A Mixed Methods Study on the Ways International Israeli High ...

International High-Tech

Entrepreneurship and Learning: A

Mixed Methods Study on the Ways

International Israeli High-Tech

Entrepreneurs Learn about Business

Opportunities

A thesis submitted to The University of Manchester for the degree of

Doctor of Business Administration

in the Faculty of Humanities

2015

Izak Zahi Fayena

Alliance Manchester Business School

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Table of Contents

List of Tables ............................................................................................................... 8

List of Figures ........................................................................................................... 10

List of Abbreviations ................................................................................................ 11

Declaration ................................................................................................................ 13

Copyright statement ................................................................................................ 14

Dedications ................................................................................................................ 15

Acknowledgments ..................................................................................................... 15

Preface ....................................................................................................................... 16

1. Introduction .......................................................................................................... 18

1.1 The Research Problem ...................................................................................... 18

1.2 The Research Purpose ...................................................................................... 20

1.3 The Research Scope and Focus ........................................................................ 21

1.4 The Research Questions ................................................................................... 22

1.5 Research Methods ............................................................................................. 23

1.6 The Structure of the Thesis .............................................................................. 24

2. Literature Review ................................................................................................. 26

2.1 Introduction ........................................................................................................ 26

2.2 Methodology of the Review ............................................................................... 29

2.3 Results of the Review ......................................................................................... 31

2.3.1 International Entrepreneurship ..................................................................... 31

2.3.2 Entrepreneurial Learning .............................................................................. 35

2.3.3 Opportunity Identification ............................................................................ 39

2.3.4 Entrepreneurial Learning Strategies ............................................................. 44

2.3.5 Learning about Opportunities ....................................................................... 51

2.3.5.1 Learning about Opportunities Literature Review: Research Objectives,

Frameworks and Findings .......................................................................................... 57

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2.3.5.2 Learning about Opportunities Literature Review: Results of Empirical

Methods ...................................................................................................................... 65

2.4 Summary ............................................................................................................. 70

3. Research Design and Methodology ..................................................................... 72

3.1 Research Paradigm ............................................................................................. 72

3.2 The Research Strategy ....................................................................................... 73

3.3 The Research Design .......................................................................................... 73

3.4 Qualitative Phase ................................................................................................ 79

3.4.1 Philosophical Assumptions of Qualitative Research .................................... 79

3.4.2 Participants and Sampling ............................................................................ 80

3.4.2.1 Qualitative Phase 1 (QUAL1) ........................................................................ 80

3.4.2.2 Qualitative Phase 2 (QUAL2) ........................................................................ 83

3.4.3 Data Collection Procedures .......................................................................... 85

3.4.3.1 Semi-Structured Interviews ............................................................................ 87

3.4.3.2 Focus Group .................................................................................................... 88

3.4.4 Data Analysis Techniques ............................................................................ 89

3.5 Quantitative Phase .............................................................................................. 90

3.5.1 Type of Research Design and Data Collection Tool .................................... 90

3.5.2 Survey Procedures ........................................................................................ 91

3.5.3 Participants and Sampling ............................................................................ 92

3.5.3.1 Population, Sample and Participants .............................................................. 92

3.5.3.2 Coverage and Sampling Error ........................................................................ 93

3.5.4 Data Collection Instruments ......................................................................... 94

3.5.4.1 Questionnaire Design ...................................................................................... 95

3.5.4.2 Reliability and Validity ................................................................................... 98

3.5.5 Pilot Survey................................................................................................. 100

3.5.6 Non-Response Error ................................................................................... 101

3.5.7 Data Analysis Procedures ........................................................................... 102

3.5.7.1 Preliminary Data Analysis ............................................................................ 102

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3.5.7.2 Data Analysis ............................................................................................... 106

3.5.7.2.1 Exploratory Factor Analysis (EFA) .............................................. 107

3.5.7.2.2 PLS-SEM ...................................................................................... 110

3.6 Summary ........................................................................................................... 122

4. Qualitative Phase Findings and Results ........................................................... 124

4.1 Qualitative Phase 1 (QUAL1).......................................................................... 125

4.1.1 Findings ...................................................................................................... 125

4.1.2 Summary .................................................................................................... 137

4.1.2.1 Entrepreneurship and International Entrepreneurship .................................. 138

4.1.2.2 Entrepreneurial Learning .............................................................................. 140

4.1.2.3 Risk Perception ............................................................................................ 140

4.1.2.4 Opportunity Identification and Exploitation ................................................ 141

4.1.3 Conclusions ................................................................................................ 142

4.2 Qualitative Phase 2 (QUAL2).......................................................................... 144

4.2.1 Findings ...................................................................................................... 145

4.2.2 Summary .................................................................................................... 162

4.2.2.1 The Process of Internationalisation and the Motivation to Internationalise . 162

4.2.2.2 The Role of the Internet in the learning Cycle of International Entrepreneurs163

4.2.2.3 How Entrepreneurs Learn about International Opportunities ...................... 165

4.2.3 Conclusions ................................................................................................ 167

5. Theoretical Framework ..................................................................................... 170

5.1 Opportunity Identification Process as a Learning Model ............................ 170

5.2 The Ways Entrepreneurs Learn in the Opportunity Identification Process174

5.3 The Conceptual Model ..................................................................................... 177

5.4 Hypotheses Development ................................................................................. 181

5.4.1 Self-Efficacy and Prior Knowledge ........................................................... 181

5.4.2 Prior Business Ownership Experience and Prior Knowledge .................... 183

5.4.3 Prior Knowledge and Learning Strategies.................................................. 185

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5.4.4 Social Networks and Learning Strategies ................................................... 187

5.4.5 The Interaction Effect between Cognitive style and Prior Knowledge ...... 190

5.5 Measures Development .................................................................................... 193

5.5.1 Learning Strategies Constructs and Measures ............................................ 193

5.5.1.1 'Learning by Networking' ............................................................................. 197

5.5.1.2 Learning by Imitating'................................................................................... 198

5.5.1.3 'Learning by Doing' ...................................................................................... 200

5.5.2 Social Networks .......................................................................................... 202

5.5.3 Prior International Knowledge.................................................................... 206

5.5.4 Prior Business Ownership Experience ........................................................ 208

5.5.5 Cognitive Style Measure ............................................................................. 210

5.5.6 Entrepreneurial Self Efficacy...................................................................... 213

5.5.7 Control Variables ........................................................................................ 214

5.6 Summary ........................................................................................................... 216

6. Quantitative Phase Findings .............................................................................. 219

6.1 Preliminary Data Analysis and Procedures ................................................... 219

6.1.1 Data Screening and Cleaning ...................................................................... 219

6.1.1.1 Accuracy of Input ......................................................................................... 220

6.1.1.2 Missing Values ............................................................................................. 220

6.1.1.3 Response Rate and Non-Response Bias ....................................................... 220

6.1.1.4 Outliers ......................................................................................................... 222

6.1.1.5 General Statistical Concerns ......................................................................... 223

6.1.1.5.1 Normality ....................................................................................... 223

6.1.1.5.2 Linearity ........................................................................................ 225

6.1.1.5.3 Multicollinearity ............................................................................ 225

6.1.1.5.4 Common Method Variance Assessment ....................................... 225

6.2 Data Analysis ..................................................................................................... 226

6.2.1 Descriptive Statistics................................................................................... 226

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6.2.2 Exploratory Factor Analysis ....................................................................... 232

6.2.3 PLS-SEM Analysis .................................................................................... 251

6.2.3.1 Measurement Model ..................................................................................... 251

6.2.3.2 Structural Model Assessment ....................................................................... 268

6.2.3.2.1 The Influence of the Control Variables ........................................ 269

6.2.3.2.2 PLS-SEM Structural Model Assessment ...................................... 272

6.2.3.3 Moderation Effect: Cognitive Style (CSI) as a Moderator Variable ............ 288

6.2.3.3.1 Moderation type Assessment ........................................................ 290

6.3 Summary ........................................................................................................... 292

7. Discussion ............................................................................................................ 300

7.1 Research Question ............................................................................................ 300

7.1.1 Entrepreneurship, International Entrepreneurship and Opportunity

Identification ....................................................................................................... 302

7.1.2 The ways Entrepreneurs Learn about Entrepreneurial Opportunities ........ 304

7.2 The Factors that Affect the Ways Entrepreneurs Learn about Opportunities314

7.2.1 Self-Efficacy and Prior Knowledge ........................................................... 315

7.2.2 Prior Business Ownership Experience and Prior Knowledge .................... 316

7.2.3 Prior Knowledge and Learning Strategies.................................................. 319

7.2.4 Moderation Effect of Cognitive Style (CSI) .............................................. 321

7.2.5 Social Networking and Learning Strategies ............................................... 323

8. Conclusions ......................................................................................................... 328

8.1 Contribution ..................................................................................................... 332

8.2 Limitations ........................................................................................................ 336

8.3 Future Research ............................................................................................... 339

8.4 Concluding Remarks........................................................................................ 343

References ............................................................................................................... 344

Appendix A: Qualitative Phase 1 (QUAL1)- Guideline for semi-structured

interviews and the focus group ............................................................................. 384

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Appendix B: Qualitative Phase 2 (QUAL2)- Guidelines for focus groups

meetings and in-depth interviews. ......................................................................... 387

Appendix C: Focus Groups and Interviews- Invitation Letter .......................... 395

Appendix D: PLS-SEM Model without the product term (interaction effect) . 396

Appendix E: PLS-SEM, measurement model assessment .................................. 397

Appendix F: Content Validity Assessment ........................................................... 417

Appendix G: Content validity assessment- Referees’ Booklet ........................... 420

Appendix H: International young ventures types ................................................ 421

Appendix I: The Questionnaire (Final English Version) .................................... 424

Appendix J: Learning strategies measures development .................................... 441

Word Count: 96,608 words including footnotes and endnotes

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List of Tables

Table ‎2.1: Frequency of the Reviewed Studies on learning about opportunities by

Journal Focus ................................................................................................................ 52 Table ‎2.2: Publications on learning about opportunities by Research Type ................ 53 Table ‎2.3: Review of 16 articles on learning about opportunities by theoretical base . 55 Table ‎2.4: Learning about opportunities: articles by research strategy, design, and data

collection techniques .................................................................................................... 67 Table ‎3.1: Mixed Method Designs Matrix ................................................................... 75 Table ‎3.2: Focus Group and Interviews by age, industry, gender, and sampling

methods ......................................................................................................................... 82 Table ‎3.3: Focus Group Participants ............................................................................ 83

Table ‎3.4: The Interviewees ......................................................................................... 84

Table ‎3.5: Focus Group and Interviews by age, industry, gender, and sampling

methods ......................................................................................................................... 85

Table ‎4.1: Main Themes by Source ............................................................................ 127 Table ‎4.2: QUAL2, Main Themes .............................................................................. 146 Table ‎5.1: Learning strategies of entrepreneurs in the opportunity identification

process ........................................................................................................................ 176

Table ‎5.2: Learning strategies – Definitions and Measures ....................................... 196 Table ‎5.3: Learning by Networking deliberately and spontaneously definitions ....... 199

Table ‎5.4: Learning by imitating deliberately and spontaneously definitions ........... 201 Table ‎5.5: learning by doing deliberately and spontaneously definitions .................. 203 Table ‎5.6: Novice, Serial, and Portfolio entrepreneurs .............................................. 211

Table ‎5.7: CSI Scoring key ......................................................................................... 213

Table ‎5.8: The models’ predictors and moderator ...................................................... 218 Table ‎6.1: Non-Response Error: one-way Anova analysis results ............................. 222 Table ‎6.2 Demographic characteristics of the respondents (table continued in 2

nd page)

.................................................................................................................................... 228 Table ‎6.3: Demographic characteristics of the respondents’ ventures ....................... 231 Table ‎6.4: LBNS Factor loadings, Communalities (h

2) and percent of variance ....... 235

Table ‎6.5: LBND Factor loadings, Communalities (h2) and percent of variance ...... 237

Table ‎6.6: LBID Factor loadings, Communalities (h2) and percent of variance ........ 239

Table ‎6.7: LBIS Factor loadings, Communalities (h2) and percent of variance ......... 241

Table ‎6.8: LBDS (final solution) Factor loadings, Communalities (h2) and percent of

variance ....................................................................................................................... 242

Table ‎6.9: LBDD (initial results) Factor loadings, Communalities (h2) and percent of

variance ....................................................................................................................... 243 Table ‎6.10: Self efficacy Factor loadings and percent of variance ............................ 244

Table ‎6.11: PK Factor loadings and percent of variance ............................................ 246 Table ‎6.12: CSI Factor loadings and percent of variance ........................................... 248 Table ‎6.13: Social networking ties: Factor loadings and percent of variance ............ 250 Table ‎6.14: Results summary of Prior Knowledge (PK) reliability and convergent

validity ........................................................................................................................ 257

Table ‎6.15: Results summary of HOC reliability and convergent validity ................ 258 Table ‎6.16: Results summary of Strong ties (Strongties) reliability and convergent

validity ........................................................................................................................ 260 Table ‎6.17: Results summary of Cognitive Style (CSI) individual indicator reliability

.................................................................................................................................... 261 Table ‎6.18: Entrepreneurial Self-Efficacy (SE) indicators and items ........................ 262 Table ‎6.19: Results summary of Entrepreneurial Self-Efficacy (SE) reliability and

validity ........................................................................................................................ 263

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Table ‎6.20: Results summary of LBNS, LBID, LBIS and LBDS ..............................265 Table ‎6.21: LBND and LBDD First-Order Constructs Reliability and Convergent

Validity ........................................................................................................................267 Table ‎6.22: Results summary of LBND HOC reliability and convergent validity .....268 Table ‎6.23: Control Variables .....................................................................................270 Table ‎6.24: The impact of the control variables (F-test) .............................................271

Table ‎6.25: The impact of the control variables (path coefficients) ...........................272 Table ‎6.26: Collinearity Assessment...........................................................................274 Table ‎6.27: R

2 and path coefficients ...........................................................................275

Table ‎6.28: Results of the Total effects ......................................................................280 Table ‎6.29: Results of Predictive Relevance (Q

2) .......................................................281

Table ‎6.30: Results of effect size (f2) ..........................................................................283

Table ‎6.31: Results of effect size (q2) .........................................................................287

Table ‎6.32: Results for Interaction Effects .................................................................289

Table ‎6.33: Results of the Hypotheses testing ............................................................297

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List of Figures

Figure ‎2.1: Research Domains ...................................................................................... 27 Figure ‎2.2: The structure of the literature review ......................................................... 30 Figure ‎3.1: Research Design ......................................................................................... 77 Figure ‎3.2: Qualitative Phase 1 ..................................................................................... 78 Figure ‎3.3: Qualitative phase 2 ..................................................................................... 79

Figure ‎3.4: The Operationalization Process ................................................................. 96 Figure ‎3.5: Modelling continuous moderator variable-The product indicator approach

.................................................................................................................................... 121 Figure ‎5.1: Opportunity identification as an entrepreneurial learning process .......... 173 Figure ‎5.2: Conceptual Model of the factors that affect the way entrepreneurs learn 181

Figure ‎5.3: The six learning strategies in the opportunity identification process ....... 219

Figure ‎6.1: Age by International activity engagement ............................................... 229 Figure ‎6.2: Respondent’s Country .............................................................................. 230

Figure ‎6.3 Number of equity partners ........................................................................ 232 Figure ‎6.4: External environment hostility perception ............................................... 233 Figure ‎6.5 Simplified version of the study’s PLS-SEM model .................................. 254 Figure ‎6.6: Typology of Moderator variables ............................................................ 292

Figure ‎6.7: Main results of the PLS-SEM structural model ....................................... 296

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List of Abbreviations

AVE Average Variance Extracted

CB-SEM Covariance based Structural Equation modeling

CFA Confirmatory Factor Analysis

CMV Common method variance

CMB Common method bias

CR Composite Reliability

CSI Cognitive Style Index

EFA Exploratory Factor Analysis

EL Entrepreneurial Learning

HCM Hierarchical Component Model

HOC High Order Construct

IB International Business

IE International Entrepreneurship

INV International New Ventures

KMO statistics Kaiser-Meyer-Olkin statistic for Factorability

K-S test Kolmogorov–Smirnov test

LOC Lower Order Construct

n.s. Not Significant

OL Organisational Learning

PCA Principal Component Analysis

PLS-SEM Partial Least Squares-Structural Equations Modelling

P-P plots Probability Plots

QUAL Qualitative Phase

QUAL1 First Qualitative Phase

QUAL2 Second Qualitative Phase 2

QUAN Quantitative Phase

SE Entrepreneurial Self-Efficacy

SEM Structural Equation Modelling

VIF Variance Inflation Factor

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Abstract

Izak Fayena. A mixed method study on the ways international high-tech

entrepreneurs learn about business opportunities (2015) DBA thesis, The University

of Manchester.

This study focuses on how entrepreneurs learn about international business

opportunities and explores the factors that affect the way they do it.

The main conclusion of the literature review was that current international

entrepreneurship research is still under development and the topic of international

entrepreneurial learning about business opportunities yet to receive widespread

attention. In addition, entrepreneurs utilise different ways to learn about the

opportunities. However, there is a lack of coherence among scholars on what learning

strategies are exactly, how many of them exist, and how they should be defined and

categorised (Kakkonen, 2010).

The research strategy of this study is based on the mixed methods approach.

The design is a two-phase, sequential mixed methods study, utilising a qualitative,

followed by a quantitative phase (Creswell et al., 2003). The qualitative phase was

split into two parts: QUAL1 and QUAL2. Each qualitative phase includes the analysis

of interviews and focus group discussions (Tashakkori and Teddlie, 1998). In the

quantitative phase, a web-based questionnaire was the chosen data collection tool

(Cobanoglu et al., 2001; Sills and Song, 2002). The study was conducted on a sample

of 178 high-tech entrepreneurs in Israel.

The results show that international entrepreneurs learn strategically about

business opportunities. They utilise different ways, means, and mechanisms to assist in

the identification process of entrepreneurial opportunities. These processes can be

considered as learning processes, and the way they are enacted can be termed as

'learning strategies'. Based on the findings of the qualitative phases (QUAL1, QUAL2)

and prior studies, six learning strategies were identified as relevant to the process of

opportunity identification. Furthermore, the quantitative phase showed that business

ownership experience and entrepreneurial self-efficacy have a significant influence on

prior knowledge on international arena. In addition, prior knowledge was found as the

most significant factor, affecting the ways entrepreneurs learn about business

opportunities, while the cognitive style was found to moderate the strength of the

relationships between prior knowledge and the learning strategies. Social networking

ties also had an impact on the ways entrepreneurs learn, however this influence is

diverse, and its statistical significance depends on the specific learning strategy.

The importance and contribution of the proposed study can be defined as follows:

Firstly, the study can help to reveal the underlying logic of opportunity identification

as a learning process. Secondly, combining different frameworks into a new

conceptual model as has been done in this study, may establish a new outlook, and

contribute to the progress of research into entrepreneurship. Thirdly, International

entrepreneurs can also benefit from these elements by acknowledging that they have a

battery of learning strategies, which are relevant to the opportunity identification

process, and most importantly, they can be taught how to learn about an idea

throughout the process of opportunity identification.

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Declaration

I declare that no portion of the work referred to in the thesis has been submitted in

support of an application for another degree or qualification of this or any other

university or other institute of learning;

Signed: Izak Zahi Fayena

Date: June 2015

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Copyright statement

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ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic

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described in this thesis, may not be owned by the author and may be owned by third

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Intellectual Property and/or Reproductions.

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Thesis restriction declarations deposited in the University Library, The University

Library’s regulations (see http://www.manchester.ac.uk/library/aboutus/regulations)

and in The University’s policy on Presentation of Theses.

15

Dedications

I would like to dedicate this thesis to my parents, Avraham, and Malka, my sons Eldar

and Eylon, my daughter Michal, my father-in-law, Ovadia, who, unfortunately passed

away recently, my brothers, Moti, Oren, and Itay, and my best friend Michael Ringart,

who have given me their unconditional love, support, and encouragement. I cannot

end without thanking my wife, Orly (the meaning in Hebrew is my light), who has

constantly, with her love, moral support, and wise remarks, encouraged me not to give

up and never to forget the importance of having a vision and the importance of strong

values that drive me on my way to the stars.

Acknowledgments

“…we know what we are, but know not what we may be”. William Shakespeare

Completing this thesis has been one of the most challenging experiences of my life,

and I personally see it as one of my greatest achievements. Along this journey, many

people such as: family, friends and colleagues, have given me their unconditional help

and support and, without their encouragement, this DBA thesis would not have been

possible.

“…as we express our gratitude, we must never forget that the highest appreciation is

not to utter words, but to live by them.” John F. Kennedy

This is the most difficult part of the dissertation, as I have more people to thank, than

space, however, I would like to thank personally some of them who have made this

journey achievable. Firstly, my research supervisor, Dr Adrian Nelson, who

challenged me, in a positive manner, to make my study more precise, and whose

advice, guidance and support encouraged me to believe in myself and in my ability to

complete this thesis. Adrian offered me his direction as well as his unlimited

knowledge in research methods, and writing style, whilst encouraging me to find my

own voice in this research domain. Our discussions and conversations were always

full of humour, interesting, and really extended beyond his formal duty as my research

supervisor. In addition, I am greatly indebted to my co-supervisor, Dr Lyndsay

Rashman, whose insightful comments led me to improve the clarity and readability of

this dissertation. I would like also to thank Dr Steve Brookes for his valuable

comments and suggestions during the end of year meetings, and Dr Ann Shacklady-

16

Smith, my former supervisor, for her feedback and guidance throughout the process of

research proposal. Since I have cited Professors Allinson and Hayes work and utilised

their Cognitive Styles Index in this study, I would like to thank them for allowing me

to use the Cognitive Style Index (CSI) in this research study. I would also like to

sincerely thank, two persons who were involved in this research study, Avishag who

helped me during the qualitative phase to organise and manage the focus group

discussions, and Heidi who proof read many of these thesis chapters. Lastly, I would

like to show my gratitude to the Manchester Business School, who have given me

opportunities to brush up on my research knowledge and skills, and the DBA office

team for their full support during my DBA Journey.

Preface

I obtained my Bachelor degree in Social Sciences in 1994 (Tel-Aviv, University,

Israel). In 1999, I obtained my MBA degree (Bar-Ilan University, Israel), and

successfully presented my MBA project on the subject of learning from experience in

business environments. I am approaching this enquiry with a background as a senior

manager in several hi-tech and low-tech firms over the years, with a strong interest in

issues such as business strategy and organisational learning. For several years, I

worked as the leading business manager for a serial entrepreneur. I continually

observed his decisions, actions, and especially his attitude toward risks, and his views

on the knowledge he needed to acquire in order to put his ideas into practice. Often,

during this cooperation, I asked myself, what made him the successful entrepreneur

while I was not? Why did I have so many good ideas for new ventures, but fail to act

upon them? Why did he never stop generating new ideas, which he followed with the

establishment of new ventures? What was the right time to do it? At what stage?

These questions led to my academic interest in this field. Based on these questions, my

experience in international business, and my observations in the field, I began to

explore more about this phenomenon. I realised at a very early stage, that my training

as a quantitative inquirer, and the fact that I am very familiar with statistical programs,

would lead me to choose the quantitative method as my main research strategy in this

study. However, as my reading on this topic progressed, I began to question my initial

decision. For many reasons, qualitative approaches give the researcher space to be

innovative and to work more within researcher-designed frameworks. This could

allow a DBA student such as myself to be more creative, and maybe to develop a

17

better framework for understanding complex phenomenon. Eventually, I was

introduced to the mixed methods strategy, and finally came to the understanding that

this was what I had been looking for. As a mixed methods study, the research would

take extra time, due to the need to collect and analyse both qualitative and quantitative

data. However, I believe that it has suited me the best, primarily because I enjoy both

the structure of quantitative research and the flexibility of qualitative inquiry.

18

1. Introduction

1.1 The Research Problem

International entrepreneurship is commonly defined as a cross disciplinary field

combining international business and entrepreneurship (Mainela et al., 2013). The

term ‘International Entrepreneurship’ was first introduced in McDougall's seminal

work (McDougall, 1989), and focused on the differences between international and

domestic entrepreneurship. In 2005, Oviatt and McDougall (2005d, p. 540), redefined

the term, and the focus of the definition was on “the discovery, enactment, evaluation,

and exploitation of opportunities across national borders to create future goods and

services”. Since then an increasing amount of literature has attempted to explain the

phenomenon of new ventures expanding their business across national borders often

called 'born global' from inception (Madsen and Servais, 1997; McDougall and Oviatt,

2003; Knight and Cavusgil, 2004). However, research in this area has either been less

than rigorous or lacking any unifying direction (McDougall and Oviatt, 2000), with

:”…theoretical inconsistencies, conflicting predictions and knowledge gaps that all

forestall the further development of International Entrepreneurship (IE) research”

(Keupp and Gassmann, 2009, p. 600). Furthermore, the role of the entrepreneur has

been unexplored, and researchers have paid little attention to international aspects of

the entrepreneur and entrepreneurial business (Acs et al., 2003).

Eckhardt and Shane (2003) explained the role and importance of opportunities in the

entrepreneurial process. They describe three ways of classifying entrepreneurial

opportunities and their implications for building and testing theories in

entrepreneurship. They argue that the field of entrepreneurship may be better served

by studies of the entrepreneurial process, including the individual as well as factors

beyond the control of the entrepreneur. Accordingly, they proposed a framework

which emphasises that “explaining the emergence and existence of entrepreneurial

opportunities is a question of fundamental importance” (2003, p. 346). However , it

was suggested that the process of identifying international opportunities remains

poorly understood (Dess et al., 2003), and that: “the entrepreneurial behaviours

focused on international opportunities have been found to be critical in IE.

International opportunities, however, are often depicted in rather abstract and

unspecified ways, and the research suffers from narrow theoretical discussion in

relation to the concept of opportunity” (Mainela et al., 2013, p. 1).

19

Despite these research disparities, some scholars have argued that the central premise

of the field is that internationalisation is a necessary condition for the survival and

development of the venture, rather than just a consequence of a managerial resolution

reached by the founders during the process of developing the venture (Coombs et al.,

2009). Based on the argument that internationalisation is a necessary condition for the

survival of the venture, it might be argued that in the increasingly knowledge-based

global economy, global competition and accelerating technological development,

ventures proactively seek to internationalise earlier in their existence and more rapidly

than in the past (Autio et al., 2000; Johanson and Vahlne, 2003).

International Business Ventures, often referred to as International New Ventures

(INV) or 'born global', (McDougall et al., 1994; Knight and Cavusgil, 2004) do not

tend to be large, are more inventive (Shane, 2003) and more nimble than established

firms (Gray and McNaughton, 2010). When they internationalise, entrepreneurial

ventures often expand faster (Oviatt and McDougall, 2005a). According to Zahra

(2005): "INVs appear to differ in the extent of their learning, but the sources of these

variations are not well defined. To fill this gap in the literature, future studies need to

examine how and when these ventures learn. Further, we need to document what INVs

learn in foreign markets. Learning is multifaceted, and we have just begun to explore

selected parts of this complex construct. Social and market learning could be

important sources of technological learning, and could serve as a key source of new

and rich knowledge enabling INVs to succeed in inter-national markets.” (2005, p. 25)

Although Zahra (2005, p. 25) focused on the entrepreneurial venture, it is argued in

this study that in order to understand the creation, failure or success of the venture, it is

first critical to understand entrepreneurial learning processes. It is suggested that

entrepreneurs should acquire context dependent knowledge such as “know-how”,

“know-who” and “know what”, before, during and after the venture creation (Aldrich

and Yang, 2013). Accordingly, entrepreneurs obtain and maintain knowledge about

foreign markets, social networks and competencies in various business environments

and cultures, the manner in which this knowledge is developed and managed might

affect their actions.

The actions of the entrepreneur and the entrepreneurial venture are the result of

complex and dynamic processes in which learning occurs at every stage. Shane and his

colleagues (Shane and Venkataraman, 2000b; Eckhardt and Shane, 2003; Shane,

20

2003) developed a framework for conceptualizing entrepreneurial action as the ‘nexus

of individuals and opportunities’. According to this framework, entrepreneurship

entails the identification and exploitation of opportunities to introduce new goods and

services, as well making efforts to organise markets, processes, and raw materials in

ways that had previously not existed (Di Gregorio et al., 2008).

This raises some more interesting questions concerning:

How international entrepreneurs identify and exploit entrepreneurial

opportunities,

How they learn about them, what their information sources are,

How this relates to improving core capabilities, and

The nature of the relationship between the way they use learning and the way

they use opportunities.

Therefore, this study focuses on the opportunity identification process and looks at it

from the perspective of various approaches; foremost the learning approach. In order

that scholars in this field fully understand the nature of the entrepreneurial process,

they need to focus on how individuals learn about the opportunity identification

process (Corbett, 2005b). This appears to address the main research problem of this

study: the relationship between learning and international entrepreneurship.

1.2 The Research Purpose

Knowledge is simply the output of a learning (or knowing) process, just as plans are

the output of the planning process (Etemad and Lee, 2003). Several scholars have

discussed the importance of learning to the business venture (Harrison and Leitch,

2005; Politis, 2005; Cope, 2005a) and the significance of the role of learning in

accelerating the market process (Kirzner, 1978).

Ardichvili et al. (2003b) suggest that entrepreneurs develop business opportunities to

create and deliver value. Entrepreneurs carefully investigate market needs and begin

with the process of opportunity development. This process might be affected by

various factors, such as: entrepreneurial alertness (Kirzner, 1978; Kirzner, 2009),

information asymmetry and prior knowledge (Shane, 2000), social networks

(Granovetter, 1973; Granovetter, 1983; Hills, 1995), personality traits and the type of

opportunity (Ardichvili et al., 2003b). Furthermore, it can be argued, that

21

entrepreneurs learn from their past successes and failures regarding opportunities, and

use their experiences to improve their future chances of success (Minniti and Bygrave,

2001). The learning process takes place between the time an opportunity is identified

and its successful exploitation (Ravasi and Turati, 2005).

Therefore, the purpose of this study is to explore the relationship between learning and

the International Entrepreneurship phenomenon. Hence, the intent of this two-phase,

sequential mixed methods study, utilising both a qualitative phase and quantitative

phase (QUAL-QUAN), is to learn about the factors that affect how entrepreneurs gain

knowledge and learn about cross-border opportunities.

1.3 The Research Scope and Focus

The focus of this research is on entrepreneurs operating in the high-tech industry in

Israel. High-tech entrepreneurship can be considered as a specific type of general

entrepreneurship which offers an exceptional research context for exploring how

international Israeli entrepreneurs learn about opportunities, due to its fast pace,

complexity, and dynamism. Prior studies have found that high-tech entrepreneurs

operate in dynamic and turbulence environments, identify opportunities fast and tend

to internationalise their ventures more rapidly (Crick and Spence, 2005). High-tech

entrepreneurship tends to be international almost from inception, often launched by

experienced individuals, who based their ideas on their previous experience and

knowledge (Braguinsky et al., 2012).

Israel is a country that, since the 1990s has been characterized by a high level of

entrepreneurship, particularly in the high tech sector (Almor and Heilbrunn, 2013).

“Israel specializes in high-growth entrepreneurship-start-ups that wind up

transforming entire global industries” (Senor and Singer, 2011, p. 23). The High-Tech

(hi-tech) industry is the major driver of the Israeli economy (Chorev and Anderson,

2006), characterized by a substantial growth rate, which is the highest of all Israeli

industrial sectors (Schwartz and Bar-El, 2007). Israel has an unusually high number

of hi-tech entrepreneurs and is among the world leaders in hi-tech start-ups, with about

4,000 hi-tech companies of which 1,500 are start-ups (Shoham et al., 2006). The

Israeli hi-tech industry includes firms engaged in software, telecommunications,

biotechnology and information technology, and due to Israel’s small size and limited

market, it is essential for these hi-tech companies to operate in global markets.

22

Consequently, the vast majority of marketing and sales activities of Israeli hi-tech

companies take place outside of Israel (Malach-Pines et al., 2004).

1.4 The Research Questions

The purpose of the first qualitative phase (QUAL1) was to formulate an interesting

research question. For this reason, the first qualitative phase (QUAL1) covered

broader content areas within this topic. Therefore, the main objectives of the QUAL1

phase can be summarised as follows:

Firstly, to elucidate the empirical definitions of entrepreneurship and

international entrepreneurship, and to examine whether or not different

entrepreneurial characteristics are needed for success in international ventures and

which such characteristics are universal.

Secondly, the question of how entrepreneurs learn was also addressed.

Thirdly, what is the entrepreneur's perception or attitude toward risk?

Finally, the question of whether or not the entrepreneur creates an opportunity

or only recognises it; was also discussed.

However, in order to depict the various aspects of the international entrepreneurship

phenomena and specifically those that relate to opportunities, the second qualitative

phase (QUAL2) takes a deeper look at several issues, which emerged during the first

qualitative phase:

Firstly, what are the factors that influence the motivation of Entrepreneurs to

Internationalise?

Secondly, what are the factors that affect their attitude towards risk and thus

toward knowledge?

During the first qualitative phase (QUAL1), it was found that entrepreneurs often use

the internet as a source of information, so it was important to gain a better

understanding of this mechanism and its role during the different stages of

entrepreneurship. Therefore, the third question was:

What is the role of the Internet in the learning cycle of International Entrepreneurs

and how does it affect the way they learn about opportunities?

23

Finally, based on the findings of the first qualitative phase (QUAL1) and based on the

research gaps that were revealed by the literature review, the following question was

developed as the overarching question for the whole study:

What are the factors that affect the way entrepreneurs learn about

opportunities in the International arena?

1.5 Research Methods

The study focuses on the individual as the unit of analysis, and addresses the

relationship between International Entrepreneurship and Learning as the main research

question.

The research strategy employed mixed methods, using a sequential exploratory

research design with an initial qualitative phase, followed by a quantitative phase

(QUAL-QUAN). Therefore, it is important to emphasise that each phase should

advance us further, as the research progresses and develops, and provide a different

aspect of the same research problem.

The Qualitative phase was subsequently split into two parts: QUAL1 and QUAL2.

The first Qualitative Phase (QUAL1) allowed us to delineate and specify the

emergence of themes based on the grounded theory approach (Charmaz, 2006). In

QUAL1, one focus group and two semi-structured, in-depth interviews were

conducted. The nine participants in the focus group were selected by means of a

'snowball technique' (Bryman and Bell, 2007). The interviewees for this stage were

selected as a 'convenience cohort sample' (Bryman and Bell, 2007).

The main purpose of the second qualitative phase (QUAL2) was to develop further

these emerging themes, and to refine the theory by developing a conceptual model,

which was tested quantitatively. The second qualitative phase (QUAL2) consisted of

one focus group, with eight entrepreneurs. In this phase, eight subsequent interviews

were conducted. Both interviewees and focus group participants were selected using

'snowball' sampling method.

24

In the quantitative phase, the target population were entrepreneurs who seek to operate

or who are already operating international entrepreneurial businesses in the high-tech

industry. The 'sampling frame' (Groves et al., 2009) consisted of a list of Israeli hi-tech

entrepreneurs1. The quantitative phase was conducted on 178 high-tech entrepreneurs

2

in Israel3. In this phase, the chosen research design was the cross-sectional survey

design (Bryman and Bell, 2007). A web-based questionnaire was the chosen data

collection tool (Cobanoglu et al., 2001; Sills and Song, 2002). The conceptual model

was statistically tested by performing the partial least squares analysis (PLS-SEM).

The PLS-SEM path modelling was selected based on its robustness and the fact that it

can analyse complex models such as in this study (Chin, 2010).

1.6 The Structure of the Thesis

This thesis is organised into eight chapters, and is structured as follows:

1. Firstly, the introduction contains a brief description of the research problem, the

research purpose, the research questions of each study phase, including the

overarching research question, and the research methods.

2. Chapter 2 is based on an extensive literature review of the international

entrepreneurship domain as well as on opportunity identification and the

intersection between them, which focuses on learning about international

opportunities.

3. Chapter 3 addresses the methodology, which is implemented in this study. This

chapter specifies the research strategy, which are the mixed method as well as the

sequential qualitative-quantitative design. Furthermore, a detailed discussion on the

justifications for the mixed methods strategy is provided. In addition, this chapter

outlines and present the sampling frame, data collection tools and data analysis

tools for each research phase.

1 This database is proprietary to the IVC organisation.

2 In this study, an international entrepreneur is either an entrepreneur who expands his business outside

his national borders, or an entrepreneur who, from the day she creates her venture, defines her target

market as global.

3 Israel is among the world leaders in the hi-tech start-up industry and the vast majority of marketing

and sales activities of Israeli hi-tech companies, which are renowned for their creativity and ingenuity,

takes place outside Israel.

25

4. Chapter 4 presents the findings and results of the first empirical phase of this study,

the qualitative phase. In this chapter, the key findings of the first qualitative phase

(QUAL1) and the second qualitative phase are reported in relation to the research

questions. Overall, this chapter discusses the development of the overarching

research question as well as the main themes that were gleaned from both

qualitative phases.

5. Chapter 5 outlines the conceptual model and the hypotheses. In addition, it

addresses the main themes of the qualitative phase as well as prior studies in this

research field. This chapter can be considered as a discussion chapter of the

qualitative findings as well as integration between the findings and prior studies.

This chapter bridges between the qualitative phase and the quantitative phase by

developing the theoretical framework, the conceptual model and the constructs to

be further measured and tested in the quantitative phase.

6. Chapter 6 presents the main Findings and results of the quantitative analysis of the

survey data. This chapter introduces the data cleaning and screening procedures as

well as the descriptive statistics and sample characteristics. In addition, the main

analysis was conducted using both Principal Component Analysis (PCA) and

Partial Least Squares (PLS-SEM) path analysis statistical techniques. The chapter

ends with a moderation analysis.

7. Chapter 7, the Discussion, discusses the interpretation of the quantitative findings

with relations to the research question. In addition, the findings of the qualitative

phase are integrated with the quantitative phase, providing a broader perspective on

the findings.

8. Chapter 8 includes a discussion of the study’s main conclusions, contributions,

limitations, and recommendations for future studies. The academic and practical

contribution is highlighted as well as the study’s limitations and suggestions for

future research on this topic.

26

2. Literature Review

2.1 Introduction

The purpose of this review is to summarise, critically, the main literature in the field of

entrepreneurial learning about business opportunities in the international arena.

Therefore, the literature review provides an overview of the international

entrepreneurship, opportunity identification, and entrepreneurial learning research, and

assesses the current research on the ways entrepreneurs learn about international

opportunities. Figure 2.1 shows the main research domains that are discussed in this

review:

Figure ‎2.1: Research Domains

International Entrepreneurship (IE) as research area has been often explored as the

intersection of two research fields: entrepreneurship and international business (IB)

(McDougall and Oviatt, 2000; Keupp and Gassmann, 2009).

IE focuses mainly on the phenomenon of young ventures that internationalise early

after inception (Rialp et al., 2014). IE is considered to be a new research discipline,

which has developed intensively over the last 25-30 years (Wach and Wehrmann,

2014). However, it is still considered an unexplored area of research, in the literature

(Weerawardena et al., 2007; Rialp et al., 2014).

International

Entrepreneurial

Learning about Opportunities

Opportunity Identification

International Entrepreneurship

Environment

Learning

27

Entrepreneurial Learning (EL) is a never-ending process. It begins with an

entrepreneur and continues throughout the life cycle of the entrepreneurship, across

the individual, group and organisation level (Crossan et al., 1999; Dutta and Crossan,

2005). Entrepreneurial opportunity is considered as a primary concept in

entrepreneurial learning research domain (Wang and Chugh, 2013) and thus,

opportunity identification can be explored as an entrepreneurial learning process

(Corbett, 2002; Corbett, 2005b; Dutta and Crossan, 2005; Lumpkin and Lichtenstein,

2005b; Corbett, 2007b)

Entrepreneurs develop business opportunities to create and deliver value (Ardichvili et

al., 2003b). Opportunities are discovered or created (Ardichvili et al., 2003a; Mainela

et al., 2013). Opportunities are discovered either through a serendipitous event or

through a deliberate search process (Bhave, 1994b; Lumpkin and Lichtenstein,

2005b), or formed through the selection, evaluation, and refinement processes

involved in identifying opportunities (Lumpkin et al., 2004; Lumpkin and

Lichtenstein, 2005b).

The positions taken in this study are that entrepreneurship can be understood as a

process of learning, and the development of entrepreneurship as a research field

requires a theory of learning. Learning is perceived as a crucial component of

entrepreneurial activity and may take place between the time an opportunity is

identified and until its successful exploitation (Ravasi and Turati, 2005).

Several important conclusions can be derived from the literature:

(1) The role of the entrepreneur is to discover, evaluate, and exploit

opportunities (Shane and Venkataraman, 2000b; Cuervo, 2005).

(2) Entrepreneurs are 'exceptional learners' (Kirzner, 1978) who learn from

everything and everyone (Smilor, 1997) permanently (Franco and Haase,

2009).

(3) The process of opportunity identification consists of two subsequent phases:

discovery and formation (Lichtenstein et al., 2003; Lumpkin and

Lichtenstein, 2005b).

28

(4) Entrepreneurial opportunities are seen as a dynamic learning process, which

are based on the individual-opportunity nexus (Dimov, 2007b) and the

interaction between the individual and his surroundings (Li et al., 2012b).

A few research gaps were discovered in the contemporary research on the relationship

between learning, international entrepreneurship, and opportunity identification:

Firstly, it is suggested that the research in this field is, to some extent, limited

and suffers from a lack of conceptual models, which bring these three research areas

together.

Secondly, the research focus is unbalanced with excessive focus on the 'why' or

'what' questions, rather than clarifying the 'how'. For example, how do entrepreneurs

learn about the cross-border opportunities? Furthermore, the current research unit of

analysis is concentrated more on the firm level, therefore, the focus on the interaction

between the individual and the environment is somewhat limited, and almost no

empirical evidence exists.

Thirdly, the interaction between learning, international entrepreneurship and

opportunity identification is complex and affected by many factors. Therefore,

research in this area, should be conducted, by using different research strategies and

methods, in order for scholars to capture the richness of the phenomena, since learning

is a crucial component of entrepreneurial activity.

This chapter is structured as follows:

Firstly, 'International Entrepreneurship’ as research fields is reviewed.

Secondly, Entrepreneurial Learning research area is introduced, highlighting

some of the key issues, which constitute the current research agenda in the field.

Thirdly, a review of prior studies related to the topic of learning strategies, and

opportunity identification are discussed.

Finally, the topic of learning about opportunities is reviewed, discussed and the

main research disparities are presented. The review of the topic of learning about

opportunities, is more specific, and provides a detailed assessment of the current

learning about opportunities research.

29

2.2 Methodology of the Review

Figure 2.2 shows a simplified version of the main research domains, starting from a

broader discussion of entrepreneurship as a research field through the entrepreneurial

learning concept, and finally discussing in details the thesis focus: how entrepreneurs

learn about business opportunities:

Figure ‎2.2: The structure of the literature review

The studies to be considered in this review were firstly identified by a process, which

combined use of electronic databases coupled with a manual search. The use of

electronic tools as a means of searching was conducted by scanning various electronic

sources: the Ebsco-host bibliographic database (Business source premier database) as

well as Scopus documents search and by searching other internet sources such as

Google Scholar.

The manual search, based largely upon citations, was conducted for identifying other

possible works in edited books of readings. This method of searching proved highly

efficient in generating a large number of articles, which contained keywords that have

become associated with the phenomenon of interest in this study: entrepreneurship,

international entrepreneurship, entrepreneurial learning, learning about opportunities,

opportunity identification, and organisational learning and opportunities.

The search was conducted in two phases. The first phase focused on the broader topic

of each research domain (i.e. international entrepreneurship, entrepreneurial learning,

opportunity identification, and learning strategies of entrepreneurs), using the

30

‘Mapping Studies Approach’ in order to review the main themes that were researched

(Kitchenham et al., 2011). The aim of this method is to identify and classify all

research related to a broad topic, and to provide an overview of a topic area.

Therefore, the starting point of this literature review was the consideration of several

previous literature reviews articles (from the last decade) that were conducted on these

research fields (Sarasvathy et al., 2013), and the most well-known, influential or most

cited studies, in which the references of the selected publications served as sources for

new material. Not only do such reviews facilitate identification of the key articles in

the subject area, they are also useful for identifying the key contributors to the field,

different methodologies and frameworks used by researchers, key topics of research,

target populations, key findings, and emerging trends for future research. However,

this review is more of a general survey and overview of the current state of knowledge

regarding international entrepreneurship, learning, and opportunity identification,

rather than as a complete identification of the entire population of studies related to

this topic.

The second phase was conducted by implementing the systematic literature review. In

contrast to “mapping studies”, this method enables the researcher to systematically

map out important literature, which is central to the topic under investigation

(Armitage and Keeble-Allen, 2008), and is driven by a specific research question,

often related with a detailed and narrow topic area (Kitchenham, 2004; Kitchenham et

al., 2011). In this study, the specific research question was how entrepreneurs learn

about business opportunities?

As this sub-section of the review focuses on a narrow research field (i.e. learning

about entrepreneurial opportunities), using a systematic review is an appropriate

approach.

In this phase, and in order to separate the studies on the relationship between

entrepreneurial learning and opportunity identification from the larger pool of

literature, the following selection criteria were established:

- The study principally and specifically focuses on the subject of the ways

entrepreneurs learn about entrepreneurial opportunities.

- The study was conducted in the domain of entrepreneurship, management and

business or international business.

31

- The study is peer-reviewed and published in an academic journal or in edited books

of reading.

- The study was published in the English language.

- The studies were published between 2002 and 2013.

In addition, combinations of several keywords were used to search for publications in

this field. These keywords include Organisational Learning and Opportunity,

Entrepreneurial Learning and Opportunity, Experiential learning or Learning from

Experience and Opportunity, Entrepreneurship and Opportunity, and International

Entrepreneurship and Opportunity.

It is pertinent to note that this phase focuses only on empirical or conceptual studies

published in peer-reviewed journals, but does not include any research reports,

unpublished dissertations, feature articles, magazine articles, nor any books or book

reviews.

The studies finally selected in this literature review are among the most relevant ones

in this research field, and demonstrate the importance of and consequently the possible

research gap this field of enquiry.

2.3 Results of the Review

2.3.1 International Entrepreneurship

Recently, more than ten reviews and special topic forums in International

Entrepreneurship (IE) research have been published (Aspelund et al., 2007; Di

Gregorio et al., 2008; Coombs et al., 2009; Cumming et al., 2009; Engelen et al.,

2009; Keupp and Gassmann, 2009; Coviello et al., 2011; Jones et al., 2011; Kiss et al.,

2012; Peiris et al., 2012; Sarasvathy et al., 2013; Kollmann and Christofor, 2014). The

growing number of literature reviews in this research field highlight the fact that the

field is evolving, developing, and can now be considered an important research

domain at the intersection of entrepreneurship and international business (McDougall

and Oviatt, 2000; McDougall-Covin et al., 2013).

Twenty years ago, Wright and Ricks (1994)) viewed international entrepreneurship as

a new and somewhat surprising thrust of international business research activity. Now,

International Entrepreneurship (IE) is viewed as a cross disciplinary field, with an

32

emerging attention of entrepreneurship researchers’ (Covin and Miller, 2013), and a

growing body of knowledge (Peiris et al., 2012). IE as a research field, initially was

developed as the intersection between international business and entrepreneurship

theory domains (Keupp and Gassmann, 2009; Mainela et al., 2013).

Recently other frameworks from various research fields, such as: strategic

management, social network, and marketing theories were included as an important

part of the IE research (Peiris et al., 2012). According to Keupp and Gassmann (2009,

p. 601) "the field is fragmented and lacks common theoretical integration, so that

progress in this field is rather uncertain...there is no unifying paradigm present within

IE, and there is great variety in the theoretical and methodological approaches".

However, Jones et al. (2011) provided a different view on the absence of a unifying

theory in the IE research domain. They reviewed 323 relevant journal articles

published in the period 1989–2009. Based on their extensive review they argued that:

“…due to the multi-disciplinary and multi-theoretical nature of IE, the continuance of

debate and theorizing is appropriate and healthy…Indeed, one might argue that

because IE is based on complex phenomena, it is perhaps unlikely that theories unique

to IE will be produced. Instead, it will continue to develop theory that spans the

domains of international business and entrepreneurship, as well as beyond.”

Peiris et al. (2012) critically reviewed 291 IE-related articles between the years 1993

and 2012. Based on their structured review they suggested that current research in the

IE field should apply integrative models that elucidate five important constructs:

entrepreneur, firm, networks, environment, and competitive advantage. By focusing on

the entrepreneur in the internationalisation process, the entrepreneurial behaviour,

which leads to opportunity identification, evaluation, and exploitation, is elucidated.

They concluded that, research in this field should highlight: “the entrepreneurial

factors that lead to the opportunity-identification process since opportunities are

identified and exploited by individuals and not firms…and to understand how

entrepreneurs evaluate and exploit these opportunities to gain competitive advantage

in international markets” (Peiris et al., 2012, p. 14).

International entrepreneurship (IE) as a term was initially defined as a "business

organisation that, from inception, seeks to derive significant competitive advantage

from the use of resources from and the sale of outputs to multiple countries" (Oviatt

and McDougall, 1994, p. 58). The focus was on ventures which are international from

33

inception and which “derive significant competitive advantage from the use of

resources and sale of outputs in multiple countries from birth” (McDougall et al.,

1994, p. 1). However, the definition limited the research to a study of new ventures,

which are small, and in the early stages of development (Autio et al., 2000).

Over the years the term has been changed and redefined mainly by adding themes

such as: “value creation” (McDougall and Oviatt, 1996, p. 293), proactivity and risk

taking of entrepreneurs (McDougall and Oviatt, 2000), opportunity identification

(Shane and Venkataraman, 2000b; McDougall and Oviatt, 2003). In addition, Zahra

and George (2002) incorporate in their proposed definition of IE, the process aspect of

international entrepreneurship, therefore, they defined international entrepreneurship

as the “process of creatively discovering and exploiting opportunities that lie outside a

firm’s domestic markets in the pursuit of competitive advantage” (Zahra and George,

2002, p. 261). Since then, the debate is still evolving.

The definition that is used in most IE studies, is the one proposed by Oviatt and

McDougall (2005d, p. 540), who maintained that IE should be defined as: "The

discovery, enactment, evaluation, and exploitation of opportunities across national

borders, to create future goods and services". However, a major critique of this

definition referred to its focus on small and medium organisations, arguing that the

nature of such study is static rather than focusing on the dynamic nature of the

phenomena. In a related way, some scholars tried to reconceptualise IE as dynamic

process which evolves over time (Coviello, 2006; Mathews and Zander, 2007).

Although definitions of IE are still evolving and the term has adopted a more

‘entrepreneurial’ notion over time, Liesch et al. (2011) observed that “the definitional

emphasis has shifted away from risk-seeking to that of opportunity identification and

exploitation as significant apparatuses of entrepreneurial behaviour” (2011, p. 852).

Gray and Farminer (2014) proposed an updated definition of IE research that includes

the dynamic aspects of international new venture development (i.e. Networking,

organisational life cycles and business model development). They defined IE as

follows: “International entrepreneurship research focuses on understanding the

enablers and barriers to the rapid internationalisation of entrepreneurial ventures that

integrate innovation and internationalisation knowledge, capabilities and networks in

dynamic and competitive business models and organizational cultures that evolve and

adapt to changing environments over time.” (2014, p. 12)

34

Recently, Peiris et al. (2012) suggested that an important facet of the entrepreneurial

behavioural process is the entrepreneurial intention. Accordingly, they redefine IE as

“the cognitive and behavioural processes associated with the creation and exchange of

value through the identification and exploitation of opportunities that cross national

borders” (2012, p. 18). Furthermore, many scholars in this field explored antecedents

of IE based on organisational, environmental, or individual variables. However, the

explanatory power for the internationalisation decision is limited when restricted to the

organisation level, mainly because the role of the entrepreneur or the entrepreneurial

team is critical when making internationalisation decision (Kollmann and Christofor,

2014).

Scholars have suggested that a significant shift in IE research is necessary, by applying

cognitive viewpoints that scrutinise how international entrepreneurs identify and

exploit opportunities in the international arena (Glavas and Mathews, 2013). In

addition, identifying opportunities is more complex in international settings, themes

such as experience, distance, prior knowledge and networking are essential to better

explain the international entrepreneurship process (Butler et al., 2010).

IE, as a research field, is often described as new, with a rapidly accumulating body of

knowledge on complex phenomena (Jones et al., 2011). The expression ‘International

Entrepreneurship’ focuses on the differences between international and domestic

entrepreneurship (McDougall, 1989), elucidating the process of recognising

opportunities (McDougall and Oviatt, 2003). The role of the entrepreneur has been

unexplored, and research has paid little attention to international aspects of the

entrepreneur and entrepreneurial business (Acs et al., 2003). Hence, drawing on the

work of Peiris et al. (2012) and previous IE literature reviews (e.g. Keupp and

Gassmann, 2009; Kiss et al., 2012), it is argued that the research on IE should focus

on:

(1) Firstly, the entrepreneur as the unit of analysis by emphasising the importance

of the entrepreneurs’ cognitive and behavioural constructs. These constructs

are highly related to their prior knowledge and experience, self-efficacy,

creativity and perseverance (perceived ability to overcome adverse

circumstance (Ardichvili et al., 2003a).

(2) Secondly, it is suggested that the entrepreneur’s dynamic capabilities,

entrepreneurial learning, knowledge, and capabilities are essential to integrate

35

and combine resources for opportunity identification and exploitation leading

to value creation and consequently to competitive advantage of the venture

(Peiris et al., 2012).

(3) In addition, the role of their social networks, institution, industry, and market

in establishing access to resources, should not be neglected (Peiris et al., 2012).

(4) International learning and networking are important predictors of IE

(Dimitratos et al., 2013).

(5) Finally, IE studies should be more versatile in the way they implement various

research methods, mainly it is suggested that the emphasis should be on

longitudinal, multilevel and mixed method studies rather than using static,

cross-sectional quantitative and qualitative studies to explore these emerging

issues (Gray and Farminer, 2014)

2.3.2 Entrepreneurial Learning

Learning is a complex, dynamic process entailing shaping the work environment,

gaining legitimacy, acquiring and exploiting resources (Aldrich and Fiol, 1994). The

study of entrepreneurship, and organisational learning, yielded a new research domain

at the intersection of these two research fields, that focus on the role of the individual

entrepreneur, and is often called: ‘Entrepreneurial Learning’ (Harrison and Leitch,

2005; Wang and Chugh, 2013).

In the entrepreneurship literature, the issue of entrepreneurial learning has been

theorised predominantly in models drawn from experiential learning (Kolb, 1984),

emphasising the importance of the reflective individual (Clarke et al., 2006), the

significance of critical incidents (Cope and Watts, 2000), the importance of action

learning (Jones et al., 2014), their previous experience (Rerup, 2005; Huovinen and

Tihula, 2008), and the role of learning modes (Corbett, 2005b). Hence, entrepreneurial

learning refers to the variety of experiential and cognitive practices, used to acquire,

retain, and utilise entrepreneurial knowledge (Young and Sexton, 2003).

However, others such as Breslin and Jones (2012, p. 295), re-conceptualised

entrepreneurial learning and defined it as an “evolutionary process in which

components of knowledge emerge and develop over time within the group of

individuals involved in the business start-up and growth.” This definition, approaches

36

the domain of entrepreneurial learning from a biological world evolution perspective,

and described the development of the field as occurring over time through three key

mechanisms of variation (of genotypes), selection (of the consequent phenotype) and

retention (of the underlying genotype) (Breslin and Jones, 2012). In this sense, the

focus of the entrepreneurial learning domain should shift from focusing on the

individual entrepreneurs to the evolution of knowledge components (Breslin and

Jones, 2012).

The entrepreneurial role in entrepreneurial learning suggests learning that is

interconnected with entrepreneurship, highlighting the role of experience and new

knowledge creation (Ireland et al., 2001), focusing on recognising and acting on

opportunities (Rae, 2006). Corbett (2007b) demonstrates that learning, and especially

learning asymmetries, affect the discovery of opportunities. Learning asymmetries is

defined as: ”the different manner in which individuals acquire and transform

information” (Corbett, 2007b, p. 114).

Entrepreneurial learning is seen as the consequences of entrepreneurial engagement

into action in order to learn and transfer this knowledge to the firm, enhancing

organisational performance (Jones et al., 2014). It is the learning which entrepreneurs

use to convert their own personal resources into organisational resources rather than

just upgrade their own personal resources and competencies (Brush et al., 2001).

Recent work argues for a view that entrepreneurial learning should be seen as a social

phenomenon (Rae, 2002), and entrepreneurs viewed as practitioners who operate

within social communities of practice (Cope, 2005a). In addition, experience becomes

a major theme in this research domain. Minniti and Bygrave (2001) suggest that this

entrepreneurial learning is built on experiences of both past and ongoing successes and

failures.

In the same vein, Sarasvathy (2002) suggests, that entrepreneurship can be considered

as the art of learning from failures and successes over time. Entrepreneurial learning

takes place as “…a result of an anticipated pressure/opportunity...” (Young and

Sexton, 2003, p. 169), and differs from learning of managers or employees in large

firms (Fenwick, 2003), by involving high level of risk and ambiguity, which are not

typical for learning by managers in large firms.

Developing knowledge about entrepreneurial learning is becoming a feature of study

in entrepreneurship (Cope, 2003a). Several scholars contend that entrepreneurial

37

learning occurs during the new venture creation process. Although a narrow definition,

this encompasses the 'learn as you go' process associated with venture creation

(Gartner, 1988), and reflects the stance taken by other authors (Van Gelderen et al.,

2005). However, the current research view, accept a broader outlook and considered

entrepreneurial learning as existing beyond the new venture creation phase and during

the opportunity identification process (Wang and Chugh, 2013) an assumption that

other theorists reject (Gartner, 1985).

There is an accepted view that entrepreneurs are action-oriented and that learning

occurs through experience and discovery (Dalley and Hamilton, 2000; Rae, 2000; Rae

and Carswell, 2000), “involving reflecting, theorising, experiencing and action”

(Taylor and Thorpe, 2004, p. 204). Moreover, identifying opportunities and problem

solving are identified as a central feature of how entrepreneurs learn (Minniti and

Bygrave, 2001; Young and Sexton, 2003). In addition, discontinuous critical learning

events have become emerging themes (Deakins and Freel, 1998; Cope and Watts,

2000; Cope, 2003a). “Entrepreneurs learn from everything” (Smilor, 1997, p. 344), by

using various mechanisms to learn. These modes, mechanisms or ways are for

example: learning through participation and vicarious learning (Lévesque et al., 2009),

learning through doing and reflection (Cope and Watts, 2000), 'learning by copying',

learning from past experience (Sardana and Scott-Kemmis, 2010), social learning

(Jones et al., 2014), and opportunity taking and learning from mistakes (Gibb, 1997).

Harrison and Leitch (2005), in their introduction for a special issue on entrepreneurial

learning, argued that the majority of articles in this special issue are conceptual in

nature, and only a few articles were considered as empirical. They call for future

research in this field to focus on robust empirical studies: “As this field develops, the

major challenge will be the design, development, and execution of robust and relevant

empirical studies, using the full range of appropriate methodologies, which address the

full range of potential studies at the interface between organizational learning and

knowledge management and the entrepreneurial context” (Harrison and Leitch, 2005,

p. 365).

Wang and Chugh (2013), reviewed the development of the research in the field of

entrepreneurial learning. They argued that EL research could be considered as

“diverse, highly individualistic, and fragmented” (Wang and Chugh, 2013, p. 2).

These research characteristics may result in disagreements in various features of EL,

38

such as its definitions. One key challenge for future research that their review revealed

is that although leading scholars in the EL research domain have called for a greater

understanding of entrepreneurs’ learning in the opportunity identification processes,

there remains a scarcity of studies on learning about business opportunities. In

addition, they call for more research on how entrepreneurs explore and exploit

opportunities, phenomenon driven research questions, and enhancing the use of mixed

methods research methods.

EL, as a research domain is mainly based on the theoretical basis of organisational

learning and entrepreneurship rather than developing new EL theory and framework

(Wang and Chugh, 2013). Furthermore, entrepreneurial learning as a research domain,

failed to explain the context in which learning takes place (Taylor and Thorpe, 2004).

This might be considered as an important research gap. Therefore, entrepreneurial

learning, should focus on situated learning perspectives (Xiao et al., 2010; Hamilton,

2011), rather than generally describing how entrepreneurs learn. The focus, then,

should be on how entrepreneurs learn in different contexts and situations (Cope,

2005a), such as the opportunity identification context.

Prior studies on entrepreneurial learning has primarily focused on the individual, as

the unit of analysis, researching mainly how entrepreneurs identify valuable

opportunities in the business environment (Minniti and Bygrave, 2001; Voudouris et

al., 2011). This might address another research gap, in which by focusing primarily on

the entrepreneur, the influence of his environment is underestimated, specifically the

interaction of the entrepreneurs and their environment. The research on entrepreneurial

learning, as with entrepreneurship, should elucidate the contextual and social

influences and move the research beyond the single-person and single-insight focus

(Dimov, 2007a). Attempting to bridge this gap, it is suggested that entrepreneurial

learning should be considered as a socially situated process, embedded in everyday

practice (Hamilton, 2011), implementing social learning process aiming to resolve the

uncertainty inherent to each stage of opportunity development (Voudouris et al.,

2011).

Thus, the entrepreneur can learn by herself, through observing others or by

networking, rather than being the act of exclusively a single person (Voudouris et al.,

2011).

39

2.3.3 Opportunity Identification

Opportunity identification for new businesses is considered one of the most significant

and unique skills for successful entrepreneurs (Ardichvili et al., 2003a). The

opportunity phenomenon is considered one of the most important concepts in the

entrepreneurship literature (Short et al., 2010). Despite that, the concept, the nature of

opportunities and their definition, are not agreed upon among entrepreneurship

scholars (Short et al., 2010), and there is a substantial fragmentation across conceptual

and operational definitions of entrepreneurial opportunity process (Hansen et al.,

2011).

Different scholars, depending on the context and approach these scholars take, define

the opportunity process in different ways. Opportunity is described in the literature as:

identified (Lumpkin and Lichtenstein, 2005b), recognised (Shane and Venkataraman,

2000b), discovered, and created (Holcombe, 2003; Alvarez and Barney, 2007b) or

constructed (Vaghely and Julien, 2010). DeTienne and Chandler (2004, p. 244)

indicate various mechanisms or processes in which opportunities are identified: active

search, passive search, fortuitous discovery, and creation of opportunities. The former

three (i.e. active search, passive search, and fortuitous discovery) is consistent with the

ontological position that opportunities exist and the role of the entrepreneur is to

reveal these opportunities. The latter, opportunity identification as creation, is

consistent with the ontological perspective that opportunities are a product of one’s

mind.

Scholars have investigated the opportunity identification process from various

approaches, for example: the network perspective (Arenius and De Clercq, 2005), the

social-capital perspective (Li et al., 2012a), heuristic-inducing approach (Van

Gelderen, 2010), cognitive approach (Zahra et al., 2005) and a social-cognitive

perspective (De Koning and Muzyka, 1999; De Koning, 2003).

Wood et al. (2014) argued that entrepreneurship research approaches entrepreneurial

opportunities as homogeneous in nature, rather than acknowledging the variance in

entrepreneurial opportunities types. Accordingly, there are different types of

opportunities, and thus, they may influence differently on the entrepreneurial actions.

Entrepreneurial opportunities are considered, primarily as a facet of the

entrepreneurial process (Shane and Eckhardt, 2005) and involve many complex

40

processes from the vague idea through to the beliefs about the risks and chances,

entrepreneurs’ knowledge and motivation, and ending with identifying business

opportunities (Grégoire et al., 2010a; Grégoire and Shepherd, 2012).

There is substantial debate about whether entrepreneurial opportunities are discovered

(Shane and Eckhardt, 2005) by alert individuals (Kirzner, 2009), or as created through

the reality construction or enactment of entrepreneurs (Alvarez and Barney, 2007a).

These different views on entrepreneurial opportunities, can be classified as follows:

firstly, studies that approach opportunity discovery and opportunity creation as

ontologically and epistemologically inconsistent (Alvarez and Barney, 2007a).

Secondly, studies that perceive them as complementary (Chiasson and Saunders,

2005), and thirdly, studies, which propose a pragmatic view, that integrate discovery

and creation in the same theoretical framework (Edelman and Yli-Renko, 2010;

Vaghely and Julien, 2010). The studies integrating opportunity discovery and creation

concentrate on the entrepreneurial behaviour rather than discussing the different

ontological positions (Mainela et al., 2013).

Hunter (2013), in the same vein, criticised the research on the opportunity

identification, by arguing that although the research spans across different disciplines,

the concept of opportunity is still lacking a comprehensive definition, and that the

theoretical frameworks in this field, failed to predict the creation, and can only explain

what capabilities exist when the entrepreneur is developing opportunities. Opportunity

in this approach is seen as a dynamic concept, situational and highly dependent on the

entrepreneur’s prior knowledge and experience.

Various definitions of opportunity offer different indications and insights upon its

meaning. Based on Venkataraman (1997), Sarasvathy et al. (2005a, p. 142) defined an

opportunity as “a set of ideas, beliefs and actions that enable the creation of future

goods and services in the absence of current markets for them”. Hence, opportunity is

identified based on perceptions and beliefs (Sarasvathy et al., 2005a). A valuable

corresponding explanation to prior studies was addressed by Grégoire and Shepherd

(2012). They refine the role of cognitive resources in the evaluation of opportunity

identification. In their view, entrepreneurs use cognitive resources to perceive process

and interpret information about an entrepreneurial opportunity, similarity matches, and

mismatches between new means of supply and market contexts. These resources, may

allow the entrepreneurs to identify different types of opportunities. This line of

41

research elucidates the role of cognitive decision making (Busenitz and Barney,

1997), meaningful pattern recognition (Baron, 2006; Baron and Ensley, 2006b; Baron,

2007), and cognitive information processing (Yingkui and Xinrui, 2012).

However, although these studies provided a useful opportunity identification

framework, they depict only one facet of this complex phenomenon. Entrepreneurs

apply different cognitive resources in different opportunities types, entrepreneurial

stages, and situations. They interact with their friends, colleagues and other

entrepreneurs along the way, in order to lessen the vagueness. Thus, cognition as an

important part of the model however, provides only a partial explanation of

entrepreneurial identification process.

Eckhardt and Shane (2003, p. 336) defined entrepreneurial opportunities as:”…

situations in which new goods, services, raw materials, markets and organising

methods can be introduced through the formation of new means, ends, or means-ends

relationships”. In their approach, entrepreneurial opportunities are discovered, and the

role of the entrepreneur is significant in constructing the means, the ends, or the

means-ends relationships. In addition, entrepreneurial opportunities are situation or

context dependent. Therefore, the situations can influence the change of the terms of

economic exchange (Shane and Eckhardt, 2005).

Sarasvathy et al. (2005a) discussed entrepreneurial opportunities from three views:

recognition, discovery, and creation. In her perspective, these three views are

legitimate and can be utilised under specific conditions of uncertainty. Such a

pragmatic approach enables an integration of these three views thus enhancing the

research on entrepreneurial opportunities. In this approach, opportunities are

recognised when the supply and demands exist and the matchup between the supply

and demands can be realised through an entrepreneurial venture. This type of

opportunity is often described as ‘arbitrage’(Mainela et al., 2013). Opportunities are

discovered when one of them (i.e. either the supply or the demand) are missing. Thus,

the ‘missing part’ should be discovered.

Opportunities can be considered as created when none of them (i.e. supply or demand)

exists. In such a situation, the supply, the demand, or both may be created. In addition,

it is important to note that this view includes recognition and discovery as required

inputs. Alvarez and Barney (2007a) explained that the differentiation between

opportunity discovery and opportunity creation relies on different types of

42

entrepreneurial behaviours. These definitions represent different conceptualizations of

opportunity-phenomena that are grounded on different approaches of entrepreneurship

and have distinguishing features (Venkataraman et al., 2012; Mainela et al., 2013).

Venkataraman et al. (2012, p. 26) argued that: “most entrepreneurial opportunities in

the world have to be made through the actions and interactions of stakeholders in the

enterprise, using materials and concepts found in the world. Opportunities are, in fact,

artifacts, and their making involves transforming the extant world into new

possibilities.” Hansen et al. (2011, p. 284) systematically reviewed the various

conceptual and operational definitions of entrepreneurial opportunity and the various

approaches to the opportunity as a process (e.g., recognition, discovery, and creation).

It was found that although the concept and its operationalisation are fragmented,

similarities do exist and hence a set of composite conceptual definitions can be

developed and addressed. Short et al. (2010) reviewed the literature on the

entrepreneurial opportunity concept. One important finding was that most of the

articles reviewed did not provide any explicit and clear definition of the concept.

The research on entrepreneurial opportunities is evolving, and over the last few years,

has provided several conceptualizations of entrepreneurial opportunities as a

phenomenon. However, in the case of international opportunities, it is suggested

therefore that the process of identifying international opportunities remains poorly

understood (Dess et al., 2003). Moreover, conceptual models that have been developed

to explore antecedents of entrepreneurial opportunity have trouble in being

implemented to study ‘young’ ventures such a start-ups (Hunter, 2013).

Mainela et al. (2013) systematically reviewed the topic of international entrepreneurial

opportunities. They argued that there is a lack of clear definitions of international

opportunity. International opportunity is defined, in their review as a: “situation that

both spans and integrates elements from multiple national contexts in which

entrepreneurial action and interaction transform the manifestations of economic

activity” (Mainela et al., 2013, p. 16). The definition contains both the discovery as

well as the creation of opportunities.

An important part of the International Entrepreneurship research is the emergence of

international new ventures (INV) (Rialp et al., 2014; Coviello, 2015), their existence

stems from opportunities to engage in the cross-border combination of resources

and/or markets (Di Gregorio et al., 2008). International new ventures are seen in this

43

perspective as the cross-border nexus of individuals and opportunities (Di Gregorio et

al., 2008, p. 186). Santos-Álvarez and García-Merino (2010) approached the research

on business internationalisation as a process of identifying and exploiting business

opportunities in an international context. In their perspective, during the identification

stage the entrepreneur’s cognitive resources play an important role when acquiring

relevant information for internationalisation. In this stage, variables such as

entrepreneurs’ alertness, causal logic, prior experience, the entrepreneur’s

environment, social networks, and institutional setting, are important for identifying

opportunities.

In this sense, the international entrepreneurial opportunity is seen as a more complex

and multifaceted concept than the International Entrepreneurship research nowadays,

would show (Mainela et al., 2013). Nevertheless, from the aforementioned discussion

of the entrepreneurial opportunity as a phenomenon, it can be concluded that

entrepreneurial opportunity is a concept that is frequently researched, from various

ontological and epistemological points of view, with highly variable interpretations,

and lack of construct clarity (i.e. definition, scope, coherence and relationships)

(Davidsson, 2012). Furthermore, Research should emphasise the importance of the

nexus between individual and opportunity (Venkataraman et al., 2012) to find patterns

of actions and interaction between the participants in this process (Mainela et al.,

2013). It is suggested that the field should move toward a cross-disciplinary approach.

Short et al. (2010) suggested that in order to enhance the research on opportunity,

studies in other related fields could reveal important insights about the opportunity. In

addition, cross-disciplinary approaches are best suited, in their perspective to the study

of entrepreneurial opportunities. In the same vein, Hunter (2013, p. 59) contended that:

”The phenomenon of opportunity spans across the disciplines of micro-economics,

psychology and cognitive science, strategic management, resource based and

contingency theories that are patched together, synchronized and added to form new

information in the form of ideas.”

The central thesis of this study is that entrepreneurial opportunity is a complex,

multifaceted and highly context dependent phenomenon. The current studies have

certainly advanced scholars' understanding of the entrepreneurial opportunity concept.

However, many of them neglect the effects that the learning and especially the

characteristics of different learning modes may have on the ways the entrepreneurs

44

identify opportunities (Corbett, 2005a). This might continue to constrain the research

in this field of enquiry, with an incomplete understanding of the individual-

opportunity nexus.

Accordingly, this study adopts the view, which was discussed by several authors such

as Johanson and Vahlne (2009) and Dutta and Crossan (2005), that the process of

entrepreneurial opportunity identification includes elements of both discovery and

creation (Ardichvili et al., 2003). It means that both can be applied and neither one is

more important than the other. The opportunity identification process is an interactive

learning process with commitment and trust being important facilitators

The research in this opportunity identification field should focus on the individual-

opportunity nexus (Venkataraman et al., 2012), emphasising processes that are often

characterised as iterative and are trial-and-error (Alvarez et al., 2013), highlighting

action and interaction (Mainela et al., 2013), implementing cross-disciplinary

approaches (Short et al., 2010), which can introduce a broader and richer

representation of this complex phenomenon.

Entrepreneurial opportunity is a complex concept, with many aspects and layers.

Hence, an approach, which can integrate, the different points of views, and present a

hybrid way of explaining the opportunity identification process, could contribute to the

progress in this field. Kyrö et al. (2011, p. 4) summarised the importance of learning

for the opportunity identification. They argued that: “Thus in spite of the efforts to

capture and analyse the differences in the understanding of opportunities and its

consequences for learning, this field of research still leaves marginal interplay between

opportunity definitions and process as well as the interplay between these and the

learning process.”

2.3.4 Entrepreneurial Learning Strategies

A search for an accepted definition of the ways entrepreneurs learn produced a myriad

of different articles in the literature. Among them, the most frequent terms used were

'learning style', 'learning behaviours', 'cognitive style' and 'learning strategy'.

Additionally, in many of the studies, scholars used the terms 'learning style' and

'cognitive style, interchangeably, whilst others consider these terms as having a

distinct meaning (Cassidy, 2004).

45

Cognitive style is defined as the person's typical or habitual mode of problem solving,

thinking, perceiving, and remembering. learning styles refer to the application of styles

in learning situations (Riding and Cheema, 1991; Cassidy, 2004), whilst strategies are

the means that may be used to cope with situations and tasks (Riding and Cheema,

1991). Learning styles are more automated, whilst strategies are optional (cf Cassidy,

2004, p. 421), and may alter based on a specific situation or a different time, and more

importantly they may be learned and developed.

Learning strategies of individuals are based upon their specific patterns of learning

activities (Vermetten et al., 1999). Learning strategies are often related to the context,

are connected to certain learning situations, specific tasks, and are personal and

habitual (Vermetten et al., 1999), existential and or reflect your situation in life

(Megginson, 1996). Warr and Downing (2000) differentiate between different types of

learning strategies. Accordingly, learning strategies can be divided into three types:

strategies that are related to cognitive processes, strategies, which are behavioural in

nature and self-regulatory strategies.

It is argued in this study, that the ways entrepreneurs learn about opportunities should

be approached from the perspective of their learning strategies. However, in the

context of entrepreneurship in general, and international entrepreneurship specifically,

there is a dearth of articles, to say the least, that focus on the learning strategies of

entrepreneurs in the opportunity identification process. In addition, there is a lack of

coherence among scholars on what learning strategies are exactly and how many of

them exist and how they should be defined and categorised (Kakkonen, 2010).

Lans et al. (2004) studied work-related lifelong learning of entrepreneurs in the agri-

food sector in the Netherlands. They distinguished between three forms of learning:

formal, non-formal and informal. Fenwick (2003) mentioned four learning behaviours:

episodic learning, continuous learning, adaptive learning and generative learning.

Both, Lans et al. (2004) and Fenwick (2003) emphasised a more strategic type of

learning which “…goes beyond the adaptation of processes or practices and will

typically challenge existing practices, leading to a re-design of existing routines,

values, principles, and starting points” (Lans et al., 2008, p. 599).

Lichtenstein et al. (2003) emphasised that rather than engaging in long-term planning,

entrepreneurs are more likely to act based on trial-and-error learning in a process of

experimentation. Experimentation is often the choice of learning for entrepreneurs, in

46

the early stages of start-ups in contrast to large and established firms. Consequently,

Learning-by-doing can be described as a transformational double loop learning

process (Cope, 2003a; Lee and Jones, 2008), which relies on the individual

entrepreneur’s mental models, including their knowledge, experience and beliefs

(Cope, 2003a).

In a more recently published article, Honig et al. (2005) studied the learning strategies

of nascent entrepreneurs in Sweden. Based on the 'theory of effectuation' (Sarasvathy,

2001b) the scholars identified six different learning strategies. They argued that these

strategies have an effect on the progression of the start-up processes. The six learning

strategies are systematic, continual adjustment, incremental, R&D, persistent and

random.

Van Gelderen et al. (2005) researched learning opportunities and learning behaviours

of small business starters. The authors define learning behaviours based on Sadler-

Smith and Beryl's (1998) definition, which is: “the approach a person tends to take to

learning opportunities” (Van Gelderen et al., 2005, p. 97). Learning opportunities can

be considered as situations that challenge the person to learn, and thus evoke learning

behaviour. They consider learning behaviours as changeable and situation dependent.

An entrepreneur can have various approaches to learning and thus have the choice to

decide which to employ. However, changeability is limited and entrepreneurs will tend

to choose from previously existing learning behaviours. They investigated four

different learning strategies: meaning-orientated learning, instruction-orientated

learning, planned learning, and emergent learning. The former two learning strategies

are based on Hoeksema et al. (1997) and the latter two on Megginson (1996) learning

strategies framework and definitions.

Hoeksema et al. (1997) distinguished between two learning strategies: the 'deep' and

the 'surface'. The deep learning strategy is focused on understanding the meaning of a

task and satisfying curiosity. In doing so, they make the tasks more coherent,

meaningful and experiential. Their approach to learning is critically orientated and

they will attempt to make a comparison between their tasks and others. In addition it

highlights the importance of work outcomes (Van der Sluis and Poell, 2002b).

The surface learning strategy is more instructionally orientated. The focus of this

strategy is on pragmatism and is based on factual data and examples. The task should

be specific, clear and instruction orientated. In addition, the essentiality of meeting

47

standards, obligations and requirements is highlighted (Van der Sluis and Poell,

2002b). Megginson (1996) distinguished between two independent learning strategies:

planned and emergent.

The ‘planned learning strategy’, includes advanced planning and discussion. The

focus is on deliberation and the forethought approach to tasks (Van der Sluis and

Poell, 2002b; Van Gelderen et al., 2005). The ‘emergent learning strategy’ is defined

by unintended exploration. The learner is open to experience, and become more

adventurous. These two strategies are legitimate and effective and no one strategy is

superior to another (Megginson, 1996).

Most of the approaches described in this section elucidate learning strategies, which

are implemented by managers or entrepreneurs. However, none of them focused

specifically on international entrepreneurship. In addition, the traditional approach to

learning suggests that effective entrepreneurial learning is learning ‘‘by doing’’.

Nevertheless, this approach might be less suited to International New Ventures, in

industry, such as the hi-tech SMEs. For them, this approach to learning is too time-

consuming a strategy, primarily because there is limited time to learn 'by doing' from

the opportunity. In addition, knowledge may become obsolete quickly, therefore, rapid

internationalisation, demands rapid learning approaches (Saarenketo et al., 2004).

Lévesque et al. (2009) studied the effects of learning on the decision to enter the

international market. Their theoretical study addressed the effect of learning on the

timing of the entry and highlighted two types of learning behaviours: learning from

participation and learning from the experience of others. Learning from participation

refers to the extent to which entrepreneurs learn by entering the industry and by

creating new, and possibly private, information. This strategy might contribute to the

accumulation of tacit knowledge. Learning from the experience of others considers the

explicit knowledge they acquire vicariously from either observing or networking with

others and exploiting pre-existing knowledge. In addition, the type of industry and the

time of entry matter. They suggest that entrepreneurs who delay entry can learn from

the experience of more experienced entrepreneurs in the industry. In other words, they

can learn from the stock of information created by others.

