Mohammad Fakhrul Alam Onik B.Sc. in Computer Science and ... · Mohammad Fakhrul Alam Onik B.Sc. in...

BUSINESS VALUE OF INFORMATION TECHNOLOGY: A COMPLEX ADAPTIVE

SYSTEMS THEORY VIEW

Mohammad Fakhrul Alam Onik B.Sc. in Computer Science and Engineering

Principal Supervisor: Dr Erwin Fielt

Associate Supervisor: Professor Guy G. Gable and

Dr. Meng Zhang

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy (IF49)

School of Information Systems

Science and Engineering Faculty

Queensland University of Technology

2019

Business Value of Information Technology: A Complex Adaptive Systems Theory View i

Keywords

Business value of IT

Complex Adaptive Systems

Coevolution

Emergence

IT-enabled Capability

Competitive Advantage

ii Business Value of Information Technology: A Complex Adaptive Systems Theory View

Abstract

There has been a long-running discourse in the Information Systems (IS)

literature examining the organisational performance impacts of information

technology; the business value of IT (BVIT). The use within and among organizations

of multi-scale digital technologies such as, cloud computing, big data, digital

platforms, and Internet-of-Things, is proliferating; altering the ways in which firms

acquire factor inputs, manage products and services, and share value with their

customers. The deployment of these digital technologies has given rise to a step-

change in the complexity, dynamism, and unpredictability of organisational

phenomena. Recent studies have begun to explore the implications of this step-change

for how value is created, with prominent IS scholars arguing the need for alternative

approaches to understanding BVIT in the contemporary economy and society.

In attention to this need, this research adopts a complexity theory lens; more

specifically a complex adaptive systems (CAS) theory view. Taking existing

theorising on BVIT as a starting point, the study develops a reconceptualisation via

CAS theory that helps to explain implicit dynamics in relation to BVIT. The CAS

theory offers a theoretical base to develop deeper understanding of the dynamic side

of BVIT phenomenon, herein focusing on two particular CAS theoretical concepts,

emergence and coevolution in the context of BVIT. This research employs a novel

theory development approach based on the ideas of Shepherd and Suddaby’s (2017)

review of methods and tools related to theory building.

First, a preliminary, high-level BVIT framework consisting of IT assets-

organisational resources, IT-enabled capabilities, and competitive advantage

constructs, is presented so as to interrelate main existing strategic perspectives on

BVIT in the literature. Subsequently, the research includes two major phases. In the

first phase, it adopts the CAS ‘emergence’ concept to explore how IT-enabled

capabilities emerge in contemporary organisations. A new interpretation of the

emergence concept as complex-emergence, containing four enabling conditions -

semi-structures, simple rules, self-organised management, and compatibility is

presented. The complex-emergence conceptual framework of IT-enabled capabilities,

Business Value of Information Technology: A Complex Adaptive Systems Theory View iii

demonstrates how IT-enabled capabilities emerge via bottom-up interactions between

the components of IT assets and organisational resources.

In the second phase, the CAS ‘coevolution’ concept is used to investigate how

these capabilities help organisations in obtaining competitive advantage. The

coevolution concept investigates the mutual evolution of IT-enabled capabilities, and

how they aid organisations to obtain competitive advantage. Two types of coevolution,

micro and macro-level coevolution, are discussed in the context of IT-enabled

capabilities, representing how capabilities improve over time within and between

organizations. Variation, selection and retention evolutionary mechanisms are

discussed to explain micro-coevolution of IT-enabled capabilities. In addition, three

distinct macro-coevolutionary dynamic phenomena; namely, Red Queen effect,

competitive exclusion, and niche separation, are discussed in relation to capabilities,

highlighting action-based competitive relationships among firms and how they vie for

advantage. Moreover, an operational NKC model is used in this phase to formalise

several strategies to manage coevolution in the IT-enabled capabilities.

The study concludes with a CAS theory based BVIT framework that

theoretically explains the dynamic path from IT assets towards creation of business

value as competitive advantage in contemporary organisations. It integrates the

emergence and coevolution lenses on IT-enabled capabilities, which together represent

action-based competitive advantage; a new BVIT framework.

iv Business Value of Information Technology: A Complex Adaptive Systems Theory View

Table of Contents

Keywords ................................................................................................................................... i

Abstract ..................................................................................................................................... ii

Table of Contents ..................................................................................................................... iv

List of Figures .......................................................................................................................... vi

List of Tables .......................................................................................................................... vii

List of Abbreviations ............................................................................................................. viii

Statement of Original Authorship ............................................................................................ ix

Acknowledgements ................................................................................................................... x

Dedication ................................................................................................................................ xi

Publications ............................................................................................................................. xii

Chapter 1: Introduction ....................................................................................... 11.1 Research background ...................................................................................................... 1

1.2 Research Motivation ....................................................................................................... 6

1.3 Research Problem and Questions ................................................................................... 7

1.4 Research Methodology ................................................................................................. 12

1.5 Research Design ........................................................................................................... 22

1.6 Chapter Summary ......................................................................................................... 25

Chapter 2: Background Literature ................................................................... 272.1 Historical Background .................................................................................................. 28

2.2 Business Value of Information Technology (BVIT) .................................................... 29

2.3 A High Level Conceptual Framework of BVIT ........................................................... 35

2.4 Complexity Theories .................................................................................................... 40

2.5 Chapter Summary ......................................................................................................... 45

Chapter 3: A Structured Literature Review on Complex Adaptive Systems Theory 473.1 Introduction .................................................................................................................. 48

3.2 Background Literature .................................................................................................. 51

3.3 Review Design .............................................................................................................. 55

3.4 The Status of CAS Theory in the IS Discipline ............................................................ 58

3.5 the Conceptual Perspective of CAS in is ...................................................................... 59

3.6 The Objectives of CAS Theory in the IS Research ...................................................... 63

3.7 The theoretical perspectives of CAS research in IS ..................................................... 69

3.8 The Methodological Approaches of CAS in IS Research ............................................ 71

3.9 Context of the CAS Theory in IS research ................................................................... 76

Business Value of Information Technology: A Complex Adaptive Systems Theory View v

3.10 Conclusion .................................................................................................................... 79

Chapter 4: An Emergence Perspective on IT-enabled Capabilities ............... 834.1 Introduction .................................................................................................................. 83

4.2 Overview of Emergence ............................................................................................... 89

4.3 Recap of Nevo and Wade (2010) Model ...................................................................... 97

4.4 A Complex Emergence Framework of IT-enabled Capabilities ................................ 107

4.5 Chapter Summary ....................................................................................................... 121

Chapter 5: A Coevolution Perspective on IT-enabled Capabilities ............. 1245.1 Introduction ................................................................................................................ 124

5.2 Overview of Coevolution ........................................................................................... 131

5.3 A prelimnary coevolution framework for IT-enabled Capabilities ............................ 137

5.4 Micro coevolution of IT-enabled capabilities within the firm ................................... 141

5.5 Macro Coevolution of IT-enabled capabilities between the firms ............................. 147

5.6 A Coevolution based framework of IT-enabled Capabilities ..................................... 154

5.7 Chapter Summary ....................................................................................................... 161

Chapter 6: Towards Strategies for Managing IT-enabled Capabilities ...... 1646.1 Introduction ................................................................................................................ 164

6.2 Applications of NKC Model in Research ................................................................... 165

6.3 Managing The Coevolution of the IT-enabled Capabilities ....................................... 174

6.4 Chapter Summary ....................................................................................................... 184

Chapter 7: Discussion and Conclusion ........................................................... 1857.1 A CAS-BVIT Framework .......................................................................................... 185

7.2 From a Static, Linear perspective to a Dynamic, Non-linear Perspective .................. 190

7.3 Key Insights Compared with Prominent, Traditional BVIT Models ......................... 192

7.4 Conclusion .................................................................................................................. 197

Appendices ............................................................................................................... 209Appendix A Abstract of the publications .............................................................................. 209

References ................................................................................................................ 213

vi Business Value of Information Technology: A Complex Adaptive Systems Theory View

List of Figures

Figure 1.1 A high level conceptual framework of Strategic-BVIT .................... 5

Figure 1.2 The position of the CAS concepts in the high-level conceptual framework of BVIT (The arrows are positioned under the concepts to represent their position in the thesis) .................................................. 11

Figure 1.3 Research Approach .......................................................................... 17

Figure 1.4 Approach to theory development in relation to BVIT ..................... 21Figure 1.5 An overview of Research Design .................................................... 23

Figure 3.1 Overview of sampling sources ........................................................ 55Figure 4.1 A Subset of the BVIT framework (Figure 1.2 in Chapter 1) ........... 84

Figure 4.2 A Subset of Research Method (see Figure 1.3 in Chapter 1) .......... 87Figure 4.3 (Nevo & Wade, 2010) ..................................................................... 97

Figure 4.4 A complex emergence framework of IT-enabled capabilities ...... 108Figure 5.1 A Subset of the BVIT framework ................................................. 126

Figure 5.2 A Subset of Research Method for Coevolution of IT-enabled Capabilities ....................................................................................... 129

Figure 5.3 A Preliminary Coevolution Framework of BVIT (With the focus on the coevolution of IT-enabled Capabilities) ..................................... 138

Figure 5.4 Micro coevolution of IT-enabled capabilities via variation, selection and retention (VSR) processes .......................................................... 145

Figure 5.5 A Coevolution Framework of IT-enabled Capabilities ................. 155Figure 7.1: A CAS-BVIT framework ............................................................. 188

Business Value of Information Technology: A Complex Adaptive Systems Theory View vii

List of Tables

Table 2.1 Typologies of IT assets and organisational resources (Adapted from (Kim, et al., 2011)) ............................................................................. 38

Table 2.2 Summary of the High Level BVIT Framework ................................ 40

Table 2.3 Basic building blocks of complex adaptive systems (CAS) theory .. 44Table 3.1 Journal/ conference and year wise distribution of CAS article ........ 59

Table 3.2 Classification of papers based on CAS concepts in IS literature ...... 61Table 3.3 CAS objectives in IS research .......................................................... 66

Table 3.4 Theoretical approaches in CAS research in IS ................................. 71Table 3.5 Overview of methodologies in CAS based IS research .................... 72

Table 3.6 Context of CAS theory in IS ............................................................. 77Table 4.1 Typologies of Emergence ................................................................. 92

Table 4.2 Simple vs Complex Emergence ........................................................ 96Table 4.3: Complex Emergence of IT-enabled Capabilities ........................... 122

Table 5.1 Application of Coevolution in Management .................................. 135Table 5.2 Application of Coevolution in BVIT related IS research ............... 137

Table 5.3 The complete coevolution Framework of IT-enabled capabilities and strategies ........................................................................................... 162

Table 6.1 NKC Applications in Management and IS Studies (Summarised from Vidgen and Bull (2011)) ................................................................... 171

Table 6.2 Translation of NKC model into the context of IT-enabled capabilities ........................................................................................ 177

Table 6.3 Strategies for managing Micro and Macro Coevolutionary Competition in Organisations ........................................................... 178

Table 7.1 Propositions related to the emergence and coevolution perspectives of the IT-enabled capabilities ........................................................... 189

viii Business Value of Information Technology: A Complex Adaptive Systems Theory View

List of Abbreviations

BVIT Business Value of IT

CAS Complex Adaptive Systems

DCT Dynamic Capabilities Theory

RBV Resource based View

CRM Customer Relationship Management

ERP Enterprise Resource Planning

IoT Internet of Things

IS Information Systems

IT Information Technology

PACIS Pacific Asia Conference on Information

Systems

ACIS Australasian Conference on Information

Systems

Business Value of Information Technology: A Complex Adaptive Systems Theory View ix

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best

of my knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made.

Signature:

Date: ________20.08.2019_________________

QUT Verified Signature

x Business Value of Information Technology: A Complex Adaptive Systems Theory View

Acknowledgements

Pursuing a PhD has been an emotional roller coaster ride. Throughout this

process I have been fortunate to enjoy immense support from a large number of people.

I would like to acknowledge my indebtedness and render my warmest thanks to

my supervisory team, Dr. Erwin Fielt, Professor Guy Gable and Dr. Meng Zhang, who

made this work possible. Specially, Erwin’s friendly guidance and expert advice have

been invaluable throughout all stages of the work. Thank you, Erwin for your critical

thoughts and suggestions that kept me focused. Further, you have challenged me

intellectually, provided a broad theoretical base, and pointed me in fruitful directions

when perplexed. Maybe most importantly, you have reminded me of the importance

of enjoying research.

I would also wish to express my gratitude to Professor Guy Gable for extended

discussions and valuable suggestions which have contributed greatly to the

improvement of the thesis. Moreover, your comments and reviews were instrumental

during my stage 2, confirmation and final seminar milestones. Thank you Guy for

showing me the craftsmanship of research. I also gratefully acknowledge financial

support that I have received from your ARC grant and supervisory top-up scholarship,

which have been crucial during my PhD journey.

I gratefully acknowledge the scholarship- QUT Postgraduate Research Award

received towards my PhD from Queensland University of Technology. I am also

grateful to the funding received through the QUT Excellence Top Up Scholarship.

The thesis has also benefited from comments and suggestions made by Dr. Sue

Nielsen and Dr. Nev Schefe who have read through the manuscript. I take this

opportunity to thank them.

I was fortunate to be surrounded by lovely colleagues in the School of

Information Systems, who constantly inspired and supported me during some of the

toughest study periods of the PhD journey. I would like to specially thank Lucky for

inspiring me a lot with positive suggestions and motivations during my journey. I also

wish to thank you Y-7 friends and colleagues Fahame, Abdul, Yancong, Ignatius,

Syed, and Sharmistha for the enjoyable memories with you that I will keep forever.

Business Value of Information Technology: A Complex Adaptive Systems Theory View xi

Dedication

I dedicate this research to-

My grandfather (Late), grandmother and my maternal uncle, Md Moslehuddin

for supporting me from my childhood till now.

My father, Md Alauddin and my mother, Fakhrunnahar Begum, for giving me

the freedom to choose my decisions in my life.

Last but not least, my beautiful wife, Dr Fatimatuz Zannat, for always inspiring

me and keeping me cheerful during the most demanding times of our lives.

xii Business Value of Information Technology: A Complex Adaptive Systems Theory View

Publications1

Conference Publications

• Onik, M. F. A., Fielt, E., & Gable, G. G. (2017). Complex Adaptive Systems Theory in Information Systems Research-A Systematic Literature Review. In the Proceedings of 21st Pacific Asia Conference on Information Systems (PACIS), Langkawi, Malaysia.

• Onik, M. F. A., Fielt, E., & Gable, G. G. (2017). Towards Complex Adaptive Systems

Roadmap for Information Systems Research. In the Proceedings of 21st Pacific Asia Conference on Information Systems (PACIS), Langkawi, Malaysia.

• Onik, M. F. A. & Fielt, E. (2016). Understanding The Dynamics of BVIT Process: A

Complex Adaptive Systems Approach. In the Proceedings of 27th Australasian Conference on Information Systems (ACIS), Wollongong, Australia.

Doctoral Consortium

• Onik, M. F. A., Fielt, E. & Gable, G. G. (2017). Understanding The Dynamics of

BVIT Creation: A Complex Adaptive Systems Approach. ISS Doctoral Consortium, QUT Brisbane.

• Onik, M. F. A., Fielt, E. & Gable, G. G. (2016). What Are Information Systems-

Information Systems as Complex Adaptive Systems. ISS Doctoral Consortium, QUT Brisbane.

1 The abstracts of the publications are available in 1)a)Appendix A.

Chapter 1: Introduction 1

Chapter 1: Introduction

The business value of Information Technology (BVIT) has been one of the

foremost concerns among Information Systems (IS) practitioners and researchers for

decades. Over the years, researchers have conceived various approaches to understand

BVIT creation mechanisms. However, the contemporary business environment has

become complex and dynamic due to advances in technologies and their deployment,

which influence the way BVIT is generated in and across organisations. This

dissertation has adopted complex adaptive systems (CAS) theory as an overarching

theoretical lens to explore the dynamics related to BVIT.

This chapter first presents an overview of the research background and the

motivation behind this study. The subsequent sections introduce the research

problems, questions and a high level conceptual BVIT framework. Lastly, the study

research methodology and research design are presented.

1.1 RESEARCH BACKGROUND

The term BVIT is commonly used to refer to the “organisational performance

impacts of information technology (IT)…” (Melville, Kraemer, & Gurbaxani, 2004, p.

287). The research suggests various measures that are used to refer BVIT, such as,

productivity enhancement, cost reduction, competitive advantage and other measures

of performance (Kohli & Devaraj, 2003; Melville, et al., 2004). Broadly, an analysis

of the BVIT literature reveals a key distinction between performance-efficiency and

performance-effectiveness (Melville, et al., 2004). Efficiency refers to an

improvement from an organisational internal perspective, such as cost reduction and

improved operational processes (Soh & Markus, 1995). In contrast, effectiveness

denotes achievement of superior strategies that may manifest in increased competitive

advantage (Barney, 1991). In sum, BVIT refers to the organisational performance

impacts of information technology at both the operational process level and the

strategic level of organisations and comprises both efficiency and competitive impacts

(Melville, et al., 2004).

2 Chapter 1: Introduction

Researchers have adopted various approaches, such as process-centric,

variance-based or intangible measures, to analyse the mechanisms by which IT

impacts organisational performance and to gauge its magnitude. The studies that adopt

variance theories explore variations in the magnitude of particular outcomes i.e. BVIT

under certain and sufficient conditions (Markus & Robey, 1983); whereas process

theories provide causal explanations of ‘how’ BVIT occurs in an organisation - the

necessary conditions, particular events and sequences (Soh & Markus, 1995). Previous

research shows that IT has indeed contributed to the improvement of organisational

performance (e.g. Hitt & Brynjolfsson, 1996; Kohli & Devaraj, 2003; Oh, Teo, &

Sambamurthy, 2012; Radhakrishnan, Zu, & Grover, 2008). Moreover, researchers

have also considered different antecedents and contextual factors, such as

organisational structure and culture, management practices, competitive environment

etc. that influence BVIT creation mechanisms (e.g. Dewan & Kraemer, 2000; Melville,

et al., 2004; Schryen, 2012). This stream of research also suggests that firms may not

be able to capture and appropriate all the value possible from IT, as value can be

competed-away or captured by end-customers in the form of satisfaction, better

quality, or lower prices (Devaraj & Kohli, 2000; Limayem & Cheung, 2008). A

contrary view in the 1990s controversially identified a productivity paradox- low

productivity growth against high spending on IT (Avison, Cuthbertson, & Powell,

1999; Brynjolfsson, 1993).

Information systems (IS) scholars have conducted conceptual, theoretical,

analytical and empirical studies with regards to BVIT. The empirical studies include

qualitative studies - case studies and field studies (e.g. Weill, 1992) - and quantitative

studies that explicate BVIT at the individual, firm and country levels of analysis

(Dewan & Kraemer, 2000; Mooney, Gurbaxani, & Kraemer, 1995). The conceptual

and theoretical research adopts competing theories and grounded observation to

describe BVIT (Mata, Fuerst, & Barney, 1995; Porter, Michael, & Gibbs, 2001).

Finally, analytical studies of BVIT use a variety of techniques to model BVIT and

derive solutions to improve organisational performance, by changing operational or

management processes in the competitive business environment (Belleflamme, 2001).

In addition, researchers have employed many theoretical lenses including

microeconomics- growth accounting (e.g. Hitt & Brynjolfsson, 1996), Tobin’s q (e.g.

Bharadwaj, Bharadwaj, & Konsynski, 1999), industrial organisation theory- game


theory (e.g. Belleflamme, 2001), socio-political perspective (e.g. Hoogeveen &

Oppelland, 2002a) and the Resource-based View (RBV) theory (e.g. Aral & Weill,

2007; Bharadwaj, 2000) to analyse the value that IT creates for organisations.

Our knowledge of BVIT derives largely from an organisation-centric

perspective on BVIT, based in internal business processes, organisational capabilities

and organisational practices (e.g. Bharadwaj, 2000; Melville, et al., 2004;

Sambamurthy, Bharadwaj, & Grover, 2003; Schryen, 2012). This research on BVIT

can largely be classified into two categories- the operational perspective and the

strategic perspective. The operational perspective on BVIT emphasises the

enhancement of internal efficiency through improving business process performance,

measures of which may include improved customer service, better information sharing

or faster inventory turnover (Melville, et al., 2004). Alternatively, the strategic

perspective is concerned with how organisations can obtain superior competitive

advantage by employing IT resources accompanied by organisational resources, such

as, non-IT physical or non-IT human resources and organisational capital resources

(Melville, et al., 2004; Wade & Hulland, 2004). In the strategic perspective, BVIT is

widely represented as competitive advantage (Barney, 2000; Bharadwaj, Varadarajan,

& Fahy, 1993; Grant, 1991). A broad discussion of the strategic perspective on BVIT

is presented in section 2.2.3 in Chapter 2.

This dissertation focuses on the strategic perspective of BVIT research, which

involves deeply analysing main strategic-BVIT models to develop a theoretical base

for this study. An analysis of how IS researchers have formulated these core strategic-

BVIT models, shows that the majority of the models have three key parts. In the first

part, the model contains aggregated variables or interdependencies between IT and

other complementary organisational resources (e.g. Melville, et al., 2004; Nevo &

Wade, 2010; Wade & Hulland, 2004). The second part contains emergent capabilities

or synergies and competencies that may improve business processes (Kim, Shin, Kim,

& Lee, 2011; Melville, et al., 2004). The third part of the model contains outcomes;

the competitive advantage that organisations obtain by deploying IT resources (Mata,

et al., 1995). It is important to note that RBV theory is used in the majority of strategic-

BVIT studies to examine the competitive advantage implications of IT resources

(Bharadwaj, 2000; Wade & Hulland, 2004).


Based on the above discussion, I present a high-level conceptual framework of

BVIT (Figure 1.1). The proposed model represents the strategic perspective of the

BVIT models. The model includes three key parts, which are termed lenses in this

dissertation: 1) IT assets and organisational resources, 2) IT-enabled capabilities and

3) competitive advantage. A more detailed discussion on the conceptual framework is

presented in Chapter 2. The framework serves simply to interrelate the way in which

the strategic perspective of BVIT is understood in existing literature. This involves

relationships between IT assets and other organisational resources, which lead to IT-

enabled capabilities, which subsequently influence firm performance, in particular

competitive advantage. A broader discussion of the proposed framework is included

in section 2.3 of Chapter 2.

As mentioned above in this section, this study focuses on the strategic side of

BVIT. The high level BVIT conceptual framework proposed here is based on the

Resource-based view (RBV) of organisations (Barney, 1991; Barney, 2001) and

following the ideas of Nevo and Wade (2010) on the strategic role of IT assets in

shaping competitive advantage (discussed in section 2.2.3 in Chapter 2). The RBV

view of the organisation is widely applied in strategic management literature, and

assumes that firms compete with each other on the basis of valuable, rare, difficult to

imitate and non-substitutable resources to achieve competitive advantage (Barney,

1991; Barney, 2001) and this study aims to explore the dynamics related to the

competitive advantage. It is important to note that, the competitive environment in

which focal firm operates has two major components- industry characteristics and

trading partners following (Melville, et al., 2004). The industry characteristics include

competitiveness, digitally enabled processes and rapid technological innovation that

shape the way IT assets are deployed in the focal firm to generate business value (Kohli

& Devaraj, 2003; Melville, et al., 2004). Moreover, when IT systems span firm’s

boundary via software applications or electronic markets and blend with the business

processes of trading partners and IT and non-IT resources of trading partners also

impact business value of IT, in particular competitive advantage of focal firm

(Mukhopadhyay & Kekre, 2002).


Figure 1.1 A high level conceptual framework of Strategic-BVIT (Emphasises the strategic perspective; competitive advantage being the ultimate

outcome) (Broadly discussed in section 2.3, Chapter 2)

The lenses in the proposed strategic conceptual framework of BVIT are briefly

described below-

1. IT assets and organisational resources:

IT assets: Anything tangible or intangible related to IT that can be used in

organizational processes for creating, producing, and offering products and services

(Wade & Hulland, 2004). Tangible IT assets are hardware, network infrastructure, or

human resources and Intangible IT assets are software, information assets, employees’

IT skills in IT functions (Melville, et al., 2004).

Organisational resources: “tangible or intangible factors of production that

organizations own, control, or have access to on a semi-permanent basis” (Nevo &

Wade, 2010, p. 164).

2. IT-enabled capabilities:

The ability to effectively use IT assets to support the organisational resources for

the benefit of organisations (Pavlou & El Sawy, 2006).

3. Competitive advantage:

Ma (1999, p. 259) defines competitive advantage as- “the asymmetry or

differential in any firm attribute or factor that allows one firm to better serve the

customers than others and hence create better customer value and achieve

superior performance”.

IT-enabled Capabilities

• IT Assets

• OrganisationalResources



1.2 RESEARCH MOTIVATION

This study concurs with recent thought, that the dynamic and turbulent

environment of modern business has changed the way IT influences the creation of

business value in contemporary organisations, and thus an alternative

conceptualisation of BVIT is required. Recent IS studies have already applied

alternative approaches to understanding BVIT, such as, Nevo and Wade (2010) who

base their arguments on a combination of system thinking and RBV to study strategic

advantage; while Tanriverdi, Rai, and Venkatraman (2010) use concepts from

complexity thinking to explore the dynamics related to strategic competitive

advantage. Although these emerging studies have begun to explore aspects of the

dynamics related to the BVIT ‘puzzle’ (Chen et al., 2014), our knowledge in this

direction remains underdeveloped and unsystematic (Schryen, 2012).

With the above context in mind, this research entails three key motivations-

First, IT has become pervasive, ubiquitous and fused with business in

contemporary organisations (El Sawy, 2003; El Sawy & Pavlou, 2008). The fusion of

IT and business in contemporary organisations has led to emerging IT-enabled

capabilities, which in turn have caused a step-change in the complexity, dynamism and

uncertainty in complex adaptive business organisations (El Sawy, Malhotra, Park, &

Pavlou, 2010; Tanriverdi, et al., 2010). The role of digital technologies is no longer

merely as functional resources, rather they play a significant role in driving strategic

change (Yoo, Henfridsson, & Lyytinen, 2010). The relationship between these

technologies and business has become reciprocal, which gives rise to emergent IT-

enabled capabilities across different levels in organisations (El Sawy & Pavlou, 2008).

Consequently, there is a need to better understand the mechanisms by which emergent

technologies, such as, artificial intelligence (AI), Internet of Things (IoT), digital twins

and Blockchain, empower organisations with IT-enabled capabilities. Thus,

understanding the emergent nature of IT-enabled capabilities and its implications for

BVIT is a key challenge for today’s organisations in the increasingly turbulent

environment.

Second, there is a growing recognition that firms’ IT enabled business processes

and capabilities are distinctive sources of value creation (El Sawy & Pavlou, 2008;

Nevo & Wade, 2010). The contemporary business environment, characterised by

unpredictability arising from non-deterministic changes in market demand, consumer


preferences, turbulent business conditions, and techonogical breakthroughs, can cause

rapid transformations in IT enabled business capabilities, which in turn influence

strategic advantage (El Sawy & Pavlou, 2008; Yoo, Boland Jr, Lyytinen, & Majchrzak,

2012). Recent discourse in IS and strategy literature has focused on the relationship

between a firm’s IT enabled business processes and capabilities, and its governance

choices such as interfirm alliances, sourcing to the market, or establishing new

collaborations using a coevolutionary perspective (Benbya & McKelvey, 2006b;

D'Aveni, Dagnino, & Smith, 2010; Tanriverdi, et al., 2010; Tiwana, Konsynski, &

Bush, 2010). These studies show that IT-enabled business capabilities influence

competitive advantage in contemporary organisations. However, the underlying

mechanisms of how these capabilities influence BVIT remain under-theorised.

Third, the-ubiquity of digital technologies, environmental turbulence, and fast-

paced organisational change, cause adaptive organisations to constantly evolve

dynamically (Morel & Ramanujam, 1999). Due to rapidly improving technologies and

intense market competition, the adaptive organisations are evolving at an accelerating

rate, and competitive advantage has become transient (Iansiti & Levien, 2004;

Tanriverdi, et al., 2010). Strategies have become perishable; the landscapes of

technologies, IT enabled processes and capabilities, products and services have

become rugged and are constantly changing (Levinthal, 1997). Subtle changes to one

of the variables, for instance reconfiguration of a digital platform or coevolution of one

of the operational processes, can stem from rapid and/or unexpected movements

between players in the business system (Burgelman & Grove, 2007). Preparing for

these ‘unknown unknowns’ requires new types of sensing frameworks or perspectives

to capture the unpredictable dynamics in the business landscape (Meyer, Gaba, &

Colwell, 2005) . Yet, few studies have focused on the dynamic perspectives and their

roles in shaping BVIT across the business landscape, and this requires further

attention. The next section presents the research problem and research questions of the

study.

1.3 RESEARCH PROBLEM AND QUESTIONS

The overarching research problem is that the competitive landscape resulting

from advances in digital technologies and their deployment in organisations is

becoming complex, characterised by increased dynamism, non-linearity and

unpredictability; and the dominant approaches for theorising BVIT in this dynamic


environment are inadequate; there is need for new holistic perspectives and non-linear

approaches to better understand this emerging context. In brief, the research problem

consists of combination of two issues- 1) complexity and dynamism in contemporary

organisations due to the deployment of the digital technologies and 2) inadequacy of

dynamic approaches to explore the dynamics. It is important to note that, the terms

dynamism, complexity, unpredictability, non-linearity have been widely used in

broader strategic management and organisational research and IS literature to explain

dynamic behaviors/ patterns of adaptive organisations (Anderson, Meyer, Eisenhardt,

Carley, & Pettigrew, 1999; McKelvey, 1997c, 2002; Merali, 2006; Merali,

Papadopoulos, & Nadkarni, 2012; Mitleton-Kelly, 2003b). In this thesis, I have used

these terms in a similar way to refer to the dynamic behaviors of complex adaptive

organisations.

The research aim is to understand BVIT in the contemporary dynamic business

environment and the research question is-

RQ 1: How is BVIT created in the dynamic business-IT environment?

Given that the contemporary conditions outlined above have caused a step-

change in the complexity, dynamism, non-linearity, and unpredictability of

relationships among various business elements; this study has particularly considered

the recommendation that to deal with these challenges, we must adopt alternative

approaches; these include holistic system thinking, complexity perspective, and

configuration theories (Nevo & Wade, 2010; Oh & Pinsonneault, 2007; Peppard &

Ward, 2004; Tanriverdi, et al., 2010; Wade & Hulland, 2004). Scholars have suggested

several alternatives, such as, chaos theory (Boisot, 2006; Burgelman & Grove, 2007),

coevolutionary theory (Altschuller, Gelb, & Henry, 2010; McKelvey, 2002),

ecodynamics (El Sawy, et al., 2010), ecosystem (Peltoniemi & Vuori, 2004),

networked view (Courtney, Merali, Paradice, & Wynn, 2008) and complex adaptive

systems (Allen, 2001; Nan, 2011; Schneider & Somers, 2006; Vidgen & Wang,

2006b).

To address this research question, this study adopts complex adaptive systems

(CAS) theory (Holland, 1995), a branch of complexity theories (Vidgen & Wang,

2009), to understand the dynamism related to BVIT. A number of researchers, Merali

(2006), Benbya and McKelvey (2006a) and McKelvey (1999) consider CAS as a

concept of complexity theory. The broader complexity literature has different views


on CAS. However, I have considered CAS as a theory following Stacey, Griffin, and

Shaw (2000) approach throughout the thesis. Few reasons to choose Stacey’s approach

and consider CAS as a theory. First, Stacey assumes that organisations can be

considered as CAS consisting of large number of agents, where each agent follows a

smaller number of simple local rules, which determine the patterns of behavior of the

organisation as whole. Second, she argues that internal dynamics of the agent

interactions are nonlinear and unpredictable, which lead to structural development of

the system. Moreover, the emergent properties of the system mutually change with

properties internal and external to the system as soon as they start emerging, which

means properties of the system mutually evolve with respect to each other over time

(Lewin, Long, & Carroll, 1999; Van Valen, 1983). It is important to mention that, CAS

theory in this study has been used as “transitional objects” (Mitleton-Kelly, 2003b, p.

4), which provides a way of thinking about dynamic creation of BVIT. The CAS theory

can also be used metaphorically, however, metaphors are limited in explaining the key

nature of system under study (Mitleton-Kelly, 2003b).

CAS theory is particularly beneficial to explain non-linear and dynamic

behaviours of adaptive organisations (Anderson, 1999). It is valuable for research

because it offers a new way of thinking about organisations as systems of interacting

agents, such as, human, IT systems, business processes, etc. and helps to explain how

order emerges from the interactions of the agents (Stacey, et al., 2000; Vidgen &

Wang, 2006b). Moreover, CAS theory is well suited to modelling the non-

deterministic behaviors of contemporary organisations characterised by sudden and

substantive changes, which cannot be represented through deterministic system

theories, such as, chaos theory, and general systems theory (Burnes, 2005). Further,

CAS helps to encode non-linear complex phenomena through mathematical models

and facilitates the conduct of computational experiments in a virtual system, which

provides researchers precision and control over the model and helps to investigate the

dynamic relationships of system components (Morel & Ramanujam, 1999).

This dissertation argues that the deployment of digital technologies gives rise to

emergent IT-enabled capabilities, which coevolve each other within organisation and

with the competitors and influence competitive advantage. Therefore, this study adopts

two concepts of CAS theory- emergence and coevolution together to investigate the

dynamics related to BVIT, in particular competitive advantage. In particular, the


emergence concept provides a way of explaining complex adaptive behaviors of

interacting system components and overall emergent patterns rise from the

components interactions (Stacey, et al., 2000), which makes it better suited to explain

the emergent rise of IT-enabled capabilities. In addition, the coevolution concept is

better suitable to explain the evolution of one domain or agent in relation to the

evolution of other related domains or agents (Kauffman, 1995a; Kauffman, 1993).

Moreover, others studies have been using these concepts to study complex

organisational phenomena- emergence (e.g. Chiles, Meyer, & Hench, 2004; Choi,

Dooley, & Rungtusanatham, 2001; Kogut, 2000; Lichtenstein, Dooley, & Lumpkin,

2006; Sawyer, 2005) and coevolution (e.g. Huygens, Van Den Bosch, Volberda, &

Baden-Fuller, 2001; Koza & Lewin, 2001; McKelvey, 2002; Pacheco, York, Dean, &

Sarasvathy, 2010). Using the emergence concept of CAS theory (Goldstein, 1999), this

study first explores the way IT-enabled capabilities emerge. It then adopts the

coevolution concept of CAS theory to explore how these IT-enabled capabilities help

organisations to achieve competitive advantage. In particular, with an emphasis on

action-based competitive relationships (D'Aveni, et al., 2010), the coevolution concept

helps to explore how organisations vie for advantage by obtaining valuable and rare

IT enabled capabilities.

The study further adopts NKC model (Kauffman, 1995a) to develop in-depth

insights on the coevolutionary dynamics of the IT-enabled capabilities within

organisation (micro level) and with the competitors (macro level). The NKC model is

particularly beneficial as an in-depth exploratory lens of investigating coevolutionary

order in organisational studies (Baum & McKelvey, 1999c; McKelvey, 1999).

Moreover, it provides a way of modelling real life coevolutionary adaptive

progressions in logical steps within computer simulations and allows to change values

(N,K,C) to test coevolutionary dynamic behaviors of CAS (McKelvey, 2002). Further,

Kauffman’s NKC model can be applied at various organisation levels of analysis

including process microstates, including people skills and experiences represented as

agents as elaborated by (McKelvey, 1999). However, few limitations are also involved

in NKC modelling- problems in operationalising complexities, defining ruggedness

and correlation of fitness landscape concept, the use of proper distribution parameter

etc. (McKelvey, 1997a). Nonetheless, NKC model has been widely used in strategic

management and organisational studies (Baum & McKelvey, 1999c; Levinthal, 1997;


McKelvey, 1999; Mckelvey, Li, Xu, & Vidgen, 2013; Siggelkow & Levinthal, 2003),

which indicates its’ potential benefits in understanding complex organisational

coevolutionary dynamics and thus is adopted in this thesis to explore the

coevolutionary dynamics of IT-enabled capabilities and its impact on competitive

advantage (Chapter 6).

The position of the emergence and coevolution concepts in the overall BVIT

framework is shown in Figure 1.2. The high level framework is developed based on

the observation on the prominent BVIT models in IS studies. Please see section 2.3 in

chapter 2 for more detailed discussion on the framework. It is important to note that

the coevolution concept addresses the relationships of at least two elements that

mutually influence each other (Volberda & Lewin, 2003). In Figure 1.2, the

coevolution concept represents both the relationships between two or more mutually

influencing IT-enabled capabilities internal to the organisation, and the reciprocal

influences of one or more IT-enabled capabilities of the focal organisation on one or

more IT-enabled capabilities of competitors, and vice versa.

Consequent research questions are,

RQ 1.1: How do IT-enabled capabilities emerge?

RQ 1.2: How do IT-enabled capabilities influence competitive advantage?

Figure 1.2 The position of the CAS concepts in the high-level conceptual framework of BVIT (The arrows are positioned under the concepts to represent their position in the thesis)

In this thesis, the CAS emergence and coevolution concepts are used to develop

explanatory theory (Gregor, 2006) on how BVIT, more specifically competitive

advantage is created in the contemporary organisations. The emergence concept helps


• IT Assets



Emergence

Coevolution


to explain the process of emergent IT-enabled capabilities (see details in Chapter 4)

and the coevolution concept helps to explain the way IT-enabled capabilities evolve in

relation to each other within organisations and with respect to the competitors and

impact competitive advantage (see details in Chapter 5).

1.4 RESEARCH METHODOLOGY

The study overall employs three research methods in different stages. In the first

stage, a background literature is conducted on broader topic areas- business value of

IT and complexity theories (Chapter 2) and a structured review is conducted on CAS

theory specifically (Chapter 3). In the second stage, a theory development approach is

adopted following Shepherd and Suddaby (2017) review on theory building methods

and tools (Chapter 4 and 5). Finally, NKC model (Kauffman, 1995a) is used to explore

and develop in-depth insights on BVIT, in particular competitive advantage (Chapter

6). The research methods are briefly described below-

1. A background literature in core topic areas- business value of IT and

complexity theories is conducted in Chapter 2. The background literature is

used to develop understanding on the current state of BVIT research and to

understand the use of broader complexity theories in within IS and

organisational studies. In addition, a structured literature review following

the guidelines of Webster and Watson (2002) is conducted on CAS theory

in Chapter 3. The structured review is used to understand CAS theory in

general (including seminal works) and within IS in particular. The structured

CAS literature review helps to identify the major CAS concepts used in

relation to BVIT studies, how they are applied, and the type of theories

developed using the CAS concepts. Moreover, Chapter 4, 5 and 6 contain

in-depth reviews on emergence, coevolution concepts and NKC model

consecutively. These reviews are specifically used to develop in-depth

understandings on the emergence (Chapter 4) and coevolution (Chapter 5)

concepts and NKC model (Chapter 6) and their use in IS studies in

particular.


2. The theory development approach based on the Shepherd and Suddaby

(2017) review of methods and tools for theory building is adopted. This

method guides the thesis to develop theories using CAS emergence (Chapter

4) and coevolution (Chapter 5) concepts. This is the core of the research

approach (Figure 1.3), summarized in this chapter and discussed in detail in

Chapter 4 and Chapter 5. I have developed explanatory theories (Gregor,

2006) adopting the CAS emergence and coevolution concepts in Chapter 4

and Chapter 5 consecutively. As a part of the theory development, I have

proposed two frameworks- a complex emergence framework of IT-enabled

capabilities (Figure 4.4 in Chapter 4) and a coevolution framework of IT-

enabled capabilities (Figure 5.5 in Chapter 5). The theory development

using emergence and coevolution concepts also yield propositions

(Summarised in Table 4.3 in Chapter 4 and 5.3 in Chapter 5).

It is important to note that, I have adopted the term ‘framework’ instead of

‘model’ to represent my proposed theoretical knowledge contributions throughout the

thesis. The term framework represents several kinds of information about a particular

situation (Minsky, 1974). The information includes about how to use the framework,

information about cause-effect relations, changes in conceptual viewpoint or synthesis

knowledge from different viewpoints (Minsky, 1974). A framework consists of nodes/

terminals representing some concepts and relations among them. The nodes are

flexible meaning new concepts can be added or concepts can be removed to describe

certain situations. In contrast, a model describes an organised set of parts/ components

and their relationships (Woodward, 2002). Each of the model components can be

generalised under certain interventions and the manipulation of each of the

components of the model changes the overall outputs. In my thesis, the term

framework is used specifically to explain BVIT from CAS conceptual viewpoint, in

particular, the CAS emergence and coevolution concepts are used in the context of IT-

enabled capabilities to explain BVIT, more specifically competitive advantage. IS

scholars have adopted one or more CAS concepts, applied them in different research

context- IS development (Vidgen & Wang, 2006b, 2009), IT use process (Nan, 2011),

intra-firm relationships in mobile ecosystem (Basole, 2009), develop frameworks and

propositions on the research context and provide in-depth explanations. In this study,


I have followed a similar approach and develop frameworks and propositions based

on CAS emergence (Chapter 4) and coevolution (Chapter 5) concepts.

3. NKC model translation into the conceptualisation of competitive advantage

based on the McKelvey (1999) approach, which is discussed in Chapter 6.

The NKC model is used as an exploratory lens to develop in-depth insights

on the coevolution of the IT-enabled capabilities via analytical NKC

components and logic. The theories developed in this stage are exploratory

in nature as it extends the NKC logics in the context of coevolutionary

dynamics of IT-enabled capabilities to broadly explain coevolutionary

dynamics in relation to the IT-enabled capabilities and how it impacts

competitive advantage.

In summary, the review on CAS theory in IS research serves broader ideas on

the concepts and features of CAS theory in relation to the BVIT studies and helps to

choose emergence and coevolution concepts for the thesis. The theory development

approach by Shepherd et al. provides an overarching guideline on how to develop

theories following a set of well-structured approaches. Finally, the NKC models serves

as an exploratory lens and gives in-depth insights on the coevolution of the IT-enabled

capabilities drawing underlying analytical logics of NKC model.

The aim of the study is to develop a better understanding of the dynamics related

to BVIT. Review of the BVIT literature reveals the few authors who have attempted

to theorise the dynamics of BVIT in their studies; e.g. the work by Nevo and Wade

(2010) on IT enabled resources using the emergence concept; the Melville, et al.

(2004) discussion focuses on the micro-macro level in their RBV based work

regarding BVIT; and Tanriverdi, et al. (2010) work on the strategic side of BVIT using

the complex adaptive business systems lens. This study uses existing theorising on

BVIT as a starting point and develops a re-conceptualisation of the theories on BVIT

using the CAS lens. In this study, I have adopted the Nevo and Wade (2010) study to

define two core constructs, then follow their ideas to apply my proposed notion of CAS

complex emergence (Halley & Winkler, 2008) to the study of the emergence of IT-

enabled capabilities (Chapter 4). In addition, based on the Melville, et al. (2004)

discussion on domains, the focal firm, competitive environment, and macro

environment, I have proposed two levels, micro (internal to firm) and macro (external


to firm) in which the IT-enabled capabilities coevolve with other IT-enabled

capabilities (Chapter 5).

There is a growing literature that offers many tools and approaches to theorising,

for example, metaphor (Cornelissen, 2005) or concepts (Dumont & Wilson, 1967). In

the management discipline, Shepherd and Suddaby (2017) review the literature on

theory building and integrate various ideas about how to build theory. They review

various methods and tools and provide explanation for how and when to use different

tools for theorising and discuss how to evaluate theories. I have constructed my theory

development approach for this study, based on their ideas. Only selected ‘tools’ are

chosen to develop my approach because of the highly conceptual nature of the

dissertation and the focus of the research. My theory development approach consists

of three major steps, 1) defining the narrative conflict, 2) building stories to construct

theories, and 3) evaluating theories. The steps are described below in the context of

this study. Figure 1.3 summarises the research approach.

1.4.1 The Narrative Conflict

Theory development starts with identifying an anomaly or a tension that

motivates the process. Narrative conflict refers to the struggle between two entities,

such as, human vs. human or human vs. nature. In relation to theory, narrative conflict

highlights a struggle between two ways of knowing, such as real world empirical

observation vs. theoretical concepts that attempt to describe the empirical world

(Shepherd & Suddaby, 2017).

In the context of this study, there is a conflict between what we explore in reality

to understand the dynamics in the context of BVIT in existing literature, and the

theories or concepts that help us to explore such dynamics. The existing literature

provides organisational perspectives of BVIT based on internal business processes,

organisational capabilities and organisational practices, from empirical observations

of the real world (Bharadwaj, 2000; Melville, et al., 2004). However, technologies are

changing, proliferating within and among organisations and altering the ways business

is conducted (Merali, et al., 2012). In this fast changing world driven by uncertainty,

dynamism and high interconnectedness, the way business value is created is

increasingly dynamic, as acknowledged by several prominent IS scholars (e.g.

Altschuller, et al., 2010; Chen, et al., 2014; Nevo & Wade, 2010; Schryen, 2012;

Tanriverdi, et al., 2010). Although emerging studies have begun to explore the


dynamics and uncertainty related to the BVIT puzzle, existing theories or concepts that

help to explore such dynamics still remain limited and underdeveloped. Moreover, the

world of business has become dynamic, strategies are perishable, and the landscape of

technology and business is rugged (Tanriverdi, et al., 2010). Preparing for ‘knowing

these unknowns’ highlights the need for rethinking existing theory and perhaps the

need to acquire new types of sensing frameworks or perspectives to capture the

unpredictable dynamics in the business landscape (Meyer, et al., 2005).

In chapter 4, I have provided a more specific example of ERP system to highlight

the narrative conflict, which is the tension between the existing literature on

emergence, whether emergence is linear or non-linear (dynamic) and the

conceptualisation of such emergence. In chapter 5, I have adopted the same ERP

system case from chapter 4 to present the narrative conflict, which is the tension

between the existing literature on coevolution, whether the internal and external

coevolution are linear or non-linear (dynamic) and the conceptualisation of such

coevolution.

1.4.2 Building Stories

Building stories refers to building a narrative framework to organise theory

development, emphasising sequential story-telling to weave together prior knowledge

with the construction of new knowledge. It involves four major stages; identifying the

core constructs, determining the narrative settings, drawing boundary conditions, and

applying disciplined imagination (theorising) (Shepherd & Suddaby, 2017). Each of

these stages is discussed below in the context of this study.

Identifying Core Constructs

Identifying the core constructs related to the study is important because it helps

to conceptually separate the phenomenon under study from other phenomena. The core

construct of this study is the business value of IT (BVIT). The study adopts existing

theorising of BVIT as a starting point and then develops a reconceptualization of

BVIT, which is broadly discussed in the next sections.

Determine the Narrative Setting: Shifting Ontology

Determining the narrative setting involves specifying a time and place within

which events occur. It is important to describe the narrative setting, the context of the

study, to explain the reasoning behind the story telling, the credibility of the theoretical


argument, and the motivations of the study. Different strategies are used by theorists

to adopt new perspectives by adjusting the philosophical setting. For this study a

shifting ontology strategy is adopted.

Shifting ontology is used to develop creative insights for the development of

theories (Shepherd & Suddaby, 2017). It refers to “changes in the ontological emphasis

that maintain epistemic-ontological alignment” (Thompson, 2011, p. 755). Ontology

refers to the nature of the phenomena under study and epistemology represents the

nature of knowledge on the phenomena; ontology deals with the nature of reality and

reflects an interpretation by an individual about what constitutes a fact (Gioia & Pitre,

1990). It is important to consider both ontology and epistemology together as an

ontological shift changes the epistemology of the study (Thompson, 2011).

In this study, shifting ontology highlights the change from a static-linear view

on BVIT to a more dynamic-complex view on BVIT. As mentioned earlier in section

1.2, in contemporary organisations, technology and business are fused in a way that

causes a step-change in the complexity of the business ecosystem (El Sawy, et al.,

2010) and thus, the way value creation in contemporary organisations has changed.

Subtle change in any of the components in the business environment, can stem not

only from rapid, but also unexpected movements between players in the business

system (Burgelman & Grove, 2007). Thus, a new perspective or alternative approach;

from a static perspective to a dynamic one; is required to better understand interactions

among the different elements in the business environment and how their relationships

influence BVIT (Tanriverdi, et al., 2010).

Figure 1.3 Research Approach

Draw Boundary Conditions: The Story’s Event Sequence

Specifying boundary conditions by relating the sequence in which events occur

is an important consideration in theory development. Theorising can involve shifting

the time sequence in a way that can help to change the ontological perspective and to

1. The Narrative Conflict:

Problematisation

Indetify the Main construct: BVIT

Draw boundary conditions: The

story's event sequence

Apply disciplined Imagination: Theorising via

Metaphor

3. The Evaluation of

Theory

Determine the narrative setting:

Shifting Ontology

2. Building Stories


better describe the relationship between constructs (Shepherd & Suddaby, 2017). This

study emphasises the time constraints via explaining event sequences in the context of

the IT-enabled capabilities. It argues that IT-enabled capabilities first emerge from the

interactions between the IT assets and organisational resources. As soon as the IT-

enabled capabilities emerge, they start coevolving with other IT-enabled capabilities

of organisations (see section 1.3). Though the time at which these events (the

emergence of the IT-enabled capabilities and then their coevolution) occur cannot be

easily explicated, the event sequences help to draw the boundary conditions. The

influence of the coevolution of IT-enabled capabilities on competitive advantage can

be considered as another event sequence.

Moreover, another boundary condition related to this study that I have

emphasised above is the strategic perspective on BVIT (the competitive advantage of

organisations), not operational efficiency (the operational perspective of BVIT). In

addition, my focus in the study is on contemporary organisations, which are operating

in moderately to rapidly changing business environments (such as retailing and

financial organisations) and are subject to the disruptive force of digitisation

(Sambamurthy, et al., 2003). The kind of IT focused on in the study includes pervasive

and generative digital technologies that are “dynamic and malleable” (Yoo, et al.,

2012, p. 1399) and provide “software-based digital capabilities” (Yoo, et al., 2012, p.

