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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))
24
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.
55
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.
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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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“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
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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.
192
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
212
Appendix B: Coding Table and Network View (2nd Study)
B.1 Coding Tables
i) Coding Table for Critical Areas of Knowledge Loss
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
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?
220
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.
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