However, the authors did not exactly define these learning strategies. They lamented

that: “the exact definition of what learning from participation includes versus what

learning vicariously includes is beyond the scope of our paper. The focus of our paper

48

is not to identify what is included in the information set available to the entrepreneur

at any point in time, but on variations in the relative role played by different learning

venues on the decision about when to enter the industry” (Lévesque et al., 2009, p.

551).

In the same vein, Schwens and Kabst (2009) researched aspects of the learning of

early internationalisers in contrast to late internationalisers. On the basis of Levitt and

March’s (1988) model, they distinguished between three learning behaviours: learning

from direct experience, learning from the experience of others and learning from a

paradigm of interpretation. Learning from direct experience refers to the extent the

firm learns 'by experiencing'. Learning from experience of others relates to the extent

foreign market knowledge is acquired through networking and learning from a

paradigm of interpretation is done through imitating best practices and routines.

Their results showed that late internationalisers are more experienced and have already

established competences; therefore, they will tend to learn from direct experience

rather than through indirect learning strategies such as learning from others. In

contrast, early internationalisers tend to prefer more indirect learning strategies, such

as: learning from the experience of others or from imitating best practices and routines

(i.e. paradigm of interpretation), which are less time consuming and allow the

entrepreneur rapid internationalisation, thus overcoming the liabilities of newness and

foreignness (Zahra, 2005). Although their research is focused on learning strategies

utilised by early as opposed to late internationalisers, it should be mentioned that these

strategies are related to the internationalisation entry phase rather than to the

opportunity identification process.

Saarenketo et al. (2004), researched dynamic knowledge-related learning processes in

internationalising high-tech SMEs. Although their research focuses on the firms, as the

research unit of analysis, it seems appropriate to refer to this research and to describe

these behaviours, as possible learning strategies of the international entrepreneur in a

fast moving environment such as the hi-tech industry. The authors, based on Huber

(1991) and Forsgren (2002), described four ways to learn in rapid internationalisation,

which will enable the entrepreneurs to rapidly acquire knowledge: learning through

networking, learning through grafting, learning through imitating and learning

through searching.

49

Applying the 'learning through networking' strategy might enable the entrepreneur to

learn through an interactive process of information exchange between the

entrepreneurs and their social networks (Johanson and Vahlne, 2009), viz., 'thinking

through taking' (De Koning, 2003). Hence, social networking is seen as a mechanism

through which opportunity can be developed (Davidsson and Honig, 2003; Mainela

and Puhakka, 2011). Entrepreneurs collect, identify and assemble relevant information

with others, in the main, in order to progress, evaluate and elaborate their intuitive idea

(Mainela and Puhakka, 2011).

'Learning through grafting' refers to the way firms acquire knowledge by getting

access to another firm’s resources by collaborating or recruiting (Huber, 1991).

Concerning entrepreneurs, after identifying the idea, they can join another founder or

team member to the entrepreneurship at this stage. In addition, they can recruit team

members with the necessary knowledge: either technological or market.

The 'Learning through imitating' strategy, considers that entrepreneurs can observe

how other international new ventures with ‘‘high legitimacy’’, that is have survived

the market feasibility stage, enter international markets and try to replicate their

behaviour.

'Learning through searching' enables entrepreneurs to search for reliable sources of

information regarding the host market, potential customers, etc. Examples of these

sources are the internet, new social media, professional magazines and business and

economic news.

Honig (2001) studied learning strategies amongst a sample of nascent entrepreneurs

and intrapreneurs. Overall, it is evident from the study findings that there are

differences between entrepreneurs and intrapreneurs in their learning strategies.

Intrapreneurs were found to employ learning strategies that focused on organisational

consensus, whilst entrepreneurs were found to utilise strategies that were more

flexible, adaptive, and less accommodating of relatively static environments.

Furthermore, four types of learning strategies were conceptualised as being relevant to

entrepreneurs and intrapreneurs: R&D strategy, continual organisational adjustment,

systematic strategy, and random strategy. Honig (2001), following Levitt and March

(1988), defined strategic learning as consisting of three subsequent processes: the

discovery, the knowledge diffusion, and the process of informed action. The discovery

process refers to the opportunity identification phase; in contrast, both knowledge

50

diffusion and informed action are elements of the post identification of an opportunity

and are related to the exploitation phase, which is the process of resource acquisition

and co-ordination. Based on this typology, Honig (2001) relates the former two

learning strategies, to intrapreneurs and the latter two, to entrepreneurs and

specifically as relevant to the opportunity identification phase ('random' and

'systematic').

The 'systematic' strategy is more typical of an entrepreneurial start-up, where the focus

is on the creative activity itself and not on a corollary of bureaucratic requirements.

The strategy refers to “the engagement of entrepreneurs in a deliberate, planned search

for a new business idea” (Honig, 2001, p. 26). This is an open-ended search and the

focus is on innovation and creation. In contrast, the 'random' learning strategy, is

highly unpredictable and flexible, and the learning is not planned, the idea just 'shows-

up' (Honig, 2001) .

Minniti and Bygrave (2001) argued that entrepreneurs acquire their entrepreneurial

knowledge through direct experience and specifically by doing or by observing. They

learn by doing in uncertain and risky environments making mistakes and therefore

possibly learning from their failures. Furthermore, Rerup (2005) argued that when a

direct experience is cognitively classified as a failure, it is likely to increase the

willingness to explore new opportunities, and to do so by changing their unsuccessful

behaviour. Whilst, a success might stimulate behavioural perseverance, so that they

will persist in continuing to do what they perceive they do best, and this might end up

in a decreased search for new opportunities (i.e. exploitation).

Huovinen and Tihula (2008), propose that failures may advance entrepreneurial

knowledge as well as forming experiences. Entrepreneurs have generally been viewed

as intention and action-orientated persons (Bird, 1988) who learn by experimental

learning or by doing through active testing, trial and error (Deakins and Freel, 1998)

and mistakes (Baum et al., 2003).

Arrow (1962), studied learning-by-doing, as the product of experience. Therefore,

learning can be performed through a process of problem solving which enforces the

necessity of an action. In addition, Arrow (1962, p. 155) argued that: "learning

associated with repetition of essentially the same problem is subject to sharply

diminishing returns…To have steadily increasing performance, then, implies that the

51

stimulus situations must themselves be steadily evolving rather than merely

repeating.”

Huber (1991) gives a good summarisation of these arguments: "In view of the above,

here a more behavioural perspective is taken: An entity learns if, through its

processing of information, the range of its potential behaviour is changed. This

definition holds whether the entity is a human or other animal, a group, an

organisation, an industry, or a society.”

2.3.5 Learning about Opportunities

In this section, as described earlier a systematic literature review was conducted in

order to review the current research on the topic of learning about entrepreneurial

opportunities. The scope of the literature was limited to the period from 2002 to 2013,

and followed general guidelines for the literature selection as described earlier in this

chapter, from which the most important is that the selected papers should be in

entrepreneurship, IE or management and business domains, and focus specifically on

the topic of learning about opportunities.

Results of this search produced a list of 16 articles that are brought together. The

studies deal with the topic of learning about entrepreneurial opportunities and draw on

the diverse range of literature on learning and opportunity identification to contribute

to both theory development and practice within the field of entrepreneurship. Table ‎2.1

shows a frequency of the 16 reviewed studies in the topic of learning about

opportunities:

Table ‎2.1: Frequency of the Reviewed Studies on learning about opportunities by

Journal Focus

Journal Type

Number

of Studies

Entrepreneurship 9 (56%)

Management & Business 3 (19%)

International Business 1 (6%)

International Entrepreneurship 3 (19%)

Total 16 (100%)

52

It can be seen that about 56% of the studies were published in Entrepreneurship

Journals. In addition to that, only approximately 19% of these studies were published

in International Entrepreneurship Journals. In contrast, 6% were published in

International Business Journals and only 19% in Management Journals. It means that a

purely managerial topic such as learning, when combined with Entrepreneurship in

International context, has not yet been achieved.

The results were investigated further by extracting the type of the study from all

articles. These results are detailed in the following table:

Table ‎2.24: Publications on learning about opportunities by Research Type

Number of

Empirical

articles

Number of

Theoretical

articles

Number

of Review

studies

Number of

Introductions to

special issues

Total

2002-2004 2 0 0 0 2

2005-2013 8 5 0 1 14

Total 10 5 0 1 16

The Table shows that the majority of the studies, which combine both International

opportunity or entrepreneurial opportunity and Learning (88%), were published

between the years 2005 and 2013. Eight of them (57%) were published in 2005 as part

of a special issue of the Entrepreneurship Theory and Practice Journal.

In 2005, Harrison and Leitch (2005) edited a special issue on the topic of:

‘Entrepreneurial Learning: Researching the Interface Between Learning and the

Entrepreneurial Context’. The special issue included articles, which were related to the

emergence of learning as an area of research in the context of entrepreneurship. The

articles emphasised conceptually and empirically, the process and outcomes of

learning in entrepreneurial contexts. Among the key themes that were included in this

4 The table shows the distribution of 16 reviewed studies. Studies that were published as books, or book

sections were excluded from this analysis.

53

special issue were ‘Opportunity recognition and exploitation as a learning process’ as

well as ‘The application of learning theories to entrepreneurship’.

Another interesting pattern is the predominance of the empirical research rather than

theoretical or reviews. The predominance of empirical research, in this literature

review, also supports the debate among scholars in the research field of

entrepreneurship that the "development of robust theoretical foundation in the

entrepreneurship domain is still evolving" (Keupp and Gassmann, 2009, p. 603) and

that the research on the phenomena of entrepreneurship is still "a widely dispersed,

loosely connected domain of issues"(Ireland and Webb, 2007, p. 891).

The review of the literature on the relationship between entrepreneurial opportunity

and learning might indicate a growing consensus in the field that learning about

opportunities occurs at the individual, group, and organisational levels. However, most

of this research focussed on the organisational level due to its promise of elucidating

the learning processes. Moreover, a sparse number of research focus on the

international context. Those studies emphasised that compared to the learning process

of large and established firms, small and young firms face considerable challenges

when they enter overseas markets (Tan, 2005).

In order to assess these studies as systematically as possible, each study was analysed

with particular emphasis on the main objective, research question/s or problem/s,

theoretical framework/s and the methodological approach.

The following table describes these 16 articles by emphasising their main research

focus (table 2.3 continued on 2nd

and 3rd

pages):

54

Table ‎2.3: Review of 16 articles on learning about opportunities by theoretical base

Title Framework/Theory

Base

Research

Question(s)/Problem(s)

Corbett

(2002)

Recognizing high-

tech opportunities: A

learning and

cognitive approach

Empirical – based on the

work of Shane (2000);

Shane and Venkataraman

(2000b), the Human

capital approach and

Baron (1998) proposition

regarding cognition.

Examination of the relationship

between an individual’s

learning mode, cognitive style,

human capital and his or her

ability to recognize and

develop opportunities in a high

technology environment.

Almeida et al.

(2003)

Start-up size and the

mechanisms of

external learning:

increasing

opportunity and

decreasing ability?

Empirical- based on

strategic management, and

organizational theories

Examination of how alliances,

the mobility of experts, and the

informal mechanisms

associated with geographic co-

location can present firms with

useful opportunities to source

technological knowledge

Corbett

(2005a)

Experiential Learning

Within the Process of

Opportunity

Identification and

Exploitation

Theoretical- based on the

experiential learning

theory (ELT)

The article makes connections

between knowledge, cognition,

and creativity to develop the

concept of learning

asymmetries in the process of

opportunity identification.

(Dutta and

Crossan,

2005)

The Nature of

Entrepreneurial

Opportunities:

Understanding the

Process Using the 4I

Organizational

Learning Framework5

Theoretical- based on two

streams of studies,

entrepreneurship and

organizational learning.

By applying the 4I

organizational learning

framework, a theoretical

framework for understanding

the nature of entrepreneurial

opportunities is developed and

introduced.

Harrison and

Leitch (2005)

Entrepreneurial

Learning:

Researching the

Interface Between

Learning and the

Entrepreneurial

Context

Review (introduction to

special issue) - reviewing

the concepts of learning

within the field of

entrepreneurship.

Reviewing the development of

the field of entrepreneurship as

a context for the emergence of

learning as an area of scholarly

attention, summarize a number

of key themes emerging from

the organizational learning

literature, among them


5 Crossan et al. (1999) developed a framework, which considered organisational learning as consisting

of four processes, which are often called the 4I model: intuiting, interpreting, integrating, and

institutionalising.

55


Base

Research


Lumpkin and

Lichtenstein

(2005a)

The role of

organizational

learning in the

opportunity-

recognition process

Theoretical- based on

Organizational Learning

Theory, Strategic Renewal

and theories about the

creation of new ventures.

Can organizational learning

(OL) enhance the process of

recognizing and pursuing new

ventures?

Politis (2005) The Process of

Entrepreneurial

Learning: A

Conceptual

Framework

Theoretical- based on

entrepreneurial learning

and experiential learning

research.

Developing a conceptual model

that explains the relationships

between the entrepreneurs’

career experience, the

transformation process, the

entrepreneurial knowledge and

recognizing and acting on

entrepreneurial opportunities.

Schildt et al.

(2005)

Explorative and

exploitative learning

from external

corporate ventures

Empirical- Organizational

Learning theory and the

concept of explorative and

exploitive learning based

on March (1991).

This study examines the

antecedents of explorative and

exploitative learning from

external corporate ventures.

Van Gelderen

et al. (2005)

Learning

Opportunities and

Learning Behaviours

of Small Business

Starters: Relations

with Goal

Achievement, Skill

Development and

Satisfaction

Empirical- entrepreneurial

learning and learning

situations of entrepreneurs

based on the DCP concept

(developmental challenge

profile) (McCauley et al.,

1994)and learning

behavior based on

Hoeksema et al. (1997)

This study investigates when

and how small business starters

learn. It specifies the situations

that offer learning

opportunities, as well as the

learning behaviours that small

business starters can employ in

order to learn from these

opportunities.

Sanz-Velasco

(2006)

Opportunity

development as a

learning process for

entrepreneurs

Empirical- Entrepreneurial

learning and the

conceptualization of

entrepreneurship as an

opportunity discovery and

opportunity development

processes.

The study illustrates how

entrepreneurial learning can be

understood from the

perspective of opportunity

development and identification

Corbett

(2007b)

Learning asymmetries

and the discovery of

entrepreneurial

opportunities

Empirical- based on

current theoretical and

empirical work in

entrepreneurship and

especially, works on

cognitive mechanism and

prior knowledge.

Empirical examination the

relationship between

opportunity identification and

learning. Specifically it defines

a relationship between how

individuals acquire and

transform information and

experience (i.e., learning) in

order to identify opportunities

56


Base

Research


Dimov

(2007b)

From Opportunity

Insight to

Opportunity

Intention: The

Importance of

Person-Situation

Learning Match

Empirical- based on

experiential learning

theory (ELT) and

entrepreneurial and

opportunity intention

concepts.

This paper explores the

intentionality that drives the

early stages of the opportunity

process, from the initial

occurrence of an idea to its

further exploration and

elaboration by a potential

entrepreneur.

(Muzychenko,

2008)

Competence-based

approach to teaching

international

opportunity

identification: cross-

cultural aspects

Theoretical - based on

applying competence-

based approach to

teaching international


The study investigates the role

of cross-cultural training in

teaching international


Bergh et al.

(2011)

Entrepreneurs

learning together: The

importance of

building trust for

learning and

exploiting business

opportunities

Empirical- building trust

in others, social learning

(Bandura, 1977) and a

learning network context.

How trust develops and how

entrepreneurs can use networks

to learn and improve their

capacity to exploit business

opportunities.

Gabrielsson

and Politis

(2012)

Work experience and

the generation of new

business ideas among

entrepreneurs: An

integrated learning

framework

Empirical- based on an

integrated learning

framework which

combines human capital

theory (Honig and

Davidsson, 2000) , and

entrepreneurial learning

research and theory

(Minniti and Bygrave,

2001)

How entrepreneurs’ work

experience is associated with

the generation of new business

ideas.

Li et al.

(2012a)

Managerial ties,

organizational

learning, and

opportunity capture:

A social capital

perspective

Empirical- the overarching

framework is based on a

social capital perspective,

as well as on studies from

organizational learning

research and networking

filed.

The study investigates how

entrepreneurs in new ventures

utilize their managerial ties to

capture opportunity. it also

explores the moderating role of

exploratory learning and

exploitative learning) in this

process.

57

2.3.5.1 Learning about Opportunities Literature Review: Research Objectives,

Frameworks and Findings

In their introduction to the Special Issue on “Entrepreneurial Learning: Researching

the Interface Between Learning and the Entrepreneurial Context”, Harrison and Leitch

(2005) addressed the research on various entrepreneurial issues through the lens of

organisational learning literature. The motivation for this special issue arises from the

rare existence of the topic of learning, to some extent, within the entrepreneurship

domain, which contributes to both theory development and practice. Therefore, the

special issue consisted of articles, which scrutinised, theoretically and empirically, the

learning process and outcomes in entrepreneurial contexts.

One important topic of interest was the ‘Opportunity recognition and exploitation as a

learning process’. Seven articles were included in the Special issue, and five of them

can be considered as highly related to the topic of entrepreneurial opportunity and

learning. On the one hand, the articles included in this special issue represent a

breakthrough in the research on the intersection between learning and entrepreneurial

opportunity. On the other, the small number of empirical articles (Harrison and Leitch,

2005), point to the fact that this topic can be considered as immature and lacking a

solid theoretical framework to increase scholars understanding of the relationships

between learning and entrepreneurship in general, and learning and entrepreneurial

opportunities in particular. In addition, the complexities of the learning construct, the

various definitions and approaches of the entrepreneurial opportunity phenomena,

might enhance the need for more appropriate research strategies and design that can

explore both differentiate concepts: entrepreneurial opportunities and learning.

Politis (2005) introduced a conceptual model of entrepreneurial learning. The model

reflects ‘the process of entrepreneurial learning as an experiential process’. The

entrepreneurial prior experience might result in the acquisition of entrepreneurial

knowledge. One major contribution of this article might be found in the distinction

between learning as a dynamic process, which transforms past experiences, and

knowledge as the outcome of the process (Harrison and Leitch, 2005). Although this

article does not relate directly the relationships between learning and opportunity, it

was included in this review, due to its emphasis on entrepreneurs’ career experience,

the transformation process, and entrepreneurial knowledge in terms of effectiveness in

recognising and acting on entrepreneurial opportunities. However, the author does not

58

make the distinction between opportunity recognition and other types of

entrepreneurial processes such as discovery and creation (Vaghely and Julien, 2010).

The author used interchangeably the terms discovery, exploitation, and recognition.

Politis (2005) adopted a positivist view, about entrepreneurial opportunities, however,

the learning process, might not be considered as a part of this ontological view.

Learning consists of both a positivist aspect as well as an interpretive aspect (Dutta

and Crossan, 2005). This is important because the concept of entrepreneurial

opportunities is often lacking in clarity and specificity (Mainela et al., 2013), and

therefore studies should clearly present and discuss their conceptualizations of

entrepreneurial opportunities. This issue of how different approaches to

entrepreneurial opportunities are related to learning was further emphasised by the

conceptual work of Dutta and Crossan (2005).

Dutta and Crossan (2005) emphasised the conceptual and theoretical foundations of

the research on entrepreneurial learning and opportunities. They examined the concept

of entrepreneurial opportunities from two opposing ontological views (i.e.

Schumpeterian versus Kirznerian viewpoints), by applying the 4I organisational

learning framework (Crossan et al., 1999) to entrepreneurial opportunities. Their main

argument was that although there are conflicting explanations of the nature of

entrepreneurial opportunities, the learning process, which is explained by the 4I

model, could lead to a pragmatic synthesis of these contradicting views. Learning in

their view, can be described as a dynamic, cyclic process, of intuiting, interpreting,

integrating, and institutionalising information at the level of the individual, the group,

and the organization.

Using the 4I framework, thus, enables the development of a theoretical framework, to

understand the process of entrepreneurial opportunities. However, although the 4I

framework provides a useful explanation of how entrepreneurs recognise or enact on

opportunities, cognitively, it does not provide an explanation of the entrepreneurial

action or behaviour, which is an important part of how entrepreneurs learn about

opportunities. They argue that the development of individual-level intuition essentially

leads to group-based learning processes (interpreting and integrating). However, this

might not be the case for every entrepreneur. Some entrepreneurs might prefer to do it

by themselves rather than to discuss it with their networks. It means that they might

59

choose to learn by doing rather than by involving shared processes at the group or

organisation level.

As opposed to the organisational learning approach of Dutta and Crossan (2005),

Lumpkin and Lichtenstein (2005) dealt with this by introducing three approaches to

organizational learning: behavioural learning, cognitive learning and action learning.

Each of the learning types is related to a specific aspect of the opportunity recognition

process, through the lens of their developed model, the creativity based approach to

opportunity recognition. In the same vein as Politis (2005), they approached

entrepreneurial learning from a positivist point of view.

Their model addresses the opportunity recognition process, as a staged process

involving both discovery and formation (Harrison and Leitch, 2005). One main

contribution of their research lies in their argument that the entrepreneurial

opportunity is a recursive process, which often begins with an insight or an idea,

through a collection and interpretation of information and an acquisition of new

knowledge and finally a formation into an opportunity is made. In a way, both

conceptual models of Dutta and Crossan (2005) and Lumpkin and Lichtenstein

(2005a) are complementary. The former elucidates the role of different types of

entrepreneurial opportunities, and the latter adds the depiction of different types of

learning types. In addition, the focus of Lumpkin and Lichtenstein (2005a) is on the

importance of creativity rather than learning.

Corbett (2005a) takes the topic of entrepreneurial opportunities and learning further. In

his theoretical article, it was argued that there is a need, within the entrepreneurship

research, to explore ‘how individuals learn and how different modes of learning

influence opportunity identification and exploitation’. In this theoretical framework,

entrepreneurship is approached as a learning process, and learning is described as a

social process (Harrison and Leitch, 2005), by which, prior experiences are

transformed through an experiential learning process, into a stock of knowledge. This

process is essential and fundamental to the learning process. One of the main

conclusions is that individuals differ in the way they learn, and hence it might

influence opportunity identification and exploitation. These learning asymmetries are

related to knowledge, creativity, and cognition. Although this article was almost the

first to explicitly focus on how individuals transform and acquire information, in the

entrepreneurial opportunity process, the emphasis is on the cognition rather than

60

evaluating other types of learning modes, such as strategies or behaviour as opposed to

styles, which are more context and situation dependent than styles.

Two additional articles by Corbett (2002); Corbett (2007a) were included in this

review. Both studies are empirical in nature and the positivist point of view to

entrepreneurial opportunity was explicitly taken. Entrepreneurial opportunities are

described as recognised, and the role of the entrepreneur is to search for them, actively

and purposefully. The results of the papers provide support for previous theoretical

arguments regarding the relationships between learning and entrepreneurial

opportunity (Corbett, 2005b), and illustrated the role that learning asymmetries plays

in the process of discovering opportunities.

Corbett (2007b) studied the individual as the unit of analysis, and explored the effect

of four independent variables (i.e. general human capital, specific human capital,

information acquisition preference, and information transformation preference) on the

number of recognised opportunities. This study contributes to the knowledge in this

field of enquiry by being the first that empirically focused on the learning processes of

entrepreneurial opportunity. However, one major limitation could be the way

entrepreneurial opportunities are measured in this study. In this study, the number of

opportunities recognised was the measure of entrepreneurial opportunity. However,

taking the positivist point of view, that opportunities exist regardless of whether

individuals are aware of their existence (Alvarez and Barney, 2007b), and that

opportunity discovery is realised through active search behaviour (Mainela et al.,

2013), it can be argued that measuring only the number of opportunities recognised,

might limit the study findings.

Schildt et al. (2005) empirically (i.e. with the use of patent citations), explored the

concept of explorative and exploitative learning, which was developed originally by

March (1991), in the process of discovering new technological business opportunities.

The main contribution of the study is that by empirically exploring factors influencing

the type of learning outcomes (explorative versus exploitative), this study has added to

our understanding of learning in entrepreneurship context. Especially, how ventures

learn from external sources. However, it shares one shortcoming concerning the

relationships between learning and entrepreneurial opportunity. Its focus is largely on

the question how firms learn and on the quality of learning through external corporate

ventures rather than how entrepreneurs learn about opportunities. Secondly, the

61

concept of explorative and exploitative learning is highly related to learning outcomes

rather than to learning types or behaviours. In addition, this concept focuses on search

activities, while it is agreed that opportunities can be defined in various types of

actions.

Almeida et al. (2003) studied the relationship between start-up size and the use of

three mechanisms of external learning, expert mobility, alliances, and informal

geographically mediated networks. One important contribution of this empirical study

lies in the argument that the start-up size increases the likelihood of learning from

another firm. This argument adopts the view that there are different ways to learn, for

example, by using learning, formal, and informal mechanisms. In addition, the focus

on start-up size as antecedents to learning mechanisms may limit the study’s findings.

Entrepreneurial ventures and start-ups often consist of only one or two founders. It

might be argued that even though the start-up size increases, they will choose to learn

from other sources or mechanisms. Furthermore, the literature nowadays suggests

more mechanisms than Almeida et al. (2003) had considered previously (e.g. Bosma et

al., 2002; Van Gelderen et al., 2005; Schwens and Kabst, 2009; Bruneel et al., 2010).

It might be argued that learning is a complex, dynamic, and recursive process, and by

suggesting, that the size of the venture influences the learning mechanisms might

depict a partial illustration of the phenomena.

Van Gelderen et al. (2005) studied when and how small business starters learn.

Specifically the study investigated learning behaviours that entrepreneurs can

implement in order to learn from opportunities. The focus of the study was on the

outcomes of the learning behaviours as well as learning opportunities. These outcomes

are described in the study as goal achievement, skill development, and satisfaction.

Their study can be considered as complementary to other studies in this review, in the

sense that their focus was on learning opportunities, learning outcomes, learning

behaviours of entrepreneurs, and their relationship with success. Their study does not

relate directly to the process of identifying opportunities. In addition, they described

four learning behaviours: meaning-oriented learning, instruction-oriented learning,

planned learning, and emergent learning.

However, these learning behaviours are described as general learning behaviours of

entrepreneurs, while it might be argued that entrepreneurs utilise various ways of

learning in different situations, contexts, and stages. These four learning behaviours

62

are described as independent of each other, while it might be reasonable to assume that

entrepreneurs might choose to learn for example by applying the meaning oriented

learning behaviour in a planned or emergent manner. Finally, as it was mentioned in

their study, the questionnaire was based on work with managers, rather than by

conducting a study in which ”…entrepreneurs are asked how and when they learn”

(Van Gelderen et al., 2005, p. 106).

By using a qualitative enquiry, among 20 entrepreneurs, Sanz-Velasco (2006)

investigated the relationships between prior knowledge and resources, such as

technology and personnel, in the entrepreneurial opportunity development process. In

this study, two views on entrepreneurial opportunities were contrasted and tested:

discovery and development. It was concluded that the term ‘opportunity development’

represents better the entrepreneurial opportunity as a process of creation of an

opportunity rather than the discovery of opportunities (Ardichvili et al., 2003a).

‘Opportunity development’, from this point of view, encompasses the identification,

the development, and the evaluation of an entrepreneurial opportunity based on market

needs. Moreover, market interaction such as an active engagement in sales, may lead

to a faster and more successful entrepreneurial opportunity development. However, the

study did not encompass explicitly any entrepreneurial learning framework rather than

emphasising the importance and the essential role of prior knowledge in the process of

‘entrepreneurial opportunity development’, in contrast to other studies in this review.

Dimov (2007b) takes the concept of ‘opportunity development’ further. This dynamic

process begins with an initial vague idea, or an insight, which is developed through a

learning process, to its further elaboration and exploitation. Domain- specific prior

knowledge and learning styles play an important role in affecting the entrepreneur’s

intentionality to act upon an opportunity. One major contribution of this empirical

work is by showing that the direct effect of prior knowledge on the probability of

developing an opportunity is subjected to the extent an individuals’ learning style

match the current situation. In other words, the match between learning style and

situation moderates the relationship between ‘Domain-Specific Knowledge’ and

‘Action Likelihood’. In addition, the role of the individual entrepreneur in this process

of opportunity development, as well as the situational characteristic of an

entrepreneurial opportunity, is highlighted. This study provides empirical evidence,

following Corbett (2005a;2007a), that different ways of learning affect the

entrepreneurial opportunities identification and development process. However, it can

63

be argued that learning styles are considered as more static, stable, and inherent ways

of learning. They are cognition based learning modes. Thus, the same entrepreneur, in

a different context of opportunity identification, may choose to acquire and process

information, and learns, in a different manner. An argument that should be further

developed and investigated.

Bergh et al. (2011) make a unique contribution, by means of adding the concept of

trust and networks to the way entrepreneurs learn about business opportunities. The

study emphasised the concept of social learning, hence learning by participating in a

network. However, for learning to become effective (i.e. identifying opportunities)

entrepreneurs, in a network, should develop mutual trust. This trust enables the

entrepreneurs to share ideas. However, the study does not provide an explanation of

how factors such as self-efficacy, or previous entrepreneurial experience, may affect

learning outcomes (i.e. cognitive, emotional, and social) and hence improve the

entrepreneur’s capability to act upon entrepreneurial opportunities.

By integrating human capital theory and entrepreneurial learning literature,

Gabrielsson and Politis (2012), provided evidence that a ‘learning mind-set’ is a

reliable predictor of the generation of new business ideas. In addition, increase in the

level of ‘breadth in functional work experience’, positively affect the generation of

new business ideas. In contrast, ‘industry-work experience’ is negatively associated to

new business idea generation. Gabrielsson and Politis (2012) view entrepreneurial

opportunity as a developmental process (Sanz-Velasco, 2006; Dimov, 2007b).

However, their dependent variable, based on Singh (2000) was measured by indicating

“…the number of new ideas, which the entrepreneur had in the last year, and could

lead to a new business or a significant part of an existing business” (Gabrielsson and

Politis, 2012, p. 56). This might limit the study’s findings mainly because new

business ideas are not necessarily related to business opportunities, especially when

entrepreneurial opportunity is defined as a developmental process. Focusing only on

indicating the number of new ideas that were generated may resulted with a partial

illustration of the entrepreneurial opportunity as a learning process, although it should

be mentioned that measuring new venture idea as a construct have considerable

analytical advantages in comparison to the entrepreneurial opportunity construct

(Davidsson and Tonelli, 2013 ).

64

Davidsson and Tonelli (2013 p. 3) explained: “the view proposed by what is now

referred to as “Discovery Theory” – cannot be identified, sampled and measured at

early stages of entrepreneurial processes. They are not identical to the “subjective

conjectures” the theory assumes entrepreneurs act on, and they represent a view that is

difficult to align with process, change, and unsuccessful outcomes”. In addition, there

is a growing agreement among scholars in this domain, that the entrepreneurial

opportunity process can be described not only as the individual-opportunity nexus

(Shane and Eckhardt, 2005) but also as a process in which entrepreneurs interact with

their social contexts and environment to develop opportunities (De Koning, 2003).

Li et al. (2012a), based on social capital and organisational learning theories, hold the

same position, emphasising that entrepreneur’s networks, as represented by their

managerial ties (consisting of ties with other firms and ties with government) are

related to entrepreneurial opportunities. In addition, the learning modes (i.e.

exploration learning versus exploitation learning) moderate the relationships between

managerial ties and entrepreneurial opportunity. One major limitation of this study

resulted from the way the authors defined social networking ties. The authors

emphasised that ties with the business community could help overcome the lack of

resources, and enhance the new venture ability to respond rapidly to the changing

market conditions, acquire useful and relevant knowledge, and thus increase their

capacity to capture more opportunities. While this makes sense, there are various types

of networking ties that may influence this process, such as strong or weak ties

(Granovetter, 1983). Entrepreneurs interact with others in their networks, which

systematically vary according to the phase of entrepreneurship (Greve and Salaff,

2003a). Thus, focusing only on one type of ties, managerial type, may represent only

one layer of the complex interaction process between the entrepreneurs and their

networks. A broader conceptualisation of the networking ties concept would be

beneficial for this study, as it might enable the authors to generalise their findings not

only to the context of China, but to other regions as well.

Muzychenko (2008), on the topic of ‘Competence Based Approach to Teaching

International Opportunity Identification: Cross-cultural Aspects’ provided some

practical implications for the relationships between entrepreneurial opportunities and

learning. Based on the relationships between opportunity identification process and

self-efficacy (Ardichvili et al., 2003a), Muzychenko argued that increase in the level

of entrepreneurial self-efficacy in international opportunity identification process,

65

could be beneficial for entrepreneurs. In addition, the author emphasised the important

role prior knowledge, experience and social networks play in the opportunity

identification process. International opportunity identification competence can hence

be developed as part of the education system. However, the study does not include any

theoretical framework to be further tested in an empirical investigation, but rather

discusses the major themes and patterns in the international-opportunity-identification

research domain.

2.3.5.2 Learning about Opportunities Literature Review: Results of Empirical

Methods

Only ten articles, from the 16 that focuses on learning about opportunities, are

empirical. A small variety of research methods characterised this review, something

that can be derived from the small size of the sample, and might reflect on both the

immaturity of this field of research and the very limited scale of research objectives

being addressed. In this context, special attention should be put on the two different

methodological approaches, the case study among the qualitative articles and surveys

among the quantitative articles.

The following table shows the frequency of research strategies, designs, and methods

in this review:

66

Table ‎2.4: Learning about opportunities: articles by research strategy, design, and data

collection techniques

Type Number

of Articles

Research Strategy Quantitative

Qualitative

Mixed Methods

7

2

1

Research

Methods/Design

Secondary Analysis (Patent documents)

Case Study

Cross-sectional

Quasi-experiment and experiment

Other

2

1

4

2

16

Data Collection Semi-structured interviews

Survey (Questionnaire)

Documents/patents citations

2

6

2

Data Analysis Regression

Systematic interpretation of emerging

concepts

GLM repeated measure model

Qualitative content analysis

7

1

1

1

From the table above it can be concluded that a quantitative research strategy was

most prevalent in the empirical research. Of the 10 empirical studies, 7 (70%) used

quantitative research strategy (Corbett, 2002; Almeida et al., 2003; Schildt et al., 2005;

Van Gelderen et al., 2005; Corbett, 2007a; Dimov, 2007b; Gabrielsson and Politis,

2012; Li et al., 2012a), while two studies used qualitative strategy (Sanz-Velasco,

2006; Bergh et al., 2011) and only one study used a mixed methods strategy. Four

articles employed a cross-sectional research design; however, only one study

employed a longitudinal cased study research design (Bergh et al., 2011). These

findings are in line with Keupp and Gassmann (2009, p. 612) who argue that "…

almost none of the dependent variables used by the quantitative subsample includes

6 Qualitative interviews among the 20 start-up ventures in the Swedish mobile internet industry

67

the measurement of a time component". Time is an important factor of the

entrepreneurship phenomena (Baron, 1998), and without it, the research is static and

might fail to understand complex processes (Coviello and Jones, 2004), such as

opportunity identification (Eckhardt and Shane, 2003).

An important trend, which was found in this small sized sample, was the emergence of

a learning perspective, i.e., using organisational learning concepts to explain various

organisational phenomena in the entrepreneurship area. In addition, the majority of the

empirical studies employed a learning perspective to explain particular phenomena.

None of the remaining papers examined facilitators of learning, i.e., studies that

examined facilitators of learning as a dependent variable. This might limit their

research conclusions; in fact, they are almost unable to recognise important

relationships, which may exist, for example, the interaction between the entrepreneur

and the environment and the entrepreneurs' social behaviour. In addition, qualitative

analysis can elucidate other layers of this phenomenon, especially when learning is

involved. In this sense, scholars should consider qualitative methods and use

approaches that require participants to think, in contrast to just responding and

reporting on past experiences (Gaglio and Katz, 2001). However, multiple-level

analysis may be more expensive and complicated, especially in relation to data

collection (Keupp and Gassmann, 2009).

Nine of the ten empirical studies implemented a qualitative or a quantitative approach,

and only one used a mixed method strategy. The only article that implemented a

mixed method strategy used a sequential qualitative-quantitative design (Corbett,

2002). Interviews were conducted first in order to explore the factors that may

contribute to the process of opportunity recognition. Based on the interview findings, a

survey, which included a quasi-experiment, was developed. However, the author did

not use the data from the interviews to enrich or strengthen the quantitative findings.

This approach might increase the internal validity of the study, however, it might have

limited one of the advantages mixed methods strategy has to offer, which is the

integration between the findings of the qualitative and the quantitative results.

Among the quantitative articles, two articles made use of patent data (Almeida et al.,

2003; Schildt et al., 2005). Patent data have received attention due to their systematic

character, the amount and quality of information, and are obtainable unceasingly

across time (Almeida et al., 2003). Patent data can be considered as secondary analysis

68

of data collected by other organisations in the course of their business (Bryman and

Bell, 2007). This type of data collection tool has drawn research intention in the

business and management field. One primary advantage of using such a tool is to

enable a longitudinal element to be designed as part of the study (Bryman and Bell,

2007). Despite that, the patent data as a data collection tool is not without critique. The

large variance in the value of patents, and the propensity to patent, the fact that the

patent depends on the nation, industry, and policy, may limit the validity and rigour of

patent-related measures (Desrochers, 1998; Schildt et al., 2005).

Two of the quantitative articles used quasi-experimental research design (Corbett,

2007a; Dimov, 2007b). This type of design has certain characteristics of the

experimental design, in which entails manipulation of a social settings as part of a

‘natural experiment’ (Bryman and Bell, 2007). In comparison to experiments, the

participants are not assigned randomly to the groups. However, in the case of these

two articles, there is no real control group, thus, the basis for comparison might be

considered as limited, and the internal validity of the findings might be lower than

expected.

The majority of the articles used the questionnaire as their data collection tool (60%)

and the survey as their preferred research method. Questionnaires are a common

research tool to collect mainly, but not exclusively, quantitative data; in this sample,

one qualitative study (Crick and Spence, 2005) and mixed method studies (Kitchell,

1995; Loane and Bell, 2006) also use this tool. However, some scholars think that the

questionnaire is not an appropriate tool for qualitative and exploratory research, and

that it has to be used in combination with other procedures in order to obtain relevant

data. Survey research may also contribute to greater confidence in the generalisability

of results. On the other hand, they are exposed to some serious biases, such as

"respondent fatigue,” low response rate, missing data, etc., (Bryman and Bell, 2005, p.

241). That is why the surveys became more meaningful when understood from the

standpoint of qualitative data, just as other statistics were most useful when compared

with content analyses or interview results.

The most prevalent statistical tool that was used in these papers was the regression

model (MLR). Moreover, many of the authors did not indicate if any of the model’s

assumptions were tested and verified, such as the measurement level of the variables

or the parametric test assumptions. In some of the papers, researchers used more than

69

one tool, and a few of them used different stats tools for different hypotheses. Almost

all of the papers included a very basic descriptive analysis; showing the means,

standard deviations and correlations.

Among the qualitative articles, only one (10%) conducted the research with the use of

a case study research design. Bergh et al. (2011) chose to conduct a single organisation

case study (Bryman and Bell, 2007). The advantages of this research design are the

investigation of real life events, strong internal validity for theory building, and a

significant level of control for the many contextual variables. However, limitations of

such a research design include that it is almost impossible to identify typical cases

which can be used to represent a certain class of objects, although some cases enable

the researcher to generate concepts and give meaning to abstract propositions (Yin,

1984). In addition, in longitudinal case studies, it might be difficult to establish how

variation is the result of real differences over the period of time or of other factors

(Bryman and Bell, 2007).

All of the qualitative articles used interviews as their main data collection tool. In

many areas of rigorous empirical investigation, there is simply no satisfactory

substitute for interviews. Yes, interviews are more costly, compared to survey or

secondary data collection (Bryman and Bell, 2007). They are often subject to potential

problems of self-reporting issues, and some data may be missed, as interviews often

rely on verbal behaviour. However, interviewing is rewarding in a sense, that with

appropriate precautions especially for language, context, interpretation, and meaning,

the research may acquire rich understandings of the phenomena.

To conclude, the methodology in most studies is characterised by collecting data from

various sources such as executive or elite individuals, patent citations and documents,

or MBA students. In most of the articles in which the survey was conducted and the

questionnaire was the selected data collection tool, the reliability and the validity of

construct was not always reported or addressed. In addition, it can be argued that

although appropriate, the single method approach of data collection or research

strategy may not fully capture the key issues and processes under investigation.

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2.4 Summary

Little research has been conducted specifically to examine learning about

opportunities as a direct function of entrepreneurship. Despite the importance of

learning about the opportunity identification process, (Dimov, 2003; Corbett, 2007b)

not many studies, have emphasised the relationship between learning strategies (or

behaviours, modes and styles) and opportunity identification and exploitation (Honig,

2001). Man (2006, p. 311) summarised this argument well, "…in order to further our

understanding of entrepreneurial learning, we need to consider it as a concrete

construct of identifiable activities or behaviours, which allow further measurement,

generalization and investigation of various individual, organizational and contextual

factors affecting them".

The review of the literature revealed that the entrepreneurial learning process is an

important factor in the entrepreneur’s pursuit of new business opportunities,

specifically in the international world of business. Based on this literature review,

some important conclusions can be reached:

Firstly, the relationship between learning and entrepreneurship, learning and

opportunity identification, and especially the relationship between learning and

opportunity identification in international entrepreneurship context, and in different

internationalisation stages, is still far from being well defined as a research topic.

Secondly, the review of the empirical methods that were conducted indicates

the lack of theory-building approaches, which would provide the basis for developing

conceptual models, and constructs.

Thirdly, the current research field is still under development. This has led to

considerable inconsistencies, which prevent a better theoretical and practical

understanding of the phenomenon.

These inconsistencies can be analysed as more than a few knowledge gaps in

contemporary research. Two can be marked as particularly important:

The first is the lack of adequately developed theoretical frameworks. Hence, a

study, which will serve to fill the gap in our current understanding about the

relationship between Learning and International opportunity identification, is essential.

71

The second is the research strategy. Researchers would benefit from the

potential synergies resulting from a more insightful combination of both quantitative

and qualitative research methods and techniques (Rialp et al., 2005). This is also in-

line with the call of Coviello and Jones (2004) for researchers to address their

methodological decisions more thoroughly and with more coherency. They indicate

the need for a multidisciplinary approach to be developed in the international

entrepreneurship field.

There are a number of situations where mixed methods research is a development over

purely qualitative or quantitative research. One such situation is when a new way of

looking at a phenomenon, is required. Here, two research traditions are combined:

entrepreneurial learning, with a predominance of the qualitative approach, and

opportunity identification with a more quantitative approach. This makes a mixed

methods approach suitable, as we can incorporate research paradigms within both

areas.

Therefore, research that focuses on the implementation of the following

recommendations will fill the research disparities, and enhance the progress in the

entrepreneurship research domain.

Firstly, integrating international entrepreneurship, opportunity identification,

and entrepreneurial learning research domains into a new theoretical framework.

Secondly, the research will emphasise the way in which they learn about cross-

border opportunities.

Thirdly, the study will focus on the individual as the unit of analysis, and

elucidate the importance of the interaction between the individual and the

environment.

72

3. Research Design and Methodology

This study was carried out using a mixed method research strategy. The purpose of

this empirical research is to develop an understanding of the role that learning plays in

the identification of new entrepreneurial opportunities in the international arena. The

main reasons for choosing this specific research strategy and design in this research

are the following:

Firstly, the proposition that mixed methods research is better able to capture

the researched phenomenon has been argued by several scholars (e.g. Tashakkori and

Teddlie, 1998; Creswell, 2009).

Secondly, the structure of this research design (Qualitative-Quantitative), makes

it possible to reach much deeper knowledge of the research problem (Jick, 1979), and

overcome the limitations of using each of the methods separately (Jick, 1979;

Tashakkori and Teddlie, 1998; Hurmerinta-Peltomaki and Nummela, 2006).

3.1 Research Paradigm

Although philosophical paradigms remain silent in research, they have an impact on

research and thus have to be clearly acknowledged (Creswell, 2009). Creswell (2009)

mentions four different worldviews: post positivism, constructivism,

advocacy/participatory, and pragmatism. Mixed methods research often falls within

the "pragmatic paradigm" (Johnson and Onwuegbuzie, 2004).

The paradigmatic approach taken in this study was based on the pragmatic research

paradigm. In general, pragmatists believe that both positivist and constructivist points

of view co-exist. The incompatibility thesis states that “…compatibility between

quantitative and qualitative methods is impossible due to the incompatibility of the

paradigms that underlie the methods” (Tashakkori and Teddlie, 2003c, p. 18).

Pragmatists contend that methods derived from opposing paradigms may be utilised in

the same study, if this mixture improves the credibility of findings (Petter and

Gallivan, 2004). Tashakkori and Teddlie (1998) summarise the view that pragmatism

forms a paradigm distinct from others (such as positivism, post-positivism and

constructivism), and that this paradigm allows the use of quantitative and qualitative

methods in social and behavioural research (Howe, 1988). Since business research is a

73

form of social and behavioural research (Easterby-Smith et al., 1991), it is more than

reasonable to state that pragmatism is applicable as a paradigm to business research.

Morgan (2007) discussed the significance of directing research attention, primarily in

social sciences, on the research problem and using the ‘pragmatic approach’ to acquire

knowledge of the problem. Instead of focusing on methods, the inquirer should use all

approaches available for researching and understanding the research problem

(Creswell, 2009). Thus, the best method is the one that answers the research questions

(Tashakkori and Teddlie, 1998), and research approaches should be mixed in ways

that offer the best opportunities for answering important research questions (Johnson

and Onwuegbuzie, 2004).

3.2 The Research Strategy

Mixed methods research is a research paradigm that combines or associates both

qualitative and quantitative methods (Creswell, 2009). Thus, it is more than simply

collecting and analysing both kinds of data; it also involves using both approaches in

tandem so that the overall strength of the study is greater than either the qualitative or

the quantitative element in isolation (Creswell and Plano Clarck, 2007). Various terms

are used for this approach, such as “synthesis, multi-method, and mixed methodology”

(Creswell, 2009, p. 205) and mixed research (Johnson et al., 2007). However,

contemporary studies use the term Mixed Methods (Tashakkori and Teddlie, 2003a;

Bryman, 2006).

Mixed methods research has become something of a growth industry (Bryman and

Bell, 2007), although this approach is still evolving and much work remains to be

undertaken (Tashakkori and Teddlie, 2003c), especially on issues such as ontological

and epistemological positions, validity and reliability issues, data analysis, and data

integration. In addition, this research approach challenges the inquirer in many ways.

These include the challenge of collecting data more extensively, the need for analysing

both qualitative and quantitative data, and the essentiality of being familiar with both

quantitative and qualitative research (Creswell, 2009).

3.3 The Research Design

Various typologies exist for classifying and identifying different types of mixed

methods designs (Creswell, 2009). Morgan (1998a) described a series of research

design for combining these two strategies, which is based on the priority (i.e. which of

74

the methods will be considered the ‘principal method’) sequence (i.e. which method

precedes the other?) model that rests on the complementary approach. One of the

problems inherent in his classification is that the researcher has to decide the priority

and the sequence, which is not always possible or desirable. Therefore, before

choosing the appropriate design for a research, four important aspects should be

considered: timing, weighting, mixing and transformative perspectives (Creswell,

2009). Timing refers to whether the different strategies are employed sequentially or

concurrently. Weighting or prioritisation considers the weight or priority to be given to

each of the strategies used in a specific research. When mixing the data, the researcher

should consider the research stage, in which the data is mixed, and the way data is

mixed (Creswell, 2009).

Johnson and Onwuegbuzie (2004) described eight mixed-method designs7. The

following table depicts these designs, by dividing them into two primary decisions:

first, the priority given to one paradigm over the other. Second, whether the research

phases are conducted concurrently or sequentially. It is important to emphasise at this

stage that according to Johnson and Onwuegbuzie (2004, p. 20), in order to be

considered a mixed methods design, ”the findings must be mixed or integrated at some

point”.

Table ‎3.1: Mixed Method Designs Matrix

Paradigm

Emphasis

Decision

Time order decision

Concurrent Sequential

Equal Status QUAL+QUAN QUAL QUAN

QUAN QUAL

Dominant Status QUAL+quan

QUAN+qual

QUAL quan

QUAN qual

quan QUAL

7 The notation used is adapted from Morse (1991) “+” indicates a concurrent design, “ ” indicates a

sequential. “qual” stands for qualitative, “quan” stands for quantitative, capital letters denote high

priority or weight and vice versa.

75

A principle of mixed methods research is that the design should answer the research

question effectively. Thus, the appropriate research design is the one that is highly

connected to the research problem and purpose.

The chosen research design in this study was the sequential qualitative – quantitative

(QUAL QUAN) (Creswell et al., 2003). Following Morse (2003, p.199), the

qualitative phase in this design is conducted first “mainly because this method

theoretically drives the study, and the quantitative methods may provide extension and

enrichment of the findings of the first phase”.

This design is particularly of importance when the study is focusing on exploring a

relatively less researched phenomenon (Tashakkori and Teddlie, 2003a), in order to

reach a better understanding of both novel phenomena and established theory

(Hohenthal, 2006).

This design comprises of two phases, both includes data collection and analysis: a

qualitative phase, followed by a quantitative phase (Creswell, 2009). In fact, this

design is used firstly to develop or refine a theory and then to test it (Morse, 2003). In

addition, this type of research design is often discussed as the “…procedure of choice

when a researcher needs to develop instruments because existing instruments are

inadequate or not available” (Creswell, 2009, p. 212). Data are mixed in a way that the

two phases are connected and may be analysed by using a theoretical framework,

implicitly or explicitly (Creswell, 2009).

This research design has many advantages: it is effective when a researcher wants to

explore a phenomenon or to develop constructs, and particularly because the

researcher can generate and test a grounded theory (Johnson and Onwuegbuzie, 2004).

However, this design is not without some challenges: a substantial amount of time is

needed to collect data for each phase, which can be problematic in some research

areas. In addition, the researcher is constantly required to make very important

decisions, for example, on what to concentrate on in the quantitative phase. The

following figure depicts a simplified version of the study’s research design.

76

Figure ‎3.1: Research Design

Final Findings of the Study

QUAL1Qualitative

Data Synthesis

Phenomena,Research Problem

Interviews

FocusGroup

Research Problem

QUAL2Interviews

Qualitative Data

Synthesis

Sample SurveyQUAN

Research Question

Hypotheses, Constructs

FocusGroup

The Qualitative part of the research is useful for theory building (Charmaz, 2000) and

for generating hypotheses and speculations (Currall and Towler, 2003). In this phase,

attributes or themes can be formed, and theoretical relationships between them,

specified by means of content analysis (Tashakkori and Teddlie, 1998; Creswell et al.,

2003). Thus, the Qualitative phase was subsequently split into two parts: QUAL1 and

QUAL2.

QUAL1 rests on two in-depth interviews and one focus group. The level of analysis

was the individual. The main purpose of this Phase (QUAL1) was to develop an

interesting research question based on the research gaps that were revealed by the

literature review and on the findings of this phase. In addition to the overarching

research question, four additional questions or objectives were planned for this phase

(QUAL1) in order to cover broader content areas within the topic. The following

figure depicts this process:

77

Figure ‎3.2: Qualitative Phase 1

Qualitative Phase 1Research

aim problem area and research

scope

Literature review

to elucidate the definition of entrepreneurship and international entrepreneurship and examine whether or not different entrepreneurial characteristics are needed for success in international ventures

to address how entrepreneurs learn and what is their attitude toward risk

to discover whether the entrepreneur constructs the opportunity enactment

Qualitative Data Synthesis

Qualitative Interpretive Lens

semi –structured

interviews

Focus Group

Research Question

The main purpose of the second qualitative phase (QUAL2) was to further develop the

emerging themes, which were gleaned and analysed through QUAL1, and to refine the

theory by addressing a conceptual model that proposes a relationship between the

constructs and activates them, so the model may be quantitatively tested. The

following figure provides a visual diagram of the main phase stages:

78

Figure ‎3.3: Qualitative phase 2

QualitativePhase 2

QUAL1 Research

Analysis and

Conclusions

Literature

review

What are the factors that

influence the motivation of

the Entrepreneur to Internationalise?

What are the factors that

affect their attitude towards

risk and thus toward the

Knowledge?

What is the role of the

Internet in the learning

cycle of International

Entrepreneurs and how it

effects the way they learn

about the opportunities?

Qualitative Data

Synthesis

QUAL2: Qualitative Interpretive Lens

Semi –

Structured

Interviews

Focus Groups

Conceptual Model (Hypotheses, Constructs)

In contrast to QUAL1, which focuses on a broader topic area of the phenomena, this

phase was narrower and focused specifically on the topics of opportunity, risk,

knowledge and learning among the selected sample.

The quantitative phase (QUAN) potentially enables the generalising of the findings

from the qualitative phase through means of a larger sample (Scandura and Williams,

2000) and to confirm the ideas induced by qualitative methods using statistical

methods (Currall and Towler, 2003). In this phase, the model is statistically tested and

the interpretation of the QUAN findings is facilitated and enriched by a re-inspection

of the QUAL data. In addition, the chosen research design is the cross-sectional

design, with the data collected at one point in time. The data collection procedures in

this phase involved creating a web-based survey and administering it online.

79

3.4 Qualitative Phase

3.4.1 Philosophical Assumptions of Qualitative Research

While quantitative research strategies and methods could comprise the basis for a

positivist, normative or functionalist paradigm, qualitative strategies and methods

could be grounded in all possible epistemological approaches (Cassell and Symon,

2004). According to Cassell and Symon (2004), there is a debate about what

constitutes a ‘qualitative method’; they believe the term ‘qualitative methods’ to be

somewhat problematic because the current range of approaches to qualitative research

are largely based on different epistemologies.

In this thesis, a more conservative approach to qualitative research is adopted, by

defining qualitative research as a research strategy belonging to the ‘interpretive’

research tradition (Goldman and McDonald, 1987), which seeks to describe and

explain the behaviour of people and the groups to which to they belong from the

various perspectives of the study subjects. In qualitative research, the researcher is

exploring a phenomenon from the ‘participants’ points of view’. The researcher does

so by examining verbal behaviour, stories, language, or by observing participants’

behaviour while engaging in their activities (Creswell, 2009). This approach addresses

the differences between people and objects in the natural sciences, the researcher’s

respondent becomes ‘the expert’ and the researcher’s role is to interpret his or her

view of the reality, empowering and giving ‘voice’ to his or her experience (Hesse-

Biber, 2010), hence the subjective meaning of social action (Bryman and Bell, 2007).

Questions of social ontology are concerned with the nature of social entities (Bryman

and Bell, 2007). Denzin and Lincoln (2007, p. 31) argued, “…that there are theoretical

variations among different qualitative approaches”. They suggested that these

approaches could be grouped into three different ontological positions: constructivist-

interpretative, critical, and feminist. A constructivist approach holds the assumption

that ‘reality’ is subjective and individuals seek subjective meanings of their specific

and personal experiences. An aim of the critical position is to expose social injustice.

This position rests on the approach that reality is “representational” and the

individual’s understating of the social world is affected by issues such as power,

control, and ideology. Feminist perspective holds the assumption that knowledge does

not exist outside the social world, and all knowledge contains a perspective (Hesse-

80

Biber, 2010). Social constructivism is typically seen as an ontological position in

qualitative research (Creswell, 2009).

Qualitative research focuses on the attempt to understand subjectively, the study

subject, and does not strive to find universal constancy. It offers a point of view to

those observing the world and trying to find meaning or to interpret phenomena in the

terms that people use (Denzin and Lincoln, 1994). While quantitative research

methods attempt to generalise, qualitative research methods attempt to learn and delve

deeper. The results of qualitative research do not indicate conclusions, which are

statistically significant, but only show directions of thinking, trends, and hypotheses.

Finally, the researcher’s role is to interpret the participants’ view of the reality,

empowering and giving ‘voice’ to their experience (Hesse-Biber, 2010). However, it is

often argued against lack of transparency in qualitative studies (Bryman and Bell,

2007). That is why the inquirer should be clear about matters such as how people were

selected for interviews or focus groups; the same is true regarding qualitative data

analysis procedures.

3.4.2 Participants and Sampling

The present study was conducted on a sample of international entrepreneurs in Israel.

Oviatt and McDougall (2005d, p. 540) defined international entrepreneurship as: “The

discovery, enactment, evaluation, and exploitation of opportunities across national

borders to create future goods and services". Thus, an international entrepreneur is

either an entrepreneur who expands his business outside his national borders, or an

entrepreneur who, from the day he or she creates his or her venture, defines the target

market as international.

3.4.2.1 Qualitative Phase 1 (QUAL1)

The sampling method for selecting the focus group participants in this stage relies on

the snowball sampling technique and the interviewees were selected based on the

convenience cohort sampling technique (Bryman and Bell, 2007).

The following table summarises the interviews and the focus group participants of

qualitative phase 1 (QUAL1) divided into several categories such as gender, industry

and others.

81


methods

Focus Group

Participants

Interviewee

"A"

Interviewee "B"

Number of participants 9 1 1

Age 36-56 51 40

Industry Various8 Medical

Imaging

Information

Security software

Gender 9 males, 1 female Male Male

Length of

Interview/Discussion

2 hr. 1.5 hr. 1 hr.

Sampling Method Snowball Convenience Convenience

The focus group consisted of one group, which represented International entrepreneurs

of various ages from a variety of industry sectors and at different stages of

internationalisation. Some of the participants were acquainted with each other,

although most of them did not work together regularly, and, most of the participants

were male with only one female.

8 The participants were from various sectors of the high-tech industry: software, medical imaging, IT,

internet, life science, bio-med, medical equipment.

82

Table ‎3.3 gives some descriptive data about the participants.

Table ‎3.3: Focus Group Participants

Indu

stry

Nu

mb o

f

En

trep

reneu

ri

al a

ctiv

itie

s

Inte

rnat

ional

Mar

ket

En

trep

reneu

rs

hip

Sta

ge

Nu

mb O

f

Em

plo

yee

s

Ow

ner

ship

Gen

der

Participant

1

Medical

Imaging

Services

2 UK 3 years in this

specific business 20 Founder Male

Participant

2

Software 3 Australia,

Hungary

In early stages of

internationalisati

on

5 Founder Male

Participant

3

Medical

Equipmen

t

2 Global Product

Development 2 Partner Male

Participant

4

Medical

Imaging

Services

1 UK 3 years in this

specific business 20 Partner

Femal

e

Participant

5

Medical

Insurance,

Tourism

4 Eastern

Europe Sales 50 Founder Male

Participant

6

Internet 1 US

In early stages of

internationalisati

on

10 Founder Male

Participant

7

IT 3 Russia 4 years in this

specific business 10 Founder Male

Participant

8

Biotechno

logy 2 Global

Product

Development 2 Founder Male

Participant

9

Biotechno

logy 1 Global 2 years 6 Partner Male

83

The two in-depth interviews were carried out in Israel, with Entrepreneurs "A" and

"B,” who are acquainted with the writer of this account. Table ‎3.4 gives some

descriptive information about the interviewees.

Table ‎3.4: The Interviewees

Interviewee

"A"

Interviewee "B"

Industry Medical

Imaging

Internet Security

Headquarter UK Israel

Global Markets UK US, Europe and Asia

Status of ownership Founder Founder

Venture Stage Sales Software development and search for

distributors

No. of Employees 20 10

Internationalisation Stage 2 years early

Venture Ownership Private Public (Israeli stock market exchange)

3.4.2.2 Qualitative Phase 2 (QUAL2)

In this phase (QUAL2), one focus group discussion was conducted, with the

participation of eight international entrepreneurs, and a further eight individual, in-

depth, semi-structured interviews that were conducted sequentially to the focus group

discussion.

The participants were Israeli entrepreneurs. In contrast to qualitative phase 1

(QUAL1), the participants in this phase (QUAL2) have essentially similar profile as

the survey respondents targeted for the quantitative phase (QUAN), which means

entrepreneurs from the Israeli High-Tech Industry, or who are involved in the

development of innovative technological solutions.

There are several arguments in favour of recruiting participants from the same industry

as the survey respondents. First, this strategy could produce more reliable measures

and specific item wording that more effectively convey our intent to the target survey

respondents (Morgan, 1997, 1998a).

84

Second, the group may serve as the first contact with potential subsequent

survey participants who will give us a better understanding of the context of the

industry, the domain, and their dimensions, than might be covered in the survey. As

with QUAL1, the participants were recruited using a 'snowball' sampling technique,

with the use of the new social media sites (i.e. Facebook, and LinkedIn) as a recruiting

platform.

New social media technologies provide new ways for recruiting research participants,

particularly for focus groups and interviews. In the case of social networks on the web,

researchers often have access to substantial data on probable and actual participants;

this may expose the inquirer to a number of significant ethical and legal issues (Fisher

et al., 2010) such as: Terms of Service (ToS) restrictions regarding the use of the

information and research validity and ethics (Fisher et al., 2010).

In this study, and taking into account these challenges, an invitation to participate in a

focus group discussion, was published in specific and public Facebook pages, web

sites, and blogs of several individuals who are deemed ‘Gurus’ or ‘Mentors‘ for

Entrepreneurs. In this way, the potential participants, with the suitable profile,

approached this research and were invited to attend. A formal letter, with the

university letterhead, invited the participants, giving a brief explanation of the research

topic and the discussion format. Confidentiality was guaranteed. The following table

summarises the participant’s characteristics:


methods

Focus Group Participants Interviews

Number of participants 8 8

Age 30-56 30-45

Industry High-Tech High-Tech

Gender 8 males 5 Males, 3 Females

Length of Interview/Discussion 2 hr. 1.5 hr. each

Sampling Method Snowball Snowball

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The interviews in qualitative phase 2 (QUAL2) were structured and constructed

similarly to the way they were conducted in the first qualitative phase (QUAL1). The

interviews were conducted subsequently to the focus group discussions. In this phase,

the interviewees were selected using the 'snowball' sampling method, based mainly on

the, Facebook, LinkedIn and personal contacts. The interviewees hold the position of

founders or co-founders from different companies in the high-tech industry, with as

wide a spread as possible in terms of age, gender, internationalisation stage,

internationalisation speed (i.e. the number of years since they established their venture

and embarked on international activity), and sub-category within the industry ( for

example, biomed, biotech, software, internet etc.).

Much effort had to be made to convince potential interviewees to be interviewed.

Prior to their invitation a 'telephone or email screening' technique was applied (Carter

et al., 2003), in order to ensure that the interviewee has the necessary profile (i.e. he or

she are entrepreneurs, they had, have operated an international venture). Only

entrepreneurs who define their main pursuit as entrepreneurship or start-up founders

were invited to the interview and considered eligible for the designation

“entrepreneur". In this way, it was ensured that only those people, who actively

involved in entrepreneurial ventures, were invited to participate, in contrast to those

who were actively involved in entrepreneurial ventures, were invited to participate, in

contrast to those who were perhaps thinking about it, but were not actively involved.

3.4.3 Data Collection Procedures

Each section of the qualitative phase includes the analysis of two data collection tools

commonly used in qualitative research: interviews and focus group discussions.

Triangulation of multiple data sources is a viable option for evaluating and improving

the validity of results (Tashakkori and Teddlie, 1998). They should show

complementary strengths rather than overlapping weaknesses (Currall and Towler,

2003). It also answers the need to obtain in-depth and personal information. The in-

depth interview enables the researcher to use an individual case study as a basis for

learning about the phenomenon of entrepreneurship in general (Stake, 1995). This is

parallel to the need to clarify broader sociological issues, which affect organisational

and managerial processes and which can be clarified in the focus groups, where there

is interaction between the participants.

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The use of in-depth interviews and focus groups in the same study might enable the

researcher to become more familiar with the contextual uniqueness of the studied

phenomena, especially in theory-driven approaches. Furthermore, focus group

discussions are a very useful source of input for in-depth interviews (Bloor et al.,

2001). Subsequent individual interviews can provide more depth and detail on topics

that were only broadly discussed during the focus groups (Morgan, 1997). In addition,

Churchill (1979) emphasised that researchers can benefit from combining focus

groups and interviews at the item-generation stage of a quantitative study.

The interviews and the focus group discussions were designed to elicit information on

topics such as entrepreneurship, international entrepreneurship, learning, risk, and

opportunity identification. These topics, although exploratory and tentative, represent

the definition and meaning of the terms Entrepreneurship and International

Entrepreneurship, which have been changed and redefined over the years.

Based on an extensive review of the literature, (see chapter 2), the research on

international entrepreneurship emphasises themes such as: “value creation”

(McDougall and Oviatt, 1996, p. 293), proactivity and risk taking of entrepreneurs

(McDougall and Oviatt, 2000; Liesch et al., 2011), and opportunity identification

(Shane and Venkataraman, 2000b; McDougall and Oviatt, 2003). In addition, the

dynamic aspects of the phenomenon highlight the importance of social networking,

innovation, internationalisation knowledge, and capabilities (Gray and Farminer,

2014). Similarly, the most persistent themes in the field of entrepreneurship were: risk-

bearing or uncertainty (Brockhaus, 1980; Hebert and Link, 1988; Koh, 1996; Miner,

1997; McMullen and Shepherd, 2006), entrepreneurial intention (Bird, 1988; Bird,

1992; Dimov, 2007b), opportunity identification (Dimov, 2003; Baron and Ensley,

2006a; Alvarez and Barney, 2007a; Dimov, 2007a, 2007b; Alvarez and Barney, 2008)

and venture creation (Bhave, 1994a; Bruyat and Julien, 2001; Aldrich, 2005; Chea,

2009).

Learning is perceived as a crucial component of entrepreneurial activity (Sarasvathy,

2002), and may take place between the time an opportunity is recognized and its

successful exploitation (Ravasi and Turati, 2005). It was concluded, from the literature

review findings that entrepreneurship research should highlight the connection

between thinking and doing (Mitchell et al., 2007), through investigating the specific

way entrepreneurs think and act in enabling the creation and realisation of

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entrepreneurial opportunities (Mitchell et al., 1997; Mitchell et al., 2002), and

facilitate the creation and realisation of new opportunities, i.e. their “thinking-doing

connection” (Mitchell et al., 2007).

The interviews and focus group guidelines (see Appendix A) were constructed

subsequent to initial reading on the topic.

3.4.3.1 Semi-Structured Interviews

The interviews were designed as semi-structured (Creswell, 2009), to elicit

information on general issues relating to Entrepreneurship. This information was

served to highlight potentially critical incidents. These critical incidents were then to

be used in the subsequent interviews to encourage the entrepreneur to expand on the

process, which led to the incident, how it was resolved, and, more importantly, what

was learned from the incident. In this type of interview, the interviewer follows a

script to a certain extent, but not as precise a script as that used for quantitative

research interviews (Aberbach and Rockman, 2002).

There are some disadvantages to conducting face-to-face in-depth interviews. For

example, interviews are more costly, compared to surveys or secondary data

collection. They may be subject to potential problems of self-reporting issues.

Moreover, some data may be missed, as interviews usually rely on verbal behaviour,

whereas implicit features of social life are more likely to be revealed because of the

observer's continued presence and ability to observe behaviour in context, rather than

just relying on what is said.

In QUAL1, two in-depth interviews were carried out in Israel, with Entrepreneurs "A"

and "B,” who are both founders of successful international ventures. In QUAL2, eight

individual, in-depth, semi-structured interviews were conducted sequentially to the

focus group discussion. In a similar way to QUAL1, the participants were Israeli

entrepreneurs; however, this time all of them were entrepreneurs from the Israeli

High-Tech Industry.

The interviews in this study were audiotaped and transcribed verbatim and as far as

possible no later than a week after the interviews were conducted. This enabled the

researcher to add notes to the transcripts, recording aspects such patterns of behaviour

and facial expressions.

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3.4.3.2 Focus Group

Focus Groups are considered a viable way of conducting qualitative research (Morgan

and Kreuger, 1998; Fontana and Frey, 2000) and have accordingly been used in

marketing, sociology and other social science research (Wooten and Ii, 2000).

Focus groups have been advocated as a tool for studying the shared experiences of

group members (Fontana and Frey, 2000). Focus groups may delve into group

members' thinking, stimulating participants to articulate their generally unarticulated

normative assumptions about certain issues (Bloor et al., 2001).

According to Bloor et al. “the group is a socially legitimated occasion for participants

to engage in 'retrospective introspection', to attempt collectively to tease out

previously taken for granted assumptions” (2001, p. 6). In this respect, focus groups

were considered an appropriate data collection method for revealing more about

complex phenomena such as entrepreneurship. Furthermore, since focus groups may

be used to “clarify, extend, qualify or contest findings on the same topic produced by

other methods” (Bloor et al., 2001, p. 90), their combined use with interviews seemed

appropriate. According to Morgan (1998b, p. 1) “focus groups are group interviews. A

moderator guides the interview while a small group discusses the topics that the

interviewer raises. What the participants in the group say during their discussions are

the essential data in focus groups.”

The major virtue of the technique lies in its explicit use of group interaction, which is

said to produce data and insights that would be less accessible in individual, serial

questioning (Hyden and Bulow, 2003), although it is not necessary to reach a

consensus of any kind (Patton, 1990). Two of the major benefits of focus groups are

their time efficiency and the built-in data quality control, since participants tend to

provide checks and nuance to each other so that extreme or faulty views are

questioned (Patton, 1990). Nevertheless, general rules of thumb have been formulated:

a homogeneous group of individuals as participants, six to ten participants per group

(Halcomb et al., 2007), an interview length of 1.5 to 2 hours. These rules were

generally followed.

The present study was conducted on a sample of international entrepreneurs in Israel.

In QUAL1, one focus group discussion was conducted, with the participants of nine

entrepreneurs of various ages from a variety of industry sectors and at different stages

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of internationalisation. In QUAL2, one focus group discussion was conducted, with

the participation of eight international entrepreneurs. The participants in this phase

(QUAL2) were entrepreneurs from the Israeli High-Tech Industry.

The focus group conversation was audio-recorded and transcribed verbatim. The

conversation was in Hebrew and was translated to English. The English translation

then served as the basis for the analysis. The focus group conducted a conversation

rather than a structured interview. The moderator, to some extent, led the group,

although the conversation was not routinized or conventionalised, but was established

in the course of the discussion. The focus group guidelines (see Appendix A) were

constructed subsequent to initial reading on the topic.

3.4.4 Data Analysis Techniques

In the Qualitative phase, an often-used approach is grounded theory. Grounded theory

serves as a way of learning about the worlds we study and a method for developing

theories to understand them (Charmaz, 2006). This approach has been traditionally

positioned as an inductive approach (Huberman and Miles, 1994); accordingly, the

analysis should develop a new theory without prior hypotheses. However, Strauss

claimed that this is almost impossible, mainly because trained researchers are

theoretically sensitized (Strauss and Corbin, 1998).

Charmaz (2006) argued that we construct our grounded theories through our past and

present involvements and interaction with people, perspectives, and research practices.

Furthermore, as Perry (1998, p. 788) points out, pure induction is impossible mainly

because: ”… Starting from scratch with an absolutely clean theoretical slate is neither

practical nor preferred.” This approach seems appropriate for implementation in this

research.

The analysis in this study was based on themes and content arising from the text itself,

according to the approach of Strauss and Corbin (1990). As defined by Strauss and

Corbin, Grounded Theory is based on field data which is collected (in this case

through semi-structured in-depth interviews and a focus group) and analysed in such a

way as to generate a theory (Strauss and Corbin, 1998). In this method, the research

starts in the field almost without any theoretical assumptions. However, Charmaz

(2006) maintains that we should at this stage adopt a pragmatist underpinning

approach to Grounded Theory, based on the assumption that any theoretical rendering

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offers an interpretive portrayal of the studied world, and not an exact picture of it

(Charmaz, 2000; Charmaz, 2006). The process also involves using several stages of

data collection and refinement, and elucidating categories of information and the

relationships between them (Charmaz, 2006). During this process, theoretical

sampling of different groups is used by the researcher to get the most out of the

similarities and dissimilarities of data, and continually relates data with emerging

categories of information (Creswell, 2009).

In this phase, general data categories were inductively identified and revealed several

'emergent' subcategories (Patton, 1990). For example, a general data category was

'entrepreneurial characteristics’ and an emerging subcategory were for example

‘innovator' or a ‘dreamer’. During the coding stage, special attention was given to

symbolic language and figures of speech, such as metaphors (Coffey and Atkinson,

1996). Especial mindfulness was given to the translation from Hebrew into English.

Finally, the data was structured by source and similarities and dissimilarities were

looked for, across the different sources. Charmaz (2006, p. 14) summarised this

process well by positing that: “The flexibility of qualitative research permits you to

follow leads that emerge. Grounded theory methods increase this flexibility and

simultaneously give you more focus than many methods.” Following Weed (2008) it

is fundamental that the researcher implement an iterative approach to the theoretical

sampling. In addition, the focus of the analysis should be on ‘meaning in context’ and,

an interpretation is considered as the ‘raw data’ for synthesis.

3.5 Quantitative Phase

3.5.1 Type of Research Design and Data Collection Tool

This phase employed a cross-sectional survey. The term ‘survey’, according to

Bryman and Bell (2007, p. 56) is reserved for “a research that employs a cross-

sectional research design in which data are collected by questionnaire or structured

interviews". In general, the sample survey has been one of the most widely used tools

in social science and management research (Tashakkori and Teddlie, 1998), yet it is

also one of the most debated techniques. The purpose of survey research is to

generalise from a sample to the target population (Creswell, 2009).

In this research, the survey was selected as the preferred procedure in the quantitative

phase, mainly because self-report surveys make efficient use of resources such as time

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and money. In addition, sample survey design rests on the probability of making

significant, statistically tested inferences, by obtaining information on only a

proportion of the target population (Cycyota and Harrison, 2002). On the other hand, it

is exposed to some serious biases, such as "respondent fatigue,” low response rate,

missing data, etc. (Bryman and Bell, 2005, p. 241). That is why surveys become more

meaningful when interpreted in light of critical qualitative information, just as other

statistics are most useful when compared to content analyses or interview results.

Questionnaires are often considered as an important element in mixed methods

(Johnson and Turner, 2003). Johnson and Turner (2003) identified three forms of

questionnaires: the first type is the unstructured, exploratory and open-ended

questionnaire, the second type is the mixed questionnaire, in which some of the

questions are open-ended. The third type, which was used in this research, is based on

a completely structured and closed-ended questionnaire.

3.5.2 Survey Procedures

The data collection in this research phase also involved creating a web-based survey,

as distinct from the paper-and-pencil questionnaire.

An email with a link to the web-survey (Hoonakker and Carayon, 2009) was selected

as a tool for distributing the questionnaire to the selected sample. The e-mail included

a brief description of the study purpose, the time needed to complete the questionnaire,

a confidentiality declaration, and a link to the web-survey. The data was then stored on

the server. This method is highly efficient compared to mail surveys, for several

reasons: it increases the speed of response; it reduces the costs of printing, posting and

coding; it saves a lot of time and could reduce errors, especially in the data entry stage

(Cobanoglu et al., 2001; Sills and Song, 2002; Umbach, 2004; Hoonakker and

Carayon, 2009).

However, there are some major weaknesses of using a web-based survey

questionnaire: the proportion of non-deliverable surveys with this method is generally

higher than for postal and fax methods, probably because people change their e-mail

addresses and internet service providers (ISPs) much more frequently than their postal

addresses (Cobanoglu et al., 2001). There is also the problem of non-response due to

'over-surveying internet users'. This issue may negatively impact on their willingness

to participate (Manfreda et al., 2008). Furthermore, the problem of spam e-mails may

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also have a negative influence on responses to web surveys. Finally, the methodology

of web surveys is probably still not sufficiently developed to take full advantage of the

possibilities available, although extensive research efforts are being made in this area

(Manfreda et al., 2008).

In summary, Sills and Song (2002, p. 28) state that for specific populations who are

“connected and technologically savvy”, such as the entrepreneurs in this study, “the

cost, ease, speed of delivery and response, ease of data cleaning and analysis weigh in

favour of the Internet as a delivery method for survey research”.

3.5.3 Participants and Sampling

3.5.3.1 Population, Sample and Participants

Sample surveys are based on two types of inference: from questions to constructs, and

from the sample statistics to the population statistics (Groves et al., 2009, p. 63). In a

research study that includes a sample survey, the researcher has to estimate the

distribution of the population being studied by making inferences from a small

proportion of that population.

In this research, the target population was Israeli high tech entrepreneurs (Chorev and

Anderson, 2006; Senor and Singer, 2011; Almor and Heilbrunn, 2013) who seek to

operate or who are already operating international entrepreneurial high tech ventures.

All respondents were founders of their businesses or part of the founding team with

full responsibility for strategic decision-making and most importantly, they were part

of the venture from the date of inception.

The sampling strategy, can be described as a nonprobability sampling strategy (or a

purposive sample) in which each participant in the target population does not

necessarily have a known and equal probability of being selected (Creswell, 2009).

The final questionnaire was distributed to various potential respondents:

1. Firstly, entrepreneurs who were included in the Israel Venture Capital

Organisation (IVC)9

list. The IVC lists more than 95% of all hi-tech companies

9 http://www.ivc-online.com/

93

in Israel, which make it a frequently used database for sampling in the context

of Israeli entrepreneurship.

2. Secondly, it was distributed to individuals who were recruited through social

networking sites such as Facebook and LinkedIn.

3. Finally, the questionnaire was distributed with the assistance of organizations

such as the Israeli Start-Up Stadium10

, and the Technological Incubators

Program in the Israeli Office of Economics.

This selection process, which includes various sources and lists, was chosen in this

study due to the low response generally experienced from Israeli senior respondents in

general and entrepreneurs specifically (Chorev and Anderson, 2006). About 75% of

the responses came through the IVC list. A sample of 5,846 entrepreneurs, closely

representing the database population of the Israeli ventures was drawn, yet only 178

respondents fully responded to the questionnaire. However, this strategy could raise

some doubts as to possible survey weaknesses, particularly with respect to coverage

error in its statistics.

3.5.3.2 Coverage and Sampling Error

Coverage error is the major concern to the quality of Web surveys (Couper, 2000).

Coverage error is the function of a mismatch between the target populations and the

frame population (Baker et al., 2004). Hence, a central concern in this study was the

question of how well the sampling frame covers the target population (Groves et al.,

2009).

The coverage might be considered as a concern if those members of the population

who do not have access to the Web or do not have an email address cannot have an

equal chance of participating in a Web-based survey. However, It is reasonable to

assume that due to the high level of internet usage in Israel, and the use of email as a

primarily mailing mechanism among entrepreneurs in Israel (Shoham et al., 2006), the

decision to implement a web-based survey in this study was appropriate.

10

http://www.startupstadium.com/

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Emails with a link to the web survey were sent to the potential respondents. This could

have led to two major concerns regarding the quality of the sampling frame. The first

is what is known as the duplication problem (Groves et al., 2009), because one person

can have many different e-mail addresses. The second is that many organisations tend

to publish in databases a general rather than a specific e-mail address, such as:

[email protected], so there is a potentially high rate of turnover of email addresses and

reliance on proprietary site lists is unsatisfactory (Grandcolas et al., 2003). Therefore,

in this study, wherever the email address was general and not specific or did not exist;

a telephone call was made to the potential respondent, in order to obtain his

permission to update his email address and his consent to participate in this study.

It is reasonable to assume that some of the respondents agreed to participate in this

survey because they had a greater interest than others in its subject matter. This fact

might have threatened the representativeness of the sample (Umbach, 2004).

Therefore, a non-response assessment was conducted (see section 6.1.1.3).

3.5.4 Data Collection Instruments

The Quantitative Phase (QUAN) was used to test and elucidate the findings from the

Qualitative Phase (QUAL1, QUAL2). The Questionnaire was written in English,

mainly because the development of the instrument was partially based on items from

other instruments, all of which were written in English. The ‘back translation method’

was used to translate the original English instruments into Hebrew.

