Competitive Advantage Strategies in Industrial Marketing

DOCTORA L T H E S I S

Jeandri Robertson C

ompetitive A

dvantage Strategies in Industrial Marketing

Department of Social Science, Technology and ArtsDivision of Business Administration and Industrial Engineering

ISSN 1402-1544ISBN 978-91-7790-791-6 (print)ISBN 978-91-7790-792-3 (pdf)

Luleå University of Technology 2021

Competitive Advantage Strategies in

Industrial Marketing Using an Ecosystem Approach

Jeandri Robertson

Industrial Marketing

Tryck: Lenanders Grafiska, 135942

135942 LTU_Robertson.indd Alla sidor135942 LTU_Robertson.indd Alla sidor 2021-03-31 09:352021-03-31 09:35

Competitive Advantage Strategies in Industrial

Marketing: Using an Ecosystem Approach

DOCTORAL DISSERTATION

By

JEANDRI ROBERTSON

15 March 2021

Submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Supervisors:

Associate Professor Tim Foster

Professor Albert Caruana

Luleå University of Technology (LTU)

Division Business Administration and Industrial Engineering

Department of Social Sciences, Technology and Arts,

Luleå, Sweden, SE-971 87

Tel: +27 (0) 83 458 8224

i

ABSTRACT

Intensified competitive pressures related to a dynamic and hypercompetitive global economy,

technological advances, unpredictable customers and competitors, and blurring industry

boundaries, have compelled industrial marketers to reconsider the strategic imperatives of the

organization, in relation to the competitive context in which it operates. As it becomes

increasingly difficult for individual firms to identify and respond to external competitive

challenges and changes independently, new organizational perspectives have been proposed to

thrive in the presence of these forces. The metaphor of the ‘ecosystem’ has increasingly been

used in research and practice to highlight the interdependencies between organizations and

their environment, providing a renewed way of thinking about the co-evolution, collaboration

and creation of value between actors. Although industrial marketers’ knowledge of ecosystems

is rapidly developing, the field is still unfolding and relatively little is known about the

differences between these ecosystems, especially from a competitive advantage perspective.

To deepen our understanding of its value from an industrial marketing point of view, the current

research sought to create conceptual and terminological clarity regarding the ecosystem

concept and examine the competitive dynamics present within and between these ecosystems.

The central research problem guiding this research is: How is competitive advantage achieved

through an ecosystem approach in industrial marketing?

Contemporary strategy literature converging at the crossroads between organizational

evolution and a fast-changing competitive landscape, proposes that relationships and the

resources and capabilities embedded within these relationships are key to the attainment of

competitive advantage. As such, theoretical streams that incorporate this line of thought are

used as lenses through which to assess competitive advantage. The following research

questions emerged:

RQ1: What are the drivers of competitive advantage through a network analysis approach to

ecosystems?

RQ2: How does social capital impact the competitive advantage of ecosystems?

RQ3: How do dynamic capabilities impact the competitive advantage of ecosystems?

RQ4: How do resource- and capability-based theories explain competition in ecosystems?

Four papers were constructed as part of the empirical part of this research. Two papers assessed

competition and competitiveness within ecosystems, with the other two examining competitive

advantage between ecosystems. Two of the papers followed a quantitative descriptive

approach, one paper followed a quantitative exploratory approach, while a qualitative

exploratory approach was utilized in the fourth paper. The respective approaches were deemed

best suited to address the respective research questions.

The research contributes to the body of knowledge in that it highlights the centrality of

knowledge exploration in leveraging, maintaining and attaining advantage. It also points to a

cyclical knowledge generating process within the ecosystem context, which centers on

exploring, then exploiting and finally, transforming of knowledge for sustainable competitive

ii

advantage. The dissertation follows a narrative of first sketching the relevance of the ecosystem

metaphor within the industrial marketing context, leading to the identification of gaps in the

research and an introduction to the research problem. The evolution of the ecosystem concept

is then reviewed, laying bare its characteristics, extant definitions and different types of

ecosystems. Theoretical perspectives of strategy, inherently embedded in theoretical

assumptions of how competition works, are then discussed. The research questions are then

introduced, including the respective papers addressing each research question, together with

the research methodology followed. The dissertation concludes with a summary of the findings,

which also provides a discussion on the research contributions, managerial implications, noted

limitations and suggested areas for future research. The four papers are presented as

Appendices, of which three have been published and the fourth is in its second round of review.

Keywords: ecosystems; competitive advantage; competitiveness; network theory; social

capital theory; resource-based view; dynamic capabilities; transient advantage; innovation;

knowledge; entrepreneurship

ABSTRACT

En allt intensivare konkurrens, kopplad till en dynamisk global ekonomi, tekniska framsteg,

oförutsägbara kunder och konkurrenter samt suddiga branschgränser, har tvingat industriella

marknadsförare att ompröva vad som är strategiskt nödvändigt för organisationen i förhållande

till den kontext den verkar inom. Eftersom det blir allt svårare för enskilda företag att identifiera

och möta upp externa utmaningar och förändringar i konkurrensen, har nya organisatoriska

perspektiv föreslagits för att företagen ska kunna lyckas under dessa förutsättningar. Metaforen

för ”ekosystemet” har i allt större utsträckning använts i forskning och praktik för att lyfta fram

det ömsesidiga beroendet mellan organisationer och deras omgivning, vilket innebär ett nytt

sätt att tänka på samutveckling, samarbete och värdeskapande mellan aktörer. Även om

industriella marknadsförares kunskap om ekosystem ökar snabbt, så är området fortfarande

under utveckling och relativt lite är känt om skillnaderna mellan olika ekosystem, särskilt när

det gäller konkurrensfördelar. För att fördjupa förståelsen för värdet av ekosystem från ett

industriellt marknadsföringsperspektiv har studierna i denna avhandling sökt skapa konceptuell

och terminologisk klarhet kring ekosystemkonceptet, samt undersöka den konkurrensdynamik

som finns inom och mellan dessa ekosystem. Det centrala forskningsproblemet för

avhandlingen är: Hur uppnås konkurrensfördelar genom ett ekosystemsynsätt inom industriell

marknadsföring?

Enligt samtida strategilitteratur är relationer, och de resurser och möjligheter som dessa

relationer innebär, nyckeln till att uppnå konkurrensfördelar. Därför används teoretiska

strömningar som innehåller denna tankegång som en lins genom vilken konkurrensfördelar kan

utvärderas. Följande forskningsfrågor har formulerats:

FF1: Vilka är drivkrafterna för konkurrensfördelar genom ett nätverksanalyssynsätt för

ekosystem?

FF2: Hur påverkar socialt kapital ekosystemens konkurrensfördelar?

FF3: Hur påverkar dynamisk kapacitet ekosystemens konkurrensfördelar?

FF4: Hur kan resurs- och kapacitetsbaserade teorier förklara konkurrens i ekosystem?

Fyra artiklar utgör en del av empirin i denna forskning. I två artiklar utvärderas konkurrens och

konkurrenskraft inom ekosystem, medan konkurrensfördelar mellan ekosystem undersöks i de

andra två artiklarna. Två av artiklarna följer ett kvantitativt beskrivande tillvägagångssätt, en

har ett kvantitativt utforskande tillvägagångssätt, medan ett kvalitativt utforskande

tillvägagångssätt användes i den fjärde artikeln. Dessa metoder ansågs bäst lämpade för att

undersöka respektive forskningsfrågor.

Forskningen bidrar till befintlig kunskap genom att den belyser vikten av kunskapsutforskande

när det gäller att utnyttja, upprätthålla och uppnå fördelar. Studien pekar också på en cyklisk

kunskapsgenererande process inom ekosystemkontexten, som fokuserar på att utforska, sedan

utnyttja, och slutligen transformera kunskap för att uppnå hållbara konkurrensfördelar.

Avhandlingen följer ett narrativ där ekosystemmetaforens relevans inom kontexten industriell

marknadsföring först beskrivs, vilket leder till identifiering av kunskapsgap och en introduktion

iii

till forskningsproblemet. Utvecklingen av ekosystemkonceptet granskas sedan och dess

egenskaper, existerande definitioner och olika typer av ekosystem beskrivs. Teoretiska

perspektiv på strategi, som är infogade i teoretiska antaganden om hur konkurrens fungerar,

diskuteras sedan. Därefter introduceras forskningsfrågorna samt de artiklar som behandlar

respektive forskningsfråga, tillsammans med den forskningsmetodik som använts.

Avhandlingen avslutas med en sammanfattning av resultaten, som också innehåller en

diskussion om bidrag till forskning, rekommendationer till beslutsfattare, begränsningar, samt

föreslagna områden för framtida forskning. De fyra artiklarna presenteras som bilagor, varav

tre har publicerats och den fjärde är under granskning i andra omgången.

iv

iv

ACKNOWLEDGEMENTS

I wish to extend my sincere gratitude to the following people for their contribution and

support:

Thank you to my supervisors, Associate Professor Tim Foster and Professor Albert Caruana,

for their assistance, guidance and encouragement throughout the study;

A heartfelt thank you to Professors Esmail Salehi-Sangari, Åsa Wallström, and Maria Ek

Styvén for their efforts on the PhD program and all the support along the way;

To Professor Leyland Pitt, thank you for your generosity and for continually conveying a spirit

of adventure and enjoyment in regard to research and scholarship;

To Caitlin Ferreira, your immense drive and optimism have inspired me – I sincerely appreciate

the camaraderie;

Thank you to all the colleagues I have had the privilege of working with – in particular to

Anna Näppä, Christine Pitt and Tess Eriksson, who are now close friends;

Thank you to my family for their unwavering support, in particular my parents, who instilled

in me that well done is better than well said;

My children, Ami and Aren, ancora imparo, you two are my anchor and my fuel;

Last, but not least, to Thomas, thank you for giving me that Dr. Seuss book, ‘Oh, the

places you’ll go’, many years ago. Little did you know where life would take us, yet you

have always been up for the adventure – thank you for living a full life with me.

v

TABLE OF CONTENTS

CHAPTER 1: OVERVIEW OF THE RESEARCH

1.1. Introduction to the Research Area 1

1.1.1. The Ecosystems Concept 1

1.1.2. Defining Ecosystems 3

1.1.3. The Relevance of Ecosystems to Industrial Marketing 5

1.1.4. Why Ecosystem Competitiveness Matters to Industrial Marketing 7

1.1.4.1. Interorganizational Collaboration 9

1.1.4.2. Dependency on Resources and Capabilities Outside of

Direct Control of One Single Organization

9

1.1.4.3. Dynamic Connections 9

1.1.4.4. Competing Beyond Traditional Industry Boundaries 10

1.2. Research Gap Identification 10

1.3. Research Scope 18

1.4. Research Problem 19

1.5. Chapter Summary 19

CHAPTER 2: LITERATURE REVIEW

2.1. The Evolution and Development of the Ecosystem Concept 20

2.2. Ecosystem Characteristics 21

2.3. Ecosystem Types 25

2.3.1. Innovation Ecosystem 29

2.3.2. Entrepreneurial Ecosystem 30

2.3.3. Knowledge Ecosystem 31

2.4. Theoretical Perspectives: Ecosystems and Competitive Advantage 32

2.4.1. Network Theory and Social Capital 34

2.4.1.1. Structural Dimension 35

2.4.1.2. Relational Dimension 35

2.4.1.3. Cognitive Dimension 36

2.4.2. Resource-Based View and Dynamic Capabilities 36

2.5. Development of Research Questions 37

vi

2.5.1. Theoretical Focus of Research Questions 38

2.5.2. Ecosystem Focus of Research Questions 38

2.6. Formulation of Research Questions 39

2.6.1. Research Question 1 is addressed by Research Paper 1 40




2.7. Delineation: Construct, Context and Units of Analysis 48


CHAPTER 3: METHODOLOGY

3.1. Introduction 50

3.2. Research Approach 50

3.3. Research Strategy and Design 53

3.3.1. Addressing Research Question 1 54

3.3.1.1. Research Design 54

3.3.1.2. Research Method 55



3.3.2.2. Research Method 56



3.3.3.2. Data and Sample 58

3.3.3.3. Measures 58

3.3.3.4. Data Analysis 59


3.3.4.1. Research Design and Research Method 59

3.3.4.2. Data Collection 60

3.4. Quality Criteria 61

3.5. Structure of Individual Papers 62

3.5.1. Paper 1 62

vii

3.5.2. Paper 2 64

3.5.3. Paper 3 65

3.5.4. Paper 4 66


CHAPTER 4: FINDINGS

4.1. Introduction 69

4.2. Findings: Research Question 1 69



4.4.1. Knowledge Creation and Innovation Performance 79

4.4.2. Knowledge Diffusion and Innovation Performance 79

4.4.3. Knowledge Absorption and Innovation Performance 80

4.4.4. Knowledge Impact and Innovation Performance 80


4.5.1. Findings Based on Ecosystem Factors 85

4.5.1.1. Actors 86

4.5.1.2. Activities 86

4.5.1.3. Alignment 87

4.5.1.4. Artifact 88

4.5.2 Findings Based on Strategic Determinants of Ecosystems 88

4.5.2.1. The Competitive Context 88

4.5.2.2. Market Attentiveness 89

4.5.2.3. Organizational Boundaries 90

4.5.2.4. The Sustainability of Strategic Advantage 90

4.6. Overview of Overall Findings 90

4.7. Theoretical Contributions 92

4.8. Managerial Implications 96

4.8.1. Interorganizational Collaboration: Leveraging Knowledge 96

4.8.2. Dependency on Resources and Capabilities Outside of Direct

Control of One Single Organization: Outside-in Strategic Orientation

96

viii

4.8.3. Dynamic Connections: Adaptation, Integration and

Reconfiguration

97

4.8.4. Competing Beyond Traditional Industry Boundaries:

Advantage for All

97

4.9. Limitations and Suggested Areas for Future Research 98


LIST OF REFERENCES

LIST OF APPENDICES

Appendix A: Paper 1 – Entrepreneurial Ecosystems and the Public Sector: A

Bibliographic Analysis

119

Appendix B: Paper 2 – Leveraging Social Capital in University-Industry Knowledge

Transfer Strategies: A Comparative Positioning Framework

135

Appendix C: Paper 3 – Innovation Performance: The Effect of Knowledge Based

Dynamic Capabilities in Cross-Country Innovation Ecosystems

149

Appendix D: Paper 4 – Competition in Knowledge Ecosystems: A Theory

Elaboration Approach using a Case Study

195

101

ix

LIST OF FIGURES

Figure 1: Organizational structure of ecosystems 6

Figure 2: Three identified research gaps in the ecosystem literature 17

Figure 3: Ecosystem typology 29

Figure 4: Overview of research questions 40

Figure 5: Schema of research problem, research questions and papers 47

Figure 6: Delineation of construct, contexts and unit of analysis 48

Figure 7: Overview of research questions 50

Figure 8: Number of annual published articles on entrepreneurial ecosystems

between 1995 to 2019

69

Figure 9: Top ten Web of Science categories in which published articles on

entrepreneurial ecosystems appear between 1995-2019

70

Figure 10: Most commonly occurring keywords on entrepreneurial ecosystems

(1995 to 2019)

