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THE DYNAMICS OF KNOWLEDGE RETENTION AND

AGING WORKFORCE IN THE OIL AND GAS INDUSTRY

SUMBAL MUHAMMAD SALEEM ULLAH KHAN

Ph.D

The Hong Kong Polytechnic University

2018

ii

The Hong Kong Polytechnic University

Department of Industrial and Systems Engineering

The Dynamics of Knowledge Retention and Aging Workforce in

the Oil and Gas Industry

Sumbal Muhammad Saleem Ullah Khan

A thesis submitted in partial fulfillment of the requirements for the

degree of Doctor of Philosophy

July 2017

iii

Certificate of Originality

I hereby declare that this thesis is my own work and that, to the best of my knowledge

and belief, it reproduces no material previously published or written, nor material that has been

accepted for the award of any other degree or diploma, except where due acknowledgment has

been made in the text.

____________________________

Name: Sumbal, Muhammad Saleem Ullah Khan

Dated: 30/06/2017

iv

Abstract

In the current age of the knowledge-based economy, the brains rather than the brawn of the

workforce contribute towards success and learning of the organizations. For organizations,

knowledge is the key component for maintaining competitive advantage and is the principal asset

of astute workers in the organizations. When these workers leave, they leave with their valuable

and much-needed skills and experience that have been accumulated over the years. Of course,

not all knowledge possessed by workers is critical, but retention of the knowledge that is rare,

non-substitutable and relevant is crucial. The literature reveals scanty research work conducted

on knowledge retention (KR). Further, organizations, in general, are not taking any measures to

retain knowledge of leaving employees. In the oil and gas industry, the majority of the workforce

will be approaching retirement age in the next 5-10 years. This mass exodus of the aging

workforce will inevitably cause massive loss of valuable knowledge. This research focuses on

this highly important issue of knowledge retention and the aging workforce in the oil and gas

industry. The main objectives of the research are i) To investigate how companies are handling

the task of knowledge retention (challenges and strategies) from pending retirees in the oil and

gas sector. ii) To investigate the dominant likelihood factors and types of knowledge lost when

employee depart in the oil and gas sector, and iii) To investigate the relationship of big data and

knowledge management regarding knowledge retention and retiring workforce issue. Semi-

structured interviews were carried out and the grounded theory approach of systematic data

inquiry were utilized for data analysis. Grounded theory is suitable when the topic is

underexplored, and the aim is to produce some fresh knowledge on the topic.

The results reveal that current oil slump has made a profound effect on KR. KR activities tend to

be inconsistent in the majority of the oil and gas companies and not much work being done

v

regarding knowledge loss from old employees because of the fall in oil prices and layoffs.

Further, the issue of an aging workforce is acuter in the upstream sector and more prevalent in

developed countries. Dominant factors of knowledge loss in companies are retirements, layoffs,

and contract workforce. The different types of knowledge possessed by departing employees

include specialized technical knowledge, contextual knowledge, knowledge of train wrecks and

history of the company, knowledge of relationships, knowledge of management and knowledge

of business systems and processes. The departing employees should be assessed against these

knowledge types, and the relevance of each knowledge should also be checked. Useful predictive

knowledge can be generated through big data that can help companies improve their knowledge

management capability. Further, a combination of the tacit knowledge of experienced staff with

predictive knowledge obtained from big data improves decision-making ability, thus, signifying

the importance of knowledge retention from experts. Technologies like big data can be useful in

the future to replace the expertise of people, but at the moment, the expertise of the employees

are vital in running the businesses smoothly.

This research has provided useful insights to managers and executives regarding the

workforce crisis, strategies and challenges related to knowledge retention, and what to look for

when the employees leave the organization. Further, this research also helps to raise the attention

of executives on the use of tacit knowledge possessed by experienced employees in conjunction

with predictive knowledge derived from the big data to make decisions for enhancing the

organizational performance.

Publications Arising from The Thesis

Journal Papers:

[1] Muhammad Saleem Sumbal, Eric Tsui, Eric W.K. See-to, (2017) "Knowledge

Retention and Aging Workforce in Oil and Gas Industry: A multi perspective study", Journal of

Knowledge Management, Vol. 21 Issue: 4, pp.907-924

[2] Muhammad Saleem Sumbal, Eric Tsui, Eric W.K. See-to, (2017) "Interrelationship

between big data and knowledge management: an exploratory study in the oil and gas sector,"

Journal of Knowledge Management, Vol. 21 Issue: 1, pp.180-196

[3] Muhammad Saleem Sumbal, Eric Tsui, Eric W.K. See-to, “Critical Areas of Knowledge

Loss When Employees Depart in Oil and Gas Industry,” Journal of Knowledge Management

(Under Review)

Conference Papers:

[1] Muhammad Saleem Sumbal, Eric Tsui, WB Lee, (2015) “Baby Boomers Retirement in

Oil and Gas - Challenges of Knowledge Transfer for Organizational Competitive Advantage,” In

Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge

Engineering and Knowledge Management, Nov 12-14, Lisbon, Portugal, pp. 168-173.

[2] Muhammad Saleem Sumbal, Eric Tsui, WB Lee, (2015) “Exploring the Relevance and

Correlation between Big Data and Knowledge Management,” 4th Hong Kong International

Conference on Engineering and Applied Sciences (HKICEAS), Dec 16-18, Hong Kong, pp. 202-

209.

[3] Muhammad Saleem Sumbal, Eric Tsui, Eric W.K. See-to, (2017) “Knowledge Loss

Assessment of departing employees in Oil and Gas Industry,” International Conference on

Information and Knowledge Management (ICIKM), Jan 13-14, Zurich, Switzerland, p. 649.

vii

Acknowledgements

I would like to thank Allah Almighty for his countless blessings and giving me the courage

and strength to finish my Ph.D. I would like to express my sincere gratitude to my chief supervisor,

Dr. Eric Tsui, for his support and invaluable advice on my research. His continuous guidance during

our discussion sessions kept me on the right route, helped me improve my research skills, and

encouraged me to proceed patiently in my Ph.D. study. I am also indebted to my co-supervisor, Dr.

Eric W.K See-to, for his valuable comments and guidance during my research. Special thanks to Dr.

Ricky Cheong and Dr. Kurt Herold who guided me on the methodology adopted for this study.

Finally, I am profoundly grateful to my beloved parents, my siblings, my wife, my beloved son,

Muhammad Haadi Khan, my friends, and colleagues for their support, understanding,

encouragement, and love throughout my Ph.D. study.

viii

TABLE OF CONTENTS

CERTIFICATE OF ORIGINALITY ............................................................................................. III

ABSTRACT ....................................................................................................................................... IV

PUBLICATIONS ARISING FROM THE THESIS ...................................................................... VI

ACKNOWLEDGEMENTS ............................................................................................................ VII

TABLE OF CONTENTS .............................................................................................................. VIII

LIST OF FIGURES ........................................................................................................................ XII

LIST OF TABLES ........................................................................................................................ XIII

CHAPTER 1. INTRODUCTION ...................................................................................................... 1

1.1 BACKGROUND ............................................................................................................................. 1

1.2 PROBLEM STATEMENT ................................................................................................................ 4

1.3 OVERVIEW OF THE RESEARCH .................................................................................................... 7

1.3.1 Phase 1 ................................................................................................................................ 8

1.3.2 Phase 2a .............................................................................................................................. 9

1.3.3 Phase 2b ............................................................................................................................ 10

1.4 SIGNIFICANCE OF RESEARCH WORK ......................................................................................... 11

1.5 STRUCTURE OF THESIS .............................................................................................................. 12

CHAPTER 2. LITERATURE REVIEW ........................................................................................ 13

2.1 INTRODUCTION .......................................................................................................................... 13

2.2 THE AGING WORKFORCE ISSUE ................................................................................................ 18

2.3 WHY IS THE KNOWLEDGE OF AGING WORKFORCE CRITICAL? ................................................. 19

2.4 KNOWLEDGE RETENTION AND AGING WORKFORCE ................................................................ 24

ix

2.5 RESEARCH CONTEXT AND MOTIVATION .................................................................................. 36

CHAPTER 3. METHODOLOGY ................................................................................................... 40

3.1 TYPES OF RESEARCH................................................................................................................. 40

3.2 QUANTITATIVE AND QUALITATIVE METHODS ......................................................................... 41

3.3 RESEARCH DESIGN ................................................................................................................... 43

3.4 THE GROUNDED THEORY APPROACH ....................................................................................... 44

3.4.1 Basic Components of Grounded Theory ........................................................................... 47

3.5 DATA COLLECTION PROCESS .................................................................................................... 51

3.6 DATA ANALYSIS PROCESS ........................................................................................................ 54

3.6.1 Coding with ATLAS.ti ........................................................................................................ 56

3.7 VALIDITY AND CREDIBILITY OF THE RESEARCH ...................................................................... 62

CHAPTER 4. RESULTS AND ANALYSIS ................................................................................... 65

4.1 CURRENT SITUATION OF THE AGING WORKFORCE .................................................................. 70

4.2 NON-HOLISTIC APPROACH TO KNOWLEDGE RETENTION ........................................................ 78

4.2.1 Mentoring and Communities of Practices Programs by far the Best Way of Retaining

Knowledge from Experts ............................................................................................................ 85

4.3 BARRIERS AND CHALLENGES REGARDING KNOWLEDGE RETENTION ..................................... 88

4.3.1 Oil Prices and Budget Constraints .................................................................................... 89

4.3.2 Multi-Perspectivity of Knowledge Hoarding .................................................................... 89

4.3.3 Opportunities to Learn ...................................................................................................... 91

4.3.4 Multicultural Environments and Language are Small Barriers........................................ 92

4.3.5 Retention of Critical Knowledge is Challenging ............................................................... 95

4.4 LARGER IMPACT OF KNOWLEDGE LOSS ON UPSTREAM SECTOR AS COMPARED TO

DOWNSTREAM AND MIDSTREAM SECTORS ..................................................................................... 96

x

4.5 DYNAMICS OF DIFFERENT GEOGRAPHICAL LOCATIONS .......................................................... 97

4.6 NEW IDEAS THAT EMERGED FROM THE STUDY ...................................................................... 103

4.6.1 Dominant Knowledge Loss Factors and Types of Knowledge Lost ................................ 103

4.6.2 Connecting Big Data with Knowledge Management ...................................................... 104

CHAPTER 5. LIKELIHOOD FACTORS AND TYPES OF KNOWLEDGE LOST WHEN

EMPLOYEES DEPART ................................................................................................................ 106

5.1 INTRODUCTION ........................................................................................................................ 106

5.2 ASSESSING KNOWLEDGE LOSS ............................................................................................... 107

5.3 LIKELIHOOD FACTORS AND TYPES OF KNOWLEDGE LOST ..................................................... 110

5.4 METHODOLOGY ...................................................................................................................... 116

5.5 RESULTS .................................................................................................................................. 119

5.5.1 Likelihood Factors of Knowledge Loss ........................................................................... 119

5.5.2 Contract Based Workers: A Great Threat to Knowledge Loss ....................................... 123

5.5.3 No Formal Knowledge Loss Assessment ......................................................................... 125

5.5.4 Critical Types of Knowledge Lost when Employees Depart ........................................... 127

5.5.5 Relevance of Knowledge .................................................................................................. 139

5.6 ANALYSIS AND DISCUSSION ................................................................................................... 141

CHAPTER 6. CONNECTING BIG DATA WITH KNOWLEDGE MANAGEMENT ........... 155

6.1 INTRODUCTION ........................................................................................................................ 155

6.2 WHAT IS BIG DATA? ............................................................................................................... 156

6.3 BIG DATA AND KNOWLEDGE MANAGEMENT ......................................................................... 161

6.4 RESEARCH CONTEXT AND MOTIVATION ................................................................................ 167

6.5 METHODOLOGY ...................................................................................................................... 170

6.6 RESULTS .................................................................................................................................. 172

xi

6.6.1 Big Data in the Oil and Gas Sector ................................................................................. 172

6.6.2 The Linkage between Big Data and KM ......................................................................... 175

6.7 DISCUSSION AND ANALYSIS ................................................................................................... 183

6.7.1 Big data: A Catalyst for Enhanced Knowledge Management Capability ....................... 184

6.7.2 Combination of Tacit Knowledge (Existing Concept) and Predictive Knowledge ......... 186

CHAPTER 7. CONCLUSION ....................................................................................................... 192

1ST STUDY: ................................................................................................................................... 192

2ND STUDY: .................................................................................................................................... 199

3RD STUDY: .................................................................................................................................... 202

APPENDIX A: CODING TABLE AND NETWORK VIEW (1ST STUDY) ............................. 205

APPENDIX B: CODING TABLE AND NETWORK VIEW (2ND STUDY) ............................. 212

APPENDIX C: CODING TABLE AND NETWORK VIEWS (3RD STUDY) ........................... 216

APPENDIX D: INTERVIEW QUESTIONS ................................................................................ 219

APPENDIX E: DETAILS ABOUT THE PARTICIPANTS IN THE RESEARCH WORK ... 220

REFERENCES ................................................................................................................................ 223

xii

List of Figures

Fig. 1.1 Global Oil and Gas Value Chain (Source:(Inkpen and Moffett, 2011)) ............................................... 6

Fig. 1.2 Overall Research Framework ............................................................................................................... 7

Fig. 2.1 Aging workforce in Oil and gas industry ............................................................................................ 37

Fig. 3.1 A Visual Representation of Charmaz Constructivist Grounded Theory (Source: Charmaz (2014)) .. 47

Fig. 3.2 The NCT Model of Analysis .............................................................................................................. 57

Fig. 3.3 The Process of computer-aided Qualitative Data Analysis ................................................................ 59

Fig. 3.4 Coding Options in ATLAS.ti .............................................................................................................. 60

Fig. 3.5 Memo function in ATLAS.ti............................................................................................................... 61

Fig. 3.6 Network View Option in ATLAS.ti .................................................................................................... 62

Fig. 4.1 List of Codes explaining the current situation of the Aging Workforce ............................................. 67

Fig. 4.2 Example of a Memo written during data collection process ............................................................... 68

Fig. 4.3 Network View for the Aging Workforce Situation ............................................................................. 69

Fig. 4.4 KR Activities mentioned by Interviewees .......................................................................................... 79

Fig. 5.1 Code table for Likelihood of Knowledge Loss ................................................................................. 120

Fig. 5.2 Network View of Likelihood of Knowledge Loss ............................................................................ 121

Fig. 5.3 Likelihood of Knowledge Loss in Oil and Gas Industry .................................................................. 144

Fig. 5.4 Global Oil and Gas Value Chain (Source: Inkpen and Moffett (2011)) ........................................... 146

Fig. 5.5 A Process for Knowledge Loss Assessment for Departing Employees ............................................ 154

Fig. 6.1 The Four dimensions of Big Data ..................................................................................................... 157

Fig. 6.2 Boom of Big Data across the globe (Source: Chen et al (2014)) ...................................................... 158

Fig. 6.3 Google graph trends for big data (blue) and data scientist (red) ....................................................... 159

Fig. 6.4 The DIKW Model (Source: Sumbal et al. (2017a)) .......................................................................... 162

Fig. 6.5 Interrelationship between Big Data and Knowledge Management................................................... 190

xiii

List of Tables

Table 2.1.The changing nature of workforce (Source: Yu & Miller (2005)) 23

Table 3.1 Details of Interviewees in the study 53

Table 5.1 Details of Interviewees in the study 118

Table 6.1 Details of Interviewees in the study 171

Table 6.2 Big Data Based Knowledge Management 189

Table 7.1 Implications of the Studies 194

1

CHAPTER 1. INTRODUCTION

1.1 Background

Knowledge in today’s economy is the most valuable, competitive, and strategic

asset which plays a vital role in fostering innovation and improving efficiency,

productivity, and competitiveness of organizations (Grant, 1996). In today's economy, the

key to success is that firms must leverage their abilities to use existing knowledge for the

creation of new knowledge (Gold et al., 2001). The potential benefits of adopting KM are

well versed in literature (Brockman and Morgan, 2003, Coleman, 1999, Wong and

Aspinwall, 2006, Halawi et al., 2006, Karkoulian et al., 2008b, Grant, 1996, Teece, 1998,

Davenport et al., 1998) and include effective decision making, improved productivity and

efficiency, responsiveness to customers, better understanding of stakeholder's

relationships, gaining competitive advantage and superior performance. Knowledge is

embedded in multiple entities in a firm, for example, individual employees,

organizational culture, policies, procedures, systems, and documents (Alavi & Leidner,

2001). The resource-based view of the firm emphasizes on human capital i.e. the

employees in the organization are the key source of competitive advantage for the

organizations. It means that organizations need to use the capabilities of their employees

and benefit from their experiences and skills (Stevens, 2010, Hashim, 2007).

Organizations have been long aware of knowledge management and ways of managing

their knowledge; however new challenges continually arise, and new strategies need to be

2

adopted to cope with these challenges. A major challenge for the organizations these days

is the changing demographics and the aging workforce.

When a long-term employee leaves an organization, it is not just a worker moving

out of the organization, but also the years of experience and knowledge is also flowing

out of the organization. Although managers fear losing their competent and experienced

employees but still people get fired and need to leave when they approach retirement

(Ball and Gotsill, 2011). Most of the time organizations don't realize they are losing a

valuable source of knowledge because of the culture prevailing in that specific

organization. Pitt-Catsouphes et al. (2009) state that according to the survey of Sloan

Center on Aging and Work, most of the employers are unaware of the baby booming

situation in their companies and thus have no figures to relate the effect of retirements on

their companies. The aging of the population can mostly affect organizations in two

ways; i) economic consequences and ii) not recognizing the potential and the capabilities

of old age employees which will result in valuable knowledge loss (Stam, 2009). Stam

(2009) further reveals that there will be a decrease in the GDP of OECD countries not

only because of the shrinking workforce in coming years but also because organizations

don't seem to be much concerned about retaining the knowledge of old employees. In

fact, organizations need to work on these false assumptions and myths that older

employees are less productive and don't have abilities to adapt and learn according to the

new trends. There are lots of evidence (Ball and Gotsill, 2011, Principi et al., 2015)

which prove these assumptions are false and there is a need for the organizations to

understand this as these factors can lead to age discrimination, disengagement, and less

productivity in the organizations. Because of this inexorable threat to the organizations

3

due to changing demographics and the graying of employees, knowledge retention has

become an essential and inevitable activity in organizations these days (Stevens, 2010,

Levy, 2011, Jennex, 2014).

Knowledge-based organizations use knowledge to generate revenue and, for this

purpose, knowledge workers possess, create and apply knowledge (Nonaka and

Takeuchi, 1995). Losing these workers means organizations will lose the much-needed

knowledge which is the basis for their competitive advantage. The knowledge of these

employees is of key importance as it may lead to a decay of organizational memory when

these employees leave, which in turn may reduce the firm’s ability to identify and use the

past knowledge for competitive advantage (De Massis et al., 2016). The success of the

organization depends on the combined capabilities of key individuals to achieve the

organizational goals (Petruzzelli and Savino, 2014). Moreover, these employees possess

the organizational knowledge, knowledge of governance and knowledge of networks and

relationships developed over a period of time within the organization and this knowledge

is the key to enhance and sustain firm’s performance (De Massis et al., 2016). Nerkar

(2003) is also of the view that knowledge creation is an evolutionary process spread over

time, and further combining current knowledge with past knowledge that evolved over

time, enhances the impact of new knowledge. Experienced employees who have been

working in organizations for a long time can combine past knowledge and current

knowledge to manage the organizational goals effectively. However, if they leave the

organization, they will take away with them the valuable knowledge accumulated over

time.

4

1.2 Problem Statement

A variety of statistical studies (Pordes, 1994, Government-of-Japan, 1999,

UnitedNationsSecretariat, 2000, United-States-GeneralAccountingOffice, 2003) have

revealed the increase in the average age of the world’ populations in the past few

decades. Not only the overall world population is growing, but the percentage of older

people has also increased significantly in the developed countries (Streb et al., 2008).

This demographic shift has made an enormous impact on the workforce composition of

organizations. There is a larger percentage of old age workers in the organizations these

days coupled with the fact that due to low fertility rates, the number of younger workers

to replace this aging workforce is quite less which might trigger a “War for Talent” in

some industries (Burke and Ng, 2006). The aging workforce can impact organizations in

various ways, and the most dominant one is the loss of important knowledge and skills

(DeLong, 2004, Calo, 2008, Bratianu and Leon, 2015) due to retirements. Also from a

corporate perspective, the shortage of younger workforce increases the importance of this

aging workforce and companies might have to keep these employees longer in the

organizations (Streb et al., 2008). Thus, the managers and executives in the organizations

not only need to safeguard their organizations against knowledge loss due to the

retirement of large cohorts of experienced employees, but they also need to handle the

demographic shift and talent shortage issues. From managerial perspectives, an important

issue in knowledge management is the retention of know-how and skills of the employees

before they retire.

5

Studies (Ball and Gotsill, 2011, Joe et al., 2013, Levy, 2011, Lesser and Rivera,

2006, Massingham, 2014) have shown that many organizations are going to lose a large

number of employees due to retirement in the next 5-10 years. Researchers have

identified retiring workers as key contributors to knowledge loss (Calo, 2008, Stevens,

2010, Ball and Gotsill, 2011), suggesting the application of comprehensive knowledge

retention strategies to avoid this knowledge loss (Leibowitz, 2009). They are of the view

that there hasn't been much work done regarding retention of employees’ knowledge

(Levy, 2011, Durst et al., 2015a, Ahmad et al., 2014) and organizations, even when

knowing that they are going to lose valuable knowledge due to retirement of employees,

don’t have formal procedures to handle this pending knowledge loss (Leibowitz,2009).

This study focuses on this aspect of knowledge retention from old age retiring workers

and targets oil and gas sector. Among major industries across the world, oil and gas

industry is one of the most complex, most important and one of the world’s largest

industry among the global industries (Inkpen and Moffett, 2011). There are three main

sectors in oil and gas namely upstream, midstream, and downstream as shown in figure

1.1. The upstream sector deals with exploration and production; midstream sector deals

with transport and trading whereas downstream sector deals with the refining and

marketing of the oil and gas. Due to changing economic, global conditions and searching

for oil and gas at remote locations, there has been an increasing number of challenges in

the oil and gas industry in the areas of exploration and production. The industry is facing

enormous challenges related to the aging workforce (normally termed as baby boomers)

complimented by high operational costs and talent shortage (Inkpen and Moffett, 2011,

Ball and Gotsill, 2011, Stevens, 2010).

6

Fig. 1.1 Global Oil and Gas Value Chain (Source:(Inkpen and Moffett, 2011))

The current research work focuses on this issue of the aging workforce and

knowledge retention by conducting research across oil and gas sector using a grounded

theory research methodology. The grounded theory approach is a systematic approach for

exploring and gaining an in-depth insight of a phenomenon. Further, a qualitative data

analysis software ATLAS.ti was used to perform the data analysis using this grounded

theory approach. The study started with the aim of how oil and gas companies are

handling the aging workforce and knowledge retention issue. The systematic data inquiry

then further allowed to discover emerging and interesting themes which led to the overall

shaping of this research in its current form. An overview of the whole research process

will be discussed in next section.

7

1.3 Overview of the Research

The title of the research work is “Dynamics of knowledge retention and Aging

Workforce in the Oil and Gas Industry”. The term dynamics means, the properties which

stimulate growth, development, or change within a system or process. So, in current

research, dynamics refers to the factors which have been affecting the knowledge

retention process within the organizations such as the current economic climate, aging

workforce, the effect of various geographical locations etc. Thus, an integrated approach

Fig. 1.2 Overall Research Framework

8

has been adopted to understand the dynamics of KR in terms of its implementation,

issues and challenges, developing a proper knowledge assessment process (using the

major factors of knowledge loss and critical types of knowledge) to perform knowledge

retention and studying the effect of new disruptive technologies such as big data. Because

of this, the research work comprised of various phases. Figure 1.2 shows the overall

research framework of this study. This research can be divided into 2 phases, Phase 1 and

Phase 2. Phase 1 provided the foundation for phase 2. The results and analysis conducted

in phase 1 helped to further determine the research areas to be explored further in phase

2.

1.3.1 Phase 1

The research objective of this phase was to investigate how companies are

handling the task of knowledge retention (challenges and strategies) from old age retiring

workers in the oil and gas sector. A grounded theory methodology was adopted, and 21

semi-structured interviews from oil and gas sector were conducted to learn about the

knowledge retention and aging workforce. The participants in the interview involved elite

informants with extensive experience from the oil and gas sector. Data collection stopped

when no new information was being added by participants and eventually leading to

saturation point. The constant comparison of data after each interview allowed in

generating the themes and concepts with dense explanations. Phase 1 has revealed

interesting outcomes which not only provided answers to the inquired research questions

but also provided some new and emerging themes to researchers for further exploring the

9

area of knowledge retention. The two areas which were considered worth investigating

include:

i) To investigate the dominant likelihood factors and types of knowledge lost

when employees depart in the oil and gas sector.

ii) To investigate the relationship of big data with knowledge management

regarding knowledge retention and retiring workforce issue.

1.3.2 Phase 2a

Results from phase 1 revealed that companies don’t have any knowledge loss

assessment procedures in practice. In most of the organizations, managers perform the

task of knowledge retention from departing employee on an ad-hoc basis. The review of

the literature further strengthened the outcomes of phase 1, revealing limited research

done in the area of knowledge loss assessment. Thus, in phase 2a, further research was

conducted to propose an outline for knowledge loss assessment by incorporating major

likelihood factors of knowledge loss, and different types of knowledge lost when

employees depart from oil and gas companies. There is also a paucity of research on the

different types of knowledge possessed by employees as the majority of literature focuses

on the importance of knowledge retention and criticality of the aging workforce but what

to look for when employees leave, is still underexplored. Thus, eleven more interviews

were conducted with oil and gas experts to find out the answers to the posed research

questions. The same grounded theory approach as used in phase 1 was used in phase 2 to

discover interesting and emerging themes from data. Results revealed that apart from

retirements and layoffs, contract workforce appears to be a dominant factor of knowledge

10

loss. Similarly, important types of knowledge discovered during the data include

specialized technical knowledge, contextual knowledge, knowledge of history and train

wrecks, knowledge of relationships, knowledge of management and knowledge of

business systems and processes. Each of these types of knowledge, has its importance

and relevance in the oil and gas sector. Moreover, these typed of knowledge are quite

different from other sectors and not discussed in literature before. Combining these

different types of knowledge with the dominant likelihood factors, an outline for

knowledge loss assessment has been provided for managers to understand what type of

knowledge to look for when employees leave the organization.

1.3.3 Phase 2b

One of the interviewees during phase 1 revealed that big data is a disruptive

technology that can eliminate the need for expertise and knowledge retention of

employees in future. Based on this statement, further investigation was performed (phase

2b) on the connection of big data to knowledge management as shown in Fig.1.2. A

review of the literature revealed that little work had been done on the interrelationship of

big data and knowledge management. However, there are hints in the literature that big

data can be linked to knowledge management around the extraction of useful knowledge

from structured and unstructured data for making an informed decision, but there is no

explanation of how the expertise of people are involved and interlinked to the big data.

Thus, with the aim of exploring this connection of the big data to knowledge

management and whether the expertise of employees can be replaced with big data and

analytics, a further study was conducted among ten major companies involved in big data

11

activities. The results revealed that at the moment, companies are in an experimental

phase in big data activities. However, there were clear examples provided by

interviewees to show that big data is an untapped source of knowledge which can help in

enhancing the KM capability of the organizations. Regarding the role of experienced

employees, big data might eliminate the need to retain experienced staff in future,

however, at the moment, insights from experienced employees are necessary to get the

true value from predictive knowledge of big data. Hence, a combination of the tacit

knowledge of experienced staff with explicit knowledge obtained from big data improves

the decision-making ability. This also signifies that retaining the knowledge of senior

experts is important as organizations need to use this knowledge along with predictive

knowledge from big data to make effective decisions.

Thus, this multiphase study led to the in-depth exploration of a significantly

important problem of knowledge retention and an aging workforce in the oil and gas

industry.

1.4 Significance of Research Work

This research makes a significant contribution towards the existing body of

literature through:

1. Little attention has been paid to the area of knowledge retention, and there is a

lack of empirical research on this topic. This research makes a significant contribution by

taking on an integrated approach to investigate the situation of old age retiring workers,

the effect of current oil prices, different knowledge retention strategies adopted and their

effectiveness in oil and gas companies across varied geographical locations. Particularly,

12

a global perspective is taken by interviewing people across a diverse range of companies

from Asia, Europe, Australia, USA, and Africa to gain a deeper understanding of the

baby boomer phenomena under different contexts.

2. This research fulfills an identified gap on the identification of critical types of

knowledge lost when employees depart in the oil and gas companies. Combining these

different types of knowledge with major likelihood factors of knowledge loss, an outline

is provided for knowledge loss assessment which is an underexplored area in the

knowledge management literature.

3. It is probably one of the first studies on the interrelationship of big data with

knowledge management and how the valuable experience of employees is vital in

undermining the real value from big data. A framework has been developed in the light of

obtained results to explain the linkage between big data and knowledge management.

1.5 Structure of Thesis

Chapter 1 provides an overview of the research with the problem statement, brief

methodology and significance of the study. Chapter 2 describes in detail the literature

review carried out to identify the research gaps and further elaborates on research context

and motivation. Chapter 3 focuses on the research design and methodology adopted for

the current study. Chapter 4, 5 and 6 cover the phase 1, phase 2a and phase 2b of the

study respectively. Finally, Chapter 7 states the conclusion and managerial implications

of the study.

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CHAPTER 2. LITERATURE REVIEW

2.1 Introduction

The literature review is defined as “a systematic, explicit, and reproducible

method for identifying, evaluating, and synthesizing the existing body of completed and

recorded work produced by researchers, scholars, and practitioners” (Fink, 2013, P.3).

The research literature review should focus on high-quality original research which in

turn helps in drawing the true outcome of the work done by previous scholars and

researchers (Fink, 2013). A good systemic review should demonstrate how much existing

research has progressed in the clarification of a particular issue, identify the gaps and

inconsistencies in the literature on a specific topic and provide the future directions for

research. The six principles of a systematic review by Jesson et al. (2011), as mentioned

in Durst et al. (2015a) were followed for this research. These include i) Defining the

scope of review, ii) performing a comprehensive research, iii) doing the assessment of

papers through reading and selection, iv) extracting and recording the information from

the articles v) synthesizing the extracted data to understand the known and unknown and

vi) writing the review.

The scope of the research involved getting to know the current research updates

on knowledge retention of aging workforce in the organizations. The search followed

specific criteria for searching articles related to knowledge retention and the aging

workforce. The inclusion criteria were the peer-reviewed research articles in English

14

language and indexed in databases such as Web of Knowledge, Scopus, ProQuest, and

ABI/Inform. These databases covered all the major journals on knowledge management

such as Journal of Knowledge Management (JKM), Knowledge Management Research

and Practice (KMRP), Journal of Intellectual Capital, VINE Journal of Information and

Knowledge Management Systems, etc. (Serenko and Bontis, 2013).

The literature review search included a combination of various words such as

knowledge loss, knowledge leakage, knowledge retention, aging workforce, retirements,

retiring workforce, old experts, and baby boomers to dig out the research articles. The

term knowledge retention has been used under various meanings in knowledge

management literature. Researchers have used this term regarding knowledge transfer

and knowledge sharing as well. For the current research, the term knowledge retention

has been used regarding activities and measures that are taken to retain the knowledge of

departing employees in the organizations. Articles which discussed in general about

knowledge transfer or sharing have not been included in this systematic review. The

literature on non-academic research and reports were also excluded. As this research

focused on knowledge retention regarding aging workforce, articles which focused on

aging workforce issue and retiring phenomena of old workers were also included in the

review to understand the current situation of aging workforce and importance of their

knowledge. These articles at times also cover the topic of knowledge retention or

emphasize on the retention of knowledge from aging employees thus revealing the

association between knowledge retention and the aging workforce. The search for

literature using the keywords mentioned above revealed several articles which were then

scanned by the researcher to evaluate their suitability for the current study. The process

15

resulted in 145 articles that were deemed suitable for the current research. To obtain a

complete picture of the research work conducted on this topic of knowledge retention,

each article was then further analyzed in depth. References provided in these articles

were also checked conducting a backward and forward search. Results revealed that:

• The literature on knowledge retention is very dispersed and boundaries not

clear. The work published in this regard is under various headings. The

different terms that have been used in literature include intergenerational

knowledge transfer, intergenerational learning, succession planning, talent

management, knowledge protection, etc.

• A second iteration of the literature review with in-depth analysis of

articles further helped in refining the search and ended up with a set of 95

articles.

• The literature search also revealed a systematic review study on

knowledge leakage conducted by Durst et al. (2015a). It focused on the

empirical studies on knowledge leakage, knowledge loss and knowledge

retention from peer-reviewed journals until August 2014. This study

helped in setting the foundation for the current research and confirming

the output of search result that little attention has been paid to the area of

knowledge retention as it states that “the topic of knowledge leakage is

still in its infancy, which calls for more testing and validation to develop

the field. It also implies that the study of knowledge leakage is rather

fragmented and influenced by those researchers’ personal interests” (P. 8).

16

• Among the 95 articles selected from literature, the oldest publication is

from 2003, and the most recent ones are from 2015 as the literature review

was conducted until 2015. Most of the articles published on knowledge

retention are from USA, Canada, Australia, Europe, and a few from South

Africa. The problem of knowledge retention in Africa is related to brain

drain through turnover of employees, and the aging workforce is not a

dominant issue in this area. However, the articles from Europe, USA,

Australia, and Canada focus on aging workforce issue revealing this as a

major concern in these countries.

• These articles have been published in a mix of journals mostly from the

Emerald and covering areas of management, nursing, human resource

management, information sciences, knowledge management, and

technology. Most articles were published in JKM (x16), VINE (x5)

whereas other journals included Harvard Business review, Academy of

Management, Organizations Science, Nursing, Learning Organization,

Management Learning, Human Resource Management, etc.; thus,

indicating the majority of research carried out in the field of management

sciences.