1398), such as, digital platforms that allow organisations to support their different

functions. These digital technologies are nearly inseparable from core products and

services (El Sawy, 2003) and are fundamentally reshaping organisations (Banker,

Chang, & Kao, 2010; Sambamurthy, et al., 2003).

Apply Disciplined Imagination: Theorising via Metaphor (Analogical Reasoning)

Shepherd and Suddaby (2017) have discussed several approaches to applying

‘disciplined imagination, such as, thought experiments (Weick, 1989), simulation

(Davis, Eisenhardt, & Bingham, 2007) or analogical reasoning (Tsoukas, 1993). This

study adopts an analogical reasoning approach via theoretical metaphors as suggested

by Tsoukas (1993).

Metaphors provide languages or vocabulary to engage, organise and describe the

phenomena under interest (Tsoukas, 1991). Metaphors and analogies guide

imagination, helping to illustrate and describe alternative conceptions of reality by


selecting certain features of it and thus forming an explanatory point-of-view of the

world (Tsoukas, 1993). Theorising via metaphors provides new perspectives, images

or concepts to develop creative insights into the social world. This study has adopted

two CAS metaphors (Mitleton-Kelly, 2003b), emergence and coevolution to

understand and theorise the dynamics related to BVIT. Based on the existing literature

on BVIT, as suggested by Mintzberg (2005, p. 362), the theorising process using CAS

metaphors provides structured descriptions of the dynamics related to the BVIT. The

study has applied CAS metaphors to use analogical reasoning to theorise the dynamics

related to BVIT and develop a CAS BVIT model. In chapter 4 and chapter 5, I have

used two CAS metaphors, emergence and coevolution respectively to theorise

dynamics related to the BVIT.

Firstly, the study reviews the existing core BVIT models (mid-range theories) to

determine to what extent the models focus on the dynamics related to the BVIT. An

examination of the BVIT research reveals that there is a growing literature in IS, which

provides hints about the development of the dynamic conceptualisation of BVIT,

though they do not provide an in-depth explanation of BVIT dynamics. This study has

adopted BVIT theorising from two such studies as a starting point, and applied CAS

metaphors for reconceptualization.

Firstly, Nevo and Wade (2010) applied a systems theory lens, more particularly

the emergence concept from systems theory, to explain how IT enabled capabilities

come into existence from the interaction between IT assets and organisational

resources. However, they conceived a static and linear approach to describe the

emergence of IT enabled capabilities. Secondly, although Melville, et al. (2004)

discussed the macro and micro environment, where macro specifies country

characteristics and micro includes focal firm and competitive environment, they

ignored how this micro-macro environment interacts and influences BVIT creation.

Although they started exploring the BVIT puzzle, they did not address the dynamics

in relation to BVIT.

Therefore, to develop and theoretically explain insights into the dynamics and

unpredictable complexities in relation to BVIT, more specifically competitive

advantage, this study has applied CAS theory. In particular, the complex emergence

metaphor of CAS theory is applied to describe how IT enabled capabilities emerge via

a bottom-up process in contemporary organisations (Chapter 4). Moreover, the


coevolution CAS metaphor is applied to describe how the IT enabled capabilities

coevolve within and among organisations and influence competitive advantage

(Chapter 5). Together these CAS metaphors- emergence and coevolution will help me

to explore the answer to the research question in section 1.3.

1.4.3 Evaluating Theory

Despite the importance of developing theories and making a theoretical

contribution, the resolution of the story (i.e., what constitutes a theory) varies widely

as does the interpretation of what represents a good story (i.e., a theoretical

contribution) (Shepherd & Suddaby, 2017). The study starts with the concern that the

growing IS literature, although it acknowledges the dynamics in relation to BVIT,

lacks conceptualisation. Therefore, this study will particularly focus on developing

new insights that help to describe the dynamics related to BVIT. So, the study begins

with identifying a problem and its associated assumptions; clarifying and focusing the

problem; and analysing, understanding and making use of analogical reasoning, as

well as evaluating the reliability of the assumptions from the literature available, and

concludes with new insights. Via the critical reasoning (Cederblom, 2012) approach,

the study evaluates what has been claimed during problematising and the insights

provided that help to reduce the research gap.

Illustrative case studies are used to conduct internal validation (Eisenhardt,

1989) of the developed theories. In chapter 4, I have adopted an ERP system case study

by Lokuge (2015) for internal validation (Eisenhardt, 1989) of my developed theories

of the emergence of the IT-enabled capabilities. I have conceptualised the case

narrative in the context of my proposed emergence framework and showed that the

concepts, analytical logic and conditions of my framework are in alignment with the

case study (section 4.4.2, chapter 4). Further, in chapter 5, I have adopted IS

development case study by Montealegre, Hovorka, and Germonprez (2014) for

internal validation (Eisenhardt, 1989) of my theories on the coevolution of the IT-

enabled capabilities. Using the case narrative, I have represented that my proposed

coevolutionary framework can provide in-depth and sense-naking insights on the

coevolution of IT-enabled capabilities and its impact on firm’s competitive advantage

(see 5.6.2, chapter 5).


Figure 1.4 Approach to theory development in relation to BVIT

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The study has thus used existing theories related to BVIT, identified the problem

(the static and linear view of BVIT), and applied CAS metaphors as analogical

reasoning to theorise and develop new insights- re-designed theories (a dynamic and

complex view of BVIT), which are explanatory type (Gregor, 2006). After then, NKC

representation of BVIT is used to explore greater in-depth insights on the dynamic

BVIT creation. The theory development flow diagram is illustrated in the following

Figure 1.4 with the research methodology embedded within it.

1.5 RESEARCH DESIGN

The research design (Figure 1.5) includes six phases:

1. Define the research problem,

2. Propose the conceptual model of BVIT,

3. Develop an emergence perspective of IT-enabled capabilities,

4. Develop a coevolutionary perspective of the IT-enabled capabilities,

5. Operationalise the coevolutionary perspective of the IT-enabled capabilities,

and

6. Present CAS based understanding of BVIT. The phases are described below-

Phase 1: Define the research problem

In this phase, I identified i) Research motivation(s), ii) Research problem &

context, iii) Research question(s), and iv) Research Objectives. Based on an extensive

review of IS and strategic management literature on BVIT, I have been able to identify

the current state of BVIT studies and the research gaps to which this study will

contribute. Topics reviewed are: i) existing BVIT studies (Chapter 2), ii) prominent

BVIT models (Chapter 2), iii) dynamic and non-linear approaches related to the

investigation of BVIT (Chapter 2), iv) complexity science and its relation to the BVIT

research (Chapter 2), and (v) CAS theory and its status in IS research (Chapter 3).

Phase 2: Propose a conceptual framework of BVIT

The BVIT conceptual framework is proposed based on the literature review of

the prominent strategic BVIT models in IS research (See Figure 1.1). This review

revealed three key parts to the models, where the 1st part contains IT related assets and


other organisational resources, the 2nd part emphasises the different capabilities

derived from IT, and the last part includes the organisational performance, competitive

advantage in particular. (See section 1.4)

Figure 1.5 An overview of Research Design

Phase 3: Develop an emergence perspective on IT-enabled capabilities

Based on the theoretical concept of Nevo and Wade (2010), a study for

understanding strategic BVIT, I have first proposed a complex emergence model of

IT-enabled capabilities, which shows how IT enabled capabilities emerge (See Chapter

Define ContextResearch Context

Research questions/ Objectives

Review Literature

Complexity TheoriesBVIT

Develop LR protocol for CAS in

ISKeyword Search

protocol documentation

Conduct review on CAS in IS

Phase 3:Emergence persp.

on IT-enabled capabilities

Data gathering and analysis protocol

Data reporting strategy Issues

Conceptual analysisTheoretical and methodological

approachesContextual Analysis Theory types

Emergence framework on IT-enabled

capabilities

Phase 4: CAS coevolutionary

persp. of IT enabled capabilities

Interpret Propositions Implications for

theory and practice

Phase 6: A CAS understanding on

BVIT

Phase 2:Propose BVIT conceptual

franework

Phase 1

NKC Translation of coevolution

Phase 5

Coevolution framework on IT-enabled

capabilities

Emergence Propositions related to IT-enabled

capabilities

Coevolution Propositions related to IT-enabled capabilities


4). This emergence model is expected to serve as an ideal approach for encoding a

bottom-up view of IT enabled business capabilities into a formal model, which can be

used to gain deeper insights related to BVIT in the next phase 4.

Phase 4: Develop a coevolutionary perspective of IT-enabled capabilities

Based on Melville, et al. (2004) discussion on domains, the focal firm,

competitive environment and macro environment to define the focus of BVIT creation,

I have proposed two levels, micro (internal to firm) and macro (external to firm) in

which the IT-enabled capabilities coevolve with other IT-enabled capabilities (Chapter

5). A Micro coevolutionary perspective based on variation, selection and retention

mechanisms is discussed in relation to the IT-enabled capabilities following Chae

(2014). Moreover, a macro coevolutionary dynamics perspective in relation to the IT-

enabled capabilities is discussed by adopting the Red Queen effect, competitive

exclusion and niche separation concepts from (McKelvey, 2002). How firms achieve

competitive advantage by obtaining valuable and rare IT enabled capabilities is also

discussed, with the emphasis on action-based competitive advantage approach

(D'Aveni, et al., 2010).

Phase 5: Operationalise the coevolutionary perspective of the IT-enabled capabilities

I have used Kauffman’s NKC model (Kauffman, 1993) to operationalise the

coevolutionary perspective of the IT-enabled capabilities in this phase. I have first

translated the coevolution of the IT-enabled capabilities using the parameters of NKC

model following McKelvey (1999). Then, I have followed the Baum and McKelvey

(1999c) approach to formalise strategies on whole-part coevolutionary competition.

Several strategies are proposed in this phase (See Chapter 6).

Phase 6: A CAS understanding of BVIT

In this phase (See Chapter 7), the emergence perspective of IT from phase 3, the

coevolutionary perspective on IT- enabled capabilities from phase 4, and the

operational NKC model of IT-enabled capabilities from phase 5, are together used to

provide and in depth answer to the research question- how is BVIT created in the

dynamic business-IT environment?


1.6 CHAPTER SUMMARY

In summary, this chapter presents an overview of the dissertation. In section 1.1,

an overview of the research related to the BVIT topic is provided; focusing on what is

BVIT, why does it matter and how it is studied via different approaches across IS

studies.

The subsequent section highlights the motivation behind this research, which is,

that the dynamic business environment has changed the way by which IT influences

the creation of business value in contemporary organisations, and thus a

reconceptualisation of BVIT is required, as advocated by prominent IS scholars.

Section 1.3 addresses the overarching research problem.

Section Error! Reference source not found. broadly discusses the overall

research question and the two sub questions. It also briefly explains that this study has

adopted CAS theory, in particular the emergence and coevolution concepts, to explore

the dynamics related to the BVIT.

Section 1.4 broadly discusses my proposed research methodology based on the Section 1.4 broadly discusses my proposed research methodology based on the

ideas of Shepherd and Suddaby (2017) regarding the methods and tools for theory

building. The subsequent section presents a brief overview of the six phases of my

proposed research design.

In the next chapter I present a review of the literature to determine the status of

BVIT research and complexity theories in the IS discipline.

Chapter 2: Background Literature 27

Chapter 2: Background Literature

Chapter 2 Summary

What was done in the previous chapter: The previous chapter sets up the

research context by defining the overarching research problem, research questions

pertaining to the problem, research method and design.

What this chapter does: This chapter presents the extant literature related to

BVIT and complexity theories to discuss the research background.

What is still outstanding in later chapters:

Chapter 3: Contains a structured literature review on CAS theory in IS.

Chapter 4: An emergence perspective of IT-enabled capabilities.

Chapter 5: A coevolutionary perspective of IT-enabled capabilities and how

it influences competitive advantage.

Chapter 6: An operational (NKC) coevolutionary model of IT-enabled

capabilities.

Chapter 7: A CAS based framework on competitive advantage and a

discussion on the overall insights that I have developed in relation to BVIT.

Chapter 2 contains an overarching literature review on BVIT and broader review

on complexity theories in IS and referral disciplines. This chapter begins with a

historical background and reviews literature on the following topics: business value of

information technology and complexity theory. The complexity theory has been

chosen as an overarching theoretical base as it helps to conceptualise a system as

complex adaptive system (CAS) (Burnes, 2005). The CAS theory is a branch of

complexity theory that helps to model a particular phenomenon by using agent based

approach and CAS concepts. As I have conceptualised BVIT using two CAS concepts-

emergence and co-evolution, therefore a background review on broader complexity

theory is presented in this chapter. Moreover, both BVIT and complexity literature

helps to better understand the studies conducted in relation to BVIT, identify the

28 Chapter 2: Background Literature

research gap and relate how CAS, a branch of complexity theory can help to better

understand the issue.

The following section first provides a historical background on BVIT related

research. Then it discusses the core BVIT models in IS literature. The following

section presents a high level conceptual framework with a particular focus on strategic

side of BVIT. After then, a review on broader complexity theories is presented. The

chapter concludes with a summary.

2.1 HISTORICAL BACKGROUND

The main objective of this study is to develop an in-depth understanding of the

dynamic side of business value of IT (BVIT) in contemporary business organizations,

which has received less attention in IS research. To do so, BVIT related literature is

explored (section 2.1) to develop a thorough understanding on various types of

research conducted on BVIT in IS. After investigating the prominent models of BVIT,

the chapter presents a high level conceptual framework on BVIT in section 2.3. The

conceptual model is intended to present a common understanding of scholars on the

strategic side of BVIT, which is how organisations achieve competitive advantage by

IT deployment (Porter & Millar, 1985).

From the review, it is identified that the traditional literature on BVIT

emphasises the more static side of BVIT (Melville, et al., 2004), where organisational

resources, capabilities, routines, etc. are considered as static over a period and

researchers adopt different approaches such as process, variance or systems

approaches to study their effect on organisational performance. Few IS research

scholars (e.g. El Sawy, et al., 2010; Tanriverdi, et al., 2010; Yoo, et al., 2012) have

addressed this particular issue – that emerging digital IT and their deployment in

organisations have given rise to a step change in relationships among different

organisational business components, such as resources and capabilities, IT

competencies and strategic potential. Consequently, they have called for alternative

methodological and conceptual approaches to deal with these changes.

The authors in this stream assert the importance of understanding the dynamic

nature of relationships between different resources and their organisations, and

between organisations and their environment (Merali, et al., 2012). They recommend

that at a more general level there is a concern with the need for systemic and complex


theory building to understand such dynamic relationships. For instance, El Sawy, et al.

(2010) advocate the use of configuration theories to describe the pattern of interactions

among environmental turbulence, dynamic capabilities and IT systems, while,

Tanriverdi, et al. (2010) adopt complex adaptive systems theory to reframe three IS

strategy quests; strategic alignment, integration and sustained competitive advantage.

Whilst they differ in focus and their prescriptions, the recommendations of these

authors draw on systems and complexity thinking for understanding dynamics related

to different organisational phenomena. This study adopts their recommendations and

adopts CAS theory, a branch of complexity theories (Burnes, 2005) to explore the

dynamics related to the BVIT. Therefore, this chapter reviews complexity theories

(Burnes, 2005) as well (section 2.4) because it is important to know the various

complexity concepts or metaphors that help to deal with the implicit dynamic and non-

linear nature of different organisational phenomena (Mitleton-Kelly, 2003b).

2.2 BUSINESS VALUE OF INFORMATION TECHNOLOGY (BVIT)

The following sections review the definition of BVIT, the theoretical paradigms

in relation to BVIT and the core models of BVIT.

2.2.1 Definition of BVIT

The term ‘business value of IT’ (denoted as BVIT throughout the dissertation)

is used to refer the organisational performance impacts of IT, including performance

enhancement, profit improvement, cost reduction and other metrics of performance

(Kohli, 2003; Lin & Shao, 2006; Melville, et al., 2004). The analysis of IS literature

reveals diverse connotations, definitions and notions of economic and non-economic

consequences of IS investments as BVIT. Scholars have embraced conceptual,

theoretical and analytical approaches to measure BVIT at different levels of an

organisation (Melville, et al., 2004). For example, many empirical studies have used

econometric approaches to explore the relationship between IT investment and

performance, such as productivity (Hitt & Brynjolfsson, 1996), return on sales

(Bharadwaj, 2000) or sales growth (Weill, 1992). Other studies go beyond financial

measures to examine intangible assets like organisational capabilities (Kohli &

Grover, 2008) or employee satisfaction (Bulchand-Gidumal & Melián-González,

2011). Much of their effort to measure BVIT is valuable, though IS literature still lacks

a consistent and widely accepted definition of IT business value (Oz, 2005). Lack of


theoretical understanding (Gable, Sedera, & Chan, 2008), inappropriate measures of

BVIT (DeLone & McLean, 1992) and inconsistencies in human judgement (Davern &

Wilkin, 2010) are some of the fundamental reasons for variations in defining BVIT.

For the purpose of this research, Melville’s definition of BVIT has been adopted-

, “…organisational performance impacts of information technology at both the

intermediate process level and the organisation wide level, and comprising both

efficiency impacts and competitive impacts ” (Melville, et al., 2004).

2.2.2 Theoretical paradigms in BVIT research

The vast majority of BVIT related IS research reveals that researchers have

followed various theoretical paradigms to trace the path of BVIT from IT investment

e.g. resources-based view (RBV), industrial organisation theory, micro-economic

theory, organisational behaviour, socio-political paradigms etc. RBV theory (Barney,

1991; Wernerfelt, 1984) has been adopted to explore the operational efficiency and

competitive advantage of firm resources (Mata, et al., 1995; Porter & Siggelkow,

2008). This particular theory is useful to better understand whether specific IT

resources and capabilities possess advantages among competing firms (Melville, et al.,

2004). IS researchers have been using the RBV for developing in-depth understanding

of BVIT (Altschuller, et al., 2010; Benitez-Amado & Walczuch, 2011; Bharadwaj,

2000; Mata, et al., 1995; Ravichandran & Lertwongsatien, 2002). As mentioned in

(Peteraf & Bergen, 2003) and cited in (Melville, et al., 2004)the “integration of

management perspective with an economic perspective” of RBV is very useful for the

development of the BVIT model.

Rooted in RBV, dynamic capabilities theory (Eisenhardt & Martin, 2000) also

has been popular to explore the strategic role of IT in shaping competitive advantage.

Dynamic capabilities are the organizational and strategic routines by which managers

alter their resource base—acquire and shed resources, integrate them together, and

recombine them—to generate new value-creating strategies (Grant, 1991). They help

firms to integrate or reconfigure organisational resources within firms to achieve

superior competitive advantage (Eisenhardt & Martin, 2000).

Microeconomic theorists have applied rich mathematical specifications and

modelling to study BVIT. The theory of production was applied to investigate the

production process and its economic impact on IT (Brynjolfsson, 1993; Brynjolfsson


& Hitt, 1998; Dedrick, Gurbaxani, & Kraemer, 2003; Hitt & Brynjolfsson, 1996).

Researchers have also used growth accounting (Brynjolfsson & Hitt, 2003), consumer

theory (Benaroch & Kauffman, 1999; Hitt & Brynjolfsson, 1996) and Tobin’s q

(Bharadwaj, et al., 1999) to understand performance implications of IT. Although the

focus of the micro economic theory is quite specific to the production process, its

application in BVIT research has enhanced our understanding on different phenomena

(Melville, et al., 2004). Devaraj and Kohli (2000) applied organisational behaviour

theory to investigate IT payoff in healthcare industry. Very few of the research

paradigms considered the notion of sociology and the socio political perspective of

BVIT (Hoogeveen & Oppelland, 2002b).

The theoretical paradigms have been applied for a better understanding of

organisational performance related to IT ( i.e. BVIT) in organizations stem from

different perspectives. Although research into BVIT leads to different streams of

thoughts on BVIT, a unified and coherent BVIT theoretical framework is still missing

in the literature, as has been acknowledged by several researchers over the years

(Melville, et al., 2004; Schryen, 2012). The next section discusses the core BVIT

models in the IS literature.

2.2.3 Core Models of BVIT

Several different BVIT models exist in the IS literature. These models differ by

research focus, phenomena under study, theoretical lens and approaches, such as,

process or variance, applied in the research (Melville, et al., 2004). However, the

prominent models of BVIT can be largely categorised into two types having two

perspectives-

1. Operational model: BVIT models that are operational and process-centric,

representing how, when and why IT investment is converted to favourable

organisational performance (Melville, et al., 2004). The models under this

category holds an operational perspective of BVIT.

2. Strategic Model: BVIT models that are strategic focusing on how

organisations can achieve superior competitive advantage over their rivals by

deploying IT systems (Porter & Millar, 1985). The majority of the strategic

BVIT models adopt RBV theory (e.g. Melville, et al., 2004; Tebboune &

Urquhart, 2016). Some models adopt dynamic capability theory (e.g. Foss &


Ishikawa, 2007; Wheeler, 2002). The models under this category holds a

strategic perspective of BVIT.

The following paragraphs briefly discuss one process centric BVIT model

(operational), one dynamic capability based BVIT model (operational) and two RBV

based strategic models of BVIT.

Soh & Markus (1995) Process-oriented BVIT Model

The Soh and Markus (1995) model of BVIT is one of the earliest prominent

BVIT models in IS literature. They followed a process-oriented approach to represent

how, when and why IT investment is converted to favourable organisational

performance. The model contains three major parts, IT conversion process, IT use

process and the competitive process. In the IT conversion process, investment is made

in IT and IT is incorporated with products and services or to redesign business

processes, better decision making and improved coordination leading to IT assets. In

the IT use process, appropriate use of the IT assets is ensured for IT better impacts to

occur. Finally, IT assets cause some impacts on organisational performance as well as

influence the competitive position of the organisation in the market. This model has

been used as an empirical guideline to develop other models, such as the Dehning and

Richardson (2002) process-centric model to represent a multi-path approach of IT-

performance measurement and Dedrick, et al. (2003) production oriented model of

organisational outcomes. However, the Soh and Markus (1995) model is criticised for

being linear as resources are considered stable in the model (Helfat & Peteraf, 2003).

To emphasise the dynamic nature of organisational resources, IS scholars have

therefore applied dynamic capability theory in their research.

Melville et al. (2004) RBV based Strategic BVIT Model

One of the most recognised BVIT models developed by Melville, et al. (2004)

is based on a resource-based view (Wernerfelt, 1984) of an IT firm. It provides a wide

view of BVIT generation process. Melville, et al. (2004) derive three domains in their

proposed model- focal firm, competitive environment and macro environment. The

model describes how each domain shape the relationship between IT and

organizational performance. The focal firm invests in IT, the competitive contains the

environment where focal firm operates and it includes industry and trading partner

characteristics and macro environment contains country factors or social and cultural

contexts that may influence BVIT. Regarding the BVIT creation mechanisms, they


focus on two important aspects. First, they argue that synergistic relationships of IT

and organisational resources lead to a high degree of complexity, but they did not

provide explanations of this complicacy. Second, they highlight the existence of micro

(focal firm) and macro levels (the broader business environment) in relation to BVIT;

however, they hardly explain the BVIT creation mechanisms.

Nevo & Wade (2010) RBV-Systems Thinking based Strategic BVIT Model

The Nevo and Wade (2010) study on BVIT is motivated by earlier literature

suggesting that analysing the effect of IT assets on the strategic potential of

organisational resources might help to identify more endurable IT-based

organisational benefits (Hackney, Burn, & Salazar, 2004). The authors develop a

theoretical model by conceptualising a path from IT assets to profitability through a

synthesis of systems theory and a resource-based view of the firm. In particular, the

work argue that it is not individual capabilities or organisational resources or IT assets

that contribute to the strategic potential, but the emergent capabilites that arise from

their combination.

In order to provide a more complete conceptual picture of BVIT, Nevo and Wade

(2010) adopt systems thinking concepts, more precisely, emergence concept with RBV

theory and propose that IT assets can be placed in a relationship with organisational

resources, thereby creating synergistic IT enabled resources, the emergent properties

(capabilites) of which facilitate an organisation to achieve profitability. They argue

that IT assets derive their business value from the impact they make on the

organisational resources with which they interact. Thus, the intrinsic capabilities of the

IT assets should not be used in isolation to infer their business value; instead, the

emergent capabilities arising from their relationships with organisational resources

should be examined and evaluated. The authors conclude that emergent capabilites

that are beneficial or positive have the potential to help IT-enabled resources to achieve

organisational goals.

Although, the authors acknowledged that there is a need to take an alternative

approach to understanding BVIT in this dynamic and contemporary business

environment and adopted systems theory based emergence concept, the emergence

concept of systems theory is largely linear (Goldstein, 1999). It is used mainly to focus

on close to equilibrium situation and used in describing simpler systems (Halley &

Winkler, 2008). In contrast, I have proposed a complex emergence perspective


(Chapter 4) that helps to explore the BVIT related dynamics which are non-linear in

nature and far from equilibrium conditions due to rapid movements of components in

business systems.

In addition to the above core models, there are many streams of BVIT models

in the literature. For example, few studies that adopt variance theories explore

variations in the magnitude of particular outcomes of performance i.e. BVIT under

certain and sufficient conditions (Markus & Robey, 1988). Another stream of BVIT

researchers focus on IS success, beyond financial measures to intangible measures

(DeLone & McLean, 1992). However, I have mainly focused on the strategic BVIT

models in this study.

Wheeler (2002) Dynamic Capability based Strategic BVIT Model

Wheeler (2002) Net-Enabled Business Innovation Cycle (NEBIC) is an applied

dynamic capability-based model for measuring, predicting, and understanding a firm’s

ability to create customer value through the business use of digital networks. It

identifies four sequenced constructs: Choosing new IT, Matching Economic

Opportunities with technology, Executing Business Innovation for Growth, and

Assessing Customer Value, along with the processes and events that inter-relate them

as a cycle. Together these four cycles provide a practical lens to understand what are

the emerging technologies, how to match them with economic opportunities and

business innovation growth and how to assess customer centric value. However, the

NEBIC model is also criticised for being too linear and static because it does not

consider what will happen if any organisational resources or capabilities change before

matching the IT capabilities with economic opportunities However, it does tell a step-

by-step practical story about how to achieve customer value via net-enabled business

innovation (Bharadwaj, El Sawy, Pavlou, & Venkatraman, 2013; Sambamurthy, et al.,

2003).

To summarise the review of BVIT related literature in the above section-

• BVIT is a well understood and established concept in the literature in relation

to stable , non-dynamic environment (Schryen, 2012). However, it has been

relatively underexplored in the context of dynamic business environment and

therefore, a stream of prominent IS scholars (e.g. El Sawy, et al., 2010;

Tanriverdi, et al., 2010) have called for adopting methodological and


conceptual alternatives in conceptualising BVIT, as contemporary digital

technologies and their adoption gives rise to a step change in complexity,

dynamism and unpredictability in social systems.

• Few research studies have been conducted to explore the dynamics related to

the BVIT puzzle using alternative conceptual alternatives (e.g. Chae, 2014;

Nevo & Wade, 2010; Tanriverdi, et al., 2010). Although the majority of the

researchers adopted the innovative approach, they rarely describe the implicit

dynamics in relation to BVIT (which has been discussed in section 2.2.3).

• This study has particularly taken motivation from two particular studies as a

starting point-

o First, the study of strategic advantage of IT enabled resources by Nevo

and Wade (2010) as a starting point to define emergence of IT-enabled

capabilities (See Chapter 4, pages 11-12).

o Second, Melville, et al. (2004) RBV based BVIT model as a starting

point to define micro (internal) and macro (external) levels of

organisations (See Chapter 5, page 1).

The above discussion provides an overview of the prominent BVIT models.

Based on the observation of the prominent models, I have proposed a high-level

conceptual BVIT framework in the next section.

2.3 A HIGH LEVEL CONCEPTUAL FRAMEWORK OF BVIT

This section broadly discusses a high level BVIT conceptual framework based

on observation on the prominent BVIT models in IS. As mentioned in the preceding

chapter, this study focuses on the strategic side of BVIT. The high level BVIT

conceptual framework is proposed that emphasises the strategic role of IT assets in

shaping competitive advantage (Bharadwaj, 2000; Wade & Hulland, 2004). The

proposed framework presents a chain of relationships involving IT assets and other

organisational resources and IT-enabled capabilities that lead to firm performance, in

particular competitive advantage. Widely applied in strategic management literature,

the RBV of the firm assumes that firms compete with each other on the basis of

valuable, rare, difficult to imitate and non-substitutable resources (Barney, 1991;

Barney, 2001).


The model also serves as a structure for the thesis. In chapter 4, the model is

used to theorise how IT-enabled capabilities emerge and in Chapter 5, the model is

used to theorise how the IT-enabled capabilities co-evolve with other IT-enabled

capabilities and influence the firm’s competitive advantage.

From the literature review on the strategic side of BVIT, it is found that the

majority of BVIT models have three key parts. In the first part, the model contains IT

related resources or assets and organisational resources (e.g. Melville, et al., 2004;

Nevo & Wade, 2010; Wade & Hulland, 2004). The second part focuses mostly on the

underlying capabilities, competencies and processes and paths that ensure how the

resources can be used or employed appropriately and efficiently in the organization so

that it can achieve improved performance (Kim, et al., 2011; Melville, et al., 2004).

The last part of the model assesses the outcomes and organisational performance via

different performance measures such as productivity, profitability and intangible

benefits (Baker, Song, & Jones, 2008; Bardhan, Krishnan, & Lin, 2013; Bharadwaj,

2000; Masli, Richardson, Sanchez, & Smith, 2011).

Based on the findings from the literature on the major RBV based BVIT

models, the proposed conceptual framework on the strategic side of the BVIT includes

three key parts; which are termed lenses in this dissertation. The lenses are described

in the following section.

2.3.1 IT Assets and Organisational Resources

IT assets and organisational resources is the first lens of the proposed

conceptual framework of BVIT. I have conceived these two constructs from Nevo and

Wade (2010), who argue that though IT has become a core contributor to business

performance, the strategic link from IT assets towards competitive advantage is

elusive. They propose that IT assets derive from business value interacting with

organisational resources. The core reason behind choosing these two particular

constructs is that; the IT assets help me to define only IT related components,

resources, capabilities or even human IT skills. Whereas, the organisational resources

only refer to the resources except IT. This differentiation is particularly important

when I apply CAS lens to explore BVIT as CAS conceptualises a system as a set of

interactions between heterogeneous components.


Following Wade and Hulland (2004), IT assets refer to anything tangible or

intangible related to IT that can be used in organizational processes for creating,

producing, and offering products and services. Tangible IT assets include hardware,

network infrastructure, and human resources, and intangible IT assets include

software, information assets, and employees’ IT skills (Melville, et al., 2004). Tangible

IT assets have been a main focus of research, possibly due to the more abstract nature

of intangible assets (Piccoli & Ives, 2005) and given the dynamic relationship between

IT assets and competitive advantage of firms are elusive (Schryen, 2012). This study

seeks to shed light on the complex and dynamic process.

Organisational resources are central to the competition between firms, and

scarce, valuable, and imperfectly imitable resources are key factors of creating

sustained competitive advantage (Barney, 1991; Wernerfelt, 1984). Grant (1991)

divide organisational resources into tangible, personnel and intangible resources,

whereas, Barney (1991) classified organisational resources into physical capital,

human capital, and organizational capital resources. The taxonomy schemes, although

they differ in terminology are almost similar in that they present physical (e.g.

infrastructure), human (e.g. individual skills) and organisational resources (e.g. rules,

organisational culture) (Kim, et al., 2011). Organisational resources in this study are

defined as, “tangible or intangible factors of production that organizations own,

control, or have access to on a semi-permanent basis” (Nevo & Wade, 2010, p. 164).

The following Table 2.1 presents a summary of the IT resources or capabilities

in IS studies. It can be observed from the table that most of the studies map physical

and human resources/ capabilities on to IT functions. Whereas, the relation of

organisational resource/ capabilities with IT function is generally lacking (Melville, et

al., 2004). The IT function involves tasks that are highly distinct from other business

functions (Kim, et al., 2011). For instance, IT function has its own structures (e.g.

hardware and IT infrastructures), its own rules (e.g. performance of IT staffs) and other

characteristics (e.g. regulation and IT budget) necessary to support different business

functions (Sambamurthy, et al., 2003). Majority of the organisational resources are

related to the governing and managing overall organisations such as, investment

decision making, coordination and control (Kim, et al., 2011). In this study, I have

considered IT assets as combinations of physical and human IT resources and


organisational resources are those which complement the IT assets for overall

functioning of organisations (Melville, et al., 2004).

Related Studies Typologies

Physical (IT) resources

Human (IT) resources

Organisational resources

(Mata, et al., 1995)

Proprietary technology

• Technical IT skills • Managerial IT skills

• Access to capital

• Customer switching costs

(Ross, Beath, & Goodhue, 1996)

Technical assets Human IT assets Relationship assets

(Bharadwaj, 2000)

Tangible resources

Human IT resources

Intangible IT-enabled resources

(Tippins & Sohi, 2003)

IT objects IT knowledge IT operations

(Melville, et al., 2004)

Technical IT resources

Human IT resources

Complementary organisational resources

(Bhatt & Grover, 2005)

IT infrastructure IT business experience

Relationship infrastructure

(Pavlou & El Sawy, 2006)

IT resources Human IT resources

Deployment of resources

(Aral & Weill, 2007)

IT assets • IT skills

• IT management skills

Culture of IT use

Table 2.1 Typologies of IT assets and organisational resources (Adapted from (Kim, et al., 2011))

2.3.2 IT-enabled Capabilities

The second lens of the proposed conceptual model is IT-enabled capabilities.

Capabilities are defined as, an “organization's ability to assemble, integrate, and

deploy valued resources” (Bharadwaj, 2000). There are organisational routines that

moderate the relationships between IT investments and different firm performance

outputs and outcomes (Aral & Weill, 2007; Bharadwaj, 2000). Much research has

attempted to understand the role of IT in enabling organisational capabilities and


reinforcing the firm’s competitive position (Kohli & Grover, 2008). Firms that are able

to utilise IT resources in support of organisational capabilities are more likely to realise

value from IT resources and the relevant competencies that they provide

(Sambamurthy, et al., 2003). Pavlou and El Sawy (2006) refer to this ability of

effectively implementing IT functionality in support of an organizational capability as

an IT leveraging competence. (Nevo & Wade, 2010) suggest IT assets and

organisational resources give rise to IT enabled resources, which have certain

emergent properties, which they name as emergent capabilities.

Following (Pavlou & El Sawy, 2006), I have defined, IT-enabled capabilities as,

the ability to effectively use IT assets to support the organisational resources for the

benefit of organisation. Chapter 4 includes broad discussion on how IT-enabled

capabilities emerge in firms.

2.3.3 Competitive Advantage

The last lens of the proposed framework includes the outcome - the impacts of

IT as competitive advantage. Ma (1999, p. 259) defines competitive advantage as- “the

asymmetry or differential in any firm attribute or factor that allows one firm to better

serve the customers than others and hence create better customer value and achieve

superior performance”. Competitive advantage is a relative concept; the advantage of

one firm over another in a given market, strategic group or market (Kay, 1993). It is

rooted in the deployment of idiosyncratic, valuable, and inimitable resources (Bhatt

& Grover, 2005). The RBV contends that firms possess heterogeneous distinct

resources, and develop and nurture valuable capabilities; therefore, they are likely to

have different potential in leveraging competitiveness (Barney, 1991; Bharadwaj, et

al., 1993; Mata, et al., 1995). A firm is said to have sustainable competitive advantage

if its value creation strategy is not being simultaneously created by one of its

competitors (Barney, 1991). The earlier perspectives on competitive advantage

focused on the external position of the firms compared to their competitors; the more

recent RBV perspective emphasises the importance of firm resources and capabilities

in achieving competitive advantage (Bhatt & Grover, 2005). It is important to note that

the competitive environment in which focal firm operates has two major components-

industry characteristics and trading partners following (Melville, et al., 2004). The

industry characteristics include competitiveness, digitally enabled processes and rapid

technological innovation that shape the way IT assets are deployed in the focal firm to


generate business value (Kohli & Devaraj, 2003; Melville, et al., 2004). Moreover,

when IT systems span firm’s boundary via software applications or electronic markets

and blend with the business processes of trading partners; the IT and non-IT resources

of trading partners also impact business value of IT, in particular competitive

advantage of focal firm (Mukhopadhyay & Kekre, 2002).

Table 2.2 summarises the high level BVIT framework.

IT assets

Anything tangible or intangible related to IT that can be used

in organizational processes for creating, producing, and

offering products and services (Wade & Hulland, 2004).

• Tangible IT assets- hardware, network infrastructure,

or human resources

• Intangible IT assets- software, information assets,

employees’ IT skills in IT functions (Melville, et al., 2004).

Organisational Resources “tangible or intangible factors of production that

organizations own, control, or have access to on a semi-

permanent basis” (Nevo & Wade, 2010, p. 164)

IT-enabled capabilities The ability to effectively use IT assets to support the

organisational resources for the benefits of organisation

(Pavlou & El Sawy, 2006).

Competitive advantages Ma (1999, p. 259) defines competitive advantage as- “the

asymmetry or differential in any firm attribute or factor that

allows one firm to better serve the customers than others and

hence create better customer value and achieve superior

performance”.

Table 2.2 Summary of the High Level BVIT Framework

2.4 COMPLEXITY THEORIES

The preceding section presents a high-level BVIT framework. This section

provides an overview of complexity theories.

The application of complexity theory has recently gained attraction in the field

of social science and organisational studies. Rooted in evolutionary biology

(Kauffman, 1995a), the term ‘complexity theories’ is a label (Burnes, 2005) that


represents a number of theories, ideas and research programmes from diverse

disciplines including physics, mathematics, thermodynamics, astrology, etc.

(Kauffman, 1995a; Kauffman, 1993; Prigogine, 1984; Waldrop, 1992). Research work

related to two major school of thoughts are prevalent in complexity theories, which

includes the scientific research conducted by the scientists of the Santa Fe Institute

(SFI) in USA, particularly the work of Stuart Kauffman (Kauffman, 1995a; Kauffman,

1993), John Holland (Holland & Mallot, 1998; Holland, 1992, 1995), Chris Langton

(Waldrop, 1992), and (Gell-Mann, 1994) on complex adaptive systems (CAS) and the

work of scientists based in Europe such as (e.g. Axelrod, 1997a; Axelrod, 1997b; Casti,

1997; Casti, 1994; Casti, 1992; Haken, 1977; Niculescu, Shin, & Whang, 2012;

Prigogine, 1984; Stengers & Prigogine, 1997), Prigogine (1984) work on dissipative

structures; work by Humberto Maturana, Francisco Varela (Varela, Maturana, &

Uribe, 1973) and Mingers (1997) research on autopoiesis, Cramer (1993) work on

chaos theory and that on economic and increasing returns by (Arthur, 1989).

Manson (2001) argues that complexity theories consist of a number of theories

from different disciplines, which together represent complexity research. One of the

significant things that strike readers when dealing with complexity theories for the first

time are the strange terms such as fitness, landscape, edge of chaos, bifurcation point,

co-evolution etc. (Kauffman, 1993; McKelvey, 1997c; Van Valen, 1983; Waldrop,

1992). Most of these terms have come from biology and mathematics and later found

their way into the strategic management and organisational science disciplines. Thus,

Burnes (2005, p. 77) mentions “without mathematics, there would be no complexity

theories”. Given the vast interest and applications of complexity theories in 1990s, a

large number of definitions and conceptualisations were developed then and are still

present in the literature (Mitleton-Kelly, 2003b; Stacey, et al., 2000).

Complexity theories have received increased attention in IS and referral

disciplines over the last two decades. Books have been written, special journals issues

on the application of complexity theories have been published, and articles applying

complexity theories have been appearing in top journals (Maguire, McKelvey,

Mirabeau, & Öztas, 2006). A few of the popular topics that have employed complexity

theories are organisational change (Burnes, 2005; Stacey, 2003), supply chain (Choi,

et al., 2001), human resource management (Colbert, 2004), software development


(Vidgen & Wang, 2006b, 2009), innovation (Habib, 2008), and organisational

management (Morel & Ramanujam, 1999), etc.

Complexity theories deal with dynamic and non-linear systems where order

emerges from the interaction of system components at the edge of chaos (Holland,

1995), more specifically systems which are constantly changing and where the cause-

effect laws do not apply (Burnes, 2005). Order emerges in the dynamic systems in an

unpredictable manner through a process of self-organisation, which is governed by a

number of simple order generating rules (Haken, 1977; Kauffman, 1993; Morel &

Ramanujam, 1999). Mitleton-Kelly (2003a) stated, “Complexity is not a methodology

or a set of tools (although it does provide both). It certainly is not a ‘management fad’.

The theories of complexity provide a conceptual framework, a way of thinking, and a

way of seeing the world”.

Despite the disparity of ideas, concepts and theories under complexity theories,

Stacey, et al. (2000) mentions that there are three key theories - chaos, dissipative

structure theory and complex adaptive systems (CAS) theory. These theories are

briefly discussed in the next subsections.

2.4.1 Chaos Theory

Chaos theory (Gleick & Berry, 1987) is a notion of dynamic systems that

represents the deterministic behaviours of complex systems (Mitleton-Kelly, 2003a).

The term ’chaos’ was first coined by Chris Langton (Waldrop, 1992) (cited in

(Mitleton-Kelly, 2003b)). Chaos theory represents the region where emergent order

coexists with disorder at the edge of chaos. When a system changes its state from a

state of order towards disorder, there can be a transition state where both order and

disorder regimes exist together; this particular region is called edge of chaos (Boisot,

2006).

However, chaos theory is distinct from complexity (Maguire, et al., 2006;

Mitleton-Kelly, 2003b) and the two concepts needs to be distinguished when applied

in research. Chaos theory describes non-linear dynamic systems in terms of complex

mathematical equations or simple rules of interactions; both types of representation

give rise to sophisticated patterns or behaviours like fractals in flower. Lorenz (1963)

found in his research on weather systems that even a small event, like the flapping of

butterfly wings can lead to unpredictable consequences. However, the chaotic system


follows rules of interaction, the pattern of behaviours or order emerges in the systems

over and over again, which leads to a coherent order (Goodwin, 1997). Because of this

closeness to deterministic behaviour, chaotic systems are quite similar to linear

systems in an ontological sense (Dooley & Van de Ven, 1999). Applying chaos theory

in social and organisational systems therefore may not always be appropriate because

organisations do not follow any simple rules or mathematical formulae. Organisational

goals, competencies and practices are always evolving in a way that changes the order

in the organisation.

2.4.2 Dissipative Structure Theory

Anderson (1999, p. 222) refers to dissipative structure as “an organized state

that arises when a system is maintained far from thermodynamic equilibrium because

energy is constantly injected into it.” Dissipative structures are mostly related to the

work of Ilya Prigogine and his colleagues (Prigogine, 1984; Stengers & Prigogine,

1997) on Bénard cell in which they showed that under appropriate conditions,

chemical molecules react and spontaneously organized themselves into a dissipative

structure (Mitleton-Kelly, 2003b). In dissipative structure, system components reach

towards a ‘bifurcation point’ from where they can spontaneously organise to create an

emergent order that cannot be predicted from the history of a previous state (Stacey,

2003). Symmetry breaking happens at this particular point and a new pattern appears

(Mitleton-Kelly, 2003b). This particular theory has been used in strategic management

and organisational studies to explore organisational transformation (Leifer, 1989;

MacIntosh & MacLean, 1999; Todd H. Chiles, Alan D. Meyer, & Hench, 2004).

However, both chaos and dissipative structure theory deal with non-linear,

dynamic and deterministic systems (Burnes, 2005; Todd H. Chiles, et al., 2004), and

complex adaptive systems (CAS) theory seeks to understand the unpredictable nature

of non-deterministic systems (Keskinen, Aaltonen, & Mitleton-Kelly, 2003; Mitleton-

Kelly, 2003b), briefly discussed in the next subsection.

2.4.3 Complex Adaptive Systems Theory

Complex adaptive systems (CAS) theory (Holland & Mallot, 1998; Holland,

1995) is a branch of complexity science that represents dynamic non-linear and non-

deterministic systems. A CAS is composed of three main elements – (1) heterogeneous

interconnected elements or agents, (2) interactions, and (3) the environment (Dooley,


1996; Holland, 1995) as summarised in Table 2.3. Agents are the basic elements that

represent individuals, objects, companies, or concepts. Each agent has its inherent

attributes and an agent’s behaviour is constrained by a set of behavioural rules, which

decide how they will interact with other agents and the environment. For example, an

agent can only interact with other agents residing within 10ft of its position.

Interactions represent the relations, activities of agents and connections among

themselves or with the environment. In CAS, connection labels the possible medium

where interaction can take place as well as the mutually adaptive behaviours of agents

(Nan, 2011). Interactions also reflect flows - the movement of information and

resources through agents’ connections (Holland, 1995). Another component of CAS

is Environment, where agents reside and interact with each other. Environment has its

own attributes, structures and rules, and its behaviour is constrained by the

environmental rules. Structure provides the ground for residing agents.

Table 2.3 Basic building blocks of complex adaptive systems (CAS) theory

There is no universally agreed upon framework for CAS theory (Curşeu, 2006);

it shares all the features and characteristics of complexity science (Mitleton-Kelly,

2003b). The CAS features and characteristics are broadly described in Chapter 3 via a

structured literature review. This dissertation has adopted the concepts of emergence

Basic Elements Description Examples

Agents Semi-autonomous entities of actions People, organisation, concepts

Attributes Properties of the agents Skills, size, etc.

Behaviour rules Rules that govern agents’ behaviours Connection pattern

Interaction Mutual behaviours of agents Cooperation, decision making

Connection Links that connects agents to interact Decision making, friendship

Flows Movement of information or resources Messages, food web

Environment Context where agents reside to interact each other

Organisation, Land scape

Structures Contour of an environment and agents interaction with it

Contextual environment


and co-evolution (Lewin, et al., 1999) to better understand the implicit complexities

and dynamics related to BVIT in contemporary business environment.

In summary-

• Complexity theories include a large number of complex concepts or metaphors

from various disciplines such as, mathematics, physics, biology,

thermodynamics. The concepts of complexity theories have been used in

strategic management and organisational science disciplines have become

more popular in the last two decades (Manson, 2001).

• Complexity theories include a wide range of ideas, concepts and theories but

the three key theories are chaos, dissipative structure and complex adaptive

systems (CAS) theory (Stacey, et al., 2000). More importantly, though the three

theories deal with non-linear and dynamic systems, only CAS theory focuses

on the unpredictable nature of non-deterministic systems (Keskinen, et al.,

2003; Mitleton-Kelly, 2003b), while the others two, chaos and dissipative

structure, focus only deterministic behaviours (Burnes, 2005).

• This study has adopted CAS theory to understand the dynamics related to the

BVIT as has been advocated by several prominent scholars (McKelvey, 1999;

Tanriverdi, et al., 2010). In particular, this study has adopted two CAS

concepts-

o First, it has adopted the complex emergence concept of CAS theory to

describe how IT-enabled capabilities emerge through a bottom-up

process in contemporary organisations (refer to motivation 1 in section

2.2.3). This is discussed in Chapter 4.

o Second, it has engaged co-evolution CAS concept to describe how IT-

enabled capabilities co-evolve with other IT-enabled capabilities

internal and external to organisation and influence competitive

advantage (refer to motivation 2 in section 2.2.3). This is discussed in

Chapter 5.

2.5 CHAPTER SUMMARY

To summarise the chapter, the review of BVIT in section 2.2 provides a high

level picture of the BVIT research in the IS discipline, including different theoretical


paradigms and core models. Section 2.2.3 introduces the key core BVIT models and

helps to narrow down the two key motivations of this study, the creation of IT-enabled

business capabilities and its reciprocal changes with other organisational capabilities

internal and external to organisations. The research motivations are broadly discussed

in section 1.2 in chapter 1.

Section 2.4 presents a high level BVIT conceptual framework based on

observation on the prominent BVIT models in IS. The high level BVIT conceptual

framework is proposed with the emphasis on the strategic side of BVIT, the role of IT

assets in shaping competitive advantage (Bharadwaj, 2000; Wade & Hulland, 2004).

The proposed framework presents a chain of relationships involving IT assets and

other organisational resources and IT-enabled capabilities that lead to firm

performance, in particular competitive advantage. The framework also serves as a

structure for the thesis. In chapter 4, the model is used to theorise how IT-enabled

capabilities emerge and in Chapter 5, the model is used to theorise how the IT-enabled

capabilities co-evolve with other IT-enabled capabilities and influence the firm’s


Section 2.4 introduces complexity theories; in particular, it introduces three

major theories, chaos theory, dissipative structure theory and complex adaptive

systems theory and justifies the choice of CAS theory as a core theory to explore the

research areas.

Overall, Chapter 2 focuses on the major BVIT studies in IS literature, helps to

describe research motivations based on the understandings on the BVIT literature. The

inclusion of a broader view of complexity theory helps to develop how complexity

theory has found it’s way into IS studies and how concepts and features of the broader

complexity theories can help us to better investigate dynamic side of BVIT.

The next chapter provides an in-depth structured literature review of CAS

theory particularly within the IS discipline. The chapter is intended to introduce with

a broader use of CAS theory in IS research and helps to apply the emergence and co-

evolution concepts in the Chapter 4 and 5 respectively

Chapter 3: A Structured Literature Review on Complex Adaptive Systems Theory 47

Chapter 3: A Structured Literature Review on Complex Adaptive Systems Theory2

Chapter 3 Summary

What was done in the previous chapter: The previous chapter sets up the

research background by introducing BVIT studies in IS, describing core BVIT

models and highlighting the motivation. It also provides a high level overview of

complexity theories and gives hint on the two CAS concepts- emergence and

coevolution that help to explore key focus of the research.

What this chapter does: This chapter presents an in-depth and structured

literature review on CAS theory in IS discipline. It introduces with key CAS

concepts & their relative contributions, theories and approaches and related context

of CAS based IS research. The chapter helps to narrow down conceptual focus on

two specific CAS concepts- emergence and coevolution.


Chapter 4: An emergence perspective of IT-enabled capabilities.




capabilities.



2 The earlier version of this chapter is published in PACIS 2017. Appendix A contains the list of PACIS publications and their abstract.

