02 47.68 0.00 0.71 0.84

CP10 <- CFP 0.03 26.74 0.00 0.58 0.76

CP18 <- CFP 0.03 25.22 0.00 0.53 0.73

COP 0.59 0.83 0.88 0.83

CP01 <- COP 0.03 20.82 0.00 0.72

CP02 <- COP 0.03 25.05 0.00 0.74

CP06 <- COP 0.02 36.87 0.00 0.80

CP07 <- COP 0.02 50.26 0.00 0.82

CP17 <- COP 0.03 28.97 0.00 0.75

3. Assessment of the convergent validity

Results in Table 6.1 indicate that all indicators of Model 2 have a loading above 0.70 (Chin

2010; Hair et al. 2017). AVE values of six constructs of functional integration are more than

0.50 (Götz, Liehr-Gobbers & Krafft 2010; Hair, Ringle & Sarstedt 2011); that is, Cost

(0.59), Innovation (0.60), Quality (0.66), Supplier (0.61), Customer (0.62) and Employee

(0.66), respectively. Four constructs of CP also have AVEs above 0.5. As a result,

convergent validity has been established in Model 2.

4. Assessment of the discriminant validity

This thesis employed three approaches to assess discriminant validity: cross-loading,

Fornell-Larcker criterion and HTMT. First, in terms of cross-loading, the results in

Appendix C.2 show that each indicator loads more highly on their own construct than on

other constructs, indicating that all constructs share more variance with their measures than

with other constructs (Chin 2010). Second, Table 6.2 presents the results regarding Fornell-

261

Larcker criterion. Each construct shared more variance with its own measurement items than

with the constructs of the different measurement items (Fornell & Larcker 1981; Götz,

Liehr-Gobbers & Krafft 2010; Hair et al. 2017). These results indicate that the discriminant

validity has been established.


Construct AVE 1 2 3 4 5 6 7 8 9 10 11

1 CCP 0.73 0.85

2 CEP 0.65 0.63 0.80

3 CFP 0.61 0.68 0.72 0.78

4 COP 0.59 0.66 0.72 0.74 0.77

5 Cost 0.59 0.38 0.43 0.45 0.49 0.77

6 Customer 0.62 0.39 0.49 0.40 0.48 0.49 0.79

7 Employee 0.66 0.38 0.60 0.44 0.48 0.46 0.71 0.81

8 Functional

Integration 0.42 0.48 0.62 0.54 0.57 0.71 0.86 0.83 0.65

9 Innovation 0.60 0.42 0.49 0.47 0.46 0.55 0.62 0.61 0.83 0.78

10 Quality 0.66 0.43 0.51 0.50 0.49 0.61 0.69 0.58 0.86 0.72 0.81

11 Supplier 0.61 0.34 0.53 0.42 0.41 0.45 0.68 0.69 0.81 0.57 0.58 0.78

Note: The square root of AVE (on bold remark) is shown in diagonal while the correlations are off-diagonal.

Last, Table 6.3 displays the HTMT values for all constructs. All LOCs of Model 2 have

HTMT values between 0.52 and 0.84, below the conservative threshold value of 0.85

(Henseler, Ringle & Sarstedt 2015). Discriminant validity was established between LOCs

and the reflectively measured constructs CCP, CEP, CFP, and COP with the HTMT value

ranging from 0.41 to 0.71, less than 0.85. HTMT values among four constructs of CP (i.e.,

CCP, CEP, CFP and COP) are below 0.90 (Henseler, Ringle & Sarstedt 2015). Furthermore,

the corresponding bootstrap confidence interval for all constructs does not include the value

of 1, indicating the HTMT value is significantly lower than 1 (Hair et al. 2017; Hair,

Sarstedt, et al. 2018). Nonetheless, it should be noted that the discriminant validity between

LOCs (Cost, Innovation, Quality, Supplier, Customer, and Employee) and their HOC

Functional Integration cannot be defined because the HOC measurement model repeats

indicators of its LOCs (Hair, Sarstedt, et al. 2018; Sarstedt, Hair, et al. 2019).

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The results of three criteria of discriminant validity signify that each indicator had higher

loadings on their own construct than on other constructs (Chin 2010). Consequently, the

measurement model assessment of Model 2 was satisfactorily accomplished; therefore, it is

reliable and valid for further analysis.



values obtained by running the bootstrapping routine with 5,000 samples in SmartPLS (Hair, Sarstedt, et al. 2018).

6.3.1.2 Assessment of Formative Measurement Models in Functional Integration

This subsection provides formative measurement model evaluation in functional integration.

Because HCM Functional Integration is reflective-formative HOC, the formative

measurements should be evaluated (Hair, Sarstedt, et al. 2018; Sarstedt, Hair, et al. 2019).

Table 6.4 presents the VIF and TOL values, showing VIF values of below five, and TOL values

of above 0.2. Thus, collinearity among the LOCs of HOC Functional Integration is not a critical

issue in this thesis (Hair et al. 2017; Hair, Risher, et al. 2018).

Table 6.4: Collinearity Test of Formative Measures of Model 2

Exogenous Construct Endogenous Construct VIF value TOL value

Cost Functional Integration 1.68 0.60

Customer 2.83 0.35

Employee 2.53 0.40

Innovation 2.45 0.41

Quality 2.88 0.35

Supplier 2.33 0.43

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Table 6.5 exhibits the six paths from LOCs to HOC Functional Integration. They are

significant and have the same weight to establish the HOC Functional Integration (Hair,


Table 6.5: Indicator Validity of Formative Measurements of Model 2

Second-Order

Constructs Paths Path Coefficient SD t-value p-value

Functional

Integration

Cost -> Functional Integration 0.17 [0.15, 0.19] 0.01 16.43 0.00

Customer -> Functional Integration 0.21 [0.19, 0.22] 0.01 29.40 0.00

Employee -> Functional Integration 0.22 [0.20, 0.24] 0.01 23.61 0.00

Innovation -> Functional Integration 0.20 [0.18, 0.22] 0.01 23.70 0.00

Quality -> Functional Integration 0.23 [0.21, 0.24] 0.01 27.73 0.00

Supplier -> Functional Integration 0.20 [0.18, 0.22] 0.01 20.24 0.00

To sum up, the assessment of the measurement model, including both reflective and

formative measures, suggests that 11 constructs used in Model 2 (10 LOCs and 1 HOC) were

reliable and valid in the context of this thesis. Therefore, the model can be used for further

analysis.

6.3.2 Assessment of the Structural Model in Functional Integration

Having confirmed that the measurement (outer) model has an adequate fit, the structural

(inner) model is now evaluated. The results of the evaluation are presented in this subsection.

Similar to strategic integration, the assessment of structural model of functional integration

covers six stages according to the guidelines from Hair et al. (2017), as per the following.

6.3.2.1 Assessment of Collinearity in Functional Integration

This subsection shows how to assess collinearity in functional integration. As shown in

Tables 6.6 and 6.7, outer and inner VIF values of indicators and constructs in Model 2 are less

than five. The results indicate no significant levels of collinearity in Model 2 (Hair et al. 2017).

6.3.2.2 Assessment of the Structural Model Relationships in Functional Integration

This subsection explains how to assess the structural model relationships in functional

integration and how to interpret the results. Path coefficients represent the strength of the

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relationships between latent variables (Sarstedt, Ringle & Hair 2017). The path coefficients

usually range from -1 to +1, with coefficients closer to +1 suggesting strong positive

relationships, and those closer to -1 suggesting strong negative relationships (Sarstedt, Ringle

& Hair 2017).

Table 6.6: Outer VIF values of Model 2

Indicator VIF Indicator VIF Indicator VIF Indicator VIF

FI01 1.61 FI13 2.19 FI25 1.84 CP07 2.17

FI02 1.98 FI14 2.14 FI26 1.89 CP08 2.43

FI03 2.26 FI15 2.20 FI27 2.39 CP09 2.57

FI04 2.03 FI16 1.54 FI28 1.71 CP10 1.59

FI05 1.93 FI17 1.69 FI29 2.47 CP12 1.95

FI06 1.52 FI18 2.13 FI30 1.81 CP13 1.94

FI07 2.31 FI19 1.95 CP01 1.96 CP14 1.62

FI08 2.48 FI20 1.91 CP02 2.01 CP15 1.74

FI09 1.65 FI21 1.75 CP03 1.61 CP17 1.48

FI10 1.64 FI22 2.31 CP04 1.77 CP18 1.47

FI11 1.90 FI23 1.92 CP05 2.01 CP19 1.46

FI12 2.15 FI24 2.35 CP06 2.09

Table 6.7: Inner VIF values of Model 2

Construct 1 2 3 4 5 6 7 8 9 10 11

1 CCP 0.00 0.00 1.97 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2 CEP 0.00 0.00 2.58 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3 CFP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

4 COP 0.00 0.00 2.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

5 Cost 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.68 0.00 0.00 0.00

6 Customer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.83 0.00 0.00 0.00

7 Employee 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.53 0.00 0.00 0.00

8 Functional

Integration 1.00 1.00 1.73 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

9 Innovation 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.45 0.00 0.00 0.00

10 Quality 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.88 0.00 0.00 0.00

11 Supplier 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.33 0.00 0.00 0.00

▪ Direct Effect in Functional Integration

Table 6.8 presents the results of bootstrapping indicating the direct effects of Model 2 (Hair

et al. 2017; Wong 2016). As displayed in Figure 6.2, seven relationships have positive direct

effects. Six of them are positive, meaningful and significant direct effects. The most

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substantial direct effects are in Functional Integration → CEP (β=0.62, t=21.86), then

Functional Integration → COP (β=0.57, t16.30), and last, Functional Integration → CCP

(β=0.48, t=10.28). Only one direct effect is weak and insignificant; this is Functional

Integration → CFP (β=0.05, t=1.40).


Path Direct Effect SD t-value p-value Significant?

Functional Integration -> CCP 0.48 [0.38, 0.57] 0.05 10.24 0.00 Yes

Functional Integration -> CEP 0.62 [0.56, 0.68] 0.03 21.86 0.00 Yes

Functional Integration -> CFP 0.05 [-0.02, 0.14] 0.04 1.40 0.16 No

Functional Integration -> COP 0.57 [0.50, 0.64] 0.04 16.30 0.00 Yes

CCP -> CFP 0.24 [0.15, 0.34] 0.05 4.89 0.00 Yes

CEP -> CFP 0.29 [0.17, 0.41] 0.06 4.74 0.00 Yes

COP -> CFP 0.34 [0.23, 0.45] 0.06 6.01 0.00 Yes

Note: The values in brackets represent the 95% bias-corrected and accelerated confidence interval of the path


▪ Mediating Effect in Functional Integration

As displayed in Model 2, there is a multiple mediation effect from constructs of CP (CCP,

CEP, and COP) to CFP (Hair et al. 2017). The model shows that CCP, CEP, and COP are

mediators that intervene in the relationship from Functional Integration to CFP. In other

words, the effect of the independent variable (HOC Functional Integration) on the dependent

variable (CFP) is mediated by third variables (e.g., CCP, CEP, and COP) (Nitzl, Roldan &

Cepeda 2016).

Following suggestions from Nitzl, Roldan and Cepeda (2016), the assessment of mediating

effects applies two steps as follows:

▪ Step a. Testing the strength of the indirect effect. As shown in Table 6.8, three paths have

positive and significant direct effects (b) on CFP. In sequence, results signify that COP

→ CFP has the most substantial path coefficient (β=0.34, t=6.01), followed by CEP →

CFP (β=0.29, t=4.74), and last, CCP → CFP (β=0.24, t=4.89).

Because Model 2 contains multiple mediation, the total indirect effect includes several

specific indirect effects (Hair et al. 2017). Table 6.9 presents three specific indirect

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effects, significantly different from zero, and their confidence interval do not include zero

(Hair et al. 2017). Hence, the results fulfill the main criterion for determining mediation

(Nitzl, Roldan & Cepeda 2016).

Table 6.9: Specific Indirect Effects of Model 2

Path Specific indirect

effect SD t-value p-value Significant?

Functional Integration -> CCP -> CFP 0.12 [0.07, 0.17] 0.03 4.32 0.00 Yes

Functional Integration -> CEP -> CFP 0.18 [0.10, 0.26] 0.04 4.56 0.00 Yes

Functional Integration -> COP -> CFP 0.19 [0.13, 0.26] 0.03 5.75 0.00 Yes



The running of PLS algorithm revealed the total indirect effect from Functional

Integration to CFP with meaningful and significant path coefficient of 0.49, t-value of

13.52 and p-value of 0.00 (Chin 1998). Accordingly, the total effect for the relationship

between Functional Integration and CFP is calculated as follows: (Hair et al. 2017)

Direct effect of Functional Integration → CFP (c) = 0.05

Total indirect effect of Functional Integration → CFP (ab) = 0.49

Total effect (c’) = direct effect (c) + indirect effects (ab)

Thus, total effect of Functional Integration → CFP = 0.05 + 0.49 = 0.54

Table 6.10 displays the direct effects, indirect effects and total effects of Model 2. This

result identifies a change between the total effect and direct effect. Direct effect from

Functional Integration to CFP is weak and insignificant (β=0.05, t=1.40), while its total

effect is greater, positive and significant (β=0.54, t=16.09).

▪ Step b. Determining the type of effect and/or mediation. As presented in Table 6.10,

results show that mediators can change a relationship between FI and CFP. The direct

effect of its relationship is weak and insignificant, but its total effect is meaningful and

significant that is mediated by CCP, CEP, and COP. Accordingly, this mediation implies

an indirect-only (full) mediation type because the effect of FI to CFP is completely

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transmitted with the help of the mediators variables (Hair et al. 2017; Nitzl, Roldan &

Cepeda 2016). Specifically, FI releases its influence only under a certain condition of

CCP (or CEP or COP) on CFP.

Table 6.10: Direct Effects, Indirect Effects and Total Effects of Model 2

Path

Direct

Effect

(a)

Direct

Effect

(b)

Indirect

Effect

(axb)

Total

Effect

(c’)

t-

value

p-

value

Functional Integration -> CCP -> CFP 0.12

[0.07, 0.17] 4.32 0.00

Functional Integration -> CCP 0.48

[0.38, 0.57] 10.24 0.00

CCP -> CFP 0.24

[0.15, 0.34] 4.89 0.00

Functional Integration -> CEP -> CFP 0.18

[0.10, 0.26] 4.56 0.00

Functional Integration -> CEP 0.62

[0.56, 0.68] 21.86 0.00

CEP -> CFP 0.29

[0.17, 0.41] 4.74 0.00

Functional Integration -> COP -> CFP 0.19

[0.13, 0.26] 5.75 0.00

Functional Integration -> COP 0.57

[0.50, 0.64] 16.30 0.00

COP -> CFP 0.34

[0.23, 0.45] 6.01 0.00

Functional Integration -> CFP 0.05

[-0.02, 0.14] 1.40 0.16


[0.42, 0.56] 13.52 0.00


[0.48, 0.61] 16.09 0.00

Table 6.11 presents the VAF value to measure the strenght of the mediators. CCP, CEP

and COP have partial mediating effects on the path from functional integration to CFP

with the VAF more than 0.20 (20%) (Hair et al. 2017). Among the three mediators, COP

has the most substantial effect on the Functional Integration → CFP (35%), followed by

CEP (33%) and last, CCP (22%).

Overall, the results from the structural model assessment of Model 2 show that HOC

Functional Integration with six LOCs has a positive and significant impact on CP. More

specifically, functional integration has an essential effect on CCP, CEP, and COP,

whereas its impact on CFP is significantly mediated by CCP and CEP as well as COP.

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Table 6.11: VAF for Mediation Effect of Model 2

Path Direct Effect

(a)

Direct Effect

(b)

Indirect Effect

(a x b)

Total Effect

(c)

VAF

(per cent)

Functional Integration -> CCP -> CFP 0.48 0.24 0.12 0.54 22.22

Functional Integration -> CEP -> CFP 0.62 0.29 0.18 0.54 33.33

Functional Integration -> COP -> CFP 0.57 0.34 0.19 0.54 35.19

6.3.2.3 Assessment of the Coefficient of Determination (R2) in Functional Integration

The assessment of the coefficient determination in functional integration is discussed in this

subsection. As shown in Table 6.12, R2 values of the endogenous constructs are in the range

from 0.23 to 0.65, indicating varying degrees of predictive capability. CFP is the largest

predictor of the structural model with the R2 value of 0.65, because this construct has impacts

not only from HOC Functional Integration but also from the three mediators’ constructs,

namely CCP, CEP, and COP. This result suggests that the four constructs (LOCs), namely

Functional Integration, CCP, CEP, and COP, can jointly explain 65% of the variance of the

endogenous construct CFP. The same model estimation also reveals the R2 value for other

latent constructs. Functional Integration explains 39% of CEP, 33% of COP, and 23% of CCP.

Subsequently, CEP and COP have weak-to-medium levels of predictive accuracy, while CCP

has a weak effect. As HOC Functional Integration has six LOCs (Cost, Innovation, Quality,

Supplier, Customer, and Employee), it explains 100% of the variance of the HOC Functional

Integration the a R2 value of 1.00 (Becker, Klein & Wetzels 2012; Hair, Sarstedt, et al. 2018).



CCP 0.23 0.16

CEP 0.39 0.25

CFP 0.65 0.39

COP 0.33 0.18

Functional Integration 1.00 0.41

6.3.2.4 Assessment of the Effect Size (f2) in Functional Integration

This subsection how the effect size is evaluated in functional integration. Table 6.13 shows

that the range of f2 values is from 0.01 to 0.64 (Henseler, Ray & Hubona 2016). The strongest

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effect size is from Functional Integration to CEP (0.64), then Functional Integration to COP

(0.49) and finally, Functional Integration to CCP (0.30). The medium effect size is disclosed

by COP on CFP (0.13). The results in Table 6.13 also demonstrate that Functional Integration

→ CFP as well as CCP → CFP and CEP → CFP have a weak effect size with the f2 values of

0.01, 0.09 and 0.09, respectively (Chin 2010; Cohen 1992; Hair et al. 2017).


Path f2 value





CCP -> CFP 0.09

CEP -> CFP 0.09

COP -> CFP 0.13

6.3.2.5 Assessment of the Predictive Relevance (Q2) in Functional Integration

The predictive relevance assessment in functional integration is provided in this subsection.

Table 6.12 presents the Q2 values after carrying the blindfolding procedure using an omission

distance, D=7 (Hair et al. 2017; Henseler, Ringle & Sinkovics 2009). The resulting cross-

validated redundancy Q2 values are positive between 0.16 and 0.41, indicating that there is

significance in the prediction of the constructs (Hair, Risher, et al. 2018). Specifically, the

exogenous construct (HOC Functional Integration) has excellent predictive relevance for all

four endogenous constructs of CP.

6.3.2.6 Assessment of Effect Size (q2) in Functional Integration

This subsection shows how to evaluate effect size in functional integration. Table 6.14

presents q2 values (Hair et al. 2017). As Model 2 applies HCM, the q2 values cannot be

identified between predictors and outcomes. The q2 values should be assessed from six

predictors LOCs to their HOC, this is Functional Integration. The six LOCs have q2 values of

0.02, indicating a small to medium predictive relevance from them to their HOC. In addition,

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the SRMR for the structural model was slightly above 0.08 (0.09 for Model 2), suggesting that

the model fit is quite good (Hair et al. 2017).


Endogenous Construct Cost Customer Employee Innovation Quality Supplier

CCP 0.01 0.00 -0.01 0.00 0.01 -0.01

CEP 0.00 0.00 0.01 0.00 0.00 0.00

CFP 0.00 0.00 0.00 0.00 0.00 0.00

COP 0.01 0.00 0.00 0.00 0.00 -0.01

Functional Integration 0.02 0.02 0.02 0.02 0.02 0.02

6.4 Discussion of Functional Integration

The findings of functional integration are discussed in this section. The objectives are to

address the research questions determined in Chapter 1 and to confirm the functional

integration hypotheses. The discussion is based on conceptual framework and hypotheses

developed in Chapter 3, current literature, and prior research findings, as well as PLS-SEM

data analysis. This section is divided into two subsections, each of them explains (i) discussion

of functional integration and company performance and (ii) discussion of mediating effect in

functional integration.

6.4.1 Discussion of Functional Integration and Company Performance

The results interpretation and the hypotheses verification of functional integration and

company performance is discussed in this subsection. The empirical evidence supports the

conceptualisation of functional integration as a HOC containing six associated LOCs: Cost,

Innovation, Quality, Supplier, Customer and Employee. The evaluations of the measurement

and the structural model have been achieved satisfactorily and signified that the model is quite

a good fit. The result demonstrates that the proposed model is considered appropriate for

exploring and predicting the integration of CSR into business strategy at the functional level

and its effect on CP among Indonesian manufacturing companies. Six LOCs of HOC

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Functional Integration can represent how CSR can be integrated at the functional level,

particularly among manufacturing companies.

The proposed hypothesis investigates the relationship between functional integration and

CP. The direction and intensity of each hypothesised relation is assessed as results of PLS-

SEM data analysis, based on the path coefficients and their corresponding t-values. If paths are

significant and follow the way hypothesised, a prior hypothesis is empirically confirmed. By

comparison, if the paths are insignificant and do not follow the hypothesised direction, they do

not support the proposed causal relationship (Götz, Liehr-Gobbers & Krafft 2010). The four

first hypotheses test the relationship between functional integration and CP particular

hypotheses as follows:

▪ Hypothesis 2a (H2a): The functional integration of CSR and business strategy has a

positive impact on customer performance.

The finding reveals that functional integration has a significant impact on customer

performance (β=0.48, t=10.24, p<0.05), supporting H2a. Similar to Model 1, customer

performance in Model 2 includes customer satisfaction, customer loyalty, and increasing

number of consumers. This finding empirically supports a relationship between functional CSR

integration and customer performance. Specifically, functional integration includes some

activities related to customers (see Table 4.4 in Chapter 4 and Table 6.2 for details), for

instance, “Our company is honest with the customers in the sale or promotion of products and

services” and “We incorporate the interests of our customers in our business decisions”. These

activities will have a positive and significant impact on this essential primary external

stakeholder by enhancing customer satisfaction, improving customer loyalty and producing

more customers. Integrating CSR activities into the operation process will enable the

company's business operations to be performed responsibly (Busaya, Kalayanee & Gary 2009).

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Particularly, integrating customers into functional integration also reflects the commitment

of companies to further engage in CSR by considering customers’ needs in relation to business

operations. Customers are more likely to trust responsible companies that operate honestly in

their activities and reflect the interests of both parties in the relationship when making

decisions. In addition, customers are more willing to engage with companies that carry out

socially responsible initiatives. Hence, customers are likely to support and reward companies

that spend the most om socially responsible programs by generating a greater satisfaction and

loyalty to them (Martínez & Rodríguez del Bosque 2013).

Moreover, Peloza and Shang (2011) argued that when CSR is implemented into a product,

customer satisfaction is greater. In functional integration, some CSR practices relate to the

product, such as developing environmentally friendly products and using eco-friendly

technologies and materials in processes, products, and packaging. The finding shows that these

activities can boost customer satisfaction.

▪ Hypothesis 2b (H2b): The functional integration of CSR and business strategy has a

positive impact on employee performance.

Current findings confirm that functional integration has a significant effect on employee

performance (β=0.62, t=21.86, p<0.05), supporting H2b. More specifically, this finding

provides new evidence that functional CSR integration enables companies to achieve better

employee performance measured by employee training, career opportunities, employee

motivation and overall social performance (see Table 6.2 and Figure 6.1).

There is a plausible explanation for this finding. One construct (LOC) of HOC Functional

Integration is Employee, with five indicators of how companies treat their employees legally

and ethically (see Table 4.4 in Chapter 4 for details). For example, “We provide procedures to

help to insure the health and safety of our employees” and “We treat our employees fairly and

respectfully, regardless of gender or ethnic background”. Workplace safety is a vital

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component of employee well-being (Sprinkle & Maines 2010). Many see it as a worthy

objective in its own right to have a working atmosphere that improves employee well-being or

job satisfaction. It can be similarly concluded that people who are happy with their jobs can

more easily align with organisational goals and work more effectively (Chenhall 2003).

Furthermore, CSR practices that directly support employees should be more likely to meet

employees’ need for safety and security. Policies and procedures that demonstrate concern for

employees and provide a good working environment can enhance company attractiveness and

decrease counter-productive actions in the workplace (Bauman & Skitka 2012). Besides,

providing employees with equal working conditions and treating them well will result in greater

employee satisfaction and, ultimately, higher productivity (Aguilera et al. 2007).

The results suggest that through incorporating CSR into business strategy at the functional

level, especially carrying out effective employee-focused CSR activities, companies can

provide their employees with appropriate training, give them good job opportunities, and thus

increase employee motivation and boost overall social performance. Notably, functional

integration has the largest effect on employee performance compared with other CP.

This finding is supported by prior studies that argued CSR can also have a positive impact

on employees. For example, according to Cochran (2007), a company with good employee

relations can lower its employee turnover rate and improve employee motivation. Ihlen (2008)

claimed that companies have a moral responsibility towards their employees over and beyond

other social actors. Adams (2011) also contended that a company’s value depends on its

relationship with its employees; thus, it is essential to count employees as the most important

stakeholder when companies implement CSR. Moreover, Dhanesh (2012) highlighted that the

adaptation of CSR to the needs of internal stakeholders will contribute significantly to

organisational engagement that can further produce other internal returns, such as increased

loyalty from employees. As previously stated, functional integration also represents the good

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will of companies to maintain good relationships with employees by treating them fairly and

respectfully. Michelon, Boesso and Kumar (2013) emphasised that a good partnership with

employees will increase a company's ability to recruit and retain employees and improve

employee commitment and effort, thus leading to efficiency and productivity enhancements.