Most of the studies on knowledge retention covered the general knowledge

retention theme (x33) and discovered that organizations are facing the aging workforce

issue and there is a lack of knowledge transfer strategies in these organizations, thus,

indicating a research gap to conduct further work on developing the area of knowledge

retention. The research is also very fragmented across different disciplines with articles

17

covering knowledge loss issue in the fields of “Biodiversity,” “Computers and Security,”

“Nursing,” “Construction” etc. Most of the empirical studies gathered statistics (Durst

and Wilhelm, 2013, Brosi et al., 2007, Bessick and Naicker, 2013, Kamsu-Foguem et al.,

2013, Dewah, 2013, Dube and Ngulube, 2013) to show that knowledge loss is a prevalent

issue, and knowledge retention needs to be performed without much focus on how in

actual knowledge retention is carried out in companies and what are the challenges and

issues in this regard. Further, the literature analysis revealed that even in top knowledge

management journals such as Journal of Knowledge Management and VINE journal of

information and knowledge management systems, only a small number of studies (x21)

have focused on this issue of knowledge retention from departing employees. The

majority of these studies have been conducted from 2011 to 2015, indicating that

researchers have started paying attention to this field. The findings of this literature

review regarding little work on knowledge retention issue are in line with the many

recent studies (Durst et al., 2015a, Daghfous et al., 2013, Ahmad et al., 2014, Goodman

et al., 2015, Pollack, 2012) identifying the need to further conduct empirical research in

this area.

The following section will discuss the existing body of knowledge on aging

workforce and knowledge retention; as per information extracted from the articles,

identifying the research gaps, followed by a section on research context and motivation

for conducting research in the oil and gas sector.

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2.2 The Aging Workforce Issue

The workforce in today's economy comprises of three main groups; Generation Y,

Generation X, and Baby Boomers. Baby boomers are employees born between 1945 and

1964 (Yu and Miller, 2005). These people were born after World War II. Their age was

the age of education. Optimism, work focus and job commitment encapsulated these

people, and they have a significant impact on businesses, economy, and the society as a

whole (Westerman and Yamamura, 2007). Generation X entails people born between

1965 to 1980 and Generation Y represents the youngest group of employees born after

1980 (Kyles, 2005). As stated by Johnson and Johnson (2010), in a survey of the Fortune

100 companies by Ernest and Young, 62% of the employees depicted a labor shortage in

upcoming years. Low fertility rates and higher longevity are causing a significant shift in

the demographics of the workforce and the competition for recruitment of skilled labor

force will be tough in the upcoming years (Strack et al., 2008, Beechler and Woodward,

2009). The fertility rates of the developed countries have fallen over the past 25-30 years

especially as the situation is terrible in Germany, Japan and Italy (Ebrahimi et al., 2008).

Aging workforce is another looming threat for organizations (Levy, 2011, Calo, 2008,

Ropes, 2015, Burmeister and Rooney, 2015, Ashworth, 2006) and the majority of

workers are near retirement age. Retirements of old workers is a primary source of

knowledge loss in organizations (Harvey, 2012, Ropes, 2015, Joe et al., 2013, Martins

and Meyer, 2012). A higher rate of retirement leads to decreased efficiency and

productivity, reduction in organizational memory and the availability of mentors for

instructing new personnel (Aiman-Smith et al., 2006). Researchers have mentioned that

19

knowledge of these retiring workers is of critical importance and needs to be retained

(Massingham, 2014, Bleich et al., 2009, Jennex, 2014, Cattani et al., 2013). The situation

has been declared as dangerous and proper strategies need to be devised to handle this

loss of knowledge through retirements (Calo, 2008, Stevens, 2010, Ropes, 2015).

2.3 Why is the knowledge of Aging Workforce Critical?

Aging may affect older employees in some ways, however, it is compensated by

the wealth of experience possessed by these employees as compared to their younger

counterparts and this experience provides a practical intelligence to them on solving ill-

defined problems which can’t be solved by looking into manuals or standard operating

procedures (SOPs) (Ebrahimi et al., 2008). Ebrahimi et al. (2008) emphasized the

importance of old age retiring workers as "Their life experience, their in-depth

knowledge of different professional environments (network of contacts, who knows

what?), and their knowledge of the culture of these environments (collection of codes,

symbols, shared significance, etc., permitting to know how to deal with who?) bestows

them differentiated attitudes to understand issues, interpret information, connect various

information and data, integrate knowledge, and finally, connect and coordinate

knowledge carriers" (P. 129). Effective knowledge management thrives on people who

can contribute and act upon the valuable information (Cowley‐Durst, 1999). Thus, people

are the true driving force for knowledge dissemination especially those who have decades

of experience and hold senior positions. Older employees perform the task efficiently, are

more reliable, possess better reading and communication skills and finally, have good

performance records and experience (Ball and Gotsill, 2011). These senior employees

20

have a good reputation, possess a lot of experience and profound insights of the

businesses, developed over an extended period of time, which new employees lack and

cannot replicate (Lesser and Rivera, 2006). Organizations take advantage of this

experience and knowledge to flourish and gain a competitive advantage over competitors.

To sustain this competitive advantage, knowledge retention of these experts is of critical

importance (Ball and Gotsill, 2011, Levy, 2011, Martins and Meyer, 2012, Daghfous et

al., 2013). Leiter et al. (2009) performed a study on the difference of work values

between generation X and baby boomers. Their study reveals that work settings of the

baby boomers are more consistent with their personal values and they are more inclined

towards knowledge sharing as compared to generation X. Busch et al. (2008) conducted a

study in IT sector and found that baby boomers take full responsibility for their work, are

not reticent to communicate with management and seniors, and don’t see competency

development as a barrier to success unlike generation X and generation Y employees.

The loss of corporate memory is significant at any level within the organization,

however, the impact is high when this happens at a senior level as senior members in the

organiation hold strategic level positions within the organization and have a sense of

intended vision for the growth of organizations. Around 50 to 90 percent of corporate

know how resides in the minds of people (Campos and Sánchez, 2003, Duhon, 1998) and

the way they perfrom different tasks is based on their years of experience. Each

organzation has people who are experts on specific problems and issues. According to

Calo (2008), such people are termed as “Wise Old Turks”, who are often aproached to

sort out the different problems within the organizations. There is direct relationship

between knowledge management effectiveness in the organizations and the ability and

21

willingness of experienced employees to share their work-related knowledge. Many of

these are seasoned veterans who are going to retire. O’Donohue (2000) in his survey

found that through the retirement of older workers, the most significant attributes lost will

be “useful experience”, “Strong work ethic” and “client knowledge”. This is further

supported by Sherman (2008) who focused on the health sector and stated that inability of

young nurses to do manual assessments along with large amount of retirements of old

nurses will lead to reduction in work efficiency, failure to rescue patients in acute

situations and a higher probability of medical errors. Thus, the senior experts are of vital

importance to maintain the organizational continuity. Jaworski (2005) stated that

mentoring is an important quality of aged workers. Further, Leibold and Voelpel (2007)

mentioned that senior employees who have been working in organizations for long time

have qualities of engagement and loyalty. Experience plays an important role for firm’s

growth and organizations need to put the age and innovation together (Nonaka et al.,

2006). The knowledge of these experts is also valuable in service sector and if these

employees leave, the inexperience personnel can lead to customer dissatisfaction and lack

of customer relationship management (Jiang et al., 2009). In the context of knowledge

management, the expert’s knowledge can be described as “...a fusion of knowing, know-

how and reflection constructed from social interaction within a specific socio-cultural

setting” (Jorgensen, 2005, P.68). Li and Gao (2003) described this knowledge as “elusive

and subjective ‘awareness’ (author’s quotes) of individual[s] that cannot be articulated in

words’’. According to a survey on ageing workforce by McKinsey (2008), majority of

the respondents in developed countries believe that ageing population will affect their

profitability, however very few are taking active steps to mitigate the negative impacts.

22

So, in the words of Kapp (2007), “organizations that are successful at transferring

their business acumen and years of work-specific experience to the incoming Gamer

generation will also be successful in their industry and in out-lasting their

competition. Those capable of this transfer will experience benefits in productivity,

quality, and profitability. Government, public, and non-profit service organizations

that are incapable of a successful knowledge transfer will experience dire results”.

Thus, the above is a strong compelling reason for retaining these knowledge assets which

have played a pivotal role in the growth of their organizations and have a higher sense of

ownership and loyalty for their respective organizations. The above discussion also

reveals that retaining baby boomers is important to properly train the next generations

and teach them about collaboration, teamwork, job commitments and personal values.

In today’s world, economies are increasingly based on knowledge. Knowledge as a

driver of productivity and economic growth has led to the evolution of the term

“knowledge-based economies”. Knowledge is well recognized as a source of competitive

advantage (Grant, 1996) especially tacit knowledge possessed by experienced employees

in the organizations (Bennet and Bennet, 2008). A company evolves naturally having its

own goals, the capability of regenerating itself and focuses on intangible resources such

as human knowledge rather on material assets (De Geus, 2002). A living company

invests in the development of employees which harvests in them, a sense of identity and

belonging to the company and further enables them to withstand dramatic changes over

time. The emphasis of companies on profits and the maximization of shareholder value

ignores the two most significant forces that affect them these days: the shift to knowledge

as the critical production factor and the changing world around the companies (De Geus,

23

2002). Companies most of the time struggle with their businesses and lay off employees;

not paying much attention to the loss of knowledge, however, organizations need to value

the knowledge of these experienced retiring workers before they walk out of the door.

However, it needs to be kept in mind that not all retiring workers and their

knowledge might be critical for the organizations but those people who have subject

matter expertise on a particular subject and possess specialized knowledge; provide the

most important basis for creating value within the organization, and such people are

trained and qualified particularly by experience (Huber, 1999). Yu & Miller (2005)

mentioned that baby boomers have worked in all three phases of a progressive economy

(from Industrial to knowledge workers) and thus have lot more experience as compared

to their counterparts, possessing a very effective set of skills in terms of management,

leadership, mentoring and coaching (Table 2.1).

Industrial Economy

(to 1980)

Service Economy

(1980-2000)

Knowledge Economy

(2000-)

Baby Boomers * * *

X Generation * *

Y Generation *

Industrial Workers Service Workers Knowledge Workers

Table 2.1.The changing nature of workforce (Source: Yu & Miller (2005))

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2.4 Knowledge Retention and Aging Workforce

Companies have been struggling to handle this aging workforce problem and

haven’t met with much success (Levy, 2011). Failure is due to the attitude of top

management, technology, and budget shortfalls, etc. while success is due to the timely

realization of the aging workforce and taking adequate measures to secure knowledge

(Bessick and Naicker, 2013, Ball and Gotsill, 2011). Companies need to invest in long-

term strategies instead of focusing on short-term objectives, and talent management

should be a key priority for the organizations (Calo, 2008). For the success of

organizations, properly managing the knowledge of individuals is a significant challenge

(Gretsch et al., 2012). Success to innovation and competitive advantage depends on the

skilled human capital of the organizations (Grant, 1991, Nonaka and Takeuchi, 1995,

Davenport and Prusak, 1998, Alavi and Leidner, 2001). Keeping in mind the shrinking

workforce and shortage of new talent (Beechler and Woodward, 2009, Ball and Gotsill,

2011, Kulik et al., 2014), organizations will have to rely heavily on their old skilled

employees and along with that organizations need to develop workforce strategies that

provide work-life balance, flexibility, autonomy, challenging work environment and

foster active participation of employees to cope with the problem of talent retention

(Jorgensen, 2003).

Knowledge management implementation is unsuccessful in most of the

organizations because they pay less attention to the role and importance of tacit

knowledge and focus on the past and present only, not the future (Fahey and Prusak,

1998). In terms of competitive advantage, intangible assets are of highest importance for

25

firms, and among these are the accumulated learning and experience of people (Bateman

and Snell, 2002) as well as the knowledge of the employees which brings value to the

organizations (Walters et al., 2002, Marr et al., 2003) and old age workers are a very

wealthy source of this knowledge. Actively addressing demographic risks to retain skills

of these employees and know-how needed to ensure future viability can provide

companies an edge over their rivals (Strack et al., 2008).

To capture knowledge of these old age retiring workers, companies need to

initiate knowledge retention activities. According to Levy (2011), knowledge retention is

a relatively new field which deals with capturing the knowledge of departing employees.

Martins and Meyer (2012) defined knowledge retention as “maintaining, not losing, the

knowledge that exists in the minds of people (tacit, not easily documented) and knowing

(experiential action manifesting in behavior) that is vital to the organization’s overall

functioning” (P. 80). Levy (2011) further stated that not much attention had been paid to

knowledge retention, a sub-discipline of knowledge management and therefore, more

research in this area is the need of the hour. Durst et al. (2015a) performed a literature

review on knowledge loss and retention. Their study revealed that despite the serious

consequences of knowledge loss regarding organizational productivity, not much

research had been done in this area. “In contrast to KM solutions, knowledge retention

takes place in a limited period of time and addresses the challenge of transforming an

expert's most valuable knowledge to an organizational asset” (Levy, 2011,P. 583).

Previous studies on the topic of knowledge retention and aging workforce are

mostly theoretical in nature for example, many studies (Calo, 2008, Strack et al., 2008,

DeLong, 2004, Beazley et al., 2002, Ropes, 2013, Slagter, 2007, Whelan and Carcary,

26

2011) provide the conceptual foundation highlighting the importance of knowledge of

baby boomers and arguing that this knowledge should be retained. De Long (2004) was

one of the first to bring the attention of organizations and researchers towards the aging

workforce issue and the lost knowledge. According to him lost knowledge is the

decreased ability of organizations to make effective decisions. He porposed that an

effective knowledge retention stratgey should have three types of processes, i)

information technology processes (Skills matrices, social network analysis, intranet sites

etc) , ii) human resources porcesses and practices (staffing profile, succession plannig,

phased retirements etc), and iii) knowledge transfer practices and recovery initiatives

(mentoring, interviewing, SOPs etc). Calo (2008) also gave a very good perspective of

the aging workforce and talent management. He is also among the early researchers to

highlight the aging workforce problem. He provided a general overview of the challenges

of talent management especially focusing on the aging workforce and their knowledge

retention. He stated that a lot of knowledge loss will occur between now and 2020

because of the retirements. During the 1980s and 1990s, organizations reduced hiring due

to the economic crisis thus creating gaps among the workforce. Organizations are now

experiencing this gap as the senior employees are approaching retirement and their

successors are young and inexperienced. Moreover, through retirements, organizations

might be making room for new employees, however, the new cohort is much smaller due

to reduction in population growth and low infertility rates. This will cause a problem of

availability of workers and finding the right talent. Moreover, the strategy of relying on

external hiring to buy the knowledge lost will be decreasingly effective because of

shrinking talent pool. Employee should not be seen as interchangeable and easily

27

replaceable parts especially when dealing with knowledge workers as the knowledge of

an old experienced worker cannot be simply extracted and transplanted into the younger

generation. Whelan and Carcary (2011) focus on integrating talent and knowledge

management and in view of retirements and challenges of competitors headhunting for

the right talent, they suggest use of mentoring programs along with rewards and

recognition for effective knowledge retention. Durst and Edvardsson (2012) in their

literature review on knowledge management in SMEs found that knowledge retention is

an underexplored area and there is need to investigate questions such as what fosters

organizations to engage in KR activities and the drivers of KR for managers to initiate

such activities. Hewitt (2008) in her study “Defusing the demographic time-bomb” stated

the need for knowledge retention from the aging workforce and suggested the use of

voluntary communities, flexible work arrangement, creative retirement policies, and

formal KR programs. The same was argued by Bell and Narz (2007) that in order to meet

the challenges of age diversity at workplace, it is important to have flexible work

arrangements and better employee engagement which helps in talent retention, improve

organizational performance and stakeholder value (Lockwood, 2007). Legas and Sims

(2011) further stated that HR needs to play a key role in designing, supporting, and

deploying strategies and training to manage this multi-generational workforce. Bursch

and Kelly (2014) stated that to handle the baby boomer’s retirement syndrome and brain

drain issue, organizations need to conduct a strategic workforce analysis and identify,

prioritize, and engage the potential retirees by creating knowledge transfer by these

people using for example, formal and informal mentoring, intergenerational work teams

and web 2.0 technologies.

28

Apart from the theoretical perspective and highlighting the aging workforce issue,

a small number of studies focused on the practical aspects of knowledge retention. The

empirical studies found in health sector focused on the upcoming issue of aging nurses

and that their knowledge needs to be retained. These studies were initiated to create

awareness on aging workforce issue in the health sector and to start some initiatives

(Lahaie, 2005), stressed on importance of past knowledge and providing guidelines and

strategies to retain the knowledge of old nurses (Bleich et al., 2009), revealing little or no

work being done on knowledge retention. A few empirical studies focused on the erosion

of cultural aspects and traditions as a result of the aging workforce. For example, Brosi et

al. (2007) emphasized that the cultural erosion was taking place because of the lost

knowledge on Canoe making in Micronesia. Similarly, Cattani et al. (2013) performed a

study on the loss of tacit knowledge of experts from the 19th century who used to

manufacture ceremonies stringed instruments in Italy. Hofer-Alfeis (2008) proposed a

debriefing process for retaining the knowledge of leaving experts. This process involves

identifying proficiency areas, transfer of codified knowledge, relationship knowledge and

lessons learned. McNichols (2010) performed research in the USA to understand the

perspectives of Generation X on knowledge retention from baby boomers in the

aerospace industry. Her findings revealed that top management support, dynamic team

environments, and effective mentoring relationships are crucial for effective knowledge

transfer. Levy (2011) conducted a case study in an Israeli company to cope with

knowledge loss from retirees. She developed a four-stage framework for knowledge

retention of retiring workers which involves initiation, scope, transfer, and integration.

Lazazzara and Bombelli (2011) conducted a study in ENI but it was from HR

29

perspective, and to understand the importance of old age workers and not to discriminate

them. Further, they emphasized that aging workforce can be used as mentors and

teachers. Dumay and Rooney (2011) performed a case study with an Australian company

to examine the outcomes of a plan to tackle a perceived human crisis in the company.

They found out that induction and training of new employees helped handle the crisis, but

the tacit or experienced knowledge dimension is still missing, and retention of this

knowledge will be a major challenge in coming years. Harvey (2012) investigated a

knowledge retention strategy for late-career nurses in Canada. He supported the use of

mutual exchange model for effective knowledge transfer to recruits falling in line with

Snowden (2002) who argued that “We always know more than we will say, and we will

always say more than we will write down.” He has the view that all tacit knowledge can

never be codified. Thus, he supports the use of narratives and story-telling in which the

experienced employees can share their situational experiences in the form of stories

through shared contexts (Snowden, 2002) helping in communicating complex ideas in

simple and memorable ways. Whyte and Classen (2012) also supported this by

conducting a grounded theory study on investigating if story telling can be used to elicit

tacit knowledge from subject matte experts (SMEs). They collected 64 stories and came

to conclusion that story telling is very effective way of retaining the knowledge of experts

and it is easier than performing the debriefing sessions, after action review etc. Agarwal

and Islam (2015) studied how libraries can manage to retain the knowledge of employees

who would leave or resign. The qualitative analysis of data from 100 respondents across

the world revealed that libraries are following many ways to retain the knowledge of

leaving experts for example hand-over training, exit interviews, storytelling, workforce

30

planning, Cops, cross-training programs. The researchers argued that most of these

processes are for general knowledge sharing and there are hardly any formal processes

for knowledge retention. Shah et al. (2014) in their study of knowledge management

practices at a Bulgarian bank also supported the use of mentoring and Cops for

knowledge retention. The work of Susanne Durst regarding knowledge retention is of

significant importance. She, along with other researchers, conducted many studies on

knowledge retention over the past few years. In their study on succession planning and

knowledge management in SMEs (Durst and Wilhelm, 2012), they collected data from a

German SME, and found that if the employees exit or get retired, the company doesn’t

have any processes to retain their knowledge. In their second study (Durst and Wilhelm,

2013) they studied the knowledge at risk within the same company and conducted 14

interviews. Based on the data collected, they developed a tool to calculate the knowledge

at risk score for the departing employees. In this tool, they used the four dimensions of

intellectual capital (human capital, social capital, structural capital and relational capital)

to measure the knowledge risk. They further stated that this tool can help in triggering the

knowledge retention measures within the organization. Durst and Bruns (2016) further

investigated the Swedish Municipality sector regarding succession planning and

knowledge retention and found that there is a lack of strategic and planned approach and

the municipality follows a sporadic approach to address the issue of succession planning.

Moreover, the impact due to retirements and knowledge loss is evident however, due to

the decentralized structure of municipality’s departments, the top management doesn’t

seem to have a clarity of this issue leading towards the lack of a non-holistic approach.

Bairi et al. (2011) focused on IT industry and stated that there are 2 causes of attrition,

31

functional and preventable. Functional is due to retirements, illness etc. while preventable

is due to death, education, and turnovers. These can be mitigated through training and

continuous education, monitory benefits, career path and job scope. Similarly, Sitlington

and Marshall (2011) supported the redesign of jobs and documentation procedures to

minimize the effect of downsizing decisions on organizational knowledge. Holtshouse

(2010) conducted a survey from business and government executives in North America,

Europe, and South America on the issue of retaining the knowledge workers in the next

10-12 years. The results revealed that the loss of knowledge from the retirements will be

of major concern in upcoming years and moreover, organizations are not much prepared

about this. Furthermore, the knowledge retention strategies to focus on include using

social networks, mentoring, self-learning courseware etc. The same was suggested by

Arif et al. (2009) who conducted a study on measuring knowledge retention in

construction industry in UAE. They used a four-level knowledge retention measuring

model which comprised of i) extent of knowledge sharing and retention from employees;

ii) documentation of this knowledge; iii) effectiveness of this documented knowledge and

finally iv) usage and retrieval of this knowledge by other organizational members. The

results indicated that the maturity level for knowledge retention is inadequate for the

studied industry. Jackson (2010) used a social software approach to retain the knowledge

of baby boomers. He stated that choice of retention strategies depends on the

organizational preferences however, the methods utilized should be easy to use, easy to

update, engaging and extendible. Using an action research approach, he devised a

retention methodology which comprised of, i) framing questions ii) recording the

responses, iii) transcribing the recordings into text, iv) loading text files into individual

32

wiki files, v) Enabling experts to review the files and finally, vi) allowing wiki pages to

be accessed and edited by all staff. The findings of Caldas et al. (2014) are also in line

with Jackson (2010) that the effectiveness of knowledge retention strategies depends on

the departure timeframe, knowledge criticality and geographical constraints. Moreover,

the timely retention of knowledge will help increase operational efficiency, reduce

cooccurrence of critical errors and encourage innovation. Schmitt et al. (2012) proposed

that organizations with high level of collaboration, strong network ties and a balance

leadership structure will have better knowledge retention. Martins and Meyers (2012)

discussed the general knowledge retention theme in the context of knowledge

management practices and explained the organizational and behavioral factors

influencing knowledge retention. They discussed some major factors to be considered

when implementing a knowledge management strategy. These factors include i)

knowledge behavior to focus on effective communication between different age groups,

ii) strategy implementation which involves value of innovativeness, openness and trust,

iii) knowledge loss risks that include identification of retirees, iv) power and politics v)

knowledge growth, vi) performance management which involves human resource

department recognizing the expertise, conducting training, development and career

success etc. Mishra and Bhaskar (2011) compared a high learning and low learning

organization in the IT industry of India, using four major processes knowledge creation,

sharing, upgradation, and knowledge retention. They found out that turnover rate is high

in IT industry and there is knowledge transfer process involving 15-30 days interaction

od departing employee with managers and going through exit interviews. However, their

study revealed that there is still need to further refine and build formal knowledge

33

retention processes for the exiting employees. Kabir (2013) focused on tacit knowledge

retention in connection with the technological advancements. His study revealed that

using advanced technology, the ability of the organizations can be enhanced to convert

useful tacit knowledge to explicit form for example through business analytics, decision

support systems etc. Working on the same line, Kim (2014) developed a K-expert

software tool that uses data mining to extract knowledge from different data sources

within the organizations, however, it hasn’t been tested. It is expected that it could be

useful for knowledge retention in future. Gotthart and Haghi (2009) conducted a case

study in Hewlett Packard facing the retirements and ageing workforce issue. They found

that Hewlett Packard used “Knowledge Briefs” (KBs) program for the retires. This

program consisted of a series of steps (submission, review, and measurement of the

quality of knowledge) to retain the knowledge of leaving experts. It had been very

successful at the company and showed a significant return on investment. Frigo (2006)

studied the retirements and ageing workforce issue on water and waste water utilities in

USA. He revealed that companies are at various stages in terms of knowledge retention

but in general, lack structured approaches. Moreover, it involves both people and

technology and an integrated knowledge retention strategy will be key to retain the

knowledge pertinent to operations and expertise. The tenure profile in the utilities sector

quantify the utility staff experience. Hermansen and Midtsundstad (2015) targeted

Norwegian companies and conducted surveys on retaining the old experts. Using

multivariate logistics and linear regression, they found that taking measures to facilitate

life-long learning, preventing health problems or reduced work capacity and financial

incentives can impact the knowledge retention processes within the organizations. Miller

34

et al. (2011) studied the retention of knowledge in university technology transfer process.

He argued that proper development and maintenance of relationships network can

significantly improve the knowledge retention in the university technology transfer

process. Daghfous et al. (2013) targeted manufacturing industry to investigate the drivers

and impacts of knowledge loss and associated retention strategies. Their study revealed

that organizations should retain knowledge through strategic coordination among units

and networking strategies.

Few studies on a general knowledge retention theme have been found in Sub-

Saharan Africa. They demonstrate that brain drain is happening because of turn-over

from the public sector to private sector (Dewah, 2013) and there is a lack of KM practices

and understanding of KM in organizations (Bessick and Naicker, 2013). These studies

(Dube and Ngulube, 2013, Dewah, 2013, Kamsu-Foguem et al., 2013, Bessick and

Naicker, 2013) will create awareness and provide the foundation for KM policy

formulation across organizations in Africa. Joe et al. (2013) found that when old experts

leave, organizations lose knowledge of business relationships, knowledge of governance,

subject matter expert knowledge, organizational knowledge and knowledge of business

processes. Massingham (2014) performed a longitudinal study in Australian company

facing aging workforce issue and tested the existing toolkits available in literature during

the study. He found out that knowledge retention works better if started well before time

and strategies like interviews, video capturing, etc. are not that successful at the end.

Moreover, it depends on the personal preferences of the departing employees regarding

knowledge transfer.

35

Similarly, few empirical studies were conducted regarding European project

SILVER – Successful Intergenerational Learning through Validation, Education &

Research. This project aims at handling the aging workforce crisis. Studies conducted in

this regard include Ropes (2015) who tested an intergenerational learning toolkit in six

knowledge intensive organizations. The toolkit was not that successful, but contained

programs like reverse mentoring, intergenerational teamwork and intergenerational

knowledge transfer of knowledge. Similarly, Bratianu and Leon (2015) focused on

intergenerational learning (IGL) to address aging workforce and retirements in four

European universities. Their study unveiled the negligence on knowledge retention, and

further, they recommended the KR strategies such as mentoring, intergenerational

research teams, intergeneration workshops to be used for IGL.

Thus, the literature review reveals that research work on the issue of knowledge

retention, especially for retiring workforce is scanty and mostly focuses on the theoretical

aspects; describing the importance of expert’s knowledge, highlighting the aging

workforce phenomena, and suggesting ways of retaining knowledge. The studies indicate

that there is more emphasis on the explanation of strategies to be adopted than the actual

implementation of these strategies in the organizations. There is a lack of research area

on how organizations are actually working on knowledge retention strategies (Durst and

Bruns, 2016) for aging workforce and what are the issues and challenges in this regards

falling in line with the outcome of the literature review on knowledge retention

performed by Durst et al (2015) stating that research work on knowledge leakage appears

to be underdeveloped in organizations whether they are private or public and thus, they

need to address knowledge loss issue of departing employees especially when critical

36

organizational members leave. Further, in the literature, there is no comparison made on

aging workforce situation especially in the context of multinational companies across

different geographical boundaries. Thus, there is a lack of holistic approach in exploring

the knowledge retention area. This research work aims to fill these gaps by conducting an

exploratory study in the oil and gas sector.

2.5 Research Context and Motivation

DePass (2012) mentioned that boomers are reaching the retirement age at a rate of

10,000 per day, every day for next 20 years. His research further revealed that this issue

is a global epidemic affecting USA (Burch and Strawderman, 2014), Japan (Halse and

Mallinson, 2009), Australia (Solnet and Hood, 2008), Europe (Ropes, 2015), China

(Kapp, 2007), and South Africa (Wessels and Steenkamp, 2009). Certain industries like

manufacturing and oil and gas are suffering more with this issue of aging workforce and

retirements as new generation isn't stepping up to join these industries because many of

them don't want to be away from home and work in harsh environments in case of oil and

gas (Ball and Gotsill, 2011) and others don't want to work in dangerous environments and

dirty factories even though the remuneration is good (Eisen, 2003). The oil and gas sector

will be significantly impacted in the next five to ten years because of the shortage of

technical people as the majority of employees are going to retire, thus triggering alarms

and raising concern regarding future projects (Ball and Gotsill, 2011, McKenna et al.,

2006, Stevens, 2010). In 2011, Microsoft conducted a third oil and gas industry

collaboration survey. The survey revealed the situation of workforce crisis depicting that

more than 40% of people are older than 50, and 66% are older than 40 (Fig. 2.1).

37

Moreover, due to fluctuating oil prices, there have also been issues of job stability in this

sector (Ball and Gotsill, 2011). The above factors intrigued the researchers to conduct

research in this area. This research makes significant research contribution to the existing

body of literature as discussed below:

1. The oil and gas are a global industry facing a severe baby boomer crisis

coupled with the fact that no new people are joining the industry.

Regarding knowledge retention, this situation poses challenges and thus

the research takes on a holistic approach to investigate the situation of old

age retiring workers, the effect of current oil prices, different knowledge

Fig. 2.1 Aging workforce in Oil and gas industry

(Microsoft-Accenture Oil and Gas Survey Source:2011: http://news.microsoft.com/download/archived/presskits/industries/

/manufacturing/docs/accenturesurvey.pdf)

38

retention strategies adopted and their effectiveness in oil and gas companies.

2. By interviewing people across a diverse range of companies from Asia,

Europe, Australia, USA and Africa, a global perspective has been added to

gain a deeper understanding of the aging workforce and knowledge

retention phenomena under different contexts.

3. Collecting data from different geographical locations helped in making a

location and sector wise (upstream, downstream, and midstream)

comparison of companies regarding knowledge retention from old age

retiring workers yielding impressive results.

Thus, the main research questions to be investigated are:

i) What is the current situation of old age retiring workers in the oil and gas sector,

due to the economic crisis and how is the oil and gas sector handling it?

ii) What strategies are being adopted for knowledge retention and what are the

challenges in their successful implementation in a global perspective?

iii) What are the organizational dynamics due to different geographical locations and

difference in upstream, downstream, and midstream sectors regarding knowledge

retention of retiring workers?

The research findings to the above research questions will be of great use for the

oil and gas sector specifically and to other sectors in general in understanding the

workforce crisis. Moreover, managers will get to know the approaches of the

organizations toward this issue and will provide a foundation to them to understand and

initiate the knowledge retention activities within their respective organizations. It will

39

further help the executives and managers to understand the influence of geographical

locations on the KR activities.

40

CHAPTER 3. METHODOLOGY

This section will cover the methodology adopted for the current study. It starts with an

overview of research design, a comparison of the different kinds of approaches used in

research and then, gives a detailed explanation of the research method used for the

current research work.

3.1 Types of Research

Business research studies can be classified into mainly three categories namely,

exploratory, descriptive, and causal research. Descriptive research is conclusive in nature,

pre-planned, very structured and aims at understanding the particular situation of a group

of people and to define their behaviors or relationships using prior knowledge (Neuman,

2005). The survey questionnaire is the most commonly used technique in this type of

research which provides statistical data. Explanatory research or causal research is also

quantitative in nature like descriptive research and attempts to measure the cause and

effect relationship among variables; thus it is also pre-planned and structured with a

defined research problem, and it's used to test or explain a theory (Neuman, 2005).

Experiments and surveys are the common tools in this type of research.

On the other hand, exploratory research is based on discovering the new ideas and

insights and can be used to clarify the nature of a problem and to find out the areas of

improvement or devise alternative strategies (Saunders et al., 2011). In other words, when

there is not enough information about the subject, exploratory study is carried out.

Quantitative researchers can use this form of study to formulate and test the hypothesis to

41

answer the “how,” “what,” “where” questions and qualitative researchers can use it when

conducting studies based on interviews, focus groups, case studies, etc.

For the current study, the gaps revealed during the literature review identified the

need for an exploratory research to be conducted to gain an in-depth insight of knowledge

retention and aging workforce phenomena. Thus, based on above discussion, this study

encompasses an exploratory approach.

3.2 Quantitative and Qualitative Methods

Creswell (2013) states that strategies of inquiry, methods and knowledge claims

define our research approaches which are then translated into practice depending on the

chosen approach which is either qualitative, quantitative, or mixed method approach.

Strategies of inquiry are the more practical way considered as they provide the directions

about what procedures to be used in the research design. These strategies of inquiry

evolved over the years due to advancement in computational power allowing analysis of

complex problems and development of more articulated ways for conducting the social

research.

According to Creswell (2013), quantitative research is an approach for testing the

objective theories through the examination of the relationship between the variables.

These variables can be measured through instruments, and then the data obtained is

analyzed through different statistical methods. In quantitative strategy, there are

experiments and surveys. Experiments are true experiments conducted on the subjects

with different conditions (Creswell, 2013) whereas survey is a non-experimental design

in which questionnaires are used to collect data from a sample of the population with the

42

intention of generalizing it to the whole population (Neuman, 2005). According to

Neuman (2005), the key factors in quantitative methods are the variables, and their

relationships with each other are normally explained in the form of a hypothesis. Thus,

the hypothesis created are subjected to empirical testing using methods described above,

and a large sample of the population is taken to test the theory and finally make a

generalizable conclusion. Thus, the main aim of quantitative methods is to work with

numbers and statistics to identify statistical relationships and significance of the findings.