The following procedure was based on the recommendations by Birnbaum et al.

(1986, p. 599):

1. The questions were initially translated into Hebrew by a paid professional

translator.

2. The professionally translated version was then translated back into English by

the researcher of this study, then back into Hebrew.

3. Finally back into English once again.

This ‘back translation’ procedure helps to ensure an accurate translation that is

decentred from a verbatim English translation (Brislin, 1970; Weeks et al., 2007).

However, it does not involve a psychometric analysis. The decentred Hebrew version

mailto:[email protected]

95

was used to collect data from Hebrew speakers, while the original English language

version was used to collect data from English speakers (Birnbaum et al., 1986).

3.5.4.1 Questionnaire Design

The data produced in surveys come from the respondents’ replies to the survey

questions. In some surveys, the researcher uses what is called 'edited response' in order

to improve on the original response obtained from measurements of underlying

constructs and the inference will be based on these data (Groves et al., 2009).

The process of developing a measurement instrument is a very complex one, tending

to be affected by numerous biases throughout this process. Therefore, right from the

start of this process of questionnaire (i.e., measurement instrument) design, both “an

extensive literature study and an initial qualitative research study to develop the survey

should prove useful” (Bryman and Bell, 2007, p. 162).

When the researcher is seeking to develop a measure of a concept, various aspects of

that concept or construct should be considered. Thus, the first step of

operationalisation in this study was the conceptualisation of each construct based on

an extensive literature review, while noting that essential features of the construct are

sometimes not revealed during the literature review. However, these conceptual

definitions proved useful as a guideline during the qualitative study.

The qualitative findings enabled this study to refine, supplement and validate these

definitions especially when collecting data from qualitative interviews, focus groups

and other sources (Bagozzi, 1994). In this stage, the development of the measurement

instruments was based on the results of the qualitative research, particularly the second

qualitative phase (QUAL2). The following figure depicts this process:

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Figure ‎3.4: The Operationalization Process

Constructs (Criterion

variables)

Qualitative Phase:QUAL1QUAL2

Literature Review

Literature Review

Measurement Instruments

Indicators/Items

Operationalization

ProcessFrom variables to Indicators

In developing this instrument, findings from this study's qualitative phase, from

previous studies and other scholars' validated measures, were used as the points of

reference. Whenever possible, multiple-item measurements were used to minimize

measurement error and to enhance the content coverage for the constructs in the model

(Schwens and Kabst, 2012).

The battery of measures comprises of six areas pertaining to learning strategies, prior

knowledge, cognitive styles, social networking ties, prior business experience, and

general information about the entrepreneur characteristics and his/her firm:

Cognitive styles was operationalised by adopting the CSI instrument (Allinson

and Hayes, 1996).

Prior knowledge was operationalised as a four-dimensional construct, on the

basis of the work of Zhou (2007), Autio et al. (2000), Hadley and Wilson

(2003), Eriksson et al. (1997), Shane (2000), Tang et al. (2012), Tang and

Murphy (2012), and Zahra et al. (2009).

Prior business ownership experience was measured based on previous scale

development work by Westhead et al. (2009).

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Entrepreneurial self-efficacy (ESE) was operationalised as a one-dimensional

construct, and adopted from the work of Arora et al. (2011). The measure is as

a well-established and reliable measure (α=0.83).

Social networks in this study reflect the strength of the networking ties

(Fernández-Pérez et al., 2012). The items and the scales were adapted from

various scholars such as Perry-Smith (2006), Collins and Clark (2003), and

Fernandez-Perez (2012). Ties strength was measured as a combination of the

duration, and closeness of the ties (Collins and Clark, 2003; Fernández-Pérez et

al., 2012).

Six learning strategies were measured in this study:

'Learning from networking' deliberately or spontaneously11

,

'learning via imitating' deliberately or spontaneously,

In addition 'learning by doing'12

deliberately or spontaneously.

The learning strategies items were developed partly by modifying scales from related

areas and partly by developing new scales (Doornbos et al., 2004). Definitions and

measures from different scholars have been adapted for the purpose measuring the six

learning strategies such as Doornbos et al. (2004); Doornbos et al. (2008); Schwens

and Kabst (2009;2012).

3.5.4.2 Reliability and Validity

There will often be a discrepancy between the measurement value and the actual value

measures. This discrepancy is known as measurement error, especially among self-

report measures, such as questionnaires (Field, 2009). Reliability and Validity are two

terms that define the 'quality of measurement' of a construct (Groves et al., 2009).

Establishing the validity of the scores in a survey helps to identify whether an

instrument might be a good one to use in survey research (Creswell, 2009, p. 149).

Validity is a function of the correlation between the response and the true value and is

11

In this study, the terms 'systematically and randomly' are used interchangeably with: 'planned and

emergent', 'deliberate and spontaneous', and 'effectual and casual'.

12 The term is used interchangeably with 'learning from direct experience', and 'experimenting'.

98

a necessary but not completely sufficient condition of a measure (Field, 2009). The

validity of a measure deals with whether or not a measure of the variable really

measures the concept in which it was designed to measure (Bagozzi, 1994; Bryman

and Bell, 2007). Typically, validity has been considered as subdivided into: content,

criterion and construct validity on the basis of their area of evidence (Onwuegbuzie et

al., 2009). The most important evaluation criteria for validity are: 'face or content

validity', and construct validity (Gefen et al., 2000). In this study, both face validity

and construct validity were assessed.

Content validity, which is also called: ‘definition validity’ , ‘logical validity’

(Newman et al., 2013a, p. 3) or ‘Face validity’, refers to the extent that the instrument

covers the content that it is designed to measure (Newman et al., 2013a). The most

common method for measuring face validity is to ask experienced judges to determine

whether or not the appearance of the measure reflects the concept concerned (Bryman

and Bell, 2007, p. 165). In this study, content validity was assessed based on

quantitative and qualitative data obtained from a panel of six expert judges. Moreover,

the approach that was taken in this study is that content validity should be addressed

by implementing a mixed methods assessment (Newman et al., 2013a). The Content

Validity Index (CVI), The Kappa Coefficient (K*) and the Item Content Validity

Index (I- CVI) have been often used, to quantify the face validity of a questionnaire

and its items (Polit et al., 2007), although they can be computed in different ways

(Rubio et al., 2003) (see appendix F for a detailed explanation and discussion). Based

on the face validity assessment, it can be concluded that the questionnaire has shown

an excellent level of relevancy, and hence can be considered as face valid. Face

validity was assessed prior to the pilot survey procedures.

Construct validity refers to what the instrument is measuring (Bagozzi, 1994) and

usually combines convergent validity and discriminant validity (McGartland Rubio,

2005). Convergent validity is defined as the extent of the fit between a measure of the

same concept developed through other methods and the construct measure used in the

study (Gefen and Straub, 2005; Bryman and Bell, 2007). Discriminant validity is

defined as the extent to which measures of a construct differ from other measures

developed by other methods (Gefen and Straub, 2005; Bryman and Bell, 2007; Hair et

al., 2013a). In this study, convergent and discriminant validity between the different

indicators of the final survey were assessed during the measurement model evaluation

with the use of PLS-SEM.

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The reliability of measures is a reflection of their consistency (Bryman and Bell,

2007). It is a measurement of variability of answers over repeated conceptual trials

(Groves et al., 2009). According to Bryman and Bell (2007), there are at least three

different meanings of the term: stability, internal reliability and internal consistency as

measured by Chronbach’s α.

Testing stability entails asking whether a measure is stable over time, the easiest way

to assess this aspect of reliability is by testing the same group of people twice with the

test-retest method (Bryman and Bell, 2007; Field, 2009; Groves et al., 2009). Internal

reliability indicates whether or not respondents' scores on any one indicator tend to be

related to their scores on the other indicators (Bryman and Bell, 2007). This aspect is

mainly applicable to multiple-indicator measures.

Reliability is often assessed by evaluating the internal consistency reliability using

tests known as Cronbach's alpha (Cronbach, 1951) and composite reliability (CR)

(Chin, 2010; Hair et al., 2013a). The figures of 0.7, or higher for Cronbach’s alpha

(Bryman and Bell, 2007), and composite reliability values equal to or above 0.7

(Fornell and Larcker, 1981; Hair et al., 2013a) are typically employed as a rule of

thumb to denote an acceptable level of internal consistency. In addition, based on the

definitions of reliability and validity, it can be concluded that if a measure is valid, it is

reliable. The opposite, however, is not necessarily true: measures can be consistent for

the wrong reasons, such as method biases (Bagozzi, 1994).

The statistical procedure that was conducted in order to evaluate the validity and the

reliability of the measures was factor analysis. As recommended by Field (2009, p.

673), factor analysis (applied in this study by conducting both EFA and PLS-SEM

analyses) is a good way to validate a questionnaire and check the reliability of the

developed scale. This technique has three main uses: identifying clusters of variables,

constructing questionnaires, and reducing a data set to a more manageable size (Field,

2009, p. 636).

3.5.5 Pilot Survey

A pilot or field survey is important before conducting the full survey for several

reasons: first, it allows us to establish the content validity of the questionnaire and if

necessary to refine the questions (Creswell, 2009), the second reason, and the prior

goal, is to assist in reducing measurement error in survey results. Pretesting deals in

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particular with response bias, that is where the response to the question deviates from

the true value by an error (Groves et al., 2009). Presser et al. (2004) argued that there

is a lack of grounded approaches and guideline for how to conduct a survey pretesting.

Web surveys, as used in this study, require “…testing of aspects unique to this mode,

such as respondents’ monitor display properties, the presence of browser plug-ins, and

features of the hosting platform that defines the survey server” (Presser et al., 2004, p.

122).

In this study, an initial review was performed by the researcher, supervisor and co-

supervisor of this study, focusing mainly on visual aspects of the questionnaire (Baker

et al., 2004). In addition, the questionnaire was subjected to a content and face validity

assessment (see Appendix F for more details). The refined questionnaire was

administered to ten entrepreneurs, who are acquainted with the author of this study, in

order to conduct a pilot test. The 10 respondents were international entrepreneurs,

from various high-tech industries. In addition, for the pretesting results to be entirely

transferable to the final survey, the pretesting procedure was conducted in a similar

way to the final survey (Presser et al., 2004). In the final stage of the pilot survey, a

telephone and email exchange as a 'debriefing method' (Hunt et al., 1982) were

conducted too. The ten respondents were asked to comment on several aspects of the

questionnaire, such as wording, question meanings, and so on. At the end of the pilot

test, questions, format and scales were improved, and comments were incorporated

into the final instrument revision (Creswell, 2009).

3.5.6 Non-Response Error

The quality of survey statistics can be threatened by an insufficient rate of response to

the survey. Non-response makes it very difficult for researchers to generalise the

results to the target population, mainly because it threatens the validity of a survey and

could introduce bias into the conclusions (Armstrong and Overton, 1977; Sivo et al.,

2006; Hoonakker and Carayon, 2009).

PLS-SEM can be considered as an approach to SEM (Hair et al., 2010; Hair et al.,

2011; Hair et al., 2013a), which specifically requires statistical rigour. In the case of

SEM analysis it is recommended to assess, among other statistical concerns, the non-

response error (Gefen et al., 2011). In addition, web surveys have been found to show,

in general, lower response rates than paper-and-pencil surveys (Couper, 2000; Sivo et

al., 2006; Bryman and Bell, 2007), but with the necessary caveat that web surveys can

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boost response numbers (Bryman and Bell, 2007), by utilising: 'repeated call backs,

long data collection periods, short questionnaire…, and persuasion letters' (Groves et

al., 2009, p. 211). In fact, for certain kind of populations the electronic version is

advisable, particularly when: 'resources are limited and the target population suits an

electronic survey' (Yun and Trumbo, 2000). Duncan (1979) suggested that researchers

could overcome potential non-response bias by carefully preplanning and designing

the program and instrument of research. In this study, two techniques were

implemented in order to increase the response rate significantly: sending an initial e-

mail notification, followed by two reminder e-mails (Couper et al., 2001)

Non-response rate is often assessed by comparing respondents who initially dropped

out of the survey, but eventually consented; with all other respondents (Couper, 2000),

or early vs. late respondents (Sivo et al., 2006). The underlying assumption is that late

respondents are similar to non-respondents (Armstrong and Overton, 1977). This

approach for assessing non-response bias is based on the assumption that individuals

who respond in later waves (i.e. responses generated by a follow-up email invitations),

are presumed to have responded because of the increased stimulus. This technique is

often called extrapolation, and was found to improve substantially the estimation of

the non-response bias (Armstrong and Overton, 1977).

The assessment in this approach is usually done by comparing the demographics (i.e.

age, education, firm age, and firm size) of the examined groups. Non-significant

differences indicate that there is no reason for concern about non-response error

(Armstrong and Overton, 1977; Sivo et al., 2006)

3.5.7 Data Analysis Procedures

3.5.7.1 Preliminary Data Analysis

The preliminary data analysis in this study covers two main issues: firstly, screening

and cleaning the data before the analysis was run and secondly assessing the match

between the data and the statistical assumptions of the analytical techniques to be

employed. Screening and cleaning the data is an important stage, in which a set of

issues are tackled and resolved before the data analysis is preformed (Tabachnick and

Fidell, 2001):

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Missing Values

The data in this study was collected using a web-based survey method. The on line

questionnaire was designed, uploaded and accessed using the “SURVEY GIZMO”

platform13

. This approach that was implemented in this study is often known as the

‘complete data approach’ (Hair et al., 2010, p. 54) or “forced-answer approach” (Hair

et al., 2013a, p. 51).

In this study, this approach was taken, for various practical reasons:

1. Firstly, this approach can be applied easily and most of the web-based software

packages provide it as a default feature.

2. Secondly, this study researched Israeli hi-tech entrepreneurs, who can be

described as very busy people and an over researched population. Enabling

them to skip the questionnaire’s items or not to complete the entire

questionnaire might result in a large number of missing values (Hair et al.,

2013a).

3. Thirdly, missing values remedies have their own disadvantages and might bias

the study’s results.

This approach might raise some concerns:

1. Firstly, the exclusion of missing values might result in an inadequate sample

size, which is not the case in this study (see also chapter 3 and chapter 6).

2. Secondly, most of the variables in this study were designed to capture

behaviour (Learning strategies), a cognitive style (CSI), Knowledge (Prior

Knowledge), and personality (Self-Efficacy). Therefore, using statistical

remedies (i.e. using imputed values as if they were actually observed) to

complete missing cases or items, might result with a loss of important

information and thus distort and bias the findings.

13

See http://www.surveygizmo.com/survey-software-features/

or direct link to the survey: http://sgiz.mobi/s3/fd5bd2163819

http://www.surveygizmo.com/survey-software-features/

http://sgiz.mobi/s3/fd5bd2163819

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Therefore, after careful consideration, it was decided that the data could be analysed

without the inclusion of missing values

Outliers

Outliers are defined as a response to an item, which distinctly differ from the other

responses to this item (Tabachnick and Fidell, 2007; Hair et al., 2010; Hair et al.,

2013a). Outliers may distort the findings, therefore they must be identified, reported

and if necessary remedied (Tabachnick and Fidell, 2007). Outliers can be described as

either univariate or multivariate (Tabachnick and Fidell, 2007), based on the number

of variables considered (Hair et al., 2010).

In this study, detection of univariate outliers was conducted by examining the

standardised scores range (i.e. z-scores). Responses with z-scores in excess of +3.29,

were considered as potential univariate outliers (Tabachnick and Fidell, 2007).

Multivariate outliers are described as unusual combinations of scores significantly

different from the multivariate mean (Tabachnick and Fidell, 2007). In this study,

multivariate outliers were identified by computing the Mahalanobis distance (D2). An

observation was considered as multivariate outlier if the computed Mahalanobis

distance 14

exceeds the critical χ2 value (with df equal to the number of variables,

p<0.001) (Tabachnick and Fidell, 2007).

Several options for reducing the impact of these anomalous values were implemented

in this study (see chapter 6 for a detailed description), among them were removing the

outlier case, changing the score, or transforming the data (Field, 2009).

General Statistical Concerns

The second part of the preliminary analysis, in this study, focuses on evaluating the

match between the data and the statistical procedures assumptions and assessing the

common method variance (CMV). Although, PLS-SEM is robust and considered as a

nonparametric statistical method (Hair et al., 2013a), it is still recommended to verify

that the data is close to normal. Extremely non-normal data might affect the

14 The Mahalanobis distance is computed through SPSS REGRESSION. The case number was used as

the DV, and the other variables as the IVs.

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probability of revealing significant relationships between some of the constructs in the

model (Henseler et al., 2009; Hair et al., 2011; Hair et al., 2013a).

There are numerous tests and methods to measure univariate normality (Hair et al.,

2010). In this study, the assumption of normality was assessed by examining the

Kolmogorov–Smirnov test, Skewness, and Kurtosis values, and by visually inspecting

the Probability Plots (P-P plots) of the sample.

Skewness and Kurtosis were chosen as the method of normality assessment for the

following reasons: firstly, the Kolmogorov–Smirnov test indicates whether the data is

normally distributed or not, however, it does not provide the indication to what extent

the data is far from being normally distributed. Secondly, PLS-SEM can be

implemented even if the data is not normally distributed; the question is how far the

data is from the normal distribution (Hair et al., 2013a). Kurtosis and Skewness values

that are close to zero are indicative of a normal distribution. Nevertheless, data might

be considered as non-normal (i.e. far from the normal distribution) if the absolute

Skewness and Kurtosis values are above the threshold of one. In addition, if non-

normality was detected, transformation of variables was considered (Tabachnick and

Fidell, 2007; Hair et al., 2010). Therefore, whenever it was necessary, transformation

of variables were used to increase their normality (Tabachnick and Fidell, 2007; Field,

2009).

Examination of Linearity was conducted. The Linearity assumption is particularly

important when the study involves Exploratory Factor Analysis (EFA) (Tabachnick

and Fidell, 2007). In the same vein as for normality, if non-linearity was found,

transformation of the variable was considered.

Multicollinearity is not an issue for concern in EFA (i.e. when the PCA is used)

(Tabachnick and Fidell, 2007, p. 614). However it should be assessed and considered

when using the PLS-SEM, which is based on the multivariate regression analysis (Hair

et al., 2013a). Collinearity assessment is necessary for PLS-SEM analysis as it is based

on Ordinary Least Squares (OLS) regression (Hair et al., 2013a), and may involve the

evaluation of the Variance Inflation Factor (VIF) values, the tolerance values, and the

conditioning index (see Tabachnick and Fidell, 2007, p. 104; Field, 2009, p. 242-

296). Hair et al. (2013a, p. 125) recommended computing the VIF measure. The VIF

indicates the degree to which the standard error has been increased due to the

existence of collinearity. A VIF value of 5 and above is indicative of a potential

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collinearity problem (Hair et al., 2011). Multicollinearity was assessed and addressed

as part of the PLS-SEM analysis (see section 6.4).

Cross-sectional studies are typically more vulnerable to the inflation of correlations by

a potential common method variance (CMV) or common method bias (CMB)

(Podsakoff and Organ, 1986; Podsakoff et al., 2003; Chin et al., 2012; Podsakoff et al.,

2012; Real et al., 2012), mainly because the same respondent responds to the items in

a single questionnaire at the same time (Malhotra et al., 2006).

In this study, the following steps were applied, in order to control for the effect of

common method variance:

1. Firstly, a statement was introduced to the respondents, notifying them that

there are no right or wrong responses, and that their anonymity and

confidentiality are guaranteed (Podsakoff et al., 2003).

2. Secondly, a statistical test was conducted to assess potential common methods

bias in the results. In this case, if a common method variance exists, available

statistical remedies may be applied to minimise the influence of this bias

(Rönkkö and Ylitalo, 2011).

There are many existing approaches to control for CMV (for a detailed review, see:

Podsakoff et al., 2012), however, there are not many studies, which introduced an

assessment of CMV with the use of PLS-SEM model (e.g., Liang et al., 2007). As

there is no evidence yet confirming that a certain approach is better than others

(Richardson et al., 2009), the Harman's single-factor test is considered to be the most

commonly and widely used statistical technique (Podsakoff et al., 2012).

In this study, the Harman's one factor test procedure (Podsakoff and Organ, 1986) was

chosen and conducted; in which all measured variables were entered into a factor

analysis. Principal axis factoring was carried out and the results of the unrotated factor

solution were examined. If a substantial degree of the common method variance was

found, either a single or one general 'factor' would emerge which would account for

“the majority of the covariance in the independent and the criterion variables”

(Podsakoff and Organ, 1986, p. 536).

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3.5.7.2 Data Analysis

The data analysis process, in the quantitative phase (QUAN) involves looking at the

data descriptively, “in order to see what the general trends in the data are” (Field,

2009, p. 18).

A descriptive analysis of data includes indications of the means, standard deviations,

and range of scores for variables of interests (Creswell, 2009) and wherever possible

presented graphically (Field, 2009). In this study, choosing the statistical model to test

the model was a complex, multi-stage procedure, as follows:

Firstly, different statistical models make different assumptions, therefore it was

important to examine the assumptions of each before deciding which statistical test

was appropriate (Field, 2009).

Secondly, different types of variables force different types of statistical tests

(Bryman and Bell, 2007). Hence, the types of variables, in this study were evaluated.

Thirdly, the size and nature of the sample are likely to impose limitations on

the kinds of techniques we can use (Bryman and Bell, 2007, p. 349).

Based on that, two main statistical analyses were performed. Exploratory Factor

Analysis (EFA) was conducted first, as part of the measures development process. The

PLS-SEM was performed including the assessment of the measurement model quality

as well as the structural model.

3.5.7.2.1 Exploratory Factor Analysis (EFA)

The exploratory factor analysis (EFA) was used in this study, for analysing the

structure of the “interrelationships among the variables” (Hair et al., 2010, p. 17) for

achieving parsimony by summarising and reducing the number of variables (Field,

2009; Hair et al., 2010) in order to prepare the scales for the hypothesis testing

(Conway and Huffcutt, 2003).

There is a debate on whether exploratory factor analysis (EFA) can be conducted in

the same analysis with confirmatory factor analysis (CFA). It has been argued that

factor solutions obtained by EFA repeatedly resulted with a low fit in confirmative

follow-up studies (Van Prooijen and Van Der Kloot, 2001). However, Anderson and

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Gerbing (1988) suggested defining these approaches as a strict dichotomy, then, in

practice it can be understood as an ordered progression.

EFA is often considered as a useful statistical method for revealing the underlying

structure of the factors in the model (Henson et al., 2004), as well as playing an

important role when establishing the validity of the study’s scales (Conway and

Huffcutt, 2003). As a closing argument, it can be contended that conducting a

separate, preparatory factor analysis (i.e. EFA: Principal Component Analysis) prior to

the PLS analysis can be considered as plausible when the use of EFA is restricted, as

in this study, to the preparation of the scales prior to the hypothesis testing (Conway

and Huffcutt, 2003, p. 149).

Recently, such an approach was applied in several PLS-SEM studies (e.g. Grace and

O’Cass, 2005; Weerawardena et al., 2006; Wilson, 2010; Ling et al., 2013; Astrachan

et al., 2014; Barbeitos et al., 2014; Kwak et al., 2014; Yu et al., 2014).

Statistical Procedures and Assumptions

Factors are defined as common underlying dimensions of a set of interrelated variables

(Field, 2009), and can be identified by observing variables that correlate exceedingly

with a group of other variables, but do not correlate with other variables, which are not

part of this group (Field, 2009).

There are several methods for exploring factors in the data set, such as: principal

component analysis (PCA) and principal factor analysis (principal axis factoring)

(Field, 2009). Both techniques, usually produced similar results and are restricted to

the sample collected, hence, generalisation cannot be achieved (Field, 2009).

In this study, PCA was chosen as the EFA technique due to the fact that the procedure

focuses only on creating linear components in the data, which encompasses

considerable amounts of common variance of the data (Field, 2009). The PCA was

conducted for each scale (only concerning the constructs of interests) separately for

the following reasons:

Firstly, the appropriateness of variables should be considered in factor analysis

(Hair et al., 2010), thus, conducting the analysis for each content domain separately

may increase the extent to which these variables are interrelated.

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Secondly, it allows the inquirer to meaningfully interpret the underlying

dimension(s), which might reflect best the conceptual underpinning of the variables in

the study (Hair et al., 2010). Although EFA is often considered as an interdependence

technique, it is less affected by not meeting the statistical assumption rather than

meeting the conceptual assumptions. Nevertheless, prior to the EFA analysis, the

statistical assumptions were examined, based on the recommendations of Tabachnick

and Fidell (2007), Field (2009) and Hair et al. (2010):

Normality: in this study, the results indicated that the data was deviated from

normality to some extent. However, factor analysis can still be conducted even though

the data does not normally distribute or have roughly normal distributions (Field,

2009). In such a case, the solution though degraded, may be meaningful (Tabachnick

and Fidell, 2007).

Linearity: in this study, Linearity was not achieved in some of the variables, therefore,

for these variables; the solution might be compromised and may be considered as a

cause for concern for the EFA results.

Outliers and missing values: No missing values were detected and a few cases with

extreme scores were found to be univariate outliers. The scores were replaced by

means of changing the scores and thereafter they were transformed. In addition, no

multivariate outliers were detected among all variables in the model.

Multicollinearity: Although, in PCA multicollinearity is not a problem (Tabachnick

and Fidell, 2007), it is important to avoid extreme multicollinearity (i.e. variables

whose correlation with other variables exceed the value of 0.9) or singularity (i.e.

variables that are perfectly correlated) (Field, 2009). In this study, prior to conducting

the EFA, multicollinearity (for the EFA analysis) was detected by inspecting the

determinant of the R-matrix, which should be greater than 0.00001 (Field, 2009, p.

648). Consequently, multicollinearity was not considered as a concern in this study.

Factorability of R: the correlation between the variables should exceed the value of

0.3; otherwise, the use of EFA is questionable. Therefore, before analysing the

separate PCA results, the inter-correlations between the variables were examined, and

items with lower level of correlations (<0.3) were omitted from the analysis (Field,

2009).

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Several tests, which are available in SPSS, were conducted, such as: the anti-image

correlation matrix and the KMO measure (i.e. Kaiser, Meyer, Olkin’s measure of

sampling adequacy) (Tabachnick and Fidell, 2007). For an EFA to meet the

factorability criteria, the anti-image correlation matrix should show small values on

off-diagonal elements and the KMO should exceed the value of 0.6 (Tabachnick and

Fidell, 2007).

In addition to the examination of the overall KMO, the diagonal elements of the anti-

image correlation matrix should be evaluated too. The value should exceed the

minimum threshold of 0.5. Variables with values below this threshold were excluded

from the analysis (Field, 2009, p. 659). The final test that was conducted and

examined was the Bartlett’s test of sphericity (Field, 2009). The results of this test

indicate whether the correlations matrix is significantly different from identity matrix

(i.e. the off-diagonal components are zero) (Field, 2009, p. 648). However, the results

of this test should be carefully examined, due to its sensitivity and dependence on the

sample size (i.e. recommended only if the sample size is smaller than the ratio of 5:1)

(Tabachnick and Fidell, 2007). In order to assert the readability and interpretability of

the PCA results, the following parameters were evaluated and reported too: factor

rotation, factor loadings, and eigenvalues (See Appendix L for more details).

Subsequently to the PCA, a reliability test, based on the Cronbach’s alpha assessment,

was conducted. A reliability test is an evaluation of the internal consistency between

the items in the same scale. The assessment of reliability of a scale is recommended

when factor analysis is used to validate a scale (Field, 2009). A value of 0.7 or higher

is an acceptable value for a reliable measure, but it is often agreed that the lower limit

for newly developed scales is 0.6 and values above 0.8 are considered as good for

established scales (Hair et al., 2010). However, it should be noted that the Cronbach’s

alpha is dependent of the number of items in the scale.

Finally, the adequacy of the sample size was evaluated. The minimum sample size for

EFA according to Hair et al. (2010) is 50 observations and the preferable size should

exceed 100. A general rule of thumb is to have at least 5:1 (a ratio of 10:1 is more

acceptable) cases per variable (Hair et al., 2010). In this study, the sample size of 178

observations meets the sample size requirements.

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3.5.7.2.2 PLS-SEM

The Partial Least Squares (PLS) approach to Structural Equation Modelling (SEM) is

a valuable and flexible tool for statistical analysis, especially when the investigated

conceptual model is considered as large and complex, as in this research (Hair et al.,

2011; Hair et al., 2012a, 2013b).

Structural Equation Modelling (SEM) focuses on prediction, with the use of path-

analytic modelling, by which concepts are modelled as latent (unobserved) variables

that are indirectly inferred from manifest variables (i.e. multiple observed measures

often termed as indicators) (Hair et al., 2013a). SEM is often described as a second-

generation multivariate technique (Fornell and Larcker, 1981), which includes two

best-known approaches: the covariance-based methodology and partial-least squares.

One approach is no more important than the other is. Instead, the chosen approach

should be designated based on the research purpose and objectives. One important

aspect of the covariance-based is that it requires a normal data distribution. On the

other hand, the PLS-SEM approach is considered as an exploratory soft-modeling

methodology and thus does not require normal data distribution, and accommodates

small sample sizes (Chin, 2010)

Justification for selecting PLS-SEM path modelling

This study examines a complex model with six criterion variables, high-order

constructs and a moderating effect. This study’s model can be considered as a complex

model mainly because complex models are defined as models which consist of more

than 10 constructs and at least 50 items (Chin, 2010).

The PLS-SEM approach was chosen as the statistical technique that was best suited

for this study purpose, for the following reasons:

Firstly and according to Gefen et al. (2011) the unique advantage of SEM in

comparison with linear regression relies upon on the fact that SEM enables the

creation and estimation of models with multiple criterion variables and their

interconnections all at once. Therefore, it allows avoiding biased and inconsistent

parameter estimates for equations (White et al., 2003).

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Secondly, PLS-SEM can converge with small sample size, as in this study, in

comparison to Covariance-based SEM (CB-SEM) that can only be applied, when the

sample size is higher than 200 (Chin, 1998; Chin, 2010; Hair et al., 2013a).

Thirdly, in situations where the main purpose of the study is prediction of a

target construct, the PLS-SEM is appropriate, in contrast to CB-SEM, which is often

used, to test theories rather than develop them, which is the case in this study

(Goodhue et al., 2012; Hair et al., 2012c; Hair et al., 2013a).

Fourthly, PLS-SEM does not require normal data distribution (Chin, 2010;

Gefen et al., 2011; Hair et al., 2013a), therefore it works competently when data is not

normally distributed, and nonparametric methods should be applied.

PLS-SEM: the two-stage approach and main procedures

The assessment of PLS-SEM models comprises of two stages: firstly, the quality of

the measurement models is evaluated, and if the measurement model examination

provides adequate results, thus verifying the reliability and validity of the item

measures used (Chin, 2010), the structural model is evaluated in the second stage

(Sarstedt et al., 2014). The main purpose of this stage is to provide evidence

supporting the theoretical model by assessing the variance explained and the extent

and significance of all path estimates (Chin, 2010).

The PLS-SEM model was assessed in both stages through the SmartPLS2 software

(Ringle et al., 2005). The software is Java-based, and the model is specified by

“…drawing the structural model for the latent variables and by assigning the indicators

to the latent variables” (Temme et al., 2010, p. 740). PLS path modelling does not rely

on any distributional assumptions, hence, parameter estimates and their statistical

significance has to be assessed by means of resampling procedures (i.e.

bootstrapping). In addition, to calculate cross-validation indices, a blindfolding

procedure was applied (Chin, 1998; Chin, 2010).

The PLS algorithm settings, which were applied in this study, were based on the

following rules of thumb to evaluate the results of the measurement and structural

model estimation (Chin, 2010; Hair et al., 2013a; Hair et al., 2013b):

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The factor weighting scheme was selected as the weighting method in stage 1

(measurement model assessment) and the path weighting scheme was chosen

as the weighting method in the evaluation of the structural model.

The data metric option was selected which z-standardised the data input (i.e., a

mean value of 0, standard deviation of 1).

The initial value for all outer weights was +1 (i.e. the starting values of the

weights for the initial approximation of the latent variable scores (Hair et al.,

2013b)).

A stop criterion of 0.00001 was selected (e.g., the sum of the measurement

model weights’ absolute changes between two iterations <0.00001)

The maximum number of iterations was defined as 300.

The bootstrap samples were conducted to estimate the PLS-SEM path model (Hair et

al., 2013a). The individual sign change option was selected, and the number of

bootstrap samples was 500.

T-test was computed for significance testing. The empirical t-value is compared to the

critical t value for a two-tailed test (Hair et al., 2013a).

Measurement model evaluation

When assessing the adequacy of the PLS-SEM measurement model, reflectively

measured constructs should be distinguished from formatively measured constructs

(Hair et al., 2013a).

Reflective indicators, reflect the same concept, thus, it is assumed that different

indicators of the same construct should be inter-correlated. The reflectively measured

constructs that were assessed, in this stage, are based on the outputs of the previously

discussed exploratory factor analysis (EFA) and are evaluated by analysing empirical

measures of the relationships between the indicators and the latent variables (Hair et

al., 2013a). This is done by systematically estimating the reliability and validity of the

constructs measures. The reliability and validity of the model were assessed as

follows:

Internal Consistency

The internal consistency reliability is evaluated by computing the composite reliability

(CR) as well as the traditional criterion, which is the Cronbach’s alpha.

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The CR Index is considered to be much more accurate than Cronbach’s Alpha

(Newman et al., 2013b), due to Cronbach’s alpha’s limitations, such as assuming equal

loadings or error terms among the measures (Chin, 1998) or the sensitivity of this

criterion to the numbers of indicators. The PLS-SEM prioritises the indicators based

on their individual reliability (Hair et al., 2013a).

The CR index, in contrast to Cronbach’s alpha does not assume all items to equally

contribute to the construct (Werts et al., 1974) and it varies between 0 and 1. In

exploratory research, values between 0.6 and 0.7 can be considered as acceptable

(Hair et al., 2013a). In spite of the critique of using Cronbach’s alpha as a measure of

internal consistency, it is recommended to report on its value, as a conventional

measure of internal consistency (Chin, 2010; Hair et al., 2013a). The lower limit for

newly developed scales is 0.6 and values above 0.8 considered as good established

scales (Hair et al., 2010).

Individual Item Reliability

The individual indicator or item reliability is assessed by closely evaluating the

indicator outer loading. Consistent with the literature, the reliability of the constructs is

measured as the standardised indicators’ loadings. As a general rule of thumb,

indicators with a loading exceeding 0.707, which represent 50% of the variance in the

observed indicator, are considered as having a sufficient individual reliability (Hair et

al., 2013a; Newman et al., 2013b). However, in social science studies, especially when

new scales are implemented, weaker outer loadings might be found (Hulland, 1999).

Therefore, it is suggested that for first time exploratory researches, a more relaxed

loadings’ values, between 0.5 and 0.6, might be considered as acceptable (Barclay et

al., 1995).

Hair et al. (2013a) recommended that indicators with weaker outer loadings (i.e.

loadings between 0.4 and 0.7) might be retained in the model if they contribute to

content validity and if when deleting them the composite reliability or the AVE do not

change substantially. Based on the aforementioned discussion and the fact that most of

the scales in this study are newly developed, the outer loading threshold within this

study is 0.5 and above. In cases when this study uses a valid and reliable measure,

such as the cognitive style index, the indicator was retained in the model only if it

contributed to the content validity and if the weaker outer loading is statistically

significant.

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Convergent Validity

The average variance extracted (AVE) is extracted to evaluate convergent validity.

Convergent validity is the extent to which indicators of a reflective construct share a

high proportion of variance. Convergent validity is assessed by evaluating the outer

loadings of the indicators and the average variance extracted (Hair et al., 2013a). This

criterion is computed as the mean value of the indicators’ squared loadings of each

construct (Fornell and Larcker, 1981). The AVE is the communality of a construct

(Hair et al., 2013a, p.103). AVE values above 0.5 are considered as satisfactory. It

indicates that the construct explains more than 50% of the variance of its indicators,

hence the variance captured by the construct exceed the variance due to error (Fornell

and Larcker, 1981).

Discriminant validity

Discriminant validity demonstrates the extent to which measures of a construct

satisfactorily differs from measures of other constructs in the model. Two tests are

commonly used in PLS models: the Fornell-Larcker criterion and the cross loadings

examination (Fornell and Larcker, 1981; Chin, 1998; Hulland, 1999). According to the

Fornell-Larcker criterion, the square root of the AVE of each construct should be

computed and then compared with the highest correlation of the other constructs in the

model. In addition, discriminant validity is assessed by examining the constructs’

cross loadings. In this assessment, the indicator’s loading on a construct should be

higher than all of its cross loadings with other constructs.

Estimating the Higher-order constructs measurement model

In this study, three constructs were designed as higher-order constructs (HOC):

Prior Knowledge (PK)

Learning by Doing Deliberately (LBDD)

Learning by Networking Deliberately (LBND)

Modelling hierarchical latent variables in PLS-SEM can be conducted by using one of

the following three approaches:

(1) The repeated indicator approach (Hair et al., 2013a)

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(2) The sequential latent variable score method or two-stage approach (Wetzels et al.,

2009) and the

(3) The hybrid approach (Wilson and Henseler, 2007; Hair et al., 2013a).

In the ’repeated indicators approach‘ (Hair et al., 2013a), the HOC is created by

reusing the indicators of the Lower Order Constructs (LOCs). The HOC is a latent

variable that “represents all the manifest variables of the underlying lower-order latent

variables” (Wetzels et al., 2009, p. 180).

PLS-SEM can determine standardised latent scores, thus, in the two-stage approach,

the latent variable scores for lower-order latent variables are obtained in a first-stage

model without the second-order construct present, saved and then used in a separate

second-stage analysis as the indicators of the HOC (Becker et al., 2012).

The hybrid approach is a mixture of the repeated indicator approach and the sequential

latent score method (Wetzels et al., 2009) and is often used in many formative-

formative and reflective –formative hieratical models (Hair et al., 2013a).

After a careful consideration of the pros and cons of each methods, and although the

repeated indicators approach is easy to implement and recommended when the

measurement model mode is a reflective-reflective HCM (Hieratical Component

Model), the two stage approach was implemented in this study, mainly because the

research focus is on the higher-level estimates, i.e., the path coefficient to and from the

higher-order constructs. In this case, Becker et al. (2012, p. 377) argued that “such

models are more parsimonious as they only incorporate the focal higher-order level

variables”.

The measurement model assessment of the HOCs in this study, based on the two-stage

approach, was conducted in a stepwise manner: In stage one, the measurement model

of each of the LOCs was estimated by linking the first-order constructs (LOCs)

directly to the final endogenous variable (s) in the model. Reliability and validity of

the LOCs was assessed, and subsequently the latent variables scores of each of the

first-order construct was saved and then used as the observed variable for the

measurement model assessment of the HOC.

The latent or factor scores are often used for statistical analysis (Field, 2009), and are

estimated in PLS-SEM, by using one of three weighting schemes: centroid, factor and

path. In this study, latent scores were determined by using the factor-weighting

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scheme during the measurement model assessment and the path weighting schemes in

the structural model assessment. The centroid-weighting scheme should not be used

for estimating higher order models (Henseler et al., 2009; Hair et al., 2012b). Hair et

al. (2013a, p. 234), recommended that the factor and path weighting schemes provide

reasonable outcomes for the HCM in PLS-SEM and typically they derive similar

results. In this study, the factor-weighting scheme was used for estimating the

measurement model parameters, mainly because in this scheme the latent variable

scores are computed as the weighted correlation coefficients between the latent

variables regardless of the causal order. Therefore, the factor scores are not affected by

the path directionality. Further, the path weighting scheme is used for the structural

model estimation, as:”…it provides the highest R2 value for the endogenous latent

variables” (Hair et al., 2013a, p. 80).

Structural model evaluation

Structural model parameters

When assessing the structural model, it should be emphasised that the PLS-SEM fits

the model to the data; as a result, the model produces best parameters estimates.

Therefore, the application of measures of goodness of fit is made redundant, in

comparison to CB-SEM models (Hair et al., 2013a).

The following five key criteria are evaluated in the structural model assessment: (1)

the path coefficient (beta coefficients) and their significance, (2) the proportion of

variance explained (R2) for all endogenous variables, (3) the effect size (f

2), (4) the

predictive relevance test based on the Stone-Geisser (Q2) and (5) the q

2 effect size.

(1) The path coefficients

The path coefficients represent the hypothesised relationship between the constructs in

the model. These standardised values ranging between +1 (representing strong positive

relationships) and -1 (representing strong negative relationships), whilst the extent the

path coefficients’ values are close to 0, the weaker the relationships (Hair et al.,

2013a). In this study, the relative importance of significant relationships was

interpreted too. The interpretation, in the same vein as is done in Ordinary Least

Squares (OLS) regression analysis, emphasises the extent the path coefficients are

relative to one another, hence, each path coefficients represent the size of the effect on

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the endogenous variable. Moreover, the significance of the path coefficients was

examined and obtained by means of bootstrapping

(2) The Coefficient Determination R2

The R2, commonly known as the ‘Coefficient of Determination’, is a measure of the

model’s predictive power (Hair et al., 2013a), calculated as the squared multiple

correlation coefficients. The R2 values range between 0 and 1. Hence, increase on the

R2 value reflects an increase in the variation in the endogenous construct that is

accounted for by the constructs linked to it. There are different opinions about what R2

values can be considered as high, and the interpretation is dependent on the research

domain. For example, Chin (1998) considered R2 values of 0.7, 0.3 and 0.2 as ‘high’,

‘moderate’ and ‘weak’, respectively. Hair et al. (2011) outlined that as rule of thumb,

in marketing research, values of 0.75 are described as substantial, 0.5 as moderate, and

0.25 as weak. In social and behavioural science, Cohen and Cohen (1983) defined R2

values of 0.25 as high, 0.09 as moderate and 0.01 as low. Cohen (1988) suggested that

any variable could be defined as small, a medium, or a large effect size when R2 is

0.02, 0.13, or 0.26, respectively. For the purpose of this study, the latter approach was

implemented for the interpretation of the R2 values.

(3) The effect size (f2)

The effect size (f 2) is measured as the change in the R

2 value, when including a

selected exogenous latent variable in the model. The effect size can be used as a

measure for assessing the impact of a selected exogenous latent variable on the

endogenous construct (Hair et al., 2013a):

Where R2 included provided from the first run (included), and R

2 excluded are the R

2

provided from the second run (excluded) of the PLS-SEM model. Following Chin

(2010), based on operational definitions for multiple regression, effect size (f 2) of

0.02, 0.15, and 0.35, were considered in this study, as having, a small, medium, or

large effect at the structural level, respectively.

(4) The Stone-Geisser (Q2)

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The Stone-Geisser (Q2) is often used in PLS-SEM analysis, specifically in reflective

measurement models, as a measure of the model’s predictive relevance (Chin, 2010).

There are two different approaches for calculating the Q2:

the cross-validated

redundancy approach and the cross-validated communality approach. Hair et al.

(2013a) suggest that the former is perfectly suited for PLS-SEM models. The cross-

validated redundancy approach relies on the parameter estimates, produced from the

structural and measurement model, in contrast to the cross-validated communality

approach, which uses the construct scores only, without including the structural model

information. The blindfolding procedure is used for obtaining the Q2

values. It is a

technique, which reuse the sample, by omitting every dth data point in the endogenous

construct’s indicators (i.e. PLS-SEM algorithm is considered by them as missing

values via pairwise deletion) and estimates the parameters with the remaining data

points (Hair et al., 2013a). The estimated parameters and the omitted data points are

used as input for the Q2 computation. The PLS-SEM model, estimate the Q

2 by using

the following equation:

Q2=

1-SSE

SSO

Where SSE represents the Sum of the squared prediction Errors and the SSO is the

Sum of the squared Observations. Following (Hair et al., 2013a), in this study, a model

with Q2

value larger than 0, was considered as having a predictive relevance for a

certain endogenous construct.

(5) The effect size (q2)

The Q2

represent the extent the path model might predict the originally observed

values. The q2 is computed as a measure of the effect size of the structural model on

the observed reflective endogenous construct:

q2=

Q2

included - Q2

excluded

1- Q2

included

Where values of 0.02, 0.15 and 0.35 indicate that an exogenous latent variable has a

small, medium, or large predictive relevance for a specific endogenous latent variable

(Chin, 2010; Hair et al., 2013a).

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Moderation Effect

PLS-SEM is chosen, in this case of moderation analysis, over traditional analysis

approaches such as multiple regression or analysis of variance (ANOVA), since the

latter are often problematic in detecting weak moderation effects (Aier, 2014). This is

because these approaches do not account for measurement errors, which in turn, might

affect the statistical power (Chin et al., 2003). In addition, PLS-SEM is often chosen

for analysis of complex relationships with interaction terms (Wilson, 2010).

Henseler and Fassott (2010) reviewed the PLS-SEM available approaches for testing

moderation effects: the first approach often named as the multi-group assessment

(MGA) or the group comparison approach and the second is the product term

approach. However, when the model includes more indicators and the sample size is

small, as is in this study, the product indicator approach should be used (Henseler and

Chin, 2010).

In this study, the product indicator approach was preferred and implemented with a

continuous moderator. In this approach, each indicator (item) of the independent

construct (X) is multiplied with each indicator (item) of the moderator construct (Z).

The product indicators create the interaction term (X.Z) (Chin et al., 2003; Wilson,

2010; Hair et al., 2013a). The following figure depicts how to set-up the interaction

term in PLS-SEM software:

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Figure ‎3.5: Modelling continuous moderator variable-The product indicator approach

Source: adopted from Chin et al. (2003, p. 28)

Hair et al. (2013b) emphasised that an important concern in PLS-SEM moderation

analysis is that, oftentimes, the moderator variables and their interaction terms are

included in the same model (i.e., “the multiplication of indicators or constructs” (Hair

et al., 2013b, p. 3)) and hence, their main effects are incorrectly understood. Therefore,

the moderation analysis should be conducted subsequently to the main effect analysis.

The researcher should first assess the main effects in the PLS-SEM path model, which

contains only main effects between constructs, excluding the moderator. In a

subsequent analysis, the product indicators term and its interaction effect are included

in the model. This process might lower the risk of mistakenly interpreting simple

effects as main effects (Henseler and Chin, 2010; Henseler and Fassott, 2010).

In a PLS-SEM model, which includes moderator variables, “…main effects change

into simple (or single) effects…Whereas a main effect quantifies the change in the

level of the dependent variable when the considered independent variable is increased

by one unit and all other independent variables remain constant (ceteris paribus), a

simple effect quantifies the change in the level of the dependent variable when the

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independent variable is increased by one unit, the interacting variable has a value of

zero, and all other independent variables remain constant” (Hair et al., 2013b, p. 3). In

PLS-SEM models, the indicator’s values should be mean-centred before the analysis,

to minimise collinearity (Chin et al., 2003; Hair et al., 2013a). In addition, the PLS-

SEM assessment should be conducted only if the interaction variable is reliable (Hair

et al., 2013a), therefore the measurement model of the cognitive styles construct (CSI)

should be evaluated, similarly to the other constructs in the model.

Based on Henseler and Fassott (2010), the evaluation and interpretation of the

moderating effects, should include the following:

(1) “The direct relations of the predictor and the moderator variable, as well as the

relation of the interaction term, with the endogenous construct” (Henseler and Fassott,

2010, p. 730).

(2) The hypothesis on the moderating effect is supported if the path coefficient

between the interaction term and the endogenous construct is significant (Hair et al.,

2013a), regardless of the values of the path coefficients between the moderator and the

endogenous variable, as well as the predictor and the endogenous variable (Baron and

Kenny, 1986).

(3) When the moderating effect is statistically significant, the strength of the identified

moderating effect has to be assessed by computing and assessing the effect size of the

moderating effect (i.e. f 2). Accordingly, moderating effects with f

2 of 0.02 may be

considered as weak, effect sizes from 0.15 as moderate, and effect sizes above 0.35 as

strong. However, it should be noted that a low effect size does not necessarily regard

the moderated effect as negligible if the resulting beta changes are meaningful (Chin et

al., 2003; Henseler et al., 2007).

Sample size

PLS-SEM perform well with small sample size (Chin and Newsted, 1999; Reinartz et

al., 2009). The ’10 times’ rule of thumb is widely considered as the criteria for

estimating the minimum sample size for PLS-SEM models (Chin, 1998; Goodhue et

al., 2012). This rule of thumb suggests that the sample size is equivalent to at least ten

times the number of arrows (structural paths) leading into a construct (Barclay et al.,

1995). Accordingly, the minimum sample size in this study should be 100

observations or higher.

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However, recently Hair et al. (2013a) suggested using the rules of thumbs which were

provide by Cohen (1992). They advocated for this estimation approach because

researchers should estimate the sample size based on the model and the data

characteristics. The sample size should be assessed by means of a power analysis.

Assuming the frequently used level of statistical power of 80%, and when the

maximum number of predictors is 4 (in this study), the minimum sample size for

detecting R2 values of at least 0.25 is 65 (p<0.05) or 91 (p<0.01). By including the

control variable, the maximum number of arrows pointing to the target construct is

nine (four predictors and five control variables). Thus, the minimum sample size

ranges between 88 (p<0.05) and 119(p<0.01).

In this study, 178 respondents responded to the survey, indicating that with respect to

the criteria the sample size (N=178) is adequate, and PLS-SEM can be applied for

estimating the model’s parameters.

3.6 Summary

The research strategy of this study is based on the mixed methods approach (Creswell,

2009). The study design is a two-phase, sequential mixed methods study, QUAL-

QUAN (Creswell et al., 2003). The sequential QUAL - QUAN design is particularly

appropriate to develop or refine a theory and then to test it (Morse, 2003), and is often

discussed as the procedure of choice when a researcher needs to develop instruments

because existing instruments are inadequate or not available (Creswell, 2009). The

Qualitative phase was split into two parts: QUAL1 and QUAL2. Each section of the

qualitative phase includes the analysis of two data collection tools: interviews and

focus group discussions (Tashakkori and Teddlie, 1998).

The first Qualitative Phase (QUAL1) allows us to delineate and specify the emergence

of themes based on the Grounded Theory approach (Charmaz, 2006). In QUAL1, one

focus group and two semi-structured, in-depth interviews were conducted. The nine

participants in the focus group were selected by means of what is usually called the

'snowball technique' (Bryman and Bell, 2007). The interviewees for this stage were

selected as a 'convenience cohort sample' (Bryman and Bell, 2007).

The main purpose of the second qualitative phase (QUAL2) was to develop further

these emerging themes, and to refine the theory by addressing a conceptual model,

which was quantitatively tested. The second phase (QUAL2) consists of one focus

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group, with eight entrepreneurs who were selected using a 'snowball' sampling. In this

phase, eight subsequent interviews were conducted and the interviewees were selected

using the 'snowball' sampling method.

In the quantitative phase, the chosen research design was the cross-sectional design

(Bryman and Bell, 2007). A web-based questionnaire was the chosen data collection

tool (Cobanoglu et al., 2001; Sills and Song, 2002). The survey was conducted on a

sample of entrepreneurs in Israel. The target population was Israeli high-tech

entrepreneurs who seek to operate or who are already operating international

entrepreneurial businesses. The 'sampling frame' (Groves et al., 2009) consists of

various lists of hi-tech entrepreneurs. The sample was selected based on a ‘purposive

sampling' method (Creswell, 2009).

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4. Qualitative Phase Findings and Results

The main purpose of the qualitative phase was to explore and illustrate the various

aspects of international entrepreneurship phenomena, and specifically those that relate

to entrepreneurial opportunities. In addition, the qualitative phase enabled the study to

identify important themes, relevant constructs, develop and present a theoretical

framework, and design the quantitative instruments with its measures and indicators.

The main purpose of the first qualitative phase (QUAL1) was to develop the

overarching research question based on the research gaps that were revealed by the

literature review. In addition, four additional questions were explored, in order to

cover broader content areas within the topic.

These questions were:

1. How do entrepreneurs define and perceive entrepreneurship and international

entrepreneurship. In addition, the question of whether or not different

entrepreneurial characteristics are needed for success in international ventures

and which of these are universal was examined.

2. Secondly, the questions of how entrepreneurs learn, was addressed too.

3. Thirdly, what is the entrepreneur's perception or attitude toward risk?

4. Finally, the question of whether or not the entrepreneur activates an

opportunity or simply recognises it was discussed too.

The main purpose of the second qualitative phase (QUAL2) was to develop the

emerging themes, which were gleaned and analysed through QUAL1, and to refine the

theory by addressing a conceptual model that proposes a relationship between the

constructs and activates them, so the model may be quantitatively tested. Thus,

QUAL2 plays a very important role in shaping the theory and being the fundamental

phase of the overall research. Based on important themes that emerged from the

QUAL1 analysis, and the literature review, the research questions addressed in this

phase were:

1. What are the factors that influence the motivation of Entrepreneurs to

internationalise?

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2. Secondly, what is the role of the Internet in the learning cycle of international

entrepreneurs?

3. Finally, how entrepreneurs learn about opportunities?

This chapter introduces the two main findings: firstly, the findings of the first

qualitative phase are introduced and discussed. It allows us to delineate and specify the

emergence of themes based on the Grounded Theory approach (Charmaz, 2006).

Secondly, the second qualitative phase findings are presented, which enable us to

refine the theory by addressing a conceptual model that was quantitatively tested.

4.1 Qualitative Phase 1 (QUAL1)

4.1.1 Findings

In this phase of the research, interviews and focus group transcripts were read

thoroughly, marked, coded, and analysed. The focus of the analysis was on the

identification of sequences of repeated phrases rather than focusing on disjointed

words or phrases, (Silverman, 2000).

The following steps were applied during the analysis:

1. Firstly, themes, symbols and recurring phrases, were mapped in the text.

2. Secondly, the gleaned themes were organised according to the content areas.

3. Thirdly, themes were kept for the main analysis, based on their importance, the

literature, and their contribution to the interpretation of the findings. At this

stage, some of the themes were found to be more important than others, thus

some were discounted.

4. The process continued in an attempt to construct a more general picture of the

subject within the context of entrepreneurship.

The analysis presents part of the transcripts as an audit trail, in order to maintain

‘meaning in context’ , transparency, integrity and trustworthiness of the synthesis

(Weed, 2008). Several central themes arose from the focus group discussion and the

interviews. Table ‎4.1 summarises the main themes from this analysis:

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Table ‎4.1: Main Themes by Source

Category Main Themes Focus

Group

Interview

with “A”

Interview

with “B”

Definition of

entrepreneurship

Entrepreneurship as creation + + +

Entrepreneurship as

parenthood + +

Entrepreneurship as fantasy + +

Entrepreneurship as a way

of life/ lifestyle + +

Entrepreneurship as a game +

Entrepreneurship as a

perpetual challenge + + +

Characteristics of

Entrepreneurs

Independence + + +

Commitment + + +

High inner locus of control + +

Dreamer/ fantasist + + +

Seeks excitement + + +

Goes against the current + + +

Lonely +

Energetic/ charismatic + + +

Versatile +

International Entrepreneur

perceived as similar to

Entrepreneur

+ + +

Opportunity

recognition

“Windows of opportunity” + +

Opportunity as “a bus to

catch” + + +

Opportunity recognition ‘by

accident’ + + +

Risk perception

Risk as embedded in the

entrepreneurial process + + +

Risk as an equation, can be

defined and measured + +

Risk level depends on the

stage of

entrepreneurship

+ + +

Dualistic approach to risk + + +

Entrepreneurial

Learning

Entrepreneurial learning as

learning from

experience

+ + +

Dualistic approach to

knowledge + + +

Learning is essential for

opportunity recognition + + +

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The following paragraphs introduce each main category and its related themes:

Entrepreneurship Definition

Throughout the interviews and the focus group, entrepreneurship was defined as

creation:

"Creating something from nothing... Actually, I find myself creating

something too… and I try to build a story around it, and in fact in that way

I feel I’m creating it" (Interviewee A).

“As I see it, an entrepreneur is a creator, and he creates it in a different

place from where he is located.” (Focus Group)

The participants saw entrepreneurship as a creation, but also as 'telling a story' at first

to themselves when they envisage the idea, and later on to others such as their friends,

family, business partners, and their surroundings in general. Their words are in the

form of a description that recalls the very act of creation:

"….the fact that I managed to create it out of nothing, that’s the great

part of being an entrepreneur" (Interviewee A).

Correspondingly, in one of the interviews, interviewee (A) compares

entrepreneurship to parenthood, procreating and raising the “infants” until they can

stand on their own business feet:

"I developed this business in England, it’s sustainable, ... I think it is like

giving birth to a child. You bring him up and when he leaves home he has the

tools, that’s something you are proud of, that’s what the business in England

is like" (Interviewee A).

Furthermore, the entrepreneurial process was described as a “fantasy”, being occupied

by something which is similar to that of a dream, one in which the dreamer can easily

imagine in his mind and cling to it until it is proven, realised, or disproved.

“Our imagination is a bit wild! We can imagine ourselves as part of the

process, and that apparently allows us to take that step, which is often

naive or stupid, and to get the experience. We aren’t afraid to try things,

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we’re not so worried by rejection, and we’re ready to put our hand in the

fire.” (Focus group)

The need to fantasise is explained in one of the interviews (A), as a consequent of the

interviewee’s need to escape the unpleasant reality of his childhood. The story of his

mother, a widow, who soared on the wings of imagination with her children,

hypothesising about what they would do with the lottery prize not only provides a

psychological explanation of the interviewee’s personal motivation to engage in the

entrepreneurial business, but also illustrates the need for creative thinking and the

ability to fantasize in entrepreneurship. Perhaps more than anything, this story clarifies

the illusoriness, which characterise promise, hidden in the future, to succeed:

"After my father died, each week my mother would buy a lottery ticket,

and everyone at home would laugh and ask, "Why do you buy a lottery

ticket?" Therefore, she said, “Buy a ticket at the beginning of the week and

then you’ll have the rest of the week to dream about it.” (Interviewee A)

However, despite this illusoriness, this fantasy often remains unrealised. Most of the

interviewees and the focus group participants describe entrepreneurship as an

inseparable part of their life; despite the price they pay, they cannot see themselves

living any other life, it is almost stronger than they are:

“Being an entrepreneur – I’ll always be an entrepreneur... at the age of 24 I

graduated from university, and since then I’ve been an entrepreneur. I

have set up companies, and then sold them...” (Focus group)

A significant proportion of the participants do not distinguish between local and

international entrepreneurship, since the two often exist in tandem, or the latter

develops from the former. Moreover, the blurring of geographical borders, particularly

in the field of hi-tech, and the accessibility of the internet, are making geographical

location secondary and thus blurring the differences between local and international

entrepreneurship:

“I have a problem with the international, let’s start with the fact that

borders don’t exist today, if you can sit here in Israel and do something

with computers, say, there’s no border... anyone who takes a broad view of

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entrepreneurship, then it’s always international, there’s nowhere else”

(Focus group)

In view of this, the entrepreneurs outlined several types of international

entrepreneurship:

‘Late starters’ – internationalise their business after they succeed in their local

business.

‘Localiser’- identify a need in a specific country and adapting a product to the

local culture.

‘Born global’- internationalise their business from the date of inception, or

think internationally from the idea stage.

They perceive international entrepreneurship as large-scale entrepreneurship mainly

because it offers the opportunity for far greater financial success. Because of this, the

international entrepreneur requires a few additional qualities and capabilities such as

acquaintance with the culture of the relevant country, the ability to adapt different

customs and international experience such as living abroad for a period. In addition,

dealing with the loneliness or unfamiliar surroundings, which are inherited in any

international business activity, is essential.

One of the characteristics of international enterprise is the need to take far greater

risks. There is a necessity for more resources; more time has to be spent at irregular

hours, and the family sacrifice required is often far greater, because of the

geographical distance and time differences. Finally, the chance of success is smaller,

both because of greater competition and because of gaps in cultural and fundamental

knowledge in the foreign country.

The research findings so far indicate that entrepreneurship is defined by the

participants as a career, personal development, or a way of life:

“I say that it’s your nature; some people want to come to work at 8 in the

morning and go home at 8 in the evening, doing something routine, and there

are those who feel in their gut that they must develop things, that’s an

entrepreneur” (Focus group)

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In addition, most of the entrepreneurs did not distinguish between entrepreneur and

international entrepreneur; it seems natural to them for every entrepreneur to

internationalise as part of their business expanding.

Characteristics of the Entrepreneur

Most of the participants emphasised that an entrepreneur should have certain

personality traits such as:

Individuality and the need for autonomy

It was clear that entrepreneurs have a very independent nature, and an obvious need

for the freedom to run their lives and their careers as they wish. For this reason, many

of them chose to leave successful careers and secure salaries:

“Again, it’s part of that thing of creation and freedom, it’s one of the

highest values here in each of us, creation and freedom that challenge us

and accompany us, because of that none of us here could get pleasure out

of being on a salary” (Focus group).

Internal Locus of control

Briefly, internal versus external control refers to the "...degree to which a person

expects that a reinforcement or an outcome of their behaviour is contingent on their

own behaviour or personal characteristics, versus the degree to which he or she

believes it is a function of fate or luck, for example it is under the control of powerful

others or just unpredictable…" (Rotter, 1990, p. 489).

It was evident that some of the participants perceive themselves as almost solely

responsible for their success or failure. Throughout the interviews and focus group,

when they explain the success or non-success of their entrepreneurial businesses, they

barely give credit to anything external to themselves. The exception in this context

was Interviewee B, who is a religious man and ascribed some of his ability to succeed

as an entrepreneur to his faith in God.

Communication skills, drive to successes and charisma

The impression created during this phase was of people who were good at

communicating, full of energy, drive, and charisma to an almost excessive degree.

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They also stated that good communication with others was essential for success as an

entrepreneur.

“At the end of the day, I think we all agree that to be an entrepreneur you

have to be a people person; you have to be able to communicate with a wide

range of people...” (Interviewee A)

The need for perpetual challenges, excitement and adrenalin

Some of the participants seem to indicate that the need for constant excitement, for

adrenalin, casts a shadow over any other human/ business activity that does not

involve a dimension of fantasy, risk, or uncertainty. So for example, sleep is perceived

by one of the interviewees as a “waste of time (A).”

Moreover, for one of the participants in the focus group, this need for excitement

creates a situation of enjoying the fantasy of establishing a new venture, while the

continuation of the business and especially its end seems less important.

The entrepreneurs see their own need for continual challenges and setting hurdles that

are always further away.

“What characterises entrepreneurs is that they’re always driving

themselves towards the next hurdle, and that’s [the] difference between

them and even the most senior of salaried managers, we’re always setting

the threshold higher and higher. Some are more extreme in this, others

less extreme, but they all set themselves challenges all the time” (Focus

group).

Determination and perseverance

In order to seize the opportunities, which arise, the entrepreneur must be determined

and persevering. Perseverance is one of the traits, which arose repeatedly as an

essential trait for entrepreneurs, both in the interviews and in the focus group. It is

essential for an entrepreneur who encounters difficulties or even failures.

“Perseverance, perseverance, it’s very strong, you always need it – even a

local entrepreneur, and even more so for international activity, particularly

when you have to learn a different culture, which is very demanding in

terms of time, and it’s a lot easier just to give up” (Focus group).

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Going against the current

Another feature of the entrepreneurs who took part in the research is their tendency to

go against the current and the views of friends and colleagues. They see themselves as

people who never give up, even if it appears as if they are 'banging their heads against

a brick wall'.

This expression, “banging your head against a brick wall”, was mentioned several

times in the focus group and the interviewees when expressing their determination to

do what they thought was right, even when they were in the minority and the future

looked uncertain.

“I think it’s in your nature. You might have to bang your head against the

wall, but you get over the wall, you just have to be ready to struggle all the

[time]” (Focus group)

Some of them saw entrepreneurship as a deviation from the norm, not only going

against the current. In both interviews as well as in the focus group it emerged that the

participants often felt that their entrepreneurship was a kind of illness or perversion, in

the sense that those around them – and they themselves – perceived that it demanded a

high price from them, but they were unable to resist.

“Entrepreneurship disease is a terrible thing... it’s a kind of egocentricity

and sociopathic, where you’re prepared to sacrifice your family just to

realise your personal ambitions” (Focus group)

Versatility, the ability to adapt to a changing reality

Another characteristic that came up could perhaps be defined as a skill rather than a

personality trait, and that is the entrepreneur’s ability to adapt to changing

circumstances, to be versatile. They also link this versatility to the ability to respond

quickly to changes, and adjust to them:

“… One day you’re a start-up enterprise, and then suddenly you’re on the

stock exchange. You have to adapt, to adjust, rediscover yourself each

time, and be versatile…” (Interviewee B)

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Opportunity Identification

The interviewees and focus group participants highlighted that internationalisation is

about creating various images of the business world, thinking globally, but being

locally responsive in a variety of social, cultural and political contexts. The

entrepreneur chooses to respond to international opportunities 'accidentally' and he

should 'run' in order not to miss the chance:

The Entrepreneur as a Dynamic Opportunity Seizer

The metaphorical description of the entrepreneur as someone who would never miss

the bus, conceals within it another trait of the entrepreneur as someone who actively

chases opportunities rather than waiting for them passively.

"The day when I have to take a bus and I see it on the other side of the

road and it’s about to leave the bus stop, and I don’t run to catch it - that

same day I’ll start to be old!" (Interviewee A)

Entrepreneurial Intuition

Some focus group participants referred to the intuitive, almost mystical ability of

entrepreneurs to identify opportunities and turn them into businesses.

“There’s something in science fiction or fantasy literature that talks all the

time about an alternative reality. I’ll go for the mystical. In my opinion

business entrepreneurs can see something that others don’t see; they see

opportunities or other things that most people miss” (Focus group).

Risk Perception: Constant Risk, Dualistic Relationship

Both interviewees and focus group participants perceive risk to be inherent in any

entrepreneurial activity, similarly to the way Cantillon (see in Murphy, 1986) defines

entrepreneurs: "Accept risk to make a financial profit rather than depend on a regular

salary for income".

One of the interviewees even argued that:

“The uncertainty of the entrepreneur’s life is what creates risk for him”

(Interviewee B).

134

It seems that in their eyes, the risk amounts to a measured cost of commitment, of

sacrificing commitment of time, money, and family relationships. “Risk is tangible;

risk is relative, personal, as opposed to uncertainty, which cannot be measured“

(Knight, 1921, p. 20).

The participants do not always use the term ‘risk’. Many times they preferred terms

such as ‘the price’ or ‘sacrifice’, referring to the cost to the entrepreneur of his

determination to persist in his entrepreneurship. Accordingly, entrepreneurs

experience a considerable degree of cognitive dissonance with respect to the cost of

their entrepreneurship to themselves and their families.

Price and sacrifice are known and clear elements at a given point in time, while risk is

something that can perhaps be measured but is actually a question mark about the

future. For the participants, risk represents the future price the entrepreneur may have

to pay and all entrepreneurship requires a certain degree of sacrifice, although the

extent can vary. In this context, failure (sometimes referred to as “falling”) is an

almost certain scenario in the life of every entrepreneur.

The participants describe various kinds of costs and difficulties, such as:

Loneliness

Often they connect the loneliness metaphor to the distance from their family, problems

with intimacy and long-distance parenthood.

“… I don’t like to admit it but I have no idea what my daughter’s nursery

school teacher is called” (Focus group).

Resource consuming

They perceive the entrepreneurial process as resource consuming, especially the

embedded financial risk (lack of regular income, mortgaging property, etc.).

“There’s no proper job, no regular income” (Interviewee B).

“… so for us to jump into the international market is like jumping into the

ocean where the water is cold and the waves are much higher and more

dangerous than we’re used to in our warm Mediterranean” (Focus group)

135

Precisely because of this huge sacrifice, the entrepreneur has far less ability to

withdraw, to stop the enterprise in mid-flight, according to one of the focus group

participants. Apparently, pulling out after sacrificing so much would create more

cognitive dissonance than the entrepreneur could deal with.

“Because of all the things I just said, being far from family, money, warm

surroundings, cultural differences, all these things make it some kind of

goal that’s clear to whoever goes into it...” (Interviewee A).

A discussion arose in the focus group regarding the link between the type of

entrepreneurship and the degree of sacrifice required. Many participants argued that

international entrepreneurship demands a much greater sacrifice in terms of health,

leisure time, money, family time, wellbeing.

“…Without the geographical limitations, the sacrifice would be significantly

smaller or even negligible. That’s a big part of the barriers...” (Focus group)

Nevertheless, some of the participants maintained that it also depended on the

character of the entrepreneur. Even a local entrepreneur could find himself sacrificing

everything for his venture. Still the economic risk created by international

entrepreneurship is greater.

“When we talk about international entrepreneurship we’re dealing with a

certain scale of demands, and there’s nothing you can do... I do the sums,

put simply if you’re working with much bigger markets there are more sales,

more projects, and naturally, a higher price to pay.”(Focus group)

Furthermore, one of the most interesting themes in the interviews is their dualistic

approach to risk. During the interviews, it became evident that although the existence

of risk motivates them, at the same time they are trying to suppress the risk, to ignore

it, since they perceive it as a limiting factor. Thus, the risk, from their point of view, is

both a motivational factor and an obstacle to entrepreneurial activity, both an

opportunity and a barrier.

“It’s better not to know, especially how difficult it is, because it will simply

put you off” (Interviewee B)

“Entrepreneurship means uncertainty, walking on a tightrope... if the fun is

gone; it’s not interesting for me” (Focus group).

136

Simon et al. (2000) conducted a survey based on a case study regarding a decision to

start a venture studied. The study explored how individuals cope with the risks

inherent in their decisions of starting ventures. The study’s findings advocate that

entrepreneurs establish ventures because they consider the risk involved in this

process, as lower than it is, mainly because of the existence of certain cognitive biases,

such as overconfidence, the illusion of control, the belief in the law of small numbers.

Therefore, entrepreneurs should continuously learn so they may avoid

misinterpretation caused by initial misperceptions.

Entrepreneurial Learning

Entrepreneurial learning is defined by the interviewees and most of the participants in

the focus group more as learning from experience, “on the go”, and less as organised

learning. Indeed, over the years, most of them completed their university studies, but

it seems that they place greater importance on day-to-day learning from experience

and especially learning from mistakes.

“…there’s nowhere to learn this, you have to experience it and learn how to

work together, how to take a venture and make it a going concern. You

don’t know how to do it until the first time you actually do it, there is no

school for that except the school of life” (Focus group).

Another important theme is the attitude to knowledge. Just as the attitude towards risk

is dualistic, similarly is the attitude towards learning. Since the knowledge generated

from the learning process causes awareness of risks, and entrepreneurs have been

shown to be especially prone to cognitive biases, which enable them to take risks with

confidence (Simon et al., 2000), they might avoid learning because they do not see it

as a necessary condition for their success. According to interviewee (A), the more

entrepreneurs know, the more they are aware of the risks, and therefore, they often

forgo the knowledge in order to allow themselves to go forward despite the risk.

"Part of my world is to close my eyes so that… The way I see it you have to

be naïve enough to take the risk…, if I knew all the answers to everything I

might not have started, it would have stopped me from putting my foot in

the water." (Interviewee A).

137

In order to be an entrepreneur, they must maintain a certain degree of naivety. In

addition, the above description bears witness to the interviewee’s being an expert at

dealing with uncertainty, bearing uncertainty according to Knight’s (1921) analysis.

Despite the above, it is evident that even learning from experience, despite the

dualistic feeling towards it, becomes more sophisticated over time. Sources of

information have been vastly increased by the internet, especially for an initial or a

specific search. In addition, the entrepreneurs read business newspapers, success

stories, and books on science fiction, philosophy and spirit, as well as learning from

marketing data and research, consultants, business networking and so on.

“Whenever I have a break I spend a lot of time reading, and on methodical

learning from books, the Internet, etc. For example I recently read a book

about choosing suitable people for operations.” (Interviewee B)

Networking came up without prompting, but the participants claimed it is an important

source of knowledge.

“It’s one of the few tools available to an entrepreneur particularly in

another territory... lots of feedback on what to do and what not to do”

(Focus group).

Since the participants maintain that they mostly learn from experience, some of them

have the feeling that they lack knowledge about how to deal with uncertainty and

failure.

4.1.2 Summary

Five main categories of themes were gleaned from this analysis:

1. Entrepreneurship and international entrepreneurship definition

2. The entrepreneur

3. Risk perception

4. Entrepreneurial opportunities, and

5. Entrepreneurial learning

The main purpose of this section is to discuss the qualitative findings in the light of the

literature, in order to clarify some of the major issues, which are in the heart of the

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current entrepreneurship research, and to lay the foundation of the conceptual model,

which is developed at the end of the second qualitative phase.

The following objectives are discussed in details:

The first objective was to elucidate the definition of entrepreneurship, and

international entrepreneurship. In addition, whether or not different entrepreneurial

characteristics are needed for success in international ventures, and which of these are

universal.

Secondly, it was also important to address how entrepreneurs learn.

Thirdly, what was the entrepreneur's perception or attitude toward risk?

Finally, the debate of whether the entrepreneur creates an opportunity or

recognises was presented and discussed.

4.1.2.1 Entrepreneurship and International Entrepreneurship

McDougall and Oviatt (2000) described international entrepreneurship as a complex

process integrating brokering, resource advantage, value creation, and opportunity

seeking through a combination of innovative, proactive and risk seeking behaviour.

From this view of international entrepreneurship, it can be inferred that acting

entrepreneurially and becoming international are dual processes, which intersect in

practice. Most of the participants and the interviewees did not distinguish between

local entrepreneur and international entrepreneur; it seems to them almost natural for

every entrepreneur to internationalise as part of expanding his business.

The characteristics of entrepreneurs were found to be as almost identical to the

characteristics of international entrepreneurs. The differences were found mainly in

the fact that internationalisation is much more demanding, thus the level of

commitment, for example, is much higher. In addition, the international entrepreneur

has to deal with different languages and cultures. It also became clear that many of the

participants in the focus group and all of the interviewees were reaching for the same

or similar metaphors when trying to structure the meaning that entrepreneurship and

entrepreneurial traits have for them. Most of these metaphors are dynamic, focusing on

various types of disruptive and difficult entrepreneurship activities.

139

Another interesting metaphor is entrepreneurship as ‘making something’. In these

metaphors, they talk of creation, fantasy, parenthood to represent how they perceive

entrepreneurship, and on the other hand, they describe their traits as going against the

current, loneliness, and having higher internal locus of control. They emphasise

process rather than objects, and their metaphors are emotionally charged. For them the

concept of entrepreneurship alludes to the process of business creation and its

complexity. Success is achieved only once the obstacles and problems have been

overcome, which also suggests the importance of creating meaning, faith and purpose

within the life the entrepreneur (Dodd, 2002). It seems likely that the traits they

emphasise, in part, may be based on qualities such as success, courage, and

commitment, which are drawn from their shared history. It can also be argued that the

way in which they perceive entrepreneurship and the entrepreneur is, to some extent,

culturally determined, and may reflect the Western iconography of the venture (Dodd,

2002).

A few of the participants and the interviewees depict entrepreneurship as a race.

Although they use this metaphor when discussing opportunity recognition, it can be

interpreted as showing that entrepreneurship carries strong connotations of

competition, as well as failure and success, and the results of this competition have

social consequences. For some of them, success does not always mean financial profit,

but might be the achievement itself or, more importantly, the recognition that their

efforts have paid off. Some of them made the analogy between a business venture and

parenting. The major implications of this refer to the mixture of pleasure and pain in

creating a new venture and nurturing its growth, as well as the complexity and length

of the process. This might be in contrast to some of the metaphors found in

management literature (Henderson, 1999) such as: evolution, selection and survival

rates, which are less personal and reduce individual perspective of the entrepreneur as

a parent.

The entrepreneurs see themselves as lonely, going against the current. They perceive

entrepreneurship as absurd, something unusual; some may perceive entrepreneurship

as iconoclasm (Dodd, 2002). However, in spite of how they perceive themselves, they

speak about their ventures as if speaking of a loved one; they express it with the

vocabulary of romance, love, and passion. This loving attachment to the business has

already been posited in the parenting metaphor, but it is underpinned by the passion,

140

which the entrepreneurs claim to feel for their venture. This immeasurable love might

restrict the entrepreneur’s ability to view his business realistically and objectively.

4.1.2.2 Entrepreneurial Learning

For the entrepreneurs, just as their attitude towards risk is dualistic, so too is their

attitude towards learning. According to them, the more entrepreneurs know, the more

they are aware of the risks; therefore, they often forgo this knowledge in order to allow

themselves to go forward despite the risk.

Nevertheless, it was also evident that learning from experience, despite the dualistic

feeling towards this, becomes more sophisticated over time and the use of the Web as

a major information source has become predominant. Surprisingly, some of them did

not emphasise their business network as an important factor in this process. This might

be part of their entrepreneurial stage, or their perception of entrepreneurship as their

own creation, so that they should take sole responsibility for its success or failure.

In this study, the perception of the entrepreneurs seems to be that learning takes place

everywhere, and affects almost everything they do. They learn, adapt, build,

transform, and generate meaning for internationalisation. They emphasise the dualism

of international and entrepreneurial processes, the dualism of being aware of the risks

inherent in any opportunity while ignoring them or even trying to forget that in every

opportunity there is a certain amount of risk

4.1.2.3 Risk Perception

Entrepreneurs have the ability to confront risk or uncertainty. This ability is usually

explained by attributing to the entrepreneur either a greater propensity to bear

uncertainty, or unique access to knowledge that renders the situation less uncertain for

the entrepreneur relative to others (McMullen and Shepherd, 2006). In either case,

uncertainty is framed as something problematic, something to be avoided or at least

reduced as far as possible. However, using the concept of entrepreneurship as a form

of expertise, Sarasvathy (2001b) argued that entrepreneurs thrive in uncertain

environments because uncertainty itself is used to create opportunities. She argued that

“entrepreneurs perceived uncertainty as a friend and an asset, eliminating the need to

overcome it” (Sarasvathy, 2003, p.210).

141

Furthermore, they do not only see uncertainty as a friend but also treat the risk

dualistically. During the interviews and the focus group, it became evident that at the

same time as the existence of risk motivates them, they are trying to suppress their

awareness of such risk, to ignore it, since they perceive it as a limiting factor. Thus

risk, from their point of view, is both an opportunity and a threat.

4.1.2.4 Opportunity Identification and Exploitation

The entrepreneurs in this study refer to international opportunity as 'out there', just

waiting for them. They state that timing is one of the key issues because of the 'limited

windows of opportunity'. In addition, the implementation of opportunity is apparently

related to having a greater presence in the market, something that is immeasurable,

and mainly due to previous experience.

It is tempting at this stage to analyse the opportunity evaluation process as a special

cognitive process (Shane and Venkataraman, 2000b) that enables entrepreneurs to

respond to international situational indicators; however, and based on Pfeffer and

Salancik (1976, p. 72), “the events of the world do not present themselves with neat

labels and interpretation... but rather we give meaning to those events”.

According to this point of view, entrepreneurs are not acting in a real external

environment, but are in fact interacting with what they perceive as the environment, as

they are constantly evaluating information, making choices about which

environmental situations to respond to and weighing up the risks, the chances, the

losses and the added value which can be created by implementing a particular

opportunity (Gartner et al., 2003). However, an important question should be raised:

if they are 'responding' to the opportunity, why do these participants see the

opportunity that exists 'out there' in the market? One answer to that could be that the

entrepreneur really believes that a 'window of opportunity' really exists, and the

process of discovering it is not ' accidental'.

The entrepreneurs in this study phase chose to recognise this 'window' and chose to

construct it as an opportunity, or even learned how to construct it as an opportunity.

They learn how to do this through dialogue, exchanges, conversations, interpersonal

relations, and co-ordinations. What is important is the never-ending process of

interaction, and the focus is on aspects of the social rather than the cognitive being.