73

Figure 11: Knowledge transfer strategy delineation, as adapted from von Krogh et

al. (2001)

75

Figure 12: Social capital university-industry knowledge transfer framework 77

Figure 13: Research model and hypotheses 81

Figure 14: Innovation ecosystem framework centered around a knowledge-based

dynamic capabilities’ approach

83

Figure 15: An overview of the findings of the dissertation, according to the four

research questions and accompanying papers

91

Figure 16: Illustrative representation of broader ecosystem-level theoretical

contributions

96

x

LIST OF TABLES

Table 1: Ecosystem definitions 4

Table 2: Thematic organization of selected industrial marketing research articles

employing an ecosystem approach

11

Table 3: Key characteristics of ecosystems 22

Table 4: Major ecosystem literature streams, ecosystem types and differences in

focus

26

Table 5: Summary of the research strategy of this dissertation 53

Table 6: Journals with most published articles on entrepreneurial ecosystems

(n=431)

71

Table 7: Ten most cited research articles between 1995 and 2019 using

“entrepreneurial ecosystem” as search term on Web of Science (n=431)

71

Table 8: An overview of social capital dimensions present 76

Table 9: Results of PLS analysis 81

Table 10: Overview of case study findings, based on ecosystem factors and

knowledge ecosystem characteristics as strategic determinants of how

competition works

85

Table 11: Summary of research contributions 94

1

CHAPTER 1: OVERVIEW OF THE RESEARCH

1.1. Introduction to the Research Area

This Chapter introduces the research area of the dissertation and discusses its relevance to

industrial marketing. Firstly, the concept of “ecosystems” is introduced and defined, and the

relevance of the ecosystem concept to industrial marketing is highlighted. In addition, extant

perspectives on the dynamics of competition in ecosystems are provided to broadly sketch the

landscape and to further also contextualize how it relates to an industrial marketing context.

Secondly, the gaps in knowledge regarding ecosystems in current industrial marketing

literature are identified and discussed. Thirdly, an overview of the research scope is provided,

followed by the overarching problem statement of the research, which culminates in clearly

articulating the research problem which this dissertation sought to answer.

1.1.1 The Ecosystems Concept

All companies cease to exist sooner or later. In 1980, the average lifespan of an S&P 500 listed

company was 35 years (Anthony et al., 2018). By 2025 the lifespan of an average company is

projected to decrease to less than 15 years (Sarkar & Kotler, 2019). There are a number of

reasons why companies figuratively die, with one growing reason being that companies do not

understand the dynamics of the competition in their ecosystems (Sarkar & Kotler, 2019). In a

fiercely competitive business environment, the performance of firms is derived from something

much larger than the companies themselves: the success of their respective ecosystems (Iansiti

& Levien, 2004; Moore, 2003). It is hard to imagine that 25 years ago, Apple was regarded a

sinking ship. In 1996 Apple was on the brink of bankruptcy and its executives, as a final attempt

at a lifeline, made a pivotal choice to “bet the firm on a novel ecosystem” (Hannah &

Eisenhardt, 2018). This ecosystem consisted of a group of firms who collectively provided

components such as an MP3 player, flash memory, the rights to digital music, and the iTunes

store. Together, this offered a seamless music experience which captivated customers and kept

Apple afloat in the crucial years before the iPhone. The ecosystem also facilitated the

cooperative creation of value with complementors, while simultaneously allowing Apple to

competitively capture a share of the value that the ecosystem created (Hannah & Eisenhardt,

2018).

Ecosystems represent loose networks of suppliers, distributors, makers of related or

complementary products and services, technology providers, outsourcing firms, or institutions

and providers which affect or are affected by the creation and delivery of the firm’s own

offerings (Iansiti & Levien, 2004). The ecosystem may also include competitors and customers,

when their actions and feedback affect the development of the firm’s own products or processes

(Fuller et al., 2019). One’s ecosystem could even comprise entities like regulatory agencies and

media outlets which may have a less immediate, but just as powerful, effect on a firm’s business

(Birkinshaw, 2019; Iansiti & Levien, 2004). To understand one’s ecosystem entails an awareness

of changes that occur with customers, suppliers, communities, and competitors, and knowing

how to respond to these changes in order to survive (Sarkar & Kotler, 2019). Organizations thus

2

need to anticipate, adapt and align to change (Day, 2020) – both internally, as well as how they

interact with their external context.

The use of an ecosystem metaphor to describe the nature of business is not new. Globally,

multinational companies like Toyota and Volkswagen have successfully been coordinating

large networks of distributors and suppliers for decades (Birkinshaw, 2019). What has,

however, changed, is that a substantial number of fast-growing companies are explicitly

positioning themselves as actors within ecosystems (Subramaniam, 2020), including Apple,

Amazon, Facebook, and Microsoft, the four largest listed companies globally (Blakeley, 2021).

Interestingly, the word ecosystem appears more than ten times more frequently in annual

reports now than a decade ago (Fuller et al., 2019), and e-commerce retail giant, Alibaba,

referenced the term 160 times in their IPO listing prospectus in 2014 – one of the world’s

largest IPO listings to date (Kelly, 2015). Companies within ecosystems are posited to have

the advantage of speed to market, market responsiveness and heightened resilience in uncertain

times (Greeven & Yu, 2020), as the dynamics of the evolving relationships between the actors

necessitate flexibility and adaptation (Möller et al., 2020).

As macro-trends like digitization, globalization and technological disruptions change the

traditional business environment, industrial marketing scholars seek to understand whether

ecosystems represent a new way of competing – challenging the conventional thinking of how

businesses capture and create value (Birkinshaw, 2019). The mechanisms and relationships

that govern interaction between the interconnected actors within ecosystems require a different

perspective (Anggraeni et al., 2007). Autio and Thomas (2020) propose that, at its core, the

ecosystem concept is an evolution from the notion of value networks. Four particular

differences between ecosystems and value networks can be highlighted. Firstly, in contrast to

value networks, ecosystems entail participant heterogeneity spanning multiple industries,

transcending the public and private sectors (Thomas & Ritala, 2021). Secondly, the system-

level output of ecosystem value propositions is regarded more generative, as innovations can

be produced unprompted (Henfridsson & Bygstad, 2013). Thirdly, ecosystems represent the

combined assets and skills of an interdependent, co-specialized group of entities which

simultaneously create value for all that form part of the ecosystem (Eisenhardt & Galunic,

2000). Finally, ecosystems mostly comprise non-contractual mechanisms with a non-

hierarchical structure (Shipilov & Gawer, 2020; Thomas & Ritala, 2021), in contrast to value

networks where relationships are often structured through formalized contracts or collaboration

mechanisms (Shipilov & Gawer, 2020).

Driven by connectivity and new modes of collaboration (Kelly, 2015), ecosystems are thus

systems of complex and loosely interconnected networks, that focus on creating and capturing

value through the integrated efforts of all the members in its environment (Fuller et al., 2019).

Participation in the ecosystem enables individual entities to commercialize products, services,

processes or ideas which they would not have been able to do by relying on their individual

competencies only (Clarysse et al., 2014; Thomas & Autio, 2020). Greeven and Yu (2020)

assert that ecosystems are about understanding the market as networks of participants, while

Jacobides et al., (2018) suggest that ecosystems facilitate value creation through relationships

3

and not through asset ownership, superior infrastructure, or physical goods. In turn, Birkinshaw

(2019) proposes that companies in an ecosystem grow the market by increasing the flow of

people and goods, in contrast to the traditional notion of each company capturing as much of

the existing market as possible to gain an advantage. Furthermore, Fuller et al. (2019) proffer

that ecosystems are engines of growth that drive organizational competitive advantage, which

heralds a shift toward dynamic, multi-firm interdependence that enables competitive and

economic advantage for all within the ecosystem. Similarly, Moore (2013) asserts that

ecosystems provide a nuanced lens on how competition is created and maintained.

To clarify the ecosystems concept, a clear definition is necessary, for if we do not define

something we cannot fully understand it and if we don’t understand it, we cannot hope to

manage it (Wittgenstein, 1953). The section to follow thus provides an overview of the various

definitions that have been offered in the literature and concludes with the definition used as

guideline in this dissertation.

1.1.2 Defining Ecosystems

The multidisciplinary nature of business, society and technology has paved the way for the

ecosystem literature to be pinned at the crossroads between a number of different disciplines

(Autio & Thomas, 2020). Aarikka-Stenroos and Ritala (2017) posit that it encompasses the

strategic management, marketing, technology, and sociology fields, while Autio and Thomas

(2020) approach it from the disciplinary perspectives of strategic management, marketing, and

information systems. Contextually-based definitions of ecosystems abound, creating

terminological and conceptual heterogeneity (Thomas & Autio, 2020). To align the various

definitions that have been put forward, Table 1 provides a summary of extant ecosystem

definitions, as found in the marketing and management literature specifically.

Based on the literature-identified definitions presented in Table 1, some conceptual

commonalities as to how ecosystems are defined emerge, namely ecosystems-as-structure,

ecosystem-based activity, and ecosystem-level output. Firstly, ecosystems-as-structure, echoes

an assertion suggested by Aarikka-Stenroos and Ritala (2017), Adner (2017), Fuller et al.

(2019), and Moore (2013), which proposes that ecosystems are configurations of co-evolving

associated actors who form an organizational structure. Secondly, ecosystem-based activity

highlights the complementarity, collaboration and interdependence of the activities between

these entities that form the organizational structure of ecosystems (Granstrand & Holgersson,

2020; Greeven & Yu, 2020; Jacobides et al., 2018). Thirdly, ecosystem-level output, defines

ecosystems as a structure through which the collective activity of all actors contributes to

generate a shared ecosystem output, e.g., creating a product, service, technology, platform, or

shared knowledge base (Hannah & Eisenhardt, 2018; Thomas & Autio, 2020; Tsujimoto et al.,

2018).

4

Table 1: Ecosystem definitions

Definitions

Ecosystems-as-structure

“A co-evolutionary business system of actors, technologies, and institutions. Actors

include the end-users or customers and user communities, developers and research

organizations, competitors, and complementors throughout the entire value chain and

network, as well as institutional actors” (Aarikka-Stenroos & Ritala, 2017, p. 2)

“The alignment structure of the multilateral set of partners that need to interact in order for

a focal value proposition to materialize” (Adner, 2017, p. 40)

“Multi-entity, groups of companies not belonging to a single organization, involving

networks of shifting, semi-permanent relationships, linked by flows of data, services, and

money. The relationships combine aspects of competition and collaboration, often

involving complementarity between different products and capabilities (for instance,

smartphones and apps). Ecosystem players co-evolve as they redefine their capabilities

and relations to others over time” (Fuller et al., 2019, p. 3)

“A new form of organization…[one] that shows promise in achieving shared purposes,

sharing value among many contributors, and in bringing the benefits of technology to a

range of people, cultures and problems far beyond what earlier systems have achieved”

(Moore, 2013, p. 3)

Ecosystem-based activity

“The network of organizations ─ including suppliers, distributors, customers, competitors,

and government agencies, and so on ─ involved in the delivery of a specific product or

service through both competition and cooperation…[each] entity in the ecosystem affects

and is affected by the others, creating a constantly evolving relationship in which each

entity must be flexible and adaptable in order to survive as in a biological ecosystem”

(Greeven & Yu, 2020, p. 1)

“A set of actors with varying degrees of multilateral, non-generic complementarities that

are not fully hierarchically controlled” (Jacobides et al., 2018, p. 2264)

Ecosystem-level output

“Groups of firms that produce products or services that together comprise a coherent

solution” (Hannah & Eisenhardt, 2018, p. 3164)

“A community of hierarchically independent, yet interdependent heterogeneous

participants who collectively generate an ecosystem output” (Thomas & Autio, 2020, p.

30)

“A product/service system, an historically self-organized or managerially designed

multilayer social network consisting of actors that have different attributes, decision

principles and beliefs” (Tsujimoto et al., 2018, p. 55)

5

Based on the three overarching commonalities between the various proposed definitions, this

dissertation mostly draws on the definitions proposed by Aarikka-Stenroos and Ritala (2017),

Adner (2017) and Thomas and Autio (2020) to define ecosystems as:

The alignment structure of a multilateral set of hierarchically independent, yet interdependent

and co-evolving actors, technologies, and institutions, that interact to collectively generate a

shared ecosystem output.