Employees who work for socially responsible companies tend to experience high self-esteem

and work motivation because they associate themselves with their organisations (Singhapakdi

et al. 2015). Hadj (2020) found that if employees positively perceive their companies’ CSR,

pride in the company can be increased. If employees feel satisfied and motivated because the

company treats them well, employees will enjoy their work, feel at home, and will not leave

the company. They are also reluctant to hold demonstrations or strikes. These conditions enable

companies to carry out their operational activities in a convenient and safe manner.

▪ Hypothesis 2c (H2c): The functional integration of CSR and business strategy has a

positive impact on operational performance.

Functional integration is found to have a significant and positive impact on operational

performance (β=0.57, t=16.30, p<0.05), supporting H2c. As explained in the previous section,

the model in Figure 6.1 involves four indicators of operational performance, namely timeline

of customer service, delivery time, productivity, and operational efficiency. In this thesis,

functional integration has the second greatest effect on operational performance after employee

performance. Three dimensions in functional integration are closely related to operations,

namely cost, innovation and quality. For example, “We increase labour productivity”, “We

have introduced innovations and improvements in production processes, logistics or

distribution” and “We improve product performance and reliability”.

The findings are supported by prior studies. CSR actions related to internal aspects of the

company, such as a commitment to quality in internal operational processes, promotion of

innovation and employee care, contribute to short-term increases in labour productivity

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(Sánchez & Benito-Hernández 2015). Additionally, companies have the opportunity to reduce

production costs, increase flexibility and improve the quality and performance of shipments

when they involve themselves in CSR initiatives, which impacts on their operational

performance in terms of cost, quality, delivery, and flexibility (Famiyeh 2017). Furthermore,

improved productivity can result from human resource management; for instance, when

companies are able to attract and retain talent as a result of their CSR commitments or when

employees become more productive due to better health and safety policies (Rasche, Morsing

& Moon 2017).

▪ Hypothesis 2d (H2d): The functional integration of CSR and business strategy has a

positive impact on financial performance.

The finding reveals that functional integration has a significant impact on financial

performance (β=0.54, t=16.09, p<0.05), supporting H2d. The finding is supported by previous

research. Companies can improve performance, such as profitability, sales growth, better return

on investment and increased market share, when they invest in CSR activities (Famiyeh 2017).

The findings of this thesis extend on previous research by showing companies integrating

CSR with specific activities at the functional level can have better financial performance

through good cash flow reports, increased sales growth and improved ROI. Particularly, in

Model 2, one critical dimension of functional integration is innovation. Some companies

benefit financially from CSR because their social and environmental efforts contribute to

innovative goods and services, which in turn boost revenue growth (Rasche, Morsing & Moon

2017).

6.4.2 Discussion of Mediating Effects in Functional Integration

With respect to mediation analysis in functional integration, this subsection presents the

three hypotheses verification as follows.

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▪ Hypothesis 5a (H5a): The relationship between functional CSR integration and financial

performance is mediated by customer performance.

Customer performance is found to have a mediation effect on the relationship between

functional integration and financial performance (β=0.12, t=4.32, p<0.05), supporting H5a.

This result supports an argument that customer satisfaction mediates between the relationship

of CSR and financial performance. CSR significantly influences financial performance, and

this relationship is partially mediated by customer satisfaction, corporate reputation, and

competitive advantage (Sindhu & Arif 2017). Customer satisfaction will influence customer

buying decisions, which can be demonstrated as improved customer buying intentions or an

increasing customer willingness to pay higher prices for the companies’ products and services

(Bhardwaj et al. 2018; Goli ´nski 2019).

Previous studies highlight that CSR affects financial performance through the mediator of

customer satisfaction (García-Madariaga & Rodríguez-Rivera 2017; Luo & Bhattacharya

2006; Saeidi et al. 2015; Xie et al. 2017). CSR activities can help companies boost their

financial performance by enhancing customer satisfaction. Thus, companies can improve the

effect of CSR efforts on companies' financial performance by using the indirect effect of

customer satisfaction.

▪ Hypothesis 5b (H5b): The relationship between functional CSR integration and financial

performance is mediated by employee performance.

Current findings indicate that the relationship between functional integration and financial

performance can be mediated by employee performance (β=0.18, t=4.56, p<0.05), supporting

H5b.

The findings of this thesis highlight that mutual trust and cooperative partnerships between

a company and its employees can provide a competitive advantage (Jones 1995). In addition,

functional integration of CSR includes many employee-related CSR activities that can build an

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environment of shared confidence that will improve employee performance (e.g., employee

motivation) (Maulamin 2017). If employees are happy at work, they tend to increase

productivity and decrease absenteeism, which have positive effects on a company’s financial

performance (Yuen et al. 2018).

▪ Hypothesis 5c (H5c): The relationship between functional CSR integration and financial

performance is mediated by operational performance.

Operational performance is indicated to have a significant mediation effect on the

relationship between functional operation and financial performance (β=0.19, t=5.75, p<0.05),

supporting H5c. This finding proves that shareholder welfare can be achieved by involving

operational priorities, such as quality, cost and delivery (Chenhall 2003). In addition,

companies are supposed to operate in a socially and environmentally responsible way in the

pursuit of economic benefits (Achda 2006).

CSP can be defined as the extent to which the expectations of stakeholders, with regards to

a company's behaviour towards the same or other relevant stakeholders, are met or exceeded.

This term also includes stakeholder managers (Husted 2000). Corporate social performance

depends on the extent to which the company meets or exceeds the expectations stakeholders

(Clarkson 1995). Where there is a high level of stakeholder satisfaction, corporate social

performance is high (Husted 2000).

Research has recorded numerous ways of generating value for stakeholders in CSR activities

while also increasing financial performance (Vishwanathan et al. 2020). For instance,

Michelon, Boesso and Kumar (2013) suggested that a company should align its CSR initiatives

to the stakeholders' likely expectations (captured by more focus on some CSR areas than

others) and then use CSR tools strategically to meet certain CSR goals. When a company

practices CSR initiatives linked to stakeholder preferences and allocates resources in a strategic

manner to those initiatives, the positive impact of its CSR initiatives on CFP will be

278

strengthened (Boesso, Favotto & Michelon 2015). Thus, managers interested in maximising

their CSR initiatives should plan their CSR initiatives according to the most important and

influential stakeholder priorities in terms of overall CSR contribution to CP (Boesso, Favotto

& Michelon 2015). Moreover, Famiyeh (2017) argued that a company can improve its

reputation, which positively influences financial performance by considering the expectations

of variety of stakeholders under stakeholder theory. When companies can develop closer

relationships with their stakeholders through CSR, those relationships will allow companies to

anticipate and prevent predictable risks (Vishwanathan et al. 2020).

This thesis extends on findings from prior studies because this thesis comprehensively

assesses the impacts of CSR integration, measured by customer, employee, operational and

financial performance. This thesis demonstrates companies can obtain many benefits through

functional CSR integration and thereby increase their competitiveness. More specifically,

based on the mediation analysis, the findings suggest that companies can achieve improved

financial performance through better performance of customers, employees and operating

because of integrating CSR into business strategy at the functional level.

Indonesia is the world's first nation to obligate companies, especially those related to natural

resources, to conduct CSR and to report their CSR activities (Maris 2014; Ridho 2018;

Sustainablesquare 2017; Waagstein 2011). At a minimum, companies dedicated to CSR should

implement principles and practices along with organisational processes to mitigate their

negative impacts and optimise their positive impacts on critical stakeholder issues (Isabelle,

Ferrell & Linda 2005). Thus, findings show that by integrating CSR, Indonesian manufacturing

companies implement CSR not only to comply with regulations but also to engage CSR in their

business operations in a manner that goes beyond any regulatory requirements.

Furthermore, there is an increasing trend in large companies in Indonesia to conduct CSR

as an integral part of the daily operations of a company (Widjaja 2011), which reflects the

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increasing and diversity of the quantity and quality of CSR activities (Azzahra 2016). The idea

of standardising CSR implementation has also begun to emerge. Some ideas are even quite

detailed on how to implement and document CSR; for example, ISO 26000 for standardisation

of CSR management. GRI governs the issue of international reporting standards, known as the

Sustainability Report, with more than 70 criteria (Maulamin 2017).

Most studies aiming to determine the relationship between corporate social performance and

financial performance reveal a positive correlation (Bernal-Conesa, de Nieves-Nieto &

Briones-Peñalver 2017; Ganescu 2012a; Lee 2008). Under stakeholder theory, it is argued that

social performance may affect financial performance (Chtourou & Triki 2017) and by

responding to the expectations of various stakeholders, a company can enhance its reputation,

which positively influences its financial performance (Famiyeh 2017). Moreover, Tang, Hull

and Rothenberg (2012) identify that companies increase profits if they implement CSR strategy

consistently, including related dimensions of CSR and starting with those more internal to the

companies.

6.5 Multi-group Analysis in Functional Integration

After completing the direct and mediation analyses, the moderating effect in functional

integration is measured to provide a more complete picture of the relationship between

functional integration and CP. This sections presenst the results of MGA in functional

integration for business strategy, CSR strategy, company size, and industry type.

6.5.1 MGA Business Strategy in Functional Integration

MGA for business strategy in fuctional integration is discussed in this subsection. Based on

the guidelines from Matthews (2017), MGA is carried out as follows:

1. Generating data groups

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Likewise strategic integration, three clusters of business strategy are used as categorical

variables: differentiation group (267 respondents), cost leadership group (154 respondents)

and no strategy group (14 respondents).

2. Analysing the MICOM

As three procedures in Step 1 MICOM have been met, this step has been established. Thus,

the following subsection explains Steps 2 and 3 of MICOM.

▪ Step 2. Compositional invariance. This thesis employed one-tailed testing, with results

presented in Appendix C.3. Comparing the correlation between the composite score of

the differentiation and cost leadership group (original correlations), the value of the 5%

quantile is always smaller than (or equal to) the value of the correlation permutation for

10 constructs (Henseler, Ringle & Sarstedt 2016). All 11 constructs have p-values

exceeding 0.05, indicating the establishment of compositional invariance.

▪ Step 3. Equality of composite mean values and variances. Appendix C.4 shows the results

for Step 3. The confidence interval of differences in means between the construct score

of the differentiation and cost leadership group does not include the original difference

in mean values as the mean-original difference for all constructs is too much above the

upper level of the confidence internal. Permutation p-values for the means are also below

0.05. Hence, there are significant differences in the mean values of latent variables across

the two groups (Hair, Sarstedt, et al. 2018). Conversely, the variance-original difference

for 10 constructs falls in the confidence interval of differences in variance between two

groups, and permutation p-values for variance are above 0.05.

Subsequently, these results signify partial measurement invariance across the two groups

(Matthews 2017), supporting the standardized path coefficient comparison across the groups

(Hair, Sarstedt, et al. 2018; Henseler, Ringle & Sarstedt 2016). Because of the mediating

effect, the total effect was used in the comparison of path coefficients. Appendix C.5 shows

281

the total effects from the permutation tests. The first two columns show the total effects in

the differentiation group and the cost leadership group, followed by their differences in the

original data set and permutation testing. All seven paths have different total effects between

two groups. Two significant total effect differences with p-values less than 0.05 are

Functional Integration → CCP and COP → CFP. These results suggest that the

differentiation group has a greater total effect than the cost leadership group in the

relationship between FI and CCP. In contrast, cost leadership has a bigger total effect than

the differentiation group in the path COP → CFP.

3. Analysing and Interpreting Permutation Results

Taking into account the results, the assessment of previous steps was continued by

examining the results of MGA (Hair, Sarstedt, et al. 2018; Henseler, Ringle & Sarstedt

2016). Table 6.15 shows the results for the PLS-MGA. There are total effect differences

between the differentiation and cost leadership group. The biggest significant differences

rely on Functional Integration → CCP (total effect difference=0.20, p=0.03) on COP →

CFP (total effect difference=0.20, p=0.04). These results suggest that total effects from FI

to CCP are bigger in the differentiation group than in the cost leadership group. Conversely,

the total effect from COP to CFP is larger in the cost leadership group than in the

differentiation group.

Table 6.15: PLS-MGA Business Strategy in Functional Integration

Totally, Table 6.16 presents PLS path estimations for the complete sample and subsamples

of business strategy. It can be shown that the total effects of both samples and the subsamples

282

are different. The significant difference is indicated by quite a big difference in the total effect

across all samples and subsamples (shown by bold marks in Table 6.16). Consequently, it is

appropriate to conduct MGA to analyse the total effect in each group of business strategy.

Table 6.16: Multi-group Results Business Strategy in Functional Integration

Path

All Samples Group 1

(Differentiation)

Group 2

(Cost)

Group 1

vs Group

2 N = 435 N = 267 N = 154


Functional Integration -> CCP 0.48 0.38-0.57 0.50 0.42-0.58 0.30 0.12-0.46 0.03

Functional Integration--> CEP 0.62 0.56-0.68 0.58 0.51-0.65 0.59 0.48-0.67 0.47

Functional Integration -> CFP 0.05 -0.02-0.14 0.52 0.44-0.59 0.46 0.32-0.57 0.24

Functional Integration -> COP 0.57 0.50-0.64 0.56 0.48-0.62 0.46 0.33-0.56 0.13

CCP -> CFP 0.24 0.15-0.35 0.30 0.21-0.40 0.21 0.08-0.34 0.17

CEP -> CFP 0.29 0.17-0.41 0.32 0.21-0.42 0.18 0.02-0.33 0.10

COP -> CFP 0.34 0.23-0.45 0.27 0.17-0.37 0.47 0.31-0.61 0.04


comparison

Appendix C.6 presents the results of parametrics and Welch-Satterthwaite tests, slightly

different from the results of PLS-MGA. The total effect difference is significant in two paths;

that is, Functional Integration → CCP and COP → CFP. In addition, at the 10% confidence

level, CEP → CFP has a significant total effect difference (0.15, p=0.09), suggesting that the

differentiation group has bigger total effects than the cost leadership group.

In sum, Table 6.17 presents the PLS multi-group results, which show similar results across

methods. Except for CEP → CFP, this path achieves the difference of total effect in parametric

and Welch-Satterthwaite tests.

Table 6.17: PLS Business Strategy in Functional Integration across Methods


Test

PLS-MGA

Test

Parametric

Test

Welch-Satterthwaite

Test

Functional Integration -> CCP X X X X

Functional Integration -> CEP

Functional Integration -> CFP

Functional Integration -> COP

CCP -> CFP

CEP -> CFP X X

COP -> CFP X X X X

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6.5.2 MGA CSR Strategy in Functional Integration

This subsection explains how to conduct MGA for CSR strategy in functional integration.

Like strategic integration, MGA CSR strategy in functional integration is carried out with three

steps (Matthews 2017). As the configural invariance (Step 1 of MICOM) has been established

(Henseler, Ringle & Sarstedt 2016), the results of Step 2 and Step 3 are presented for each

group comparison of CSR strategy.

1. Proactive versus Reactive in Functional Integration

▪ Step 2. Compositional invariance. One-tailed permutation testing was employed because the

proactive group is assumed to have a bigger impact of functional integration on CP than the

reactive group. Appendix C.7 presents the results of Step 2 MICOM for comparing these

two groups. The value of the 5% quantile is always smaller than (or equal to) the value of

the correlation permutation for all constructs (Henseler, Ringle & Sarstedt 2016). This result

is also confirmed by most p-values exceeding 0.05, except Supplier which has a p-value

below 0.05. Accordingly, compositional invariance has not been established for all eight

constructs.

▪ Step 3. Equality of composite mean values and variances. Appendix C.8 displays the results

for Step 3. Since the confidence interval of mean differences between the proactive and

reactive groups does not include the original difference in mean values, there are significant

differences in the mean values of latent variables across the two groups (Hair, Sarstedt, et

al. 2018). The mean-original differences are above the upper level of the confidence interval,

implying significant variances across the two groups.

In contrast, the confidence intervals for differences in variance between the construct score

of these two groups do not include the original difference in variance values, particularly in

the eight constructs. The original differences in variance values are less than the lower level

of the confidence interval. In addition, this result is supported by permutation p-values for

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variance below 0.05, indicating significant differences in the variance values of latent

variables across the two groups (Hair, Sarstedt, et al. 2018). Except for Supplier, CCP and

COP, these three constructs have p-values greater than 0.05 and their variance original

differences fall within the confidence interval.

Because these results indicate significant differences in both the means and variance values

across the two groups, the comparison of the standardized path coefficient across the groups

using MGA was supported with evidence (Hair, Sarstedt, et al. 2018; Henseler, Ringle &

Sarstedt 2016). The total effect was used in the comparison of path coefficients because of the

mediation effect, with results presented in Appendix C.9. The first two columns show the total

effects in the proactive group and reactive group, followed by their differences in the original

data set and the permutation testing, respectively. All seven paths have different total effects

between two groups, but not all of them are significant. The significant total effect differences

are shown by Strategic Integration → CEP (p=0.03). At the 10% confidence level, the

significant total effect difference lies on Strategic Integration → COP (p=0.07).

Given the outcome, the assessment of the previous measures was continued by running PLS-

MGA (Hair, Sarstedt, et al. 2018; Henseler, Ringle & Sarstedt 2016), with results in Table 6.18.

At the confidence level of 10%, the largest and significant total effect differences rely on two

paths: Functional Integration → CEP (total effect difference=0.11, p=0.07) and Functional

Integration → COP (total effect difference=0.12, p=0.08). In both paths, the reactive group has

a bigger total effect than the proactive group.

Table 6.18: PLS-MGA Proactive and Reactive in Functional Integration

285

The parametric and Welch-Satterthwaite tests are carried out to gain more confidence in the

final results achieved (Hair, Sarstedt, et al. 2018). Appendix C.10 shows results for the

parametric and the Welch-Satterthwaite tests, which are consistent with the permutation and

PLS-MGA tests and identify significant total impact differences between the two groups. More

specifically, at the 10% level, the total effect is different between two groups in the Functional

Integration → CCP and Functional Integration → CFP, which shows that the reactive group

has a greater total effect than the proactive group.

Table 6.19 summarises the PLS multi-group results. Four methods produce similar results,

which signify the total effect differences of the impact of Functional Integration on CEP and

on COP.

Table 6.19: PLS Proactive and Reactive in Functional Integration across Methods


Test

PLS-MGA

Test

Parametric

Test

Welch-Satterthwaite

Test

Functional Integration -> CCP

Functional Integration -> CEP X X X X


Functional Integration -> COP X X X X

CCP -> CFP

CEP -> CFP

COP -> CFP

2. Proactive versus Accommodative in Functional Integration

▪ Step 2. Compositional invariance. One-tailed testing is employed as proactive groups are

considered to have a bigger impact than accommodative groups. Appendix C.11 shows the

results of the permutation run in comparing the proactive and accommodative groups. The

consequence of comparing the correlation between the composite score of these two groups

means that the value of the 5% quantile for all constructs is equal to the value of the

permutation correlation (Henseler, Ringle & Sarstedt 2016). Most p-values exceed 0.05,

except for Quality, Functional Integration and CEP with p-values below 0.05. Based on

these results, compositional invariance has not been established for all constructs.

286

▪ Step 3. Equality of composite mean values and variances. Appendix C.12 presents the

results for Step 3. The confidence intervals of differences in means between the construct

score of the proactive group and accommodative group do not include the original difference

in mean values. Significant differences occur as the mean-original differences are above the

upper level of the confidence interval (Hair, Sarstedt, et al. 2018).

Regarding variance, some original difference in variance values fall outside the confidence

interval, but the others do not. The confidence intervals for Cost, Supplier, CCP and CEP

include the variance value original differences with permutation p-values greater than 0.05.

Hence, these results indicate that there are significant differences in the variance values of

latent variables across the two groups (Hair, Sarstedt, et al. 2018).

Appendix C.13 shows the total effect from the permutation test. At a 10% confidence level,

Functional Integration → CFP has a significant difference across two groups (total effect

difference=0.12, p=0.08). As these results imply substantial differences across the two groups,

they support running PLS-MGA (Hair, Sarstedt, et al. 2018) with results in Table 6.20. Only

one path has significant total effect differences, which implies that the accommodative group

has the stronger total effect than the proactive group; this is, Functional Integration → CFP

(total effect difference=0.12, p=0.08).

Table 6.20: PLS-MGA Proactive and Accommodative in Functional Integration

To increase confidence in the end results achieved, parametric and Welch-Satterthwaite tests

are conducted, with results in Appendix C.14 (Hair, Sarstedt, et al. 2018). Their results are in

line with results from the permutation test and PLS-MGA, showing significant differences in

total effects between proactive and accommodative group. Specifically, total effect difference

287

in Functional Integration → CFP is significant, which reveals that the accommodative group

has a bigger total effect than the proactive group.

The PLS multi-group results are summarised in Table 6.21. Four methods yield identical results

that show the total effect differences on CEP and COP as the impacts of functional integration.

Table 6.21: PLS Proactive and Accommodative in Functional Integration across Methods


Test

PLS-MGA

Test

Parametric

Test

Welch-Satterthwaite

Test



Functional Integration -> CFP X X X X

Functional Integration -> COP

CCP -> CFP

CEP -> CFP

COP -> CFP

3. Accommodative versus Reactive in Functional Integration

▪ Step 2. Compositional invariance. Because the accommodative group tends to have a greater

impact than the reactive group, one-tailed testing is employed with results in Appendix C.15.

The value of the 5% quantile for all constructs is smaller than (or equal to) the value of the

permutation correlation (Henseler, Ringle & Sarstedt 2016). Most have p-values above 0.05,

except for CCP and CEP, which have p-values below 0.05. CEP has a 5% quantile higher

than its original correlation. Thus, compositional invariance has not been established for all

composites.

▪ Step 3. Equality of composite mean values and variances. The composite’s equality of mean

values and variances across groups is assessed in Step 3 (Henseler, Ringle & Sarstedt 2016)

with results in Appendix C.16. Mostly, the original differences in mean values fall outside

the confidence interval. The mean-original differences are well above the upper level of

their confidence interval, suggesting substantial differences in the mean values of latent

variables between the two groups (Hair, Sarstedt, et al. 2018). However, five constructs have

288

mean original differences inside the confidence interval (i.e., Cost, Customer, Quality, CCP

and COP).

In terms of variance, most of the original difference in variance values falls within the

confidence interval with permutation p-values above 0.05. Only Quality has an original

difference in variance value outside its confidence interval (p-value =0.04). Accordingly,

these results imply a substantial difference both in means and variance values across the two

groups and support the necessity of comparing the standardized path coefficient across the

groups (Hair, Sarstedt, et al. 2018; Henseler, Ringle & Sarstedt 2016). Appendix C.17

presents the total effects from permutation tests. Although seven paths have different total

effects between two groups, their permutation p-values are above 0.05, indicating

insignificant differences.

To further analyse group-specific effects, PLS-MGA was run with the results in Table 6.22

(Hair, Sarstedt, et al. 2018; Henseler, Ringle & Sarstedt 2016). There are differences of total

effects across the two groups, significant with p-values above 0.05. Then, parametric and

Welch-Satterthwaite tests are conducted to complete MGA results (Hair, Sarstedt, et al. 2018).

Appendix C.18 shows their results are similar to PLS-MGA, indicating no significant

differences in total effects between two groups.

Table 6.22: PLS-MGA Accommodative and Reactive in Functional Integration

The results for MGA of CSR strategy in functional integration are presented in Tables 6.23

and 6.24. Both tables show that in most of the seven paths, the accommodative group dominates

with larger total effects than the reactive and proactive groups. Specifically, a bold remark in

Table 6.24 shows that in three group comparisons, the accommodative group has a significantly

289

larger total effect than the proactive group in Functional Integration → CFP. Nonetheless, in

some paths, the proactive group has a bigger and significant total effect in Functional

Integration → CEP and Functional Integration → COP compared to the reactive group. Thus,

the results suggest that CSR strategy moderates the impacts from functional integration to CP.

Table 6.23: MGA Results CSR Strategy in Functional Integration

Path

All Samples Group 1

(Reactive)

Group 2

(Proactive)

Group 3

(Accommodative)

N = 435 N = 145 N = 180 N = 110

β CI β CI β CI β CI

Functional Integration -> CCP 0.48 0.38-0.57 0.38 0.18-0.53 0.44 0.32-0.54 0.51 0.34-0.62

Functional Integration -> CEP 0.62 0.56-0.68 0.61 0.50-0.69 0.49 0.40-0.57 0.50 0.35-0.60

Functional Integration -> CFP 0.54 0.48-0.61 0.45 0.31-0.55 0.43 0.32-0.51 0.55 0.41-0.64

Functional Integration -> COP 0.57 0.50-0.64 0.56 0.44-0.65 0.44 0.31-0.52 0.54 0.38-0.65

CCP -> CFP 0.24 0.15-0.34 0.24 0.08-0.39 0.33 0.20-0.43 0.21 0.06-0.36

CEP -> CFP 0.29 0.17-0.41 0.27 0.09-0.45 0.22 0.10-0.34 0.28 0.11-0.45

COP -> CFP 0.34 0.23-0.45 0.38 0.20-0.54 0.37 0.25-0.50 0.31 0.12-0.49


Table 6.24: Comparison of CSR Strategy in Functional Integration

Path

Proactive versus

Reactive

Proactive versus

Accommodative

Accommodative versus

Reactive

Proactive Reactive Proactive Accommodative Accommodative Reactive

Functional Integration -> CCP 0.44 0.38 0.44 0.51 0.51 0.38

Functional Integration -> CEP 0.49 0.61 0.49 0.50 0.50 0.61

Functional Integration -> CFP 0.43 0.45 0.43 0.55 0.55 0.45

Functional Integration -> COP 0.44 0.56 0.44 0.54 0.54 0.56

CCP -> CFP 0.33 0.24 0.33 0.21 0.21 0.24

CEP -> CFP 0.22 0.27 0.22 0.28 0.28 0.27

COP -> CFP 0.37 0.38 0.37 0.31 0.31 0.38

Note: the green colour means the significant difference in each comparison.