The information and bias of the researcher in this type of study are not known to the

participants, and similarly, the characteristics of the participants are also hidden from the

researcher.

The qualitative research as described by Creswell (2013, p.4) is the "means for

exploring and understanding the meaning, individuals or groups ascribe to a social or

human problem." The qualitative study aims at exploring a topic and giving an

opportunity to the participants to respond in their own words and thus tends to be very

rich and explanatory in nature (Punch, 2013). In qualitative research, the researcher wants

to explain the phenomena and context in detail within the research settings. The

researcher is not worried about generalization and keeps the research approach more

flexible and able to change. The characteristics of the different qualitative methods vary,

and their utilization depends on the subject and discipline (Milena et al., 2008).

Based on the above discussion, a qualitative approach has been adopted for the

current study to gather detailed and rich information on the underexplored topic of

knowledge retention. The next section will describe in detail the research design for the

study.

43

3.3 Research Design

Naturalistic concepts can characterize qualitative approach such as closeness

(Patton, 1987), common-sense understanding, everyday life (Bogdan and Biklen, 1992),

and a set of interpretive practices that makes the phenomena under study visible

(Creswell, 2013). Qualitative analysis involves interpretation of mainly non-numerical

data that may not be easy to reduce and interpret as compared to a quantitative approach.

The role of the researcher in a qualitative study is of central importance as the researcher

is placed in the world of phenomena, unveiling the interpretations that make the world

visible and allowing the researcher to employ an inductive approach and let the concepts

and explanations emerge from data (Bogdan and Biklen, 1992). The research method is

generally selected for its appropriateness in exploring a particular research topic. There is

no “best” method but most suitable for a certain type of study. So, in other words, a

method has no superiority, but some tend to be more appropriate for a particular study

than the others (Creswell, 2013). The qualitative research method is effective when the

topic is underexplored and has obtained a little academic attention.

There are different qualitative approaches described in the literature, and the most

common ones are ethnography, phenomenology, grounded theory, symbolic interaction

and ethnomethodology (Bogdan and Biklen, 1992). Ethnography attempts to describe a

culture in which the researcher gets immersed in the culture as an active participant.

Phenomenology focuses on understanding an event, activity or phenomena as perceived

by the study population. It focuses on people’s subjective experiences and interpretations

of the world. Symbolic interaction is concerned with the human interaction and the

44

meanings that individuals give to their experiences through different symbols.

Ethnomethodology focuses on the methods people use to understand and carry out their

everyday activities. Finally, grounded theory approach is an inductive approach and a

systematic way of inquiry. It follows a systematic procedure for data analysis and

involves development of codes from data. These codes then combine into categories.

Then, the interaction and relationship among these categories produces a cohesive

explanation of the whole phenomena under study. Glaser and Strauss developed

grounded theory method in the 1960s during a period when qualitative approaches were

considered nonscientific. Grounded theory has since achieved wide acceptance and

popularity because of its systematic way of inquiry and rigor. This technique is suitable

when there is scarce knowledge available on some topic, and the main aim is to produce

some fresh knowledge on that topic through the lens of participants involved in the study;

thus the term grounded theory arises as the outcomes are grounded in data by following a

systematic procedure (Charmaz, 2014). The current study adopts this grounded theory

building approach (Charmaz, 2014) for data analysis. The details on grounded theory

approach will be discussed further in the next section.

3.4 The Grounded Theory Approach

It is defined as "an initial systematic discovery of theory from the data" (Glaser

and Strauss, 1967,p.3) “which emerges from the bottom up..... from many disparate

pieces of collected evidence that are interconnected" (Bogdan and Biklen, 1992,p.3).

Glaser and Strauss introduced grounded theory in 1967. In their book, “The Discovery of

Grounded Theory,” they proclaimed a subversive message regarding systematic

45

qualitative analysis and intended to explain the usefulness of grounded theory in

constructing abstract theoretical explanations. It was considered of great importance in

the emergence of qualitative research as a credible research approach. The aim of Glaser

and Strauss was to move qualitative study to the realm of explanatory theoretical

frameworks instead of merely conducting the descriptive study. The researcher seeks to

produce fresh and new knowledge instead of following others to verify something.

Setting a hypothesis before field work distorts and researchers might force the data into

predetermined ideas. The practice of theory generation is worth encouraging to serve the

purpose of knowledge building. None should expect perfect grounded theory to be

generated in the first place but a stepping stone for the researchers (Charmaz, 2014).

Thus, grounded theory facilitates researcher's creativity to generate theory using a series

of systematic steps. Glaser and Strauss were the original developers of grounded theory.

However, over the years, grounded theory developed in a variety of ways. There are three

main approaches to grounded theory: the Glaserian inductive approach, the Straussian

inductive-deductive approach, and the Charmaz constructivist approach (McCallin,

2004).

In the Glaserian approach, we start data collection by entering the field without any

preconception. In the Straussian approach, we have a preliminary review of literature

before beginning data collection. There is a third approach called the constructivist

approach coined by Charmaz which is similar to the Straussian approach in practice, but

it emphasizes that the theory is not generated but constructed both by the interviewers

and the participants. According to Charmaz (2014), "We are part of the world we study,

the data we collect and analysis we produce. We construct our grounded theories

46

through our past and present involvements and interactions with people, perspectives,

and research practices" (P.17). Further, she explains that "Research participant's implicit

meaning, experiential views, and the researcher's finished grounded theories are

constructions of reality" (P.17). Grounded theory methods help in increasing flexibility

because they build upon the real data i.e. what is happening? The grounded theory

provides a clear focus on what is going on without losing the details of the overall

scenarios just like a camera with many lenses where in which you first get the whole

scene, but then you shorten focal points to bring the key scenes closer (Charmaz, 2014).

Shaping and reshaping of data through grounded theory refines the data and increases the

knowledge. For the current study, the authors followed the Charmaz Constructivist

grounded theory approach as shown in figure 3.1.

The constructivist approach has been used extensively by researchers in leading journals

(Corbet-Owen and Kruger, 2001, Little et al., 2016, Kreiner et al., 2015, Gustafsson et

al., 2003, Mazmanian et al., 2013), and is supported by researchers (Evans, 2013, Deady,

2011, Glaser, 1978, Glaser, 1998) as it stays true to the concepts initially displayed by

Glaser and Strauss (1967). Moreover, it provides a combination of flexibility and rigor

through the incorporation of literature review and allowing the researchers greatest

amount of freedom to proceed in their desired directions while sticking close to data

(Evans, 2013). In the case of using software for qualitative data analysis which is the case

for current research, the researchers (Bringer et al., 2006, Friese, 2014, Evans, 2013) also

supported the use of the constructivist approach. Finally, the constructivist approach

applies for the current study as researchers try to make meaning through interaction with

research participants (Hussein et al., 2014) thus acknowledging the mutual creation of

47

Fig. 3.1 A Visual Representation of Charmaz Constructivist Grounded Theory

(Source: Charmaz (2014))

knowledge by researcher and research participants (Charmaz, 2014). The basic structure

of the grounded theory as explained by Charmaz (2014) is further explained in next

section.

3.4.1 Basic Components of Grounded Theory

a. Theoretical Sampling: Selecting the study participants by relevance to the study

objectives. Mason (1996) states "Theoretical sampling is concerned with constructing a

sample which is meaningful theoretically, because it builds in certain characteristics or

criteria which help to develop and test your theory and explanation." According to Glaser

(1978), using theoretical sampling, data collection process is performed for generating

48

theory where the analyst collects, codes and analyzes the data simultaneously. Based on

this analysis, the researcher decides on what data to collect next to develop his theory as

it emerges. Thus, theoretical sampling is performed by using the concepts, categories and

theoretical relationships which are found in a previous analysis of data. As concepts and

theories emerge, sampling boundaries might change to include different people talking

about different things, same people talking about different things and different people

talking about same things.

b. Coding: Coding is a process of deriving concepts from data. The coding in

grounded theory is to define what data are about. Star (2007) mentions that "a code sets

up a relationship with your data and with your respondent." Coding involves naming the

data segments in a way that categorizes and summarizes a piece of data (Charmaz, 2014).

The codes should be assigned in a way that they point out to the actions, feelings, and tell

about the progression.

Moreover, the codes should stick close to data. Coding everything early in the

research aligns the research direction and helps the researcher to get to the focused

categories quickly. According to Charmaz (2014), coding in grounded theory involves

two steps:

i) An initial phase of naming each word, line, or segment of data and

ii) A focused or selective phase using the most frequent or significant initial

codes.

Initial coding can be performed either line by line, word by word or incident by

incident. Glaser (1978) and Charmaz (2014) advocated the use of the line by line coding

to develop the codes. Thus, line by line analysis has been utilized for this study as it

49

seemed more appropriate and one can see the actions and maintain a flow of the events.

Initial coding helps in achieving the fit and relevance. After initial coding is performed

then, focused coding is performed which emphasizes the selection of important and

relevant codes. These codes can be considered as tentative categories for further

exploration and theoretical sampling.

c. Constant Comparative Method: It helps the researcher to generate concepts

from data by identifying the conceptual similarities and differences through steady and

continuous comparison of data right from the beginning till the end of data collection

process. In this way, the researcher achieves saturation as the researcher can take a

holistic view to raising data to concepts to make the concepts richer and fuller.

Moreover, through this constant comparison, the researcher would be sensitive to the

pattern or phrasal changes and will be able to observe the differences in the categories.

d. Memoing: When collecting data, notes are written to help researcher develop

the ideas to link together the concepts and categories. This helps to prevent the ideas from

slipping away. These notes are called memos, and they play a major role in integrating

the concepts and in formulating a cohesive explanation for the inquired research

questions.

e. Treatment of Literature: Ideas from literature can be treated as subjects for

dialogues as these can help the grounded theory researchers in positioning their research

by comparing it to existing literature. Doing a constant comparison with the existing

literature is a practice of grounded theory methodology. Through this comparison, the

freshly generated knowledge can be positioned on a large map of knowledge that

explains the phenomena under study.

50

In practice, grounded theory research is not a linear process, and might involve going

back and forth a number of times to refine the concepts and categories as grounded

theorists stop and think about the data whenever ideas occur to them and the realization

of analytical connection can happen anytime during the research process(Charmaz,

2014). The quality of a study based on grounded theory depends on the data collection

method, and quality of data gathered as the depth and scope of the data makes a

difference. The study which incorporates rich, substantial and relevant data obviously

stands out. If there is limited data collected during the study, it might not make

persuasive and definitive statements. Thus, according to Charmaz (2014), following main

points need to be considered during data collection:

i) Enough Background Data: About persons, processes, and settings to understand and

portray the full range of the contexts of the study.

ii) A detailed description of the participant's views.

iii) Does data reveal the phenomena under study with in-depth detail and unveils the

meaning that’s lying beneath the surface?

iv) Does the data enable the researchers to develop analytic categories?

According to Mäkelä and Turcan (2007) “Grounded theory research can have many

outcomes (Eisenhardt, 1989; the present paper’s Appendix). It can lead to, among others,

‘causal theory,’ wherein relationships of mutually interacting constructs are explained, or

‘process theory,’ wherein the explanation specifically focuses on sequences of temporally

evolving action, changes in which can be traced to structural and environmental

changes (Strauss and Corbin, 1998, p. 163). Potential outcomes also include mere

51

building blocks of theory, for instance, concepts, typologies, and suggestions for

facilitating statistical research, such as ideas for potentially valid measurement

items"(P.21). Furthermore, researchers (Thornberg, 2012, Charmaz, 2014) agreed that it

is not necessary to generate a new theory using grounded theory, however, to serve

purpose of grounded theory, outcomes should be grounded in data (following the

systematic process), should provide a cohesive explanation of the research problem and

can be described in terms of broad conceptual frameworks/ propositions. For current

research, the same approach was followed, and researchers used propositions and

frameworks to explain the findings and draw the explanation of whole knowledge

retention phenomena along with discussion in the respective sections.

3.5 Data Collection Process

Interviewing is the most common technique employed in grounded theory for data

collection as according to Charmaz (2014), interviews fit the grounded theory approach

quite well. For the current study, intensive interviewing has been used to collect the in-

depth knowledge from the participants. Interviewing is quite flexible, and you can

communicate with participants easily. Moreover, the interviewees can be contacted later

for clarification of some answers. Rubin and Rubin (2011) further support interviews

stating that qualitative interviewing helps obtain rich data to build theories that describe a

setting or explain a phenomenon in a sequence, with the help of examples and

experiences collected during the interviews. Thus, interviews provide flexibility in some

ways and moreover, provide an opportunity for interactive dialogue with the respondents

for a detailed exploration of the topic. Semi-structured interviews were used for data

52

collection. Respondents were asked open-ended and probing questions to get an in-depth

insight of the research questions (Gill et al., 2008). To cover the global perspective of the

oil and gas sector, 21 interviews were conducted in Phase 1 of the study. The sampling

size in grounded theory approach depends on the saturation of concepts and categories

(Creswell, 2013). The researcher can stop collecting data when gathering fresh data no

longer sparks new insights and reveals no new information (Creswell, 2013).

The population for the study was the people in oil and gas companies who

represented “elite informants” and deemed as appropriate to answer the research

questions. For the selection of participants, the main point kept in mind was the years of

experience they had in oil and gas sector and how much relevant knowledge they can

provide on the research topics. Thus, participants were selected based on their vast

experience. The participants represented “elite informants” meaning they were at key

positions and most of them directly involved in knowledge management activities within

their organizations. Elite interviewing is a well-known technique with the aim of yielding

insightful information (Marshall and Rossman, 2011) from the participants. The

participants were contacted through LinkedIn profiles, emails, and available contact

points of the research team. Table 3.1 provides details about the participants regarding

their positions, experience, and location of the companies. From the table, it can be seen

that majority of the participants were at senior level with more than ten years of

experience in oil and gas. Few of the participants had experience of fewer than ten years.

Moreover, 12 out of these 20 participants were either KM managers or KM Directors in

their respective organizations thus providing a firm basis for the validity and reliability of

53

Table 3.1 Details of Interviewees in the study

Interviewee Years of

Experience Position

Company Location No of employees

in Company

1 10 Managerial/

Consultant A USA 20k-50k

2 30 Managerial/

Consultant B USA 50k-100k

3 8 Managerial C Russia 50k-100k

4 7 Senior Manager

D Australia 50k-100k

5 35 Senior Manager

E Netherlands 50k-100k

6 8 Senior Drilling

Engineer F Pakistan 20k-50k

7 6 Junior manager

G Nigeria 0-20k

8 30 Chief Drilling

Engineer H Pakistan 0-20k

9 15 Managerial

I Middle East 20k-50k

10 8 Managerial J Middle East 20k-50k

11 20 Director K Indonesia 0-20k

12 10 Senior Drilling

Engineer L Middle East 50k-100k

13 10 Managerial

M UK 20k-50k

14 32 Managerial

N India 20k-50k

15 40 Decommissioning

Manager O UK 0-20k

16 26 Director P USA 50k-100k

17 16 Managerial Q Thailand 0-20k

18 7 Managerial

R Italy 50k-100k

19 10 Managerial/

Consultant S UK 0-20k

20 9 Managerial

T Norway 0-20k

21 3 Managerial

U Middle East 20k-50k

54

collected data. Further, the participants are from different geographical locations such as

America, Europe, Middle East, Asia, Africa and Australia. This distribution allowed to

collect adequate knowledge on the knowledge retention from a global respective and to

make a comparison between developing and developed countries. Therefore, these

participants satisfied the criteria regarding experience, relevance, and knowledge required

to answer the research questions.

The interview questions (Appendix D) were sent in advance to the participants to

have a look and respond in case of any queries. The average duration of the interviews

was around 50 minutes. Before the start of the interview, the purpose of the interview

was explained to the participants and how the information collected from the interviews

will be used. The consent of the participants was taken to record the interviews.

Participants also provided feedback on the interview questions, and some modifications

were also made to the questions based on the input from the participants. Notes (memos)

were also taken during the interviews to note down the important points that could help in

data analysis and seemed interesting for further investigation. The interviews were

transcribed to analyze the data using grounded theory approach as discussed above.

To confirm the accuracy, the participants were sent a copy of their transcribed

interviews and asked for feedback. In some cases, the amendments were made in the

transcribed data as per feedback received.

3.6 Data Analysis Process

The interviews and analysis of data were performed simultaneously using

constant comparison method of the grounded theory described in the previous sections.

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During the data collection process, new ideas emerge as a result of in-depth interaction

with participants, due to which sometimes the modification of interview questions may

be necessary (Creswell, 2013). This is the characteristic of grounded theory approach

which helped researchers to remain flexible to the changes emerging in the data and make

modifications accordingly. Data quality is of key importance in grounded theory

approach as the depth and scope of the data makes a difference. The constant comparison

of data allows saturating the emerging concepts. Data collection ends when no new

information is being added from the participants. For the current research, interviews

covered the oil and gas companies from developed and developing countries. Interviews

were continued until the saturation point was reached and no new insights were being

revealed for the inquired research questions. Thus, theoretical saturation was achieved as

no new concepts emerged from the interviews. The analysis of qualitative data entails the

identification of themes, constructs, and patterns that unveil the perspectives of the

participants (Berg, 1989). The details of this analysis procedure will be discussed now. A

Computer Assisted Qualitative Data Analysis Software (CAQDAS) called ATLAS.ti

(version 7.1.6) was used to code and organize the full data. For this purpose, a student

license of the software was purchased.

ATLAS.ti is a powerful qualitative data analysis tool which portrays the analysis

process through coding and can create concept maps to visualize the actual meanings

delivered from the data (Muhr and Friese, 2004). ATLAS.ti is based on grounded theory

approach and offers a well-organized and systematic approach for coding and analysis of

data substantiating the rigor and quality of the study (Piecka, 2008). The use of this

software allows to do tasks in a more efficient way such as combining all the transcribed

56

files, linking the files, searching for any relevant information, facilitating the annotation

of codes, memos and categories, thus eliminating the need of laborious paperwork and

manual record keeping and tracking of all the codes and notes. Thus, ATLAS.ti simplifies

the task of analyzing data systematically by integrating large volumes of data (Saillard,

2011) and assists in increasing the validity of study at the conceptual stage of analysis

(Friese, 2014).

3.6.1 Coding with ATLAS.ti

According to Friese (2014), the computer assisted data analysis can be performed

through a method called “Computer-Assisted NCT analysis” as shown in figure 3.2. NCT

stands for the three essential components of the method which are noticing things,

collecting things, and thinking about things. The heavy arrows in the middle indicate that

this NCT is a non-linear process and involves moving back and forth between noticing,

collecting, and thinking.

These three essential components are described below:

i) Noticing things: It refers to the process of searching for interesting things in data

for example when reading the transcripts. To note down these important

things, the researcher assigns codes to the data segments revealing interesting

information. Moreover, the researcher may also write down the notes on these

interesting points.

ii) Collecting Things: As the data collection progresses and the researcher goes

through more data, he comes across things which are similar to the ones

noticed before. So, these similar things can be grouped under the same code

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Fig. 3.2 The NCT Model of Analysis

(Source: http://www.quarc.de/qualitative-data-analysis-with-

atlasti/companion-website/figures/chapter-5.html )

name. Things which are different can be assigned with new code names, and

thus, this is the coding process as performed in grounded theory by always

comparing the new data with the previous data and looking for similarities and

differences. Thus, this collecting process involves grouping of codes to generate

themes and categories for analysis purpose. The care should be taken here that it

is not necessary to attach a new label to everything one notices across data but

carefully analyze the data and if it fits within an already existing code, assign it to

that code. This process helps in preventing too many codes and ends up with a

well-structured coded data to explain the research questions. Any coding

procedure can be used depending on the choice and research design. As the

current study follows Charmaz approach, the “initial coding and focused coding”

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technique as described in earlier sections have been used. This technique has also

been adopted and recommended by Friese (2014) for Analysis with ATLAS.ti.

iii) Thinking about things: The researcher needs to remain engaged in the thinking

process right from the beginning. It involves thinking about coming up with

good code names, sorting and defining the relationships among the codes and

development of categories for codes. Thus, this process involves finding

patterns and relations among data and integration of all the concepts to

develop a comprehensive picture to answer the research questions.

Thus, these three basic steps are applied in ALTAS.ti. It starts with preparing the data and

creating a project file. The transcripts are uploaded in the ATLAS.ti. After this, the

coding of data starts (noticing things) and then, sorting and structuring the data to unveil

the patterns and relationships (collecting process and thinking process). In addition to

noticing, collecting, and thinking, the researchers also need to perform the writing

(memos) to write down his finding and ideas being developed to explain the relationships

and patterns. These memos might also include drawings and mind maps thinking about

the missing links for further investigation. These memos then serve as building blocks

when presenting and discussing the results in the thesis. This whole process, when used

in the actual scenario, can be described by figure 3.3. It is clear from the figure that this

process of computer aided data analysis is not a linear process but a recursive process and

involves moving back and forth between the different stages as shown in the figure

below.

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Fig. 3.3 The Process of computer-aided Qualitative Data Analysis

(Source:http://www.quarc.de/qualitative-data-analysis-with-atlasti/companion-

website/figures/chapter-5.html )

The software ATLAS.ti provides an easy and efficient method for coding the data.

Figure 3.4 shows a screenshot of 3 coding options, Enter Code Name (free code), Code in

Vivo and Select Code(s) from the list. Researchers give the majority of the code names

called free codes; some codes could be in vivo codes which are the statements or words

from the original data and are deemed appropriate as a code name.

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Fig. 3.4 Coding Options in ATLAS.ti

The third option is to assign a code to the new data segment by selecting from the already

existing list of codes generated by the researchers. The names of some of the codes are

changed or merged as the analysis and data collection progresses. The memo (Fig 3.5)

making is another important function of the software. It helps in writing down the notes

and key points as the data collection progresses (Friese, 2014). These memos keep a

record of the thought process of the researcher and all the significant findings during the

data collection and help in theoretical sampling, generating concept maps and in writing

the analysis of the results. Along with coding and categorizing the textual sources of data,

the researchers also utilized the visual capabilities of the software inherent in network

view features of the software.

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Fig. 3.5 Memo function in ATLAS.ti

The ATLAS.ti offers the flexibility of creating semantic networks comprising of nodes

and links; the codes represent the nodes and labeled links represent the relationships

between the codes (Friese, 2014). After the completion of coding from textual data, the

network views helped in understanding the overall scenario of knowledge retention and

the aging workforce in oil and gas industry. The role of network view is quite important

in determining the linkages among codes and doing it graphically or through visual

representations is much easier than just looking at the list of codes. The network view

also helped the researchers in refining and merging of codes conveying similar meanings.

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Figure 3.6 shows a network developed at initial stages of research to understand the aging

workforce situation.

Fig. 3.6 Network View Option in ATLAS.ti

3.7 Validity and Credibility of the Research

The quality of findings in a qualitative research depends on the authenticity and

trustworthiness of data (Mertens, 2014). The criteria for this trustworthiness involves

credibility, transferability, confirmability, and dependability, which serve as evidence to

the other researchers regarding the authenticity of research and its adherence to

appropriate standards. Guba and Lincoln (1989) refer to creditability as the groundedness

of findings and conclusion into the data. For the findings from data, they should be

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plausible to individuals from whom the data has been collected (Miles and Huberman,

1994). Such authenticity is referred to as internal validity (Mertens, 2014) in qualitative

research. Thus, to enhance the credibility of the study, member checks were performed as

described earlier. The transcribed data was verified by participants, and in some cases,

some amendments were made to the data. Transferability is related to the generalization

of study results to similar situations and contexts (Mertens, 2014). Although, it depends

on the reader to assess the degree of similarity, however, the researcher should provide

sufficient details for the readers to make that assessment. To enhance the transferability

of the current research, various measures were used such as the use of low inference

descriptors for example verbatim, peer review, and triangulation. As the participants in

the study belonged to different companies and different locations, it helped in

understanding the phenomena in depth and without bias. Stating the direct quotations

from the interviews of participants also helped in understanding the phenomena in detail.

The results and analysis were shared and discussed with peers to get insights regarding

interpretations and conclusions. Thus, all these methods helped in strengthening the

external validity and transferability of results. Confirmability refers to the objectivity of

the data and its interpretations. This is achieved through describing in detail, the methods

and procedures used to collect data for the current study. Dependability deals with the

appropriateness of inquiry process (Neuman, 2005). In the current research, the

researcher used a systematic methodology of grounded theory for data collection and

evaluation. A fundamental characteristic of grounded theory approach is following a

systematic process of data collection and analysis for underexplored topics (Charmaz,

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2014) thus adhering to the concept of dependability of the study. Thus, in the light of

above discussion, the current research work fulfills the validity and credibility criteria.

Thus, this chapter focused on the methodology and explained in detail the data

collection and analysis procedures. The grounded theory process and rationale behind its

selection was discussed in detail. Then the data collection process was described, and

details of the participants were provided. Semi-structured interviews were conducted with

the participants to collect the data. Further, the use of grounded theory methodology with

qualitative data analysis software, ATLAS.ti, was explained. ATLAS.ti eases the process

of coding and analysis of data using its powerful features such as memo function,

network views, code manager, etc. Finally, the validity and the credibility of the research

was discussed. The next chapter now describes the results and analysis of data, carried

out using the procedures outlined in this chapter.

65

CHAPTER 4. RESULTS AND ANALYSIS

This section will describe in detail the results and analysis performed using ATLAS.ti.

The main research questions to be investigated are:

1. What is the current situation of old age retiring workers in the oil and gas

sector, due to the economic crisis and how is the oil and gas sector handling

it?

2. What strategies are being adopted for knowledge retention and what are the

challenges in their successful implementation in a global perspective?

3. What are the organizational dynamics due to different geographical locations

and difference in upstream, downstream, and midstream sectors regarding

knowledge retention of retiring workers?

To answer these research questions, the data was collected over a period of 8

months as the process of theoretical sampling was followed. As data had to be collected

from participants across different geographical locations, it took time for the participants

to respond and then decide dates for interviews. A couple of times dates had to be

changed because of the change in the schedule of the participants. Overall, this data

collection process was quite challenging and time-taking.

The coding in ATLAS.ti started with the creation of a new project called “Atlas_

Analysis”. The first primary document was uploaded. The coding process started

following the Charmaz (2014) approach, and initial coding was performed doing a line by

line analysis of the data segments. After conducting the initial four interviews and

performing the initial coding, some patterns started to emerge in the data and thus

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following the steps of Friese (2014) for data analysis in ATLAS.ti, families were created

based on the codes constructed. The codes which explained the similar phenomena were

put together to construct a family. These codes got saturated as the data collection

progressed. The data collection stopped at the 21st interview when all the categories

developed from the codes got saturated, and no new information was being added.

Here is an example to explain the complete process of coding and categorization

using ATLAS.ti. A category was constructed as “Current_Situation” containing all the

codes which explained the current situation of the aging workforce to answer the first

research question. Figure 4.1 shows the different codes which explain the situation of the

aging workforce. Here the prefix “AW_Situation” is for aging workforce situation and is

used at the start of all the codes which explain the aging workforce situation. This helps

in categorizing and grouping the codes in ATLAS.ti. Moreover, a color (red) has also

been assigned to this category. These steps help in organization and retrieval of desired

data in an easier and comfortable manner. At the start, there were few codes in this

category, and as more data was collected, further codes were generated through line by

line analysis of data. The data segments which described the similar concept or idea as

coded previously were assigned one of the existing codes. Data segments which

described new idea were assigned new codes. There was merging and changing of code

names during the coding process. For example, a code initially created with a name

“Realize Knowledge Loss When Prices boom” was merged with the code “Short term

benefits against knowledge loss” as they both revealed the same concept. Similarly, a

code name “Reduction in Drilling Operations to Reduce Cost” was changed to

“Reduction in Operations” to make it more concise. Some of the invivo codes that were

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Fig. 4.1 List of Codes explaining the current situation of the Aging Workforce

taken as expressed in data are “Accelerated knowledge loss” and “Worst Recession ever

since 1980s”. At the end, this category comprised of 14 codes as shown in figure 4.1. The

groundedness of codes is displayed numerically under the column “Grounded”. This

number shows how many times this code has appeared in the data. This groundedness of

codes reveals the agreement and consensus across the interviewees on the given topic and

paves the way for theoretical saturation. These codes thus describe the overall situation of

the aging workforce, for example, the first code “layoffs and loss of expertise” indicates

that in oil and gas industry, companies are laying off employees and the majority of them

are the old and senior workers. Through the layoffs of these employees, the loss of

expertise is taking place. The thinking process on what these codes describe and how

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these are interlinked was performed through writing the memos. An example of a memo

is shown in figure 4.2 below which explains the researcher’s thought process during the

initial data collection. The text in red color are the questions that were further clarified in

subsequent interviews.

Fig. 4.2 Example of a Memo written during data collection process

The network view of the ATLAS.ti was used to see the relationships of codes and

categories visually. The network view for the aging workforce situation is shown in

figure 4.3. Similarly, following the above coding procedure, all the other categories were

created. The memos were simultaneously written, and network views were drawn to see

the relationships visually. The main categories that emerged from data in relation to the

research questions are:

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a. Current Situation of the Aging Workforce

b. Non-Holistic Approach to Knowledge Retention

c. Barriers and Challenges Regarding Knowledge Retention Activities

d. Dynamics of Different Geographical Locations

A detailed discussion for each of these categories will be carried out in next

section. This discussion will revolve around the codes that constitute the categories as

these codes represent the crux of the whole study.

Fig. 4.3 Network View for the Aging Workforce Situation

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4.1 Current Situation of the Aging Workforce

The network view for this category is shown in figure 4.3. There are 14 codes that

constitute to this category, and the relationships of these codes with each other are shown

in the figure. This view helps in understanding the current situation of the aging

workforce in a more concise way. There was a consensus among the interviewees that

aging workforce, approaching retirement, is a problem in oil and gas sector and there are

challenges in this regard. A major challenge in this regard that exacerbated the

phenomena further was the fall in oil prices as interviewee 1 stated:

“One of the biggest thing you should address is current downturn in oil and gas.

People are going from, you know, having 18 assets drilling down to 2 assets. Right. So,

the number of people they need is much less, the number of expertise they need is much

less ……… The problem is they (baby boomers) are being laid off. The oil prices are so

low that baby boomers are the most expensive staff they have irrespective of their

knowledge or not”

Thus, most of the people near retirement have been forced into early retirements for cost

saving purposes. The reason behind this as expressed by interviewee 4 is:

“Yes, there is a large range of baby boomers leaving the organization and normally you

would say that’s gonna impact us from a knowledge capacity but I guess, in the current

economic climate, that kind of goes out of the window a little bit……we have gonna cash

flow moving in the business again. In order to do that, you need to let go from a

workforce transition and undertake redundancies, and that involves a lot of baby

boomers because they are the ones preventing cash flows, because, they have got annual

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leave, long service leave and they are very highly paid individuals in the business. That

really does prevent the cash flow”

Thus, it reveals that old and senior workers are the highly-paid ones and

companies choose them as their prime target to reduce the costs. As the oil prices fall, the

operations become expensive, profit gets very low and cannot meet the operating costs of

the projects. Because of this, companies reduce their operations and they also layoff the

staff as they don’t want to pay them. Thus, early retirements are used to reduce the

overhead costs. Interviewee 13 from the UK explained this redundancy phenomenon as:

“I am in Aberdeen particular, huge downswing and something like 60,000 people

have been made redundant…… As a result, focus is not on future. More recent data from

Oil & Gas UK indicates that 160,000 UK workers will have been made redundant from

the industry by the end of 2016. This recession is by far the worst I have seen, having

been in the industry (oil and gas) since the 1980s"

The management doesn't seem to be much concerned about these retirements as

they are more concerned about profits and revenues and thus will let the person (retiree)

go. Thus, the interviewees agreed that management and companies are under budget

constraints and they don’t have much options to save the business. As interviewee 1

stated:

“They (management) are aware of it, but it's literally holding onto too many staff,

and it’s not happening ... ... you go from 500 wells to 80 wells a year, you are gonna lose

a lot of operational staff but you also don’t need a big central group for operations for so

many less wells… ... That's the biggest challenge”

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Interviewee 1 explained this accelerated knowledge loss because of low oil prices

and focus of companies on short term benefits as he stated that:

“Instead of the other way around where people are deciding to retire, it’s the

companies actually who are deciding for them to retire. So, the focus on knowledge

retention is actually more challenging in this era ...… So, they (companies) are focusing

on short-term benefits because they are making such losses that they are just executing

and removing people not focusing on what the long-term impact would be. So, knowledge

loss is probably more accelerated in these two years”

Interviewee 20 expressed his concern by saying that companies are maintaining a

fish bone structure where they just want to have a structure to perform the business at the

current moment. He stated that:

“It is such a cut off that you want to just have the structure which can perform

businesses for that time. And that is full stop. It’s really serious because it’s a question.

Are you surviving as a company or you are losing?”

Because of this scenario, the loss of expertise was well accepted by all the

interviewees. They were of the view that, at the moment, companies are not paying

attention to this knowledge loss issue but looking to save their businesses, however, they

will realize this when this bust cycle comes to an end and oil prices boom again.

Companies have slowed in recent years because of this oil slump, and thus, they are not

feeling the pain of losing valuable knowledge. They will realize that they have lost

important knowledge once things get better and operation are resumed. For the time

being, companies have reduced their operations, so the workload is less but when oil

prices get better in 1-2 years, the operations that were stopped will be continued, and at

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that point, companies will go again into hiring. At that point, the companies might not get

the same experts who worked for them previously. So, in that sense, knowledge is lost as

there is no guarantee of getting back the same experts. The competitors might have hired

them.