Interviewee “A,” for example, chose to discover the 'window of opportunity' in the

142

UK, because he could envisage how the situation might be in the future (Gartner,

1993; Johannisson, 2000).

Moreover, the construction process is never-ending; even during the interviews and

the focus group discussion, they were conducting a dialogue with the interviewer,

generating meaning, giving expression to their internationalisation process,

opportunity implementation and understanding of what it means for them to define

themselves as ‘international entrepreneurs'. They see entrepreneurship as creation, but

also as 'telling a story', first to themselves when they envision an idea, and later to

others such as friends, family, business partners, and others around them. In this

dynamic process, they kept an 'open mind', altered and tuned, observed, and most

importantly learned from their previous experience, but also from others.

4.1.3 Conclusions

From the preceding analysis of this Qualitative Phase (QUAL1), four major

conclusions can be derived:

(1) International Entrepreneurship is a firmly integrated process by means of

which entrepreneurs envisage and realise the creation of their business as an

international entity. For the participants, there is no clear dichotomous

distinction between entrepreneurship and international entrepreneurship, or

between the qualities needed by a successful entrepreneur of each type. In their

opinion, internationalisation is an extension of what they have done in the

home market. Although they emphasise that they perceive a greater risk mainly

because they have to commit more resources, at the same time they believe that

they can see far more opportunities. They speak about the global market as

something that is more spacious, fresh, and promising rather than in relation to

the local context. Hence, the international entrepreneur, in this study, desires to

expand his dreams and find new possibilities. However, he/she is willing to

consider those when doing so he might have to dilute his desire for autonomy

and independence.

(2) The entrepreneurial process can be defined, moreover, as a dynamic learning

process. Entrepreneurs learn mainly from experience; however, their approach

to the knowledge they need to acquire was dualistic, similar to their approach

towards the risk inherent in any entrepreneurial action. The entrepreneurial

143

process begins with the entrepreneur’s need to take risks in order to fulfil his

vision, through constant learning that might influence the perceived risk. The

output of this constant learning is knowledge. However, a dualistic approach to

knowledge can be extrapolated from this analysis, since the entrepreneur

experiences two contradictory forces: the need to know on the one hand (i.e. to

know the risk), and correspondingly the desire to avoid the knowledge of it

(i.e. its consequences).

(3) In this phase, it was not clear from the analysis whether opportunities are

recognised or in fact created, which means that the opportunity search process

can be perceived as a socially constructed activity. Some depict the

entrepreneurial opportunities as 'windows' to be opened; the timing is crucial –

they must ‘catch the buses’. These 'windows' are ready for opening, and the

only constraints are resources and willingness for self-sacrifice. Moreover,

during the internationalisation process, they improve on their entrepreneurial

skills, taking calculated risks, utilizing network contacts and acting on the

specialised knowledge that has been constructed during their previous

experience.

(4) It was also found that international entrepreneurship is a complex process,

which cannot be explained only by a behavioural analysis of the entrepreneur.

International Entrepreneurship may also be explored as a contextualised and

socially constructed activity, which occurs through rapid interaction and

dialogue between the self and the environment. This interaction can be seen as

a relational process occurring and being constructed in relation to learning,

conversations, events and experiences.

The qualitative findings of this phase (QUAL1) elucidate the importance of

internationalisation, attitudes towards risk and opportunities, and the relationship

between opportunities and learning. Based on the literature review, which emphasised

the need to investigate the ways entrepreneurs learn about international opportunities,

and the conclusions of this study, the following research question was set as the

overarching research question:

What are the factors that affect the ways entrepreneurs learn about opportunities in

the international arena?

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4.2 Qualitative Phase 2 (QUAL2)

The main objective of the first qualitative phase (QUAL1) was to formulate a primary

research question based on the findings and results of this phase (QUAL1) and to build

on the research gaps that were revealed during the literature review. In QUAL1 phase,

the findings highlighted that entrepreneurs perceive entrepreneurship as an integrated

process that facilitates the creation of their business as an international entity.

Therefore, based on the findings of the first qualitative phase (QUAL1) and the

overarching question for the whole study, it is clear that further investigation of

international entrepreneurs is needed, focussing on two processes: entrepreneurial

learning process and entrepreneurial opportunity, and the relationship between these

two processes.

Thus, the second qualitative phase was developed to elucidate the following

supplementary research questions:

1. What are the factors that might affect the motivation of entrepreneurs to

internationalise?

2. Secondly, what is the role of the Internet in the learning cycle of International

Entrepreneurs?

3. Finally, what are the ways they learn about opportunities?

In addition, various aspects of knowledge, risk, entrepreneurship definition, and

entrepreneurial characteristic, which were gleaned from the QUAL1 findings, were

discussed in detail. This allows the researcher of this study, firstly, to elaborate on

each of the themes, and secondly, to validate the findings of the first qualitative phase

that relate to these issues.

The QUAL2 phase includes an analysis of themes that arose from the text itself,

similarly to what was done in the first phase (QUAL1).

The structure of this section is as follows. Firstly, the findings and results of the

second qualitative phase (QUAL2) are addressed in detail. Secondly, the findings that

are most strongly related to the study objectives are discussed in the light of the

relevant literature in order to develop a conceptual framework for the study. The

purpose of this model is to reveal more aspects of the process of learning about

opportunities and the factors that affect that learning.

145

4.2.1 Findings

In general, many of the themes that were gleaned in the first qualitative phase analysis

(QUAL1), and are related to the category of entrepreneurship definition and

entrepreneurial characteristics, are similar to the themes that emerged from this

analysis (QUAL2), which might increase the validity of the findings. Table ‎4.2

summarises the main themes gleaned from each data source (table 4.2 continued on 2nd

pages):

Table ‎4.2: QUAL2, Main Themes

Category Main Themes

Focus

Group

B

Interviews

(A-H) QUAL1

International

Entrepreneurship

Definition

'battlefield fighting'

'game'

'born international' by nature

Become international from 'Idea-

generation' or from 'first- action'.

+

+

+

+

+

+

+

+

+

International

Entrepreneur

Characteristics

Entrepreneur as 'executer'

Entrepreneur as suffering from

'unrecoverable illness'

'Self-efficacy'

Courage

Committed

Persistent ( i.e. 'banging your

head against the wall')

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

Typology of

International

Entrepreneurship

Replication

Localisation

Internationalisation: International

from inception

+

+

+

+

+

+

Drivers of

International

Entrepreneurship

The desire to 'make money'

The need to make 'a change in

the world'

+

+

+

+

146

Category Main Themes

Focus

Group

B

Interviews

(A-H) QUAL1

Type of

knowledge

Market Knowledge

Technological Knowledge

Business Knowledge

+

+

+

+

+

+

Entrepreneurial

Learning

Intuition

Mentors: ' Professionals' versus '

the ‘average person'

Learning from experience and

mistakes

Learning by 'doing and copying'

+

+

+

+

+

+

Opportunity

Recognition

and/or Enactment

'Something from nothing'

' Making it Happen'

Entrepreneurial idea as a 'desire

to a change'

Constant alertness

+

+

+

+

+

+

+

+

+

Types of

International

Entrepreneurs

'Head in the clouds, feet firmly

planted on the ground'

The Excel Entrepreneur versus

the Extremer (sportive)

The Serial Entrepreneur versus

the Novice' Entrepreneur

+

+

+

+

+

147

Definition of International Entrepreneurship

In the first phase, three of the most frequent themes that were related to the definition

of entrepreneurship were entrepreneurship as creation, parenthood and a game.

Similarly, in the second qualitative phase, these themes also emerged from the text.

However, some of the themes reflect a more aggressive approach, such as defining

entrepreneurship in the international arena as a 'battlefield':

"… fights all the time on a battlefield…. he needs to know how to smooth

things over, cut corners... adjust, adapt to an ever-changing environment..."

(QUAL2, Interviewee A)

Entrepreneur was described as a 'warrior', they never give-up, just as in a game, they

do everything they can to compete, to win: they plan their moves, design strategies,

and above all stay alert and focused:

“…Like chess, one with moves, you need to foresee the moves, build a new

strategy; I’ll put money on you, who will back this horse, who will march

forward with you, who will drop out.” (QUAL2, Interviewee A)

In every game, or 'war', in order for you to meet your targets, you need to be

persistent, even if this means ignoring the opinions of those around you; it is almost

like 'banging your head against a brick wall':

"Sometimes these experts will explain to you why it won’t work, usually that

won’t cause me to stop, I’ll just look for another expert, there is no

formula, there are no set rules, a lot depends on my beliefs, some sort of

intuition that I can’t explain." (QUAL2, Interviewee N)

The study participants were asked to point to the moment when they think they

became international entrepreneurs. The responses indicate that there is a process

occurring over time, of which the entrepreneurs are not completely aware. In addition,

it seems this point in time changes from one entrepreneur to the next.

“From the moment I came up with the idea and decided to execute it, and

started to act to execute it” (QUAL2, Focus Group)

148

Thus, a distinction can be made between two groups. The first group emphasise the

importance of entrepreneurial action, and accordingly they define themselves as

international entrepreneurs only from the moment they started to act on the idea:

"I think that the moment I started looking for resources to realise it,

that’s the point at which I moved to the entrepreneurship stage." (QUAL2,

Focus Group)

The definition of 'action' is varied; the following are some examples:

From the moment they ‘shook on it’ with a partner;

From the moment they signed the company foundation documents;

From the moment there is a product;

From the moment there is an international customer;

From the moment there are suppliers abroad;

From the moment there are sales abroad;

From the moment, there are offices abroad.

The second group, which consisted of most of the participants does not

necessarily define the exact moment, and some of them even stated that they

were 'born global', which means that they consider their entrepreneurship

action as international from the moment they thought of or envisioned the idea.

In addition, some of them even claim that for them entrepreneurship is always

international entrepreneurship:

"Today I certainly define myself as an international especially when I look

at my current company that is active worldwide…." (QUAL2, Interviewee H)

Moreover, it seems that a considerable number of the study participants place no

importance on the size of the enterprise, or on the number of partners involved with

regards to the transition into actual entrepreneurship in general and international

entrepreneurship specifically:

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"Q – So does it make any difference - the size of this thing, whether I am

one person, two people, or whether I have offices or not?

A – It does not matter." (QUAL2, Focus Group)

Characteristics of International Entrepreneurs

The participants and interviewees described the ‘classic entrepreneur’ as someone who

executes entrepreneurship. Therefore, in their view, it is not enough for a person to

have good ideas, however good they are; they need to take steps in order to execute

their ideas. The action itself makes the difference between an idea and

entrepreneurship. In his or her opinion, only someone who takes action, and is willing

to take a risk, can be considered an entrepreneur.

"I wake up in the morning with an idea and I try to execute it… that means

there are many ‘back drawer entrepreneurs’, that’s what I call them, that’s

what they call themselves. An actual entrepreneur is someone who knows

how to take an idea and realise it, execute it...."(QUAL2, Interviewee A)

Nevertheless, there were those who argued that the entrepreneurship started at the idea

stage:

" I call it [entrepreneurship] from the moment I was in Pnai Li [Thailand], I

call it [entrepreneurship] even before that, when we were sitting in the

living room chatting; for them we were just chatting, but I already saw

something that could exist tomorrow" (QUAL2, Interviewee H)

Similarly, to the first qualitative phase (QUAL1) findings, the participants, and

interviewees perceive the entrepreneur’s traits as unique; innate in their personality.

This unique combination of innate traits differentiates between an entrepreneur and

everyone else:

"… I tell you I meet with entrepreneurs with amazing enterprises but I

don’t buy it, I wouldn’t put my money on them...." (QUAL2, Interviewee A)

They almost admit that it is something that they cannot control:

"It’s the nature of the entrepreneur, he is a self-motivator. I can’t stop,

I’m not able to." (QUAL2, Focus group)

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By contrast, a few of them even described it as an illness, but while illness can often

be cured, entrepreneurship is incurable:

"Now this entrepreneurship is chronic, it’s an illness….” (QUAL2, Focus

Group)

Similarly, to what was found in the first phase (QUAL1), there are no major

differences between the characteristics of international entrepreneurs and domestic

entrepreneurs. Entrepreneurs are characterised as having a high level of self-

confidence, self-efficacy, and courage. The entrepreneur is perceived as a 'hero', a

'warrior', even a 'super-hero' like Superman. Another theme is that the success of

entrepreneurs is affected by their actions, because they can control reality, they have

the 'power of divinity'. In addition, failures are excluded from this equation. Their

attitude is that they did everything in their power to achieve their goal, but

unfortunately, they failed. Accordingly, they interpret their failures as something

positive; they call it 'experience'.

Typology of International Entrepreneurship

Many of the interviewees and focus group participants argued that there is no clear

definition of international entrepreneurship. Rather, they discuss several types of

internationalisation strategies for entrepreneurship. Firstly, they agreed that there are

local enterprises which are 'replicated as is’ for international markets. An example of

this is case of the Internet or software industries that are from the start developed in

English, and need no or almost no international adaptation:

"It’s to really understand the meaning of what is called the ‘global village’,

that is to say the fact that I am in Israel developing software, the minute

that I have written it in English, then from the perspective of using the

software, there is practically no significance to the fact that I am in

Israel." (QUAL2, Focus Group)

Secondly, there are home based entrepreneurs who established their operation in the

local market, and after a certain amount of time, became international. They generally

develop and produce products that may or may not require localisation for the

international market:

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"I think that in a way there is a whole spectrum between these two

poles...and I think that a good international entrepreneur knows how to

utilise the ability to be global when needed and local when needed."

(QUAL2, Focus Group)

As already mentioned, many study participants argued that they perceive and define

international entrepreneurship as international from inception, for the following two

reasons: the ‘global village’ and the small size of the Israeli market:

"From the start when I knew I wanted, that there was something here I

wanted to develop, I didn’t think about the Israeli market because the

Israeli market for such a product is not developed so that it’s blatantly

uneconomical and from the start I was looking at the world." (QUAL2,

Interviewee H)

There were those who differentiated between international entrepreneurship and

global entrepreneurship. International entrepreneurship is aimed at one or more

countries while global entrepreneurship is aimed at the whole world.

The same conclusion was made in the literature. Entrepreneurs can be distinguished by

the type of new venture they have established or are in the process of establishing. One

commonly used criterion, which is highly relevant to the purposes of this study, is the

speed and level of the new venture internationalisation. Aspelund and Moen (2005)

divide international new ventures into four categories: born global, early international,

late international and late global. Others distinguish between domestic new ventures

and international new ventures (McDougall, 1989; McDougall et al., 2003).

Drivers and Motivations for International Entrepreneurship

Throughout the interviews and the focus group, two main factors were identified as

influencing the entrepreneur’s motivation to be involved in entrepreneurship:

Financial incentives – they emphasised constantly the desire to “make money” and to

maximise their profit. The international market is perceived as offering a better chance

for success on a large scale. In addition, implicit in their words is the correlation they

make between financial factor and the desire to have an 'effect', to make a 'change'.

They referred to both 'rational’ factors such as maximising profits and 'emotional and

subjective’ ones such as the desire to introduce a change:

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"What I want is for a lady in Honolulu to buy, to stretch out a hand to the

shelf and take down one of my products and enjoy it, and that was my first

motivation, which is what I imagined, I imagined a woman somewhere in the

world putting out a hand for my product."(QUAL2, Interviewee H)

Perhaps in order for the entrepreneur to justify the risks he is taking, he has to be

emotionally connected to his entrepreneurship. This assumption is reminiscent of the

themes that emerged from the text of the first qualitative phase, such as

entrepreneurship as parenthood and entrepreneurship as your 'loved one'.

Psychological / Sociological – the international market is perceived as much more

exciting, interesting, and challenging than the domestic market. In addition, there is

the desire to make a change, to leave one’s mark on the world. This desire was found

to be one of the most important drivers for becoming an entrepreneur:

"Q – What motivates you?

A – The need to change, I wanted to affect and change the world. (QUAL2,

Interviewee N)

Environmental conditions- a common argument among all of the focus group

participants and interviewees concerned some of the environmental conditions, which

are related directly to the small size of the Israeli market.

"This is the world, the world, it’s a lot, its potential ...it’s money, and

success, and reputation, and excitement, and interest... once, I had to go on

my own to Orange County and everything was really big and I was still really

young and this was my first international experience. It was so exciting and

a bit scary, I also remember getting completely lost on the roads and I was

also really frustrated and tired but I think it helped me to grow..."

(QUAL2, Interviewee B)

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Internationalisation is an inevitable stage in the development life cycle of any

entrepreneurship:

"I think that internationalism is simply a natural development, a function of

everything that happens. Nowadays I think that the market has to be

global, international ... in my opinion a market which is only local doesn’t

exist unless you are involved in traditional local industry." (QUAL2,

Interviewee H)

It seems that the process of internationalising the entrepreneurship can be carried out

in stages, that is, first you establish your local business and after a certain period of

time you decide to internationalise, or you conceive the entrepreneurship as

international from the day of inception:

"I don’t think it’s possible that I’ll suddenly decide to internationalise, it

doesn’t seem to me that there is a point where there’s a shift, a move, a

transition, that is not something planned." (QUAL2, Interviewee H)

Types of Knowledge

Knowledge is considered the output of a learning process. In the opinion of the

interviewees and participants, the knowledge required for international

entrepreneurship includes the following:

Business Knowledge: laws, regulations, business & technical writing, patents, how to

raise capital from funds and so forth:

"Where I personally felt I had to learn the most and the hardest

knowledge process I underwent was with regards to the whole issue of

building a business plan and raising capital, because I came up with a

fantastic idea and I was told, good but where is the business plan? How will

it be realised? What will the profit be, all sorts of questions, I said what

do you want from me, you’re the smart people that’s why I came to you, this

is the idea, why can’t you relate to it? And then I understood that you need

a lot of knowledge." (QUAL2, Focus Group)

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Market knowledge: such as information about the target audience, their needs, and

characteristics:

"You need to understand for instance the consumption habits of the place

when you arrive, to understand how they are used to consuming things and

what they are willing to pay for and what they are not willing to pay for,

how much they are willing to pay for each thing in relation to the financial

structure or financial situation of the place to which you are directing your

market." (QUAL2, Focus Group)

Cultural knowledge:

bridging cultural gaps:

"When I wanted to understand what was happening in China I read a book

about China, I sat and learned... I wanted to understand for example what

personal opportunities there might be with the Chinese, how they act, what

they do, why some succeeded and some failed in China." (QUAL2, N)

Another important area of knowledge, which was found in the first qualitative phase,

deals with risk. The study participants emphasised two main concepts of what the

entrepreneur is risking:

Alternative resources (income, time, commitment)

Alternative cost:

"The risk is what you could have earned somewhere else, which is to say if I

had stayed a salaried worker in another place of work, I could have perhaps

earned more." (QUAL2, Focus Group)

Interestingly, it should be said that, at this stage, in some cases the topic of risk or

aspects relating to risk were not raised in the interviews or discussed explicitly. This

issue was only discussed without prompting when the subject arose explicitly. The

conscious awareness of the link between knowledge, the development stage of the

enterprise and the concept of risk is not consistent among the study participants. Many

of them appear to agree that the risk increases the more the enterprise progresses and

gets closer to success, which means that there is more to lose, more hours and

resources have been invested, and more concessions have been made up to that point.

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Nevertheless, there were some study participants whose perception of risk; was in fact

particularly high at the start of the venture:

"In my particular case the greatest risks were always in the transition

stages and not towards the end." (QUAL2, Focus Group)

The relationship between knowledge and the perception of risk is not linear, although

it seems that the perception of risk increases as knowledge increases (usually at more

advanced stages of the entrepreneurship):

"The risk is greater the closer you are to success" (QUAL2, Focus Group)

"When you get close to the end you’re willing to risk more." (QUAL2, Focus

Group)

However, there is a tendency for some of the study participants (as it was found in the

first qualitative phase, QUAL1, of the study) to ignore their accumulated knowledge,

by delaying or neutralising it, to prevent it from interfering with the progress of the

process. For these entrepreneurs, knowledge is rather a tool for dealing with risk and

they are continually re-examining the lie of the land at each stage of the enterprise.

"In my opinion the more knowledge I accumulate, the more it exposes risks

but it also minimises these risks because I can neutralise them by knowing

more about them. I think that trying to avoid this knowledge is really a

sort of suicide.” (QUAL2, Interviewee Y).

Entrepreneurial Learning

Learning is characterised in this phase by a combination of learning from experience

and learning from various sources along the way, “on the job,” as well as from partial

and complete successes and failures. There were also entrepreneurs who learnt from

exposure to the world through their work as salaried employees and the work of their

parents abroad during their childhood.

"I’m still learning, let’s put it this way, I’m always in the process of learning,

I didn’t learn formally, my learning was ONGOING through doing." (QUAL2,

Focus Group)

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Apparently, those who started their entrepreneurial experience after working as

salaried employees, or those who can be defined as 'late starters' (i.e. who

internationalise a few years after the establishment of the entrepreneurship), were

more likely to learn in an organised and methodical manner than those entrepreneurs

who were 'born global'.

Even so, there were those who told us that, despite having studied in an organised

manner, they found that the most effective learning was learning from experience.

"I have a feeling that my learning was slightly more formal in this matter

because I really did devote my MBA to entrepreneurship…, I can say that...

academically I learnt...I got many tools that..., but... I think I got the most

significant part of my learning on how to be an entrepreneur from

experience, simply by trying … it was the things that are more at the level

of experience and less from studying in a classroom." (QUAL2, Focus

Group)

"You learn a lot on the job and on the way you pay the price of your

mistakes." (QUAL2, Interviewee H)

Below are some of the ways of learning identified from the interviews with

entrepreneurs:

Spontaneous versus planned learning

Some entrepreneurs confessed that they had not studied during their entrepreneurial

career and that most of their learning was non-methodical and based on intuition and

luck. There were even those who argued that learning from experience was no

indicator for the future:

"I don’t...usually I don’t apply a structured method but rather it’s very

intuitive, I always know my competitors in Israel, and globally as well: until

we started penetrating into Canada, we worked the London market and we

were already acquainted with the American competitors, North Americans,

Europeans." (QUAL2, Interviewee A)

"I didn’t necessarily learn about the opportunity in a methodical manner, it

was more intuitive, with a lot of gut feeling." (QUAL2, Interviewee N)

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Although the study participants understood that the kind of structured information

obtained from surveys, studying business management and so forth, is relevant to

entrepreneurship, they often ignore this knowledge, or failed to mention it. For them,

this type of knowledge is necessary, but can be learnt over time. For example,

Interviewee A, only agreed at the end of the interview, after repeated questions, to

describe what he learnt on the entrepreneurial course he took when he was already an

entrepreneur; he agreed that he learnt things which are essential but claimed that most

entrepreneurs, himself included, learn in the ‘hard way’ by doing, rather than in an

organised manner:

"I don’t believe you can learn to be an entrepreneur, I really don’t, either

you have it or you don’t. Now if you have it we can improve it, make you a

professional, sharpen it and upgrade you…” (QUAL2, Interviewee A)

Also H (QUAL2, Interviewee H), who attended a course on how to write a business

plan at a business development centre, felt that she was already familiar with the

course content from self-learning:

"… I already knew pretty much how to prepare a business plan and this only

confirmed for me that I already knew what I was doing and that I didn’t

need the course, because it had nothing much to teach me." (QUAL2,

Interviewee H)

A minority of the study participants reported learning in a structured and methodical

manner. This seems to characterise entrepreneurs who are ‘late starters’

(internationally). Moreover, the need for money from funds often dictates more

methodical work, but it seems, as Interviewee A (QUAL2, Interviewee A) admitted,

that if it were not for the investment funds he would have stuck to learning from

experience and gut feelings.

"…Q – So why are you more methodical now?

A – Because it is required, the funds want method.

Q – Otherwise, you would not necessarily do it like that.

A – No, definitely not, I hate bureaucracy.

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A – I think at heart it would remain the same thing - dreaming about an

idea, not waiting for anyone to execute it in the most naïve or silliest way

and hoping for a miracle." (QUAL2, Interviewee A)

Learning from mentors, others and through personal contacts

Entrepreneurial learning based on interactions with the social and business

environment seems to be at the heart of any entrepreneurial process, especially in its

initial stages:

"Talk about it with at least a hundred people, listen to all the opinions, some

will direct you one way, some another... go and tell people, each one might

make a small switch in your thinking, and sometimes one word may cause an

idea to succeed or fail at the end of the day." (QUAL2, Interviewee N)

Some of the entrepreneurs stressed that they were not afraid of revealing their

entrepreneurial idea since they believed that the chance of someone else “stealing” the

idea was negligible:

"I am one of those who believes that you need to talk about it, and that

helps to promote the idea, in contrast to the approach that says let’s keep

it close to the chest because it’s dangerous, maybe someone will steal my

idea. I think that there are very few people who could realise it, so I’m not

so worried." (QUAL2, Interviewee H)

Others, (for instance N and H) explained their willingness to be interviewed for the

study by saying that they were always glad of human interaction with the chance of

making new acquaintances and acquiring business information [networking]:

"By the way, one of the reasons we’re sitting here is that I never say no to

meetings because you never know what will come out of it." (QUAL2,

Interviewee H)

There are several ways of learning from others, primarily through their social

networks such as friends from school, the army, and university, or through initiating

contact with professionals by phone or mail. Forums and articles on the Internet can

also be a useful tool:

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"I always check with several people who are close to me, and check with

other serial entrepreneurs." (QUAL2, Interviewee N)

Learning by searching the Internet

It seems that many entrepreneurs make extensive use of information from the Internet,

using search engines, reading articles, professional journals and reports, as well as

checking out their competitors’ sites. Additionally, social networks are used in order to

reach mentors and business partners:

"Look, the world has changed a lot since the Internet, there’s so much

information at your fingertips and you don’t have to pound the pavement

and go to libraries. Of course you need to know how to manage this

information because there is an overload and sometimes it isn’t relevant and

it’s certainly imprecise, but it is a good starting point." (QUAL2,

Interviewee H)

The process of opportunity identification: recognition versus creation

Similarly to the QUAL1 findings, when the entrepreneurs described the way they

arrived at the entrepreneurial idea, there were elements of both creating an opportunity

and recognising an opportunity, and it was difficult to distinguish between the two:

"In most cases I create something from nothing, yes in most cases it’s

really an idea; sometimes, by the way, it comes to me in a dream. I wake up

in the morning with an idea that came to me in a dream; I get up and know

how something new can be done." (QUAL2, Interviewee H)

Whether the opportunity is recognised or created, the entrepreneurial idea seems to

stem from a desire to change reality and find solutions, and what distinguishes

entrepreneurs from those who are not entrepreneurs is the ability to envision and find

solutions:

"I think it’s like the laws of physics that were always there only they hadn’t

been discovered. The opportunity is there, but not everyone sees it, and

those who see it are clever…, I see things that others do not, there is no...

“(QUAL2, Focus Group)

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"I was in the middle of an exercise on the artificial intelligence course...

and suddenly I got the idea for a new business, and... from that moment I

didn’t hear anything else. The idea came from something that the teaching

assistant said, and I sort of didn’t hear the rest of the exercise! Then I

went out and started to put together a team and I went for it." (QUAL2,

Focus Group)

Types of international entrepreneurs

Concerning these study findings, various types of entrepreneur are mentioned in the

literature. Andersson (2000 p. 63) classified entrepreneurs into three categories: "the

marketing entrepreneur who implements an international push strategy, the technical

entrepreneur who implements a technical development and creates an international

pull strategy, and the structural entrepreneur who implements an international

restructuring of an industry".

One of the interviewees could serve as a model of the type that takes more calculated

risks. She could be described as having her “Head in the clouds, with feet firmly

planted on the ground,” mostly because she is both an entrepreneur and a salaried

employee, who only recently quit her salaried job in order to focus on

entrepreneurship:

"I’m just consuming more knowledge and responding to it accordingly...I also

define it as having my head in the clouds and my feet firmly planted on the

ground…I think I have both, that’s why I’m so tall." (QUAL2, Interviewee

H)

The entrepreneur, who does not take a step or make a move, without first examining it

in an organised manner, could be described as the ‘spread-sheet’ or ‘Excel’

entrepreneur:

"...I believe in an overload of information... I’d rather know things a second

before. I’m not one of those who don’t want to know if they have cancer, I

want to know, perhaps I can fight it. It’s a matter of character; it could be

that if I know more, I’ll have a better chance of fighting it." (QUAL2,

Interviewee N)

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The findings of this phase (QUAL2) highlighted several types of entrepreneurs:

(1) The dreamer, fantasist, who may nevertheless act either spontaneously or in a

planned manner; this type of entrepreneur is described in the study as having

his 'Head in the clouds, feet firmly planted on the ground'.

(2) The 'Excel Entrepreneur', who bases her entrepreneurial actions on routines

and financial calculations, versus the 'Extremist', who trusts her intuition and is

not afraid to take calculated risks, even at high levels. Interestingly, it was

evident in the study that all the entrepreneurs who were categorised as

'extremists' were also active in extreme sport activities, such as cycling and

driving sports cars, and even competed in sporting events such as the triathlon.

This could increase the inherent tension between planned and improvisational

entrepreneurs. In their inductive study on organisational learning and

improvisation, Miner et al. (2001) argued that the use of improvisational

learning, in contrast to planned learning, might explain how entrepreneurs

respond to day-to-day changes and pressures in the early stages of the new

venture as they engage with opportunity.

(3) The Serial Entrepreneur, who tends to describe himself as an entrepreneur by

profession, in contrast to the ‘novice entrepreneur’. These two differ in their

entrepreneurial experience, which can be crucial to their success.

A number of researchers have reached similar conclusion about typologies of

entrepreneurs, based on a systematic classification of types that have characteristics or

traits in common. For example, Tang et al. (2008) developed a typology of

entrepreneurs based on their different personality characteristics and level of alertness.

This model enabled them to demonstrate why nascent entrepreneurs start new

businesses. The resulting matrix identifies four types of entrepreneur: the true believer,

the clueless, the practical, and the reluctant. They found that the four types of

entrepreneurs “…differed across three key entrepreneurial characteristics: need for

achievement, risk-taking propensity, and commitment” (Tang et al., 2008, p. 273). In

addition, Miner (2000) refers to a four-way psychological typology of business

founders. He distinguishes between personal achievers, real managers, expert idea

generators, and empathic super sales people. The findings are extended to indicate that

those who can be classified as one or more of the defined types are more likely to be

entrepreneurs.

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4.2.2 Summary

The main objectives of the second qualitative phase were the following:

Firstly, to investigate further the main themes, which were found in the first qualitative

phase (QUAL1), such as entrepreneurial opportunities, knowledge and learning, and

the relationships between them.

Secondly, to explore the following supplementary research questions,

1. Firstly, what factors might affect the motivation of entrepreneurs to

internationalise?

2. Secondly, what is the role of the Internet in the learning cycle of International

Entrepreneurs and finally, what are the ways they learn about opportunities?

4.2.2.1 The Process of Internationalisation and the Motivation to Internationalise

The study findings highlighted the concept of ventures that are internationalising from

inception. The 'born global' and the 'INV' concepts are the most frequently discussed

types in the international entrepreneurship literature (McDougall and Oviatt, 2003;

Aleluia, 2009). Internationalisation is not a linear, incremental, unidirectional path.

Firms can have a domestic focus for many years and then internationalise very

quickly. On the other hand, firms may de-internationalise to focus on the domestic

market.

Based on the various definitions given above, it seems that the most comprehensive

definition of the early internationalisation phenomenon that is in line with the findings

and results of this phase (QUAL2) is the 'born global' definition (see Appendix H for

more details). The focus in this definition is on the small, technology oriented firm,

which seeks to internationalise from the day of its inception. In this type of firm,

internationalisation will not start without entrepreneurial action. Internationalisation

must be triggered by someone, the entrepreneur (Andersson, 2000). The entrepreneur

does not act in a vacuum: he or she is an important part of his/her environment, but

most importantly, he or she constantly interprets the environment.

The entrepreneurs in this study indicate three main drivers of internationalisation:

psychological, financial, and environmental. The first group of factors are part of the

cognitive approach to entrepreneurship action. Many of these study participants

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indicate their intention to create a change in the world, to have an 'effect'. They

perceive the global market as more exciting, but also more challenging and demanding

greater levels of personal and business commitment. They described the international

market in a very positive way, with the emphasis on opportunities rather than risks.

This might be because as entrepreneurs they are influenced by cognitive biases

(Baron, 2004), of which a powerful one is the self-efficacy bias (Krueger, 2005).

Efficacy beliefs (Bandura, 1977) have been found to greatly influence entrepreneurial

behaviour, and improving the perceived possibility of action, is hence considered as a

necessary condition to encourage specific results (Krueger, 2005).

The second group of factors mentioned by these study participants is related to the

motivation or intention to improve their economic status. Krueger et al. (2000)

describe this intention toward behaviour as critical to understanding other antecedents:

”These include situational role beliefs, subsequent moderators, including the perceived

availability of critical resources, and the final consequences, including the initiation of

a new venture (or lack thereof)” (Krueger et al., 2000, p. 413). In addition, the

intention to become an entrepreneur is a better predictor than personality or

employment status variables, for example.

The last group of factors refers to the environment. The most frequent theme in this

study’s analysis was found to be the indication of networking, and particularly

personal contacts, as factors that might influence the entrepreneur’s decision to

internationalise in the early stages. In network approaches, the emphasis is on

relationships between either people or businesses. In business networks, the role of

networking is especially important for building the internationalisation competencies

of entrepreneurs (Hinttu et al., 2004).

4.2.2.2 The Role of the Internet in the learning Cycle of International

Entrepreneurs

In this study, interestingly, in both qualitative phases (QUAL1, QUAL2), use of the

Internet as a source of information was described by most of the study participants as

an important tool, despite the fact that they primarily made use of basic Internet

services such as accessing a repertoire of websites, search engines and social

networks. They are mainly concerned with the Internet’s function as a means of

communication and an information source (Mostafa et al., 2005).

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The study findings indicate that the Internet plays a significant role in entrepreneurs’

information search activities. The convenience, accessibility and anonymity of the

Internet, as well as the wealth of data available at no cost, meet the needs of

entrepreneurs, especially in the pre start-up phase (Shoham et al., 2006). At this stage,

entrepreneurs have limited resources and tend to seek out all possible information

rather than conduct more focused searches. In addition, decision makers such as

entrepreneurs consider not only the quality of the source but also its accessibility. It is

the accessibility of the source that consistently determines usage: decision makers are

busy and often lacking in time, therefore they will prefer the most readily accessible

channel (Carlson and Gordon, 1998), such as the Internet. In addition to its value as an

important impersonal source of information (Loane, 2005), “the use of the Internet as

an international distribution channel has expanded rapidly” (Arenius et al., 2005, p.

282).

While the Internet is recognised as a key resource, especially for ventures operating in

a geographically peripheral economic region, it can be argued that this role may be

redundant if the entrepreneur does not have the necessary competences to exploit its

potential (Durkin and McGowan, 2001). When properly applied, “the Internet can

provide a way to counteract the negative effects of foreignness and resource scarcity,

and thereby contribute to the increased speed of internationalisation” (Arenius et al.,

2005, p. 279).

To sum up, this study addresses the supplementary research question dealing with the

role of the Internet. It was found, both in this study and in the literature, that the

Internet plays an important role in the process of creating international ventures.

Entrepreneurs use the Internet primarily to gather specific information about their idea,

competitors, and potential customers, and general information about their

environment. They also use the Internet as a platform to establish, develop, and

maintain their personal and social networks, via specialised web sites, such as the new

social media sites (e.g., Facebook, LinkedIn, and Twitter), discussion forums, and

email. In fact, the Internet is their default option and procedure of choice for gathering

information. Its accessibility and ease of use allow entrepreneurs to overcome their

lack of resources, especially in the early stages of the entrepreneurship. The Internet is

perceived not only as an effective tool for gathering information and building

knowledge, but also as a useful and effective networking tool. Shoham et al. (2006)

point out that the Internet has changed the information search practices of

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entrepreneurs in recent years and has become a key factor in start-up planning at all

venture stages.

4.2.2.3 How Entrepreneurs Learn about International Opportunities

The concept of opportunity plays a key role in entrepreneurship in general (Shane and

Venkataraman, 2000b; Krueger, 2003) and in international entrepreneurship

specifically. As mentioned in the findings and results chapter (QUAL2), entrepreneurs

either discover or create their opportunity in the international market.

Entrepreneurs recognise an opportunity either by searching for it or by discovering it.

This phase is described in the interviews and focus group as an exploratory stage, in

which opportunities are being explored and measured. Searching for opportunity is

thus described as a deliberate action. Others indicated that for them, recognition of the

opportunity was not a deliberate process. They might have accidentally identified the

opportunity, for example, because of a business offer, request, or change in plans.

However, as mentioned above, opportunities can also be created. Interestingly, it was

found that the same entrepreneur may on one occasion search for or recognises an

opportunity, and on another occasion may create the opportunity.

Opportunity creation is described in this phase as the vaunted 'epiphany' or "eureka

moment", known also as the 'aha phenomenon'15. They detect a pattern, make a move,

and live at the intersection of thought and action, where they somehow manage to

think deeply and innovatively. In the view of Oviatt and McDougall (2005b),

entrepreneurs either discover or create the opportunity, and exploit the possible future

development of the opportunity. Entrepreneurs constantly develop and enhance their

services and products in order to meet the demands of the market.

Kirzner (1999, 2009) refers to the entrepreneur’s alertness to the entrepreneurial

opportunity. According to this view, an entrepreneur may be ready to discover an

opportunity, sometimes even without knowing what to look for. In this study, one of

15

The eureka effect (Greek: heureka, "I have found") is any sudden unexpected discovery, or the

sudden realisation of the solution to a problem, resulting in a eureka moment (the moment of

unexpected discovery), also dubbed as "breakthrough thinking". The eureka effect is also known as the

aha phenomenon, and it is similar to an epiphany (Wikipedia)

bword://!!ARV6FUJ2JP,Eureka%20%28word%29/

bword://!!ARV6FUJ2JP,Greek%20language/

bword://!!ARV6FUJ2JP,Discovery%20%28observation%29/

bword://!!ARV6FUJ2JP,solution/

bword://!!ARV6FUJ2JP,Epiphany%20%28feeling%29/

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the interviewees told that during his vacation he was suddenly exposed to a potential

business opportunity. He was sensitive to his (altered) environment. Alertness not only

gives entrepreneurs the ability to discover an opportunity, but also enables them to be

aware of their networks. When a business opportunity is discovered, their alertness

assists them to match the opportunity with their personal network. Ardichvili et al.

(2003b, p. 105) identify the entrepreneur’s “personality traits, social networks and

prior knowledge as antecedents of entrepreneurial alertness to business opportunities.

Entrepreneurial alertness, in its turn, is a necessary condition for the success of the

opportunity identification triad: recognition, development, and evaluation.”

The findings of both qualitative phases (QUAL1, QUAL2), highlighted the fact that

entrepreneurs do not necessarily act impulsively. In fact, they actually take their time,

learning about and analysing their products and markets before making a move. They

differ in the way they acquire knowledge and learn about these opportunities, the

speed of internationalisation, and their internationalisation strategy.

It was also found in these phases that international entrepreneurs should acquire

specific knowledge in order to be able to operate internationally. This knowledge may

be based on prior knowledge and may be acquired through a dynamic and interactive

process of learning. Some of these study participants indicate that while evaluating an

idea, their working experience allows them to understand their target audience, their

demands, and needs. Moreover, during the opportunity identification process, they

may count on their existing contacts in the industry, which could help them to bridge

the gap between their existing knowledge and the knowledge required to operate a

successful international entrepreneurship.

The way entrepreneurs learn is complex and might be affected and changed due to the

stage of the entrepreneurship: market entry, establishment, and the operational phase.

Given the complexity of learning mechanisms from international ventures (Autio et

al., 2000; Zahra et al., 2000; Yeoh, 2004), it could be argued that international

entrepreneurs learn about international opportunities and foreign markets by following

a path of entrepreneurial learning, and accordingly implementing several types of

learning behaviours, mechanisms, modes and styles.

Accordingly, entrepreneurs might learn by doing, mainly from their prior experience,

in the process of identifying entrepreneurial opportunities, if they have the relevant

experience inventory. This entrepreneurial learning process should include both

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successes and failures, and moves from the individual level to the organisational level,

even in the early stages when an opportunity has just been recognised, but no

established venture yet exists (Dutta and Crossan, 2005). Thus, the entrepreneur in this

view can be considered as the organisation, and her social networks and environments

are important components in her learning process.

Entrepreneurs also learn vicariously from others' experience, and use their knowledge

inventory and experience to explore new knowledge, particularly knowledge that is

required for learning about international opportunities. However, entrepreneurs must

be aware of the fact that vicarious learning might be risky. It is vulnerable to the

influences of behavioural and social biases of inference as well as the problems of

experiential learning from samples and noisy data, and is thus usually far from ideal

for causal inference (Denrell, 2003)

To sum it up, it is evident that three types of learning in the process of identifying

entrepreneurial opportunities were addressed by the study participants. Firstly,

entrepreneurs learn by doing or experimenting (Cope and Watts, 2000) as well as

learning from experience and learning from failures (Corbett, 2005a, 2007a).

Secondly, the role of social networks in the learning process was elucidated,

(Forsgren, 2002; Johanson and Vahlne, 2003; Haahti et al., 2005). Finally, they learn

from others, many times by copying or imitating best practices (Gibb, 1997; Cope and

Watts, 2000; Politis, 2005; Pittaway and Cope, 2007a; Holcomb et al., 2009;

Voudouris et al., 2011). These learning behaviours might be implemented in a

structured or a planned manner and or in an unintended or spontaneous manner

(Honig, 2001).

4.2.3 Conclusions

International operations are perceived as much more demanding and challenging than

domestic operations. So what are the factors that might affect the motivation of

entrepreneurs to internationalise?

The study findings highlighted the importance of factors such as greater control of

one’s destiny, increased satisfaction, more money and creating a legacy of wealth for

their family (Alstete, 2002). For them, the execution of their ideas is essential to their

self-esteem and self-satisfaction, in other words, it is essential for their "internal

reward system”. Perceived financial incentives also have an impact on the

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entrepreneur’s willingness to increase risk in order to facilitate growth, and his

innovation aspirations. Cognitive factors, such as personal needs (Corman et al.,

1988), need for achievement (Collins et al., 2004), alertness to the environment, the

centre of interest, causal logic, and prior experience (Santos-Álvarez and García-

Merino, 2010), and self-efficacy (Bandura, 1977) were also found to be correlated to

the motivation to entrepreneurship and internationalisation.

The study findings emphasised the importance of the entrepreneur’s environment,

especially the size of the home market, the country's level of social security (Hessels et

al., 2008) and success stories in the same industry, their social networks, such as close

friends and family, mentors and colleagues. International entrepreneurs, particularly in

early stages of the entrepreneurship, need to search for relevant business information,

which is related to business opportunities in an international context.

The research findings make it clear that although the entrepreneurs indicate that they

are highly interested in internationalisation, they devote very little time and effort to

gathering information on this subject, compared to the risk involved in international

entrepreneurship (Cooper et al., 1995). As a result, decisions related to

internationalisation are handicapped by uncertainty and a poor understanding of the

appeal, barriers, and available support services.

For the study participants the internet is used as their main source of information. In

the early stages, entrepreneurs have limited resources and tend to seek out all possible

information rather than conduct more focused searches. In addition to the use of the

Internet as an information collecting activities, the entrepreneurs participating in this

study indicate that their preferred strategy for bridging the gaps in their limited

knowledge is to make use of the internet as their main networking platform. For an

example, they use the internet as a substitute for doing international fieldwork, such as

conducting face-to-face meetings, attending conventions and trade shows, and meeting

with international prospects.

Learning was also found as the key reasons for international expansion. Ventures that

compete in international markets can learn new skills related to R&D, production and

marketing (Zahra et al., 2000). This process can be depicted as an iterative dynamic

process, which is triggered by critical incidents. It is dependent on several factors such

as the entrepreneur's prior knowledge, business, industry and international experience,

social networks, entrepreneurial type, level of alertness and their level of self-efficacy.

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The entrepreneurs in this study must overcome improper 'habits of mind' through a

process of experiential learning whether their own direct experience, or vicariously

from the experiences of others (Fiol and Lyles, 1985; Huber, 1991). In addition, they

have to perform a myriad of trial and error activities, so that they can learn from their

own successes and mistakes. They also learn by doing and copying and through their

social networks.

To sum up, given that learning occurs at the individual, group and organisational level,

and that entrepreneurs are involved in some kind of process at each of the levels,

(Dutta and Crossan, 2005), it becomes important to understand the entrepreneurial

learning strategies adopted by international entrepreneurs at different stages of

internationalisation and the extent to which they are effective and suitable. However,

there is insufficient consensus with regard to the factors that explain the way

entrepreneurs learn.

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5. Theoretical Framework

This study focuses on the opportunity identification process and looks at it from the

perspective of various approaches; foremost the learning approach. According to

which, opportunity identification is a dynamic, recursive and complex learning

process. Furthermore, the nature of the entrepreneurial process might be better

understood by focusing on how entrepreneurs learn and how different ways of learning

affect the opportunity identification process (Corbett, 2005b).

This chapter is structured as follows: firstly, a theoretical framework of the

opportunity identification as a learning approach is illustrated. Secondly, the 'ways

entrepreneurs learn' and specifically the differences between 'strategies' and 'styles' is

addressed and debated., and finally, a conceptual model and hypotheses that will be

further tested in the quantitative phase of this study are developed (QUAN).

5.1 Opportunity Identification Process as a Learning Model

This study defines learning as a crucial component of entrepreneurial activity. The

positions taken in this study are that entrepreneurship can be understood as a process

of learning, and a theory of entrepreneurship requires a theory of learning. Though

learning cannot assure success, and what is learned may be false (Minniti and

Bygrave, 2001), it is the learning process that matters, and eventually entrepreneurs

will increase their chances for success. Furthermore, this research conceptualises

opportunity identification as a dynamic, continuous, cyclical, recursive and iterative

process. In addition, the opportunity identification, in this study is explored as an

entrepreneurial learning process (Corbett, 2002; Corbett, 2005b; Dutta and Crossan,

2005; Lumpkin and Lichtenstein, 2005b; Corbett, 2007b).

This study introduces a model, which is based on the qualitative findings and the

premises from the following frameworks:

Firstly, the 4I model (Crossan et al., 1999), which was integrated by Dutta and

Crossan (2005) to explain the process of opportunity identification.

Secondly, the creativity-based model of opportunity recognition (Lumpkin and

Lichtenstein, 2005b) and the experiential learning model within the opportunity

identification and exploitation process (Corbett, 2005b).

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The main purpose of the proposed model is to provide a hybrid way to view how

entrepreneurs transform information into international entrepreneurial identification.

These transformation processes, might in turn, enable them to acquire the necessary

and critical knowledge for their success. Therefore, this study attempts to design a

conceptual model of international entrepreneurial learning about opportunities. It

consists of factors that may influence the entrepreneurs' learning, and allow this study

to empirically investigate hypothesises that are derived from this model.

The focus of this model is on the following:

The model focuses on the individual and his or her networks.

The role of the entrepreneur is to create or discover, evaluate, and exploit

opportunities (Shane and Venkataraman, 2000b; Ardichvili et al., 2003b;

Cuervo, 2005).

The process of opportunity identification consists of two subsequent phases:

discovery and formation (Lichtenstein et al., 2003; Lumpkin and Lichtenstein,

2005b). The model highlights the former stage, which is the identification of


Opportunities can be discovered and/or created. Although these conflicting

views of entrepreneurial opportunities derived from two opposing ontological

positions, Dutta and Crossan (2005, p. 425), explained that: “by applying the 4I

organisational learning framework to entrepreneurial opportunities, we were

able to not only resolve the apparently, but also to achieve a level of pragmatic

synthesis between them”.

The model focuses on the beginning of new learning, which is sensing the idea

and continues with interpreting it until a business opportunity is identified

(Crossan et al., 1999; Dutta and Crossan, 2005).

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The following figure depicts the opportunity identification process as an

entrepreneurial learning approach:

Figure ‎5.1: Opportunity identification as an entrepreneurial learning process

Insight

Idea Opp.

Preparation

Incubation

Evaluation

Elaboration

Feed forward (Exploitation)

Int.

Intuition

•Deliberate

•Unintended

Feed –back (Exploitation)

It can be argued that the opportunity process begins with a cognitive preparation of the

entrepreneur (Lumpkin et al., 2004; Corbett, 2005a; Dutta and Crossan, 2005;

Lumpkin and Lichtenstein, 2005a).

The prior knowledge (Shane, 2000) and the experience of the entrepreneur (Corbett,

2005a) develop their alertness and awareness in revealing existing market needs, or

conversely, influencing the entrepreneurs' consciousness in gathering information

(Lumpkin and Lichtenstein, 2005b). Consequently an idea might emerge (Franco and

Haase, 2009). This might be considered as the beginning of a new learning process

(Crossan et al., 1999). At this stage the learning is very much cognitive (Corbett,

2005b).

When the idea is generated, the entrepreneur moves to the next stage of incubation

(Lumpkin and Lichtenstein, 2005b). According to Ward (2004, p. 179): “…it is

important to note, however, that an idea for a new product is not the same as the

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finished product itself, as the idea is explored, modified, transformed, extended, or

even rejected on the basis of additional exploratory thought processes”.

During this incubation stage, the entrepreneur is more attuned to discover

opportunities either because of a systematic search or unintendedly (Corbett, 2005a;

Lumpkin and Lichtenstein, 2005a). The end of the on-going incubation process is

when the entrepreneur becomes aware of a possible solution to a problem that

disturbed them. This moment in time, is often called the 'eureka moment' or the ' Ah-

Ha moment'. This sudden moment assimilates the idea into an insight. Insights often

occur recursively throughout the discovery process (De Koning, 2003) and might

consist of the recognition of an opportunity or be influenced by their social networks

(De Koning, 2003; Lumpkin and Lichtenstein, 2005b).

Experiencing the 'eureka moment' will lead the entrepreneur toward the second phase

of the opportunity process, which is the formation. At this phase, the entrepreneur is

interpreting the idea, especially aspects that were not revealed in earlier stages, and

might determine a more detailed planning and feasibility study of the availability of

the necessary resources such as money, time and especially knowledge (Lumpkin and

Lichtenstein, 2005b). In addition, the entrepreneurs might question their own beliefs

and insights, and look for legitimacy in order to decide whether this idea is worth

pursuing. This is done either internally or in an interactive conversation process

(Crossan et al., 1999) with their social networks, especially stockholders (Dutta and

Crossan, 2005; Lumpkin and Lichtenstein, 2005b) or close friends and family

members (Bhave, 1994b; Singh, 2000). Moreover, they will continue to interpret and

transform information in a recursive manner, refining its meaning and enabling the

entrepreneur to develop their vague idea into a vision of a new venture. This can be

called as an entrepreneurial learning process (Dutta and Crossan, 2005).

Adopted from Dutta and Crossan (2005) and consistent with March’s model (1991), it

can be argued that international entrepreneurs confront two distinct pressures or

forces: the first is the feed-forward process, in which, in any opportunity identification

stage, the entrepreneur learns and acquires knowledge based on existing resources

such as financial, time and most importantly prior knowledge. Once the opportunity is

identified, the second force becomes more relevant, with the power to explore new

knowledge, which is not related to current resources. Levinthal and March (1993)

argued that maintaining adaptiveness requires both exploiting existing competencies,

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which are for example: complex combinations of assets, knowledge, and skills (Floyd

and Lane, 2000), and exploring new ones. Moreover, these two surfaces of learning

are inseparable (Levinthal and March, 1993) demanding sustaining a reasonable level

of exploration and exploitation (March, 1991; Levinthal and March, 1993). By doing

so, entrepreneurs carry out exploration activities to decrease their ignorance, and

consequently move their attention to exploitation activities in order to increase

revenue (Choi et al., 2008).

The theoretical framework, which was discussed above described the process from a

cognitive preparation of an entrepreneur until the vague idea is considered by the

entrepreneur as an entrepreneurial opportunity. This process is dynamic, complex and

can be considered as a learning process about ideas and opportunities. Apparently,

entrepreneurs learn about opportunities, the learning can be cognitive, behavioural or

actionable (Lumpkin and Lichtenstein, 2005b), however, the question of how they

learn in the opportunity identification process is still the most interesting one.

5.2 The Ways Entrepreneurs Learn in the Opportunity Identification Process

The view taken in this study is that entrepreneurs are action orientated, and thus they

take decisions, at almost any entrepreneurial process stage, especially when they

evaluate their ideas. They do not just learn about opportunities, they learn strategically

(Honig, 2001) about the opportunities. This strategic learning is complex and

developed over time, and most importantly can be changed as a matter of the

entrepreneurs' choice.

The qualitative findings of this study elucidated the importance of learning from

experience, directly or from others, as well as the significance of learning by doing. In

the same vein, Schwens and Kabst (2009), who focused on the learning behaviours of

early as opposed to late internationalised firms, identified that 'learning from direct

experience as one of the important behaviours of international learning behaviour. In

addition, learning from direct experience, learning by experimentation or learning-by-

doing has been generally recognised as the main entrepreneurial learning behaviour

(Ulrich and Cole, 1987; Cope and Watts, 2000; Raffo et al., 2000; Man, 2006;

Holcomb et al., 2009).

The 'Learning Strategies' about the opportunity in this study, are conceptualised as an

integration of two groups of strategies, behaviours based and cognition based. The first

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group, contains three strategies, which are based on the qualitative findings of this

study and the work of Saarenketo et al. (2004) and Schwens and Kabst (2009). The

second group contains two strategies, which are related to the way entrepreneur's

acquire the information, organise and reflect on it (Holman et al., 2001), and were

implemented from the work of Honig (2001) (i.e. random and systematic).

It is important to mention at this stage, that two strategies, which were found in the

qualitative results: searching and grafting, were not included in the following

categorisation for the following reasons: firstly, learning through grafting (Saarenketo

et al., 2004) was excluded, mainly for the reason that entrepreneurs, especially in the

early stages, lack resources, and thus, this specific strategy will rarely be found. In

addition, learning through searching (Saarenketo et al., 2004) can be illustrated as a

necessary stage while entrepreneurs learn by doing. Therefore, this specific strategy

cannot replace the role of learning from experience and thus can act only as a first step

before learning by doing. Thus, Table ‎5.1, demonstrates a six-way typology of the way

entrepreneurs might choose to learn about the opportunities in a cross-border context:

Table ‎5.1: Learning strategies of entrepreneurs in the opportunity identification

process

Spontaneous Deliberate

Learning by doing16

(Schwens and Kabst, 2009)

Learning by doing

spontaneously

Learning by doing

deliberately

Learning by networking

(Saarenketo et al., 2004) Learning by networking

spontaneously

Learning by

networking

deliberately

Learning by imitating

(Saarenketo et al., 2004)


spontaneously


deliberately

It is argued in this study that in a rapid and accelerated internationalisation,

entrepreneurs might be able to choose to learn through experiencing ('by searching', 'by

16

Will also be used in this study interchangeably as 'by experience' or 'by experimenting'

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doing', 'by experimenting', 'from failures and successes'), via networking ('from the

experience of others'), and through imitating ('paradigm of interpretation'). In addition,

they can do it in a systematic or a random manner (Honig, 2001). These six strategies

are legitimate, effective, and each one of the former three (i.e. 'by doing', 'by

networking', by imitating') can be combined with the latter two ('spontaneous',

'deliberate'). However, there is no one strategy, which is superior to another.

In order to engage in a learning process, regarding the opportunity, entrepreneurs

should integrate strategies from both groups; for example, learn via networking in a

planned and systematic manner or randomly. Entrepreneurs, that integrate strategies

from both groups are able to 'proactively reflect' on past events, and thus, eventually

learn how to learn (Cope and Watts, 2000) about entrepreneurial opportunities.

Moreover, the integration of both groups of strategies, can be explained through the

lens of the 'Effectuation and Causation' theory (Sarasvathy, 2001b).

Sarasvathy (2001b) in general, distinguished between two decision-making processes

of new venture creations: effectuation and causation. She defined them as follows:

"Causation processes take a particular effect as given and focus on selecting between

means to create that effect. Effectuation processes take a set of means as given and

focus on selecting between possible effects that can be created with that set of means"

(2001b, p. 245).

Implementing this logic for the study’s purposes, it can be argued that an individual’s

choice of information source (i.e. doing, networking and imitating) should be

integrated with the entrepreneur’s choice of how to acquire this information (i.e.

random versus systematic). For example, an entrepreneur who chooses to learn by

doing can learn by planning or learn in an emergent manner. The following abstract

example should help in clarifying and distinguishing between the two types of

processes.

An entrepreneur has a wonderful novel idea. They now need to learn about it and to

decide at the end of this process, whether this brilliant idea is an enviable business

opportunity or not. There are several possible ways this mission can be accomplished.

For the sake of this example, let us choose between only two: learning by doing in a

planned and a systematic way, versus learning by doing in a random manner. Based on

the Sarasvathy (2001b, p. 245) examples, it can be argued that the entrepreneur might

choose between two processes: learning by doing by causation, which is in the same

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vein as learning by doing in a random manner, and learning by doing by effectuation,

which is similar to learning by doing in a systematic way. In the first, the entrepreneur

envisions the opportunity, in their mind. All the entrepreneur needs to do is list the

resources needed, plan for them, and then systematically acquire the relevant resources

in order for them to learn about the opportunity. For example, decide in advanced that

there is an opportunity in the United States. This is a process of causation. It begins

with a given idea, and focuses on planning and systematically selecting between

effective ways to learn about the specific opportunity.

In the second, the entrepreneur does not envision the opportunity. Based on a vague

idea they have in mind, the entrepreneur starts to look for possible opportunities,

realises what are the available resources in their possession, such as past experience

and knowledge, and then begin with learning about the markets in general, their needs

and gaps, and finally an opportunity sometimes emerges, usually, different from the

vague idea. This opportunity is often more innovative and sometimes it even enables

the entrepreneur to create a new market. In this way, the entrepreneur learns by doing,

without initial planning and a systematic acquisition of resources, such as knowledge.

It begins with available (or unavailable) resources, capabilities and mechanisms and

focuses on learning one of many possible opportunities. This is the process of

effectuation according to Sarasvathy (2001b, p. 245).

5.3 The Conceptual Model

Zahra et al. (2005, p. 135) citing Shane and Venkataraman (2000b, p. 218) suggested

that entrepreneurship research addresses three key questions:

(1) “Why, when, and how do opportunities for the creation of goods and services

come into existence?

(2) Why, when, and how do some people and not others discover and exploit

these opportunities?

(3) Why, when, and how are different modes of action used to exploit

entrepreneurial opportunities?”

This study focuses on the third question, from the learning point of view. By applying

this approach, in order to understand entrepreneurship action we must first understand

how entrepreneurs learn (Minniti and Bygrave, 2001). This study's literature review

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addressed several conclusions, based on the research gaps, which were revealed. Two

of the main conclusions were as follows:

Firstly, future research on international entrepreneurship might, focus on the

development of a new entrepreneurial learning framework and a conceptual model

combining different research approaches, and

Secondly, focus on the individual and their interaction with the environment.

The following conceptual model, takes the challenge that was set by Zahra et al.

(2005) further, and hence, was designed and devoted to the study of the way

entrepreneurs learn about the international opportunity. In addition, and with relations

to the literature review conclusion, the conceptual model integrates between three

different approaches to entrepreneurial learning about opportunities: experiential,

cognitive, and social networking:

Experience is studied as one of the major learning antecedents, especially in reference

to the way entrepreneurs identify opportunities. Prior studies identified various types

of experiences, which may be utilised as a learning opportunity. Rae and Carswell

(2001) proffered that the notion of experiential learning is important in

entrepreneurship, and that experience can bring into being new meaning, and bring

forth subsequent change in thinking and behaviour by critically reflecting on particular

incidents (Cope, 2003b), however, learning is a more complex process and people do

not inevitably learn only from experience (Rae and Carswell, 2001).

Learning can be seen as a cognitive process (Santos-Álvarez and García-Merino,

2010) of gathering and interpreting information, acquiring and structuring knowledge,

in long-term memory (Young and Sexton, 2003). Learning may be regarded as a self-

reinforcing process affected by emotional, motivational and personality factors (Cope

and Watts, 2000; Fenwick, 2003; Man, 2006) and that new ventures are profoundly

influenced by their founder(s), whose cognitive template and decision-making style

play an important role in all aspects of the venture’s lifecycle (Lichtenstein et al.,

2003). Zahra et al. (2005) proposed that research on international entrepreneurship

should focus on applying a cognitive approach to examine the way entrepreneurs

recognise opportunities. Entrepreneurs often identify opportunities where others do not

and foresee patterns and trends that others fail to recognise (Allinson et al., 2000b).

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Another stream of research on entrepreneurial learning is the networking approach.

At the centre of this approach stands the acknowledgment that the social networks of

the entrepreneur act as a bridge between the environment and the entrepreneur, allow

the entrepreneur to acquire knowledge that is essential for the international

opportunity identification (Harris and Wheeler, 2005), reduce risks, and, on the other

hand, spur innovative behaviour of entrepreneurs (Sharma and Blomstermo, 2003). In

addition, they provide an opportunity for learning, as well as establishing trust,

legitimacy and commitment (Johanson and Vahlne, 2009). Entrepreneurs develop by

interacting with other individuals and groups in their social life (Gibb and Ritchie,

1982). They interact with their surroundings through a broader spectrum of networks

such as family and friends, mentors, suppliers, bankers, customers, etc. These

interactions are important especially in the opportunity identification phase during the

start-up stage (Boussouara and Deakins, 1999; Man, 2006).

These three approaches to entrepreneurial learning represent different dimensions of

this phenomenon in general, and specifically might enable us to elucidate the

entrepreneurial identification process. Therefore, to understand better learning

strategies of international entrepreneurs, a conceptual model, which combines these

different approaches, seems to be appropriate.

The following figure depicts the main factors that might influence the learning strategy

of entrepreneurs, in the international opportunity identification process:

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Figure ‎5.2: Conceptual Model of the factors that affect the way entrepreneurs learn

The model reflects the influence of three types of factors:

1. Cognitive variables: prior international knowledge (Reuber and Fischer, 1997;

Michailova and Wilson, 2008; Chandra et al., 2009), and entrepreneurial self-

efficacy constructs as predictors (e.g. Bandura, 1978; Zhao et al., 2005), and

cognitive styles construct as a moderator (Allinson and Hayes, 1996; Sadler-

Smith and Badger, 1998; Allinson et al., 2000b; Sadler-Smith, 2004; Cools and

Van den Broeck, 2007).

2. Networking variable: the social networking constructs of the international

entrepreneur (Arenius and De Clercq, 2005; De Carolis and Saparito, 2006;

Agndal and Chetty, 2007; Ellis, 2008; Ellis, 2011).

3. Experiential variable: the prior business ownership experience construct

(Ucbasaran et al., 2003; Westhead et al., 2005b;a; Ucbasaran et al., 2009).

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Whereas the current study posits that each of the constructs (i.e. Business ties, Strong

ties, professional and social ties and prior knowledge) has a direct relationship with

learning strategies, and entrepreneurial self-efficacy and prior business ownership

experience has a direct relationship with prior knowledge, it is argued that there is an

interaction effect between cognitive styles and the prior knowledge construct.

The model hypothesises that Prior Knowledge and the three social networking

variables will affect the ways entrepreneurs learn. Due to the model’s high complexity,

the large number of criterion variables (i.e. six dependent variables that represent six

different learning strategies), and the underlying assumption in this model that each of

the predictors affect each of the criterion variables in the same direction, the

hypotheses in this study, are formulated as more general than concrete. For example,

instead of formulating six different hypotheses for the relationships between prior

knowledge and the six criterion variables, only one hypothesis was formulated

whereas the term ‘the ways entrepreneurs learn’ reflects the six criterion variables.

5.4 Hypotheses Development

5.4.1 Self-Efficacy and Prior Knowledge

Self-efficacy refers to the belief in the individual capability to organise and perform

actions required to manage a specific task or a situation, considering that beliefs might

affect the individuals’ motivation and actions (Bandura, 1978). Self-efficacy differs

from the concept of ‘locus of control’. Locus of control refers to the individuals’

“general belief in the authority of their own actions across a variety of tasks and

situations, while self-efficacy relates to an individual’s self-confidence in specific

tasks and situations” (Wilson et al., 2007, p. 389).

Previous studies have shown that entrepreneurs, with a high-level of self-efficacy, may

believe in their capability to successfully develop ideas into business opportunities

(Dimov, 2010), and hence are more likely to pursue this action, whether or not they

objectively own the relevant resources (Arora et al., 2011), and have a greater belief in

their success.

In addition, it was found that self-efficacy is correlated with learning opportunities

(Zhao et al., 2005), learning goal orientation (Yi and Hwang, 2003) and learning

strategies (Pintrich, 2000; Walker et al., 2006; Liem et al., 2008; Diseth, 2011).

Entrepreneurial self-efficacy may influence the way entrepreneurs learn and hence the

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entrepreneurial outcomes. According to Bandura (1978, p. 278): “Self-precepts also

determine how much effort people will expend and how long they will persist in the

face of obstacles or aversive experiences. Because knowledge and competencies are

achieved through sustained effort, any factor that leads people to give up readily can

have personally limiting consequences.”

Furthermore, Kauppinen and Juho (2012) from the perspective of individual

entrepreneurial attributes, argued that entrepreneurial self-efficacy, in addition to other

personality traits, may be viewed as an explanation of why an entrepreneur could

successfully internationalise their business. Entrepreneurs with a high level of self-

efficacy can cope very well with adverse environments, mainly because they have a

strong belief in their capabilities to overcome environmentally harsh conditions, such

as in the global market (Bullough et al., 2013). In addition, according to Peiris et al.

(2012, p. 17), entrepreneurial learning is an important”… element that connects the

constructs such as previous knowledge, self-efficacy and creativity and uncovers how

individuals combine these constructs with existing knowledge stocks”.

Bandura (2012, p. 20) explained that: “Self-efficacy operated as a motivator,

regardless of whether attainments supposedly fell substantially, moderately, or

minimally short of the assigned goal or even exceeded it”. Thus, entrepreneurs with

high level of self-efficacy will be motivated to internationalise their business. One

important factor in their decision is their level of international knowledge, therefore;

they will be motivated to acquire more knowledge about their markets. Hence, it can

be argued that Self-efficacy is related to the entrepreneur’s prior knowledge. In the

context of entrepreneurial opportunity identification process, individuals with a high

level of self-efficacy are expected to enjoy the challenge of learning new business

opportunities, by developing their self-confidence throughout the process of

identifying the opportunities, and thus enhancing their motivation to acquire more

knowledge, which is highly relevant to the identified opportunity.

Therefore:

H1: Entrepreneurial self-efficacy level will have a positive effect on the level of the

‘prior knowledge’.

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5.4.2 Prior Business Ownership Experience and Prior Knowledge

The concept of prior experience plays an important role in entrepreneurial research

(Reuber and Fischer, 1999). Experienced entrepreneurs have acquired valuable and

important knowledge about the markets, the necessary resources, and the availability

of social networks (Politis and Gabrielsson, 2005).

Experienced entrepreneurs are more effective in establishing and maintaining social

and business networks (Shane and Khurana, 2003; Politis and Gabrielsson, 2005), and

their international experience also enables greater awareness about opportunities

(Westhead et al., 2001; Yeoh, 2004) and the speed of market entry (Oviatt and

McDougall, 2005d). They have the opportunity to learn from their past mistakes,

avoid further mistakes and enable them to become more insightful and fast in taking

subsequent entrepreneurial actions (Farmer et al., 2011).

Several researchers such as: Westhead and Wright (1998b), Ucbasaran et al. (2003);

Ucbasaran et al. (2006) have distinguished between novice and habitual entrepreneurs.

These groups of entrepreneurs may think differently (Westhead et al., 2005b;a), and

differ in their knowledge structures, the amount of deliberate practice, cognitive

abilities, and behavioural patterns (Corbett et al., 2007; Mitchell et al., 2009;

Markowska, 2011).

Consistent with Westhead and Wright (1998b, p. 173), and Brigham and Sorenson

(2008), “novice entrepreneurs are entrepreneurs with no previous entrepreneurial

business ownership experience. Habitual entrepreneurs, either serial or portfolio, are

individuals with minority or majority business ownership experience prior to their

current venture. Serial and portfolio are subsets of habitual entrepreneurs. Serial

entrepreneurs have founded, not concurrently, different ventures. Portfolio

entrepreneurs have concurrent possession in two or more businesses” (Brigham and

Sorenson, 2008, p. 2).

In a way, habitual entrepreneurs are more experienced than novice entrepreneurs are.

Specifically, they are familiar with certain situations that can enable them to

‘automatically’ grasp the relevant issues and recognise meaningful signs or patterns

that have information value. Based on their past experience, their behaviour is linked

to the active, deliberate and systematic opportunity search (Santos-Álvarez and

García-Merino, 2010). Repeated exposure to the complexity of real problems, for

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example, past experience in founding ventures, will develop more Expert

Entrepreneurial Schema than will novice entrepreneurs (Blume and Covin, 2011).

Experts have more developed schema, richer in content, appreciate the relevance of

information and are able to ‘connect the dots’, identifying meaningful patterns in

comparison to novice entrepreneurs (Baron and Ensley, 2006b). Expert entrepreneurs

reject the use of predictive information, choose not to rely on external inputs, instead

they learned to filter information from external sources. In addition, they prefer to do

the things by themselves (Read and Sarasvathy, 2005). However, expert entrepreneurs

such as serial entrepreneurs might engage in less information search than corporate

managers of the same age, or novice entrepreneurs (Kaish and Gilad, 1991), most

probably due to hubris or reduced ambitions (De Koning and Muzyka, 1999).

Despite that, it is most likely to assume that an entrepreneur might devote time and

effort to seek information, to select sources of information, following up these sources

and finally deciding whether the information is relevant and useful to the available

opportunity, closely matching patterns acquired through experience (Gaglio and Katz,

2001). The prior business ownership experience of an entrepreneur can be perceived

as a reflection of the idiosyncrasy of entrepreneurial knowledge (Fuentes Fuentes et

al., 2010). Venkataraman (1997) argued, that the prior knowledge, which leads to

opportunity discovery, is idiosyncratic in nature, and might be a result of various

sources such as work experience, personal events, and education.

Michailova and Wilson (2008) studied experiential knowledge in small international

firms. They argued that internationally experienced entrepreneurs possess international

experiential knowledge about foreign markets, institutions, and culture. This unique

knowledge reduces uncertainty (Johanson and Vahlne, 1977) and hence increases

their ability to identify opportunities, mobilise resources and act upon it. Therefore,

prior experience, enables the individuals to acquire knowledge which influences their

ability to recognise opportunities (Shane, 2000), and to organise and manage new

ventures (Corbett, 2002; Shane and Khurana, 2003).

Previous start-up or business ownership experience, provides valuable learning

opportunity for the entrepreneur in terms of making more methodological market entry

decisions as they learn to generate and consider various alternatives (Gruber et al.,

2008). In addition, experienced entrepreneurs are exposed to information that prove

essential throughout the start-up process, and is considered by them as valuable mainly

185

because it may enable the entrepreneurs to utilise their relevant resources such as time

and effort more efficiently (Dimov, 2010). In situations when they lack the necessary

information, for example about an international market, more experienced

entrepreneurs will most likely learn from their experience in previous ventures

wherein information is never fully available and thus they should acquire as much new

knowledge as they can to reduce uncertainty and to learn about the identified

opportunity. On the other hand, novice entrepreneurs, although appreciating the

necessity of knowledge in a rapid and dynamic environment, such as the high-tech

market, do not necessarily have the required level of resources (time and money) to

invest in the process of acquiring this information. It is therefore, suggested that:

H2: Prior business ownership experience will be negatively related with ‘prior

knowledge’.

5.4.3 Prior Knowledge and Learning Strategies

Acquiring information, using various sources of information and different information

seeking behaviours (Welsch and Young, 1982), is an important aspect of the

entrepreneurial behaviour (Cooper and Artz, 1995), mainly because the intent of the

entrepreneurs to eliminate or reduce the effect of the uncertainty through acquiring

plausible and reliable information.

Prior entrepreneurial knowledge mainly reflects the existing stock of the

entrepreneur’s experiences on the target market, the gap between the problems some

customers experience and the existing solutions (Shane, 2000). As a construct, it

reflects the duration, importance, and diversity of experiences, which are obtained

through job and life experiences. In addition it plays an important role in identifying

international opportunities in international new venture (Ardichvili et al., 2003b; Zhou

et al., 2010), and is critical for a sustainable process of internationalisation (Fletcher

and Prashantham, 2011). By entering the 'knowledge corridor' (Ronstadt, 1988)

entrepreneurs are able to identify certain opportunities and not others (Shane, 2000)

which might increase the ability of an entrepreneur to identify opportunities (Shane,

2000).

This study adapts the approaches of Eriksson (Eriksson et al., 1997; Eriksson et al.,

2000), Ardichvili (2003b) and Shane (2000), who view prior knowledge as

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idiosyncratic in nature, personal, multidimensional, and integrate both objective and

experiential knowledge (Fletcher and Prashantham, 2011).

Objective knowledge, which is acquired through training or from data sources such as

market research, government statistics, bank bulletins or company reports (Nonaka,

1994; Fletcher and Prashantham, 2011), can be replicated and transferred. Experiential

knowledge is costly, country-specific, cannot be easily transferred and replicated, and

is often viewed as a driving force in the internationalisation process (Eriksson et al.,

1997), in contrast to the objective knowledge, which is viewed as having a minor

influence on the internationalisation process (Johanson and Vahlne, 1977). In addition,

the convergence of knowledge, which is driven by the entrepreneurs' special interest in

a specific area or domain, and knowledge, which refers to their job experience in a

specific task or industry, is essential for enabling the entrepreneur to engage in

successful opportunity identification (Ardichvili et al., 2003b).

Shane and Eckhardt (2005) explained that prior knowledge, such as: markets,

technologies and customers, might generate an absorptive capacity, which enables the

entrepreneurs to better assess the value of the information acquired (Cohen and

Levinthal, 1990), and therefore are better able to learn about opportunities. In addition,

the information acquired can be shared and thus, may influence the knowledge access

ability needed in order to improve market performance (Matsuno et al., 2002). This is

especially of importance when studying international new ventures. These ventures,

which lack sufficient resources, such as prior experience and relevant knowledge, tend

to enrol in learning through less structured procedures (Zhou et al., 2010)

Moreover, the extent to which international entrepreneurs develop international

networks by for example “attending trade shows, visiting international markets, and

establishing international business and social contacts, in addition to monitoring

export markets, and exploring international business opportunities” (Zhou et al., 2010,

p. 900), is considered to play a key role in the international market knowledge

acquisition process, and can provide the entrepreneurs benefits such as growth

opportunities from early internationalisation (Zhou et al., 2010). On the other hand,

less prior knowledge may lead the entrepreneurs to rely on external sources of

knowledge (Fernhaber et al., 2009) such as other entrepreneurs and ventures in their

environment.

187

H3: Prior knowledge will be positively associated with the ways entrepreneurs learn

about entrepreneurial opportunities17

.

Haunschild and Miner (1997) distinguished between three distinct modes of selective

imitation: frequency imitation, trait imitation, and outcome imitation. Frequency

imitation relates to copying very common practices, trait imitation is copying practices

of others with certain features, and finally outcome imitation is based on a practice's

apparent impact on others. For example, learning from success and abandoning

practices that led to failures. Uncertainty can be linked to a lack of experience,

therefore, and according to Fernhaber et al. (2009) new ventures with less international

experience are likely to be facing greater uncertainty and, therefore, rely more on

other's knowledge, hence learn vicariously from counterpart's ventures in their

environments, in order to interpret their own situations and act accordingly (Fernhaber

et al., 2009), learn from the experience of others or by paradigm of interpretation

(Schwens and Kabst, 2009).

Entrepreneurs that operate in international markets might be repeatedly exposed to

foreign nationals (Michailova and Wilson, 2008) in various circumstances, either

through direct interaction with their networks in foreign markets, or through observing

the behaviour of others, which will enable imitation of complex behavioural patterns

by others (Davis and Luthans, 1980), either in an unplanned or planned manner.

Therefore, the fourth hypothesis is:

H4: Prior knowledge leads to more learning by imitating than learning by doing, and

similarly, learning by networking.

17

Due to the model’s high complexity, the large number of criterion variables, and the underlying

assumption in this model that each of the predictors affect each of the criterion variables in the same

direction, only one hypothesis was formulated whereas the term ‘the ways entrepreneurs learn’ reflects

the level of each of the six entrepreneurial learning strategies about opportunities. For example, H3:

Prior knowledge will be positively associated with the level of learning by networking deliberately, and

similarly learning by networking spontaneously, learning by imitating deliberately, learning by

imitating spontaneously, learning by doing deliberately, and learning by doing spontaneously.

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5.4.4 Social Networks and Learning Strategies

The amount of uncertainty reduced through information search depends on the

entrepreneur’s information processing capabilities, as well as on the availability of the

information (Kang and Uhlenbruck, 2006). The entrepreneur interprets the

environment, as well as the acquired information (Shaver and Scott, 1991; Kang and

Uhlenbruck, 2006). A high level of networking activity is necessary, both to maintain

and initiate the social networks (De Koning, 2003), and to facilitate the way

entrepreneurs learn about entrepreneurial opportunities, especially in an international

context.

Prior studies have emphasised many dimensions of this influence. For example, the

role of mentors (Sullivan, 2000) and directors (Deakins et al., 2000), professionals

(Deakins and Freel, 1998), social ties (Ellis, 2008) and personal relationships (Harris

and Wheeler, 2005). Most of them approached the relationships between social

networks and entrepreneurship in different entrepreneurial situations such as domestic

entrepreneurship, analysing these relationships based on the firm as the unit of

analysis. Not many have provided an extensive analysis focusing on the role of social

networks in the opportunity identification process, and only few of them have focused

on the international opportunity identification process.

Several studies have addressed the relationship between networking and collecting

information for internationalisation. Whilst information is just one of the required

resources for international opportunity identification, one of the challenges

entrepreneurs face, is the lack of available resources such as financial and knowledge

needed for processing this information. The social network of an entrepreneur can

cost-effectively facilitate their access to relevant information (Singh, 2000), affect

their entrepreneurial intention (Fernández-Pérez et al., 2014a) and hence, direct them

to deliberately seek for an interesting gap in the market, which has not yet been

discovered by others. It means that their networks such as mentors, informal industry

networks, and participation in professional forums may positively affect the

opportunity recognition process (Ozgen and Baron, 2007).

Coviello and Munro (1995) found that foreign market decisions such as: the target

market and entry modes, were triggered by opportunities offered by alliance partners,

rather than as a result of a systematic search. Either way, these repeated interactions

might increase their ability to learn, incrementally through a cycle of observation. This

189

study, based on prior studies, distinguishes between two types of networks: strong and

weak ties. Entrepreneurs should develop and promote both types, as it is found to be a

robust predictor for opportunity discovery and exploitation of nascent entrepreneurs

(Davidsson and Honig, 2003).

Weak ties, in this study, are measured as two separate and independent variables:

business ties, social, and professional ties (see section 5.5 for more details). Most

people have more weak ties than strong (Granovetter, 1973). Weak ties, in general are

loose relationships between individuals, and are useful in acquiring information that

would otherwise be unobtainable or expensive to discover, as opposed to strong ties

that would be derived from family and close friends relationships, which provide

secure and consistent access to resources (Granovetter, 1973). Weak ties can facilitate

the exchange of simple and codified knowledge, whilst strong ties are used for the

exchange of tacit and complex knowledge, through high levels of interaction

communication, and trust, thereby reducing the risks of opportunism and facilitating

complex knowledge transfer (Lowik et al., 2012).

Networks are important not only as a factor that might affect the number of

opportunities being identified (Singh, 2000), but as a factor which may facilitate the

way they acquire and transform the information. Through membership in a network,

individuals might acquire new and relevant knowledge (Inkpen and Tsang, 2005), thus

influence the way entrepreneurs learn.