48 Chapter 3: A Structured Literature Review on Complex Adaptive Systems Theory

3.1 INTRODUCTION

Complex adaptive systems theory (CAS) offers a new way of thinking about

systems of interacting agents and how order emerges in systems from the interactions

of agents, which is very useful in dynamic environments where organisations and

information systems have to be responsive and adaptive. While CAS offers valuable

insights and has been widely applied in related disciplines like management and

organisational studies, its application in the information systems (IS) discipline is

limited (Mitleton-Kelly, 2014). In this chapter, I set out to better understand and

advance the use of CAS in IS by conducting a literature review.

A special branch of complexity theory, CAS is used to investigate how complex

systems become adaptive to their environment and how properties emerge from the

interactions of the system components (Vidgen & Wang, 2006b). CAS can be valuable

for research because of its suitability for modelling non-deterministic behaviors, the

possibilities of sensitivity analysis, the use of mathematical and computational models,

and its multi-level nature (Stacey, 2003).

Cybernetics, chaos theory, and general systems theory all focus on deterministic

dynamic systems, where a set of equations are used to determine how a system changes

(i.e. experimenting with linear behaviour under some predefined conditions) in a given

time space. CAS provides another way of simplifying a complex system into a formal

system (Anderson, 1999). Organisations today potentially face sudden and substantive

change. Reasons include digitization, globalization, process re-engineering, quality

improvement, and greater workforce diversity. Organisations need to be more adaptive

and responsive to these dynamics (Cohen, 1999). CAS theory is well suited to

modelling the non-deterministic behaviors of such dynamic systems, which cannot be

represented through a deterministic set of equations. By undertaking sensitivity

analysis and varying the assumptions of basic properties of CAS components e.g.

fitness value, schemata or population dynamics, it is possible to explore and better

describe the complex behavior of dynamic systems (as opposed to evidencing causal

relationships).

Further, CAS can usefully represent complex systems through mathematical or

computational models and such models are crucial for the analysis of dynamic

processes that are too complex to be understood with language (Morel & Ramanujam,

1999). CAS helps to encode non-linear complex phenomena through mathematical


expressions and facilitates the conduct of computational experiments in a virtual

system. Thus the computational approach provided by CAS gives researchers

precision as well as control over the implemented model and helps to investigate the

myriad contingencies that may arise from the dynamic relationships of system

components. Such contingencies are difficult to explore in field studies and more

readily realized in a virtual system. Moreover, CAS is inherently multi-level in nature,

facilitating investigation of collective-level behavior that emerges from the lower-level

interactions of systems components.

Although CAS was first popularized in the field of evolutionary biology, several

CAS principles have been widely applied in the strategic management discipline to

understand the dynamic behaviors of complex systems e.g. supply chain networks

(Choi, et al., 2001), leadership (Schneider & Somers, 2006) and organisational

learning (Kane & Alavi, 2007). The application of CAS also has a long history in the

social sciences and organisational studies (Anderson, 1999; Brown & Eisenhardt,

1998; Morel & Ramanujam, 1999). For decades, organisational researchers have

employed concepts from complexity theory such as adaptation, self-organisation and

evolution (Casti, 1994; Gell-Mann, 1994) to analyse dynamic non-linear phenomena

in complex systems.

The employment of CAS theory in the Information Systems (IS) research

domain is more recent. The importance of CAS and encouragement of its broader

adoption in IS has been argued in several journal special issues, such as Journal of

Information Technology (Merali & McKelvey, 2006) and Information Technology &

People (Jacucci, Hanseth, & Lyytinen, 2006). More recently, IS researchers have

employed CAS principles to investigate the complex behaviour of processes like agile

software development (Vidgen & Wang, 2006b; Wang & Conboy, 2009), information

systems development (Allen & Varga, 2006; Kautz, 2012) and organisational

knowledge processes (Habib, 2008; Merali, 2002). These research areas involve a

myriad of complex processes and activities; CAS principles and concepts align well

with the exploration of such unpredictable patterns. The concepts of CAS theory have

also been used in conjunction with other theory e.g. resource based theory (RBV)

(Barney, 1991) to study sophisticated business processes like the generation of

business value using IT (Nevo & Wade, 2010). Moreover, CAS provides a new way

of exploring contemporary dynamic phenomenon, such as- IT use processes (Nan,


2011), systems dynamics (Hildebrand, Hofstetter, & Herrmann, 2012), and agile

processes (Vidgen & Wang, 2009). In addition, CAS concepts and ideas are used for

theory building, sometimes in conjunction with case study research (e.g. Nan, 2011).

While CAS has found its inroad into IS, the first impression is that its use is

limited and fragmented. A search of mainstream IS journals and conferences revealed

few papers (36 papers) that apply CAS theory3. However, a total of 220 papers

encountered in the sample search, which suggests that the CAS theory is relevant in IS

discipline. Moreover, a further investigation of the papers reveals that our

understanding is limited with respect to what CAS theory is, why CAS theory is

applied and how CAS theory is applied. These fundamentals require attention in order

to advance our understandings on CAS theory. Moreover, several scholars in IS (e.g.

Merali, 2006; Vidgen & Wang, 2009) also suggested the limited use and inadequate

understanding of the theory in IS discipline.

This chapter entails a structured literature review of CAS studies in Information

Systems. Guided by, Levy and Ellis (2006), Webster and Watson (2002) and Paré,

Trudel, Jaana, and Kitsiou (2015) suggestions in relation to developing structured

literature review, the aim of the review includes following-

1. To systematically collect, analyse and synthesise all CAS theory related

literature within the IS discipline,

2. To understand the current status of CAS theory in IS,

3. To provide a firm foundation to the basic understanding of CAS theory,

4. To identify theoretical and methodological approaches related to the CAS

application in IS, and

5. To identify what research exists (i.e. what is already known?) and where new

research is needed (i.e. what is needed to be known?).

I believe this is the first such review comprehensively focusing on CAS theory

in IS discipline.

3 This statement about the status of CAS research in IS discipline is further discussed broadly in the rest of the paper.


The remainder of this paper proceeds as follows. The section 3.2 contains an

overview of the CAS theory. Subsequently, I present the review design used to conduct

the review in section 3.2. I then address the current status (section 3.4) of and

conceptual perspectives (section 3.5) on CAS theory. The following section 3.6

overviews the objectives of CAS theory in IS. The theoretical and methodological

approaches to the use of CAS in IS are presented in section 3.7 and section 3.8

respectively. Finally, the context of CAS theory in IS is discussed in section 3.9 and

the chapter concludes with a summary in section 3.10.

3.2 BACKGROUND LITERATURE

A special branch of complexity science, CAS is a new way of thinking about

systems of interacting agents and how order emerges in systems from the interactions

of agents. Authors such as, Holland (1995), Gell-Mann (1994) and Dooley (1997) were

among the first to inspire organisational researchers to adopt CAS theory for

investigating non-deterministic, dynamic phenomena in organisations.

There are many accounts of CAS theory, the general view across the broader

community of CAS scholars being that there is no single theory of CAS (Anderson, et

al., 1999; Vidgen & Wang, 2006b). In practice, few scholars explicitly represent a

CAS theory. Nonetheless, all imply that a CAS is composed of agents that interact.

Holland (1995) alludes to a definition of CAS as- a single coherent system that

emerges over time from the interactions of its components (agents) and adapts and

organizes itself within an environment. A CAS is composed of three main elements –

(1) heterogeneous interconnected elements or agents, (2) interactions, and (3) the

environment. He also explains the basic characteristics and mechanisms of CAS

through its seven basics- aggregation, flows, nonlinearity, diversity, tagging, internal

models, and building blocks. The basic building blocks of CAS are agents (Dooley,

1997). Each agent has inherent attributes and schemata, which are the cognitive

structures by which agents choose to interact with other agents and with the

environment. Interactions represent the relations: activities of agents and connections

among agents or with the environment. Environment is the space in which the agents

reside. Authors like Cilliers (1998) and Mitleton-Kelly (2003a) describe the major

features and principles of CAS, such as open systems, non-linearity, feedback loops,

etc. In short, CAS refers to a system that is adaptive to its environment where


properties emerge in the system from the interactions of its components (Vidgen &

Wang, 2006b).

Though a widely accepted definition of CAS theory is yet lacking, review of the

views of main CAS theorists reveals several key concepts and properties that

characterise CAS. These concepts and principles are used to interpret how complex

non-linear systems with interacting agents function to produce orderly but

unpredictable behaviour (Alaa & Fitzgerald, 2013). Many of the concepts are

prominent in previous theoretical frameworks; for example, the concepts of

emergence, connectivity, interdependence, and feedback are familiar from systems

theory. Nonetheless, CAS theory extends these theories with additional concepts like

co-evolution, self-organisation and edge of chaos, which enriches systems thinking

concepts and emphasizes their inter-relationship and interdependence. The CAS

concepts are tightly associated with each other, bringing into question any attempt to

isolate and concentrate on one concept such as emergence or co-evolution, and exclude

the others (Mitleton-Kelly, 2003a).

Below, the major CAS concepts are described, cross-referenced with the core

papers such as- (Holland, 1995), (Morel & Ramanujam, 1999), (Mitleton-Kelly,

2003a), (Cilliers, 1998) and (Gell-Mann, 1994) from which they have been derived.

As discussed in the Chapter 1, the emergence and coevolution CAS concepts are

adopted in this study. The emergence CAS concept helps to explain the emergent rise

of IT-enabled capabilities from the interactions of IT assets and organisational

resources in chapter 4 and the coevolution concept provides a way of explaining the

evolutionary dynamics among various IT-enabled capabilities inside organisation and

with the competitors and its effect on competitive advantage in chapter 5. These two

concepts are widely used in IS referral disciplines to the study of similar complex

phenomena- emergence (e.g. Chiles, et al., 2004; Choi, et al., 2001; Kogut, 2000;

Lichtenstein, et al., 2006; Sawyer, 2005) and coevolution (e.g. Huygens, et al., 2001;

Koza & Lewin, 2001; McKelvey, 2002; Pacheco, et al., 2010) and they are better suited

to explain the core focus of this study, which is understanding the dynamics of BVIT,

in particular competitive advantage. The reason to include the other CAS concepts in

the review is to provide background for the explication of the status of CAS in IS.

Co-evolution- Each component (agent) of the CAS environment influences each

other component; in turn, the environment influences all components of which it is


comprised (Anderson, 1999). When a component changes to ensure best fit in the

landscape, the environment also changes, and those changes are likely to result in

further system changes and this process goes on causing continual changes in the

system (Kauffman, 1993).

Emergence- refers to the phenomenon whereby macroscopic patterns arise from

the interactions of micro-level components (Morel & Ramanujam, 1999). CAS

components interact with each other; their interactions collectively giving rise to

emergent properties.

Self-organisation- is the ability of complex systems to evolve dynamically in an

organized form, changing their internal structures and patterns of behaviour, from the

interactions of autonomous agents (Anderson, et al., 1999). “Self-organisation can

lead to fundamental structural development...is ‘spontaneous’ or ‘autonomous’,

arising from the intrinsive iterative nonlinear nature of the system”- (Stacey, 2007, p.

196).

Fitness landscape- conceptualises a CAS system as having N decision variables

(agents) and K interactions among the variables (Kauffman, 1993). Each configuration

of a set of decision variables is associated with a fitness value representing

performance if that particular configuration is enacted. The system uses different

techniques e.g. – hill climbing or long jumps, in the fitness landscape to find positions

with higher fitness value. When, there is little interaction among decision variables

(i.e. low K), the resulting fitness landscape is “smooth”. Higher interactions (i.e. high

K) among the variables cause the landscape to be “rugged”.

The edge of chaos- Systems that are too simple are static, and systems that are

too active become complex or chaotic. The edge of chaos is a region between these

two, where a system is neither too simple nor too complex, and can undertake

productive activity (Miller & Page, 2009, p. 129). “Organisations never quite settle

into a stable equilibrium but never quite fall apart, either” (Brown & Eisenhardt, 1998,

p. 12).

Dynamism and non-linearity- CAS is different from a traditional process model

consisting of interrelated variables that result in deterministic outcomes; it is

comprised of interconnected autonomous agents, which show non-linear behaviors.

The whole is greater than the sum of its parts (Holland, 1995). As a dynamic approach,


the tenets of CAS theory are used to model complex social systems and to investigate

and understand the dynamic properties of complex systems.

Adaptation- is the process through which CAS maintains fitness in the

competitive landscape (Kauffman, 1993). A CAS changes its structure in the fitness

landscape through its interactions with other CAS. Adaptation by a CAS changes the

structure of another CAS, which further leads to a process of mutual adaption.

As a branch of complexity science, CAS has received increased attention in

diverse academic fields including strategic management and organisational science.

The concepts and principles of CAS have been used to study a growing range of

contemporary IS phenomena such as agile software development (Vidgen & Wang,

2009), IT supported organisational processes (Canessa & Riolo, 2006) and IT enabled

organisational learning (Kane & Alavi, 2007). Several IS referent disciplines,

including strategic management (Burgelman & Grove, 2007), organisation science

(Frank & Fahrbach, 1999) and management science (Rivkin & Siggelkow, 2007), have

also benefitted from the use of concepts and features of CAS. Several journal special

issues have been published, such as Journal of Information Technology (Merali &

McKelvey, 2006) and Information Technology & People (Jacucci, et al., 2006) to

demonstrate the value of CAS while encouraging its broader adoption in IS research.

Significant arguments advocating for increased employment of CAS theory in

information systems management and organisational studies, include: limited success

of traditional theories, non-linear changes to organisational environments that require

increased appreciation of dynamic formal methods e.g. simulation, emphasis on

bottom-up agent-based and rule governing behaviours and increasing appreciation of

the need for holistic research (Maguire, et al., 2006).

Yet research employing CAS in the IS discipline remains limited and

fragmented. A search of top IS outlets revealed few papers that apply CAS theory.

Possible reasons are several, the foremost likely being that CAS is not well understood

by many IS researchers. There seems to exist confusion about the concepts central to

the theory; those concepts not being intuitive nor easy to measure, in particular as they

are tightly intertwined (Vidgen & Wang, 2006b). Further, researchers may be aware

of CAS theory but are unsure of its potential. Those who attempt CAS research often

face difficulties knowing where to start, what methodology to follow, etc. A common

mis-conception is that the sole purpose of CAS is to model real world phenomena


computationally, many perceiving computational modelling as foreign or irrelevant to

IS research. To better understand CAS theory and make it more accessible to IS

researchers, this review of the IS literature using CAS theory is developed. This review

contributes to laying the foundation of CAS theory in IS research by synthesising the

current literature. The next section presents the review design.

3.3 REVIEW DESIGN

A review of the prior literature provides a firm foundation to understand the

current status of a research field. It facilitates theory development, represents the areas

where a plethora of research exists and helps to discover areas where research is

needed (Webster & Watson, 2002). Following the review guidelines by Levy and Ellis

(2006) and Webster and Watson (2002) the review followed a structured method

consisting of three steps to extract, analyse and report the literature based findings. In

the extraction phase, a methodological search were conducted to identify and extract

relevant articles from the IS top outlets to include them in this review. In the

subsequent analysis phase, the articles were prepared for analysis; an appropriate

classification and coding scheme was developed and maintained to match the study

objectives and conducted the analysis accordingly. In the findings and interpretation

phase, findings on the papers were gathered and interpreted for unfolding deeper

insights on the CAS in IS. The next sections describe each phase of the research design

in detail.

Figure 3.1 Overview of sampling sources

(1) IS senior scholars basket of

8 journals

(2) Top 3 IS journals

I&M, I&O and DSS

(3) Top 2 IS referent

Management

journals MS and

OS

+ + +

2 top AIS conferences

ICIS and ECIS

(i) Journals (ii) Conferences


3.3.1 Extraction of Papers

A well-structured method is important for a comprehensive literature review

(Levy & Ellis, 2006) - a step-by- step procedure of collecting, synthesising and

analysing relevant data from the body of knowledge. As the aim of this review is to

investigate and synthesise CAS based IS research only within IS discipline, the

literature sources have been targeted at the IS community only. Selecting targeted a

set of outlets within a predetermined scope, has been practised in past literature studies

(e.g. Fielt, Bandara, Miskon, & Gable, 2014; Zhang & Gable, 2014). Thus,

academically refereed, full text papers were searched from the top IS outlets, that

includes a list of top IS journals and selected conferences. The study started in 2014

and hence the selected outlets were defined based on the information available then.

The extraction and analysis of the articles were continued since then and reported

herein based on the data extracted from the selected outlets through to December 2018.

The review explored a complete set of high quality papers from top IS outlets.

Thus, it included articles published in the AIS Senior Scholars’ Basket of Eight

Journals4 endorsed by the Association for Information Systems (AIS) as high quality

journals in the IS discipline. An extended search in other well established IS journals

was conducted, such as Information and Management (I&M), Information and

Organisation (I&O) and Decision Support Systems (DSS), given their high quality and

high receptivity to the social research in IS. As the application of CAS also has a long

history in the social sciences and organisational studies (Anderson, 1999; Brown &

Eisenhardt, 1998; Morel & Ramanujam, 1999), two journals on management and

organisational studies, Management Science (MS) and Organisational Science (OS)

were considered as both of them are also highly cited within IS discipline. Thus, a total

of 13 top IS journal outlets is included in this review.

Given the relative limited use of CAS theory in IS and to ensure that the literature

reviewed is as current and inclusive as possible, the proceedings from the top IS

conferences affiliated with the AIS were also included. The review surveyed two IS

4 4Senior Scholars’ Basket of Eight journals are- MIS Quarterly (MISQ), Information Systems Research (ISR), Journal of Management Information Systems (JMIS), European Journal of Information Systems (EJIS), Information Systems Journal (ISJ), Journal of the Association for Information Systems (JAIS), Journal of Information Technology (JIT), and Journal of Strategic Information Systems (JSIS). See further details at- https://aisnet.org/?SeniorScholarBasket


conferences- International Conference on Information Systems (ICIS) and European

Conference on Information Systems (ECIS), representative of quality conferences in

IS (Webster & Watson, 2002). The review commenced search with the basket of eight

journals, then the top 5 IS related management journals, later extending to the

mentioned AIS conferences (see Figure 3.1).

In order to identify publications that use CAS as a core theory, the search was

conducted using keyword search (Levy & Ellis, 2006), i.e. the use of a specific word

or phrase5 - “Complex Adaptive System*”6 in the title, abstract and full text sections

of the databases. The Proquest database returned 53 articles for seven of the basket of

eight journals, with 44 articles found from Science Direct database for JSIS. The

Science Direct database also returned 61 articles for I&M, I&O and DSS. Further, the

JSTOR database returned a total of 12 articles for MS and OS journals. Additionally,

ICIS and ECIS returned 59 and 45 conference papers respectively. Thus, the initial

search yielded 274 articles in total, which mentioned complex adaptive system*

somewhere in the text. All the papers were carefully reviewed to anticipate their

relevance to this study. Although a comprehensive approach was followed to extract

relevant articles that are most suited for this study, the review acknowledges that there

may be some papers relevant to this study, still unidentified and excluded due to the

defined scope and applied research approach. This can be expected in any literature

review (Webster & Watson, 2002).

3.3.2 Analysing the Papers

The review employed a formal coding scheme to ensure a consistent, structured

process for extracting and recording all relevant information from the pool of papers.

I analysed the title, abstract, keywords and full text of the articles for the relevance of

content. To be included in the sample, a candidate article must have adopted CAS as

a base theory or used at least one concept of CAS to theorise phenomena. In terms of

duplicate publications, articles published in conference proceedings and journals, the

review considered the journal paper as normally conference proceedings are extended

5 Please note that ‘keyword’ refers here to the search string, not to the keywords property of the documents that we searched (as stated, we searched in the title, abstract and full text properties of the documents). 6 The asterisk symbol ‘*’ used in the Boolean keywords of the search string combination allows for the inclusion of derivatives in the search criteria.


and published as journal articles. No research in progress papers was considered in the

sample set.

For analysing the contents of the papers, a combined a top-down and bottom-up

approach was followed so that the relevant information from the paper can be

identified. It also ensures the legitimacy of the analysis. A database was maintained of

the codified contents. A total of 40 articles (24 journals and 16 conference papers)

were selected from the original dataset of 274 papers after the analysis. The database

fields populated are - keywords, research topics, concepts from CAS, journal/

conference name, year of publication, definitions, methodologies, theoretical

approaches, objectives, and important key findings and notes extracted (a summary for

later reference) from each research article.

3.4 THE STATUS OF CAS THEORY IN THE IS DISCIPLINE

This section presents an overview of CAS theory literature in the IS discipline.

A descriptive overview of the literature helps to present the detailed research findings

as it positions the data-contexts from which the analysis is drawn (Fielt, et al., 2014).

Recall that a total of 40 articles on CAS theory were extracted from the pool of

IS outlets as a result of our analysis, where 24 are journal articles and 16 are conference

papers. Table 3.1 illustrates the number of CAS articles published in the sampled

outlets. The frequency of CAS related studies in IS is relatively small. One of the

earliest articles that embraced CAS theory appeared in EJIS 2002 (Merali, 2002).

Nothing on CAS was published in the subsequent three years (2003-2005). In 2006,

eight articles were published, five of which appeared in a special issue of Journal of

Information Technology (JIT) on Complexity Science (that special issue was intended

to evidence the significance of, and encourage the adoption of, complexity theory in

IS). The number drops to 3 in 2007 then ranges between 3 and 4 over the subsequent

7 years (2008-2014). The number of studies varies across journals; JIT publishing the

most (7 - 5 of which are in the special issue), followed by DSS (5 papers) and

subsequently JAIS, MISQ and ISR (each with 2). ISJ and JMIS have published nothing

on CAS. The two prominent AIS conferences ICIS and ECIS published 16 (40%) of

the total set of 40 papers over the past 13 years. ICIS first published two CAS articles

in 2008 and has averaged 1/year since, whereas ECIS first published on CAS in 2006


(averaging 0.5/year since). Relatively, these are not large numbers, with CAS research

representing a niche area of research in IS.

Outlet 2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Total

MISQ 1 1 2 ISR 1 1 2

JMIS 0 EJIS 1 1 ISJ 0

JAIS 1 1 2 JIT 5 1 1 7 JSIS 1 1 2 I&M 1 1 2 I&O DSS 1 1 1 1 1 5 MS 1 1 OS 0

ICIS 2 1 2 1 1 1 2 1 11 ECIS 2 1 1 1 5 Total 1 0 0 0 8 3 3 4 4 2 4 2 3 1 1 40

Table 3.1 Journal/ conference and year wise distribution of CAS article

3.5 THE CONCEPTUAL PERSPECTIVE OF CAS IN IS

The previous section addresses the status of CAS theory in IS discipline. This

section presents current understandings on CAS theory by exploring concepts used in

IS studies.

The review followed a combined top-down and bottom-up approach to identify

the key CAS concepts used in IS research. It commenced with identifying key concepts

from the core papers on CAS like- (Holland, 1995), (Morel & Ramanujam, 1999),

(Mitleton-Kelly, 2003a) and (Gell-Mann, 1994), subsequently moving to the sample

CAS-IS papers to identify the similar CAS concepts used. It screened the core CAS

and CAS-IS papers back and forth several times to get the list of key CAS concepts.

Six main concepts of CAS in IS papers were identified as a result of this analysis.

Based on the 6 key concepts, the CAS-IS studies were then grouped and represented

in a tabular framework (see Table 3.2) using the concept matrix suggested by Webster

and Watson (2002). Note that some papers use multiple CAS concepts in combination

to explain particular phenomena; therefore, the same paper may appear in multiple

places in the table.


CAS concept Reference Summary of research contributions

1. C

o-E

volu

tion

(Allen & Varga, 2006)

Through an agent based view of the organisation, discussed the co-evolution of IS and the importance of understanding it for strategic thinking and decision making

(Benbya & McKelvey, 2006b)

Focus on co-evolution based self-organized emergent behaviour and structure of sustainable IS alignment

(Vessey & Ward, 2013)

Sustainable IS alignment occurs when organisation’s IS co-evolve with the organisation to meet its goals

(Kim & Kaplan, 2006)

IS engagement is a co-evolutionary process, where software systems, vendors and organisations adapt dynamically with the changing nature of one another CAS theory to incorporate evolutionary and teleological motors, and actor-network theory to incorporate dialectic motors to understand how systems and organisations co-evolve

(Vidgen & Wang, 2009)

Volberda's three principles of co-evolving systems are used to understand agile software development

(Tanriverdi, et al., 2010)

IS strategy should be co-evolved with the evolving, rugged, competitive landscape to maximize a firm’s agility for achieving profitability

(Chae, 2014)

Uses co-evolution concept to explain the process of IES innovation. The steps of coevolutionary processes are variation, selection and retention

2. E

mer

genc

e

(Curşeu, 2006)

In virtual teams, team cognition, trust, cohesion, and conflict are interdependent states that emerge from the interactions of team members

(Nan, 2011)

Seeks to highlight the emergent properties that rise from the individual- level IT use behavior- patterns to collective- level IT use patterns

(Lanham & McDaniel Jr, 2008)

How heterogeneous IT use emerges at individual and group levels

(Basole, 2009) Seeks to discover the relations of existing and emerging segments of the mobile system


Seeks to understand the emergent capabilities of agile software development teams

(Kautz, 2012)

Uses emergence concepts along with other concepts for comprehending the characteristics and practices of the IS development process

(Förderer, Kude, Schütz, & Heinzl, 2014)

Seeks to investigate the emergence of generativity from the interactions between platform and partners in micro and macro levels

(Huang, Rand, & Rust, 2016)

Seeks to investigate how different innovation speeds and qualities emerge from the competition when firms differing in IT capabilities competing in markets varying in uncertainty under different conditions

(Someh, Songhori, Wixom, & Shanks, 2018)

Focuses on the embeddedness of data and non-data teams within an organizational network, and it examines how the two teams connect and collectively influence organizational performance

3. S

elf-

Org

anis

atio

n

(Vidgen & Wang, 2006b)

Self-organisation with other CAS concepts- coevolution, edge of chaos, etc. to investigate system level emergence of agility


Use the principles of co-evolving systems to develop more coherent understandings of the properties of agility e.g. emergent capabilities

(Kautz, 2012)

Uses self-organisation concepts along with other concepts for comprehending the characteristics and practices of the IS development process


Use self-organisation along with other CAS concepts to investigate how sustainable IS alignment occurs in organisations


(Nan & Lu, 2014)

Use the self-organisation concept to explore the possibility of orderly crisis management of organisations

4. F

itnes

s lan

dsca

pe (Hahn &

Lee, 2010) Use NK model to investigate how knowledge overlaps influence information systems development processes under some pre-defined conditions


Use fitness landscape, suggests shift to three research quests of IS strategy in the highly dynamic and co-evolving competitive performance landscapes of products and services

(Rivkin & Siggelkow, 2007)

Develop simple, intuitive rules of thumb that allow to examine two interaction patterns and determine which warrants greater investment in broad exploration

5. D

ynam

ism


Focus on co-evolutionary based self-organized emergent behaviour and structure of IS alignment to uncover dynamics of IS alignment

(Canessa & Riolo, 2006)

Discuss how the dynamics generated by agent based modelling can be utilised to gain deeper understanding of computer mediated communication

(Curşeu, 2006)

Conceives the virtual team as CAS and seeks to provide better understanding of virtual team dynamics through artificial simulation

(Hanseth & Lyytinen, 2010)

Use CAS to recognize factors that generate the dynamics associated with the bootstrap and adaptability problems in information infrastructure

(Hildebrand, et al., 2012)

Develop a simulation model to provide explanations to viral marketing dynamics in social network

6. T

he e

dge

of c

haos


Use edge of chaos and five other CAS concepts to explore the system level emergence of agility


Draw edge of chaos and other CAS concepts to build an organizing framework for agile software development

(Kautz, 2012) Uses edge of chaos to explain the region of complexity in OMS project

Table 3.2 Classification of papers based on CAS concepts in IS literature

From the analysis, the review has identified six major CAS concepts that are

frequently used in IS research. The concept of emergence has been used most in 9

studies within 40 papers, while co-evolution (7 studies) is the second highest one.

Self-organisation and dynamism, are used in 5 studies. However, it is too difficult to

assess how many times all of these concepts are used in IS studies as the CAS concepts

are highly intertwined with each other (Vidgen & Wang, 2006b). For instance, the

concept of adaptation is implicit within the concept of coevolution as co-evolution

takes place when some components adapt to each other over a specific period of time

(Mitleton-Kelly, 2003a). The analysis of the papers also reveals that not all the CAS

concepts from the core CAS studies and referent disciplines are explicitly represented

in the IS research. For instance, feedback process is an inherent characteristic of CAS,

which is frequently used in the CAS based IS studies but is not explicitly described in

research.


Overall the review shows that the majority of IS studies has adopted one or more

CAS concepts and then extended the concept(s) to explore the phenomena under study.

The particular section informs how CAS concepts are used for theory development

related to different dynamic phenomena. In addition, it also helps to relate the

contributions derived using each of the CAS concepts. The conceptual perspective

helps to identify the CAS concepts that are frequently used in IS research and

determine the concepts that can be adopted in my dissertation, in this case, the study

overall two key CAS concepts, emergence and coevolution. Moreover, it also

addresses the concepts related studies in IS, which helps to determine, how the

emergence and coevolution concepts are used in IS studies, such as, to broadly explore

the phenomena like dynamic relations over fitness landscape(Tanriverdi, et al., 2010)

and explain dynamic processes of evolution of system components (Chae, Koh, &

Prybutok, 2014).

It is important to highlight that, the CAS concepts are highly interrelated and

they are sometimes used together or separately in literature. For instance, the self-

organisation concept is used independently (Kauffman, 1995a; Kauffman, 1993) and

together within emergence concept (Goldstein, 1996, 2005) (I have adopted the later

view in this thesis) in broader complexity literature. This particular scenario brings to

attention that CAS related literature is highly inconsistent as acknowledged by other

scholars (Stacey, et al., 2000; Vidgen & Wang, 2006a). A broader conceptual

framework consolidating all CAS concepts by complexity theory experts and thought

leaders may resolve this inconsistent interpretation.

The primary reason of exploring the CAS concepts and their relative

contributions is to understand how the concepts can be applied in understanding a

complex phenomenon, in particular in the context of BVIT. However, after the

analysis of the CAS concepts in IS studies and specially in relation to the BVIT studies,

it is found that majority of the BVIT studies in strategic management discipline adopt

coevolution and fitness landscape concepts to explore BVIT, particularly competitive

advantage. Besides, the emergence concept have been used few cases (e.g. Huang, et

al., 2016) to explore strategic side of BVIT. Based on the above observation, I have

chosen two key concepts- emergence and coevolution, to explore the dynamics related

to the BVIT (broadly discussed in Chapter 4 and 5). The next section broadly discusses

the objectives of CAS theory in IS research.


3.6 THE OBJECTIVES OF CAS THEORY IN THE IS RESEARCH

This section presents the objectives of CAS theory in IS. To identify the major

objectives with CAS theory in IS literature, I first analysed the introduction section of

the papers, later exploring their full text. The preliminary analysis identified two major

goals associated with the use of CAS in IS. Subsequent analysis of the full text

identified six more objectives. The analysis also discerned a range of distinctive

characteristics among the objectives. I categorised the objectives according to their

characteristics, yielding three groupings. The first group contains two objectives,

which are more specifically the key goals of applying CAS theory in IS; I named this

group goals. The second group consists of three objectives, which are more

specifically the types of theory generated from applying CAS in IS research; I labeled

this group as theory types. The remaining three objectives I grouped as stages (see

following).

The purpose of this section is to show how these objectives of CAS theory

helps to guide my research direction. It also represents the type of IS theory this thesis

emphasises at the end of the section.

3.6.1 Objectives as Goals

My analysis suggests two key goals of CAS theory use in IS research: (i) to develop

novel theory and (ii) to test existing theory through simulation.

Theory Building

The elements and concepts of CAS are used for developing new theories in IS research.

Nan (2011) develops a theoretical framework drawing on the concepts and the

analytical tools of CAS; A CAS model of IT use that encodes a bottom-up IT use

process into three interrelated elements- agents, interactions and environment. Agent

based modeling (ABM) is performed for computationally representing and examining

the CAS model of IT use. The CAS model is operationalised and the analytical tool

ABM is demonstrated through a theory-building exercise, translating an interpretive

case study of IT use to a specific version of the CAS model. This theory building study

represents the bottom-up nature of IT use process, further demonstrating that collective

level patterns of outcomes are logical and often unpredictable as a consequence of

individual level behaviors. Chae (2014) conceptualises IT enabled services (IES) as

CAS with such properties and behaviors as diverse adaptive elements, nonlinear


interaction, self-organisation, and adaptive learning, and IES innovation as a co-

evolutionary process of variation, selection, and retention (VSR). He proposes a novel

framework of IES and IES innovation and develop a set of theoretical propositions

(theories) using business analytics (BA) as a new kind of decision support service.

This particular objective helps me to analyse and understand the processes of theory

building using CAS concepts in every surveyed paper.

Theory Testing

The key elements of CAS - agents, interactions and environment, allow researchers to

capture interactions among the basic entities of actions and relationships, and between

these entities and an environment in a virtual platform, and analyse their patterns of

behaviors under certain conditions. CAS theory serves as an ideal approach to encode

real life processes or activities into the computational model and enable analysis of the

properties, complex mechanisms, and dynamics in a virtual environment (Merali,

2004). As it is hard to represent every aspect of real life phenomenon in a virtual

environment, researchers specify a set of assumptions that portray the theoretical logic

of real life and test them in the simulation. For example, Hahn and Lee (2010) argue

that knowledge overlaps between business and IS, play an important role in the

Information Systems Development (ISD) process. Using an NK fitness simulation,

they seek to investigate how knowledge overlaps influence IS performance under

various levels of interdependencies, distributions of interdependencies, and levels of

inter-unit trust. The results of the simulation are analysed to developing deep

theoretical insights. The findings yield a set of testable propositions, which are further

tested in the simulation environment. Basically, my analysis of the papers shows that

majority of the papers that engage computational modeling and simulation test

hypothesis and theories by modeling them in the virtual environment. I will broadly

discuss this issue at the end of this section.

3.6.2 Objectives as Theory Types

My analysis suggests that applying CAS theory in IS yields three types of theory-

exploratory, explanatory, and design theory, which are also objectives of CAS in IS

research.


Exploratory Theory

Most CAS studies that use empirical data are exploratory in nature. These studies

typically first entail one or multiple case studies to collect data about the phenomenon

and then apply the CAS lens to explore theoretical insights from the data. Vidgen and

Wang (2006b) argue that the theoretical foundation of agile software development

(ASD) has not been articulated systematically, and propose a conceptual framework

to study agile software development based on CAS. They follow an interpretive

approach for collecting empirical data through a case study of an ASD team. They

identify several agile practices from the CAS perspective as a result of analysis.

Moreover, CAS studies that include simulation are also exploratory in nature, as a real-

life phenomenon is encoded in the computational model; and by running the model in

simulation it can be clearly depicted how a process unfolds and evolves over time;

something much more difficult to understand through verbal communication or

observation. This theory type is highly related to chapter 6, where I have adopted NKC

model as a lens to explore the coevolutionary dynamics of IT-enabled capabilities in

micro and macro levels of organisations. The NKC models helps to translate the

components of NKC and analytical logic to develop strategies based on the simulation

outcomes from (Kauffman, 1995a) simulation. A detailed discussion is available in

Chapter 6.

Papers Objectives of CAS

Goals Theory Types Stages

The

ory

build

ing

The

ory

test

ing

Exp

lora

tory

Exp

lana

tory

Des

ign

Con

cept

ualis

atio

n

CA

S Fr

amew

ork

or m

odel

ing

Sim

ulat

ion

(Merali, 2002) ✓ ✓ ✓ ✓ (Allen & Varga, 2006) ✓ ✓ ✓ (Benbya & McKelvey, 2006b) ✓ ✓ ✓ ✓ (Canessa & Riolo, 2006) ✓ ✓ ✓ ✓ ✓ ✓ ✓ (Curşeu, 2006) ✓ ✓ ✓ ✓ ✓ ✓ ✓ (Kim & Kaplan, 2006) ✓ ✓ (Merali, 2006) ✓ ✓ ✓ (Vidgen & Wang, 2006b) ✓ ✓ ✓ ✓ (Wang & Vidgen, 2007) ✓ ✓ ✓ ✓ (Habib, 2008) ✓ ✓ ✓ ✓ ✓ ✓ ✓


(Lanham & McDaniel Jr, 2008) ✓ ✓ ✓ (Basole, 2009) ✓ ✓ ✓ ✓ ✓ ✓ ✓ (Vidgen & Wang, 2009) ✓ ✓ ✓ ✓ (Wang & Conboy, 2009) ✓ ✓ ✓ ✓ (Hahn & Lee, 2010) ✓ ✓ ✓ ✓ ✓ ✓ ✓ (Hanseth & Lyytinen, 2010) ✓ ✓ ✓ ✓ (Tanriverdi, et al., 2010) ✓ ✓ ✓ (Nan, 2011) ✓ ✓ ✓ ✓ ✓ ✓ ✓ (Grover, 2012) ✓ ✓ (Hildebrand, et al., 2012) ✓ ✓ ✓ ✓ ✓ ✓ ✓ (Kautz, 2012) ✓ ✓ ✓ ✓ (Merali, et al., 2012) ✓ ✓ (Khanna & Venters, 2013) ✓ ✓ ✓ (Vessey & Ward, 2013) ✓ ✓ ✓ (Förderer, et al., 2014) ✓ ✓ ✓ ✓ (Nan & Lu, 2014) ✓ ✓ ✓ ✓ (Ozer & Anderson, 2015) ✓ ✓ ✓ ✓ ✓ ✓ ✓ (Huang, et al., 2016) ✓ ✓ ✓ ✓ ✓ ✓ ✓ (Chae, 2014) ✓ ✓ ✓ ✓ - (Schramm, Trainor, Shanker, & Hu, 2010)

✓ ✓ ✓ ✓ ✓ ✓ ✓

(Wang, Gwebu, Shanker, & Troutt, 2009)

✓ ✓ ✓ ✓ ✓ ✓ ✓

(Khouja, Hadzikadic, Rajagopalan, & Tsay, 2008)

✓ ✓ ✓ ✓ ✓ ✓ ✓

(Klashner & Sabet, 2007) ✓ ✓ ✓ ✓ (Sherif & Xing, 2006) ✓ ✓ ✓ ✓ (Adler, Baets, & König, 2011) ✓ ✓ ✓ ✓ ✓ ✓ ✓ (Rivkin & Siggelkow, 2007) ✓ ✓ ✓ ✓ ✓ ✓ (Someh, et al., 2018) ✓ ✓ ✓ ✓ ✓ ✓ (Tanriverdi & Lim, 2017) ✓ ✓ ✓ ✓ (Schilling, Beese, Haki, Aier, & Winter, 2017)

✓ ✓ ✓ ✓

(Marjanovic & Cecez-Kecmanovic, 2017)

✓ ✓ ✓ ✓

Table 3.3 CAS objectives in IS research Explanatory Theory

The concepts and principles of CAS are suitable for explaining complex phenomena

and thus IS researchers have been using it for analysing sophisticated organisational

processes like agile software development (ASD) and IS alignment or service

platforms. The objective of this type of research is to explore something that was

imperfectly or poorly understood beforehand (Gregor, 2006). The purpose is to

identify and explain how and why particular phenomenon happens using CAS lens.

For instance, Wang and Vidgen (2007) argue that the agile processes are marked by


some chaotic processes and are contrasted to waterfall approaches. They use the edge

of chaos concept from CAS to analyse the role of structure and planning in software

development (SD) processes. They gather data on the project structure and planning

of SD processes of two teams from a major IT company, who often use both agile and

waterfall approaches and compare them from a CAS perspective. The analysis shows

structure and planning is essential to agile processes and takes different forms from

the waterfall model. CAS is predominantly used as an explanatory theory to describe

complex phenomena in IS studies, which is revealed though my analysis. I have found

that majority of the IS studies have used one or more CAS concepts to broadly explain

phenomena under study.

Design Theory

Only one paper was identified that used CAS theory to derive design

principles; more precisely, as design theory in IS research. Hanseth and Lyytinen

(2010) argue that contemporary IT systems involve complexity that extends beyond

what can be addressed by traditional design approaches. They seek to develop a design

theory based on CAS theory that tackles information infrastructures’ dynamic

complexity. By exploring the design histories of infrastructures and reviewing the

principles of CAS theory, and illustrated by analysing the history of Internet exegesis.

3.6.3 Objectives as Stages

Applying CAS theory in IS research involves two main stages; (i) conceptualisation

and (ii) modeling of the phenomena of interest. Some studies involve only stage 1

conceptualisation. Studies that entail stage 2 modeling of the phenomena must be

preceded by stage 1. A third stage (which demands prior completion of the 1st two

stages) may follow, which entails computationally representing the phenomena from

the conceptual model in a simulation of the virtual environment; we broadly name this

stage simulation. Very few of the studies engage in this stage of the research. These

three stages represent three CAS objectives.

Conceptualisation

The analysis shows that most of the IS studies use CAS or CAS concepts to

conceptualise phenomenon and theoretically explain them. For example- Vidgen and

Wang (2006b) conceptualise agile software development as CAS and propose a

theoretical framework. The framework is used as a sensitizing device for collecting


data and carrying out analysis in an interpretive case study. As a result of their analysis,

several agile practices are identified, and reflected on, from the theoretical perspective

of CAS.

CAS Framework or Modeling

The analysis of the papers suggests that the CAS concepts and key elements are used

for conceptual or theoretical frameworks or modeling of complex business processes

like IT use (Habib, 2008; Nan, 2011) or computer information systems (Canessa &

Riolo, 2006). For example, Curşeu (2006) uses the CAS perspective to integrate the

literature on emergent states in virtual teams (VT). She uses the concept of emergence

to develop a CAS framework and provide a new theoretical understanding for some of

the phenomena of VTs’ dynamics that were previously studied in isolation. By

combining the insights from the CAS framework with the empirical data, the study

seeks to provide a basis for matching emergence in VT with the virtual simulations.

Simulation

As an analytical theory, CAS provides a way of encoding and presenting real

life complex processes through a computational model, then in virtual simulation

(Merali, 2004). The elements of CAS theory, agents, interactions and environment,

have been applied in IS research to understand the underlying complexities of different

contexts, computer information systems (Canessa & Riolo, 2006), virtual teams

(Curşeu, 2006), viral marketing dynamics (Hildebrand, et al., 2012), innovation

diffusion (Schramm, et al., 2010), decision making (Adler, et al., 2011) etc.

Researchers have employed different computational modeling or simulation

techniques in IS research, such as Agent based modeling (ABM) (Bonabeau, 2002),

NK modeling (Kauffman, 1993) and MySQL simulation. The theoretical propositions

of real life processes are outlined under certain assumptions and conditions in the

model. The model is executed to explore a wide range of possible contingencies that

are difficult to assess in a laboratory setting or through field studies (Nan, 2011).

From Table 3.3, it can be observed that CAS studies that adopt the simulation

method appear relatively more versatile than the other studies. The simulation based

CAS studies employed in attention to all but 1 of the 8 objectives. The reason seems

clear, as a real world complex phenomenon can be conceptualised theoretically (1.

conceptualisation), modelled (2. conceptual model), explained (3. explanatory)

computationally represented, run (4.simulation), and tested in simulation with specific


assumptions (5. theory testing), and the results of the simulation are analysed (6. theory

building) for in-depth understandings about the phenomena (7. explanatory) (e.g. Nan,

2011). This review also found that a limited number of design research studies have

been conducted in IS discipline using CAS theory. I identified only a single study i.e.

(Hanseth & Lyytinen, 2010) that used CAS to derive design principles, a.k.a. design

theory. This may suggest an opportunity to further apply CAS theory in design science

research.

In summary, the identification of the CAS theory objectives in IS research

helps me to better shape my research direction. Firstly, in a higher level, the objective

as goal helps me to develop my thesis as a theory building exercise. It helps me to

understand how CAS theory can be used to develop theories in IS from the analysis of

the CAS based IS studies. Secondly, the theories developed from my thesis (in Chapter

4 and 5) are explanatory type (Gregor, 2006) as it helps to explain why BVIT process

has become dynamic in contemporary organisations and how we can use CAS features

and concepts to better understand the complex process. In addition, NKC model

adopted in Chapter 6 is used as an exploratory lens to further develop greater insights

on the coevolution perspective of IT-enabled capabilities and its impact on competitive

advantage. The theories in this chapter are exploratory in nature, which follow my

proposed theory types in section 3.6.2. Moreover, the CAS objectives also has guided

how to conceptualise BVIT process via two CAS concepts- emergence and

coevolution, develop CAS based framework and guidelines.

3.7 THE THEORETICAL PERSPECTIVES OF CAS RESEARCH IN IS

In the preceding section, I discussed different methodological approaches that

researchers follow in CAS-IS research. In this section, I present the theoretical

approaches of applying CAS in IS research. For the theoretical approaches, it was

necessary to consider the full text of the surveyed papers. As a result of the analysis, I

identified two major theoretical approaches- only CAS theory and CAS with other

theories. Of the 40 papers, 34 papers used one or more of the CAS concepts and

principles. Only 6 papers used other theories along with CAS theory.

3.7.1 CAS Theory Only

This class of research engages only the basic concepts or principles of CAS

theory to conceptualise complex phenomena and for analysis to develop in-depth


understanding. These papers followed all the methodological approaches reported in

this chapter, empirical (e.g.Kim & Kaplan, 2006; Vidgen & Wang, 2006b), conceptual

(e.g.Allen & Varga, 2006; Benbya & McKelvey, 2006b) and computational (e.g. Hahn

& Lee, 2010; Nan, 2011). Tanriverdi, et al. (2010) (mentioned earlier) conceptualise

business systems as CAS in order to theorise about IS strategic alignment. In another

study, Allen and Varga (2006) explain the construction and development of IT systems

from the co-evolutionary perspective of CAS. They conceptualise organisations as

CAS and individuals as agents to develop understandings of the evolution of IS from

the interactions of agents and other constructs, such as IT systems in organisations.

The analysis of the theoretical perspective of CAS theory helps me to apply CAS

theory to conceptualise BVIT and later on, I have used CAS concepts- emergence and

coevolution for developing in-depth understanding on BVIT.

3.7.2 CAS with Other Theories

The objectives of studies that use CAS with other theories are similar to those which

use CAS alone. For example- Benbya and McKelvey (2006b) present a view of IS

alignment in organisations drawing on the co-evolution concept of CAS theory,

especially focusing on co-evolution based self-organized behaviour, which provides

important insights on the emergent nature of IS alignment. This view considers

business/ IS alignment as a series of adjustments at three levels of analysis- individual,

operational and strategic. Drawing on scale free dynamics theory and principles of

adaptation, they suggest several enabling conditions to speed up the adaptive co-

evolutionary dynamics among the three levels.

Theoretical Approaches Studies

CAS theory only (34 out of 40)

(Allen & Varga, 2006);(Canessa & Riolo, 2006);(Curşeu, 2006); (Merali, 2006);(Vidgen & Wang, 2006b);(Wang & Vidgen, 2007);(Habib, 2008);(Basole, 2009);(Vidgen & Wang, 2009); (Wang & Conboy, 2009);(Hahn & Lee, 2010);(Hanseth & Lyytinen, 2010);(Tanriverdi, et al., 2010);(Nan, 2011); (Grover, 2012);(Hildebrand, et al., 2012);(Kautz, 2012);(Merali, et al., 2012);(Khanna & Venters, 2013);(Vessey & Ward, 2013);(Förderer, et al., 2014);(Nan & Lu, 2014);(Ozer & Anderson, 2015);(Huang, et al., 2016);(Chae, 2014);(Wang, et al., 2009);(Khouja, et al., 2008);(Sherif & Xing, 2006);(Adler, et al., 2011);(Rivkin & Siggelkow, 2007); (Schramm, et al., 2010); (Klashner & Sabet, 2007)


CAS with other theories (6 out of 40)

(Merali, 2002);(Benbya & McKelvey, 2006b);(Kim & Kaplan, 2006);(Lanham & McDaniel Jr, 2008); (Schramm, et al., 2010); (Klashner & Sabet, 2007)

Table 3.4 Theoretical approaches in CAS research in IS

3.8 THE METHODOLOGICAL APPROACHES OF CAS IN IS RESEARCH

This section presents the methodological approaches researchers follow to

conduct CAS-based IS research. The purpose is to determine the nature of CAS

research by identifying the research approaches in the IS literature. In order to identify

the methodological approaches, I analysed the research method or methodology

section of the papers. Following Chen and Hirschheim (2004), I classified the papers

into two broad methodological classes, empirical and non-empirical. In addition, I

added another category termed computational; those papers that use computational or

simulation method to model real world phenomena.

The empirical papers were classified into sub categories using the framework

developed by Chen and Hirschheim (2004). In addition to the original sub categories

of survey, case study, experiment, field study, action research, I have included another

sub category as archival analysis following Fielt, et al. (2014). I also categorised the

computational studies based on the computational modeling (simulation) approach.

The majority of the papers followed agent based modeling (ABM) Therefore, I

classified computational studies into two sub-categories; agent based modeling and

other approaches.

Studies Empirical Conceptual Computational

Cas

e st

udy

Surv

ey

Exp

erim

ent

Arc

hiva

l an

alys

is

Fiel

d St

udy

Act

ion

rese

arch

O

ther

s

Age

nt b

ased

m

odel

ing

Oth

ers

(Merali, 2002) ✓

(Allen & Varga, 2006)

✓


✓

(Canessa & Riolo, 2006)

✓

(Curşeu, 2006) ✓

(Kim & Kaplan, 2006)

✓

(Merali, 2006) ✓


✓


(Wang & Vidgen, 2007)

✓

(Habib, 2008) ✓ ✓

(Lanham & McDaniel Jr, 2008)

✓

(Basole, 2009) ✓ ✓


✓

(Wang & Conboy, 2009)

✓

(Hahn & Lee, 2010) ✓

(Hanseth & Lyytinen, 2010)

✓


✓

(Nan, 2011) ✓ ✓

(Grover, 2012) ✓

(Hildebrand, et al., 2012)

✓

(Kautz, 2012) ✓

(Merali, et al., 2012)

✓

(Khanna & Venters, 2013)

✓


✓

(Förderer, et al., 2014)

✓

(Nan & Lu, 2014) ✓

(Ozer & Anderson, 2015)

✓

(Huang, et al., 2016)

✓

(Chae, 2014) ✓

(Schramm, et al., 2010)

✓

(Wang, et al., 2009) ✓

(Khouja, et al., 2008)

✓

(Klashner & Sabet, 2007)

✓ ✓

(Sherif & Xing, 2006)

✓

(Adler, et al., 2011) ✓

(Rivkin & Siggelkow, 2007)

✓

Table 3.5 Overview of methodologies in CAS based IS research

The analysis of the papers based on the classification of research

methodologies yielded 16 empirical papers in total of which 13 were case study based

papers, 1 field study based, 1 action research based and 1 used social network data,


which is positioned under others column within empirical method (see column 8 of

Table 3.5). No surveys, experiments or action research studies were encountered in

the sample of papers.