With a concise definition of ecosystems determined, the relevance of the ecosystems concept

to industrial marketing in particular is discussed next.

1.1.3 The Relevance of Ecosystems to Industrial Marketing

From a scholarly perspective, academe in the industrial marketing field, particularly from a

business network perspective, have steadily started to adopt the ecosystem concept in their

vocabulary over the past decade (Aarikka-Stenroos & Ritala, 2017; Akaka et al., 2013; Frow

et al., 2016; Möller & Halinen, 2017; Möller et al., 2020; Wieland et al., 2016; Wilkinson &

Young, 2013). As a way to describe the increased interdependence between networks of

customers, suppliers, buyers, and producers of complementary and competing services or

products (Adner, 2017; Autio & Thomas, 2020; Clarysse et al., 2014; Jacobides et al., 2018;

Kapoor, 2018; Kapoor & Lee, 2013; Vargo & Lusch, 2017), the interest denotes a “notable

shift in the conceptual focus of industrial marketing and management – from networks toward

ecosystems” (Aarikka-Stenroos & Ritala, 2017, p.23).

The conceptual shift in focus from networks to ecosystems is mainly driven by two factors.

First, it heralds a new way of coordinating multiple interdependent, diverse and co-evolving

entities with the shared goal of bringing a focal value proposition to fruition (Fuller et al., 2019;

Jacobides et al., 2018). Akaka et al. (2013, p. 8) assert that for industrial marketers, the

“ecosystems view emphasizes the integration of skills to develop new knowledge...to apply

resources in a more effective, efficient, and sustainable manner.” In addition, Hannah and

Eisenhardt (2018) contend that ecosystems and networks differ in the sense that networks are

composed of the ties among a set of firms that for instance, shape the flow of resources and

information in an industry, but doesn’t necessarily entail organizational interdependence. In

contrast, ecosystems revolve around an output and not a structure of ties, which also reflects

the interdependence among actors to deliver and secure the ecosystem’s overall performance

(Hannah & Eisenhardt, 2018).

Second, the adoption of an ecosystem approach is driven by necessity, as the accelerated

change of the business environment is increasingly prompting organizations to realign their

structures (Teece, 2014) and create novel capabilities (Narayanan et al., 2009) to keep pace

with changes and achieve or maintain competitive advantage (Guerrero et al., 2019). In a

progressively interconnected world, dramatic shifts in technology have changed the way in

which products and services are designed, produced, distributed, evaluated and consumed

(Kumar et al., 2015). Kumar et al. (2015, p.470) propose that “a web of entities rather than

6

predominantly a single firm coordinates a set of activities that delivers utility to mutually

connected consumers.” In addition, there is a growing consensus among scholars that

ecosystem thinking demands a different theoretical and empirical approach from what has

previously been employed in relationship and network studies (Aarikka-Stenroos & Ritala,

2017; Anggraeni et al., 2007; Greeven & Yu, 2020). As such, more research is warranted better

to understand how organizations who form part of these ecosystems are organized for

marketing agility, which has been indicated as a research priority by the Marketing Science

Institute for 2020-2022.

Figure 1: Organizational structure of ecosystems

Source: Adopted from Fuller et al. (2019)

From an organizational structure perspective, ecosystems can be pegged as sitting between two

extremes along a spectrum of market fluidity. Figure 1 provides an illustrative representation,

developed by Fuller et al. (2019), which depicts different organizational structures and

relationships that firms can have between their products and the eventual consumer. On the

one end, we find vertically integrated companies or static supply chains, while on the other

end, we find open, competitive markets, where customers can combine products according to

their changing patterns of need (Fuller et al., 2019). Making use of an ecosystems approach

thus necessitates a shift away from a traditional, static, company-centric strategic approach.

Fueled by an increased interest in the phenomenon, a number of journals in the field of

marketing have issued calls for papers on ecosystems for the period 2020-2021, ranging in

focus from innovation ecosystems (Industrial Marketing Management, Journal of Business

Research, Journal of Cleaner Production), knowledge ecosystems (International Business

Review), entrepreneurial ecosystems (Review of Managerial Science), to platform ecosystems

(European Journal of Marketing).

As can be observed from the various contexts in which the ecosystem term is conceptually

being applied, Thomas and Autio (2020) contend that this conceptual proliferation hinders a

7

deepened understanding of the phenomenon and the potential value it can add to theory and

practice. In addition, Aarikka-Stenroos and Ritala (2017), and Thomas and Autio (2020)

highlight that the dynamics of competition within these ecosystems, particularly from an

industrial marketing perspective, seems to be an unexplored area. Some scholars view the rise

of the ecosystem concept as an opportunity for creating new competitive advantage (Jacobides

et al., 2018; Kelly, 2015), while others contend that ecosystems thinking is changing the rules

of competitive strategy (Birkinshaw, 2019; Greeven & Yu, 2020; Jacobides, 2019; Thomas &

Autio, 2020). The dynamics of competitive strategies in these different ecosystems is thus still

uncharted territory in industrial marketing literature.

Several areas relating to the dynamics of competitive strategy within ecosystems have been

earmarked for further inquiry. First, despite conceptual proliferation, there is consensus that

each ecosystem type collectively contributes to generate a common ecosystem output which

ensures superior enterprise performance and longevity (Thomas & Autio, 2020). In the

industrial marketing literature, performance has been closely linked to the development of

inimitable capabilities and sustained competitive advantage (Moorman & Day, 2016). The

importance of understanding how competitive advantage is achieved in ecosystems has been

addressed by some in industrial marketing literature (Aarikka-Stenroos & Ritala, 2017;

Rohrbeck et al., 2009), yet, conceptual and empirical enquiry to contribute to our theoretical

understanding of the phenomena is still in short supply (Möller & Halinen, 2017). Second,

although we have a good understanding of the organizational interconnectedness of networks

within ecosystems (Zahra & Nambisan, 2012), little is known about the dynamic capabilities

of the actors within, and how that impact the ecosystems’ competitiveness (Jacobides et al.,

2018). Third, the collaborative, cooperative and competitive nature of relationships within

ecosystems have been established (Thomas & Autio, 2020); however, a deeper understanding

of how these relational resources impact the competitiveness of the ecosystem is still lacking

(Kapoor & Lee, 2013). Last, there is agreement in the strategic management literature that the

shared goal of entities within ecosystems is the creation and capturing of value (Moore, 1993).

Knowledge regarding what the respective strategic drivers of competitiveness in ecosystems

from an industrial marketing perspective would be, is, however, not yet clear.

To further probe the relevance of ecosystem competitiveness to industrial marketing, the

section to follow provides a more in-depth explanation of the dynamics of competition and

how this plays out in the ecosystems context from an industrial marketing perspective.

1.1.4. Why Ecosystem Competitiveness Matters to Industrial Marketing

The dynamics of competition inherent within ecosystems has been explicitly articulated since

the term’s inception into the business management literature. Thirty years ago, Rothschild

(1990, p. xi), argued that a “capitalist economy can best be comprehended as a living

ecosystem. Key phenomena observed in nature – competition, specialization, cooperation,

exploitation, learning, growth, and several others – are also central to business life.” James F

Moore, generally regarded as the pioneer of the ecosystem concept in contemporary business,

wrote in his seminal 1993 Harvard Business Review article, “Predators and Prey: A New

8

Ecology of Competition”, that “it’s competition among business ecosystems, not individual

companies, that’s largely fueling today’s industrial transformation. Managers can’t afford to

ignore the birth of new ecosystems or the competition among those that already exist” (p.76).

It is thus necessary to acknowledge that organizations form part of a bigger ecosystem

(Peltoniemi, 2004). In his follow-up book, “The Death of Competition”, Moore (1996) further

directly refers to the end of traditional competition between individual products and services,

inferring that competition instead plays out between ecosystems spanning multiple

traditionally defined product markets (Cennamo, 2019).

Thomas, Sharapov and Autio (2018) argue that organizations seldom operate in perfectly

competitive markets that are characterized by isolated transactions between toe-to-toe

competing firms, producing substitutable products. Instead, it operates within hyper-networked

structures that consists of co-specialized, complementary organizations that co-create value

(Jacobides et al., 2018). All actors impact the competitiveness of the ecosystem in the business

environment. Just like a species can flourish when all the elements are in balance and favorably

contribute to the health of the ecosystem, companies grow and excel when the dynamics of the

ecosystem contribute to its competitive advantage. Conversely, a species can become extinct

when access to key elements in its ecosystem are withdrawn, just like firms can cease to exist

if some elements in its ecosystem change unfavorably. Iansiti and Levien (2004, p.69) stress

the “shared fate” of the ecosystem community as a whole, as the performance of individual

members is tied to the overall performance of the ecosystem.

Teece (2007) contends that the ecosystem should monitor and react to its environment,

evaluating what shifts are occurring and how it affects its dynamic capabilities and thus, its

ability to build sustainable competitive advantage (Kotler & Sarkar, 2019). As the nature of

competition shifts to an organization’s capacity and ability to build relationships within an agile

ecosystem, the resources and capabilities of the actors in the ecosystems are of equal

importance (Adner, 2017; Zahra & Nambisan, 2012). Competition is thus not necessarily only

approached from an external perspective where competitors are those vying for a similar

customer base or market anymore. An ecosystems approach implies that competitiveness is

dynamic and entails an interorganizational, interdependent perspective to assess potential

opportunities for growth and commercialization from within the ecosystem, its resources and

capabilities (Pellikka & Ali-Vehmas, 2016).

Two distinct, yet interacting levels of competition, are implied: competition within the

ecosystem and competition across ecosystems. Competition within ecosystems relates to the

assurance of position, the security of activities and roles, and the distribution and capturing of

value in the ecosystem (Adner, 2017). Competition across the ecosystem refers to the

“collective advantages in creating and capturing value relative to rival constellations of actors”

(Adner, 2017, p.49). Tensions can arise if there is increased competitiveness between partners.

Adner (2017, p.49) argues that this serves “to enhance the value creation advantage of the

ecosystem” which contributes to certain actors “maintaining a (leadership) position in the face

of competitive partners, the importance of whose contribution is increasing, and who may

desire to change roles or revenue capture” (Adner, 2017, p.49). At the core of this tension is

9

the search for alignment in order to achieve competitive advantage. Employing traditional

strategies that rely on the value, rarity, and inimitability of resources is exchanged for

multilateral partnerships and stronger relationships, as sustainable advantage focuses as much

on maintaining relationships as it does on keeping competitors at bay (Adner, 2017; Jacobides

et al., 2018).

These competitive dynamics thus play a central role in better understanding the strategic

imperatives inherent in ecosystems. Four overarching competitive considerations of

ecosystems are identified and proposed to have an impact on the evolving nature of strategic

competitiveness within industrial marketing.

1.1.4.1. Interorganizational Collaboration

First, interorganizational collaboration explicitly highlights the interdependency among

partners in ecosystems. It underscores the coordination and dynamics of activities, whether

cooperatively, collaboratively or competitively, to both create and capture value within these

exchange networks (Adner & Kapoor, 2010). This represents a move away from defining

competition as the ability to establish superiority over other external competitors, to evolve

towards investing in connections, including cooperative and competitive relationships, in order

to build the overall competitiveness of the ecosystem (Aarikka-Stenroos & Ritala, 2017).

1.1.4.2. Dependency on Resources and Capabilities Outside of Direct Control

of One Single Organization

Second, the dependency on resources and capabilities outside of the direct control of any one

particular entity in an ecosystem approach, builds on the necessity for reliance on

interorganizational and interdependent relationships (Pellikka & Ali-Vehmas, 2016). It further

highlights a challenge to extant perspectives of competitive advantage, as it theorizes that

strategic competitiveness moves away from a central foci, hub or executive decision-making

team, to the ecosystem as a whole (Jacobides et al., 2018). An industry-specific positioning

approach to building competitive advantage, traditionally built on product differentiation and

asset ownership, thus becomes less central to gain competitive advantage (Fuller et al., 2019).

Competitiveness in this context is thus evolving towards interdependent ‘networks of

networks’, where influence, complementarity and super-modularity have a bigger impact on

the cumulative network effects of the entire ecosystem than full ownership or control (Fuller

et al., 2019; Jacobides et al., 2018).

1.1.4.3. Dynamic Connections

Third, competitiveness is established through dynamic connections, where the larger the

ecosystem, the greater the ability to interact with potential complementary actors (Möller et al.,

2020). The growth of the ecosystem thus supports a higher level of interaction (Satell, 2019).

This indicates a move away from a static approach to competitiveness, where high barriers to

entry and maximization of bargaining power within the value chain facilitated advantage

10

(Tsujimoto et al., 2018). Within the ecosystem context, these tactics may weaken connections

with other existing and potential actors in the ecosystem (Fuller et al., 2019). Sustainable

competitiveness is rather sought through an evolving approach towards a change in the

configuration of actors within an ecosystem, for the purpose of establishing multilateral,

dynamic relationships and alignment strategies, both vertical and horizontal, for sustainable

competitiveness (Adner, 2017; Tsujimoto et al., 2018).

1.1.4.4. Competing Beyond Traditional Industry Boundaries

Fourth and finally, from a competitive perspective, success within ecosystems spans beyond

traditional industry boundaries and is determined by innovating or creating value for other

actors in the ecosystem as much as innovating for one’s own interest, regardless the industry

or sector (Jacobides, 2019). This suggests a move away from considering competitors as those

who target and compete for the same or similar customers, markets or industries only, to an

evolving perspective towards competing across multiple industries which transcend the

traditional boundaries between business, public and private sectors (Fuller et al., 2019; Thomas

& Autio, 2020).