Before the start of the data collection, the major issue of knowledge loss was

thought to be retirement and researcher planned to focus on retirement. However, the

impact of oil prices was found to be an important factor for knowledge loss from aging

workforce, and valuable inputs were received from interviewees as discussed above. This

financial crisis forced the organizations to focus on short-term benefits (Ball and Gotsill,

2011) exacerbating the knowledge loss from aging workforce (Massingham, 2008). In

such situations, the expulsion of employees on short notice minimizes the chances of

retaining critical knowledge. Further employees are also not willing to share knowledge

(Daghfous et al., 2013) when they are being laid off. It also draws the attention of

researchers to the fact that employee knowledge is of least importance for companies

when it comes to reducing the costs and overheads in the organizations. This approach

makes things work temporarily but can have devastating effects in the long run (Calo,

2008). The oil and gas industry is considered the pioneer in KM (Grant, 2013), yet the

budget constraints and cost factors make companies perform their businesses in a

traditional way, and knowledge management initiatives are put aside. Based on this, it is

proposed that,

P1: Financial constraints and fluctuating oil prices accelerate knowledge loss in

oil and gas companies by putting a stop on knowledge management activities and

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focusing more on short-term business benefits which can have calamitous impact in

future.

Apart from the financial crisis and people forced into retirements or layoffs, in

general, the problem of aging workforce is well accepted in oil and gas industry. A high

aging profile has raised concerns among the companies. From the results, it is revealed

that this aging workforce termed as baby boomers, is a problem in oil and gas sector. This

generation was born after the World War II. No doubt, after a war, a lot of babies are

born, and this phenomenon occurs probably to protect the civilization. So, people from

this generation were born from 1945 to 1964 (Yu and Miller, 2005). Now, these people

are in the age range of 72 to 53 years. Most of them have retired, and rest will be

approaching retirement in the next 5 to 10 years making this generation the last working

generation in the industry at the moment (Stevens, 2010). From the interviews, it

becomes clear that a lot of people will be retiring in the next few years from the oil and

gas companies in developed countries. Interviewee 5 from Netherlands, who is the

director and KM lead at a supermajor company, mentioned that:

“We do have an aging profile in the company, and the whole industry has this as well.

So, it’s a known issue… what we have seen is people retiring at a rate that we have never

seen before and a lot of experience is walking out of the door, and we have to deal with

that”

There was a strong consensus among the interviewees from the developed

countries that aging workforce is a real problem in the companies located in Europe,

USA, and Australia. Some companies don’t have middle age people, either they have old

workers or very young workers as interviewee 3 stated that:

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“It is a challenging situation. We have a lot of old people going to retire and no

people in the middle; others are very young. So, it is a big task to take knowledge of

senior employees”

This also happens because of the cyclic nature of the oil and gas sector (Ball and

Gotsill, 2011). When oil prices fall, the recruitment stops, creating gaps regarding age

and experience between employees. When the businesses gain momentum, the

recruitments are again started. This cyclic nature also promoted the contract-based jobs

which have now become very common in the oil and gas industry. Many of the baby

boomers are also on contract jobs, working on projects and when a project finishes, they

leave at a relatively short notice and thus, there are no ways to have knowledge transfer

sessions with them. Interviewee 13 mentioned about this cyclic nature as:

“No job stability in oil and gas sector. There really never has been job stability,

which is why the proportion of contract workers, even for highly trained professionals, is

very high”

As compared to aging workforce issue in developed countries, the interviewees

from developing countries such as Pakistan, India, Indonesia, Thailand and Nigeria and

UAE stated that aging workforce is not a huge issue as most of the workforce comprises

of young people. As interviewee 6 mentioned that:

“Generally, people are in the young age, the ones who are running the system. But the

ones who are about to retire, I believe only very few (maybe) about 5% or so”

However, an interesting situation in these countries was found in the government

companies. In state-owned companies, the shrinking workforce crisis exists as the

recruitments are not made on time because of budget constraints and government strict

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recruitment policies. Thus, a huge age and experience gap exists between the aging

workforce and the younger employees revealing an interesting situation regarding

knowledge gap created when the senior workers will retire. As interviewee 11 stated that:

“Yes, it is a problem; we don’t have much new people joining oil and gas.

Recruitment system for state-owned enterprises is different than private owned

(companies). So, sometimes have a shrinking workforce problem”

Further interviewee 17 from Thailand stated that her company expanded rapidly

in past years. Most of the senior employees were transferred abroad to manage the

operations, creating workforce gap in the company. Because of this, there are also few

people left to train the junior staff. Same was depicted by interviewee 12 that, in case of

expansions, there are limited options for the companies regarding employees, and it can

create workforce issue if not enough people available. This can cause problems when

junior people take over after the senior employee retires. Because of less experience and

lack of expertise, they might not be able to perform the tasks well enough, causing delays

in the operations (Massingham, 2008).

The importance of this aging workforce has been well recognized by all the

interviewees. They are considered the key players in the companies. Their contribution

has been recognized in terms of expertise and solution providing capabilities. They know

the companies’ businesses very well and provide different insights and perspectives to

make it progress. Interviewee 9 stated the importance of these employees as:

“I call them (baby boomers) knowledge banks …… Knowledge banks that have

deep experience in managing situations …… I always say that when you face a trouble,

and you have 20 or 25 years’ experience, within few seconds you will look at the problem

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that you face from multiple angles and look at it from different perspective. When you

judge on the situation, you take a decision based on lots of inputs that you have in your

mind. However, the new generation don’t have all those angles of experience”

Interviewee 2 emphasized the importance of these employees as:

“In most cases, they are a largely untapped source of real expert domain

knowledge that majority of companies have yet to use effectively”

Moreover, the interviewees established that oil and gas industry is a very

knowledge intensive industry where knowledge is of core importance, especially in petro-

technical areas (Sheehan and Stabell, 2007). There was a consensus among the

interviewees that these huge sources of knowledge need to be retained within companies

or their knowledge needs to be captured before it walks out of the door. Results further

revealed that in current oil slump, learning gap will increase when these old workers are

tossed out as there will be more work pressure on the remaining employees and not

enough time left sharing. The interviewees were of the view that these old age workers

bring competitive advantage to the companies as through their expertise, they can reduce

cost, make effective decisions, and enhance the operations (Burmeister and Rooney,

2015). They are the key members in devising the overall strategy and work plan (Ball and

Gotsill, 2011). These workers are unique as they have been working in the industry for

quite a period of time and have gone through various phases of how the businesses

progressed, technology and process improvements over the years and they are used to

having the things change frequently (Yu and Miller, 2005, Ebrahimi et al., 2008).

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4.2 Non-Holistic Approach to Knowledge Retention

The next category focused on the knowledge retention strategies adopted by

companies to cater this knowledge loss. Interesting insights were revealed by

interviewees on this. The coding table for this category can be found in Appendix A.

There were a total of 53 codes with the most dominant code as “No Well-Defined

Processes for Knowledge Retention”. There was inconsistency in the responses of the

interviewees, and most of the activities mentioned were related to everyday knowledge

sharing, and only a few companies have well-structured knowledge retention processes

focusing on retirees. According to interviewee 2:

"Oil and gas companies do have knowledge transfer programs …... These

programs kind of come and go, depends on whether there is a good executive sponsor for

these or not. Over a period of time, these (programs) just tend to disappear if people are

not interested or don't see value in it, and the program just goes away. They may be

resurrected again later, but it’s not consistent”

The companies from developing countries, as mentioned earlier in the category “Aging

Workforce Situation”, are not having this problem. However, these companies are at

initial stages of knowledge management and even there are companies where no activities

are happening related to KM. For example, there are some knowledge management

initiatives at company F, G, and K whereas company H and M stated that there is nothing

such as knowledge management going on in their companies. Thus, knowledge retention

is a step further in such cases. Company P although in a developing country is a big

multinational company and has a very well-structured knowledge management program

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with special focus on knowledge retention. The company has a dedicated KM team for all

knowledge management activities. On the other hand, the companies from developed

countries although have proper knowledge management setups but there are

inconsistencies across the board regarding implementation of the KM activities and

Fig. 4.4 KR Activities mentioned by Interviewees

especially the knowledge retention for retirees. Figure 4.4 shows the different knowledge

sharing activities as stated by the interviewees. It is evident from the figure that it is a

fragmented structure of activities and most of these activities are for general everyday

knowledge sharing but no activities for knowledge retention from the departing

employees apart from few companies. Thus, the absence of a holistic approach turned out

to be a major concern in terms of knowledge retention. Interviewee 4 from Australia

described this as:

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“KM process is kind of owned by HR persons, …. they have exit interviews, they

have knowledge transfer processes and whole series of knowledge templates …... Now,

how much actually that gets taken up? I am not too sure about it, I haven’t seen a lot of it

used. It doesn’t get heavily promoted by HR … ... It is more up to the individual function

within business unit and it is more up to the manager who is seeing people leaving the

organization and how they want to handle that”

Interviewee 1 also described the situation in similar words as he stated that:

“Even though every company has a KM manager, the closest they get to execution

of knowledge management in a good way is communities of practice, but they don't take

this step further to individual knowledge capture”

Interviewee 15 also confirmed the absence of any knowledge retention activities

stating that:

“When an employee gives the notice to quit, the management team decides if he is

critically important and to keep him or not. In most cases, he is let go. There are no

formal knowledge retention procedures, and no active role is played by KM section”

There are a few programs by companies which are worth mentioning regarding

knowledge retention. These programs provide a hint that companies are concerned about

the aging workforce and taking measures to handle the situation. The retention of critical

knowledge (ROCK) is a formal program for capturing the knowledge of retirees in

company E. This whole process comprises of a number of steps as discussed below:

i) This process follows “Trigger a 55 policy” which means that when an employee

turns 55, provided his knowledge is valuable, the knowledge retention process

of the employee starts.

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ii) This process involves a range of debriefing sessions by departing expert with the

junior employees.

iii) The topics of these sessions are brainstormed with the audience to know which

topics might be of interest for the employees and what they want to know.

Accordingly, the topics are prioritized.

iv) The number of debriefing sessions is decided by the number of topics on which

the experts need to talk.

v) The employees from the company attend the session, and it is broadcasted as well

to other locations. The experts deliver the lecture on the topic and questions

are asked by the junior employees. The sessions are also video recorded and

later made available in the form of short videos and reports on the internal

portals of the company.

vi) Mind maps are created for the debriefing sessions of the experts. These mind

maps are further discussed with the experts and amendments made if required.

vii) When it is not possible to have a debriefing session with the departing employee,

the KM team works with the colleagues and concerned department of the

employee. Questions related to employee’s skills, working approach, peers,

etc. are inquired to form a knowledge base and identify the core capabilities of

the employee.

This whole process is running successfully at the company. Also, during the

restructuring of the organization, the team helps managers to decide which areas are

critical and which areas can have job cuts in case of budget shortfalls. Similar to above

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debriefing process, company Q also has a program which can be termed as “Knowledge

Retention from Experts”. It involves:

a. Identification of critical person based on scarcity of knowledge

b. Interview colleagues to find out the area of expertise and what they want to

learn from experts/retiree

c. Prepare theme for interview

d. Interview retiree

e. Extract knowledge nuggets

f. Develop a knowledge book

g. Ask expert/retiree to review

h. Publish to portal

i. Make announcement to all staff to access the published material

So, in this process, the interviews are conducted by KM team and there is no

interactive session of junior employees with retirees. The knowledge nuggets that are

extracted from the interview can then be accessed by employees through portals.

Company D also has a formal mentoring process which is on hold because of the oil

slump. As there are transitions and layoffs taking place and along with that, no more

inductions are taking place, thus the process has come to a halt. This process is called

“Horizon and Graduate” and in this program, the new graduates are attached to an expert

to learn the skills through on job training.

There are also initiatives in the companies to bring back retirees as lecturer and

consultant, but it is not a regular or formal process apart from company C which has a

special program called Total Professeurs Associés (TPA) for retirees to become lecturers.

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The company also introduced the official expert career path two years ago along with the

regular managerial path. For this expert career, knowledge transfer and networking are

essential parts. Moreover, these activities are also part of the annual performance

appraisal report. Same initiatives are also being exercised in company P. In company D;

there is a 2-year gap before a retiree can join back to make room for the next generation

to move up and maintain the hierarchical flow of the organization. Thus, from above

discussion, the findings are comparable to existing literature that companies have

traditional ways of knowledge transfer such as lessons learned databases, portals,

communities of practices, etc. (DeLong, 2004), however, these methods focus on long-

term knowledge transfer (Levy, 2011) and sharing but are not appropriate for knowledge

retention from the employees who are leaving within months or weeks. In some

companies, knowledge management is linked to HR department, whereas most of the

companies have proper KM teams and setups. When employees leave, the HR aspects

normally cover the reasons for leaving jobs rather than focusing on the knowledge to be

lost. If knowledge retention procedures can be embedded with HR exit processes (Calo,

2008), then it could be much more beneficial for the organization. In general,

organizations lack proper knowledge retention methods for departing employees

(Daghfous et al., 2013). Some super majors have well-structured programs such as

ROCK (retention of critical knowledge), Horizon and Graduate, “Knowledge Retention

from Experts”, bringing retirees as lecturers thus showing the concern of these companies

for retaining the knowledge of employees, however, these programs are not consistent

across the board. If we compare these programs of knowledge retention to the existing

literature, these can be related to the knowledge debriefing process explained by Hofer-

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Alfeis (2008), Leibowitz (2009), Jennex (2014), Cheung et al. (2007) and Durst and

Aggestam (2017). Hofer-Alfeis (2008) did not focus on brainstorming the topics with

audience, however, he focused on identifying the proficiency areas and targeted transfer

of codified knowledge, relationships knowledge and lessons learned from the experts to

the younger employees. Similarly, Cheung et al (2007) used systematic approach for

knowledge auditing in transport industry. The method is composed of 8 phases namely, i)

orientation and background study, ii) cultural assessment, iii) in-depth investigation, iv)

building knowledge inventory and knowledge mapping, v) knowledge network analysis

and social network analysis, vi) recommendation of knowledge management strategy, vii)

deploying KM tools and building collaborative culture, and viii) continuous knowledge

re-auditing. This approach is good to identify the critical knowledge areas within the

organizations however, it needs to be performed periodically and moreover, it cannot be

used for the departing employees at the last moment because it is time taking. However,

if this is performed on regular basis, it can greatly help in identifying the knowledge to be

captured from the departing employee. Jennex (2014) used a scoring method to identify

the criticality of employees using three criteria i) likelihood of knowledge loss, ii)

consequence of knowledge loss and, iii) quality of knowledge loss. However, the scoring

is much subjective and not a rigorous method. Moreover, it is an initial step before

knowledge retention. Most of the managers are already aware of the key people who are

going to leave. Thus, the ROCK session seems much more authentic in this case in terms

of following a systematic procedure for retention of critical knowledge.

Because of this inconsistency, knowledge retention, most of the time is

performed on an ad-hoc basis (Leibowitz, 2009). There have been indications of bringing

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retirees as consultants and mentors in few companies, but it is also not across the board.

Moreover, companies need to let their employees go for retirement in order to maintain a

general work pattern of the organizations. Bringing back retirees might be expensive but

oil and gas companies, when they are doing well, can expand their workforce and hire

more people (Inkpen and Moffett, 2011) and rehire or bring important persons back as

consultants. In order to avoid bringing back retirees and reduce the costs, the companies,

however, should try to capture and transfer the knowledge well in advance before the

retiree leaves the company for example as done in ROCK process by company E. The

above discussion thus leads to the second proposition:

P2: Although oil and gas companies have traditional ways of knowledge transfer,

there is negligence on the part of most companies with well-established and structured

knowledge retention programs for departing employees.

4.2.1 Mentoring and Communities of Practices Programs by far the Best Way of

Retaining Knowledge from Experts

The interviewees were also inquired about the best way of retaining the

knowledge from experts. Mentoring and Cops were considered as the best way although

mentoring was a more preferred way among the interviewees. Interviewee 2 was of the

view that;

“Mentoring is the best way if companies have time to do so"

He worked on a project aimed at providing real time expert advice and solution to

the people in the field. The project was called “In Touch” in which the key experts in the

company were moved to 15 centers across the world. The job of these experts was to be a

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mentor and point of contact for knowledge sharing with the people in the field. This

program is running successfully at the company and a lot of oil and gas companies tried

to adopt this, however, there wasn’t much success due to inconsistent approach.

Interviewee 1 opined a similar idea and supported virtual peer assist, in which the field

people consult with experts at different locations to perform a review of the operations

before an important decision is made. Interviewee 9 supported mentoring stating that,

“I think those who reach 60, they should be moved to training and advisory

departments so that they continue to do workshops and train others……. For example, in

an oil company, you might have 7-8 fields, each field works separately. People who are

managing (this) field are different from the other field. So, if you have those

(experienced) people and you let them move in between the fields to provide advice, they

are going to gain more knowledge about the problems that are happening in different

areas and they are going to utilize this knowledge to teach others about these lessons

learnt"

The interviewees from the companies M and O in the UK supported mentoring

and suggested the use of lunch and learn sessions in which junior employees could

interact with experts in a non-formal way. Results further revealed that face to face

interaction is best during on job training and mentoring where junior employees can try to

discuss real field scenarios with the experts where they have a context to solve the

problem. This will help the senior workers to recall the important events in field work

and share their good and bad experiences as interviewee 6 stated that:

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“The best way is of course face to face. You got to put them in a situation. You

have got to ask them to share their knowledge and their experience on the matter. You

have got to sit with them and listen to them. That is the best way”

Interviewee 1 emphasized the use of communities of practices and said that

experts normally receive naive questions from first hires, and thus companies should

focus on transferring top 20% of expert elements to the experts who are one level down.

According to interviewee 3, use of social networks and Cops is the best way to connect to

experts for gaining knowledge. Interviewee 9 stated that communities of practices if

managed properly could be very useful especially in oil and gas sector as people are

dispersed across the globe and thus they need to communicate through some common

forum to get answers to their questions, some of which will come from different time

zones.

Interviewee 5 stated the usefulness of Cops as:

“We have got hundreds of active communities making huge savings for the

company. If we see a nice conversation, we provide bonuses to the employees”

Above discussion reveals that under normal circumstances, when layoffs are not

happening, the best way is to assign experienced people the roles of coaches, mentors or

experts (Karkoulian et al., 2008a) who are able to guide and teach young employees.

Through mentoring, the deep tacit knowledge possessed by experienced employees is

explored when they are asked questions in a specific context (Snowden, 2002). This is

challenging because of high mobility of the workforce (Inkpen and Moffett, 2011) in the

oil and gas sector and also because of time availability. Mentoring can work best when

people are specifically assigned to these roles and they are solely dedicated to training

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their juniors and the younger generation. Thus, the process should be started well ahead

of the retirement of senior employees. For this reason, communities of practices (Cops)

seem to be a more reasonable and practical solution. The experienced employees need to

play a proactive role in communities of practices (Wenger et al., 2002) for these to work

effectively. This is also supported by the fact that oil and gas companies are spread across

different geographical locations and thus they need some common forums for

communicating with people dispersed across the globe. As people progress through their

careers, they move into higher positions such as managers or senior executives. Thus,

they spend less time in the field and more in the office. As their interaction with people in

the field is primarily virtual, therefore, usage of Cops under such scenarios is the best

way (Scarso et al., 2009). Now there are challenges in these communities of practices as

well. Sometimes, experts are asked naive questions which might affect the purpose of

Cops and cause participants to lose interest. However, it is hard to define some criteria

for posing the questions on these forums. Companies thus need to make sure employees

are provided with basic knowledge through training sessions. Based on above discussion,

it is proposed that:

P3. Cops and mentoring, if executed properly, seem to be the best way of

knowledge retention in the oil and gas sector.

4.3 Barriers and Challenges Regarding Knowledge Retention

The results unveil a significant number of barriers and challenges in terms of

knowledge retention from departing employees. The coding tables and network view for

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this category are provided in Appendix A. In total; there were 35 codes which were then

further grouped into subcategories according to their relevance.

4.3.1 Oil Prices and Budget Constraints

Lack of central funding and budget constraints are a big barrier for knowledge

retention activities. This point has been thoroughly discussed in the above sections during

the discussion on the impact of oil prices and non-consistent KR activities. Here it has

been mentioned again to include in the list of main barriers regarding knowledge

retention.

4.3.2 Multi-Perspectivity of Knowledge Hoarding

Multiple perspectives on knowledge hoarding were discovered during the

interviews. Some of these already mentioned in literature while some new factors also

emerged. During the layoffs and termination of employees, the knowledge retention

cannot be performed because there is short notice for the company to take any measures

and moreover, employees are mostly not willing to share any knowledge. The other

perspective of this knowledge hoarding is the classical “knowledge is power” as revealed

by most of the employees. Although the majority of senior workers like to share still

there are people who think knowledge is their job security, and if they share this, they can

be replaced. Interviewee 15 described this as:

“Some people consider knowledge is power and if one shares knowledge, you lose

your value to the company. Hence your job is at risk especially when most engineering

jobs are contract based these days”

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Interviewee 5 describes this as:

“I think It is a bit of mix. Certain cultures will share. Certain cultures will not

share. Within the Dutch culture where I am working now, you get people who are open to

sharing whereas some people are not. There is a spectrum of people and that I have seen

in every country”

The trust element of the older generation is also different as they are cautious to

take the risk and need more face to face interaction in developing the relationship. As

compared to that, younger generation tends to trust and get involved with each other

faster through social networks interaction. In the Middle East, the situation is more

interesting as the majority of subject matter experts are expatriates. They consider their

knowledge as their job security, and if they share, they can be replaced by local

workforce, but this is not true for all cases. The results further revealed that in the case of

old workers, in general, it is same, but people from developed countries are more open in

sharing knowledge as compared to people in developed countries as depicted by

interviewee 6:

“When it comes to people of these ages, it is more or less the same but when it

comes to culture, yes, I would give an edge to the people who are from Europe. They are

relatively more willing to share the knowledge compared to people in our areas, but

again, I mean, generally, the trend is same”

Interviewee 5 who is the KM lead at company E, revealed some interesting

information about Japanese colleagues stating that:

“Japanese don't feel like they can ask a question from other person …. their

culture demands if they are senior engineers, they should know how to do it”

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The interviewee further mentioned that it is a generic issue within the Japanese

culture for the workers to admit that they do not know something related to their job. So,

they had a hard time to deal with this problem and managed this using ghost writers to

share questions from these colleagues. It was a long time ago, but still, they are not able

to overcome this cultural barrier.

4.3.3 Opportunities to Learn

Time for knowledge sharing is a big challenge. Time isn’t always enough

especially when there is work pressure on the employees during the recession when the

majority of employees are tossed out. Interviewee 13 explained this as:

“When times are hard, people are less inclined to share. This can be seen in

cutbacks of lessons learning sessions and the overhead cost of those who would gather

such information”

Also, knowledge sharing depends on the personality and context, and it is all

about the ability to conversate and engage in conversations. Thus, it is subject to the

craving of younger employees to learn and ask questions. Interviewee 6 described this

as:

“The nature of the people of these ages (baby boomers) is that they generally

don't share knowledge on their own. You have got to put some effort into that ……. It

depends on the interpersonal skills of the people to take the knowledge from them. If you

generally get them involved in stories or you ask them to share their experience on

certain thigs, then they try to open up and share the knowledge on that subject, but of

course, it is an art to get knowledge from them”

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Moreover, some people are natural teachers and mentors, and there are personal

drivers i.e. some people enjoy sharing while some do not. Interviewee 1 explained this as:

“It almost depends on the personality and role of the employee. Some companies

define their SMEs (subject matter experts) ……. They take one person and put him on a

very high leadership position and they are placed as a discipline lead and an example

and their main propose is to share knowledge throughout the organization…… They are

respected, they are active, they are social, and they know what they are talking about”

Results further indicate that incentives do play a role in such activities.

Recognition is the best incentive that fosters knowledge sharing. Older employees are

more than happy to share knowledge when their efforts are acknowledged. For example,

company E acknowledges the knowledge sharing efforts of an employee through a

personal letter from a senior manager at a level higher than employee's boss. This

recognition is more valuable to the employee’s career than any other thing. Some

companies also include knowledge sharing as a performance indicator in performance

reports, and an important incentive for promotion for example company C, D and O.

Interviewee 16 from company O in the USA described this as:

“If you are a technical person and you want to be promoted on the technical

career ladder, you have to clearly be sharing your technical knowledge”

4.3.4 Multicultural Environments and Language are Small Barriers

Multicultural environments and language do not seem to a barrier in oil and gas

companies. Most of the interviewees agreed that it is very common in oil and gas for

people from different cultures and background to work together and there is a lot of

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mobility in this sector. Employees keep on rotating on different projects across the globe

and English is used as a common medium for communication. Interviewee 6 described

this as:

“It’s a kind of different world. I mean here we have people from Germany,

Canada, US, from Arab countries, from Egypt, Hungry, so, I mean, we have all sort of

people working with us, and working style is such that when it comes to rig side,

everybody tries to mingle in and generally it’s not a problem …… Yes, language barriers

do come in on certain occasions. But, I mean, the kind of industry we have, we have

developed a way to interact with people”

Interviewee 2 also shared the similar thoughts as he said:

“Most people I know work overseas for part of their careers. I haven’t heard

language being the main barrier. An engineer or a professional manager from a different

country generally knows how to speak English. There has not been a big cultural issue. I

worked with Iraqis, Arabs, Chinese, and Egyptians and I think we were generally on the

same page, although there was some difference in approaches”

Thus, the language which was initially thought to be a barrier (Riege, 2005) is not

a big barrier in this sector. A few of the interviewees mentioned that this could be a bit of

barrier but overall, if employee has international exposure and has worked in different

environments which is common in multinational companies (Klitmøller and Lauring,

2013), language and culture don’t seem to be a problem. English is spoken and

understood by almost all employees. Thus, a shared language enhances communication

and promotes knowledge exchange (Welch and Welch, 2008). The mobility of employees

plays a key role in this regard, and it also helps in enhancing the learning and sharing

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experience through interaction with people from different backgrounds. The findings

related to knowledge sharing are in line with previous studies that sharing of knowledge

is a characteristic thing (Riege, 2005), some people like to share while some do not

(Martins and Meyer, 2012). It is related to the fear factor or confidence of the

employees. People who are comfortable in interacting with other people, knowing more,

perform better, and execute faster, have no problem in sharing their knowledge. On the

other hand, people who think sharing is the only thing between their job and getting laid

off will never share (Seidler-de Alwis and Hartmann, 2008, Ranjbarfard et al., 2014).

Further, some people have natural ability to teach and be mentors (Martins and Meyer,

2012) although this percentage tends to be very small. Companies need to identify such

people and provide them with opportunities to disseminate knowledge. The fear factor or

job security is a bigger barrier in developing countries as compared to developed

countries. It is because of the prevailing culture, and more importantly, job security is of

more importance in developing countries (Marshall and Van Adams, 2016) because if

you lose your job, there is no support from the government. Further, there are few

opportunities and job market is very competitive (Beechler and Woodward, 2009). Thus,

employees in developed countries tend to be more open towards knowledge sharing as

the culture of trust, openness, and friendliness is more prevalent there. Similarly as

mentioned in previous studies, incentives should be maintained for knowledge sharing

(Bock et al., 2005). If employees are asked to do it as part of their regular jobs or after

hours, sharing won't happen effectively. Incentives play a key role in motivating the

employees and success for knowledge (Ajmal et al., 2010, Ranjbarfard et al., 2014).

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4.3.5 Retention of Critical Knowledge is Challenging

Results show that it is not easy to capture the critical knowledge of employees.

There are a number of factors that play their part in this process and make it a challenge.

Most of the time, it is challenging to know what to capture from the retirees and what to

ask them. It is not possible to codify all the knowledge of these experts as according to

interviewee 5,

“It is very hard to write everything down ......The problem is figuring out what is

critical. You really don’t know what’s going to be critical for future…. We would never

envision the oil prices will shoot to 150$ and we can never envision, the oil prices will

fall down to 20$ so quickly. All the scenarios as such we are not predicting. Nobody can

predict the oil prices, and it’s the market place and political and geo-political influences

going as well…... There are some areas on which just talking is not enough, you need to

provide some more detailed documentation…...Getting somebody to write the full

description of how something is done just before the retirement is quite hard”

Even when the interview videos are captured, these need to be cut down into

smaller versions for the employees to view. As an interview can last for an hour or two or

even more, thus it is important to extract the important points or “knowledge nuggets”

from the videos. Also, it is important to understand that not all the past knowledge is

critical. It means companies need to understand what to capture from the departing

employee. Thus, identification of critical knowledge of employees (Joe et al., 2013) is a

major concern which needs to be achieved through proper assessment of employees.

However, results show that there are no knowledge loss assessment processes for the

employees.

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4.4 Larger impact of knowledge loss on upstream sector as compared

to downstream and midstream sectors

This category focused on sector comparison. There were total 11 codes in this

category. The coding table and the network view for this category are provided in

Appendix A. There was a consensus among interviewees that upstream sector is the one

that is explicitly losing people. Results indicated that the downstream and midstream

sector of the companies are working fine and not losing workers. There are certain

downstream activities that are very challenging and knowledge intensive, but they are

operating fine right now. However, in the upstream sector, this is not the case as

interviewee 10 stated:

“I think it’s (knowledge loss due to layoffs and early retirements) more in

upstream. I mean, in downstream, when you get things sort of set up, it is pretty straight

forward. I would say it is happening, but it is on a smaller scale”

Moreover, interviewee 5 draws attention toward the challenges of upstream sector

as he mentioned that:

“I think what we see at the moment, because of low oil prices, the upstream is

suffering and downstream is doing well….so, at the moment, businesses not doing well in

the upstream where we are making the biggest reductions in staff members …...but the

baby booming problem is across the board. There are number of people who are opting

to leave the organization voluntarily, and that’s equally across upstream and

downstream sector”

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Also, the upstream sector is a knowledge intensive sector as interviewee four

described that the key areas where the companies don't want to lose knowledge are

explorations, drilling, and earth sciences especially reservoir management predominantly

as this is the core business. Thus, it is clear that upstream sector is the largest sector

where majority of the operations such as drilling and exploration are performed, and thus

it has a high number of employees (Gould et al., 2007). These operations are very

expensive, and thus in the case of low oil prices, it is not profitable to go for such

operations. As the oil prices fall, the operations stop in the upstream sector for cost

saving purposes, therefore the employees working there have no more work to do, and

they are made redundant (Sampath and Robinson, 2005, Green and Jackson, 2015,

Labban, 2014). Thus, this sector is the first one to see the impact of low oil prices.

Moreover, the expertise required for this sector are also very critical (Hjelle, 2012)

especially when companies need to explore in frontier locations (especially deep waters)

posing higher environmental risks (Grant, 2013). Thus, it is proposed that:

P4. The aging workforce issue is same across all sectors; however, in recent

years, the upstream sector has suffered greater knowledge loss because of layoffs and

early retirements of employees.

4.5 Dynamics of Different Geographical Locations

This section will draw a comparison among the companies regarding developing

and developed countries. The aspects of aging workforce, knowledge retention, and talent

shortage will be compared here.

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In developing countries such as Pakistan, India, Indonesia, Thailand, UAE and

Nigeria, the aging workforce does not seem to be a big problem as most of the employees

working there are either young or middle aged. However, there is an issue of the

knowledge gap between employees in government organizations. Interviewee 13

mentioned that because of oil slump in the 1980s, hiring was reduced and people were

laid off. The organizations are experiencing that gap now. Also because of strict

recruitment policies, the employees who replace a senior worker are quite young and far

less experienced than their predecessor. Further results indicate the knowledge

management is at an early stage in developing countries and there are either few or no

measures at all in the organizations. Thus, there are no knowledge retention activities for

people who are laid off or who get retired. Organizations lack the understanding of

knowledge loss from departing employees.

On the contrary, in developed countries, proper knowledge management practices

exist within the organizations and KM activities are quite streamlined as evident from the

results. However, less attention has been paid to knowledge retention and especially to

the knowledge lost through retirements. There are only a few organizations that have

formal procedures in practice for retaining the knowledge of departing employees. Rest

of the organizations take no measures or leave it to the managers if they want to do some

knowledge capture from their leaving employee. Aging workforce is a dominant issue in

developed countries as interviewee 20 stated that:

“This (aging workforce) is the problem of other areas like Europe and probably

United States, Canada and Australia, probably the same”

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Shrinking workforce is another issue in developed countries causing knowledge

gaps. Low fertility rates in developed countries is a major reason for this (Beechler and

Woodward, 2009). The new cohort called baby-bust generation is smaller by 16 % than

baby boomers, thus the broader problem is not only the availability of workers but also

the reduction in population growth in developed countries (Burmeister and Rooney,

2015, Stevens, 2010). According to responses from the interviewees, fewer people are

joining oil and gas these days as interviewee 5 stated that:

“Twenty years ago, we had some 2000 chemical engineers graduate yearly and in

2009, that was down to 240. So, it just isn’t popular and seem to be not going green as it

should be”

According to interviewee 19 from the UK:

“Issue is now we have got two demographic time bombs. Aging workforce and not

enough people coming through the other end”

Interviewee 1 was of the view that currently as companies are not hiring, not

many people required but in future, as the prices go high, it might be challenging as many

young people do not look oil and gas as their preferred industry. They are more

environmentally focused and prefer other industries. There are also concerns about job

stability as interviewee 21 stated that:

“IOCs and large service companies (and some NOCs) tend to be very good at

developing and recruiting new talent out of university, but they often then find it difficult

to retain it for long. Repeated down cycles over recent decades, in which many leave the

industry, have resulted in the dearth of talent in the mid/senior space- which is a well-

acknowledged issue now”

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These down cycles result in layoffs and contract based jobs causing future

apprehensions for young employees (Inkpen and Moffett, 2011). The perception of oil

and gas is also not good as in the past years, there have been spills and leakage incidents

(Morgan et al., 2014) in oil and gas sector. In order to handle such issues, company E has

based its centers in India and China for manpower and talent acquisition. The company

has a big recruitment section in India historically shifting recruitment away from West.

Now more people from India, China, and Far Eastern countries are joining the company.