The analysis also reported 14 computational based studies in total, in which 13

studies adopted ABM approach and only one study followed more than one approach,

Basole (2009) used both MySQL and simulation together. Moreover, I found 10

conceptual papers. Noted that all 4 studies using multi-method (greyed boxes in Table

3.5), used 2 methods. Two papers used case study and simulation methods together to

develop a computational model based on the case data; I marked them as

computational studies because the ultimate objective of these studies is to build and

simulate computational models based on empirical evdience. For the same reason I

have placed another study into the category computational studies, that used archival

data for developing a computational model. One paper used field data to develop

conceptual understanding of a phenomenon; I classified this as conceptual study

because the overall intention of this study is to develop conceptual understanding on

the phenomenon under study.

3.8.1 Empirical

The empirical papers contain observations and data (primary or secondary

data) that provide strong evidence for testing theories. Typically, one or multiple case

studies are conducted to gather empirical data about the phenomenon. CAS is used as

a theoretical lens to provide an in depth theoretical description of the phenomena

(Courtney, et al., 2008; Merali, 2004). The description contains detailed explanation

of the phenomena; what it is, how, why, when and where. For example, Vidgen and

Wang (2006b) propose a theoretical framework of agile software development using

CAS. An interpretive case study is conducted to gather data on a software development

process. The framework is used as a sensitizing device for data collection and analysis.

Several agile practices are identified and reflected on from the theoretical perspective

of CAS.

3.8.2 Non-empirical or Conceptual

The non-empirical papers develop new concepts and theories; I refer to these

as conceptual papers. CAS theory is used to conceptualize a phenomenon, explore it


and then provide an in depth explanation of it. One or more CAS concepts are used to

investigate and provide theoretical statements on the phenomena. The outcome of the

analysis generates new insights, concepts and theories. For example, Tanriverdi, et al.

(2010) employ CAS to theorize about IS strategic alignment. They suggest

organisations consider three quests of IS strategy- strategic alignment to co-evolution,

integration to reconfiguration and sustained competitive advantage to renewal; in the

competitive performance landscapes of products and services, which are highly

dynamic and co-evolve in nature. It is important to note that, this study on BVIT using

CAS theory is a conceptual research.

3.8.3 Computational

These studies employ computational models to represent the phenomena under

study, and using empirical or non-empirical data (Davis, et al., 2007), test models to

gain deep understandings of the phenomena and develop new concepts and theories.

As described above, the computational studies may use empirical data, yet we

categorise them as computational because the ultimate objective of these studies is to

build and simulate computational models based on empirical evidences. For example,

Nan (2011) uses secondary empirical case data to explore bottom-up emergence of IT

use in organisations. She develops a computational model using ABM to represent the

IT use process, operationalises the model using empirical data and emulates it in a

virtual platform for studying the properties and mechanisms of bottom-up IT use

processes. For instance, her findings from the simulation reveal that the IT-based

organisational transformation does not depend on organisations with high employee

learning rates, high IT flexibility, and low workplace rigidity; it can occur in any

organisation permitting mutually adaptive interactions among human actors, IT

features, and environmental structures.

The analysis shows that case studies make up the majority (13 out of 16) of

empirical studies in CAS. They are a popular and appropriate empirical research

method for a new area of research (Yin, 2013). and and help to understand the

dynamics present is a single setting and also help to generate novel theory (Eisenhardt,

1989)., which is the focus of the majority of the studies under the analysis. In addition,

case studies are also good to “explore complexities that are beyond the scope of more

'controlled' approaches” (Gillham, 2010). Moreover, the analysis of the set of papers

reveal that the majority of the papers are exploratory in nature containing how and why


research questions to investigate complex contemporary phenomena; these types of

questions deal with operational links that need to be traced over time (Yin, 2003).

Case studies help researchers to gather primary evidence from the research settings

(Gillham, 2010), and using the CAS lens, they can inductively develop theory that is

grounded in the evidence.

The 14 computational CAS studies reviewed are truly exploratory in nature.

The computational papers employ simulation methods to virtually represent real world

problems with some predefined constructs and their relationships under some

conditions and then run the model for superior insight into complex theoretical

relationships among constructs, especially when challenging existing empirical data

limitations (Zott, 2003). According to Davis, et al. (2007, p. 483), “Simulation is

particularly suited to the development of simple theory”. My analysis also showed that

though the studies are exploratory, the main focus of the computational studies under

my investigation is to develop theory and extension to the existing theories. CAS

studies deal with phenomena involving multiple interacting components and

processes, feedback loops, and bottom-up emergence, and simulation is especially

useful for studying these type of situations as it is likely to reveal complexities that are

difficult to identify and understand using other methods.

My analysis identified methodological triangulation (Denzin, 1978) - the use

of multiple methods. There are several overriding purposes for methodological

triangulation. The preliminary purpose is to eliminate or reduce biases and ensure the

validity of the study (Jonsen & Jehn, 2009). Another purpose is to ensure the richness

and in-depth understanding of the study. These papers use case study and the

simulation methods together; the case study is conducted as an exploratory device to

gather primary evidence on the phenomena, the gathered data are inductively analysed

using CAS lens to identify simple theories that have modest empirical grounding such

that propositions are likely to be correct, but are limited by weak conceptualisation

(Davis, et al., 2007). Using the data with the simple theories’ constructs, relationships

are defined in the simulation model and run in the virtual environment to uncover non-

intuitive explanations of the simple theories. Using the case data in the simulation

model and analysing it by running the model enriches the understanding of the

researchers and also ensures that the simulation result satisfies the theoretical logics of

the case findings, and thus establishes the validity as well (Davis, et al., 2007).


Therefore, the analysis also explains that the case study together with the simulation

method (2 out of 14 computational studies) provides a better way of investigating

complex phenomena.

The analysis of the methodological approaches of CAS theory has facilitated

my research design (section 1.5 in chapter 1); in particular, the phase 3 and phase 4-

developing emergence and cooevolution perspectives on IT-enabled capabilities

respectively of my research design. The analysis provides me ideas on how to apply

CAS concepts- emergence and coevolution in a conceptual way and develop new

insights, concepts or theories in relation to the dynamics of BVIT. Moreover, the

analysis of the computational papers also provides ideas on how to conceptualise and

translate simulation outcomes for generating new insights. Though, I followed both

McKelvey (1999) and Baum and McKelvey (1999c) studies in strategic management

area as guidelines (section 6.3.1 and 6.3.2 in chapter 6) to understand the translation

process and developing strategies respectively, it was important to check back and

forth whether the way simulation is used to generate insights in IS is same as strategic

management.

3.9 CONTEXT OF THE CAS THEORY IN IS RESEARCH

For the contextual analysis, I categorized the papers based on the topics. A

single paper sometimes contains multiple topics; I put them in more generic type to

cover the multiple topics. Table 3.6 contains the list of major CAS research topics

identified from the review. The most highly represented topics identified are agile

Software Development, information structures, conceptualisation of IS domain, each

of which are 10% (4 out of 40). A more general focus-area is information systems

development (ISD) (7.5%), which could be considered to subsume agile software

development. Together they encompass more than 37% of CAS related IS studies in

the sample. The modern ISD process is considered a combination of complex activities

(Highsmith, 2000). Turbulent business environments, changing customer

requirements, pressure for short-time delivery, and rapid evolution of information

technologies make the ISD process more complex (Baskerville, Levine, Pries-Heje,

Ramesh, & Slaughter, 2001; Benbya & McKelvey, 2006b). The debate between agile

versus plan-driven software practices in organisations reflects the lack of theoretical

understanding of the ISD process (Highsmith, 2000). It is in this context that CAS can


provide deeper insights. These are some of the primary reasons for conducting CAS

based research in the ISD domain. Topic References # Percent

(%) Agile Software Development

(Vidgen & Wang, 2006b); (Wang & Vidgen, 2007); (Vidgen & Wang, 2009); (Wang & Conboy, 2009) 4 10

Information Structures (Hanseth & Lyytinen, 2010); (Khanna & Venters, 2013); (Schilling, et al., 2017); (Marjanovic & Cecez-Kecmanovic, 2017) 4 10

Conceptualization of IS Domain (Merali, 2006); (Grover, 2012); (Tanriverdi, et al., 2010); (Merali, et al., 2012) 4 10

Information Systems development (Allen & Varga, 2006); (Hahn & Lee, 2010); (Kautz, 2012) 3 7.5

IT Use Process (Canessa & Riolo, 2006); (Lanham & McDaniel Jr, 2008); (Nan, 2011) 3 7.5 Information Systems Alignment (Benbya & McKelvey, 2006b); (Vessey & Ward, 2013) 2 5.0

Organisational Knowledge Processes (Merali, 2002); (Habib, 2008) 2 5.0

Social Network (Hildebrand, et al., 2012); (Nan & Lu, 2014) 2 5.0 Decision making (Rivkin & Siggelkow, 2007); (Adler, et al., 2011) 2 5.0 Information Systems Engagement (Kim & Kaplan, 2006) 1 2.5

IT- enabled service (Chae, 2014) 1 2.5 Service Platform (Förderer, et al., 2014) 1 2.5 Virtual Teams (Curşeu, 2006); (Marjanovic & Cecez-Kecmanovic, 2017) 1 2.5 Innovation Diffusion (Schramm, et al., 2010) 1 2.5 Knowledge Sharing (Wang, et al., 2009) 1 2.5 Copyrighted information goods (Khouja, et al., 2008) 1 2.5

ICT use in mission critical infrastructure (Klashner & Sabet, 2007) 1 2.5

Knowledge creation (Sherif & Xing, 2006) 1 2.5 Inter firm relations of mobile ecosystem (Basole, 2009) 1 2.5

IS capabilities (Schilling, et al., 2017) 1 2.5

Table 3.6 Context of CAS theory in IS Another major topic is conceptualization of the IS domain as CAS (12%).

Merali, et al. (2012)) argue that the IS domain has sufficient adaptive capacity to

evolve in the emerging competitive landscape, challenging the increased turbulence,

uncertainty and dynamism of IS research. The field of IS research I s highly diversified

and dynamic, with new topics emerging constantly, thus the landscape of IS research

is always changing with scholars shifting attention to investigate new IS and IS

phenomena. Grover (2012, p. 259) mentions that the IS field is currently adapting to

the complex research environment and the field itself is responsive, and exhibits

rational adaptation and learning behaviours to deal with new research topics and

emerging phenomena. This dynamism leads IS researchers to conceptualize the IS

domain as a complex system.

Another focus of CAS research is the IT Use Process 8.6% (3 out of 36). CAS

as a theory is inherently multi-level in nature and facilitates exploration of macro-level

properties that emerge from micro-level dynamics. The traditional IT use literature

tends to capture snapshots of discrete elements of the IT use construct, such as users,

system or tasks using variance based models as suggested by the recent system usage


literature (Burton-Jones & Straub Jr, 2006). Several researchers e.g. Nan (2011) is

dissatisfied with this fragmented understanding of one or two selective elements of the

IT use construct and seeks to explore more comprehensive conceptualizations of this

construct. As CAS helps to study collective level phenomena where emergent

properties arise from lower level elements, researchers become more interested in this

theory.

Other papers address such topics as Information Systems Alignment,

Organisational Knowledge Processes, Social Networks, decision-making and

Information Structures (2 papers each or 5.8%). Topics represented by a single paper

(3%) are virtual teams, IT-enabled service, information systems engagement,

innovation diffusion, knowledge sharing and service platforms.

One of the key findings is that there is a lack of studies directly related to the

BVIT. Few studies can be considered within BVIT umbrella. For instance, Tanriverdi,

et al. (2010) study on the strategic advantage of firms over competitive performance

landscape, in which authors suggest that firms operate in a complex adaptive business

systems. The firms follow coevolution quest that continuously identifies profitable

positions over landscape, reconfiguration quest that supports the profitable positions

and renewal quest that supports dynamic and agile changes due to positioning. Overall

firms attempt to achieve a superior position in contemporary environment via

dynamically aligning IS and corporate strategies. The strategic alignment of IS and

business study by Benbya and McKelvey (2006b) can also be considered as a BVIT

related work. Moreover, prominent management scholars, such as, Rivkin and his

colleagues (e.g. Rivkin, 2000, 2001; Rivkin & Siggelkow, 2007) have been working

on Kauffman’s simulation model (Kauffman, 1993) to derive strategies for

organisations to obtain better competitive advantage on the landscape of products and

services for the last two decades. Therefore, it can be argued that IS scholars more

recently have started realising the potential of adopting a dynamic view on strategic

perspective of BVIT, but the number of studies is still limited and the core dynamic

mechanisms related to the BVIT remain underexplored, which I am attempting to

focus in my thesis.


3.10 CONCLUSION

This chapter explores the current understanding on CAS theory in IS research.

This chapter is distinct from Chapter 2 in terms of the contents of the review items.

This chapter only contains CAS related IS and IS referral studies- strategic

management and organisational research and the surveyed studies are used to

understand CAS theory in general (including seminal works) and within IS in

particular. Though the applications of CAS theory in IS discipline have achieved

increased attention in recent years, the current body of knowledge regarding CAS

theory within the IS discipline remains limited and fragmented. The lack of maturity

of CAS theory within IS research has been recognised by other researchers over the

years (e.g. Merali, 2004, 2006; Mitleton-Kelly, 2014; Tanriverdi, et al., 2010; Vidgen

& Wang, 2009). Therefore, this chapter synthesises and explores CAS based IS studies

to develop a better understanding of the what (conceptions of CAS theory in section

3.5), why (objectives of CAS theory in Section 3.6), and how (theoretical and

methodological aspects of CAS theory in section 3.7 and 3.8). To my knowledge this

is the first such comprehensive review of CAS related IS studies.

It is important to note that there are still confusions around CAS- whether this

is a theory or a concept of complexity theories in the broader complexity literature. A

number of researchers such as, Merali (2006) and McKelvey (2002) consider CAS as

a concept of complexity science and few researchers, such as, Stacey, et al. (2000)

consider CAS as a theory; I have adopted Stacey et al.’s view, which is mentioned in

section 1.4. According to Stacey, et al. (2000), any review on CAS theory may

implicitly contain ontologically inconsistent interpretations of CAS, which may draw

invalid conclusions. These ontological and teleological assumptions are not easy to

derive from individual papers, but requires an in-depth analysis of the core body of

complexity science research over many years by complexity experts and this is a

limitation of my CAS review on IS.

Section 3.5 provides an overall overview of the key CAS concepts used in IS

research. This section is intended to consolidate the major CAS concepts and then

choose the concept(s) that can be used to explore the dynamics of BVIT. I have

adopted emergence and coevolution concepts to study the dynamics of BVIT (a more

detailed discussion is below). Section 3.5 broadly analyses the objectives of CAS

theory in IS research and classifies them as goals, theory types and stages. This section


is highly relevant to the Chapter 4, 5 and 6 as section 3.6.1 establishes the base that I

have used emergence and coevolution concepts for theory building studies in chapter

4 and 5 consecutively. Moreover, section 3.6.2 highlights that I have used emergence

and coevolution concepts to develop explanatory theories (Gregor, 2006)and NKC

model to develop exploratory theories (Chapter 6). Section 3.7 presents that CAS

theory can be used in isolation or with other theories. This section is intended to relate

that this thesis has only used CAS theory, more specifically, emergence, coevolution

concepts and NKC model; other dynamic theories/ concept such as, cynefin framework

(Merali, 2002) can also be used with CAS to develop insights from different viewpoint

(Woodward, 2002). Section 3.8 represents that CAS can have three major types of

methodologies- empirical, conceptual and computational. In this study, I have adopted

conceptual approach in applying emergence and coevolution concepts in Chapter 4

and 5 consecutively and a combination of computational and conceptual approach in

applying NKC model in the context of coevolutionary dynamics of IT-enabled

capabilities in Chapter 6. Finally, section 3.9 aims to reflect upon the context of CAS

based studies. The analysis of the context helps me to identify BVIT related studies

which adopt CAS concept and adopt their approach in my thesis, which is described

in below paragraph.

The overall observation of the literature on CAS related studies in IS is that

BVIT has not been explicitly explored. However, there are still some studies that

particularly emphasise the strategic perspective of the BVIT, competitive advantage

(e.g. Rivkin, 2000; Rivkin, 2001; Rivkin & Siggelkow, 2007; Tanriverdi, et al., 2010).

Another crucial observation is that the concept of emergence has been used most in 9

studies within 40 papers, while co-evolution (7 studies) is the second highest one

(section 3.5). There are some related works in relation to the strategic perspective of

BVIT, competitive advantage both in IS and strategic management mentioned in

section 3.9, that highly used coevolution concept, NKC theorising (Tanriverdi, et al.,

2010) and simulation outcomes (Rivkin, 2001). Section 3.9 reflects that the

coevolution concept is used in many studies in strategic management and

organisational studies (Chae, 2014; Rivkin, 2001), but not in abundant in IS studies

(Benbya & McKelvey, 2006b), in particular, in the context related to BVIT. Moreover,

the emergence concept though not highly used, but have been adopted to explore how

different innovation speeds emerge from different innovation speeds of firms with


varying IT capabilities and their role in competitive advantage (e.g. Huang, et al.,

2016). Beyond that, the emergence (systems thinking based) concept has also been

used in exploring competitive advantage in traditional IS study (e.g. Nevo & Wade,

2010).

Based on the observation of the CAS literature in IS, I have decided to adopt two

CAS concepts- emergence and coevolution to explore the dynamics related to the

BVIT. The emergence concept is highly beneficial in explaining emergent patterns/

behaviors from the interactions of system components (Stacey, et al., 2000) and hence,

I have chosen the emergence concept in explaining the emergence of IT-enabled

capabilities (Chapter 4). Further, the coevolution concept is widely used to explain

evolutionary adaptive dynamics between one or more domains (McKelvey, 2002), and

therefore, I have chosen the coevolution concept to explain micro and macro level

coevolutionary dynamics of IT-enabled capabilities and their impact on competitive

advantage (Chapter 5).

As such, the literature review of CAS in IS also provides a starting point for how

this study can build on and extend existing work on CAS in IS. Taking Nevo and Wade

(2010) systems thinking based study on competitive advantage as a starting point, I

have adopted the emergence concept to explore how IT-enabled capabilities emerge

from the interactions between two elements IT assets and organisational resources,

which is broadly discussed in chapter 4. I have also adopted coevolution concept to

further explore how these IT-enabled capabilities coevolve with other IT-enabled

capabilities in organisations and with competitors and influence competitive

advantage. I have adopted Melville, et al. (2004) RBV based model on competitive

advantage as a starting point because it helps me to define two levels- micro (internal

to firm) and macro (external to firm) in organisations and then, I have applied

coevolution lens in both with the argument that IT-enabled capabilities coevolve in

these two levels and ultimately influence competitive advantage, which is broadly

discussed in chapter 5. Moreover, as discussed in section 3.6.3, the simulation model

has the potential to develop deeper insights on the phenomenon under study and it has

been widely used for theory development on competitive advantage, therefore, I have

also used simulation outcomes from Kauffman’s NKC model (Kauffman, 1993) and

followed Baum and Mckevely’s approach (Baum & McKelvey, 1999c) to propose

some strategies in managing the coevolution of the IT-enabled capabilities, which is


broadly discussed in chapter 6. It is important to highlight that Chapter 4, 5 and 6

contain in-depth reviews on emergence, coevolution concepts and NKC model

respectively. These reviews are specifically used to develop in-depth understandings

on the emergence (Chapter 4) and coevolution (Chapter 5) concepts and NKC model

(Chapter 6) and their use in IS studies in particular.

Chapter 4: An Emergence Perspective on IT-enabled Capabilities 83

Chapter 4: An Emergence Perspective on IT-enabled Capabilities

Chapter 4 Summary

What was done in the previous chapter: The previous chapter presents an

in-depth structured literature review of CAS theory in IS discipline.

What this chapter does: This chapter presents a complex emergence

perspective on IT-enabled capabilities. It highlights how IT-enabled capabilities

emerge dynamic and non-linear way from the interactions between IT assets and

organisational resources.





capabilities.



4.1 INTRODUCTION

In this chapter, an emergence perspective of IT-enabled capabilities is discussed.

Based on the ideas of Nevo and Wade (2010) as a way of understanding IT-enabled

capabilities, this chapter will focus on first subset of the BVIT model presented in the

Figure 1.2 in Chapter 1. Nevo and Wade (2010) apply systems thinking (Ackoff, 1971)

together with RBV (Barney, 1991) to explore strategic potential of IT enabled

resources and their effect on competitive advantage in organisations. They have

adopted a systems thinking based emergence concept (Ackoff, 1971) that is simple and

84 Chapter 4: An Emergence Perspective on IT-enabled Capabilities

linear (Goldstein, 1999; Halley & Winkler, 2008) and considered IT assets and

organisational resources as stable and the outcome of their interactions give rise to the

intended IT enabled resources, which leads to an equilibrium situation. However,

prominent IS scholars (e.g. El Sawy, 2003; El Sawy, et al., 2010; El Sawy & Pavlou,

2008; Tanriverdi, et al., 2010) argue differently and suggest that in contemporary

organisations, IT assets, organisational resources, capabilities and competencies are

continuously changing and thus their interactions need to be considered as dynamic

and non-equilibrium in nature (Merali, et al., 2012). These IS scholars further suggest

to adopt holistic and systemic approaches to understand such dynamic and non-linear

interactions.

Consequently, in this study, I have taken a dynamic and non-linear view as

suggested by the prominent IS scholars. In particular, I have proposed a CAS theory

based emergence (complex emergence) perspective (Goldstein, 1999) to study how

IT-enabled capabilities emerge from the interactions between IT assets and

organisational resources. I have adopted a complex emergence perspective as it

conceives a non-linear and dynamic view of the phenomenon under study (Goldstein,

1996; Halley & Winkler, 2008). In addition, I have adopted Nevo and Wade (2010)

model to explain the two concepts, IT assets and organisational resources. The

complex emergence lens is applied into the first subset of the generic BVIT framework

presented in the Figure 1.2 in Chapter 1 (Figure 4.1). The research subquestion for this

study is-

Research subquestion 1.1: How do IT-enabled capabilities emerge?

Figure 4.1 A Subset of the BVIT framework (Figure 1.2 in Chapter 1)


• IT Assets



Emergence


Using Shepherd and Suddaby (2017) ideas on theory development, I have

developed my research method for this study. A broad discussion of the overall

research method is discussed in Figure 1.4 in the Chapter 1. For this chapter, a subset

of the research method, specifically focused on emergence is applied as shown in

Figure 4.2.

In brief, the research method includes-

1. The narrative conflict: As described in section 1.5.1 in the Chapter 1, the

overarching narrative conflict of this study is the tension between the existing

literature on BVIT and the conceptualisation of BVIT. I provide a more

specific example in relation to ERP system (IT assets) and emergence of IT-

enabled capabilities, which highlights that adoption of a new feature triggers

new IT-enabled capabilities (e.g. ERP-enabled logistics management) that

further triggers other IT-enabled capabilities (e.g. ERP-enabled production

management> purchase management> sales order management and so on).

However, current IS literature conceives that such phenomenon occurs in a

linear way, but there are chains of phenomena occur because of the new feature

adoption in ERP system (as discussed in more detail below). So, the narrative

conflict for this chapter is the tension between the existing literature on

emergence, whether emergence is linear or non-linear (dynamic) and the

conceptualisation of such emergence.

2. Building stories: It involves four stages-

o Identifying core constructs: Three major constructs are defined for this

chapter- IT assets, organisational resources and IT-enabled capabilities.

They are broadly discussed in section 2.3 in Chapter 2.

o Determine the narrative settings: For this study a shifting ontology

strategy is adopted to determine the narrative settings. In this chapter,

shifting ontology highlights the change from a static, linear view to a

dynamic, non-linear view on the complex emergence of IT-enabled

capabilities.

o Draw boundary conditions- The story’s event sequence: The event

sequence here is- first, IT assets and organisational resources interact

in a dynamic and non-linear way and second, their interactions give rise


to the IT-enabled capabilities, which can be unpredictable. This is

briefly discussed in the first paragraph of this section.

o Apply disciplined imagination- theorising via metaphors (analogical

reasoning): In this chapter, complex emergence metaphor of CAS

theory is applied to describe how IT-enabled capabilities emerge from

the interactions between IT assets and organisational resources. The

complex emergence theorising of the IT-enabled capabilities is broadly

discussed in section 4.4.

3. New insights: The application of complex emergence perspective gives new

insights in relation to the dynamic rise of IT-enabled capabilities. The theories

developed using CAS complex emergence are explanatory type theories

(Gregor, 2006) as they help to provide greater explanations on how IT-enabled

capabilities dynamically emerge from lower level interactions of IT assets and

organisational resources. Few propositions are proposed in this phase. I have

also internally validated the proposed propositions using an exemplary case

narrative as first. In section 4.4.2, the narrative has established an internal

validity of the proposed complex emergence framework as it helps to provide

a good understanding of the dynamics of underlying relationships that is ‘why’

and ‘how’ the emergence phenomenon is happening (Eisenhardt, 1989). It is

important to note that, the internal validation using the case study is a first step

of the validation process.


Figure 4.2 A Subset of Research Method (see Figure 1.3 in Chapter 1)

Nevo

& W

ade

(2010

)Sim

ple

Emerg

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1.Th

e Nar

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raw

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&

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Disc

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r

Comp

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Emerg

ence

New

Insig

hts:

-Com

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merge

nce

of IT

enab

led

capa

bilitie

s

Core

BV

IT

mode

l

3. Ne

wIn

sight

s (Th

eory

) an

d Eva

luatio

n

Exist

ingTh

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Re-d

esign

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eory

Analo

gical

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oning

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CAS T

heor

y

Static

& L

inear

Dyna

mic a

nd

comp

lex

How

do IT

en

abled

ca

pabil

ities

em

erge?


To exemplify the narrative conflict in relation the tension between the existing

literature on BVIT and the conceptualisation of BVIT (section 1.5.1), I use enterprise

resource planning (ERP) system as an example. ERP system is a customisable and

standard application software that integrates core business processes within and across

functional areas in an organisation The ERP solution provides capabilities that ensure

customers business needs and can adapt with the changing needs dynamically so as to

provide the services and products for their customers efficiently. Although emerging

research have focused on understanding the factors and their role in contributing to

ERP system benefits (Seddon, Calvert, & Yang, 2010; Staehr, Shanks, & Seddon,

2012) from a linear point of view, little work has been done from a dynamic point of

view to gain deeper understanding on the process that contributes to the business value

i.e. the conceptualisation of BVIT requires rethinking from dynamic viewpoint. As a

first step, in this chapter, I have adopted CAS emergence concept to conceptualise the

dynamic process of emergent IT-enabled capabilities. The narrative conflict here is

that the existing literature argues that IT-enabled capabilities emerge in a linear way

(Nevo & Wade, 2010). However, in real world, the IT assets and organisational

resources are continuously changing and thus it impacts the emergent rise of the IT-

enabled capabilities and makes it dynamic. For instance, adopting the ERP system,

such as, Epicor in inventory management triggers improved logistics, which further

triggers better production management via Epicor. The improved production

management provided by Epicor may also improve better purchase management,

which may further improve sales order and quote management. This example shows

that adoption of ERP (IT system) improves better logistic management (IT-enabled

capabilities), which further may improve production management, and then, purchase

management and so on. It reflects the emergent nature of IT-enabled capabilities in

ERP system in contemporary organisations, which requires re-conceptualisation from

a dynamic perspective. Therefore, the narrative conflict in this chapter is the tension

between the existing literature on emergence, whether emergence is linear or non-

linear (dynamic) and the conceptualisation of such emergence. It is important note that,

emergence phenomenon can also conceptualised as being simple and linear such as,

Nevo and Wade (2010) did in their study on the emergence of IT enabled resources

and its’ impact on the competitive advantage. However, in this study, I have only

focused on the non-linear type of emergence in contemporary organisations and this


conceptualisation is also consistent in the ERP system example that I have used as a

narrative conflict in this chapter.

The rest of the chapter is as follows- first, section 4.2 presents an overall

overview of the emergence concept and its applications in IS and broader

organisational studies. The next section 4.3 provides a recap of Nevo and Wade (2010)

model of BVIT and addresses the complex emergence concept of CAS and highlights

the enabling conditions related to it. The following section 4.4 presents a complex

emergence model of IT-enabled capabilities and the chapter concludes (section 4.5)

with a chapter summary.

4.2 OVERVIEW OF EMERGENCE

This section presents an overview of the concept of emergence , firstly . in

relation to CAS studies. Then it briefly highlights the typologies of emergence with

the emphasis on complex emergence. Finally, a review is presented of the use of the

emergence concept in CAS studies across IS and the organisational research domain.

4.2.1 The Basics of Emergence

The term ‘emergent’ (and by extension ‘emergence’) was coined in 1875 by the

philosopher Lewes (1875) to discuss on the changing nature of causality. Since then,

the idea has been proposed as a supplement to Darwin’s selection theory to describe

the mechanistic and incremental view of evolution (Goldstein, 2011). Emergence is

broadly defined as “coming into being of qualitative novelty” (Bunge, 2003), with the

view that a whole is formed from the interactions of parts. Casti defines emergence as

“the way the interactions among system components generates unexpected global

system properties not present in any of the subsystems taken individually” (Casti, 1997,

p. 91).

Emergence has been a central phenomenon in many studies that are concerned

with CAS (Goldstein, 2011), self-organization (Nan & Lu, 2014), and the origins of

novel entities, properties, or processes (Bunge, 2003). The notion of a “whole before

the parts” can be traced back to Aristotle (Hovorka, 2013), but the modern conception

of the dynamic perspective of emergence is credited to philosopher G. H. Lewes

(Goldstein, 1999). In the dynamic emergent perspective, emergence is broadly

concerned with the properties of dynamic systems that arise from interactions of the

parts but cannot be predicted from the properties of those parts (Casti, 1997).


Emergence phenomena are characteristic of complex adaptive systems (CAS)

(Mitleton-Kelly, 2003b). The concept of emergence in CAS has very diverse scientific

and mathematical roots: cybernetics, solid state/ condensed matter physics,

evolutionary biology, artificial intelligence, artificial life, etc. There are four major

school of thoughts that influence the way emergence is studied:

1. Complex adaptive systems theory; research conducted by Santa Fe Institute

researchers which explicitly uses the concept of emergence to define macro-

level properties arising from interacting agents (Weisbuch & Ryckebusch,

1991).

2. Nonlinear dynamical systems theory and Chaos theory; both theories use a

the concept of ‘attractor’, a special behaviour to which system evolves and one

kind of attractor is called strange attractor (Young, 1991), which is classified

as an emergent phenomenon (Goldstein, 1996; Young, 1991).

3. The synergetics school, which focuses primarily on emergence related to

physical systems. They used an order parameter that highlights which macro

level phenomena a system exhibits (Haken, 1977).

4. Far-from-equilibrium thermodynamics; The studies in thermo-dynamics

by Prigogine and his colleagues refer to emergent phenomena as dissipative

structure, arising from far from equilibrium conditions (Nicolis, Prigogine, &

Nocolis, 1989; Prigogine, 1984; Stengers & Prigogine, 1997).

In summary, the emergence concept refers to two important aspects: a global

behaviour or pattern firstly, that arises from the interactions of local elements and

secondly that cannot be traced back to the individual elements (Bunge, 2003; De Wolf

& Holvoet, 2004).

4.2.2 Typologies of Emergence

Studies of different types of emergence can be found in a IS and organisational

studies related literature. All these types of emergence share the similar conception

that novel structure or properties or behaviours arise at high level from as a result of

interactions in constituent components at lower level (Bunge, 2003; Goldstein, 2000).

However, researchers differentiate types of emergence based on the emphasis on

research focus and context. Goldstein (2011) derives a set of six prototypical

conceptualisations on emergence- phase transition, self-organising physical systems,


mathematical, computational, social and biological emergence. Lichtenstein and

McKelvey (2011) develop a typology that defines four increasingly demanding

definitions of emergence, and use this typology to organise a review of the complexity

literature, focusing on computational models that have been utilised by management

scholars (Burt, 1992; Mintzberg & McHugh, 1985; Wasserman, 1994). Moreover, Hovorka

(2013) defines three forms of emergence and provide both research exemplars and a

framework for categorizing emergent phenomena to better articulate and refine how

researchers understand emergent phenomena in Information Systems. Table 4.1

summarises the above-mentioned conceptualisations and typologies.

Authors Typologies of Emergence

Goldstein (2011) identified at least six prototypes on emergence with the emphasis on the research into the emergence phenomenon in complex systems.

1. Phase transitions, e.g. symmetry breaking (Anderson, 1972), change of orders, (Holland & Mallot, 1998), reformalisation of groups and criticalisation (Mckelvey, et al., 2013). 2. Self-organising physical systems, where dissipative structure exists (Nicolis, et al., 1989); far from equilibrium conditions (Meyer, et al., 2005) and self-organisation (Ashby, 1968). 3. Mathematical emergence, e.g. nonlinearity; phase space (Morel & Ramanujam, 1999); bifurcations; attractors; and chaos (Gleick & Berry, 1987).

4. Computational emergence, e.g. neural network (Holland & Mallot, 1998), artificial intelligence, game of life (Sigmund, 1994) and computational mechanics. 5. Social emergence, e.g. social networks (Nan & Lu, 2014), cooperation (Sawyer, 2005), system usage (Nan, 2011).

6. Biological emergence, e.g. new species, morphogenesis; symbiogenesis; and hierarchical constructions (Kauffman, 1995a; Kauffman, 1993).


Lichtenstein and McKelvey (2011) classify four types of emergence according to heuristics developed by philosophers focusing on exploring nature and properties of emergence.

7. Type 1- emergent network; explanations of collective actions of social structure, such as, social network emergence (Burt, 1992; Wasserman, 1994).

8. Type 2- emergent hierarchies; an emergent property or structure is defined as ‘different in kind’ from its components, e.g. emergent strategy formation (Mintzberg & McHugh, 1985).

9. Type 3- emergent causalities. Upward effects that cause interactions between system components to constitute higher structures including the non-linear effects that the emergent structures may have on its components. It depicts coevolutionary causal effects both ways bottom-up and top down. Studies related to institutional emergence, such as, where scholars examine how macro-level (institutional) structures supervene on micro-level (individual) behaviour (Contractor et al., 2000). 10. Type 4- emergent purposeful CAS. it combines type 2 and type 3 emergence together. It combines purposefulness and multiple causal cycles which makes this emergence non-linear, e.g. (Siggelkow, 2002) study on Vanguard group, which includes multiple coevolving causalities such as material, final, formal and efficient across multiple levels.

Hovorka (2013) develops a framework highlighting two major types of emergence in IS research.

1. Simple emergence - properties of the whole can be predicted by knowing the properties of the parts.

o Perceptual emergence (internal)- something that was obscured becomes visible to the researchers. o Associative emergence (external)- parts are aggregated such that the properties of the whole can be predicted by analysing the properties of the constituent parts (e.g. Burton-Jones & Straub Jr, 2006).

2. Complex emergence- Properties of the whole are distinct from its constituent components.

o Emergence as process- Focuses on patterns, timing, and intensity of interactions of constituent parts. Interactions may be planned or unplanned (e.g. Nan, 2011). o Constituent parts are combined or fused such that the properties of the whole are distinct from the properties of the parts, and the parts themselves are transformed (e.g. Nevo & Wade, 2010).

Table 4.1 Typologies of Emergence

Based on the above discussion, it is evident that extant literature on complexity

science, IT and organisational studies provide different typologies of emergence with

common understanding that novel properties arise from the interactions between lower


level components. Though the emergence concept has been used across different

disciplines and various typologies have been suggested, still it shares some common

characteristics, such as, macro level properties from micro level interactions,

unpredictability, and dynamical in the sense of coming to be over time (Goldstein,

1999).

However, this study particularly focuses on the emergence of IT-enabled

capabilities based on the ideas of the Nevo and Wade (2010) study on IT enabled

resources and their strategic contribution to competitive advantage. I have proposed

that the authors’ ideas about exploring the emergence of IT enabled resources and

synergistic capabilities are largely linear (Simple emergence) as they scarcely consider

the changes in IT assets and organisational resources from which the IT enabled

resources appear. Consequently, I have proposed a dynamic lens, in particular a

complex emergence perspective that considers the dynamic and continuous changes in

the components of IT assets and organisational resources which together give rise to

the emergent IT-enabled capabilities. Therefore, I have considered simple vs. complex

emergence and adopted a complex emergence perspective in the context of IT-enabled

capabilities. The following section briefly discusses the differences between simple

vs. complex emergence in relation to IS and organisational research.

4.2.3 The Use of Emergence in IS and Organisational Research

Emergence has been used in disciplines ranging from biology to physics and

organizational studies to Information Systems (IS). The emergence concept has gained

popularity in IS recently to understand novel phenomena where technologies,

business, and organisations are all interacting within business ecosystem (El Sawy, et

al., 2010). It has been used along with resource based view theory to explore how

synergies from IT enabled resources positively influence competitive advantage in

organisations (Nevo & Wade, 2011; Nevo & Wade, 2010). The applications and

contributions derived using emergence concept in IS research have been broadly

discussed in Chapter 3. Here, I have focused on different emergence models and their

use in extant research, whereas the Chapter 3 focuses only contributions derived using

emergence concept in IS research. Emergence is an important construct in studies of

organizational dynamics and leadership in particular (e.g. Benbasat, Goldstein, &

Mead, 1987; Chiles, et al., 2004; Goldstein, 1996; Lichtenstein & McKelvey, 2011;

MacIntosh & MacLean, 1999). Lichtenstein, et al. (2006) use emergence via time


series analysis along with multi-level longitudinal analysis in the context of

organisations.

One of the dominant models of emergence, the dissipative structure model

(Prigogine, 1984; Stengers & Prigogine, 1997) of emergence has widely used in

organisational studies. For instance, the Chiles, et al. (2004) study on music theatre

which adopts the dissipative structure model of emergence, reveals that the sundries

related to country music have been attracting over 6 million visitors annually to the

town of Branson, Missouri. This collective behaviour has arisen without any

centralised hierarchical guiding facility; it appears in a self-organised way.

Following Chiles, et al. (2004), Lichtenstein and Plowman (2009) use the ideas

of emergence in leadership and identify four conditions of emergence, the

disequilibrium state, amplifying actions, self-organisation and stabilising feedback.

The studies by Chiles, et al. (2004), Lichtenstein and Plowman (2009) and Lichtenstein

and McKelvey (2011), show that they understand emergence as involving self-

organising logic or process, entailing a combined set of configurations that are neither

planned nor created through human design (Chiles, et al., 2004).

In fact, the majority of organisational scholars holds the similar view that self-

organisation processes are related to the emergence concept (Goldstein, 2011). Self-

organisation processes appear spontaneously when command and control mechanisms

are relaxed or absent. Such a perspective suggests a passive leadership style in self-

organised based emergence (Goldstein, 2011). Goldstein (2011) has suggested to

consider self-organising logic in emergence model recognisable in majority of

complexity-based research related to emergence for better constructional operations.

Based on the self-organising logic, emergence can be classified into two high

level categories (Table 4.1) (Halley & Winkler, 2008; Hovorka, 2013)-

Simple emergence

The emergent phenomena whose properties are close to linear and relatively

predictable from the constituent components. The model properties can be modelled and

can be predicted from the interactions of components. For instance, Nevo and Wade

(2010) discuss the emergence of IT enabled resources (e.g. IT enabled customer

service), which are combinations of IT assets (e.g. IT systems, IT people) and

organisational resources (department, unit, business processes). The properties of IT


enabled customer service can be predictable, such as, fast/slow response to customer

inquiries.

Complex emergence

The emergent phenomena might exhibit non-linear behaviour and are often

unpredictable. The emergent order tends to remain in a far from equilibrium region

containing self-organised logic in more than a level in lower level from where the order

emerges (Goldstein, 1996). For instance, in Orlikowski (1996) study on organisational

transformation, she identified that different organisational practices of actors such as,

leadership emerges in a way in relation to contingencies, breakdowns or opportunities

and any unintended consequences, that is, in an unpredictable way. Although the

combinations of parts, e.g. actors’ actions or decisions can be understood, the trajectory

of situated change was not predictable by knowing the characteristics of the technology

or the work practices.

The key differences between simple and complex emergence lie in a few important

characteristics. Simple emergence is largely linear and the emergent patterns stay close

to an equilibrium state, while complex emergence produces novel patterns in a region

which is far from equilibrium state, a region between stability and instability, the edge

of chaos (Waldrop, 1992). The complex emergence gives rise to unpredictable patterns

because of complex causality. Complex causality refers to the ambiguity concerning the

nature of the causal connections between actions and results (Lippman & Rumelt, 1982).

The emergent patterns might not be as expected from the interactions in constituent

components is lower levels. The simple emergence is similar to the ‘Simple’ or ‘Known’

domain of Cynefin framework (Snowden, 2002), in which the relationship between

causes and effect is pretty obvious. In a similar way, complex emergence refers to the

‘Complex’ Cynefin domain, in which the relationship between cause and effect can be

perceived in retrospect, but not in advance. For instance, the use of new incident tracking

support system (ITSS) has improved the rate of resolving incidents, which was an

expected outcome for Zeta (Orlikowski, 1996). To understand complex emergence,

suppose, ITSS is a cloud based modular ERP and a series of new modules, such as, sales

and marketing, inventory, finance modules are introduced in the system, the expected

benefits may vary due to the various dynamics, such as, IT maturity, organisational

learning, organisational innovation, change management (Teo, 2017).


The following Table 4.2 summarises the differences in properties of simple and

complex emergence based on Halley and Winkler (2008), Goldstein (1996) and

Hovorka (2013)-

Characteristics Simple emergence Complex emergence

Type of systems Simple system Complex system

Linearity Largely linear Non-linear

Equilibrium state Close to

Equilibrium

Far from Equilibrium

Self-organisation No Yes

Predictability of

properties

Yes No

Causality Simple Complex

Table 4.2 Simple vs Complex Emergence

Based on the above discussion of complex emergence and following Nevo and

Wade (2010) ideas on the emergence of IT enabled resources (Chapter 2), this study

argues that the creation of IT-enabled capabilities from the interaction of IT assets and

organisational resources is non-linear and has largely exhibit unpredictable properties.

Therefore, the complex emergence perspective is adopted following Halley and

Winkler (2008), Goldstein (1996) and Hovorka (2013) to provide insights into the

dynamic side of the emergence of IT-enabled capabilities.

It is very important to note that, this study does not disregard the existence of

simple emergence of IT-enabled capabilities such as Nevo and Wade (2010) did in

their study on the emergence of IT-enabled capabilities. The authors only focused on

the simple emergence of IT-enabled resources in organisations and they have

acknowledged that their study intuitively contribute to the discussion on dynamic type

of resources as raised by other IS researchers (Wade & Hulland, 2004). In a similar

way, in this chapter, I have narrowed down my focus only on the complex emergence

of the IT-enabled capabilities. I have acknowledged that the emergence of IT-enabled

capabilities scenario may also contain simple emergence, which can be dealt with

generic systems view point as the way Nevo and Wade (2010) did in their study.


4.3 RECAP OF NEVO AND WADE (2010) MODEL

The Nevo and Wade (2010) model is broadly discussed in the Chapter 2 (see

Figure 4.3). In brief, they have argued that IT assets derive their strategic potential via

interacting with commodity type organisational resources and influence competitive

advantage. They propose that when the components of IT assets and organisational

resources are compatible, and managers support (‘integration effort’) the interactions,

then IT enabled resources and their emergent capabilities emerge as a result. The

compatibility between IT assets and organisational resources and support from

managers, which is termed ‘integration effort’ are the two key enabling conditions of

the emergence of IT enabled resources.

This study proposes that the emergence of IT-enabled capabilities from the

interactions of IT assets and organisational resources are non-linear. It is non-linear

and dynamic because the components of IT assets and organisational resources are

always changing, and they give rise to IT-enabled capabilities, which are sometimes

unpredictable in nature. Therefore, I propose to adopt complex emergence lens to

explore the dynamic emergence of IT-enabled capabilities.

Figure 4.3 (Nevo & Wade, 2010) Model on Competitive Advantage

In this study, to understand the complex emergence of IT-enabled capabilities-

o I have adopted similar ideas suggested by Nevo and Wade (2010), i.e. the

compatibility condition as an enabling condition that ensures the interactions

between IT assets and organisational resources. However, I have replaced the

integration effort condition with self-organised management, as ’integration

effort’ is planned and it is considered to be prone to command-control

Organisational Resources

IT Assets

Compatibility

Integration effort

Value

Rarity

Non-subsitutability

Inimitability

Rea

lised

Syn

ergy

Sust

ainb

le C

ompe

tetit

ive A

dvan

tage

Systems TheoryResource

Based View


mechanism (Goldstein, 2011). The self-organised management condition is

neither planned not controlled through human design and supports the non-

linear emergence of IT-enabled capabilities. The two enabling conditions are

broadly discussed in the next section.

o I have applied the complex emergence concept to understand the emergence of

IT enabled capabilities and the dynamics related to it. In particular, I have

followed Goldstein (2011) ideas of self-organising, logic based complex

emergence in the context of IT-enabled capabilities.

The next section provides a broader overview of the adoption of the complex

emergence concept in the context of IT-enabled capabilities.

4.3.1 Adoption of Complex Emergence in IT-enabled Capabilities Context

As discussed in the previous section, complex emergence involves complex

causality that refers that the causal connections between actions and outcomes are not

same, they can be non-deterministic (Goldstein, 2011; Lippman & Rumelt, 1982).

Consistent with this logic, I have proposed that complex causality logic needs to be

considered while exploring IT-enabled capabilities via the complex emergence lens,

as the resultant capabilities might not be as expected.

The two enabling conditions and the complex causality logic are briefly

discussed below-

Compatibility between IT assets and Organisational Resources

To interact between each other and to develop synergistic relationships,

components need to be compatible with each other (Singh, 1997). Compatibility

between two components is defined as “an assessment of the ability of a system’s

components to interact—that is, form a relationship; it is not an assessment of the

outcome of the interaction” (Nevo & Wade, 2010). Compatibility must exist between

IT assets and the organisational resources with which they are combined (Markus &

Robey, 1988). Orlikowski (1996) case study on the Zeta corporation reflects the notion

of compatibility in the context of new incident tracking system software (ITSS) and

the customer service department (CSD). The ITSS software is developed in a way that

it is compatible with operational and business requirements of the CSD. The

compatibility notion is inherent in alignment literature (Peppard & Breu, 2003), in

which IT and business elements need to be aligned. Moreover, the compatibility


condition has been recognised as an essential factor in building synergistic

relationships between multiple components in different circumstance (Sarkar,

Echambadi, Cavusgil, & Aulakh, 2001). In summary, once the IT assets and

organisational resources are compatible, they interact and their interactions give rise

to IT-enabled capabilities. As the components of the IT assets and organisational

resources are continuously changing, this can be theorised using the complex

emergence perspective as it acknowledges implicit dynamic changes in the constituent

components (Halley & Winkler, 2008).

Self-organised Management

“Self-organization is the ability of interconnected autonomous agents of a

complex adaptive system to evolve into an organized form without external force”-

(Vidgen & Wang, 2009). Agents are autonomous because they can intervene in the

status of the environment and take appropriate actions from the perceptions of

environment (Mitleton-Kelly, 2003b). In the context of agile management, for

instance, self-organisation refers to the spontaneous formation of a group to achieve

some goals; the group members decide within themselves what to do, how to do it,

assess the context of dynamics and initiate proper actions based on their consciousness

of the environment (Vidgen & Wang, 2009).

From the traditional systems perspective, management is largely planned-driven

and more prone to control-command; it is considered as a mechanism for transforming

the organisation’s numerous parts into an organized whole in order to organise

unrelated and unused components in a manner that can accomplish more goals

(Johnson, Kast, & Rosenzweig, 1964). The integration effort condition in Nevo and

Wade (2010) BVIT model highlights the role of management in estimating a

relationship between IT assets and organisational resources that is congruent with

organisation goals.

This study argues that ‘integration effort’ is planned driven and controlled by

traditional managerial practices, following a traditional leadership style (Goldstein,

2011) and proposes that the emergence of IT-enabled capabilities is facilitated by self-

organisation logic, which is neither planned, controlled nor created through human

designs and composed of a set of ‘hodgepodge’ configurations (Chiles, et al., 2004).

Self-organisation signifies a departure from the traditional command and control

philosophy driven by hierarchical bureaucratic organizations Anderson (1999);


Volberda and Lewin (2003). Goldstein (2011) refers to such a perspective, as a

“laissez-faire” or passive leadership style.

Researchers have acknowledged that it is difficult to predict emergent order in

complex adaptive organisations (Anderson, 1999). Thus, this study proposes a new

condition, which is termed as, ‘self-organised management’ that is characterised as

spontaneous, dynamic and relaxed, all of which support dynamic emergence of IT-

enabled capabilities. It works as relaxed and flexible management practices that

facilitate the dynamic changes in IT assets and organisational resources and the related

emergent IT-enabled capabilities. The idea of self-organised management is widely

used in agile development, where it is widely referred as self-organisation and

considered as truly emergent management practices that arises depending on situations

(Vidgen & Wang, 2006b, 2009; Wang & Vidgen, 2007). I have adopted a similar idea

of the self-organisation here, but termed as self-organised management in this research

context. The underlying assumption entails, while IT assets and organisational

resources change continuously, the emergent IT-enabled capabilities also change with

them and self-organised management serves as a catalyst providing necessary support

for the interactions to occur.

Accordingly, self-organised management is different from the traditional

integration effort that is deliberately decided, it dynamically adjusts in a way that

provides necessary conditions for the dynamic interactions between IT assets and

organisational resources (Stacey, 2003). For the purpose of this study, self-organised

management is defined as flexible and spontaneous managerial practices that support

and guide the dynamic relationships between IT assets and organisational resources

following Stacey (2003).