Competition within ecosystems is, however, not a “zero-sum game” (Thomas & Autio, 2020,

p.27). The competitive focus is on trying to meet as many customers’ needs as possible

(Cennamo & Santalo, 2013). The competitive behaviours within ecosystems are thus different

(Cennamo, 2019). Competitive behaviour scenarios which have been researched include the

subsidization of one customer segment to support another (Rochet & Tirole, 2003); varied

degrees of participant openness to the ecosystem (Boudreau, 2010); different levels of

exclusivity (Cennamo & Santalo, 2013); as well as strategic tactics like platform envelopment

(Eisenmann, et al., 2011). With the exception of Hannah and Eisenhardt (2018), research into

competition among interdependent ecosystems is scant. Substantive research has been

conducted to understand the unique characteristics of ecosystems and how they collaboratively

facilitate value creation for ecosystem-level outputs (Adner, 2017; Autio & Thomas, 2020;

Clarysse et al., 2014). Surprisingly little is, however, known about the respective and

comparative competitive strategies or theories that could be employed to assess these strategies

within industrial marketing.

To further probe these competitive considerations, the section to follow focuses on specific

areas of inquiry within the ecosystem literature to identify research gaps. Drawing on both the

extant broader business and strategic management literature, as well as more specifically the

marketing and industrial marketing literature in particular, ecosystems and the dynamics of

competition within ecosystems are discussed in the section to follow. In doing so, pertinent

research gaps are identified and presented.

1.2. Research Gap Identification

Ecosystems are considered a new way of depicting the competitive environment of business

(Jacobides et al., 2018; Jacobides, 2019; Kotler & Sarkar, 2019). Having emerged from the

11

business and strategic management fields, the ecosystem concept has received substantive

scholarly attention in these fields. From an industrial marketing perspective, the earliest article

that refers to the ecosystem concept in the industrial marketing literature, is an article by

Bengtsson and Kock (1999), titled “Cooperation and competition in relationships between

competitors in business networks”, which was published in the Journal of Business &

Industrial Marketing. The industrial marketing literature has seen a rapid increase in ecosystem

literature from 2015 onwards, which included a special issue with a research emphasis on

ecosystems in the Journal of Business Research in 2016. Up until 2016, most of the studies in

the industrial marketing field conceptualized the ecosystem concept metaphorically, and

loosely defined it as a complex and broad system with many diverse actors (Aarikka-Stenroos

& Ritala, 2017).

Through the emergent service ecosystem stream, the authors Vargo and Lusch strongly pushed

to integrate service-dominant logic and the ecosystem analogy with industrial marketing

research (e.g., Lusch et al., 2016, Vargo & Lusch, 2016; Vargo et al., 2015). Important to also

note is that the ecosystem concept was often used in parallel with dynamic capabilities (Teece,

2007) to sketch the dynamic nature of the broader market environment. As a framework from

which to identify new and emerging topics, Aarikka-Stenroos and Ritala (2017) developed

thematic categorizations based on industrial marketing research articles that used an ecosystem

approach. Table 2 provides an overview of their thematic categorization, using selected articles

as reference.

Table 2: Thematic organization of selected industrial marketing research articles employing

an ecosystem approach

Research themes

linked to ecosystems

The role of ecosystems in the

field of study

Examples of the role of ecosystems

in the focal research and

references

Markets and

industries

An ecosystem is a way in which

a market is structured; it is a

dynamically evolving structure.

• The ecosystem relates to a layer of

“institutional systems and their

dynamics (e.g., distribution channels

and networked ecosystems)”

(Möller, 2013, p.324).

• The market ecosystem has balance

and symmetry, but this can be

disrupted via market shaping and

scripting as an actor introduces new

ideas or new business model

elements to which “the market

ecosystem” responds by seeking to

recover and create a stasis once more

(Storbacka & Nenonen, 2011).

• An ecosystem enables “discovering

opportunities” and “market learning”

(Storbacka & Nenonen, 2015).

12

Firms learn, discover, and

acquire information from the

“market,” that is, the

“ecosystem.”

The market orientation implies

that “the whole ecosystem” is a

source of information.

• Knowledge acquisition from the

internal and external actors of a

value co-creation

ecosystem via social media and a

market orientation strategy builds a

firm's competitive advantage

(Nguyen et al. 2015).

• “Market sensing” is the ability of a

firm to “anticipate [the] future

evolution of markets and detect

emerging opportunities based on

information collected from its

business ecosystem” (Mu, 2015,

p.154).

Value co-creation Value is co-created in a systemic

way by diverse actors.

• Multiple stakeholders or actors

contribute to value co-creation in the

ecosystem via their divergent

resources and resource integration

practices (Ekman et al., 2016; Frow

et al., 2016; Pera et al., 2016;

Singaraju et al., 2016; Storbacka et

al., 2016).

• There is a systemic perspective on

value co-creation rather than isolated

investigations on one level

(Meynhardt et al., 2016).

Value chains and

value networks

An ecosystem is an evolution of

the value network: the firm

chooses and operates a network

of collaborating actors who help

provide an offering.

• Ecosystems are hub-, firm-, or

product-centric value chains or

networks with vertical, horizontal,

and diagonal relationships (Søilen et

al., 2012).

• A firm “chooses an ecosystem” that

comes with both opportunities and

risks and either enables or challenges

survival (Töytäri et al., 2015).

• An ecosystem is related to value

chain transformations (Lampel &

Germain, 2016)

Business models

Business models are embedded

in an ecosystem context.

Firms' business models and

ecosystems co-evolve.

• Business models differ in terms of

how firms relate to the surrounding

ecosystem, that is, other players

(Benson-Rea et al.,2013).

• Firms must constantly develop their

business models, taking into account

the co-evolution of the business

model and ecosystem; dynamics are

emphasized (Muzellec et al., 2015).

13

Competition and collaboration

occur on an ecosystem level.

• Competition, collaboration, and co-

opetition occur on the ecosystem

level, and business models can be

designed to create as well as

appropriate value in this context

(Ritala et al., 2014).

Innovation and R&D Ecosystem actors are

contributors to innovation.

New tools and methods are

needed to enable ecosystem

actors to contribute.

Market innovations are the result

of co-creation and

institutionalization by ecosystem

actors.

• Interpreters, for example designers,

who are outside of or distant from

the focal ecosystem can question

conventions and thus trigger

innovation and change (Verganti &

Öberg, 2013).

• Knowledge from business

ecosystems is relevant to open

innovation processes and

R&D (Lind et al., 2012).

• The ecosystem approach puts

forward new approaches such as

crowdsourcing (e.g., Simula &

Ahola, 2014) and various other tools

and methods to enable dispersed

actors' contributions (Van

Bockhaven et al., 2015).

• Market innovation requires the

institutionalization of new practices

and the emergence of common

templates reflecting shared problems

and solutions. Problems and

inconsistencies in an ecosystem

trigger the emergence of new

solutions that create change, but the

institutions in the ecosystem can also

help in achieving and realizing

institutional change (Kjellberg et al.,

2015).

• Innovation is a process that unfolds

through changes in the institutional

arrangements that govern resource

integration practices in ecosystems

(Koskela-Huotari et al., 2016)

• Innovation can be considered a

social process in the ecosystem “by a

group of actors in which a company's

borders and the distinction between

the internal and external disappear,”

and thus, innovation is co-created by

all actors through a set of practices

(including symbolic, linguistic, and

14

Radical innovation requires a

(business) ecosystem, though

this is often absent.

material practices) and resource

integration (Mele & Russo-Spena,

2015, p.43).

• The main external innovation

barrier for radical innovation is a

lack of support from an ecosystem

(Sandberg & Aarikka-Stenroos,

2014).

• In cases of radical innovation,

ecosystems must be created (Yami &

Nemeh, 2014) or modified (Aarikka-

Stenroos & Lehtimäki, 2014).

Start-ups and

entrepreneurship

A new business requires support

from multiple actors and

institutions.

Ecosystems, as industry clusters,

support entrepreneurship.

• The ecosystems around innovative

start-ups (Baraldi et al., 2014;

Boehm & Hogan, 2013; Purchase et

al., 2014), universities (Jahanmir,

2016; Janeiro et al., 2013), and

investors (Lutz et al., 2013) are

studied as innovation ecosystems.

• Clusters created by economic

policies are less prone to innovation

than the spontaneous ecosystems that

emerge from private entrepreneurial

initiatives (Letaifa & Rabeau, 2013).

Branding and

legitimacy

The social processes that occur

between multiple stakeholders in

an ecosystem are meaningful.

• Brand and goodwill are earned in

ecosystems that include multiple

stakeholders ─ firms must re-

legitimize their businesses within

their ecosystems (Sheth & Sinha,

2015).

• Multiple stakeholders, even those

that are distant, opposing, and at the

periphery of an ecosystem, can

contribute to the co-creation of a

brand (online and offline) via their

values, cultural complementarities,

and valuable adjustments at the core

of the ecosystem (Gyrd-Jones &

Kornum, 2013).

• Multiple actors realize the

transition occurring in the service

ecosystem (Letaifa et al., 2016).

Source: Adopted from Aarikka-Stenroos and Ritala (2017)

As per Table 2, seven established and nascent themes in the area of industrial marketing

emerge. These themes include overarching topics that relate to markets and industries, the co-

15

creation of value through a diverse and interacting set of actors, the collaborative efforts of the

value chain, the creation and capturing of value through business models within an ecosystem

context, the generation of an innovative output through innovation and R&D developments in

an ecosystem, the instantiation of entrepreneurial ventures and start-ups through support within

an ecosystem, and finally branding and legitimacy, which emphasize end-user brand

engagement which leads to an ecosystem built around a brand. Besides the articles by Nguyen

et al. (2015) and Ritala et al. (2014), none of these articles explicitly address how competitive

advantage emerges or is achieved through an ecosystems approach.

To further probe the literature for articles with a focus on competitive advantage and

ecosystems, a Web of Science database search on the topic of ecosystems and competitiveness

was run. The search yielded 368 research articles in the general field of business management

between the years 2000 and 07 July 2020. Of the research articles identified, the most cited

article, with 634 citations, was an article by Vargo and Lusch (2016), titled “Institutions and

axioms: An extension and update of service-dominant logic”, published in the Journal of the

Academy of Marketing Science. Assessing the rest of the research articles, it is clear that

research on ecosystems and competitiveness in the marketing field in particular, is not

extensive, with 19 articles that have been published in marketing and marketing-related

journals on the focal topic since 2000.

On closer inspection, six articles have reviewed ecosystems and competitiveness from a

services marketing perspective over the last five years (Cheng et al., 2018; Guillemot & Privat,

2019; Minkiewicz et al., 2016; O’Connor & Cooke, 2020; Parris et al., 2016; Vargo & Lusch,

2016). From an industrial marketing perspective, barring one recent article by Bacon et al.

(2020) which explores the conditions for knowledge transfer success, examining how

knowledge transfer differs in coopetitive versus non-competitive ecosystem partnerships in the

Journal of Business Research, no research articles on ecosystems and competitiveness have

been published in industrial marketing journals. To ensure a comprehensive overview of the

topic and potential research gaps in the area of ecosystems and competitiveness, additional

articles from the fields of business, management, and strategy were also included to provide

broader reach and topical clarity. The broader assessment of the literature yielded three

particular areas for future research, which is discussed in more depth in the following section:

Gap 1: Clarity regarding the ecosystem metaphor

In assessing business ecosystems as a perspective for studying the relations between firms and

their business networks, Anggraeni et al. (2007) suggested further development of the

ecosystem concept from a research perspective, to gain better insight into the roles and

strategies of companies regarding the nature of competitive relationships between them. Oh et

al. (2016) conducted a critical examination of innovation ecosystems, and proposed that more

clarity is needed regarding the differences between different ecosystems. More recently

Thomas and Autio (2020) developed an organizing typology of ecosystems and asserted that

more empirical research is needed to gauge how different ecosystems compete.

16

To mature the ecosystem metaphor as a research concept and perspective for studying the

relationships between firms, their networks, and actors in industrial marketing, more clarity

and review of the concept in general is thus needed (Aarikaa-Stenroos & Ritala, 2017;

Anggraeni et al., 2007). A concise representation of conceptual differences, terminological

applications and distinctions between different ecosystems, would serve better to highlight its

value for industrial marketing and management (Oh et al., 2016). Clarity regarding the

ecosystem metaphor would serve to gain better insight into how different ecosystems compete,

as well as the nature of the competitive relationships between the different ecosystem types

(Thomas & Autio, 2020).

Gap 2: Theories to assess the dynamics of competitive advantage within and across

ecosystems

Adner (2017) assessed the ecosystem structure as an actionable construct for strategy, and

suggested that the consolidation of competitive dynamics across multiple levels of interaction

within ecosystems is an area not yet explored in the literature. Similarly, Jacobides et al. (2018)

present a theory of ecosystems and propose that firms gain from others participating in an

ecosystem, but cannot fully control them. The authors suggest that future research should

investigate what that implies for the way in which they attain advantage and how frameworks

like the resource-based view (RBV) and dynamic capabilities would be valuable for firms in

this dynamic context (Jacobides et al., 2018). Looking at social exchanges, networks and

relationships, Theodoraki et al. (2018) employed a social capital approach to the development

of sustainable entrepreneurial ecosystems, and proposed that research is needed to explain how

the dimensions implicit in social networks impact the competitive advantage within

ecosystems.