The participant from company E further mentioned that there is too much competition for

talent these days and the big recruitment companies are also after talent. At the moment,

because of oil slump, companies are doing minimal or no recruitment. However, talent

shortage will be apparent in future after this down cycle. Company C also has a close

collaboration with the universities as they have a shortage of young graduates. The

company is now providing grants to universities and initiating joint programs with

scholarships to hire students to graduate and get recruited at the company. They also

arrange seminars in universities to promote the image of oil and gas sector as interviewee

from company C stated that:

“What is really worrying, it’s O&G image for young generation. For them, it

looks like business responsible for pollution and global warming. It looks not so “sexy”.

X’s (company name) strong commitment to responsible energy is a key point to improve

the image. Also, we sponsor educational programs and conduct free-to-attend

seminars/webinars to promote petrotechnical disciplines”

In company Q, there is a problem of recruitment in geosciences. New graduates

are not coming in this field as there are not many programs offered by universities in this

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field and thus fewer graduates. They have also initiated a program at local universities to

attract new talent and fill this shortage. These statements show that companies need to

realize the job related future apprehensions of the younger generation (Inkpen and

Moffett, 2011), and take timely and active measures regarding offering more job stability

to workers (Beechler and Woodward, 2009). The shrinking workforce indirectly puts

more pressure on the companies to retain and preserve the departing employees and their

knowledge. Similarly, for hiring new graduates, there is a need for more collaboration

with universities. Also, seminars and job fairs need to be conducted on a regular basis to

attract new talent in this sector. The above discussion leads to the proposition:

P5. The knowledge retention issue due to the aging workforce and talent crisis is more

acute in developed countries as compared to developing countries.

The situation of Middle East is also very interesting. Although there were only 5

interviewees to talk about the situation in the Middle East, some interesting and worth

mentioning information was revealed. The knowledge management practices are not very

well established in this area and the environmental conditions and cultural influences

impact the knowledge dissemination (Mohamed et al., 2008). The aging workforce is not

an issue in this region, but layoffs are taking place and no work being done in terms of

knowledge retention. The Middle East is a rich oil country thus a lot of super majors in

this area and working as shareholders with local companies. This region also has a lot of

expatriates working here (Smith et al., 2007). These expatriates are at key positions and

are hired because of their expertise. Knowledge of these expatriates is a kind of job

security for them and thus not much sharing of knowledge takes place as according to

interviewee 21 from Saudi Arabia:

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“In these regions, companies are still buying in large numbers of expats to fill key

knowledge gaps, or bring in new capabilities altogether that do not exist in company yet.

These expats tend to be underused, not operating to their full capacity, and their function

is in part to disseminate knowledge and upskill the local workforce. In theory, at least. In

practice, this is rarely done well and leads to disengagement on both sides, and short

tenures”

Companies in the Middle East are also changing shareholders from West to East.

China is buying a lot of assets in the Middle East as China is energy hungry. Due to oil

slump, also there was a cut in the share of super majors and China’s share increased by

buying the assets left by super majors. Finally, as the standard of education is rising in the

Middle East, the companies in the Middle East want the local workforce into jobs

(Metcalfe and Murfin, 2012). Also, because of the down cycles of oil, the expatriates

prove to be expensive for them. These factors are quite interesting to be explored further

as the current study had a limited number of interviewees from the Middle East due to

limited availability of resource and time. The above arguments can be further

strengthened by conducting more interviews from this region.

This concludes the first study as the answers to the inquired research questions

have been described concisely. The chapter started with stating the research questions.

Then the results were described and their analysis performed in the light of existing

literature. First, the aging workforce situation was discussed, followed by results and

discussion on KR strategies of the companies. After this, the main barriers and challenges

regarding knowledge retention were explored and finally, a comparison was made among

companies from different geographical locations. The next section will focus on the new

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ideas that emerged during data collection and intrigued the researcher to further explore

this knowledge retention area with new objectives.

4.6 New Ideas that Emerged from the Study

The beauty of grounded theory approach lies in the fact that when researchers

stick close to data, they get some interesting ideas to explore further. From the responses

of interviewees on different challenges, some new idea emerged to extend further and

explore the knowledge retention phenomena. These ideas formed the basis for conducting

two more studies entailed in chapter 5 and chapter 6. The context and development of

ideas for these studies will be explained in next sections.

4.6.1 Dominant Knowledge Loss Factors and Types of Knowledge Lost

Results show that it is not easy to capture the critical knowledge of employees.

The inconsistency of the knowledge retention procedures as discussed in section 2

appears to be a major challenge. Also, it is challenging to know what to capture from the

retirees and what to ask them as there are no knowledge loss assessment procedures to

assess what to capture from the employee. Some interviewees mentioned that managers

of the employees naturally think about it and have an idea about the knowledge of the

employee but agreed that a structured and systematic approach is always better to know

the critical knowledge areas. Interviewee 1 stated that an employee works through

different positions and in different areas in oil and gas and thus he might possess different

expertise and skills (Joe et al., 2013). Therefore, it is important to break down the job

history of the employee according to different skills he possesses. Accordingly, suitable

replacements matching with those skills areas need to be made.

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These results provided an interesting idea to researchers for further study on the critical

areas of knowledge loss when employees leave. If companies can understand the major

factors of knowledge loss and what to capture from employees, knowledge retention can

be performed in a better way. A literature review has further revealed the dearth of

knowledge on these topics, which provided the foundation to conduct another series of

interviews to explore the critical areas of knowledge loss when employees depart and the

major factors of knowledge loss apart from retirements and layoffs explored in the

previous study.

4.6.2 Connecting Big Data with Knowledge Management

The efficient use of advanced technology is equally challenging in the oil and gas

sector. Among these technologies, big data is a disruptive technology and has huge

potential to contribute towards enhanced performance of the companies. The fast pace of

technology has paved the way for efficient monitoring of the operations through the use

of sophisticated sensors and equipment. These sensors can generate a variety of

voluminous data related to the operations such as reservoir management, drilling, etc. and

can be used to enhance the efficiency of operations largely. Also, on the other hand, there

is the challenge of protecting this data not to fall in the hands of competitors. In relation

to big data, during the 3rd interview, the participant from company J made an interesting

statement that companies need to pay attention to the new trends such as big data for

efficient production from the oil reserves. As the oil prices caused operations to close and

laying off employees, new technologies such as big data can play their part here. This

idea was fascinating and raised some questions in the minds of the researchers. The first

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question was if there is a relationship between big data and knowledge management? Can

big data play any part in knowledge retention and cater for loss of knowledge from staff?

These questions intrigued the researcher to further explore the area of big data from the

perspective of knowledge management. Thus, another series of interviews were

conducted to understand this relationship between big data and knowledge management.

Big data is a new field and literature review further revealed that not much work has been

done on the linkage of big data and KM.

Thus, these two new ideas emerging as an extension of the previous study on

knowledge retention in oil and gas industry, will be further explored in next chapters.

Chapter 5 covers how to better perform knowledge retention by determining the critical

areas of knowledge loss for departing employees and the dominant likelihood factors.

Chapter 6 focuses on the connection between big data and KM and provides some

interesting insights whether experts knowledge can be compensated through big data or

not.

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CHAPTER 5. LIKELIHOOD FACTORS AND TYPES

OF KNOWLEDGE LOST WHEN EMPLOYEES

DEPART

5.1 Introduction

Organizations sometimes fail to capitalize on the benefits of intellectual capital as

they don’t realize its importance (Massingham, 2008). According to Ulrich (1998), the

most important rule is to preserve knowledge which requires a proper vision and strategy.

Retaining and successfully identifying valuable industry and company knowledge can be

very challenging for organizations (Durst and Wilhelm, 2013, Massingham, 2008, Bender

and Fish, 2000). Organizations can lose their competitive advantage through the loss of

knowledge workers. NASA lost the knowledge about moon exploration as the majority of

their employees retired or dead (Jennex, 2014). The changing workforce demographics

have raised concerns among companies to retain their valuable knowledge workers (Ball

and Gotsill, 2011). Organizations need to assess the impact of this demographic trend on

each business unit, location, and job function (Strack et al., 2008). In this regard, it is

important to identify the critical areas of knowledge loss in the companies. Determining

the key "at risk" knowledge is a significant step towards the knowledge retention process

(Leibowitz, 2009). When critical knowledge workers leave the organization, they can

take the knowledge that is vital for the organization along with them. The literature

reveals that there aren’t any systematic procedures in the organizations to assess the

knowledge of departing employees (Jennex, 2014). Moreover, the departing employees

might possess different types of knowledge related to the organization; however, the

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research on this subject area is scanty (Joe et al., 2013). Identifying the major causes of

knowledge loss and types of knowledge possessed by employees is of vital importance as

organizations can then take measures to retain that knowledge.

Thus, this chapter will further extend the knowledge retention study and will focus on

identifying the major factors of knowledge loss in oil and gas industry and further

determine the different types of knowledge possessed by departing employees. Based on

the results, a process for assessing knowledge loss is proposed.

5.2 Assessing Knowledge Loss

In organizations, the manager should be responsible for identifying the at-risk

positions and then devise a plan for succession planning focusing on the identification of

the successor and facilitating the transfer of knowledge from the incumbent workers to

the successor (Calo, 2008). Employees leave for various reasons such as job change,

retirement, disability, etc. (Martins and Meyer, 2012). These employees gain a wealth of

experience and knowledge when working for a long time in a particular industry. When

these employees intend to leave, a key point for the organizations is to capture, share and

apply their knowledge in a best possible way to foster knowledge creation and innovation

(Leibowitz, 2009). Massingham (2008) builds on this IC theory and studies the impact of

knowledge loss when critical employees leave. He states that due to the departure of an

employee, an organization loses human capital knowledge, relational capital knowledge,

structural capital knowledge, and social capital knowledge. Loss of human capital

knowledge results in decreased organizational output and productivity. Loss of social

capital knowledge reduces the organizational memory. Loss of structural capital

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knowledge diminishes organizational learning, and finally, loss of relational capital

knowledge may produce disrupted external knowledge flows. Thus, it implies that

organizations might lose business opportunities and have a fall in the revenue when

critical employees leave (Leibowitz, 2009, Daghfous et al., 2013) and along with that,

organizations might also suffer from lower productivity and workflow disruptions

(Burmeister and Rooney, 2015, Martins and Meyer, 2012).

Jennex (2014) defines the risk of knowledge loss as the possible impact on the

organization regarding efficiency and productivity due to loss of an expert or knowledge

worker. To alleviate this risk, there is need to have a proper assessment of knowledge

loss to understand the criticality of an employee’s knowledge. Literature review reveals

that only a few studies have been conducted on the evaluation of knowledge loss. The

nuclear industry was the first to take initiatives on assessment of knowledge loss as the

industry has been confronted with a looming issue of an aging workforce. International

Atomic Energy Agency (IAEA) published a guide "Risk Management of Knowledge

Loss in Nuclear Industry Organizations" (Kosilov et al., 2006) to reduce the issue of

knowledge loss. The criticality of the loss of knowledge due to the departure of the

employee is determined from 2 factors namely; time until retirement and position

criticality. Position criticality entails if the knowledge required for that position is critical

and if there are any suitable replacements available for that position. The same process

has also been successfully implemented at 2 energy companies in US-Tennessee Value

Authority and Duke Energy. This process, however, only focuses on the retirees. Based

on the work of Kosilov et al. (2006), Jafari et al. (2011) developed a more comprehensive

model. They developed a model for knowledge loss risk management targeting the

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project-based organizations. This model incorporates PMBOK risk management

approach and Kosilov et al.’s (2006) knowledge risk assessment framework. They first

used the method by Kosilov et al. (2006) to determine the risk of knowledge loss from

departing employees. Then, using the expert’s opinion, they rated these risk factors

according to their impact on project cost, quality, and duration. Another contribution was

from Durst and Wilhelm (2013) who worked on a process for assessment of knowledge

loss in an SME. They developed an instrument for SMEs facing knowledge attrition

issue. They used four dimensions of intellectual capital i.e. human capital, relational

capital, structural capital, and social capital. A percentage criterion was further used to

estimate the score for a departing employee. The percentages were assigned as 30%

human capital, 30% relational capital, 30% social capital and 10% structural capital.

Based on this, a score can be determined for an individual. However, there are no fixed

criteria for these percentages and the percentages of each dimension need to be

determined by each SME individually. The next main contribution was from Jennex

(2014), who extended the work of Kosilov et al. (2006). He used the resource-based view

of the firm in his model stating that knowledge management (KM) tends to use the

resource-based view of the organization (Wernerfelt, 1995) with knowledge as the

resource and KM as the process used to manage this resource. Viewing knowledge as a

resource makes KM an appropriate tool to capture and retain knowledge from departing

individuals and making it available to the rest of the organization. Jennex used a formula

for knowledge risk assessment using traditional risk approach. This model of Jennex

(2014) uses three factors namely likelihood, consequence, and quality of knowledge loss

to determine the risk of knowledge loss. Likelihood of knowledge loss entails the reason

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for the departure of an employee such as retirement, turnover, job rotation, etc. The 2nd

factor, the consequence of knowledge loss, checks the skills profile of departing

employee and then checks how many replacements are available for those skills set of

departing employee. Finally, quality of knowledge loss involves the factors such as time

remaining in the departure of an employee, willingness to share knowledge, etc. The

concerned managers of the employee need to assign a score for each factor and based on

that score, the knowledge criticality of the employee is determined.

5.3 Likelihood Factors and Types of knowledge Lost

Above are the few studies conducted on assessment of knowledge loss and it is

evident that the research in this area is still in its infancy. Especially in the studies

mentioned above, little attention has been paid to understand the most significant

likelihood factors of knowledge loss within the organizations. Employees leave the

organization because of different factors such as retirement, job change, disability, death,

etc. (Daghfous et al., 2013). The likelihood factors regarding knowledge loss might vary

for each industry depending on the business performance, economic situation, and the

geographical location of that industry. If organizations want to prepare well in advance

for succession planning, it is important to know the dominant likelihood factors which

could lead to knowledge loss.

Oil and gas sector has a unique place among the multinational corporations as

most industries are running because of oil and gas. Oil and gas industry is considered to

be the pioneer in the area of knowledge management and other organizations used them

as examples in term of KM initiatives. However as discussed in previous chapter along

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with a number of studies (McKenna et al., 2006, Grant, 2013, Shuen et al., 2014, Inkpen

and Moffett, 2011, Sumbal et al., 2017b, Sampath and Robinson, 2005, Gould et al.,

2007) which indicate that the aging workforce issue hasn’t been handled well in this

sector. Because of considerably high percentage of aging workers, retirements might be

of major concern for oil and gas and an important likelihood factor for knowledge loss in

oil and gas sector. Turnover is another factor of knowledge loss and can be categorized as

voluntary and involuntary turnover (Price, 1977). Voluntary turnover is, when an

employee himself switches to some other company and resigns, whereas involuntary

turnover is linked to organizations choosing to dismiss the employees (Abbasi and

Hollman, 2000). There are two other categories of turnover as well; functional and

dysfunctional turnover (McEvoy and Cascio, 1987). In dysfunctional turnover,

organizations fail to retain the critical or “star” employees whereas in functional turnover

is linked to shedding the poor performers (Dalton et al., 1981). There is a negative

relationship between job turn-over and individual performance of the employees

(Jackofsky, 1984). Employees with god performance will stay in company longer (Wells

and Muchinsky, 1985) unless there is some redundancy carried out because of the

financial issues within the organizations. Generally, employee’s proficiency has positive

effect on their work motivation and self-confidence (Dreher, 1982) and thus lead to job

stability and less turnover. Moreover, organizations face the issue of bearing both direct

and indirect expenses associated with turnover. The direct expenses involve new

recruitment, training, and exit interviews of departing employees whereas indirect

expenses involve decreased employee motivation and loss of valuable organizational

knowledge (Frank et al., 2004, O'Connell and Kung, 2007). Thus, this could be an

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important factor in terms of knowledge loss in the oil and gas sector. Job rotation is

another important factor mentioned in literature. In terms of multinational corporations,

there are numerous hierarchical level with many specialists which often leads to

decreased freedom and flexibility (Brunold and Durst, 2012). Hence, coordination is

much more complex as compared to local companies. Also, there is heavy movement of

employees across globe and job rotation is quite particular to this industry. Job rotation is

defined as “lateral transfer of employees among a number of different positions and task

within jobs, where each requires different skills and responsibilities” (Brunold and Durst,

2012, P.182). Job rotation helps employees learn, become more knowledgeable and

versatile through exposure of working at various geographical locations (Eriksson and

Ortega, 2006) in case of MNCs. It might also be used as a preparation to hold top

management positions (Huang, 1999). However, there are also risks associated with job

rotation if the predecessor’s knowledge is not well documented before departing and the

newcomer has to struggle finding the right knowledge to fulfill the tasks efficiently

(Brunold and Durst, 2012). Thus, it might be interesting to explore job rotation as a factor

contributing to knowledge loss in the oil and gas sector.

In order to combine the internal and external knowledge, firms especially

multinational corporations rely on various sources (Ferraris et al., 2017) for example

competitors, customers, databases, suppliers, cops etc. Oil and gas companies usually

have various multi-million-dollar projects going on in various parts of the world. In order

to avoid any losses and remain competitive at the same time, these companies need to

make highly informed decisions regarding the execution of these projects. In such

decisions, expert insights from the people with right experience, are of utmost

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importance. Most of these people are senior workers in the oil and gas industry. Tsang et

al. (2016) stated that knowledge leakage occurs when an employee leaves the

organizations and this risk of knowledge loss can impact the organizations in terms of

both financial and non-financial losses. Thus, such types of risk related to the intellectual

capital should be managed properly to ensure the competitiveness and sustainability

within the organizations. In the process of succession planning, the aim of the

organizations should not be on finding a suitable successor but they also need to focus on

the retention and transfer of the critical knowledge of the departing employee (Durst and

Aggestam, 2017). Ward and Wooler (2010) argue that organizations should stay in touch

with these alumni as they might be needed in future for helping out the organizations in

important matters however, it requires time and effort. As such, these workers possess “a

substantial volume of tacit knowledge, operational heuristics, stores and organizational

history” (Jackson, 2010, P.908). Parise et al. (2006) found that organizations do not focus

on the complete skill set of the employees and only manage to capture small portion of

the individual’s knowledge (Jennex, 2014). It is important to know the complete skill set

of the employees which made them successful. Organizations which are successful on

retention of critical knowledge have higher capabilities of assimilating and acquiring new

knowledge. Therefore, it is necessary to analyze the capabilities and skills of these

employees because they might be working on different positions and in different areas

during their career and thus, might have variety of expertise in different domains (Sumbal

et al, 2017). Levy (2011) conducted a multi-case study of seven organizations in Israel.

She proposed that to carry out knowledge retention effectively, these three steps are

necessary, i) Identifying the critical knowledge, ii) Capturing of critical and

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undocumented knowledge and, iii) integrating the retained knowledge for reuse in the

organization’s business processes. The first step “identifying the critical knowledge”

draws the attention towards the unique types of skills and expertise possessed by the

employees whether it is the expertise related to management, relationships or technical

areas (Joe et al., 2013). Alavi and Leidner (2001) discussed a knowledge taxonomy that

provides a possible context on different types of knowledge that the departing employees

might possess (Joe et al., 2013). This knowledge taxonomy is described as, i) declarative

or explicit knowledge (know about), ii) Procedural or tacit knowledge (know how), iii)

causal knowledge (know why), iv) conditional knowledge (know when) and v) relational

knowledge (know with). This distribution of knowledge types provides a broader context

that could be useful for the organizations, however, there is a need to further refine and

elaborate on these knowledge types on individual level. Eucker (2007) further stated that

organizations lose knowledge of know-how, know-what and know-who through the loss

of a knowledge worker. Durst and Wilhelm (2013) developed a process to measure the

risk of knowledge from a departing employee, based on lost structural capital, human

capital, relational capital and social capital. Daghfous et al (2013) found out that

knowledge loss can occur in terms of architectural and component knowledge.

Architectural knowledge comprises of firm-wide procedures which integrate the different

components of the firm to run the operations smoothly whereas component knowledge

encompasses sub-routines or discrete aspects of a firm’s operations. Similarly, Jennex

(2014) mentioned the competency profile of the employees listing the unique

competencies and skills while assessing their knowledge. This competency profile checks

if an employee is a key contributor for products/services offered by organization or if the

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skills of the employee are in demand by the organization and last but not the least, if he

has knowledge related to organizational events and projects etc. Bratianu and Leon

(2015) also categorize organizational knowledge as cognitive, emotional, and spiritual

knowledge. Cognitive knowledge is rational knowledge that resides in the words and

behaviors, emotional knowledge is the unconscious knowledge generated through

sensory systems and transformed into feelings. Finally, spiritual knowledge encompasses

the professional and cultural values that guide our behavior and decisions. Hence, these

studies provide a general and broader description of knowledge types mentioning about

the unique skills of the departing employees, however, these studies do not dwell into the

identification of individual knowledge types of the departing employees especially in

terms of evaluating the various expertise of the employees according to work performed

over the years.

Thus, this research tries to bridge this gap by using a grounded theory approach to

determine the major factors of knowledge loss and the different types of knowledge

possessed by employees. This will help the organizations in carrying out the assessment

of knowledge loss in a more appropriate way by checking if the departing employee

possesses knowledge related to critical areas. No previous studies have been found on

this identification of critical knowledge and the types of knowledge to retain, from the

departing employees except for one study. This study by Joe et al. (2013) was conducted

in New Zealand, and it focused on SMEs to investigate the knowledge lost when old

experts leave the organizations. The current research work focuses on oil and gas sector.

The oil and gas industry is unique regarding its operations and geographical boundaries.

The operational units of the companies are spread across different geographical locations

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in the world, and people work in a variety of different environments for example

companies have installations under the sea, for example, North Sea, in snowy regions

such as Arctic, in plain areas and deserts, for example, Middle East. Thus, the type of

experiences and knowledge gained over a period of time might be quite variable

especially for retirees who spent around 20-30 years working in oil and gas. Further, oil

and gas sector comprise of different sectors such as upstream, midstream, and

downstream, each performing a different function. Because of these reasons, the types of

knowledge possessed by employees in oil and gas sector were bit different as compared

to those in SMEs as it will be discussed in the results section. Thus, this chapter further

extends the research work conducted on knowledge retention and aims to produce some

fresh knowledge on the subject by continuing the grounded theory approach and

interviewing some of the respondents again, to gain insights on the dominant likelihood

factors and critical areas of knowledge loss. Based on the above discussion, the two main

research questions for the phase 2a are:

Q 1: What are the dominant likelihood factors of knowledge loss in oil and gas industry?

Q 2: What are the critical types of knowledge lost when employees depart from the oil

and gas industry?

Based on the responses received to these research questions, a framework for

assessing the knowledge loss has been proposed at the end.

5.4 Methodology

Grounded theory methodology has been continued for this study as it is an

extension of the previous study of knowledge retention and an aging workforce (Phase 1).

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Grounded theory has a capacity of exploring a research topic in detail. Apart from

investigating under-explored areas, grounded theory is particularly useful for generating

new perspective to explain the phenomena under the study thus, contributing towards

fresh knowledge in this area of study (Charmaz, 2014). 11 semi-structured interviews

were conducted with oil and gas experts to explore the research questions. The

interviewees were from the original group of 20 interviewees in the first phase. For the

current study, they were contacted back to have another session of interviews with the

researcher. Most of them were kind enough to respond back and provide some valuable

information on the topic. A few of them could not manage time. Also, those interviewees

were selected which, in the previous study, mentioned that this aging workforce and

knowledge retention is a serious issue in their companies. So, the interviewees in the

current study mostly represented companies from USA, Russia, Europe, Thailand, UAE,

and Australia. Further as mentioned earlier, these interviewees had an extensive amount

of experience and most of them directly involved in knowledge management activities

within their respective organizations. Table 5.1 shows the details of these 11 interviewees

for this phase 2a of the research work. The interviews were recorded and transcribed

afterward. Few of the interviewees preferred replying to emails, and thus questions were

sent to them. Interviewees were contacted back several times to clarify answers in the

case of any ambiguities and if any further insights were required. The interpretation and

analysis of data were performed using ATLAS.ti. Through line by line analysis of

interviews, codes were generated which highlighted the main concepts and themes. The

data from each subsequent interview was compared using constant comparison method,

to find out the similarities and differences in the data. Further refinement of data was

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performed to identify the codes pertinent to the current research and able to provide the

answers to the research questions. The data collection strategy was quite flexible and

used open-ended and probing questions. This approach helped in staying close to data

and generating codes and categories grounded in data. Thus, it was possible to look for

Interviewees Years of

Experience

Position Company Location

1 10 Managerial/Consultant A USA

2 7 Managerial/KM

Coordinator B Australia

3 35 Director/ KM Lead C Netherlands

4 20 Director D UK

5 32 Managerial E UK

6 7 Managerial F Italy

7 16 Managerial/KM Lead G Thailand

8 9 Managerial H Norway

9 10 Managerial/Consultant I UK

10 8 Managerial J Russia

11

10 Senior Drilling Engineer K UAE

Table 5.1 Details of Interviewees in the study

emerging themes and constructs beyond the ones identified in the prior literature

(Daghfous et al., 2013). When additional data wasn’t providing any new insights about

the inquired research questions, data collection was stopped i.e. saturation point was

reached (Pandit, 1996). The coded data were then sorted out to come up with categories

of higher abstraction to identify the likelihood of knowledge loss and types of knowledge

lost when employees depart. Memos were also written during the coding to understand

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and explain the relationships among the codes generated from the interview data

(Charmaz, 2014). These memos served as building blocks for analysis of coded data and

also guided the researchers towards subsequent data collection on emerging themes and

concepts. The results of the study are supported by having a dialogue with the existing

literature and by conducting a discussion on similarities and differences.

5.5 Results

The results on likelihood and critical types of knowledge have been categorized

into various categories according to the responses obtained from the interviewees and

will be discussed further now.

5.5.1 Likelihood Factors of Knowledge Loss

The code table and network view of this category are shown in figure 5.1 and 5.2.

The responses from the interviewees indicated that in oil and gas sector, aging workforce

and retirements are an important issue, similar to what was revealed in phase 1. Thus,

retirements and layoffs won’t be discussed here in detail as these already have been

covered in phase 1. There are a lot of senior employees approaching retirement and

posing a challenge for the companies regarding knowledge loss. For example,

interviewee 2 stated that:

“Yes, there is a large range of baby boomers leaving the organization, normally

you would say that’s gonna impact us from a knowledge capacity”

Layoffs also came out as a major knowledge loss factor in the oil and gas industry

as the industry is facing a bust cycle because of the fall in the oil prices as mentioned by

Interviewee 8:

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Fig. 5.1 Code table for Likelihood of Knowledge Loss

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Fig. 5.2 Network View of Likelihood of Knowledge Loss

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“Like if you look at the companies, many companies are laying out people,

usually it goes 30-40 percent of the young or whatever age they choose, they are going to

retire. In addition, they are giving packages to the older people because they are more

expensive. Thus, according to that, they will take the knowledge (away) without passing

their knowledge”

The turnover didn’t turn out to be a key element of knowledge loss according to

the responses by interviewees. Interviewee 2 explained this as:

“My experience is that not many people leave the top oil and gas companies.

Generally, the remuneration in oil and gas companies is very good ……. So, I think once

people come in within the organizations and are employed, there has to be a pretty good

reason. I think generally the remuneration is so good that people will get to stay even

though they generally have less enjoyment and less career satisfaction”

Interviewees were of the view that senior employees hardly switch to other

companies and there might be rare cases; however, millennials are a different case, they

do change jobs frequently as mentioned by interviewee 1:

“Absolutely that is becoming an equal challenge for the younger generation.

Millennials are not typically lifers anymore. They don’t work in a company for life. So,

you have the next generation which is coming. They will stick around for 2 or 3 years and

then get a better position in another oil company. They might actually come back to the

same company 2 or 3 times over the years as they kind of float around……. At least the

last couple of years, it has happened less with the older people because, honestly, I know

a lot of people in each company that even get a better offer from other oil and gas

companies but still have a very traditional sense of loyalty to the company”

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Moreover, there was a consensus among interviewees that the knowledge of

senior and expert people is quite critical as compared to younger employees. Other staff

can replace younger employees in case they leave, and the tasks they regularly perform

are not that much knowledge intensive and can be handled and learned by the successor

employees. Younger employees thus lack experience and this has been explained by

interviewee 2 as:

“There might be some younger people who have the knowledge around some

particular process but generally, these are the more mature ones and (senior) employees

who have been there for some time”

5.5.2 Contract Based Workers: A Great Threat to Knowledge Loss

Another important factor of knowledge loss discovered during the interviews was

the knowledge loss from contract-based employees. It was kind of interesting for the

researcher as contract workforce has not been discussed in the literature as an important

factor of knowledge loss. Interviewee 2 described it as a major knowledge loss factors as

he stated that:

“Within our area of business that we operate in at the moment, we have a

significant contract force, not employees, we have someone (for example) on contract for

one or two years or 6 months. They might have actually stayed for 5 years as we keep on

renewing them. So, one day they might leave as they are not an employee, and you know

that contract workforce is a huge amount of knowledge in there that we will lose…….

These contractors are with us and some walk out of the door in days’ notice. So, that

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doesn’t give us much time to get the knowledge out of them. That’s one thing you might

want to think about in future. Contractors vs Employees is a real hot topic”

Interviewee 1 also gave an interesting statement on important knowledge

possessed by contract workforce and signified their importance in the following words:

“When I worked in company X, I was continuity of the department that I was

working in because I was working in one department for 8-9 years. I was a transition

between 2-3 bosses that all came in with no experience in the field of what I was doing

i.e. knowledge management. So, I trained 2 or 3 bosses coming through but without me

being there, they would not have had the people to discover and understand the past

because some of them would; come and try to do the same thing that had been tried two

years before but they weren’t aware. So, there is a lot of dependency on longer term

contractors in corporations”

Thus, contract workforce has a significant impact on the oil and gas companies. It

plays a crucial role in companies and not really understanding the dependency on the

contractors is kind of interesting. Interviewee 8 explained this situation as:

“I was working in such situation (as a contractor), and I wasn’t asked to report or

share and they lost everything. They just got some kind of model and there was no

information in it. But sometimes they ask you to report ….... (Of course) it takes a lot of

time, but it is good and this is the record of what you have been doing”

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5.5.3 No Formal Knowledge Loss Assessment

Responses from the interviewees revealed that companies are lacking in any type

of formal knowledge assessment procedures for departing employees as interviewee 5

stated that:

“The HR aspect of leavers and dissatisfaction is done, but not knowledge

retention. It is a fast-moving industry and most jobs are contract-based jobs, so

unfortunately not much processes to share”

Normally it is a quick notice when an employee departs especially job change or

layoffs, leaving little time for the company to conduct any assessment and generally the

manager of departing employee decides if the employee is critically important and to

retain his/her knowledge. In most cases, no knowledge assessment is performed as

interviewee 9 mentioned that:

“I don’t think organisations proactively identify critical knowledge at all,

secondly, if they do, they don’t do much about it. Are you aware ISO 9000 has a new

section called organisational knowledge? …… To become accredited, you have to

identify the knowledge you need to perform your business. So, it is too early to see what

the impact of that is”

However, two of the interviewees stated that their companies generally keep track

of the critical employees and they make sure to have enough replacements available for

the critical persons, but still no formal knowledge assessment processes. The

interviewees agreed that there should be a structured approach to do the assessment of

knowledge loss to decide on knowledge retention in a better way as interviewee 1 stated

that:

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“I have seen this done in pockets but that has never been applied structurally

throughout the organization but that is the way to do it”

Interviewees stated that although managers naturally think of knowledge areas

that are important and the skills an employee might possess. However, it is better to have

a structured approach to identify the key knowledge areas of employees which will

eventually help in efficient knowledge retention. On this criticality of knowledge,

interviewees agreed that there is need of identifying the critical knowledge areas as

interviewee 1 stated that:

“When you are interviewing someone for exit, you are going through their whole

work history. You need to go through all the jobs they did, all their disciplines and

backgrounds, so, one person could have 4-5 different expert areas …… There is (need of)

transfer of tacit knowledge on very specific specialized topics and expertise areas but

there is also contextual knowledge for example somebody has worked in Siberia and

understands how the working environment is in Siberia”

Interviewee 7 stated the same idea that:

“If we break down the type of knowledge and expertise he possesses and capture

it, it will cover everything”

Interviewees agreed that managers should make this practice of breaking the job

history of employee and should not only focus on the most recent knowledge as the

employees in oil and gas might possess critical knowledge from the past which could still

be useful as interviewee 9 stated that:

“I think that it (identifying critical knowledge) is useful to do this although line

managers invariably are only concerned with recent knowledge, I have found critical

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knowledge held by experts that was accumulated and deployed almost 10-20 years in the

past”

Thus, identification of critical knowledge will make it easier to understand the types of

knowledge that are most important in oil and gas sector and will help managers to

understand what knowledge is vital and needs to be retained for the organization. The

identification of this critical knowledge depends on several factors such as roles and

responsibilities of the employees, the discipline in which they are working and finally,

the locations they have been working on. Also, there would be series of tasks performed

by an employee since (s)he joined the company. It is unlikely for people to do the same

job as they move to higher positions as well as take on various roles during their career

thus culminating different types of knowledge. The next section will discuss the results

obtained regarding different types of critical knowledge possessed by employees in the

oil and gas industry.

5.5.4 Critical Types of Knowledge Lost when Employees Depart

The interviewees were asked questions about the different types of knowledge

that the departing employees might possess. A range of knowledge types was discovered

and will be discussed in this section. The code table and network view for this category

are provided in Appendix B.