Ties, either strong or weak are important sources of information for the entrepreneurs.

What matters is the strength of the ties. Individuals who communicate with each other

frequently or who have a strong emotional attachment are more likely to share

knowledge than those who communicate infrequently or who are not emotionally

attached (Reagans and McEvily, 2003). In a way, repeated and enduring exchange

relationships, can lead the entrepreneur to prioritize social networking as the chosen

source of learning regarding the opportunity identified (Inkpen and Tsang, 2005).

Moreover, Entrepreneurs that have developed social and business networks might gain

greater accessibility to diverse types of information that would not otherwise be

encountered (Wilkinson et al., 2005). Weak ties, as one type of inter-personal network,

might connect between diverse sources of information and international opportunities

(Harris and Wheeler, 2005; Komulainen et al., 2006; Ellis, 2011). Accordingly, it can

be argued that entrepreneurs with a high strength of weak ties are more likely to have

190

in place, a foreign business and a social network, and are more likely to have

developed the skills needed to identify and negotiate with firms in a different culture

(Reuber and Fischer, 1997). As a result, they will tend to learn in any available way

about entrepreneurial opportunities:

H5.1: Business ties (Businessties) will have a positive effect on the ways entrepreneurs

learn about entrepreneurial opportunities.

H5.2: Professional and Social (Pro&Socialties) will have a positive effect on the ways


Weak ties enable the entrepreneurs to unintendedly discover opportunities without a

deliberate search (Chandra et al., 2009). However, having a network of weak ties does

not necessarily lead the entrepreneur toward generating good business ideas (De

Koning, 2003). An entrepreneur may have contacts, which sometimes, are too small or

not diverse enough, thus are not sufficient to provide new information or perspectives

for the entrepreneurs, and therefore, they may not be able to identify and develop the

opportunity (Porac et al., 1989).

Entrepreneurs perceive strong ties as contacts, which are derived from long lasting

relationships, common background, collective experience, and common culture.

International entrepreneurs, manage strong ties through maintaining contacts, in a way

they can increase access to their network contacts and promote business objectives

through informal and prompt access to their contacts (Dashti and Schwartz, 2009).

Consequently, entrepreneurs with high strength of strong ties will tend to learn about

the opportunities through their networks and less by themselves, either by doing or by

observing the behaviours of best practices, i.e. by imitating.

H5.3: Strong ties will have a positive effect on the learning through networking

strategies

H5.4: Strong ties will have a negative effect on the learning through imitating and

Doing strategies

5.4.5 The Interaction Effect between Cognitive style and Prior Knowledge

There is a lot of evidence that cognitive styles are important when researching

individual preferences, decision making or learning styles in organisational behaviours

191

and management research domains (Hodgkinson and Sadler-Smith, 2003), and they

are also an excellent indicator of entrepreneurial attitudes (Bouckenooghe et al., 2005;

Kickul et al., 2009).

The research on cognitive style is related to individual differences in the cognitive

processes involved in simple perceptual and sorting tasks, as well as to more complex

tasks such as problem solving, decision making, and judgment; and, studies that are

aimed at integrating different cognitive styles (Kozhevnikov, 2007). Brigham and De

Castro (2003) and Brigham et al. (2007) found evidence of significant correlations

between a more intuitive style (i.e. based on the CSI model) and an increased number

of prior businesses owned and/or founded. In addition, Brigham and Sorenson (2008)

found that different entrepreneurial ownership patterns may be related to different

cognitive decision-making styles

Cognitive styles can be seen in different ways: preferences or habitual strategies,

attributes or preferences, no matter the perspective, it is evident that it might determine

the way individuals frame, approach and solve problems (Brigham et al., 2007), and

even remember and think (Knockaert et al., 2011b). However, many researchers in this

field asserted that the literature is inconclusive on the definitions of styles (Sadler-

Smith and Badger, 1998; Leonard et al., 1999), dimensions of styles (Cassidy, 2004;

Knockaert et al., 2011b), and measuring styles (Leonard et al., 1999).

According to Kickul et al. (2009), a cognitive style is often perceived as a multiple

dimensional phenomenon and may affect the individual preference for information

processing, learning types, and decision making. These preferences are highly related

to entrepreneurial action and behaviours. In addition, it can lead individuals to focus

on specific tasks and knowledge rather than on others. The entrepreneurial process is

conceptualised as a nonlinear, iterative stage based on processes including idea

generation, opportunity identification, planning, establishment, and implementation of

the new venture.

Different cognitive styles are related to different entrepreneurial stages. They found

that two distinct styles, based on Allinson and Hayes (1996) analytic and intuition

styles influence entrepreneurial self-efficacy and thus influence their ability to identify

opportunities. Olson (1985) proffered that different approaches to information

processing, which he called 'thinking modes', are more effective at different stages of

the entrepreneurial lifecycle, and that entrepreneurs are dissimilar in information

192

processing. These two thinking modes are intuitive, which is described as holistic and

simultaneous thinking; and, analytic, which is considered rational and sequential

processing. Naturally, people vary in their information processing strengths (Kickul et

al., 2009). Therefore, the opportunity identification stage may be linked with

entrepreneurs' tendency toward intuitive thinking, due to the need to confront

unexpected events and problems.

Chandra et al. (2009) studied first time international opportunity identification. They

considered the first time international opportunity was recognised either by a

discovery process (e.g. serendipitously) or a systematic and deliberate search. They

concluded that there are relationships between their prior international experience and

the opportunity identification mode (i.e. discovery versus systematic search).

Accordingly, their results reinforce this conclusion by showing that firms with little or

no prior international knowledge tend to make use of opportunity discovery rather than

deliberate/systematic search. In contrast, firms with extensive prior international

experience were found to deliberately search for their first international opportunity. In

addition, their findings suggest that international opportunity discovery involves a

problem solving process, which matches between pre-existing means and new ends.

Moreover, Shepherd and DeTienne (2005) explained that individuals with more

knowledge also appear to think in a more intuitive way, accordingly, they are able to

take decisions in a more automatic, unintended processing manner, and therefore

faster, rather than through a more systematic processing (Logan, 1990).

Learning strategies can be defined as the individual's procedures for acquisition and

development of knowledge in any context. Entrepreneurs must learn how to become

strategic learners. They have to learn how to learn (Hoeksema et al., 1997). In other

words, strategic learners should continuously acquire knowledge, integrate it, and

transfer what they learn in order to solve and facilitate novel challenges, to achieve the

best results from learning. However, a skilful individual, or an entrepreneur in our

case, is not necessarily a good strategic learner and the chosen strategy is based on

“…their cognitive based and affective understanding of the meaning of the initial

trigger” (Marsick and Watkins, 2003, p. 134).

Entrepreneurs seek to acquire the necessary knowledge regarding the opportunity they

identified, notwithstanding, their individual thinking preferences and their decision

making style, when they confront a complex problem such as identifying an

193

opportunity, might influence the impact their knowledge has on the way they learn

about opportunities. In other words, prior knowledge interacts with the entrepreneurs

cognitive styles, and thus moderates the relationships between prior knowledge and

the way the entrepreneurs prefer to learn about the identified opportunity.

Hence:

H6: Cognitive Style (CSI) will moderate the impact of Prior Knowledge on the

ways entrepreneurs learn about entrepreneurial opportunities.

5.5 Measures Development

The battery of measures developed to test the above hypotheses comprises of six areas

pertaining to learning strategies, prior knowledge, cognitive styles, social networking

ties, prior business experience, and general information about the entrepreneur

characteristics and his/her firm.

Despite the fact that some scholars suggested that research in entrepreneurship should

focus on how entrepreneurs learn (Reuber and Fischer, 1999), and which

entrepreneurial learning mechanisms they implement (Cope, 2003b), the majority of

research on learning strategies in general has been conducted in educational settings

(Holman et al., 2001). Little research to date has specifically considered

entrepreneurial self-regulated learning (O'Shea and Buckley, 2010) and the learning

strategies that an entrepreneur may be engaged in. Moreover, the fact that no valid

instrument exists for measuring the inventory of learning strategies in the process of

opportunity identification, has motivated this study to develop an instrument for

measuring entrepreneurial learning strategies.

In developing this instrument, findings from this study's qualitative phase, from

previous studies and other scholars' validated measures, were used as the points of

reference. In addition, this study operationalized cognitive styles, prior knowledge,

and prior business ownership experience based on previous scale development work

by a number of scholars. Moreover, whenever possible, multiple-item measurements

were used to minimize measurement error and to enhance the content coverage for the

constructs in the model (Schwens and Kabst, 2012).

194

5.5.1 Learning Strategies Constructs and Measures

This study sought to understand better the habitual patterns or preferred methods, by

which, international entrepreneurs learn about an identified opportunity. Learning

strategies can be seen as the concrete form of individual learning as a combination of

behaviour and intentionality within the context of entrepreneurial learning, and may

reflect the entrepreneur's real-life application of learning capabilities (Gielnik, 2004).

Each pf the learning strategies measures was designed to assess underlying learning

abilities, and not only learning preferences (Gielnik, 2004), and reflects also the

intention of the individual to learn, or in other words, whether he/she learns

spontaneously or deliberately (Eraut, 2000).

Spontaneous learning refers to random, flexible, unplanned, unintended activities

(Megginson, 1996), highly unpredictable (Honig, 2001) or a sudden realisation

(Marsick and Watkins, 2001). In contrast, deliberate learning refers to planned,

systematic (Honig, 2001) and intended activities (Megginson, 1996), which are

performed with the explicit goal of learning in mind (Doornbos et al., 2008).

Based on the study findings so far, there is a methodological need to integrate the

approaches of various scholars, in order to develop an instrument that is

comprehensive and can tap entrepreneurial learning strategies.

The instrument was developed partly by modifying scales from related areas and

partly by developing new scales (Doornbos et al., 2004). In addition, the questionnaire

is based on multi-item scales, with cross-validation of other methods (Holman et al.,

2001), and a well-established measure development process that balances between

completeness and parsimony (Barringer and Bluedorn, 1999) and includes item

selection criteria.

The table below summarises the main studies upon which this research is based. It is

worth mentioning at this point that an integrative approach has been adopted in this

study; in other words, definitions and measures from different scholars have been

adapted for the purpose of this study:

195

Table ‎5.2: Learning strategies – Definitions and Measures

Other

sources

Measures

Source(s) Definition Scale Learning strategy

(Haunschild,

1993;

Barringer and

Bluedorn,

1999)

Doornbos

et al.

(2008);

Schwens

and Kabst

(2009)

(Bandura,

1977);

Saarenketo

et al.

(2004),

Schwens

and Kabst

(2009)

5-point Likert scale,

from 'Not at all' through

'Quite often'


(deliberate or

spontaneous)

Schwens and

Kabst (2009)

Burgel and

Gordon

(2000); (Ellis

and Pecotich,

2001) Yeoh

(2004)

Doornbos

et al.

(2008)

(Saarenketo

et al., 2004)



'Quite often'

Learning by

networking(deliberate

or spontaneous)

(Deakins and

Freel, 1998;

Minniti and

Bygrave,

2001; Yli-

Renko et al.,

2001; Yli-

Renko et al.,

2002)

Doornbos

et al.

(2008),

Gielnik

(2004),

Tang et al.

(2007)

Doornbos et

al. (2008)



'Quite often'

Learning by

doing(deliberate or

spontaneous)

The measure development process was based on the recommendations of various

authors, such as: Van der Sluis et al. (2002); (2002b; 2003), Holman et al. (2001),

Barringer and Bluedorn (1999), Doornbos et al. (2004); (2008), and on the following

considerations:

(1) In defining the measure and designing the instrument, an attempt has been

made to include sufficient dimensions of learning strategies, in order to reflect

the overall essence of the ways in which entrepreneurs learn about overseas

opportunities, while keeping the number of dimensions manageable and

theoretically relevant (Barringer and Bluedorn, 1999, p. 422).

(2) Multiple-item measures are necessary for the following reasons: Firstly, they

may enable the researcher to capture the various dimensions of the constructs

196

adequately and accurately. Secondly, the measurement error may be reduced;

and thus, the measurement reliability and validity may be increased(Churchill,

1979). Accordingly, the dimensions of learning strategies were selected, based

on two sources: the qualitative analysis findings of this study (QUAL1,

QUAL2), and the extensive literature review on the topic of entrepreneurial

learning strategies.

(3) Hence, the focus has been on identifying the areas of learning strategies most

relevant to the pursuit of opportunity identification processes. As a result,

using a typology of entrepreneurial learning strategies (i.e. the six identified

strategies), most likely to be put into operation by the international

entrepreneur under study.

(4) The items were developed based on the approach that items can be adapted

from different validated measures and can be combined in one instrument. In

addition, the adopted items were modified to fit entrepreneurial settings (Van

Der Sluis and Poell, 2002a; Van der Sluis et al., 2003)

(5) In this study, a separate multiple-item scale was designed to measure each of

the six learning strategies. Following the approach of Holman et al. (2001),

exploratory and confirmatory factor analyses were conducted to examine

whether the six factors could be distinguished, as originally hypothesised in

this study. Originally, it was suggested that the six learning strategies, based on

the 3-by-2 matrix, are independent of each other (Van der Sluis and Poell,

2002b), and thus, an individual can learn about a business opportunity using all

kinds of learning.

(6) Consequently and based on the factor analysis, a new scale consisting of a

different number of items was distributed.

In summary, the learning strategies measures were designed to indicate the extent to

which the entrepreneur has learned about the international opportunity with the use of

the following strategies: 'Learning from networking' deliberately or spontaneously18

,

18

In this study, the terms 'systematically and randomly' are used interchangeably with: 'planned and


197

'learning via imitating' deliberately or spontaneously, and 'learning by doing'19

deliberately or spontaneously. All responses were obtained on a 5-point Likert scale,

from 'Not at all' through 'Quite often' (Holman et al., 2001) to 'always'. A report on the

development of the six scales can be found in Appendix J.

5.5.1.1 'Learning by Networking'

For the purpose of this study, items from various studies were adopted and modified to

capture the entrepreneurial context (Doornbos et al., 2008). Moreover, learning via

networking emphasises the entrepreneur's social networks as the ‘Entrepreneur’s

Learning Community’ (Thompson, 2011), and focus on the importance of the

interaction with others, as well as to the learning itself (Doornbos et al., 2004). Their

learning through interaction reflects their habitual method of learning that may be

performed through guidance, coaching and mentoring, and can be explicit, and entail

direct or close interpersonal relationships (Doornbos et al., 2004). The learning is

triggered by the need to acquire critical information through professional networks,

personal contacts and learning from suppliers and customers (Doornbos et al., 2008).

Accordingly, individuals will find learning opportunities from their interactions with

fellow entrepreneurs who are undergoing the same development opportunity, using

different media, such as new social media platforms and crowdsourcing, and different

individual and communities such as: mentors, management 'gurus', professional

experts and academics (Thompson, 2011).

Based on the aforementioned, the Learning by Networking Deliberately and Learning

by Networking Spontaneously were designed as a multi-item construct, consisting of

four items each, and were measured on a 5-point Likert-type scale. The respondents

were asked to rate the extent to which they engaged in learning activities. For

example, “I engaged with others in a deliberate and systematic inquiry regarding an

idea, in order to be able to study it in depth” (Doornbos et al., 2008), and “When I

chatted with people I know, they came up with interesting new ideas that I had not

thought of previously” (Doornbos et al., 2008). Table ‎5.3, summarises the definition:

19

The term is used interchangeably with 'learning from direct experience', and 'experimenting'.

198

Table ‎5.3: Learning by Networking deliberately and spontaneously definitions

Nominal Definition Operational

Definition

Scale Scale development

Learning By

Networking

deliberately

Entrepreneurs can

deliberately gain access

to new knowledge bases

created by others through

partnerships and network

relationships, without

precisely having to go

through all of their

experiences. At its best,

collaboration may offer a

faster track to

international markets.

The extent the

entrepreneur

engages in

deliberate

learning by

networking

activities

5-point

Likert scale,

from 'Not at

all' through

'Quite often'

to ‘always’.

The original items

were subjected to

the following

procedures: Face

validity

assessment, Pilot

study, Factor

analysis and PLS-

SEM measurement

model assessment

Learning By

Networking

spontaneously

Entrepreneurs can

spontaneously gain

access to new knowledge

bases created by others

through partnerships and

network relationships,

without precisely having

to go through all of their

experiences. At its best,

collaboration may offer a

faster track to

international markets.

The extent the

entrepreneur

engages in

spontaneous

learning by

networking

activities.

5-point

Likert scale,

from 'Not at

all' through

'Quite often'

to ‘always’.

The original items

were subjected to

the following

procedures: Face

validity

assessment, Pilot

study, Factor

analysis and

PLS-SEM

measurement

model assessment

5.5.1.2 Learning by Imitating'

Learning may be enhanced through isomorphic or mimetic trends (DiMaggio and

Powell, 1983), mainly by imitating best practices (Haunschild and Miner, 1997).

Bandura (1977) developed the concept of vicarious learning, suggesting that

individuals observe the outcome of actions that others undertake, and imitate those

that appear to produce positive outcomes, while avoiding those that produce negative

outcomes. Schwens and Kabst (2009) measured learning from paradigms of

interpretation, later on defined as imitating (Schwens and Kabst, 2012), by

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conceptualizing a 3-item-scale20

regarding the degree to which imitating foreign

market best practices facilitated the acquisition of foreign market knowledge.

In this study, one of the items was adapted. This item is important, mainly because it

emphasises the international aspect of imitation, by demonstrating that this mechanism

significantly influenced the speed at which a start-up could acquire the relevant

international knowledge (Schwens and Kabst, 2012) that made possible an earlier

venturing into the foreign market.

Based on the aforementioned, Learning by Imitating Deliberately was designed as a

multi-item construct, consisting of four items and measured on a 5-point Likert-type

scale. In the same vein, Learning by Imitating Spontaneously was designed as a

multi-item construct, consisting of three items and measured on a 5-point Likert-type

scale.

The respondents were asked to rate the extent to which they engaged in learning

activities. For example, “I generated new ideas by monitoring or purposefully

observing colleagues,” and “I observed others that turned out to be unexpectedly

informative.” The following table summarises the definitions:

20

Adapted from (Haunschild, 1993)

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Table ‎5.4: Learning by imitating deliberately and spontaneously definitions


Definition

Scale Scale

development

Learning by

imitating

deliberately

Entrepreneurs can

deliberately observe how

other firms with ‘‘high

legitimacy’’ enter

international markets and

try to observe the

outcome of actions that

others undertake, and

imitate those that appear

to produce positive

outcomes while avoiding

those that produce

negative outcomes

(Bandura, 1977;

Saarenketo et al., 2004).

The extent the

entrepreneur

engages in

deliberate

learning by

imitating

activities.

5-point

Likert scale,

from 'Not at

all' through

'Quite often'

to ‘always’.

The original items

were subjected to

the following

procedures: Face

validity

assessment, Pilot

study, Factor

analysis and PLS-

SEM measurement

model assessment

Learning by

imitating

spontaneously

Entrepreneurs can

spontaneously observe

how other firms with

‘‘high legitimacy’’ enter

international markets and

try to observe the

outcome of actions that

others undertake, and

imitate those that appear

to produce positive

outcomes while avoiding

those that produce

negative outcomes

(Bandura, 1977;

Saarenketo et al., 2004)

The extent the

entrepreneur

engages in

spontaneous

learning by

imitating

activities.

5-point

Likert scale,

from 'Not at

all' through

'Quite often'

to ‘always’.

The original items

were subjected to

the following

procedures: Face

validity

assessment, Pilot

study, Factor

analysis and

PLS-SEM

measurement

model assessment

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5.5.1.3 'Learning by Doing'

'Learning from direct experience' or 'Learning by doing' is not a straightforward

concept. Entrepreneurs may learn by active experimentation through testing their

theories and hypotheses (Kolb, 1984), trying things, carrying out decisions" and

implementing ideas by themselves (Gielnik, 2004), from failures and mistakes

(Petkova, 2009), from direct experience (Schwens and Kabst, 2009) and from almost

everything (Smilor, 1997). Hence, this construct should reflect the individual actions

that the entrepreneur takes by him- or herself, from the moment he/she generates or

recognises the idea until it is perceived as an opportunity. The focus should be on the

'Do it yourself ' aspect, rather than on 'do it with or by others'. It should be

acknowledged that, although errors are often associated with stress, frustration, and the

perception of helplessness (Petkova, 2009); they can alert entrepreneurs to incorrect

assumptions and beliefs (Daft and Weick, 1984) and become the starting point of a

process of learning from mistakes, so that new knowledge can be acquired. In

addition, some entrepreneurs may deliberately plan their actions beforehand, while

others may prefer to act spontaneously rather than thinking things through first.

Based on the aforementioned, the Learning by doing deliberately is designed as a

multi-item construct, consisting of seven items, and is measured on a 5-point Likert-

type scale. In the same vein, Learning by Doing spontaneously, is designed as a multi-

item construct, consisting of seven items and is measured on a 5-point Likert-type

scale. The respondents were asked to rate the extent to which they engaged in learning

activities. For example “When I had an idea, I preferred to actively and systematically

search, by myself, for information on this topic,” and “Without prior planning I

reflected on a sudden event that enabled me to think of this new idea. The following

table summarises the definitions:

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Table ‎5.5: learning by doing deliberately and spontaneously definitions


Definition

Scale Scale

development

Learning By

doing

deliberately

This construct reflects the

individual actions that the

entrepreneur takes by

him- or herself

Entrepreneurs may learn

by intended active

experimentation through

testing their theories and

hypotheses and from

direct experience, trying

things, carrying out

decisions" and

implementing ideas by

themselves from failures

as well as from mistakes.

The extent the

entrepreneur

engages in

deliberate

learning by

doing activities.

5-point

Likert scale,

from 'Not at

all' through

'Quite often'

to ‘always’.

The original items

were subjected to

the following

procedures: Face

validity

assessment, Pilot

study, Factor

analysis and PLS-

SEM measurement

model assessment

Learning by

doing

spontaneously

This construct reflects the

way Entrepreneurs learn

without prior planning

from direct experience,

trying things, and

implementing ideas by

themselves from failures

as well as from mistakes.

The extent the

entrepreneur

engages in

spontaneous

learning by

doing activities.

5-point

Likert scale,

from 'Not at

all' through

'Quite often'

to ‘always’.

The original items

were subjected to

the following

procedures: Face

validity

assessment, Pilot

study, Factor

analysis and PLS-

SEM measurement

model assessment

5.5.2 Social Networks

Social networks are viewed as the relationships an entrepreneur maintains at the

individual, organisational, inter-organisational, regional, and national level (Griffith

and Harvey, 2004), in order to provide and receive support and access to resources

such as information, capital, and other influential contacts (Dashti and Schwartz,

2009). However, the term is still vague, and various scholars have addressed this

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theme from different approaches, and there are methodological challenges in this

research area (Johannisson, 1995).

Social networks are often characterised by the strength of the ties; ties strengthen and

facilitate the exchange and flow of detailed information (Krackhardt, 1992) and is

affected by mutual trust and common history and demand a high level of tie

maintenance (Fernández-Pérez et al., 2012). However, measures or scales are

relatively diverse and scarce (Petróczi et al., 2007). Several authors criticized the

definition of tie strength as a continuous variable (Krackhardt, 1992). They argued that

tie strength should be conceptualised as separate constructs, different in kind rather

than degree, for example some distinguished between strong and weak ties (Rowley et

al., 2000) others between personal and professional ties (Fernández-Pérez et al.,

2014b) or mentors, informal industry networks, and participation in professional

forums (Ozgen and Baron, 2007).

There is still a debate what the dimensions of ties strength are. Scholars approached

this from different perspectives, depending mainly on the purpose of research. Some

scholars assessed the closeness, duration, and frequency of each relationship (Perry-

Smith, 2006). Others suggested slightly different dimensions, for example tie strength

as a combination of the duration, frequency, and intensity of the ties (Capaldo, 2007).

Marsden and Campbell (1984) have refined and tested Granovetter’s measures

(Granovetter, 1973) for tie strength. They found that frequency and duration, as

indicators of tie strength, are related to the same content and thus are highly

correlated: ”the use of frequency as a measure of strength will tend systematically to

overestimate the strength of ties between persons who are neighbours or co-workers,

while the use of duration as a measure of strength will overestimate the strength of ties

between relatives…” (Marsden and Campbell, 1984, p. 499).

Closeness is related to how entrepreneurs are willing to share information, which in

the light of opportunity identification process might be an important aspect.

Researchers have used a single measure of tie strength, such as closeness (Seibert et

al., 2001) or frequency (Granovetter, 1973; Nelson, 1989). However, relying only on a

single measure such as closeness, although found as the best indicator of tie strength

(Wiese et al., 2011), may not capture all the relevant dimensions of the tie strength.

Furthermore, Hansen (1999) measured the weakness of an interdivisional tie as the

average of the frequency and closeness scores. The findings have showed a high level

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of correlation between these two dimensions, suggesting that these two measures

reflect the same underlying construct.

The strength of the social networking ties, which are relevant to the opportunity

identification process was measured in this study as follows: firstly, two dimensions

reflect the strength of the ties: closeness and duration. Ties are characterized by

emotional intensity (i.e. closeness) and relationships that develop over time (i.e.

duration). Secondly, following Hansen (1999) and Levin and Cross (2004) approach,

the measure of tie strength employed a work related meaning, thus the focus was on

the opportunity identification process rather than a general question about

relationships’ closeness and duration. Thirdly, respondents were asked to report on the

duration and the closeness of a specific and separate category of contacts as opposed

to asking the respondents to specify the names of their contacts. This approach might

enhance recall and improve accuracy and reliability. In addition, the respondents were

not restricted with the number of categories; they are asked to refer to each one of the

indicated categories. This procedure is implemented in order to encourage the

respondents to refer to their weaker contacts, since pretesting revealed that

respondents tended to refer firstly to stronger contacts.

The contacts types were designed to represent three different social networking types:

1. Strong ties, such as kinship and family relationships,

2. Business ties, such as business partners, associates and business colleagues,

and

3. Professional and Social ties such as mentors, professional forum members

(Ozgen and Baron, 2007), and new social media (i.e. Facebook, LinkedIn,

twitter) members.

Therefore, and in order to capture the full meaning of social networking in the process

of learning about opportunities, these three constructs should be treated as three

different factors rather than one general construct measuring social networking. This

approach is similar to the approach taken in two recent studies, by Ozgen and Baron

(2007) and Fernández-Pérez et al. (2014b).

Fernández-Pérez et al. (2014b) distinguished between professional and personal

networks. Professional networks consisted of three kinds of social networks: business

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networks (focused on the extent to which information and/or support is obtained from

customers, suppliers, competitors, and investors); support from mentors and

professional forums (the extent to which individuals made use of seminars,

conferences, workshops and technical publications as sources of information and

support). Personal networks variable reflects the reliance on close friends (work-

related or otherwise) and family members as sources of information and support.

Ozgen and Baron (2007) studied the impact of three social sources of opportunity

related information: mentors, informal industry networks, and participation in

professional forums. It was found that “all three sources had direct, positive effects on

opportunity recognition by entrepreneurs” (2007, p. 174). Most importantly is the

influence of the two social sources of information (i.e. mentors and professional

forums), which were not systematically investigated in previous research, and their

inclusion in a study can enable entrepreneurs in identifying opportunities.

Considering these three variables and the eight contact categories, the respondents

were asked to assess the strength of their ties in the following dimensions: duration,

and closeness.

The respondents were asked to focus on their personal and social entrepreneurial

characteristics, which are relevant to the way they identified a particular business

opportunity, and specifically how they learnt about it. They were asked to recall from

their memory the best memorable case, of identifying a business opportunity and to

indicate in the following questions how (i.e. duration and closeness) they interact with

their contacts (the eight contact types):

Duration

The item and the scale were adapted from Perry-Smith (2006):

How many years has each contact category been in existence?(1) less than 2 years, (2)

2 to 5 years, (3) 5 to 10 years, (4) more than 10 years.

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Emotional intensity or closeness

The item and the scale were based on Collins and Clark (2003) and Fernandez-Perez

(2012):

On average, how would you qualify the closeness of your relationship with each

contact category? This item was measured on a seven-point Likert scale, ranging from

(1) ‘Distant’ to (7) ‘Close’.

Ties strength was measured jointly as the average of the standardised values of

duration, and emotional intensity (Collins and Clark, 2003; Fernández-Pérez et al.,

2012) for each of the eight contact type. Composite variables provide a richer

description of the concept while at the same time maintaining parsimony in

multivariate models (Hair et al., 2010). This procedure is also known as item

parcelling (Bandalos, 2002), and is useful in management and business studies (e.g.

Weerawardena, 2003; Grace and O’Cass, 2005); mainly when a researcher would

prefer to reduce the complexity of the model. The procedure is also useful with latent

variables analysis such as exploratory factor analysis (Little et al., 2002b) and is also

applicable in PLS models (e.g. O'Cass and Weerawardena, 2009).

It should be acknowledged at this stage that the items were subjected to exploratory

factor analysis (see chapter 6) in order to validate that (as hypothesised in this study)

the eight contact types can be grouped into three social networking variables.

5.5.3 Prior International Knowledge

Prior knowledge as the sum of all stocks of prior information, at a given moment in

time, that an individual may, intentionally or not, acquire during the course of his life

(Ko and Butler, 2006; Arentz et al., 2012). Most scholars in this field approached

prior knowledge as multidimensional and a broad construct (Shepherd and DeTienne,

2005).

Prior knowledge relevant to the opportunities identification process may reflect

different aspects of knowledge, such as knowledge about markets, including services

needed, and the customers’ needs (Shane, 2000), knowledge about institutional,

market and internationalisation knowledge (Eriksson et al., 1997), or technological,

market and internationalisation knowledge (Fletcher and Harris, 2011). According to

De Clercq et al. (2012) the most commonly used framework in early literature for

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classifying foreign knowledge types is business market knowledge, institutional

knowledge, and internationalisation knowledge (Eriksson et al., 1997). However, most

studies focus exclusively on international market knowledge, and internationalising

knowledge, but surprisingly, examination of institutional knowledge (De Clercq et al.,

2012), or social knowledge (Zahra et al., 2009) are rarely found.

Social Knowledge is an important aspect of prior knowledge, especially in the

contexts of international opportunity identification, mainly because opportunities in

foreign markets are culturally grounded (Zahra et al., 2009). Social knowledge might

help the entrepreneurs in this process, by assessing what they have observed and

learned, and do so in the context of the cultures that dominate the markets they target

(Sohn, 1994). Accordingly, entrepreneurs with high levels of social knowledge, know

whom to contact to get relevant information about market opportunities and the types

of products needed to capitalize on these (Zahra et al., 2009).

Prior knowledge consists of two dimensions: objective and experiential knowledge

(Chandra et al., 2009). Rao and Monroe (1988) argued that operationally, prior

knowledge has been defined either in terms of what people perceive they know

(subjective knowledge), or in terms of what knowledge an individual has stored in his

memory (objective knowledge). Objective knowledge is acquired through commonly

used methods of collecting and transmitting information, as opposed to subjective

knowledge, which is experiential in nature, and idiosyncratic (Eriksson et al., 1997).

However, objective and subjective knowledge, although conceptually distinct,

empirically are highly correlated, and are thus difficult to separate operationally (Rao

and Monroe, 1988). Evidently, what people perceive they know is likely to depend on

what they actually know, and how confident they are in their memory.

Thus, for the purpose of this research, prior knowledge is defined to encompass the

amount of accurate information held in memory about the foreign markets, as well as

their self-perceptions of this knowledge (i.e., what they believe they know) (Rao and

Monroe, 1988). Furthermore, because no single item fully captured the construct of

prior knowledge of the entrepreneur (Eriksson et al., 1997; Hadley and Wilson, 2003;

Shane, 2003; Zhou, 2007; Tang and Murphy, 2012), this study operationalizes prior

knowledge as a multifaceted construct, which reflects experiential as well as objective

knowledge, and focuses on foreign market knowledge, which is an essential condition

for the identification of opportunities.

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Based on prior research, the items were adapted and modified to measure these four

types of knowledge from the following scholars: firstly, three items are adapted from

Zhou (2007), Autio et al. (2000), Hadley and Wilson (2003) and Eriksson et al.

(1997), to capture foreign institutional knowledge. Seven items were used to

operationalize foreign business knowledge. Four items (i.e. items 1-4) were adopted

from Zhou (2007) and three items (i.e. 5-7) are based on Shane (2000), Tang et al.

(2012) and Tang and Murphy (2012). Four items were modified based on Zhou (2007)

to measure internationalisation knowledge, and finally, six items were designed based

on Zahra et al. (2009) to capture the social dimension of prior knowledge.

This construct is composed of four dimensions, which reflect prior knowledge of

foreign markets: Foreign institutional knowledge (“Possess knowledge about foreign

language and norms”), Foreign business knowledge (“Know how to serve foreign

markets”), Internationalisation knowledge (“Have international business experience”)

and Social knowledge (“Understand the history of the countries you have entered”).

The prior knowledge items were subjected to reliability and validity assessments,

using exploratory factor analysis. The respondents were asked to indicate the extent to

which they agree to the following statements, on a five point Likert scale, ranging

from (1) = “strongly disagree” to (5) = “strongly agree.”

5.5.4 Prior Business Ownership Experience

Entrepreneurial experience is an important factor of the opportunity identification and

exploitation process (Robson et al., 2012), especially in the international context.

Internationally experienced individuals are more aware of learning opportunities in

foreign markets; may have had more opportunities to accumulate and leverage their

specific human capital (Robson et al., 2012), may transfer learning from an initial firm

to a subsequent new firm (Robson et al., 2011), and can better benefit from

information than those with less prior experience can (Yeoh, 2004; De Clercq et al.,

2012). Westhead and Wright (1998b) summarised the importance of studying the

influence of prior business ownership on learning by suggesting that: “there is a need

to probe more deeply into the ‘quality’ rather than the ‘quantity’ of prior

entrepreneurial experience. Moreover, there is a need to know what, whether, and how

habitual entrepreneurs learn from previous business ownership experiences.’’ (1998b ,

p. 17).

209

Prior business ownership experience, can be viewed as prior entrepreneurial

experience (Reuber and Fischer, 1997, 1999), or prior start-up experience (Davidsson

and Honig, 2003; Delmar and Shane, 2006; Farmer et al., 2011). To date, most of the

studies, which included prior business ownership, in their models, considered prior

business experience as consisting of three categories: novice, serial, and portfolio

entrepreneurs (Westhead and Wright, 1998b; Alsos and Kolvereid, 2011).

This study defines habitual (i.e. serial and portfolio) and novice entrepreneurs, in line

with Westhead et al. (2005c); (2009):

“Novice entrepreneurs are individuals with no prior business ownership experience as

a business founder, an inheritor, or a purchaser of an independent business, but who

currently own a minority or majority equity stake in an independent business that is

new, purchased, or inherited.

Serial entrepreneurs are individuals who have sold or closed a business in which they

had a minority or majority ownership stake, and they currently have a minority or

majority ownership stake in a single independent business that is new, purchased,

and/or inherited.

Portfolio entrepreneurs are individuals who currently have minority and/or majority

ownership stakes in two or more independent businesses that are new, purchased

and/or inherited. Minority business ownership was included to represent the large

number of team-based business start-ups and purchases” (Westhead et al., 2009, p.

666).

The level of business ownership experience is measured, by asking respondents “to

indicate the total number of businesses in which they had prior minority or majority

business ownership, either as a business founder or a purchaser” (Ucbasaran et al.,

2008, p. 161). In addition, instead of referring to the number of businesses, this study

adopts the view that the focus should be on the prior start-up experience.

210

Therefore, the respondents were asked to indicate the following:

Please indicate the total number of start-up ventures; you had equity stakes in, as

a founder, or an inheritor, a purchaser or part of the founding team:

0/1/2/more than 2:________.

Please indicate the total number of start-up ventures, you currently have equity

stakes in, as a founder, or an inheritor, a purchaser or part of the founding team:

0/1/2/more than 2:________.

The following Table ‎5.6 summarises the different categories and the coding schema:

Table ‎5.6: Novice, Serial, and Portfolio entrepreneurs

Number of start-ups owned in the past

Number of start-ups currently owned

0 1 2

0 N/A N/A N/A

1 Novice Serial Serial

2 Portfolio Portfolio Portfolio

Based on practical reasons, and the purpose of this study, the PLS-SEM analysis

included the binary variable “NOVICE” (1=’novice’, 0= ‘serial and portfolio’) as an

indicator of the prior business ownership’s construct (Westhead et al., 2009).

5.5.5 Cognitive Style Measure

In most of the studies, cognitive style is defined, in general, as consisting of multiple

dimensions, including decision-making, learning, personality, and awareness (Kickul

et al., 2009). However, the literature has been inconclusive on the definition of styles,

and the amount of style dimensions that should be included (Knockaert et al., 2011a).

One instrument that measures cognitive style is the Cognitive Style Index of Allinson

and Hayes (1996). The Cognitive Style Index (CSI) is a self-report questionnaire,

which was designed to assess the tendency of an individual toward the intuitive-

analytic continuum. The CSI has received substantial consideration in studies business

211

and management (Armstrong and Hird, 2009; Armstrong et al., 2012) and

entrepreneurship (Allinson and Hayes, 2012).

Recently, there has been a growing debate, among researchers, who used the CSI,

concerning the number of factors, which might reflect underlying styles most

effectively (Backhaus and Liff, 2007). Several authors, such as Hodgkinson and

Sadler-Smith (2003) and Kickul et al. (2009) argued that, in contrast to the Allinson–

Hayes Cognitive Style Index, the two-factor model with correlated factors, provides a

better approximation of responses to the CSI than the unifactoral model.

Despite the critique on the CSI, in this study, the chosen index is based on the original

analytic–holistic classification of cognitive style (Hayes and Allinson, 1994; Allinson

and Hayes, 1996). This perspective views cognitive style principally as a unitary

construct, which represents personality characteristic traits, and proposes that the

cognitive style reflects the tendency toward one of the poles of the continuum,

suggesting that “the more analytical an individual, the less intuitive he or she will be,

and vice versa” (Allinson and Hayes, 2012, p. 3). The chosen operationalisation of

cognitive style, is adopted from Allinson and Hayes (1996), for the following reasons:

Firstly, the CSI is broadly used in entrepreneurship research (Hayes and

Allinson, 1994; Dutta and Thornhill, 2008).

Secondly, for reasons of parsimony and elucidation, research has shown that

the bipolar, superordinate classification is a useful simplification (Brigham et al.,

2007; Dutta and Thornhill, 2008; Kickul et al., 2009), and is well established in the

literature.

Thirdly, previous research (Allinson and Hayes, 1996; Sadler-Smith et al.,

2000; Brigham and Sorenson, 2008) have shown that the CSI demonstrated acceptable

reliability and both construct and concurrent validity. In addition the CSI has shown a

significant number of relationships between the CSI and various types measures

(Sadler-Smith et al., 2000).

Fourthly, the relatively small number of items and its brief format enables the

researcher to incorporate it into a larger research questionnaire, and the respondents to

complete it in only 5-10 minutes; additionally, the item analysis can be performed by

the researcher, without the need for a formal administration by an expert (Allinson

and Hayes, 1996).

212

Finally, Armstrong et al. (2012), despite their criticism regarding the use of

uni-dimensional instruments, such as the CSI, confessed that the analysis–intuition

dimension addresses an aspect of style as being important in the context of decision

making and that:" there is significant scope for cross-validating previous research

findings using more business and management focused instruments such as the KAI21

(Kirton, 1976), and the CSI” (Armstrong et al., 2012, p. 254). Therefore, and

following Allinson and Hayes (2012), this study, based on extensive research

(e.g.,Sadler-Smith et al., 2000; Murphy et al., 2001; Van den Top, 2010; Hammad

2012), supports and retains the idea of the CSI as a measure of a single dimension.

The CSI is a 38-item self-report questionnaire and the procedure, which is

implemented in this study, enables the researcher to reduce the 38 items into a single

total score to assess the extent to which an individual is intuitive or analytical.

Each item has ‘true’, ‘uncertain’ and ‘false’ response options, and the respondents are

asked to indicate whether, or not the statements are indicative of their own attitudes or

behaviour. On this scale, values of 0, 1 or 2 are assigned to each response, depending

on the polarity of the item.

The following Table ‎5.7 demonstrates the scoring key method:

Table ‎5.7: CSI Scoring key

Key Response Scoring

Analytic items Intuitive items

T True 2 0

F

?

False

Uncertain

0

1

2

1

Source: Allinson and Hayes (2012)

Table 5.7 displays that for a response of ‘True’ (T), two points are given if an item is

‘analytic’ (e.g. ‘in my experience, rational thought is the only realistic basis for

making decisions’), and zero is given if an item is ‘intuitive’ (e.g. ‘I make many of my

21

Kirton Adaption/Innovation Inventory

213

decisions on the basis of intuition’). For a response of ‘False’ (F), two points are given

if an item is ‘intuitive’, and zero is given if an item is ‘analytic’. In all cases, one point

is given for a response of ‘Uncertain’ (?). The score range is between 0 (intuitive) and

76 (analytic). However, the cognitive style of most people, involves elements of both

intuition and analysis. Hence, the CSI continuum involves five conceptual styles,

which are representative of the full range: Intuitive (score range: 0-28), Quasi-Intuitive

(score range: 29-38), Adaptive (score range: 39-45), Quasi-Analytic (score range: 46-

52) and Analytic (score range: 53 – 76). At the extremes are the pure cases of

‘intuition’ and ‘analysis’ respectively. The full exercise of either precludes the

adoption of the other (Allinson and Hayes, 2012, p.9-10).

The items are partly presented in the final questionnaire, due to copyrights (see the

questionnaire in appendix I) and were adopted from Allinson and Hayes (2012);

Hammad (2012) and personal email exchange with the authors.

5.5.6 Entrepreneurial Self Efficacy

Entrepreneurial self-efficacy has been broadly defined as the extent of individuals’

beliefs in their capabilities, to perform successfully specific tasks required for

incepting a new venture, and their expectations toward the outcomes of establishing

such a venture (McGee et al., 2009; Pihie and Bagheri, 2013). While there is a broad

consensus among researchers that self –efficacy is an important factor influencing

behaviour (Bandura, 2012), there remain discrepancies in the definition,

dimensionality, and measurement of entrepreneurial self-efficacy (McGee et al., 2009,

p. 965). In addition how exactly perceived self-efficacy influences behaviour, is still

shrouded in mystery, although it is acknowledged that self-efficacy influences the

individuals’ selection of an action and it can be considered as both the antecedent

(Zhao et al., 2005), and the consequence of an action choice (Pihie and Bagheri,

2013).

The entrepreneurial self-efficacy measure is mainly operationalised as a

multidimensional construct, capturing different entrepreneurial tasks (Zhao et al.,

2005; Arora et al., 2011; Pihie and Bagheri, 2013). However, some of the measures

assessed entrepreneurial self-efficacy using more managerially focused items such as

marketing, accounting, and personnel management, which may not capture the

specific tasks entrepreneurs are mostly confronted with. Thus it raises an important

question: Does entrepreneurial self-efficacy require a broad construct (i.e. general self-

214

efficacy as a construct), due to the complexity of the entrepreneurial activity tasks, and

skills, or a specific measure, in which the explanatory value of self-efficacy is

enhanced by its specificity (Kasouf et al., 2013), and thus researchers should use task-

specific measures of self-efficacy (Barbosa et al., 2007a). Furthermore, the

dimensionality of the construct has not yet been fully established, and researchers are

still utilising single item measures (McGee et al., 2009) as well as different underlying

dimensions by introducing different entrepreneurial actions and tasks.

In this study Entrepreneurial self-efficacy (ESE) is operationalised as a one-

dimensional construct. The measure was designed to capture entrepreneurial self-

efficacy in general but is highly appropriate for opportunity identification tasks (Arora

et al., 2011).

The measure can be considered as a well-established and reliable measure (α=0.83)

(Zhao et al., 2005). Arora et al. (2011, p. 10), based on Zhao et al. (2005, p. 1268)

administered the entrepreneurial self-efficacy measure by asking respondents to rate

how confident they were in: “Successfully identifying new business opportunities”,

“Creating new products”, “Thinking creatively”, and “Commercializing an idea or

new development”.

The respondents rate their responses on a 5 points Likert scale ranging from (1)-‘no

confident’ to (5)-‘complete confidence’.

5.5.7 Control Variables

Control variables are an important part of quantitative studies. In quantitative studies,

the researcher is trying to control several variables, which might have an effect on the

dependent variable(s), so that the real influence of the independent variable on the

dependent variable can be concluded (Creswell, 2009). Thus, this might enable the

researcher, to some extent, to account for alternative explanations for the relationships

between the models’ variables. Typically, control variables are special types of

independent variable, which may be, for example, demographic or personal (e.g., age

or gender) that need to be controlled (Creswell, 2009).

In this study, two types of control variables were selected: firstly variables, which

represent important entrepreneurial demographic such as education and age. These

variables were chosen primarily because this research focuses on the individual as the

unit of analysis and it is evident that these characteristics could have an effect on the

215

opportunity identification process. Secondly, a study, which examines individual-level

phenomena should control for firm level and industry effects (Corbett, 2007b; Peiris et

al., 2012). Therefore, to control for this potential effect, variables such as firm age,

firm size and the level of hostile environment, were selected.

In this study, education was measured as the highest degree of formal education

(Gielnik et al., 2011). The respondents were asked to choose one of the following:

High School or less, Diploma Studies, Bachelor degree (B.A), Master degree (M.A),

Doctorate, and Other (e.g. professional qualifications). During the data, screening and

cleaning stage (see chapters 3 and 6), the variable education was recoded to correct for

normality, and the new variable consisted of four educational levels ranging from 1-

(High school or less) to 4 (Doctorate).

Age was measured as a response to the question: ‘Please indicate your age (in years)’.

During the data screening stage, age was transformed using the log 10 plus 1 (i.e.

lg10+1).

The second group of control variable represent two important characteristics of the

venture that might potentially influence the way entrepreneurs learn: the firm size and

firm age. Firm size is controlled primarily due to the potential effect of the firm size

on the process of identifying opportunities (Gielnik et al., 2012). Firm size was

measured based on the number of employees (Gielnik, 2010; Li et al., 2012a; Zheng et

al., 2012). Some authors advised to use the natural logarithm of number of employees

as an indicator of the business size, in order to account for skewed distribution within

the data. In this study, this transformation technique was implemented too (Musteen

and Datta, 2011; Zheng et al., 2012).

Firm age was controlled too, to account for the fact that at the time of the data

collection each venture was at a different stage of development (Dimov, 2010).

Furthermore, there is evidence, which shows that there is a potential impact on the

opportunity recognition (Gielnik et al., 2010). In addition, in many studies, firm age is

often measured by the years since the firm was established (e.g., Li et al., 2012a). The

firm age was computed, as the number of years between the date of the research (i.e.

2013) and the date of the venture inception. The respondents were asked to respond to

the question: “Please indicate the year this venture was established formally

(YYYY)”. During the data screening stage, the firm age variable was transformed

using the lg10+1 transformation technique.

216

Finally, a variable, which measures the extent to which the environment is perceived

as hostile, was selected as an additional control variable. The external environment

may act as a trigger for opportunity identification (Choi and Shepherd, 2004). In

addition, it might affect the way they learn about the opportunities. Based on Li et al.

(2012a, p. 12), the respondents were asked “to assess to what extent her venture has

faced external threats about survival and development, using a five-point Likert scale,

ranging from (1)-‘totally disagree’ to (5)- ‘totally agree’”.

5.6 Summary

Based on the findings of the qualitative phase (QUAL1, QUAL2) and the literature

review, a theoretical framework, which was the integration of various theories in the

field of learning and opportunity identification, was described. These frameworks

were as follows: Firstly, the 4I model (Crossan et al., 1999), which was integrated by

Dutta and Crossan to explain the process of opportunity identification (Dutta and

Crossan, 2005). Secondly, the creativity-based model of opportunity recognition

(Lumpkin and Lichtenstein, 2005b) and the experiential learning model within the

opportunity identification and exploitation process (Corbett, 2005b). The main

purpose of the framework was to provide a hybrid way to view how entrepreneurs

transform information into international entrepreneurial identification. These

transformation processes, might in turn, enable them to acquire the necessary and

critical knowledge for their success.

The theoretical frameworks provided the theoretical basis for the development of the

conceptual model. Based on the findings of the qualitative phase (QUAL1, QUAL2)

and prior studies, a conceptual model was developed through the integration of three

different approaches: experiential, cognitive, and social networking. These three

approaches to entrepreneurial learning represent different dimensions of this

phenomenon in general, and specifically might enable us to elucidate the international

opportunities identification process. The model reflects the influence of the following

factors:

217

Table ‎5.8: The models’ predictors and moderator

Construct Type of construct References

Cognitive styles Moderator (Allinson and Hayes,

1996; Sadler-Smith and

Badger, 1998; Allinson et

al., 2000b; Sadler-Smith,

2004; Cools and Van den

Broeck, 2007)

Social networking: Strong

ties, Business ties,

Professional and Social ties

Predictors (Arenius and De Clercq,

2005; De Carolis and

Saparito, 2006; Agndal

and Chetty, 2007; Ellis,

2008; Ellis, 2011),

Prior knowledge Predictor (Reuber and Fischer, 1997;

Michailova and Wilson,

2008; Chandra et al., 2009)

Prior business ownership

experience construct22

Predictor (Ucbasaran et al., 2003;

Westhead et al., 2005b;a;

Ucbasaran et al., 2009)

Entrepreneurial self-

efficacy

Predictor (Bandura, 1977; Zhao et

al., 2005; Liem et al.,

2008)

The model was based on the argument that international entrepreneurs utilise different

ways, means, and mechanisms to assist in the acquisition, storage, and use of

information, processing it, interpreting it, and finally storing it as knowledge. This

process can be considered as a learning process, and the way it is done can be termed

as 'learning strategies'. However, there is a lack of coherence among scholars on “what

22

Novice versus habitual (i.e. serial and portfolio)

218

learning strategies are exactly, how many of them exist, and how they should be

defined and categorised” (Kakkonen, 2010, p. 88).

The model introduces a battery of six learning strategies that entrepreneurs can

implement when they learn strategically about the opportunity:

Figure ‎5.3: The six learning strategies in the opportunity identification process

Accordingly, in a rapid and accelerated internationalisation, entrepreneurs might be

able to choose to learn by doing23

, by networking24

, and by imitating25

. In addition,

they can do it in a systematic or a random26

manner.

These six strategies are legitimate, effective, and each one of the former three (i.e. 'by

doing', 'by networking', 'by imitating') can be combined with the latter two

('deliberate', 'spontaneous'). However, there is no one strategy, which is superior to

another. Entrepreneurs who learn strategically about opportunities can 'proactively

reflect' on past events, and thus, eventually learn how to learn (Cope and Watts, 2000).

23

'by searching', 'by doing', 'by experimenting', 'from failures and successes'

24 'from the experience of others'

25 'paradigm of interpretation'

26 Referred also to 'planned' and 'emergent', 'deliberate' and 'spontaneous', and 'effectual' and casual'.

Learning By Doing

Deliberately

Spontaneously

Learning By Networking

Deliberately

Spontaneously

Learning By Imitating

Deliberately

Spontaneously

219

6. Quantitative Phase Findings

The main purpose of this phase (QUAN) is to empirically validate, and test the

conceptual model, which was derived and developed based on the findings of the

literature review and results of the qualitative phase (QUAL).

This chapter introduces the following two main sections:

Firstly, a preliminary data analysis is presented (i.e. data screening and

cleaning procedures is provided).

Secondly, the results of the primary data analysis are addressed. In this section,

descriptive statistics are provided and examined. Subsequently, results of an

exploratory factor analysis are presented, the findings from which produced a new set

of latent variables that served as an important source of measures for the main

analytical procedure, utilising PLS-SEM.

The PLS-SEM was run in two stages: in the first stage, the measurement model was

introduced and the reliability and validity of the model were assessed. In the second

stage, the structural model results were further elaborated and finally the role of the

cognitive style (CSI) as a moderator was introduced and statistically assessed.

6.1 Preliminary Data Analysis and Procedures

The preliminary data analysis in this study covers two main issues: firstly, screening

and cleaning the data before the analysis was conducted, and secondly assessing the

match between the data and the assumptions underlying the statistical techniques

employed.

6.1.1 Data Screening and Cleaning

In this study, data was screened using mainly the SPSS (version 20) FREQUENCIES

and SPSS GRAPH procedures. In the first stage, data was checked for detection of

potential errors during the data entering process. Data was screened first by means of

frequencies and coding appropriateness.

Cleaning the data was done by using data transformation, as a remedy for outliers or

failure to normality, linearity and homoscedasticity, as well as cases removal if

necessary (Tabachnick and Fidell, 2007).

220

6.1.1.1 Accuracy of Input

The data was entered by the respondents, directly onto the web-based questionnaire.

Then, the data was imported and saved as an SPSS data file (.sav). Accuracy of input

was examined by evaluating the plausibility of the range of values (i.e. minimum and

maximum values (Tabachnick and Fidell, 2007).

Based on these assessments, it was concluded that there is no concern about the

accuracy of the input.

6.1.1.2 Missing Values

In this study, there were no missing values. The respondents were asked to respond to

all of the items in the questionnaire. This means that the study was restricted to

respondents who had entirely completed the web-based questionnaire. This approach

is well known as the ‘forced-answer’ procedure (Hair et al., 2013a).

6.1.1.3 Response Rate and Non-Response Bias

In this study, 178 completed questionnaires were collected. The initial sampling frame

included 7,336 email contacts. 1,850 invitations were returned due to incorrect email

addresses or companies, which no longer existed. In addition, in many cases, the

emails were returned due to a high level of internet security restrictions, which caused

the invitation emails to be rejected. After two main data collection waves, 330

responses were acknowledged. However, only 178 participants completed the survey.

Consequently, a survey response of 178 was obtained from 5,486 email invitations

sent. Therefore, the effective response rate was approximately 3.2%.

Plausible response rates for detailed on-line cross-sectional survey studies range

between 12% and 25% (Sauermann and Roach, 2013) and when compared to other

survey methods, response rates of web based survey have been estimated to be around

11% (Manfreda et al., 2008; Sánchez-Fernández et al., 2012). However, the relatively

low-response rate (i.e. 3.2%) is not uncommon (Frippiat et al., 2010) in surveys sent to

221

high-tech Israeli27

entrepreneurs (Shoham et al., 2006) via emails (Cook et al., 2000)

and hence can be considered as an acceptable response rate size.

To investigate further the implications of the relatively low response rate, a non-

response bias assessment was conducted. There are many reasons for a high level of

non-response rate, among them technical and confidentiality issues can be considered

as highly relevant for web-based survey (Couper, 2000).

In this study, these two issues were not found to be issues for concern for the

following reasons: firstly, no technical or communication problems were reported at

any time and secondly a declaration of confidentiality was introduced to the

participants before responding to the survey. In order to assess the non-response bias,

two groups were created. The groups were formed according to the time they

completed the survey28

. The first group consisted of 75 respondents (i.e. early

respondents), who responded to the first wave and the second group included 103

respondents (i.e. late respondents). Finally, a One-Way Anova was conducted. The

following table shows these results:

Table ‎6.1: Non-Response Error: one-way Anova analysis results

SSBa SSW

b F p

Age 190.633 26759.822 1.254 .264n.s

Education .354 139.871 .445 .506n.s

Firm Age 31.972 2837.427 1.983 .161n.s

Firm Size 25.119 27001.937 .164 .686n.s

- Age is measured in years. Education level: 1 (High school or less)-4 (Doctorate). Firm Age is

measured as the number of years from inception date. Firm size is measured as the number of

employees. The factor is RespDate (i.e. response date: 1-group one, early respondents. two- group 2,

late respondents)

- SSB: Sum of Squares Between the groups, SSW: Sum of Squares within the groups.

- n.s.: non-significant, *(p<0.1), ** (p<0.05), *** (p<0.01)

27

See for example page 165 in: (Chorev and Anderson, 2006)

28 The first group consists of respondents that responded to the survey between June 17

th and June 20

th,

2014 (First wave). The second group included responses between June 20th

and August 27th

, 2014

(second and third waves). This grouping classification is because the first wave lasted only three days

and was the largest in comparison to waves two and three. According to Armstrong and Overton (1977,

p. 397):” Persons who respond in later waves are assumed to have responded because of the increased

stimulus and are expected to be similar to non-respondents.”

222

The results of the one-way Anova show that no significant differences were found

between the two groups of respondents. It can be concluded that non-response error is

not a major concern in this study.

6.1.1.4 Outliers

Prior to the analysis, the six learning strategies construct (i.e. LBDD, LBND, LBID,

LBIN, LBNS and LBDS)29

, the moderator (CSI), the predictors (Prior Knowledge,

Strong ties, Professional and Social ties and Business ties, Self-Efficacy and Prior

Business Ownership experience) and the control variables (e.g. firm size, firm age, age

and education), were examined through various SPSS procedures for identification of

univariate and multivariate outliers.

Five cases (cases: 34, 92, 93, 119, 144) with extremely high standardised values for

firm size were found to be univariate outliers. After careful examination of these cases

(for inconsistency or incorrect data entry), it was decided not to remove the cases from

the analysis, but to change the score using the next mean plus two standard deviations

(Field, 2009, p. 153). Therefore, for these cases the score, which represents the

number of employees, was changed to 55.

Five cases (cases: 46, 99, 107, 111, and 171) were detected as potential univariate

outliers for the variable age. After a careful examination of their responses and a

telephone communication with one of them (Age=80), it was decided that these cases

should be considered as part of the sample, and the best approach should be not to

remove these cases or to change the score. These scores were kept for further

examination.

Four cases (cases: 47, 95, 119, and 144) were identified as potential univariate outliers

of firm age. For example, case number 47, reported that the venture has existed for 26

years, whilst the mean score for the variable was 3.7955 and the standard deviation

4.80633. These scores were changed and replaced by using the mean plus two

29

LBDD: Learning by doing deliberately, LBDS: learning by doing spontaneously, LBND: learning by

networking deliberately, LBNS: learning by networking spontaneously, LBID: learning by imitating

deliberately, LBIS: learning by imitating spontaneously.

223

standard deviations equation. Hence, these outliers were replaced with the 13.34

score.

No other univariate outliers were detected among the other constructs, mainly since

Likert-scales had been used for measuring the responses.

Multivariate outliers were examined by calculating the Mahalanobis distance through

SPSS REGRESSION with all the variables (i.e. all the indicators of the predictors,

criterions, moderator and control variables). In addition, variables such as age, firm

size and firm age were transformed prior to searching for multivariate outliers

(Tabachnick and Fidell, 2007, p. 98). The criterion for multivariate outliers was χ2

(100) 30

=149.449, at p<0.001. The evidence suggests that there are no influential

cases, which exceed this criterion; hence, the issue of multivariate outliers is not a

cause for concern in this study.

6.1.1.5 General Statistical Concerns

PLS-SEM is considered a robust (Gefen and Straub, 2005), and a non-parametric

statistical method (Henseler et al., 2009; Hair et al., 2011; Hair et al., 2013a). EFA can

be conducted when the data is normally distributed, although in situations when the

EFA does not serve for statistical inference purpose, even if the data does not meet the

normal distribution requirements, it is still useful (Tabachnick and Fidell, 2007; Hair

et al., 2010). The concern in factor analysis is in many ways not meeting the

underlying conceptual assumptions rather than not meeting the statistical assumptions

(Hair et al., 2010), however, it is recommended that researchers should carefully

assess the data distributional characteristics, especially when using a relatively small

sample size (Reinartz et al., 2009; Ringle et al., 2012; Hair et al., 2013a).

6.1.1.5.1 Normality

Normality was assessed by statistical and graphical methods (Tabachnick and Fidell,

2007; Field, 2009). Evaluation of the Kolmogorov–Smirnov (K-S) test for all of the

items of interests revealed that the K-S test was highly significant, indicating that the

variable’s distributions deviate from normality. A visual examination of the

30

χ2 with degrees of freedom equal to the number of variables, in this case it equals

the number of items/indicators which is 107.

224

Probability Plots (P-P plots) and more importantly an assessment of the Skewness and

Kurtosis measures revealed the following:

Education level displayed Skewness value of -0.023 and Kurtosis value of -0.721,

which can be considered as close to normality.

Age provided Skewness value of 0.502 and Kurtosis of -0.408. In this case, the Age

variable was transformed by using the lg10 plus 1 (i.e. lg10+1). The transformed

variable resulted with a Skewness of -0.029 and kurtosis of -0.750.

Firm size showed highly positively skewed distribution (2.391) and a high positive

value of kurtosis, pointing a heavy-tailed distribution (Field, 2009). A log

transformation improved substantially the results of the Skewness (0.328) and kurtosis

(-0.287), which can be interpreted as indicating a data distribution, which is now only

slightly far from normal.

Firm age showed a positively skewed distribution (1.601) and a high positive value of

kurtosis (2.23). The chosen transformation (i.e. lg10+1) improved substantially the

results: the skewness was reduced to 0.176 and the kurtosis to -0.876, both indicating a

substantial distribution improvement.

The six learning strategies and Prior Knowledge showed significant K-S results,

however, it was decided not to transform these variables for the following reasons:

Firstly, in many cases the direction of the skewness and or kurtosis differed

among items that were part of the same scale, hence, preforming transformation might

have weakened the EFA analysis results.

Secondly, EFA results, are still interpretable although can be considered as

problematic if the data is too far from the normal distribution.

Thirdly, the main analysis is conducted by using the PLS-SEM algorithm,

which is considered as a robust statistical method. In this cases, when the method has a

robustness to departure from normality, the variables should be kept in their original

form (Hair et al., 2010).

Finally and more importantly, these constructs are measured on a Likert-based

scale, and by transforming some of the variables and not others (e.g. transforming

LBND items and not transforming LBIS items), may affect the comparability of the

findings in the main analysis results interpretation (Tabachnick and Fidell, 2007).

225

The other constructs of interests showed kurtosis and skewness values, which are only

slightly far from normal.

6.1.1.5.2 Linearity

Multivariate analysis such as EFA is based on correlation assessment, hence linearity

between the variables is evaluated by inspecting scatterplots of the variables (Hair et

al., 2010). If linearity is not achieved, the EFA solution may be degraded but still

meaningful (Tabachnick and Fidell, 2007, p. 613).

In this study, the number of variables (i.e. items) can be considered as large, to some

extent. Therefore, it is recommended to screen only pairs of variables through

Bivariate Scatterplots that are likely to depart from normality (Tabachnick and Fidell,

2007). The variables that were chosen as “worst case” are those where Skewness

exceeded substantially the absolute value of 1, and belong to the same scale (i.e. hence

we can assume that they were supposed to correlate).

Variables were inspected as linearly related to some extent, however a few items of

the LBDD constructs, especially items LBDD1, 2 and 5 did not demonstrate an oval

shape of the Scatterplots hence, the variables can be considered as not linearly related.

However, as it was explained in paragraph 6.1.1.5.1 (normality) it was decided not to

transform or to remove these items at this stage, but rather to deal with these items as

part of the EFA and the PLS-SEM analysis (the measurement model assessment

stage).

6.1.1.5.3 Multicollinearity

Based on the collinearity diagnostics assessment (see Table ‎6.26: Collinearity

Assessment) it can be suggested that no Multicollinearity was evident.

6.1.1.5.4 Common Method Variance Assessment

Following Podsakoff and Organ (1986) recommendations, a principal axis factoring

solution was conducted using SPSS with all the model’s variables. The results of the

un-rotated factor solution indicated that no single factor emerged. In the principal axis

factoring solution, 34 factors were extracted, of which 31 factors had eigenvalue

greater than 1. In addition, no single factor accounted for the majority of variance. The

226

first factor accounted for only 9.929% of the variance, and only 71.60% of variance

was extracted by the entire factor solution.

These results indicate that a common method bias is not considered as a cause for

concern in this study.

6.2 Data Analysis

This section addresses the analytical results of the quantitative phase (QUAN). The

analysis was conducted by using two main statistical techniques: the Exploratory

Factor Analysis (EFA) and the PLS-SEM approach. Although, the measurement

model adequacy is often assessed directly in PLS-SEM models, this study conducted a

separate exploratory factor analysis, in which each construct of interest was explored

through PCA with varimax rotation (Wilson, 2010). In addition, descriptive statistics

is presented, aiming to show the demographic characteristics of the sample.

6.2.1 Descriptive Statistics

The following tables (6.2 and 6.3) show the demographic characteristics of the

respondents and their ventures. The participants were mostly comprised of males

(89.3%). It must be noted, however, that this percentage is not representative of the

percentage of male and female entrepreneurs in Israel, which was in 2010, for

example, 1:1.6 (Menipaz et al., 2011; Almor and Heilbrunn, 2013).

The majority of Israeli undergraduate students enter college at the age of 21-22 and

many of them work for their living and consequently acquire their first work

experience during their academic studies (Almor and Heilbrunn, 2013). Moreover, in

2012, 70 academic institutions were operating in Israel (Almor and Heilbrunn, 2013).

This might explain the fact that the majority of the respondents in this sample

(approximately 87%) had obtained a bachelor degree (i.e. B.A.) or higher. The same

proportion (e.g. 84%) of academic degrees among high-tech entrepreneurs was

described by Schwartz and Malach-Pines (2007).

The respondent’s ages in this sample ranged between 21 and 80 years old. The Mean

age is 43.22 (Std. Deviation=12.339), which is in line with other studies in Israel (e.g.,

Schwartz and Malach-Pines, 2007), the Median is 41.5 while the Mode 38. It can be

seen that the majority of the entrepreneurs in our sample were older than 30 years old

(68% are between 31 and 55) while only 16% are younger than 30. Furthermore, 16%

227

are older than 56. Even though these finding are surprising to some extent, this is in

line with the GEM 2010 report which indicated that among the 55-64 age group (i.e.

among males), entrepreneurial activity31

is on the rise in comparison with the 45-54

age group (Menipaz et al., 2011).

Table ‎6.2 Demographic characteristics of the respondents (table continued in 2nd

page)

Variable Category Frequency Percent

Position in

Company

Founder 80 44.9

Chairman 2 1.1

Principal Owner 11 6.2

Managing Directors 71 39.9

Other 14 7.9

Total 178 100

Gender

Male 159 89.3

Female 19 10.7

Total 178 100

Entrepreneurial

Experience

Novice 30 16.9

Habitual 148 83.1

Total 178 100

Age

(Regrouped)

21-30 28 16

31-55 121 68

>56 29 16

Total 178 100

31

Potential entrepreneurial activity rate – The percentage of the total population of adults age 18-64

(not including all of the actual entrepreneurs) who are not involved in entrepreneurial activity, but have

a positive perception of their personal entrepreneurial abilities and see entrepreneurial opportunities in

the area where they live (Menipaz et al., 2011)

228


Education

1.00 = "High School or less/Diploma

Studies/professional qualifications" 22 12.4

2.00 = "Bachelor degree (B.A)" 64 36

3.00 = "Master degree (M.A)" 66 37.1

4.00 = "Doctorate" 26 14.6

Total 178 100

The following figure depicts the mean age by the international activity engagement

(i.e. whether or not the respondents are engaged currently in any international

activity):

Figure ‎6.1: Age by International activity engagement

It can be seen that the mean age of entrepreneurs who are engaged in international

activity is higher than those who are not currently engaged in such activity. This might

indicate the importance of levels of prior experience and knowledge in international

entrepreneurship (Shane and Khurana, 2003; Zhou, 2007).

40.458 (years)

45.8 (years)

35.1 (years)

229

Interestingly, the majority of the respondents defined themselves as experienced

entrepreneurs. Approximately, 83% of the sample can be classified as habitual

entrepreneurs (i.e. serial and portfolio) and about 17% as novice (Westhead et al.,

2005c; 2009). Furthermore, most of the respondents were in a senior position in their

ventures. Approximately 85% held the position of founder or managing director in

their ventures. As shown in Figure ‎6.2 most of the respondents (93%) were from

Israel (mainly from Tel Aviv) and a few of them lived outside Israel (i.e. in the United

States, Turkey, Canada, and the UK):

Figure ‎6.2: Respondent’s Country

With respect to their ventures’ characteristics (see Table ‎6.3), most of the ventures

employ less than six employees (54.5%) and of them about 60% employ less than

three employees. In this study, the majority of the respondents (62%) indicated that

their venture’s age was three years or less. These descriptive statistics are in line with

prior research, which revealed that in Israel, a high proportion of adults are either

actively involved in starting a business or are owners/managers of a business which is

less than three and a half years old (Schwartz and Malach-Pines, 2007).

The vast majority of marketing and sales activities of Israeli hi-tech companies take

place outside of Israel (Malach-Pines et al., 2004). In this study, more than 54% of the

sample reported that the proportion of their overseas sales was 10% and less, while

approximately 38% stated that the proportion of their overseas sales was 51% and

above. Based on the fact that most of the ventures in this study are relatively young

(62% were less than 3 years) and 38% were more mature (venture’s age range between

230

4 and 11 and above), and that the majority of the entrepreneurs responded that their

venture was engaged in an international activity (76%) the description of the ventures’

overseas sales in this study was considered as representative of the activities of Israeli-

hi-tech ventures.

Table ‎6.3: Demographic characteristics of the respondents’ ventures


Number of equity partners

Less than 3 partners 86 48

3 partners and above 92 52

Total 178 100

Overseas sales percentage

10% and less 95 53

11%-50% 15 9

51% and above 68 38

Total 178 100

Number of Employees

up to 6 employees 97 54.5

between 6 and 25 65 36.4

26 and above 16 9.1

Total 178 100

Venture age (years)

3 years and less 111 62

between 4 and 10 years 53 30

11 years and above 14 8

Total 178 100

Furthermore, the majority of the entrepreneurs in this sample stated that they founded

the venture with business partners.

231

The following figure depicts the distribution of resposnes for the statement whther

their venture was established solely or in partnership:

Figure ‎6.3 Number of equity partners

The external environment may act as a trigger for opportunity identification (Choi and

Shepherd, 2004). Business opportunities are often derived from a new product, which

creates or adds value for the market (Shane and Venkataraman, 2000b), however

entrepreneurs might face uncertainty over the value of these new products (Choi and

Shepherd, 2004). In this study, the respondents were asked to rate to what extent their

ventures have faced external threats about survival and development:

n=178

232

Figure ‎6.4: External environment hostility perception

As expected, the majority of the entrepreneurs in this study (approximately 63%

agreed) perceived their external environment as hostile.

6.2.2 Exploratory Factor Analysis

The PCA extraction with varimax rotation through SPSS was conducted for each one

of the following scales: learning by networking deliberately (LBND), learning by

networking spontaneously (LBNS), learning by imitating deliberately (LBID),

learning by imitating spontaneously (LBIS), learning by doing deliberately (LBDD),

learning by doing spontaneously (LBDS), entrepreneurial self-efficacy (SE), prior

knowledge (PK), and the Networking ties. These scales reflect the main constructs of

interests in this study.

233

Prior to conducting the PCA analysis, the match between the data and the assumptions

was examined, verifying that a factor analysis can be conducted, with the necessary

precautions (Tabachnick and Fidell, 2007). All scales were subjected to PCA with

orthogonal rotation (varimax). Furthermore, variables were ordered and grouped by

size of loading to facilitate interpretation. Loadings below 0.45 (20% of variance)

were not presented. Furthermore, various statistics were assessed for each of the

scales.

Firstly, a close inspection of the correlation matrix was conducted in order to

assess whether there were variables, which were highly correlated (r>0.9) with one or

more other variables.

Secondly, the correlation matrix determinant was examined too, in order to

check whether multicollinearity was a concern (determinant of the correlation matrix

should be bigger than 0.00001).

Thirdly, the Kaiser-Meyer-Olkin measure of the sampling adequacy was

evaluated. A KMO above 0.5 is considered as the minimum acceptable level (Field,

2009).

Fourthly, Bartlett’s test of sphericity was evaluated. The χ2 value should be

significant, p<.05, indicating that correlations between items are sufficiently large (i.e.

the R-matrix is not an identity matrix) for PCA.

Finally, an initial analysis was conducted to obtain eigenvalues for each

component.

Subsequent to the factor analysis, the resultant factor solutions were further analysed

by means of Cronbach’s alpha. Cronbach (1951) suggested that the alpha should be

applied separately to items related to different factors (i.e. sub-scales). As part of the

reliability test, if the deletion of an item increases the Cronbach’s alpha, the item was

omitted even if it was included in the PCA solution (Field, 2009).

234

LBNS (Learning by Networking Spontaneously)

LBNS was designed as a multi-item scale, consisting of four items. The PCA with

orthogonal rotation (varimax) was performed on 4 items for a sample of 178

entrepreneurs. The PCA statistics assessment was considered adequate with the

confirmation of satisfactory measures values. In addition, only one component had

eigenvalue over Kaiser’s criterion of 1 (2.587) explaining 64.683% of the variance.

Table ‎6.4 shows the factor loadings (only one component was extracted therefore the

solution was not rotated):

Table ‎6.4: LBNS Factor loadings, Communalities (h2) and percent of variance

Item Factor

Loadings

h2

(communalities)

q141 LNBS1: When I chatted with people I know,

they came up with interesting new ideas that

I had not thought of previously.

.801 .641

q142 LBNS2: When I discussed an idea, about an

issue that bothers me, with people I know,

we came up with new solutions for this

problem, unexpectedly.

.813

.661

q143 LBNS3: I started to think differently about

an idea due to the contribution of an

experienced colleague.

.753 .568

q144 LBNS4: When I brainstormed with people, I

know about how to approach a shared

problem, we learned from each other

spontaneously.

.847 .718

% of variance

Eigenvalues

Cronbach’s

alpha

64.683

2.587

0.817

235

The four items were clustered on one component; suggesting that these items relate to

the same scale, therefore it can be concluded at this stage that LBNS is a unitary

construct (Cronbach’s alpha=0.817).

Learning by Networking Deliberately (LBND)

Learning by networking deliberately (LBND) was designed as a multi-item scale,

consisting of four items. The PCA revealed that LBND is a two-component construct.

The PCA with orthogonal rotation (varimax) was performed on 4 items for a sample of

178 entrepreneurs. The PCA statistics assessment was considered adequate with the

confirmation of satisfactory loadings on the 2 components

An initial analysis was run to obtain eigenvalues for each component in the data. Two

components had eigenvalues over Kaiser’s criterion of 1 (1.057 and 2.025) and in

combination explained 77.03% of the variance. The scree plot justified retaining both

components for the final analysis.

The following table shows the items, the factor loadings after rotation, communalities

extracted (before the rotation), eigenvalues, and the percentage of variance explained

by the two factors (components). Items q131 and q132 (Cronbach’s alpha=0.670)

clustered on the same component (LBND1) suggest that this component represents

learning by networking from weak network ties such as experts and network contacts

in general (i.e. ‘learning from others’). The second component (LBND2) represents

learning by networking from close relationships such as personal contacts (Cronbach’s

alpha=0.696).

236

Table ‎6.5: LBND Factor loadings, Communalities (h2) and percent of variance

Item Rotated Factor Loadings h2

LBND1(learning

from weak network

ties)

LBND2(learning

from close/strong

networks)

q131 LBND1: I engaged with others

in a deliberate and systematic

inquiry regarding an idea, in

order to be able to study it in

depth

.898 .807

q132 LBND2: I made an effort to

contact an expert in this field

to get their reactions to my

idea.

.802 .735

q133 LBND3: I discussed my

idea(s) with people I know.

This enabled me to acquire

information and to take

decisions regarding my

business ideas.

.888 .750

q134 LBND4: I deliberately

consulted with my personal

contacts, regarding an idea. I

realised that this was a feasible

business opportunity.

.888 .790

% of variance

Eigenvalues

Cronbach’s alpha

26.416

1.05

0.670

50.613

2.025

0.696

237

Learning by Imitating Deliberately (LBID)

LBID was designed as a multi-item scale, consisting of four items. The PCA with

orthogonal rotation (varimax) was performed on 4 items. Inspection of the correlation

matrix revealed that the item LBID2 had low level of correlations (<0.3) with the other

three. In addition, the item LBID1 had correlations, which were low too. The highest

correlation value was 0.306, which can be considered as very close to the minimum

threshold level of 0.3 (Field, 2009). The communalities of these two items were below

the threshold of 0.5 (LBID1=0.367 and LBID2=0.033). Variables with communities of

lower than 0.50, should be considered for omission from the analysis (Hair et al.,

2010). Their correlations and communalities illustrate the instance that these two

variables are poorly accounted for in the factor solution32

. Therefore, these two items

were eliminated from the analysis33

.

A second run of the analysis with items q153 and q154 revealed that the PCA statistics

assessment was considered adequate with the confirmation of satisfactory measures

values. An analysis was run to obtain eigenvalues for each component in the data.

Only one component had an eigenvalue over Kaiser’s criterion of 1 (1.638) explaining

81.908% of the variance.

32

The PCA solution with the four items did not provide a better solution. Furthermore, this solution was

further examined by means of scale’s reliability, using the Cronbach’s alpha measure. The results of

the reliability analysis were consistent with the results of the PCA, providing evidence that q151 and

q152 should be removed from the scale.

33 In addition, reliability tests for the four items: q151-q154 were conducted. The results indicated a

poor level of Cronbach’s alpha (0.487). In addition, the Cronbach’s alpha if the items: q151 and q152,

deleted was improved substantially (Cronbach’s alpha=0.779).

238

The following table shows the results (only one component was extracted therefore the

solution was not rotated):

Table ‎6.6: LBID Factor loadings, Communalities (h2) and percent of variance

Item Factor

Loadings

h2

(communalities)

q151 LBID1: I generated new ideas by

monitoring or purposefully observing

colleagues.

Removed

q152 LBID2: I tracked the policies and tactics

of other entrepreneurs or start-ups with

best practice in my industry.

q153 LBID3: I deliberately acquired

knowledge about the foreign market

through following the example of best

practices firms

.905 .819

q154 LBID4: I deliberately acquired

knowledge about the foreign market

through imitating entrepreneurs that are

perceived as having best practices.

.905 .819

% of variance

Eigenvalues

Cronbach’s

alpha

81.908

1.638

0.779

It can be concluded that the two items were clustered on one component; suggesting

that these items relate to the same scale, having high loadings only on a single factor

(LBID) (Cronbach’s alpha=0.779).

239

Learning by Imitating Spontaneously (LBIS)

LBIS was designed as a multi-item scale, consisting of three items. The PCA with

orthogonal rotation (varimax) was performed on 3 items. Inspection of the correlation

matrix revealed that the item LBIS3 had low levels of correlations (<0.3) with the

other three. The highest correlation value was 0.270, which is below the minimum

threshold level of 0.3 (Field, 2009). In addition, the communality of this item was

below the threshold of 0.5 (LBIS3=0.346). The low level of correlation and

communality indicate that this variable is poorly accounted for in the factor solution34

.

Therefore, item q163 was omitted from the analysis.

A second run of the analysis with items q161 and q162 revealed that the PCA statistics

assessment was considered adequate with the confirmation of satisfactory values. An

analysis was run to obtain eigenvalues for each component. Only one component had

an eigenvalue over Kaiser’s criterion of 1 (1.580) explaining 79.003% of the variance.

Table ‎6.7 shows the results (only one component was extracted therefore the solution

was not rotated).

It can be concluded that the two items (q161, q162) were clustered on one component;

suggesting that these items relate to the same scale (Cronbach’s alpha=0.732), having

high loadings only on a single factor (LBIS).

34

The three items solution was further examined by means of scale’s reliability, using the Cronbach’s

alpha measure. The results of the reliability analysis were consistent with the results of the PCA,

providing evidence that q163 should be removed from the scale (Cronbach’s alpha including

q163=0.640 and Cronbach’s alpha with the exclusion of q163=0,732).