As such, it is posited that once conceptual clarity is gained, empirical research can be conducted

to better explain and explore relationships between constructs within ecosystems. This,

however, necessitates suitable theoretical frameworks (Adner, 2017; Autio & Thomas, 2020;

Jacobides et al., 2018). The second identified gap thus relates to the exploration of appropriate

theories to assess the dynamics of competition and competitive advantage of ecosystems.

Different theoretical perspectives have been proposed, mostly focusing on traditional

theoretical approaches customarily used to assess competition. These suggested theoretical

approaches include the resource-based view of the firm theory (Barney, 1991; Penrose, 1959;

Wernerfelt, 1984), dynamic capabilities (Teece et al. 1997), and network theory research (Burt,

2001; Lin, 2008), which centers on the dynamics of structural, relational and cognitive

dimensions of relationships within ecosystems, for example social capital (Adner, 2017;

Anggraeni et al., 2007; Autio & Thomas, 2020).

Gap 3: Empirical research regarding strategies for competitiveness in ecosystems

In their study of the management of innovation ecosystems to create and capture value in ICT

industries, Pellikka and Ali-Vehmas (2016) assert that more empirical research is necessary,

both qualitative and quantitative, to assess what strategies are being employed across different

17

ecosystem types. Similarly, Aarikka-Stenroos and Ritala (2017) propose that research and data

collection methods, with which to understand ecosystem-based business and innovation

activities and their management, should be developed. More empirical research with carefully

planned research designs in various contexts is needed to generate a deeper understanding of

the dynamics and co-evolution, as well as the open and evolving boundaries, of ecosystem

entities and the competitive advantage inherent in each of them (Aarikka-Stenroos & Ritala,

2017). Bacon et al. (2020) conducted a comparative analysis of knowledge transfer

configurations to assess coopetition in innovation ecosystems, and suggest that more analyses

are required regarding the conditions for competitive advantage within ecosystems. Finally,

Granstrand and Holgersson (2020) carried out a conceptual review of innovation ecosystems,

and propose that more empirical research is necessary to assess the strategic drivers of

competitiveness in other ecosystems.

Thus, to assess the implications of the emerging ecosystems approach and the way in which it

reflects in industrial marketing, more empirical research is necessary to assess strategy

formulation for competitiveness within and across ecosystems (Bacon et al., 2020; Granstrand

& Holgersson, 2020; Pellikka & Ali-Vehmas, 2016). Empirical analysis, both qualitative and

quantitative, would serve to generate a more holistic understanding of the drivers, factors and

conditions for competition and competitive advantage from an ecosystem perspective

(Aarikka-Stenroos & Ritala, 2017; Thomas & Autio, 2020). Figure 2 illustrates these three

gaps in the current literature, of which the one gap builds on the other. In other words,

addressing the first gap will have implications on the second, which will similarly also impact

the third research gap.

Figure 2: Three identified research gaps in the ecosystem literature

18

An ecosystems approach thus heralds a shift in the business environment (Kotler & Sarkar,

2019), with industry evolving from a distinct group of similar contenders, competing to

produce common end-products in a vertically integrated manner, to companies co-evolving in

non-permanent clusters of semi-connected, non-hierarchical relationships that span the

boundaries of traditional industry (Iansiti & Levien, 2004). Relying on assumptions and

traditional ‘plan and execute’ strategies from the classical perspective of the business

environment, would thus not position one in the best possible position to capitalize on

opportunities to create and capture value (Kapoor, 2018; Satell, 2019), which remains the

cornerstone of competitive advantage (Porter, 1985).

Instead, ecosystems’ competitive considerations and its implications for industrial marketing,

require responsiveness to an ever-changing market environment (McColl-Kennedy et al.,

2020). Competitive strategies would need to incorporate collaboration and cooperation with

others (McColl-Kennedy et al., 2020), including competitors, a reliance on the resources and

capabilities of those that you are collaborating and cooperating with (Quero Gervilla et al.,

2020), and alignment in activities to ensure that strategic decisions lead to beneficial outcomes

for all in the ecosystem (Hannah & Eisenhardt, 2018). Central to this strategic quandary would

be to explore how competitive advantage is achieved through an ecosystems approach in

industrial marketing.

1.3. Research Scope

The scope of the research relates to the extent to which this dissertation will explore the

research area with this study, and specifies the parameters within which the study will be

operating. Consolidating the competitive dynamics across multiple levels of interaction, using

multiple approaches to measure a number of constructs from an ecosystem perspective, would

be an arduous task for any doctoral dissertation. In addition, the three identified research gaps

extend across various disciplines and across a number of different types of ecosystems. As

such, a full exploration of all areas would extend beyond the scope of this dissertation and a

narrowing of focus for the purposes of this study is necessary. Three particular aspects across

and within specific ecosystems, pertinent to industrial marketing, are focused on.

First, the proliferation of the ecosystem concept across different disciplines and fields of study

warrants a thorough review of the literature to seek conceptual clarity. This would serve to

identify specific types of ecosystems and their characteristics. Second, building on the types of

ecosystems identified, a focused inquiry on competition and how it relates to competitive

advantage, both within and across specific ecosystems, is warranted. Third, specific theoretical

frameworks and strategies that relate to attaining advantage through resources, dynamic

capabilities and social networks, need to be employed for empirical analysis. The overarching

research problem and accompanying problem statement are introduced next.

19

1.4. Research Problem

The preceding sections have established that little is known about the achievement of

competitive advantage through the emerging ecosystems approach within industrial marketing.

The crux of the research problem that this dissertation thus seeks to answer is as follows:

Problem Statement:

How is competitive advantage achieved through an ecosystems approach in industrial

marketing?

The nature of ecosystems, competitive considerations, as well as research gaps identified, lead

to a number of potential avenues for further assessment. To provide clarity in terms of the

research direction to take, two key issues need to be addressed first:

1. The first issue is the challenge of conceptual heterogeneity of the ecosystem term, an

issue frequently highlighted in the literature (Adner, 2017; Jacobides et al., 2018;

Shipilov & Gawer, 2020, Thomas & Autio, 2020).

2. The second issue, closely linked to the conceptual heterogeneity, is the proliferated use

of the concept to depict different types of ecosystems (Adner, 2017; Jacobides et al.,

2018; Thomas & Autio, 2020).

Clarity regarding these two key issues would serve to better delineate the particular research

questions to address the overarching research problem of how competitive advantage is

achieved through an ecosystems approach in industrial marketing. To address these two issues,

a thorough literature review is necessary to assess the origins of the ecosystems concept, as

well as its use and typological application in the literature. These respective areas are elaborated

on in the literature review chapter to follow, starting with the evolution and development of the

ecosystem concept, as well as its characteristics. An assessment of the various literature-

proposed ecosystem types is also presented.

1.5. Chapter Summary

Chapter 1 served to introduce the research area of the dissertation and discuss its relevance to

industrial marketing. The ecosystems concept was first introduced, after which the concept was

defined, and its relevance to industrial marketing discussed. The pertinence of the ecosystems

concept to competitiveness in an industrial marketing context was also further considered. It is

argued that ecosystems are strategic vehicles for growth and innovation, that drive competitive

advantage in industrial marketing and provide a nuanced lens on how competition is created,

maintained and advantage achieved. Extant perspectives on the dynamics of competition in

ecosystems were provided to broadly sketch the landscape. The gaps in industrial marketing

literature and knowledge regarding ecosystems were identified and discussed – both from a

broader business management perspective, as well as specifically within the industrial

marketing stream. The scope of the research was presented, followed by the overarching

problem statement and research problem, which this dissertation seeks to answer.

20

CHAPTER 2: LITERATURE REVIEW

In this Chapter, the extant ecosystem literature is reviewed to track its evolution and

development. Emphasis is placed on the conceptualization of the ecosystem construct in the

management and marketing literature, which leads to a review of ecosystem-linked research

themes in industrial marketing literature specifically. This is followed by a discussion of the

characteristics of ecosystems in more detail, which serves as the basis for a consolidated

typology of the different types of ecosystems, including the respective ecosystems pertinent to

this doctoral dissertation. This is followed by a discussion of the literature-identified

importance of studying the competitive advantage of ecosystems from an industrial marketing

viewpoint. The Chapter then provides an overview of the theoretical perspectives of

competitive advantage, focusing in particular on the network and social capital theories, as well

as RBV theory and dynamic capabilities framework. The development of the research

questions is then discussed, together with an introduction to the four accompanying papers that

address each respective research question.

2.1. The Evolution and Development of the Ecosystem Concept

Ecosystems in the natural world are a familiar concept to most. Originally invented by the

British botanist Arthur Tansley in 1935, the word was used as a framework to describe the

interaction between contained communities of living organisms and the elements in their

localized environment (Golley, 1993). Tansley contended that as the organisms in an

ecosystem continuously coevolve to adapt to external changes and disruptions, they influence

each other and their terrain through sharing, competing, creating and collaborating for

resources in order to survive (Kelly, 2015). In 1986, the sociologist Amos Hawley introduced

the ecosystem term into social science, referring to an ecosystem as an “arrangement of mutual

dependencies in a population by which the whole operates as a unit and thereby maintains a

viable environmental relationship” (Hawley, 1986, p. 26). A few years later the business

strategist James F. Moore noticed growing parallels with the world of commerce and applied

the ecosystem concept to an increasingly dynamic and interconnected business environment,

in his seminal 1993 Harvard Business Review article. Moore (1993, pp. 75-76) proposed that:

“successful businesses are those that evolve rapidly and effectively. Yet innovative

businesses can’t evolve in a vacuum. They must attract resources of all sorts, drawing

in capital, partners, suppliers, and customers to create cooperative networks... I

suggest that a company be viewed not as a member of a single industry but as part of a

business ecosystem that crosses a variety of industries. In a business ecosystem,

companies co-evolve capabilities around a new innovation: They work cooperatively

and competitively to support new products, satisfy customer needs, and eventually

incorporate the next round of innovations.”

Although initially slow to gain momentum, interest in the phenomenon has grown rapidly in

recent years (Kelly, 2015). According to Google Scholar, Moore’s 1993 article had

accumulated 60 citations by the year 2000, and another 462 citations were added between 2001

21

and 2010. Over the last decade, since 2011, the article has, however, been cited 2814 times,

with 1942 of these citations occurring between 2016 and July 2020. The intensified scholarly

interest in ecosystems generally revolves around increased interest to understand the

cooperative and competitive interaction between a group of firms that depend on each other’s

activities for shared positive outcomes (Jacobides et al., 2018). As such, the ecosystem

approach has received a lot of attention in disciplines such as strategic management (Adner,

2017; Ansari et al., 2016; Autio & Thomas, 2020; Dattée et al., 2017; Thomas & Autio, 2020),

innovation and technology management (Clarysse et al., 2014; Gawer & Cusumano, 2014;

Ritala et al., 2013), and more recently, industrial marketing and management literature

(Aarikka-Stenroos & Ritala, 2017; Möller, 2013; Möller et al., 2020; Vargo et al., 2015;

Wilkinson & Young, 2013).

Over the past decade, the ecosystem concept has been given many different labels and monikers

to capture the essence of the approach in the industrial marketing and management literature.

Conceptualizations include the ‘innovation ecosystem’ (Ritala et al., 2014), ‘platform

ecosystem’ (Perks et al., 2017), and ‘service ecosystem’ (Vargo et al., 2015). In addition,

scholars have emphasized different aspects of an ecosystem, as determined by the unit of

analysis. Topics range from marketing and systemic value creation (Vargo et al., 2015),

managerial motives and resources for value co-creation (Pera et al., 2016), to co-creation

practices for health care ecosystems (Frow et al., 2016). Despite the widespread interest, some

contend the term has been conceptually and semantically overstretched in its application to

varying contexts (Aarikka-Stenroos & Ritala, 2017; Autio & Thomas, 2020; Fuller et al.,

2019).

Thomas and Autio (2020, p.2) argue that “despite extant proliferation, the concept nevertheless

describes collectives that are distinctive in that they uniquely combine participant

heterogeneity, coherence of ecosystem output, participant interdependence, and non-

hierarchical governance.” Considering the conceptual heterogeneity, the following section

seeks more clarity regarding the ecosystem concept and what it encapsulates, by conducting a

review of the literature regarding the nature and concomitant characteristics of ecosystems.

2.2. Ecosystem Characteristics

The previous section discussed the evolution of the ecosystem concept and highlighted that

there are diverse conceptualizations of ecosystems in current ecosystem research and streams.

The most frequently referenced ecosystem types are business ecosystems (Moore, 1993; 1996),

entrepreneurial ecosystems (Autio et al., 2014), innovation ecosystems (Zahra & Nambisan,

2011), knowledge ecosystems (Clarysse et al., 2014), platform ecosystems (Hein et al., 2019),

and service ecosystems (Akaka & Vargo, 2015). While reference is made to different

conceptualizations of ecosystems, the shared purpose of most ecosystem types is to collectively

co-generate some common output with other actors, while remaining an autonomous entity

(Adner, 2017). The ecosystem metaphor further also provides a vehicle to describe the

coordinated efforts and actions of hierarchically independent organizations (public, and/or

private sector) and individual actors for different value creating purposes, across different

22

spaces (physical or virtual), and in different formations (Fuller et al., 2019; Hannah &

Eisenhardt, 2018; Jacobides et al., 2018). As the different members within an ecosystem co-

evolve over time, their capabilities and relations to others are redefined in order to keep pace

with an ever-changing market and customer base (Helfat & Raubitschek, 2018). In most

ecosystems member relationships combine aspects of both collaboration, as well as

competition, where complementarity between capabilities and ecosystem-level output is

central, for example the complementarity of capabilities found in smartphones and applications

(also referred to as apps) (Fuller et al., 2019).