5.5.4.1 Specialized Technical Knowledge

All interviewees mentioned the specialized technical knowledge as one of the

most important knowledge that the employees could possess. If a departing employee

possesses this knowledge, it should be captured and retained. This knowledge comes

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under various domains in oil and gas. Each area in oil and gas has a different suite of

technical skills and knowledge associated with it. For example, a maintenance engineer

will have tremendous experience in the area of operations and maintenance and this

experience is different to the person who has got specialist technical knowledge on R&D

or development projects. Interviewee 1 described this technical skill dependency on the

areas and functions as:

“When it comes to drilling, ability to understand the environment and to run the

team correctly, to have the experience of when things go wrong, how to handle something

that is stuck down the hole, from an engineering point of view, it is the ability to track

large projects. And be able to make sure everything is on point”

The technical knowledge is spread over the different areas of the value chain. The

petro-technical areas in upstream sector are of crucial importance in oil and gas

companies. First, there is an exploration of reserves, then appraisal, after assessment, the

development of infrastructure starts. After development, the production phase initiates

which involves extraction of oil and gas. Finally, the decommissioning takes place when

the reserves are finished. According to interviewees, critical knowledge areas where

companies don’t want to lose knowledge are the areas that can bring competitive

advantage for example in exploration and production area; the knowledge of how to

interpret seismic data and how to bring up hydrocarbon for production, etc. It involves

understanding the sub-surface and embracing the sub surface calculations. Sub-surface, in

oil and gas, means, in a well or below the surface of the ground. The sub-surface

structures are understood through seismic surveys to look for hydrocarbons. The drilling

knowledge involves understanding the rock structures where the well is to be drilled. A

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properly drilled well is essential for efficient production of hydrocarbons. Also in the

upstream sector, most of the projects are in partnership and organizations work with

contractors to provide expertise in different technical areas. Interviewee 8 further

highlighted the importance of this technical knowledge as he stated that:

“So, when I talk about specialized technical knowledge, then, I will put

uncertainty and volume in place, then, how you define reserves and report standards,

how you meter error when measuring producing volumes and then how you apply new

technology to enhance well productivity and well drainage. Then, challenges when you

change the apprenticeships of assets. Changes in business models used by operators…...

how you verify field and well data for the public domain. You know, you have to report

your reserves to the state and so on. This reporting data is very important, once you

report that to state, you have to stick on that. When you talk about each of these items,

there are sub-items, for example, uncertainty in volume and place and as I said definition

of reserves is very important”

The technical domain also covers the operations and maintenance of the plants

and equipment i.e. facilities management. Such type of knowledge can be used by

employees to keep the equipment running, for example, aging plants or replacing the

major components in the refineries and ensuring smoother operation after the

replacements. There is also critical knowledge required for major component overhauls

such as gas compressors when the reservoirs deplete. Apart from this, technical

knowledge can also involve, specialized research & development knowledge, chemical

processing expertise, seismic interpretations, handling of the field and well data, etc.

Especially the companies which venture into research and development and work on

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bleeding edge technologies need to keep knowledgeable people. On the whole, there was

an agreement among the interviewees that subject matter experts with critical technical

skills possess true tacit knowledge as they perform all sorts of field work and work in the

form of teams and exchange and share knowledge with each other.

5.5.4.2 Contextual Knowledge: Knowledge of working at different places

The heavy movement of employees during their career, across various

geographical locations, helps them in gaining the knowledge of working in different

environments under different conditions. This knowledge of different geographical

locations was deemed critical by all the interviewees. This knowledge involves the

experience of working at different geographical locations, understanding the

environmental conditions and political situations, etc. Interviewee 1 signified the

importance of this knowledge as he stated that:

‘‘It is easier to drill in West Texas than in Angola for many reasons obviously.

For example, if you are drilling in Angola, you need to bring someone; you need to bring

people who have experience with the security, location things like that to protect your

office. It is whole different dynamics; the logistics are harder to get things into the

country. So, all those factors; if you would put someone from West Texas in Angola, they

would not have the breadth of knowledge to understand the political sensitivities, and the

security measures that are needed to ensure that the operations can be handled and

done”

Similarly, it is essential to know the additional resources required in specific areas

and accessibility options to carry out operations. Also, it is important to understand the

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ecological effects of carrying out the operations in different environments. For example,

interview 10 provided the example of the Arctic that it's susceptible to pollution and such

contextual knowledge about the environment is very important. The companies need to

carefully select the future operations and locations for oil and gas activities as it is

becoming more challenging and expensive to dig the oil out of the earth. Similarly, for

initiating new projects, companies are in competition with projects in different areas as

interviewee 4 mentioned:

“When you are looking for new developments in a particular part of the world;

when seeking funding for development from management team, you have to be aware that

you are in competition with other areas of the world for funding”

Interviewees mentioned that the geology of different areas is quite unique and this

knowledge provides information of what is under the surface. This knowledge is quite

critical for exploration and drilling for example the executions of operations in the

Middle East will be different from the operations carried out in West Africa or the Gulf

of Mexico. Thus, it is important to keep track of such information as interviewee 1 stated

that:

“It is not only geographical but it is also types of sub surface …... So, if you have some

experience of working in (for example) Oman, you would be able to work easier or have

better understanding of West Texas. So, a lot of companies, have knowledge bases, where

they compare the sub-surface between different locations around the world and they

actually draw experience… they would bring in people with experience in that context”

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Knowledge of the political situation of different areas is also of vital importance

as companies can put their projects at risk if the situations are unstable as interviewee 8

mentioned:

“Why many many people are coming to the North Sea, because the political

situation is stable. For example, I was once involved in a certain area in South of

Europe, and the states were not so stable. So, for example, if you are in Southern Europe,

they can announce the session and after 1 year, the state will say no, no we will not go

for that ……. So, in that sense, yes political knowledge for starting new initiatives is

important and like taxation etc. It is relevant to political system if it stable or unstable”

5.5.4.3 Knowledge of Management

Knowledge of management involves managing people and handling large projects

and teams. It was interesting to discover that although departing employees might possess

this type of knowledge, it was not considered as much important by the interviewees.

Interviewee 4 reflected on this knowledge type and stated that knowledge of management

is important but then it again depends on the attitude of the successors. The previous

managers what they are doing now, learned all that 4 or 5 years ago when they got

promoted. So, it takes time but you learn it over time. Some of the lessons learnt might be

lost but then the new managers over time catch up with all the processes and activities.

The knowledge of management tends to be different from other types of

knowledge such as subject matter expertise. Interviewee 2 commented on it as:

“Management side is a kind of little bit different, it is more generic, So, it is less

concerned there I guess from a subject matter expert leaving. But when you start seeing

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the technical people leave, that is where true tacit knowledge is and that’s where you can

hit problems……. You don’t tend to feel a huge impact when a GM leaves from a

knowledge perspective. You might see a different way of everyone working, a different

approach or may be a different strategy but you haven’t lost the true subject matter

expertise, technical knowledge because that person is a manager and they have got

technical people under them. So, I think, it is very different senior management

knowledge loss as opposed to subject matter knowledge loss”

Thus, the management knowledge was given less weight regarding importance as

compared to other known types such as technical knowledge and contextual knowledge.

Thus, it might be easier to replace managers than technical experts and the departing

manager probably might have an assistant who can maintain the continuity by passing on

the history of the work. Interviewee 2 stated that there is a transition taking place in their

company and they have been slowly moving people out but normally no huge impact is

seen regarding knowledge loss when somebody leaves from a top management position

as the replacement manager has all the true subject matter experts working under him.

Management is generally a facilitator and further, few people can learn these types of

skills. Good people skills are also necessary for this type of knowledge as interviewee 9

mentioned:

“It’s important but it is less important as no one has got the job to be done …... I

have seen managers with terrible people skills. Yes, it’s a role to facilitate things to be

done. But as such doing those things, I don’t see management doing it.”

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Also, because management is a natural ability, it is hard to transfer or retain this

type of knowledge. A lot of aspects related to management are personality based.

Interviewee 1 explained this as:

“You cannot extract lessons of how to manage different people or how to manage

challenges within the workforce and all those types of things. You can do some exit

interviews regarding that but it is very hard to ask someone how do you manage people”

5.5.4.4 Knowledge of Train Wrecks and History of the Company

This knowledge, mostly possessed by senior employees was also considered of

key importance. Train wrecks are basically the reverse of best practices and are used to

mention the bad events that happened in the past. According to interviewee 1:

“Most people don’t admit to the train wrecks; most people cover up the train

wrecks all the way up to management but that is where train wrecks get repeated.

Because no one has shared the knowledge of what went wrong”

The knowledge of these train wrecks includes the bad experiences of operations

or performing activities in a specific way that didn’t work. Similar to this is the

knowledge about the history of the company as described by interviewee 4:

“In 1986, I was working for X (company) oil, and the oil prices died and we had

to make a 3rd of the company redundant in a day. We lost about 800 people in 1 day. The

management had this process (of handling layoffs). It is exactly the same problem that

our managers faced in 2014 when the prices of oil died again. If you have the history of

working through that sort of problems, it may not be your responsibility to execute the

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new change but at least you can say, hold on, we tried that and it worked very well. I

suggest you do something similar now”

Similarly, if something was tried in the past but didn’t work as expected, then the

employee knows the history and circumstances of that event. This knowledge is possibly

useful in the event of newer person inadvertently drifting along a path which had been

discouraged in the past. Thus, this past knowledge of history and train wrecks, mostly

possessed by senior experts, can avoid “reinventing the wheel” and in turn save time and

efforts. Interviewee 9 agreed that this is an important part of the corporate memory and

is equally valid for the type of fault or maintenance issue that occurs only sporadically

(have experienced maintenance schedule of once every 10 years for a specific plant). So,

the persons who worked on these 10 years back will know how to fix these. Similarly,

interviewee 6 highlighted the importance of this knowledge stating that accuracy and

reliability of data collected in the past could be well determined by talking to those who

were present at the time of data collection.

5.5.4.5 Knowledge of Networks and Relationships

There were some interesting viewpoints from interviewees on this knowledge

type. Some interviewees considered this important while some took it from the view that

it is not as useful regarding knowledge retention. However, in general, there was an

agreement that this knowledge is important. Interviewee 6 highlighted the importance of

this knowledge stating that it is like an ecosystem. The ecosystem is losing something

important, and the equilibrium is affected when a person with strong relationships, built

on trust is leaving. Further results show that this knowledge is crucial because that is how

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a job is done and thus it is important to inquire from a departing employee; who (s)he

works with and why. There are a lot of activities within the organizations, and it is

important to know the main contacts, to deal with different matters and to know the

expert people. Also, this knowledge has its relevance in technical areas and from the tacit

point of view as interviewee 2 mentioned that:

“There might have been three meetings today, and none of it is documented. But

in a months’ time, that person might get called in a meeting to help out with a particular

meeting or problem, and there he'll say that yes, I have heard and understood this from

the previous meeting. So, if it was just discussed in the meeting and not documented, then

you are relying on the person of what is captured in his head and what he can

remember”

According to interviewee 1, people working in the field operations tend to have

strong connections and networks with their colleagues.

“The engineers and drillers who are out there in the field, they have a much

stronger connection. The production people as well. Everyone that is a part of the asset

in the field has a stronger connection”

To further emphasize the relationships knowledge, the interviewee recalled his

experience of working in Norway and said that the circle is so small that all the

Norwegians almost worked in each company at least one time in their career. So, their

personal network within the companies is like that they would call someone to form a

competitor or expert in a different location or country. It is complimented by the fact that

in Norway, most oil and gas companies have joint ventures.

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The external networks of relationships are also important along with internal

networks. For example, if an employee working in the Gulf of Mexico comes across a

problem, he will approach the people working in the same region i.e. Gulf of Mexico for

solutions rather than people from other regions. Moreover, to handle the governments and

regulatory contracts, the knowledge of networks and relationships is also important. It

could be cumbersome for companies if they don’t have the right permits to operate as

interviewee 9 stated that:

“Local governments, regional government, national governments, you need to

know the regulatory mind field certainly in order to operate. You can’t operate without

the permissions on environmental regulations, industry regulations, and of course the

government and (for that knowledge of networks is important)”

Interviewee 4 had a different opinion and considered the knowledge of

relationships as something personal, stating that:

“Your network is the individuality of yourself. And your relationships are

individual to yourself. You kind of create those. The fact that I get on really well with

somebody, doesn’t necessarily mean that it’s going to be of any use to next person

coming in. That is a waste of time. That is how I approach it. Other people may approach

it differently”

However, most of the interviewees disagreed that previous contacts can be of no

use. For example, interviewee 10 was of the view that nowadays most of the activity is

team based activity, so in these teams, experts have experience and contacts, but on the

other hand, for the same team in different projects, there are younger people and these

people participate in meetings, emails, etc. and thus in this way they get to know the

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contacts and have the know-how of the relationships. So, in this way, this knowledge of

relationships can be propagated. Same was agreed by interviewee 9 as he said that the list

of previous role holder’s network could serve as a starting point for a new person in

building the relationships. The responses further revealed that the knowledge of

relationships is also dependent on the job profile of the person and the impact he has on

operational work, for example, people who are in business development and supply chain

areas can have a higher network of relationships as compared to other areas.

5.5.4.6 Knowledge of Business Processes

One of the interviewees mentioned that senior experts possess the knowledge of

business processes which relate to knowing the different processes in the organizations

and how these are interlinked to understand the real value of the business, however, there

wasn’t a strong consensus on this one. This type of knowledge was not considered

important by the interviewees, and the reason is that most of the knowledge related to

business processes is in documented form and can be easily retrieved and used. In large

organizations, all the business processes are well documented and well structured. Thus,

it is not difficult to understand and comprehend these processes even if the experienced

employees leave as interviewee 4 mentioned that:

“Procedures on how systems work are written down and are, therefore, available

for the people to pick up and use….. Someone else can pick these up and use them”

Further, the results revealed that this knowledge might be important if the

company has not defined and documented those processes correctly and that normally

happens in companies of smaller scale. So, in smaller companies, this might be a

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problem, but from the mid-size to the larger ones, most of them have their processes

streamlined and well documented as interviewee 8 stated that:

“This is standard and how each company adopted and then usually you have

certain directorates of oil and gas by state, and they announce certain procedures, and

accordingly, businesses and systems are defined within the system let’s say for small,

medium and big size companies ……this one they (successor employees) can pick easily”

5.5.5 Relevance of Knowledge

The previous section described the different types of knowledge to be captured

and retained from the departing employees, however; another key factor to be kept in

mind is the relevance of this knowledge. This emerged out as an important category

during the coding process. The code table and network view for this category are

provided in appendix B. Relevance of knowledge is described by interviewee 1 as:

“You need to put together a risk assessment profile overall within the corporation

as related to specific skill set and dependency on that skills set. Then, you can go after

the individual that might be relevant because larger corporations have too many people

to do it on an individual basis….. That’s where a competency model is very important to

know where everyone is from an expertise point of view and then say ok we have, you

know; one senior reservoir engineer at level 5 and everyone else is at level 3 that is a risk

for our organization. So, we need to focus on the knowledge transition in that specific

area”

Responses from the interviewees revealed that an active 'scanning the horizon' for

trends on technical and business practices would be helpful to estimate what knowledge

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will be relevant in future. Moreover, it is important to have a sound understanding of the

probable future projects of the organizations. The relevance and scope of knowledge

might also differ depending on the location, for example, interviewee 8 mentioned that:

“Ideally it should be based on the knowledge for that process …. You have big

companies, small companies and you have different processes within. You have offshore,

onshore... Now in specialized technical knowledge, you have very difficult fields or easy

fields. The Middle East has easy fields. You have one well in the Middle East and for that

one well in the Middle East, you have 10 wells in the North Sea. But in the United States,

you have 100 wells in the same area. So, in the Middle East it doesn’t matter that much

but in the United States you need to have a knowledgeable guy who can predict drainage,

and so on, so they will try to keep this guy. So, it depends on in which area you talk about

and according to that, you have to do most out of it”

Further, the relevance of knowledge also depends on the goals and strategy of the

company taking into consideration several factors as explained by interviewee 6:

“What could be considered today a critical knowledge it could not be the same

tomorrow. And what is considered today less important as knowledge could mean to lose

a fantastic opportunity for tomorrow, we could think of a multi-dimensional model with

the following dimensions: the age of the personnel, the employee satisfaction, the time,

the velocity at which a knowledge becomes obsolete, the business objectives, etc. It

depends mainly on the company’s strategy. The fact is there are few companies that are

really able to identify and value their critical knowledge. And when I speak about critical

knowledge I refer mainly to the critical people”

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5.6 Analysis and Discussion

In organizations, employees create intellectual capital and further influence it

through their mindset, organizational values, beliefs and skills (Brunold and Durst, 2012).

Companies can lose their competitiveness if they do not take adequate measures to retain

the potential and expertise of their employees (Kupi et al., 2008). Especially

multinational corporations (MNCs) need to be more concerned about this as a

multinational corporation "consists of a group of geographically dispersed and goal

disparate organizations that includes its headquarters and different national subsidiaries"

(Ghoshal and Bartlett, 1990,P. 603). The organizational structure and dynamics because

of international competitors, markets and locations make MNCs more prone to challenges

of retaining critical knowledge to remain competitive (Brunold and Durst, 2012). Job

rotation is common in MNCs. It is quite a normal practice in oil and gas as employees

profoundly move across different geographical locations; and thus, it is inherent in the oil

business. Also, if the person switches from one place to another, he is still in the

organization and can be contacted if required.

Turnover does not seem to be major knowledge loss factor in the oil and gas

sector as the pay packages are quite good; especially in the top companies. Thus, regular

employees will not leave the job unless they get some really good offer and better career

prospects (Guan et al., 2014) in rival top companies. Turnover is even less for senior

workers, in fact, almost negligible because the job stability and bonding of these old age

experts with the company (Leiter et al., 2009) prevents them from changing jobs

frequently. Also, when employees get to a certain level in the organization, the

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compensation for transitioning into other company is high regarding losing pension and

other benefits (Munnell et al., 2006). On the contrary, retirements and layoffs seem to be

a major influential factor regarding knowledge loss (Martins and Meyer, 2012).

Highly experienced and senior employees have high salaries and thus tend to be

quite expensive for the organizations (Young et al., 2014) during the low times. To

reduce the costs and overhead, they are the first target of the companies for layoffs. In

addition to that as indicated in the previous study, a lot of people in oil and gas are

approaching the retirement age, thus, making retirement a dominant factor of knowledge

loss (Ball and Gotsill, 2011, McKenna et al., 2006) in oil and gas in upcoming years. In

such scenario, it is easier for organizations to devise some strategies to assess the

knowledge of the employees as the organizations know in advance about the departure of

the employee. Also, there is enough time to break down the job history of employee and

identify the critical knowledge areas of knowledge loss. Therefore, it is important for

companies and managers to understand the importance of a proper process for the

assessment of knowledge loss. Moreover, conducting such assessment requires a

proactive role to be played by managers in the organizations (Calo, 2008).

The millennials or younger generation, however, have different issues regarding

knowledge loss and are very different from older experts or baby boomers. These days,

oil and gas companies have stopped offering permanent and life type jobs even though

they imply they do (Inkpen and Moffett, 2011). The working patterns and lifestyles tend

to be different these days, and the younger generation is more concerned about

maintaining a work-life balance (Stevens, 2010). The Millennials are not looking for

permanent careers in the industry; they are not lifers anymore as they have the drive to

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move up faster (Liebermann et al., 2013). As millennials are normally at early stages of

the career, they might need the basic knowledge relevant to do their job and thus, their

work is not that knowledge intensive. Millennials are therefore not that critical regarding

knowledge loss. Moreover, the contract-based jobs are more prevalent in oil and gas

sector which does not provide any incentive to stay in oil and gas sector for a longer

period of time. Also, because of the fluctuating oil prices, oil and gas sector lacks job

stability (Ball and Gotsill, 2011). The contract workforce might be one of the major

issues in upcoming years because of retirements and a shortage of workforce and

companies might have to buy the knowledge most of the time from outside (Becker and

Smidt, 2015). The retiring generation was probably the last generation to work for a

lifetime in the companies. The next generation would be moving around and switching

jobs more often. Thus, companies need to get prepared for losing people with certain

expertise on a regular basis. Further, organizations need to look for how to retain that

expertise for staying competitive. Companies don’t seem to assess the dependency on

contractors (Mearns and Yule, 2009) before laying them off. Losing specialized

contractors mean losing all aspects of the expertise provided by those contractors. On the

other hand, it is equally challenging to have a knowledge retention process with the

contractors. They are living off the fact that they have knowledge and skills for which

they are hired unlike the regular employees of the company. If they give away this

knowledge, then they are not needed anymore.

From the above discussion, it appears that there are three domains interlinked

with each other regarding knowledge loss in the oil and gas sector. On one hand, the

aging workforce is a looming threat, and many of the senior workers are going to leave

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Fall in Oil

Prices

Knowledge

Loss in Oil

and Gas

Industry

Contract Based

Workers

Retirements

Lay offs High Turnover

No Stable Career

Aging Workforce

Millennials

the industry. Then, there is this new generation, the Millennials, working in oil and gas

but they are not very stable, and they change jobs frequently. Finally, the career prospects

and job market in oil and gas sector is not very promising. There are no permanent jobs,

the majority of people working in this sector are on contract-based jobs causing

apprehension regarding career prospects for the new generation. Then, there is a problem

of the higher number of layoffs due to oil crash. These factors pose a lot of challenges for

the oil and gas industry regarding knowledge loss as shown in figure 5.3.

Fig. 5.3 Likelihood of Knowledge Loss in Oil and Gas Industry

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The process of succession planning appears to be quite easy in theory. Precisely

understanding what critical knowledge needs to be retained is the challenge for

organizations. It is important to understand that critical knowledge in the companies is

associated with the methods exercised in the companies. These methods are owned and

patented by people who have worked in the oil and gas companies for a long time and

possess huge experience (Daghfous et al., 2013) emphasizing the importance of senior

workers. Six knowledge types were identified by the interviewees which the departing

employees might possess.

The first one is technical knowledge; which is related to the core operations no

matter what discipline you are working in oil and gas. Subject matter expertise is at the

core of the organizations. In such type of expertise, there can be a high reliance on a

single individual for example when there is a single or loan expert for a specific area

(Jennex, 2014, Durst and Wilhelm, 2013). If we look at the value chain of oil and gas

(Fig 5.4) as described by Inkpen and Moffett (2011), there are three main sectors in oil

and gas namely upstream, midstream and downstream. This knowledge, as revealed in

results, is spread across all these sectors and in areas such as reservoir management,

exploration, production, drilling, maintenance, etc.

There are also research and development sections in bigger companies, working

with innovative technologies and the experts working in these research groups also

possess the relevant subject matter expertise. All these areas are part of the upstream

sector thus signifying its importance, and the interviewees also put more emphasis on the

petro-technical areas regarding specialized technical knowledge. It might be because the

majority of the interviewees represented the upstream sector and considered this as the

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core business area for oil and gas companies. However, the areas of downstream and

midstream equally hold their importance regarding subject matter expertise as pointed out

by interviewees 1, 2 and 4. Further, the global demand for oil will be on the rise, and

along with conventional oil resources, companies are venturing into new discoveries and

Fig. 5.4 Global Oil and Gas Value Chain (Source: Inkpen and Moffett (2011))

development of non-conventional resources (Mills, 2008). These new frontiers require

more input from the expertise point of view as operations become more expensive

(Sorrell et al., 2010, Shuen et al., 2014). Moreover, the advancement in technology is

bringing more sophisticated techniques, and the people with top technical skills will be

most valuable for the company (Shuen et al., 2014, Nordin et al., 2014).

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The knowledge of networks and relationships has been considered necessary for

enhanced business performance (Groth, 2003, Inkpen and Tsang, 2005) and by contacting

the right persons, the job is done more smoothly. The importance of this relationships

knowledge is well versed in the literature (Hohenthal et al., 2014, Merrill et al., 2008,

Inkpen and Tsang, 2005, Tsai, 2001). As evident from the results, this knowledge was

considered important in oil and gas sector as well. In general, people in oil and gas are

dependent on each other to share and learn through the exchange of knowledge.

However, regarding networking, the people in the field are more active and possess a

stronger network of people as in the field, they interact with a lot of people as they move

across the globe throughout their career. These people will not remain in the field forever

as they progress and eventually get office positions but that relationship and bonding with

the people stay with them for the rest of their career. When required, this networks

knowledge eventually helps them to approach the right contact and refer their colleagues

to the right knowledge source (Joe et al., 2013). Moreover, through maintaining and

using these social and cultural networks, organizational and collective learning improves

(Daghfous et al., 2013). In the oil and gas industry, when new projects are initiated across

different countries, the knowledge of networks plays a role in interacting with the local

bodies and governments regarding different regulatory requirements (Fisher and White,

2000). The trust factor is crucial in these networks (Hohenthal et al., 2014). Trust is

developed through healthy interaction and relationships and employees when they have

worked with somebody and have developed good relations (Huxham and Vangen, 2013),

they will value their opinion more than any other expert. This statement reveals an

interesting linkage that even if knowledge of contacts and relationships is captured from a

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departing person, the trust element he developed with these contacts is missing. This

shows that apparently, the relationships and network knowledge is linked to an individual

person and not the company. As long as an employee is there in the company, he might

use his contacts to get the job done, but after he leaves, that network of relationships

might not prove to be much effective for the company. Based on the importance and

usefulness of networks, there could be different scenarios in this regard. a) Retirement:

the person leaving could very well have given a good hand-over of all aspects of his

work, including introducing his replacement to these contacts. Thus, the use of former

contacts can be maintained, and the new person can build up trust without starting from

scratch, b) Leaving for a better opportunity: there may well have been a good hand-over,

as in case “a” above, or not. Arguably, the best employee leaving scenario is one where

the person leaving tells everybody he meets that the firm he just left was a good one, and

he would be happy to go back one day. That is especially true if someone is leaving

because of being made redundant. iii) Redundancy: In this case, the departing person is

less likely to assist the organization with contacts. The contacts are retained, although

they might have to be followed up almost like a cold call. Overall, the trust level is highly

important, and losing a good employee will slow down the organization to some extent.

On the other hand, the reputation of the organization is also important. If it is a good

reputation, there is a better chance of the organization retaining some trust with the

contacts of the exiting employee.

Knowledge of management is another type of knowledge that is unique and

cannot be retained in full as revealed from the results. This knowledge is, however,

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necessary as it involves managing people and projects. To utilize this knowledge, either

the organization can keep the departing employee, or the successor can be trained through

training or workshops to improve his/her management skills (Mintzberg and Gosling,

2002), as management skill is something natural and every person owns a distinct style to

handle the people and projects. Aspects like quantitative intelligence, ability to engage

people personally, competitiveness, etc. are innate qualities of the persons (Elmuti, 2004).

The successor might take some time to catch up and improve his skills through learning

on the job. This also might slow down the things a bit. Thus, if possible, it is always a

good choice to choose a successor who has relevant management experience for the

vacant position. The concept of job shadowing might also be useful to engage the

successor with the incumbent worker in learning the tasks and gaining some experience

before the guy leaves. The contextual knowledge is another important knowledge type

which is very relevant and pertinent to the oil and gas industry. In this industry,

employees keep on moving and work at various locations around the world (Inkpen and

Moffett, 2011). Because of the variation in culture and the infrastructure in that area, each

part of the world performs the job slightly differently. It involves the knowledge of

political situations, cultural knowledge, knowledge of the environment and geographical

conditions (Mearns and Yule, 2009), and to be aware of different constraints across

varied geographical locations (Brunold and Durst, 2012). The knowledge on these

various aspects plays a major role in oil and gas operations and should be retained from

departing employee. Companies can keep this knowledge in their knowledge bases.

Based on this, they can compare the knowledge on different geographical locations to

determine the best approaches to perform operations. The political stability of the

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location is important for the operations to be executed in a smooth way (Longwell, 2002,

Mearns and Yule, 2009). Delays might occur in the processes if the situations are

unstable. The delay or stoppage of the operations will cause cost overruns (Ruqaishi and

Bashir, 2013) and even lives of the employees might also be at risk.

Knowledge of history and train wrecks is another important type of knowledge to

be retained. The companies have a record of best practices, in general; however, these

bad experiences also need to be recorded and shared so that they do not get repeated. This

can be termed as classic lessons learning for the avoidance of repeat blunders. However,

it should be performed in a non-career threatening way. Interviewee 4 provided a nice

example of using this past knowledge to handle the downturn in 2014 using the

experience of 1986 economic crisis. It also reveals that such knowledge resides with the

experienced and aged workers (Ebrahimi et al., 2008) showing the importance of these

workers. Further, the data in oil and gas tends to be of key importance and collected over

a period of time (Sorrell et al., 2010) especially the seismic interpretations as it takes 10-

15 years for an exploration project to get to an end. Thus, concerned people who were

present during data collection and know the history of the data can make a difference and

thus their knowledge is of key importance. This knowledge of the history of the company

coincides with the organizational knowledge as described by Joe et al. (2013) as they

state that through knowing the history, employees understand the organization as a

whole, how it used to operate and why it functions the way it does today. It is knowledge

of all the changes that organizations went through. Such knowledge is beneficial when

introducing major changes in the structure and functioning of the organization. Further,

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this knowledge tends to be highly tacit, not documented and even the person himself

might not be aware of such knowledge. For sure, this knowledge resides with the people

who stay with the company for a very long time. Thus, this is mature knowledge that

cannot be collected easily. The bigger lesson is to reserve funds in good times and apply

when the bad times come to retain the staff that is key to the organization. However, it is

easier said than done.

Knowledge of business processes determines the patterns in which the processes

are interlinked with each and work together to produce the specific outcome (Rosemann

and vom Brocke, 2015). This knowledge is not considered that critical as the new persons

can learn the processes because these are routine procedures and not something highly

“tacit.” Most of the companies follow some standard procedures and have well

documents SOPs and flow charts. However, the knowledge of business processes should

be kept up to date as this knowledge is normally documented but not updated regularly

(Joe et al., 2013). If the documentation is incomplete, a certain amount of knowledge

cannot be replicated. Knowledge of processes is important to understand how things are

done around in an organization following some pattern. Knowledge of processes might

be necessary especially:

a) When an employee transitions out and he is the only one knowing the processes or

b) The company transitions out of the system into something new.

Further, if the processes are not as holistic and not as combined and systematic as one

would like it to be, the execution of operations becomes challenging for organizations.

Due to the limitation of resources, organizations need to prioritize the critical

knowledge areas to work on. The relevance of knowledge is thus important. It also

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depends on how well spread are the subject matter experts regarding a particular

knowledge area. If the expertise related to a knowledge domain lies with one person who

is a single point of contact, then the company might get into trouble upon his departure

(Jennex, 2014). Thus, the relevance and criticality of knowledge are determined by the

number of replacements available for a departing employee (Kosilov et al., 2006).

Several factors need to be considered when retaining the knowledge of employee such as,

how good is the network of the employee, how often he meets with people, how long he

has been working in the organizations and at what positions (Jennex, 2014). Thus, it is

quite a challenge to perform a proper assessment of the employee’s knowledge.

Organizational goals and strategy must always be the focal point (Leibowitz, 2009) when

conducting such assessment. Every job entails a specific set of roles and responsibilities.

Thus, an employee performs a series of task to meet the requirements of a job and (s)he

could have gone through various positions since joining the company. Further, the

location of the organization also plays a part here. The type of expertise required for

projects carried out at one location might be different from other locations. For example,

the exploration projects in the Middle East are very different from those carried out in the

Arctic or the North Sea. Thus, all these factors contribute towards the relevant knowledge

of an employee and need to be considered when performing the assessment for

knowledge loss. Moreover, it is important to break down the employment history of

employee according to different positions and expertise he has gained over time. In other

words, it is important to have a one-one mapping of critical knowledge. Through this,

organizations can identify the closest person with similar expertise and skill set to serve

as a replacement for the employee or to whom knowledge of the departing person could

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be transferred. The relevance of knowledge as discussed earlier needs to be kept in mind

as all the past knowledge of employee might not be important for the organization and

thus one-one mapping needs to be performed accordingly. In the light of this discussion,

figure 5.5 provides a general outline that can be followed by managers. The six

knowledge types identified in this study can be checked for the departing employees.

After the knowledge type is identified then, the relevance of that knowledge can be

checked. The manager of the departing employee needs to play a proactive role in this

process; along with assistance from the peers of the departing employee. The peers

through their experience of working with the departing employee can truly identify and

evaluate the knowledge of the person.

This chapter thus concludes the phase 2a of the study on identification of

dominant knowledge loss factors and critical types of knowledge lost when employees

depart from the oil and gas industry. The next chapter covers the phase 2b of the study on

the linkage of big data to knowledge management and if the knowledge workers and their

expertise can be replaced by big data.

.

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Fig. 5.5 A Process for Knowledge Loss Assessment for Departing Employees

Knowledge

of Trains Wrecks

Likelihood of Knowledge Loss

• Retirements, Lay-offs (Dominant Factors of Knowledge Loss and companies need

to pay special attention to this)

• Turnover, Job Change (Not considered as major factors of knowledge loss but need

to be addressed in case such situations occur)

• Contract Workforce (Might be major factor in near future and not much awareness

among companies for this knowledge loss)

Relevance of Knowledge type possessed by employees: For knowledge types

identified, check if

1. Knowledge is aligned with goals and objectives of organization

2. Enough number of replacements with similar knowledge/expertise are available

3. Needed in future projects

Identifying Critical Knowledge Areas of Employee: Need to check

if the employee possesses knowledge related to following

knowledge types

Specialized Technical

Knowledge

Knowledge of Train

Wrecks

Knowledge of

Relationships

Knowledge of

Management Knowledge of

Business Processes

Decision: If knowledge needs to be retained or not

Contextual

knowledge

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CHAPTER 6. CONNECTING BIG DATA WITH

KNOWLEDGE MANAGEMENT

6.1 Introduction

During the 3rd interview, the participant from company J made an interesting

statement that companies need to pay attention to the new trends such as big data for

efficient production from the oil reserves. As the oil prices caused operations to close and

laying off employees, new technologies such as big data can play their part here. Further,

this intrigued the researchers to conduct a study on big data. This study is thus an

extension of the 1st study on knowledge retention and the aging workforce. In this study,

the researcher has made an attempt to move a step forward in the research work about

knowledge retention to investigate the connection between knowledge management and

big data and if this potential of big data for knowledge creation can be used to replace the

knowledge workers.