6.5.3 MGA Company Size in Functional Integration

How to perform MGA for company size in functional integration is discussed in this

subsection. Similar with strategic integration, MGA company size in functional integration are

conducted in three steps (Matthews 2017). Since all three procedures of Step 1 MICOM have

been met (Henseler, Ringle & Sarstedt 2016), the following presents Step 2 and Step 2

MICOM.

290

▪ Step 2. Compositional invariance. Appendix C.19 presents the results for Step 2 MICOM

and shows that the value of the 5% quantile is always equal to the value of the correlation

permutation (Henseler, Ringle & Sarstedt 2016). Most constructs have permutation p-values

over 0.05, but the p-values for four constructs are below 0.05. Thus, these results suggest

that the compositional invariance is not established for all 11 constructs.

▪ Step 3. Equality of composite mean values and variances. Appendix C.20 shows the results

of Step 3 MICOM. Most confidence intervals do not include the original difference in mean

values, while the mean-original differences of the seven constructs (i.e., Cost, Employee,

Innovation, Quality, Supplier, Functional Integration and CEP) are much greater than the

upper level of their confidence interval, implying significant variances across two groups.

Additionally, permutation p-values for the means of these seven constructs are less than

0.05, suggesting significant differences in the mean values of latent variables across the two

groups (Hair, Sarstedt, et al. 2018). However, the original difference in variance values for

the 10 constructs falls inside their confidence interval. They also have p-values above 0.05,

implying no significant variances across two groups.

Based on the results, the full measurement variance is not established. As these results

support partial measurement invariance, the standardized path coefficient across the groups

using a MGA can be compared with confidence (Hair, Sarstedt, et al. 2018; Henseler, Ringle

& Sarstedt 2016). Due to the mediation effect, the total effect is used in the comparison of path

coefficients presented in Appendix C.21. All seven paths have different total effects between

two groups. Total effect original differences for five paths fall within their confidence levels

with permutation p-values above 0.05. But the original difference of total effect for the other

two paths falls outside their confidence interval: Functional Integration → COP and CCP →

CFP. With permutation p-values less than 0.05, these results indicate significant differences

across the two groups, whereby large companies have a greater total effect than SMEs.

291

Taking into account the results, the assessment of previous steps is continued by examining

the results of MGA (Hair, Sarstedt, et al. 2018; Henseler, Ringle & Sarstedt 2016) with results

presented in Table 6.25. There are differences in path coefficients between large companies

and SMEs, but not all differences are significant. Two paths have significant differences:

Functional Integration → COP (total effect difference=0.15, p=0.04), and CCP → CFP (total

effect difference=0.19, p=0.03). These differences show that large companies have stronger

total effect than SMEs.

Table 6.25: PLS-MGA Company Size in Functional Integration

Generally, Table 6.26 presents PLS path estimations for the complete sample and

subsamples of company size. Total effects of all samples (pooled data) are different from total

effects in subsamples. Accordingly, the results confirm that the total effects evaluation should

be conducted across two groups as it provides a deeper analysis.

Table 6.26: MGA Results Company Size in Functional Integration

Path

All Samples Group 1 (Large) Group 2 (SME) Group 1

vs

Group 2 N = 435 N = 297 N = 138



Functional Integration -> CEP 0.62 0.56-0.68 0.63 0.57-0.68 0.59 0.48-0.67 0.25

Functional Integration -> CFP 0.54 0.48-0.61 0.56 0.49-0.62 0.52 0.39-0.61 0.29


CCP -> CFP 0.24 0.15-0.34 0.33 0.23-0.43 0.13 -0.03-0.26 0.03

CEP -> CFP 0.29 0.17-0.41 0.26 0.15-0.38 0.31 0.15-0.46 0.35

COP -> CFP 0.34 0.23-0.45 0.30 0.18-0.41 0.39 0.24-0.53 0.21


comparison

292

To obtain comprehensive results, the parametric and Welch-Satterthwaite tests are carried

out, with results in Appendix C.22 (Hair, Sarstedt, et al. 2018). The results support the MGA

results, showing significant differences in Functional Integration → COP and CCP → CFP.

Overall, Table 6.27 summarises the PLS multi-group results, which show similar results

across methods. It can be concluded that total effects between large companies and SMEs differ

significantly in the relationship between functional integration and CP. Accordingly, the results

from multi-methods approach can provide confidence in the final results obtained (Hair,


Table 6.27: PLS Company Size in Functional Integration across Methods


Test

PLS-MGA

Test

Parametric

Test

Welch-Satterthwaite

Test




Functional Integration -> COP X X X X

CCP -> CFP X X X X

CEP -> CFP

COP -> CFP

6.5.4 MGA Industry Type in Functional Integration

This subsection explains how MGA for industry type is conducted in functional integration

with three steps (Matthews 2017). Because the configural invariance (Step 1 MICOM) has

been met (Henseler, Ringle & Sarstedt 2016), the results of Step 2 and Step 3 MICOM is

explained as follows.

▪ Step 2. Compositional invariance. As non-ESI group is perceived to have less strategic

integration impact on CP than the ESI group, this thesis employed a one-tailed permutation

test, and results are presented in Appendix C.23. The value of the 5% quantile is always

equal to the value of the correlation permutation for all constructs (Henseler, Ringle &

Sarstedt 2016). Also, this result is confirmed by most p-values exceeding 0.05. Thus,

compositional invariance is established for 11 constructs.

293

▪ Step 3. Equality of composite mean values and variances. The composite’s equality of mean

values and variances across groups is assessed in Step 3 (Henseler, Ringle & Sarstedt 2016)

(see Appendix C.24). The confidence interval of differences in means between the construct

score of ESI and non-ESI groups include the original difference in mean values, indicating

insignificant differences across the two groups (Hair, Sarstedt, et al. 2018). The 11

constructs also have permutation p-values for variance above 0.05.

Most original difference of variance values fall within the confidence interval of differences

in variance between the construct score for non-ESI and ESI groups. Besides, permutation

p-values for variance are 0.05. In the case of CCP and CFP, their original difference of

variance values falls outside their confidence level, exceeding the upper level of the

confidence level. These two constructs have permutation p-values below 0.05. These results

suggest that significant differences in the variance values of latent variables across the two

groups was achieved (Hair, Sarstedt, et al. 2018).

To confirm the results, the total effect was compared (Hair, Sarstedt, et al. 2018; Henseler,

Ringle & Sarstedt 2016), with results in Appendix C.25. The first two columns present the total

effects in non-ESI and ESI groups, followed by their differences in the original data set and the

permutation testing, respectively. One path, CEP → CFP, has a significant different total effect

(0.22, p=0.04). At confidence level 10%, two paths have significant total effect differences:

Functional Integration → CCP (0.15, p=0.06) and Functional Integration → CFP (0.09,

p=0.08). These results imply that the non-ESI group has a bigger total effect than the ESI group.

Next, the assessment was continued by analysing the effects of MGA by running PLS-MGA

(Hair, Sarstedt, et al. 2018; Henseler, Ringle & Sarstedt 2016), with results shown in Table

6.28. There are differences between the ESI and non-ESI groups, but not all differences are

significant. At the 5% confidence level, only CEP → CFP has a significant total effect

difference (total effect difference=0.22, p=0.03). At 10% confidence level, the total effect

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differences of two paths are significant: Functional Integration → CCP (total effect

difference=0.15, p=0.06) and Functional Integration → CFP (total effect difference=0.09,

p=0.1). These results show that in these relationships, non-ESI group have higher total effect

than ESI group.

Table 6.28: PLS-MGA Industry Type in Functional Integration

As shown in Table 6.29, total effects in complete sample (pooled data) are different from

total effects in subsamples of industry type (shown by bold marks in Table 6.29). Thereby,

MGA is appropriate for evaluating the differences between non-ESI and ESI groups.

Table 6.29: MGA Results Industry Type in Functional Integration

Path

All Samples Group 1

(ESI)

Group 2

(Non-ESI) Group 1

vs Group 2 N = 435 N = 228 N = 107



Functional Integration -> CEP 0.62 0.56-0.68 0.59 0.51-0.65 0.66 0.58-0.71 0.13

Functional Integration -> CFP 0.05 -0.02-0.14 0.50 0.41-0.58 0.59 0.50-0.66 0.10


CCP -> CFP 0.24 0.15-0.34 0.24 0.10-0.36 0.23 0.14-0.32 0.46

CEP -> CFP 0.29 0.17-0.41 0.19 0.05-0.34 0.41 0.31-0.52 0.03

COP -> CFP 0.34 0.23-0.45 0.38 0.24-0.51 0.28 0.17-0.39 0.15


comparison

Appendix C.26 presents the results of the parametric and Welch-Satterthwaite tests of

industry type in functional integration. Results are in line with MGA results. Three paths have

significant differences of total effect between ESI and non-ESI groups: Functional Integration

→ CCP, Functional Integration → CFP and CEP →CFP. Table 6.30 summarises the PLS multi-

group results, showing that MGA results across methods are slightly similar.

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Table 6.30: PLS Industry Type in Functional Integration across Methods


Test

PLS-MGA

Test

Parametric

Test

Welch-Satterthwaite

Test





CCP -> CFP

CEP -> CFP X X X

COP -> CFP

6.5.5 Discussion of Multi-Group Analysis in Functional Integration

The discussion of MGA in functional integration are presented in this subsection. The

purposes are to answer the research questions and to verify the hypotheses. The discussion is

based on the conceptual framework and hypotheses outlined in Chapter 3, existing literature,

and research findings from prior studies, as well as PLS-SEM data analysis outcomes. The

discussion is divided into four subsections: (i) moderating effect of business strategy in

functional integration, (ii) moderating effect of business strategy in functional integration, (iii)

moderating effect of company size in functional integration, and (iv) moderating effect of

industry type in functional integration.

6.5.5.1 Moderating Effect of Business Strategy in Functional Integration

This subsection presents moderating effect analysis of business strategy in functional

integration. In relation to MGA in functional integration, there are several hypotheses whether

business strategy has a moderator effect in the relationship between functional integration and

CP as per the following.

▪ Hypothesis 7a (H7a): Business strategy moderates the impact of functional CSR

integration on customer performance.

Current findings reveal the relationship between functional integration and customer

performance is significantly influenced by business strategy (total effect difference=0.20,

p=0.03), supporting H7a. This finding shows that the differentiation group has a bigger total

296

effect than the cost leadership group. The finding is fairly justified. By offering higher quality,

better efficiency, or unique features, the differentiation strategy creates products or unique

services (Galbreath 2009; Porter 1985; Valipour, Birjandi & Honarbakhsh 2012). Companies

with a differentiation strategy focus on recognising consumer needs and adding value to

customers (Sun & Pan 2011), which ultimately boosts customer satisfaction and loyalty.

▪ Hypothesis 7b (H7b): Business strategy moderates the impact of functional CSR

integration on employee performance.

The result suggests the impacts of functional integration on employee performance are not

influenced by business strategy (total effect difference=0.00, p=0.47), thereby not supporting

H7b.

▪ Hypothesis 7c (H7c): Business strategy moderates the impact of functional CSR

integration on operational performance.

The result implies there is no difference impact of functional integration on operational

performance caused by business strategy (total effect difference=0.09, p=0.13), thereby not

supporting H7c.

▪ Hypothesis 7d (H7d): Business strategy moderates the impact of functional CSR

integration on financial performance.

The finding indicates that the relationship between functional integration and financial

performance is not influenced by business strategy (total effect difference=0.06, p=0.24),

thereby not supporting H7d.

Overall, business strategy affects only customer performance in the functional integration,

which suggests that the differentiation group has more significant total effects than the cost

leadership group. Business strategy does not mediate the relationship between functional

integration and other performance, namely employee, operating and financial performance.

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There is a plausible explanation for these findings. Differentiation is the ability to provide

unique and superior value to the buyer in terms of the service (product) itself (i.e., design,

quality), marketing approach, delivery system, or after-sales service. It, therefore, permits a

company to charge a premium price, which leads to superior profitability, provided a

differentiator’s cost position is not so far above that of competitors so as to offset its price

premium (O'Farrell, Hitchens & Moffat 1992). A company following a differentiation strategy

aims to build a belief in consumers' minds that their goods or services possess superior features

relative to those of their rivals in terms of brand and credibility, reliability, design features and

efficiency (Sashi & Stern 1995). In most developing countries, companies implementing the

differentiation strategy do not focus on a single dimension but simultaneously emphasise

multiple dimensions, such as image, consumer loyalty, quality, innovation and service level

(Kim, Nam & Stimpert 2004). Moreover, compared to companies with cost leadership strategy,

companies with differentiation strategy have better socially responsible supply chain

management (Hoejmose, Brammer & Millington 2013), involved in functional CSR

integration.

In addition to moderating the effect of business strategy, other findings reveal the total effect

from COP to CFP is more significant in the cost leadership group than the differentiation group.

In manufacturing systems, the economies of scale and efficiency could be considered as key

examples of manufacturing skills associated with cost leadership strategy (Porter 1985). Lower

cost, although not neglecting quality, service and other areas, emphasises the ability of the

company to design, produce and sell a standardised product or service more efficiently than its

competitors with an emphasis on reaping cost advantages from all sources (O'Farrell, Hitchens

& Moffat 1992). One of four indicators of operational performance is operational efficiency.

Thus, it can be understood that companies with cost leadership strategy have a higher total

effect on operational performance compared with those with differentiation strategy.

298

6.5.5.2 Moderating Effect of CSR Strategy in Functional Integration

The hypotheses verification regarding moderating effect of CSR strategy in functional

integration is explained in this subsection. In terms of CSR strategy, hypotheses relate to

whether the impact of functional integration on CP is influenced by CSR strategy, as per the

following:

▪ Hypothesis 9a (H9a): CSR strategy moderates the impact of functional CSR integration


The finding reveals that the impact of functional integration on customer performance is not

significantly different across the three groups of CSR (reactive, accommodative and proactive

group), thereby not supporting H9a.

▪ Hypothesis 9b (H9b): CSR strategy moderates the impact of functional CSR integration


The empirical evidence shows that there are differences in total effects across three groups

of CSR strategy, particularly when comparing reactive and proactive groups. At the 10%

confidence level, the result indicates that the reactive group has a bigger total effect than the

proactive group (total effect difference=0.11, p=0.07). This suggests that the impact of

functional integration on employee performance is moderated by the CSR strategy, supporting

H9b.

▪ Hypothesis 9c (H9c): CSR strategy moderates the impact of functional CSR integration


The result indicates that differences of total effects across the three groups of CSR strategy

are significant. More specifically, the accommodative group has a greater total effect than the

proactive group at the confidence level of 10% (total effect difference=0.12, p=0.08). In other

299

words, CSR strategy moderates the impact of functional integration on operational

performance, supporting H9c.

▪ Hypothesis 9d (H9d): CSR strategy moderates the impact of functional CSR integration


The finding shows that the reactive group has a bigger total effect than the proactive group

at the confidence level of 10% (total effect difference=0.12, p=0.08). Thus, CSR strategy

moderates the impact of functional integration on financial performance, supporting H9d.

Totally, the findings suggest that CSR strategy has an influence on the relationship of

functional integration and CP, particularly on employee, operating and financial performance.

In particular, the reactive group has a more significant total effect than the proactive group on

employee and operational performance, while the accommodative group has a greater total

effect than the proactive group on financial performance.

The reactive strategy indicates that companies fulfil their economic responsibilities, and

they apply CSR at the basic level required to meet their regulatory compliance (Lee 2011;

Maignan et al. 1999; Torugsa, O'Donohue & Hecker 2013). Companies following the

accommodative strategy support certain ethical responsibilities, particularly those of their

stakeholders, without initiating voluntary actions for the common good (Ganescu 2012b). The

findings show that although companies implement CSR at a minimum to fulfil regulatory

compliance, they already make an effort to adopt values and norms along with organisational

processes to minimise their negative impacts and maximise their positive impacts on important

stakeholder issues (Isabelle, Ferrell & Linda 2005). Because companies focus on economic

responsibly, they carry out several activities to pursue their goals, such as cost savings,

enhancing employee motivation, increasing efficiency, improving productivity and

maximising profit.

300

Three corporate paradigms are relevant to implementation of the CSR system: first, the

organisation provides merely lip service and coercion, and second, the organisation makes

effort to comply. CSR is enforced because, for example, there are rules, legislation and

regulations that require it, and because there is market driven control. Third, companies take

steps beyond compliance towards enforcement. CSR is applied because there is a genuine

(internal driven) drive from within (Wibisono 2007, cited in Maulamin 2017).

6.5.5.3 Moderating Effect of Company Size in Functional Integration

In terms of company size, this subsection presents several hypotheses verifications whether

company size has a moderating effect on the relationship between functional integration and

CP.

▪ Hypothesis 11a (H11a): Company size affects the impact of functional CSR integration


The finding shows that the impact of the functional integration on customer performance is

not influenced by company’s size (total effect difference=0.08, p=0.24), thereby not supporting

H11a.

▪ Hypothesis 11b (H11b): Company size affects the impact of functional CSR integration


The relationship between functional integration and employee performance is not

significantly different across large companies and SMEs (total effect difference=0.05, p=0.25),

thereby not supporting H11b.

▪ Hypothesis 11c (H11c): Company size affects the impact of functional CSR integration


301

Current findings reveal that the relationship between functional integration and operational

performance is significantly moderated by company size (total effect difference=0.15, p=0.04),

supporting H11c. This suggests that large companies have better COP than SMEs.

Larger companies have a greater social impact because of greater visibility (Dupire &

M’Zali 2018). They tend to have more power to monitor their operating environment and use

large-scale mass manufacturing techniques, which minimise the task uncertainty (Chenhall

2003). Moreover, the existence of economies of scale often leads to a cost advantage for larger

companies over small companies, assuming that large companies have the most efficient

facilities, distribution systems, service organisations and other functional units for its size

(Porter 1980b; Reverte, Gómez-Melero & Cegarra-Navarro 2016).

▪ Hypothesis 11d (H11d): Company size affects the impact of functional CSR integration


The result suggests that the impact of functional integration on financial performance does

not differ by company size (total effect difference=0.04, p=0.29), thereby not supporting H11d.

Many studies examine the connection between company size and CSR. For example, Aras,

Aybars and Kutlu (2010) argued that company size has a significant relationship on CSR. Tang,

Hull and Rothenberg (2012) also contend that larger companies may have stronger motives for

engaging in CSR activities and can handle complicated and fast CSR engagement strategies

better, because they are more familiar with diverse operations. Nonetheless, this thesis provides

a new empirical finding, particularly when analysing the moderating effect of company size in

the relationship between functional CSR integration and CP. The results demonstrate that large

companies have significantly a stronger impact from functional integration on operational

performance.

302

6.5.5.4 Moderating Effect of Industry Type in Functional Integration

This subsection provides the hypotheses verifications whether industry type moderates the

relationship between functional integration and CP.

▪ Hypothesis 13a (H13a): Industry type affects the impact of functional CSR integration on


Findings demonstrate the relationship between functional integration and customer

performance is significantly influenced by industry type (total effect difference=0.15, p=0.06),

supporting H13a.

▪ Hypothesis 13b (H13b): Industry type affects the impact of functional CSR integration on


The finding suggests that the relationship between functional integration and employee

performance does not differ by the industry type (total effect difference=0.06, p=0.13), thereby

not supporting H13b.

▪ Hypothesis 13c (H13c): Industry type affects the impact of functional CSR integration on


The relationship between functional integration and operational performance is not

significantly different according to the industry type (total effect difference=0.02, p=0.38),

thereby not supporting H13c.

▪ Hypothesis 13d (H13d): Industry type affects the impact of functional CSR integration on


The results suggest there is a different impact of functional integration on financial

performance by industry type at the confidence level of 10% (total effect difference=0.09,

p=0.10), supporting H13d.

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In general, the findings indicate that there is a significant difference of total effect based on

industry type. Specifically, the results suggest that non-ESI groups have higher total effects on

customer and financial performance than ESI groups. In the relationship between employee

and financial performance, non-ESI groups have a stronger total effect than ESI groups. There

is a plausible justification for this finding. The process of prioritisation and strategic approach

with regard to environmental CSR activities reinforces the CSR and CP relationship more in

ESI than in non-ESI companies (Boesso, Favotto & Michelon 2015). 'Business exposure'

should also be identified when assessing CSR; that is, the extent to which a company is

vulnerable to its environment (Michelon, Boesso & Kumar 2013). The industry in which a

company operates can also affect the pressures from various stakeholder groups it deals with.

Consumer product companies, for instance, encounter their greatest exposure and pressure

from customer groups, while companies in industrial sectors, such as utilities and oil and gas,

face the biggest push from stakeholders concerned about environmental effects. Conversely,

companies in industries such as consumer goods, utilities, and oil and natural gas are under

intensive scrutiny from various stakeholders (i.e., they face considerable business exposure),

so they are more likely to engage in certain CSR acts than companies in other industries

(Michelon, Boesso & Kumar 2013). Other industries, especially the newer manufacturing and

service industries, have significantly smaller environmental effects and are associated with less

visible environmental issues (Reverte, Gómez-Melero & Cegarra-Navarro 2016).

6.6 Results for all Tested Hypotheses in Functional integration

Based on evidence derived from the findings, this subsection shows 15 significant and

supported hypotheses that explain the relationship between functional CSR integration and CP,

the mediating effects, and the moderating effects on this relationship. However, the remaining

(eight) hypotheses that refer to the moderating effects are insignificant and hence unsupported.

304

The results of all hypothesised relationships in the functional integration of CSR into business

strategy are presented in Table 6.31.

Table 6.31: Final Results of Hypothesis in Functional integration


H2a The functional integration of CSR and business strategy has a positive impact on


Supported

H2b The functional integration of CSR and business strategy has a positive impact on


Supported

H2c The functional integration of CSR and business strategy has a positive impact on


Supported

H2d The functional integration of CSR and business strategy has a positive impact on


Supported

H5a The relationship between functional CSR integration and financial performance is

mediated by customer performance.

Supported

H5b The relationship between functional CSR integration and financial performance is

mediated by employee performance.

Supported

H5c The relationship between functional CSR integration and financial performance is

mediated by operational performance.

Supported

H7a Business strategy moderates the impact of functional CSR integration on customer

performance.

Supported

H7b Business strategy moderates the impact of functional CSR integration on employee

performance.

Not

Supported

H7c Business strategy moderates the impact of functional CSR integration on operational

performance.

Not

supported

H7d Business strategy moderates the impact of functional CSR integration on financial

performance.

Not

supported

H9a CSR strategy moderates the impact of functional CSR integration on customer

performance.

Not

supported

H9b CSR strategy moderates the impact of functional CSR integration on employee

performance.

Supported

H9c CSR strategy moderates the impact of functional CSR integration on operational

performance.

Supported

H9d CSR strategy moderates the impact of functional CSR integration on financial

performance.

Supported

H11a Company size moderates the impact of functional CSR integration on customer

performance.

Not

supported

H11b Company size moderates the impact of functional CSR integration on employee

performance.

Not

supported

H11c Company size moderates the impact of functional CSR integration on operational

performance.

Supported

H11d Company size moderates the impact of functional CSR integration on financial

performance.

Not

supported

H13a Industry type moderates the impact of functional CSR integration on customer

performance.

Supported

H13b Industry type moderates the impact of functional CSR integration on employee

performance.

Not

supported

H13c Industry type moderates the impact of functional CSR integration on operational

performance.

Not

supported

H13d Industry type moderates the impact of functional CSR integration on financial

performance.

Supported

305

6.7 Conclusion of Functional Integration

This first part of this chapter presented results regarding the proposed hypotheses and

conceptual model previously developed in Chapter 3. Following guidelines for assessing

measurement and structural models, a HCM using a repeated-indicator approach was

employed, generating reliable and robust results.

After completing the reflective and formative measurement assessments, the reliability and

validity of structural models were checked. The findings indicate the reliability and validity of

the measurement and structural models are satisfactory. Thus, Model 2 can represent how CSR

is integrated at the functional level, which consists of six dimensions of functional integration

and four dimensions of CP with their indicators. Moreover, the hypothesised model of

functional integration of CSR (Model 2) was verified based on the statistical analysis. The

model was also evaluated for the mediating impact, which supported confirmation of the

hypotheses. The significance and relevance of coefficient paths of the structural model and

mediating effects were empirically identified.

MGA was also conducted to determine whether business strategy, CSR strategy, company

size and type of industry moderate the functional integration of CSR and its effect on CP.