Complex Causality

The complex emergent phenomena involves unpredictability that is caused by

disruptions in the chain of cause and effect in the component relationships (Goldstein,

1996). Stacey (1996) addresses this particular phenomenon and argues that emergence

in complex systems indeed demands complex causality. In fact, the causal explanation

in complex systems is indeed problematic, though Goldstein (1996) argues that the

unpredictability might not be the only reason behind this problem; the emergent

phenomenon itself might be responsible for the causal ambiguity because a radically

new pattern arises due to the emergent phenomenon. Regarding the relationship


between unpredictability and the unique outcome that is due to the emergence, it can

be argued that this unpredictability comes from the way emergence can incorporate

randomness into the formation of totally new order (Stacey, 1996). According to

Nicolis (1989), a system under the influence of randomness within an unpredictable

environment may create a temporary emergent structure (complex emergence) for a

small period of time.

Therefore, complex emergent phenomena seem to violate the conventional

notion of causality, whereby something new and completely different emerges from

the causal chain (Goldstein, 1996). This study follows that complex emergence

involves complex causality which refers to a particular ontological specification, a

different understanding of causality than traditional approaches to explore cause and

it also includes a unforeseeable outcomes (Byrne, 2005; Byrne, 2011). Because,

according to Bunge (2003), linear causality (traditional) remains trapped in the chain

between cause and effect, where complex causality can direct the emergent phenomena

in multiple directions. For the purpose of the study, complex causality is defined as,

the causality concerning the emergence of unpredictable outcomes from the results of

interactions between components of the IT assets and organisational resource

following Lippman and Rumelt (1982). Further, I have assumed that as the outcomes

of certain contexts are unforeseen, decision makes intervene (self-organised

management), investigate while making necessary adjustments based on semi

structures and simple rules (Snowden, 2002), which are discussed in section 4.3.3.

The preceding discussion addresses two enabling conditions compatibility and

self-organised management and complex causality logic that facilitate complex

emergence of IT-enabled capabilities. However, Goldstein (2011) suggests that the

emergence concept contains few ‘folklores’- myths, which must be addressed before

applying it in organisational context. The next section highlights these folklores.

4.3.2 Folklores related to Emergence

One of the renowned scholars in complexity research Goldstein (2000) has

argued that the notion that emergence contains conceptual snares and ambiguous

threads is derived from misinterpretations. He advocates that these ‘folklores’ must

be addressed before applying complexity theory in organisational contexts. This

section first briefly discusses the folklores.


The four folklores addressed by Goldstein (2000) are-

1. Complexity arises suddenly from simplicity: This folklore concerns that

complexity can manifest as an emergent and random dynamic phenomenon

spontaneously and suddenly from very much simpler dynamics. This

interpretation is quite similar to the mathematical ‘chaos’ theory (Prigogine,

1984), where technical chaos is considered as an unpredictable emergent

phenomenon raised through simple mathematical equation. Such a sudden and

unpredictable outcome from the interactions of system components however

does not mean novelty arises without any cause (Goldstein, 1999). Looking

closely at the system reveals some intermediary phases, which can be explained

by the dissipative structure model of a Bérnard cell (Nicolis, et al., 1989;

Prigogine, 1984; Stengers & Prigogine, 1997). In brief, while many complexity

scholars argue that complexity arises from suddenly from simplicity (e.g.

Kauffman, 1993), this may not always be the case and complexity may rise

from intermediary complexifying operations, such as bifurcation point

(Prigogine, 1984).

2. Order for free: The emergence of a new form or structure in complex adaptive

systems is thought to obtain new order without any pre-set design rules

(Kauffman, 1995a). Kauffman (1993) in his book of ‘Origins of Order: Self-

Organization and Selection’ argues that Darwinian natural selection is a “single

singular force” that is inadequate to identify, stress and incorporate the

possibility that emergence in a complex adaptive system (CAS) exhibits order

spontaneously. He claims that the spontaneous order in the CAS occurs through

a self-organised emergent process. Emergent patterns and properties arise from

the interactions of system elements and are greater than the sum of their parts

and it is difficult predict the emergent nature by investigating individual

elements. Emergence is the process that creates new order together with self-

organisation (Kauffman, 1993). In summary, Goldstein argues against the

many scholars who consider that orders are free (e.g. Kauffman, 1993) thus,

there is a necessity to understand what enables orders to be arose.

3. The edge of chaos: The edge of chaos is a zone between total order and

complete disorder. It is the zone in which a system shows bounded instability

because it shows stable and instable behaviours at the same time. It is assumed


to be stable, as the system shows emergent patterns. It is called unstable at the

same time, as the system’s future state is unpredictable- the system is far from

equilibrium condition (Anderson, 1999). Burgelman and Grove (2007) also

observe that there is order in chaos and chaos in order. The new order consists

of agents’ attributes that impose tension which causes emergent of the new

order. Agents creates the ordered region through self-organising processes

(Morel & Ramanujam, 1999). Cramer (1993) refers to the region as critical

complexity. Systems on the edge of chaos tend to change, adapt and self-

organise constantly to create new configurations with the ever changing

business environment (Goodwin, 1997). The edge of chaos is considered as a

sweet-spot between order and chaos as the system can self-organise

spontaneously to keep itself in an ordered state, though it changes frequently.

4. Emergence only takes place through self-organisation: The ‘self-organisation’

folklore is very similar to folklore ‘order for free’. However, order for free as

discussed in point 2 to the situation in which emergent order from the

interactions of components is thought of as free, it’s an outcome (Weiss, 1987).

Self-organisation is the process that creates emergence out of the order (De

Wolf & Holvoet, 2004). The close association of the concepts of emergence

and self-organisation in CAS come from the physical and life sciences (Allen,

Maguire, & McKelvey, 2011). Prigogine’s work on dissipative structures in

molecules (Nicolis, et al., 1989), Haken’s articulation of synergistic (Haken,

1977), and Maturana and Varela’s concept of autopiesis (Varela, et al., 1973);

- all of these scholars are agreed upon the fact that self-organisation is not a

part of priori design, rather it emerges as an outcome of interactions among a

system’s components. Self-organisation is thus a part of the emergent process

that connotes properties of CAS such as, unplanned and spontaneous situations

(Goldstein, 2011). Accordingly, Goldstein argues that whether emergence

always takes place through self-organisation process or any other

constructional operations are related to it, which he later on describes through

self-transcending constructions (Goldstein, 2000).

Given that Goldstein (2000) makes clear that complex emergence phenomena do

not happen spontaneously, it is necessary to understand what enables complex

emergence to take place. Goldstein (2005) proposes an alternative conceptual


explanation self-transcending construction, that allows us to understand how emergent

order arises out of yet transcends the lower level antecedent substrate (Goldstein,

2011), other scholars in organisational studies propose the concept of ‘semi-structure’

(such as, Brown and Eisenhardt (1997)) and Eisenhardt and Sull (2001) discuss the

notion of ‘simple rules’ that may also help to explain how complex emergence takes

place. The folklores help to reduce confusions around the emergence concept and also

provide a way to establish the fact that self-organisation is one of the enabling

conditions of complex emergence.

4.3.3 Enabling Conditions of Self-organised Complex Emergence

This section discusses the ideas of ‘semi-structures’ (Brown & Eisenhardt,

1997) and ‘simple rules’ (Eisenhardt & Sull, 2001) and how they can enable self-

organised based complex emergence. The proposed enabling conditions semi-

structures and simple rules together resolve the ambiguities in theorising complex

emergence.

Semi-structures

The first folklore discussed above debatable idea is that complexity arises

spontaneously and suddenly out of simple or random dynamics, which is similar to the

idea that emergent patterns arise from simple mathematical operations, referred to as

chaos (Young, 1991). An outcome via emergence that suddenly arises distinctively

from the constituent components does not necessarily mean that there are no

intermediary rules or operations or stages related to it. Complexity thought leaders

frequently mention intermediary operations, such as, bifurcation point, criticalisation,

iterative and recursive operations, and feedback processes. (Chiles, et al., 2004;

Goldstein, 1999; MacIntosh & MacLean, 1999; Prigogine, 1984). In a very similar

way, the second folklore is concerned that a focus on ‘order for free’ tends to neglect

the indispensable role of the ‘containers’ and other ‘constraining and constructional

operations’ involved in emergence, like the distance separating two neighbouring

currents is on the order of the vertical height of the container in a Bénard cell (Bergé,

Pomeau, & Vidal, 1986). The fourth folklore highlights the existence of self-

organisation in creating emergent order. In fact, the majority of the emergence

literature that talks about self-organisation, agrees that self-organisation and

emergence occur together (De Wolf & Holvoet, 2004).


From the above discussion it is clear that the emergence phenomenon involves

intermediary phases that gives rise to the emergent patterns or structures or behaviours,

which are acknowledged in complexity science related research (Holland & Mallot,

1998; Kauffman, 1993; Lissack, 1999; MacIntosh & MacLean, 1999). Moreover,

Kauffman (1993) view on emergence entails that emergence causes the patterns along

with self-organisation. Combining the above two ideas ensures that the emergence and

self-organisation are complement each other (De Wolf & Holvoet, 2004; Goldstein,

2011).

Based on the literature of emergence related to self-organisation, this study

acknowledges that the emergent phenomena involve at least four stages (Goldstein,

2011; Lichtenstein & Plowman, 2009),

1. Disequilibrium state- a period of disequilibrium that triggers the seeds of new

order (McKelvey, 2001) and pushes the system in to a highly dynamic state

(Anderson, 1999).

2. Positive feedback- in the disequilibrium state, small actions or events amplify

(Maruyama, 1963) similar events i.e. the emergence of one order amplifies the

likelihood of similar emergent order via positive feedback loop (Morel &

Ramanujam, 1999).

3. Recombination- in this stage, new correlation between existing components

arises; the systems comes into being in this stage (Lichtenstein, et al., 2006).

4. Stabilising feedback- coordinating mechanisms that stabilises the new

emergent order in this stage for maximum period of sustainability until next

emergence takes place (Chiles, et al., 2004).

This study adopts the view that to be able to change from one emergent order to

another one, the emergent orders must have a level of flexibility that ensures that they

can change from the existing order to the another one with relatively low chaos. To

emphasise this particular condition, this study adopts the idea of semi-structures by

(Brown & Eisenhardt, 1997) in relation to continuous change in complex system.

Semi-structures are defined as some prescribed or determined features such as

responsibilities, organisational goals, project deadlines, etc. of adaptive organizations

(Brown & Eisenhardt, 1997). The semi-structures support non-deterministic features


that arise from dynamic interactions between components of complex adaptive

systems (Brown & Eisenhardt, 1997; Goldstein, 1999).

The semi-structures facilitate the continuous emergence of orders by supporting

the level of flexibility required for continuous/frequent change. This ensures that the

design of a complex system is flexible so that it can accommodate the complex

emergence by avoiding any types of rigidity that hinders the emergence of IT-enabled

capabilities following the intermediary phases mentioned above. The semi-structures

contain some features, such as, time intervals, rules for coordinating sustainability of

emergent orders (discussed in the next paragraph) and recombination logic so that the

emergent orders can stay at the edge of chaos region (Goldstein, 1999). The concept

supports the intermediary stages of emergence so that it can exhibit frequently

changing patterns.

Simple rules

The above discussion argues that emergent order requires flexibility and semi-

structures are required to provide some predefined features that ensure the emergent

orders shift continuously from one existing state to another emergent state. However,

here I argue that the interim phases as acknowledged above follow to some extent

some rules that give rise to the emergent patterns, specifically complex emergence. In

this study, I propose that complex emergence follows simple rules (Eisenhardt & Sull,

2001; Kauffman, 1995a) that guide the emergent patterns in a direction without

confining complex emergence. The simple rules can be thought of as some non-rigid

(flexible) guidelines or schemas that direct complex emergent phenomena towards the

edge of chaos, between stable and non-stable regions meaning the emergent patterns

will be in a such state that a small change can trigger unintended changes. The simple

rules can be compared to heuristics (Cohen et al., 1996) that self-organised

management uses as guidelines and decision rules (Levy, 2000) along with the

predefined features (semi-structures) to facilitate the unpredictable complex

emergence of IT-enabled capabilities consistent with the organisational goals. Davis,

Eisenhardt, and Bingham (2009) use simulation to show that a simple rules strategy is

essential in unpredictable environments. Eisenhardt and Sull (2001) conjecture that

simple rules enable flexible, yet coherent capture of opportunities related to

organisational processes, such as, product development and internationalisation. Their


discussion of different simple rules (heuristics) includes boundary rules, how-to rules,

priority rules, timing rules and exit rules.

Kauffman (1995a) explains that the emergent pattern caused by the sensitivity

of small changes follows some rules, which might involve redundancies. Stacey (1996)

also conceives the similar ideas that the balance between canalisation and redundancy

in complex systems makes emergent patterns more robust in the face of turbulent

changes and guides them to operate at the edge of chaos. The use of simple rules in

directing organisational strategies to capture fleeting opportunities has been popular in

the strategic management literature (Brown & Eisenhardt, 1997; Eisenhardt & Sull,

2001; March, 1991). The simple rules work as implicit guidelines (Senge, 1990) or

coordinating mechanisms (Lichtenstein, 2004) so that the spontaneous fluctuations

towards new emergent order in complex emergence stays at the point of the edge of

chaos region (Waldrop, 1992). Combining the above views, for this study, simple rules

are defined as, organisational heuristics that work as guidelines and decision rules to

facilitate complex emergence of IT-enabled capabilities.

The following section discusses the complex emergence perspective in the

context of IT-enabled capabilities.

4.4 A COMPLEX EMERGENCE FRAMEWORK OF IT-ENABLED CAPABILITIES

This section adopts the complex emergence perspective to understand the

emergence of IT-enabled capabilities in contemporary organisations. Based on the

ideas of Nevo and Wade (2010), this study proposes that IT assets and organisational

resources interact together and their interactions give rise to IT-enabled capabilities

(Chapter 1). These interaction are dynamic, meaning that the components of IT assets

and organisational resources can be changed while they are interacting (Weisbuch &

Ryckebusch, 1991). As mentioned in section 4.2.3, in this study I have only focused

on the complex type of emergence. The simple emergence is not disregarded here, but

due to the research focus, the simple type of emergence is not included in the

framework. If a simple emergence becomes complex, decision makes intervene (self-

organised management), investigate while making necessary adjustments similar as

suggested by (Mitleton-Kelly, 2003b) in complexity study and in cynefin framework

(Snowden, 2002).


For example-an organisational resource such as a customer service department

(CSD) provides answers to the customer inquiries currently at a slow rate. However,

with the use of a customer retention management anaytics tool, e.g. saleforces.com (an

IT asset, SFC), resulting in a new relationship (SFC enabled CSD, IT-enabled

capability ), CSD can answer the inquiries at a high rate. Now, if any module, for

instance a Twitter channel is added to the saleforces.com SFC then that customers who

use Twitter can now inquire through salesforce.com (SFC) to the CSD. Due to this

added option, CSD needs to adapt to the new features of SFC which might trigger new

emergent capabilities, such as, Twitter focused advertising. In a similar way, any

requirement changes on the CSD side may influence SFC to change its options

accordingly, which also can cause new issues such as, system failure, low response

rate, etc. (Orlikowski, 1996). The proposed model refers to these type of instances as

emergent occurences as contemporary organisations are always changing which

provokes different dynamic events in the system .C onsequently the relationships

between IT assets and organisational resources are affected, which trigger further

changes in IT-enabled capabilities.

Figure 4.4 A complex emergence framework of IT-enabled capabilities

Figure 4.4 presents a complex emergence framework of IT-enabled capabilities,

where it emerges from the interactions between IT assets and organisational resources.

The intersected spheres indicate that IT assets and organisational resources are mostly

indistinguishable and it is difficult to separate them from each other (Bharadwaj, et al.,

2013; El Sawy, 2003; El Sawy, et al., 2010). The upward directed arrow from the

IT Assets OrganisationalResources

Complex Emergence


Semi-structures

Simple Rules


Compatibility

Enabling Conditions

Complex Causality


common zone of the sphere represents that the two entities involve in a relationship

that gives rise to IT-enabled capabilities. The figure also includes the enabling

conditions (in short phrases)- semi structures, simple rules, self-organised

management, complex causality and compatibility. The enabling conditions are highly

interrelated to each other and they enable the relationships between IT assets and

organisational resources, which in turn cause the rise of IT-enabled capabilities. In this

conceptual model, I have proposed that the emergence of IT-enabled capabilities is

actually complex in nature as the components of IT assets and organisational resources

are continuously changing and and these changes make the relationships between them

dynamic and non-linear. Therefore, complex emergence concept can be better suited

to describe this dynamic complex emergence of IT-enabled capabilities.

4.4.1 Enabling Conditions of Complex Emergent IT-enabled Capabilities

In the section 4.3.1, it was discussed that IT-enabled capabilities might not be

realised without the presence of enabling conditions. From the Nevo and Wade (2010)

study, I have adopted the compatibility condition - that the components of IT assets

must be compatible with organisational resources to give rise emergent IT-enabled

capabilities. However, I have proposed replacing the integration effort, as it is planned

driven and controlled by managerial hierarchy with a new enabling condition, self-

organised management, which refers to relaxed and flexible management practices

facilitating the dynamic changes in IT assets and organisational resources while they

are interacting. Combined with the enabling conditions of complex emergence from

section 4.3.3, I have proposed a complex emergence framework of IT-enabled

capabilities (Figure 1.3). The following sections build the theoretical case for the

framework, linking the enabling conditions with the complex emergence of IT-enabled

capabilities and a set of propositions is developed.

IT assets and Organizational Resources Compatibility

The complex emergence of IT-enabled capabilities depends on the mutual

compatibility between the components of IT assets and organisational resources

(Singh, 1997). The notion of compatibility can be seen in different forms across

various organisational studies. An early study for example by Markus and Robey

(1983) provided examples of compatibility between IT assets and organisational

processes. They deemed an inquiry system with a decentralised architecture to be

compatible with a department such as research and development characterised by non-


routine work. In a similar way, a centralised MIS with optimisation models was seen

as compatible with departments characterised by routine work processes. The

compatibility notion is inherent in alignment literature (Peppard & Breu, 2003), and

has been recognised as an essential factor in building synergistic relationships in

different circumstances (Sarkar, et al., 2001). However, Moore and Benbasat (1991)

has argued that the use of compatibility in terms of need is a source of confusion with

another concept (e.g. relative advantage) and hence they have suggested to

conceptualise compatibility as a ‘need free’ variable. I have also taken a similar view

to Moore and Benbasat (1991) on compatibility notion for this study.

The compatibility notion can be seen in digital platform technology. When a new

software module or extension is introduced to an existing digital platform architecture,

such as, Amazon Elastic cloud, this particular module needs to interoperate with the

core functionality of the structure (Tiwana, et al., 2010). If the underlying program is

developed using Unix code, then the newly added module must also be developed by

Unix code so that they interact to form a mutually compatible relationship. However,

because of the connectedness and interdependence between IT assets and

organisational resources in contemporary complex adaptive business systems

(Tanriverdi, et al., 2010), the change in the platform module also needs to be

compatible with business routines; for instance, if a CRM system that manages

customer retention information is built on the digital platform. Now suppose a block

of code is added to the digital platform to enhance its customer retention process. This

particular change in the platform must be made in such a way that it still can continue

the relationship with the CRM system that controls the customer related information.

In other words, any change in the customer retention process needs to be compatible

with the CRM system so that CRM enabled customer retention capability can be same

as before the change. This example is particularly at technical level.

An example of compatibility in relation to complex emergence in organisational

context is as follows. For instance, a financial organisation has an ERP system (IT

assets) that facilitates sales and marketing, payment processing and supply chain

processes (organisational resources). Suppose, the institution has added a new HRM

module to automate the existing HRM processes, e.g. employee application

lodgement, application assessment and application decision processes. Therefore, the

company now has obtained new ERP enabled HRM capabilities that automate the


mentioned processes. However, after the integration of the new HRM module in the

ERP system, the company identifies that even though the HRM module automates the

HRM processes and is compatible with the ERP system as well as with the HRM

routines and the above mentioned other processes still, this upgrade causes issues in

the employee payroll system. This is because of an incompatibility between the new

ERP-enabled HRM and the existing payroll process (since the HRM processes are

linked with the payroll process). Therefore, the compatibility condition addresses the

feasibility of the relationship between IT assets and organisational resources, though

the emergent IT-enabled capabilities might not result in desirable outcomes (causal

complexity) (Pentland, Feldman, Becker, & Liu, 2012). MacIntosh and MacLean

(1999) discuss how, in the complex emergence state, components constantly adapt to

each other to create configurations that ensure their compatibility as well as with the

ever changing environment that triggers further emergence. Moreover, (Churchman,

1968) highlights that though IT systems enable different capabilities, such as rapid

data analysis for decision making, if the users are unable to interpret the meaning of

the data, it can cause more harm than good. Hence,

Proposition 1: Greater compatibility between IT assets and organisational

resources can positively influence the complex emergence of IT-enabled capabilities.

Self-organised Management influencing IT-enabled Capabilities Complex Emergence

Self-organised management is defined in this study as flexible and spontaneous

managerial practices that support and guide the dynamic relationships between IT

assets and organisational resources following Stacey (2003). In the context of the

complex emergence of IT-enabled capabilities, self-organised management is needed

because it addresses that the notion that, as the components of IT assets and

organisational resources change continuously, the emergent IT-enabled capabilities

also change, and that these changes and self-organised management serve as a catalyst

providing necessary support for the interactions to happen in a manner that is

congruent with organisational goals (Anderson, 1999).

The self-organised management constantly monitors the status of IT assets and

organisational resources and facilitates the emergence of IT-enabled capabilities by

monitoring changes or failures or any emergent contingencies and ensuring that

emergent IT-enabled capabilities are consistent with organisational goals, otherwise


the new IT-enabled capabilities may cause massive unpredictable consequences

(Perrow, 1984), such as the failure in the overall business processes (Avison, Wilson,

& Hunt, 2003). In the ERP-enabled HRM example provided in the above discussion

of compatibility condition, the payroll process suffers from some inconsistencies due

to incompatible relations between the new ERP-enabled HRM processes and existing

payroll processes. In this case, self-organised management carries out actions in such

a way that the unpredictable inconsistency could be adjusted instantly so that both

ERP-enabled HRM processes and payroll process are consistent with organisational

goals.

Another example of the self-organised management can be seen in agile software

development (Vidgen & Wang, 2009). For instance, any failure in one of the

components in ERP system during development can cause unbound effects on the

organisational processes associated with it. In such a case, an agile driven team

spontaneously forms into small groups, defines local rules (described in the next

section) for each group with lists of possible tasks and then initiates the failure

mitigation process (Vidgen & Wang, 2006b). However, researchers admitted that it is

difficult to accurately predict the effects of interactions in contemporary business

environments (Bharadwaj, et al., 2013; Grant, 2003). Thus, self-organised

management follow simple rules (Eisenhardt & Sull, 2001) (described in the next

section), which are some pre-defined routines, e.g. organisational goal, process

objectives etc., and defined local rules (Vidgen & Wang, 2009), so that IT-enabled

capabilities can be directed to the local and global optima (Dooley, 1997). Self-

organised management therefore, both sets up the context, defines and controls certain

relationships and facilitates overall interactions (Markus & Robey, 1983) so that the

interaction outcomes (i.e. IT-enabled capabilities) do not go beyond control, otherwise

organisational operations would be severely influenced (Perrow, 1984).

Building on the above, I propose that self-organised management acts as an

enabler of the complex emergence of IT-enabled capabilities by facilitating the

relationships between IT assets and organisational resources and providing

organisational context of those interactions. Hence:

Proposition 2: Self-organised management to ensure the relationship between

IT assets and organisational resource can positively impact the complex emergence

of IT-enabled capabilities.


The preceding discussion also suggests that self-organised management by

ensuring the facilitation of the relationships between IT assets and organisational

resources can have a positive effect on the compatibility between the two components.

Self-organised management provides spontaneous consultation, brainstorming and

arranges flip through sessions with organisational resources such as department,

groups, and individuals (Markus & Robey, 1983) to ensure that they follow simple

rules (Eisenhardt & Sull, 2001) and they maintain the IT assets according to

organisational goals and routines (Pentland, et al., 2012) even though components of

both of the entities are continuously changing. Accordingly, it can be argued that self-

organised management ultimately provides support and organisational context to make

IT assets and organisational resources compatible. Hence:

Proposition 3: Self-organised management to ensure the relationship between

IT and business can positively impact their compatibility.

Semi-structures facilitating Complex Emergence of IT-enabled Capabilities

In section 4.3.3, I have discussed that for complex emergence to take place, it is

required to have semi-structures in the complex system. Semi-structures are defined

as some prescribed or determined features such as, responsibilities, organisational

goals, project deadlines, etc. of adaptive organizations (Brown & Eisenhardt, 1997).

The semi-structures support non-deterministic features that arise from dynamic

interactions between components of complex adaptive systems (Brown & Eisenhardt,

1997; Goldstein, 1999). Semi-structures exhibit partial order and are flexible and lie

between the extremes of very rigid and highly chaotic organisation. Brown and

Eisenhardt (1997) identified that managers with successful product market portfolios

exhibit opportunistic, proactive and agile characteristics and exhibit semi-structures so

that they can direct firms towards effective competition.

Semi-structures support the intermediary phases (section 4.3.3) of the complex

emergence of IT-enabled capabilities. In the hypothetical example of ERP-enabled

HRM process, the intermediary phases related to it will be following-

1. In the disequilibrium period, the new ERP-enabled HRM capabilities, such as,

automated application lodgement, assessment and approval trigger the

unpredictable emergence of new order, i.e. the inconsistencies in payroll

processes,


2. In the positive feedback stage, the emergent ERP-enabled HRM capabilities

(assuming that they are consistent with organisational goals, can be changed if

the goals change) amplify the fluctuations of inconsistencies in payroll

processes if they are not corrected.

3. If the payroll related inconsistencies are identified, they start making

correlations with the ERP-enabled HRM capabilities until any further changes

occur in both of the components.

4. Coordinating mechanisms (self-organised management) will try to continue

this order of relationships between ERP-enabled HRM and payroll processes

for maximum period of sustainability until the next emergence takes place

(Chiles, et al., 2004).

From the above discussion, it is evident that the complex emergence of IT-

enabled capabilities requires to be aligned with the predefined organisational goals so

that any unpredictable disruptions can be kept to a minimum level (Terry, 2012).

Accordingly, the predefined features in organisation, such as, organisational goals,

routines, policies, are provided by semi-structure (Brown & Eisenhardt, 1997). When

any complex emergence of IT-enabled capabilities arises, self-organised management

matches the emergent capabilities with the semi-structure to facilitate the IT-enabled

capabilities. Self-organised management defines experimental routines and strategic

initiatives, which are neither rigidly planned nor chaotically react, when new IT-

enabled capabilities emerge (Brown & Eisenhardt, 1997). Thus, semi-structures

support the intermediary phases of the complex emergence of IT–enabled capabilities.

The underlying argument is that change readily occurs in IT assets and

organisational resources that trigger complex emergence of IT-enabled capabilities,

such as, incorrect approval of an employee application in the HRM example mentioned

above. Semi-structures are sufficiently rigid features so that emergent IT-enabled

capabilities can be directed towards organisational goals. Too little structure makes it

difficult to coordinate emergent order and too much structure makes it hard to facilitate

the emergent order towards organisation goals. Flexible semi-structures accommodate

the emergence order following simple rules (discussed in next section). Garud (1997)

identifies that semi-structures can accommodate high rates of successful innovation

while maintaining industry standards. However, sustaining the semi-structured state

can be challenging as it is dissipative equilibrium (Brown & Eisenhardt, 1997) and


thus self organised management ensures constant managerial vigilance of semi-

structures to avoid slipping into pure chaos or pure structure (Morel & Ramanujam,

1999).

Another argument entails that the expected IT-enabled capabilities might not

always be emerged because of the continual changes in the components. Without a

grasp of the future, change becomes inefficient, unpredictable and problematic

(Holland, 1995). Semi-structures provide the features that help to accommodate the

emergent IT-enabled capabilities as well as support them to be consistent with

organisational goals. Hence,

Proposition 4: Semi-structures to ensure the match between predefined goals

with emergent IT-enabled capabilities can positively influence the complex

emergence of IT-enabled capabilities.

Simple Rules supporting Complex Emergence of IT-enabled Capabilities

Simple rules are organisational heuristics (Cohen, et al., 1996) that self-

organised management uses as guidelines and decision rules (Levy, 2000) along with

the predefined features (semi-structures) to facilitate the unpredictable complex

emergence of IT-enabled capabilities consistent with the organisational goals. In

organisations, managers craft simple rules as heuristics to guide a few strategically

important processes, such as product innovation, partnering, alliances, or new market

entry so that organisations can obtain fleeting opportunities (Eisenhardt & Sull, 2001)

in the competitive business environment. In the context of IT-enabled capabilities

(internal to organisation), self-organised management defines simple rules in the case

of unpredictable emergent IT-enabled capabilities to facilitate them to be consistent

with organisational goals. The simple rules can be remarkably effective in guiding the

IT-enabled capabilities adaptation to changing organisational goals (Jarzabkowski,

2004).

In the presence of semi-structures, such as, plans, standards, responsibilities for

certain activities, self-organised management define and use simple rules to guide the

complex emergent IT-enabled capabilities while establishing bounds that can prevent

organisations falling off at the edge of chaos (Brown & Eisenhardt, 1997; Eisenhardt

& Sull, 2001). In the dynamic business environment, where, IT assets and

organisational resources are always on the move, which trigger unpredictable IT-


enabled capabilities, the formulation of simple rules is strategically important.

Organisations utilise simple rules as a means of grasping opportunities from dynamic

markets (Eisenhardt & Sull, 2001). It can be said that the simple rules guide the

emergent IT-enabled capabilities towards organisational goals and that helps

organisation to deal with other competitors in dynamic and high velocity markets

(Jarzabkowski, 2004).

The above discussion entails that complex emergence requires simple rules as

enablers to guide the complex emergent IT-enabled capabilities towards organisational

goals. As the complex emergence is characterised as unpredictable, so formulating

simple rules requires rapid response from self-organised management, but the simple

rules must be well defined because random rules with no clues about organisational

routines can bring negative consequences (Jarzabkowski, 2004). Well defined simple

rules can direct the complex emergent IT-enabled capabilities consistent with

organisational goals.

Proposition 5: Well-defined simple rules can positively impact the complex

emergence of IT-enabled business capabilities.

Complex Causality Logic acknowledging Unpredictability

Complex causality refers to the state, when the outcomes of specific actions are

unpredictable (Stacey, 1996). For the purpose of the study, complex causality is

defined as, the causality concerning the emergence of unpredictable outcomes from

the results of interactions between components following Lippman and Rumelt (1982).

The complex causality aspect of the complex emergence concept in the context of IT-

enabled capabilities is vital because it helps to consider the non-linear and surprising

nature of capabilities. It acknowledges the idea that even the expected IT-enabled

capabilities emerge from the interactions between IT assets and organisational

resources, it might not be beneficial for overall organisational functions. In the HRM

example above, the discussion of IT assets and organisational resources reveals that

although the ERP-enabled HRM capabilities are thought be beneficial for the overall

organisation, they trigger issues in payroll processes. IT-enabled capabilities viewed

via the lens of complex emergence may produce unpredictable outcomes as they arise

from the interactions between IT assets and organisational resources, which are

difficult to know about ahead of the time. The complex causality can be thought as a

part of the complex emergence of the IT-enabled capabilities that enforces self-


organised management to consider about any unforeseeable orders in the emergence

of the IT-enabled capabilities as shown in Figure 4.4.

The preceding section describes a process starting from two individual entities,

in this case, IT assets and organisational resources; both of them interact together and

their interactions give rise to emergent IT-enabled capabilities. The proposed

relationships are considered as non-linear, because components of both of the entities

always change in organisations and that influences the emergent IT-enabled

capabilities. Because of this non-linear nature, I have used the complex emergence

lens which helps to theorise non-linear and unpredictable emergent patterns from

components’ interactions. When IT assets and organisational resources become

compatible, they are eligible for interactions. However, due to the existence of non-

linearity, I have proposed that self-organised management works as an enabler and

facilitates the emergent IT-enabled capabilities by defining simple rules (enabler)

following semi-structures (enabler) congruent with the organisational goals. The

complex causality characterises any unpredictable outcomes of specific actions related

to the emergence of IT-enabled capabilities.

4.4.2 A Narrative Exemplary Case to Explain the Complex Emergence of IT-enabled Capabilities Framework and Internal Validation

In this section, ERP system has been considered as a case narrative to briefly

show how the framework actually provides a narrative explanation on complex

emergence phenomena in practical organisational context. Moreover, the case study

also helps to provide a first step internal validation of the proposed propositions and

framework.

In recent years, due to the technological innovation, the nature of ERP systems

has experienced considerable change by becoming more open, modular, cloud-based,

collaborative, complex and networked and ecosystem based structure (Cusumano,

2010; Nambisan, 2013; Tiwana, et al., 2010). IT firms (e.g. SAP, Microsoft, Epicor)

has been increasingly taking leverage of service platforms and providing ERP

solutions as software as a service (SaaS) to co-create business value. Platform structure

serves an operant source to allocate a wide variety of software applications and

modules on a remarkable scale (Tiwana, et al., 2010). ERP system is now connected

to robotic systems and mobile applications specially manufacturing and production


industries to streamline between design and manufacturing and contributes to the

business value creation. The business value creation process has dramatically changed

over the years due to the such ERP integration in contemporary organisations (Teo,

2017). Below, I have adopted empirical qualitative studies from a published thesis to

explain how my proposed framework can help to better explain the dynamic


Lokuge (2015) conducted nine case studies in various of organisations in two

phases to understand how organisations innovate through enterprise systems (ES) and

digital technologies and to investigate the role of ES in supporting innovation. The

first phase involved four case organisations- logistics, multinational, energy and

farming, where SAP enterprise systems modules- Materials Management, Sales and

Distribution, Financials and Controlling modules were implemented in between 1997-

2008. She tested two propositions; proposition 1- ES facilitates innovation and

proposition 2- digital technologies facilitates innovation. I am using her test case

description as narrative to show how my proposed framework can help better

explaining the complex emergence of IT-enabled capabilities. I have made few

interpretations because the test cases were conducted with researchers aims and foci,

whereas my thesis has different focus.

According to my proposed framework, the IT assets in her case studies are SAP

system and digital technologies- such as, analytics, intelligence software, cloud

computing and mobile technologies. In addition, the organisational resources are

human resources, their skills, organisational strategies, business processes etc. The

study identified that radical innovation and incremental innovation were attained

through ES. The radical innovation is relevant to my framework it is characterised as

discontinuous and leads to greater uncertainty and paradigm shift (Latzer, 2009),

which is very close to the nature of complex emergence. It was found that due to radical

innovation in the SAP (IT assets) from the legacy systems, the interrelated core

business processes, employee responsibilities and organisational structure

(organisational resources) were improved in the multinational organisation.

The scenario was same for the logistics, farming and energy organisations. The

IT and technical inexperience, high cost, high risk, unpredictability and technological

uncertainty were evident in all case organisations, which refers that radical innovation

was evident. The radical innovation further triggers innovation in the mobile and


analytic technologies, such as, cloud computing (energy organisation) and mobile

application (farming organisation), which further trigger changes in the roles and

responsibilities of organisations.

The analysis of the case data illustrates that-

• Due to the implementation of the SAP system, the roles and

responsibilities of the employees were dramatically improved and they

had to learn how to use the new system to do their tasks. The associated

digital technologies also affected employees’ job tasks and they adjusted

themselves with it over time. The both paradigms refer to compatibility

in between IT assets and organisational resources. They also help to

validate the proposition 1, which refers that greater compatibility

between IT assets and organisational resources can improve the complex


• The case data provide evidences on the continual support, trainings and

assistance from the SAP implementation team to ensure the SAP

products rightly match with the core business processes and

organisational strategies. This scenario refers to self-organised

management. The case data also reveal the decision makers support

during and after ES implementation, especially in the radical innovation

help to improve better process performance in logistics, multi and energy

organisations. This validates both proposition 2 and 3.

• Complex causality was evident in the case studies. Complex causality is

defined as the causality concerning the emergence of unpredictable

outcomes from the results of interactions between components of the IT

assets and organisational resource. In the farm case study, the case data

shows that even though the ES is expected to improve the business

efficiency, it was so marginal that the company intends to acquire (self-

organised management) new solution- mobile technologies, analytics

and big data. Moreover, the logistics and multi organisations also show

similar outcomes- the respondents share that stand alone digital

application can solve their internal business related issues, which were

expected to be solved by ES, but the ES did not. Therefore, the


organisations had to proceed with digital technologies. Both of the

scenario present the fact the the ES was expected to improve the certain

capabilities (IT-enabled capabilities), however, the ES failed to do so,

which refer to the complex causality in relation to the IT-enabled

capabilities.

• Because of the SAP implementation, organisations thought they would

get substantial operational efficiency and business benefits; the case data

shows that they had got process efficiencies. However, the business

benefits were marginal, all the four organisations had to further invest in

the mobile and digital technologies for better customer reach (logistics),

to improve productivity (multinational) and productivity improvement

and new business opportunities (energy). The case data reveals that

managers needed to rethink about SAP enabled capabilities and their

effect in the business value as the SAP integration in the business was

creating little to less value. All the case organisations had to adopt new

technologies- cloud based mobile app (logistics), BI (multinational),

third party IT applications (energy) and analytics and big data (farming)

to enhance the SAP enabled capabilities that actually contributed largely

in the organisational and business benefits. The case scenario actually

represents that the organisations had identified the complex causal nature

of emergence of SAP enabled capabilities as the capabilities were not

sufficiently contributing in the organisational benefits. In addition, the

scenario also helps to understand that organisations had to react

proactively (simple rules and semi-structures) and invested in the

integrations of digital technologies which ultimately contributed to the

business value and that also validate the proposition 4 and 5.

The above narrative helps to understand how emergence of IT-enabled

capabilities can be complex and also it helps to validate the framework of the complex

emergence of IT-enabled capabilities in a limited way by explaining it through case

description. Verifying relationships in theory development research is crucial and

important because researchers are unable to conduct any statistical test. The narrative

has established an internal validity of the proposed complex emergence framework

because it helps to provide a good understanding of the dynamics of underlying


relationships that is ‘why’ this phenomenon is happening (Eisenhardt, 1989). It is

important to note that, the internal validation using the case study is a first step of the

validation process. An in-depth empirical case study can be conducted to further

validate the propositions and the framework.

4.5 CHAPTER SUMMARY

Based on the ideas of Nevo and Wade (2010), this chapter argues that IT-enabled

capabilities emerge as result of interactions between IT assets and organisational

resources. The underlying assumption behind phenomena entails that the components

of the IT assets and organisational resources are always changing in contemporary

organisations, due to changes in business requirements, organisational goals,

innovation, and strategic formulation. Therefore, the emergence of IT-enabled

capabilities becomes non-linear and dynamic in nature. Consequently, I have proposed

that a dynamic lens, the complex emergence concept of CAS theory can provide new

insights into this particular dynamic phenomenon.

Section 4.2 presents an overview of the emergence concept and its use in IS and

broader organisational research. It also presents different emergence typologies from

which the complex emergence concept has been chosen as an overarching lens to

explore IT-enabled capabilities.

Section 4.3.1 recaps the Nevo and Wade (2010) study on strategic IT-enabled

resources and their influence on competitive advantage. Using a complex emergence

perspective two enabling conditions compatibility and self-organised management are

discussed here along with complex causality logic that supports unpredictability

related to complex emergence phenomena. In addition, some ‘folklores’, the

misconceptions around the emergence concept suggested by Goldstein (2000)are

discussed. The section ends with proposing two enabling conditions, semi-structures

and simple rules, that help us to understand how complex emergence takes place based

on self-organising logic.

Section 4.4 presents a complex emergence framework of IT-enabled

capabilities. The complex emergence framework reveals new insights as propositions

from the enabling conditions. Section 4.4.2 includes a case narrative to briefly show

how the framework actually provides a narrative explanation on complex emergence

phenomena in practical organisational context. Moreover, the case study also helps to


provide a first step internal validation of the proposed propositions and framework.

The framework and the associated propositions are presented in Table 4.3.

Complex Emergence Framework of IT-enabled Capabilities

Propositions

Proposition 1: Greater compatibility between IT assets and organisational resources, can

positively influence the complex emergence of IT-enabled capabilities.

Proposition 2: Self-organised management to ensure the relationship between IT assets and

organisational resources, can positively impact the complex emergence of IT-enabled capabilities.

Proposition 3: Self-organised management to ensure the relationship between IT and business,

can positively impact their compatibility.

Proposition 4: Semi-structures to ensure the match between predefined goals with emergent IT-

enabled capabilities, can positively influence the complex emergence of IT-enabled capabilities.

Proposition 5: Well-defined simple rules can positively impact the complex emergence of IT

enabled business capabilities.

Table 4.3: Complex Emergence of IT-enabled Capabilities

The next chapter argues that once IT-enabled capabilities emerge they start

coevolving with other IT-enabled capabilities and that this influences competitive

advantage. I have applied a coevolutionary CAS perspective to explore the coevolving

IT Assets OrganisationalResources

Complex Emergence


Semi-structures

Simple Rules


Compatibility

Enabling Conditions

Complex Causality


nature of IT-enabled capabilities and I discuss how they influence competitive

advantage.

124 Chapter 5: A Coevolution Perspective on IT-enabled Capabilities

Chapter 5: A Coevolution Perspective on IT-enabled Capabilities

Chapter 5 Summary

What was done in the previous chapter: The previous chapter presents a

complex emergence perspective of IT-enabled capabilities. It also discusses few

enabling conditions related to the emergence of the IT-enabled capabilities.

What this chapter does: This chapter presents a coevolution perspective on

IT-enabled capabilities. It addresses how IT-enabled capabilities coevolve in two

levels- micro and macro levels of organisations.


Chapter 6: An operational (NKC) coevolutionary framework of IT-enabled

capabilities.



5.1 INTRODUCTION

This chapter presents a micro and macro co-evolutionary perspective on IT-

enabled capabilities and will focus on the second subset of BVIT framework (Figure

5.1) presented in Figure 1.2 in Chapter 1. Based on the RBV view of organisations,

(Melville, et al., 2004) develop an integrated model of BVIT, in which authors argue

that the locus of value generation in organisations is the focal firm where IT resources

are deployed (micro level). Moreover, external factors such as the competitive

environment, including industry characteristics and trading partners, as well as the

macro7 environment denoting country and meta country factors, such as, social and

7 McKelvey (1997c, p. 360) argues that coevolution takes place at multiple levels and makes a distinction between coevolution within the firm as microcoevolution and coevolution between firms and their niche as macrocoevolution. In this study, coevolution of IT-enabled capabilities within firm is

Chapter 5: A Coevolution Perspective on IT-enabled Capabilities 125

cultural contexts are pertinent to the value BVIT generation (macro level). Also,

although Melville, et al. (2004) acknowledge that BVIT creation in the micro and

macro levels of organisations has become dynamic and complex, their approach to

understanding the creation of BVIT is static (Schryen, 2012) and they do not describe

the dynamic mechanisms related to the BVIT creation. Accordingly, I propose an

approach to understanding BVIT that accounts for the dynamic mechanisms related to

the value generation.

In contemporary organisations, the adoption of digital technologies in business

gives rise to a step change in the dynamism and unpredictability of the elements in

business systems (Tanriverdi, et al., 2010). A stream of prominent IS scholars (El

Sawy, et al., 2010; Nevo & Wade, 2010; Oh & Pinsonneault, 2007; Tanriverdi, et al.,

2010) has suggested that the dominant approaches to dealing with the unpredictable

dynamics are inadequate and have called for new methodological and conceptual

perspectives for dealing with such dynamic context. These scholars agree that a

holistic system perspective needs to be adopted; e.g. (Nevo & Wade, 2010) uses

systems thinking, (Oh & Pinsonneault, 2007), (Tanriverdi, et al., 2010) and (El Sawy,

et al., 2010) adopts complexity thinking, that is better suited to explore the dynamic

relationships among different components of organisations and environment (Merali,

et al., 2012). Consequently, in this study, I have adopted a CAS perspective,

specifically a CAS coevolution (Figure 5.1) perspective (Koza & Lewin, 2001; Lewin,

et al., 1999; Volberda & Lewin, 2003) to explore how IT-enabled capabilities change

each other at micro (internal to organisation) and macro (external to organisation)

levels and how they influence performance and competitive advantage8 or

organisations. In particular, I have proposed that IT-enabled capabilities change with

other internal and external IT-enabled capabilities in contemporary organisations and

their interplay influences efficiency (micro) and competitive advantage (macro) of

organisations following the idea of Melville, et al. (2004). The research subquestion

for this study is:

considered micro level coevolution of the IT-enabled capabilities and coevolution of IT-enabled capabilities between two or more firms is considered as macro level coevolution.


Research subquestion 1.2: How do IT-enabled capabilities coevolve and

influence competitive advantage?

Figure 5.1 A Subset of the BVIT framework

Based on the ideas of Shepherd and Suddaby (2017) related to theory

development, I have developed my research method for this study. A broad overview

of the research method is discussed in Figure 1.4 in the Chapter 1. For this chapter, a

subset of the research method is applied as shown in Error! Reference source not

found..

In brief, the research method includes- In brief, the research method includes-

1. The narrative conflict: As described in section 1.4.1 in the Chapter 1, the

overarching narrative conflict of this study is the tension between the existing

literature on BVIT and the conceptualisation of BVIT. Drawing the same ERP

example from Chapter 4, section 4.1, in this chapter, I have described the

narrative conflict in relation to the coevolution concept. Suppose, the ERP

system provides organisation with these mutually related capabilities- logistic

management, production management, purchase management, and sales order

management. If the logistic management capability changes because of an

update in the ERP, it will affect the other capabilities mentioned above because

of the mutual causal relationships, which is a micro coevolution in

organisational setting (Mckelvey, 1999). In a similar way, if the changes in the

logistic capability triggers a change in the competitors’ capability, it is called

macro coevolution (Mckelvey, 1999). However, existing BVIT literature in IS

considers these mutual causal changes within firm and with the competitors as

a linear process. So, the narrative conflict becomes the tension between the


• IT Assets



Coevolution


existing literature on coevolution, whether the internal and external

coevolution are linear or non-linear (dynamic) and the conceptualisation of

such coevolution.

2. Building stories- It involves four stages-

o Identifying core constructs: Two major constructs are relevant to this

chapter- IT-enabled capabilities and competitive advantage. They are

broadly discussed in section 2.3 in Chapter 2.

o Determine the narrative settings: For this study a shifting ontology

strategy is adopted to determine the narrative settings. In this chapter,

shifting ontology highlights the change from a static, linear view to a

dynamic, non-linear view on the coevolution of the IT-enabled

capabilities, which is briefly discussed in the first paragraph of this

section.

o Draw boundary conditions- The story’s event sequence: The event

sequence here is- first, IT-enabled capabilities coevolve each other

within organisation, which is denoted as micro coevolution. Moreover,

IT-enabled capabilities from two or more organisations mutually

influence each other, which is termed as macro coevolution. Second,

the micro and macro level coevolution of the IT-enabled capabilities

influence competitive advantage. It is important to note that, though IT-

enabled capabilities can mutually change with other organisational

elements, such as, routines, capabilities, strategies, resources (Koza &

Lewin, 2001; Lewin, et al., 1999), I have considered the mutual changes

between IT-enabled capabilities only. This is broadly discussed in

section 5.4 and 5.5.

o Apply disciplined imagination- theorising via metaphors (analogical

reasoning): In this chapter, coevolution metaphor of CAS theory is

applied to describe how IT-enabled capabilities coevolve in micro and

macro levels of organisations and how they influence BVIT, in

particular competitive advantage. The theorising of the coevolution of

IT-enabled capabilities and their influence on competitive advantage is

discussed in section 5.4, 5.5 and 5.6.


3. New insights: The application of coevolutionary perspective gives new

insights in relation to the coevolution of IT-enabled capabilities, explains the

way they affect competitive advantage and advances some new strategies,

which are discussed in section 5.4, 5.5 and 5.6. Moreover, an exemplary case

study is used to validate the framework in section 5.6.2. The theories developed

in this chapter using CAS coevolution concept are explanatory type theories

(Gregor, 2006) as they help to provide greater explanation on how IT-enabled

capabilities coevolve in micro and macro levels and their impact on



Figure 5.2 A Subset of Research Method for Coevolution of IT-enabled Capabilities

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Based on the RBV view of organisations, Melville, et al. (2004) review past

empirical research related to BVIT. Their BVIT model integrates research assessing

both the implications of IT at the operational level internal to the organisation (referred

to as efficiency) as well as at the strategic level (that addresses how IT helps

organisations to achieve competitive advantage). In brief, their model contains three

domains: focal firm, competitive environment and macro environment.

1. The focal firm is the core organisation that acquires and deploys IT resources

within the organisation. BVIT is generated by the deployment of IT and

complementary organizational resources within business processes. The

applications of IT resources, such as, business applications, database, IT

skills, etc. and complementary resources, such as, non-IT human resources,

production, sales processes, etc. may improve business processes, which in

turn may impact overall organisational performance (Brynjolfsson & Hitt,

2000).

2. The competitive environment refers to the environment in which the focal

firm operates and comprises industry characteristics and trading partners

(Mukhopadhyay, Kekre, & Kalathur, 1995). The industry characteristics,

such as business policy, regulation, and technological change influence the

way in which IT is applied in the focal firm to generate business value (Kohli

& Devaraj, 2003). In addition, trading partners play a role in the BVIT

generation when IT spans focal firm boundaries and includes IT and non-IT

resources from trading partners (Mukhopadhyay, et al., 1995).

3. The macro environment refers to the macro factors, such as, government

initiative related to technological development for the improvement of

organisational performance and broader social, political and cultural

contexts that may influence organisational performance (Dewan & Kraemer,

2000).

This study takes the Melville, et al. (2004) BVIT model and the three domains

as a starting point. However, my study argues that BVIT is generated at two major

levels of contemporary organisations - the micro and macro levels, where, micro

signifies the focal firm (as in the Melville, et al. (2004) model) that acquires and

deploys IT resources that may improve its operational performance, . The macro level

defines the industry within which focal firm is operating and competing with other


firms via investing and deploying IT resources to achieve superior advantage over its

rivals (Porter & Millar, 1985). For the purpose of the study, I have considered two

domains- the competitive environment and macro environment together as macro

level.