240

Table ‎6.7: LBIS Factor loadings, Communalities (h2) and percent of variance

Item Factor

Loadings

h2

(communalities)

q161 LBIS1: I observed others that turned out to

be unexpectedly informative.

0.889 0.79

q162 LBIS2: When I watched other entrepreneurs,

I noticed that I could perform a task in a

similar way.

0.889 0.79

q163 LBIS3: I did not plan ahead; however, when

the idea just emerged, I made a connection to

a successful or unsuccessful venture.

Removed

% of variance

Eigenvalues

Cronbach’s

alpha

79.003

1.580

0.732

Learning by Doing Spontaneously (LBDS)

Learning by Doing Spontaneously (LBDS) was designed as a multi-item scale,

consisting of seven items. The PCA with orthogonal rotation (varimax) was performed

on seven items.

The results show that LBDS is a two-component construct, and all the seven items

showed good results, however, Cronbach’s alpha (0.589) was lower than the minimum

level of 0.6. Due to low item-to-total correlations, the items for the scale for one of the

LBDS components (LBDS1) were omitted from the subsequent analysis.

A second PCA analysis was conducted, this time without the inclusion of q181-183.

The PCA assessment was considered adequate with the confirmation of satisfactory

values. This time, only one component had an eigenvalue over Kaiser’s criterion of 1

(2.094) explaining 52.361% of the variance. The scree plot was slightly ambiguous

and showed inflexion that would justify a two-component solution. However, it was

decided to retain the one component solution due to the Kaiser’s criterion and the

241

convergence of the scree plot. Table ‎6.8 shows the results for the final one component

solution:

Table ‎6.8: LBDS (final solution) Factor loadings, Communalities (h2) and percent of

variance

Item Factor

Loadings

h2

(communalities)

q184 When the idea showed up, I kept it to myself,

and I did not have a plan of how to acquire

the relevant information about it; I just did it.

.738 .545

q185 I learnt unintentionally, and without prior

planning, from relevant literature (such as

professional journals, business and

managerial books, professional websites)

about this new idea.

.680

.462

q186 I scanned the Internet for business advice, and

unintentionally, I got this new idea.

.708 .502

q187 When I thought about this idea, I realised that

I learnt from my mistakes, but it did not affect

the way I executed it.

.765 .586

% of variance

Eigenvalues

Cronbach’s

alpha

52.631

2.094

0.696

Learning by Doing Deliberately (LBDD)

Learning by doing deliberately (LBDD) was designed as a multi-item scale, consisting

of seven items. The PCA with orthogonal rotation (varimax) was performed on seven

items. Inspection of the correlation matrix revealed that the items LBDD3 (q173) and

LBDD7 (q177) had low level of correlations (<0.3) with the other items (Field, 2009).

Therefore, items q173 and q177 were omitted from the analysis. An initial analysis

was carried out to obtain eigenvalues for each component. Two components had

eigenvalues over Kaiser’s criterion of 1 (1.517) and in combination explained

69.486% of the variance.

242

The scree plot justified retaining both components for the final analysis. The first

factor (LBDD1) represents learning by doing deliberately from mistakes (items q172

and q174), whilst Items q171, 175 and 176 clustered on the same component

(LBDD2) suggesting that this component represents learning by active search.

A subsequent reliability test was conducted after the PCA procedure. The two sub-

scales (the two factors) were examined and the Cronbach’s alpha of the subscale

LBDD1 (q172 and q174) was 0.808 and for the second sub-scale (LBDD2) the

Cronbach’s alpha was 0.639, which is above the minimum threshold of 0.6 for newly

developed scales.

Table ‎6.9: LBDD (initial results) Factor loadings, Communalities (h2) and percent of

variance

Item Rotated Factor

loadings

h2

LBDD1 LBDD2

q172 LBDD2: When I took action with regard to this

business opportunity, I reflected on my previous

mistakes, and tried to learn from them.

.913 .844

q174 LBDD4: When I thought about the new business

opportunity, I deliberately learnt from my mistakes.

.911 .830

q171 LBDD1: When I had an idea, I preferred to actively

and systematically search, by myself, for information

on this topic.

.787 .541

q175 LBDD5: I scanned the Internet to get information that

is more relevant on the new idea that I identified.

.762 .624

q176 LBDD6: I learnt systematically by reading relevant

literature (such as professional journals, business and

managerial books, professional websites).

.731 .636

% of variance

Eigenvalues

Cronbach’s alpha

39.14

1.957

0.816

30.345

1.57

0.639

243

Entrepreneurial Self-Efficacy (SE)

Entrepreneurial Self-efficacy (SE) is defined in this study as the extent to which the

entrepreneurs are confident in their capability to act upon entrepreneurial situations

(Arora et al., 2011), such as identifying opportunities. A Self-efficacy measure was

designed as a four items scale, in which the respondents were asked to rate (on a 5-

point Likert scale) how confident they were in four situations, which are highly

relevant for entrepreneurs. Table ‎6.10 summarises the PCA results:

Table ‎6.10: Self efficacy Factor loadings and percent of variance

Item Factor

Loadings

se1 Successfully identifying new business

opportunities

.803

se2 Creating new products .704

se3 Thinking creatively .691

se4 Commercializing an idea or new development”. .630

% of variance

Eigenvalues

Cronbach’s

alpha

50.359

2.014

0.668

The PCA with orthogonal rotation (varimax) was performed on four items. The PCA

assessment was considered adequate with the confirmation of a satisfactory measure.

An initial analysis was conducted to obtain eigenvalues for each component in the

data. Only one component had eigenvalue over Kaiser’s criterion of 1 (2.014)

explaining 50.359% of the variance.

The PCA resulted in a one-factor solution. The Cronbach’s alpha value of this scale

was 0.668.

244


Prior knowledge was operationalised as a multidimensional construct consisting of

four dimensions: foreign institutional knowledge (3 items), foreign business

knowledge (4 items), internationalisation knowledge (4 items) (Eriksson et al., 1997)

and social knowledge (6 items) (Zahra et al., 2009).

The PCA with orthogonal rotation (varimax) was performed on 17 items in total. The

initial factor analysis of these 17 items produced a solution with several cross loading

items: InterKnow1, 3 were cross-loaded on component one and two. Variables with

cross-loadings exceeding 0.4, were considered as candidates for deletion (O'Cass,

2002). Moreover, the difference between the two factors’ loadings was very small

(less than 0.2), hence these two items were omitted, and the PCA was re-run.

The second run revealed similar results: three items (ForKnow4, InterKnow4 and

SocialKnow4) were cross-loaded on a second factor. These items were dropped

sequentially from the analysis due to small difference between the two factor loadings.

The final PCA run was performed on 12 items. The PCA assessment was considered

adequate with the confirmation of satisfactory measures.

Three components had eigenvalues over Kaiser’s criterion of 1 (1.132) and in

combination explained 68.061% of the variance. The scree plot justified retaining the

three components for the final analysis. The following table shows the results for each

of the three components:

245

Table ‎6.11: PK Factor loadings and percent of variance

Item Rotated Factor loadings

SocialKnow InstKnow ForiegnKnow

SocialKnow1

SocialKnow2

I understand the history of the

countries I have entered

I understand the key values that

people share in the countries I

have entered

0.790

0.888

SocialKnow5 I am aware of the key beliefs in

the national cultures of the

countries entered

0.834

SocialKnow6 I understand accepted standards

of how people behave in those

countries

0.734

InstKnow3 I possess knowledge about

foreign government agencies

.869


foreign business laws and

regulations

.796

SocialKnow3 I am aware of national attitudes

regarding foreign investment

.632


foreign language and norms

.607

ForigenKnow2 I know how to serve foreign

markets

.800

ForiegnKnow1 I possess knowledge about

effective marketing in foreign

markets

.716

ForiegnKnow3 I am familiar with customer

problems in other countries

.703

InterKnow2 I have the ability to determine

foreign business opportunities

.556

% of variance

Eigenvalues

Cronbach’s

alpha

43.193

5.183

0.871

15.435

1.852

0.811

9.433

1.132

0.768

246

The first factor (SocialKnow) represents Knowledge, which is based on social

interaction and relationships (SocialKnow1, 2, 5, 6).

Items InstKnow1, 2, 3, and SocialKnow3 clustered on the same component

(InstKnow) suggesting that this component represents Prior Knowledge about norms,

regulations and attitudes in foreign markets.

Finally, the last component (ForiegnKnow) reflects the acquired knowledge, which is

related to the foreign markets (items ForiegnKnow1, 2, 3 and InterKnow2).

A subsequent reliability test for each one of these three subscales produced

Cronbach’s alpha value for SocialKnow of 0.871, InstKnow of 0.811 and

ForiegnKnow of 0.768.

Cognitive Style (CSI)

The Cognitive Style Index (CSI) (Allinson and Hayes, 1996) was operationalized

based on the original CSI instrument, which consists of 38 items.

Following Allinson and Hayes (1996), the 38 items were grouped into six parcels

(CSP1:CSP6), as presented by Sadler-Smith et al. (2000, p. 177) and Hammad (2012,

p. 166). Parcels are items which were combined into small groups of items within a

specific scale or a subscale (Little et al., 2002a).

The CSI’s Parcels were computed as the simple sum of several items, which are

conceptually similar and assess the same construct. The PCA with orthogonal rotation

(varimax) was performed on the six parcels:

247

Table ‎6.12: CSI Factor loadings and percent of variance

Items Parcel Factor Loadings

11, 19, 20, 21, 22, 27, 33 and 35 CSP1 .800

2, 8, 10, 12, 15 and 32 CSP2 .743

5, 9, 16, 17, 24 and 34 CSP3 .675

1, 25, 26, 28, 29 and 39 CSP4 .672

4, 6, 18, 31, 36 and 38 CSP5 .506

3, 7, 13, 14, 23 and 37 CSP6 .733

% of variance

Eigenvalues

Cronbach’s alpha

48.03

2.892

0.779

The PCA statistics assessment was considered adequate with the confirmation of

satisfactory measures values. An initial analysis was run to obtain eigenvalues for each

component in the data. Only one component had eigenvalue over Kaiser’s criterion of

1 (2.892) explaining 48.23% of the variance. The PCA resulted in a one-factor

solution. The Cronbach’s alpha value of this scale was 0.779, indicating that the CSI

scale can be considered as a reliable scale.

Social Networking Ties: Strong ties, Business ties and Professional & Social ties

Social networking ties strength was operationalized in this study as three separate

measures: Strong ties, Business ties, and Professional & Social ties.

Ties strength was measured jointly as the average of the standardised values of

duration and closeness (Collins and Clark, 2003; Fernández-Pérez et al., 2012) for

each one of the eight contact categories. Consequently, eight composite measures

were computed, in a way that each composite variable is the average of two items: the

standardised duration score and the standardised closeness score. The procedure is also

248

useful with latent variables analysis such as exploratory factor analysis (Little et al.,

2002b).

The PCA with orthogonal rotation (varimax) was performed twice:

Firstly, a separate PCA with varimax rotation was conducted on each one of

the three sub-scales (using the composite variables): Strongties, Businessties, and

Pro&Socialties. This PCA revealed that each subscale produced a unitary factor

solution.

Secondly, a PCA with varimax rotation was performed on the eight parcels

together. The initial factor analysis of these eight items produced a three factors

solution as it was assumed and designed in this study. The PCA statistics assessment

was considered adequate with the confirmation of satisfactory measures values.

Three components had eigenvalue over Kaiser’s criterion of 1 (1.329) and in

combination explained 69.741% of the variance. The scree plot justified retaining the

three components for the final analysis.

The results of the separate and joint PCA revealed the same solution: three separate

networking ties components, reflecting the type of the contacts: strong or personal

(called “Strongties” in this study, Cronbach’s alpha=0.785) measured by two

indicators (zkinshipties and zfriendsties), business component (“Businessties”,

Cronbach’s alpha=0.772), measured by three indicators (zcollegties, zbizassoties and

zbizpartties) and Professional & Social component (“Pro&Socialties”, Cronbach’s

alpha=0.642) measured by three indicators (zproforumties, zmentoringties and

zsocialties).

The following Table ‎6.13 presents the results:

249

Table ‎6.13: Social networking ties: Factor loadings and percent of variance

Composite

Variable

Item/Contact category Factor Loadings

Strongties Businessties Pro&Socialties

Zkinshipties Kinship relationships (family

members, relatives) .895

Zfriendsties Friendship and neighbouring

relationships .890

Zsocialties Social networks members

(Facebook, Twitter, LinkedIn,

etc.)

.776

zmentoringties Mentoring and coaching

relationships .742

zproforumties: Professional forums

relationships such as seminars,

conferences, workshops, and

technical publications

.738

Zbizpartties Business partners and co-

founders .831

Zbizassoties Current and former business

associates: customers suppliers,

professional experts

.800

Zcollegties Current and former collegiality

(such as co-workers)

relationships

.833

% of variance

Eigenvalues

Cronbach’s

alpha

16.609

1.329

0.785

31.193

2.495

0.772

21.939

1.755

0.642

250

Summary

The EFA uncovered a range of valid measures directly linked with entrepreneurial

behaviour in the context of opportunity identification. The results showed that

Learning by networking spontaneously (LBNS) is a unitary construct (Cronbach’s

alpha=0.817) consisting of four items.

Learning by networking deliberately (LBND) consists of two factors

(components): LBND1, which represents learning by networking from weak network

ties (Cronbach’s alpha=0.670). The second component (LBND2) represents learning

by networking from close relationships such as personal contacts (Cronbach’s

alpha=0.696).

Learning by imitating deliberately (LBID) (Cronbach’s alpha=0.739), and

learning by imitating spontaneously (LBIS) (Cronbach’s alpha=0.732), are both unitary

constructs consisting of two items each.

Learning by doing spontaneously (LBDS) is a unitary construct (Cronbach’s

alpha=0.696) consisting of four items.

Learning by doing deliberately (LBDD), is consisted of two components

(factors). The first factor (LBDD1) represents learning by doing deliberately from

mistakes (two items, Cronbach’s alpha=0.808). The second component (LBDD2),

represents learning by active search (three items, Cronbach’s alpha = 0.639).

The PCA analysis of entrepreneurial self-efficacy (SE) (4 items, Cronbach’s

alpha=0.668), and cognitive style (CSI) (38 items, Cronbach’s alpha=0.779) resulted in

a one-factor solution each.

The results of prior knowledge (PK) showed a three-factor solution. The first

factor (SocialKnow) represents Knowledge, which is based on social interaction and

relationships (four items, Cronbach’s alpha=0.871). The second factor (InstKnow)

represents Prior Knowledge about norms, regulations, and attitudes in foreign markets

(four items, Cronbach’s alpha=0.811). The last component (ForeignKnow) reflects the

acquired knowledge, which is related to the foreign markets (four items, Cronbach’s

alpha=0.768).

251

Three components of networking ties were revealed: Strong ties (Strongties)

measured by two indicators (Cronbach’s alpha=0.785), business component

(“Businessties,” Cronbach’s alpha=0.772), measured by three indicators, and

Professional & Social component (“Pro&Socialties,” Cronbach’s alpha=0.642)

measured by three indicators.

6.2.3 PLS-SEM Analysis

This section is structured as follows: firstly, an evaluation of the PLS-SEM

measurement model is presented. Secondly, the structural model was estimated and

evaluated, using several recommended statistics (such as R2, Q

2, and q

2). In addition,

bootstrapping and blindfolding procedures were applied. Finally, an evaluation of the

interaction term and the moderation model are shown and discussed.

6.2.3.1 Measurement Model

This section discusses the evaluation of the quality of the study’s measurement model.

The assessment of the reflectively measured constructs was conducted on the study’s

model, which consists of six criterion variables:

Learning by networking deliberately (LBND,

Learning by networking spontaneously (LBNS),

Learning by imitating deliberately (LBID,

Learning by imitating spontaneously (LBIS),

Learning by doing deliberately (LBDD), and

Learning by doing spontaneously (LBDS)

In this PLS-SEM model, the constructs were designed as reflectively measured,

therefore constructs’ internal consistency reliability was assessed by various statistical

criteria, such as indicators loadings and composite reliability, whilst the validity of the

constructs was evaluated in terms of their convergent and discriminant validity (Chin,

2010; Hair et al., 2013a).

In contrast to an OLS analysis (i.e. ordinary least squares regression analysis) and

although the model includes six criterion variables, it is only necessary to conduct a

single PLS-SEM procedure, in that the technique can consider the underlying

structural relationships of all the latent variables at once. Thus, this PLS-SEM model

constrains the new construct and measures to its nomological network of constructs.

252

This might reduce estimation bias that can be affected by minor modelling or item

selection errors (Chin, 2010). In addition, a model, which includes a large set of

factors, as in this study, which focuses on the complex interrelationships among these

factors, closely mirrors the study context rather than explaining the covariance of a

relatively small set of measured items based on a few underlying latent constructs

(Chin, 2010).

253

Figure 6.5: Simplified version of the study’s PLS-SEM model

254

Three variables were measured as high order constructs, Prior Knowledge (PK),

Learning by doing deliberately (LBDD), and Learning by networking deliberately

(LBND). The following steps were conducted in order to assess the validity and

reliability of these constructs in the model, at the same level of the construct

abstraction (Chin, 1998; Chin, 2010):

(1) A first-order measurement model was run, without including the second order

construct (Becker et al., 2012; Wright et al., 2012)

(2) Reliability and validity of the first-order measurement model was assessed

(3) A new data file was created with the latent variable scores of the first order

constructs

(4) A second order factor with the latent variable scores as indicators was constructed

(5) Reliability and validity of the second order measurement model was assessed.

Steps (1) to (4), which are related to the measurement assessment of the first order

constructs, are discussed briefly in this section. A detailed results report is discussed in

relation to the measurement assessment for each of these three high order constructs:


This study operationalizes prior knowledge as a multifaceted construct, which reflects

knowledge as an essential condition for the identification of opportunities. This

construct was designed, as part of the questionnaire, consisting of four dimensions:

foreign institutional knowledge, foreign business knowledge, internationalisation

knowledge (Eriksson et al., 1997) and social knowledge (Zahra et al., 2009). However,

the exploratory factor analysis (EFA) revealed that prior knowledge is composed of

three facets instead of four. The facets were renamed as Foreign Knowledge

(ForiegnKnow), Institutional Knowledge (InstKnow), and Social Knowledge

(SocialKnow).

Furthermore, Prior knowledge in this PLS model was measured as an HOC (i.e. High-

Order Construct) which represent or reflect the Low-Order-Constructs (LOCs) (i.e.

ForiegnKnow, InstKnow and SocialKnow) that can be treated as the product of the

reflective relationships through the prior knowledge construct. Thus by measuring

Figure ‎6.5 Simplified version of the study’s PLS-SEM model

255

High Order Constructs (HOC), the PLS model might be considered as more

parsimonious. This design resulted in a reflective-reflective HCM (Hair et al., 2013a).

As discussed earlier, the measurement quality of PK was conducted in two stage using

the sequential latent scores approach (Wilson and Henseler, 2007): firstly, a first-order

measurement model was tested, without including the second order construct (Becker

et al., 2012; Wright et al., 2012), then, reliability and validity of the first-order

measurement model was assessed. Secondly, a second order factor with the latent

variable scores as indicators was constructed, and reliability and validity of the second

order measurement model was assessed.

In general, the results of the first run of the PLS model measurement assessment for

the first order constructs of Prior Knowledge (PK) were considered adequate with the

confirmation of satisfactory reliability and validity values. Reliability (α>0.6, CR>

0.6), convergent validity (AVE>0.5, outer loadings>0.5, p<0.0001), and discriminant

validity (Fornell-Larcker Criterion, √AVE>correlations, as well as the cross loadings

assessment that shows that each reflective indicator is loaded higher on the construct it

was designed to measure), provided evidence for the constructs’ reliability and validity

(see Appendix E, Table 6.18A, the Fornell-Larcker criterion assessment, and, Table

6.19A, Cross-Loadings). The following Table ‎6.14 summarises the results of the outer

loadings analysis, internal consistency, and convergent validity measures values:

256

Table ‎6.14: Results summary of Prior Knowledge (PK) reliability and convergent

validity

Construct(s) Indicator(s) Outer

Loadin

gs

T-

Statistics

P-

Value

CR AVE Cronbach’s

alpha

ForiegnKnow

ForiegnKnow1 0.6794 9.7637 0.0001 0.86 0.6078 0.7881

ForiegnKnow2 0.8787 47.9664 0.0001

ForiegnKnow 3 0.7316 11.5595 0.0001

InterKnow3 0.8139 27.2296 0.0001

InstKnow

InstKnow1 0.7726 16.8985 0.0001 0.875 0.637 0.8109

InstKnow2 0.8758 41.05 0.0001

InstKnow3 0.7702 18.1662 0.0001

SocialKnow3 0.7711 20.0818 0.0001

SocialKnow1 0.6778 8.3191 0.0001 0.908 0.7154 0.871

SocialKnow2 0.8702 25.788 0.0001

SocialKnow SocialKnow5 0.9328 57.6334 0.0001

SocialKnow6 0.8802 25.6464 0.0001

Thus, the results suggest that the three reflective constructs ForiegnKnow, InstKnow,

and SocialKnow proved to have a satisfactory level of reliability and validity and can

be used to test (see step 5) PK as a second order construct in the next sub-section.

In the next stage, the assessment of Prior Knowledge as ‘higher order construct’

(HOC) was established. This evaluates whether the first order constructs (LOCs) load

onto their theorised second order construct. The following table shows the values of

the second order (HOC) outer loadings, the CR, the Cronbach’s alpha, and the AVE:

257

Table ‎6.15: Results summary of HOC reliability and convergent validity

HOC Indicators

(first order

constructs)

Outer

Loadings

T-

Statistics

P-

Value

CR Cronbach’s

alpha

AVE

Prior

Knowledge

(PK)

ForiegnKnow 0.8915 44.1568 0.0001 0.8642 0.7644 0.6810

InstKnow 0.8409 22.0626 0.0001

SocialKnow 0.7357 8.9065 0.0001

Evaluation of the second order indicators outer loadings revealed that all the indicators

demonstrated statistically significant (p<0.0001) satisfactory loadings (>0.5). The

measurement model of the HOC showed that the criteria for Cronbach’s alpha

(0.7644) and composite reliability (CR=0.8642) were met. The AVE had a value

(0.6810) which is above the threshold of 0.5. The criteria for reliability and convergent

validity of the HOC were met.

Discriminant validity for the HOC was further evaluated and interpreted. Tables 6.21A

and 6.22A (see Appendix E, Table 6.21A and 6.22A) show the discriminant validity

assessment of the HOC model. The results indicate that all diagonal elements (square

root of AVE) based on the Fornell-Larcker criterion, exceed the lower-left triangle of

inter construct correlations. In addition, a close examination of the constructs cross

loadings revealed that each reflective indicator is loaded higher on the construct it was

designed to measure. Thus, discriminant validity for the reflective Prior Knowledge

HOC was established (Gefen et al., 2000).

It can be summarised that the results of the PK HOC measurement model assessment

confirmed that Prior Knowledge construct (PK HOC) could be included in the model.

258

Social Networking Ties: Business, Strong and Professional & Social Ties

The measure of the strength of the social networking ties, which are relevant to the

opportunity identification process, was measured jointly as the average of the

standardised values (z-score transformations) of duration and emotional intensity

(Collins and Clark, 2003; Fernández-Pérez et al., 2012) for each one of the eight

contact types.

The results of the EFA model revealed three separate networking ties components,

reflecting the type of the contacts: strong or personal (called “Strongties” in this

study) measured by two indicators (zkinshipties and zfriendsties), business component

(“Businessties”), measured by three indicators (zcollegties, zbizassoties and

zbizpartties) and Professional & Social ties component (“Pro&Socialties”) measured

by three indicators (zproforumties, zmentoringties and zsocialties).

The results show that the indicators of Strongties, Businessties, and Pro&Socialties,

thus are well above the minimum acceptable level for outer loading (outer

loadings>0.5, p<.00001). In addition, the composite reliability values of all the three

constructs: Businessties=0.8628, Strongties=0.8928 and Pro&Socialties=0.8023,

demonstrate a high level of internal consistency reliability. The same conclusion can

be made by evaluating the Cronbach’s alpha values. Furthermore, the AVE values are

well above the required minimum level of 0.5 (Businessties=0.6778,

Strongties=0.8701 and Pro&Socialties=0.5744). Thus, these three constructs have a

high level of convergent validity. Finally, the Fornell-Larcker criterion and the cross

loadings were assessed (see Appendix E, Tables 6.21A and 6.22A). Overall, the

results provide evidences for adequate discriminant validity. The following table

summarises the results of the PLS-SEM model outer loadings analysis:

259

Table ‎6.16: Results summary of Strong ties (Strongties) reliability and convergent

validity


Loadings

T-

Statistics

P-

Value

CR Cronbach’s

alpha

AVE

Businessties

zbizassoties 0.8791 26.403 0.0001 0.8628 0.7727 0.6778

zbizpartties 0.8297 16.4584 0.0001

zcollegties 0.7565 11.5563 0.0001

Strongties zfriendsties 0.9616 17.6881 0.0001 0.8928 0.7847 0.8701

zkinshipties 0.8304 9.2309 0.0001

Pro&Socialties zmentoringties 0.79 11.5477 0.0001 0.8023 0.6418 0.5744

zproforumties 0.6501 5.0482 0.0001

zsocialties 0.8279 11.6551 0.0001

In summary, the measurement quality of Strongties, Businessties, and Pro&Socialties,

provides evidence for the satisfactory level of their reliability and validity.

Cognitive Style (CSI)

The Cognitive Style Index (CSI) was measured by using six parcels CSP1-CSP6

(Sadler-Smith et al., 2000). As described earlier in this dissertation (see chapter 5),

parcels are variables, which were combined into small groups of items within a

specific scale or a subscale (Little et al., 2002a). Following Allinson and Hayes (1996)

each of the CSI’s Parcels was computed as the simple sum of several items, which are

conceptually similar and assess the same construct.

The results show that all of the CSI loadings exceed the threshold of 0.5. The loadings

are statistically significant (p<0.0001). The composite reliability (CR=0.8411) and

Cronbach’s alpha (0.7795) are well above its critical value of 0.70 and 0.6

respectively, thus confirming internal consistency and reliability.

260

Table ‎6.17: Results summary of Cognitive Style (CSI) individual indicator reliability

Construct Indicator(s) Outer

Loadings

T-

Statistics

P-

Value

CR Cronbach’s

alpha

AVE

CSI

CSP1 0.8385 16.6344 0.0001 0.8411 0.7795 0.474

CSP2 0.7908 10.9249 0.0001

CSP3 0.6313 6.9627 0.0001

CSP4 0.5788 5.3324 0.0001

CSP5 0.5682 5.3803 0.0001

CSP6 0.6521 7.6253 0.0001

The average variance extracted (AVE) has a value of 0.474. Although this value is

slightly below the threshold of 0.5, it was decided to retain all the indicators in the

model for the following reasons:

Firstly, the CSI is a well-established measure of cognitive style (Allinson and

Hayes, 1996; Allinson et al., 2000b; Allinson and Hayes, 2000; Allinson et al., 2001;

Allinson and Hayes, 2012).

Secondly, all of the other measurement model quality criteria such internal

consistency and discriminant validity, were met.

Thirdly, omission of an indicator should be done only if it does not affect the

content validity of a measure (Hair et al., 2013a).

In the case of the CSI measure, omitting one of the reflective indicators (parcels)

might change the meaning of the measure and measurement parameters. In addition,

table 6.21A (see Appendix E, Table 6.21A) demonstrates that the square root of the

AVE for the CSI construct is higher than the correlations of the construct with other

latent variables in the path model. Table 6.22 (see Appendix E, Table 6.22A) shows

that the indicators: CSP1:CSP6, have the highest value for the loading with its

corresponding construct CSI. Overall Fornell-Larcker criterion, as well as the cross

loadings evaluation, provides evidence for the satisfactory level of discriminant

validity of CSI.

261

Entrepreneurial Self-Efficacy (SE)

Entrepreneurial Self-efficacy (SE) was designed to assess the belief itself and the

actual probability of executing various entrepreneurial actions (Arora et al., 2011), by

asking respondents to rate (on a 5-point Likert scale) how confident they were in four

situations, which are highly relevant for entrepreneurs:

Table ‎6.18: Entrepreneurial Self-Efficacy (SE) indicators and items

Indicator Item

se1 Successfully identifying new business opportunities

se2 Creating new products

se3 Thinking creatively

se4 Commercializing an idea or new development.”

An exploratory factor analysis (EFA) revealed that these four items are uni-

dimensional and only one factor emerged. In addition, SE was designed as a reflective

measure.

A PLS-SEM analysis was conducted. The composite reliability value of the reflective

construct (SE) in the model is 0.7995, which can be regarded as having high levels of

internal consistency and reliability. The same conclusion can be made about the

Cronbach’s alpha value (0.668) which is above the minimum threshold of 0.6. Overall,

these values demonstrate a very good internal consistency. The AVE is 0.5025, which

indicates a satisfactory level of convergent validity. In addition, table 6.21A (see

Appendix E, Table 6.21A) demonstrates that the square root of the AVE for the SE

construct is higher than the correlations of the construct with other latent variables in

the path model. Table 6.22A (see Appendix E, Table 6.22A) shows that the indicators

se1, se2, se3, and se4, have the highest value for the loading with its corresponding

construct SE. Overall Fornell-Larcker criterion, as well the cross loadings evaluation,

provides evidence for the satisfactory level of discriminant validity of SE. The

following table summarises the reliability and convergent validity assessment:

262

Table ‎6.19: Results summary of Entrepreneurial Self-Efficacy (SE) reliability and

validity

Construct Indicator(s) Outer

Loadings

T-

Statistics

P-

Value

CR Cronbach’s

alpha

AVE

SE

se1 0.7393 10.2077 0.0001 0.7995

0.668

0.5027

se2 0.5855 5.5086 0.0001

se3 0.6684 8.3114 0.0001

se4 0.8206 15.1664 0.0001

Learning Strategies

Six separate multiple-item scales were designed to measure each of the six learning

strategies: 'Learning by networking' deliberately or spontaneously35

, 'learning by

imitating' deliberately or spontaneously, and 'learning by doing'36

deliberately or

spontaneously. Each of the six scales was subjected to an exploratory factor analysis

(EFA) and reliability test.

The reflective learning by imitating deliberately (LBID) construct was measured by

two indicators (q153, q154), learning by networking spontaneously (LBNS) construct

was designed as a reflective construct, measured by four indicators (q141-q144),

learning by imitating spontaneously (LBIS) construct was measured by two reflective

indicators (q161, q162), Learning by Doing Spontaneously (LBDS) was measured as a

reflective construct consisting of four items (q184, q185, q186 and q187). Learning by

networking deliberately (LBND) and Learning by doing deliberately (LBDD) in this

PLS model were measured as HOCs (i.e. Higher-Order Constructs) which represent or

reflect their first order constructs.

The results of the PLS-SEM measurement model assessment for LBID, LBNS and

LBIS provides satisfactory values of the outer loadings (outer loadings>0.5

35

In this study, the terms 'systematically and randomly' are used interchangeably with 'planned and


36 The term is used interchangeably with 'learning from direct experience', and 'experimenting'.

263

sig<0.0001), the CR (>0.6), Cronbach’s alpha (>0.6) and the AVE (>0.5). These

values, confirmed that these three constructs have a high level of internal item

reliability, internal consistency and convergent validity. Moreover, table 6.21A (see

Appendix E, Table 6.21A) demonstrates that the square root of the AVE for the LBID,

LBNS, LBIS construct are higher than the correlations of each construct with other

latent variables in the path model. Table 6.22A (see Appendix E, Table 6.22A) depicts

that the reflective indicators, for each of these constructs, have the highest value for

the loading with their corresponding constructs Overall Fornell-Larcker criterions, as

well the cross loadings evaluation, provide evidence for the satisfactory level of

discriminant validity of LBIS.

The results of the first run of learning by Doing Spontaneously (LBDS) PLS model

revealed that the AVE value of LBDS was 0.4864, which is lower than the acceptable

level for the AVE. In situations like this, it is recommended to omit the item with the

lower loading and to re-examine the AVE value. If the AVE increased marginally and

the removal of the item does not affect substantially the content validity of the

construct, then it is recommended to omit this item from the model (Hair et al.,

2013a). Table ‎6.20 summarises these values:

264

Table ‎6.20: Results summary of LBNS, LBID, LBIS and LBDS


Loadings

T-

Statistics

P-

Value

CR Cronbach’s

alpha

AVE

LBNS

q141 0.8018 21.5817 0.0001 0.8796 0.8173 0.6466

q142 0.8187 23.5668 0.0001

q143 0.7533 11.9272 0.0001

q144 0.8404 25.7105 0.0001

LBID

q153 0.8884 21.5817 0.0001 0.90 0.7791 0.8182

q154 0.9203 23.5668 0.0001

LBIS

q161 0.9026 33.2571 0.0001 0.8824 0.7342 0.7895

q162 0.8742 25.3317 0.0001

LBDS

q184 0.5889 2.9431 0.00340 0.778 0.6039 0.5445

q185 0.8608 5.0404 0.0001

q186 0.7387 3.8635 0.00013

q187 omitted n/a n/a

The analysis was rerun without q187 (see Table ‎6.20 above). Removing q187 from

LBDS scale resulted with a higher level of AVE (0.545) in comparison to the initial

AVE value (0.48) (i.e. AVE including q187 in the model).

The overview of the final item loadings displayed a satisfactory item loading values

(>0.5) although the loading value of q184 (0.5889) decreased when compared to the

first run value (0.61). In addition, the omission of the q187 item resulted with a

substantial decrease in the value of Cronbach’s alpha (from 0.69 to 0.60). However,

the Cronbach’s alpha value although decreased, is above the threshold for newly

developed scales. In addition, an acceptable level of CR was obtained. Thus, it was

decided to retain the construct and their corresponding items. It can be concluded that

the measure of LBDS reflective construct has an acceptable level of internal

consistency and convergent validity.

265

Finally, the discriminant validity of LBDS was assessed. Table 6.42A and 6.43A (see

Appendix E, Tables 6.42A and 6.43A) show the results of the Fornell-Larcker

criterion assessment and the cross-loadings analysis respectively. The results

confirmed that the LBDS construct obtained a satisfactory level of discriminant

validity.

In the same vein as was done with Prior Knowledge (PK), the measurement quality of

LBND and LBDD were conducted in two stages using the sequential latent scores

approach (Wilson and Henseler, 2007). Firstly, a first-order measurement model was

run, without including the second order construct (Becker et al., 2012; Wright et al.,

2012), then, reliability and validity of the first-order measurement model was assessed.

Secondly, a second order factor with the latent variable scores as indicators was

constructed, and reliability and validity of the second order measurement model was

assessed.

In general, the results of the first run of the PLS model measurement assessment for

the first order constructs of LBND and LBDD were considered adequate with the

confirmation of satisfactory reliability and validity values. Reliability (α>0.6, CR>

0.6), convergent validity (AVE>0.5, outer loadings>0.5, p<0.0001), and discriminant

validity (Fornell-Larcker Criterion, √AVE>correlations, as well as the cross loadings

assessment that shows that each reflective indicator was loaded higher on the construct

it was designed to measure), provided evidence for the constructs’ reliability and

validity (see Appendix E, Table 6.36A, 6.46A, 6.37A and 6.47A, which shows the

results of the Fornell-Larcker criterion and Cross-Loadings assessment).

266

Table ‎6.21: LBND and LBDD First-Order Constructs Reliability and Convergent

Validity

HOC Construct(s)

(First

Order)

Indicator(s) Outer

Loadings

T-

Statistics

P-

Value

CR Cronbach’s

alpha

AVE

LBND

LBND1

q131 0.8815 20.1696 0.0001 0.858 0.6698 0.7513

q132 0.8518 13.8592 0.0001

LBND2

q133 0.7267 6.4399 0.0001 0.8436 0.6963 0.7334

q134 0.9689 41.0292 0.0001

LBDD

LBDD1 q172 0.924 39.0846 0.0001 0.9156 0.8157 0.8443

q174 0.9136 41.5881 0.0001

LBDD2 q171 0.6648 5.0787 0.0001 0.8016 0.6392 0.576

q175 0.7678 5.033 0.0001

q176 0.8345 4.9603 0.0001

Furthermore, the results suggest that the two first-order-reflective constructs of LBND

(LBND1 and LBND2) and LBDD (LBDD1 and LBDD2), proved to have a

satisfactory level of reliability and validity and can be used to test LBND and LBDD

as second order constructs.

The assessment of LBND and LBDD as ‘higher order constructs (HOCs) was

established using the sequential latent scores (i.e. two-steps) approach (Wilson and

Henseler, 2007). In this stage, the emphasis is on the HOC only, based on the latent

variable scores of the first-order constructs, that were computed in the first stage.

These scores were used as reflective indicators (Mode A) of the second-order

construct in the second stage. The following table shows the values of the second

order (HOC) outer loadings, the CR, and the AVE:

267

Table ‎6.22: Results summary of LBND HOC reliability and convergent validity

HOC Indicators (first

order constructs)

Outer

Loadings

T-

Statistics

P-

Value

CR AVE

LBND

HOC

LBNDL1 0.8013 4.3677 0.00002 0.7859

0.6473

LBNDL2 0.8078 4.4785 0.00001

LBDD

HOC

LBDD1 0.6653 3.5357 0.00044 0.7099 0.5527

LBDD2 0.8141 4.7773 0.00001

Evaluation of the second order indicators outer loadings revealed that all the indicators

demonstrated significant (p<0.0001) satisfactory loadings (>0.5). The measurement

model of the HOCs (LBDD and LBND) showed that the criteria for composite

reliability for both constructs (CR>0.6) was met. The AVE had the values (0.6743 for

LBND and 0.5527 for LBDD), which are above the threshold of 0.5. Thus, the criteria

for reliability and convergent validity of the HOC were met.

Discriminant validity for the HOC were further evaluated and interpreted. Tables

6.42A and 6.43A (see Appendix E, Tables 6.42A, 6.43A, 6.46A, and 6.47A) show the

discriminant validity assessment of the LBND HOC and LBDD HOC model

accordingly.

The results indicate that the indicators shared more common variance with their own

constructs than with other constructs, thus demonstrating a satisfactory level of

discriminant validity for the LBND HOC and LBDD HOC as second order constructs.

Based on the above assessment, it can be concluded that the second-order reflective

constructs, LBDD HOC and LBND HOC proved to have a satisfactory level of

reliability and validity.

268

Prior Business Ownership Experience

The PLS-SEM model requires at least ordinal scaled data for the model’s indicators,

but also works well with binary coded data such as dummy-coded indicators (Hair et

al., 2013a), although it should be considered with caution.

This study operationalizes prior business ownership experience as a single-item

construct (BizExp). Based on practical reasons, and the purpose of this study, the PLS-

SEM analysis included the dummy-coded variable “NoviceDummy” (1=’novice’, 0=

‘serial and portfolio’) as an indicator of the prior business ownership’s construct. The

use of single-item measures in a PLS-SEM is an accepted procedure, mainly due to

pragmatic considerations, and when they are used to measure observable

characteristics, however it might reduce the level of predictive validity (Hair et al.,

2013a, p. 48).

In addition, as the concept of prior business ownership experience is highly

homogenous, and is based on two items, predictive validity could not be severely

affected, in comparison with multi-items measures. Due to the fact, that the dummy

coded variable is based on two items, and that these two items were adopted from

several well-established studies (Ucbasaran et al., 2008; Westhead et al., 2009), it was

decided to use these indicators as the measure of BizExp in the PLS-SEM model,

although their reliability and validity cannot be assessed directly in the current model.

6.2.3.2 Structural Model Assessment

The second step of the PLS-SEM analysis is the assessment of the model’s parameters.

This study assesses these parameters as applied into the nomological network of the

model (Chin, 2010). Nomological network is a representation of the constructs of

interest in a study, their observable manifestations, and the interrelationships among

and between these constructs (Chin, 2010).

This section is structured as follows: firstly, the influence and inclusion of control

variables in the structural model is discussed and assessed. Secondly, the structural

model (i.e. path analysis) is evaluated. Finally, the moderation effect of the cognitive

style index on the relationships between the predictors and each of the six criterion

variables was examined.

269

6.2.3.2.1 The Influence of the Control Variables

Before testing the PLS-SEM model, the influence of control variables on the six

different learning strategies was examined. Control variables are often included in PLS

path models, accounting for some of the target construct’s variation (Hair et al.,

2013b), and should be discussed as part of the main analysis (Atinc et al., 2012). In

this study, the following five control variables were included: age, firm age, firm size,

hostile environment perception, and education. The following table provides

descriptive statistics for the control variables:

Table ‎6.23: Control Variables

Age

(years)

Venture

age (years)

Venture Size

(Likert scale)

Environment

Hostility Perception

(Likert scale)

Education

(Likert scale)

N Valid 178 178 178 178 178

Missing 0 0 0 0 0

Mean

43.22 3.5787 1.6705 3.74 2.5393

Median

41.5 2 1 4 3

Mode

38 1 1 4 3

Std. Deviation 12.339 4.02633 0.81726 1.09 0.89007

- The descriptive statistics shows the statistics parameters before any transformation was

conducted

- Age (entrepreneur age in years), Venture age (venture age from inception date in years),

venture size (1=up to 5 employees, 2=between 5 and 10, 3=above 11 employees), Environment

Hostility (1=total disagree that the environment is hostile, 5=totally agree), Education (Likert

scale ranging from 1 to 4)

In the PLS-SEM model, the control variables were linked directly to the criterion

variables (endogenous variables). To test the effect of the control variables, the

following steps were taken:

(1) Three nested models were designed: the hypothesised model, which includes the

predicators, the criterion variables and the block of the five control variables, the

second model was the hypothesised model without the inclusion of the control

variables and finally the third model was a PLS-SEM model, which included only the

control variables

270

(2) Subsequently, the three PLS-SEM models were analysed by means of their R2 and

their path coefficients values

(3) The impact of the control variables, as a block of variables, was tested by

analysing the increase in R2 relatively to the base model (i.e. the model with the

control variables only).

This is done by computing an F-test (Tabachnick and Fidell, 2007; Chin, 2010), which

tests the significance of the incremental change in R2

, using the following formula

adopted from Chin (2010):

R22-R

21

K2-K1

1-R2

2

N-K2-1

Where F is the incremental F ratio, R2

2 is the model that includes the additional LVs,

R21 is the baseline model, K2 and K1 are the number of LVs in each model,

respectively, and N is the sample size. Table ‎6.24 summarises these results:

Table ‎6.24: The impact of the control variables (F-test)

R2 (base

model- only

control

variables)

R2 (only

predictors)

R2(Full

model)

F (full model-

only CR)

F(full model-

predictors

only)

LBID 0.058 0.043 0.158 4.988124*** 4.589074***

LBND

HOC

0.104 0.128 0.181 3.948718** 2.174359 n.s.

LBDS 0.071 0.061 0.074 0.136069 n.s. 0.471706 n.s.

LBDD

HOC

0.038 0.136 0.226 10.20155*** 3.906977***

LBIS 0.131 0.108 0.218 4.672634*** 4.726343***

LBNS 0.115 0.126 0.235 6.588235*** 4.787451***

- n.s. (non- significant), *(p<0.05), **(p<0.01), ***(p<0.001)

F=

271

Table ‎6.24 shows that the full model is to be preferred over the base model, which

includes only the control variables.

The predictors, significantly, except in the case of the LBDS criterion, add explained

variance to the endogenous latent variables. However, the control variables,

significantly, increase the R2 of four of the six criterion variables in the model.

The R2 of the LBND HOC and LBDS constructs, increases when adding the control

variables, but the change in the R2, is not significant for both of them. Moreover, the

control path and the path significance were further analysed. Table ‎6.25 shows these

results:

Table ‎6.25: The impact of the control variables (path coefficients)

LBDD HOC LBDS LBID LBIS LBND HOC LBNS

Age -0.1867* 0.1052(n.s) -0.2832*** -0.287*** -0.1352(n.s.) -0.2382***

Education 0.0628(n.s) 0.0172(n.s) -.0839(n.s) -0.1677* -0.123(n.s) -0.2363***

Firm Age -0.1092(n.s) 0.0479(n.s) -.0329(n.s) 0.0362(n.s) 0.024(n.s) -0.0512(n.s)

Firm Size -0.0909(n.s) -0.044(n.s) -.1152(n.s) 0.0065(n.s) -0.1662* -0.0512(n.s)

Hostile

Environment

0.1757(n.s) -0.0468(n.s) 0.0706(n.s) 0.0417(n.s) 0.1498* 0.0564(n.s)

- Bootstrapping with 5000 resamples, Individual change, path weighting

- n.s. (non-significant), *(p<0.05), **(p<0.01), ***(p<0.001)

The inclusion of control variables is still a debatable topic. On the one hand their

inclusion might reduce the power of analysis (Becker, 2005), increased model

complexity and, hence, may demand a larger sample size for estimating the PLS path

model (Hair et al., 2013b).

On the other hand omitting control variables, only because their influence is not

significant (p>0.05) is a questionable practice (Breaugh, 2008). What matters is

whether they may infect a predictor variable's effect on a criterion variable, and if

there is a theoretical justification of their inclusion (Breaugh, 2008). Moreover, even

when the impact of control variables is significant, the researcher should use this

finding with caution when running advanced analyses such as PLS-SEM multi-group

analysis (Hair et al., 2013b).

272

In this study, after careful consideration of the pros and cons of control variable

inclusion in PLS-SEM analysis, it was decided to analyse the full model (the model

including control variables) for the following:

Firstly, the sample size of this study meets the required sample size for PLS-

SEM models (see chapter 3).

Secondly, the full model demonstrates a substantive increase in the model’s R2

for all of the endogenous criterion variables, except LBDS.

This means that the full model represents significant explanatory improvements

over the base model. Although, their path coefficients are not significant for most of

the criterion variables, except the impact of age, which in most of the models, provides

significant path coefficients. However, in PLS-SEM analysis, this conclusion would

be kept, whether or not the path coefficient between the control variable and the

endogenous latent variable is statistically significant (Kock, 2011).

6.2.3.2.2 PLS-SEM Structural Model Assessment

The assessment of the structural model results addresses the relationships between the

constructs and the model’s predictive capabilities (Hair et al., 2013b). The first step,

before reporting on the assessment results is examination of collinearity among

predictor constructs by computing the VIF value.

A VIF indicates whether a predictor construct has a strong correlation with the other

predictors in the model (Field, 2009). VIF above 5, indicates a potential problem of

collinearity (Hair et al., 2013a). The assessment was done as follows:

Firstly, the latent variable scores were extracted by running the PLS-SEM

model and this was saved for further examination.

Secondly, the latent variable scores were imported into the SPSS software.

Thirdly, a multiple regression analysis was run for each part of the model.

In this study, two separate OLS regressions analyses were run in SPSS: firstly, BizExp

and SE as predictors of PK and secondly, CSI, PK, Strongties, Pro&Socialties, and

Businessties as predictors of each one of the learning strategies constructs. Table ‎6.26

shows the results:

273

Table ‎6.26: Collinearity Assessment

First Set Second Set

Constructs VIF Constructs VIF

BizExp 1.007 PK 1.128

SE 1.007 Businessties 1.19

Pro&Socialties 1.107

Strongties 1.077

As can be seen, all VIF values are clearly below the threshold of five. Therefore, it can

be concluded that in this structural model, collinearity was not a cause for concern.

Path coefficients and determination coefficients (R2)

Falk and Miller (1992) suggested that the variance explained for endogenous latent

variable should be more than 0.1.

As can be seen in the following table (the table continued on 2nd

, 3rd

, and 4th

pages),

only one (R2

LBDS=0.0624) of the seven endogenous constructs did not meet the

threshold of 0.1. The variance explained of the LBNS has the highest value

(R2

LBNS=0.2363), which can be described as satisfactory, considering the complexity

of the model. The variance explained for the LBID construct was 0.1355, 0.2121 for

LBDD HOC, 0.2163 for LBIS, 0.1481 for LBND, and 0.1576 for PK.

274

Table ‎6.27: R2 and path coefficients

Learning by Imitating Deliberately (LBID)

R2 0.1355

Path

Coefficients β

t

values

p values Significance

Levels

Businessties -> LBID 0.1703 2.1386 0.0325164 *

PK -> LBID 0.2138 2.518 0.0118333 *

Pro&Socialties -> LBID -0.0286 0.516 0.6058772 n.s.

Strongties -> LBID -0.1035 1.6333 0.1024689 n.s

Learning by Doing Deliberately (LBDD HOC)

R2 0.2121

Path

Coefficients β

t

values


Levels

Businessties->LBDD

HOC 0.2881 3.4785 0.0005085

***

PK -> LBDD HOC 0.2885 3.2523 0.0011524 **

Pro&Socialties->

LBDD HOC -0.1763 2.094 0.0363102

*

Strongties->LBDD

HOC 0.0507 0.8607 0.3894446

n.s.

275

Learning by Doing Spontaneously (LBDS)

R2 0.0624

Path

Coefficients β

t

values


Levels

Businessties -> LBDS 0.0895 1.1205 0.2625546 n.s.

PK -> LBDS 0.0629 0.9967 0.3189584 n.s.

Pro&Socialties ->LBDS 0.1771 1.8516 0.0641422 n.s.

Strongties -> LBDS -0.1309 1.665 0.0959754 n.s.

Learning by imitating Spontaneously (LBIS)

R2 0.2163

Path

Coefficients β

t

values


Levels

Businessties -> LBIS 0.0437 0.808 0.419129 n.s.

PK -> LBIS 0.2453 3.5142 0.000445 ***

Pro&Socialties -> LBIS 0.1433 2.2067 0.0273803 *

Strongties -> LBIS -0.1285 1.8889 0.0589631 n.s.

276

Learning by Networking Deliberately (LBND HOC)

R2 0.1481

Path

Coefficients β

t

values


Levels

Businessties -> LBND 0.171 1.832 0.0670109 n.s.

PK -> LBND 0.191 2.1087 0.0350201 *

Pro&Socialties->LBND 0.0798 1.1618 0.2453722 n.s.

Strongties -> LBND 0.1157 1.2761 0.2019795 n.s.

Learning by Networking Spontaneously (LBNS)

R2 0.2363

Path

Coefficients β

t

values


Levels

Businessties -> LBNS 0.1533 1.7932 0.0730014 n.s.

PK -> LBNS 0.1448 1.8396 0.0658862 n.s.

Pro&Socialties-> LBNS -0.0341 0.6555 0.5121761 n.s.

Strongties -> LBNS 0.1533 1.7932 0.0730014 n.s.


R2 0.1576

Path

Coefficients β

t

values


Levels

BizExp -> PK -0.2183 3.0232 0.0025139 **

SE -> PK 0.3128 4.0997 0.00004 ***

n.s. not significant, *p<0.05, **p<0.01, ***p<0.001.

Bootstrapping settings: 5,000 samples, 178 cases

277

The interpretations of the results in relation to the hypotheses are further discussed in

the discussion chapter (see chapter 7).

Looking at the path coefficients results for the Learning by imitating deliberately

construct (LBID), it can be concluded that the path PK LBID has the highest

positive relationship value (β=0.2138, p<0.05), suggesting that Prior Knowledge (PK)

is the most important construct, followed by Business ties LBID (β=0.1703,

p<0.05), while Strongties negatively influences the LBID (β=-0.1035, p>0.1), it can be

considered as a small and non-significant effect. In addition, Pro&Socialties LBID

has the smallest non-significant negative impact (β= -0.0286, p>0.1).

The results for the Learning by doing deliberately construct (LBDD HOC) showed

that impact of the Prior Knowledge construct was the highest PK LBDD HOC

(β=0.2885, p<0.01), followed by the strength of the Businessties construct (β=0.2881,

p<0.001). The strength of Pro&Socialties LBDD HOC (β=-0.1763, p<0.05) had a

negative influence on the intention to learn by doing deliberately. It can be suggested,

therefore, that as the strength of this type of ties increases, the intention to learn by

doing deliberately decreases. The effect of the strength of the kinship and friendship

relationships, Strongties LBDD HOC (β=0.049, p>0.1), was non-significant.

The percentage of the explained variance of the Learning by doing spontaneously

(LBDS), as indicated earlier in this section, is very low (R2=0.0624). In addition, the

importance of the Business Ties construct (β=0.0895, p>0.1), and the Prior Knowledge

(β=0.0629, p>0.1) which are very low and not statistically significant. The relative

importance of the Pro&Socialties was found to be the highest, although non-

significant, (β=0.1771, p<0.1), followed by Strongties (β=-0.139, p<0.1), suggesting

that the intention to learn by doing spontaneously is most positively influenced (non-

significant effects) by the strength of the professional and social ties as well as

influenced negatively by the friendship and kinship ties.

The relative importance of the Prior Knowledge construct (PK) for the Learning by

imitating spontaneously (LBIS) is the highest, among the four predictors (β=0.2453,

p<0.001), followed by Pro&Socialties (β=0.1433, p<0.05). In contrast, Strongties (β=-

0.1285, p<0.1), and Business ties (β=0.0437, p>0.1) had very small, non-significant

effect on LBIS.

278

The Learning by networking deliberately (LBND) construct (R2

LBND=0.1481) is

influenced mostly by the Prior Knowledge construct (PK). The results show that

impact of PK (β=0.191, p<0.05) explained the largest amount of variance. However,

the constructs Business Ties (β=0.171, p<0.1), Pro&Socialties (β=0.0798, p>0.1), and

Strongties (β=0.1157, p>0.1) displayed a minimal effect compared to the other

constructs as illustrated by low and non-significant values of their path coefficients.

The analysis of the structural model relationships, showed that the most influential

latent variable on Learning by networking spontaneously (LBNS) was PK (β=0.2656,

p<0.1), however the effect was non-significant. The strength of the Business Ties

(β=0.1533, p<0.1) and Pro&Socialties (β=0.1448, p<0.1) were relatively less

important and non-significant, as can be seen by inspecting their path coefficients

values. In addition, the effect of the Strongties (β=-0.0341, p>0.1) was statistically

non-significant.

The last endogenous latent variable in this model is PK. The results show that the

relationships between BizExp (β=-0.2183, p<0.001), SE (β=0.3128, p<0.001) and PK

were relatively influential and statistically significant. BizExp is a dummy variable, in

which (1=Novice and 0=Habitual entrepreneurs) and SE represents the level of

entrepreneurial self-efficacy. Accordingly, it can be seen that novice entrepreneurs

tend to gain or acquire less knowledge (negative relationship) and that Self-Efficacy

has a positive moderate influence on PK.

The assessment of the structural model should consider the examination of other types

of relationships: the total effects (i.e. the summation of the direct and the indirect

effect between an exogenous and an endogenous construct) (Hair et al., 2013a). The

assessment of total effects provides an investigation of the effect on a target construct

via all mediating constructs and thus depicts a richer inspection of the relationships in

the structural model. The examination of total effects of BizExp and SE on the six

target variables, demonstrates interesting results.

The results of the total effects show that eight out of the twelve total effects displayed

statistically significant. Prior business ownership experience (BizExp) has a significant

total effect on most of the learning strategies, except LBDS. The total effects of SE on

the learning strategies are significant at least at 5% level except the total effect on

LBDS and LBND HOC, which are non-significant. However, the values of all total

effects are low (<0.1) indicating that SE and BizExp are important predictors of PK,

279

however, their contribution to the prediction of the learning behaviours is

questionable.

Table ‎6.28: Results of the Total effects

n.s. not significant, *p<0.05, **p<0.01, ***p<0.001.

Bootstrapping settings: 5,000 samples, 178 cases

A PLS-SEM Blindfolding procedure was conducted to evaluate the model’s predictive

relevance for each of the reflective endogenous constructs. The blindfolding procedure

is a resampling technique that produces cross-validated redundancy measures for each

of the endogenous constructs (Hair et al., 2013a) and is used only in reflective

measurement models. The procedure systematically “deletes and predicts every data

point of the indicators in the reflective measurement model of endogenous constructs”

(Hair et al., 2013a, p. 199). An omission distance (D) between 5 and 10 should be

chosen. In this study, the number of observations is 178 and the omission distance,

D=7, was chosen (Hair et al., 2013a; Sarstedt et al., 2014) .

Total

Effect

t

values

p

values

Significance

Levels

BizExp -> LBDD HOC -0.063 2.0488 0.041004 *

BizExp -> LBDS -0.0137 0.6956 0.487003 n.s.

BizExp -> LBID -0.0467 1.7779 0.076029 *

BizExp -> LBIS -0.0536 2.1256 0.034027 *

BizExp -> LBND -0.0417 1.6629 0.09696 *

BizExp -> LBNS -0.058 2.1757 0.030046 *

SE -> LBDD HOC 0.0902 2.2491 0.024941 *

SE -> LBDS 0.0197 0.6577 0.511034 n.s.

SE -> LBID 0.0669 1.9415 0.05276 n.s

SE -> LBIS 0.0767 2.516 0.012182 *

SE -> LBND 0.0598 1.5643 0.118381 n.s

SE -> LBNS 0.0831 2.5239 0.011915 *

280

Table ‎6.29: Results of Predictive Relevance (Q2)

Endogenous Construct SSO SSE Q2

PK 534 475.2234 0.1101

LBDD HOC 356 337.0938 0.0531

LBND 356 310.2482 0.1285

LBID 56 316.1667 0.1119

LBIS 356 293.439 0.1757

LBNS 712 603.1466 0.1529

LBDS 534 549.2571 -0.0286

The Stone-Geisser’s Q2=1-SSE/SSO.

Q2>0 the model has predictive relevance for this construct

The omission distance, D=7.

The cross-validated redundancy values for all seven endogenous constructs are above

zero (PK=0.1101, LBDD HOC=0.053, LBND=0.1285, LBID=0.11119, LBIS=0.1757,

LBNS=0.1529), except LBDS=-0.0286, providing support for the model’s predictive

relevance. As discussed earlier, the predictive relevance demonstrates the predictive

validity of a complex model (Chin, 2010).

An assessment of the effect size (f2) addresses an exogenous construct’s contribution

to an endogenous construct’s R2 value. The results, concerning all the relationships in

the model, are presented in Table ‎6.30 in a way that the target endogenous latent

variables appear in the first row of the table below, whereas the predictor constructs,

which are directly linked to the endogenous construct, are in the columns.

The results show that most of the effect sizes can be described as ‘small’, based on

suggestions from Chin (2010). The effect sizes of Strongties on the entire criterion

endogenous variables are lower than the minimum threshold of 0.02, indicating that

the contribution of this construct to the variance explained of each of the criterion

variables is less than required to contribute significantly. The same conclusion can be

made when analysing the effect sizes of the Business Ties construct (except the effect

size of Businessties on LBDD HOC, LBNS, and LBID, which are above 0.02).

However, it can be concluded that Prior Knowledge (PK) plays an important role as a

predictor construct of the six learning strategies.

281

The effect sizes of PK are the largest among the five-predictor variables, although they

can be considered as ‘small’ (except the contribution to the variance explained of the

LBDS construct, which is not surprising due to the low level of R2). In addition, the

effect size of the entrepreneurial self-efficacy (SE), can be described as small,

however, the effect size value (f 2

=0.116) is the second largest among all the

constructs in the model, indicating the importance of self-efficacy as a predictor of

PK.

282

Tab

le 6

.30:

Res

ult

s of

effe

ct s

ize

(f2)

PK

LB

DD

HO

C

LB

DS

R

2in

clu

ded

R2

exclu

ded

f2

R2

incl

ud

ed

R2

exclu

ded

f2

R2

incl

ud

ed

R2

exclu

ded

f2

SE

0.1

576

0.0

60

0.1

16

Biz

Exp

0.1

576

0.1

110

0.0

55

PK

0.2

12

0.1

42

0.0

89

0.0

624

0.0

59

0.0

04

Str

on

gti

es

0.2

12

0.2

10

0.0

03

0.0

624

0.0

50

0.0

13

Pro

&S

oci

alt

ies

0.2

12

0.1

86

0.0

33

0.0

624

0.0

52

0.0

11

Bu

sin

esst

ies

0.2

12

0.1

54

0.0

74

0.0

624

0.0

55

0.0

08

283

Tab

le 6

.30:

conti

nu

ed

LB

ND

HO

C

LB

NS

LB

ID

LB

IS

R2

incl

ud

ed

R2

exclu

ded

f2

R2

incl

ud

ed

R2

exclu

ded

f2

R2

incl

ud

ed

R2

exclu

ded

f2

R2

incl

ud

ed

R2

exclu

ded

f2

SE

Biz

Exp

PK

0.1

481

0.1

44

0.0

04

0.2

363

0.1

78

0.0

76

0.1

35

5

0.0

97

0.0

45

0.2

163

0.1

65

0.0

65

Str

on

gti

es

0.1

481

0.1

37

0.0

13

0.2

363

0.2

35

0.0

02

0.1

35

5

0.1

26

0.0

11

0.2

163

0.2

02

0.0

18

Pro

&S

oci

alt

ies

0.1

481

0.1

43

0.0

06

0.2

363

0.2

19

0.0

23

0.1

35

5

0.1

35

0.0

01

0.2

163

0.1

99

0.0

22

Bu

sin

esst

ies

0.1

481

0.1

47

0.0

01

0.2

363

0.2

21

0.0

20

0.1

35

5

0.1

12

0.0

27

0.2

163

0.2

15

0.0

02

f2=

(R2

incl

ud

ed-R

2ex

clu

ded

)/(1

-R2

incl

ud

ed),

f2 v

alues

of

0.0

2, 0.1

5, an

d 0

.35 i

ndic

ate

smal

l, m

ediu

m o

r la

rge

effe

ct r

espec

tivel

y.

284

The Q2 values were estimated by the blindfolding procedure, which can provide an

assessment of the extent the path model predicts the observed values (Hair et al.,

2013a). As in the case of f 2, which is based on the changes in R

2 values, changes in Q

2

values can estimate the relative influence of the structural model on the observed

measures for each endogenous latent variable (Chin, 2010).

The final key criterion, which demonstrates the quality of the structural model, is the

q2 effect size. The q2 effect size is a relative measure of the exogenous construct’s

predictive relevance, for a specific endogenous latent variable (Chin, 2010; Hair et al.,

2013a) or the relative change in the Q2 after omitting a specific exogenous from the

path model. This procedure is done similarly to the computation of Q2, by running the

blindfolding algorithm twice: the first run includes the exogenous construct in the

model, however, in the second run, this latent variable is deleted, and the model is re-

estimated (Hair et al., 2013a).

Table ‎6.31 below, summarises the key results. The q2 of PK, when omitting the SE

was (0.0778) and after deleting BizExp (0.0389), suggesting that both variables have a

relative predictive relevance to PK, however they can be described as having a small

effect.

The q2 results for LBDD HOC suggest that both PK (0.0347) and Business Ties

(0.0254) have a relative predictive relevance (small effect size), however, the q2 values

of Strongties (-0.0051) and Pro&Socialties (0.0124), indicate that these two constructs

have non-significant effect size for LBDD HOC.

The q2 results for LBDS show that none of the constructs has q2 values, which are

above the threshold of 0.02. The q2 values of Businessties (-0.0293), PK (-0.0691),

Strongties (0.0087) and Pro&Socialties (0.0154), indicate that these four constructs

have no significant effect size for LBDS.

The q2 analysis of LBND HOC revealed that the effect sizes of Strongties (0.0192),

Pro&Socialties (0.0107), and Businessties (0.0002) are lower than the threshold of

0.02. However, the effect size of PK (0.0340), can be considered as having a small

predictive relevance. It can thus be suggested that only PK has a relative predictive

reliance for LBND HOC.

The effect size values for LBNS demonstrate that only PK has a meaningful effect size

(q2=0.0476), although small. The relative predictive relevance of Strongties (0.0013),

285

Pro&Socialties (0.0137) and Businessties (0.0122), indicate no significant effect for

the endogenous construct LBNS.

It was found that the effect sizes for LBID are as follows: PK (0.0427) has a small

effect size on LBID. However, the effect sizes of Strongties (0.0125), Pro&Socialties

(0.0057), and Businessties (0.0193) are lower than expected and demonstrate that

these three drivers' constructs have meaningful relative predictive relevance for LBID.

Examination of the q2

effect sizes for LBIS revealed that only PK (0.0581) has an

effect size for LBIS. The effects sizes of the Strongties (0.0144), Pro&Socialties

(0.0180), and Businessties (0.0013) are very low and do not meet the minimum level

of 0.02.

286

Table ‎6.31: Results of effect size (q2)

PK

LBDD HOC

LBDS

Q2included Q2

excluded q2 Q2included Q2


excluded q2

SE

0.1101 0.0409 0.0722

BizExp

0.1101 0.0755 0.0390

PK

0.0531 0.0202 0.0347 -0.0286 0.0425 -0.0691

Strongties

0.0531 0.0579 -0.0051 -0.0286 -0.0376 0.0087

Pro&Socialties

0.0531 0.0414 0.0124 -0.0286 -0.0444 0.0154

Businessties

0.0531 0.0254 0.0293 -0.0286 -0.0293 0.0007

287

Table 6.31: Continued

LBND HOC

LBNS

LBID

LBIS

Q2included Q2




excluded q2

SE

BizExp

PK 0.1285 0.0989 0.0340 0.1529 0.1126 0.0471 0.1119 0.0703 0.0468 0.1757 0.1287 0.0581

Strongties 0.1285 0.1118 0.0192 0.1529 0.1518 0.0007 0.1119 0.1008 0.0125 0.1757 0.1638 0.0144

Pro&Socialties 0.1285 0.1192 0.0107 0.1529 0.1413 0.0129 0.1119 0.1068 0.0057 0.1757 0.1609 0.0180

Businessties 0.1285 0.1283 0.0002 0.1529 0.1426 0.0120 0.1119 0.0948 0.0193 0.1757 0.1746 0.0013

q2=(Q2included-Q

2excluded)/(1-Q2

included),

q2 values of 0.02, 0.15, and 0.35 indicate small, medium or large effect.

288

6.2.3.3 Moderation Effect: Cognitive Style (CSI) as a Moderator Variable

One important goal of this study is to understand how cognitive style influences the

strength or the direction of the direct relationship between the prior knowledge (PK)

and the ways entrepreneurs learn about the business opportunities. This type of

research goal often requires moderation analysis.

The moderation effect was created using the SmartPLS software, by running the

‘Create Moderating Effect’ procedure37

(Hair et al., 2013a) In this procedure, the CSI

construct was specified as the moderator variable, for each of the six learning

strategies, and the PK as the predictor variable. The full model now, includes the CSI

construct as the moderator latent variable. Table ‎6.32 shows the path coefficients of

the interaction terms and the R2 of each of the six learning strategies after including

the moderator:

Table ‎6.32: Results for Interaction Effects

LBDD

HOC

LBDS LBID LBIS

LBND

HOC

LBNS

Path Coefficients

PK 0.2513*** 0.0207n.s 0.1901** 0.212*** 0.2438*** 0.2298***

CSI (moderator) 0.1387* -0.1123n.s 0.1755** -0.0304n.s 0.2111** -0.0128n.s

PK*CSI (interaction term) 0.2155*** 0.2235*** 0.2068*** 0.1233** -0.2049*** 0.1575**

R2 (including the moderator) 0.2682 0.1187 0.1991 0.2312 0.2197 0.2585

R2 (without the moderator)) 0.2121 0.0624 0.1355 0.2163 0.1481 0.2363

f 2 0.0767 0.0639 0.0794 0.0194 0.0918 0.0299

- Bootstrapping with 500 resamples, Individual change, path weighting scheme

- n.s. (non-significant), *(p<0.05), **(p<0.01), ***(p<0.001)

37

The PLS-SEM algorithm was run using the path weighting scheme, no missing values and the

indicators values were mean-centred before multiplication

289

The results display the standardised path coefficients after the inclusion of the

moderator (CSI) in the model, and can be interpreted as follows: a change of one

standard deviation would imply a change in the impact of the PK construct on the

Learning strategies constructs, by the size of the interaction term.

The interaction term (PK*CSI) has a positive significant effect on LBDD HOC

(0.2155, p<0.001). The relationship between PK and LBDD HOC has a value of

0.2513. If the CSI increases by one standard deviation point, the relationship between

PK and LBDD HOC would increase by the size of the interaction term (0.2155) and

obtain the value of 0.4668 (0.2513+0.2155). Thus, when the CSI increases (i.e. the

entrepreneur tends to be more analytical), the PK becomes more important for the

prediction of LBDD HOC. The effect size is 0.0767, suggesting a low moderating

effect (f 2>0.02).

The influence of PK on LBDS was very low (<0.1) and non-significant (p>0.1), as

well as the impact of the moderator on LBDS (β=-0.1123, p>0.1) (i.e. negative

influence). However, the interaction term effect (CSI*PK) effect was statistically

significant (β=0.2235, p<0.001), suggesting a significant and meaningful moderating

effect, but with a low effect size (f 2=0.0639).

The influence of PK on LBID, before the inclusion of the moderator (i.e. the model

without the interaction term) was 0.2135. The path coefficient after the inclusion of

the moderator decreased slightly (β=0.1901, p<0.05), but the value of the coefficient

determination increased from 13.55% to 19.91%, indicating a small effect size (f

2=0.0794). The moderator effect is meaningful and significant (β=0.2068, p<0.001),

therefore a one standard deviation increase in CSI will affect the effect of PK on LBID

directly by substantially increasing its effect from 0.2068 to 0.3969).

The effect of PK on LBIS after the inclusion of the CSI construct as the moderator

reduced slightly from 0.24531 to 0.212, while similarly increasing the R2 from 0.2163

to only 0.232, indicating that the effect size is slightly below the minimum level of

0.02. However, the moderator effect is meaningful and significant (β=0.1233, p<0.05)

although its strength of association is questionable.

290

The results of the relationship between the PK construct and the LBND HOC strategy

show a significant value of 0.1909. Adding the interaction term to the model, resulted

in an increase of the beta value to 0.2438 (p<0.001) and the R2 from 0.1481 to 0.2197.

These results indicate that the strength of the moderator effect on the relationship

between PK and LBND HOC is the largest (f 2=0.0918) of the six learning strategies.

In addition, and surprisingly, the effect of the moderator although significant (β=-

0.2049) is negative, suggesting that if the CSI score becomes higher, this would imply

that the effect of PK on LBND HOC would decrease by the size of the interaction term

(β= -0.2049) and obtain the value of 0.2438-0.2049=0.0389. Hence, when the scores

on the cognitive style are higher (i.e. indicating that the entrepreneur tends to be more

analytical) the PK becomes less important for the explanation of LBND HOC.

The effect of PK on LBNS after the inclusion of the CSI construct as the moderator

reduced from 0.26566 to 0.22298, while slightly increasing the R2 from 0.2363 to only

0.2585, indicating that the effect size is slightly above the minimum level of 0.02 (f

2=0.0299). However, the moderator effect is meaningful and significant (β=0.1575,

p<0.05).

6.2.3.3.1 Moderation type Assessment

Sharma et al. (1981) presented a typology of moderator variables with a framework for

identifying their presence and type. They suggested that moderator variables are of

two types: they either influence the strength of the relationship or modify the form of

the basic model. The following typology was drawn by the authors, showing the

difference between these two types of moderator variables:

291

Figure ‎6.6: Typology of Moderator variables

Related to

Criterion and/or

Predictor

Not Related

to Criterion

and Predictor

No Interaction

With

Predictor

1.

Intervening, Exogenous, Antecedent,

Suppressor, Predictor

2.

Moderator

(Homologizer)

Interaction

With

Predictor

Variable

3.

Moderator

("Quasi" Moderator)

4.

Moderator

(“Pure” Moderator)

Source: Sharma et al. (1981, p.292)

This moderator typology is based on the following regression equation:

Y = a+ β1X+ β2Z+ β3XZ +e

Where Y is the endogenous variable (i.e. the criterion); X is the predictor variable; and

Z is the suspected moderator. The classification distinguishes between Predictors,

which are not defined as moderators and therefore are related directly to the criterion

variable and do not interact with the predictor.

The second type is the Homologizer, which influences the strength of the relationship;

however, the interaction is not significant.

The third type is “pure” moderator, which significantly interacts with the predictor,

but do not show a significant effect with the criterion.

Finally, if the suspected moderator changes the form of the relationships between the

predictor and the criterion, significantly interacts with predictor and his direct

292

relationship with the criterion is significant, it can be defined as “quasi-moderator.”

Therefore, the following evaluation38

assessment steps were implemented using the

PLS-SEM algorithm:

(1) Whether the suspected moderator Z interacts with the independent and the

interaction exhibits significant effect.

(2) Whether the suspected moderator Z is related to the predictor variable, and/or

the criterion variable, and it exhibits significant effect.

In this study, the findings of the interaction model exhibited that in the case of the

three spontaneous learning strategies (i.e. LBIS, LBNS, and LBDS) the CSI represents

a variable that significantly interacts with the predictor variable (PK) and is

significantly related to the criterion variable.

In this case, when the moderator is significantly related to the criterion and the

interaction term is significant too, it can be concluded that the CSI, as the moderator,

acts as a “quasi-moderator.” However, with relations to the three deliberate learning

strategies (i.e. LBDD, LBND HOC, and LBID) the CSI construct plays the role of

“pure moderator.” It has significant interaction with PK, but the moderator itself has

no significant relations with the criterions (Sharma et al., 1981).

6.3 Summary

The main purpose of the quantitative phase was to test statistically the conceptual

model that was presented in chapter 5 of this thesis. Prior to this analysis, data was

screened and cleaned through various methods such as missing values evaluation,

outliers identification, and the extent that variables of interests meet the assumption of

multivariate analysis, especially EFA and PLS-SEM. No missing values were detected

(this study implemented a “forced-answers” approach) (Hair et al., 2013a).

Cleaning the data was done by using data transformation, as a remedy for outliers or

failure to normality, linearity and homoscedasticity, as well as removal of cases if

38

For detailed explanation about the steps for assessing the type of moderation effect, please refer to

Sharma et al. (1981, p. 295)

293

necessary (Tabachnick and Fidell, 2007). A few cases with extreme scores on firm

size, and firm age were found to be univariate outliers. The scores were replaced by

means of changing the scores. In addition, no multivariate outliers were detected

among all variables in the model.

Normality was evaluated by assessing the Skewness and Kurtosis measures, by

visually inspecting the probability plots and histograms and by evaluating the K-S test

significance. Overall, most of the variables presented a slight Skewness and or

Kurtosis (values between + 1). However, all of the items resulted with a significant K-

S test values, indicating that the data is deviated from normality. Four of the variables

were transformed to reduce the extreme Skewness and Kurtosis. Linearity was not

achieved in some of the variables, especially concerning three items of Learning by

doing deliberately (LBDD), which may be considered as a cause for concern for the

EFA results. However, it was evident that collinearity among the predictors constructs

is not a concern of this study.

Consequently, a survey response of 178 was obtained from the email invitations sent.

The effective response rate was approximately 3.2%. Although this response rate is

lower than expected, it is not uncommon (Frippiat et al., 2010) in surveys sent to

entrepreneurs (Shoham et al., 2006) via emails (Cook et al., 2000). Furthermore, non-

response bias assessment was conducted. The results of the one-way Anova showed

that non-response error is not a major concern in this study. This study applied the

Harman single-factor test (Podsakoff and Organ, 1986) to assess the extent to which

common method variance (CMV) exists. The results of the un-rotated factor solution

indicated no single factor is found to explain more than 50% of the variance. Based

on the preliminary data analysis, it can be asserted that the study's data can be accepted

as valid for the analysis.

The data analysis was conducted in three stages:

Firstly, Exploratory Factor Analysis (EFA) was conducted. The EFA was

conducted using the Principle Component Analysis (PCA). Prior to conducting the

PCA main analysis procedures, the match between the data and the assumptions was

examined, verifying that a factor analysis can be conducted, with the necessary

294

precautions. In addition, subsequent to the factor analysis, the resultant solutions were

further analysed by means of a test of the reliability of the measures (i.e. Cronbach’s

alpha). The PCA enabled this study to reduce the number of variables (i.e. items) and

to develop reliable scales for each of the constructs of interests.

Secondly, the constructs under investigation were further analysed using PLS-

SEM. The PLS-SEM measurement model (outer model) demonstrated an adequate

level of reliability, in terms of Cronbach’s alpha, Composite Reliability (CR), and

AVE (Average Variance Extracted). In addition, convergent validity and discriminant

validity was achieved and demonstrated. For three constructs (LBND, LBDD, and

PK), a separate measurement model assessment stage was conducted. These three

constructs were designed as High Order Constructs (HOC) and were measured by

using latent variable scores. The measurement model assessment confirmed that the

constructs under investigation LBNS, LBID, LBIS, LBND (HOC), LBDD (HOC), PK,

SE, BizExp, Strongties, Businessties, Pro&Socialties have a satisfactory internal item

reliability, internal consistency and convergent validity as well as a satisfactory level

of convergent and discriminant validity. The measurement model of the LBDS

construct was partially supported in the first run. The AVE value of LBDS was lower

than the threshold of 0.5. The analysis was rerun without indicator q187. The overview

of the final item loadings, CR, and Cronbach’s alpha displayed a satisfactory level of

reliability.

The structural model analysis (inner model) included the assessment of the main and

moderation effects (interaction effect). The following Figure ‎6.7 summarises the main

results of the PLS-SEM structural model.

295

Control Variables

Learnin

g Strategies

1. 1. The Six Learning Strategies are treated as six different criterion variables.

2. 2. Their measurement model is not illustrated here.

Soci

al n

etw

ork

ing

Var

iab

les

Ho

stile

En

viro

nm

ent LB

NS

R2=0

.23

63

LBID

R

2 =0.1

35

5

LBIS

R

2=0

.21

63

LBD

S R

2 =0.0

62

4

LBD

D H

OC

R

2 =0

.21

21

LBN

D H

OC

R

2 =0.1

48

1

Edu

cati

on

Firm

Siz

e A

ge

Firm

Age

Lear

nin

g St

rate

gie

s V

aria

ble

s:

LBN

D:

Lear

nin

g b

y N

etw

ork

ing

Del

iber

atel

y LB

NS:

Lea

rnin

g b

y N

etw

ork

ing

Spo

nta

neo

usl

y LB

ID:

Lear

nin

g b

y Im

itat

ing

Del

iber

atel

y LB

IS:

Lear

nin

g b

y Im

itat

ing

Spo

nta

neo

usl

y LB

DD

: Le

arn

ing

by

Do

ing

De

liber

atel

y LB

DS:

Lea

rnin

g b

y D

oin

g Sp

on

tan

eou

sly

Pri

or

Bu

sin

ess

Exp

erie

nce

Self

-Eff

icac

y

Co

gnit

ive

styl

e (m

od

erat

or)

Stro

ng

ties

B

usi

nes

s ti

es

Pro

fess

ion

al &

So

cial

ties

Pro

&S

oci

alt

ies-

>L

earn

ing

Str

ate

gie

s:

Pro

&S

oci

alti

es -

> L

BID

β =

-0.0

28

6 (

n.s

.)

Pro

&S

oci

alti

es -

> L

BD

D H

OC

β =

-0.1

763

(*)

Pro

&S

oci

alti

es -

> L

BD

S β

=0

.1771

(n

.s.)

Pro

&S

oci

alti

es -

> L

BIS

β =

0.1

43

3 (

*)

Pro

&S

oci

alti

es -

> L

BN

D H

OC

β =

0.0

798

(n.s

.)

Pro

&S

oci

alti

es -

> L

BN

S β

=-0

.034

1(n

.s.)

Str

on

gti

es -

> L

earn

ing

Str

ate

gie

s:

Str

on

gti

es -

> L

BID

β =

-0.1

035

(n.s

.)

Str

on

gti

es -

> L

BD

D H

OC

β=

0.0

507

(n

.s.)

Str

on

gti

es -

> L

BD

S β

=-0

.130

9 (

n.s

.)

Str

on

gti

es -

> L

BIS

β=

-0.1

28

5 (

n.s

.)