Notably, with respect to functional integration, the results support 14 of 23 hypotheses, while

nine hypotheses were not confirmed.

The findings highlight that by concerning themselves with environmental and social issues,

companies can achieve competitive advantage (Moczadlo 2015). Notably, the findings provide

empirical evidence that contingency theory can be used to assess whether companies that match

their strategies with environmental requirements will achieve superior performance (Kotha &

Nair 1995).

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II. The Combined CSR Integration

After investigating the integration of CSR into business strategy at the strategic and

functional levels, the relationship between both integrations is examined and presented in the

second part of Chapter 6. Because the descriptive analyses of strategic and functional CSR

integration were discussed in Chapter 5, this part presents PLS-SEM analysis of the combined

CSR integration.

6.8 Model Assessment in The Combined CSR Integration

This section explains how to assess the model in the combined CSR integration. Model 3

depicts the relationship between strategic and functional integration, which contains two

HOCs, Strategic Integration and Functional Integration (see Figure 6.3). HOC Strategic

Integration comprises three LOCs, while HOC Functional Integration consists of six LOCs.

Model 3 has four constructs of CP (see Table 4.10 and Figure 4.7 for details). The evaluation

of Model 3 covers two steps (the assessment of the measurement model and structural model)

using PLS-SEM algorithms and bootstrapping procedures.

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Figure 6.3: Model of CSR Integration and Company Performance (Model 3)

6.8.1 Assessment of the Measurement Model in The Combined CSR Integration

The measurement model evaluation in the combined CSR integration is provided in this

subsection. Since Model 3 involves reflective-formative HCM, the assessment of the

measurement (outer) model contains reflective and formative measure evaluation. The results

are presented below.

6.8.1.1 Assessment of Reflective Measurement Model in The Combined CSR Integration

This subsection presents the reflective measurement model assessment in the combined

CSR integration. The assessment of Model 3’s measurement models include four criteria as

per the following (Götz, Liehr-Gobbers & Krafft 2010; Hair et al. 2017; Sarstedt, Ringle &

Hair 2017).


Table 6.32 presents the resulting evaluation of Model 3’s structural model, which indicates

that all indicators of strategic integration, functional integration, and CP have a significant

308

indicator reliability of above 0.50 (Götz, Liehr-Gobbers & Krafft 2010; Hair, Risher, et al.

2018; Sarstedt, Ringle & Hair 2017).

Table 6.32: Reflective construct assessments of Model 3

Construct SD t-

value

p-

value

Indicator

reliability

Convergent



alpha

Composite

reliability ρA

Aligning CSR with company’s strategy (Aligning) 0.67 0.88 0.91 0.88

SI01 <- Aligning 0.02 33.62 0.00 0.65 0.80

SI02 <- Aligning 0.02 44.52 0.00 0.68 0.82

SI03 <- Aligning 0.03 25.02 0.00 0.53 0.73

SI04 <- Aligning 0.01 58.70 0.00 0.76 0.87

SI05 <- Aligning 0.02 51.36 0.00 0.72 0.85

Support from top management (SuppTM) 0.74 0.91 0.93 0.91

SI06 <- SuppTM 0.02 51.71 0.00 0.73 0.85

SI07 <- SuppTM 0.01 66.16 0.00 0.78 0.88

SI08 <- SuppTM 0.02 45.09 0.00 0.71 0.84

SI09 <- SuppTM 0.01 73.80 0.00 0.77 0.88

SI10 <- SuppTM 0.02 43.46 0.00 0.70 0.83

Developing effective communication (EffCom) 0.75 0.91 0.94 0.91

SI11 <- EffCom 0.02 40.53 0.00 0.70 0.83

SI12 <- EffCom 0.02 53.94 0.00 0.72 0.85

SI13 <- EffCom 0.02 58.46 0.00 0.77 0.88

SI14 <- EffCom 0.01 63.83 0.00 0.77 0.88

SI15 <- EffCom 0.01 64.55 0.00 0.76 0.87

Cost 0.59 0.83 0.88 0.84

FI01 <- Cost 0.03 23.49 0.00 0.53 0.73

FI02 <- Cost 0.03 20.13 0.00 0.49 0.70

FI03 <- Cost 0.02 32.57 0.00 0.65 0.81

FI04 <- Cost 0.02 46.56 0.00 0.67 0.82

FI05 <- Cost 0.02 37.97 0.00 0.62 0.79

Innovation 0.60 0.83 0.88 0.84

FI06 <- Innovation 0.03 25.68 0.00 0.53 0.73

FI07 <- Innovation 0.03 26.12 0.00 0.62 0.79

FI08 <- Innovation 0.02 40.20 0.00 0.67 0.82

FI09 <- Innovation 0.03 29.59 0.00 0.59 0.77

FI10 <- Innovation 0.02 34.47 0.00 0.60 0.77

Quality 0.66 0.87 0.91 0.87

FI11 <- Quality 0.03 24.93 0.00 0.61 0.78

FI12 <- Quality 0.02 33.42 0.00 0.66 0.81

FI13 <- Quality 0.02 40.60 0.00 0.70 0.84

FI14 <- Quality 0.02 34.42 0.00 0.66 0.81

FI15 <- Quality 0.02 43.16 0.00 0.68 0.82

309


Supplier 0.61 0.84 0.89 0.84

FI16 <- Supplier 0.03 29.14 0.00 0.56 0.75

FI17 <- Supplier 0.03 27.24 0.00 0.54 0.74

FI18 <- Supplier 0.02 44.32 0.00 0.68 0.82

FI19 <- Supplier 0.03 28.06 0.00 0.62 0.79

FI20 <- Supplier 0.02 40.81 0.00 0.65 0.81

Customer 0.62 0.85 0.89 0.85

FI21 <- Customer 0.03 31.33 0.00 0.62 0.78

FI22 <- Customer 0.02 34.78 0.00 0.65 0.81

FI23 <- Customer 0.02 38.47 0.00 0.67 0.82

FI24 <- Customer 0.03 20.95 0.00 0.52 0.72

FI25 <- Customer 0.03 29.83 0.00 0.64 0.80

Employee 0.66 0.87 0.91 0.87

FI26 <- Employee 0.02 36.97 0.00 0.65 0.80

FI27 <- Employee 0.02 49.47 0.00 0.72 0.85

FI28 <- Employee 0.03 26.32 0.00 0.57 0.76

FI29 <- Employee 0.02 48.73 0.00 0.74 0.86

FI30 <- Employee 0.02 34.70 0.00 0.61 0.78

Company customer performance (CCP) 0.73 0.81 0.89 0.82

CP12 <- CCP 0.01 66.53 0.00 0.77 0.87

CP13 <- CCP 0.02 54.13 0.00 0.73 0.86

CP14 <- CCP 0.02 48.53 0.00 0.69 0.83

Company employee performance (CEP) 0.65 0.82 0.88 0.82

CP04 <- CEP 0.02 43.04 0.00 0.65 0.81

CP05 <- CEP 0.02 44.90 0.00 0.70 0.84

CP15 <- CEP 0.02 41.75 0.00 0.66 0.81

CP19 <- CEP 0.03 28.32 0.00 0.58 0.76

Company financial performance (CFP) 0.61 0.84 0.89 0.84

CP03 <- CFP 0.02 32.77 0.00 0.58 0.76

CP08 <- CFP 0.02 39.10 0.00 0.69 0.83

CP09 <- CFP 0.02 47.88 0.00 0.71 0.84

CP10 <- CFP 0.03 25.90 0.00 0.57 0.76

CP18 <- CFP 0.03 25.89 0.00 0.53 0.73


Company operational performance (COP) 0.59 0.83 0.88 0.83

CP01 <- COP 0.03 20.82 0.00 0.52 0.72

CP02 <- COP 0.03 24.71 0.00 0.54 0.74

CP06 <- COP 0.02 36.04 0.00 0.64 0.80

CP07 <- COP 0.02 49.66 0.00 0.67 0.82

CP17 <- COP 0.02 30.19 0.00 0.56 0.75

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2. Assessment of internal consistency (construct) reliability

As displayed in Table 6.32, all reflective measures of strategic and functional integration

and CP have Cronbach’s alphas well above the threshold value of 0.70 and are satisfactory

(Bagozzi & Yi 2012; Hair et al. 2017; Hair, Ringle & Sarstedt 2011). CR for all Model 3

constructs fall between 0.70 and 0.95 (Bagozzi & Yi 2012; Hair et al. 2017). Furthermore,

all constructs have a reliability coefficient ρA ranging from 0.82 to 0.91 (Benitez et al.

2020). Hence, these results support the establishment of the internal consistency and

reliability of Model 3.


Table 6.32 presents loadings for all indicators of functional integration and CP, which are

more than 0.70 (Chin 2010; Hair et al. 2017). AVE values for all constructs of strategic

integration, functional integration, and CP fall within 0.59 and 0.75, above the threshold

value of 0.50 (Götz, Liehr-Gobbers & Krafft 2010; Hair, Ringle & Sarstedt 2011). Model

3, thus, has satisfactorily achieved convergent validity.

4. Assessment of discriminant validity

Three approaches are employed to evaluate discriminant validity in Model 3, namely cross-

loading, Fornell-Larcker criterion and HTMT. Cross-loading results in Model 1 and Model

2 show that each indicator loads more on its own composition than on other constructs (see

Appendices B.7 and C.2). As Model 3 includes identical constructs and indicators with both

models, the cross-loading test is therefore not replicated.

Table 6.33 displays the results of Fornell-Larcker criterion, showing that each construct

shared more variance with its own measurement items than with the constructs of the

different measurement items. Nonetheless, there is a small variation for the Aligning-

SuppTM. Because the difference is small (0.01), it may be ignored (Rahim & Magner 1995).

There is also a little difference in HOC and its LOCs. The square root of AVE is less than

311

the correlations, particularly for EffCom-Strategic Integration, Customer-Functional

Integration, Innovation-Functional Integration and Quality-Functional Integration. It should

be noted, however, that HOCs should demonstrate the selective validity of all other models,

with the exception of their own HOCs, of which they are part (Sarstedt, Hair, et al. 2019).


Note: The square root of AVE (on bold remark) is shown in diagonal while the correlations are off-diagonal.

Table 6.34 presents the HTMT values for all constructs, which fall between 0.31 and 0.83,

below the threshold value of 0.85 (Henseler, Ringle & Sarstedt 2015). Discriminant validity

was established between LOCs and the reflectively measured construct CCP, CEP, CFP,

and COP with the HTMT values ranging from 0.33 to 0.71, less than the threshold of 0.85.

HTMT values among four constructs of CP (i.e., CCP, CEP, CFP and COP) are below 0.90.

Nonetheless, the discriminant validity between LOCs and their related HOC Strategic

Integration, as well as HOC Functional Integration, cannot be determined because the HOC

measurement model repeats indicators of its LOCs (Hair, Sarstedt, et al. 2018; Sarstedt,

Hair, et al. 2019).

To sum up, the reflective measurement assessment of Model 3 has been established. As a

result, it is reliable and valid for further analysis.

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values obtained by running the bootstrapping routine with 5,000 samples in Smart PLS (Hair, Sarstedt, et al.

2018).

6.8.1.2 Assessment of Formative Measurement Models in The Combined CSR Integration

The formative measurements of HCM Strategic Integration (Model 1) and HCM Functional

Integration (Model 2) have been assessed in the previous chapters. The results show that

collinearity is not a critical issue (Hair et al. 2017; Hair, Risher, et al. 2018), and three LOCs

of HOC Strategic Integration and six LOCs of HOC Func2tional Integration have a similar

weight to form their related HOC (Hair, Sarstedt, et al. 2018).

6.8.2 Assessment of the Structural Model in The Combined CSR Integration

The structural model assessment of Model 3 is presented in this subsection that includes six

stages (Hair et al. 2017) as per the following.

1. Assessment of Collinearity in CSR Integration

The results indicate no significant levels of collinearity detected among the indicators and

the constructs in Models 1 and 2 (see 5.3.2.1 and in 6.3.2.1 for detail) (Hair et al. 2017).

2. Assessment of the Structural Model Relationships in CSR Integration

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Figure 6.4 shows the direct effects as results from the PLS algorithm of Model 3, and Table

6.35 presents the direct effects as determined by bootstrapping procedures (Hair et al. 2017;

Wong 2016).


The results show that 11 of 12 paths have positive direct effects. Although nine paths are

positive, only seven relationships have meaningful and significant direct effects with a path

coeeficient of at least 0.20 (Chin 1998). One path has a negative direct effect and is not

significant: Strategic Integration → CCP (β=-0.01, p=0.80).

Although the relationship between strategic and functional integration has a significant

direct effect at the 10% confidence level (p=0.06), it is not meaningful while its path

coeficient is 0.00, below the minimum value of 0.20 (Chin 1998). Accordingly, the result

suggest that CSR integration can be implemented either separately (i.e., Models 1 and 2) or

sequentially (Model 3).

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Path Direct Effect SD t-

value

p-

value Significant?

Strategic Integration -> CCP 0.03 [-0.07, 0.13] 0.05 0.55 0.58 No

Strategic Integration -> CEP 0.40 [0.31, 0.49] 0.04 9.17 0.00 Yes

Strategic Integration -> CFP -0.01 [-0.09, 0.07] 0.04 0.25 0.80 No

Strategic Integration -> COP 0.15 [0.05, 0.27] 0.06 2.72 0.01 Yes

Strategic Integration -> Functional Integration 0.00 [0.00, 0.00] 0.00 1.90 0.06 Yes

Functional Integration -> CCP 0.46 [0.34, 0.57] 0.06 7.95 0.00 Yes

Functional Integration -> CEP 0.39 [0.30, 0.48] 0.04 8.96 0.00 Yes

Functional Integration -> CFP 0.06 [-0.03, 0.14] 0.04 1.36 0.17 No

Functional Integration -> COP 0.48 [0.38, 0.58] 0.05 9.24 0.00 Yes

CCP -> CFP 0.24 [0.14, 0.34] 0.05 4.54 0.00 Yes

CEP -> CFP 0.30 [0.16, 0.43] 0.07 4.37 0.00 Yes

COP -> CFP 0.34 [0.23, 0.45] 0.06 5.97 0.00 Yes


coefficients obtained by running the bootstrapping routine with 5,000 samples in Smart PLS.

In general, the mediation analysis in Model 3 produces similar results as Models 1 and 3.

There are mediating effects from three constructs of CP as mediators (CCP, CEP and COP)

in the relationship between both HOC Strategic Integration and HOC Functional Integration

and CFP. Because the mediation analysis of Models 1 and 2 can provide detailed and robust

results as previously discussed in Chapter 5 and 6 the assessment of the mediating effect is

not replicated in Model 3.

3. Assessment of the Coefficient of Determination (R2) in CSR Integration

Table 6.36 shows R2 values of the endogenous constructs ranging from 0.23 to 0.65, which

are not substantially different from the R2 values of Model 1 and Model 2. CFP is the largest

predictor of the structural model, with an R2 value of 0.65. As can be seen in Figures 5.1,

6.1 and 6.2, this construct is impacted by not only HOC Strategic Integration and HOC

Functional Integration but also three mediators, namely CCP, CEP, and COP. As a result,

this construct get the highest R2 value since the greater the number of predictor constructs,

the higher the R2 (Hair, Risher, et al. 2018). This result suggests that both HOCs can jointly

explain 65% of the variance of the endogenous construct CFP. The same model estimation

also reveals R2 values for other latent constructs: two HOCs explain 50% of CEP, 34% of

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COP, and 23% of CCP. Subsequently, CEP and COP has weak-to-medium levels of

predictive accuracy, while CCP has a weak effect. Three LOCs, in particular, shape HOC

Strategic Integration depicting the variance of the HOC Strategic Integration by 100% with

an R2 value of 1.00. Likewise, HOC Functional Integration gets an R2 value of 1.00 because

it consists of six LOCs (Becker, Klein & Wetzels 2012; Hair et al. 2017; Hair, Sarstedt, et

al. 2018).



CCP 0.23 0.16

CEP 0.50 0.31

CFP 0.65 0.39

COP 0.34 0.19

Functional Integration 1.00 0.41

Strategic Integration 1.00 0.62

4. Assessment of the Effect Size (f2) in CSR Integration

Table 6.37 demonstrates that the range of f2 values falls between 0.00 and 0.23. Among 12

relationships, the strongest effect size is from Functional Integration to COP (0.23), while

the weakest effect size is from Strategic Integration to CCP (0.00). Totally, five paths have

a medium effect size, and four paths have a weak effect size. However, two paths have f2

values of less than 0.02, indicating that there is no effect (Hair et al. 2017).


Path f2 value





Strategic Integration -> CCP 0.00

Strategic Integration -> CEP 0.21

Strategic Integration -> CFP 0.00

Strategic Integration -> COP 0.02

Strategic Integration -> Functional Integration 0.04

CCP -> CFP 0.08

CEP -> CFP 0.08

COP -> CFP 0.13

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5. Assessment of the Predictive Relevance (Q2) in CSR Integration

Table 6.36 presents the resulting cross-validated redundancy Q2 values after running the

blindfolding procedure using an omission distance D=7 (Hair et al. 2017; Henseler, Ringle

& Sinkovics 2009). Q2 values of all constructs fall between 0.16 and 0.62. As they are

positive Q2 values, they indicate a significant prediction of the constructs. Specifically, the

exogenous constructs (HOC Strategic Integration and HOC Functional Integration) have

satisfactory predictive relevance for all four endogenous constructs of CP.

6. Assessment of Effect Size (q2) in CSR Integration

Table 6.38 shows results of the q2 calculations (Hair et al. 2017). Because q2 values fall in

the between 0.00 and 0.12, they suggest a small to medium predictive relevance from

exogenous constructs (two HOCs) to four endogenous constructs of CP.


Endogenous Construct CCP CEP CFP COP

Functional Integration 0.12 0.09 0.00 0.11

Strategic Integration 0.00 0.10 0.00 0.01

Finally, the SRMR for the structural model was slightly above 0.08 (0.09 for Model 3),

suggesting that the model fit is quite good (Hair et al. 2017).

6.9 Model Comparison

To further examine the relationships in Models 1, 2, and 3, the direct effects of these three

models are compared. This section presents the direct effects resulting from PLS-SEM data

analysis. As shown in Table 6.41, there is a difference in direct effects, particularly in the

relationship between strategic integration and CP. For example, Strategic Integration → CCP

has a positive and significant direct effect in Model 1 (β=0.30, p=0.00), but its direct effect is

insignificant in Model 3 (β=0.03, p=0.58). Strategic Integration → CEP has a positive and

significant direct effect in Model 1 and Model 3. However, the direct effect in Model 1 is

stronger (β=0.63) than in Model 3 (β=0.40). Similarly, Strategic Integration → COP has a

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bigger and significant direct effect in Model 1 (β=0.43) than in Model 3 (β=0.15). Moreover,

Strategic Integration → CFP has a larger total effect in Model 1 (β=43) than in Model 3

(β=0.17); both total effects are significant.

Models 2 and 3 have slight similar direct effects in the relationships between functional

integration and CP. Even though, the direct effects in Model 2 are more significant than in

Model 3. For instance, the direct effect of Functional Integration → CEP in Model 2 (β=0.62)

is greater than in Model 3 (β=0.39). Furthermore, Table 6.39 shows that, in these three models,

the relationships between CCP, CEP and COP to CFP have nearly the same direct effects.

Table 6.39: Direct Effects and Total Effect Comparison across Three Models

Path

Model 1 Model 2 Model 3

Direct

Effect

Total

Effect

Direct

Effect

Total

Effect

Direct

Effect

Total

Effect

Strategic Integration -> CCP 0.30 0.30 0.03 0.03

Strategic Integration -> CEP 0.63 0.63 0.40 0.40

Strategic Integration -> CFP 0.01 0.43 -0.01 0.17

Strategic Integration -> COP 0.43 0.43 0.15 0.15

Functional Integration -> CCP 0.48 0.48 0.46 0.46

Functional Integration -> CEP 0.62 0.62 0.39 0.39

Functional Integration -> CFP 0.05 0.54 0.06 0.45

Functional Integration -> COP 0.57 0.57 0.48 0.48

Strategic Integration -> Functional Integration 0.00 0.00

CCP -> CFP 0.25 0.25 0.24 0.24 0.24 0.24

CEP -> CFP 0.30 0.30 0.29 0.29 0.30 0.30

COP -> CFP 0.35 0.35 0.34 0.34 0.34 0.34

Table 6.40 displays the specific indirect effects across the three models and shows the

differences among them. The difference in indirect effects of Models 1 and 3 falls between

0.07 and 0.10. Specifically, the mediating effects in Model 1 have a larger difference compared

to Model 3. For example, the indirect effect from SI to CFP mediated by CEP is more

substantial in Model 1 (β=0.15) than in Model 3 (β=0.05). Nonetheless, the difference in the

indirect effects of Model 2 and Model 3 is not substantial, between 0.01 and 0.06. For instance,

the indirect effect from functional integration to CFP mediated by CEP is 0.12 in Model 2 and

0.11 in Model 3.

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Table 6.40: Specific Indirect Effects Comparison across Three Models

Path Model 1 Model 2 Model 3

Strategic Integration -> CCP -> CFP 0.08 0.01

Strategic Integration -> CEP -> CFP 0.19 0.12

Strategic Integration -> COP -> CFP 0.15 0.05

Functional Integration -> CCP -> CFP 0.12 0.11

Functional Integration -> CEP -> CFP 0.18 0.12

Functional Integration -> COP -> CFP 0.19 0.16

Because CP has been measured in four dimensions, each model has differences in the

strength of mediating effect among those four dimensions. In Model 1, the greatest mediating

effect lies on CEP, followed by COP and last, CCP. But, in Model 2, COP has the most

significant mediating effect, followed by CEP and CCP.

Generally, the results reveal that the indirect effects are greater if Models 1 and 2 are carried

out separately rather than aligned in Model 3, because alignment can impact the total effect.

Especially, as presented in Table 6.39, total effects from HOC Strategic and Functional

Integration to CFP are bigger if both models (Models 1 and 2) are conducted separately.

Since the results indicate that Models 1 and 2 should be applied independently, MGA is not

evaluated in Model 3 with the assumption that Models 1 and 2 have provided an acceptable

and robust result. Table 6.41 summarises the MGA results for both models, showing varying

results. Particularly, bold marks indicate a significant total effect. Business strategy can

moderate the relationship from strategic integration to CCP and CFP in Model 1, but only

moderates the relationship between functional integration and CCP in Model 2. The effect of

strategic integration on CCP and CFP in Model 1 can be moderated by CSR strategy.

Conversely, in Model 2, CSR strategy can moderate the impact of functional integration on

other CP; that is, CEP and COP and COP. Company size moderates the path from strategic

integration to three aspects of CP (i.e., CCP, COP and CFP). But company size can moderate

the impact of functional integration on only one CP, COP. In Model 1, industry type can

moderate the relationship between strategic integration and CFP only, whereas in Model 2, it

moderates the impact of functional integration on CCP and CFP.

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Despite the variations in the moderating impact of Models 1 and 2, the findings show that

company performance as a result of both CSR integrations is influenced by business and CSR

strategies adopted by a company, as well as the company size and the type of industry. Those

four variables can therefore be considered as contingent variables in CSR integration.

However, these results are slightly different from those for strategic integration that show

that the proactive group has the largest total effect between the three CSR strategy groups.

Findings in functional integration are different, with the assumption that the proactive group

would get a greater total effect than the other two groups (i.e., reactive and accommodative

groups).

Table 6.41: MGA Results Model 1 and Model 2

6.10 Discussion of The Combined CSR Integration

This subsection offers a review of the combined CSR integration results explained in the

previous section. The aim is to answer the research questions as outlined in Chapter 1. The

discussion is based on the conceptual framework and hypotheses outlined in Chapter 3, current

literature, and research findings from prior research, as well as PLS-SEM data analysis.

▪ Hypothesis 3 (H3): Strategic CSR has a positive relationship with functional CSR

integrationy.

Research findings reveal that strategic integration has a positive relationship with functional

integration (β=0.00, t=1.90, p<0.10), supporting H3. However, the connection is not

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meaningful as it is zero. Thus, this result suggests that strategic integration can be implemented

either separately or sequentially with functional integration. Either strategic or functional

integration could be conducted individually because they both have positive and significant

total effects on CP. This means that companies could implement functional integration without

completing strategic integration. However, if they are carried out sequentially, strategic

integration has no significant impact on customer performance. An organisation cannot pass

every level, and the stages can progress sequentially or concurrently. The implementation

process thus tends to be circular rather than linear (Lindgreen et al. 2011).

The findings suggest both strategic and functional integration can be conducted either

separately or sequentially. However, each implementation of either strategic or functional

integration gives a positive and more meaningful overall impact on CP. This means companies

could implement functional integration without completing strategic integration.

6.11 Conclusion of Combined CSR Integration

This section provided findings related to the hypotheses and conceptual model previously

outlined in Chapter 3, particularly regarding the relationship between strategic and functional

integration. Following the guidelines for the evaluation of measurement and structural models,

a HCM used a repeat-indicator approach, producing accurate and robust results. The findings

indicated that the reliability and validity of the measurement and structural models were

satisfactory. Thus, CSR was appropriate to be integrated into business strategy and operations

at the strategic and functional levels. The former represents a ‘top-down’ approach, whereby

companies begin with their competitive strategy to define company policy as guidance for

organisational work at the lower level (Skinner 1969). In this thesis, strategic integration

represents CSR integration conducted at the strategic level and comprises three dimensions

(i.e., aligning with the company’s strategy, gaining support from top management and

developing effective communication). The latter, functional integration reflects CSR

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integration at the functional level by coordinating the six functions of companies: Cost,

Innovation, Quality, Supplier, Customer and Employee.