The rest of the chapter is structured as follows; the next section Error!

Reference source not found. presents an overall overview of the coevolution concept

and how it has been used in IS and organisational research. Section 5.3 presents a

preliminary coevolution framework of IT-enabled capabilities. After then, micro and

macro coevolution in relation to IT-enabled capabilities are discussed (section 5.4 and

5.5). A coevolution based framework of IT-enabled capabilities with the focus on how

IT-enabled capabilities influence BVIT in micro and macro levels is proposed in

section 5.6. The chapter concludes with a summary.

5.2 OVERVIEW OF COEVOLUTION 5.2 OVERVIEW OF COEVOLUTION

The previous section briefly presents the research motivation, research question and

research method of the chapter. This section first addresses the basics on

coevolutionary CAS concept. Then it presents a review of the IS and organisational

research that adopt the coevolution CAS concept.

5.2.1 The Basics of Coevolution

Evolution in general is often thought of as progress or improvement. It is

defined as “cumulative and transmissible change” in the components of a system

(Murmann, 2003). The concept of ‘coevolution’ is related to the evolution concept.

Coevolution is the evolution of one domain or entity that is partially dependent on the

evolution of other related domains or entities (Ehrlich & Raven, 1964; Kauffman,

1995a; Kauffman, 1993; Mitleton-Kelly, 2003b).

The term ‘coevolution’ was first coined by Ehrlich and Raven (1964) in

reference to biological evolution when looking at the relationship between the patterns

of evolution of plants and butterflies, and describing the simultaneous, reciprocal

evolution of interacting populations. Coevolution is characterised as the reciprocal

evolutionary process of natural selection (Kauffman, 1993). Coevolution is closely

related to the notion of ecosystem. An ecosystem in biology means, “each kind of

organism has, as parts of its environment, other organisms of the same and of different

kinds ... adaptation by one kind of organism alters both the fitness and the fitness


landscape of the other organisms” (Kauffman, 1993, p. 242). The way each element

influences and is in turn influenced by all other related elements in an ecosystem is

part of the process of coevolution.

Basic notions of coevolution are homogeneous across natural and social science

literature, reflecting that the evolution of one domain or entity is partially dependent

on the evolution of other related domains or entities (Kauffman, 1995a; McKelvey,

1999). However, coevolution can happen within the entities of a system (Mitleton-

Kelly, 2003b). Therefore, the interpretations of the concept of co-evolution vary

depending on the applications. Coevolution is a dynamic concept that has been used

mostly in natural science and evolutionary biology but the concept has been extended

into diverse fields in social sciences as an applied metaphor or interpretive lens to

understand the dynamic mechanisms of complex evolving phenomena. Though the

concept has been widely used to understand evolving relationships among biological

entities, it is now used also in reference to analogically similar dynamics between

entities within system (endogenous) or systems that coevolve (exogenous) (Mitleton-

Kelly, 2003b).

5.2.2 Types of Coevolution

McKelvey (2002) mentioned total six types of coevolution from evolutionary

biology and organisational studies literature in his seminal paper on coevolutionary

dynamics. Each type of coevolution defines distinctive mutual-reactive relationships

between multiple entities. They are briefly discussed below-

1. Coevolution between mutation (change) rate and environment- This particular type

of coevolution defines reciprocal relationships between two different entities and

with the environment. For instance, the more the ERP develops, the more

organisations develop services; the more they develop their services the faster the

ERP develops, and so on.

2. Predator/ prey coevolution: This coevolutionary type is mainly used in the study

of species within ecosystems. The increase in number of rabbits influences the rise

of foxes in that particular ecosystem. The faster the development of IT within

firms, the faster the value obtained through IT is diminished by the development

of new IT in organisations.


3. Supernormal coevolution: This refers to the coevolutionary phenomena in which,

the cause of coevolution accelerates the coevolutionary process itself. For instance,

the more Engineering graduates are hired, causes the tendency of the firm to hire

more Engineering graduates and so on.

4. Inbreeding and population size: The more breeding within a species of small

population, the more the species become isolated from others;. For instance, the

more research collaboration within a small group of researchers, the more the

narrowly restricted the types of research, - the more intellectually inbred it

becomes, the more collaboration with other research groups becomes restricted and

the more new researchers are excluded from the group and so on.

5. Symbiotic coevolution: This is where a species causes coevolution with other

species with which it is connected. For instance, the more that a large firm hires

surrounding suppliers, the more they survive and grow; the more that the suppliers

survive and grow, the easier it is for the large firm to survive and grow in its

competitive context, and so on.

6. Micro-macro coevolution refers to coevolution between a population and its

environment (Ehrlich & Raven, 1964). For example- Kauffman (1993) discusses

how a species emerges from micro level to macro level that involves RNA to DNA,

to protein sequences, to molecules, to cells, to organisms and species.

Pagie (1999) proposes three types of coevolution in the context of biology,

namely competitive, mutualistic and exploitative. Competitive coevolution occurs

between species which are limited by the same resources. In this case the organisms

are forced to change in such a way that they can either take advantage of or acquire

that resource more efficiently. Mutualistic coevolution, on the other hand, comprises

reciprocal relations where all the participants benefit from the interaction and change

in the direction of better compatibility. Exploitative coevolution comprises relations

where all the participants do not benefit from the interaction.

Based on the above discussion on the types of coevolution it is evident that the

concept of coevolution has different typologies across different disciplines. However,

in the context of organisational studies, two major types of coevolution, micro and

macro coevolution that are addressed across different studies (Chae, et al., 2014; Koza

& Lewin, 2001; Lewin, et al., 1999; Lewin & Volberda, 1999; Melville, et al., 2004;


Volberda & Lewin, 2003). Therefore, in this study, I have discussed micro and macro

level coevolution in the context of IT-enabled capabilities.

5.2.3 Applications of Coevolution in Strategic Management and IS Discipline

The preceding section addresses different types of coevolution. This section

highlights the use of CAS coevolution concept across Management and IS studies. As

discussed in Chapter 3, the use of CAS theory is very recent, and it has been used in

strategic management and organisational studies since 1960s and then has been applied

into IS research in the last two decades. Therefore, I have presented firstly an overview

of the use of coevolution in organisational studies and then in IS research.

Coevolution in Management Research

The concept of coevolution has been used to analyse the competitive advantage

of nations (Porter, 1990), strategic management (Barnett & Hansen, 1996), strategic

alliances (Koza & Lewin, 2001), new organisational forms (Lewin, et al., 1999), rent

appropriation and capability development (Coff, 2010), entrepreneurship (Pacheco, et

al., 2010), and the management of collaboration among business units in a firm

(Eisenhardt, 2000). Error! Reference source not found. briefly summarises the use

of coevolution concept in management studies.

Based on the observation of the management studies that have applied Based on the observation of the management studies that have applied

coevolution, the studies mainly focus on Type 1 and Type 6 coevolution in section

Error! Reference source not found., where an aspect of firm studied in relation to its

environment and with other competing firms. Majority of the studies explored here has

considered multiple levels and dynamic nature of coevolutionary process. The nature

of relationship has been considered important to some extent in these studies.

Moreover, other researchers have identified and classified different types of

relationships in their research. For instance, Baum and McKelvey (1999c) have used

Heylighen and Campbell (1995) competitive configuration to derive different types of

interactions such as- zero-sum, super competitive and hypercompetitive.


Authors Summary

(Lewin, et al.,

1999)

Analyse the effect of the environment on the organizational form

with emphasis on the dynamic and multi-level nature of coevolution.

They contend that the coevolution concept in the domain of management

is the inherent integration of an external (selection oriented) focus with an

internal (adaptation oriented) focus.

(Lewin &

Volberda, 1999)

Explore the relationship between firms and their environment with the

interaction between industry selection pressures and firm level adaptation

resulting in new organizational forms. Micro and macro level coevolution

are discussed, where micro characterises change involving inter-

organisational elements and macro signifies relationships between a firm

and one or more components of the external environment.

(McKelvey,

1999)

Explores strategic moves by the firm in response to changes in its

environment, such as, competitors, technology, market and government

policies and how they relate to competitive advantage. He also proposes

ideas on micro and macro levels of competencies in organisations.

(Eisenhardt,

2000)

Uses the coevolution concept to analyse collaboration management

between business units within a firm and how this maximises benefits

from cross-business synergies. The business units are termed as

coevolving species in this research, though the nature of the relationships

is not well explored.

(Coff, 2010) Explores how capabilities and bargaining power coevolve in the context

of rent appropriation.

Table 5.1 Application of Coevolution in Management

The above analysis on the application of coevolution in management studies

helps me to understand and define multiple levels- micro and macro levels in this

study. Moreover, following Lewin, et al. (1999), I have identified six properties of

coevolutionary models used in the management research: multilevel effects,

multidirectional causalities, nonlinearity, positive feedback, path- and history-

dependencies, and adaptation principles. The identification of these properties helps


me to propose my coevolution framework of IT-enabled capabilities and I have defined

the relevant properties in my proposed framework (see section 5.3).

Coevolution in IS Research

In IS research, coevolution has been used to study IT and business alignment

(Benbya & McKelvey, 2006b; Peppard & Breu, 2003; Vessey & Ward, 2013), IS

engagement (Kim & Kaplan, 2006), information systems development (Hovorka,

2013), business process management (Vidgen & Wang, 2006a), the co-design of

organizations and information systems (Nissen & Jin, 2007) and offshore outsourcing

(Lahiri & Kedia, 2011). Section 1.5 in Chapter 3 broadly discusses the concept of

coevolution, the contributions pertaining to the use of the concept and also the contexts

in which the concept is applied within IS research. Error! Reference source not

found. briefly summarises the use of coevolution concept in IS studies.

Authors Authors Summary

(Peppard &

Breu, 2003)

Both of these studies explore the strategic alignment of IS and

business using the coevolution concept. Benbya and McKelvey

(2006b) consider three major levels, individual, operational and

strategic, involved in the strategic alignment and (Peppard &

Breu, 2003) address key factors related to the strategic alignment

process of IS and business. Both of the studies draw heavily on

complexity thinking (Mitleton-Kelly, 2003a).

(Benbya &

McKelvey,

2006b)

(Vidgen &

Wang, 2006a)

The authors have applied coevolutionary framework in

BPM area to explore whether the conceptual boundary between

IT and business can be divided. The conceptual study has

considered four species that may exhibit coevolutionary

relationships- business processes, software components, IT

developers and business users. They have provided some

guidelines for facilitating coevolutionary development.

(Tanriverdi, et

al., 2010)

Use coevolution concept to address how a firm dynamically

repositions itself, and identifies profitable product-market

positions in the competitive landscape.


(Chae, 2014) Defines the IT enabled service innovation process as

coevolutionary process involving three stages, variation,

selection and retention following Aldrich (2006) evolutionary

model.

Table 5.2 Application of Coevolution in BVIT related IS research

In IS research, the dynamic nature of coevolutionary process is addressed as

well. Like management research discussed multiple levels are often explored and

reciprocal relationships among multiple levels and environment are considered in the

studies. One important observation is, the use of the coevolution concept is limited in

IS research and majority of the studies adopt the conceptualisation of coevolution from

the key management studies, such as- Lewin and his colleagues (Koza & Lewin, 2001;

Lewin, et al., 1999; Volberda & Lewin, 2003) and Mckelvey and his colleague (Baum

& McKelvey, 1999c; McKelvey, 1997c, 1999, 2002).

An analysis of both of the management and IS literature related to the

coevolution concept reveals that majority of the application of coevolution in BVIT

related studies are in management research (e.g. Baum & McKelvey, 1999c; Coff,

2010; McKelvey, 1999), in particular on competitive advantage and a few number in

IS research (e.g. Chae, 2014; Tanriverdi, et al., 2010). The overall analysis on the

coevolution concept based management and IS studies addresses that

conceptualisation of the coevolution is consistent across different fields and the

properties of coevolutionary frameworks are also mostly same. From this review, I

have mainly developed my perception based on McKelvey (1997c) work and defined

micro and macro levels in this study. In addition, Lewin and his colleagues (Koza &

Lewin, 2001; Lewin, et al., 1999; Volberda & Lewin, 2003) works help me to define

the properties of my proposed coevolution framework of IT-enabled capabilities

(section Figure 5.3).

5.3 A PRELIMNARY COEVOLUTION FRAMEWORK FOR IT-ENABLED CAPABILITIES

The previous section reviews literature related to the use of the coevolution

concept across strategic management and the IS discipline. This section presents a high

level overview of a preliminary coevolution based framework of BVIT.


As discussed in the Introduction, this study has adopted the Melville, et al.

(2004) model as a starting point to explore the coevolution perspective on IT-enabled

capabilities. Melville, et al. (2004) model provides a foundation for the micro and

macro levels of my proposed coevolution based BVIT model, whereas, the coevolution

framework by Lewin, et al. (1999) helps to describe the dynamic relationships

between IT-enabled capabilities across these levels.

Figure 5.3 presents a preliminary version of my coevolution framework of BVIT.

The model has two major parts, micro level and macro level. The micro level presents

the focal organisation, where investment is made on IT assets. As discussed in chapter

3, IT assets and organisational resources together give rise to emergent IT-enabled

capabilities in an organisation. The IT-enabled capabilities according to the proposed

model coevolve with each other when the focal organisation responds to the changing

business environment over time (Wade & Hulland, 2004). The full framework is

discussed in Section 5.6.

Figure 5.3 A Preliminary Coevolution Framework of BVIT (With the focus on the coevolution of IT-enabled Capabilities)

The macro level part of the framework represents the broader industry within

which the focal organisation is operating and competing to obtain a superior

competitive advantage via the potentials provided by IT-enabled capabilities



Focal Firm

IT-enabled Capabilities Other Firms

Micro Coevolution

Macro Coevolution

Dashed Line: Micro coevolution; Solid Line: Macro coevolution

Focal Firm'sCompetitive Advantage

Mutual Adaptation


(Bharadwaj, 2000). From an RBV perspective of organisation (Barney, 1991), when

the IT-enabled capabilities become valuable (value), idiosyncratic to the focal

organisation (rare), difficult to imitate (inimitable) and non-substitutable, then they

have the potential to lead the focal organisation to attain competitive advantage. The

solid line presents the macro level coevolution of IT-enabled capabilities, where IT-

enabled capabilities of the focal firm changes the IT-enabled capabilities of the

competing firms and vice versa. The dashed line presents the micro coevolution of IT-

enabled capabilities, where an IT-enabled capability mutually adjusts with another IT-

enabled capability within the focal firm.

A few important key points related to the proposed coevolution based BVIT

framework are noted here-

• This study has only considered coevolution of IT-enabled capabilities for the

purpose of the research. Non IT-enabled capabilities can coevolve between

themselves or other capabilities, routines, strategies, but they are not the

focus of this study.

• The study has integrated both micro and macro levels together in relation to

the BVIT generation process in organisation. In this study, I have combined

and conceptualised competitive environment and macro environment

domains together as macro level and the focal firm domain as micro level

from Melville, et al. (2004) study. The micro level defines the focal

organisation that invests, acquires and deploys IT resources. The macro level

refers to the broader industry landscape where the focal organisation

competes for superior competitive advantage over its’ competitors using the

potential of IT resources.

• The study follows Melville, et al. (2004) study to acknowledge the ideas of

micro and macro levels in relation to BVIT. The focal organisation attempts

to obtain superior overall organisational performance to its competitors via

investing in IT resources to gain competitive advantage (Porter, 2008).

• Melville, et al. (2004) RBV based model is considered as one of the most

prominent BVIT models in IS research (Kohli & Grover, 2008; Schryen,

2012). The RBV based view provides a robust framework to examine the


strategic side of organisational performance expressed as competitive

advantage (Bharadwaj, 2000) in this study.

• The coevolution framework based on Lewin, et al. (1999) is used to provide

an approach to define and explore the implications of IT-enabled capabilities

in deriving competitive advantage for a focal organisation. The framework

also helps to represent the dynamic relationships between IT-enabled

capabilities and their reciprocal effects on the overall performance of

organisations.

• It is important to note that non IT-enabled capabilities and other

organisational resources, competencies and capabilities co-evolve (Volberda

& Lewin, 2003), but for the purpose of this work, I focus on the co-evolution

of IT-enabled capabilities only.

My study diverges from the traditional adaptation–selection debates by

developing a more general theory of firm-industry coevolution (e.g. Lewin, et al.,

1999; McKelvey, 1999, 2002). This study argues that IT-enabled capabilities coevolve

within firms with other IT-enabled capabilities and as well as with other firms. The

direction of these changes is not unidirectional. Moreover, the theorising of the

coevolutionary framework is consistent with sufficient conditions suggested by Lewin,

et al. (1999) and McKelvey (2002) for coevolution to occur-

• It has considered IT-enabled capabilities as heterogeneous element (Lewin, et

al., 1999; McKelvey, 2002). The IT-enabled capabilities are characterised as

heterogeneous, e.g. IT-enabled service delivery, IT-enabled customer

management, etc.

• Multidirectional causality between IT-enabled capabilities in micro and macro

level (McKelvey, 1999) and mutual and simultaneous influences to each other

(Lewin, et al., 1999) is considered in this study. IT-enabled capabilities change

with each other internal to the focal organisation as well as with the changes in

the IT-enabled capabilities of the competing firms in the industry. Changes in

the IT-enabled capabilities are multi-directional within firm and across

competing firms.


• Elements must have adaptive capability (McKelvey, 2002). IT-enabled

capabilities have the adaptive capability to change with overall business

environment (Wade & Hulland, 2004).

• There needs to be an initiating punch or event to start coevolution (Lewin, et

al., 1999; McKelvey, 2002). Any change in organisational goals, or the

business environment or in the IT-enabled capabilities or other resources, will

trigger changes in the related IT-enabled capabilities. For instance, a change in

the supply chain process cause changes in IT-enabled productions (Swafford,

Ghosh, & Murthy, 2008).

The next sections provide a broad discussion of micro and macro coevolution

perspectives in relation to BVIT.

5.4 MICRO COEVOLUTION OF IT-ENABLED CAPABILITIES WITHIN THE FIRM

The preceding section provides a preliminary version of my co-evolution

framework for BVIT. This section specifically discusses micro coevolution

perspectives in the context of IT-enabled capabilities within the firm. As mentioned

in the previous section, I have only focused the coevolution of IT-enabled capabilities

in this study.

Micro (internal) coevolution of firms refers to the coevolution of intrafirm

resources, capabilities and competencies (Lewin & Volberda, 1999). In this study, I

define micro coevolution for BVIT as the mutual changes among different IT-enabled

capabilities internal to focal firm. A change in one of the IT-enabled capabilities will

likely bring reciprocal changes in other IT-enabled capabilities. Several studies

consider the coevolution of intrafirm resources, dynamic capabilities and

competencies in an intrafirm competitive context (Barnett & Hansen, 1996;

Burgelman, 1994; Galunic & Eisenhardt, 1996). Galunic and Eisenhardt (1996) study

selection and adaptation at the intra-corporate (intra firm) level of analysis. The

Burgelman (1994) coevolution based model shifts the locus from the firm as whole to

the strategic action and views managing intra-organisational processes as a means by

which the firm can achieve learning benefits from external and internal selection.

McKelvey (1997c) recognises that coevolution within the firm level i.e. micro

coevolution, involves processes of variation, selection and retention operating within


the organisation and interacting with similar processes at the population level. Based

on this logic, I have conceptualised the micro coevolution of IT-enabled capabilities

involves three processes, variation, selection and retention (VSR). The IT-enabled

capabilities take co-evolution journeys proceed through a continuous cycle of

variation, selection and retention, and generate evolution of the focal firm in the

direction of better fit to the selection environment (Volberda & Lewin, 2003). In this

study, I have adopted (McKelvey, 1997c) ideas, followed similar approach as Chae

(2014) and discuss a micro coevolution perspective of IT-enabled capabilities within

the firm.

The stages are described below-

Variation

Variation refers to the change of current routines or forms or structures (Aldrich,

2006). Capabilities are often seen as collection of routines (Nelson & Winter, 1982).

They help organisations to perform basic functional activities (Collis, 1994) and IT-

enabled capabilities are a combination of higher level routines that help organisations

to reliably perform and extend their characteristic output options (Salvato & Rerup,

2011). In today’s organisations, Variation of IT-enabled capabilities provides the basic

elements for adaptation of organisations with the continuous changes in a complex

business environment (Axelrod & Cohen, 2000). In organisational settings, managerial

intentionality deliberately causes variations of IT-enabled capabilities through

decision making, reconfiguring, or imitating (Salvato & Rerup, 2011; Wheeler, 2002).

Moreover, informal and less structured improvisational activities may also lead to

variation of IT-enabled capabilities (Axelrod & Cohen, 2000; Volberda & Lewin,

2003).

Variation creates novelty in IT-enabled capabilities (Lewin & Volberda, 1999).

IT-enabled capabilities are improved via variation, though all variations might not be

beneficial for the functionalities of organisations (Salvato & Rerup, 2011).

Nevertheless, variation is essential as it enables organisations to adapt and fit with the

changing business environment. For instance, big data based analytics though

perceived to be recent, in fact evolved from s predecessors such as data mining and

business intelligence driven decision support in the early 2000s (Chen, Chiang, &

Storey, 2012).


Two broad categories of variation can be seen in the literature; exploitation and

exploration (March, 1991). Exploitation is based on existing IT assets, modifying their

interaction, and pursuing incremental innovation. In the context of IT-enabled

capabilities, exploitation refers to the enhancement of existing IT-enabled capabilities

by modifying or adding new IT-enabled capabilities via incremental innovation. For

instance, cloud computing can be integrated into existing ERP system as a service

delivery ‘platform’, which can enable cloud based data delivery (new IT-enabled

capability) from existing client-server based data delivery. IT professionals can

incrementally introduce new data services, such as SaaS and Internet of Things (IoT)

analytics (Davenport, Barth, & Bean, 2012) into the cloud, which entails new IT-

enabled capabilities.

Exploration of IT entails “importing energy” in organisations (Anderson, 1999),

such as, implementing big data based analytics (Chen, et al., 2012) or cloud computing

(Battleson, West, Kim, Ramesh, & Robinson, 2016) or even a new IT professional for

improvisation (Axelrod & Cohen, 2000). For instance, CRM focuses mainly on

customer data through a configuration of relational databases and other related

technologies, and their impact is limited to interactions between an organization and

its customers. Big data technology, such as Hewlett Packard’s (HP) big data platform

provides an opportunity in terms of scale and impact to manage abundant data and

domain-specific analytics, such as e-commerce and market intelligence and

organisation specific predictive analytics. An organisation may want to import HP’ big

data platform and with existing CRM for analysing consumer market demands or

discovering new customers and new business opportunities (new IT-enabled

capabilities).

Selection

Selection is the filtering mechanism to choose or eliminate technological

configurations based on their fitness or outcomes (Axelrod & Cohen, 2000). In the

context of IT-enabled capabilities, selection refers to the mechanism of choosing or

eliminating IT-enabled capabilities or routines that are part of an IT-enabled capability

(Nelson & Winter, 1982).The least efficient and effective routines are either

abandoned or changed, or a firm is likely to not be able to survive in the long run. The

most efficient and effective routines help firms to obtain competitive advantage

(Nelson & Winter, 1982).


Selection of IT-enabled capabilities can be driven by favouring certain IT-

enabled capabilities over others (Weeks, 2002). Favouring particular IT-enabled

capability is involved as organisation mainly favours higher performing IT-enabled

capabilities over existing IT-enabled capabilities with the focus on cost reduction

(Neirotti & Raguseo, 2017). The rise of digital technologies, such as a cloud based

CRM platform (e.g. salesforce.com+ replaces traditional ERP systems based CRM

system in managing customer related information in contemporary organisation

(Cusumano, 2010).

The selection mechanism can be internal or external (Aldrich, 2006). Internal

selection involves reconfiguring or choosing IT-enabled capabilities in such a way that

they become useful for the functionalities of organisation (Chae, 2014). For instance,

when Domino’s Pizza first launched its mobile application, customers faced the issue

of checking the real time status of the pizza delivery. Domino’s recognised this issue

and installed a tracker in the delivery person’s car and added an option in the mobile

application that shows the real time location of the pizza delivery driver. So, the

combination of the tracker and the mobile applications enables customers to track real

time delivery of the pizza.

External selection happens when the selection mechanism is determined by

factors external to organisation. In the context of IT-enabled capabilities, external

selection refers to the mechanism that determines particular IT-enabled capabilities

based on the external market. For instance, where the overall business environment

has become more competitive, uncertain and dynamic, and where everything in

business organisations is connected, there is a strong demand for combinable data

technologies, such as data-analytics, Mongo DB, Splunk etc., which can overpower

the previous data driven technologies, such as business intelligence (Yoo, et al., 2010).

The selection of such new IT-enabled digital capabilities, like IoT driven analytics

instead of traditional Excel based analytics helps firms to improve capabilities such as

reporting (Davenport & Harris, 2007).

Retention

Successful selected IT-enabled capabilities might be retained by organisations

for future potential use (Aldrich, 2006). In organisational settings, particular routines,

strategies, organisational memory and technology artifacts are some examples of

retention (Pentland, et al., 2012). In the case of IT-enabled capabilities, some are


retained for future variations. For instance, when changes in a business environment

will impact the variation of IT-enabled capabilities, and certain retailed IT-enabled

capabilities, such as, data mining and analytic techniques (Bitterer, 2011) will become

a part of the new variation.

Summary of the Micro coevolution of IT-enabled Capabilities

In brief, the three mechanisms, variation, selection and retention together can

help to understand the evolution of a particular IT-enabled capability in organisations

(McKelvey, 1997c). Figure 5.4 represents micro coevolution of IT- enabled

capabilities. The bidirectional dashed arrows represent the micro coevolution of IT-

enabled capabilities. When the evolutionary process of a particular IT-enabled

capability influences the evolutionary process of another IT-enabled capability within

a focal organisation, then it is called as micro coevolution of IT-enabled capabilities.

The micro coevolution of IT-enabled capabilities is simply illustrated in the following

figure-

Figure 5.4 Micro coevolution of IT-enabled capabilities via variation, selection and retention (VSR) processes

For instance, an ERP system may contain three major modules, such as supply

chain, logistics and inventory control that facilitate business processes that are highly

related to each other. , Thus the related capabilities provided by the ERP system are

ERP-enabled supply chain, ERP-enabled logistics and ERP-enabled inventory control.

If the supply chain module is updated to accommodate new features that will automate

warehouse management processes, this means that a variation of the ERP-enabled

supply chain capability has taken place and a new capability has been selected for

supply chain via ERP; an evolutionary process related to the ERP-enabled supply

chain. Now, as the ERP-enabled supply chain capability gets updated, the other ERP

enabled capabilities are required to adjust to it as they are highly associated. That



VSR VSR


means, the evolution of the ERP-enabled supply chain capability triggers changes in

the other associated capabilities. This might require the logistics and inventory control

ERP modules to undergo variation-selection processes so that they can adjust to the

change and help the organisation to operate appropriately. The variation of one IT-

enabled capability provides the raw material for the adaptation of the other IT-enabled

capabilities (Axelrod & Cohen, 2000). This particular example highlights the role of

evolutionary process of one IT-enabled capability in triggering further evolution in

other IT-enabled capabilities and thus it is a coevolution at a micro level.

Variation creates novelty as well as heterogeneity in IT-enabled capabilities

(Chae, 2014). Once an IT-enabled capability evolves through the processes of

variation, selection and retention, it triggers further evolution in other IT-enabled

capabilities with which it is related. Therefore, the IT-enabled capability starts

coevolving with other IT-enabled capabilities in the organisation (micro level). As the

emergence of the IT-enabled capabilities are not entirely predictable and pre-

determined (see Chapter 4), the micro coevolution is likely to be unpredictable. The

variation mechanism provides options to choose certain IT-enabled capabilities via

reconfiguration or importing. Selection facilitates variation internally and externally

by selecting IT-enabled capabilities internal to organisations based on performance

criteria and organisational goals (Weeks, 2002). Finally, retention helps to retain

certain IT-enabled capabilities for future variations with other social and

organisational elements. Thus, these three mechanisms work together to ensure that

IT-enabled capabilities mutually change with other IT-enabled capabilities (micro

coevolution) in a way that is beneficial for the efficiency of organisation. Based on the

above discussion, I propose-

Proposition 1a: The evolutionary processes- variation, selection and retention

can improve IT-enabled capabilities of focal firm.

Proposition 1b: Micro coevolution of IT-enabled capability occurs when

evolutionary improvement of one IT-enabled capability causes evolution in the

associated IT-enabled capabilities.


5.5 MACRO COEVOLUTION OF IT-ENABLED CAPABILITIES BETWEEN THE FIRMS

The preceding section discusses micro coevolution of IT-enabled capabilities.

This section presents the macro coevolutionary process related to IT-enabled

capabilities and the way it influences BVIT, particularly competitive advantage. Macro

coevolution of IT enabled capabilities refers to the process by which firms’ IT-enabled

capabilities coevolve reciprocally with the IT-enabled capabilities of other firms

(McKelvey, 1999). The focus of macro coevolution of is on firms existing in a

coevolutionary competitive context to obtain IT-enabled capabilities that help them to

achieve competitive advantage (Lewin & Volberda, 1999; McKelvey, 1997c).

Scholars have been using the coevolutionary lens to explore different dynamic

coevolutionary phenomena in macro level in organisational context, such as, the Red

Queen effect (Barnett & Sorenson, 2002), evolutionary stability (Lewin, et al., 1999),

competitive exclusion (Kauffman, 1995a), coevolutionary imbalance (Madhok & Liu,

2006) and Niche separation (McKelvey, 2002). There are myriads of such phenomena

that are related to coevolution and separating such coevolutionary dynamic phenomena

from each other is difficult as they are highly intertwined and implicit within

coevolutionary relationships (Maruyama, 1963; McKelvey, 2002).

In this section, I have focused on three particular macro coevolutionary dynamic

phenomena (interrelated processes), the Red Queen Effect, competitive exclusion and

Niche separation dynamics in the context of IT-enabled capabilities, following the

ideas of coevolutionary organisational dynamics by (McKelvey, 2002). In general, the

competitive exclusion, Red Queen competition and niche separation coevolutionary

dynamics temporarily dominate in IT enabled firms and influence firms’ competitive

advantage (McKelvey, 2002). The coevolutionary dynamics can be generalizable

across coevolutionary relationships in an organisational context (Lewin & Volberda,

1999; McKelvey, 2002). In particular, these three dynamics have been chosen as they

reflect competitive dynamic relationships among components over the coevolutionary

landscape which are looking to achieve some fitness value (Kauffman, 1995a), IT-

enabled capabilities in this case are the components. Moreover, these three dynamics

together represent an action based approach to developing strategic capabilities

(Voelpel, Leibold, Tekie, & Von Krogh, 2005), which in turn may contribute to


competitive advantage (McKelvey, 2002). The macro coevolutionary dynamics are

described below.

Red Queen Effect

Evolutionary theorists portray how entities dynamically interact and coevolve

with one another via Red Queen competition. The term “Red Queen effect” was

originally introduced by Van Valen (1973) based on the conversation between the Red

Queen and Alice in Lewis Carroll's Through the Looking Glass. In that story, Alice

realizes that although she is running as fast as she can, she is not getting anywhere,

relative to her surroundings. The Red Queen responds: “Here, you see, it takes all the

running you can do, to keep in the same place. If you want to get somewhere else, you

must run at least twice as fast as that!”(Carroll, 1960). Van Valen (1973) has used this

analogy to describe interactions between participants in dynamic systems and how

they maintain relative fitness amongst them in coevolutionary relationships. Since

then, the Red Queen effect has been used to explain dynamic and chaotic behaviours

in a variety of setting ranging from biology to organisational studies (Derfus, Maggitti,

Grimm, & Smith, 2008; Kauffman, 1995a).

In the context of IT-enabled capabilities, the Red Queen effect mainly describes

the macro coevolutionary relationships among firms competing for IT-enabled

capabilities in a business landscape (McKelvey, 1997c; Tanriverdi, et al., 2010)

because it is closely related to the notion of competition as firms compete for valuable,

rare, inimitable and non-substitutable IT enabled capabilities (Grant, 1991). In the

complex adaptive business system, each firm’s adaptation moves to achieve particular

IT-enabled capabilities (e.g. other organisational resources and capabilities) can cause

reciprocal movement of the rival firms over the landscape and other institutional and

external factors, such as technological advancement, market demand, etc. together

change the competitive landscape’s topography (Tanriverdi, et al., 2010).

In the context of macro coevolutionary relationships, the Red Queen can be seen

as a challenge in which, a firm contests to match or exceed the performance or fitness

of its rivals by adopting, reconfiguring or innovating new IT-enabled capabilities

(Baum & McKelvey, 1999c; Kauffman, 1995b; McKelvey, 1999). In these contests,

acquiring new IT-enabled capabilities may improve competitive advantage of the focal

firm over the competitors in the landscape. The only way rivals can improve their

strategic advantage relative to the focal firm is by acquiring at least similar or better


IT enabled capabilities so that the rival firm can exceed the focal firm in the

competition of achieving strategic advantages. For instance, the Commonwealth Bank

of Australia (CBA) has superior competitive advantage over its’ rival banks, Westpac,

NAB and ANZ due to an easy to use and all-in-one mobile banking application,

according to the recent Forrester report (Zhi Ying Ng, 2018). The app-enabled

capabilities such as, money transfer using mobile number, paying bill by taking a photo

of it and setting up future payments make the CBA app better at attracting more

customers and likely to achieve better competitive advantage over the rival banks. If

the rival banks want to be ahead in the competition, then according to Red Queen

analogy they need to come up with better IT-enabled capabilities than the CBA’s.

Once the rival achieves a better advantage than the focal firm, it waits for some

time period, complexity researchers term the waiting period as Nash equilibrium

period (Kauffman, 1993) subsidiary, before it starts to looking for more improvement

in the IT-enabled capabilities driven by selection of new IT-enabled capabilities and

niches as well as by managerial adaptation that determines the scope of the

organisation (Madhok & Liu, 2006). Again, it triggers further search for improving

IT-enabled capabilities. Therefore, in the macro coevolutionary run for achieving

better IT-enabled capabilities, each firm is forced by the rival firms in an industry to

participate in a continuous action and development race such that firms end up racing

as fast as they can to keep ahead in the competition (Barnett & Sorenson, 2002).

Researchers have discussed that firms that are more than active in sensing the

competitive position of the rivals and responding accordingly is likely to improve their

competitive advantage (Banker, Cao, Menon, & Mudambi, 2013; Overby, Bharadwaj,

& Sambamurthy, 2006); also that firms that are slower than their rivals in creating

opportunities via IT enabled capabilities are likely to experience relatively lower

competitive advantage (Overby, et al., 2006; Sambamurthy, et al., 2003). However,

the Red Queen effect can have negative impacts or consequences (Barnett & Hansen,

1996); for example. frequent changes in the IT-enabled capabilities might affect the

sustainability of competitive advantage. Recognising IT-enabled capabilities is a time

consuming task, and selecting and adapting the new IT-enabled capabilities in the

business can be difficult due to different organisational factors, such as, the cognitive

receptiveness of the local managers (Szulanski, 1996). Therefore, (Voelpel, et al.,

2005) suggests that rather than running harder than the competition, it is better to run


differently and act more smartly than rivals by developing certain organisational

capabilities; technology, consumer, profitability and business system infrastructure

sensing capabilities.

The longer a firm can monopolise a market and enjoy first-mover advantages

based on a new competitive action, the greater the benefits it enjoys (Lieberman &

Montgomery, 1988). Rapid response to the competitive actions of the rivals require

awareness, sense and agile response to the actions (Overby, et al., 2006). More recent

tools, such as, big data and predictive analytics (Fan, Lau, & Zhao, 2015), text and

web mining (Anica-Popa & Cucui, 2009) provide firms with IT-enabled capabilities

and competitive intelligence to predict rivals action accurately.

In sum, the Red Queen effect help organisation to achieve superior IT-enabled

capabilties so that it can obtain better competitive advantage than its rivals (McKelvey,

1999). The coevolution of IT-enabled capabilities via Red Queen competition changes

and gradually improves competitve advantage of firms. Hence-

Proposition 2a: The Red Queen competition can improve IT-enabled

capabilities of firms in a macro coevolutionary relationship.

Proposition 2b: The Red Queen Effect can positively influence the competitive

advantage of firms in coevolutionary relationships

Competitive Exclusion

The competitive exclusion principle implies that if two firms in an industry have

the same resource pool, the firm with a faster internal mutation/ change rate is likely

to evolve faster than their competitors in a new niche market and likely to surpass its

rivals’ fitness (Hardin, 1960). Given this analogy, the faster are coevolutionary

dynamics, the less likely firms will be held hostage to the law of competitive exclusion.

In brief, the law entails that two firms competing for the same IT-enabled capabilities

or competitive market position cannot coexist together. One of them will find a smart

way to survive in the competition (Voelpel, et al., 2005).

In the context of IT enabled capabilities where firms are in coevolutionary

relationships, competitive exclusion entails that competing firms are continuously

evolving their existing IT enabled capabilities so that they do not lock into a law of

competitive exclusion. For instance, among Australia’s pizza retailers, Domino’s has

been continuously ahead, in an unprecedented manner, of its competitors regarding


innovations, new IT enabled features and marketing (Rice & Martin, 2017). A recent

report (Hatch, 2018) shows that Domino’s profits are up by more than 30% year-on-

year. The other competing pizza retailers are hardly able to compete with Domino’s in

the market in terms of IT-enabled services within Australia, although the Australian

local pizza joints reign supreme over the big chains. Illustrative of this is Domino’s

mobile application discussed above. Pizza Hut is one of Domino’s closest

competitors,, however, the basic pizza ordering iOS for Pizza Hut is rated poorly for

service ( only 1.9 out of 5.0 rating in Apple store (and only 39 users rated), whereas,

Domino’s pizza ordering app scores 4.5 out of 5 (about 4000 customers rated)).

Although the local pizza shops are not offering such innovative services via IT

for their customers, they are avoiding the competitive exclusion (i.e. not locking

themselves into a position where they are unable to coexist with big chains such as

Dominos) by offering other incentives, such as discounted price, local flavour,

weekend or lunch or dinner time deals, etc. Overall in the Australian pizza retailer

industry in terms of IT-enabled capabilities no one has beaten Domino’s. The pizza

retailers who are weaker in the competition migrate into different niche market

(described in next section) (McKelvey, 2002).

The above example illustrates how competitive exclusion of IT-enabled

capabilities coevolve at the macro level and how it controls a firm’s competitive

position in the pizza industry. In the contemporary business environment, competitive

exclusion law forces a firm to engage in rapid and relentless innovations in IT based

products and services that can help the firm to achieve competitive advantage (Lewin

& Volberda, 1999). Successful firms are likely to compete with the rivals by creating

Niche (discussed in the next section) opportunities (Khanna & Venters, 2013) for the

customers to avoid head-to-head competition in the competitive landscape. Depending

on customer demand and product competition in the market, dominant firms mostly

survive in the Red Queen competition via IT-enabled capabilities (Sambamurthy,

2000); the weaker firms may get away from the competition due to competitive

exclusion (McKelvey, 2002). The stronger firms having better IT-enabled capabilities

obtain superior competitive advantage over the weaker firms and a few firms dominate

the competition via IT-enabled capabilities as well as well reputation regarding their

products and services (Roberts & Dowling, 2002; Selnes, 1993). Hence-


Proposition 3a: Firms with better IT-enabled capabilities in competitive

exclusion can achieve superior competitive advantage than the firms with weaker IT-


Proposition 3b: The competitive exclusion law can influence firms with weaker

IT-enabled capabilities to compete to obtain improved IT-enabled capabilities.

Niche Separation

McKelvey (2002, p. 5) defines Niche in the context of resources as, “…a pool

of relevant resources- coevolves in its elaboration in conjunction with the elaboration

of the characters describing newly forming species”. For instance, Facebook is not just

a social media platform; as people started using it, the features of Facebook have

coevolved with the skills, demands and preferences of its users, and now it has become

one of the most popular platforms for news, marketing and advertisement, and

communication. From a social media platform, Facebook has become a platform for

news and advertisement i.e. Niche.

As mentioned at the beginning of this section, the three coevolutionary dynamics

are highly related to each other. In the Red Queen competition, when competitive

exclusion situation starts to dominate, pressure starts to increase and favours niche

separation and the beginning of coevolution of new niches and species (McKelvey,

2002). The coevolution in the new niche temporarily stops further coevolution, until

any new species enters into the new niche. For instance, in shared economy based

hospitality industry, Airbnb provides a network platform where customers can

collaboratively make use of under-utilized inventory via fee-based sharing. Individuals

can rent their empty rooms or entire house at reasonably lower costs than traditional

hotels and consumers can benefit by renting at a lower cost. This disruptive innovation

has become one of the most popular and demandable in an IT enabled sharing economy

niche in the hospitality industry (Zervas, Proserpio, & Byers, 2017). However, a few

competitors of the Airbnb, such as Homestay, and Flipkey by Tripadvisor are also

booming in the hospitality industry. Suppose that both of these Airbnb competitors are

in a competitive exclusion situation with almost similar IT-enabled capabilities

looking to achieve superior competitive advantage over each other. Let’s further

assume that, Airbnb introduces a new business, providing an option for booking air

tickets along with accommodation booking from the same Airbnb portal (new niche)

as customers travelling overseas might find it very useful and that new IT-enabled


capability would bring improved competitive advantage that its competitors. It is

important to note that weaker competitors of Airbnb such as WWOOFing (Working

Weekends On Organic Farms) create niche markets; for example the hosts expect

guests to stay a minimum of two weeks and that the guests do work on the farm, and

get a place to stay, and in most cases food as well.

The basic argument of niche separation in the context of IT enabled capabilities

is that, when firms reach a competitive exclusion situation, they create new IT-enabled

capabilities to increase their competitive advantage in a smarter way than their

competitors. Industries such as banking and finance have recently started creating new

IT-enabled capabilities (niches) to outdo their competitors. The banking industry has

been invaded by Fintech companies, that use technology as a catalyst to offer various

financial services to the end users more efficiently (Dapp, Slomka, AG, & Hoffmann,

2014). To survive in the competition, banks have been trying to achieve competitive

advantage over the Fintechs via IT innovation, collaboration, and a wide range of IT

enabled products and services. For instance, The Commonwealth Bank of Australia is

planning to launch private and permissioned block-chain solutions which will enable

banks to process payments more quickly and more accurately while reducing

transaction processing costs and the requirement for exceptions (Eyers, 2017). This

particular new IT-enabled capability will enable the CBA likely to exceed their

competitors in obtaining competitive advantage. Moreover, this IT-enabled capability

will create further IT enabled niche capabilities such as smart contracts or the direct

transfer of money from investor to issuer, etc. using block-chain. These niche

capabilities will likely influence CBA’s competitors such as, ANZ and Westpac to

come up with new IT-enabled initiatives to improve their competitive advantage in

the banking landscape. Hence,

Proposition 4: Firms with IT-enabled niche capabilities will likely to achieve

superior competitive advantage than their competitors.

Summary of the Macro coevolution of IT-enabled Capabilities

In summary, the above discussion on the three coevolution related dynamics,

Red Queen effect, competitive exclusion and niche separation shows that they are

interwoven together, each continuously influencing the other in a circular way

(McKelvey, 2002). In the context of IT-enabled capabilities, the Red Queen effect

stimulates firms in competition to acquire, reconfigure or innovate new IT-enabled


capabilities to improve existing fitness value (competitive advantage) at the macro

level (Lewin, et al., 1999). However, in a particular period, if competing firms the

point where they have almost the same IT-enabled capabilities and they try to achieve

same level of competitive advantage in the market, these firms face a situation in which

they compete for almost similar competitive advantage and market position; this is

called a competitive exclusion situation. Firms with stronger IT-enabled capabilities

may stay in the competitive exclusion situation, whereas firms with relatively weaker

IT-enabled capabilities attempt to achieve better IT-enabled capabilities via creating

new niche IT-enabled capabilities. However, while the majority of the competitors

look to achieve better competitive advantage, the focal firm with stronger IT-enabled

capabilities may find itself locked in a situation if the firms with relatively weaker IT-

enabled capabilities want to obtain better competitive advantage via niche, which

Kauffman (1993) terms as Evolutionary Stable region, where firm itself is a sole

competitor. Firms in Evolutionary Stable regions compete with the niche firms

creating new IT-enabled capabilities and this spiral of dynamic relationships to achieve

competitive advantage continues. The next section presents a complete coevolution

framework of IT-enabled capabilities.

5.6 A COEVOLUTION BASED FRAMEWORK OF IT-ENABLED CAPABILITIES

This study argues that IT-enabled capabilities coevolve at micro and macro

levels of organisation after they emerge from the interactions between IT assets and

organisational resources. Based on the argument, the preceding section highlights the

micro and macro coevolution in relation to IT-enabled capabilities and how they

influence BVIT. This section presents a coevolution based framework of IT-enabled

capabilities (Figure 5.5). The preliminary framework (Figure 5.3) only described the

position and existence of micro and macro level coevolution of the IT-enabled

capabilities. Figure 5.5 is a complete version that represents micro and macro level

dynamic mechanisms of the coevolutionary process related to the IT-enabled

capabilities.

The coevolution based framework of IT-enabled capabilities represents both

micro and macro coevolution together and shows how the three dynamic

coevolutionary phenomena fit. The two rectangle overlap as firms exist within a

competitive environment of other firms. One rectangle represents a focal firm and the


other represents other firms in the business landscape. The focus is the coevolutionary

relationships via IT-enabled capabilities with the other firms. The bidirectional dashed

line presents micro coevolution of IT-enabled capabilities internal to the focal firm. In

the micro-coevolution (discussed in section 5.4), the IT-enabled capabilities coevolve

with other IT-enabled capabilities within the focal firm. The bidirectional solid line

represents macro coevolution of IT-enabled capabilities. IT-enabled capabilities of one

firm influence IT-enabled capabilities of another firm via macro coevolution

(discussed in section 5.5).

Figure 5.5 A Coevolution Framework of IT-enabled Capabilities

The micro coevolution of IT-enabled capabilities is driven by variation, selection

and retention evolutionary processes, where evolution of an IT-enabled capability may

cause evolution of the other IT-enabled capabilities with which it is connected. The

mutual adaptation between two or more IT-enabled capabilities in focal firm level is

referred to as micro coevolution (Axelrod & Cohen, 2000). Moreover, the IT-enabled

capabilities also coevolve with the IT-enabled capabilities of other firms. Three related

coevolutionary dynamics can be highly related with the macro coevolution of IT-

enabled capabilities between firms. The Red Queen causes a focal firm to compete to

obtain IT-enabled capabilities superior to its competitors. The Competitive Exclusion

principle ensures that if two firms (for instance a focal firm and a competing firm) aim

to achieving competitive advantage via similar IT-enabled capability, then they can



Focal Firm


Micro Coevolution

Macro Coevolution



VariationSelection Retention

Red QueenCompetitive Exclusion

Niche Separation

Mutual Adaptation


hardly co-exist together. In this circumstance, if the focal firm for instance achieves

the competitive advantage, the competing firm looks for achieving niche IT-enabled

capabilities so that it can achieve competitive advantage in an alternative way (Kemp,

Schot, & Hoogma, 1998). Both macro and micro coevolution of IT-enabled

capabilities together helps firms to achieve competitive advantage. The relation

between the micro and macro coevolution of IT-enabled capabilities is broadly

discussed in the next section.

5.6.1 Relation between Micro and Macro Coevolution

In summary, the foundation premise is that micro coevolutionary order in IT-

enabled capabilities within firm emerges in the context of macro coevolutionary

selectionist competitive pressure, which is termed as nested coevolutionary effect

(McKelvey, 1997c). The coevolution of IT-enabled capabilities refers to the fact that

firms coevolve with other firms because of the changes in IT-enabled capabilities via

macro coevolutionary process (Baum & McKelvey, 1999c; McKelvey, 1999).

Whereas, in the micro coevolutionary process, IT-enabled capabilities, evolution of an

IT-enabled capability influences other IT-enabled capabilities with which it is related

and vice versa.

In the competitive business environment, environmental selection forces, such

as, market changes, consumer needs, new technologies, competitors’ strategies cause

variations in business product/technology (Madhok & Liu, 2006). Such variations in

the product and technologies trigger changes in the IT-enabled capabilities at micro

level and which happens continually. In contrast, coevolutionary changes in the IT-

enabled capabilities at micro level are infrequent and punctuated because an

organization cannot constantly change its internal structure, routines and strategies

(Hannan & Freeman, 1984). However, micro coevolution can outpace over macro

coevolution as well. For instance, if the focal firm has the tendency to upgrade its

operations more frequently via selecting new IT-enabled capabilities than faster than

the external competition with the other competitors (Madhok & Liu, 2006).

In the contemporary business environment, in particular macro coevolution of

IT-enabled capabilities outpacing micro coevolution is more likely to occur and poses

a significant challenge (Madhok & Liu, 2006). A faster pace of the external macro

level coevolution of IT-enabled capabilities relative the internal micro level

coevolution may cause negative effect on the focal firm’s competitive advantage. For


instance, if the IT-enabled capabilities of the focal firm do not coevolve with the

coevolution of the competitor’s IT-enabled capabilities, there is a high chance that the

competitive advantage of the focal firm will be negatively influenced (Madhok & Liu,

2006; McKelvey, 1997c; Morel & Ramanujam, 1999). For instance, although CBA

(suppose, it is a focal firm) is one of the top banks in Australian banking industry with

the topmost share in the market ($93B), has not yet adopted leading edge technology

like, Apple Pay to provide payment service to its customers. Whereas, its competitor,

ANZ is the first of the big banks (CBA, ANZ, NAB, Westpac) which has teamed with

Apple and provide a contactless payments so that customers can use their iPhone or a

smartwatch to pay for purchases (ANZ.com) and this it eventually has achieved better

competitive advantage than the focal firm CBA. Recently, CBA has signed agreement

with Samsung to provide payment service via mobile system (Davidson, 2018).