Str

on

gti

es -

> L

BN

D H

OC

β =

0.1

157 (

n.s

)

Str

on

gti

es -

> L

BN

S β

= 0

.153

3 (

n.s

.)

Bu

sin

esst

ies>

Lea

rnin

g S

tra

teg

ies:

Bu

sin

esst

ies

-> L

BID

β =

0.1

70

3 (

*)

Bu

sin

esst

ies

-> L

BD

D H

OC

β =

0.2

881

(***)

Bu

sin

esst

ies

-> L

BD

S β

=0

.0895

(n.s

.)

Bu

sin

esst

ies

-> L

BIS

β =

0.0

43

7 (

n.s

.)

Bu

sin

esst

ies

-> L

BN

D H

OC

β =

0.1

71 (

n.s

.)

Bu

sin

esst

ies

-> L

BN

S β

=0

.1533

(n.s

.)

PK

->

Lea

rnin

g S

tra

teg

ies:

PK

->

LB

ID β

=0

.2138

(*)

PK

->

LB

DD

HO

C β

=0.2

88

5 (

**)

PK

->

LB

DS

β=

0.0

629

(n.s

.)

PK

->

LB

IS β

=0

.2453

(***)

PK

->

LB

ND

HO

C β

=0.1

91

(*)

PK

->

LB

NS

β=

0.1

448

(n.s

.)

SE

->

PK

β =

0.3

128

(***)

Biz

Exp

->

PK

β =

-0.2

18

3 (

**)

PK

*C

SI-

>L

earn

ing

Str

ate

gie

s:

PK

*C

SI-

>L

BD

D H

OC

β=

0.2

15

5(*

*)

PK

*C

SI-

>L

BD

S β

=0

.22

35

(**)

PK

*C

SI-

>L

BID

β=

0.2

068

(**)

PK

*C

SI-

>L

BN

D H

OC

β=

0.1

23

3 (

*)

PK

*C

SI-

>L

BIS

β=

-0.2

049

(**)

PK

*C

SI-

>L

BN

S β

=0

.15

75

(*)

Prior Knowledge

HOC

Fig

ure

6.7

: M

ain r

esult

s of

the

PL

S-S

EM

str

uct

ura

l m

odel

296

The following Table ‎6.33 shows the overall findings of the hypotheses testing (the

path coefficients and p values are reported in the figure above (table continued on 2nd

page):

Table ‎6.33: Results of the Hypotheses testing

Effect Type Hypotheses Results

Mai

n E

ffec

ts

H1: Entrepreneurial self-efficacy level will have a positive effect

on the level of the prior knowledge.

Supported

H2: Prior business ownership experience will be negatively

related with prior knowledge.

Supported

H3: Prior knowledge will be positively associated with the ways


Partially

Supported

H4: Prior knowledge leads to more learning by imitating than

learning by doing, and similarly, learning by networking.

Partially

Supported

H5.1: Business ties (Businessties) will have a positive effect on

the ways entrepreneurs learn but entrepreneurial opportunities.

Partially

Supported

H5.2: Professional and Social (Pro&Socialties) will have a

positive effect on the ways entrepreneurs learn about


Partially

Supported

H5.3: Strong ties will have a positive effect on the learning

through networking strategies.

Not Supported

H5.4: Strong ties will have a negative effect on the learning

through imitating and Doing strategies

Not Supported

Mo

der

atio

n

Eff

ect

H6: Cognitive Style (CSI) will moderate the impact of Prior

Knowledge on the ways entrepreneurs learn about


Supported

297

The results indicate that of the nine hypothesised relationships in the research model,

two were supported, five were partially supported, and two were not supported.

The findings indicate that:

1. Entrepreneurial self-efficacy was positively related (β =0.3128***) to the level of

prior knowledge (H1 was supported).

2. In addition, a negative, and statistically significant relationships between prior

business ownership experience (BizExp) and prior knowledge (β =-0.2183**) were

found (H2 was supported).

3. Prior knowledge was positively related to four of the six learning strategies,

however, the effect of prior knowledge on learning by doing spontaneously (β=0.0629,

n.s.), and learning by networking spontaneously (β=0.1448, n.s.) was small and not

statistically significant (H3 partially supported). Furthermore, the effect (as measured

by the path coefficient value) of prior knowledge on learning by imitating

(deliberately, β=0.2138*, and spontaneously β=0.2453**) was greater than the effect

of prior knowledge on learning by doing spontaneously (β=0.0629, n.s.) and learning

by networking (deliberately, β=0.191*, and spontaneously, β=0.1448, n.s.), however,

it was lower than learning by doing deliberately (β=0.2885**) (H4 partially

supported).

4. In two of the relationships, the effect of Business ties on the learning strategies can

be described as positive, influential, and stronger, compared to the other types of

networking ties, (LBDD: β=0.2881**, and LBID: β=0.1703*). In four of the

relationships (LBDS: β=0.0895, n.s., LBIS: β=0.0437, n.s., LBND: β=0.171, n.s., and

LBNS β=0.1533, n.s) the effect was non-significant (H5.1 partially supported).

5. The effect of professional & social ties on the six learning strategies was in general

small and in most of the cases non-significant. The quantitative results indicated that

professional & social ties negatively influenced learning by imitating deliberately

(LBID, β= -0.0286, n.s.), and positively influence learning by networking deliberately

(LBND β=0.0798, n.s.), but the impact was very small and non-significant. In

addition, the influence of professional & social ties on learning by doing deliberately

298

(LBDD, β=-0.1763*) was negative and significant, but affect positively the three

spontaneous learning strategies (LBDS: β=0.1771, n.s., LBIS: β=0.1433*, and LBNS:

β=0.1448, n.s.) (H5.2 partially supported).

6. The effect of strong ties on the six learning strategies was in general small and in all

of the cases non-significant. Strong ties negatively influences learning by networking

deliberately (LBND, β=-0.0341, n.s.), and positively learning by networking

spontaneously (LBNS, β=0.1157, n.s.) (H5.3 not supported).

The effect of strong ties on learning by imitating deliberately (LBID, β=-0.1035, n.s.),

learning by doing deliberately (LBDD HOC, β=0.049, n.s.), learning by doing

spontaneously (LBDS β=-0.1309, n.s.), and learning by imitating spontaneously (LBIS

β=-0.1285, n.s.) can be considered to have small and non-significant effect (H5.4 not

supported).

7. Finally, the results indicated that Cognitive Style (CSI) moderated the impact of

Prior Knowledge on the ways entrepreneurs learn about entrepreneurial opportunities

(PK*CSI->LBDD HOC β=0.2155***, PK*CSI->LBDS β=0.2235***, PK*CSI-

>LBID β=0.2068***, PK*CSI->LBND HOC β=-0.2049***, PK*CSI->LBIS

β=0.1233**, PK*CSI->LBNS β=0.1575**). The moderating relationships increased

(i.e. the relationships between Prior Knowledge and the Learning Strategies) when the

cognitive style index score was higher, (i.e. the entrepreneur tends to be more

analytical). Overall, it was found, in this study, that the cognitive style significantly

moderates the relationships between prior knowledge and the learning strategies (H6

supported).

299

7. Discussion

This chapter discusses the main findings and results of the study. The discussion is

conducted by integrating the findings of both phases: the qualitative and quantitative,

in the context of prior studies in this field of enquiry (Creswell, 2009). This approach

encourages a broadening of understanding by using one approach to better

“…understand, explain, or build on the results from the other approach” (Creswell,

2009, p. 205).

The interpretation of the qualitative findings, in the light of the literature and the

findings of the quantitative phase enabled this study to reveal important facets of the

ways entrepreneurs learn in the opportunity identification process.

This chapter is organised as follows:

The first sub section introduces the overarching research question and the main

themes of this study, including a review of the main findings of the ways

entrepreneurs learn about international opportunities (7.1).

Secondly, the pertinent results for the factors that affect the ways entrepreneurs

learn about entrepreneurial opportunities are discussed (7.2).

7.1 Research Question

Di Gregorio et al. (2008) based on Shane and Venkataraman (2000b) defined that

“entrepreneurship entails the discovery, evaluation and exploitation of opportunities to

introduce new goods and services, as well making efforts to organise markets,

processes, and raw materials in ways that had previously not existed” (2008, p. 189).

The entrepreneurial behaviours, examined in this study, focused on international

opportunities, which play an important role in the domain of international

entrepreneurship research (Mainela et al., 2013; Muzychenko and Liesch, 2015).

However, there is a miss-match between the importance of this topic and the extent of

theoretical and empirical development in relation to the concept of entrepreneurial

opportunities (Jones et al., 2011; Mainela et al., 2013).

300

Learning is multifaceted, complex, dynamic, and it has been argued by many writers

that learning is an important attribute of International New Ventures (INV) (Rialp et

al., 2014), however the concept of how and when entrepreneurs learn and what they

learn in foreign markets is not fully captured and discussed in the literature (Zahra,

2005; Weerawardena et al., 2007; Rialp et al., 2014)).

The literature review, revealed that the topic of how international entrepreneurs

identified entrepreneurial opportunities, how they learn about them, what their

information sources are, and how this relates to the factors that might predict the way

they learn, have been discussed in the literature in sometimes vague and abstract ways.

The research domain suffers from limited theoretical and empirical discussion in

relation to the concept of the ways entrepreneurs learns and the factors that affect the

way they learn. This study focused on the opportunity identification process and

looked at it from the perspective of the learning approach (Corbett, 2005a). Hence, the

main research question of this study examined the relationship between learning,

opportunity identification, and international entrepreneurship.

The main purpose of this two-phase, sequential mixed methods study was to learn

about the factors that affect how entrepreneurs gain knowledge and learn about

international opportunities. Based on the findings of the first qualitative phase

(QUAL1) and based on the research gaps that were revealed by the literature review,

the following question served as the overarching question for the whole study:

What are the factors that affect the way entrepreneurs learn about opportunities in

the international arena?

In order to answer this overarching question, firstly, the definitions of

entrepreneurship, international entrepreneurship, and opportunity identification are

discussed. Secondly, a discussion, which integrates the qualitative and the quantitative

findings, is presented to elucidate, from the findings of this study, the ways

entrepreneurs learn about international business opportunities.

Exploring the ways entrepreneurs learn about international opportunities is not

straightforward, because there are different ways to approach the learning processes of

entrepreneurs in general. Thus, it is important to understand what factors determine

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entrepreneurial learning, how entrepreneurs learn in any given entrepreneurial stage,

and what conditions affect the extent of learning from a specific entrepreneurial action

(Petkova, 2009).

7.1.1 Entrepreneurship, International Entrepreneurship and Opportunity

Identification

The process of internationalisation is often explained by the ‘Uppsala’ model. The

Uppsala internationalisation model (Johanson and Vahlne, 1977; Johanson and

Vahlne, 1990), often named as the ‘Stages’ model (Madsen and Servais, 1997),

maintains that firms usually tend to become international in a slow and incremental

manner. This sequential model focuses its attention on international entry mode

strategies and elucidates the importance of market information acquisition in order for

the firm to progress through the model (Johanson and Vahlne, 1977; Madsen and

Servais, 1997; Johanson and Vahlne, 2009).

However, the existence of new international ventures challenges the rationale of the

‘Uppsala’ model. These types of ventures internationalise rapidly, in comparison to

what is expected from the stages model (Welch and Luostarinen, 1988). They are

often termed 'Born Global’ or ‘International New Ventures' (INV), (see Appendix H)

skip some important stages and are internationalised at a very early stage of their life-

cycle. It has, moreover, been argued that ‘Born Global’ ventures are flexible in their

internationalisation modes (Rialp et al., 2005), which are adapted to the needs of the

individual market and clients (Sharma and Blomstermo, 2003; Melén and Nordman,

2009).

The findings from the entrepreneurs in this study confirmed that internationalisation is

a matter of a decision, or a strategy in many cases, but for them, living in Israel and

working in the High-tech industry, being international from inception (INV or ‘Born

Global’) is almost inevitable. McDougall and Oviatt (2000) described international

entrepreneurship as a complex process integrating value creation and opportunity

seeking, through a combination of innovative, proactive and risk seeking behaviour.

The findings of the qualitative phase support this argument, by corroborating that

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acting entrepreneurially and becoming international are dual processes that intersect in

practice.

In addition, and in line with the qualitative findings, opportunities are discovered or

created (Ardichvili et al., 2003a; Chiasson and Saunders, 2005; Dutta and Crossan,

2005; Alvarez and Barney, 2007a; Edelman and Yli-Renko, 2010; Venkataraman et

al., 2012; Mainela et al., 2013). Opportunities are discovered either through a

serendipitous event or through a deliberate search process (Bhave, 1994a; Lumpkin

and Lichtenstein, 2005a), or formed through the selection, evaluation, and refinement

processes involved in recognising opportunities (Lumpkin et al., 2004; De Clercq et

al., 2005; Lumpkin and Lichtenstein, 2005a). In order to seize the opportunities, which

arise, the entrepreneur must be determined and persevering. The participants described

the process of identifying international opportunities as something that was

constructed as a reactive process of responding to the unexpected. They seize

opportunities and have the ability to identify them by using their exceptional ability to

sense opportunities.

In this interpretation, the opportunity is thought to exist separately from the

entrepreneur (Dutta and Crossan, 2005). However, a more recent approach considers

these themes as analytically connected, and contends that opportunities are a

consequence of the dynamic socioeconomic environment and characteristics of the

entrepreneur. Accordingly, entrepreneurs pursue business opportunities, although

vaguely perceived, by taking decision and acting upon them in order to create a

successful venture (Dutta and Crossan, 2005; McMullen and Shepherd, 2006).

Hebert and Link (1988) review the history of economic thought about

entrepreneurship, and conclude that opportunity creation, risk embracement, and being

responsive to existing circumstances are important facets of entrepreneurial action.

What is perceived as a risk or a desirable opportunity depends on what stands out as

relevant for specific entrepreneurs. This highlights the notion of risks and

opportunities that are both highly personal and influenced by specific situations, and

as a result develop with experience.

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The literature distinguishes between entrepreneurs who identify an entrepreneurial

idea, which they perceive as the answer to a need they identify from within, and those

who create “something from nothing” from a sort of vision that comes to mind. Both

types were represented in the study. In the interview with participant H, they exist in

tandem. He mentioned that at some time the idea just showed up, as a sort of “light

bulb” goes on in his head; or in other situations, he was more methodical, and

purposefully focused his thoughts to initiate the idea.

The findings of this qualitative analysis show that several types of international

entrepreneurs can be identified. Most of the participants reported that they intended to

internationalise early in their entrepreneurship life cycle. Their motivation to do so is

derived mainly from two groups of factors: internal motivational factors and external

motivational factors. They perceive early stage internationalisation to be an almost

inevitable requirement for their success as entrepreneurs. However, they are aware of

the risks involved in internationalisation, and that they may lack many of the

resources, which are essential for internationalisation in the early stages. These

resources include finance, time, effort, and above all, knowledge. In order to bridge

this knowledge gap, they have to learn.

The entrepreneurs in this study (in both phases) learn in ways similar to those

described by Smilor (1997, p. 344): "…entrepreneurs are exceptional learners. They

learn from everything… they learn from other entrepreneurs. They learn from

experience. They learn by doing. They learn from what works, and more importantly,

from what does not work.”

7.1.2 The ways Entrepreneurs Learn about Entrepreneurial Opportunities

Entrepreneurial learning is a never-ending process. It begins with an entrepreneur and

continues throughout the life cycle of the entrepreneurship, across the individual,

group and organisation level, by focusing on the opportunity identification (Crossan et

al., 1999; Dutta and Crossan, 2005). The learning process takes place between the time

an opportunity is recognised and its successful exploitation (Ravasi and Turati, 2005).

It was found in this study that entrepreneurs learn from their past successes and

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failures regarding a given opportunity, and use their experiences to improve their

future chances of success (Minniti and Bygrave, 2001).

The study findings support the importance of 'learning by doing' as a learning

mechanism in an entrepreneurial context, an aspect that has been widely discussed in

the literature on entrepreneurial learning (Minniti and Bygrave, 2001; Rae and

Carswell, 2001; Cope, 2003a).

The role of the mentor is also emphasised in this study. Mentors serve as an important

source of information and as knowledge providers, but their most important function is

to assist entrepreneurs to better analyse, understand and interpret the environment

(Sardana and Scott-Kemmis, 2010). Learning from others, such as mentoring, may

also be deemed a learning type or mechanism. The way entrepreneurs learn is complex

and might be affected and changed due to the stage of the entrepreneurship: market

entry, establishment, and the operational phase. In general, entrepreneurial learning as

described in this study, is either unorganised or unmethodical learning (the role of

intuition was highly highlighted in both qualitative phases) or structured and planned.

Some entrepreneurs in this study were engaged in unstructured activities, with little

regular feedback. They must gather information that helps them identify opportunities

and then must try to assemble resources and create a new venture, all in an iterative

process in which they learn about the business and its aspects (Cooper et al., 1995). In

addition to information collecting activities, entrepreneurs in this study indicate that

one of their preferred strategies for filling the gaps in their limited knowledge is to

make use of their social ties. As a first step, they might consult with experts and

professionals such as accountants, lawyers, patent editors and so on; then in the early

stages, due to their lack of resources, they will use the Internet as a replacement tool

instead of doing international field work such as holding face to face meetings,

attending conventions and trade shows, or meeting with international prospects.

In this study, based on the qualitative phase (QUAL) and the literature, it was argued

that in a rapid and accelerated internationalisation, entrepreneurs might be able to

choose to learn through experiencing ('by searching', 'by doing', 'by experimenting',

'from failures and successes'), via networking ('from the experience of others'), and

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through imitating ('paradigm of interpretation'). In addition, they can do it in a

systematic or a random manner. These six strategies are legitimate, effective, and each

one of the former three (i.e. 'by experiencing', 'via networking', 'through imitating') can

be combined with the latter two ('planned', 'emergent'). However, there is no one

strategy, which is superior to another.

In order to engage in a learning process, regarding the opportunity, entrepreneurs

should integrate strategies from both groups; for example, learn via networking in a

planned and systematic manner or randomly. Entrepreneurs, that integrate these

actions (i.e. 'doing', 'networking', and 'imitating') with these capabilities (i.e.

'systematic' and 'random') are able to 'proactively reflect' on past events, and thus,

eventually learn how to learn (Cope and Watts, 2000)

The following paragraphs discuss the results concerning each of the three groups of

learning strategies: learning by doing, learning by imitating and learning by

networking.

Learning by Doing

Entrepreneurship scholars often consider the start-up process as a process of trial and

error (Cope and Watts, 2000; Politis, 2005) and thus entrepreneurs preferred choice of

learning behaviour is learning by doing. However, entrepreneurs may not learn simply

by doing things (Petkova, 2009). 'Learning by doing’, involves 'doing' and some

experimentation but at the same time requires repetition of a particular task (Petkova,

2009). The repetition of the task, in a process of learning, will enable the entrepreneur

to find the most efficient way of performing the task and to become proficient in

performing it (Anzai and Simon, 1979). Therefore, 'learning by doing' (Thompson,

2011) can be considered as “learning by repetition of efficient practices” (Petkova,

2009, p. 346), they learn by 'doing and doing and doing' (Greeno and Simon, 1988),

through a process of reflection and review (Cope and Watts, 2000).

In this dynamic process, they should keep an 'open mind', observe, and learn from

their previous experience, and from others. Largely, learning from experience, as

perceived by the entrepreneurs in this study is essential since much of

entrepreneurship takes place in an environment of uncertainty. However, they agreed

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that learning from experience somehow might limit the personal development and

knowledge of the entrepreneurs.

Entrepreneurs who participated in the qualitative phases of this study emphasised

experience as a crucial factor in their entrepreneurial actions, decisions and processes,

and consequently stressed the importance of learning by doing as an experience-based

learning, i.e. learning from direct experience. Implicit in their words was the role of

mistakes and failures in the process of learning. The quantitative findings strengthen

these results by emphasising that this type of learning strategies involved learning

from direct and own experience, mainly at the initial stage of the venture’s life cycle.

This type of learning emphasised the importance of knowledge or information that was

codified and easy to access (Voudouris et al., 2011).

The quantitative findings (based on the EFA and the PLS-SEM analysis) revealed that

a learning by doing spontaneously strategy represents learning by reflection based on

thoughts, relevant literature and an unplanned and occasional internet surfing (the

unplanned learning from mistakes item was omitted during the PLS-SEM analysis due

to low AVE value). For example, one of the items derived from the Qualitative phase

exemplifies this idea: I learnt unintentionally, and without prior planning, from

relevant literature (such as professional journals, business, and managerial books,

professional websites) about this new idea. This may highlight the fact that some of

the entrepreneurs do not necessarily have a clear business idea, they had a vague

solution about a known need or a problem, and while they were reading an article or

even a newspaper, they experienced what is often called the “Eureka moment.”

The PLS-SEM structural model results indicated that the learning by doing

spontaneously (LBDS) showed a low level of predictive relevancy, thus the predictors

do not contribute to the construct’s variance explained proportion. The second learning

by doing strategy consisted of two main components or drivers for learning: learning

by doing deliberately from mistakes, and learning by active search.

The significant results and the high loadings value of LBDD furthermore confirmed

the importance of learning from mistakes (for example item 172: “When I took action

with regard to this business opportunity, I reflected on my previous mistakes, and tried

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to learn from them”), and the need to be actively involved in a systematic search for

information in the opportunity identification process (for example, item 171: When I

had an idea, I preferred to actively and systematically search, by myself, for

information on this topic). These two components highlight the importance of

experimenting and the role of failures and mistakes as important learning facilitators

of entrepreneurs, especially when the entrepreneur seeks deliberately to learn about the

opportunity.

Prior studies also emphasised learning from experience as an important learning mode

of entrepreneurs. For example Levitt and March (1988) whose concepts were later

applied by Schwens and Kabst (2009) to the field of entrepreneurship. The second is

the application of the experiential learning theory (ELT), developed by Kolb (1984) to

the field of entrepreneurship in general, and opportunity recognition, specifically, by

Corbett (2005b, 2007b) and Lumpkin and Lichtenstein (2005b).

While a substantial body of organisational learning research exists, only recently has

there been an emphasis on learning from success and failure experiences (Miner et al.,

1999; Haunschild and Sullivan, 2002; Corbett et al., 2007; Kim and Miner, 2007). The

unique aspect of this study is that this learning strategy is also legitimate and relevant

to the way entrepreneurs learn about opportunities. Ellis et al.(2009) defined

experience-based learning as the “process of drawing lessons from experience via

continuous improvement of one’s knowledge structures” (2009, p. 543).

Entrepreneurs, make use of 'explicit feedback', from self, experts, managers and

colleagues, as their major tool for performance interpretation and evaluation. In

addition, when the entrepreneurs learn by doing they have to define how this situation

differs from previous experiences by implementing one of the following four types of

strategy: critical thinking, hypothesis testing, evaluation of results, and critical

reflection (Robinson and Wick, 1992).

Corbett (2005b) developed a theoretical model that uses Experiential Learning Theory

(ELT) which was developed by Kolb (1984), and the creativity-based models of

opportunity recognition (Lumpkin et al., 2004). This model which was developed by

Lumpkin et al. Lumpkin et al. (2004), to directly address how individuals acquire and

transform information within the process of opportunity identification and

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exploitation. He transposed Kolb's learning modes onto Lumpkin et al.'s model and

suggested that individuals with diverse learning modes will achieve better results

during different phases of the opportunity recognition process. In this view of the

relationship between entrepreneurship and learning, Corbett (2005b) proposes that

entrepreneurs who use different learning styles in different entrepreneurial phases are

more likely to be successful. In other words, they adapt and learn as they progress

through the process of entrepreneurship. Therefore, learning by doing in this context

of opportunity identification, is the process of transforming prior experience and

knowledge into new knowledge. Moreover, Corbett (2005b) found that when initially

identifying an opportunity, specific human capital is more useful than general human

capital. However, as the entrepreneurship progresses over time, general human capital

becomes more useful. This means that the entrepreneur should learn when to rely on

either specific or general human capital.

In this study, participants reported that another important aspect of their learning is

learning from past failures. Interestingly, they not only learn from their own failures

but they learn from success stories of others (Chuang and Baum, 2003). This view of

organisational learning shows that learning from failures can be effective in the

context of opportunity recognition (Dutta and Crossan, 2005).

This may not be because they perceive learning from failures as more effective than

learning from success, but mainly because the information about organisational

failures is often concealed from publication, making the details of failures less

accessible. Moreover, the experience of failure is "more likely than experience with

success to produce two of the necessary conditions for experiential learning discussed

above: the motivation to alter knowledge, and ability to extract meaningful knowledge

from experience" (Madsen and Desai, 2010, p. 454).

Learning by Networking

Learning by networking is defined in this study as the extent to which the entrepreneur

uses network learning to gain knowledge about the international market (Schwens and

Kabst, 2012). The term “network” is employed in several ways in the literature, in

order to illustrate connections between players who can be individuals or organisations

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(Coviello and Cox, 2006; Kontinen and Ojala, 2011). Ellis (2008; 2011) noted that

research using the network model of internationalisation elucidates the interaction

between organisations. However, important social relations at the level of the

individual entrepreneur have been ignored (Ellis, 2008; Ellis, 2011; Kontinen and

Ojala, 2011).

Entrepreneurs in general and international entrepreneurs specifically, operate in an

uncertain and dynamic environment, and are constantly lacking in resources such as

finance, knowledge, and entrepreneurial and international experience. The lack of

resources challenges the entrepreneur, and may affect her ability to internationalise.

The international entrepreneur, particularly in the early stages, has a struggle to

overcome these obstacles. One effective approach to be considered is the

establishment, maintenance, and development of networks. Researchers who focus on

early-internationalised firms emphasise the importance of the relationship between

internationalisation and networking (Coviello and Munro, 1995; Oviatt and

McDougall, 2005a; Coviello, 2006). The reasoning behind the need to develop

networks is based on the possibility of acquiring the knowledge necessary to operate

internationally (Ghauri et al., 2005). Countless articles have emphasised the important

role of networking in the internationalisation process. However, most of them dealt

with the antecedents of networking or networking as a mechanism to acquire

knowledge. Based on the findings of this study (QUAL2), it was evident that

entrepreneurs choose to learn from experienced people that they are familiar with, and

even admire, mainly because as entrepreneurs they perceive themselves as lonely

individuals, thus the opinion of others, for example may give them the right and

necessary feedback.

An individual’s network of relationships may yield new resources, such as knowledge

(Agndal et al., 2008). Entrepreneurs, in the early stages of internationalisation, will

need to acquire mainly technological knowledge and market knowledge (Nordman and

Melén, 2008). Accordingly, when the organisation is the entrepreneur herself, or

sometimes a small group of founders, networks can replace the firm at the generation,

transfer, and recombination stages of knowledge acquisition, especially when

considering entering the uncharted territory of new markets.

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Schwens and Kabst (2009) argued that early internationalising ventures learn about

foreign markets through various mechanisms such as networking and by imitating39

.

Loane and Bell (2006) suggest that where leading entrepreneurs observe gaps, they

will acquire valuable knowledge or resources, for example leveraging existing

networks, building new networks when required, or recruiting highly experienced

managers who already possess a certain amount of knowledge about the target market,

in order to internationalise rapidly and gain 'first mover' advantage.

Considering that many of the 'born global' firms are small and did not have extensive

networks from the start, “the contacts in their emerging networks were important for

rapid internationalisation to secure business and to source knowledge” (Kudina et al.,

2008, p. 43). Therefore, networks are an important success factor, since they help the

company to learn about the overseas market, and to some extent are responsible for the

success of/demand for its new products (Kudina et al., 2008). When entering foreign

markets, the role of networks becomes more important and ventures that lack existing

social networks are able to identify business opportunities by using their weak ties.

The reliability of the contact is important when they realise these business

opportunities and establish new networking ties for entering foreign markets

(Kontinen and Ojala, 2011).

Recently, some scholars have raised the topic of how entrepreneurs learn about

international opportunities through their existing ties with others (Ellis, 2011).

Exchange opportunities were typically identified via existing relationships linking

researchers, innovators and others interested in a particular technology (Sharma and

Blomstermo, 2003; Komulainen et al., 2006).

The quantitative results revealed similar conclusions. The significant, and high

loadings values of the Learning by networking deliberately (LBND) construct

elucidate the role of ties as a source of knowledge in the opportunity identification

process. LBND is a two-component construct: learning from others (for example item

q131:“I engaged with others in a deliberate and systematic inquiry regarding an idea,

39

The exact term they used was paradigms of interpretation (Schwens and Kabst, 2009)

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in order to be able to study it in depth”) and learning from personal contacts (for

example item q134:”I deliberately consulted with my personal contacts, regarding an

idea. I realised that this was a feasible business opportunity”). Accordingly,

information acquired from managers’ networks of friends, relatives and other contacts

enabled firms to enter foreign markets (Zain and Ng, 2006). The role of others was

also represented in the learning by networking spontaneously (LBNS), for example

item q141:” When I chatted with people I know, they came up with interesting new

ideas that I had not thought of previously”

Accordingly, industry connections and friendship ties (Crick and Spence, 2005), prior

social ties (Wong and Ellis, 2002), former employees, dealer networks, migrating

customers, and family members (Ellis and Pecotich, 2001) were all used to identify

new exchange opportunities.

Both the information and the networks themselves can contribute to their success in

identifying potentially valuable opportunities for new ventures (Ozgen and Baron,

2007). The entrepreneurs, throughout the process of establishing the entrepreneurial

venture rely on their networks as an important source of acquiring knowledge in the

most efficient way (Greve and Salaff, 2003b).

Learning by Imitating

The internationalisation stage theory regards experience at an organisational level as

the most important factor for determining the firm’s international expansion pattern

(Johanson and Vahlne, 1977). Firms may learn directly from foreign market

experience and indirectly via observation of foreign firms or from interaction with a

foreign partner (Johanson and Vahlne, 1990; Sapienza et al., 2005).

Levitt and March (1988) reviewed the literature on organisational learning, and

distinguish between three types of learning from experience: the first is learning from

direct experience, the second is learning from the experience of others, and the third

type is learning from a paradigm of interpretation. Organisations learn from direct

experience through two main mechanisms: trial-and-error experimentation, and the

organisational search (Levitt and March, 1988). Entrepreneurs also learn from the

experience of others. When they do so, they often rely on their ‘paradigm of

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interpretation’ (Schwens and Kabst, 2009). This type of learning refers to how

organisations develop conceptual frameworks or paradigms for interpreting that

experience (Levitt and March, 1988, p. 319). In this type of learning, the entrepreneurs

learn by imitating legitimized routines. This routine enables the organisation to

generate and distribute knowledge within the boundaries of the firm (Schwens and

Kabst, 2009).

The role of using networks and their imitations is emphasised by Forsgren (2002) who

states that, in order for ventures to reduce the liability of foreignness (i.e. the entrant

venture is at a disadvantage in comparison with local competitors), they often rely on

networks or imitations rather than direct learning, for example. In this study learning

by imitating is defined as the extent to which the entrepreneur learns by imitating other

routines that are perceived to be best practices in the local market (Schwens and

Kabst, 2012). As one of the participants described it:

""I personally went to learn from the experience of others." (QUAL2,

Focus Group)

The quantitative results confirmed the Schwens and Kabst (2009) argument that

imitations constitute a valuable mechanism for overcoming resource constraints and to

manage the liabilities of foreignness.

Learning by imitating deliberately (LBID) consists of intended learning from best

practices of other ventures and entrepreneurs, for example: “I deliberately acquired

knowledge about the foreign market through following the example of best practices

firms” (item q153).

The same conclusion can be reached regarding learning by imitating spontaneously

(LBIS). The entrepreneurs who implement this strategy understand the importance and

even effectiveness of mimicking the behaviours of others. They observe best practices

and know that an opportunity may result due to this observation (for example, item

q161:”I observed others that turned out to be unexpectedly informative”).

They prefer to learn in this way since venturing into cross border markets early in the

firm's lifecycle is a risky move. If the perceived risk of internationalising is initially

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lower than not internationalising, the founders have to implement mechanisms that

will enable them to overcome this gap.

Novice entrepreneurs, particularly those who have a low level of international

experience, have to acquire new knowledge more rapidly, so they may mimic the

behaviours of others. This example of vicarious learning can shorten the entrepreneurs'

learning cycle for developing the necessary skills and acquiring new knowledge,

especially market knowledge.

Learning by imitating may enable the entrepreneurs to increases both legitimacy and

learning efficiency (Schwens and Kabst, 2009). Imitating differs from copying mainly

because by imitating the entrepreneurs adapt “best practices” routines, which could

allow them to learn faster from than for example, learning by doing. In addition, they

increase the efficiency of learning by using practices and routines that are directly

provided the prerequisites of the foreign market. By learning by imitating the

entrepreneurs ‘put their faith’ on established routines of perceived best practices, and

thus save the time of developing own routines, methods and practices (Schwens and

Kabst, 2012).

Krueger (2005, p. 108) summarised well the importance of understanding how

entrepreneurs learn about entrepreneurial opportunities: “Obviously, if the "heart" of

entrepreneurship is this orientation toward seeking opportunities, developing a much

deeper understanding of this cuts to the very essence of entrepreneurship. If we

understand how we learn to see opportunities, we unlock much of the heretofore

"black box" of entrepreneurship” (2005, p. 108).

7.2 The Factors that Affect the Ways Entrepreneurs Learn about Opportunities

The literature review revealed that the process of identifying entrepreneurial

opportunities is affected by various factors. Among the most important ones are, prior

knowledge (Shane, 2000), social networks (Hills, 1995, Granovetter, 1973,

Granovetter, 1983), and human capital (Ucbasaran et al., 2008). Furthermore, the

entrepreneur’s personal traits, her entrepreneurial alertness, prior knowledge and

experience, and her personal and social networks were among the most important

themes that emerged from the analysis of this qualitative phase. These themes were

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mostly mentioned with reference to the decision to internationalise and the ways

entrepreneurs learn about entrepreneurial opportunity.

Based on the findings of the qualitative phase and prior studies, a conceptual model

was designed to examine the relationships between Prior business ownership

experience (BizExp), Entrepreneurial self-efficacy (SE), and Prior knowledge (PK)

In addition, the relationships between Prior knowledge (PK), the strength of strong ties

(Strongties), Business ties (Businessties), professional & social ties (Pro&Socialties),

and the six learning strategies, were investigated too.

Furthermore, the extent the cognitive style of entrepreneurs moderates the

relationships between prior knowledge (PK) and the learning strategies were

statistically tested.

The following paragraphs discuss the major findings of the quantitative phase

regarding these relationships in the context of prior studies and the findings of the

qualitative phase. In addition, the implications of the quantitative findings in light of

the literature and the qualitative findings are discussed in the next chapter (Chapter 8).

7.2.1 Self-Efficacy and Prior Knowledge

Entrepreneurial self-efficacy plays an important role in entrepreneurship mainly

because it enables entrepreneurs to overcome difficulties during the entrepreneurship

process in general and other challenging processes such as opportunity identification

specifically (Krueger and Dickson, 1994; Barbosa et al., 2007a; McGee et al., 2009;

Pihie and Bagheri, 2013).

The qualitative findings elucidate the fact that many of these study participants

indicate their intention to generate a change in the world, to have an 'effect'. They

perceive the global market as more exciting, but also more challenging and demanding

greater levels of personal and business commitment. They described the international

market in a very positive way, with the emphasis on opportunities rather than risks.

This might be because as entrepreneurs they are influenced by cognitive biases

(Baron, 2004), of which the most central is self-efficacy.

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The quantitative results indicated a positive, and significant relationship between

entrepreneurial self-efficacy and prior knowledge (β =0.3128***). Therefore, H1 was

supported. In addition, the effect size value (f2 =0.116) indicating the importance of

self-efficacy as a predictor of PK. Accordingly, it might be suggested that

entrepreneurs with high level of entrepreneurial self-efficacy, during the opportunity

identification process, have a greater belief in their ability to acquire knowledge and

their perspective of how much they know about the international market. This is

particularly important in high-tech industries, which force the entrepreneur to be more

innovative, creative, and sophisticated. Efficacy beliefs have been found to greatly

influence entrepreneurial behaviour, and improving the perceived feasibility of certain

courses of action is therefore seen as vital to encourage identification of opportunities

(Krueger, 2005).

Entrepreneurial self-efficacy, in a way, creates mental schemas that frame the

perception of the business opportunities. Before acting entrepreneurially, an

entrepreneurial opportunity must be perceived, and an intention to pursue that

opportunity (Krueger, 2005) might be formed. In turn, intentions are driven by critical

attitudes and beliefs such as self-efficacy (Krueger, 2005). Thus, entrepreneurs with

high level of self-efficacy may tend to act upon an opportunity; therefore, indirectly

their level of self-efficacy affects the way they learn about opportunities.

7.2.2 Prior Business Ownership Experience and Prior Knowledge

In this study, entrepreneurs with prior industry experience deliberately search out

opportunities in the global market, based on their experience or store of unique

knowledge.

Given that entrepreneurial actions start in a variety of ways, at different times, the

varied duration of the entrepreneurial life cycle, and the variety of paths they follow,

understanding how different types of entrepreneurs act was deemed beneficial for this

study. In other words, the entrepreneurial types are a very important aspect of

international entrepreneurship, and might have an impact on the way they identify

opportunities, learn, and act on them.

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Westhead, Ucbasaran and Wright (2005b) argue that we should make an explicit

distinction between prior independent business ownership experience and other types

of experience related to entrepreneurship. Prior business ownership could involve

founding a business, inheriting one, or purchasing an independent business. An

important aspect of why such a distinction is needed is indicated by the fact that

experienced entrepreneurs, such as serial and portfolio entrepreneurs, may be able to

leverage their prior business ownership experience in order to identify and exploit

additional business opportunities. In addition they might differ in their decision

making, actions and performance (Westhead et al., 2005b).

Novice entrepreneurs are defined in this study as individual entrepreneurs who

establish a new venture with no prior business ownership experience. Habitual can be

defined as individuals with prior business ownership experience, either in serial or

portfolio entrepreneurship (Ucbasaran et al., 2001). It was learned from the qualitative

phase that some of the study participants had been involved in entrepreneurship from a

very young age, some from childhood, often in several enterprises at the same time, or

a series of enterprises one after the other without a break. For them entrepreneurship is

apparently a way of life, a profession in some cases.

Huovinen and Tihula (2008) described habitual entrepreneurs as persons who have

experience owning at least two different firms. Habitual entrepreneurs who own the

venture temporally, are defined as serial entrepreneurs, whilst entrepreneurs who

owned it simultaneously are addressed as portfolio entrepreneurs (Westhead and

Wright, 1998b). Portfolio entrepreneurs are motivated to found new ventures for the

following reasons: firstly, they aim to increase their wealth or profit (Carter, 1998),

and secondly it might be due to a survival strategy (Iacobucci et al., 2004). In addition,

novice and habitual entrepreneurs differ in their level of experience (Westhead et al.,

2005b), in a way that previous entrepreneurial experience can enable the identification

of business opportunities and hence increase the likelihood of exploiting these

opportunities (Westhead et al., 2005a).

Ucbasaran et al. (2008) contend that business ownership experience has long been

acknowledged as a significant facet of entrepreneurship. This experience might be

acquired sequentially or concurrently (Ucbasaran et al., 2006; Ucbasaran et al., 2010)

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and enable the entrepreneur to identify and pursue business opportunities. Moreover,

experienced entrepreneurs, such as portfolio entrepreneurs, may have the ability to

identify more opportunities and leverage the resources required to pursue opportunities

(Westhead et al., 2005a). International new ventures reflect the dominant role of the

founder (Reuber and Fischer, 1997; Oviatt and McDougall, 2005a).

The quantitative results confirmed a negative, and significant relationships between

prior business ownership experience (BizExp) and prior knowledge (β =-0.2183**).

Therefore, H2 was supported. In addition, the effect size value (f2 =0.055) represents

the prior business ownership experience contribution to the variance explained in PK

(R2) value.

The effect size of prior business ownership experience (BizExp) indicates the

importance of prior business ownership experience as a predictor of PK, although

entrepreneurial self-efficacy has a larger effect size than prior business ownership

experience (BizExp), suggesting its relative importance to the prediction of Prior

Knowledge (PK). These findings are in line with prior studies, which highlighted

experience as a key contributor to the development and growth of entrepreneurial

knowledge (Minniti and Bygrave, 2001; Rae and Carswell, 2001; Politis, 2005; Rerup,

2005; Politis, 2008).

Westhead et al. (2005a) conclude, for example, that business ownership experience

appears to enable individuals to process new information more effectively than

inexperienced individuals. The engagement of entrepreneurs in other businesses as

founders and owners enables them to develop relevant skills and attitudes (Politis,

2008). Conversely, novice entrepreneurs, are likely to lack the necessary experience,

especially in the opportunity identification process, and therefore are not able to

transform it into knowledge. Novice entrepreneurs, although appreciating the

importance of Knowledge about foreign markets, often lack resources such as time

and money. Thus, they will try to fill the gap in their knowledge stocks by relying on

their prior experience or the experience of others, rather than investing in the

demanding process of acquiring the necessary and relevant knowledge. Therefore,

internationally experienced entrepreneurs enter foreign markets more rapidly than

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their counterparts (Autio et al., 2000) and this facilitates greater awareness of

opportunities (Westhead et al., 2001).

The study findings imply that prior business ownership experience can be a valuable

source of learning. Prior experience, as measured in this study, affects the knowledge

of entrepreneurs and this in turn, may increase the understanding of methods and

principles for acting in opportunity identification contexts (i.e. learning), where often

the identification process often appears complex and stressed.

7.2.3 Prior Knowledge and Learning Strategies

Extant literature in International Entrepreneurship focuses on the importance of prior

knowledge, and its influence on entrepreneurs’ internationalisation decisions.

According to dictionary definitions, knowledge is familiarity, awareness, or

understanding gained through experience or study (Merriam-Webster, 2010).

Some authors elucidate the role of prior international knowledge in reducing

uncertainty, increasing commitment to international markets, and selecting more

psychically distant markets (Johanson and Vahlne, 1977), others, emphasised the

relevancy of prior international knowledge to the perception of entrepreneurial

opportunities in general (Oviatt and McDougall, 2005a) and international

opportunities specifically (Evers and O’Gorman, 2011).

Three concepts were found to be highly related to explaining differences in

opportunity identification among entrepreneurs: knowledge (Ardichvili et al., 2003b),

creativity, and cognition (Ward, 2004). Shane (2000) and Ardichvili et al. (2003b)

emphasise the importance of the prior knowledge factor in explaining why different

individuals tend to recognise different opportunities.

Renko et al. (2012) contended that the word knowledge implies accuracy in knowing

truthful information about market needs and the means to satisfy those needs.

However, the concept of knowledge relates to what we know, but does not necessarily

demonstrate what we think we know and do not know.

Shepherd and DeTienne (2005) explained that individuals with higher level of prior

knowledge recognise more entrepreneurial opportunities, mainly because their ability

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to make a connection between different concepts (Arentz et al., 2012), hence, enabling

entrepreneurs to identify opportunities (Chandra et al., 2009).

Chandra et al. (2009) argued that there are three main drivers of the opportunity

recognition process identified in the literature: prior knowledge, international network

structure, and a firm’s entrepreneurial orientation. Prior knowledge is an important

factor in the opportunity identification process because it creates a knowledge corridor

that allows opportunities to be recognised or not (Venkataraman, 1997; Ardichvili et

al., 2003b).

In the qualitative phase, the focus group discussions touched on the need for a certain

degree of maturity and business understanding, in order to cope with the complexity

and the adjustments necessary in international entrepreneurship. On the other hand,

there were those who recalled successful international enterprises that were undertaken

by young, inexperienced people with a successful idea. Internationalisation requires a

complex structure of knowledge, as an entrepreneur you must be aware of the need to

acquire various types of knowledge Ardichvili et al. (Ardichvili et al., 2003b).

In this study, prior knowledge was measured as a second-order construct, which

consisted of three facets: foreign, institutional, and social knowledge, which were

found as relevant in the opportunity identification process. International entrepreneurs,

especially from the high-tech industry, driven by their special interest, invest their

resources, mainly, time and effort, to engage in a learning process that might advance

and develop their capabilities, in that way, acquiring deep knowledge about their topic

of interest. In addition, during this process, their level of entrepreneurial self-efficacy,

prior business ownership experience, and their special interest in a specific topic might

lead to the identification of a new entrepreneurial opportunity.

The quantitative results indicated that prior knowledge has a significant and positive

effect on four out of six learning strategies. It was found that the effect of prior

knowledge on learning by doing spontaneously is very small and non-statistically

significant (β=0.0629 (n.s.)). This indicates that the predictors in this model, among

them prior knowledge, do not contribute to the explained variance of the Learning by

doing spontaneously (LBDS) construct. An explanation for that may lie in the

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possibility that learning by doing is by default a deliberate mechanism. Doing

something is a result of a decision to do it, thus, this strategy does not exist in the

opportunity identification process, and therefore, prior knowledge (PK), as well as

other predictors, cannot contribute to the construct’s explained variance.

Furthermore, the effect size values of prior knowledge (PK) leads to the conclusion

that prior knowledge (PK) plays an important role as a predictor construct of the six

learning strategies. The effect sizes of prior knowledge (PK) are the largest among the

five-predictor variables, although they can be considered as ‘small’ (except the

contribution to the variance explained of the learning by doing spontaneously (LBDS)

construct, which is not surprising due to the low level of R2). In addition, based on the

q2 effect size (which is a relative measure of the exogenous construct’s predictive

relevance, for a specific endogenous latent variable (Chin, 2010)), it can be concluded

that for most of the vicarious learning strategies (learning by networking deliberately,

and learning by imitating spontaneously and deliberately), only the prior knowledge

construct (PK) has a meaningful effect size. It means that the prior knowledge

construct is relevant in predicting the ways entrepreneurs learn.

7.2.4 Moderation Effect of Cognitive Style (CSI)

One important goal of this study was to understand how entrepreneurs’ cognitive style

(CSI) influences the strength or the direction of the direct relationship between prior

knowledge (PK) and the ways entrepreneurs learn about the business opportunities. In

an effort to answer this question, the study assessed and evaluated the findings of the

moderating effect of cognitive style (CSI) on the relationships between prior

knowledge (PK) and the six learning strategies. Overall, it was found, in this study,

that cognitive style significantly moderates the relationships between prior knowledge

and the six learning strategies.

These findings can be explained by the findings of other studies, which emphasised

that cognitive style has a direct influence on an individual's approach towards

information for the purpose of making decisions (Dutta and Thornhill, 2008). In

addition, an individual’s cognitive style may affect the preference for different types

of learning (Kickul et al., 2009). Furthermore, entrepreneurs’ cognitive styles are

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effective in different phases of the business venturing process (Olson, 1985; Barbosa

et al., 2007b), such as: opportunity identification, venture creation and growth

(Mitchell et al., 2002). This study provides further evidence to indicate that

entrepreneurs’ cognitive style affects the strength of the influence of prior knowledge

on the learning strategies, in other words, cognitive style moderates the relationships

between prior knowledge (PK) and the six learning strategies.

Prior knowledge is an essential ingredient in the process of learning about

opportunities. This process is experiential in nature with a massive influence of

cognition. Cognitive styles affect modes of information processing such as selecting,

decoding, and organisation. Therefore, it was not surprising to find out that cognitive

style partially determines these relationships. In order for entrepreneurs to learn about

opportunities, they need extensive prior knowledge. If their style is more analytical,

for example, they probably will rely more on their prior knowledge rather than on their

intuition. Cognitive style has been frequently researched as an important determinant

of individual behaviour (Sadler-Smith and Badger, 1998; Kickul et al., 2009) and

conceptualised as the preferred heuristic that individuals employ when they solve

problems (Brigham et al., 2007).

Entrepreneurs perceive and interpret information about foreign markets. This in turn

forms their entrepreneurial cognitions, which are the knowledge structures that

entrepreneurs use in the opportunity identification process (Mitchell et al., 2002), that

ultimately enable the discovery and exploitation of new business opportunities. As

entrepreneurs, during the opportunity identification process, process the information

and use their prior knowledge, they develop the sense of how to learn about the

opportunity, thus the cognitive style plays the role of a moderator between the

knowledge schemas and learning strategies. Cognitive style can be defined also as a

preferred approach to information processing (Allinson et al., 2000a). Hence,

entrepreneurs with different cognitive styles will differ in the ways they process

information and even engage in concrete experiences.

Since styles are often defined as stable and almost unchangeable, strategies in general

and learning strategies specifically, are typically the function of the interaction

between the individual and the situation (Sadler-Smith and Badger, 1998). In the

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specific context of opportunity identification, the role of cognitive style, which is the

function of the individuals’ disposition (Sadler-Smith and Badger, 1998), is to enhance

the influence of the knowledge on the learning strategies; thus entrepreneurs with

different styles may differ in the effect of prior knowledge on the ways they learn in a

specific situation of learning about opportunities. Kickul et al. (2009, p.441)

summarised the relationships between prior knowledge, cognitive style and learning

by stating that: “An individual’s cognitive style may influence the preference for

different types of learning, knowledge gathering, information processing, and decision

making, many of the critical behaviours with which an entrepreneur is confronted on a

daily basis. In addition, it can lead individuals to direct their attention to specific areas

of knowledge and certain tasks, and reduce the extent to which they focus on other,

similarly important, knowledge and tasks.”

This study empirically investigated how entrepreneurs’ prior knowledge affect the

ways they acquire and transform information (i.e., learn) and how this process

interacts with their cognitive style, in the opportunity identification process. The

findings of the interaction effect, suggests that it is not just what the entrepreneur

knows about the foreign market, but also the process through which they process that

information (i.e. their cognitive style), that matters with respect to learning about


7.2.5 Social Networking and Learning Strategies

As the entrepreneur goes through the process of identifying an opportunity, from an

idea and conception through opportunity realization and implementation, social

networks are one of the major sources of ideas and information (Granovetter, 1973;

Burt, 1992; Singh, 2000). Networks, contacts or connections are often informal work

and non-work connections that may include: professionals, friends, family and

colleagues from prior jobs (Greve and Salaff, 2003b). Based on the strength, diversity,

size, and quality of the networks, it is argued in this study, that networks play an

important role in providing support and assistance in multiple aspects, especially in

understanding the way entrepreneurs learn about the identified opportunities.

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Greve and Salaff (2003b), citing Wilken (1979), propose a 3-stage model, which

identified critical social network activities and characteristics of entrepreneurial firms.

At the initial stages of entrepreneurship, the motivation phase, whether it is domestic

or international, entrepreneurs rely on their social networks, particularly their close

circle of personal contacts (Greve and Salaff, 2003b), to learn about the opportunity.

In the second phase, the planning, throughout the discussions with their network

contacts, they learn and consequently acquire missing knowledge, mainly for

clarifying an initially vague opportunity. This is a process of interpretation, in which

they develop the business idea into a fully-fledged business vision. Once they have

decided to establish the venture, their focus is on planning. Now they mobilize their

social networks and consequently they actively enlarge their entrepreneurial networks

and can use 'weaker ties' to obtain new or unique information to which their close

friends and family members cannot get access. In the last phase, the entrepreneurs

establish a new venture. In this phase, entrepreneurs are inclined to concentrate on key

persons in their networks, those who are able to provide resources and commitment

(Greve and Salaff, 2003b). In the same vein, the qualitative findings elucidated three

types of social networking ties:

o “Family and friends” – family members and friends, who are not,

necessarily, entrepreneurs or business people,

o "Professionals" – professionals who can provide professional support

and guidance in the relevant entrepreneurial fields such consultants,

mentors etc.

o "Business ties" - business colleagues or partners, other entrepreneurs or

venture’s founders, who are acquainted with the entrepreneur based on

business relations, direct or indirect.

It was found in the qualitative phases, that establishing social networking ties is

considered an important factor in any entrepreneurial stage, however, it requires

resources such as time, effort and in many cases the knowhow to do it in the right way.

In the process of starting, a new venture the entrepreneur is seeking not only the

resources of equipment, space, and money, but advice, information, and reassurance.

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Consequently, the help and guidance received from both the formal networks (e.g.

banks, accountants, lawyers) and the informal networks (e.g. family, friends, business

contacts) is required for the entrepreneur. However, the best resource for developing

the business (Vesper, 1980; Birley, 1985) and identifying opportunities (Ozgen and

Baron, 2007) are the informal relations.

The entrepreneurs in the qualitative phase generally agreed that social networking is

very important, however there was a broad disagreement about which type of

networking is most important. Some advocated that all three (i.e. strong, business, and

professional and social ties) are important, but they are useful in different

entrepreneurial stages. Probably, there is no right or wrong way of establishing your

own networking ties. However, relationships can differ in character and strength. The

diversity of relationships means that one knows people who do not know each other.

Emotional strength can vary from strong to weak (Aldrich, 1999). Aldrich expects

successful nascent entrepreneurs to have a diverse network of relationships with many

strong ties. Such a network is important, since no individual sets up a firm solely by

himself or herself.

The quantitative findings revealed that all three types of networking ties exist in our

sample. The findings of the EFA showed that each of these three factors is a unipolar

construct. However, the findings of the PLS-SEM analysis were not completely in

line, with the study’s qualitative findings, and the hypotheses. The effect of strong ties

on the six learning strategies was in general small and in all of the cases non-

significant. However, although the effect was non-significant, the results indicated an

interesting pattern, that might be worth to re-examining in future studies. Strong ties affect

negatively learning by doing spontaneously (LBDS, β=-0.139, p<0.1) and learning by

imitating spontaneously (LBIS, β=-0.1285, p<0.1). When the strength of strong ties

increases, the tendency to learn strategically in a spontaneous manner by imitating and

doing, decreased.

Strong Ties are of less importance than the other constructs, as illustrated by the low and

non-significant values of their path coefficients. Furthermore, the effect sizes of strong

ties on the entire criterion endogenous variables (i.e. the six learning strategies) are

lower than the minimum threshold of 0.02, indicating that the contribution of this

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construct to the variance explained of each of the criterion variables is less than

expected. One explanation for that might be because most of the entrepreneurs in this

sample can be considered as internationally experienced entrepreneurs. There is also a

relationship between the international experience of the senior management team and

international networks ((Oviatt and McDougall, 1995; Freeman et al., 2006).

Internationally experienced entrepreneurs rely less on their strong ties because they are

less useful in order to form and to acquire the relevant resources for

internationalisation.

The same conclusion can be made about Professional & Social ties. Professional &

Social ties are defined in this study as a special type of weak tie. The effect of

professional & social ties on the six learning strategies was in general small and in

most of the cases non-significant, except for the influence of professional & social ties

on learning by doing deliberately (LBDD, β=-0.1763, p<0.05), which was negative

and significant. Interestingly, while the effect of professional & social ties on learning

by doing deliberately (LBDD) is significant and negative, professional & social ties

affect positively learning by imitating spontaneously (LBIS: β=0.1433, p<0.05).

Entrepreneurs, especially in the high-tech industry maintain network relationships on a

frequent basis with their professional ties such as their mentors and consultants. This

frequent communication might yield interesting insights about market or technological

opportunities, without planned or methodological behaviour to accomplish this task. In

general, the influence of professional & social ties on learning strategies were

relatively less important, as can be seen by inspecting their path coefficients values

and their effect sizes (f2), indicating that the contribution of this construct to the

variance explained of each of the criterion variables is less than expected.

In comparison to professional & social ties, the effect of Business ties on the learning

strategies can be described as stronger and influential, although, in only two of the

relationships (LBID: β=0.0895*, and LBDD: β=0.2881***) the effect was statistically

significant. The results indicated that when the strength of the business ties increases

the entrepreneurs choose to learn, based on their observation of best cases, or by doing

it by themselves. In addition, by analysing the effect sizes of business ties it can be

seen that effect size (f2) of business ties on learning by doing deliberately (LBDD

HOC), learning by networking spontaneously (LBNS), and learning by imitating

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deliberately (LBID) are above the criterion value of 0.02, indicating that the

contribution of this construct to the variance explained of each of these criterion

variables is as expected, and business ties, as well as their prior knowledge, play an

important role in predicting the way entrepreneurs choose to learn in the opportunity

identification process. Burt (1992, p. 62) explained that "the information benefits of a

network define who knows about the opportunities, when they know and who gets to

participate in them". Consequently, high tech entrepreneurs who maintain their

business networks are more likely to yield new information, which can lead to the

discovery of entrepreneurial opportunities.

The study findings showed that the influence of strong ties is less important than the

two types of weak ties: business ties and professional and social ties. One possible

explanation for that might be related to this study’s sample characteristics and the

diversity in entrepreneurial stages of the entrepreneurs in this sample. Reuber and

Fischer (1997) concluded that the more internationally experienced the managers are,

the more likely they are to form the networks required for internationalisation. Thus,

they will rely more on their business networks rather than on their strong ties.

Furthermore, 'weak ties', such as business ties, may give better access to new and

unique information compared to strong ties, largely due to the infrequency of the

individual's interactions with their 'weak ties' (Granovetter, 1973). Furthermore, in

smaller firms, such as international new ventures, it is not unfeasible to argue that

personal networks and business networks may overlap (Agndal et al., 2008).

Moreover, 'strong ties' are important in the start-up phase, although 'weak ties' make a

greater contribution to accruing and developing cross-border knowledge. ‘Weak ties’,

especially business network relationships, play an important role in the early

internationalisation stage, when firms learn by their experiences (Johanson and

Vahlne, 2003).

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8. Conclusions

In this chapter the main conclusions, contribution, and implications of this study for

both research and practice are discussed. The limitations of the current study are

presented and discussed followed by a discussion on the future directions that the

research in this field of enquiry would benefit from.

This study approached the opportunity identification process as a learning process,

where international entrepreneurs learn strategically about opportunities. The findings

of each phase of this study elucidate the importance of the relationships between

international entrepreneurship and learning. Based on a literature review, the findings

of the qualitative, and the quantitative phase; some important conclusions can be

reached here:

1. Firstly, little research has been carried out regarding the relationship between

Learning and International Entrepreneurship (IE) in different internationalisation

stages, and specifically the topic of international entrepreneurial learning about

business opportunities, has not received widespread attention in mainstream

management journals. In addition, the dynamic nature of the domain of

'Organisational Learning', and the fact that the current International

Entrepreneurship Research is still under development, has led to considerable

inconsistencies which prevent a better theoretical and practical understanding of

international entrepreneurship, the learning about business opportunities, and the

relationship between them. Furthermore, these inconsistencies have resulted in a

number of knowledge gaps in contemporary research, which formed the basis for

the justification for this research. This study expands upon our limited knowledge

on the intersection of the field of International Entrepreneurship and Organisational

Learning theory. Therefore, a study, which serves to fill the gap in current

understanding about the relationship between Learning about opportunities and

International entrepreneurship, is of inherent value.

2. The second is the research strategy. Researchers would benefit from the potential

synergies resulting from a more insightful combination of both quantitative and

qualitative research methods and techniques (Rialp et al., 2005). This is also in-line

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with the call of Coviello and Jones (2004) for researchers to address their

methodological decisions more thoroughly and with more coherency. They indicate

the need for a multidisciplinary approach to be developed for this field.

There are a number of situations where mixed methods research is an advance on

purely qualitative or quantitative research. One such situation is when we have seen

a new way of looking at a phenomenon, such as international entrepreneurship.

Here, two research traditions are combined: international business, dominated by

quantitative research, and entrepreneurship, with its more qualitative approach. This

makes mixed methods suitable, as we can incorporate earlier research within both

areas and seek to integrate the two. Another situation is when there are mixed

results in testing theories. One such situation is the question of the relationship

between international expansion and experience (Johanson and Vahlne, 1990),

where mixed methods could be used to develop and test such theories.

3. The study findings revealed that entrepreneurs learn mainly from their prior

experience, by copying or doing, and from their personal and social networks. In

the interview with participant A “QUAL1, A,” he describes entrepreneurial learning

mainly as learning from experience, and less as organised learning. However, over

the years entrepreneurs have shaped the way they learn, by implementing different

learning strategies. It seems that they place greater importance on the day-to-day

learning from their own and others’ experience, and especially on learning from

their own mistakes and others’ successes, rather than other sources.

In contrast to that, it was concluded that learning by networking is a legitimate

and even an effective strategy, because it enables the entrepreneurs to tap into the

knowledge base of their network partners, without relying on their own experiential

knowledge (Schwens and Kabst, 2012). In fact, this strategy might help them to

overcome, more rapidly, risks and liabilities of foreignness that they confront prior

to their initial entry into foreign markets. No matter which strategy is used, viewed

up close, opportunity identification is not a single point in time, but a process that

begins and ends with an intense focus on learning. In this process, entrepreneurs are

strategic players, who act, promptly, spontaneously or deliberately, assess what

they have achieved, and live at the intersection of dream and action, where they are

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able to be more innovative than others. Identifying opportunities, although not

always successful, is not the impulsive act it can seem to be. A lot of the knowledge

they acquire is based on their actual experience. Surprisingly, this knowledge is not

necessarily acquired through a formal education or a structured learning such as

seminars. Although some of them claimed to have woken up with a great vision,

most of them are continually collecting and processing the information they seem to

find around them. They are alert to their environment, but they do not try to go it

alone, and they know how to collaborate well.

Entrepreneurs do listen to others - selectively. Some of the entrepreneurs in

this study indicated that they were able to identify international opportunities using

their intuition. They actually described this opportunity identification process as a

complex process which begins with a gut feeling that there is something interesting

over there’. In other words, learning begins when the entrepreneur’s intuition, based

on prior experience and the ability to recognise a pattern revealed in external

events, indicates that a business opportunity exists (Dutta and Crossan, 2005). The

entrepreneur uses these patterns to make sense, or interpret an insight or an

emerging idea.

Crossan et al. (1999) emphasise the importance of the environment in this

process, especially in the interpretation process. They suggest, "The nature or

texture of the domain within which individuals and organisations operate, and from

which they extract data, is crucial to understanding the interpretive process. The

precision of the language that evolves will reflect the texture of the domain, given

the tasks being attempted” (Crossan et al., 1999, p. 528). Furthermore, different

entrepreneurs may interpret the same pattern differently, because the same stimulus

can arouse different meanings for different people.

Interpretation can be fortified by "sharing it with a group, who can then engage

in joint exploration, interpretation, and integration of the idea, to develop it into a

shared understanding of a feasible business proposition" (Dutta and Crossan, 2005,

p. 435). Learning is seen as a combination of stocks and flows of knowledge.

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Some of the entrepreneurs in this study obtained the insight, the idea, upon

which their business venture was established, through an intuitive process. Based

on their prior experience and cognitive orientation, they perceived patterns

regarding the need in their target market. At this stage, the perceived image of the

insight or the idea is developed by the entrepreneur into a metaphor (Dutta and

Crossan, 2005). These metaphors are the basis for their story; from now on, they

are ready to tell their story, as a way of sharing thoughts and acquiring the

necessary knowledge so that they can move to the next stage (Hurst et al., 1989).

During this opportunity identification process, entrepreneurs might choose to fill

the gaps in their knowledge, using their personal contacts and social networks and

later on by their organisational learning, mainly through a process of dialogue and

conversation with their co-founders and social network members. They make use of

their social networks including professionals such as lawyers and accountants, and

their family and friends, each group at a different stage and for a different purpose,

as their 'external knowledge providers'.

Politis (2005) defines entrepreneurial learning as a dynamic experiential

process in which individuals continuously develop the kind of knowledge which is

mainly associated with recognising and acting on opportunities (Shane and

Venkataraman, 2000b). This type of knowledge is necessary in order to be effective

when starting new ventures (Politis, 2005), and for managing new ventures

(Aldrich, 1999). In the course of this process, the entrepreneur’s experience is

transformed into knowledge (Minniti and Bygrave, 2001). Such knowledge is

therefore strongly influenced by the entrepreneur’s prior experience, and the level

of prior experience is linked to the entrepreneur's effectiveness in recognising

opportunities. Prior experience consequently enables entrepreneurs to evaluate new

information, and through a transformation process to acquire knowledge.

4. It can be argued, based on the foregoing discussion and the empirical evidence of

the study, that entrepreneurs actively look for new international opportunities, as

such they are deemed to be action-oriented (Rae, 2000). The entrepreneurial

learning process is crucial for international entrepreneurs, because they constantly

need to find opportunities and overcome problems (Minniti and Bygrave, 2001).

Entrepreneurs learn in several ways; Gibb (1997), similarly to the findings of this

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study, suggests that they learn by copying, solving problems, experimenting and by

learning from mistakes. In addition, they may learn through doing and reflection

(Cope and Watts, 2000). Entrepreneurial learning in this study is viewed as

experiential learning and vicarious learning. Experiential learning emphasises the

role of experience while vicarious learning is done through observations on the

behaviours and actions of others (Holcomb et al., 2009). Furthermore,

entrepreneurs may learn and consequently acquire knowledge "by direct

experience, by observing the actions and consequences of others and by explicit

codified sources such as books, papers, etc…" (Holcomb et al., 2009, p. 171).

5. No matter what learning strategy or mechanism is used, the level of prior

knowledge showed significant relationships with learning strategies. In this study,

entrepreneurial learning can be seen as path dependent. It depends on the

entrepreneur’s prior experience, prior knowledge, cognitive styles, their self-

efficacy, and their use of social networks. Entrepreneurial learning affects the way

they identify and act upon opportunities and later on the formation and

development of the venture, and the knowledge they acquire (Cope, 2005a;

Holcomb et al., 2009; Sardana and Scott-Kemmis, 2010). Eventually, the

importance of learning strategies in the opportunity identification process is through

understanding that not only do entrepreneurs learn how to “connect the dots,”

(Baron and Ensley, 2006a) but the way they connect those dots evolves over time

as well (Holcomb et al., 2009, p. 171), and thus and most importantly they brush-up

and shape the way they learn how to learn about entrepreneurial opportunities.

8.1 Contribution

This study and its results provide significant contributions to the research on

entrepreneurial learning about opportunities in a cross-border context, which has

implications for academics as well as for practitioners. A study of International

Entrepreneurship and its domain, defined here in terms of knowledge, learning,

entrepreneurial action, and entrepreneurial opportunities, is relevant for several

audiences, such as scholars in the fields of entrepreneurship, international

entrepreneurship and entrepreneurial learning, decision makers, and entrepreneurs at

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various entrepreneurial stages. The importance and contribution of the study can also

be defined as follows:

Firstly, despite the importance of learning about the opportunity identification

process, (Dimov, 2003, Corbett, 2007), the literature review revealed that in the

context of entrepreneurship research domain, there is a dearth of research, to say the

least, that focuses on the learning strategies of entrepreneurs in the opportunity

identification process. Therefore, understanding the relationship between learning

strategies and capabilities and the entrepreneurial opportunity in an international

environment, especially among hi-tech entrepreneurs, can help to reveal the

underlying logic of opportunity identification as a learning process. Scholars in this

field show disagreement on what learning strategies are exactly, how many of them

exist, and how they should be defined and categorised (Kakkonen, 2010), thus

focusing on how entrepreneurs learn strategically about opportunities, defining that

these strategies are independent, learnable and changeable is what makes this study

unique.

Secondly, this research integrates two emerging areas: entrepreneurial learning

and international entrepreneurship. While entrepreneurial learning has attracted

significant academic attention among leading scholars, its theoretical and empirical

foundation is still under development. The same conclusion can be made concerning

the international entrepreneurship research domain. Therefore, the study contributes to

theory building, which offers a new framework to explain the ways entrepreneurs

learn about entrepreneurial opportunities, by introducing a conceptual model that

combines research domains such as International Entrepreneurship, and

Entrepreneurial Learning. Furthermore, combining different frameworks into a new

conceptual model such as developed in this study may establish a new outlook, and

contribute to the progress of research into entrepreneurship.

Thirdly, by elucidating the 'how' question, such as how entrepreneurs learn

about cross-border opportunities, this study highlights an interesting facet of the

entrepreneurial learning process. This is done by focusing on the interaction between

the individual and the environment, and by conducting a mixed methods research

strategy. The study design was a two-phase, sequential mixed methods study,

333

qualitative-quantitative design (Creswell et al., 2003). This approach could contribute

both to the progress of research methodology in the entrepreneurship domain by

introducing a rarely used strategy, and by assisting scholars to gain something from

the advantages of each research strategy, thus enabling researchers in this field to

develop a greater understanding of different aspects of the same phenomena in one

study. In addition, the insights from the two qualitative phases enabled this study to

transform abstract concepts into concrete constructs.

Fourthly, along with the implications discussed above, an important

contribution of this study is the development of the scales used to measure the six

learning strategies included in the study. These scales are unique to this study and

demonstrated good reliability and preliminary evidence of validity. Future research

may benefit by using these scales to study additional aspects of the interface between

strategic learning, as a more general construct, and these six learning strategies "…in

order to further our understanding of entrepreneurial learning, we need to consider it

as a concrete construct of identifiable activities or behaviours, which allow further

measurement, generalization and investigation of various individual, organizational

and contextual factors affecting them" (Man, 2006, p. 311).

The practical contributions to entrepreneurs includes providing a framework for

learning about opportunities, and based on the research findings, providing guidelines

for entrepreneurs who aim from the day of inception to internationalise and wish to

learn from the experience of others. For example, entrepreneurs, especially from the

high-tech industry, should realise that learning about opportunities might provide a

basis for the development of necessary knowledge for being effective in identifying

opportunities (Politis, 2005). In addition, they should be aware of the ways they can

learn about new entrepreneurial opportunities.

One major contribution of this study, embedded in the findings, is that effective ways

of coping with the liability of newness can best be learned by doing (Smilor, 1997;

Cope and Watts, 2000), by networking, or by imitating, and not necessarily through

education.

334

Educators can also benefit from these results. Entrepreneurs learn continually, mainly

from experience, and modify their behaviour and the way they learn about

opportunities accordingly. The ways entrepreneurs learn about opportunities, or in

other words, their learning strategies, can be developed as part of entrepreneurship

educational programmes. In addition, eentrepreneurs can learn how to learn, especially

in the process of transforming a vague idea into a business opportunity. The fact that

they can learn how to learn might enable them to become strategic learners.

Casting entrepreneurs as strategic learners about opportunities may contribute to their

strategic capability and their strategic behaviour “Distinguishing characteristics of

strategic learning are that it is learning without questioning and without unlearning in

advance. It starts from a current set of basic assumptions and ends with a new set of

basic assumptions” (Kuwada, 1998, p. 719).

Enabling the entrepreneur to incorporate prior knowledge and their social networking

ties in a way that this strategic learning capability might yield competitive advantages

and performance benefits (Anderson et al., 2009). In addition, and following Corbett

(2005b), the role of the educator is to assist each nascent entrepreneur to uncover their

learning strategies that best fit his or her strengths as a learner. Furthermore, by

providing nascent entrepreneurs, with information on the ways entrepreneurs learn

about international entrepreneurial opportunities, educators can help them understand

the need for learning about opportunities.

This is an important conclusion mainly because entrepreneurs place their attention in

the early stages in creating business plans rather than focusing on learning more about

how to adapt their original ideas in response to market or technological forces. Thus,

educators can train nascent entrepreneurs, for example, by enhancing their ability to

learn in different opportunity identification situations by using scenarios, role plays,

and strategic games that tap each entrepreneur’s ability to transform their knowledge,

networks and experience into an effective identification of an opportunity: “ In other

words, while recognizing that learning asymmetries exist, entrepreneurs should

attempt to stretch out of their comfort zone and try to learn in new ways” (Corbett,

2007a, p. 115).

335

8.2 Limitations

Research on learning about entrepreneurial opportunities can be conducted using a

variety of research settings, samples, contexts and designs. Of course, conducting an

effective research study is a complex procedure, and the researcher needs often to

strike a balance between various constraints such as time and money, and the rigour of

the study. Therefore, it is valid to argue that any empirical research suffers to a degree

from limitations. This is particularly the case, when the study is one of the first

attempts to model the relationships between the factors that affect the ways

entrepreneurs learn about opportunities. In the following paragraphs several

limitations are outlined and addressed.

This study implemented a cross-sectional design with the use of qualitative interviews

and focus groups and a web-based questionnaire. Although, the study was conducted

as a mixed methods study, a fact that might increase the validity and reliability of the

study findings, there are some limitations that should be outlined here:

1. The study was limited to the high-tech industry. The high-tech industry is

highly characterized as influenced by a dynamic, competitive, ambiguous, and

uncertain environment. Hence, generalising the study’s conclusions outside the

context of a technological business environment may be inappropriate. In addition, the

external validity (generalisability) of the study’s findings should raise a concern,

because this study is focused on Israeli entrepreneurs in the high-tech industry. Thus,

the results of this study should be interpreted with a caveat, due to the focus on a

single site (Israel) and a single industry.

2. The analysis was confined to the study of six specific learning strategies.

Obviously, the notion of learning strategies is a much broader multidimensional

construct, and other dimensions of these constructs might be relevant, especially if

other studies focus on entrepreneurs from various industries. It means that measures of

learning strategies could benefit from further refinement and replication. It should also

be outlined that the relationships found in this study are relevant for this study only.

The findings could not show that the learning strategies are persistent and indicative of

entrepreneurial learning across contexts. Further, it is entirely possible that a particular

336

strategy is more suitable for the novice entrepreneur, while another is more suitable for

serial or portfolio entrepreneurs.

3. Another methodological limitation is related to the fact that in the

quantitative phase, the respondents were asked to recount, based on their experience,

how they have identified opportunities and learn about them. This practice, in some

cases might be considered as problematic, mainly because it enables the respondents

to collapse this complex, dynamic and multistage process, into one moment in time

(Gaglio and Katz, 2001).

4. The survey recruitment method and the sampling frame could be considered

as a potential methodological limitation. In particular, the responses collected

included a very small number of observations from women entrepreneurs, not

representative of the proportion of female participation in entrepreneurship in Israel.

Thus, the effect of gender on the predictors or the criterion variables could not be

assessed and analyzed. In addition, 178 responses were collected, which may be

considered as a relatively small sample based on the prospective total number of

entrepreneurs operating in Israel. Furthermore, the sample was not randomly selected,

thus, external validity and measurement reliability could be limited.

5. The final scale of two constructs (they were originally measured on a scale

consisting of more than two items): learning by imitating deliberately, and learning by

imitating spontaneously, consisted of only two items. This could, to some extent, limit

the degree these measures are reliable and valid (Gefen et al., 2011). However, it

should be noted that these constructs showed acceptable levels of validity and

reliability in this study.

6. The study does not represent an entirely inclusive picture of entrepreneurial

learning about opportunities because further research on larger and broader samples in

different environments, cultures, and industries may yield a model with broader

applicability. The study findings in both phases are based on the extensive experience

of many Israeli founders of new ventures. However, this fact may yield another

limitation. The study may not be representative of less successful or failed

entrepreneurs. This limitation confines the generalisability of the findings, therefore

337

future studies, should include in their data, failed entrepreneurs, who can shed much

light on the learning from failure in the opportunity identification process.

7. The qualitative phase included the collection of data from entrepreneurs with

the use of focus group discussions and semi-structured interviews. During the

interviews and the focus group discussions, the participants were asked, based on their

knowledge and experience, to define entrepreneurship and international

entrepreneurship. The main purpose of these discussions was not to suggest an

alternative definition of entrepreneurship, but mainly to learn about their perception

about entrepreneurship. Throughout these discussions, the researcher can tap into their

minds and glean important ways of speech, symbols, and to provide interesting

insights about the conventional discourse on entrepreneurship, which for example is

highly, rooted in the heroic myth of entrepreneurship discourse. In this interpretation,

there are no ‘facts’ other than those that are existed in their language. This approach

questions what : “…is said and, more importantly, what is not said and how these texts

create myths about entrepreneurship” (Ogbor, 2000, p. 607). The main advantage of

this technique is to provide baseline and a clear understanding of the researcher about

the meaning of entrepreneurship and international entrepreneurship to the

interviewees. However, the major limitation of this technique embedded in the fact

that the entrepreneurs do not provide nominal or operational definition of

entrepreneurship nor exact statement of the phenomena. Thus, this study cannot

provide, based on their opinions, a new outlook or a new definition of

entrepreneurship and international entrepreneurship,

8. this study focused on the outcomes of strategic learning, by introducing six

actual mechanisms and behaviours that the entrepreneurs apply to enhance their own

learning about opportunities. However, this conceptual approach highlights the

importance of the outcome of learning strategically, rather than focusing on learners’

distinctive self-directed competence that enhance their efforts to apply their own

strategic learning mechanisms (Tseng et al., 2006). Learning strategies in this study

were defined as observable behaviour that can be chosen by the entrepreneurs in a

specific situation or a context. This approach is in line with “contemporary theories of

self-regulation in educational psychology, which argue that self-regulated learners

differ from others who do not engage in strategic learning” (Tseng et al., 2006, p. 79).

338

Thus, the basic argument, in this study, that entrepreneurs learn strategically about

opportunities should be reinvestigated in different contexts and populations, and in

longitudinal research designs: “…that it is not what learners do that makes them

strategic learners but rather the fact that they put creative effort into trying to improve

their own learning. This is an important shift from focusing on the product—the actual

techniques employed—to the self-regulatory process itself and the specific learner

capacity underlying it” (Tseng et al., 2006, p. 81).

The broader concept of learning strategies, as discussed in this study, has created

considerably more opportunities for future research rather than limiting the findings

and results of this study. The study emphasised the fact that the topic of learning

strategically about opportunities, in international and high-tech context, suffers from

insufficient understanding of the underlying mechanisms and processes, but this

should motivate researchers to make headway into understanding other aspects of

learning strategies of entrepreneurs.

8.3 Future Research

As the research on entrepreneurship, international entrepreneurship, entrepreneurial

learning and opportunity identification, is still under developed and evolving, the

findings of this study may be considered as the first indication of the appearance of

one of the first swallows40

. On the other hand, the study has produced as many

questions as it has answers, and many uncovered layers that are waiting to be revealed.

Thus, further exploration and enquiry with regard to the topic of learning among

entrepreneurs in general and learning about opportunities, specifically is necessary and

some of the interesting and unexplored areas are mentioned in the following

paragraphs.

Strategic learning researchers showed the relationships between strategic learning

capabilities and performance (Sirén et al., 2012). The six learning strategies, which are

in the focus of this study, can be viewed as the output or the consequence of strategic

40

According to a sailing superstition, swallows are land-based birds, so their appearance informs a

sailor that he is close to shore. http://en.wikipedia.org/wiki/Swallow

339

learning capability. This might be in line with the argument that international

entrepreneurship requires dynamic capabilities to be mobilized, integrated, and applied

across all entrepreneurial stages, and specifically during the opportunity identification

process. Thus, future studies may benefit from researching the relationships between

these learning strategies and the number of identified opportunities, as an acceptable

indicator for the effectiveness of such a strategy.

Researchers will have the opportunity to extend this study by examining how

entrepreneurs are able to integrate differentiated capabilities throughout the

opportunity identification process. Furthermore, by examining similar phenomena in

other non-high tech environments, researchers may broaden our understanding about

the differences and similarities between entrepreneurs from different industries.

This study illuminates the importance of the learning that occurs as entrepreneurs

identify new opportunities throughout the process of shaping their ideas (Corbett,

2007b). However, entrepreneurship is a complex, multilevel process, therefore, by

extending the examination of learning strategies beyond the opportunity identification

process into other entrepreneurial processes, researchers may uncover and elucidate

further layers or stages of this unique and evolving phenomenon. The results of this

study should encourage researchers to continue to explore other facets of the learning

process with respect to opportunity identification and the entire process of

international entrepreneurship.

When considering international new ventures, Voudouris et al. (2011) argue that

international learning orientation is crucial to international new ventures, since it has

an impact on the way entrepreneurs identify opportunities. Moreover, entrepreneurial

learning is seen as a continuous learning process, which interacts with opportunities to

create learning loops. Interestingly, in the early stages of new venture creation, the

entrepreneur is a lone individual; any team or organisation learning cannot be

observed, as they are absent at this stage. Yet starting a new venture is a long-term

process and at a later stage, when they have a fully established company, these

processes will enable the entrepreneur to transfer individual knowledge to the

collective level (Corbett, 2005b; Dutta and Crossan, 2005). Following that, it might be

important, to focus on the transition of learning from the individual to the

340

organisational level. Thus, researchers may prefer to delve deeper into the study of

international learning about opportunities by focusing on the role of the entrepreneur

in this process, as well as on the transition process from the individual to the team and

organisation level. This transition is reflected in changes in collective actions and

behaviour (Zhang et al., 2006).