The hypothesised model of CSR integration into business strategy (Model 3) was verified

based on the statistical analysis. The significance and relevance of coefficient paths of a

structural model were empirically identified, which supports the proposed hypothesis. Strategic

integration has a minor impact on functional integration, according to the findings. Based on

the results, the findings suggest that Models 1 and 2 can be applied separately to have a greater

impact than combining as Model 3.

In terms of mediating effects, Models 1 and 2 produce approximately similar results, which

confirm that the relationship between both strategic and functional integration and financial

performance can be mediated by customer, employee, and operational performance. However,

in terms of the moderating effect, both models generate different results. The results also

indicate that business strategy, CSR strategy, company size and industry type have different

impacts on CP resulting from integration. In other words, there is a contingency in the

company's performance as an impact of CS integration, which depends on business strategy,

CSR strategy, company size, and industry type.


This chapter discusses results related to functional integration and the combined CSR

integration, based on data analysis conducted with SPSS 26 and SmartPLS 3. HCM, using a

repeated-indicator approach, was employed in both integrations, producing reliable and robust

results. The assessment of reflective and formative measurement achieved satisfactory results.

The findings reveal that functional integration and the combined CSR integration have a

significant impact on company performance.

Furthermore, the hypothesised model of functional integration (Model 2) and model of the

combined CSR integration (Model 3) were verified based on the statistical analysis. To

322

complete the data analysis, MGA was also conducted to examine whether business strategy,

CSR strategy, company size, and type of industry moderate the functional integration of CSR

and the combined CSR integration, as well as their effects on CP.

This chapter also presents the comparison between Model 1, Model 2, and Model 3. In terms

of mediating effects, Models 1 and 2 generated similar results. They revealed that the

relationship between both strategic and functional integration and financial performance can

be mediated by customer, employee, and operational performance. Nonetheless, regarding

moderating effects, both models resulted distinctive results.

In total, the findings supported 16 of 24 hypotheses, while 8 hypotheses were not

confirmed. These findings suggest that stakeholders should be considered in CSR integration.

When companies incorporate their interests in CSR implementation, as well as business

strategy and operations, companies can achieve a better performance that will enhance their


In relation to the contingency, the findings show that business and CSR strategy, company

size, and industry type can influence company performance because of functional integration

and the combined CSR integration.

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CHAPTER 7: CONCLUSIONS AND IMPLICATIONS

This chapter provides a conclusion of this thesis. An introduction is presented first, followed

by a summary and several key findings of this thesis. Next, this chapter discusses contributions

and implications, as well as the limitations of this thesis and directions for future research.

7.1 Introduction

The introduction to this thesis is summarised in this section. It begins by summarising the

research background, objectives, and research questions of this thesis. The research

methodology is then briefly explained. Finally, it summarises the findings of this thesis.

CSR, as one of the two key aspects of NMS (Baron & Diermeier 2007), is the most

frequently used term for highlighting the connection between companies and society (Branco

& Rodrigues 2006), and has received much attention over the past three decades. Arguably, a

company’s CSR should be aligned or integrated with its business strategy to further develop

its competitive advantage. Such integration is crucial to enhance companies social,

environmental, and financial performance (Carroll & Shabana 2010; Dey & Sircar 2012;

Galbreath 2006; Ganescu 2012b; Hasan et al. 2018; Marín, Rubio & de Maya 2012; Porter &

Kramer 2011; Torugsa, O'Donohue & Hecker 2013). Despite the potential impact of such

integration on CP, research on how CSR and business strategies can be integrated remains scant

(Rangan, Chase & Karim 2012).

The key objectives of this thesis were to investigate how companies integrate CSR into

business strategy and functions and to examine the impact of integrations on organisational

performance in the context of the Indonesian manufacturing sector. The primary research

questions of this thesis are:

1. To what extent are CSR and business strategy integrated?

2. How does such integration impact on a company’s performance?

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These two primary research questions were complemented by the following subsidiary

research questions:

(i) To what extent does CSR integrate into business at the strategic and functional

levels?

(ii) To what extent does CSR integration affect company performance?

(iii) To what extent does social performance mediate the relationship between CSR

integration and financial performance?

(iv) To what extent do business and CSR strategies moderate the impact of CSR


(v) To what extent do company size and industry type moderate the impact of CSR


This thesis used quantitative research approach with a survey method to investigate how

manufacturing companies in Indonesia integrate CSR into their business strategy and functions,

and to identify the impact of such integration on their CP. A total of 514 questionnaires were

returned and after data screening, 435 responses remained in the data set, with a final effective

response rate of 41.23%.

This thesis developed a theoretical framework for integrating CSR with business strategy

and functions based on the literature review using stakeholder and contingency theories. Then,

it used survey data from 435 Indonesian companies of varying size and across different

manufacturing industries to test the hypotheses developed based on the theoretical framework.

SPSS 26 and SmartPLS 3 were employed for the statistical analyses to discover and evaluate

the direct, mediating and moderating effects. The findings of this thesis provide a deeper

understanding of how CSR can be integrated into business strategy at the strategic and

functional levels and the effect of these integrations on CP.

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7.2 Thesis Summary

This section explains how the findings of this thesis can help to address the research

questions. It starts by outlining how each of the five research questions can be answered. It

then goes on to describe a summary of hypothesised relationship in CSR strategic and

functional integrations into business strategy.

As mentioned previously, this thesis proposed and tested the models that depicted the

relationship between the integration of CSR into business strategy and the impacts of this

integration on company performance. Based on the results, this thesis has provided a clear

response to Research Question One (RQ1), that is, the extent to which CSR can be integrated

into business strategy. To the best of my knowledge, this thesis is the first to investigate how

market and non-market strategies are aligned by integrating CSR into business strategy.

Business strategy represents the market strategy and CSR reflects the NMS. The findings

demonstrate that the integration of market and non-market strategies can be conducted at both

strategic and functional levels for companies’ financial and social benefit.

Moreover, the findings support the hypothesis that CSR integration in companies has a

meaningful impact on their customer, employee, operating and financial performance. As a

result, this thesis extends findings from previous studies by measuring company performance

across these four performance areas at the strategic and functional levels. It is one of the first

to examine the performance implications of integrating CSR into business strategy and

functions, both empirically and comprehensively. The findings of this thesis provide empirical

evidence that CSR practices, including CSR integration, can be a means to improve company

performance (Hur, Kim & Woo 2014). The findings are thus appropriate to answer Research

Question Two (RQ2), that is, the extent to which integration of CSR affects company

performance.

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The findings of this thesis further reveal that strategic CSR integration has a positive effect

on functional CSR integration, although this effect is small. These findings show that CSR

integration at either the strategic or functional level has a positive and significant effect on

company performance. This relationship indicates that companies need to coordinate activities

and arrange them smoothly. They may integrate CSR at both levels, but have to ensure that

CSR integration at the strategic level can provide guidance for conducting integration at the

functional level. Hence, CSR is carried out in daily business operations and is a way of working

throughout a company. These findings suggest that through CSR integration, CSR can be

regarded as companies’ commitment to accounting for the social and financial effects of their

operations, and consistently ensuring that their stakeholders benefit from these effects.

Investigation of the social performance effect of CRS is highly critical when evaluating the

mechanisms through which CSR integration influences financial performance. The interaction

between stakeholders was proposed in the model using stakeholder theory. The results offered

a more in-depth understanding of how a company’s non-financial performance (customer,

employee, and operational performance) can mediate the effect of CSR integration on financial

performance. Accordingly, the findings of the mediation analysis can provide a robust answer

to Research Question Three (RQ3), that is, the extent to which social performance mediates

the relationship between CSR integration and financial performance. This thesis highlights that

when companies integrate CSR into business strategy, they should consider stakeholders

interests in the integration.

In addition, the findings suggest that CSR should be considered as a long-term investment,

rather than a temporary activity disconnected from a company's operational activities. A

company can consider CSR as their responsibility to stakeholders, whose support is needed to

run its business, including employees, suppliers and customers. Because CSR is a long-term

investment, the company cannot expect results from CSR over a short period. The benefits of

327

CSR instead need to be seen over the longer-term. Hence, the company must carry out CSR

consistently and continuously and adopt it as its lifestyle—as a way of working—so that all of

its functions are motivated and accustomed to doing CSR.

Furthermore, this thesis conducted MGA to explore the moderating impact of contingency

variables on the relationships between CSR integration and company performance. The results

show that variation exists in the relationship between CSR integration and company

performance, due to the influence of a company’s business and CSR strategy. Business strategy

can determine how companies run their business to achieve their goals, including whether they

are pursuing differentiation or cost leadership strategy. This thesis finds that in relation to

strategic and functional CSR integrations, companies adopting differentiation strategy achieve

better customer performance compared to those with cost leadership strategy. Surprisingly, this

thesis also discovered that in strategic CSR integration, companies with differentiation strategy

have a greater effect on financial performance than those that use cost leadership strategy.

Because business strategy is a crucial determinant of CSR performance (Yuan et al. 2020), this

thesis highlights that business strategy can moderate the impact of CSR integration on company

performance as a result of this integration.

The results of this thesis further highlight that CSR strategy moderates the relationship

between strategic CSR integration and company performance. The findings reveal that

proactive groups have the most substantial total effect on customer and financial performance

at the strategic level. In contrast, at the functional level, the reactive group achieves the greatest

total effect on employee and operational performance, while the accommodative group has the

most significant total effect on financial performance.

Furthermore, the findings of this thesis support the argument that company performance is

contingent on their business and CSR strategies. Specifically, the findings of the moderation

analysis provide empirical evidence to address Research Question Four (RQ4), that is, the

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extent to which business and CSR strategies moderate the impact of CSR integration on


In addition to business strategy and CSR strategy, this thesis applied other contingency

variables (i.e., company size and industry type) as control variables. Company size, determined

by the number of employees, may indicate organisational resources and capacity. This thesis

discovers that company size has a significant moderator impact on the relationship between

CSR integration and company performance. The results, especially at the strategic level,

indicate that large companies have better company performance than SMEs on customer,

employee, operating and financial performance. At the functional level, the results show that

large companies have a more substantial total effect on operational performance than do SMEs.

Industry type is classified by the kind of products that companies manufacture. The findings

indicate that industry type can moderate the impact of CSR integration on company

performance. Particularly at the strategic level, non-ESI groups have a more significant total

effect on financial performance than those of ESI groups. Nevertheless, at the functional level,

the results suggest slight differences. Non-ESI groups were found to have a more significant

total effect on customer and financial performance than ESI groups. These findings of the

moderation analysis thus support a clear response to Research Question Five (RQ5), that is, the

extent to which company size and industry type moderate the impact of CSR integration on


The results of all hypothesised relationships in CSR strategic and functional integration into

business strategy are provided in Table 7.1.

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Table 7.1: Final Results of Hypothesis of CSR Integrtation


CSR integration-company performance relationship

H1 Strategic CSR integration has a positive impact on (a) customer

performance, (b) employee performance, (c) operational performance and

(d) financial performance.

Supported

H2 Functional CSR integration has a positive impact on (a) customer

performance, (b) employee performance, (c) operational performance and

(d) financial performance.

Supported

H3 Sstrategic CSR integration has a positive relationship with functional CSR

integration.

Supported

Mediating effects

H4 The relationship between strategic CSR integration and financial

performance is mediated by (a) customer performance, (b) employee

performance, and (c) operational performance.

Supported

H5 The relationship between functional CSR integration and financial

performance is mediated by (a) customer performance, (b) employee

performance, and (c) operational performance.

Supported

Moderating effects

H6 Business strategy moderates the impact of strategic CSR integration on (a)

customer performance, (b) employee performance, (c) operational

performance and (d) financial performance.

H6 (a,d) supported

H6 (b,c) not

supported

H7 Business strategy moderates the impact of functional CSR integration on

(a) customer performance, (b) employee performance, (c) operational


H7 (a) supported

H7 (b,c,d) not

supported

H8 CSR strategy moderates the impact of strategic CSR integration on (a)



H8 (a,d) supported

H8 (b,c) not

supported

H9 CSR strategy moderates the impact of functional CSR integration on (a)



H9 (a) not supported

H9 (b,c,d) supported

H10 Company size moderates the impact of strategic CSR integration on (a)



Supported

H11 Company size moderates the impact of functional CSR integration on (a)



H11 (a,b,d) not

supported

H11 (c) supported

H12 Industry type moderates the impact of strategic CSR integration on (a)



H12 (a,b,c) not

supported

H12 (d) supported

H13 Industry type moderates the impact of functional CSR integration on (a)



H13 (a,d) supported

H13 (b,c) not

supported

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There are 13 main hypotheses and 47 specific hypotheses because each main hypothesis is

broken down into three to four supporting hypotheses. Based on the findings in Chapters Five

and Six, a total of 31 hypotheses (66%) are statistically supported, while the remaining 16

hypotheses (34%) are statistically insignificant and thus unsupported. Overall, all nine main

hypothesis related to the relationship between CSR integration and company performance

(H1a,b,c,d, H2a,b,c,d and H3) and six hypotheses of the mediating effect (H4a,b,c and H5a,b,c)

were supported. Regarding the moderating effect, among the 32 hypotheses (H6a,b,c,d -

H13a,b,c,d), 16 hypotheses (50%) were supported, while the rest were not supported. Through

the proposed and verified models, as well as supported hypotheses, this thesis emphasises that

CSR should be integrated with company strategy and implemented comprehensively to the

company's operational activities and decision-making processes.

Table 7.2 summarises the results of hypotheses verification, particularly regarding

mediating and moderating effects. The findings of strategic integration are similar to functional

integration. Nonetheless, having been influenced by the contingency variables, strategic and

functional integrations reveal different findings for the moderating effects.

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Table 7.2: Summary of Hypotheses Verification

Propositions Strategic

Integration

Functional

Integration

The integration of CSR and business strategy has a positive impact on customer

performance.

√ √

The integration of CSR and business strategy has a positive impact on employee

performance.

√ √

The integration of CSR and business strategy has a positive impact on operational

performance.

√ √

The integration of CSR and business strategy has a positive impact on financial

performance.

√ √

The relationship between integration and financial performance is mediated by


√ √



√ √



√ √

Business strategy moderates the impact of integration on customer performance. √ √

Business strategy moderates the impact of integration on employee performance. - √

Business strategy moderates the impact of integration on operational

performance.

- -

Business strategy moderates the impact of integration on financial performance. √ -

CSR strategy moderates the impact of integration on customer performance. √ -

CSR strategy moderates the impact of integration on employee performance. - √

CSR strategy moderates the impact of integration on operational performance. - √

CSR strategy moderates the impact of integration on financial performance. √ √

Company size affects the impact of integration on customer performance. √ -

Company size affects the impact of integration on employee performance. √ -

Company size affects the impact of integration on operational performance. √ √

Company size affects the impact of integration on financial performance. √ -

Industry type affects the impact of integration on customer performance. - √

Industry type affects the impact of integration on employee performance. - -

Industry type affects the impact of integration on operational performance. - -

Industry type affects the impact of integration on financial performance. √ √

7.3 Conclusions

This section presents several conclusions that can be drawn based on the key findings of this

thesis. The first conclusion is that strategic integration between a company’s CSR and business

strategy can have a positive impact on its social and financial performance. Based on findings

presented in Chapter 5, strategic CSR integration has positive simultaneous effects on four

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aspects of a company’s performance, namely customer, employee, operational and financial

performance. Thus, when a company starts integration at the strategic level by aligning CSR

with its objectives, gaining support from top management, and communicating CSR effectively

to its internal and external stakeholders, it can gain not only social but also financial benefits.

The second conclusion drawn from this thesis is that functional integration between CSR

and business can have a substantial impact on a company’s social and financial

performance. Based on the findings presented in Chapter 6, this thesis highlights that if

companies integrate CSR at the functional level through six key dimensions, namely Cost,

Innovation, Quality, Supplier, Customer and Employee, they can benefit from this integration

through customer, employee, operational and financial performance.

Based on these conclusions, this thesis emphasises that CSR integration has a positive

impact on company performance at several organisational levels, including financial

performance at the strategic level, operational performance at the operational level, and

customer and employee performance at the tactical level. In the pyramid of CSR (Carroll 1991),

philanthropic responsibility is placed on top. This means companies will carry out activities

related to philanthropic responsibility if they already fulfil the three other responsibilities on

the pyramid below, that is, economic, legal and ethical responsibilities. The primary goal of

companies is indeed to gain profits. However, they also have to consider how to run their

business legally and ethically through integrating CSR among their core values and activities.

This has changed in recent decades as companies are expected to fulfil social and

environmental targets to meet the needs of all stakeholders and to ensure their own long-term

existence (Cazeri et al. 2018). Because stakeholders, such as customers and employees, have

to be satisfied prior to any improvements in financial performance, it is essential that CSR

practices are evaluated, aligned with the interests of stakeholders, and implemented to their

333

satisfaction (Bhattacharya, Korschun & Sen 2009). This concern can be resolved by CSR

integration as defined in this thesis.

Furthermore, the integration of CSR, both at the strategic and functional levels, reflects that

companies want to engage deeply and continuously in CSR. Strategic integration can reflect

CSR ‘talk’, similar to the type of symbolic impression management strategies that include the

different ways in which the company interacts with its external stakeholders, such as customers

(Rasche, Morsing & Moon 2017). Functional integration, in contrast, can indicate CSR ‘walk’

that involves substantive and behavioural practices within the company, such as modifying

manufacturing processes to reduce environmental impacts or improving working conditions

through the company’s supply chain (Rasche, Morsing & Moon 2017). Companies that ‘walk’

CSR invest in responsible business behaviour and incorporate CSR in key business processes

to achieve measurable results (Rasche, Morsing & Moon 2017). Thus, this thesis

comprehensively describes how companies can ‘talk’ and ‘walk’ CSR. Instead of conducting

CSR in discretionary activities, such as donation and compliance, this thesis highlights that

companies should carry out CSR comprehensively and commit to it by aligning CSR with

business strategy and implementing CSR in their business operations. In doing so, companies

are undertaking an endless improvement change and continuous development and, at the same

time, increase their contributions to stakeholders socially and financially. CSR integration also

represents a leap in CSR practices. Companies shift from traditional CSR, which is carried out

through discretionary and short-term activities with no impact on financial performance, such

as donations and charities, to strategic CSR, which has a significant impact on financial

performance.

Donation and charity activities are easily imitated by competitors because they are

momentary and customised to their needs. In contrast, the integrated implementation of CSR

across all company functions is not easily copied by competitors. It takes a long time and

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sufficient resources to integrate CSR into value chain activities. As a result, CSR integration

may serve the competitive advantage of a company, suggesting a unique and precise

differentiator from competitors.

Third, it can be concluded that there is a positive relationship between strategic and

functional integration. As explained in Chapter 6, although the impact is not large, this finding

shows that integration of CSR at the strategic level would have an impact on integration of

CSR at the next level, such as at the functional level. The result confirmed an argument that

companies show various levels of CSR concern (Öberseder, Schlegelmilch & Murphy 2013).

Particularly in the context of the Indonesian manufacturing industry, this finding implies that

CSR integration is more easily implemented at the functional level than the strategic level. It

also suggests that many activities related to the requirements of the manufacturing sector have

been carried out to date, such as environmental legislation, labour laws, ISO and supply chain

management. Nonetheless, the companies do not know that these activities are also linked to

CSR.

The fourth conclusion is a positive link between a company’s social performance and its

financial performance. By investigating the mechanism of the relationship between CSR and

financial performance in greater depth using multiple mediations, the findings in Chapter 5

provide crucial evidence that strategic CSR integration has the most significant direct and

mediating effect on employee performance. The findings in Chapter 6 highlight that in

functional CSR integration, employee performance has the second greatest direct and

mediating effect after operational performance. With respect to stakeholder theory, this finding

reveals that employees are important internal and primary stakeholders who are most impacted

by CSR integration. These findings suggest that companies should implement CSR with

internal primary stakeholders before undertaking CSR with external stakeholders. Employees

can see how companies operate their business in a responsible, legal, and ethical manner while

335

pursuing economic profit. If they are satisfied with what companies are doing to them, they

will then be motivated to work better, more effectively and more productively.

Notably, the findings of this thesis also show that operational performance has the second-

highest direct effect and the second greatest mediating effect on strategic and functional CSR

integrations. To date, there has been little research analysing the impact of CSR on operational

performance. Thus, this thesis offers critical new findings, particularly in non-market studies

and the manufacturing sector, that CSR integration can have a positive impact on operational

performance. Functional integration involves CSR in all activities along the value chain, which

relate to suppliers, production, employees, and customers. If companies conduct these activities

well and responsibly, they will undoubtedly have an impact on operational performance.

In addition to employee and operational performance, this thesis provides empirical

evidence that CSR integration has a significant and positive impact on customer performance,

which plays a significant role in the mechanism through which CSR affects financial

performance. A company’s dedication to CSR integration reflects that it will treat its customers

well and produce goods responsibly. Consequently, customers can be satisfied with and loyal

to that company; they are likely to repeat buying, increase their buying volume and promote

the products to others (through word-of-mouth) which, in turn, can improve financial

performance. Satisfied and loyal customers can also provide an excellent image of the

company, which will boost its reputation.

Overall, the mediation analysis showed that customer, employee, and operational

performance represents a mechanism underlying the relationship between CSR integration and

financial performance. Strategic and functional CSR integrations contribute to customer

performance, which in turn leads to better financial performance. Likewise, they positively

influence employee performance, which then impacts financial performance. Similarly, they

lead to operational performance, which in turn affects financial performance. Through an

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examination the potential mediating role of social (i.e., customer and employee) and

operational performance, the mechanisms through which CSR integration significantly impacts

on financial performance can be better understood. Accordingly, this thesis provides one well-

researched empirical mechanism through which CSR integration has been found to influence

financial performance including customer, employee, and operational performance.

Furthermore, regarding stakeholder theory, the results offer new key findings that

customers, employees, and other stakeholders are more supportive of socially responsible

companies. Thus, by conducting ethical, legal, and responsible activities, companies have the

potential to increase their competitive advantage.

Last, this thesis concludes that the impact of CSR integration on company performance can

be moderated by several contingent variables. In terms of CSR strategy, this thesis finds that

CSR strategy affects the impact of CSR integration on company performance in different ways.

Especially in relation to strategic CSR integration, this thesis proposes that CSR in those

proactive companies can have a more substantial impact on customer and financial

performance. Companies that implement proactive strategy fully understand their social

obligations and actively participate in meeting the needs of stakeholders (Ganescu 2012b),

while ensuring economic, social and environmental development that exceeds the level needed

to comply with government regulations (Torugsa, O'Donohue & Hecker 2013; Wagner, Lutz

& Weitz 2009).

Proactive strategy indicates that companies do more than what is legally required.

Particularly in Indonesia, companies can be rewarded by their excellent efforts in CSR

implementation. Such rewards can certainly increase the company’s reputation from

stakeholders’ perspectives, including customers, shareholders, and investors. Customers will

have more trust in the companies, while shareholders and investors will be more interested in

investing their money in them. These impacts can increase the stock price of companies.

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Surprisingly, in functional CSR integration, the findings of this thesis reveal that reactive

companies have better results in terms of employee and operational performance. In contrast,

accommodative companies obtain a greater impact than others. Companies that follow reactive

strategy focus on economic responsibility and practice CSR merely to comply with regulations

(Lee 2011; Maignan et al. 1999; Torugsa, O'Donohue & Hecker 2013). Consequently, such

companies perform CSR limited to the level of charity, which is temporary and tailored to

needs, such as donations in response to natural disasters and building village roads. However,

these activities are more visible, which shows that they have met CSR regulations. Thus,

companies can concentrate on their internal process and plan how they can achieve high profit.

In Indonesia, regulations exist regarding employees. For example, the Labour Law of the

Republic of Indonesia No. 13 of 2013 (UU Ketenagakerjaan Repubik Indonesia No 13 Tahun

2013) regulates labour working hours, decent wages, social security and occupational safety

and health. In meeting this regulation, companies treat their employees well. Employees can

work well and be productive because they feel safe and secure. As a result, operational

performance can increase.

7.4 Theoretical Contributions

This section explains how this thesis can contribute to the literature. First, it enriches the

literature by integrating two theories (i.e., stakeholder and contingency theories) to create a

conceptual framework. Such theories describe how constructs and indicators are

operationalised and related to each other. This thesis integrates CSR, measured through several

activities related to primary stakeholders at the strategic and functional levels. The impact of

CSR integration is measured for several stakeholders (i.e., employees, customers, and

shareholders). The findings highlight that the stakeholder relationship is the key mechanism

through which companies can gain financial benefits from CSR integration. Additionally, this

thesis provides empirical evidence that stakeholder relationships should be considered when

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evaluating the mechanism between CSR integration and financial performance as it can be

mediated by customer and employee performance (in addition to operational performance).

Accordingly, this thesis represents the first attempt to understand the essential factors of

company strategy, which affect CSR-company performance relationships: stakeholder theory

and contingency theory.