It was found that there is a connection between knowledge retention of experts

and big data. The knowledge of the experts is necessary to understand the patterns and

prediction revealed from big data. Big data cannot replace the deep tacit knowledge of

experts and leverage on big data involves both predictive knowledge and tacit knowledge

of the employees. Thus, this knowledge of experts plays its part in big data activities and

thus, needs to be retained. On the other hand, big data can enhance the knowledge

management capability of the organizations as big data is a unique, heterogeneous and

distinctive source of knowledge for the organizations.

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6.2 What is Big Data?

Big data is a buzz word that is encapsulating organizations and media these days.

The advancement in IT has resulted in an explosion of big data created from various

sources such as social media, sensors and technological instruments, transaction records,

etc. The three words that are previously used to brand big data are volume, velocity, and

variety. Nowadays a fourth V namely veracity is also used to represent big data

(Schroeck et al., 2012a). The volume of data is high as a tremendous amount of data is

being generated from multiple sources (Waller and Fawcett, 2013). Velocity indicates the

speed at which big data is being generated. Due to the explosion of multiple data sources

as a result of advancement in technology, the data generation rate is far higher than

before, and according to Sagiroglu and Sinanc (2013), 5 exabytes of data is generated in

2 days which is equivalent to the total data generated by humans until 2003. According to

IBM, 40 zettabytes of data will be created by 2020 which is 300 times more than the data

we had in 2005 and an estimated 2.5 quintillion bytes of data are created each day

through different sources (Price, 2015). Most of the companies nowadays have huge

repositories of data and in the USA, on average companies have 100 terabytes of data

stored in their repositories. Variety means that data can exist in various forms (structured,

semi-structured or non-structured) and is generated through different ways. According to

IBM, 30 billion postings are shared every month on Facebook, the global size of the data

in the health sector is 150 exabytes, more than 4 billion hours of video are played on

YouTube every month, and active users on Twitter post 400 million tweets each day

(Sagiroglu and Sinanc, 2013). Finally, veracity entails if the data is of good quality and

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useful for effective decision making. No doubt that effective decision making depends on

the quality and accuracy of data and if the data is not accurate, it is hard to get desired

results. IBM Institute of business value provides an overview of big data using 4 Vs as

shown in figure 6.1. There are other definitions of big data as well, a notable one to

mention is by Apache Hadoop stated as “datasets which could not be captured, managed

and processed by general computers within an acceptable scope” and based on this

definition, McKinsey & Company considered big data as “next frontier for innovation,

competition and productivity” (Chen et al; 2014, p. 173). Internet of things is also a key

player in the rapid growth of big data. Chen et al. (2014) describe this boom of big data

through figure 6.2.

Fig. 6.1 The Four dimensions of Big Data

(Source: https://www.slideshare.net/ibmsverige/building-confidence-in-big-data)

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Fig. 6.2 Boom of Big Data across the globe (Source: Chen et al (2014))

Being a relatively new technology, many organizations lack adequate skills and

resources for carrying out big data activities (Schroeck et al., 2012a), However, it is

evident that organizations have started investing in big data activities. Figure 6.3 shows

the Google graphs for the terms “big data’’ and “data scientist.” There were no searches

made for data scientist until 2012, and the only little search was done for big data until

2011. After that, there is a sharp rise in the search for “big data.” Thus, organizational

awareness regarding big data is increasing now. Still it is a long way to go until the

organizations can fully capture the potential of big data as according to the survey done

by IBM (Schroeck et al., 2012b), 24% of the organizations haven’t begun working on big

data, 28% are in the pilot and implementation phase and 47% are planning big data

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Fig. 6.3 Google graph trends for big data (blue) and data scientist (red)

(Source: http://www.google.com/trends/explore#q=big%20data%2C%20data%20scientist&cmpt=q&tz=Etc%2FGMT-8)

activities. So, it means more than 50% of the organizations are not performing any

productive big data activities. According to Davenport (2014), as compared to traditional

data sets mainly used for handling internal business decisions, big data thrives on

improving the services and operations to meet customer demands. An organization can

pay more attention and get more benefit from big data initiatives by moving analytics

away from IT and integrating it into core business and operational functions (Davenport

et al., 2013). McKinsey & Company after research on 5 core industries of US namely

health, retail, manufacturing, public sector administration and global personal location

data, came to the conclusion that big data has huge implications regarding improving the

productivity and competitiveness of enterprises and public sectors (Manyika et al., 2011).

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McKinsey & Company have emphasized that big data can create value in some ways

(Manyika et al., 2011) which include:

i) Creating Transparency.

ii) Enabling experiments to be conducted for discovering the needs and improving

the performance.

iii) Segmenting populations to customize actions.

iv) Supporting or replacing the human decision making through automated

algorithms.

v) The innovation of new products and services.

In healthcare, the main potential of big data lies in clinical decision support

systems, analyzing disease patterns and improvement of public health, and using

analytics, the expenditure of the American medical industry can be reduced by 8%.

Further, they say that retailers can increase their profit by large margins almost like 60%

if they properly utilize the big data for price optimizations, product design, product

placement, etc. Similarly, in manufacturing, the big data analytics can improve sales

support, demand forecasting, production operations, etc. In personal location data, urban

planning, smart routing, and improved business models are a few applications of big data.

Thus, big data encompasses almost every sphere of life. Integrating all this valuable

knowledge obtained through big data with proper actions and business goals can result in

excellent knowledge management.

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6.3 Big Data and Knowledge Management

Knowledge management is a well-known term which has thoroughly been

discussed in the literature, and it is almost an integral part of every organization in today's

world based on the knowledge economy. In fact, knowledge management is one of the

most important strategic factors in the organizations (Spender, 1996). In classical

literature, knowledge management has been described through 4 principal activities

namely creating, storing, transferring and applying knowledge (Alavi and Leidner, 2001).

According to Wiig (1997), knowledge management can be defined as the ability of the

organizations to intelligently use its knowledge assets for overall success and improved

performance. Gupta et al. (2000) defined knowledge management as a process that helps

organizations find, select, organize, disseminate and transfer relevant information and

expertise necessary for activities such as problem-solving, dynamic learning, strategic

planning, and decision making. The above definitions reveal that central idea is the

creations and dissemination of knowledge using technology and people to achieve

organizational goals. Proper creation and dissemination of knowledge results in improved

performance of employees, business success, and higher competitive advantage.

Knowledge management capability of an organization as defined by Chuang (2004) is

"the ability to mobilize and deploy KM-based resources in combination with other

resources and capabilities” (P. 460), to improve business and gain competitive advantage.

If we talk about the resource-based perspective of knowledge management,

technological, cultural and structural resources are the sources of organizational

capability (Gold et al., 2001). Varun and Davenport (2001) also emphasize that culture,

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technology, and strategy are sources for business growth as enablers of KM. Big data is

generated through various technologies and is a source of business growth in the

organizations; thus, it can be termed as an enabler of KM in the light of the above

discussion. If we compare the definition of KM and big data regarding value creation, a

link can be established on the generation of knowledge from big data for enhanced

organizational performance, thus, in line with the objective of knowledge management.

Further, if we look at the DIKW model (Ackoff, 1989), knowledge is considered separate

from data and information (Fig 6.4). Here it is important to understand the conversion of

raw data into information and information to knowledge. Data is usually the discrete facts

without any context, but when this data is given some context, it becomes information.

Fig. 6.4 The DIKW Model (Source: Sumbal et al. (2017a))

This information using personal experience and intuition is then converted to knowledge.

The aim of both big data and knowledge management is to use knowledge for improved

performance. However, the difference lies in the way it is performed. Big data is obtained

from a variety of sources and is normally structured, semi-structured or unstructured.

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Using analytics, this data is organized in some meaningful pattern to reveal some

meaning thus turning the data into information. Finally, this information is interpreted

and analyzed by business intelligence tools or data analysts to unveil some actionable

knowledge termed as knowledge management. Thus, this analogy helps in determining

the link between big data and knowledge management where we have data and

processing and interpretation of this data using analytics results in effective knowledge

management. According to the knowledge-based view of the firm, the amount of

knowledge within the organization determines the value of the organization (Grant,

1996). The sustainability of an organization is determined through the knowledge

accessible within the organization and reconfiguration of available knowledge-based

resources to gain competitive advantage.

Knowledge based resources serve as the basis for competitive advantage because

these tend to be valuable, difficult to imitate, scarce, and cannot be substituted by

competitors (Barney, 1991). The purposeful use of stocks in the organizations for

knowledge flow across the organizations (Teece, 1998) can bring competitive advantage

to the companies (Zack, 1999, Grant, 1996) to obtain a superior position among the

market competitors. Big data also falls under this category and through the development

of big data approaches (Vance, 2011), there is a similar interest among organization to

focus on big data and use it to gain competitive advantage. One of the goals of

knowledge management is to integrate the information from various sources and extract

knowledge from it to make decisions (Lamont, 2012). Similarly, in this era of big data,

valuable knowledge can be generated through analysis of data generated from various

sources. Bose (2009) describes the decision making through analytics as “knowledge

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management initiative in which the organization's best practices for each decision-

making process are pushed to the desktops of end users as embedded logic within

analytic applications” (P.157).

Business intelligence is an upcoming trend to analyze structured and unstructured

data using techniques such as data mining, statistical analysis, etc. for improved business

performance. Managers can maximize their efficiency by putting into practice, the

acquired knowledge gained through analytics (McAfee et al., 2012). For example, Fuchs

et al. (2014) did research in the tourism industry and developed a destination

management information system. This system helps in valuable knowledge creation for

organizational learning through extracting structured and unstructured data from web-

based sources such as social media, online surveys, etc.; and applying analytics on it to

provide intelligent services to customers such as hotels, guides, recreational service,

etc.The e-businesses are also taking advantage of big data and companies are

incorporating real-time knowledge management through big data significantly improving

the businesses (McAfee et al., 2012). For example, Amazon.com is a big e-business

giant. It is highly customer focused, and it keeps on optimizing itself continuously.

Customers generate an enormous amount of online data. Through the application of

machine learning algorithms, Amazon gathers the knowledge on personalized preferences

and trends and uses it to improve the services and processes for the customers. Similarly,

Ali Baba, another e-business giant, provides loans through automatic analysis of

transaction data without any human intervention. Through this way, loans of 30 million

RMB have been granted. Similarly, in the health sector, the machine learning algorithms

are used to unveil patterns for anticipating the infectious diseases in babies (Davenport et

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al., 2013) thus helping in decision making regarding birth. Similarly, Chen et al. (2014)

stated that using big data; the organization can enhance their production efficiency for

example in sales, optimization of commodity prices, in marketing, prediction of customer

behavior, etc. These examples on big data reveal a new form of knowledge management

in which big data and analytics play a critical role for effective knowledge generation.

Effective decision making is one of the goals of knowledge management, and it is also

performed through the knowledge generated from big data providing a linkage between

the two (Murdoch and Detsky, 2013). A survey by IBM reveals that top enterprises are

successful as they use big data and analytics 5% more than their rivals and this success

comes from the competitiveness explored in the big data (LaValle et al., 2011).

Companies need real-time decisions in today’s competitive world. The current market is

affected by a lot of factors and disruptions, such as oil prices, force majeure for example

earthquake, epidemics, political disaster and in such scenarios, the market strategy and

decisions can be made real time using big data (LaValle et al., 2011).

If there is inconsistency in new knowledge creation, it can reduce the performance

of the organizations. Thus, it is imperative for organizations to continuously strive for

new knowledge creation (Choi and Lee, 2002). Moreover, to exacerbate this knowledge

creation process, the key enablers of KM such as people, processes and organizations

need to work coherently. What type of knowledge is important and unique for

organizations is explained through the studies conducted on intellectual capital and

intangible resources of the organizations (Bassi, 1997, Hall, 1992). The knowledge based

theory (Grant, 1996) then explains how these resources can support organizations in

capturing the market and gain competitive advantage (Choi and Lee, 2002). Finally, the

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knowledge creation and learning using these resources are explained by the classical

SECI model by Nonaka and Takeuchi (1995). The traditional SECI model incorporates 2

types of knowledge, tacit and explicit. Tacit knowledge, relatively difficult to be codified

resides in heads of employees (Gore and Gore, 1999) whereas explicit knowledge is the

structured knowledge that can be documented and easily transmitted to others (Duffy,

2000). The conversion between tacit knowledge and explicit knowledge through the 4

processes (socialization, externalization, combination, and internalization) leads to

knowledge creation and learning. Also, the type of knowledge strategies adopted by firms

significantly influence the KM processes (Zack, 1999). As the business environments and

scenario change, the reconfiguration of knowledge takes place through continuous

learning and knowledge acquisition. Organizations these days should possess this

'dynamic capability'' of adapting themselves to new trends, shape the businesses and

further focus on learning and creating new knowledge to sustain their competitive edge

(Fuchs et al., 2014). Teece et al. (1997) describe this concept of dynamic capability as

''ability to integrate, build and reconfigure internal and external competencies to address

rapidly changing environments" (P. 516). Based on this concept of dynamic capability,

big data is a new addition in the business environments. It is a source of new knowledge

creation and learning. It provides an opportunity to explore the hidden patterns inside the

data and use it for predicting the markets and improve business processes and

information through informed decisions.

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6.4 Research Context and Motivation

To get a clear picture of the previous work on big data and knowledge

management, an extensive literature review was performed using various databases such

as Web of Knowledge, Scopus, ProQuest, and ABI Inform. It was found that very little

work has been conducted on the linkage of big data and knowledge management. When

searching articles, more attention was paid to the journals about knowledge management

such as Journal of Knowledge Management, VINE Journal of Information and

Knowledge Management Systems, Knowledge Management Research and Practice, etc.

Various combinations of keywords were used to search the articles. These keywords

included big data, analytics, knowledge management, knowledge generation, knowledge

creation, etc. Articles from the field of computer sciences and non-academic reports were

not included. As big data is a new field, a total of 60 articles were found. These articles

were then further reviewed. Out of these articles, only 2 articles were found that focused

on explaining the relationship between big data and knowledge management. The first

study was by (Chan, 2014) entitled as “Big Data Customer Knowledge Management.”

Customer knowledge mainly comprises of 3 types. The first one is the knowledge of the

customer. It is derived from the experiences of customer’s interaction with the firm and

involves using the firm’s products and services, and experience of customers with the

marketing, sales, and services of the firms. The second one is knowledge about the

customer which involves knowing the purchasing patterns, demographics, and behavior

of customers. It is obtained from various sources such as data mining firms, public

records and credit bureaus, etc. Finally, the third one is knowledge for customers, and it

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involves information about firm’s products and services. The customer knowledge

management (CKM) thus revolves around the customer knowledge creation, acquisition,

sharing, utilization, and storage. The traditional customer knowledge management

revolves around the records within the organizations whereas big data customer

knowledge management focuses on external platforms such as social media and machine

to machine (M2M) communication, web logs, call centers, emails, and surveys, etc. This

study provides a conceptual framework for integration of CKM with big data channels.

The authors propose that massive volumes of a variety of structured and unstructured

data about customers can be transformed into valuable business insights through the

application of analytics which can then be fed into traditional CKM model of knowledge

creating, sharing and utilization. Thus, this study just proposed a conceptual framework

based on the literature review to link big data and knowledge management.

The second study was conducted by Erickson and Rothberg (2015) entitled “Big

Data and Knowledge Management, establishing a conceptual foundation.” They used the

big data report from McKinsey Global Institute (Manyika et al., 2011) on various

industries. With this information on these industries, they used a strategic protection

factor framework for developing the link between intellectual capital, big data and

knowledge management. The study indicates that a natural connection exists between

KM, IC, and big data as all the three fields deal with some sort of intangible assets in the

form of data, knowledge, information or intelligence. As mentioned in the study, the

McKinsey Global Institute (MGI) report described the “Ease of Capture” for value

potential of big data using four indicators namely talent, IT intensity, data-driven

mindset, and data availability (Erickson and Rothberg, 2015). The authors argued that

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these factors have some relation with common knowledge concepts, for example, talent is

related to human capital and tacit knowledge. Staff and managers with right analytical

and technical skills are necessary to make the most out of big data. IT intensity is related

to structural capital and is used for managing data, information, and knowledge. Data-

driven mindset relates to the human capital again and focuses on the knowledge of

managers and leaders to understand and invest in big data. Finally, data availability is

linked to the precursors of knowledge i.e. enough data is available to extract some

valuable knowledge from it. Thus, through such linkages, the study made an attempt to

illustrate the linkage of big data to knowledge management.

These 2 studies provide a hint about the connection between big data and

knowledge management and reveal that the research on this topic is scanty. Most of the

work as discussed in literature review section provides an overview and some theoretical

evidence of a connection between big data and KM, thus unveiling the gaps regarding the

empirical investigation on this topic. This study, thus, aims to address this gap and

focuses on how companies and employees establish a link between big data and

knowledge management through some practical evidence of value creation and

leveraging on big data. Thus, the main research question addressed in this study was:

Q1: What is the interrelationship between big data and knowledge management in a

knowledge intensive industry?

The oil and gas industry is one of the most important and largest industries in the

world (Inkpen and Moffett, 2011). At the same time, it is one of the most complex

industries with business spread across all parts of the world and having installations at

challenging locations (Longwell, 2002) such as Arctic, North Sea etc. Further, it is a

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highly knowledge-intensive industry and produces large volumes of data in day to day

operation, especially in the exploration and production areas, and with new technologies

and sensors, this data is being generated at a far higher rate (Farris, 2012). The data is of

critical importance in the oil and gas sector, and engineers and expert rely on this data to

predict if oil exists at some location and if it is feasible to drill a well (Inkpen and

Moffett, 2011). Moreover, oil and gas operations tend to be very expensive costing

millions of dollars (McKenna et al., 2006). Thus, accurate and timely decisions are of real

importance with data playing a key role. All these factors provided a strong context for

the researchers to conduct research in this sector.

6.5 Methodology

As this study is an extension of previous work on knowledge retention and the

aging workforce, the interviewees from the same group were selected based on the

criteria that their organizations were conducting some work on big data. As big data is a

new field and companies are at initial stages, only 9 out of 20 interviewees from the

original group said that their companies were doing some work on big data. One more

interviewee, not part of the original group, was also included based on his expertise.

Thus, these 10 interviewees (Table 6.1) were then further inquired of some questions on

the linkage of big data to knowledge management and if big data can replace the need for

retaining the knowledge workers or their knowledge. Although the sample size is small,

the detailed responses from the interviewees helped in developing and understanding the

relationship to get the answer to the research question. There was a high degree of

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Table 6.1 Details of Interviewees in the study

coherence in the answers allowing for saturation and also helped in shaping a framework

to explain the relationship between big data and knowledge management. The inclusion

of more interviewees could have yielded more insights, but due to resource constraints

and time limitations, no more interviewees could be included. The same grounded theory

approach was continued here, and ATLAS.ti was used for coding and analysis of data.

The interviews were recorded and later transcribed. The transcribed interviews were

verified from the interviewees and changes were made in some cases by them.

Unambiguous answers were also cleared from the interviewees. Through careful coding

Interviewee Years of

Experience Position Company Location

No of

employees in

Company

1 8 Managerial A Russia 50k-100k

2 30 Managerial B USA 5k-10k

3 10 Managerial/

Consultant C USA 20k-50k

4 6 Junior Manager D Nigeria 5k-10k

5 16 Managerial E Middle East 20k-50k

6 10 Managerial F Middle East 20k-50k

7 7 Managerial/KM

Coordinator G Australia 50k-100k

8 26 Director/ KM

Lead H USA 50k-100k

9 35 Director/ KM

Lead I Netherlands 50k-100k

10 10 Managerial J Australia 50k-100k

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of the transcribed data using ATLAS.ti, the categories were created to understand the

underlying concept. These categories served as building blocks to constitute the

framework on the linkage of big data to knowledge management. The concepts that

emerged from data were compared with existing literature to look for the similarities and

differences. The new insights that emerged from this study are then further discussed in

the light of practical examples provided by the interviewees.

6.6 Results

This section will describe in detail the results obtained from the interviews. The

results are categorized according to the categories developed during the coding process in

ATLAS.ti. A total of 42 codes were generated during the coding process. There were 2

broad categories conceived as a result of sorting these codes. The first category is big

data in oil and gas sector and the second one is linkage of big data to KM.

6.6.1 Big Data in the Oil and Gas Sector

The code table and network view for this category are in appendix C. The oil and

gas sector has been long working with data. The term “Big Data” has come into existence

since the last decade and got popularity in the organizations. Big data terminology is well

known in oil and gas sector as interviewee 9 from a super major company stated that:

“We have been active in one particular from of big data for many many years

which is seismic interpretations …. Massive data collections around 3D and 4D maps.

We have lots of data processing on that in oil and gas. That is huge data base... So, some

areas we are very good at big data and that is quite structured data. Problem we see is

we are using same people who looked at structured data to look at unstructured data and

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that didn’t really work …. So, now we have VPs, analytics, human resource, the technical

part of X, the IT part, they all work together (on big data)?”

Interviewee 8 also described the familiarity of organizations with big data as:

“We have got big data, and we have had big data and we deal with big data. Now it’s

like the rest of industries, (and) other organizations are catching up. To summarize, big

data has been there from day one. Talking about petrotechnical, the amount of data that

we produce, it’s always been like that”

Big data is the point of major concern in oil and gas sector. The amount of data

being generated is very high in oil and gas companies and with the advanced technology

and tools, the data generation rate has surged especially in the areas of drilling and

exploration. Interviewee 10 stated the importance of data in oil and gas sector as:

“I think in oil and gas to some extent; most oil and gas executives understand

they are critically dependent on data and information. Because what is happening is far

below the surface and you have to use all these very expensive methods to collect the

data. So, they are very aware data is expensive”

Thus, the term big data is well known in oil and gas however the amount of work

being done in this regard is another issue. Using big data to improve the business is still

at an early stage in most of the oil and gas sector as interviewee 2 stated that:

“It really varies. Bigger companies, they generally have been through the cycle of

looking at Hadoop and looking at some of the analytic tools and may be have settled on

standards ... So, they are playing around with tools. They are trying it in a very limited

way, they are trying to understand how to use these”

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Interviewee 7 also supported the fact that companies have huge volumes, but they

lack the understanding of how to use the data properly:

“I think it is the right problem to tackle. With all these data driven oilfields, smart

oilfields, digital oilfields, whatever you call them, they all have this rich access to big

data but they (companies) really don’t know how to use it”

Seven interviewees mentioned that they have dedicated units and teams working

on big data. Rest of the interviewees stated that IT departments in their companies handle

the big data operations. Further, the interviewees agreed that the top management is

aware of the benefits associated with big data and fully supports it. Interviewee 8

described this as:

“They (management) sponsor the big data program. They are aware and support

it, they are the stewards that own the data”

Interviewee 1 also suggested the similar ideas about the involvement and concern

of top management as he stated that:

“Touching all the digital sphere, we have support from most top executives. There

are dedicated digital officers for all divisions, including exploration and production, and

chief digital officer who directly reports to our CEO. So, for big data, top management is

fully involved”

The above results provide an excellent overview of big data in oil and gas sector.

Oil and gas companies generate an enormous amount of data. They have been focusing

on big data and most companies have dedicated sections and teams for carrying out big

data activities. However, from the responses of the interviewees, it seems that companies

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are in a trial phase regarding big data and are not fully leveraging on big data. This will

be discussed further in the next section.

6.6.2 The Linkage between Big Data and KM

The code table and network view for this category are provided in Appendix C.

Interviewee 1 from Company A stated that they started work on big data in 2015 and the

company is in the trial phase with big data activities. The company has initiated around

15 big data projects, and there has been some good progress in some of the areas in this

regard. Also, he stated that big data can be linked to knowledge management in terms of

value creation. There is one project on predictive maintenance of rotating machines such

as turbines and pumps. In general, the company has a standard maintenance schedule of 6

months in which the machines are stopped and inspected for any required maintenance.

An enormous amount of data is generated by different sensors mounted on these

machines. This data is analyzed using the software to check the various parameters of the

machines. The required maintenance of the machines is then performed based on the

analysis of these parameters. This project is in the application phase. Another project in

the trial phase is the autonomous monitoring and analysis of the satellite images of

underwater installations. A large number of images of these installations are continuously

being captured, and machine learning algorithms are used to analyse these sheer volumes

of the images automatically. The analysis is performed to check the oil leakages in the

underwater installations to cater water pollution. Thus, leakages can be detected with ease

and in short time to protect the environment as well as loss of oil. Another project in the

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trial phase is analysing and gathering information about competitors through the publicly

available information using text mining and semantic analysis techniques.

Interviewee 2 from company B was of the view that big data and knowledge

management are quite related to each other. It is all about the management of knowledge.

The hidden patterns inside the data reveal useful knowledge and how this knowledge is

propagated and used comes under the knowledge management umbrella. He provided an

example of a project to explain this linkage. This project was on identification of

parameters that constitute a good fracturing job. Fracturing process involves opening the

fractures in deep rock formations and widening them by injecting liquids and chemicals

at high pressure. This fracturing process allows easier extraction of oil and gas; however,

fracturing jobs tend to be very costly. Thus, knowing the optimal parameters for good

fracturing job is the key to success. There are about 32 parameters to be considered when

performing fracturing. Thus, in this project of understanding critical parameters, the team

collected data about these parameters (pressure, flow rate, proppant size etc.) for

fracturing jobs performed in different fields. The analysis of these parameters using

analytics and through expert insights helped in understanding the most critical

parameters. Interviewee 2 highlighted the importance of experienced insights for the

analysis of data as he stated that:

“Having the right knowledge from experienced people and using it for analysis of

data is extremely valuable and people don’t realize it that much”

From the results, it was found that there was a difference in fracturing jobs

performed in different fields. The operating parameters that worked at one place did not

yield the same results at another place. These parameters were dependent on the geology

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and structure of the formations at different places. This information could not be

discovered by just merely looking at data. Thus, it is not only the volume of data but also

a variety of data and how this data is analyzed to gain valuable knowledge. The tacit

knowledge of the experienced employees is also used in this as the analysis provides the

patterns, but the interpretations of these patterns need to be performed by employees who

have worked in such processes and understand the technicalities.

Interviewee 3 from company C stated that big data is a new technology and

software vendors sell it to the companies like a wonderful black box. Thus, companies

assume that they can feed data to this black box and get wonderful solutions but there is

need to change this perception. He described the linkage of big data to KM as:

“Organizations don't typically brand the big data initiatives with knowledge

management; however, big data can be linked to knowledge management very clearly

around decision making. If you provide the right basis for the decision making... You

analyze the data, you give a prediction and you execute against what the prediction is

saying, then you analyze with an after-action review if it went well and then you feed it

back to the big data people over there making predictions, to make sure prediction is

better next time. Thus, at the end, big data is purely the specific information that has been

provided for your decision making and can be vetted by the community of experts, and

you can put knowledge management personal element in there to give an extra filter and

not just leave it to machines because there is a feedback loop necessary”

He provided an example of an experiment performed in his company. In this

experiment, the data analysis for a drilling operation revealed that there was a 70%

chance of multiple failures in the next one hour. Based on this prediction through

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analytics, a decision could have been made by the in charge to shut down the well and

change motor or take a risk and continue the work. Instead of putting it to an individual

decision, the company placed it on a group to discuss the prediction with experts based

on their expertise and then decide whether the prediction is right or not, and suggesting a

way to execute it. Then, if it happened or not, the experts would again discuss and feed

the conclusion to the data analysts generating reports from big data. Thus, in this case, the

knowledge of experts was used to converge fast on decision making and in real time. The

interviewee further stated that the knowledge related to sub-surface is essential when

drilling wells. Through comparison of subsurface data using analytics, the knowledge

obtained during an operation at a specific location can be used to perform a similar

operation at another location. For drilling operations, this will help in executing a well

faster. If a well is executed faster, it will save the cost of the rig which can be thousands

of dollars per day. Moreover, drilling a well faster means getting into production more

quickly and having a more rapid return on investment. Thus, it is all about optimization,

and it is possible through the application of valuable knowledge gained from data along

with utilizing the knowledge of experts. The interviewee further stated that people get

excited about analytics and they think that analytics are the solution to everything but

analytics cannot do all the work. Thus, analytics can fail on predictions. This

understanding on why predictions fail is associated with how the organizational culture

supports big data and how much people understand this value generation from big data.

The interviewee from company D stated that big data is of critical importance in

every industry. In oil and gas, it is of key importance although not being used to its full

potential. He explained the reservoir facilities management to elaborate the linkage

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between big data and knowledge management. A significant amount of data is gathered

through various instruments and sensors installed in the reservoir. This data tells about

the strengths and weaknesses of the reservoir. Through analysis of this data, the operating

parameters can be set for optimal production, and it can also be known why there is a

difference, for example, in the initial production plan and actual production going on.

Thus, using more sophisticated sensors, large amounts of reservoir data can be collected

and then analyzed for enhanced performance. The interviewee further added that there is

an issue of integrating the data from the assets. To understand the asset correctly, it is

vital to store, organize and define this data in the proper way. There is a lot of historical

data on the structure of the surface and sub-surface properties, and it is an important

source for drilling wells. If this data is managed properly, it can be tracked easily for

future use.

The interviewee from company E was of the view that oil and gas companies have

always been working on data, for example, in the case of reservoir modeling and sub-

surface analysis. Now with the advent of big data, they are further trying to enhance it.

He provided an example of big data utilization on CAD diagrams of the field. The

metadata of the drawings is taken to see what parts of the drawing are affected by health,

safety, and environmental (HSE) concerns. He further was of the view that companies in

the Middle East are trying to figure out how to effectively use big data as there hasn’t

been much progress in this area. Thus, its linkage with knowledge management is a step

ahead, and it will take some time for the companies to comprehend this. The interviewee

from another company F in the Middle East revealed that big data and analytics help in

having structured knowledge material that can be used for improving the business. He

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said that there is a lot of data generated during oil and gas operations. However, it is the

component of valuable knowledge that needs to be extracted out of it, for example,

analysis of reservoir data from new and old oil fields for enhancing the core operations.

Using big data in a structured and meaningful way is still in its infancy in the Middle East

companies; however, benefits and value associated with this data are well understood by

the companies.

The interviewee from company G stated that big data is all about decision making

and this decision making is carried out by analysis of authoritative data as he stated that:

"Companies won't make decisions based on poor data. They want authoritative

definitive data that's probably trended over a long period of time so that they can, you

know, feel comfortable in decisions they are making on process improvement, so, that’s

where it comes in and thus the tacit knowledge of decision will come from that explicit

knowledge (from big data)"

He provided an example of a pump which is not able to maintain its performance.

Even after multiple replacements, if the pump is still not working properly, it will affect

production. From the data, they can analyze if there is need to redesign the pump or fit

new equipment. The interviewee further supported the previous finding on the integration

of data stating that:

“I think integration is the biggest challenge of all, we have got different

applications, different vendors, ……. We don’t have metadata aligned with document

management systems and share point collaboration solution, we don't have security

aligned. We don’t have the information architecture aligned, so, we can’t even integrate

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between our instructional information which is really simple. So, can you imagine how

they struggle when they are dealing with the (complex big data) systems they have got”

Company H representative mentioned that they have achieved some success in

big data projects and have a large dedicated team working on big data. On linkage of big

data to knowledge management, she stated that:

“One strong use of big data is to create knowledge that we didn’t even realize

we already had, that was buried or hidden in data and data analytics can actually find

that knowledge………So, I don’t call data as knowledge management but it certainly can

enhance knowledge management”

She provided example of hardware maintenance stating that generally, they have

knowledge manuals or books which tell them when to perform scheduled maintenance

and how to maintain a piece of equipment, so they know this already but through big

data, they can have advanced predictive knowledge if some equipment needs

maintenance apart from regular schedule and if there is any issue with the equipment

while it is running, and thus they can take measures accordingly to prevent failure. Thus,

the ability to generate predictive knowledge from big data can help to timely work on a

piece of equipment which can avoid stoppage of operation and save time as well as avoid

cost overruns for the company.

The interviewee from a company I revealed interesting insights on big data and

KM linkage. His company conducted experiments with IBM Watson and found that there

is a linkage between big data and the knowledge of the employees. The interviewee

provided some examples of the projects related to big data, going on in the company. The

first project is monitoring of machines for example compressors. Using different

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monitoring systems, the data about the compressors are collected from various locations

to know the optimal parameters for good performance. If a compressor is malfunctioning

at some location, these parameters can be compared to solve the problem and get

maximum operating efficiency. The interviewee stated that they did not have enough data

on compressors about a year ago, but now because of data availability, they can sort out

the problem quickly, shut down the compressor for a shorter time and put them on track

after maintenance. Through the help of technology and analytics, it is possible to collect

data from multiple locations, analyze it and make improved decisions. The interviewee

further added that engineers who have expertise in this area could build on the linkage of

different parameters for the compressors. Another project that utilized the big data and

analytics was the development of a catalyst for gas liquefication. A catalyst speeds up the

chemical process for gas liquefication. The R&D department of the company works in

the preparation of catalysts, and some parameters are considered in this process. Through

a mass experimentation of combining various parameters to form catalysts, the company

analyzed which catalysts are more efficient. This mass experimentation has accelerated

the process of developing the catalysts. Now, the company can produce a catalyst in 13

months which, before, took years to develop. The interviewee further added that:

“It is the union of both sides, it is critical to get both sides, the experienced

people don't know how to handle data and data scientists don't know the business, so,

together when they work, they can challenge each other and that’s the best way to do

these projects”

The interviewees agreed that the tacit knowledge of experienced employees is

necessary to understand the output results from analytics and to make a final decision.

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Analytics lack the human intelligence and experience and cannot always be true as

interviewee 3 stated that:

“Exactly this is the last step in the process. Big data could support the whole

bunch of pre-work for decision making but in the end, it is the expert that needs to make

the call on the direction what needs to happen. Big data could prepare a lot of

information, and help the decision-making process be faster but it will not replace the

expertise”

Interviewee 10 from company J also shared similar thoughts:

“This is the other point that we touched on before. Yes, this is where the aging

workforce experience links to the big data. You have all these young people, who are

data analyst but experience is necessary”

At another point, the interviewee stated that:

“In oil and gas, you have the aging workforce that has all the knowledge of the

business, but they don’t have knowledge of new big data techniques. You need the young

gun to handle all these fancy techniques”

Thus, the expertise of employees seem to play an important role in effective

decision making through big data and analytics.