Ecosystems thus entail a big shift from static, company-centric and traditional perspectives of

strategy and competitive advantage, to dynamic and collaborative value creation (Kapoor,

2018). The literature asserts that this loosely connected configuration of relationships creates

options for new path exploration and exploitation that a traditional company might not have

the time, risk tolerance or resources to create and capture alone (Kapoor, 2014; 2018). Drawing

from the extant literature on ecosystems, Table 3 provides a summary of key factors which

outline the nature of ecosystems and their characteristics.

Table 3: Key characteristics of ecosystems

Factor Characteristic Description

Actors Network oriented

Diverse

Connected

• Encompasses overlapping networks of networks,

rather than discrete, linear value chains (Fuller et al.,

2019; Kapoor, 2018)

• Multiple actors, co-specialized at times, who create,

scale, and serve markets in ways that are beyond the

capacity of any single organization or individual

(Autio & Thomas, 2020; Granstrand & Holgersson,

2020)

• Embedded ecosystem actors are directly and

indirectly connected through their resource-integrating

interactions (Frow et al., 2019; McColl-Kennedy et al.,

2020)

Activities Externally focused

Interdependent

(Complementary/

Collaborative)

• Focus is on activities that extend beyond individual

company borders (Adner, 2017, Peltier et al., 2020)

• Presence of complementarities and interdependencies

between actors to create focal value proposition

(Akaka et al., 2013; Kapoor, 2018)

• Actors are motivated to collaboratively co-create

among firms, customers, stakeholders and other

ecosystem members (Hannah & Eisenhardt, 2018)

• The nature of the interdependence between actors

23

Coopetitive;

Cooperative;

Competitive

(i.e., cooperative, competitive, or coopetitive), which

impacts the ecosystem strategy to follow (Trischler et

al., 2020)

Alignment Dynamic

Emergent

Influence based

• Ecosystem relationships and capacities coevolve

rather than being static (Moore, 1996; Quero Gervilla

et al., 2020)

• Generates and embraces unanticipated shifts,

reversals, and unintended consequences (Iansiti &

Levien, 2004; Trischler et al., 2020)

• Shaped by partial influence rather than full ownership

or control of assets and resources (Akaka et al., 2013;

Vargo & Lusch, 2017)

Artifacts Holistically created

Upstream and

downstream

• The aim is to collaboratively combine firms’

individual offerings into a coherent, customer-facing

solution, product, knowledge repository (e.g.,

knowledge ecosystem) (Adner, 2006; Jacobides et al.,

2018)

• Focal innovation or solution often covers the full set

of components (upstream) and complements

(downstream) that support it (i.e., innovation

ecosystems, platform ecosystems, service ecosystems).

• Customers use the end product, not the firm (Fuller et

al., 2019; Kapoor, 2018)

Four distinct factors are differentiable in Table 3, with accompanying ecosystem characteristics

to describe how these traits contribute to the nature of ecosystems. These characteristics are

discussed in more detail below.

Actors Ecosystems mostly comprise a diverse and interconnected set of actors, that formally

and informally interact in a non-hierarchical manner, to co-create mutual benefits, i.e. ‘value’

(Autio & Thomas, 2020; Ganstrand & Holgersson, 2020; Jacobides et al., 2018). The actors

can consist of innovative organizations (e.g., research institutes, science and technology parks,

universities, industry partners), entrepreneurial organizations (e.g., start-ups, venture

capitalists, public sector agencies), or innovative and entrepreneurial processes (e.g., new

business model platforms, service or digital platforms). Ecosystems typically bring together

multiple networks and actors from different industries and sizes in order to create, scale, and

serve markets in ways that are beyond the capacity of any single organization or any traditional

industry (Greeven & Yu, 2020). The diversity and collective ability of each member in the

ecosystem to learn, adapt, and innovate together, are key determinants of long-term success

(Birkinshaw, 2019).

24

Activities To meet increasing customer demands and ensure the long-term health of the whole

ecosystem, all ecosystem members need to collaborate, so that all can derive mutual benefit

(Peltier et al., 2020). Interests, goals and values are aligned for the materialization of a focal

value proposition (Akaka et al., 2013). The concurrent presence of complementarities and

interdependencies between the diverse set of actors is one of the key characteristics of

ecosystems (Kapoor, 2018). Actor complementarity creates or enhances the value proposition

and therefore facilitates an economic relationship between offers (Adner, 2017). In turn,

interdependence implies a structural relationship between offers relating to how they are

connected for the purpose of value creation (Kapoor, 2018). Change in one offer may thus

impact on the value that is contributed by the other, as the actors’ respective offers are often

connected through system-level architecture (McColl-Kennedy et al., 2020).

Alignment Jacobides et al. (2018, p.2257) conceive the ecosystem as “an economic community

of interacting actors that all affect each other through their activities, considering all relevant

actors beyond the boundaries of a single industry”. This implies dynamic relationships where

a responsiveness to changes, both on a micro- and macro-level, would impact on the

performance of not only individual ecosystem members, but also the community as a whole

(Iansiti & Levien, 2004). Teece (2007) contends that this environment affects the ecosystem’s

dynamic capabilities and also its ability to build sustainable competitive advantage. No pre-

defined blueprint exists for ecosystem management (Autio & Thomas, 2020); rather, the

emergence and evaluation of opportunities for value creation and capturing necessitates

adaptive strategies to achieve competitive advantage. Similarly, when collaborating with others

and co-evolving based on a responsiveness to changes in one’s environment, the indirect

influence of others on the ecosystem, as well as the influence that the ecosystem exerts on

others, plays a more central role than asset ownership or full control (Fuller et al., 2019).

Although limited in its empirical support, previous research has also explored ecosystem

management from the point of view of ‘hub’ or ‘keystone’ firms, who serve to provide network

stability through managing knowledge mobility and appropriating innovation (Azzam et al.,

2017; Dhanaraj & Parkhe, 2006). Adner (2017, p.42) proposes that “the ecosystem is defined

by the alignment structure of the multilateral set of partners that need to interact in order for a

focal value proposition to materialize”. Alignment structure is defined as “the extent to which

there is mutual agreement among the members regarding positions and flows,” which then

becomes the objective, pursued through a firm’s “ecosystem strategy” to “secure its role in a

competitive ecosystem” (Adner, 2017, p. 47).

Artifacts An artifact represents the products, services, intangible resources, technological and

non-technological resources, or business models and solutions that serve as ecosystem outputs

(Granstrand & Holgersson, 2020). The purpose of an ecosystem is to create or exploit the

connection between a core product or solution, its components, and the complementary

products or services, that would jointly provide value for its customers (Jacobides et al., 2018).

An ecosystem may thus consist of collaborative actors that co-evolve through complementary

and competitive activities, to provide artifacts that address customer needs (Granstrand &

Holgersson, 2020). As new ways are forged to create value, ecosystems lead to the discovery

of new ways in which to create and capture value (Subramaniam, 2020). Central to this process

25

of value co-creation is an understanding of how competitive advantage is achieved through the

interplay of the various actors’ dynamic capabilities, the coordination of relational resources,

or the implementation of strategic mechanisms inherent to an ecosystems approach (Bacon et

al., 2020).

With the development of the ecosystem concept and its unique characteristics considered, the

section to follow offers an overview of different types of ecosystems as found in the literature.

The purpose of this section is to assess the differences and commonalities between different

ecosystem types to determine whether they can be coalesced. In addition, assessing the

differences and commonalities between different ecosystem types can aid in establishing

further theoretical and empirical clarity regarding the concept from an industrial marketing

perspective.

2.3. Ecosystem Types

Before adopting the ecosystem term as a standalone concept (Adner, 2017; Ansari et al., 2016;

Hannah & Eisenhardt, 2018; Kapoor, 2018), major research streams employed the term in

service of established and nascent concepts. Established concepts include the business

ecosystem (Moore, 1993; 1996), innovation ecosystem (Iansiti & Levien, 2004), and platform

ecosystem (Parker & Alstyne, 2008), while more nascent notions include the entrepreneurial

ecosystem (Ács et al., 2008) and service ecosystem (Vargo & Akaka, 2012). The various

ecosystem approaches differ in terms of relevant focal actors, configuration of activities,

alignment structures, and ecosystem output or nature of its artifacts. An overview of the major

ecosystem literature streams, their respective focus areas, including selected references to

academic literature and practical examples, are provided in Table 4.

26

Table 4: Major ecosystem literature streams, ecosystem types and differences in focus

Ecosystem stream Focus areas Practical examples

Business ecosystem

Comprises both

upstream and

downstream value

network actors,

related technologies

and institutions.

• Emphasizes collaboration

and supply chain aspects

(Adner, 2017; Iansiti &

Levien, 2004)

• Highlights the co-evolution

of competition and

collaboration (Moore, 1993)

Alibaba

Innovation ecosystem

An ecosystem

consisting of actors,

technologies,

and institutions that

enable innovation.

• Firm-centric innovation

ecosystems related to the

focal actor and its technology,

platform, brand, etc.,

connecting the various actors

or stakeholders around it

(Autio & Thomas, 2014;

Rohrbeck et al., 2009)

• National or regional

innovation systems (Clarysse

et al., 2014; Thomas & Autio,

2020)

• Technological innovation

systems (Adner & Kapoor,

2016; Almanopoulou et al.,

2019)

Silicon Valley

Modular ecosystem

An ecosystem that

consists of

independently

designed components

yet function as an

integrated whole in

terms of its value

offering.

• Mostly narrow in scope,

consisting of focal firm(s),

with complementors and

suppliers. Customer either

adopts or accepts ecosystem

output, which would not be

viable if it did not meet

customer needs (Kapoor,

2018)

• Customers choose among

the components and/or how

they are combined as modules

to make up a particular

platform or service offering

(Hannah & Eisenhardt, 2018)

Apple iOS

Platform ecosystem

Ecosystems centered

around a technology-

mediated digital

platform.

• Typically governed by a

platform leader that facilitates

connections to various sides

of markets for value exchange

and creation (Gawer &

Uber (including Uber

Eats, Uber Freight,

Uber Health, Uber

Everything)

27

Cusumano, 2014; Greeven &

Yu, 2020)

Service ecosystem

Ecosystems that focus

on value co-creation

in service exchange

and resources.

• Key concept in S-D logic,

provides the unit of analysis

for value co-creation activities

among actors integrating

resources and exchanging

services within dynamic

networks, facilitated by

institutional arrangements

(Vargo & Lusch, 2017).

• Nested within or part of a

larger system, where systems

influence each other, thus

evolving the service

ecosystem (Trischler et al.,

2020).

Fitbit

Entrepreneurial ecosystem

Ecosystems that

enable the emergence

and

growth of new

businesses to heighten

the competitiveness of

a region.

• Start-up and entrepreneurial

ecosystems that are often

located in particular

geographical areas or around

a certain industry

(Ács et al., 2017; Isenberg,

2010)

Triple Helix model of

university-industry-

government

partnerships, e.g., the

Research Triangle

Park in North

Carolina, USA

Knowledge ecosystem

Ecosystems where the

main interest and

outcome is the

creation and

exploration of new

knowledge through

joint research work,

collaboration, or the

development

of a shared knowledge

base.

• Large number of actors

grouped around knowledge

exchange or a central non-

proprietary resource for the

benefit of all actors (Järvi et

al., 2018; Valkokari, 2015).

• Usually confined to a

specific geographic locality

and encompasses research,

education to share explored

knowledge, and technology

transfer (Van der Borgh et al.,

2012).

High Technology

Campus Eindhoven

28

According to Table 4 there are seven major ecosystem literature streams, each pointing to a

different ecosystem type. A brief overview of each of these ecosystem types is provided to

highlight their respective focus areas and properties.

Business ecosystems emphasize the economic outcomes of business relationships, and focus

on customer value creation (Valkokari, 2015). Business ecosystems further encompass the

cooperation of different companies to jointly deliver a product or service to a customer, which

no single firm would have been able to accomplish individually (Adner, 2006; Iansiti & Levien,

2004; Kumar et al., 2015). Innovation ecosystems are distinguished from business ecosystems,

in that their goals are innovation-related (Aarikka-Stenroos & Ritala, 2017), and they are often

analyzed from a technological systems perspective (Markard & Truffer, 2008). Innovation

ecosystems explore new knowledge in order to exploit it for the creation and capturing of value

(Valkokari, 2015), and its members include both individual actors, as well as private and public

organizations (Clarysse et al., 2014). Modular ecosystems comprise a set of components

(upstream) and complements (downstream) that support and contribute toward the building of

a bigger, central, value proposition or architecture (Adner & Kapoor, 2010). A modular

ecosystem has a clear supply-push and value production emphasis (Adner, 2017; Hannah &

Eisenhardt, 2018; Jacobides et al., 2018). Platform ecosystems revolve around technological

platforms and platform architecture that connect multiple sides of markets, like users,

advertisers, and content providers (Gawer & Cusumano, 2014; Kapoor, 2018). By connecting

to the platform, complementors can generate complementary innovation and gain access to the

platform’s customers (Jacobides et al., 2018). As an emergent and rapidly growing stream,

service ecosystems represent “relatively self-contained, self-adjusting systems of resource-

integrating actors connected by shared institutional logics and mutual value creation through

service exchange” (Vargo & Akaka, 2012, p. 207). The focus of entrepreneurial ecosystems is

the creation and growth of new businesses in a particular geographical area to heighten the

competitiveness of the region or grow its economic outlook (Isenberg, 2010; Roundy et al.,

2018). A knowledge ecosystem consists of users, producers and supporters of new knowledge

(Järvi et al., 2018). The members of knowledge ecosystems are organized around joint

knowledge search and exploration, in order to contribute to the joint knowledge base

(Valkokari, 2015).