Second, this thesis enhances the theory on CSR by highlighting a novel finding that CSR

integration benefits organisations socially and financially. Notably, this thesis contributes to

the literature by developing a framework that enables researchers to identify the different types

of benefits provided by CSR integration, and the effect these benefits have on the stakeholder-

company relationship. A broadened stakeholder perspective is critical to thoroughly assess the

benefits of CSR integration. Findings demonstrated that the impacts of CSR are not restricted

to a single stakeholder domain (Bhattacharya, Korschun & Sen 2009). Nevertheless, CSR

integration benefits several stakeholders (i.e., managers, employees, customers and

shareholders) with tangible and intangible benefits.

Last, this thesis explored the theory of contingency and its consequences in the relationship

between CSR integration and company performance. This thesis applied four variables of

contingency that can moderate the effect of CSR integration on company performance. The

results of the moderation analysis show that the impacts of CSR integration in four dimensions

of company performance are contingent on business strategy and CSR strategy adopted by a

company, along with its size and industry. The moderated mediation analyses provide adequate

support for the related hypotheses. Hence, this thesis offers new insights into CSR from a

contingency perspective.

7.5 Methodological Contributions

How this thesis offers several contributions from a methodological perspective is discussed

in this section. First, this thesis contributes to the literature by addressing knowledge gaps about

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CSR in developing countries, particularly Indonesia, where the manufacturing industry

contributes the most to the national economy.

Second, the integration of non-market strategies with competitive market strategies is

critical in strategic management. Such a mechanism, however, remains unknown to both

practitioners and researchers, particularly in emerging markets (Yoo 2015). This thesis

examined how the integration of non-market strategies (CSR in this thesis) with business

strategies is applied at two levels, strategic and functional. More specifically, it explored how

integration is achieved by including a variety of business strategy and CSR strategy practices,

such as integrating CSR into strategic planning, concentrating on manufacturing operations and

addressing stakeholder concerns. The integration should be used to improve the overall

performance of a company, rather than just affecting financial performance.

Third, this thesis used quantitative research with a survey method. As discussed in the

previous chapter, conceptual and qualitative research have dominated the literature on CSR

integration. The integration of CSR in SEM is rarely undertaken to explore the interaction

between CSR integration and other target constructs, such as company performance. To

address this gap, this thesis employed PLS-SEM in a more sophisticated way to simultaneously

perform several advanced analyses, such as higher-order modelling, multiple mediation, MGA

and MICOM. In doing so, this thesis contributes to a deeper understanding of the relationship

between CSR integration and company performance.

Fourth, this thesis highlights the significance of including mediator and moderator variables

into PLS-SEM models and analyses. Within the conceptual literature, these variables are often

discussed, but empirical evidence remains scarce. For mediator variables, in addition to

operational performance, this thesis includes primary stakeholders, employees and customers,

to analyse their effects on the CSR-CFP relationship. This thesis also focuses on the moderating

effects of contextual variables that can explain CSR-CFP relationship heterogeneity. As the

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proposed model can be conducted at the strategic and functional levels, CSR integration into

business strategy can be conducted comprehensively in the organisation and can have an effect

on company performance across financial and non-financial aspects.

Fifth, this thesis fills a substantial gap by comprehensively examining the impact of CSR

integration on company performance including financial and non-financial aspects, and by

analysing the potential mediating role of customer, employee, and operational performance in

the relationship between CSR integration and financial performance. Furthermore, this thesis

investigates the possible moderating effect of business and CSR strategies, as well as company

size and industry type.

7.6 Managerial Implications

This section lists several significant managerial implications provided by this thesis in

addition to contributions to theory and methodology. First, the findings of this thesis can

provide a better understanding of CSR implementation, especially in Indonesia. As shown in

several prior studies, most Indonesian companies conduct CSR through donation and charitable

practices, which are sporadic, temporary, and undertaken only when necessary. These activities

are immediately visible to external stakeholders, such as the local community surrounding the

companies.

However, this thesis generated different results. As explained in Chapter 5, in terms of CSR

strategy, four of 20 items had a mean value less than four, and they referred to philanthropic

responsibility. These results indicated that respondents’ perception of philanthropic

responsibility is less than other three responsibilities that had value more than four (see

Appendix B.2). As a result, the findings of this thesis back up the PER-05/MBU/04/2021 that

encourages everyone to recognise that CSR is more than just donation and community

development (Santia 2021).

341

Furthermore, since CSR deals more with a company’s internal activities, CSR must be

prioritised for internal stakeholders, rather than the community (Radyati 2014). The thesis

findings can inspire companies, particularly Indonesian manufacturing companies, to return to

implementing CSR within the company by treating its closest stakeholders well, those who

interact directly and daily with business operations: employees. Indeed, this requires

commitment and a great deal of resources, such as time, money, and people. However,

companies should understand that through CSR integration, they can maintain their business

sustainability and experience the benefits of CSR for a long time, not only financially but also

socially and operationally.

Second, the findings of this thesis can guide how companies meet their economic, legal, and

ethical responsibilities by integrating CSR. The integration of CSR at the strategic level

emphasises what top management should do, which can serve as a guide for CSR integration

at the next level. Meanwhile, at the functional level, CSR integration closely relates to the

company's daily business operations in six related functions (i.e., production, quality, R&D,

purchasing, marketing and human resources). Strategic CSR integration has 15 indicators and

functional CSR integration has 30 indicators. They can be used as suggestions to monitor

whether the companies already conduct them in their CSR implementation. In this way,

companies can adopt formalised CSR practices and thereby establish the procedures and tools

that are aligned with their corporate strategy (Bocquet et al. 2013). In doing so, CSR is

integrated upstream to downstream or along the value chain, covering all functions within the

company and relating to the company's internal activities.

Accordingly, these findings support PER-05/MBU/04/2021 as mentioned in Chapter 2. In

this new regulation, CSR (TJSL) refers to actions that represent company commitments to

sustainable development through delivering benefits to the community, economy, social, and

environments, as well as legal and governance principles that are more integrated and part of

342

the company's business approach (Jogloabang 2021). The findings of this thesis show that CSR

can be integrated into and be a part of the company’s business strategy and operations.

Third, understanding whether CSR integration is successful in improving company

performance is essential, not only for researchers, but also for managers who have been

practicing or are being motivated to engage in CSR. The findings of this thesis can encourage

manufacturing companies to increase their level of CSR implementation. They will not conduct

CSR as a mere charity, philanthropy, or compliance activity (as many currently do) or just to

meet regulations, but they will integrate CSR into business strategy. As a result, they will

achieve better financial and non-financial performance, which in turn could enhance their

competitive advantage. Thus, investing in CSR to boost the financial performance of an

organisation makes good business sense. Validating the model will help managers understand

why they should be paying attention to CSR integration, and what to expect from their attempts

to achieve social performance, beyond financial performance. When managers know what

benefits they can get from CSR integration, they will be motivated to implement it effectively.

As stated in PER-05/MBU/04/2021, the impacts of CSR (TJSL) should be targeted and

measured (Jogloabang, 2021). Hence, the findings of this thesis are in line with this regulation

by discovering that the effects of CSR integration can be evaluated comprehensively in four

aspects: customer, employee, financial, and operational performances.

Moreover, the findings of this thesis provide several suggestions for policymakers, such as

the Indonesian government. First, the government releases laws mandating companies to

practice CSR, and provides clear, systematic, and complete guidance. Accordingly, companies

should know to what extent they should implement CSR and the scope of CSR they can

conduct.

Second, the government should communicate regulations and guidance on their official

website, at association meetings, annual gatherings, or awards appreciation more often and

343

effectively. In doing so, large companies and SMEs will also recognise the regulation and

understand how to comply with it.

Third, the government can be a facilitator, by providing seminars, workshops and training

to companies on how to integrate CSR with business activities. Furthermore, to ensure that

companies, mostly SMEs, understand how to meet the regulations, the government can work

with universities and/or non-profit organisations (e.g., NGOs) to provide training to companies.

Training could cover, for example, how companies can implement occupational health and

safety, create a comfortable work environment, control product and process quality using

statistical process controls and communicate their CSR activities.

Fourth, the government can reward companies that have successfully implemented CSR. It

should be noted that the award is not for companies that make many donations, but companies

that conduct a sustainable business while involving the surrounding community. Thus, the

award can motivate companies to run their business in a responsible, legal, and ethical manner

that makes a significant impact to their stakeholders. Indeed, there have been many awards

given to companies that perform CSR well. Nonetheless, the awards are usually given by a

third party that does do not relate directly to the company, such as the media. It would be better

if the awards are provided by the government as the party issuing CSR regulations. Hence, the

government would be acting consistently with the regulations by monitoring and measuring

their implementation.

Last, the government can also provide incentives, which can motivate companies to conduct

CSR better. For instance, the government may facilitate the licence for new company branches,

reduce taxes and offer the company to foreign investors. Through these activities,

manufacturing companies in Indonesia will be more motivated to implement CSR strategically

and effectively. More specifically, they will be encouraged to integrate CSR into their business

strategy and operations. As a result, manufacturing companies in Indonesia will achieve better

344

company performance and appreciation from the government. These benefits are conducive to

enhance their competitive advantage and make their business sustainable.

7.7 Research Limitations and Directions for Future Research

This section outlines research limitations in this thesis, which can be seen as opportunities

for further research. First, this thesis focused on the integration of CSR. However, knowledge

of the antecedents of CSR integration is equally essential. Future research could address this

issue by interviewing executives and managers to comprehensively explore their perceptions

in detail.

Second, this thesis used a quantitative survey to address the proposed hypotheses and

research questions. As a result, this thesis relied on the information provided by respondents as

single informants in their companies. The collected data reflected managerial evaluation (or

perception) of organisational ways of working and business outcomes. Use of self-report

measures, although commonly used in behavioural and strategy research, may raise some

questions about the findings (Boesso & Kumar 2009). Hence, future research could overcome

this shortcoming by involving multiple informants, such as combining surveys of employees

and customers to obtain a better description of CSR implementation and business performance.

Third, in the data collection process, this thesis employed a cross-sectional approach. Using

this approach, customer strategy, CSR practices, and company performance were gathered at a

single point in time to clarify their relationship. A longitudinal study is required to provide an

in-depth understanding of the relationship between CSR integration and company

performance. Longitudinal data enables the researcher to analyse certain aspects of a mediation

model that are not available in cross-sectional data, such as whether the effect is stable over

time and whether evidence exists for one of the essential causality conditions (MacKinnon,

Fairchild & Fritz 2007).

345

Fourth, this thesis emphasised on a single industrial field, the manufacturing sector, and

used one region, Java, which might restrict data generalisability. Future research may overcome

this limitation by covering more than one industrial sector and one geographical area, such as

including the service sector both within and outside of Java, enabling data generalisability and

comparison of results.

Fifth, this thesis conducted a survey without case studies. To understand the mechanism

underlying the relationships between the variables investigated in this thesis, future research

can use case studies to enrich our understanding of these relationships. Case studies can also

help discover whether the effect is stable over time and whether evidence exists for one of the

essential causality conditions.

Last, this thesis used subjective measurement due to a lack of published company reports

and reluctance from respondents to discuss their company’s performance. Future research can

overcome this limitation by using objective data from multiple sources or validating subjective

dependent variables with objective measures to further verify the proposed research model.

Despite these limitations, this thesis could be seen as pioneering, as it represents a starting

point for the integration of CSR and the benefits of such integration across several aspects of

company performance in manufacturing companies. The results of this thesis provide empirical

evidence that companies can reap the benefits of CSR integration, which will enhance their



This section provides a summary of this chapter. It summarises the thesis's introduction.

Then, the entire thesis is concisely explained by describing how the thesis' findings can address

the research questions. Accordingly, it is clear that the findings of this thesis can answer five

research questions. This chapter also discovered that 31 of 47 hypotheses (66%) are statistically

346

supported, while 16 hypotheses (34%) are not. This means that the findings of this thesis can

confirm the majority of hypotheses.

This chapter highlights nine key findings in this thesis. Accordingly, the research objectives

can be achieved in a satisfactory manner. In addition, this chapter shows how this thesis can

make a theoretical and methodological contribution. This chapter offers various managerial

implications, not only for managers but also for policymakers. Last, this chapter lists research

limitations that can be seen as potential areas for further research.

347

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Production Economics, vol. 171, pp. 417-426.

Zikmund, W, D'Alessandro, S, Winzar, H & Babin, B 2017, Marketing Research, Cengage

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385

APPENDICES

Appendix A.1: Concept Definition and Concept Measurement Used in This Thesis

Concept Conceptual Definition Conceptual Operational / Measurement

Business strategy How a company competes and positions

itself successfully in the market (Bowman

& Helfat 2001).

Cost leadership, differentiation, focus (Porter

1985).

Cost leadership Cost leadership companies need to control

costs tightly, refrain from incurring too

many expenses from innovation or

marketing, and cut prices when selling their

products (Porter 1985).

Lowering production costs, improving

productivity, maximizing capacity utilization,

investment in production facilities, large-scale

facilities, process improvements, cost

minimization, achieve/maintain lowest inventory

(Chi 2015; Torugsa, O'Donohue & Hecker

2012).

Differentiation This strategy requires the development of

goods or unique services from unmatched

by relying on customer loyalty to the brand

(Porter 1985); Differentiation strategy

offers customers unique products or

services that are differentiated in such a

way that customers are willing to pay a

price premium that exceeds the additional

cost of the differentiation (Chi 2015).

Developing technology for new products for

increased economic and social inclusiveness

(Bhattacharyya 2010; Moon et al. 2011);

releasing new or improved products launched to

the market, introducing changes in company

products (Martinez-Conesa, Soto-Acosta &

Palacios-Manzano 2017).

Corporate Social

Responsibility

Companies’ ability to be socially

responsible to the development and growth

of the society where they run the business

(Adeneye & Ahmed 2015); A continuous

business agreement to have an ethical

behaviour and to benefit sustainable

economic development and at the same

time to enhance the life quality of

employee, their families, the local

community as well as wide society (World

Business Council for Sustainable

Development, cited in Moir 2001).

Economic, legal, ethical, and philanthropic

responsibilities (Carroll 1991); measurement of

four CSR classifications (Maignan & Ferrell

2000; Marín, Rubio & de Maya 2012).

Integration The synergy between competitive strategies

that seek superior performance in the

marketplace and NMS that shape the

competitive environment (Baron 1997).

The greater the level of integration across these

two strategic levels (CSR strategy and company

strategy), the closer the strategic clusters of these

two levels prove proximate and consequently

generating higher levels of synergies (Marques-

Mendes & Santos 2016).

Strategic

integration

How companies engage CSR in

management systems (Werre 2003);

Indication of the existing level of

interconnectedness between the CSR

strategy and the company strategy

(Marques-Mendes & Santos 2016).

CSR is integrated by triggering, maintaining, and

sharing a set of core dominant values (Marques-

Mendes & Santos 2016); The inclusion of social

responsibility objectives in the business strategy

(Ganescu 2012b).

Aligning CSR

with company

strategy

Companies incorporate CSR into

company’s vision and mission in this stage

(Guadamillas-Gómez, Donate-Manzanares

& Škerlavaj 2010).

Establishing CSR as one of the main long-term

goals of the company, evaluation of strategic

business decision in terms of CSR criteria,

creating a code of conduct describing desired

386

Appendix A.1 (continued)

behaviour (Guadamillas-Gómez, Donate-

Manzanares & Škerlavaj 2010; Werre 2003);

enriching customer lives, ensuring employees

consider social issues when performing business

duties, taking responsibility for the

organisation’s impact on the world, highlight

CSR in their strategy statements and employee

value policies (Lindgreen et al. 2011).

Gaining support of

top management

Senior managers determine strategy and

without their support become critical

barriers to CSR implementation (Werre

2003).

Team meeting with top-management with CSR

as a fundamental topic, organizing CSR efforts,

creating CSR steering committee with regular

meeting (Guadamillas-Gómez, Donate-

Manzanares & Škerlavaj 2010; Martinez-

Conesa, Soto-Acosta & Palacios-Manzano 2017;

Werre 2003).

Effective

communication

Companies should develop an effective

communication to generate a clear

perception that CSR is an aspect of strategic

importance for the company (Guadamillas-

Gómez, Donate-Manzanares & Škerlavaj

2010).

CSR reports, detailed information on the

company’s web, and continuous communication

within the organisation in order to further

increase the awareness of CSR (Guadamillas-

Gómez, Donate-Manzanares & Škerlavaj 2010;

Martinez-Conesa, Soto-Acosta & Palacios-

Manzano 2017; Werre 2003).

Functional

integration

Converting these concepts into actual

practices and processes (Lindgreen et al.

2011).

The development of CSR activities depends on

how they are internally managed and integrated

into business practices (Marques-Mendes &

Santos 2016).

Quality

Companies focus on the conformance

dimension of quality with aims to get

advantage by stabilizing the quality of the

product at a predetermined level according

to competition (Theodorou & Florou 2008).

Providing high performance design, offer

consistent and reliable quality, and conformance

to product design specification (Chi 2015);

providing product with more quality than those

of the rival companies (Marín, Rubio & de Maya

2012); fitting customer needs for products and

provide a product of excellent quality

(Boubakary & Moskolaï 2016); Implementing a

quality management such as ISO or total quality

management (TQM) (Martinuzzi & Krumay

2013; Ward et al. 1995).

Productivity in the

value chain

Enhancing the social, environmental, and

economic capabilities of supply chain

members (Vitolla, Rubino & Garzoni

2017).

Value chain dimension consists of suppliers,

customers and specific tools (Witek-Hajduk &

Zaborek 2016).

Stakeholders Groups and individuals who can affect, or

are affected by, the achievement of

companies mission (Freeman 2010).

Internal stakeholders are the owners, managers,

employees of a company, who reside inside the

boundary of the company, while the external

stakeholders of a company are the suppliers,

customers, communities and government

(Freeman 2010).

387


Suppliers Companies implement some practices to

develop strong and lasting relationship with

suppliers.

Treating suppliers, regardless of their size and

location, fairly and respectfully, incorporating

the interests of suppliers in company business

decisions, informing suppliers about

organisational changes affecting our purchasing

decisions (Lindgreen, Swaen & Maon 2009).

Customers Companies practice some activities to

enhance the relationship with customers.

Informing customers about appropriate use and

risks of products, taking the necessary steps to

avoid customer complaints, giving response to

customer complaints (Martinez-Conesa, Soto-

Acosta & Palacios-Manzano 2017); providing

all customers with the information needed to

make sound purchasing decisions, satisfying the

complaints of customers about products or

services, and incorporating the interests of our

customers in business decisions (Lindgreen et al.

2009).

Employee Corporate socially responsible activities

aimed at employees (Witek-Hajduk &

Zaborek 2016) that reflects how

organisations treat employee in their

activities (Ganescu 2012b).

Health and safety, working conditions, and equal

treatment in the workplace (Chen, Feldmann &

Tang 2015; Lindgreen et al. 2011; Torugsa,

O'Donohue & Hecker 2012; Witek-Hajduk &

Zaborek 2016); employee development

(Ganescu 2012; Lindgreen et al. 2011).

Community Commitment to improve community well-

being through discretionary business

practices and contributions of corporate

resources (Kotler & Lee 2005); Companies’

practices that relate to communication,

cooperation, and support for local and

wider social partners (Rangan, Chase &

Karim 2012; Witek-Hajduk & Zaborek

2016).

Funding for social or environmental programs as

an extension of its business interests through

strategic corporate philanthropy initiatives,

charitable donation, sponsorship of local

community initiatives, developing local talent

(Griffin & Prakash 2013; Liket & Maas 2015;

Martinuzzi & Krumay 2013; Rangan, Chase &

Karim 2012).

Performance

measurement

system

A set of related measures-described by rules

and procedures for capture, compilation,

presentation and communication of data

that in combination reflect key

performances and characteristic of a

selected process effectively enough to

allow intelligent analysis leading to action

if needed (Ljungberg 1994, cited in

Wibisono 2011).

Five levels are proposed for managing the

performance of manufacturing company:

financial perspective, customer perspective,

manufacturing competitive priorities, internal

process, and resource availability (Wibisono

2011).

Corporate

financial

performance

Measuring the results of a firm's policies

and operations in monetary terms (Business

Dictionary 2017).

These results are reflected in the firm's return on

investment, return on assets, value added, etc.

(Business Dictionary 2017).

388


Accounting-based

measures

Capturing a firm's internal efficiency in

some ways; accounting returns are subject

to managers' discretionary allocations of

fund to different projects and policy

choices, and reflect internal decision-

making capabilities and managerial

performance (Chi 2015; Cochran & Wood

1984)

Return on assets (ROA), return on equity (ROE),

earnings per share (EPS); market share, profit

margin, sales growth (Chi 2015).

Return on

Investment (ROI)

How profitable a company’s capital

investment is in generating revenue (Chi

2015)

Net Profit x 100

Total Capital Employed (Wibisono 2011)

Profit One operational measure of the efficiency

of a firm with regard to the profitable use

of its total asset base (Dess & Robinson

1984).

After tax return on total asset (Dess & Robinson

1984).

Sales growth Sales growth are a very good reflection of

the value that firms create and share with

society, as this implies an increase in

economic growth (Stoian & Gilman 2017).

An increase in the value of goods and services

delivered to consumers (Stoian & Gilman 2017).

Delivery On time delivery Number of product delivery on time x 100%

Total product

(Wibisono 2011)

Corporate social

performance

A business organisation’s configuration of

principles of social responsibility,

processes of social responsiveness, and

policies, programs, and observable

outcomes as they relate to the firm’s

societal relationships (Wood 1991).

Social dimension The social dimension refers to the impacts

generated by the company on the social

systems within which they works (GRI

2013)

The Social Category includes the sub-

Categories: Labour Practices and Decent Work,

Human Rights, Society, and Product

Responsibility (GRI 2013)

Social impact on

customers

Influence of socially responsible actions

which refers to customer as the external

field (Commission of the European

Communities 2001, cited in Ferraz,

António & Gallardo-Vázquez 2016)

Reduced customer complaints, improvement on

customer service, and increased customer

loyalty (Bernal-Conesa, de Nieves-Nieto &

Briones-Peñalver 2017).

Social impact on

employees

Influence of socially responsible actions

which refers to employee as the internal

field (Commission of the European

Communities 2001, cited in Ferraz,

António & Gallardo-Vázquez 2016).

Reduced absenteeism, increase of employee

satisfaction, employee participation in decision-

making processes, employee motivation, reduces

the number of accidents, employees' loyalty and

morale (Chi 2015; Ganescu 2012b; Ghasemi,

Nazemi & Hajirahimian 2014; Martinez-Conesa,

Soto-Acosta & Palacios-Manzano 2017).

389


Strategic

integration

The extent to which a manufacturing

company makes use of interactions with

other intra-organisational units to make its

program objectives and practices consistent

with its internal and external requirements

(Swink, Narasimhan & Kim 2005).

390

Appendix A.2: Questionnaire - English Version

The Integration of Corporate Social Responsibility (CSR) with Business Strategies

and Its Impact on Company Performance: An Investigation of the Indonesian

Manufacturing Industry

QUESTIONNAIRE

We invite you to participate in this research with purposes to investigate the integration of corporate

social responsibility (CSR) and business strategies into manufacturing operations and to identify how

such integration impacts company performance. We ask your assistance to give a perception or an

opinion how the company run its business operation and implement CSR.

This research is conducted by Esti Dwi Rinawiyanti (principal researcher) as a part of Doctor of

Philosophy and supervised by Dr Charlie Huang and Professor Sharif As-Shaber from RMIT

University, Australia. The principal researcher is a permanent lecturer at University of Surabaya who

gets a scholarship from Indonesian government (LPDP)

This research has obtained an approval from BCHEAN RMIT Ethics Committee. If you would like to

get more information about this research, please contact the researcher as the following:


Dr Charlie Huang

We expect your honest opinions, perceptions, and information for this research. It is an ANONYMOUS

survey and all information will be treated in STRICTEST CONFIDENCE, and no person or business

will be identified.

Participation in any research project is voluntary. If you decide to take part, you will continue to

complete the questionnaire and return it to the researcher. By completing the survey and return it to the

researcher, you consent to the research team collecting and using information from you for the research

project by maintaining the anonymity.

Please answer ALL questions by CROSSING (X) the appropriate box, which BEST describes the

condition and the situation in your company accurately.

PART ONE: BUSINESS STRATEGY

Please indicate the importance of each strategy in your company.

(From 1 = least important to 5 = most important)

No. Statement 1 2 3 4 5

1. Pursuing operating efficiency

2. Controlling the product quality

3. Developing/refining existing products

4. Innovation in manufacturing processes

5. Emphasis on the efficiency of securing raw materials or components (e.g.,

bargaining down the purchase price)

6. Developing brand identification

7. Having cooperative and supportive channels of distribution

8. Creating new product development

9. Providing customer service capabilities

10. Innovation in marketing techniques and methods (e.g., public relations, sales

promotion, direct marketing)

391


PART TWO: CSR STRATEGY

Please indicate how much you agree or disagree with each of the following CSR strategy.

(From 1 = strongly disagree to 5 = strongly agree)


1. We strive to lower our operating costs.

2. We closely monitor employees’ productivity.

3. Top management establishes long-term strategies.

4. We have a procedure in place to respond to every customer complaint.

5. We continually improve the quality of our products.

6. Internal policies prevent discrimination in employee’s compensation and

promotion.