This study has presented a comprehensive conceptual model, which was derived from

the qualitative phase, and designed based on prior research in this field. While this

model consists of factors that affect the ways entrepreneurs learn about entrepreneurial

opportunities, other factors, which were not included in this model, mainly due to

methodological concerns, could explain a bigger part of the criterion variables’

variance. Therefore, it is suggested that future research should explore the influence of

other factors that may affect entrepreneurial learning. These factors may include the

following:

Firstly, the diversity of the founders’ backgrounds can stimulate creativity,

which provides a basis for learning (Politis, 2005).

Secondly, their previous experience, such as previous start-up experience

(Reuber and Fischer, 1999), extent of management experience (Gartner, 1988; Taylor,

1999) or industry-specific experience (Shane, 2003), heuristics (Holcomb et al., 2009),

personal (e.g. perception, intelligence, experience, necessity and motivation) and

contextual factors (e.g. learning environment) (Franco and Haase, 2009) may increase

their ability to overcome problems and to deal with the liability of newness.

Entrepreneurs transform their experiences into knowledge, by either exploiting or

exploring knowledge (March, 1991; Schildt et al., 2005). Exploration means that

individuals learn from experiences by exploring new possibilities, and exploitation is

related to learning from experiences by exploiting old routines. Both are essential to

sustain learning (March, 1991). The mode of transformation utilised by an

entrepreneur (i.e., exploitation or exploration) is largely dependent on three factors:

the outcome of previous entrepreneurial events (Minniti and Bygrave, 2001), the

predominant reasoning of the entrepreneur (Sarasvathy, 2001b; Politis, 2005), and the

entrepreneur’s career orientation (Politis, 2005). It is recommended that other studies

341

could explore the role of these learning modes in the opportunity identification phase,

mainly as how exploration and exploitation modes are related to learning strategies.

Furthermore, when developing an empirical investigation of learning about

opportunities, future research may prefer to use a longitudinal research design, as more

appropriate to investigate a phenomenon, which can be described as a temporal

process (Long and McMullan, 1984), in order to identify entrepreneurial opportunities

(Singh et al., 1999) with many stages (Lumpkin and Lichtenstein, 2005a) and loops

(Dutta and Crossan, 2005). It would be useful to examine whether the model that was

developed and tested in this study, would be as valid in other techniques for empirical

investigation, such as longitudinal studies, experiments, or simulations (Eckhardt and

Shane (2003). These types of design fundamentally require “…that informants must

do the thinking rather than report their perceptions about what they believe about how

they thought in the past” (Gaglio and Katz, 2001, p. 107).

Prior studies defined prior business ownership experience as one of the human capital

variables (Farmer et al., 2011). This form of experience helps entrepreneurs to

overcome liabilities of newness (Delmar and Shane, 2006) and therefore, “the more

that entrepreneurs start firms or work in an industry, the better they become at

organising firms, acquiring resources, attracting customers and suppliers, and hiring

employees” (Delmar and Shane, 2006, p. 220). Notwithstanding, for the purpose of

this study, a distinction was made between novice and habitual (i.e. Serial and

portfolio entrepreneurs).

Future studies, may benefit from comparing between the use of different learning

strategies during the opportunity identification process, and their prior business

ownership experience. For example, a comparison could be conducted between two

groups: novice and habitual, or entrepreneurs and nascent entrepreneurs.

8.4 Concluding Remarks

The main objective of this study was to identify and empirically investigate the factors

affecting the ways entrepreneurs learn about international opportunities. Each phase

had different objectives (i.e. QUAL1- research question, QUAL2- conceptual model,

QUAN- statistical inference). In order to achieve the overarching aim of this research

342

study, a sequential qualitative quantitative, mixed-methods design, was conducted.

Drawing upon the literature regarding the learning strategies of entrepreneurs during

the opportunity identification process, as well as the qualitative phase findings, a

conceptual model and a set of hypotheses were developed.

The research model was specified as a complex model with six criterion variables,

some of them were designed as high order constructs. The model was tested among

178 high-tech entrepreneurs, in Israel. Exploratory Factor Analysis (EFA) and the

Partial Least Squares-Structural Equations Modelling (PLS-SEM) were the chosen

analytical tools, in the quantitative phase. The results confirmed that the constructs

under investigation displayed satisfactory internal item reliability, internal consistency

and convergent validity as well as a satisfactory level of convergent and discriminant

validity. The structural model assessment indicated that most of the proposed

hypotheses were supported or partially supported. Finally, the qualitative findings

were used to help the interpretation of some of the quantitative results. This re-

interpretation of the quantitative findings revealed once more the value of mixed

methods research.

This study is unique; in the sense that, it provides empirical evidence on the ways

entrepreneurs learn about international opportunities. The first qualitative phase

(QUAL1) findings showed that most of the participants intended to internationalise

early in their entrepreneurship life cycle. They perceived early stage

internationalisation to be an almost inevitable requirement for their success as

entrepreneurs. However, they are aware of the risks involved in internationalisation,

and that they lacked many of the resources, among them knowledge, which is essential

for internationalisation in early stages. In order to bridge this knowledge gap, they

have to learn.

The findings of the second Qualitative phase (QUAL2) highlight the fact that founding

a new international venture requires a remarkable amount of resources, such as effort

and commitment. Moreover, it was evident that it is important to understand the

learning strategies adopted by international entrepreneurs and the extent to which they

are effective and suitable. Entrepreneurs, particularly international entrepreneurs in the

high-tech industry, learn by doing, however they have to acquire new knowledge more

343

rapidly, so they may also seek to shorten their learning cycle by, mimicking the

behaviours of others, or learning through their networks. This process can be depicted

as an iterative dynamic process and is dependent on several factors such as the

entrepreneur's prior knowledge, business experience, social networks, and level of

self-efficacy.

The study findings highlighted the relative importance of prior knowledge in relation

to most of the criterion variables. Hence, prior knowledge plays a critical role by

predicting the ways entrepreneurs learn about opportunities. Furthermore, it was found

that prior business ownership experience and Entrepreneurial Self-Efficacy affect the

level of entrepreneur’s prior knowledge. In addition, it was shown that the cognitive

style moderates significantly the strength of the relationships between prior knowledge

and the six criterion variables.

The relationships between prior knowledge and the ways they learn about

opportunities are significantly moderated by their cognitive style. Hence, this study

emphasised why it is important to explore the opportunity identification process from

the entrepreneurial learning point of view, and by that, introduce a new outlook on the

ways international high-tech entrepreneurs learn.

344

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382

Appendices

Appendix A: Qualitative Phase 1 (QUAL1)- Guideline for semi-structured

interviews and the focus group

Research Questions (for the Interview)

1) What are the different entrepreneurial characteristics needed for success in

international ventures?

2) How do you understand, or what do you understand by entrepreneurial

learning/organisational learning? Then how does EL/OL connect to/ affect the

entrepreneur's perception of risk?

3) How do you define risk perception? (Establish whether there are similarities or

differences) What are the different stages? What are the main factors that affect

changes in perception?

The Discussion

Meet and Greet (5 minutes)

Introducing the Interviewer

Introducing the “rules of the game” protocols, ethical considerations, and

confidentiality.

Being an Entrepreneur – General (10 minutes)

How did you come to entrepreneurship? What led you to it? (Moderator

– check for the use of the word "opportunity"; did they study, did they

come to the subject by chance, did they identify a need, or did they

create a need? And so on.)

What do you love about entrepreneurship? What don’t you like? Why?

(Check if the following come up in discussion: success, risks,

organisational learning, international entrepreneurship as opposed to

entrepreneurship in Israel, and probe accordingly.)

383

International Entrepreneurship (30 minutes)

When you think of international entrepreneurship – what comes into

your mind?

Tell someone who has not experienced it what international

entrepreneurship involves.

What is the path to international entrepreneurship?

Which type of people does it suit? Why? Which qualities characterise

them? Which types of behaviour characterise them? Which values?

Which motivations?

Which type of people does it not suit? Why?

Convince someone to enter the field.

Convince someone why they shouldn’t enter the field. (Moderator –

check if and how the issue of risk is raised.)

What is good about it? What do you enjoy?

What is problematic? What is difficult?

How is it similar or different from “regular” entrepreneurship?

What are you most proud of in your entrepreneurial career? Give some

anecdotes.

What do you most regret in your entrepreneurial career? Why?

Assume that your friend is about to enter the field of international

entrepreneurship – what tips would you give him/her?

384

Organisational learning (15 minutes)

What information sources do you use in your field of entrepreneurship?

(Internet? Newspapers? Other?)

How do you learn in the organisation? Give an example.

Give an example of a failure you have learnt from; give as many details

as possible.

Give an example of a success you have learnt from, as above.

Do you use networking in your field? In what way? When?

Risk Perception

How would you define risk in your field? Give examples.

End of the interview (5 minutes):

Thank the interviewee and express appreciation of his willingness to

cooperate and to allocate time for this interview.

Emphasise again confidentiality issues.

385

Appendix B: Qualitative Phase 2 (QUAL2)- Guidelines for focus groups meetings

and in-depth interviews.

Primary Research Question

Are there any differences between various types of international entrepreneurs

(i.e. / e.g. 'born global' versus 'late starters') in the way they view and learn

about opportunities in the international field?

Supplementary research questions

What are the factors that influence the motivation of the entrepreneurs to

internationalise?

What are the factors that affect their attitude towards risk and thus towards

knowledge?

What is the role of the Internet in the learning cycle of international

entrepreneurs and how does it affect the way they learn about opportunities?

Research aims and objectives

Aims

1. Construction of a conceptual model or models

2. Defining research hypotheses to be examined in a quantitative study (QUAN)

3. Building constructs

Objectives of the Qualitative Research Phase (QUAL2)

In what way/s do entrepreneurs define the concept “international entrepreneurship”

and the point in time when they perceive entrepreneurship as international?

What are the factors that influence the motivation of the entrepreneurs to

internationalise?

386

What are the factors that affect their attitude towards risk and thus towards

Knowledge?

What is the role of the Internet in the learning cycle of international

entrepreneurs and how does it affect the way they learn about opportunities?

Discussion

Dear Participant,

This session is part of a thesis study being conducted at the Manchester Business

School. The study addresses the topic of International Entrepreneurship. The session is

audio recorded. Confidentiality will be maintained and the data will be used for

research purposes only. Your cooperation is appreciated and we thank you in advance.

a. Introductions and warm-up (10 minutes)

Presenting “the rules of the game”

Introducing the moderator and participants – one minute each

If you could meet any person, living or dead, who would you like to meet?

Why?

How do you define yourselves in terms of occupation?

b. Being an international entrepreneur – general (20 minutes)

Entrepreneurship as perceived by the subjects – mind mapping

Now we are going to peek inside ourselves, at our feelings and thoughts on a particular

topic. I will show you how it works:

(The moderator presents an example of the technique using a different subject, and

explains how to create a semantic map of the subject: Being an international

entrepreneur.) You have to write down all the associations aroused by this phrase:

387

“Being an international entrepreneur”. You can also make drawings or jot down

(thoughts, feelings, and events) the first things that come into your head. Start in the

middle of the sheet and then move towards the edges, to represent the order of your

associations.

Draw a circle around a particular part of the semantic map that is

meaningful for you.

Present and discuss the maps.

What feelings did you write down?

What thoughts?

What pictures did you draw?

What did you mark? Why? What feeling does it relate to?

What can we learn from these maps about this subject?

What do you like about international entrepreneurship? What don’t you like?

Why? (Moderator: check if any discussion arises on success, risk,

organisational learning, overseas versus local entrepreneurship, and encourage

accordingly.)

Try to give a short definition of international entrepreneurship – as you see it.

Of all the definitions given – which do you think is the best? Why?

What is international? One country or more than one? What is the difference

between international and global?

If the subject were entrepreneurship without “international,” would the results

be different? How? How does international entrepreneurship differ from local

entrepreneurship?

Tell somebody who has never experienced international entrepreneurship what

it involves.

388

What types of people are suitable for it? Why?

What types of people are not suitable for it? Why?

What would you consider a successful international entrepreneur? Successful

international entrepreneurship?

Is there a gap between your vision as an international entrepreneur and reality?

Explain.

What do you think explains your success / failure in international

entrepreneurship?

What are you most proud of in your career in international entrepreneurship?

Give some anecdotes.

What do you most regret in your career in international entrepreneurship?

Why?

Let’s assume that a friend of yours is about to enter the field of international

entrepreneurship. What tips would you give him/her?

c. International entrepreneurship on a time axis (20 minutes)

I would like to ask each of you to draw the development of your

entrepreneurship along a time axis, from the time entrepreneurship first came

into your life, until today. (Like an ECG diagram with difficulties.) Please

indicate significant points in time along your path as entrepreneurs.

(After they have finished.) Make sure that you have indicated when you began

to be an entrepreneur. Why have you chosen that particular point? Do the same

for international entrepreneurship. What marks the start of your international

entrepreneurship? Why?

Make sure that you indicate when you began to be an international

entrepreneur. What marks the start, how many years passed between each

stage, what was your age at each stage?

389

Look at the map you have drawn:

How did you get to international entrepreneurship? What led you to it?

(Moderator: check the use of the word opportunity, whether they studied, got

there by chance, identified a need or created a need, and so on.)

Was it a development of what you were doing previously? Was it a new

direction?

Was it something planned or by chance?

Is international entrepreneurship a development from local entrepreneurship, or

is it something different?

What does “creating a new enterprise,” mean to you? What is – from the day of

creating it? From the day of the idea? Establishing the company? New

ownership? A new product?

What is new? 0 years? 3 years? More? New for who?

Which of you was aiming for international activity from the day you started

your business? (Ask those who put up their hands.) So how long did it take you

to start this activity? Did you work in stages, that is, first Israel, then gradual

development overseas, or did you start the overseas development immediately?

Why? What motivated you?

What makes an enterprise become international? (Encourage probing.) As soon

as you think of it? When you obtain an export license? When you start

addressing overseas customers? When a company is registered overseas?

When they move overseas physically? When there’s more than one person?

Something else? What was it like with you?

How big should a business be to be called an enterprise?

Did you stop being a local entrepreneur when you became international?

What is the role of the home market?

390

d. Entrepreneurial learning and perceptions of risk (20 minutes)

What type of knowledge did you need in connection with international

entrepreneurship?

How did you collect / obtain information / learn about your field of

international entrepreneurship along the way? (Literature? Which? Internet?

Press? Experience? Other?)

What do you mean by learning from experience? Explain, give examples

How does learning from experience differ from other learning?

What is structured learning? How does it differ from learning from experience?

Go back for a minute to your drawings of your professional development and

try to remember what knowledge you needed and how (if at all) you attained it.

Is there a difference between the knowledge you need and the stage of

entrepreneurship where you are? What is it?

Does international entrepreneurship require specific knowledge? Do you think

there is a link between organisational learning and international

entrepreneurship? How is this manifested in your daily activity? How does it

differ from local entrepreneurial activity?

Were there stages when you preferred not to learn? Not to know? Why?

Explain.

Which do you learn more from, successes, or failures?

Give an example of a failure that you learned from, specify as much as

possible.

Give an example of a success that you learned from, as above

Do you use networking in your field? In what way? When?

391

e. Identifying or creating opportunities (20 minutes)

When I say entrepreneurial opportunity – what do you think of? (Pay attentions

to which terminology they use – identify or create.)

Are opportunities identified? Created? Grasped? Explain, what is the

difference between them?

How do you identify an opportunity? Give an example.

How do you create an opportunity? Give an example.

On your time, show when you identified or created an enterprise; tell us about

it.

Do they each require different types of learning?

f. Perception of risk (15 minutes)

How would you define risk in your field? Give examples.

When you’ve finished, mark on your chart where you had a low perception of

risk, and where a high perception.

Tell me what you’ve marked. Where were the risk points? Why there,

specifically?

In your opinion, what determines the risk?

What is the relative weight of financial and other risks?

Do you think the perception of risk changes at different stages of the process?

Explain.

g. International entrepreneurship learning and risk perception (15 minutes)

Now look at the chart you made – you marked points on your international

entrepreneurship timeline, points concerning knowledge and learning, and

392

points concerning risk. Can we understand anything about the link between

them?

Is the perception of risk linked to the form of learning? Or vice versa? How?

Is the perception of risk linked to the “age” of the enterprise? How?

Is the type of learning linked to the “age” of the enterprise? How?

Do you think there is a difference between entrepreneurs who started out with

an international enterprise and those who became international entrepreneurs

as a stage in their development, with respect to the perception of risk and the

type of learning? Explain.

Conclusion (5 minutes).

393

Appendix C: Focus Groups and Interviews- Invitation Letter

Manchester Business School

The University of Manchester

Booth Street West

Manchester M15 6PB

Tuesday, 26 April 2011

Dear Participant,

Thank you for participating in this study. This Focus Group Discussion is part of a

thesis study being conducted at the Manchester Business School.

The study addresses the topic of International Entrepreneurship.

The focus group discussion will take place in Kfar-Sava, 14 Ha'taas St., at 19:00, and

will last approximately 1.5 hrs.

Confidentiality will be maintained and the data will be used for research purposes

only. Your cooperation is appreciated and we thank you in advance.

Best Regards,

Izak Fayena,

DBA Student

394

Appendix D: PLS-SEM Model without the product term (interaction effect)

395

Appendix E: PLS-SEM, measurement model assessment

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897

-0.0

778

q161

-0.0

302

-0.1

839

0.1

873

0.0

821

0.1

476

0.1

017

0.4

175

0.8

968

0.2

408

0.3

485

0.1

371

0.1

959

0.1

65

-0.0

616

q162

0.0

875

-0.1

023

0.2

327

0.1

771

0.2

564

0.1

797

0.3

492

0.8

806

0.1

704

0.2

493

0.1

748

0.2

331

0.1

683

-0.0

458

q184

0.0

146

-0.1

443

0.0

273

-0.0

053

-0.1

761

0.5

401

-0.1

242

0.0

688

-0.1

145

0.0

31

0.1

041

0.0

15

-0.0

559

-0.0

128

q185

0.1

147

-0.0

70.1

011

0.1

511

0.0

002

0.8

722

-0.0

369

0.0

498

0.0

601

0.0

001

0.1

42

0.0

341

-0.0

531

0.0

083

q186

0.1

628

0.0

433

0.1

357

0.0

661

-0.0

107

0.7

447

0.1

713

0.2

417

0.0

744

0.1

79

0.1

015

0.0

756

0.0

33

-0.1

248

se1

0.0

918

-0.1

892

0.2

594

0.1

945

0.2

575

-0.0

058

0.1

741

0.2

641

0.1

607

0.1

765

0.1

711

0.7

292

0.1

349

-0.0

123

se2

0.0

013

-0.0

704

0.2

835

0.0

875

0.1

277

0.0

005

0.0

184

0.0

656

0.0

095

0.0

573

-0.0

814

0.5

9-0.0

012

0.0

135

se3

-0.0

238

-0.1

875

0.2

326

0.1

788

0.2

03

0.0

131

0.1

634

0.1

752

0.0

599

0.0

652

-0.0

704

0.6

753

0.0

716

-0.1

104

se4

0.1

459

-0.0

92

0.3

434

0.2

137

0.1

663

0.1

359

0.1

091

0.1

573

0.1

441

0.1

488

0.0

723

0.8

216

0.1

855

-0.0

64

sock

now

10.0

618

-0.0

627

0.1

842

0.3

189

0.0

499

-0.0

609

0.0

02

0.0

82

0.1

214

0.0

297

0.0

735

-0.0

707

0.6

675

0.0

498

sock

now

20.0

451

-0.0

717

0.3

118

0.3

337

0.0

938

-0.0

382

0.0

482

0.1

635

0.1

599

0.1

606

0.0

937

0.1

139

0.8

655

0.1

188

sock

now

30.2

321

-0.1

048

0.5

146

0.7

68

0.2

605

0.0

664

0.2

11

0.1

785

0.1

37

0.1

576

0.0

183

0.1

683

0.4

861

0.0

216

sock

now

50.0

983

-0.0

44

0.4

772

0.3

955

0.1

417

0.0

082

0.1

075

0.1

921

0.2

426

0.3

186

0.0

796

0.1

553

0.9

362

0.1

508

sock

now

60.1

727

-0.0

326

0.4

884

0.4

144

0.1

701

-0.0

424

0.0

416

0.1

579

0.1

051

0.1

59

0.1

111

0.1

966

0.8

843

0.1

055

zbiz

assotie

s0.8

702

0.0

556

0.3

596

0.2

923

0.2

317

0.1

623

0.0

731

0.0

571

0.1

925

0.1

668

0.2

622

0.1

335

0.1

107

0.0

514

zbiz

parttie

s0.8

284

0.1

46

0.2

091

0.2

307

0.2

071

0.1

215

0.0

915

0.0

206

0.1

801

0.1

29

0.0

682

0.0

698

0.0

869

0.1

123

zcollegtie

s0.7

759

0.1

132

0.2

261

0.2

558

0.1

583

0.0

727

-0.0

167

-0.0

248

0.1

297

0.0

853

0.1

437

-0.0

245

0.0

854

0.1

388

zfrie

ndstie

s0.1

415

0.0

563

-0.0

853

-0.0

042

0.0

657

-0.0

647

-0.0

805

-0.0

705

0.1

308

0.0

574

0.2

749

-0.1

014

0.1

321

0.9

68

zk

inship

tie

s0.0

162

-0.0

552

0.0

312

0.0

675

0.0

851

-0.0

192

0.0

035

-0.0

225

0.0

472

0.0

20.1

43

0.0

368

0.1

126

0.8

166

zm

entorin

gtie

s0.1

942

0.0

174

0.0

104

-0.0

121

-0.0

001

0.0

881

0.0

467

0.1

666

0.1

083

0.1

572

0.7

711

0.1

201

0.1

587

0.2

352

zproforum

tie

s0.2

282

0.0

908

-0.0

012

-0.0

273

-0.0

422

0.2

299

-0.0

138

0.0

834

0.1

375

0.0

961

0.7

302

0.0

198

-0.0

115

0.0

554

zsocia

ltie

s0.0

459

-0.0

918

-0.0

149

0.0

467

-0.0

079

0.0

499

0.0

517

0.1

479

0.1

461

0.2

197

0.7

873

-0.0

197

0.0

89

0.2

813

Socia

lKnow

Strongtie

sL

BD

SL

BID

LB

IS

LB

ND

HO

CL

BN

SS

EB

usin

esstie

sC

SI

Forie

gnK

now

InstK

now

LB

DD

HO

C

397

Table 6.21: Prior Knowledge (PK) HOC, discriminant validity- Fornell-Larcker

Criterion

Diagonal elements represent square roots of AVEs Lower left triangle represent the correlations between the constructs

Pro&

Socialites

BizExp Single Item

Businessties -0.2152 0.823286

CSI 0.0667 0.1217 0.684398

PK HOC -0.2458 0.3219 -0.0517 0.825227

LBDD HOC -0.0588 0.2492 0.0941 0.3038 0.743371

LBDS 0.2596 0.1524 -0.0319 -0.0479 -0.0361 0.760986

LBID -0.0557 0.0752 0.0476 0.1768 0.3565 -0.0406 0.904544

LBIS 0.0217 0.0308 -0.1693 0.2076 0.2266 0.0629 0.4334 0.888538

LBND HOC -0.0157 0.017 0.023 0.0668 0.118 -0.0384 0.1564 0.2206 0.724224

LBNS -0.0589 0.163 -0.1325 0.2618 0.2549 0.0816 0.2948 0.3397 0.4787 0.804114

Pro&Socialties 0.0449 0.1844 -0.018 0.0466 -0.0196 0.2485 0.0452 0.1798 0.1827 0.2182 0.759868

SE -0.0836 0.0944 -0.1993 0.3269 0.2704 -0.0434 0.1723 0.2417 0.0006 0.1663 0.0535 0.708802

Strongties 0.0554 0.1061 0.0221 0.0312 0.0767 0.0325 -0.0569 -0.0597 0.2703 0.0491 0.2717 -0.0598 0.898387

StrongtiesLBDS LBID LBIS LBND HOC LBNS SEBizExp Businessties CSI PK HOC LBDD HOC

398

Table 6.22: Prior Knowledge (PK) HOC, Discriminant Validity- Cross Loadings

Pro&

Socialites

LBDD1LS 0.1524 0.1469 0.1717 0.6652 -0.0135 0.2672 0.0739 0.0905 0.081 0.0547 0.1425 0.0959

LBDD2LS 0.2117 0.0137 0.2721 0.8142 -0.06 0.2671 0.2436 0.0907 0.2756 -0.0711 0.2488 0.028

LBND1LS 0.0359 0.0366 0.079 0.138 -0.1359 0.1734 0.2277 0.8013 0.497 0.1851 0.0136 0.2631

LBND2LS 0.2652 0.1968 0.2383 0.3257 0.1657 0.2922 0.1615 0.8078 0.398 0.1052 0.192 -0.0334

CSP1 0.0745 0.8348 -0.0781 0.0687 -0.0077 -0.0139 -0.1967 0.0222 -0.1809 0.0243 -0.1356 0.0757

CSP2 0.1837 0.795 0.0458 0.1549 -0.1107 0.0802 -0.1692 0.0041 -0.0368 -0.091 -0.1983 -0.0098

CSP3 0.0451 0.6237 -0.0485 -0.0674 0.0535 -0.0475 -0.0932 -0.0069 -0.1137 0.0449 -0.1282 -0.0053

CSP4 0.0588 0.5863 -0.0942 0.0694 -0.037 0.143 -0.0526 0.0169 -0.0438 0.0182 0.011 -0.0461

CSP5 -0.0311 0.5707 -0.0757 0.023 -0.1045 0.0394 -0.0051 0.0748 -0.1097 -0.0287 -0.1865 -0.0762

CSP6 0.1455 0.6498 -0.0303 0.0796 0.0481 0.0489 -0.0851 -0.0234 -0.0173 0.0622 -0.0608 0.1606

ForigenKno

wL0.3174 -0.0933 0.8946 0.2522 0.1304 0.1283 0.1906 0.0452 0.2867 0.0092 0.3559 -0.0457

InstKnowL 0.3142 0.0121 0.8444 0.3263 0.1168 0.2302 0.1406 -0.0499 0.1052 0.0085 0.244 0.0191

SocialKnowL 0.1439 -0.0366 0.727 0.1676 -0.0266 0.0775 0.184 0.1973 0.2503 0.1081 0.1861 0.134

q141 0.1221 -0.0863 0.1869 0.1586 0.094 0.1823 0.281 0.4274 0.8074 0.1628 0.0888 0.0454

q142 0.0687 -0.1993 0.2363 0.2218 0.0528 0.1812 0.2962 0.2855 0.8174 0.1988 0.1538 0.0391

q143 0.1469 -0.0812 0.187 0.2358 0.0827 0.3566 0.2645 0.3584 0.7513 0.1206 0.1572 0.0072

q144 0.1876 -0.0507 0.2274 0.2054 0.0775 0.2407 0.2488 0.4809 0.8378 0.2093 0.1378 0.0643

q153 0.1443 0.1007 0.1393 0.3436 0.0543 0.8884 0.383 0.1938 0.307 0.0542 0.2211 -0.024

q154 -0.0006 -0.0016 0.1796 0.3055 0.019 0.9204 0.4006 0.1015 0.2327 0.0252 0.1005 -0.0749

q161 -0.0294 -0.1867 0.1478 0.1504 0.1097 0.4169 0.9027 0.2414 0.3488 0.1402 0.1969 -0.0603

q162 0.0882 -0.1079 0.2249 0.2584 0.1817 0.3498 0.8741 0.1473 0.2493 0.1769 0.235 -0.0449

q184 0.0154 -0.1501 0.0312 -0.1711 0.6042 -0.1239 0.0691 -0.1433 0.0308 0.0999 0.0135 -0.0102

q185 0.1145 -0.0737 0.0949 -0.0025 0.8432 -0.037 0.0487 -0.113 0.0001 0.1296 0.0328 0.0087

q186 0.1646 0.0416 0.0931 -0.0117 0.7458 0.172 0.2406 -0.1111 0.1797 0.0939 0.0759 -0.1238

q187 0.0619 -0.0187 -0.0204 -0.0858 0.5514 0.1282 0.1332 -0.0254 0.1004 0.0227 -0.0697 -0.0264

se1 0.0943 -0.1921 0.2333 0.2598 -0.0113 0.175 0.2635 0.0563 0.1763 0.1753 0.739 -0.0102

se2 0.0008 -0.0793 0.1886 0.1285 0.0023 0.0202 0.0631 0.0082 0.0565 -0.0858 0.5863 0.0148

se3 -0.0231 -0.1905 0.174 0.202 0.0133 0.1632 0.1763 -0.0267 0.0643 -0.0726 0.6682 -0.1067

se4 0.1476 -0.1002 0.3147 0.1677 0.1137 0.1106 0.157 -0.031 0.1486 0.0663 0.8204 -0.0602

zbizassoties 0.8743 0.0565 0.3249 0.2328 0.1614 0.0745 0.0554 -0.0175 0.1656 0.2535 0.135 0.0496

zbizpartties 0.8275 0.1476 0.2186 0.2078 0.1135 0.0927 0.0196 0.0386 0.1296 0.058 0.0702 0.1088

zcollegties 0.7697 0.1159 0.2479 0.1569 0.0656 -0.0151 -0.0264 0.0489 0.0852 0.1295 -0.0251 0.1353

zfriendsties 0.1401 0.0573 0.0091 0.0646 -0.0675 -0.0798 -0.0708 0.2962 0.0577 0.2888 -0.1002 0.9613

zkinshipties 0.0158 -0.0611 0.0624 0.083 -0.0128 0.0036 -0.0226 0.1481 0.0195 0.1491 0.0372 0.831

zmentoringti

es0.1956 0.0131 0.0433 -0.0014 0.0785 0.0471 0.1661 0.1089 0.157 0.7692 0.1233 0.2334

zproforumtie

s0.2281 0.0836 -0.0055 -0.0463 0.2224 -0.0136 0.0828 0.074 0.0959 0.6844 0.0203 0.0547

zsocialties 0.0471 -0.0942 0.0503 -0.0087 0.0547 0.052 0.1475 0.2078 0.2197 0.824 -0.0169 0.2773

LBID LBIS LBND HOC LBNS SE StrongtiesBusinessties CSI PK HOC LBDD HOC LBDS

399

Table 6.36: LBND, first order constructs, Fornell-Larcker criterion

Pro&

Socialites

BizExp Single Item


CSI 0.0661 0.1212 0.690145

PK HOC -0.2462 0.3257 -0.0561 0.824924

LBDD HOC -0.0585 0.2482 0.0919 0.3073 0.743438

LBDS 0.0486 0.1517 -0.0484 0.1104 -0.0421 0.735595

LBID -0.0558 0.0732 0.0544 0.1809 0.3568 0.033 0.904544

LBIS 0.0216 0.0294 -0.1625 0.2055 0.226 0.1577 0.4334 0.888538

LBND1 -0.1378 0.2624 0.1991 0.244 0.3268 0.1692 0.2879 0.1642 0.866833

LBND2 -0.021 0.0307 0.0315 0.0724 0.1312 -0.1356 0.1677 0.2228 0.2771 0.856388

LBNS -0.059 0.1619 -0.1284 0.2563 0.2542 0.0912 0.2947 0.3397 0.3963 0.4947 0.804114

Pro&Socialti

es0.0386 0.195 0.0001 0.0397 -0.0228 0.1515 0.0404 0.1753 0.1061 0.1872 0.2126 0.762693

SE -0.0834 0.092 -0.1873 0.3267 0.2705 0.0587 0.1731 0.2424 0.2011 0.0119 0.1666 0.0519 0.708661

Strongties 0.0554 0.1058 0.0223 0.0292 0.0775 -0.0546 -0.0558 -0.0591 -0.0435 0.2696 0.0487 0.256 -0.0582 0.899722

SE StrongtiesLBDS LBID LBIS LBND1 LBND2 LBNSBizExp Businessties CSI PK HOC LBDD HOC

400

Table 6.37: LBND, Cross loadings of first order constructs

Pro&

Socialites

LBDD1LS 0.1523 0.1464 0.1754 0.6661 0.0039 0.2673 0.0739 0.2542 0.1021 0.081 0.0575 0.1428 0.0966

LBDD2LS 0.212 0.0083 0.2728 0.8135 -0.0591 0.2671 0.2436 0.2374 0.0953 0.2756 -0.0752 0.2492 0.0279

CSP1 0.0741 0.8246 -0.0753 0.0687 -0.0061 -0.0139 -0.1967 0.154 0.0339 -0.1808 0.029 -0.1357 0.0747

CSP2 0.1829 0.776 0.048 0.155 -0.1081 0.0803 -0.1693 0.1333 0.0059 -0.0367 -0.085 -0.1988 -0.0122

CSP3 0.0453 0.6425 -0.049 -0.0672 0.0563 -0.0474 -0.0932 0.1303 0.0028 -0.1137 0.0499 -0.1292 -0.0058

CSP4 0.0583 0.6227 -0.0921 0.0695 -0.0437 0.1431 -0.0526 0.1506 0.023 -0.0438 0.0251 0.0102 -0.0464

CSP5 -0.0312 0.5573 -0.0756 0.0231 -0.0993 0.0394 -0.0051 0.1215 0.0734 -0.1097 -0.0306 -0.1864 -0.077

CSP6 0.1451 0.6817 -0.0303 0.0797 0.0508 0.049 -0.0852 0.1528 -0.0168 -0.0173 0.0696 -0.0618 0.1607

ForigenKno

wL0.3181 -0.0963 0.8913 0.2522 0.1364 0.1283 0.1906 0.1875 0.0544 0.2867 0.0102 0.3551 -0.0447

InstKnowL 0.3143 0.0043 0.8554 0.3264 0.1244 0.2302 0.1405 0.2908 -0.0372 0.1052 0.0061 0.2444 0.0201

SocialKnowL 0.1441 -0.0414 0.7179 0.1675 -0.0181 0.0775 0.184 0.1096 0.2006 0.2503 0.1038 0.1862 0.1336

q131 0.1983 0.158 0.2327 0.2936 0.1527 0.2104 0.1668 0.8805 0.1587 0.3286 0.0713 0.2512 -0.1275

q132 0.2598 0.1889 0.1885 0.2723 0.1402 0.293 0.1156 0.853 0.3303 0.3604 0.1149 0.0899 0.061

q133 0.1008 0.0756 0.0916 0.2079 -0.1604 0.218 0.2447 0.3421 0.7265 0.4054 0.0468 0.0383 0.0684

q134 0.0015 0.0115 0.0562 0.0867 -0.1092 0.1279 0.1861 0.2179 0.969 0.4628 0.2135 0.0009 0.3071

q141 0.1217 -0.0833 0.1828 0.1585 0.0932 0.1824 0.281 0.344 0.437 0.8074 0.1595 0.0893 0.0441

q142 0.069 -0.1944 0.2343 0.2217 0.0541 0.1813 0.2963 0.2492 0.2954 0.8174 0.1964 0.154 0.0401

q143 0.1474 -0.0765 0.1829 0.2357 0.0793 0.3567 0.2646 0.3525 0.3736 0.7512 0.1181 0.1571 0.0073

q144 0.1882 -0.0528 0.223 0.2053 0.0673 0.2408 0.2488 0.3341 0.4887 0.838 0.2066 0.1383 0.0631

q153 0.1445 0.1066 0.1407 0.3437 0.0434 0.8886 0.383 0.32 0.204 0.307 0.0522 0.2214 -0.0234

q154 -0.0004 0.0004 0.1836 0.3055 0.0184 0.9202 0.4007 0.2102 0.1073 0.2326 0.0233 0.1016 -0.0736

q161 -0.0291 -0.1838 0.1459 0.1502 0.1047 0.4169 0.9029 0.1607 0.2422 0.3488 0.1385 0.1982 -0.0597

q162 0.0886 -0.1 0.2244 0.2583 0.1804 0.3498 0.8739 0.1294 0.1483 0.2493 0.1757 0.2351 -0.0446

q184 0.0159 -0.1392 0.0302 -0.1714 0.5662 -0.1239 0.0691 -0.0568 -0.1357 0.0308 0.1025 0.0133 -0.009

q185 0.1145 -0.0673 0.0979 -0.0024 0.8609 -0.037 0.0487 0.1581 -0.1015 0.0001 0.1367 0.0326 0.0088

q186 0.1651 0.0457 0.0929 -0.0116 0.7494 0.172 0.2406 0.1769 -0.0959 0.1797 0.0981 0.0761 -0.1233

se1 0.0953 -0.1882 0.2332 0.2597 -0.0063 0.175 0.2634 0.188 0.0651 0.1763 0.1726 0.7413 -0.0093

se2 0.001 -0.0645 0.1863 0.1285 0.0017 0.0203 0.063 0.0095 0.0088 0.0565 -0.0835 0.5814 0.0154

se3 -0.0231 -0.1866 0.1753 0.2021 0.0164 0.1632 0.1763 0.1039 -0.0224 0.0642 -0.0717 0.6691 -0.105

se4 0.1482 -0.087 0.3127 0.1676 0.1328 0.1107 0.157 0.2184 -0.0207 0.1486 0.0694 0.8207 -0.0585

zbizassoties 0.8769 0.0569 0.3259 0.2328 0.1625 0.0746 0.0553 0.27 -0.0039 0.1657 0.2584 0.1353 0.0488

zbizpartties 0.8254 0.1462 0.2205 0.2078 0.1191 0.0928 0.0195 0.2124 0.0436 0.1297 0.0632 0.0709 0.1072

zcollegties 0.7682 0.1143 0.2499 0.157 0.0698 -0.015 -0.0265 0.1318 0.0535 0.0852 0.1376 -0.0259 0.1337

zfriendsties 0.1395 0.0577 0.008 0.0647 -0.0667 -0.0798 -0.0708 -0.0357 0.2967 0.0578 0.2812 -0.1006 0.9582

zkinshipties 0.0156 -0.0507 0.0627 0.0832 -0.0182 0.0036 -0.0226 -0.0478 0.1524 0.0194 0.1456 0.037 0.8372

zmentoringti

es0.1961 0.019 0.041 -0.0013 0.0878 0.0471 0.1661 0.0726 0.1164 0.157 0.7688 0.1244 0.2325

zproforumtie

s0.2286 0.0963 -0.0062 -0.0461 0.2292 -0.0136 0.0828 0.1281 0.0842 0.0959 0.711 0.0204 0.0544

zsocialties 0.0482 -0.0907 0.0501 -0.0086 0.0515 0.052 0.1475 0.0521 0.2118 0.2197 0.8052 -0.0161 0.2755

StrongtiesLBID LBIS LBND1 LBND2 LBNS SEBusinessties CSI PK HOC LBDD HOC LBDS

401

Table 6.42: LBDS first order constructs- Fornell-Larcker criterion

Pro&

Socialites


CSI 0.0614 0.1196 0.680147

PK HOC -0.2464 0.3237 -0.0512 0.824985

LBDD HOC -0.0585 0.2482 0.0995 0.3046 0.743438

LBDS 0.0446 0.1473 -0.0595 0.1001 -0.0529 0.737902

LBID -0.0557 0.0734 0.0534 0.1778 0.3567 0.0389 0.743438

LBIS 0.0217 0.0295 -0.1669 0.2071 0.2261 0.1614 0.4333 0.888538

LBND HOC -0.0188 0.024 0.0315 0.0692 0.125 -0.1478 0.1623 0.2231 0.728766

LBNS -0.0589 0.1618 -0.1323 0.2608 0.2542 0.0952 0.2947 0.3396 0.4851 0.804114

Pro&Socialti

es0.0423 0.1868 -0.0222 0.0435 -0.0207 0.14 0.0433 0.1779 0.1829 0.2166 0.761118

SE -0.0838 0.0918 -0.2032 0.3282 0.2697 0.048 0.1718 0.2411 0.005 0.1662 0.0509 0.708872

Strongties 0.0554 0.1081 0.0122 0.0287 0.0769 -0.0538 -0.0573 -0.0599 0.2686 0.0493 0.267 -0.0602 0.897942

Strongties

BizExpSingle

Item

LBDS LBID LBIS LBND HOC LBNS SEBizExp Businessties CSI PK HOC LBDD HOC

402

Table 6.43: LBDS first-order constructs, Cross-Loadings

Pro&

Socialites

LBDD1LS 0.1524 0.1469 0.1717 0.6652 -0.0135 0.2672 0.0739 0.0905 0.081 0.0547 0.1425 0.0959

LBDD2LS 0.2117 0.0137 0.2721 0.8142 -0.06 0.2671 0.2436 0.0907 0.2756 -0.0711 0.2488 0.028

LBND1LS 0.0359 0.0366 0.079 0.138 -0.1359 0.1734 0.2277 0.8013 0.497 0.1851 0.0136 0.2631

LBND2LS 0.2652 0.1968 0.2383 0.3257 0.1657 0.2922 0.1615 0.8078 0.398 0.1052 0.192 -0.0334

CSP1 0.0745 0.8348 -0.0781 0.0687 -0.0077 -0.0139 -0.1967 0.0222 -0.1809 0.0243 -0.1356 0.0757

CSP2 0.1837 0.795 0.0458 0.1549 -0.1107 0.0802 -0.1692 0.0041 -0.0368 -0.091 -0.1983 -0.0098

CSP3 0.0451 0.6237 -0.0485 -0.0674 0.0535 -0.0475 -0.0932 -0.0069 -0.1137 0.0449 -0.1282 -0.0053

CSP4 0.0588 0.5863 -0.0942 0.0694 -0.037 0.143 -0.0526 0.0169 -0.0438 0.0182 0.011 -0.0461

CSP5 -0.0311 0.5707 -0.0757 0.023 -0.1045 0.0394 -0.0051 0.0748 -0.1097 -0.0287 -0.1865 -0.0762

CSP6 0.1455 0.6498 -0.0303 0.0796 0.0481 0.0489 -0.0851 -0.0234 -0.0173 0.0622 -0.0608 0.1606

ForigenKno

wL0.3174 -0.0933 0.8946 0.2522 0.1304 0.1283 0.1906 0.0452 0.2867 0.0092 0.3559 -0.0457

InstKnowL 0.3142 0.0121 0.8444 0.3263 0.1168 0.2302 0.1406 -0.0499 0.1052 0.0085 0.244 0.0191

SocialKnowL 0.1439 -0.0366 0.727 0.1676 -0.0266 0.0775 0.184 0.1973 0.2503 0.1081 0.1861 0.134

q141 0.1221 -0.0863 0.1869 0.1586 0.094 0.1823 0.281 0.4274 0.8074 0.1628 0.0888 0.0454

q142 0.0687 -0.1993 0.2363 0.2218 0.0528 0.1812 0.2962 0.2855 0.8174 0.1988 0.1538 0.0391

q143 0.1469 -0.0812 0.187 0.2358 0.0827 0.3566 0.2645 0.3584 0.7513 0.1206 0.1572 0.0072

q144 0.1876 -0.0507 0.2274 0.2054 0.0775 0.2407 0.2488 0.4809 0.8378 0.2093 0.1378 0.0643

q153 0.1443 0.1007 0.1393 0.3436 0.0543 0.8884 0.383 0.1938 0.307 0.0542 0.2211 -0.024

q154 -0.0006 -0.0016 0.1796 0.3055 0.019 0.9204 0.4006 0.1015 0.2327 0.0252 0.1005 -0.0749

q161 -0.0294 -0.1867 0.1478 0.1504 0.1097 0.4169 0.9027 0.2414 0.3488 0.1402 0.1969 -0.0603

q162 0.0882 -0.1079 0.2249 0.2584 0.1817 0.3498 0.8741 0.1473 0.2493 0.1769 0.235 -0.0449

q184 0.0154 -0.1501 0.0312 -0.1711 0.6042 -0.1239 0.0691 -0.1433 0.0308 0.0999 0.0135 -0.0102

q185 0.1145 -0.0737 0.0949 -0.0025 0.8432 -0.037 0.0487 -0.113 0.0001 0.1296 0.0328 0.0087

q186 0.1646 0.0416 0.0931 -0.0117 0.7458 0.172 0.2406 -0.1111 0.1797 0.0939 0.0759 -0.1238

q187 0.0619 -0.0187 -0.0204 -0.0858 0.5514 0.1282 0.1332 -0.0254 0.1004 0.0227 -0.0697 -0.0264

se1 0.0943 -0.1921 0.2333 0.2598 -0.0113 0.175 0.2635 0.0563 0.1763 0.1753 0.739 -0.0102

se2 0.0008 -0.0793 0.1886 0.1285 0.0023 0.0202 0.0631 0.0082 0.0565 -0.0858 0.5863 0.0148

se3 -0.0231 -0.1905 0.174 0.202 0.0133 0.1632 0.1763 -0.0267 0.0643 -0.0726 0.6682 -0.1067

se4 0.1476 -0.1002 0.3147 0.1677 0.1137 0.1106 0.157 -0.031 0.1486 0.0663 0.8204 -0.0602

zbizassoties 0.8743 0.0565 0.3249 0.2328 0.1614 0.0745 0.0554 -0.0175 0.1656 0.2535 0.135 0.0496

zbizpartties 0.8275 0.1476 0.2186 0.2078 0.1135 0.0927 0.0196 0.0386 0.1296 0.058 0.0702 0.1088

zcollegties 0.7697 0.1159 0.2479 0.1569 0.0656 -0.0151 -0.0264 0.0489 0.0852 0.1295 -0.0251 0.1353

zfriendsties 0.1401 0.0573 0.0091 0.0646 -0.0675 -0.0798 -0.0708 0.2962 0.0577 0.2888 -0.1002 0.9613

zkinshipties 0.0158 -0.0611 0.0624 0.083 -0.0128 0.0036 -0.0226 0.1481 0.0195 0.1491 0.0372 0.831

zmentoringti

es0.1956 0.0131 0.0433 -0.0014 0.0785 0.0471 0.1661 0.1089 0.157 0.7692 0.1233 0.2334

zproforumtie

s0.2281 0.0836 -0.0055 -0.0463 0.2224 -0.0136 0.0828 0.074 0.0959 0.6844 0.0203 0.0547

zsocialties 0.0471 -0.0942 0.0503 -0.0087 0.0547 0.052 0.1475 0.2078 0.2197 0.824 -0.0169 0.2773

LBID LBIS LBND HOC LBNS SE StrongtiesBusinessties CSI PK HOC LBDD HOC LBDS

403

Table 6.46: LBDD first order constructs- Fornell-Larcker criterion

Pro

&Socialites

BizExp Single Items


CSI 0.0642 0.1228 0.687168

PK HOC -0.2463 0.323 -0.0536 0.825045

LBDD1 -0.0778 0.2111 0.0107 0.272 0.918858

LBDD2 0.0017 0.1521 0.1462 0.1728 0.1125 0.758947

LBDS 0.0487 0.15 -0.0552 0.1081 -0.059 -0.0015 0.73641

LBID -0.0558 0.073 0.0519 0.1779 0.2671 0.2704 0.0304 0.904544

LBIS 0.0216 0.0289 -0.1662 0.207 0.2437 0.0776 0.1566 0.4334 0.888538

LBND -0.0169 0.0207 0.0241 0.0661 0.0906 0.0933 -0.1523 0.1586 0.2216 0.725879

LBNS -0.0589 0.1615 -0.1301 0.2606 0.2758 0.0861 0.0902 0.2947 0.3397 0.4811 0.804114

Pro&Socialti

es0.038 0.1941 -0.0052 0.0409 -0.0757 0.0546 0.1529 0.04 0.1749 0.1776 0.2121 0.762693

SE -0.0835 0.091 -0.1946 0.3276 0.2482 0.1441 0.0584 0.1726 0.2419 0.0023 0.1664 0.051 0.708731

Strongties 0.0554 0.1058 0.017 0.0306 0.0279 0.0955 -0.0535 -0.0553 -0.0589 0.2671 0.0484 0.2541 -0.0571 0.900222

SE StrongtiesLBDD2 LBDS LBID LBIS LBND HOC LBNSBizExp Businessties CSI PK HOC LBDD1

404

Table 6.47: LBDD first-order constructs, Cross-Loadings

Businessties CSI PK HOC LBDD1 LBDD2 LBDS LBID LBIS LBND HOC LBNSPro&Socialti

esSE Strongties

LBND1LS 0.036 0.0355 0.0791 0.1031 0.1071 -0.14 0.1734 0.2277 0.8013 0.4971 0.1812 0.0137 0.2608

LBND2LS 0.265 0.1983 0.2383 0.2356 0.2573 0.1666 0.2923 0.1615 0.8078 0.398 0.109 0.1923 -0.0337

CSP1 0.0747 0.8291 -0.0778 0.0172 0.0945 -0.0075 -0.0139 -0.1967 0.022 -0.1808 0.0296 -0.1356 0.0743

CSP2 0.1841 0.7899 0.0458 0.0794 0.1651 -0.1101 0.0803 -0.1692 0.0039 -0.0368 -0.0842 -0.1986 -0.0133

CSP3 0.045 0.6381 -0.0485 -0.1305 0.0506 0.0558 -0.0474 -0.0932 -0.0071 -0.1137 0.0505 -0.1285 -0.006

CSP4 0.0591 0.6046 -0.0941 0.0002 0.117 -0.0446 0.1431 -0.0526 0.0167 -0.0438 0.026 0.0107 -0.0466

CSP5 -0.031 0.5613 -0.0755 -0.0146 0.0603 -0.1009 0.0394 -0.0051 0.0747 -0.1096 -0.0314 -0.1865 -0.0774

CSP6 0.1457 0.6589 -0.0303 0.0292 0.0967 0.0502 0.049 -0.0852 -0.0236 -0.0173 0.0706 -0.0612 0.1608

ForigenKno

wL0.317 -0.0934 0.8938 0.2229 0.1449 0.1363 0.1283 0.1906 0.0449 0.2867 0.0104 0.3556 -0.0443

InstKnowL 0.3141 0.0093 0.8447 0.2509 0.2358 0.1238 0.2302 0.1405 -0.0503 0.1052 0.0059 0.2442 0.0205

SocialKnowL 0.1438 -0.0387 0.7279 0.202 0.0309 -0.0185 0.0775 0.184 0.1972 0.2503 0.1027 0.1862 0.1335

q141 0.1223 -0.0854 0.1869 0.2165 0.0006 0.092 0.1824 0.281 0.427 0.8075 0.1591 0.0891 0.0435

q142 0.0686 -0.1963 0.236 0.2113 0.1102 0.0531 0.1813 0.2963 0.2853 0.8172 0.1962 0.1539 0.0406

q143 0.1467 -0.0795 0.1868 0.2694 0.0622 0.0787 0.3567 0.2646 0.358 0.7514 0.1177 0.1572 0.0073

q144 0.1873 -0.0513 0.2274 0.1935 0.1055 0.0669 0.2408 0.2488 0.4805 0.8379 0.2062 0.138 0.0625

q153 0.1443 0.104 0.1392 0.2531 0.2649 0.0409 0.8886 0.383 0.1934 0.307 0.0517 0.2213 -0.0232

q154 -0.0006 -0.0017 0.1798 0.2323 0.2278 0.0161 0.9202 0.4006 0.1012 0.2327 0.0229 0.1009 -0.073

q161 -0.0294 -0.1854 0.1479 0.1833 0.0237 0.1041 0.4169 0.9028 0.2412 0.3488 0.138 0.1975 -0.0594

q162 0.088 -0.1053 0.2249 0.2544 0.1201 0.1789 0.3498 0.874 0.1472 0.2493 0.1753 0.235 -0.0444

q171 0.1062 0.0072 0.1099 0.1147 0.6648 -0.0099 0.2088 0.0418 0.085 0.0778 0.0834 0.1212 0.0844

q172 0.2186 -0.0329 0.2587 0.924 0.1295 -0.0588 0.2501 0.2194 0.0743 0.2458 -0.0884 0.2757 0.0258

q174 0.1679 0.0553 0.2407 0.9136 0.0756 -0.0495 0.2404 0.2288 0.0927 0.2616 -0.0495 0.1774 0.0254

q175 0.1073 0.0677 0.1544 0.2046 0.7678 -0.0579 0.2819 0.1384 0.1459 0.1862 -0.0225 0.1504 0.0455

q176 0.1312 0.2093 0.1305 -0.0205 0.8345 0.0475 0.1514 0.01 0.0072 -0.0324 0.0672 0.0748 0.0887

q184 0.0151 -0.145 0.0309 -0.0243 -0.2647 0.5745 -0.1239 0.0691 -0.1433 0.0308 0.1032 0.0133 -0.0085

q185 0.1145 -0.0724 0.0947 -0.0642 0.0737 0.8617 -0.037 0.0487 -0.1132 0.0001 0.1379 0.0328 0.0089

q186 0.1644 0.0444 0.093 -0.0307 0.0221 0.7446 0.172 0.2406 -0.1114 0.1798 0.0987 0.0761 -0.1231

se1 0.0939 -0.1914 0.2331 0.262 0.1091 -0.0072 0.175 0.2635 0.056 0.1763 0.1713 0.7402 -0.0089

se2 0.0007 -0.0724 0.1881 0.1212 0.0615 0.0026 0.0203 0.0631 0.0082 0.0565 -0.0832 0.584 0.0157

se3 -0.0231 -0.1898 0.1739 0.1394 0.1679 0.0179 0.1632 0.1763 -0.0268 0.0642 -0.072 0.6683 -0.1042

se4 0.1474 -0.0949 0.3145 0.1658 0.0749 0.131 0.1107 0.157 -0.0313 0.1486 0.0695 0.8207 -0.0577

zbizassoties 0.8729 0.0571 0.3247 0.2138 0.1211 0.1616 0.0746 0.0554 -0.0179 0.1657 0.2589 0.1352 0.0485

zbizpartties 0.8288 0.147 0.2186 0.1834 0.1215 0.1176 0.0928 0.0196 0.0383 0.1297 0.0631 0.0706 0.1065

zcollegties 0.7702 0.1157 0.2478 0.0946 0.1459 0.0695 -0.015 -0.0264 0.0487 0.0852 0.1389 -0.0254 0.133

zfriendsties 0.1405 0.0558 0.0095 0.0285 0.0728 -0.0661 -0.0798 -0.0708 0.2964 0.0578 0.2799 -0.1004 0.9566

zkinshipties 0.0158 -0.0593 0.0626 0.02 0.1149 -0.0171 0.0036 -0.0226 0.1482 0.0194 0.1448 0.0371 0.8401

zmentoringti

es0.1954 0.015 0.0436 -0.0302 0.0357 0.0876 0.0471 0.1661 0.1088 0.157 0.7656 0.1238 0.2321

zproforumtie

s0.2277 0.0891 -0.0056 -0.1302 0.0839 0.2295 -0.0136 0.0828 0.0738 0.0959 0.7159 0.0204 0.0542

zsocialties 0.0465 -0.0937 0.0505 -0.0234 0.0134 0.052 0.052 0.1475 0.2078 0.2197 0.804 -0.0165 0.2746

405

Appendix F: Content Validity Assessment

Figure 2: Different types of content validity indexes

Source: (Polit et al., 2007, p. 494).

Table 2: Evaluation of I-CVIs with Different Numbers of Experts and

Agreement

Source: Polit et al. (2007, p. 465)

In addition a minimum value of S-CVI should be above .80 (e.g. excellent content

validity is >.9).

406

Findings

The debate on how and what to compute when assessing the face validity of a

questionnaire is still evolving, and there are various approaches to it (Polit and Beck,

2006; Beckstead, 2009). Yet, the Content Validity Index (CVI), The Kappa

Coefficient (K*) and the Item Content Validity Index (I- CVI) have been often used,

to quantify the face validity of a questionnaire and its items (Polit et al., 2007),

although they can be computed in different ways (Rubio et al., 2003). Moreover, the

approach that was taken in this study is that content validity should be addressed by

implementing a mixed methods assessment (Newman et al., 2013a). Therefor

decisions on items (i.e., omitting, revising or conserving them) should not exclusively

be based on computing indexes but also on the overall consideration by the judges and

by considering qualitative observations on items or alternative wording suggested for

them.

However, while reading the findings of this analysis it should be emphasised that it is

only a quantification of a small sample size of referees. Beckstead (2009, p.1281)

summarised the main limitation of this approach by lamenting that:”… sampling of

experts is merely a mechanism for obtaining an estimate of the item’s (or

instrument’s) relevance to the construct at hand. There is no escaping the law of large

numbers; the quality (i.e., precision) of an estimate is a function of sample size”.

In the first iteration the I-CVI 41

and K* 42

were computed following the

recommendations of various authors such as: Polit and Beck (2006); Polit et al. (2007)

and Fleiss et al. (1981). In addition, the evaluation of each item face validity was

adopted from Polit et al. (2007, p. 465) as it is shown in Table 2. Overall the

interpretation item’s face validity was done based on the magnitude of weighted

kappa. The Evaluation critetria, based on the aformentioned authors is like that of

41

Content Validity of individual items: Proportion of content experts giving item a relevance rating of 3

or 4

42

k*-kappa designating agreement on relevance: k*=(I-CVI- pc)/(1- pc).

407

unweighted kappa: K*>0.74 or so indicate excellent agreement, for most purposes,

K*>0.60 or so represent good agreemen and K*>0.40 signifies fair agreement.

The number of items was reduced for this analysis by purposefully selecting for

evaluation 57 items from the questionnair. These items are related to the main

varibales of the conceptual model or items that are related to a newly developed

meaure in this study.

Six judges were selected. All of them possess a doctoral degree, five of them are from

academia, and one is an expert in survey design. The experts have proven experience

in instrument design and construction. All the judges who were selected and invited to

assess the questionnaires' items completed the task. In this stage, all items were

evaluated, even if acceptable indices were obtained. Of the 57 items assessed, Seven

were considered to have insufficient item content validity (CVI < .70 and Kappa <

.0.74). All of these items are related to the dimensions of the Learning Strategies.

For each of the seven items, based on the comments and amendments of the reviewers,

seven revised items were proposed for a second iteration by the supervisors of this

research. In this study, the second iteration was done with only two judges but without

computing again the quantitative indexes of each of the revised items. This was done

for the following: firstly, the number of revised items 7 is considered low, compared to

the total number of the items in this questionnaire. Secondly, all of these items have

index values of K*=0.56, which might be considered according to prior work as

having a fair evaluation. Thirdly, the average of the I-CVIs for all items on the scale

(S-CVI/Ave) was computed too. This was done by calculating the average of items on

a scale that achieves a relevance rating of 3 or 4 by all the experts. It was found in this

study that the S-CVI/Ave is above 0.90, which is considered as face valid (Polit et al.,

2007). In addition, qualitative assessment of all of the items was conducted. Even if

the item was assessed, based on the quantitative indexes, as face valid, but one of the

judges made a comment on this item, it was re-evaluated and in some cases rephrased.

408

Appendix G: Content validity assessment- Referees’ Booklet

Dear:_____

Doctorate Research: Content Validity Assessment

I hope this email finds you well,

It is my great pleasure to send you this electronic form, on which you are asked to

assess the face validity of this study’s questionnaire. The questionnaire is part of my

doctoral research project, which I am conducting at The University of Manchester

Business School, U.K. The aim of this study is to explore the factors that affect the

ways entrepreneurs learn about business opportunities in the international arena.

On the attached hyper link: http://194.90.28.59/userinfo.aspx,

you are asked to assess whether the items are face valid. This will enable revisions

to improve and refine the instrument.

In addition, please see attached three files, which may assist you in this process:

The full version of the questionnaire

An Introduction letter

An Abstract of this Study

I look forward to hearing from you,

With best wishes,

Izak Fayena

409

Appendix H: International young ventures types

Venture

Type

Scholars Definition Classification criterion

Born Global (Knight and

Cavusgil,

2004)

Small, technology-

oriented companies that

operate in international

markets from the earliest

days of their existence. In

addition they can be

described as highly

specialised global ‘niches’

and the type is particularly

prevalent among SMEs

located in small, open

economies that face the

double jeopardy of

targeting narrow ‘niches’

in small domestic markets

(Bell et al., 2001)

Internationalising (by

licensing, Alliances,

exporting) within two

years of inception

.

Between 3 and 5 years

from start-up as a

classification criterion

International

New Venture

(Oviatt and

McDougall,

2005a)

INV is a business

organisation that from

inception seeks to derive

significant competitive

advantage from the use of

resources and the sale of

outputs in multiple

countries.

Firms that are global from

inception or,

internationalise within

two years of

establishment

(Madsen et

al., 2000;

Knight and

Cavusgil,

2004; Zhou et

al., 2010)

INVs are defined as

independently operating

SMEs with export sales.

Firms with export sales

that represent at least 20%

of their total sales within

3 years of inception

410

Venture

Type


Born-Again

Global

(Bell et al.,

2001; Bell et

al., 2003)

These are firms that are

well established in their

domestic markets, with

apparently no great

motivation to

internationalise, but which

have suddenly embraced

rapid and dedicated

internationalisation.

Predominantly,

'traditional' firms, whose

internationalisation

process was prompted by

a 'critical incident'

International

Start-up

(Johnson,

2004)

An ‘international start-up’

was defined for purposes

of this study as a new

venture that exhibits an

innate propensity to

engage in a meaningful

level of international

business activity at or near

inception, with the

intention of achieving a

strategic competitive

advantage.

Firms were considered to

be international start-ups

if they internationalised

within five years of

inception, and

international sales

represented a minimum of

20% of total revenue over

the first five years of their

international activity,

thereby indicating

substantive international

business intensity and

commitment

International

Venture

(Kuemmerle,

2002)

International

entrepreneurship is

defined as the

development of INVs or

start-ups that, from their

inception, engage in either

HBA or HBE activities or

both, thus viewing their

operating domain as

international from the

initial stages of the firm’s

operation.

- The ventures are built

from one rather than

several home bases.

- The need for rapid

growth of the venture

- The venture’s

international character can

be explained by the

entrepreneur’s prior

exposure to international

environments.

411

Venture

Type


Micro-

Multinational

(Dimitratos

et al., 2003)

A micro multinational is a

small- or medium-sized

firm that controls and

manages value-added

activities through

constellation and

investment modes in more

than one country. The

mMNE is a new type of

internationalised small

firm; it is different from

the ‘global small firm’ and

the international new

venture.

Unlike large

multinationals, the

mMNE is likely to: (1)

not necessarily ‘own’

value-added activities

abroad; (2) engage in

higher degrees of

constellation formations;

(3) possess a higher range

of value-added activities

abroad represented by

‘knowledge intensive

assets’. According to its

objectives, the mMNE

can be classified as: (1)

network seeker; (2)

market hunter; (3)

flexibility pursuer; (4)

resource tracker; (5)

global market chaser; (6)

learning seeker; (7)

competition player; or a

combination of any of

these.

412

Appendix I: The Questionnaire (Final English Version)

Doctorate Research: International Entrepreneurship and Learning

Dear colleague,

Thank you for participating in this study. This questionnaire is part of my doctoral

research project being conducted at The University of Manchester Business School,

U.K. The research addresses the topic of International Entrepreneurship, and

specifically, what are the factors that affect the ways entrepreneurs learn about

business opportunities. The questionnaire is directed to entrepreneurs who are actively

involved in a start-up venture(s) as founders or part of the founding team. You have

been selected from a list provided by the IVC database in Israel. Filling in the

questionnaire should last between 20- 30 minutes.

If your entrepreneurial experience includes more than one business venture, please

indicate this, but relate in your answers to the most memorable venture.

Confidentiality and anonymity will be maintained and the data will be used only for

research purposes. To express our appreciation, if you agree to participate you will

receive a summary report of the key findings from this study, which should be

interesting and informative for all entrepreneurs. Your cooperation is appreciated and

we thank you in advance.

Izak Fayena, DBA Student, Manchester Business School

Email: [email protected]

Tel: +972-52-7506260

mailto:[email protected]

413

What are the factors that affect the ways entrepreneurs learn about

business opportunities?

CONSENT FORM

If you are happy to participate, please complete the consent form below

Please

Initial

Box

1. I confirm that I have read the attached information sheet on the above

project and have had the opportunity to consider the information and ask

questions and had these answered satisfactorily.

2. I understand that my participation in the study is voluntary and that I am

free to withdraw at any time without giving a reason

3. I agree to take part in the above project

414

Part One – Introduction

In this questionnaire, I would like to ask you to share with me your personal

experience as an entrepreneur. This research project focuses on how entrepreneurs

identify business opportunities in the international arena. Of particular interest is how

you learn as an entrepreneur about your ideas until they were considered business

opportunities. For example, when you have an idea for a new venture, it might be

vague at the beginning, but you feel you want to delve into it and to see whether it is

viable. At this point, you might execute your ideas without prior planning or you

might do it deliberately and systematically. In addition, you might base your decision

on your intuition, personal experience or your friends, family and colleagues. Any of

these strategies is legitimate, and might be considered as one of your general

entrepreneurial behaviours, however in this study I am interesting in the specific

behaviours, which entrepreneurs acquires and implements in order to learn about

business opportunities.

Part Two- personal demographics and venture characteristics

This section contains a number of questions about you and your role. The information

is solely used to gain an idea of what sorts of people have responded to this survey.

Please be assured that this information will only be used for statistical purposes, and

not to identify individuals. All information will be kept strictly confidential and no

individuals will be identified personally.

Please refer to the only one and most recent business venture.

415

Are you the (please tick the appropriate boxes):

Respondent position

Founder of the venture

Chairman

Principal owner

Managing director

Other

Please state

(1) Please tell me whether you started, purchased, or inherited this venture alone or

with other equity partners? Please tick:

Alone

With others

If with others, how many equity partners does this business have?

(2) Please tell me about the total number of start-up ventures; you have had equity

stakes in, as a founder, or an inheritor, a purchaser or part of the founding team:

(3) Please tell me about the total number of start-up ventures, you currently have

equity stakes in, as a founder, or an inheritor, a purchaser or part of the founding

team:

(4) Please tell me about your gender:

Male

Female

416

(5) Please tell me about your age (in years):

(6) Please describe the highest level of your formal education:

Education Level

High School or less

Diploma Studies

Bachelor degree (B.A)

Master degree (M.A)

Doctorate

Other (e.g. professional qualifications)

(7) Please tell me about the number of employees (i.e. including the owners, full time

and part time employees) you currently employ in your venture:

(8) Please tell me about the year this business was established formally:

(9) Has your venture engaged in an international activity? If Yes Please tell me about

the year this venture engaged in its first international activity (YYYY):

(10) Please tell me what percentage of your total sales are from overseas (%):

417

The following questions relate to how confident you feel in a range of business

activities. Please rate how confident you are in:

Please tell me about the extent to which your venture has faced external threats about

survival and development:

Part three- Learning strategies

In the following section, we would like to ask you to share with us the story of how

you identified a particular business opportunity, and specifically how you learnt about

it. Please recall from your memory best memorable case, of identifying a business

opportunity. Please, focus on the time when you thought about the idea, the information

(11) Entrepreneurial Self-Efficacy

No

confidence

1 2 3 4

Complete

confidence

5

(11.1) Successfully identifying new

business opportunities

(11.2) Creating new products

(11.3) Thinking creatively

(11.4) Commercializing an idea or new

development

(12) Environment

Totally

disagree

1 2 3 4

Totally

agree

5

(12.1) The venture has faced external threats about

survival and development

418

you needed and how you collected and analysed it. Please use the space below to write

down, a short description of this actual business opportunity:

The following questions relate to how frequently you engaged in a range of learning

activities. I would like to ask you how frequently you engaged in these learning

activities from the time you first thought about this business idea till it has been

identified, by you, as a business opportunity:

(13) Learning by Networking Deliberately

Not

at all

1 2 3 4

Always

5

(13.1) I engaged with others in a deliberate and systematic

inquiry regarding an idea, in order to be able to study it in

depth

(13.2) I made an effort to contact an expert in this field to get

their reactions to my idea.

(13.3) I discussed my idea(s) with people I know. This enabled

me to acquire information and to take decisions regarding my

business ideas.

(13.4) I deliberately consulted with my personal contacts,

regarding an idea. I realised that this was a feasible business

opportunity.

419

(14) Learning by Networking Spontaneously

Not

at all

1 2 3 4

Always

5

(14.1) When I chatted with people I know, they came up with

interesting new ideas that I had not thought of previously.

(14.2) When I discussed an idea, about an issue that bothers me,

with people I know, we came up with new solutions for this

problem, unexpectedly.

(14.3) I started to think differently about an idea due to the

contribution of an experienced colleague.

(14.4) When I brainstormed with people I know about how to

approach a shared problem, we learned from each other

spontaneously.

(15) Learning by Imitating Deliberately

Not

at all

1 2 3 4

Always

5

(15.1) I generated new ideas by monitoring or purposefully

observing colleagues.

(15.2) I tracked the policies and tactics of other entrepreneurs or

start-ups with best practices in my industry.

(15.3) I deliberately acquired knowledge about the foreign market

through following the example of best practices firms

(15.4) I deliberately acquired knowledge about the foreign

market through imitating entrepreneurs that are perceived as

having best practices.

420

(16) Learning by Imitating Spontaneously

Not

at all

1 2 3 4

Always

5

(16.1) I observed others that turned out to be unexpectedly

informative.

(16.2) When I watched other entrepreneurs, I noticed that I

could perform a task in a similar way.

(16.3) I did not plan ahead; however, when the idea just emerged,

I made a connection to a successful or unsuccessful venture.

(17) Learning by Doing Deliberately

Not

at all

1 2 3 4

Always

5

(17.1) When I had an idea, I preferred to actively and

systematically search, by myself, for information on this

topic.

(17.2) When I took action with regard to this business

opportunity, I reflected on my previous mistakes, and tried

to learn from them.

(17.3) When I had an idea, I kept it to myself, and planned solely

how to acquire the relevant information about it.

(17.4) When I thought about the new business opportunity, I

deliberately learnt from my mistakes.

(17.5) I scanned the Internet to get more relevant information on

this new idea that I identified.

(17.6) I learnt systematically by reading relevant literature (such

as: professional journals, business and managerial books,

professional websites).

(17.7) I analysed my mistakes. This can help me improve the

way I identify business opportunities.

421

Part four- social networks, prior knowledge, entrepreneurial experience and

cognitive style.

In the following section, we would like to focus on your personal and social

entrepreneurial characteristics, which are relevant to the way you identified a

particular business opportunity, and specifically how you learnt about it. Please recall

from your memory best memorable case, of identifying a business opportunity.

Please, focus on the moment you thought about the idea, and try to memorise who

were the persons, if any you have consulted with, what was your knowledge about the

markets, regulations and what was the information you needed and how you collected

and analysed it. In addition, I believe that people differ in the way they think about

(18) Learning by Doing Spontaneously

Not

at all

1 2 3 4

Always

5

(18.1) Without prior planning I reflected on a sudden event that

enabled me to think of this new idea.

(18.2) Although I was alerted and tuned in to new information, I

didn't actively search for it.

(18.3) When I thought about the new business idea, it resulted

from spontaneous thinking on my past mistakes.

(18.4) When the idea showed up, I kept it to myself, and I did not

have a plan of how to acquire the relevant information about

it; I just did it.

(18.5) I learnt unintentionally, and without prior planning, from

relevant literature (such as professional journals, business

and managerial books, professional websites) about this new

idea.

(18.6) I scanned the Internet for business advice, and

unintentionally, I got this new idea.

(18.7) When I thought about this idea, I realised that I learnt

from my mistakes, but it did not affect the way I executed it.

422

problems. Therefore, this section includes items, which are designed to identify your

own approach.

The following questions relate to how (i.e. duration, frequency, and closeness) you

interact with different contact types with regards to this specific business idea:

(20) Please tell me, with regards to each category, how many years has each type of

contact been in existence?

Duration

less

than

2

years

1

2 to

5

years

2

Over

5 less

than

10

years

3

Over

10

years

4

Kinship relationships (family members, relatives)

Friendship and neighbouring relationships

Social networks members (Facebook, Twitter,

LinkedIn, etc.)

Mentoring and coaching relationships

Business partners and co-founders

Current and former business associates: customers,

suppliers, professional experts (i.e.: advisors and

consultants, bankers, board directors, venture

capitals, lawyers, accountants)

Current and former collegiality (such as co-

workers) relationships

Professional forums relationships such as seminars,

conferences, workshops, and technical publications

423

(21) On average, how frequently do you communicate with each contact category43

?

Frequency

Never

1 2 3 4 5 6

Very

Often

7




LinkedIn, etc.)





consultants, bankers, board directors, venture capitals,

lawyers, accountants),

Current and former collegiality (such as co-workers)

relationships,



(22)

43

This part (the frequency dimension) was omitted from the web version after the pilot survey, see more

details and explanations in chapter 3.

424

On average, how would you qualify the closeness of your relationship with each

contact category?

Closeness

Distant

1 2 3 4 5 6

Close

7




LinkedIn, etc.)





consultants, bankers, board directors, venture

capitals, lawyers, accountants)

Current and former collegiality (such as co-workers)

relationships



425

(23) Please indicate the extent, to which you agree to the following statements:

Foreign institutional knowledge

Strongly

disagree

1 2 3 4

Strongly agree

5

I possess knowledge about foreign

language and norms


business laws and regulations


government agencies

Foreign business knowledge

Strongly

disagree

1 2 3 4

Strongly agree

5

I possess knowledge about effective

marketing in foreign markets

I know how to serve foreign markets

I am familiar with customer problems in

other countries

I have a rich knowledge of markets such

as: supplier relationships, sales techniques,

and capital equipment requirements

426

Internationalisation knowledge

Strongly

disagree

1 2 3 4

Strongly agree

5

I have international business experience

I have the ability to determine foreign

business opportunities

I have experience in dealing with foreign

business contacts

I have the capability to manage

international operations

Social knowledge

Strongly

disagree

1 2 3 4

Strongly agree

5

I understand the history of the countries I

have entered

I understand the key values that people

share in the countries I have entered

I am aware of national attitudes regarding

foreign investment

I know key factors that determine people’s

decision-making processes in other

countries

I am aware of the key beliefs in the

national cultures of the countries entered

I understand accepted standards of how

people behave in those countries

427

(24) People differ in the way they think about problems. Below are 38 statements44

,

designed to identify your own approach. If you believe that a statement is true about

you, answer T.

If you believe that it is false about you, answer F.

If you are uncertain whether it is true or false, answer?

This is not a test of your ability, and there are no right or wrong answers. Simply

choose the one response, which comes closest to your own opinion. Work quickly,

giving your first reaction in each case, and make sure that you respond to every

statement. Indicate your answer by completely filling the appropriate opposite the

statement:

Statements T F ?

(1) In my experience, rational thought is the only realistic basis for

making decisions

(2) To solve a problem, I have to study each part of it in detail

(3) I am most effective when my work involves a clear sequence of tasks

to be performed

(4) I have difficulty working with people who ‘dive in at the deep end’

without considering the finer aspects of the problem

Due to copyrights, only 4 items are presented for

illustrative purposes only.

44

C. W. Allinson & J. Hayes 1996. All rights reserved. No part of this document may be reproduced in any form of printing or

by any other means, electronic or mechanical, including, but not limited to, photocopying, audio-visual recording and

transmission, and portrayal or duplication in any information storage and retrieval system, without permission in writing from the authors.

428

(25) Finally, if you have anything further say about the ways you learn about business

opportunities, please use the space below to add your comments and thoughts:

Thank you for participating in this research. To express our appreciation, please let us

know if you are interesting in receiving a summary of the findings of this study:

Would you like to receive the results of this research? Yes No

Name (Optional) _______________________________________________________

Email (Optional) _______________________________________________________

Thank you again for your participation in this research.

429

Appendix J: Learning strategies measures development

'Learning by networking'

The following questionnaire items have been adopted and modified from Doornbos et

al. (2008):

1) 'The extent Learning by networking is deliberate' (i.e. systematic, planned,

effectual)

o I engaged with others in a deliberate and systematic inquiry regarding an

idea, in order to be able to study it in depth (Doornbos et al., 2008) .

o 'I have been deliberately looking for people's reactions as a source for

reflection' (Doornbos et al., 2008).

o I discussed my idea(s) with people I know. This enabled me to acquire

information and to take decisions accordingly, regarding my business

ideas (Doornbos et al., 2008).

o 'By deliberately consulting with my personal contacts, such as: family,

friends and professional experts, regarding an idea, I have realised that it is

a feasible business opportunity' (Doornbos et al., 2008).

2) 'The extent learning by networking is spontaneous (i.e. random, unintended,

casual)

o 'When chatting with others, they came up with interesting ideas'

(Doornbos et al., 2008).

o 'When discussing an issue with people I know, we came up with new

o ideas unexpectedly' (Doornbos et al., 2008).

o 'I am starting to think differently about a subject due to the contribution of

a colleague who I know' (Doornbos et al., 2008).

o 'When brainstorming about how to approach a shared problem, we learned

from each other spontaneously' (Doornbos et al., 2008).

430

Learning by Imitating

The following items were designed:

3) The extent learning through imitating is deliberate

o '…generating new ideas by monitoring or purposefully observing

colleagues' (Doornbos et al., 2008)

o '…tracking the policies and tactics of other entrepreneurs or start-ups with

best practices in my industry' (Barringer and Bluedorn, 1999)

o '…deliberately acquiring knowledge about the foreign market through

imitating firms and/or entrepreneurs that are perceived as having best

practices' (Schwens and Kabst, 2009)

4) The extent Learning through imitating is spontaneous

o '…observing others that turned out to be unexpectedly informative'

(Doornbos et al., 2008).

o Watching other entrepreneurs, I noticed that I could perform a task in a

similar way' (Doornbos et al., 2008).

o I do not plan ahead; however, if an idea just shows up, I can sometimes

make a connection to a successful or unsuccessful venture that I came

across (Haunschild and Miner, 1997).

Learning By Doing

The following items have been adapted from Doornbos et al. (2008), Gielnik (2004)

and Tang et al. (2007):

5) 'The extent to which Learning by doing is deliberate'45

o When I have an idea, I prefer to actively and systematically search, by

myself, for information on this topic (Doornbos et al., 2008).

o When I take action with regard to a business opportunity, I reflect by

myself, and from my direct experience, on what happened and try to learn

from the mistakes that were made (Doornbos et al., 2008).

45

(i.e. from direct experience, by experimenting)

431

o When I have an idea, I keep it to myself, and plan how to acquire the

relevant information about it.

o I purposefully learn from personal business, entrepreneurial or managerial

mistakes, when I think about a new business opportunity.

o I scan the Internet to get more relevant information on a new idea or a new

opportunity that I have identified (Gielnik, 2004).

o I learn systematically by reading relevant literature (such as: professional

journals, business and managerial books, professional websites) (Gielnik,

2004).

o Analysing my mistakes can help me improve the way I identify business

opportunities' (Gielnik, 2004).

6) 'The extent Learning by doing is spontaneous'

o I reflect without prior planning, based on my actions and on an event that

suddenly gave me new ideas (Doornbos et al., 2008)

o I am alert and tune in to new items of information, but I don't actively

search for them (Tang et al., 2007).

o I know that I should learn from personal business, entrepreneurial or

managerial mistakes, when I think about a new business opportunity;

however, I only do it without prior planning.

o If an idea just shows up, I keep it to myself, but I do not have a plan of

how to acquire the relevant information about it; I just do it.

o I learn unintentionally, and without prior planning, from relevant literature

(such as professional journals, business and managerial books,

professional websites).

o I scan the Internet for business advice, or I may, unintentionally, get new

ideas (Gielnik, 2004).

o I should learn from my mistakes, but it is unnecessary to analyse the

mistakes and to change the way that I identify opportunities accordingly.

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