Thomas and Autio (2020) recently proposed a typology of ecosystems to further probe what

the differences and commonalities between these various types of ecosystems are. Their

typology relies on two dimensions. The first dimension refers to ecosystem-level output and

the second refers to research emphasis. Ecosystem-level output, or shared goals, takes three

main forms, namely an ecosystem value offering targeted at a defined audience, a business

model innovation, such as a new start-up venture, or new research-based knowledge (Thomas

& Autio, 2020). The second dimension, research emphasis focuses on the emphasis of scholarly

attention, in which case the authors differentiated between community dynamics, output co-

creation, and interdependence management.

For the purposes of this dissertation the three conceptual commonalities identified from the

terminological components in the previous section, namely ecosystems-as-structure,

29

ecosystem-based activities, and ecosystem-level output, are used to replace their second

dimension with three areas of terminological emphasis. The purpose of this synthesis is two-

fold. First, to clarify any potential overlapping ecosystem characteristics that are present in

more than one type of ecosystem, and second, to explore whether there are distinctive and

unique characteristics within these types of ecosystems that could cumulatively shed light on

the phenomenon as a vehicle for competitive advantage within industrial marketing. In short,

the delineation clarifies the research emphasis and the types of ecosystems that this dissertation

focuses on. Figure 3 presents the adapted ecosystem typology.

Figure 3: Ecosystem typology

Source: Adapted from Thomas and Autio (2020)

Based on the conceptual commonalities as derived from the definitions, the ecosystem

typology as adapted from Thomas and Autio, 2020, suggests that there are mainly three

overarching ecosystem types, namely innovation ecosystems, entrepreneurial ecosystems, and

knowledge ecosystems. These three types of ecosystems are now discussed in more detail.

2.3.1. Innovation Ecosystem

Due to resource constraints and an increased demand for specialization, individual-level firms

find it challenging to develop and commercialize a technology-based offering from beginning

to end on their own (Clarysse et al., 2014). This has given rise to the emergence of “complex

constellations of organizations” (Walrave et al., 2018, p. 6) in the form of innovation

ecosystems. Innovation ecosystems consist of multiple stakeholder approaches to co-create and

30

produce a focal value proposition. It often entails shared technological compatibilities in order

to co-align, which may be in the form of a platform or a service (Adner, 2017; Autio & Thomas,

2014; Jacobides et al., 2018). Valkokari (2015) suggests that intermediaries play an important

bridging role between actors within an innovation ecosystem, through facilitating interaction

and building interdependencies. Interaction among actors in innovation ecosystems has the

main goal of creating, delivering and appropriating value in the form of an overarching

common offering, which Walrave et al. (2018) refer to as the ecosystem’s value proposition.

An innovation ecosystem value proposition is the focal goal or performance achieved for end

users, based on the contribution and combination of activities of all the actors in the ecosystem

(Ulaga & Reinartz, 2011).

Echoing previous suggestions (Jacobides et al., 2018; Thomas & Autio, 2020), and based on

the identified overarching characteristics of innovation ecosystems, it is proposed that

innovation ecosystems comprise four types of ecosystems: ‘business ecosystems’, with an

emphasis on the interactive flow of activity between members of an ecosystem to deliver an

ecosystem value offering targeted at a defined audience (Iansiti & Levien, 2004); ‘modular

ecosystems’, with an emphasis on the collective and co-produced value proposition of the

ecosystem targeted towards a defined audience (Hannah & Eisenhardt, 2018); ‘platform

ecosystems’, which emphasize the centrality of structural alignment of all members of the

ecosystem to bring the focal value proposition to fruition (Teece, 2020); and ‘service

ecosystems’, where the service offering to a defined audience is the focal ecosystem value

proposition around which actor activities and exchange of resources are centered (Vargo et al.,

2020). Different configurations may constitute different ecosystems (Adner, 2017). The

heterogeneity of innovation ecosystem members provides complementary resources and

capabilities for the shared benefit of the whole ecosystem. Innovation ecosystems thus

encapsulates an ecosystem that exhibits a consistent ecosystem-level offering of value or

ecosystem-level output, that is innovation-driven and targeted towards a defined audience.

2.3.2. Entrepreneurial Ecosystem

An entrepreneurial ecosystem differs from an innovation ecosystem in the sense that it doesn’t

focus on providing an ecosystem value offering targeted at a defined audience (Thomas &

Autio, 2020). Instead, entrepreneurial ecosystems cultivate a shared knowledge base by

employing and organizing technological advances and infrastructure in new ways, which

facilitate some form of ecosystem output to create, deliver, and capture value, for example

business model innovation or the ventures that embody them. As a result, these business models

can be applicable in any sector and targeted at any audience (Ács et al., 2013; Isenberg, 2010;

Spigel, 2017). Thomas and Autio (2020) suggest that an entrepreneurial ecosystem’s audience

is predominantly internal, as it consists of new ventures that leverage the business model

experience of others to discover new practices to capitalize on (Autio et al., 2018).

Conceptually, entrepreneurial ecosystems comprise the connection between regional economic

development strategy, entrepreneurial activity and innovative initiatives associated with job

creation and urban revitalization (Ács et al.,2008; Ács et al., 2017; Adner, 2017; Audretsch &

Belitski, 2017; Isenberg, 2016; Roundy, 2017; Roundy et al., 2018; Spigel & Harrison, 2018).

31

In the entrepreneurial environment, various potential and existing interconnected

entrepreneurial actors need to be interlinked to reach a common performance goal. Mason and

Brown (2014, p.5) define all these actors as “entrepreneurial organizations (e.g. firms, venture

capitalists, business angels, banks), institutions (universities, public sector agencies, financial

bodies) and entrepreneurial processes (e.g. the business birth rate, numbers of high growth

firms, levels of ‘blockbuster entrepreneurship’, number of serial entrepreneurs, degree of

sellout mentality within firms and levels of entrepreneurial ambition) which formally and

informally coalesce to connect, mediate and govern the performance within the local

entrepreneurial environment.” Brown and Mason (2017) argue that the nonlinearity of the

entrepreneurial ecosystem concept has been key in its evolution. Audretsch et al. (2019) build

on this idea by underlining that the entrepreneurial ecosystem is directly affected by network

externalities, governmental support, knowledge spillovers and the turbulent competitive

environment in which it exists. Changes in government policy, for example, can have an

irrevocable impact on the developmental trajectory of an entrepreneurial ecosystem (Brown &

Mason, 2017).

The entrepreneurial ecosystem comprises clusters with various firms, universities, science

parks, and governmental agents and agencies, which collaboratively form the structure within

which the entrepreneur should navigate their path. As an example, public sector incubators

often involve universities, as connections to universities are considered to provide new

knowledge and opportunities for innovation which firms could then access and exploit (Mason

& Brown, 2017). The Triple Helix model (Etzkowitz & Leydesdorff, 2000) is an example of

an interaction cluster between university, industry and government which represents the

embeddedness of actors in an ecosystem. Universities develop contractual agreements with

industry partners to conduct research in particular areas and as such, exploit the economic

potential of the opportunity. With an increased demand from knowledge-producing institutions

like universities to commercialize their knowledge for economic gain (Miller & Ács, 2013),

the Triple Helix model is characterized by reciprocal relationships among university-industry-

government, in which each attempt to enhance the performance of the other (Leyden & Link,

2015).

2.3.3. Knowledge Ecosystem

The central activity and output of a knowledge ecosystem includes the collaborative exploration

of new knowledge and collective knowledge exchange processes” (Thomas & Autio, 2020;

Van der Borgh et al., 2012). Knowledge is used as the most important medium of interaction

among ecosystem members (Iansiti & Levien, 2004). The ecosystem-level output of

knowledge ecosystems are generally research-based knowledge and associated applications.

Knowledge ecosystem members jointly create and explore new knowledge as a shared

resource, with Järvi et al. (2018) stating that knowledge ecosystems mostly occur in pre-

competitive and pre-commercialization settings. As a result, this type of ecosystem is generally

removed from knowledge exploitation and commercialization, both considered to be

downstream activities (Valkokari, 2015).

32

Knowledge ecosystems are often multi-stakeholder, geographic clustered organizations that

benefit from their physical proximity or location close to universities, research institutes or

technology hubs (Clarysse et al., 2014). Companies locate in these particular geographic

hotspots in order to develop and exchange tacit knowledge, for example, during the early

growth and development phases of a project’s R&D (Van der Borgh et al., 2012). During these

phases the knowledge exchange intensity, as well as the effectiveness and efficiency of the

other participants, greatly depends on social interaction among actors in the ecosystem and

thus, physical proximity facilitates this interaction (Gupta et al., 2007). In this respect,

knowledge ecosystems are “organizations comprising diverse actors bound together by a joint

search for valuable knowledge while having independent agency also beyond the knowledge

ecosystem” (Järvi et al., 2018, p.1524).

From an emphasis perspective, the research focus of this dissertation is centered on how

competitive advantage is achieved through an ecosystems approach in industrial marketing.

The synthesized ecosystems typology further concentrates the focus to explore only innovation,

entrepreneurial and knowledge ecosystems as the ecosystem-level units of analysis. The

section to follow discusses some theoretical perspectives regarding ecosystems and

competitive advantage to provide a lens through which the research problem can be assessed.

2.4. Theoretical Perspectives: Ecosystems and Competitive Advantage

Recent events, like the Coronavirus pandemic, have exposed the vulnerabilities of firms and

organizations whose competitive advantage is solely nested in the ownership of unique

physical assets (Greeven & Yu, 2020). Cruise lines, airlines, manufacturing and traditional

retail have all struggled to swiftly adjust their business models to continue to deliver value to

their customers. There are, however, companies that have created competitive advantage by

leveraging their ecosystem of partnerships, networks and hierarchically independent business

relationships, to swiftly adapt their offering to a disrupted environment and market. This

ecosystem is aligned to the analogy of natural ecosystems – a community of living organisms

that adapt and evolve with their environment, to collectively operate as a unit (Smith & Smith,

2015). Using the ecosystem metaphor in a marketing context, the purpose of the analogy is to

highlight that the goal of the unit is to jointly improve performance, and performance measures

are at the heart of marketing strategy (Mintz & Currim, 2013).

Metaphors, akin to the ecosystem concept, have long provided analogies through which to

express competitive strategy frameworks within marketing (Cornelissen, 2003; Hunt &

Menon, 1995; Rindfleisch, 1996). By stimulating creativity through “metaphoric transfers”

(Hunt & Menon, 1995, p.88), metaphors develop marketing knowledge by enabling the transfer

of concepts, propositions, and theories from other disciplines. Research provides examples of

the symbiotic use of metaphors to describe and provide constructive, reflective and systematic

value to the literature on competitive advantage (Achrol, 1996; Arndt, 1985; Eliashberg &

Chatterjee, 1985). From an industrial marketing perspective, competitive advantage in

ecosystems accounts for the creation of a differentiated value proposition that attracts not only

the end customer, but also the required partners to bring the value proposition to fruition

33

(Colombo et al., 2019). Traditionally, it has been proposed that competitive advantage is driven

by the value creating strategy of a single organization, where successfully implemented

strategies will lead to superior performance (Barney, 1991). In turn, this would facilitate

competitive advantage to “outperform current or potential players” (Passemard & Calantone,

2000, p. 18). According to Kumar et al. (2015), the ecosystem relies on a web of entities that

coordinate a set of activities to deliver utility to mutually connected customers. The ecosystem

perspective thus acknowledges the importance of the demand-side of how firms create value

and take into account the different elements that contribute to value. In addition, it also takes a

macro view of the external actors that contribute to the focal firm’s value creation (Kapoor,

2018) ─ not just as rivals or competitors, but also as cooperatives.

Fast-changing economic, technological and competitive developments in today’s business

environment compel firms to reexamine traditional strategies to achieve and increase their

competitive advantage (Day, 2014). Strategies could entail focusing on the interrelationships

and interdependencies among businesses (Porter, 1998), developing or capitalizing on superior

skills, or leveraging superior resources (Day & Wensley, 1988). Identifying and exploiting the

potential of interrelationships create opportunities to reduce costs or enhance differentiation

(Porter, 1998), while superior skills encompass greater resources relating to abilities,

competences and human talent, with superior resources implying “greater stock of financial

and other capital, better productive capacity, better location, access to supply and the like”

(West et al., 2015, p.495). All three of these strategies imply a process model of competitive

advantage, where marketing strategy regards competitive advantage as an ongoing and agile

process.

From a theoretical perspective, a growing number of scholars have raised calls to better

understand two central notions in recent years.

1. In a web of interdependent networks, what is the impact of social relations on ecosystem

competitiveness (Thomas & Autio, 2020)? Network theory has been proposed to study

intermediaries’ interactions within and between ecosystems (Clarysse et al., 2014;

Hayter, 2018). As ecosystems by definition consist of interdependent actors and entities

which interact, based on complementarities, research using network theory contributes

to a better understanding of how social relations and network analysis apply in

networked environments (Venkatraman & Lee, 2004). Research assessing the impact

of social capital as a network theory approach in ecosystems has, however, been

limited, with no research to date empirically examining how social capital embedded

in networks relates to the attainment of competitive advantage of ecosystem structures

(Theodoraki et al., 2018).