7. We seek to comply with all laws regulating hiring and employee benefits.

8. All our products meet legal standards.

9. Our contractual obligations are always honoured.

10. Managers are informed about relevant environmental laws.

11. We have a comprehensive code of conduct.

12. We are recognised as a trustworthy company.

13. Fairness toward co-workers and business partners is an integral part of the

employee evaluation process.

14. We have a proper procedure for employees to report any misconduct at work.

15. Members of our company follow professional standards.

16. We give adequate contributions to charities.

17. We encourage partnerships with local businesses and schools.

18. We give a donation for sport and/or cultural activities.

19. A program is in place to reduce the amount of energy and materials wasted in our

business.

20. We encourage employees to join civic organisations that support our community.

PART THREE: THE STRATEGIC INTEGRATION

Please indicate how much you agree or disagree with each of the following statements.



1. We establish CSR as one of the main long-term goals of our company.

2. Objectives have been established relating to social and environmental aspects.

3. Mechanisms are available for evaluating the results of the objectives.

4. CSR strategy is well aligned with corporate vision and mission.

5. Continuous improvement and/or preventive actions are made in the area of CSR.

6. Top management formulates and shares a clear vision and core corporate values

with regards to CSR.

7. Top management remains responsive to the issues related to CSR.

8. Top management provides mentoring and coaching to managers to develop

decision-making skills that integrate CSR criteria in evaluating options.

9. We systematically organize CSR efforts.

10. We conduct team meeting regularly with top-management with CSR as a

fundamental topic.

11. CSR strategies and goals are clearly communicated to all employees.

12. We use IT by intensifying the company's presence on the Internet and social

networks to communicate CSR.

13. We communicate CSR activities within the company through multiple channels,

such as face-to-face meetings, formal communications from senior managers, and

a company newsletter.

14. We create CSR report with detailed CSR activities information.

15. We provide CSR information on the company’s web.

392


PART FOUR: THE FUNCTIONAL INTEGRATION

Please indicate how much you agree or disagree with each of the following statements.



1. We achieve/maintain the lowest production cost.

2. We reduce material costs.

3. We reduce overhead costs.

4. We increase labour productivity.

5. We increase capacity utilization.

6. We actively conduct product innovation to improve the product and service.

7. We develop environmentally friendly products.

8. We use eco-friendly technologies and materials in our processes, products, and

packaging.

9. We produce high-quality products which use raw materials up to standard and do

not use hazardous materials.

10. We have introduced innovations and improvements in production processes,

logistics or distribution.

11. We reduce defective rates.

12. We improve product performance and reliability.

13. We implement quality control circles.

14. Our products and/or services satisfy national and/or international quality

standards.

15. We enforce strict product quality control procedures.

16. We treat suppliers, regardless of their size and location, fairly and respectfully.

17. We seek to provide training to our suppliers and partners in business activities.

18. We incorporate the interests of our suppliers in our business decisions.

19. We inform our suppliers about company changes affecting our purchasing

decisions.

20. We strive to enhance stable relationships of collaboration with our suppliers.

21. We provide our customers with accurate and complete information about our

products and/or services.

22. We provide a prompt response to the complaints of our customers about products

and/or services.

23. We incorporate the interests of our customers in our business decisions.

24. We provide responsive and fair after-sales service.

25. Our company is honest with the customers in the sale or promotion of products

and services.

26. We provide procedures to help to insure the health and safety of our employees.

27. We treat our employees fairly and respectfully, regardless of gender or ethnic

background.

28. We incorporate the interests of our employees in our business decisions.

29. We provide our employees with salaries that properly and fairly reward them for

their work.

30. We update the social and environmental knowledge through the training of our

employees.

PART FIVE: THE COMPANY PERFORMANCE

Please indicate your company performance relatively COMPARES TO COMPETITORS OVER

THE LAST THREE-YEAR PERIOD

No. From 1 = much longer to 5 = much shorter 1 2 3 4 5

1. Compared to our competitors, our timeline of customer service is

2. Compared to our competitors, our delivery time is

From 1 = much worse to 5 = much better 1 2 3 4 5

3. Compared to our competitors, our cash flow report is

4. Compared to our competitors, our training of employee is

393


5. Compared to our competitors, career opportunities in our company are

From 1 = much lower to 5 = much higher 1 2 3 4 5

6. Compared to our competitors, our productivity is

7. Compared to our competitors, our operational efficiency is

8. Compared to our competitors, our profit is

9. Compared to our competitors, our return on investment (ROI) is

10. Compared to our competitors, our sales growth is

11. Compared to our competitors, our customer complaints are

12. Compared to our competitors, our customer satisfaction is

13. Compared to our competitors, our customer loyalty is

14. Compared to our competitors, our increasing number of consumers is

15. Compared to our competitors, our employee motivation is

16. Compared to our competitors, our employee turnover is

From 1 = very unsatisfied to 5 = very satisfied 1 2 3 4 5

17. Overall, with the operational excellence, we are

18. Overall, with the financial performance, we are

19. Overall, with the social performance, we are

PART SIX: COMPANY AND DEMOGRAPHIC INFORMATION

Please choose ONE ANSWER by CROSSING (X) the appropriate option for the following questions.

The main product of the company:

[ ] Food and drinks [ ] Tobacco processing

[ ] Textiles and apparel [ ] Leather, leather goods, and footwear

[ ] Paper and articles of paper [ ] Chemicals and articles of chemicals

[ ] Furniture [ ] Rubber, articles of rubber, and plastics

[ ] Automotive [ ] Metal goods, not machinery and equipment

[ ] Consumer goods [ ] Computers, electronics, and optical goods

[ ] Machinery and electrical equipment [ ] Handicraft

[ ] Pharmaceuticals, chemical, and traditional medicine products

[ ] Others: _________________________________

Number of employees in your company: [ ] < 20 [ ] 20-99 [ ] 100-499

[ ] 500-999 [ ] > 1.000

Please indicate the turnover for the previous financial year (2017) (in Rupiahs):

[ ] < 2.5 billion [ ] 2.5 - 50 billion [ ] > 50 billion [ ] Prefer not to answer

The age of company: [ ] < 5 [ ] 6-10 [ ] 11-20 [ ] 21-50 [ ] > 50

The company’s ownership: [ ] State-ownership [ ] Private [ ] Multi-national company

Town/city of the company: [ ] Surabaya [ ] Sidoarjo [ ] Gresik [ ] Pasuruan

[ ] Mojokerto [ ] Jakarta [ ] Bekasi [ ] Tangerang

[ ] Others: ____________

Is the company located in an industrial estate? [ ] Yes [ ] No

394


Your position in the company: [ ] Owner [ ] CEO [ ] Director

[ ] Senior Manager [ ] Middle Manager [ ] Assistant Manager

[ ] Team Leader [ ] Others: ______________

How many years have you been working in this company (years):

[ ] < 5 [ ] 6-10 [ ] 11-20 [ ] > 20

Your age (years):

[ ] < 25 [ ] 25-30 [ ] 31-40 [ ] 41-50 [ ] 51-60 [ ] > 60

Your highest education level: [ ] Secondary education [ ] Bachelor [ ] Diploma

[ ] Post-graduate (e.g., Master, Doctor)

PLEASE RETURN THE COMPLETED QUESTIONNAIRE IN THE PRE-PAID ENVELOPE

PROVIDED.

THANK YOU VERY MUCH FOR YOUR TIME AND CO-OPERATION

395

Appendix A.3: Questionnaire – Indonesia Version

Integrasi Tanggung Jawab Sosial Perusahaan (CSR) dengan Strategi Bisnis serta

Dampaknya pada Kinerja Perusahaan: Investigasi pada Industri Manufaktur di

Indonesia

KUESIONER

Kami mengundang Anda untuk berpartisipasi dalam penelitian ini yang bertujuan untuk

mengidentifikasi bagaimana tanggung jawab sosial perusahaan (CSR) diintegrasikan dengan strategi

bisnis dalam operasi manufaktur serta untuk menganalisisis bagaimana integrasi tersebut berdampak

pada kinerja perusahaan. Kami memohon kesediaan Anda untuk memberikan persepsi atau pendapat

bagaimana perusahaan menjalankan operasi bisnis dan mengimplementasikan CSR.

Penelitian ini dilakukan oleh Esti Dwi Rinawiyanti (peneliti utama) sebagai bagian dari program doktor

(Doctor of Philosophy) yang dibimbing oleh Dr Charlie Huang dan Profesor Sharif As-Shaber dari

RMIT University, Australia. Peneliti utama adalah seorang dosen tetap di Universitas Surabaya dan

mendapatkan dana pendidikan dan penelitian dari Lembaga Pengelola Dana Pendidikan (LPDP).

Penelitian ini mendapatkan persetujuan dari Komite Etika Penelitian RMIT University. Jika Anda

menginginkan informasi lebih lanjut mengenai penelitian ini, Anda dapat menghubungi peneliti melalui

kontak berikut:


Dr Charlie Huang

Mohon pertanyaan-pertanyaan dalam kuesioner ini dapat dijawab dengan sejujur mungkin. Silakan

memberi TANDA SILANG (X) pada kotak yang sesuai untuk menandakan pilihan mana yang paling

sesuai dengan kondisi dan situasi di perusahaan Anda. Kuesioner ini konfidensial dan bersifat rahasia.

Kami akan bertanggung jawab atas segala informasi yang telah diberikan. Survei ini ANONIM dan

tidak ada data yang dapat diidentinfikasi, baik nama Anda ataupun merek produk dan nama perusahaan

Anda.

Partisipasi dalam penelitian ini bersifat sukarela. Jika Anda memutuskan untuk berpartisipasi, mohon

kesediaan Anda untuk mengisi dan mengembalikan kuesioner yang telah dilengkapi, yang berarti Anda

menyetujui tim peneliti menggunakan informasi dari Anda untuk penelitian dan publikasi dengan

pengertian bahwa anonimitas akan dipertahankan.

BAGIAN PERTAMA: STRATEGI BISNIS

Berikan tanda silang di bawah nilai yang sesuai dengan sudut pandang perusahaan Anda mengenai

betapa pentingnya strategi bisnis di bawah ini.

(Dari 1 = paling tidak penting sampai dengan 5 = paling penting) No. Pernyataan 1 2 3 4 5

1. Menciptakan efisiensi operasi

2. Mengendalikan kualitas produk

3. Mengembangkan/menyempurnakan produk yang ada

4. Berinovasi dalam proses manufaktur

5. Menekankan pentingnya efisiensi dalam memperoleh bahan mentah atau komponen

produksi (misal: tawar-menawar dalam menentukan harga pembelian)

6. Mengembangkan identitas merek

7. Adanya saluran distribusi yang kooperatif dan suportif

8. Menciptakan produk baru

9. Menyediakan pelayanan prima bagi pelanggan

10. Berinovasi dalam teknik dan metode pemasaran (misal: promosi penjualan, direct

marketing, public relation)

396


BAGIAN KEDUA: IMPLEMENTASI CSR

Berikan tanda silang di bawah nilai yang menunjukkan seberapa Anda setuju atau tidak setuju dengan

strategi CSR di bawah ini. (Dari 1 = sangat tidak setuju sampai dengan 5 = sangat setuju) No. Pernyataan 1 2 3 4 5

1. Kami berusaha untuk menurunkan biaya operasi.

2. Kami memantau produktivitas karyawan dengan ketat.

3. Manajemen tingkat atas menetapkan strategi jangka panjang.

4. Terdapat prosedur untuk menanggapi setiap keluhan pelanggan.

5. Kami terus meningkatkan kualitas produk.

6. Terdapat kebijakan internal perusahaan yang mencegah terjadinya diskriminasi

dalam pemberian upah/gaji dan promosi karyawan.

7. Kami berusaha untuk mematuhi semua hukum yang mengatur rekrutmen dan

kesejahteraan karyawan.

8. Semua produk kami memenuhi standar hukum.

9. Kami selalu mematuhi kewajiban kontrak kami.

10. Para manajer telah memahami undang-undang lingkungan yang terkait dengan

operasi perusahaan.

11. Kami memiliki kode etik yang komprehensif.

12. Kami diakui sebagai perusahaan yang terpercaya.

13. Keadilan bagi rekan kerja dan mitra bisnis kami merupakan bagian penting dari

proses evaluasi karyawan.

14. Terdapat prosedur yang layak bagi karyawan untuk melaporkan pelanggaran kode

etik yang terjadi di tempat kerja.

15. Tiap karyawan terutama yang memegang posisi/jabatan telah mengikuti standar

profesional.

16. Kami memberikan sumbangan yang memadai untuk badan amal.

17. Kami mendorong adanya kemitraan dengan perusahaan lokal dan sekolah.

18. Kami memberikan sumbangan untuk kegiatan olahraga dan/atau budaya.

19. Terdapat program untuk mengurangi jumlah energi dan bahan baku yang terbuang

dalam bisnis kami.

20. Kami mendorong karyawan untuk bergabung dengan organisasi masyarakat yang

mendukung komunitas sekitar.

BAGIAN KETIGA: INTEGRASI STRATEGIS

Berikan tanda silang di bawah nilai yang menunjukkan seberapa Anda setuju atau tidak setuju dengan

pernyataan di bawah ini. (Dari 1 = sangat tidak setuju sampai dengan 5 = sangat setuju) No. Pernyataan 1 2 3 4 5

1. CSR ditetapkan sebagai salah satu tujuan jangka panjang utama perusahaan kami.

2. Sasaran perusahaan kami berkaitan dengan aspek sosial dan lingkungan.

3. Terdapat mekanisme untuk mengevaluasi hasil dari sasaran perusahaan.

4. Strategi CSR sejalan dengan visi dan misi perusahaan.

5. Terdapat perbaikan berkelanjutan dan/atau tindakan pencegahan di bidang CSR.

6. Manajemen tingkat atas menyusun dan berbagi visi dan nilai-nilai inti perusahaan

yang jelas dengan mempertimbangkan CSR.

7. Manajemen tingkat atas terus bertindak responsif terhadap isu-isu yang terkait

dengan CSR.

8. Manajemen tingkat atas menyediakan bimbingan dan pelatihan untuk para manajer

dalam mengembangkan keterampilan pengambilan keputusan yang mengutamakan

CSR sebagai salah satu kriterianya.

9. Kegiatan CSR dikelola secara sistematis.

10. Kami secara teratur mengadakan rapat tim dengan manajemen tingkat atas dengan

CSR sebagai bahan pembicaraan utama.

11. Strategi dan tujuan CSR telah disampaikan dengan jelas kepada seluruh karyawan.

397


12. Kami menggunakan teknologi informasi (IT) untuk meningkatkan visibilitas

perusahaan di internet dan jejaring sosial untuk mengomunikasikan CSR.

13. Kami mengomunikasikan kegiatan CSR dalam perusahaan melalui berbagai saluran,

seperti pertemuan tatap muka, komunikasi formal dari manajer senior serta

buletin/majalah perusahaan.

14. Kami membuat laporan CSR yang berisikan informasi mendetail mengenai kegiatan

CSR.

15. Kami menyediakan informasi tentang CSR pada halaman web perusahaan.

BAGIAN KEEMPAT: INTEGRASI FUNGSIONAL

Mohon berikan nilai yang menunjukkan seberapa Anda setuju atau tidak setuju dengan pernyataan di

bawah ini. (Dari 1 = sangat tidak setuju sampai dengan 5 = sangat setuju) No. Pernyataan 1 2 3 4 5

1. Kami telah mencapai/mempertahankan biaya produksi terendah.

2. Kami menurunkan biaya bahan baku.

3. Kami menurunkan biaya overhead.

4. Kami meningkatkan produktivitas tenaga kerja.

5. Kami meningkatkan pemanfaatan kapasitas.

6. Kami secara aktif melakukan inovasi produk untuk meningkatkan produk dan

layanan.

7. Kami mengembangkan produk ramah lingkungan.

8. Kami menggunakan teknologi dan bahan-bahan ramah lingkungan dalam proses

manufaktur serta pada produk dan kemasan kami.

9. Kami memproduksi produk-produk berkualitas tinggi yang menggunakan bahan

baku yang memenuhi standar dan tidak menggunakan bahan-bahan berbahaya.

10. Kami telah mengadakan inovasi dan perbaikan dalam proses produksi, logistik atau

distribusi.

11. Kami mengurangi tingkat kerusakan produk.

12. Kami meningkatkan kinerja dan keandalan produk.

13. Kami menerapkan gugus kendali mutu.

14. Produk dan/atau jasa kami memenuhi standar kualitas nasional dan/atau

internasional.

15. Kami menerapkan prosedur pengendalian mutu produk yang ketat.

16. Kami memperlakukan pemasok dengan adil dan penuh hormat terlepas dari ukuran

dan lokasi perusahaan pemasok.

17. Kami berusaha untuk memberikan pelatihan kepada pemasok dan mitra kami dalam

kegiatan bisnis.

18. Kami mempertimbangkan kepentingan pemasok kami dalam pengambilan

keputusan bisnis.

19. Kami menginformasikan para pemasok mengenai perubahan yang terjadi pada

perusahaan yang mempengaruhi keputusan pembelian kami.

20. Kami berusaha untuk meningkatkan hubungan kolaboratif yang stabil dengan para

pemasok.

21. Kami menyediakan informasi yang akurat dan lengkap tentang produk dan/atau jasa

kami kepada para pelanggan.

22. Kami merespons dengan cepat keluhan pelanggan tentang produk dan/atau jasa kami.

23. Kami mempertimbangkan kepentingan pelanggan dalam pengambilan keputusan

bisnis.

24. Kami menyediakan layanan purna jual yang responsif dan adil.

25. Kami jujur pada pelanggan dalam penjualan atau promosi produk dan/atau jasa.

26. Kami menyediakan prosedur untuk membantu memastikan kesehatan dan keamanan

karyawan.

27. Kami memperlakukan karyawan secara adil dan dengan penuh hormat terlepas dari

jenis kelamin atau latar belakang etnis.

398


28. Kami mempertimbangkan kepentingan karyawan dalam pengambilan keputusan

bisnis.

29. Kami menyediakan penggajian yang sepatutnya dan adil sebagai imbalan untuk

pekerjaan karyawan.

30. Kami memperbarui pengetahuan sosial dan lingkungan dengan memberikan

pelatihan bagi karyawan.

BAGIAN KELIMA: KINERJA PERUSAHAAN

Berikan penilaian Anda terhadap kinerja perusahaan Anda jika DIBANDINGKAN DENGAN PARA

PESAING SELAMA TIGA TAHUN TERAKHIR.

No. Dari 1 = lebih lama sampai dengan 5 = lebih singkat 1 2 3 4 5

1. Dibandingkan dengan para pesaing, masa tunggu layanan pelanggan kami…

2. Dibandingkan dengan para pesaing, waktu pengiriman kami…

No. Dari 1 = jauh lebih buruk sampai dengan 5 = jauh lebih baik 1 2 3 4 5

3. Dibandingkan dengan para pesaing, laporan arus kas kami…

4. Dibandingkan dengan para pesaing, pelatihan karyawan kami…

5. Dibandingkan dengan para pesaing, peluang karir di perusahaan kami…

No. Dari 1 = jauh lebih rendah sampai dengan 5 = jauh lebih tinggi 1 2 3 4 5

6. Dibandingkan dengan para pesaing, produktivitas kami…

7. Dibandingkan dengan para pesaing, efisiensi operasional kami…

8. Dibandingkan dengan para pesaing, laba (profit) kami…

9. Dibandingkan dengan para pesaing, laba atas investasi (ROI) kami…

10. Dibandingkan dengan para pesaing, pertumbuhan penjualan kami…

11. Dibandingkan dengan para pesaing, keluhan pelanggan kami…

12. Dibandingkan dengan para pesaing, kepuasan pelanggan kami…

13. Dibandingkan dengan para pesaing, loyalitas pelanggan kami…

14. Dibandingkan dengan para pesaing, peningkatan jumlah konsumen kami…

15. Dibandingkan dengan para pesaing, motivasi karyawan kami…

16. Dibandingkan dengan para pesaing, turnover karyawan kami…

No. Dari 1 = sangat tidak puas sampai dengan 5 = sangat puas 1 2 3 4 5

17. Secara keseluruhan, kami … dengan keunggulan operasional perusahaan.

18. Secara keseluruhan, kami … dengan kinerja keuangan perusahaan.

19. Secara keseluruhan, kami … dengan kinerja sosial perusahaan.

BAGIAN KEENAM: INFORMASI PERUSAHAAN DAN DEMOGRAFI

Berikan SATU JAWABAN saja dengan memberikan TANDA SILANG (X) pada kotak yang sesuai

untuk tiap isian di bawah ini.

Jenis produk utama perusahaan:

[ ] Makanan dan minuman [ ] Tembakau dan olahannya

[ ] Tekstil dan pakaian [ ] Kulit, barang berbahan kulit dan alas kaki

[ ] Kertas dan barang berbahan kertas [ ] Bahan kimia dan barang berbahan kimia

[ ] Mebel [ ] Karet, barang berbahan karet dan plastik

[ ] Otomotif [ ] Barang-barang konsumsi (sabun, dll)

[ ] Komputer, elektronik, dan barang optik [ ] Mesin dan peralatan listrik

[ ] Kerajinan tangan [ ] Lain-lain: ___________________________

[ ] Obat-obatan, produk obat kimia dan obat tradisional

[ ] Barang berbahan logam bukan mesin dan peralatan

399


Jumlah karyawan di perusahaan: [ ] < 20 [ ] 20-99 [ ] 100-499 [ ] 500-999 [ ] > 1.000

Omzet perusahaan untuk tahun keuangan sebelumnya (2017) (dalam rupiah):

[ ] < 2,5 miliar [ ] 2,5 - 50 miliar [ ] > 50 miliar [ ] Memilih untuk tidak menjawab

Umur perusahaan (tahun): [ ] < 5 [ ] 6-10 [ ] 11-20 [ ] 21-50 [ ] > 50

Kepemilikan perusahaan: [ ] Milik negara (BUMN) [ ] Swasta [ ] Perusahaan multinasional

Kota lokasi perusahaan: [ ] Surabaya [ ] Sidoarjo [ ] Gresik [ ] Pasuruan

[ ] Mojokerto [ ] Jakarta [ ] Bekasi [ ] Tangerang [ ] Lain-lain: ______________

Apakah perusahaan Anda berada di area industri? [ ] Ya [ ] Tidak

Jabatan Anda di perusahaan: [ ] Pemilik [ ] CEO [ ] Direktur [ ] Senior Manager

[ ] Middle Manager [ ] Assistant Manager [ ] Team Leader

[ ] Lain-lain: ______________

Berapa lama Anda telah bekerja di perusahaan (tahun): [ ] < 5 [ ] 6-10 [ ] 11-20 [ ] > 20

Umur Anda (tahun): [ ] < 25 [ ] 25-30 [ ] 31-40 [ ] 41-50 [ ] 51-60 [ ] > 60

Tingkat pendidikan tertinggi Anda: [ ] Sekolah Menengah [ ] Sarjana [ ] Diploma

[ ] Pascasarjana (misal: S2, S3)

TERIMA KASIH ATAS WAKTU DAN KERJASAMA ANDA

MOHON MENGEMBALIKAN KUESIONER YANG TELAH DIISI LENGKAP DENGAN

MENGGUNAKAN AMPLOP PRABAYAR YANG TELAH DISEDIAKAN.