6.7 Discussion and Analysis

Results reveal that oil and gas operations are data intensive and organizations

have been working with data for long. However, the term big data is more widely used

and recognized nowadays, as the fields are equipped with advanced technology and

sophisticated tools generating large amounts of data (Feblowitz, 2013). The challenge

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faced by oil and gas companies is to make proper use of the big data for value creation.

Companies, in general, are well aware of the potential and benefits associated with big

data (Anand, 2013). Most of the companies involved in the study are super majors and

have a clear understanding of the linkage between big data and knowledge management.

However, the big data projects have recently started, and companies are in experimental

phase in most of the cases with few projects in the application phase. According to results

and codes generated from the data, the linkage of big data and KM can be explained

through the following main categories.

6.7.1 Big data: A Catalyst for Enhanced Knowledge Management Capability

There has been a lot of discussion on knowledge being different from data and

information (Alavi and Leidner, 2001, Davenport and Prusak, 1998) where knowledge is

placed at the highest level, derived from information and information derived from data.

Knowledge is a justified belief that helps in making effective decisions (Nonaka and

Takeuchi, 1995) and is not a stock but a flow which is embedded in day to day

organizational processes (Fahey and Prusak, 1998). Further, this knowledge is

everywhere in the organizations and in various forms (Ball and Gotsill, 2011). Big data is

one form of this hidden untapped knowledge. The large pools of data from various

sources can be analysed for generating predictive knowledge (Bose, 2009) that can be

used for improving various operations (McAfee et al., 2012) as shown in results, in case

of predictive maintenance of machines in company A and H where the data from sensors

mounted on machines, is used to predict any required maintenance apart from regular

maintenance. This helps to avoid loss of equipment by timely repairing and maintenance.

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Same is the case for company A using satellite images to track oil traces in water. By

processing the high-resolution images using analytics, any potential leakages can be

found in quick time and the timely decision can be made to avoid cost overruns as well as

protecting the environment. The same applies to the examples from companies D, B and

I. Thus, improved decision making, which is one of the goals of knowledge management

(Bassi, 1997), is clearly supported by these examples of predictive knowledge generation

from big data. Here we can say that big data is being used as a catalyst for enhancing the

knowledge management capability of an organization falling in line with the knowledge

based theory of the firm (Grant, 1996) which implies that knowledge based resources are

most difficult to replicate and complex, heterogeneous resources are the basis for superior

performance. This is evident from the example of developing catalyst in the case of

company D, where mass experimentation analysis helped in reducing development time

of catalyst from years to months eventually helping the organization to speed up its

process of gas liquefication. Same was the case for fracturing job in company B. The

point here is to understand that data possessed by these companies was unique and

inimitable which became a resource for valuable knowledge and utilizing this

heterogeneous resource helped companies in achieving superior performance supporting

the knowledge base theory of the firm. At the same time, the utilization of this

knowledge from big data also enhances the knowledge management capability of the

organizations. Thus, in the words of Teece et al. (1997), big data can be seen as the

reconfiguration capability of the organizations regarding the capacity to absorb the

untapped knowledge in big data and infer generalizable cause and effect relationships

with existing knowledge for enhanced performance. Moreover, integration of knowledge

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and analyzing it from various perspectives to make decisions is another goal of

knowledge management (Lamont, 2012). Same way, data needs to be consistent, and in

an integrated form as in this form, it is useful and easier to extract knowledge out of it

(LaValle et al., 2011). Furthermore, the analysis of data integrated from multiple sources

supports this goal as it was for the case of company D in which data was collected from

different geographical locations and then compared to understand the optimal parameters

for compressors. Similar was the case for company C to converge faster on decisions for

drilling operations through comparing the data of similar sub-surfaces.

According to Cowley‐Durst (1999), an efficient KM system disseminates

knowledge to the right people at right time and focuses on people who can act upon and

create value from that knowledge. Thus, people support is also crucial for proper

application of predictive knowledge generated from big data which brings us to the next

theme that emerged from the results.

6.7.2 Combination of Tacit Knowledge (Existing Concept) and Predictive

Knowledge

The tacit knowledge of employees needs to be combined with the predictive knowledge

from big data. This is another important aspect of the connection between big data and

knowledge management. According to Hair Jr (2007), the first step in big data analysis is

automated learning through machine learning algorithms. These algorithms attempt to

discover the non-obvious hidden patterns of information with the potential of creating

new knowledge. The second step then involves testing and confirming of these

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relationships. This step involves “human learning” requiring human insights to check,

revise and approve or reject the knowledge revealed through the analytics (Hair Jr, 2007).

The traditional concept is that knowledge is created and held by individuals; however, in

the case of analytics, it is different, as, at first instant, the knowledge is created through

large volumes of heterogeneous data and then is vetted by experienced employees to

understand and decide if it could be useful or not. This can be seen for example in the

case of company B in which analytics were used to understand the critical parameters for

a fracturing job. Same was the case for company D for maintenance of compressors.

Similarly, the case of company C is an excellent example of combing tacit knowledge

and predictive knowledge for informed decision making. The analytics applied to the

sensor data predicted the failure of drilling operation; a group of experts then further

vetted this prediction where they used their knowledge to decide on the prediction. This

knowledge of experienced employees is the tacit knowledge (Nonaka and Takeuchi,

1995) and is regarded as one of the most important concepts in the field of knowledge

management.

The results of the study reveal the connection of this existing tacit knowledge

concept with the new concept of predictive knowledge using analytics, emphasizing that

the predictions cannot always be correct and thus need judgment from experts. With the

advancement in the field of analytics, the researchers are trying to make machine learning

algorithms more robust and autonomous so that decisions can be made in real time

without human intervention. However, for this to happen, it is required to consistently

make the algorithms learn to mimic the behavior of experts. This learning comes through

vetting of the predictions by experts and feeding back the information about the accuracy

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of predictions into the systems to make them more robust. However, from the results, it is

apparent that autonomous decision making is still a step further and needs more time to

get mature. Interviewees agreed that the role of experts is crucial to embark on the real

value from predictive knowledge and not blindly follow the predictions. It shows that on

the whole, learning is still a human function and this learning needs to be continuously

mimicked in algorithms to make the predictive knowledge more and more trustworthy.

This can be further explained through the example from company C, using the traditional

SECI model.

The predictive knowledge (explicit knowledge) obtained from analytics indicated

a 70% chance of failure in the drilling operation. This predictive knowledge was

transformed into tacit knowledge when the experts discussed this prediction and decided

about it. After decision implementation, the output was further discussed thus having an

exchange of tacit to tacit knowledge or socialization. Then, after discussing what went

right or wrong and how to perform this task next time, this knowledge was codified

(externalization) to be used in the future. Further, this codified knowledge was feedback

to the analysts to combine it with existing knowledge (combination) for better

predictability next time. This codified knowledge will be further used by data scientists

and experts in the future to learn and update their existing knowledge (internalization).

Thus, a whole cycle of socialization, externalization, combination, and internalization has

been completed starting from the knowledge generated from big data to final decision

making. Following this discussion, table 6.2 and figure 6.5 have been drawn to elaborate

this linkage between big data and knowledge management.

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Table 6.2 Big Data Based Knowledge Management

Further, it is revealed from the discussion that big data cannot be a replacement for the

tacit knowledge of the employees. It is quite evident from the results that expert insights

play a crucial role in final decision making from the predictive knowledge generated

through big data. Thus, the influence of expert’s knowledge cannot be negated. Thus, in

the current scenarios of knowledge loss through retirements and layoffs, retention of

knowledge is crucial, and big data and analytics cannot compensate the knowledge loss

of departing experts. Also, organizations are at early stages in big data initiatives and may

be in future when organizations get advanced and mature in big data application; it might

be possible to replace the knowledge workers through cognitive automation. Thus, with a

large number of layoffs and aging workforce problem, big data and analytics might

provide new frontiers for accessing institutional knowledge to compensate limited expert

staffing (Feblowitz, 2013). This will though challenge the classical concepts of

knowledge management such as preservation of tacit knowledge and human capital for

Big Data-based Knowledge Management

1. Processing of data to extract useful knowledge. High analytical skills required

for knowledge extraction. Knowledge extraction is performed in real time but

may be codified later.

2. Principally knowledge creation, knowledge discovery, and reasoning with

knowledge which later can transform to Nonaka’s SECI model.

3. Machine-focused initially to generate predictive knowledge using analytics and

people focused on later stage to check, revise and approve or reject the

predictions.

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Fig. 6.5 Interrelationship between Big Data and Knowledge Management

Big Data (Structured,

Unstructured, Semi-Structured) from

Multiple Sources

Processing of Big Data (Analytics)

Knowledge Creation and

Discovery through smart algorithms

Predictive

Knowledge (Machine

Focused)

Transformation into SECI Model of

knowledge creation and learning through:

1. Experts’ insights (Tacit knowledge)

and opinions for decision making

2. After implementation, performing after

action reviews to learn and codify the

new knowledge

3. Updating the existing knowledge

bases/algorithms for future use and

other people to learn

Use of tacit knowledge

and human learning to

confirm, understand and

reject/revise the

predictive knowledge

(People Focused)

Improved Decision Making/

Business Performance

191

organizational success. Some areas of concern that need to be addressed regarding big

data and analytics in organizations include the applicability of big data such as what can

be achieved from big data is mostly unclear (Russom, 2013) as the responses reveal that

companies are doing a varying number of activities with big data and there is not much

coherence. Another significant challenge for organizations is the integration of data from

multiples sources (LaValle et al., 2011) to extract the true value out of data. Technology

infrastructure is not a problem for companies and companies can afford massive data

storage and data processing facilities, but they lack the data specialists to perform

analytics and thus there is need to hire more data scientists (Davenport and Patil, 2012).

Also, from the results, it is clear that most of the companies included in the study are

super majors and from developed countries. It will be interesting to have a comparison

among the developing and developed countries on big data initiatives. At the moment, it

seems that companies from developed countries have a stronger focus on big data and can

relate it to big data in a more convincing way. This argument can be further strengthened

by involving more companies from developing countries.

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CHAPTER 7. CONCLUSION

This research work focused on the issue of knowledge retention in oil and gas

industry. Three studies were conducted to meet the following objectives:

• To investigate how companies are handling the task of knowledge

retention (challenges and strategies) from old age retiring workers in oil

and gas sector.

• To investigate the dominant likelihood factors and types of knowledge lost

when employee depart in oil and gas sector.

• To investigate the relationship of big data with knowledge management

regarding knowledge retention and workforce issue.

The grounded theory methodology was adopted to conduct the studies. The results of the

first study led to the formulation of objectives for the next two studies. Overall, the

research work revolves around the knowledge retention and the aging workforce in oil

and gas sector and pivots around the knowledge based theory of the firm maintaining that

skilled human capital leads to success and innovation of the organizations and further

contributes towards achieving competitive advantage. This research work is of

significance importance for the oil and gas industry. From this research work, a number

of implications have been identified for managers and executives in the oil and gas

sector, as shown in table 7.1.

1st Study: The 1st study on knowledge retention and the aging workforce is one of few

empirical studies conducted in this area and the first one to investigate the knowledge

retention issue across varied geographical locations across the globe. Moreover, it is

193

Study Implications

Study 1: Knowledge

Retention and Aging

Workforce

• Organizations need to give serious thoughts to the

knowledge loss caused by retirements and layoffs of

senior employees. They should focus on long term

objective instead of short term focus on cost overruns

and forcing early retirements and layoffs of senior

employees.

• The upstream sector is suffering the most through these

retirements and layoffs. It is a very knowledge intensive

sector and losing experts in this area can have

devastating effects on the exploration and production

operations in future.

• Organizations, in general, don’t have any knowledge

retention programs for the departing employees and

need to pay attention to this. Some formal knowledge

retention programs have been mentioned which can be

used by managers and organizations as a basis to start

knowledge retention programs in their companies.

• Managers and executive need to promote the image of

oil and gas sector to attract younger talent. The war for

talent will be on the rise in future considering the mass

exodus of boomers and not many new people joining oil

and gas. Thus, proper recruitments and offering

permanent jobs to employees should be one of the key

priorities of the oil and gas companies.

194

Study 2: Likelihood

Factors and types of

knowledge lost when

employees depart

• The contract workforce and retirements are major

factors of knowledge loss and require the attention of

the organizations. Moreover, through knowing these

factors, organizations can prepare well in advance to

retain knowledge loss from these factors.

• Managers and executives can assess the criticality of

employees against the six knowledge types discovered

in the study. If an employee possesses any of these

knowledge types, it might be important to retain that

knowledge.

• The relevance of each knowledge should be checked to

decide whether to retain knowledge or not depending on

the future organizational goals and strategy.

Study 3: Big Data and

Knowledge Retention

• Big data is a catalyst for enhancing the knowledge

management capability of the organizations and an

inimitable source of competitive advantage providing a

strong reason for executives and managers need to

invest in big data initiatives.

• Managers need to make sure that expertise of the

employees are well utilized in big data activities for

improved decision making. The knowledge of the

experienced employees plays a key role in this regard.

• The organizations need to hire the skilled people and

build dedicated units and teams for performing the big

data activities.

Table 7.1 Implications of the Studies

probably the first study of its kind conducted in oil and gas sector. Oil and gas industry is

unique because of its knowledge intensive operations and business spread across different

195

geographical locations. The study provides useful insights to researcher and managers on

the issue of the aging workforce and knowledge retention. Further, it explains in detail

the measures companies are adopting to handle this problem and the various challenges

in this regard. The research questions in this study were:

i) What is the current situation of old age retiring workers in the oil and gas

sector, due to the economic crisis and how is the oil and gas sector

handling it?

ii) What strategies are being adopted for knowledge retention of retirees and

what are the challenges in their successful implementation in a global

perspective?

iii) What are the organizational dynamics due to different geographical

locations and difference in upstream, downstream, and midstream sectors

regarding knowledge retention of retiring workers?

The study demonstrated that workforce crisis is quite evident in the oil and gas

sector and the aging workforce situation is same across all the three sectors namely

upstream, midstream and downstream. A huge number of employees are going to retire

soon. The companies in developed countries are mainly faced with this shrinking

workforce issue because of a huge number of retirees and not many people joining the

industry. However, companies in developing countries are not having this aging

workforce issue, but they lag in knowledge management practices. Also, the oil slump

has accelerated the knowledge loss, especially in upstream sector, through layoffs, and

the majority of these layoffs include the senior employees. The findings are in line with

196

the existing literature stating the aging workforce as an inexorable threat in terms of

knowledge loss and that their knowledge needs to be preserved (Levy, 2011, Calo, 2008,

Martins and Meyer, 2012, Durst and Wilhelm, 2013, Ropes, 2015, Burmeister and

Rooney, 2015). Moreover, the results are compatible with the organizational learning

theory and continuiy management (Beazley et al., 2002). The organizational learning

theory fits well to explain the outcomes regarding retirements and lay offs in the current

research work. The literature on organizational learning encompass knowledge creation,

knowledge transfer and knowledge retention which are central to aging workforce issue

as the organizational learning research supports that "learning by doing" and accumulated

experience are key for improved organizational performance (Argote, 1996, Simon, 1991,

Bennet and Bennet, 2008, Argote, 1999, Hayes and Clark, 1986, Joskow and Rose, 1985,

Darr et al., 1995). The accumulation of knowledge based on experience is described

through "learning curve" mentioned in many studies (Adler and Clark, 1991, Argote and

Epple, 1990, Argote, 1996), however, there is variation in learning curve rates. The

research studies (Iun and Huang, 2007, Lahaie, 2005, Massingham, 2008, Lin, 2000,

Eucker, 2007) show that retirements and layoffs might have significant effect on

organizational learning in terms of both productivity and quality. The current research

work is thus, falls in line with the organizational learning theory and enhances our

understanding on the aging workforce issue especially in the context of multinational

corporations across varied geographical locations.

With the passage of time, employee’s awareness about the explicit and tacit

knowledge broadens and they can easily accomplish the tasks as their learning grows.

The concept of “deep smarts” (Leonard and Swap, 2004), coined in earlier litrature is

197

useful in this regard. “Deep samrts” also taken as “expert judgement” is a process of

comprehenidng the complex situations and decision taking based on contextual and

implicit knowledge gained through formal education and experience in business contexts.

This concept entails the strtaegic element that knowledge of these experts promotes the

capacity to innovate. Organizations, thus, need to take care of both “Productivity Risks”

and “Capacity Risks” (Strack et al., 2008). The Capacity risk involves retirements and

loss of knowledge and expertise whereas productivity risk is effect of old age workers

(still on pay-roll) on the organization as whole as there might be issues of health, phased

retirements, career satisfaction etc. for the old age employees.

The practices regarding knowledge retention appear to be inconsistent. The oil

prices turn out to be a major decisive factor regarding knowledge retention activities and

workforce stability. Even when there is no oil slump, the major factors causing

inconsistencies in knowledge retention programs include budget shortfalls and lack of

interest from organizations in developing proper knowledge retention programs. Only a

few organizations have proper succession planning strategies whereas, in most of the

organizations, it is done on an ad-hoc basis. Some formal knowledge retention programs

of super majors have been discussed which can be used as an example by managers to

initiate the knowledge retention activities in their companies. According to theory of

Erikson (1994), older workers feel satisfied and they want to pay back to the

organizations by imparting their knowledge gained over the years. The cohort theory

(Ropes, 2013) says that people who grow at same time encounter similar life experiences

which shapes their behavior, opinions, attitudes, and values. Cohort theory is used to

understand the various aspects of employees such as learning styles, motivation, and

198

ways of working etc. In current research, this theory is useful to understand how, the

aspects of age and experience help employee’s to gain expertise in different areas during

their career. Durst et al. (2015b) argued that critical knowledge within the organizations

can be assessed systematically by combining various methods starting from a macro level

of intellectual capital charting to sorting out an inventory list of critical knowledge items

(Knowledge audit by (Cheung et al., 2007)) and measuring the knowledge risk score for

each of these items (Durst and Wilhelm, 2013). The debriefing process by Hofer-Alfeis

(2008) is also a well-structured process that have been successfully tested and can be

used as a starting step for the organizations. Same is for the knowledge risk measurement

model by (Jennex, 2014) that can initially help in prioritizing the critical persons with

respect to the time availability, knowledge criticality and quality of the knowledge. Thus,

these methods can be combined with the KR procedures identified (ROCK, TPA,

“Knowledge Retention from Experts” etc.) in this study to further strengthen the

knowledge retention process. Considering the mass exodus of the aging workforce from

the industry, the measure adopted by companies are not up to the mark. Thus, the study

calls for the attention of managers and executive to address this issue. There is a genuine

need of adopting proactive strategies in this regard as the consequences of failing to do so

are not that hard to predict.

Limitations and Future Work: The study has limitations as well. It might lack

generalizability for only focusing on 1 sector. Due to lack of resources and time

constraints, the comparison among companies from developed and developing countries

on the issue of knowledge retention is not much comprehensive and needs to be further

strengthened on the aspects of political situations, expatriates impact and local versus

199

non-local companies. Future research can also focus on the comparison of service

provider companies and operator companies regarding knowledge retention activities and

workforce issue. The data collected during the study also provided new ideas to the

researchers for further research on assessment of knowledge loss and what are the critical

knowledge loss areas when employees depart. This issue was addressed in the 2nd study

as an extension of this work on knowledge retention.

2nd Study: The 2nd study brings the attention of managers and executives to the

critical areas of knowledge loss when employees depart. The main contribution of the

study is the identification of different knowledge types and exploring the dominant

knowledge loss factors. The research questions for this study were:

i) What are the dominant likelihood factors of knowledge loss in oil and gas

industry?

ii) What are the critical types of knowledge lost when employees depart from

the oil and gas industry?

The study indicated that knowledge loss due to retirements because of an aging

workforce is inevitable soon. Thus, retirees are a major factor of knowledge loss followed

by layoffs of employees because of oil slump. These layoffs are dominated by especially

those near retirements thus making layoffs a second dominant factor for knowledge loss.

Another key element of knowledge loss discovered during the study is over-reliance on

contractors. Too much dependence on contractors can be devastating in the future after

the last generation of baby boomers head back to their homes. This high proportion of

contract workforce calls for the attention of executives and managers to understand its

200

impact on organizations regarding knowledge loss. Further, this situation demands a

fervent role by management in providing stable and permanent jobs to the workforce. The

experienced and seasoned employees are critical for the oil and gas industry, and they

possess a variety of knowledge. When these employees leave, the organizations can lose

different types of knowledge such as specialized technical knowledge, contextual

knowledge of working across different geographical locations, knowledge of

relationships, knowledge of business processes and finally, knowledge of train wrecks

and history of the company. All these knowledge types are gained through expansive

learning (Ropes, 2013) linked to problem solving through high level of knowledge and

competence (Engeström, 2001). These findings are consistent with the existing literature

on different knowledge types such as Joe et al (2013), Frigo (2006), Leibowitz (2009)

and Daghfous et al. (2013). Frigo (2006) in his study on water utilities found that three

types of knowledge are critical to retain in utilities sector. These are technical knowledge,

structural knowledge, and social relationships knowledge. Joe et al (2013) discovered 5

knowledge types when old experts leave knowledge intensive organizations. These are

subject matter expertise; knowledge of business relationships/social networks;

organizational knowledge; knowledge of business systems and value chains; and finally,

knowledge of governance. The current study findings are in line with these studies. The

specialized technical knowledge in this study can be put under subject matter expertise

matching with the findings of Joe et al. (2013) who take subject matter expertise tied to

knowledge, skills, and experience of individuals and might involve operational, strategic

and scientific knowledge (Inkpen and Moffett, 2011). The contextual knowledge is linked

to experience gained through working at various locations and becoming sensitive to

201

cause-effect relationships and environmental cues (Sanders and Ritzman, 1992). The loss

of employees with good network of relationships leads to negative impact on informal

networks of "transactive memory" as transactive memory is associated with knowledge

of "who knows who what" and contributes towards performance outcomes (Lewis, 2004).

Learning at workplaces through interactions, group discussions, reflection helps in

knowledge building process (Van den Bossche et al., 2006) and variety of expertise as

explored in this study. The outcomes in terms of various knowledge types are in line with

the organizational learning and socio-technical systems theory. The socio-technical

systems theory (Trist and Bamforth, 1951, Emery and Trist, 1960) states that learning

capability of organizations is influenced by human resources, combined with financial

and physical resources as well as task design, knowledge and group structures. The

human resources levels can have a direct impact on organizational efficiency as these

levels can be disrupted by turnover, retirements and lay-offs and especially those people

who are knowledgeable and highly experienced in work teams.

Thus, organizations need to focus on these areas when performing knowledge

retention. If a departing employee possesses any of these knowledge types, the relevance

of knowledge also needs to be checked as not all knowledge might be important and the

importance of a specific knowledge type depends on the organizational goals and strategy

in future. Based on the findings of the study, an outline was provided to assess the

departing employees for different knowledge types.

This study can help managers and executives to understand the different types of

knowledge that might be lost when employees depart. They can perform the knowledge

retention much better as they know what to look for from the departing employees.

202

Further, the study draws the attention of managers and executives towards the dominant

factors of knowledge loss. Apart from retirements and layoffs, contract workforce

appears to be a big factor of knowledge loss in oil and gas sector. Organizations need to

pay special attention to this as a bigger portion of the workforce in oil and gas comprises

of contract workers. Moreover, by knowing the major factors of knowledge loss,

organizations can prepare well in advance for succession planning and knowledge

retention from the employees.

Limitations and Future Work: The study has limitations as well. The focus of

the research was the oil and gas industry, to determine the areas of critical knowledge

loss. Thus, the types of knowledge discovered, and their relevance might be very

particular to this industry for example contextual knowledge of working at different

locations. Exploring other industries might unveil more interesting insights on different

types of knowledge possessed by employees. Manufacturing industry might be good as it

is also facing an aging workforce issue and new people are not joining the industry.

Contract workforce appears to be another important topic that can be explored further as

in this study, it has been restricted to as an important factor of knowledge loss only. A

detailed study can be further performed on issues and challenges for knowledge retention

with a focus on contract workers.

3rd Study: The 3rd study was an offshoot from the 1st study. In the 1st study, it

was stated by one of the participants that big data could be a new avenue for

organizations to work on in order to handle this knowledge loss and workforce crisis.

Thus the 3rd study focused on this linkage of big data to knowledge management and if it

could be a replacement for the knowledge workers in the organizations. Little work has

203

been done on the interrelationship between big data and knowledge management, and this

study is one of the first studies in this connection. The main research question of the

study was:

i) What is the interrelationship between big data and knowledge management in a

knowledge intensive industry?

Results of the study show that oil and gas is a highly knowledge intensive sector

and big data is well known and well supported in this area. The big data term is more

widely used now, and companies have started to focus on big data. Most of the work on

big data is at initial stages, and companies are in experimental phase. It will take some

time for the companies to utilize big data to its full potential. Results reveal a clear

linkage between big data and knowledge management. Big data through its capability of

generating predictive knowledge helps in efficient decision making and enhances the

overall knowledge management capability of the organization. Big data as an intangible,

heterogeneous resource is the basis of competitive advantage for the companies.

The predictive knowledge generated through data is converted into actionable

knowledge by incorporating the tacit knowledge of experts. The insights from experts

have emerged as an important factor in final decision making from the knowledge

generated through big data. In future, there is the possibility of autonomous decision

making using more smart algorithms, however, at the moment, there is no clear evidence

on the ability of big data to compensate for the knowledge of experienced employees

thus, revealing to the managers and executives, the importance of retaining the

knowledgeable and experienced people in the organizations. Integration of data remains a

challenge for the organizations, and more dedicated efforts are required to extract true

204

value out of big data. As organizations will dwell more into big data initiatives, it will

assimilate well into the organization culture developing a know-how of this field among

employees and understanding the limitations and capabilities of big data.

Limitations and Future Work: The limitation of this study includes a small

sample size as big data is relatively a new field and thus not many companies working on

it. Including more companies could have revealed more interesting insights. This study

does not make a comparison among the upstream, downstream, and mid-sectors

regarding big data activities. Most of the examples provided by the participants relate to

the upstream sector as majority of the participants were from upstream sector. Also, the

responses of the participants could be somewhat biased as they might not possess the

equal expertise both in big data and KM fields. The oil and gas sector has been focused

on because of its intensive data operations. These results draw the attention of managers

and executives to invest in big data initiatives and capitalize on it. It is also evident that

big data applicability is not only limited to oil and gas but other data intensive as sectors

as well and especially social data and public domain data can be used for informed

decision making. From example, Google use data analytics to get to know about spread

of Flu in 2009 and further predicted the sources of influenza spread (Ginsberg et al.,

2009). Using customer data, big data analytics can contribute towards better planning and

forecasting and target the customers for improved performance such as targeted

marketing and customer-based segmentation. Thus, the conclusion that big data can act as

a catalyst for enhancing the Km capability of the organizations and improved decision

making can be generalized to other sectors as well. It would be interesting to conduct

future studies in other knowledge intensive sectors such as retail and finance.

205

Appendix A: Coding Table and Network View (1st Study)

A.1 Coding Tables

i) Coding Table for Non-Holistic Knowledge Retention

206

ii) Coding Table for Barrier and Challenges in Knowledge Retention Activities

207

iii) Coding Table for Sector Comparison

208

A.2 Network Views

i) Network View for Non-Holistic Knowledge Retention

209

ii) Network View for Barriers and Challenges in Knowledge Retention Activities

210

iii) Network View for Sector Comparison

211

iv) Network View for New Emerging Ideas

212

Appendix B: Coding Table and Network View (2nd Study)

B.1 Coding Tables

i) Coding Table for Critical Areas of Knowledge Loss

213

ii) Coding Table for Relevance of Knowledge

214

B.2 Network View

i) Network View for Critical Areas of Knowledge Loss

215

ii) Network View for Relevance of Knowledge

216

Appendix C: Coding table and Network Views (3rd Study)

i) Coding table for Big Data in Oil and Gas Sector

ii) Coding table for linkage of Big Data to KM

217

C.2 Network View

i) Network View for Big Data in Oil and Gas Sector

218

ii) Network View for Big Data and KM Linkage

219

Appendix D: Interview Questions

Interview Questions for Knowledge Retention and Aging Workforce

a. What is the current situation of aging workforce in the company? Are there too

many people going to retire?

b. Old age workers/baby boomers are a valuable source of knowledge for the

organization. What is your opinion and the general perception about them in the

organization?

c. Are there any processes to retain knowledge from them? Are these successful?

d. How can the knowledge of the baby boomers be retained in the best possible

way?

e. Do employees get enough opportunities to learn from boomers?

f. How is the behavior of these baby boomers towards knowledge sharing and

retention? Do incentives play any role?

g. What are the barriers and challenges in retaining the knowledge of these experts?

h. Are there any knowledge assessment processes to evaluate the knowledge of the

leaving employees?

i. Is the situation of aging workforce same in all the three sectors namely upstream,

downstream and midstream sectors?

j. Are there enough new people available to replace the older employees?

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Appendix E: Details about the Participants in the Research Work

i) Details about the Participants in the Research Work —Phase 1(Table 3.1)

Interviewees Roles and Responsibilities

1 Extensive experience in the capacity of knowledge manager

in the oil and gas industry. Worked at a super major in the

capacity of knowledge, innovation and information advisor

for 10 years until Jan 2016. Currently serving as senior

knowledge and information advisor for a number of energy

companies in the USA.

2 Extensive experience in the capacity of Chief Technologist,

Global energy division at a multinational corporation.

Active involvement in implementing the KM initiatives in

energy sector involving super majors. Currently serving as a

senior solution architect, enterprise intelligence and big data

in the oil and gas sector. More than 30 years of experience

in oil and gas industry.

3 Manager Geosciences and information systems at a large oil

and gas company. Involved in knowledge management

initiatives at the company and key note speaker at

international forums. The key area of the operations is

exploration and production section in the organization.

4 7 years of experience at an American multinational energy

giant and currently based in Australia. Started in the

capacity of information management consultant, then served

as the web, collaboration and knowledge management

coordinator and currently serving as project and team lead,

information management governance, and strategy.

5 15 years’ experience of working as KM lead in a super

major. Worked as KM lead in the company’s global

solution section from 2005 to 2009. Currently working as

projects and technology KM lead from 2009 to present and

coordinating all the globally spread KM teams of the

company.

6 Senior drilling engineer with 8 years of experience in the oil

and gas upstream sector. Involved in initiating and

managing the KM activities at the company and team lead

for setting up the KM initiatives at the company. Along

with that involved in planning drilling activities,

development of new systems in the organization, corporate

reporting, services management etc.

221

7 A sub-surface analyst with 6 years of experience in a super

major and involved in data handling and management of

exploration and production operations, project planning, and

tracking, liaising with governments and contractors and

coordinating in KM activities at the organization.

8 Chief drilling engineer with 8 years of experience in oil and

gas industry in the upstream sector. Key duties include

project planning, management, and execution of drilling

operations. Involved in training and seminars to teach and

train younger employees.

9 15 years of experience in the oil and gas industry and

worked in the capacity of client support and systems

engineer, information leader and digital oil fields head

advisor. Directly remained involved in KM initiatives at the

company and delivered seminars, workshops, and

presentations on IT and KM topics.

10 Team lead and principal consultant to major oil and gas

companies in UAE providing consulting and technical

oversight of solutions in the areas of enterprise solutions

and knowledge management.

11 20 years of work experience at a big national energy

company in the capacity of manager knowledge

management, Asset Management Head of Division and IT

senior manager. Currently working as KM consultant at the

company. Involved in running and managing the KM

program at the company and integration of knowledge

management and asset management.

12 Senior drilling engineer at the UAE set-up of a super major

company. 10 years’ experience of working in the upstream

sector and involved in activities such as optimization of

drilling operations, technical support to management,

handling, and analysis of drilling projects.

13 A chartered engineer and Project Manager by profession

and with 10 years as KM manager for a major oil and gas

service company in Aberdeen. Holds an MSc degree in KM.

The author of various international articles and book

chapters.

14 Having more than 38 years total working experience and

mostly in Onshore oil & gas industry. Extensively worked

in well services, stimulation and completion operations.

Worked in the capacity of installation manager at a big

national energy company in India.

15 Decommissioning specialist and working in Oil and Gas

since 1974. Worked in the capacity of senior project

engineer, project manager, and decommissioning specialist.

222

Currently working as a decommissioning manager in an

international engineering company. Also, actively remained

involved in KM initiatives within the organization.

16 Director of Career Management and Knowledge

Management at a large multinational company and has been

on this post for the company since March 2014. Previously

held various management positions in the area of

knowledge management within the company. Overall more

than 25 years of experience in the field of KM.

17 Senior Manager at the technology and knowledge

management division of the multinational company in

Thailand. Had been at this position since April 2000 and has

been coordinating and carrying out all the knowledge

management activities within the organization.

18 Knowledge management expert and project systems

coordinator with more than 8 years of experience at a large

multinational oil and gas company in Italy. Involved in

development of lesson learned systems, Cops, Wiki pages,

and in design and implementation of information systems

architecture under share point.

19 Knowledge management consultant and trainer with more

than 10 years of experience. Providing consultancy to the

companies in oil and gas sector on knowledge management

particularly knowledge retention strategies, organizational

learning, and enhanced performance.

20 Senior Reservoir management and engineering lead at

Norwegian oil and gas company. Extensive experience in

the oil and gas industry and worked as research scientist,

reservoir engineering consultant and actively remained

involved in R&D projects as well as KM activities. Also

worked as a consultant, visiting lecturer, and trainer at

various forums and universities.

21 Head of research at executive search section at one of the

world’s largest oil companies in Saudi Arabia. Involved in

implementing a number of process improvements, created

tools to improve data capture/presentation, developed a

prospectus to promote search internally, led the adoption of

new tools to engage passive candidates, widening the

sourcing channels, and actively engaged in in knowledge

transfer initiatives.

223

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