In a word, macro-micro ‘coevolutionary imbalance’ may compromise firm

overall competitive advantage at global level (Madhok & Liu, 2006). In a different

arena, Burgelman (1994) has found in his study on Intel that the internal selection

mechanism by mimicking market mechanisms and allocating the manufacturing

resources according to the sales margins played a key role to make the company as a

successful microprocessor company. For another perspective, it can be said that

selection mechanism related to IT-enabled capabilities at the micro level enables focal

firm to adapt itself with the changing IT-enabled capabilities of the competitors at the

macro level.

In sum, macro and micro coevolution of IT-enabled capabilities occur

simultaneously (McKelvey, 1997b), which constitutes the coevolutionary process. The

process occurs via a dynamic framework consisting of ongoing iteration of three

processes, variation, selection and retention internal to the firm referred as micro level.

The macro level coevolution of the IT-enabled capabilities causes the micro

coevolution (McKelvey, 1997b). The initiating punch for the coevolution can be

internal to the focal firm. For instance, Commonwealth Bank of Australia’s (CBA)

innovation lab has recently partnered with a Fintech company on a project where

natural language processing (NLP) and artificial intelligence (AI) to automate the

process of reviewing regulations and thus saving hundreds of manual processing hours

and cost related to the processing. This might encourage the other competitor banks

such as, NAB, ANZ and Westpac to come up with innovative ideas and engage digital


technologies such as, BI or machine learning techniques to bring value for

organisations.

In addition, the initiating trigger can come from broader market. For example,

block chain technology can be used for highly secured money transfer and record

keeping and thus it can subsequently reduce the cost of transaction processing. The

secured money transfers and record keeping via block chain technology can be a great

option for banks in competition to adopt to provide innovative and better services to

the customers. In general, to achieve competitive advantage, it is important for the

focal firm to adjust and manage the tensions between micro-macro levels and match

the rates of coevolution constructively (Madhok & Liu, 2006) so that it can obtain

superior competitive advantage over business landscape (Tanriverdi, et al., 2010).

Hence, I propose that,

Proposition 5: Well balanced micro and macro coevolutionary processes of IT-

enabled capabilities can enable focal firm to achieve superior competitive advantage

over rivals.

5.6.2 A Narrative Exemplary Case to Explain the Coevolution of IT-enabled Capabilities Framework and Internal Validation

This section adopts an empirical case study as a narrative to show how my

proposed coevolution framework of the IT-enabled capabilities helps to provide a

narrative explanation on coevolution phenomena in practical organisational context.

In addition, the case study also helps to provide a first step internal validation of the

proposed framework.

I have adopted Montealegre, et al. (2014) case study on U.S. National Oceanic

and Atmospheric Administration (NOAA) as a narrative to represent how my proposed

coevolutionary view on the IT-enabled capabilities can generate in-depth insights and

the case data also is used for internal validation. The authors adopted a coevolutionary

view to understand information systems (IS) development process. They investigated

dynamic processes of changes in IS development and how the processes change

(coevolve influencing each other) over time. In the first step, I have adopted their study

for validating my developed explanatory theories on the micro coevolution of IT-

enabled capabilities. However, this study does not provide a wider view of the macro

environment, therefore, I have made some assumptions to discuss how my proposed


macro coevolutionary view of IT-enabled capabilities work. In fact, majority of the

empirical study on coevolution mainly focuses on one of the coevolutionary

persepectives- micro and macro. Because, coevolutionary studies are longitudinal

(Lewin, et al., 1999) and based on the emphasis on myriad of events among various

components, it is significantly difficult and requires substantiative effort to conduct a

micro and macro coevolutionary study of a phenomenon.

Montealegre, et al. (2014) identified three distinct theoretical domains- 1)

Organization information services choreography (OISC), service interactions and

collaborations are managed via this domain, 2) Organization information services

orchestration (OISO), service processes are selected and interact though this domain

and 3) Organization information services instrumentation (OISI), via which services

are developed and architected. These three domains together form the coevolutionary

core of the Information services and changes in one of the domains trigger changes in

other.

I have particularly focused on the OISO domain as it is relevant with the context

of the IT-enabled capabilities. The OISO domain focuses on how business and

information service interact together and how information service capabilities9 (IT-

enabled capabilities) dynamically adapt to meet the changing needs of organisation.

The case study reveals that the IT-enabled capabilities in the information services can

be easily influenced by the users based on the strategic and tactical business

requirements of the business. The IT-enabled capabilities are selected based on three

fundamental aspects of the business- business goals, business context and business

dynamics. If a capability does not meet the requirements or any unplanned situation

occurs, then a different IT-enabled capability is chosen. Apparently, when an IT-

enabled capability is changed, the relative IT-enabled capability in the information

services also bears the effect and if it does not meet the three aspects mentioned above,

it is also changed by organisations. In brief, the IT-enabled capabilities are constantly

changing to meet business requirements and continuous information service

improvement via variation, selection and retention process. It also proves that micro

coevolution of the IT-enabled capabilities occurs when unplanned exception happens

9 Information Service are assumed as service provided via the means of IT systems. In other words, IT systems enable the business organisation with capabilities, which are IT-enabled capabilities to provide services in different levels in organisations.


in one of IT-enabled capabilities or IT-enabled capabilities do not align with the

broader business aspects (Benbya & McKelvey, 2006a), which is in alignment with

my proposed propositions related to the micro coevolution of IT-enabled capabilities.

To describe macro coevolution, suppose, there is another government

department X which is competing with the NOAA in developing IS. Further assume

that, both NOAA and X are in the process of implementing the same information

system, A. As both of the departments are different in operations and business needs,

it is quite normal that the system solution A will be different in both organisations

accordingly. However, the competitive advantage of the organisation will be how well

the system A adaptive with the changing business requirements and provides all the

needs for the end users and meets broader business goals. It is evident that, macro

coevolution is also affected by several key factors such as, political, economic,

cultural, demographic and technological etc. (Derfus, et al., 2008; Lewin, et al., 1999;

Montealegre, et al., 2014). In NOAA, the system A provides a special user engagement

capability which enables users to personalise dashboards and share the dashboard via

online service which ensure seamless operational efficiency within internal department

communication. However, the same system in organisation X, does not provide the

same feature and it may cause less user flexibility and hinder operational efficiency.

In this situation, the organisation X will try to provide the dashboard sharing option by

allowing customers to deploy dashboard via online, providing option to customise

them or even with the option to design their own dashboard in the cloud and deploy it

in the core information system. In brief, the dashboard sharing and conjuring capability

in NOAA may trigger further improvement in organisation X dashboard or even

inspire them to come up with unique solution such as, integrating power BI to input

customer data in the dashboard.

Therefore, organisations in competition are always in the run to improve their

IT-enabled capabilities which in terms improve their internal efficiency and

competitive advantage in the competitive business landscape. This is in alignment with

the red queen competition, which helps to improve IT-enabled capabilities as well as

competitive advantage. The above example also indicates that firms with relatively

weaker capabilities tend to provide added feature such as power BI integration option

for better CRM experience and thus create niche capabilities (IT-enabled), which

focuses on improving advantage in the market and internal efficiency. Though both


organisations possess the same IS, because of their different type of operations, the

competitive exclusion hardly applies here. However, if one of the organisations locks

into competitive exclusion situation, firms with relatively weaker IT-enabled

capability definitely acquires or innovates new IT-enabled capability such as power BI

integration to improve its business value and thus the hypothetical example helps to

internally validate my proposed propositions on the macro coevolution of IT-enabled

capabilities.

The above narrative helps to understand how ceovolution of IT-enabled

capabilities can be complex and also it helps to limitedly validate the framework of the

coevolution of IT-enabled capabilities by explaining it through the empirical case

description. The narrative has helped to establish an internal validity of the proposed

coevolution framework as it helps to provide a good understanding of the dynamics of

underlying relationships that is ‘why’ this phenomenon is happening (Eisenhardt,

1989). It is important to note that, the internal validation using the case study is a first

step of the validation process. An in-depth empirical case study can be conducted to

further validate the propositions and the framework.

5.7 CHAPTER SUMMARY

This chapter presents a coevolution perspective of IT-enabled capabilities and

how it influences competitive advantage. It argues that IT-enabled capabilities

coevolve internal to the firm with other IT-enabled capabilities (micro level) and

coevolve externally with the competitor’s IT-enabled capabilities (macro level).

Section Error! Reference source not found. presents an overall overview of

coevolution concept and its use in IS and strategic management research. It also

presents different coevolution typologies from which micro and macro coevolution

concepts have been chosen as an overarching lens to explore IT-enabled capabilities.

Section 5.3 presents a preliminary coevolution framework of IT-enabled Section 5.3 presents a preliminary coevolution framework of IT-enabled

capabilities, where the loci of micro and macro level coevolution of IT-enabled

capabilities are discussed. The micro coevolution focuses on the mutual changes in

two IT-enabled capabilities within the firm, whereas, macro coevolution signifies how

a firm’s IT-enabled capability changes the IT-enabled capability/es of other firms and

vice versa.


Coevolution Framework of IT-enabled Capabilities

Proposition

Proposition 1a: The evolutionary processes- variation, selection and retention can improve IT-

enabled capabilities of focal firm.

Proposition 1b: Micro coevolution of IT-enabled capability occurs when evolutionary

improvement of one IT-enabled capability causes evolution in the associated IT-enabled capabilities.

Proposition 2a: The Red Queen competition can improve IT-enabled capabilities of firms in macro

coevolutionary relationship

Proposition 2b: The Red Queen Effect can positively influence competitive advantage of firms in

coevolutionary relationships

Proposition 3a: Firms with better IT-enabled capabilities in competitive exclusion can achieve

superior competitive advantage than the firms with weaker IT-enabled capabilities.

Proposition 3b: The competitive exclusion law can influence firms with weaker IT-enabled

capabilities to compete for obtaining improved IT-enabled capabilities.

Proposition 4: Firms with IT-enabled niche capabilities will likely to achieve superior competitive

advantage than their competitors.

Proposition 5: Well balanced micro and macro coevolutionary processes of IT-enabled

capabilities can enable focal firm to achieve superior competitive advantage over rivals.

Table 5.3 The complete coevolution Framework of IT-enabled capabilities and strategies



Focal Firm


Micro Coevolution

Macro Coevolution



VariationSelection Retention

Red QueenCompetitive Exclusion

Niche Separation

Mutual Adaptation


Section 5.4 presents three processes, variation, selection and retention related to

the micro coevolution of the IT-enabled capabilities and section 5.5 presents three

macro coevolutionary dyanmics, Red Queen effect, competitive exclusion and niche

separation related to the macro coevolution of the IT-enabled capabilities and how the

macro coevolution influences a firm’s competitive advantage.

Section 5.6 presents a complete coevolution based framework of IT-enabled

capabilities. The section also discusses how micro and macro levels coevolution

together influence a firm’s competitive advantage. In addition, section 5.6.2 includes

an empirical case study to show how the proposed coevolution framework provides a

narrative explanation on the coevolution of IT-enabled capabilities organisational

context. This section also provides an internal validation of the proposed framework

and propositions. The complete coevolution framework of the IT-enabled capabilities

and firms’ competitive advantage and propositions in the chapter are presented in

Figure 5.5.

164 Chapter 6: Towards Strategies for Managing IT-enabled Capabilities

Chapter 6: Towards Strategies for Managing IT-enabled Capabilities

Chapter 6 Summary

What was done in the previous chapter: The previous chapter proposes a

co-evolution perspective of the IT-enabled capabilities and how it influences


What this chapter does: This chapter presents an operational approach

related to the co-evolution of the IT-enabled capabilities. In particular, it shows how

a co-evolutionary operational model, NKC model can be used to formalise strategies

in managing co-evolutionary competition in organisations.




6.1 INTRODUCTION

In this chapter, I have mainly focused on an operational approach related to the

co-evolution of the IT-enabled capabilities discussed in the preceding chapter. The

main goal specifically is to show that the co-evolution perspective of the IT-enabled

capabilities can result in more concrete strategies that can be tested by researchers

empirically to and used as guidelines for managers. In order to do so, I have used

Kauffman (1993) coupled fitness landscape model, NKC model to formalise strategic

aspects of co-evolutionary competition in organisations. I have translated the NKC

model of co-evolutionary complexity into an organisation context via IT-enabled

capabilities as “parts” or elements of firms similar as McKelvey (1999), where the

author has conceptualised firms in competitions that are related to each other via value

chain competencies. The approach taken here substitutes linear deterministic history

model by non-linear numerical simulation models (Baum & Singh, 1994) with

particular focus on co-evolutionary competition relationships among competing firms.

Chapter 6: Towards Strategies for Managing IT-enabled Capabilities 165

It is important to note that in the macro co-evolutionary interaction, a move by one of

the firms over landscape changes fitness value of relative firms with which it is

connected (McKelvey, 1999).

Specifically, my formulation of strategies are based on the Kauffman’s NKC

model to further explore co-evolutionary competition with these questions- 1) How to

manage co-evolution of the IT-enabled capabilities? 2) What levels (micro and macro)

of complexity might affect the overall adaptive success of firms comprising a co-

evolving system via IT-enabled capabilities? The analysis is completely based on the

simulation results obtained by Kauffman (1993, chapter 6). Kauffman’s theory allows

an interweaving between Porter’s co-evolutionary pocket discussion (Porter & Millar,

1985) and the RBV and competence based views (Teece, Pisano, & Shuen, 1997;

Wernerfelt, 1984)and multi-coevolutionary views of organisations (Baum &

McKelvey, 1999c; McKelvey, 1999). The key aim is to further unfold the potential of

CAS theory by extending the NKC model in the coevolutionary context of IT-enabled

capabilities and develop strategies similar to (McKelvey, 1999) and (Baum &

McKelvey, 1999c). The theories as form of strategies developed in this chapter are

exploratory in nature as it provides a way to explore greater in-depth of the theoretical

insights from the coevolutionary perspective of IT-enabled capabilities using NKC

model.

The next section 6.2 first introduces the NKC model- its’ key the variables and

parameters, assumptions and how the NKC model operates in computational

environment. This section also briefly discusses the research conducted using this

model in IS and referral disciplines. The following section 6.3 contains the

operationalisation of the NKC model components and strategies developed based on

the NKC simulation outcomes following Baum and McKelvey (1999c). The chapter

concludes with a summary (section 6.4). The next section presents a brief overview of

the NKC model and then it shows the applications of NKC model in management and

IS research.

6.2 APPLICATIONS OF NKC MODEL IN RESEARCH

This particular section presents NKC model and NKC model based research.

First, section 6.2.1 briefly introduces the key components, variables, assumptions of

the NKC model and highlights how the NKC model operates in computational


environment, which is the core of this chapter. The subsequent section 6.2.2 briefly

discusses how the model is used in IS and referral management disciplines for theory

development. The theory development approach that I have adopted in this chapter

based on the study of Baum and McKelvey (1999c) on whole-part coevolutionary

relationships and McKelvey (1999) research on rugged landscape, which are also

included in section 6.2.2.

6.2.1 The NKC Model

Kauffman (1993) introduced NK model as a theoretical tool to model complex

adaptive systems (e.g. species, organisations), where N is the number of genes that

represent an agent and K is the number of couplings between these genes in the species.

Each configuration of a set of genes can take A possible states and is associated with a

fitness value, which can be interpreted as performance if that particular configuration

is implemented. The species uses different types of search strategies like long jump,

hill climbing or trial-error search to find better position (better fitness) over the

landscape. The two parameters (N and K) of the NK model allow a modeller to create

tuneable fitness landscapes of varying degrees of complexity on which to test the

performance or fitness value of various search strategies. When there is little

interaction among the agents (i.e., low K), the resulting fitness landscape is “smooth”;

for instance, when K=0, the landscape has a single peak and thus smooth sides.

Conversely, when the agents are highly interdependent (i.e., high K), for K=N-1 the

resulting fitness landscape is “rugged”.

NKC (Kauffman, 1993) is an extension of the original NK model introduced to

study the coevolution among different species over the landscape. In Kauffman’s NKC

model, each individual gene is also considered to depend on the genes in the other

agents through C coupling with which it interacts. Hence, the adaptive move by one

agent may deform the fitness landscapes of its partners S; altering C, with respect to

N changes the landscape of the interacting agent species. Here, C refers to the

connection between the set of genes from two species S1 and S2. Co-evolution among

two species is modelled in NKC by signing to each gene in the genotype of species 1

“external” inputs from C genes in species 2, and vice versa, in addition to the K

“internal” inputs, which is only the difference between NK and NKC model

(Kauffman, 1993; Kauffman & Johnsen, 1991). If C > 0then a move by one species on

its landscape. It will influence the landscapes of its co-evolutionary partners.


The model assumes all external (C) and internal (K) interactions among agents

are so complex that it is only appropriate to assign random values from a uniform

random distribution to calculate their effects on the total fitness of landscape.

Therefore, for each of the possible K+C interactions, assuming that the number of

states an agent can take is A = 2, a table of 2(K+1+CxS) fitness contributions is created,

with all entries in the range [0.0, 1.0], such that there is one fitness for each possible

combination of K and C interactions for a specific agent. The fitness contribution of

each agent within an experiment is found from its individual table. These contributions

are then summed and normalized by N to give the actual fitness of the agent. The sum

of all the fitness contributions of all agents reflects the total fitness of the landscape.

Kauffman (1993) used random hill-climbing search to evolve each agent in turn,

i.e. each agent uses the current context of the other neighbour agents to determine

whether a random alteration to its configuration represents progress. From a given

configuration, an agent can alter one agent’s state randomly and can calculate the

resulting fitness. If the resulting fitness value is greater than the agent’s current fitness,

in the current environment, the agent adopts a new configuration and it causes a co-

evolutionary change in other agents’ configurations, which are connected to the current

agent; this is repeated for number of generations. They show how both inter

connections (C) and intra connections (K) epitasis affect a co-evolving system,

particularly in the attainment of Nash equilibria: “a combination of actions by a set of

agents such that, for each agent, granted that the other agents do not alter their own

actions, its action is optimal” (Kauffman, 1993).

6.2.2 Applications of the NKC Model in Management and IS Research

This section presents applications of NKC model in management and IS studies.

As discussed in Chapter 3, the use of CAS theory is very recent in IS discipline and

thus the NKC model has been used in few occasions within IS field, which will be

discussed briefly in this section.

Table 6.1 summarises the applications of the NKC model in various management

and IS studies. Kauffman (1993) NKC model has been used by a number of studies to

explore aspects of organisations and their adaptation in the business environment (e.g.

Levinthal, 1997; Rivkin & Siggelkow, 2007). Researchers have used the NKC model

as a theoretical lens to conceptually describe specific co-evolutionary relationships

among different components over fitness landscape (Ahouse et al., 1991; Levitan,


Lobo, Schuler, & Kauffman, 2002). Although there is a limited exploitation of NKC

model in social studies (Vidgen & Bull, 2011), a review of the related studies reveals

important insights on how and why NKC model has been used by researchers. Majority

of the studies have applied NKC model as a conceptual lens to management research

(e.g. Baum & McKelvey, 1999c; McKelvey, 1999). Other studies extend the NKC to

capture aspects of social systems and establish that management are not blindly

roaming over the fitness landscape, rather the landscape can be tuned to add different

parameters and explore their effect on the overall fitness value of the landscape (e.g.

Hordijk & Kauffman, 2005), while others go beyond extending the model,

implementing and testing the model in simulation environment (e.g. Levitan, et al.,

2002). Marion (1999) reconceptualises a case study of the microcomputer industry

using the NKC lens. Only one study uses the NKC in a qualitative manner to analyse

case study data of inter-firm networks in Japan (Colovic & Cartier, 2007b).

In IS discipline, a very few studies have been identified that are used NKC

model, specifically to the research on competitive advantage. For instance, Curşeu

(2006) used NKC model to interpret virtual team behavior and the dimensions of

cognition, trust, cohesion, and conflict. Moreover, Vidgen and Wang (2006a) have

used NKC model concepts to discuss how different elements of business process

management co-evolve each other. There are other instances in IS which indirectly use

theoretical interpretation of NKC model, like Tanriverdi, et al. (2010) who actually

have not used NKC directly rather they applied fitness landscape thinking to develop

strategies in managing IS alignment.

Author Application Research Method; Implications

(Ahouse, et al., 1991)

Co-evolution of firm strategies and co-evolution of belief systems have been proposed using NKC. In belief systems, species represent individuals who have N beliefs where each belief is evolved by K other beliefs. Each individual’s beliefs are also affected by C beliefs of other individuals (species). Fitness is measured as cognitive consistency. The model is also applied for firm strategies.

Conceptual; guidance on how to apply NKC in competitive strategy and belief systems.


(Kauffman, 1995a)

Technological co-evolution, where new technologies enter and influence others and vice versa which create opportunity for further technologies.

Conceptual; examples of coevolution.

(Caminati, 1999)

NKC is used to describe technological co-evolution, and how complementarity of technologies increases over time and how technology from one sector can influence technology in another sector.

Conceptual; in the early stage of innovation technologies were considered as chaotic as they compete for dominance. Calminati hypothesises that the technologies co-evolve over time by tuning K and C in the complex region.

(Baum & McKelvey, 1999b)

Kauffman (1993) co-evolutionary findings are used to interpret strategies for managing co-evolving competition in organisations so that the organisation can achieve maximum fitness.

Conceptual; by tuning K and C vale four co-evolutionary strategies are identified.

(McKelvey, 1999)

Porter and Millar (1985) value chain competency has been conceptualised using NKC lens to speculate how co-evolutionary value chain competency of one firm influence another competing firm.

Conceptual; Firm should match internal complexity K with environment and focus on moderate external competency C for maximum performance.

(Marion, 1999)

Apply NKC to reconceptualise an empirical case study on microcomputer industry to understand how an industry moves toward a stable regime.

Conceptual with reinterpretation of an empirical case study; Microcomputer industry fitness is a function of two interrelated activities: interdependence among actors in the network and the agreement on standards. Any changes in technology have less effect on the fitness function as industry focuses on incremental fitness increases.

(Chang & Harrington, 2000; Chang & Harrington Jr, 2003; Harrington

Co-evolution of the stores competing in the retail chains serving customers in different markets. Each store has N store practices, where K represents relationships between practices and C represents interactions among competing practices of other stores.

Theoretical development with computer simulation; centralisation of the stores is best in low market heterogeneity and decentralisation of the stores is best in high heterogeneity.


& Chang, 2005)

Fitness value is the customers who make purchases proportional to their ideal practices offered by the store. A headquarter species control the behaviours of competing stores characterised by centralised and decentralised form.

(Levitan, et al., 2002)

NKC is used to model the web of interactions within and between groups.

Theoretical development with computer simulation; For short search periods, larger organizations perform better as larger interconnected groups can test with a larger number of alternative strategies. For longer periods, smaller groups with a small number of external connections perform best as they can exploit random opportunities.

(Colovic & Cartier, 2007a)

NKC is used as an alternative lens to re-conceive qualitative case study of exploration and exploitation in nine inter-firm networks in Japan.

Case study; Different patterns of network, like colony, herd, pack and migratory are identified as a result of NKC reconceptualisation.

(Ganco & Agarwal, 2009)

Investigate the relationship between firms’ entry characteristics and their subsequent performances as contingent on environmental turbulence and stage of industry life cycle by simulating industry as an NKC landscape.

Theoretical development with computer simulation; Diversifying entrants outperforms entrepreneurial start-ups when turbulence is high.

(Curşeu, 2006)

CAS is used to conceptualise virtual team effectiveness. NKC model is considered to interpret virtual team behavior and the dimensions of cognition, trust, cohesion, and conflict.

Conceptual; neural network models are better alternative to NKC model and it is more suitable to model diverse agents and capture relationships among them.

(Vidgen & Wang, 2006a)

Co-evolution of business processes and Web services technology.

Conceptual; A business process infrastructure may become chaotic if processes and Web services are simple (low K) but interconnected (high C) and freeze if they are internally complex (high K) but loosely coupled (low C).


Table 6.1 NKC Applications in Management and IS Studies (Summarised from Vidgen and Bull (2011))

Among the NKC models described above, two of the models Baum and

McKelvey (1999a) study on whole-part co-evolutionary relationships and McKelvey

(1999) study on value chain competencies and how they can be managed to foster

competitive advantage are particularly relevant to this study for developing strategies

to manage IT-enabled capabilities. McKelvey’s model (McKelvey, 1999) provides as

a theoretical base to relate the co-evolutionary relationships among IT-enabled

capabilities with the competitive advantage of organisations. Baum’s model (Baum &

McKelvey, 1999a) serves as a guideline to formalise and propose strategies for

managing co-evolutionary competition of organisations (see 6.3).

The models are briefly summarised below-

McKelvey’s co-evolutionary lens on firm’s competitive advantage

One of the earliest models based on the core notion of complexity science is

McKelvey (1999) co-evolutionary based conceptual model on competitive advantage.

Built on the notion of NKC model developed by biologist Kauffman (1993) offering

universal principles explaining Darwinian natural selection theory, McKelvey

proposes that multi-co-evolutionary complexity in firms is defined by moving natural

selection processes in side firms and down to the parts level using examples of Porter’s

value chain level. The model focuses on the microstate activities by agents and the

assumptions of stochastic idiosyncratic microstates and how they co-evolve are

analysed. Competitive advantage is defined as a dependent variable and described

through Nash equilibrium conditions in NKC models. He translates the Kauffman’s

model into value chain level description, by taking particular attention on how value

chain landscape can be modelled, the underlying assumptions on NKC models to value

chain perspective and how the NKC models can better explain different aspects of the

competitive advantages with respect to different conditions.

Various computational models from the Kauffman (1993) book are directly

translated into value chain competencies and a range of strategic approaches which

most likely to foster competitive advantages that are derived from the outcomes of the

NKC models. Mckelvey has suggested a number of expected and surprising strategies,

like- “moderate complexity fares best and external co-evolutionary complexity sets an


upper bound to advantages likely to be gained from internal complexity” (McKelvey,

1999, p. 294).

Baum’s whole-part co-evolutionary competition lens in organisations Baum’s model has some relevance with the McKelvey (1999) model. First,

both of the studies have used Kauffman (1993) NKC model outcomes. Second, both

studies have formalised some strategies to manage co-evolutionary relationships in

organisational level, while Mckelvey’s strategies are particularly related to

competitive advantage, Baum’s strategies are related to managing whole-part co-

evolutionary competition.

Baum and McKelvey (1999a) review some unsuccessful attempts to

understand whole-part relationships in the management literature and use Kauffman

(1993) NKC coupled fitness landscape model to formalise some aspects of co-

evolutionary whole-part competition in organisations. The central argument is that

units of organizational evolution are nested and overlapping, such as individuals and

groups in organisation are integral parts and when they take part in evolution it’s hard

to demarcate any cleavage among them. The structure and patterns of relationships

among them arise from the interactions among various units responding to a series of

organisational goals, shift in the environment and different internal-external dynamics.

The features, such as, some individuals compete or co-operate, or individuals interact

with multiple groups for completing some specific tasks, and thus lower level units

typically out evolve and influence higher levels complicating co-evolution

considerably.

Drawing evolutionary theories, Baum and McKelvey proposed that the

inherently faster pace of evolution at individual and face-to-face-group levels

undermines the emergence of integrated, cooperative organization-level systems and

explains the inefficiency of organisational alignment strategies due to the faster pace

lower level co-evolution among organisational units. They proposed four alternative

“structure-tuning” strategies for managing whole-part co-evolutionary competition in

organizations by employing the Kauffman (1993) NKC model.

The reason for reviewing the NKC model related literature was to identify how

NKC model has been applied in different management studies, learn about the current

practices and applications of NKC and apply it to the BVIT study. The review of the

NKC model on management and organisational studies give several insights-


• NKC model has been mostly exploited for conceptually modelling to explore

co-evolutionary management phenomena, such as whole-part relationships in

organisational units (Marion, 1999), in economics (Kauffman & Macready,

1995). The C represents the connections of couple fitness landscapes of agents

in the system, such that adaptive moves by one agent interact (more or less

profoundly) with the fitness landscapes of other agents in the system. C

provides a natural way to couple different agents' landscapes; that is, each trait

of agent1 depends on K other of its own traits and of C other traits of agent2

and thus, C represents the number of agent2's traits that might co-evolve with

a given trait of agent1. Kauffman’s NKC framework thus affords a dynamic

model of couple landscapes whose ruggedness and richness of coupling can be

tuned and different organisational phenomena, which are irregular, and

sensitive to initial conditions, occasionally can cause unpredictable behaviours

can easily be represented using the NKC framework (Kauffman, 1993).

• Kauffman (1993 Chap. 6) carried out a series of co-evolutionary simulation

using the NKC model. In the simulation, Kauffman changes the N, K and C

values to explore how the resulting landscapes behave, analyses the common

patterns or behaviours to identify and understand the best strategies in different

combinations of N, K and C and then apply them to the analysis of

organisational phenomena. Once a basic NKC model is constructed different

patterns, findings and insights that can be achieved. Extensions to the NKC

model allow researchers to add features through tuning model parameters that

capture aspects of systems under study and acknowledge that researchers can

intervene through tuning the model parameters.

• The above review has only dealt with a fraction of research at the intersection

of NKC model and competitive advantage. In particular, the analysis represents

that the management studies have probed in to strategic challenges posed by

interdependencies between practices. Critically taking stock of this body of this

research as well as the field more broadly, it is fair to say that the research up

to now has been fairly fragmented and specifically focused on the strategic

perspectives, such as, what are the best strategies to choose, how to choose a

set of strategies in different situations to achieve better advantages than the

competitors and so on.


6.3 MANAGING THE COEVOLUTION OF THE IT-ENABLED CAPABILITIES

This section discusses the operationalisation of the NKC model into the context

of IT-enabled capabilities in section 6.3.1. The subsequent section 6.3.2 presents the

strategies developed based on the NKC simulation outcomes following Baum (1999).

Chapter 5 broadly discusses on micro level (internal) and macro level (external)

co-evolution of IT-enabled capabilities and its impact on competitive advantage. That

is, the IT-enabled capabilities do not merely evolve, they co-evolve with other IT-

enabled capabilities internal to organisations and with the competitors’ IT-enabled

capabilities. In a co-evolutionary process, the fitness landscape of one agent is altered

by the adaptive moves made by other agents at the macro level. At the micro level, the

co-evolution mechanism addresses mutual changes among the IT-enabled capabilities,

like, changes in ERP –enabled logistics (e.g., more accurate inventory data) is likely

to bring changes in E-commerce-enabled sales (e.g., showing items in or out of stock).

In contrast, the macro level co-evolution process features non-linear and dynamic

changes that pressurise organisation in competition to continually improve the IT-

enabled capabilities to maintain its fitness relative to the competitors (Baum & Singh,

1994). For example, when a firm can show the inventory levels of their stores on the

website, its direct competitors will likely follow.

McKelvey (1999) used Kauffman’s NKC model that allows to model value

chain competencies and suggest various strategies that may foster competitive

advantage. Based on McKelvey (1999) approach, I have used NKC model to

operationalise the co-evolutionary perspective of the IT-enabled capabilities in this

study. I have first translated the co-evolution of the IT-enabled capabilities using the

parameters of NKC model (Table 6.2). Then, I have followed Baum and McKelvey

(1999c) approach to formalise strategies on whole-part co-evolutionary competition.

Consistent with Kauffman (1993) as well as McKelvey (1999), I have conceived the

macro co-evolution as a process that couples the NK fitness landscapes of different

firms. However, I have made assumption that these landscapes of firms are connected

via one or more IT-enabled capabilities of the firms. In sum, to understand the strategic

relationships between firms in competition via macro co-evolutionary relationship, I

have translated Kauffman (1993) model of co-evolutionary complexity into a firm

context via IT-enabled capabilities as “parts” or elements of firms similar as


McKelvey (1999), where the author has conceptualised firms in competitions which

are externally connected to each other via value chain competencies. The approach

taken is similar as Kauffman (1993) did in evolutionary biology and McKelvey (1999)

followed Kauffman’s in modelling value chain competencies and I have followed

McKelvey (1999) in the context of IT-enabled capabilities. The proposed model

substitutes linear deterministic history model by non-linear numerical simulation

models (Baum & Singh, 1994) with particular focus on co-evolutionary competition

relationships among competing firms.

The section first translates Kauffman’s NKC model to firms with particular

attention to (1) how IT-enabled capabilities landscapes might be modelled following

McKelvey (1999) (section 6.3.1) and (2) how assumptions underlying Kauffman’s

models can be used to derive basic strategies to increase landscape’s fitness same as

(Baum & McKelvey, 1999c) and how these basic strategies related to the fitness

landscape can be used to develop more concrete level strategies to managing micro

and macro co-evolutionary competition in organisations (section 6.3.2).

6.3.1 Translation of NKC Key Assumptions in IT-enabled Capabilities Context

Kauffman (1993, chapter 6) presents a series of simulations and outcomes

generated from the simulations using the NKC model. The simulation in the

Kauffman’s model features a dynamic model of coupled fitness landscapes, whose

ruggedness can be tuned by changing values of N, K and C. McKelvey (1999) has

suggested that these assumptions can be applicable in the context of firms in

competition and thus he has applied the Kauffman’s model landscape into the context

of value chain competencies. As mentioned earlier, in this study, I have followed

McKelvey (1999)’s approach to translate the key assumptions of the NKC model into

the context of IT-enabled capabilities. Table 1.2 summarises the translation of NKC

model parameters into the context of IT-enabled capabilities and key assumptions that

I have made. For the purpose of readily understandable, I have mentioned both

Kauffman (1993) and McKelvey (1999) assumptions of each parameters of the NKC

model and I have presented my assumptions.

NKC Parameters and assumptions

Translation into the the IT-enabled capabilities context


S • Represents a species in Kauffman’s term, that is treated as a

homogeneous entity. Mckelvey uses it to denote number of firms.

• I have also used it to represent number of firms over the landscape.

N • It is the number of genes that represent an agent (section 1.2.1). In NKC

model, it represents an independent “part”. Mckelvey’s value chain

model denotes the number of subunits, production stations, value chain

units, competencies, and so forth.

• For the simplicity of my model, I have used it to represent number of

(heterogeneous) IT-enabled capabilities or number of agents that

represents the heterogeneous IT-enabled capabilities.

K • In Kauffman’s terms it measures epistatic links that inhibit change.

Mckelvey uses it to represent internal co-evolutionary density among

parts within a firm.

• I have used K to represent the internal co-evolutionary density among

various IT-enabled capabilities within a single firm.

C • Kauffman uses it to represent coevolving pair (gene or species). In

Mckelvey’s model, it is used to represent agents/microagents between a

pair of competing coevolving firms.

• I have used C to represent the number of interdependent IT-enabled

capability that connects between a pair of competing coevolving firms. In

broad sense, it measures external co-evolutionary density among firms in

competition

A • In Kauffman’s model, it is used to denote number of alleles (alternative

states) that a gene may take. Mckevely uses it to assume that any

alternative adaptive walk that agent takes to improve its fitness is limited

within two options- fitness remain unchanged or changed.

• I have used same assumption as Mckelvey did. The fitness value of the

IT-enabled capabilities are limited to two options- either the value will be

same or it will be changed to obtain a better fitness value.

W • Represents the total fitness value of all N agents. It is the average of all

its N agents, W=1/ N*Σwi,j. This parameter is used to denote overall

fitness value of NK landscapes both in Kauffman and Mckelvey.

Adaptive

walk

Evolution can be thought of as an adaptive walk over the fitness

landscape of the IT-enabled capabilities. At each time period during

simulation, a firm improves its IT-enabled capabilities (agents) by


surveying all the other one-change neighbouring agents and randomly

selects one that offers improved fitness. The agent stays same, if none

offers better fitness.

Table 6.2 Translation of NKC model into the context of IT-enabled capabilities

6.3.2 Strategies to Managing Micro and Macro Co-evolutionary Competition in Organisations

In this section, I have first discussed four basic strategies based on Kauffman

(1993) NKC model simulation results proposed by (Baum & McKelvey, 1999c).

(Baum & McKelvey, 1999c) suggest four basic strategies that represents how co-

evolutionary relationship can be tuned so that organisations can achieve increased

effectiveness (fitness value). The authors also discuss the underlying rationale behind

the strategies observed from Kauffman’s simulation and they have proposed some

specific operationalisations of these propositions in the context of firms.

In this study, I have followed Baum’s approach (Baum & McKelvey, 1999c)

and propose four concrete strategies to managing micro and macro co-evolutionary

competition of IT-enabled capabilities in organisations. The strategies are proposed in

relation to the Strategy A and Strategy B of Baum and McKelvey (1999c) in Table 6.3.

The rightmost column of Table 6.3 contains the mapping between C and K with my

proposed coevolution framework of IT-enabled capabilities, the middle column

contains the rationale behind the NKC model and leftmost column contains Baum’s

strategies.

Strategies proposed by Baum and McKelvey (1999c) following Kauffman’s NKC model

Rationale behind the model

Proposed concrete strategies to manage co-evolutionary competition

Strategy A: Raise organisation’s K when C is high

When C is high- the likelihood of reaching Nash equilibria is low. High K agents get higher mean pre nash equilibria

Strategy 1: When macro co-evolution is High, Increase micro co-evolution of IT-enabled capabilities. • Strategy 1.1: Increase Variation

by continual incremental


if paired with low K agents

reorganising and reconfiguring of IT-enabled capabilities.

• Strategy 1.2: Aid selection mechanism by employing multiple, inconsistent and changing performance measures of IT-enabled capabilities.

Strategy B: Lower organisation K when C is low

When C is low- the likelihood of reaching Nash equilibria is high. Lowering K increases access to local optima with high fitness peaks at Nash equilibria

Strategy 2: When Macro Coevolution is Low, Lower Micro Coevolution of the IT-enabled Capabilities. • Strategy 2.1: Aid selection

mechanism by adopting comparable performance measures of the IT-enabled capabilities.

• Strategy 2.2: Adopt Modular IT-enabled capabilities.

Strategy C: Balance organisation K and C

Already implied via strategy A and B

Match between C and K is discussed via the proposed strategies 1 and 2.

Strategy D: Lower C

When C is low, K can be lowered, which can increase access to local optima with high fitness peaks at Nash equilibria

Placed as future work.

Table 6.3 Strategies for managing Micro and Macro Coevolutionary

Competition in Organisations

Strategy 1: When macro co-evolution is High, Increase micro co-evolution of IT-enabled capabilities

When the external complexity (macro co-evolution) of the IT-enabled

capabilities is high, then one way of balancing the macro co-evolution is to increase

the internal complexity (micro coevolution) of the IT-enabled capabilities. The

strategy is proposed based on the ‘nested coevolutionary effect’ by McKelvey (1997c),

where the author argues that micro co-evolutionary mechanisms appear within the

context of macro co-evolution with the underlying principle to attain external fitness

value. In the context of this study, this means that macro level and micro level co-

evolution of the IT-enabled capabilities jointly influence competitive advantage.

Madhok and Liu (2006) in the context of multi-national companies have argued that

macro co-evolution outpacing micro co-evolution may negatively impact on the


overall competitive advantage. They further suggest that internal micro level co-

evolution is in fact a part of managerial adaptation efforts at the external macro level

changes. There are two ways in which organisations can increase micro level co-

evolution of the IT-enabled capabilities (internal to organisation) to balance with the

macro level co-evolution between two or more organisations, which relate to variation

and selection and are described in the following sections.

Strategy 1.1: Increase Variation by continual incremental reorganising and reconfiguring of IT-enabled capabilities

The first proposed strategy to increase micro co-evolution is to increase

variations in the internal IT-enabled capabilities. As discussed in the previous chapter,

variation is to create novelty in the internal IT-enabled capabilities (Volberda &

Lewin, 2003). In the context of IT-enabled capabilities in organisational settings,

organisation can increase internal micro co-evolution via variation mechanism by

continual incremental reorganisation and reconfiguration of exiting IT-enabled

capabilities (Chae, 2014). The terms continual refers to the fact that the business

environment is continually changing, which requires the organisation to adjust its

internal sets of resources, capabilities and competencies (Levinthal, 1997). In addition,

incremental implies that the continual changes take place in the IT-enabled capabilities

does not pose significant threat to any existing elements of the business (Wolfe, 1994).

The incremental reorganising of the IT-enabled capabilities involve minor

modification of the interactions of IT-enabled capabilities or pursuing incremental

innovation (Chae, 2012). In the service science literature, incremental formulation

results in many successful services. For instance, United Parcel Service (UPS) is very

well known to adopt incremental reorganising variations into logistic management.

UPS preliminarily focuses on improving package delivery service, but later on they

emphasise on delivering specialised service offerings, such as inbound logistic

management, outbound logistic management and reverse logistic management via

incrementally modifying existing capabilities (Sawhney, Balasubramanian, &

Krishnan, 2004). Organisations today for instance can modify ERP-enabled service

delivery capability with the emphasis on enterprise wide data management, reporting

and visualisation by incrementally adding predictive analytics. Such reorganising can

actually increase connections with the existing IT-enabled capabilities, such as, ERP-

enabled customer data management, which is a way to increase internal connections


among IT-enabled capabilities. In Kauffman’s term Kauffman (1995a), variations via

incremental reorganising is a safer strategy as agent (climber) keeps moving to high

peaks.

According to Kauffman (1995a) simulation, variations via reconfiguration can

be compared as combinations of many short walks and occasional long jumps over

fitness landscape that provides an optimised strategy for finding solutions. The

incremental reconfiguration of the IT-enabled capabilities involves finding alternative

solutions than what organisations currently have. The nature of this particular

variations can be demanding as it deals with the existing uncertainties present in

business (Birkinshaw, Bessant, & Delbridge, 2007). Incremental reconfiguration

involves bringing new resources, such as, big data or cloud computing and combine

and reconfigure the existing architectures to improve existing set of capabilities (Chen,

et al., 2012). For instance, IBM in its early days in the 1990s was suffering from

immense market and financial losses due to its reliance on mutation for service

innovation (O'Reilly III, Harreld, & Tushman, 2009). However, IBM realised this

particular issue and in response, it introduced variations via reconfiguring emerging

business opportunities with the existing service innovation strategies that gave IBM

long term business growth with innovative services (O'Reilly III, et al., 2009).

Strategy 1.2: Aid selection mechanism by employing multiple, inconsistent and changing performance measures of IT-enabled capabilities

The second strategy to increase the micro co-evolution of the IT-enabled

capabilities is to guide the selection mechanism by adopting multiple, inconsistent and

changing performance measures of various IT-enabled capabilities based on similar

idea proposed by Baum and McKelvey (1999c). In the context of IT-enabled

capabilities, multiple, inconsistent and changing performance measures of the IT-

enabled capabilities can be initiated locally, i.e., it is an ‘exploitative’ search, in which

locally (within firm boundary) best known good IT-enabled capabilities are identified,

routinized, extended and evaluated that may lead to positive outcomes. However, if

the time is limited, then ‘exploratory’ search, experimenting new IT-enabled

capabilities outside from organisations likely dominates, which may lead to globally

best results (March, 1994). Meyer (1994) argues that if the overall performance of an

organisation is considered to be uncertain, then multiple, inconsistent and differential

performance measures can facilitate organisational adaptation because the


measurement approach sets up an upper limit of performance on a given dimension,

the dimension loses prominence and performance in other domain increases

accordingly (Baum & McKelvey, 1999c). Moreover, he suggests that if the

performance is a fixed target, then multiple, inconsistent and differential performance

measures can facilitate organisational adaptation (Baum & McKelvey, 1999c). When

multiple, inconsistent and changing performance measures are initiated in the context

of the IT-enabled capabilities it slows down the speed of evolution by increasing

reconfigurations of the IT-enabled capabilities locally or bring over new IT-enabled

capabilities that ultimately increase internal connections (Baum & McKelvey, 1999c).

Strategy 2: When Macro Coevolution is Low, Lower Micro Coevolution of the IT-enabled Capabilities

In contrast with the proposed Strategy 1, when the macro co-evolution of the

IT-enabled capabilities is low (low C), then the way to fit between macro and micro

co-evolution is to reduce the micro co-evolution of the IT-enabled capabilities

(decrease K). Basically, macro co-evolution is characterised as market-driven and

constantly changing, in contrast, micro co-evolution internal selection pressurise, for

instance on the routines or organisational structure are not intense always (Cyert &

March, 1963). However, micro co-evolution can outpace macro co-evolution in some

cases. For instance, in an automobile industry, Toyota and Honda exhibit superior

capabilities in new product offering than the other competitors (Fujimoto & Clark,

1991). The Japanese firms have a short product development life cycle. Usually the

Japanese car manufacturers offer new features in the vehicle or newly designed vehicle

in every five years, where US competitors redesign their cars every seven years. Such

internal variation and retention speed by the Japanese car manufacturers enables them

to offer new features or new cars in the US market faster than the local US

manufacturers, which means macro co-evolution is slower than the micro co-

evolution. In such case, faster micro co-evolution can shape the landscape faster than

the external macro co-evolution (Madhok & Liu, 2006).

There are two ways in which organisations can lower micro level co-evolution

of the IT-enabled capabilities (internal to organisation) to balance with the macro level

co-evolution between two or more organisations, which relate to variation and

selection and are described in the following sections.


Strategy 2.1: Aid selection mechanism by adopting comparable performance measures of the IT-enabled capabilities This proposed strategy is almost opposite to the strategy 1.2. Baum and

McKelvey (1999c) argues that adopting comparable performance measures can bring

benefit in organisational level from an evolutionary view point as it simplifies the

range of possible configurations for evolution to occur in particular multi divisional

organisations where the performance of each division is represented by same metric,

such as, sales and the performances are comparable. In fact, the adoption of

comparable performance enhances evolution of capabilities at the organisational level

by improving coexisting processes as organisations inquire or reconfigure new

capabilities (exploration) and improve capabilities that are effective (exploitation)

(March, 1994).

In the context of the IT-enabled capabilities, the selection of the IT-enabled

capabilities can be aided by comparable performance measures so that the chosen set

of the IT-enabled capabilities can be beneficial for organisations. The comparison

between similar capabilities has been popular in measuring process performance in

particular in manufacturing industry (Chen & Chen, 2004). It helps practitioners to

determine whether or not two similar processes are equally capable. In a similar way,

during the selection process, the two or more IT-enabled capabilities can be compared

to determine whether both generate similar level of performance for organisations.

Suppose, organisation may have two different implementing options- traditional ERP

system and salesforce CRM to improve customer service delivery process i.e. ERP-

enabled and salesforce CRM enabled customer service. The organisation can adopt a

performance measure of the two chosen capabilities such as, rate of service to

determine the one is capable of improving customer service process. Such comparable

performance measures can be considered to simplify the optimisation problem for

complex organisations (Baum & McKelvey, 1999c). In terms of the NKC model,

adoption of comparable performance measures lowers internal complexity K

(Kauffman, 1993), which helps organisations to lower K when C is low.

Strategy 2.2: Adopt Modular IT-enabled capabilities Organisations can adopt modular IT-enabled capabilities (Rai, Venkatesh,

Bala, & Lewis, 2010) to decrease micro co-evolution, when the macro co-evolution is

high. Modular systems are composed of elements, which can independently perform

distinctive functions (Simon, 1962). As modular elements can evolve autonomously


without hampering overall structure of the system, they are often robust in the case of

any changes than tightly coupled systems (Pil & Cohen, 2006). In a modular system,

like an ERP system consisting of different modules- sales, customer management,

service delivery and so forth; each of the module provides some capabilities to support

particular business functions. These capabilities are called modular capabilities (Pil &

Cohen, 2006). In a similar way, if the above definition is matched with the concept of

IT-enabled capabilities, then, it can be said that, each of the ERP modules (i.e. IT

assets) interact with some organisational resources, such as, business units, operational

employees, processes, etc. and their interactions give rise some IT-enabled

capabilities, such as, ERP(sales module)-enabled sales, ERP(service delivery module)-

enabled service delivery process etc. In contemporary environment, IT has become

highly modular and continuously changing in nature and that is making the IT-enabled

capabilities evolving faster (Tiwana, et al., 2010).

The modular IT-enabled capabilities can be used to meet with changing

business demands- provide organisations with far more competencies to grow rapidly

and profitably. Rai, et al. (2010) identified that Delivery Corp encapsulated services

from core legacy systems using component based architectures. They further found

that the organisation developed separate subsystems that enable providing various

services- a subsystem that handles brokerage, a subsystem that handles forwarding and

so on. The separation of the services via different application components makes it

easier for the organisations to maintain loose coupling that lower connections (K)

among services so that they can be combined efficiently. Modular IT-enabled

capabilities can be found in digital platform architecture (Tiwana, et al., 2010). By

promoting partitioning of different modular capabilities on the core platform,

organisations can reduce the cost of innovation as the new system does not need to be

changed or innovated from scratch as new modules can be introduced without limiting

effects on the core structure (Gawer, 2009).

Modular capabilities helps to lower down connections that means it maintains

loose coupling among different components and hence different IT-enabled

capabilities. However, developing modular IT-enabled capabilities is a longer process

and it is often hampered by organisational tendency to acquire immediate business

needs rather than long term capabilities. Nonetheless, organisations can adopt

developing modular IT-enabled capabilities to lower down the connections K between


various IT-enabled capabilities in organisations and thus balance evolutionary

connection C.

6.4 CHAPTER SUMMARY

This chapter presents how a co-evolutionary operational model, NKC model

(Kauffman, 1993) can be used to formalise strategies in managing micro and macro

co-evolutionary competition in organisations, in particular in the context of IT-enabled

capabilities. Chapter 5 broadly discusses the micro level (internal) and macro level

(external) co-evolution of IT-enabled capabilities and its impact on competitive

advantage. This chapter has extended the discussion on coevolution by translating

NKC model into the context of IT-enabled capabilities (section 6.3.1) and developing

four strategies based on the simulation results obtained by Kauffman (1993, chapter 6)

following Baum and McKelvey (1999c) approach (section 6.3.2). The strategies are

proposed in relation to the Strategy A and Strategy B of Baum and McKelvey (1999c).

The key aim of proposing strategies is to provide guidance for managers so that they

can better manage the micro and macro coevolution of IT-enabled capabilities in

organisations.

Few limitations are related to the use of the outcomes of NKC model. Any

computational model can never represent a real world scenario. Therefore, the

parameters in NKC model represents a very simplified representation of the events in

a firm. So, the validity of the model has little validity. Nevertheless, NKC models can

provide very useful insights about co-evolutionary relationships (McKelvey, 1999). In

addition, the moves in the NKC model is conceived as random and their adaptive value

is randomly determined. However, in real life these events are less random (McKelvey,

1999).