400

Appendix A.4: Mann-Whitney Test of Business Strategy

401

Appendix A.5: Kruskal-Wallis Test of Strategic Integration

402

Appendix A.6: Normality Test of Business Strategy

403

Appendix A.7: Skewness and Kurtosis for All Items

404

Appendix A.8: Kruskall-Wallis Test of Company Performance

405

Appendix A.9: Mann-Whitney Test of Strategic Integration

406

Appendix A.10: Mann-Whitney Test of Company Performance

407

Appendix A.11: Ethics Approval

408

Appendix A.12: PCA Constructs of Strategic Integration

Construct &

Indicator

Cronbach’s

Alpha KMO Eigen Value

Total Variance Explained

Cumulative (%) Communalities Loading

Aligning 0.88 0.87 3.35 70

SI01 0.64 0.80

SI02 0.68 0.83

SI03 0.55 0.74

SI04 0.76 0.87

SI05 0.71 0.84

SuppTM 0.91 0.89 3.69 73.82

SI06 0.73 0.85

SI07 0.78 0.88

SI08 0.72 0.85

SI09 0.77 0.88

SI10 0.69 0.83

EffCom 0.91 0.88 3.73 74.51

SI11 0.69 0.83

SI12 0.73 0.85

SI13 0.77 0.88

SI14 0.77 0.88

SI15 0.77 0.88

409

Appendix A.13: PCA Constructs of Functional Integration

Construct &

Indicator

Cronbach’s




Cost 0.83 0.77 2.99 59.69

FI01 0.56 0.75

FI02 0.56 0.75

FI03 0.69 0.83

FI04 0.61 0.78

FI05 0.56 0.75

Innovation 0.84 0.80 3.02 60.31

FI06 0.52 0.72

FI07 0.65 0.81

FI08 0.69 0.83

FI09 0.58 0.76

FI10 0.57 0.75

Quality 0.87 0.84 3.31 66.13

FI11 0.60 0.78

FI12 0.66 0.82

FI13 0.71 0.84

FI14 0.66 0.81

FI15 0.67 0.82

Supplier 0.84 0.82 3.05 61.06

FI16 0.52 0.72

FI17 0.56 0.75

FI18 0.70 0.83

FI19 0.64 0.80

FI20 0.63 0.80

Customer 0.84 0.86 3.11 62.09

FI21 0.61 0.78

FI22 0.66 0.81

FI23 0.67 0.82

FI24 0.52 0.72

FI25 0.65 0.80

Employee 0.87 0.86 3.29 65.84

FI26 0.63 0.80

FI27 0.71 0.85

FI28 0.58 0.76

FI29 0.74 0.86

FI30 0.62 0.79

410

Appendix A.14: PCA Constructs of Company Performance

Construct &

Indicator

Cronbach’s




CCP 0.44 0.71 2.26 56.57

CP11 0.12 -0.35

CP12 0.76 0.87

CP13 0.74 0.86

CP14 0.63 0.80

CEP 0.63 0.80 2.60 52.00

CP04 0.66 0.81

CP05 0.72 0.85

CP15 0.65 0.81

CP16 0.02 -0.15

CP19 0.54 0.73

CFP 0.84 0.83 3.08 61.56

CP03 0.57 0.75

CP08 0.71 0.84

CP09 0.74 0.86

CP10 0.56 0.75

CP18 0.50 0.71

COP 0.83 0.76 2.95 58.93

CP01 0.57 0.75

CP02 0.60 0.77

CP06 0.63 0.79

CP07 0.64 0.80

CP17 0.51 0.72

411

Appendix B.1: Descriptive Analysis of Business Strategy

Variable Mean SD BS01 BS02 BS03 BS04 BS05 BS06 BS07 BS08 BS09 BS10

BS01 4.53 0.64 1.00

BS02 4.68 0.61 0.33** 1.00

BS03 4.29 0.71 0.26** 0.27** 1.00

BS04 4.26 0.71 0.29** 0.30** 0.43** 1.00

BS05 4.31 0.76 0.37** 0.26** 0.33** 0.22** 1.00

BS06 4.14 0.82 0.19** 0.18** 0.32** 0.31** 0.25** 1.00

BS07 4.25 0.72 0.32** 0.31** 0.34** 0.33** 0.35** 0.39** 1.00

BS08 4.00 0.87 0.12** 0.20** 0.40** 0.43** 0.21** 0.31** 0.29** 1.00

BS09 4.54 0.71 0.24** 0.37** 0.27** 0.34** 0.21** 0.24** 0.35** 0.27** 1.00

BS10 4.25 0.72 0.18** 0.24** 0.36** 0.33** 0.25** 0.38** 0.41** 0.36** 0.40** 1.00

412

Appendix B.2: Descriptive Analysis of CSR Strategy

413

Appendix B.3: Descriptive Analysis of Strategic Integration

414

Appendix B.4: Descriptive Analysis of Company Performance

415

Appendix B.5: Outer Model Loadings and Cross Loadings of Model 1

Indicator Construct

Aligning EffCom SuppTM CCP CEP CFP COP

SI01 0.80 0.59 0.68 0.19 0.44 0.36 0.34

SI02 0.82 0.62 0.65 0.26 0.53 0.33 0.35

SI03 0.73 0.47 0.54 0.24 0.41 0.27 0.31

SI04 0.87 0.65 0.73 0.24 0.51 0.34 0.39

SI05 0.85 0.70 0.76 0.23 0.50 0.32 0.36

SI06 0.77 0.66 0.85 0.20 0.51 0.30 0.32

SI07 0.73 0.71 0.88 0.22 0.53 0.36 0.38

SI08 0.66 0.65 0.84 0.23 0.48 0.31 0.32

SI09 0.73 0.78 0.88 0.29 0.58 0.40 0.41

SI10 0.66 0.76 0.83 0.26 0.50 0.37 0.33

SI11 0.66 0.83 0.77 0.32 0.55 0.40 0.41

SI12 0.61 0.85 0.69 0.22 0.44 0.30 0.27

SI13 0.64 0.88 0.70 0.24 0.50 0.35 0.34

SI14 0.67 0.88 0.75 0.21 0.50 0.33 0.31

SI15 0.63 0.87 0.65 0.17 0.42 0.30 0.29

CP01 0.28 0.24 0.28 0.45 0.48 0.45 0.72

CP02 0.28 0.23 0.26 0.44 0.46 0.46 0.73

CP03 0.34 0.30 0.32 0.50 0.56 0.76 0.58

CP04 0.48 0.47 0.51 0.45 0.81 0.55 0.54

CP05 0.48 0.41 0.45 0.49 0.83 0.55 0.56

CP06 0.33 0.28 0.30 0.54 0.56 0.63 0.81

CP07 0.36 0.31 0.33 0.55 0.57 0.64 0.82

CP08 0.32 0.38 0.35 0.53 0.56 0.83 0.56

CP09 0.33 0.31 0.33 0.50 0.52 0.84 0.56

CP10 0.25 0.24 0.26 0.62 0.54 0.76 0.60

CP12 0.26 0.25 0.25 0.87 0.55 0.59 0.64

CP13 0.20 0.19 0.22 0.85 0.53 0.54 0.53

CP14 0.25 0.25 0.24 0.84 0.54 0.61 0.53

CP15 0.42 0.43 0.48 0.57 0.81 0.60 0.59

CP17 0.37 0.36 0.38 0.53 0.64 0.61 0.75

CP18 0.32 0.30 0.34 0.50 0.63 0.73 0.58

CP19 0.51 0.48 0.49 0.52 0.76 0.62 0.61

Note: the blue remark indicates the loading of the indicator on its construct.

416

Appendix B.6: Varimax-Rotated Common Factor Matrix of Business Strategy

Code Item Factor loadinga

Communalities Factor 1 Factor 2

BS01 Pursuing operating efficiency 0.81 0.67

BS02 Controlling the product quality 0.79 0.66

BS03 Developing/refining existing products 0.58 0.45 0.53

BS04 Innovation in manufacturing process 0.60 0.48

BS05 Emphasis on the efficiency of securing raw materials or

components (e.g., bargaining down the purchase price)

0.67 0.49

BS06 Developing brand identification 0.72 0.53

BS07 Having cooperative and supportive channels of distribution 0.59 0.49

BS08 Creating new product development 0.74 0.56

BS09 Providing customer service capabilities 0.43 0.59 0.53

BS10 Innovation in marketing techniques and methods (e.g.,

public relations, sales promotion, direct marketing)

0.72 0.53

Total

Sum of squared loadings (eigenvalue) 4.32 1.14 5.46

% of variance 43.17 11.43 54.59


417

Appendix B.7: Collinearity of Business Strategy

Item

Unstandardized

Coefficients

Standardized

Coefficients t-

value Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

BS01 0.09 0.07 0.08 1.32 0.19 0.63 1.58

BS02 0.09 0.07 0.07 1.15 0.25 0.66 1.53

BS04 -0.12 0.06 -0.11 -1.91 0.06 0.66 1.51

BS05 0.04 0.06 0.04 0.72 0.47 0.72 1.39

BS06 0.04 0.05 0.04 0.65 0.51 0.67 1.49

BS08 -0.02 0.06 -0.02 -0.25 0.80 0.63 1.60

BS08 0.13 0.05 0.14 2.58 0.01 0.70 1.43

BS10 0.12 0.06 0.11 1.93 0.05 0.71 1.41

Note: Dependent Variable: profit

418

Appendix B.8: Notification from SmartPLS about Small Sample Size

419

Appendix B.9: Varimax-Rotated Common Factor Matrix of CSR Strategy

Code Item

Factor loadinga

Communalities Factor

1

Factor

2

Factor

3

CS01 We strive to lower our operating costs. 0.79 0.62

CS02 We closely monitor employees’ productivity. 0.70 0.59

CS03 Top management establishes long-term strategies. 0.49 0.42 0.47

CS04 We have a procedure in place to respond to every

customer complaint.

0.45 0.46

CS05 We continually improve the quality of our

products.

0.50 0.48 0.50

CS06 Internal policies prevent discrimination in

employee’s compensation and promotion.

0.56 0.40

CS07 We seek to comply with all laws regulating hiring

and employee benefits.

0.73 0.59

CS08 All our products meet legal standards. 0.73 0.62

CS09 Our contractual obligations are always honoured. 0.68 0.55

CS10 Managers are informed about relevant

environmental laws.

0.69 0.51

CS11 We have a comprehensive code of conduct. 0.73 0.59

CS12 We are recognised as a trustworthy company. 0.68 0.52

CS13 Fairness toward co-workers and business partners

is an integral part of the employee evaluation

process.

0.66 0.51

CS14 We have a proper procedure for employees to

report any misconduct at work.

0.70 0.63

CS15 Members of our company follow professional

standards.

0.58 0.50

CS16 We give adequate contributions to charities. 0.66 0.54

CS17 We encourage partnerships with local businesses

and schools.

0.75 0.62

CS18 We give a donation for sport and/or cultural

activities.

0.85 0.74

CS19 A program is in place to reduce the amount of

energy and materials wasted in our business.

0.45 0.51 0.55

CS20 We encourage employees to join civic

organisations that support our community.

0.76 0.60

Total

Sum of squared loadings (eigenvalue) 8.12 1.82 1.16 11.11

% of variance 40.62 9.12 5.79 55.53


420

Appendix B.10: Collinearity of CSR Strategy

Item

Unstandardized

Coefficients

Standardized

Coefficients t Sig. Collinearity Statistics

B Std. Error Beta Tolerance VIF

CS01 0.01 0.05 0.02 0.30 0.77 0.81 1.24

CS02 -0.01 0.06 -0.01 -0.26 0.80 0.68 1.47

CS06 -0.01 0.05 -0.01 -0.18 0.86 0.64 1.55

CS07 -0.01 0.07 -0.01 -0.08 0.94 0.48 2.10

CS08 0.07 0.07 0.07 0.99 0.32 0.44 2.28

CS09 -0.09 0.08 -0.07 -1.08 0.28 0.48 2.09

CS10 0.11 0.07 0.10 1.72 0.09 0.56 1.78

CS11 0.03 0.07 0.03 0.45 0.66 0.47 2.13

CS12 0.11 0.06 0.10 1.66 0.10 0.56 1.78

CS13 -0.09 0.06 -0.09 -1.44 0.15 0.55 1.81

CS14 -0.04 0.07 -0.04 -0.53 0.60 0.44 2.30

CS15 0.12 0.07 0.11 1.80 0.07 0.51 1.98

CS16 0.08 0.05 0.10 1.62 0.11 0.58 1.72

CS17 0.06 0.05 0.07 1.08 0.28 0.53 1.90

CS18 0.11 0.05 0.14 2.16 0.03 0.50 2.00

CS20 0.05 0.05 0.07 1.13 0.26 0.58 1.73

a. Dependent Variable: profit

421

Appendix B.11: Step 2 MICOM Proactive and Reactive of Strategic Integration

Composite Original

Correlation

Correlation

Permutation

mean

5.0% Permutation

p-values

Compositional

invariance

established?

Aligning 1.00 1.00 1.00 0.03 No

EffCom 1.00 1.00 1.00 0.74 Yes

SuppTM 1.00 1.00 1.00 0.47 Yes

Strategic _Integration 1.00 1.00 1.00 0.35 Yes

CCP 1.00 1.00 1.00 0.03 No

CEP 1.00 1.00 1.00 0.30 Yes

CFP 1.00 1.00 1.00 0.30 Yes

COP 1.00 1.00 0.99 0.24 Yes

422

Appendix B.12: Step 3 MICOM Proactive and Reactive of Strategic Integration

423

Appendix B.13: Permutation Test Proactive versus Reactive of Strategic Integration

424

Appendix B.14: Parametric and Welch-Satterthwaite Tests Proactive and Reactive of

Strategic Integration

425

Appendix B.15: Step 2 MICOM Proactive and Accommodative of Strategic Integration

Composite Original

Correlation

Correlation

Permutation Mean 5.0%

Permutation

p-Values

Compositional

invariance

established?

Aligning 1.00 1.00 1.00 0.90 Yes

EffCom 1.00 1.00 1.00 0.50 Yes

SuppTM 1.00 1.00 1.00 0.09 Yes

Strategic _Integration 1.00 1.00 1.00 0.00 No

CCP 1.00 1.00 1.00 0.73 Yes

CEP 1.00 1.00 1.00 0.54 Yes

CFP 1.00 1.00 1.00 0.04 No

COP 1.00 1.00 0.99 0.35 Yes

426

Appendix B.16: Step 3 MICOM Proactive and Accommodative of Strategic Integration

427

Appendix B.17: Permutation Test Proactive and Accommodative of Strategic Integration

428

Appendix B.18: Parametric and Welch-Satterthwaite Tests Proactive and

Accommodative of Strategic Integration

Note: Pro=Proactive; Accom=Accommodative

429

Appendix B.19: Step 2 MICOM Accommodative and Reactive of Strategic Integration

Composite Original

Correlation

Correlation


Permutatio

n p-Values

Compositiona

l invariance

established?

Aligning 1.00 1.00 1.00 0.27 Yes

EffCom 1.00 1.00 1.00 0.54 Yes

SuppTM 1.00 1.00 1.00 0.22 Yes

Strategic Integration 1.00 1.00 1.00 0.00 No

CCP 1.00 1.00 1.00 0.02 No

CEP 1.00 1.00 1.00 0.06 Yes

CFP 1.00 1.00 1.00 0.42 Yes

COP 1.00 1.00 0.99 0.25 Yes

430

Appendix B.20: Step 3 MICOM Accommodative and Reactive of Strategic Integration

431

Appendix B.21: Permutation Test Accommodative and Reactive of Strategic Integration

432

Appendix B.22: Parametric and Welch-Satterthwaite Tests Accommodative and Reactive

of Strategic Integration

Note: Accom=Accommodative.

433

Appendix B.23: Step 2 MICOM Company Size of Strategic Integration

Composite Original

Correlation

Correlation


Permutatio

n p-Values

Compositiona

l invariance

established?

Aligning 1.00 1.00 1.00 0.45 Yes

EffCom 1.00 1.00 1.00 0.02 No

SuppTM 1.00 1.00 1.00 0.13 Yes


CCP 1.00 1.00 1.00 0.04 No

CEP 1.00 1.00 1.00 0.14 Yes

CFP 1.00 1.00 1.00 0.09 Yes

COP 1.00 1.00 1.00 0.22 Yes

434

Appendix B.24: Step 3 MICOM Company Size of Strategic Integration

435

Appendix B.25: Permutation Test Company Size of Strategic Integration

436

Appendix B.26: Parametric and Welch-Satterthwaite Tests Company Size of Strategic

Integration

437

Appendix B.27: Step 2 MICOM Industry Type of Strategic Integration

Composite Original

Correlation

Correlation

Permutation

mean

5.0% Permutation

p-values

Original

Correlation

established?

Aligning 1.00 1.00 1.00 0.60 Yes

EffCom 1.00 1.00 1.00 0.86 Yes

SuppTM 1.00 1.00 1.00 0.46 Yes


CCP 1.00 1.00 1.00 0.72 Yes

CEP 1.00 1.00 1.00 0.75 Yes

CFP 1.00 1.00 1.00 0.18 Yes

COP 1.00 1.00 1.00 0.86 Yes

438

Appendix B.28: Step 3 MICOM Industry Type of Strategic Integration

439

Appendix B.29: Permutation Test Industry Type of Strategic Integration

440

Appendix B.30: Parametric and Welch-Satterthwaite Tests Industry Type of Strategic

Integration

441

Appendix C.1: Descriptive Analysis of Functional Integration

442

Appendix C.2: Outer Model Loadings and Cross Loadings of Model 2

443

Appendix C.3: Step 2 MICOM Business Strategy of Functional Integration

Construct Original

Correlation

Correlation


Permutation

p-Values

Original

Correlation

established?

Cost 1.00 1.00 0.99 0.42 Yes

Customer 1.00 1.00 1.00 0.35 Yes

Employee 1.00 1.00 1.00 0.75 Yes

Innovation 1.00 1.00 1.00 0.45 Yes

Quality 1.00 1.00 1.00 0.00 No

Supplier 1.00 1.00 1.00 0.09 No

Functional Integration 1.00 1.00 1.00 0.04 No

CCP 1.00 1.00 1.00 0.40 Yes

CEP 1.00 1.00 1.00 0.35 Yes

CFP 1.00 1.00 1.00 0.16 Yes

COP 1.00 1.00 1.00 0.28 Yes

444

Appendix C.4: Step 3 MICOM Business Strategy of Functional Integration

445

Appendix C.5: Permutation Test Business Strategy of Functional Integration

446

Appendix C.6: Parametric and Welch-Satterthwaite Tests Business Strategy of

Functional Integration

447

Appendix C.7: Step 2 MICOM Proactive and Reactive of Functional Integration

Construct Original

Correlation

Correlation


Permutation

p-Values

Original

Correlation

established?

Cost 1.00 1.00 0.99 0.15 Yes

Customer 1.00 1.00 1.00 0.42 Yes

Employee 1.00 1.00 1.00 0.28 Yes

Innovation 1.00 1.00 1.00 0.30 Yes

Quality 1.00 1.00 1.00 0.76 Yes

Supplier 1.00 1.00 1.00 0.01 No

Functional Integration 1.00 1.00 1.00 0.17 Yes

CCP 1.00 1.00 1.00 0.38 Yes

CEP 1.00 1.00 1.00 0.65 Yes

CFP 1.00 1.00 1.00 0.31 Yes

COP 1.00 1.00 0.99 0.21 Yes

448

Appendix C.8: Step 3 MICOM Proactive and Reactive of Functional Integration

449

Appendix C.9: Permutation Test Proactive and Reactive of Functional Integration

450

Appendix C.10: Parametric and Welch-Satterthwaite Tests Proactive and Reactive of

Functional Integration

451

Appendix C.11: Step 2 MICOM Proactive and Accommodative of Functional Integration

Composite Original

Correlation

Correlation


Permutation

p-Values

Original

Correlation

established?

Cost 1.00 1.00 1.00 0.81 Yes

Customer 1.00 1.00 1.00 0.11 Yes

Employee 1.00 1.00 1.00 0.68 Yes

Innovation 1.00 1.00 1.00 0.07 Yes

Quality 1.00 1.00 1.00 0.05 No

Supplier 1.00 1.00 1.00 0.24 Yes

Functional Integration 1.00 1.00 1.00 0.01 No

CCP 1.00 1.00 1.00 0.21 Yes

CEP 0.99 1.00 1.00 0.00 No

CFP 1.00 1.00 1.00 0.06 Yes

COP 1.00 1.00 1.00 0.08 Yes

452

Appendix C.12: Step 3 MICOM Proactive and Accommodative of Functional Integration

453

Appendix C.13: Permutation Test Proactive and Accommodative of Functional

Integration

454

Appendix C.14: Parametric and Welch-Satterthwaite Tests Proactive and

Accommodative of Functional Integration

455

Appendix C.15: Step 2 MICOM Accommodative and Reactive of Functional Integration

Composite Original

Correlation

Correlation


Permutation

p-Values

Original

Correlation

established?

Cost 1.00 1.00 0.99 0.28 Yes

Customer 1.00 1.00 1.00 0.56 Yes

Employee 1.00 1.00 1.00 0.21 Yes

Innovation 1.00 1.00 1.00 0.48 Yes

Quality 1.00 1.00 1.00 0.11 Yes

Supplier 1.00 1.00 1.00 0.47 Yes


CCP 1.00 1.00 1.00 0.05 No

CEP 0.99 1.00 1.00 0.01 No

CFP 1.00 1.00 1.00 0.63 Yes

COP 1.00 1.00 0.99 0.26 Yes

456

Appendix C.16: Step 3 MICOM Accommodative and Reactive of Functional Integration

457

Appendix C.17: Permutation Test Accommodative and Reactive of Functional

Integration

458

Appendix C.18: Parametric and Welch-Satterthwaite Tests Accommodative and

Reactive of Functional Integration

459

Appendix C.19: Step 2 MICOM Company Size of Functional Integration

Composite Original

Correlation

Correlation


Permutation

p-Values

Compositional

Invariance

Cost 1.00 1.00 1.00 0.22 Yes

Customer 1.00 1.00 1.00 0.32 Yes

Employee 1.00 1.00 1.00 0.90 Yes

Innovation 1.00 1.00 1.00 0.02 No

Quality 1.00 1.00 1.00 0.67 Yes

Supplier 1.00 1.00 1.00 0.83 Yes


CCP 1.00 1.00 1.00 0.01 No

CEP 1.00 1.00 1.00 0.01 No

CFP 1.00 1.00 1.00 0.02 No

COP 1.00 1.00 1.00 0.21 Yes

460

Appendix C.20: Step 3 MICOM Company Size of Functional Integration

461

Appendix C.21: Permutation Test Company Size of Functional Integration

462

Appendix C.22: Parametric and Welch-Satterthwaite Tests Company Size of Functional

Integration

463

Appendix C.23: Step 2 MICOM Industry Type of Functional Integration

Composite Original

Correlation

Correlation


Permutation

p-Values

Compositional

Invariance

Cost 1.00 1.00 1.00 0.93 Yes

Customer 1.00 1.00 1.00 0.14 Yes

Employee 1.00 1.00 1.00 0.15 Yes

Innovation 1.00 1.00 1.00 0.21 Yes

Quality 1.00 1.00 1.00 0.16 Yes

Supplier 1.00 1.00 1.00 0.86 Yes


CCP 1.00 1.00 1.00 0.82 Yes

CEP 1.00 1.00 1.00 0.30 Yes

CFP 1.00 1.00 1.00 0.22 Yes

COP 1.00 1.00 1.00 0.94 Yes

464

Appendix C.24: Step 3 MICOM Industry Type of Functional Integration

465

Appendix C.25: Permutation Test Industry Type of Functional Integration

466

Appendix C.26: Parametric and Welch-Satterthwaite Tests Industry Type of Functional

Integration

467

Appendix D: Thesis Related Publications

Journal Articles (published):

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2021). Integrating corporate social

responsibility into business functions and its impact on company performance: Evidence

from the Indonesian manufacturing industry. Social Responsibility Journal (ABCD B-

ranked, Q2 in Scimago, under review).

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2020). Adopting management control

systems through CSR strategic integration and investigating its impact on company

performance: evidence from Indonesia. Corporate Governance: International journal of

business in society, https://doi.org/10.1108/CG-04-2020-0150 (ABCD C-ranked, Q2 in

Scimago).

Proceedings:

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2021). The Impacts of Corporate Social

Responsibility on Small and Medium Enterprises Performance. Presented at International

Conference Project Management (ICPM), 12-13 June 2021, Malang, Indonesia (online):

Journal of International Conference Proceedings (JICP), p. 39.

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2019). The Impact of Firms’ Strategic CSR

integration on Their Organisational Performance: Evidence from Indonesian Manufacturing

Firms. Presented at the 16th International Conference on Business Management (ICBM),

12-14 December 2019, Melbourne, Australia: Proceeding Book, p. 112.

▪ Rinawiyanti, E.D., Huang, C. and As-Saber, S. (2019). The Integration of Social

Responsibility into Business Operation: Case Study of Indonesian Manufacturing Industry.

Presented at the International Conference on Informatics, Technology, and Engineering

(InCITE), 22-24 August 2019, Bali, Indonesia: IOP Conference Series: Materials Science

and Engineering 703 (2019) 012016, pp. 1-6. doi:10.1088/1757-899X/703/1/012016.

468

Conference Presentations:

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2021). Integration of Corporate Social

Responsibility for Improved Company Performance: Evidence from the Indonesian

Manufacturing Industry. Presented at the 3rd International Conference on Informatics,

Technology, and Engineering (InCITE), 25-26 August 2021 (online).

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2021). Clustering of corporate social

responsibility in the Indonesian manufacturing industry: How far can you go? Presented at

the 18th International Symposium on Management (INSYMA), 27-29 May 2021 (online).

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2021). Benefits of corporate social

responsibility on small and medium enterprises. Presented at the Australia and New Zealand

International Business Academy (ANZIBA) Conference 2021, 17-19 February 2021

(online).

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2019). Strategic Integration of CSR with

The Company’s Strategy and Its Effect on The Firm’s Performance: Evidence from the

Indonesian Manufacturing Industry. Presented at the 33rd Annual Australian & New Zealand

Academy of Management (ANZAM) Conference, 3-6 December 2019, Cairns, Australia.

▪ Rinawiyanti, E.D., Huang, C. and As-Saber, S. (2018). A Brief Overview of Integrated CSR

Practices Assessment in A Developing Country: A Case Study of Manufacturing Industry

in Indonesia. Presented at the Governance and Sustainability (GAS) Conference, 2

November 2018, Melbourne, Australia.

▪ Rinawiyanti, E.D. (2017). Integrating Corporate Social Responsibility into Business

Strategy and Its Impact on Company Performance. Presented at the 7th Annual Business

Ethics Network (ABEN), 7-10 December 2017, Melbourne, Australia.

469

Achievements:

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2021). Clustering of corporate social

responsibility in the Indonesian manufacturing industry: How far can you go? The Best

Paper Award in Strategic Category at the 18th International Symposium on Management

(INSYMA), 27-29 May 2021 (online).

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2020). Let’s Integrate CSR. People Choice

Award at Three Minutes Presentation (3 MT), College of Business and Law, RMIT

University, 11 August 2020, Melbourne, Australia.

▪ Rinawiyanti, E.D., Huang, X. and As-Saber, S. (2018). Integrating Social Responsibility

into Business Operations and Its Impact on Organisational Performance: Evidence from the

Indonesian Manufacturing Industry. Runner Up at RMIT HDR Poster Competition, 23

October 2019, Melbourne, Australia.

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