The next chapter presents a CAS-BVIT framework and discusses important

insights from overall dissertation. It also addresses the limitation of the study and

discusses future research opportunities.

Chapter 7: Discussion and Conclusion 185

Chapter 7: Discussion and Conclusion

This chapter explains the dynamic mechanisms of BVIT, in particular

competitive advantage through a CAS based framework. This is followed by a detailed

discussion on the insights that I have obtained from the study. The structure of the

chapter is as follows. The first section presents the study CAS conceptual framework

on BVIT. The subsequent section presents implications of the proposed CAS

framework by comparing it with major extant BVIT models. The chapter concludes

by explaining the theoretical contributions, practical implications, limitations and

potential future research.

7.1 A CAS-BVIT FRAMEWORK

This section presents a CAS-BVIT framework that reflects how I have applied a

CAS theoretical lens to view how BVIT contributes to strategy in contemporary

organisations’, how IT-enabled capabilities emerge from IT assets and organisational

resources, and how these capabilities influence competitive advantage. This CAS view

of the BVIT sees competitive advantage as the outcome of the complex emergence and

coevolution of IT-enabled capabilities (Figure 7.1). It presents a consolidated version

of the whole BVIT creation process from a CAS lens including the complex emergence

and coevolution of IT-enabled capabilities. It includes all the components of proposed

two frameworks together to instantiate a wide view and broader understanding of the

dynamic way of BVIT creation process. The proposed CAS based BVIT framework

contains two key foci,

1. From IT assets and organisational resources to IT-enabled capabilities

via complex emergence, and

2. From the IT-enabled capabilities towards competitive advantage via

micro and macro coevolution.

In the first part of the CAS-BVIT framework, I argue that IT assets and

organisational resources come together in a relationship that gives rise to IT-enabled

capabilities, as an emergence process following ideas the from the Nevo and Wade

(2010) study on the strategic advantage of IT assets. IT assets are defined as those that

are used to store, process and disseminate information (Wade & Hulland, 2004), and

186 Chapter 7: Discussion and Conclusion

organisational resources are other tangible or intangible factors owned by

organisations (Helfat & Peteraf, 2003).

I argue that the emergence of IT-enabled capabilities from the interactions

between IT assets and organisational resources is non-linear and dynamic in nature. I

propose a complex emergence perspective (Halley & Winkler, 2008) that helps to

explain the dynamic rise of IT-enabled capabilities. Additionally, I suggest that four

enablers (see section 4.3 in Chapter 4): compatibility (Nevo & Wade, 2010) - enables

the interactions between the two entities- IT assets and organisational resources, self-

organised management (Vidgen & Wang, 2009)- spontaneous managerial practices

that support the interactions, semi- structures (Brown & Eisenhardt, 1997)- provide

flexible structures to accommodate unpredictable emergence of the IT-enabled

capabilities, and simple rules (Eisenhardt & Sull, 2001)- provide schemas to guide the

unpredictable emergence in a way that is beneficial for organisational processes. All

of the enablers together help the complex emergence of the IT-enabled capabilities to

occur. Overall, a complex emergence model of IT-enabled capabilities (Figure 7.1)

and several propositions (Table 7.1) are proposed in this study.

Regarding the second part of the CAS-BVIT framework, I argue that the IT-

enabled capabilities start mutually changing with other IT-enabled capabilities at the

micro level (internal) and macro level (external) of organisations10. I argue that a

coevolution perspective (Lewin, et al., 1999) on IT-enabled capabilities can better

explain the reciprocal changes. Based on the coevolution framework by Lewin and his

colleagues (Koza & Lewin, 2001; Lewin, et al., 1999; Lewin & Volberda, 1999), I

propose a coevolution perspective on IT-enabled capabilities that explains how IT-

enabled capabilities coevolve in micro and macro levels of organisations and influence

competitive advantage of organisations. Following Chae (2014), at the micro level,

three mechanisms - variation, selection and retention, explain coevolution of the IT-

enabled capabilities internal to the organisation. Following McKelvey (2002), at the

macro level, three macro coevolutionary dynamics - Red Queen effect, competitive

exclusion and niche separation, explain the competitive actions that organisations

undertake to obtain superior competitive advantage over competitors. This suggests

10 It is important to note that, though IT-enabled capabilities can mutually change with other organisational elements, such as, routines, capabilities, strategies, resources (Koza & Lewin, 2001; Lewin, et al., 1999), I have considered the mutual changes between IT-enabled capabilities only.


that coevolution of the IT-enabled capabilities actually follows a ‘nested’ mechanism

(McKelvey, 1997c) in which macro coevolution triggers micro coevolution of the IT-


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Figure 7.1: A CAS-BVIT framework

The second part also addresses how the overall coevolution of the IT-enabled

capabilities influence competitive advantage of organisations. It reveals how, due to

the macro level competitive action-reaction, firms are highly prone to stay in the Red

Queen competition and are incentivised to take a variety of actions, such as adoption

of new IT-enabled capabilities or reconfiguration of the new ones that erode their own

and competitors’ advantage (D'Aveni, et al., 2010). The three macro coevolutionary

dynamics together represent an action based approach to developing strategic

capabilities (Voelpel, et al., 2005). Therefore, firms in the competition show rivalrous

behaviour to achieve superior IT-enabled capabilities and thus competitive advantage

over others (Chen, 1996). Such behaviour, aimed at achieving better IT-enabled

capabilities than rivals, erodes firms’ existing advantage as well as affecting the rivals’

advantages; this incessant rivalry resulting in a temporary advantage (D'Aveni, et al.,

2010). The competitive dynamics also increase hyper-competition in the industry (Lee,

2010). Under these circumstances, a firm on a dynamic business landscape can pursue

new, temporary advantages by availing itself of valuable, rare and nearly inimitable

IT-enabled capabilities and striving to concatenate a series of temporary advantages

over time, although consistently achieving new temporary advantages will be highly

challenging (D'Aveni, et al., 2010; Tanriverdi, et al., 2010). Thus, I propose a micro-

macro co-evolution model of IT-enabled capabilities (Figure 7.1) and several

propositions (Table 7.1).

As an extension of the second foci as well as to develop new insights from

analysing the outcomes of simulation model, in particular Kaufman’s NKC model

(Kauffman, 1993), I have followed Mckelvey’s approach (McKelvey, 1999) and

propose four high level operational strategies (section 6.3.2 in Chapter 6). The

strategies (new insights) are proposed based on the underlying rationale of Kaufman’s

NKC model. The proposed strategies focuses on the conceptual understanding of the

coevolution of the IT-enabled capabilities and serve as a way for managing micro and

macro coevolutionary competition in organisations.


Propositions related to the emergence perspective of the IT-enabled capabilities

Proposition 1 Greater compatibility between IT assets and organisational resources can positively influence the complex emergence of IT-enabled capabilities.

Proposition 2 Self-organised management to ensure the relationship between IT assets and organisational resources can positively impacts the complex emergence of IT-enabled capabilities.

Proposition 3 Self-organised management to ensure the relationship between IT and business can positively impacts their compatibility.

Proposition 4 Semi-structures to ensure the match between predefined goals with emergent IT-enabled capabilities can positively influence the complex emergence of IT-enabled capabilities.

Proposition 5 Well-defined simple rules can positively impact the complex emergence of IT enabled business capabilities.

Propositions related to the coevolution perspective of the IT-enabled capabilities

Proposition 1a

Focal firm can improve IT-enabled capabilities via variation, selection and retention evolutionary processes.

Proposition 1b

Micro coevolution of IT-enabled capability occurs when evolutionary improvement of one IT-enabled capability causes evolution in the associated IT-enabled capabilities.

Proposition 2a

The Red Queen competition can improve IT-enabled capabilities of firms in macro coevolutionary relationships.

Proposition 2b

The Red Queen Effect can positively influence competitive advantage of firms in coevolutionary relationships.

Proposition 3a

Firms with better IT-enabled capabilities in the macro level competition can achieve superior competitive advantage than firms with weaker IT-enabled capabilities.

Proposition 3b

The competitive exclusion law can influence firms with weaker IT-enabled capabilities to compete in obtaining improved IT-enabled capabilities.

Proposition 4 Firms with IT-enabled niche capabilities will more likely to achieve superior competitive advantage than their competitors.

Proposition 5 Well-balanced micro and macro coevolutionary processes of IT-enabled capabilities can enable the focal firm to achieve superior competitive advantage over rivals.

Table 7.1 Propositions related to the emergence and coevolution perspectives of the IT-enabled capabilities


In the next section, I will address how my proposed dynamic, non-linear

perspective is more useful for understanding BVIT in contemporary organisations than

the existing static, linear perspective.

7.2 FROM A STATIC, LINEAR PERSPECTIVE TO A DYNAMIC, NON-LINEAR PERSPECTIVE

One of the underlying arguments of the thesis is that the contemporary business

environment is no longer simple, linear and predictable. The deployment of digital

technologies has given rise to complexities, dynamism and unpredictability in

contemporary organisations (Merali, et al., 2012), which requires methodological and

conceptual alternatives to deal with these changes. Based on this argument, this study

has taken a dynamic and non-linear approach and adopted CAS theory (Dooley, 1996)

to explore the underlying dynamics related to BVIT generation.

One of the most prominent theories used to investigate the business value of IT,

the RBV approach, argues that firms possess IT resources that are valuable and rare

and that can provide firms with long term competitive advantage if the firms are able

to protect against resource imitation, transfer or substitution (Barney, 1991; Grant,

1991). However, the RBV has been criticised for being a static concept, unable to

adequately capture the nature of a dynamic business environment (Eisenhardt &

Martin, 2000; Priem & Butler, 2001). Considerations such as how resources are

developed, how they are integrated in the firm, how they are configured to obtain

competitive advantage in the dynamic business environment, have been under-

explored in the literature (Wade & Hulland, 2004). Therefore, the concept of dynamic

capabilities attempts to bridge this gap by adopting organisational and strategic

processes that help firms to manage resources into productive assets in the changing

business environment (Eisenhardt & Martin, 2000; Teece, et al., 1997). The dynamic

capabilities help a firm to adjust its resources so that it can maintain the sustainability

of the firm’s competitive advantage, which might be eroded in the dynamic

environment (Helfat & Peteraf, 2003).

A few prominent models in the IS literature partially account for the dynamics

related to BVIT generation, such as the Sambamurthy, et al. (2003) strategic model

linking IT competence to a firm’s performance via competitive actions, Tanriverdi, et

al. (2010) complex adaptive business systems based model of a firm’s strategic

competitive advantage, and the dynamic capability theory based models emphasising


competitive advantage (e.g. Kim, et al., 2011; Schwarz, Kalika, Kefi, & Schwarz,

2010; Wheeler, 2002). However, the underlying organisational dynamics driving the

relationships among IS and other organisational resources remain unclear, though they

are understood to be complex in nature (Tanriverdi, et al., 2010). How IT resources

and their related capabilities continuously emerge and evolve over time and how they

influence competitive advantage in the dynamic environment is still opaque. The

strategic value of resources lies in their inherent complexity, and attempts to explicate

causal explanations of this complexity are limited in the literature (Colbert, 2004);

neither are RBV and dynamic capabilities theories clear about this. Consequently, this

study has adopted a dynamic theory, CAS theory, that acknowledges such issues and

acknowledges that IT driven capabilities may stem from causal ambiguity, complex

relationships and/ or unpredictable path dependencies to influence competitive

advantage (Colbert, 2004).

Based on the Schumpeterian dynamics of disequilibrium (Schumpeter, 1934),

the CAS theory serves as an approach to explore the dynamics related to the emergence

of IT-enabled capabilities, from non-linear relationships between IT assets and

organisational resources. The CAS theory holds a dynamic opportunistic logic in this

study similar to Sambamurthy, et al. (2003), who suggested in relation to firm

performance, that superior firms achieve competitive advantage through the

continuous creation of valuable and rare IT-enabled capabilities and competitive

actions (D'Aveni, et al., 2010). This logic in the context of this study suggests that

competitive advantage can be eroded if rivals obtain superior IT-enabled capabilities

or uncover new market opportunities that threaten the focal firm’s competitive

advantage (Sambamurthy, et al., 2003). The CAS theory thus first draws attention to

the dynamic and complex emergence of IT-enabled capabilities. It then argues that

firms follow a coevolutionary strategic mechanism at the micro level that reconfigures

and adjusts various IT-enabled capabilities. Moreover, firms also allow coevolutionary

adaptation of the IT-enabled capabilities at the macro level, which helps firms look for

windows of opportunity in achieving unique IT-enabled capabilities (Barney, 1991)

superior to its competitors. The fit between micro and macro level coevolution of the

IT-enabled capabilities helps a firm to achieve greater competitive advantage over its

rivals. It also implies that the adoption of new IT-enabled capabilities may largely

influence and change other related IT-enabled capabilities at the micro level (Lewin,


et al., 1999). The dynamics at both micro and macro levels, together provide a firm

with better detection and exploitation opportunities (Mata, et al., 1995), i.e. identifying

and deploying IT-enabled capabilities so that it can acquire superior competitive

advantage over its rivals.

In sum, the complex emergence of the IT-enabled capabilities and the

coevolution between different IT-enabled capabilities inside organisations represent

the non-linear internal dynamics related to the IT-enabled capabilities. In addition, the

macro level coevolution of the IT-enabled capabilities between two or more firms

portrays external competitive dynamics related to the IT-enabled capabilities. The

micro coevolution occurs as a ‘nested’ mechanism within the macro coevolution of the

IT-enabled capabilities (McKelvey, 1997c) that a firm undertakes to outperform its

competitors in the dynamic environment and achieve competitive advantage via

obtaining valuable, rare and nearly inimitable IT-enabled capabilities (Barney, 1991;

Bharadwaj, 2000).

7.3 KEY INSIGHTS COMPARED WITH PROMINENT, TRADITIONAL BVIT MODELS

In this section, I have specifically addressed the limitations of the prominent,

traditional BVIT models, Nevo and Wade (2010), Melville, et al. (2004) and Wheeler

(2002), and discussed what I have added to each model to deal with the limitations.

Moreover, I have also discussed several specific implications that derive from my

proposed CAS-BVIT framework in section 7.3.1.

Nevo and Wade’s Model vs. The CAS-BVIT Framework

The RBV and systems thinking based BVIT model by Nevo and Wade (2010),

maintains that IT-enabled resources emerge from interactions between IT assets and

organisational resources which have strategic potential that can contribute to the firm’s

competitive advantage. However, the systems thinking based emergence concept used

in the Nevo and Wade’s model is static and linear in nature. One of the key reasons is

that the underlying components of the IT assets and organisational resources are

assumed to be stable and unchanged during the time when they are in the relationship.

This also results in the predictability of the outcomes of the relationships, i.e. IT

enabled resources and the emergent capabilities. These assumptions become

questionable in the dynamic business environment, where every element of an


organisation is constantly changing (El Sawy, et al., 2010; Tanriverdi, et al., 2010). In

a dynamic and complex environment, at the time any measures or hypothesized

emergent relationships are captured, one or more of the components of an organisation

will exhibit emergent moves, which may change the relationships between the

components, and in the new state previously hypothesised relationships are unlikely

to be valid (Tanriverdi, et al., 2010).

My proposed complex emergence view addresses the limitations of Nevo and

Wade’s model, which are the components of IT assets and organisational resources are

stable and the outcomes of their relationships- the emergent capabilities are

predictable. My proposed view considers the ‘dynamic and non-linear’ changes in the

components of the IT assets and organisational resources, that give rise to the IT-

enabled capabilities, which may not result in expected outcomes. Moreover, my

proposed view also focuses on the ‘unpredictable’ nature of emergent IT-enabled

capabilities via the complex emergence lens. The overall complex emergence view

and the inclusion of additional enabling conditions- self-organised management, semi-

structures and simple rules with the existing compatibility condition, addresses in

dealing with the limitations of the Nevo and Wade model.

Melville’s Model vs. The CAS-BVIT Framework

Melville, et al. (2004) RBV based BVIT model focuses mainly on the locus of

IT business value in three domains: focal firm, competitive environment, and macro

environment. However, the authors are relatively opaque in describing the underlying

dynamic mechanisms, such as, how a focal firm acts or reacts with regard to the

changing competencies of rivals, how resources are reconfigured or developed over

time and their influences on BVIT in their model. Although, the authors acknowledge

that “The macro environment is dynamic and complex” (Melville, et al., 2004, p. 310),

they focus on the macro factors such as, telecommunication structure, which

potentially shape the BVIT of organisation and thus they underexplore the dynamic

mechanisms related to the BVIT generation. Besides, they are silent about the dynamic

mechanisms in relation to the focal firm as well.

My proposed CAS-BVIT framework addresses this limitation by identifying the

latent mechanisms via which organisations obtain competitive advantage in the

dynamic business environment. This involves explicating intra-firm and inter-firm

action based competitive processes (D'Aveni, et al., 2010) related to IT-enabled


capabilities, which ultimately help firms to obtain competitive advantage. In particular,

the CAS-BVIT framework includes coevolutionary mechanisms at two levels - the

micro and macro11 levels of organisations. The micro level coevolution explains how

IT-enabled capabilities are improved over time via variation, selection and retention

mechanisms (Aldrich, 2006) and the macro level coevolution explains three key

competitive dynamic mechanisms, Red Queen effect, competitive exclusion and niche

separation towards achieving competitive advantage via acquiring valuable and rare

IT-enabled capabilities. These added mechanisms in the two levels-micro and macro

levels help to deal with the limitation.

Wheeler’s NEBIC Model vs. The CAS-BVIT Framework

Rooted in RBV, the dynamic capability theory focuses on the ability of a firm to

achieve new forms of competitive advantage by renewing its resources in congruence

with the changing business environment (Teece, et al., 1997). One of the major applied

dynamic capability focused BVIT models, the Wheeler (2002) model of Net-Enabled

Business Innovation Cycle (NEBIC) argues that net-enabled organisations reconfigure

their internal and external resources to employ digital networks to exploit business

opportunities. Therefore, the net enabled dynamic capabilities engage processes and

routines that help organisations to turn IT into customer value (Sambamurthy, et al.,

2003). In the NEBIC view, a firm possesses four capabilities that help the firms to

create customer value - choosing emerging information technologies, matching

economic opportunities with emerging technologies, executive business innovation for

growth, and assessing customer value. NEBIC is an applied dynamic capability theory.

The term ‘dynamic’ implies a tautological notion of an organisational capability that

allows for continuous adaptation, otherwise the addition of ‘dynamic’ is either obsolete

or misleading (Burisch & Wohlgemuth, 2016); however, the NEBIC theory does not

include the notion of ‘continuous adaptation’. Although the dynamic capability theory

has the potential to contribute to understanding competitive advantage, it does not

include the concept of ‘complex system interaction effects’ (Colbert, 2004). Moreover,

NEBIC theory is driven by predefined capabilities and routines that help a firm to

create customer value.

11 For the purpose of the study, I have considered two domains- the competitive environment and macro environment together as macro level. It is briefly discussed in section 5.1, chapter 5.


My proposed CAS-BVIT framework addresses the above mentioned three

limitations of the NEBIC model. First, the proposed coevolutionary view, in particular

the macro level coevolution of the IT-enabled capabilities suggests that because of the

competition among firms, each firm’s adaptation move changes the adaptation move

of rival firms and thus firms continually adapt to obtain superior advantage over their

rivals. Moreover, the micro coevolution, the variation of the IT-enabled capabilities

provides the raw material for the continuous adaptation of the other IT-enabled

capabilities (Axelrod & Cohen, 2000). Second, both of the proposed views- complex

emergence and coevolution in the context of IT-enabled capabilities deal with complex

interaction effects among components. The complex emergence view suggests that the

dynamic and non-linear interactions between IT assets and organisational resources

give rise to emergent IT-enabled capabilities. The coevolution view deals with the

reciprocal interactions between two or more IT-enabled capabilities between firms in

competition at micro level and inside firm in micro level. Third, the underlying logic

of spontaneous responsiveness of firms in coevolutionary relationships by developing

or reconfiguring particular IT-enabled capabilities makes my CAS-BVIT framework

different from the partially planned12 and predictable opportunistic logic of NEBIC

theory (Pavlou & El Sawy, 2010). In addition, the emergent IT-enabled capabilities

are characterised as unpredictable in nature as discussed in section 7.1.

7.3.1 Implications of the CAS-BVIT Framework

The previous section highlights the limitations of the traditional BVIT models

and how I have dealt with these limitations using a CAS perspective. Following, I

discuss some implications of my proposed CAS-BVIT framework.

Dynamic and unpredictable view of contemporary business environment:

The business environment has become less predictable in nature, which requires

organisations to be super responsive, by understanding the impact and likelihood of

disruptions (Coutu, 2002). A small change in the organisation can cause large-scale

effect, like the butterfly effect (Lorenz, 1963), according to which, a flap of butterfly

wings can change the course of weather. In a similar way, my proposed view

acknowledges that a small change in any component of the IT assets (e.g. software

12 Choosing emergent technologies must be planned and predictable, whereas the value provided by a particular technology might not be as expected and thus I have used ‘partially’


applications, or IT skills of a developer) or organisational resources (e.g. management

practices, business functions), can give rise to unexpected IT-enabled capabilities,

which might or might not be beneficial for overall organisational functions (Gleick &

Berry, 1987). So, basically my CAS view supports such notions that a small tweak,

such as a small IT investment can bring greater competitive advantage to an

organisation. Or it can cause negative consequences; for example a small shift in the

downstream of consumer market can become amplified and cause bankruptcy among

equipment manufacturers (Lee, Padmanabhan, & Whang, 1997).

A novel IT management style: Such a point of view actually challenges the

traditional IT management style, such as long term planning, IT strategy formulation,

evaluation and implementation and control (Rosenhead, 1998). So the question

becomes, how should we look at IT management? Stacey (1996) suggests that this type

of creative disorder in organisations needs to be taken to heart by managers. Learning

about the fundamentally unpredictable future of contemporary organisations can be

fostered so that managers not only focus on their success relative to pre-determined

targets; they need to be reflective in the light of unfolding events and use assumptions

(mental models (Senge, 1990)) to set up various actions (Rosenhead, 1998). Rather

than trying to consolidate stable equilibrium, organisations need to seek for a region

of instability and welcome disorder in the system as it will provoke IT management

creativity, which may lead to a better competitive position. Moreover, a combined

adoption of ordinary and extraordinary IT management, in which the former refers to

the day-to-day activities and the latter presents required actions to accommodate open-

ended change, can be adopted to handle such unpredictable situations (Rosenhead,

1998).

Facilitates longitudinal research: The proposed coevolution perspective of the

CAS-BVIT framework provides a basis for longitudinal research on firm adaptation

in general with the emphasis on the emergence and coevolution of the IT-enabled

capabilities. The need for conducting longitudinal studies on organisational adaptation

on is not new (Tushman & Anderson, 1986; Van Valen, 1983). At the organisational

level, longitudinal research requires details of firm specific strategic and evolutionary

events with time series data (Lewin, et al., 1999). The proposed framework includes

both internal and external coevolutionary dynamic mechanisms that together serve as

sources of insights for managers to identify the existence of different dynamic events


(such as, VSR and Red Queen, Niche Separation) in relation to IT-enabled capabilities.

As a major barrier for longitudinal studies is the absence of time series data on

adaptation events (Lewin, et al., 1999), managers need to record the dynamic

mechanisms of specific events (e.g. the transition from ERP to salesforce based

customer management- event due to Red Queen competition- dynamics) and allocate

time in the organisational portfolio, which can be used to conduct longitudinal

research.

7.4 CONCLUSION

The dissertation started with an overarching research problem; that is, how BVIT

can be understood in the contemporary, dynamic business environment. The high level

research question is-

RQ 1: How is BVIT created in the dynamic business-IT environment?

To address this research question, the study takes a complex adaptive system

approach, more specifically it adopts emergence and coevolutionary perspectives

together, to explore BVIT in a dynamic environment. Using the emergence concept of

CAS theory, first, the study explores the way IT-enabled capabilities emerge. It then

adopts the coevolution concept of CAS theory to explore how these IT-enabled

capabilities help organisations to achieve competitive advantage.

Consequent research questions are,

RQ 1.1: How do IT-enabled capabilities emerge?

RQ 1.2: How do the IT-enabled capabilities coevolve and influence competitive

advantage?

The resultant frameworks and propositions from the emergence and

coevolutionary perspectives together provide a holistic overview of how BVIT, in

particular, competitive advantage is created in contemporary organization (Figure 1.1).

The proposed perspectives may stimulate analysis and understanding of the role of IT

in organizational strategy by pointing to foundational aspects and suggesting emergent

relationships. The theoretical contributions, suggestions for future research, and

limitations, are presented next.


7.4.1 Theoretical Contributions

The study has several theoretical contributions. First, it has provided a deeper

understanding of how BVIT is created in the modern dynamic business environment

and has shown the limitations of existing models of BVIT (as summarised in section

7.3). Second, CAS theory has been elaborated by its application to BVIT; in particular,

the concepts of emergence and coevolution have been rigorously applied to IT enabled

capabilities. Third, The development of the framework can be considered as

contributing to BVIT theory, comprising an ontological shift from a static-linear view

on BVIT to a more dynamic-complex view on BVIT. Fourth, the new insights from

NKC simulation model on coevolution of IT-enabled capabilities contributes to the

foundation of developing BVIT theory based on simulation model. Fifth, using a CAS

lens, my study contributes to the action based competitive advantage literature on

BVIT. Sixth, the structured literature review on CAS theory in IS contributes to the

knowledge body of both CAS theory and IS literature. Last, the dissertation contributes

to theory building in information systems by developing a novel research method

based on the ideas of Shepherd and Suddaby (2017) in theory development. Each of

these are discussed below.

Complex emergence as a way to understand the unpredictable rise of IT-

enabled capabilities: It has been widely agreed that CAS theory can capture dramatic

changes occurring in social institutions (Cohen, 1999; Lewis, 1994). Yet there is a

growing concern that as emergent events are unpredictable (Goldstein, 1996), there is

no assurance that the outcome of interest (which, in this study, is the emergence of the

IT-enabled capabilities) will occur, Indeed unforeseen consequences are a defining

characteristics of CAS (Mitleton-Kelly, 2003b). Therefore, this study proposes that the

use of emergence in prior BVIT studies to understand competitive advantage e.g.

Nevo and Wade (2010) is not clearly explained. More recent ideas of complex

emergence are concerned about this potential ‘black box’ of unpredictability (Pavlou

& El Sawy, 2011). The notion of complex emergence helps us to understand the

unpredictable nature of the emergence of the IT-enabled capabilities, as well as to

understand the enabling conditions (e.g. simple rules) that can potentially serve as a

countervailing force to take advantage of the unpredictable phenomena (in this study,

the IT-enabled capabilities).


Coevolution as a way to understand micro and macro level changes in IT-

enabled capabilities and competitive dynamics between firms in competition: The

coevolutionary perspective of the IT-enabled capabilities offers new ways of managing

the processes of their development in organisations. Recognising the tensions between

various IT-enabled capabilities and their evolutionary adaptations is more likely to be

effective than the traditional approaches that are premised on rationally planned and

controlled processes through which the IT-enabled capabilities adapt in business

systems (Tanriverdi, et al., 2010). This is particularly a very important issue given the

increasingly dynamic business environment in which information systems connect

people, places and organisations. Moreover, the study suggests that IT-enabled

capabilities continually coevolve internally and externally with organisations. Thus the

key is to identify valuable and rare IT-enabled capabilities, as well as to initiate

strategic variations for competitive success in organisations (Volberda & Lewin,

2003). Organisations should invest in dynamic capability, which enables them to

sense, seize and reconfigure different unique IT-enabled capabilities (Eisenhardt &

Martin, 2000). In another way, organisations can initiate strategic variation via

improvisation that can open the door allowing radical innovation of IT-enabled

capabilities to occur, hence, competitive advantage (Chae, 2014).

CAS-BVIT framework as a dynamic way to understand BVIT: The

proposed CAS-BVIT framework (Figure 7.1 in section 7.1) helps to capture the logic

of how IT assets derive business value via creating IT-enabled capabilities towards

competitive advantage. The proposed dynamic and non-linear approach to understand

the dynamics related to BVIT research, contributes to the overall BVIT research in IS.

A majority of existing BVIT studies in the IS literature have considered a static

perspective and thus my proposed dynamic approach will bring further attention to the

IS scholars who are very interested in the BVIT related research.

The framework makes explicit how emergence and coevolution and related

concepts (such as enabling conditions) operate to achieve BVIT for competitive

advantage. The propositions developed via the emergence and coevolution

perspectives can be considered as contributions to the existing knowledge. The

propositions related to the complex emergence might be beneficial for empirical

testing to examine the unpredictable effect of various IT-enabled capabilities on

overall organisational success. The proposed enabling conditions - simple rules, semi-


structures, and self-organised management can be used as tools to manage possible

unforeseen outcomes raised from the IT-enabled capabilities in organisations. In a

similar way, the propositions related to the micro and macro level coevolution of the

IT-enabled capabilities reveal important insights on coevolutionary adaptation of IT-

enabled capabilities in dynamic environment. These propositions can also be

empirically tested in contemporary organisations. Moreover, exploring the micro

coevolutionary dynamics- variation, selection and retention of the IT-enabled

capabilities together with macro coevolutionary dynamics- Red Queen effect,

competitive exclusion and niche separation will help managers to practically

investigate the dynamics in organisations.

Conceptual understanding on multilevel CAS view and dynamic fit between

micro-macro levels: The attempt to integrate multiple levels of research in a single

framework is significant in IS as the discipline suffers from fragmentation and some

incoherence (Robey, 2003). In addition, the constructive tension between micro and

macro levels and flow of different resources from outside into inside organisations has

been emphasised in major strategy research (Barney, 2001; Priem & Butler, 2001), and

this is focused on in the proposed framework via IT-enabled capabilities.

The proposed coevolutionary perspective can serve as an important foundation

for understanding dynamic fit between between the micro and macro coevolution of

the IT-enabled capabilities and it also puts managers in the central role to help achieve

this dynamic fit (Madhok & Liu, 2006). Managers in organisations scan for changes

in the business environment that may drive an opportunity or challenge for

organisations. So managers play a role in influencing internal relationships between

different IT-enabled capabilities as well as adjusting external relationships with other

competitors by scanning these companies’ benefits that are driven by IT-enabled

capabilities. From an evolutionary point of view, this particular phenomenon ensures

requisite variety, necessary information to adjust (fit) the micro and macro coevolution

of the IT-enabled capabilities (Ashby, 1968) and a balance between internal and

external selections (Miner, 1994).

A novel way of learning about BVIT theory from simulation model: I have

adopted the simulation outcomes of Kauffman’s coevolutionary NKC model

(Kauffman, 1993) as a way of getting new insights of the coevolution perspective on

IT-enabled capabilities. Based on the underlying rationale of NKC model and


following Mckelvey’s approach (McKelvey, 1999), I have proposed some

propositions that can be helpful in managing the coevolution of IT-enabled

capabilities. Based on the observation of the rationale of NKC, I have found that it is

crucial for organisations to balance the coevolution of IT-enabled capabilities in

between the micro and macro levels so that the coevolutionary balance helps

organisations to achieve competitive advantage, which is also acknowledged by

McKelvey (1999) in the context of value chain coevolutionary competencies in

competing firms. This simulation based theorising shows a novel way of learning

about BVIT theory, which is relatively uncommon in IS although it has been popular

in strategic management discipline (e.g. Baum & McKelvey, 1999c; Rivkin, 2000).

Although the derived propositions might be dismissed by some on the basis of being

developed by biologists, the developed propositions can be practised in organisational

settings for further validation and can be used for critical strategic decision making.

A new discussion on action based competitive advantage via the CAS lens:

The study contributes to the research stream related to action based competitive

advantage (D'Aveni, et al., 2010). The deeper insights discussed through the three

coevolutionary dynamic mechanisms, Red Queen, competitive exclusion and niche

separation, help to explain how organisations erode each other’s competitive

advantage and achieve temporary competitive advantage over the dynamic landscape.

The concept of temporary competitive advantage is illuminated via the CAS lens and

it is hopeful to open new discussions in relation to this particular concern.

A new conceptual theory building approach: The research approach for

theory building derived particularly for this study, based on the review by Shepherd

and Suddaby (2017) is unique and can potentially be applied to other IS and

organisational studies. My application of these ideas for exploring the dynamics of

BVIT provides a first illustration of how this approach can be used. Using “thickest

descriptive literature” (Mintzberg, 2005, p. 362), I have attempted to develop theories

of BVIT. The integration of conceptual descriptions as a way of describing complex

dynamics is a particular feature of the approach based on Shepherd and Suddaby

(2017). In particular, the research method can be adopted for theory building studies

in IS research.

Structured literature review on CAS theory in IS research as a future point

of reference: Finally, the structured literature review on CAS theory in the IS


discipline contributes to the overall knowledge body on CAS theory and IS theories.

To date, as far as I am aware, this is one of the very first structured literature review

within the IS field that has captured a variety of aspects related to CAS theory, such

as, concepts applied in IS research, related contributions, objectives, different

theoretical perspectives, and methodologies as well as context. The information

gathered on the applications of CAS theory in IS domain will serve as a knowledge

repository of CAS related IS research, hence it is expected to advance the use of CAS

theory in future IS research. For example, in the strategic management field, the

potential benefits of CAS theory such as, conceptual and simulation modelling or

fitness analysis or landscape behaviour analysis can be used in IS research.

7.4.2 Implications for Practice

The dissertation is highly abstract and conceptual, so the direct practical

implication may be more limited, but more concrete and applied follow-up work can

result in interesting, new strategies and guidelines. For example, complexity thinking

can be combined with RBV theory to develop principles for managing strategic human

resource management (Colbert, 2004). These principles, which are dynamic in nature

can be tested in organisational settings. On a high level, this can be helpful to examine

the practical implications of unfolding situations in planning and managing basic

aspects of organisations (Mintzberg, 1989).

The developed strategies based on NKC model in chapter 6 (section 6.3.2)

exhibit relationships with managerial actions to impact the organisation’s competitive

advantage. Anyone can adopt these strategies and apply them in their organisations

because of their intuitive scale-free nature- top strategy level, or mid-level, or at the

level where various IT-enabled capabilities interact. For instance, Benbya &

McKelvey (2006a) applied such dynamic strategies in IS-business alignment.

Admittedly, this example is not related to managing IT-enabled capabilities for better

competitive advantage, per se, it illustrates the real world potential of applying such

dynamic managerial actions similar to what I have proposed. Arguably, it is better to

initiate the proposed strategies by IS managers so that they can help organisations to

achieve better competitive advantage.

Moreover, the emergence and coevolutionary perspectives of IT-enabled

capabilities proposed in the thesis can provide a basis for a constructive and

penetrating dialogue among practitioners about the unfolding events and tensions


related to IT-enabled capabilities that develop in organisations. The proposed enabling

conditions in the emergence perspective and the dynamics in the coevolutionary

perspective can serve as guidance for managers for technology alignments and change

management activities. McKelvey (1999) suggests that organisation should

synchronise concurrent exploration and exploitation, a balance between innovation in

IT and process improvements for the improvement in productivity. Further,

Kauffman’s NKC model suggests that organisations should mix long jumps

(exploration) and adaptive walk (exploitation) with the implication of radical

innovation in IT systems for better process improvement. The above suggestions are

in align with my micro coevolutionary view on IT-enabled capabilities (see section

5.4). The case narrative used in Chapter 4 (section 4.4.2) suggests that radical

innovation in Enterprise Systems performs better than incremental innovation in the

case organisations. The above examples, admittedly highly relevant to managerial

actions and complexity leaderships that might guide IS managers to better synchronise

and manage IT-enabled capabilities for continuous improvement in organisational

productivity.

One of the core contributions of the CAS theory is developing mental models

(Senge, 1990) via the application of complex emergence and coevolution concepts.

My proposed emergence and coevolution frameworks can help practitioners visualise

IT-enabled capabilities map in their organisations. For example, firm can categorise

IT-enabled capabilities into operational, strategic and support and then analyse how

direct and indirect ties among these capabilities shape their position in the business

landscape in comparison to their competitors. Moreover, firm can use the emergence

framework to predict any future IT-enabled capabilities. This will also help firm for

better capability management for competitive market positions. In a similar way, the

coevolution framework can help firm to map the capabilities in micro and macro levels

and then manage them accordingly with current organisational goals to obtain better


Describing the dynamic mechanisms underlying BVIT is an important

contribution for IS practitioners who seek to understand how BVIT is created in

contemporary organisational contexts via IT-enabled capabilities. The relationships

between IT assets and organisational resources depends on the degree of compatibility

between these two, and it also influences IT enabled capabilities. Thus, it alerts


managers to consider not only the IT assets in which they invest, but also the

relationships with the organisational resources with which these assets are fused. They

can also be aware of the importance of simple rules and semi-structures for the

emergence of IT-enabled capabilities which are conducive for overall performance.

Moreover, managers can mind map a portfolio of the dynamics related to IT enabled

capabilities to better realise the state and importance of each capability and how it

brings advantage.

7.4.3 Limitations

My study acknowledges several limitations. The thesis has considered CAS as a

theory following (Stacey, et al., 2000), however, several IS and management

researchers, (Benbya & McKelvey, 2006a; McKelvey, 2001; Merali, 2006) consider

CAS as a concept of complexity theory. There are two major school of thoughts- the

European school led by Prigogine, Allen, Cramer, Haken, and Mainzer (Allen, 2001;

Cramer, 1993; Haken, 1977; Prigogine, 1984) and among many others and North

American school rooted in the work of Kauffman, Lorenz, Anderson, Holland, Arthur

(Anderson, 1972; Arthur, 1989; Holland & Miller, 1991; Kauffman, 1993; Lorenz,

1963) among many others influence the way CAS is defined within complexity

theories. Further, a number of complexity scholars, (Merali, 2004; Stacey, et al., 2000)

warn about using CAS in interpreting engineering type of systems, including IT

systems, as the system components remain unchanged while they are interactions.

Such interpretation can cause intuitive inconsistencies in the ontological and

teleological foundations of CAS theory. Consequently, any literature review on CAS

may become ontologically and teleologically inconsistent, which is a limitation of my

structured review on CAS. An in-depth study by complexity experts gathering

evidences on the evolution of the complexity theories from root into various disciplines

and mapping them in a coherent and consolidated framework may resolve such

inconsistencies.

This study has only emphasised the complex emergence of IT-enabled

capabilities. However, IT-enabled capabilities can rise via simple emergence process

similar as Nevo and Wade (2010) studied. My study is not disregarding them, however,

due to the research focus on understanding the dynamic aspect of BVIT creation, such

simple emergence phenomena are not considered in the thesis, which is a limitation of

thesis.


The conceptual line between the emergence and coevolutionary perspectives is

drawn to enable the conceptual understanding of the two foci of the study.

Theoretically, it is difficult to draw such distinctions as CAS concepts are highly

intertwined and such distinction can be ambiguous in the use of complexity theories

(Mitleton-Kelly, 2003b). However, I have made an assumption that the emergence and

coevolution of the IT-enabled capabilities occur separately to better understand the

dynamic mechanisms related to the BVIT, which is a limitation.

In chapter 4 (section 4.4.2) and 5 (5.6.2), I have adopted multiple empirical case

studies to validate my proposed frameworks and propositions, which is known as

internal validity (Eisenhardt, 1989). However, internal validity in this thesis is just a

first step and quite limited. Scholars (McKelvey, 1999; Merali, 2006) followed similar

approaches in their research to validate their proposed theories. A dedicated empirical

case study can be conducted in future to establish concrete validation of the proposed

theories in this research.

Moreover, the use of CAS theory is questioned by scholars, such as not being

applicable in organisational studies where traditional streamlined theories, such as

RBV or dynamic capabilities theory are preferred, to explore BVIT. More broadly,

CAS raises ontological and epistemological issues in relation to research and

knowledge accumulation. Because of the continuous and rapid changing nature of the

CAS components, it is often assumed that at the time when the information is collected,

CAS is in a temporary equilibrium state, which means the gathered knowledge on the

relationships is unlikely to be valid because components might change during that

short window of stability (Tanriverdi, et al., 2010). Therefore, while the attractions of

the CAS theory are apparent, this limitation is particularly implicit in CAS based

research. Another limitation of the research includes the consideration of coevolutionary

relationships only between IT-enabled capabilities at micro and macro levels of

organisations. In organisational settings, IT-enabled capabilities actually coevolve

with other organisational elements, such as, routines, capabilities, strategies, resources

(Koza & Lewin, 2001; Lewin, et al., 1999), but these coevolutionary relationships are

not considered in this study, which can be investigated in future research.


In addition, the study is highly conceptual and based on the existing literature.

Further development of theory and empirical testing is required in future research,

which will help to test the proposed framework and related propositions.

Furthermore, I have focussed on the notion of temporary advantage in the

context of contemporary organisations, but there are many organisations who are

actually achieving sustainability of their competitive advantages in these dynamic

environments (Bharadwaj, et al., 2013). This study does not consider the how the

temporary advantage can become sustainable nor indeed what ‘temporary’ means,

time-wise, which can be explored in future research.

7.4.4 Future Research

The study has drawn on many case studies, hypothetical examples and prior

relevant research in information systems to illustrate and explain claims.

Consequently, the dissertation provides many examples that offer preliminary

empirical illustration regarding the viability of the proposed frameworks. While

grounded in relatively new theories in the IS discipline and supported by anecdotal

evidence, the usefulness of the unified model may be further evaluated by

operationalizing the conceptual constructs and empirically testing the propositions as

well as conducting simulation for exploration and testing. As a first step, in Chapter 6,

I have shown how to develop deeper insights based on simulation outcomes Therefore,

the study calls for future empirical research based on the theoretical perspectives

developed in the context of BVIT to get better insights and empirical support for the

arguments, assumptions and models. Further, operationalising CAS perspectives can

be challenging and problematic (Rivkin & Siggelkow, 2007; Siggelkow, 2002). Thus,

I suggest that researchers seeking to test the conceptual models should employ, as a

methodological compromise to operationalise different variables and constructs.

Furthermore, the inconsistent interpretations of CAS and the invalid ontological and

teleological foundations of CAS theory will require to be addressed by an in-depth

engagement of the whole body of work via complexity experts.

I believe that the proposed perspectives in this dissertation can be applied in

different ways. First, an operational model of the emergence perspective on IT enabled

business can give insights into phenomena which are difficult to understand via

theories. Future studies can also extend my integrated CAS framework to large

organisations, where there are multiple levels, highly digitalised and they depend


highly on competition in niche markets, to test my model in real life to make sense or

for decision making.

One of the key insights from the proposed CAS theory is that the things we plan

and make decisions about in relation to the organisational context, do not always work

as planned. There is a huge potential for research, particularly in this area of

unpredictability. As this study suggests that the concept of sustainability is being

eroded via unpredictability, research into identifying and reducing unpredictability to

some extent would positively contribute to the research on BVIT. Future research may

be able to shed more light on this important issue by exposing the barriers that prevent

sustainable competitive advantage in contemporary organisations.

The research asserts that there is ongoing Red Queen competition among

contemporary firms for enhancing IT-enabled capabilities over their competitors.

However, there is very little research that has focused on the firms in the competitive

exclusion region or niche separation and this is mainly focused in the strategic

management and marketing literature (Kemp, et al., 1998). There is a possible

opportunity to investigate these dynamics in empirical settings to better understand

these dynamics and how they influence competitive advantage in real life.

In sum, an empirical case study with the emphasis on the conceptual claims in

the study as well as validating the hypotheses in a real life case study would establish

a strong base from this study. A simulation study such as Nan (2011) can also be

conducted as a way of validating the assumptions in the conceptual models as well as

the hypotheses.

This dissertation has explored the dynamics related to the business value of IT

(BVIT)—in particular, competitive advantage—in contemporary organisations. It

adopts CAS theory as a lens to explore the emergence of different IT-enabled

capabilities, their coevolution, and their influence on competitive advantage. The study

highlights the dynamic mechanisms via which IT-enabled capabilities emerge in

organisations. It also provides explanation on the coevolutionary dynamic mechanisms

in relation to the IT-enabled capabilities and discusses the influence of IT-enabled

capabilities in shaping competitive advantage. In the future, these mechanisms will be

challenged and adapted, but until then, they provide plausible guidance to researchers

to consider about taking a complex and dynamic view on contemporary organisations


in the quest of obtaining new knowledge and in-depth insights on the generation of

BVIT.


Appendices

Appendix A

Abstract of the publications

Publication Details Abstract

Onik, M. F. A., Fielt, E., & Gable, G. G. (2017). Complex Adaptive Systems Theory in Information Systems Research-A Systematic Literature Review. In the Proceedings of 21st Pacific Asia Conference on Information Systems (PACIS), Langkawi, Malaysia.

A special branch of complexity science, complex adaptive systems (CAS), is a way of thinking about systems of interacting agents and how order emerges in systems from the interactions of agents. Though CAS has been widely used in management and organizational studies for decades, it has been employed in the Information Systems (IS) research domain only more recently to investigate complex phenomena like agile software development, bottom-up IT use process, and systems dynamics. The aim of this research is to conduct a review of CAS studies within the IS discipline, particularly focusing on how CAS concepts are used for theorizing complex phenomena and the context of the use. To achieve this, we survey papers published in top outlets between 2002 and 2014, conduct in-depth analysis and categorize the contributions of the papers sampled by mapping them with the relevant CAS concepts. The review suggests that CAS has attracted limited interest within IS due to confusion with its central concepts, inherent complexities and possible ontological and epistemological issues with knowledge accumulation. We identify some promising IS research areas that can be studied using CAS and propose some guidelines for future researchers.

Onik, M. F. A., Fielt, E., & Gable, G. G. (2017). Towards Complex Adaptive Systems Roadmap for Information Systems Research. In the Proceedings of 21st Pacific Asia Conference on Information

Complex adaptive systems (CAS) theory conceptualises a system composed of heterogeneous agents, which interact with each other to adapt to the environment. CAS concepts have been applied in several Information Systems (IS) referent disciplines over the last decade to study complex phenomena in strategic management, social science and organisational research. The application of CAS theory in IS is more recent, wherein researchers have studied complex phenomena including agile


Systems (PACIS), Langkawi, Malaysia.

processes, systems dynamics and IS alignment. Though CAS has gained some traction with IS researchers, general understanding of the potential of CAS, and its methodological and theoretical applications in IS research, is yet partial and fragmented. The aim of this study is to develop a roadmap for applying CAS in IS research, to analyse the key research objectives with CAS in extant IS research, and to identify methodological and theoretical approaches that researchers follow in conducting CAS-based IS research. To achieve this, we review IS papers published 2002-2014 inclusive in top IS outlets. We analyse the papers based on a supportive theoretical framework and identify eight main objectives of applying CAS, three methodological approaches, and two theoretical approaches related to CAS-based research in the IS discipline. The study reports several valuable observations, including the relative versatility of computational studies over other studies, the minimal use of CAS in design research, methodological triangulation, and theoretical triangulation in IS research. We propose several guidelines for future researchers.

Onik, M. F. A. & Fielt, E. (2016). Understanding The Dynamics of BVIT Process: A Complex Adaptive Systems Approach. In the Proceedings of 27th Australasian Conference on Information Systems (ACIS), Wollongong, Australia.

There has been a long-running discourse in the information systems literature around the business value of IT (BVIT). Researchers have adopted a myriad of conceptual, theoretical and analytical approaches to evaluating the tangible and intangible measures of BVIT. Little research however, has explored richer theoretical explanations of the dynamic nature of the BVIT processes in contemporary dynamic business environments. To address this gap, this research in progress seeks to explore how BVIT is created as a bottom-up emergent process. In pursuit of such understanding, this research embraces complex adaptive systems (CAS) theory to develop a bottom-up conceptual model of BVIT. The agent-based modelling (ABM) technique is introduced as an analytical tool for computationally representing and examining the CAS model of BVIT. Operationalization of the CAS model and ABM modelling will be demonstrated through a theory building exercise.

Onik, M. F. A., Fielt, E. & Gable, G. G. (2017). Understanding The Dynamics of BVIT Creation:

Dramatic change is occurring in the structure of business, governmental and non-profit organisations due to the advent of complex technologies. These changes are engendering the emergence of business eco-systems as complex adaptive systems linking


A Complex Adaptive Systems Approach. ISS Doctoral Consortium, QUT Brisbane.

firms, innovations, processes and services in a network of increasingly interconnected, interdependent and diverse entities; IS has become fused with the business environment in a way that they are indistinguishable. Thus, the competitive organisational context is becoming more complex and less predictable suggesting the need for more dynamic models of BVIT in IS. In this fast changing, dynamic environment, traditional approaches to theorising and conceptualising BVIT have reached their limits. Beyond static or discreet views of BVIT or episodic events that lead to BVIT, there is need for more holistic theoretical logic concerning the bottom-up emergence of BVIT from resource level to organisational level. This research-in-progress paper seeks to contribute to BVIT research by proposing a novel framework especially suited to the examination of the bottom-up dynamic nature of BVIT by extending the analytical elements of complex adaptive systems (CAS) theory. By varying the assumptions of basic properties of CAS components e.g. fitness value, schemata or population dynamics, it is possible to model and theorize emergent, non-deterministic and co-evolutionary behaviours of dynamic systems.

Onik, M. F. A., Fielt, E. & Gable, G. G. (2016). What Are Information Systems- Information Systems as Complex Adaptive Systems. ISS Doctoral Consortium, QUT Brisbane.

The rise of the Internet and advancement in the technologies has given rise myriad of complexities in the current information systems (IS) in organisations. The contemporary IS are open ended, more diverse, internet-based and fast changing according to the demand. Organisations are adopting advanced technologies at the fundamental level and increase their capabilities, like processes, information, and expertise. It also helps organisations to expand their business, maximise performance and increase revenue. However, organizations now-a-days are considered as a globally distributed systems consisting of networks of internet-enabled IS. Thus, the competitive context and structure of IS is progressively becoming more complex and less predictable and it is being questioned whether the traditional approaches to theorising and conceptualising are efficient enough to deal with this new context. To address the issue, this study adopts the connectionist network framework of IS developed by (Merali, 2004) and proposes that the contemporary IS can be conceptualised as complex adaptive systems (CAS) in order to better understand the unpredictable complexities arising from the highly interconnected


world of distributed IS. We would like to explore whether the CAS theory can contribute to the discourse on contemporary IS characterised by internet-enabled technologies.

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