2. If firms benefit from the participation of others in an ecosystem, what does that convey

about how they attain advantage (Helfat & Campo-Rembado, 2015; Jacobides et al.,

2018; Jacobides, 2019)? As existing frameworks like the RBV mostly revolve around

owned resources, how should this be approached when the resources exist at the level

of the ecosystem? Furthermore, when linking RBV with dynamic capabilities and

ecosystems, what resources and capabilities are valuable within this dynamic

ecosystem context in industrial marketing?

34

To contribute to the literature and address these calls from a theoretical perspective, the section

to follow briefly discusses social network theory, with a particular focus on social capital, as

well as RBV and dynamic capabilities.

2.4.1. Network Theory and Social Capital

Network theory focuses on patterns of connectivity and actor ties, while the ecosystems

approach sets the boundaries around the entities and actors that need to interact in order for the

value proposition to materialize (Adner, 2017). In ecosystems, specialized members provide

specific resources for the value creation in the ecosystem (Thomas & Autio, 2013). By doing

this, the total cost of provided services is reduced within the ecosystem and the feasibility of

the whole ecosystem is boosted (Theodoraki et al., 2018). Members of the ecosystem

collaborate with each other to strengthen individual performance and contribute according to

their core competencies, which results in all benefiting from the value that is created by the

ecosystem.

Rooted in the structural holes approach by Burt (2004), and the strong and weak ties

perspective by Granovetter (1983), social capital theory fundamentally proposes that network

ties provide access to resources (Nahapiet & Ghoshal, 1998). As a concept that provides a

foundation for describing and characterizing interrelationships, both within and between

organizations, social capital has drawn increased interest from industrial marketing and

management scholars (Batt, 2008; Carmona-Lavado et al., 2010; Hsieh & Tsai, 2007). As a set

of resources rooted in relationships (Andriani, 2013), social capital conceptually provides a

measure through which to assess the cooperative reciprocity of associations (Batt, 2008). With

limited consensus on how social capital should be defined, Lin (2008) proposes that it is

constructed of social obligations and connections between members of a group, which builds

on Coleman's (1988, p. 98) assertion that social capital is defined by its function, i.e., “it is not

a single entity, but a variety of different entities that have two characteristics in common: they

all consist of some aspect of a social structure, and they facilitate certain actions of individuals

who are within the structure.” Bourdieu (1986, p. 248) emphasizes that it is “the aggregate of

the actual or potential resources which are linked to possession of a durable network of more

or less institutionalized relationships of mutual acquaintance and recognition.” Putnam (2000,

p. 19) focuses on the mutually beneficial characteristics of the connection, by stating that

“social capital refers to connections among individuals – social networks and the norms of

reciprocity and trustworthiness that arise from them.”

In spite of alternative perspectives, a common thread is the notion of member interaction that

facilitates the creation and maintenance of embedded social assets. Nahapiet and Ghoshal

(1998) distinguish between three social capital dimensions: structural, cognitive, and relational.

35

2.4.1.1. Structural Dimension

The structural dimension of social capital reflects the “pattern of relationships between the

network actors” (Inkpen & Tsang 2005, p. 152) and concerns the network ties, network

configuration, and the network stability. Network ties refer to the specific ways in which actors

are related. These ties are a principal aspect of social capital as they influence resource

combinations and resource exchange, which concomitantly affects innovation (Tsai &

Ghoshal, 1998). Strong ties have shown to be fundamental for the knowledge transfer of

complex, high-quality and tacit knowledge (Uzzi & Lancaster, 2003), whereas weak ties are

more conducive to the transfer of explicit knowledge (Hansen, 2002). The strengthening of ties

may also reach a threshold, where extra time and effort ploughed into a relationship only lead

to marginal or declining returns (Reagans & McEvily, 2003).

The pattern of links among network members is determined by the configuration of a network

structure (Filieri & Alguezai, 2014). Configuration elements include hierarchy, density, and

connectivity. As a consequence of its impact on access to and contact among the members of

the network, these elements have a bearing on the agility and ease with which knowledge can

be exchanged. Network density varies from dense to sparse, where McFadyen et al. (2009)

argue that sparse networks provide diverse knowledge and is the optimal configuration for the

creation of knowledge. Lazer and Friedman (2007) provide evidence that network density, over

time, reduces the diversity of information available in a network, which reduces long-run

innovation. According to Inkpen and Tsang (2005), the overall corporate structure has the

potential to inhibit or facilitate the connectivity between certain members within the network.

Network stability refers to any change of membership within the network, where a highly

unstable network may “limit opportunities for the creation of social capital because when an

actor leaves the network, ties disappear” (Inkpen & Tsang 2005, p. 153). It has been suggested

that network stability has widespread implications on the transfer of knowledge within a

network.

2.4.1.2. Relational Dimension

In contrast to the structural dimension, the relational facet focuses on the direct ties and

outcomes of interactions (Inkpen & Tsang, 2005). As such, this dimension is critical in building

trust and confidence between actors, which, for the purposes of an ecosystem-configuration of

interdependence between actors, relies on the willingness of the actors to share their knowledge

(Schofield, 2013). Trust, however, implicitly entails risk – risk of exposing one’s ignorance or

lack of knowledge (Kang & Hau, 2014), risk related to cost if the other party is found to be

untrustworthy (Rousseau et al., 1998), and risk of exploitation due to a knowledge partner’s

opportunistic behaviour (Holste & Fields, 2010). The transfer of knowledge is inhibited and

reduced in relationships that lack relational trust, as literature asserts that knowledge sources

will limit their engagement to known and trusted partners in such instances (Kang & Hau,

2014).

36

2.4.1.3. Cognitive Dimension

The final social capital dimension is termed the cognitive dimension. This dimension

represents those resources that provide shared representation, interpretation, understanding and

meaning among all entities involved (Nahapiet & Ghoshal, 1998). In their contextual focus on

knowledge transfer relevance, Inkpen and Tsang (2005) focus particularly on two aspects of

this dimension among network members, namely shared goals and shared culture. De Wit-de

Vries et al. (2018, p. 7) assert that shared goals are needed to “reach a common understanding

of the desired output” and to align for a shared interpretation of the results (Tsai & Ghoshal,

1998). The absence of shared goals creates ambiguity and hampers the cause and effect

differentiation of the knowledge exchange (Davenport, et al., 1998; Partha & David, 1994).

From an ecosystem perspective, successful interrelationships between members within an

ecosystem would be governed by clearly communicated shared goals (Robinson & Malhotra,

2005).

Shared culture represents the degree to which behavioral norms determine relationships. Gulati

et al. (2000, p.205) closely relate this to tie modality, which is defined as the “set of

institutionalized rules and norms that govern appropriate behaviour in a network”. At times,

these rules are clearly stipulated in formal contractual format, but most often they are

informally agreed upon, as is often the case with organizations in an ecosystem where actors

are not necessarily seen as formally connected to each other via contractual arrangements

(Trischler et al., 2020). Shared culture may create ‘excessive expectations of obligatory

behavior’, which could result in either a fixed mindset and an unwillingness to explore beyond

the borders of the network, or free riding on the opposite side of the spectrum. Where cultural

compromises need to be made, conflict often follows. Similarly, if one of the parties is

inflexible in relation to their way of doing things, cultural conflict and stifled knowledge

transfer are often the by-products. Being too heavily weighted in one dimension, while having

too little of another social capital dimension may, however, also lead to bad performance (Yang

et al, 2011). Assessing the impact of social capital on performance measures within an

ecosystem context would thus serve to highlight its effect on competitive advantage.

2.4.2. Resource-Based View and Dynamic Capabilities

Competitive advantage focuses on the firm’s resources and capabilities, which also form the

basic components of the RBV (Akter et al., 2020). Although sometimes viewed as synonymous

(Newbert, 2008), it is important to distinguish between resources and capabilities (Wu et al.,

2016). Resources could be basic tangible resources (e.g., machinery), or basic intangible

resources (e.g., organizational policies and procedures, or employee knowledge and skills) that

coherently fit together (Hunt, 2000). Capabilities refer to the firm’s ability to use its resources

to bring a desired outcome to fruition (Amit & Schoemaker, 1993). From a theoretical

perspective, the RBV theory views firms as a bundle of these resources and capabilities

(Wernerfelt, 1984), and argues that firms’ resources and capabilities will differ in nature and

extent (Nath et al., 2010). Research asserts that the survival of a firm is dependent on its ability

to create new resources, develop and expand its capabilities, and ensure that its capabilities are

37

inimitable to maintain competitive advantage (Day & Wensley, 1988; Helfat & Peteraf, 2009;

Prahalad & Hamel, 1990).

Nath et al. (2010) argue that merely possessing superior resources does not equate to attaining

competitive advantage. Instead, the key lies in the capability of the firm to manage its scarce

resources and to effectively employ its capabilities to enable superior performance, by

complementing an existing resource-capability framework (Makadok, 2001; Morgan et al.,

2009; Peteraf, 1993). Capabilities are thus different from resources as they are intertwined with

the tacit knowledge embedded within organizations, making it more difficult to transfer and

therefore more inimitable (Makadok, 2001).

In turn, dynamic capabilities are defined as an organization’s ability to “integrate, build and

reconfigure internal and external competencies in response to rapid environmental changes”

(Teece et al., 1997, p. 516). As a theoretical framework, it has received considerable currency

to study the capacity to flex tangible and intangible assets for competitiveness (Eisenhardt &

Martin, 2000; Nonaka, 1994; Teece et al., 1997). Although it is generally considered that the

dynamic capabilities perspective is built on the foundations of the RBV of the firm, Teece et

al. (1997) explicitly differentiate them from the static orientation of RBV (Akter et al., 2020).

RBV focuses on current resources and operational capabilities, whereas dynamic capabilities

emphasize the meaningful improvement and adaptation of these resources. In the pursuit of

continuous competitive advantage, dynamic capabilities provide a means through which to

renew and reconfigure the assets and capabilities of an organization or ecosystem ─ particularly

in the face of a changing environment (Teece, 2014; Zollo et al., 2013). According to Akter et

al. (2020), dynamic capabilities provide a strong theoretical foundation to sense and seize

opportunities, e.g., technological progress. Dynamic capabilities could thus shed light on how

an ecosystem approach could provide competitive advantage in the face of dynamic

environment and competitive changes (Schilke et al., 2018).

Following the review of the ecosystem concept in literature, a clarified definition,

conceptualization, delineation of ecosystem types, and the theoretical perspectives with which

to study them, the next section discusses the development of the four research questions that

guided this dissertation.

2.5. Development of Research Questions

To answer the overarching research problem, the study was broken down into four separate

research questions, each addressed by a different paper. As a whole, the four research questions

and papers offer insight into the research problem relating to how competitive advantage is

achieved through an ecosystems approach in industrial marketing. As it has been suggested

that competition in ecosystems inherently operate on two different, but interacting levels

(Adner, 2017; Jacobides et al., 2018; Kapoor, 2018), it is proposed that competitive advantage

both within the ecosystem, referring to “the security of activities, positions, and roles, which

affects the distribution and capture of value across positions” (Adner 2017, p. 49); and between

ecosystems, relating to “collective advantages in creating and capturing value relative to rival

38

constellations of actors” (Adner, 2017, p. 49) is assessed. In addition, the terminological

delineation and proposed ecosystem typology in the preceding section, together with the

theoretical frameworks highlighted due to literature-identified competitive considerations,

enable the development of four research questions. The two sections to follow first provide an

overview of the theoretical focus of the research questions, followed by the type of ecosystem

focus of the research questions.

2.5.1. Theoretical Focus of Research Questions

Recent years have seen a steady increase in interest among industrial marketing scholars and

practitioners relating to the ecosystems concept. As a means to characterize a variety of value

creating interactions and relationships between interconnected organizations, ecosystems differ

from value chain and supply chain paradigms by including both vertical and horizontal

relationships between actors (Bacon et al., 2020; Jacobides et al., 2018). It is also distinguished

from value networks and other value creation-oriented constructs, as it focuses on value

appropriation and use (Kapoor, 2018).

Building on these differentiated ecosystem approaches from an industrial marketing

perspective, a review of the literature and theoretical perspectives regarding ecosystems points

out that little is known about how competitive advantage is achieved through an ecosystems

approach (Bacon et al., 2020; Granstrand & Holgersson, 2020; Pellikka & Ali-Vehmas, 2016).

In addition, empirical assessments have been undertaken in other streams of literature, yet from

an industrial marketing perspective, empirical analysis is still limited (Aarikka-Stenroos &

Ritala, 2017). Considering the nature of competitive advantage, the literature suggests the use

and application of suitable theoretical frameworks to assess these strategies within ecosystems.

Theoretical approaches that have been suggested include network approaches such as social

capital theory, that focus on the competitive advantage of interdependent relationships (Burt,

2001; Lin, 2008), revolving around the dynamics of structural, relational and cognitive

dimensions of relationships within ecosystems (Adner, 2017; Anggraeni et al., 2007; Autio &

Thomas, 2020); as well as the RBV (Barney, 1991; Penrose, 1959; Wernerfelt, 1984), and

dynamic capabilities (Teece et al., 1997) theoretical frameworks. Based on these literature-

identified calls for more research using these theories, the research questions will employ either

one of these two theoretical approaches: network and social capital theory, or RBV and

dynamic capabilities theoretical frameworks.

2.5.2. Ecosystem Focus of Research Questions

The research problem guiding the inquiry of this dissertation seeks to address how is

competitive advantage achieved through an ecosystems approach in industrial marketing? To

contribute to the maturing of the ecosystem approach as a research concept and perspective for

studying the relations between firms and their networks in industrial marketing (Aarikaa-

Stenroos & Ritala, 2017), the conceptual differences and distinctions between different

ecosystems should also be taken into account (Oh et al., 2016). The ecosystem typology, as

adapted from Thomas and Autio (2020), proposes three main types of ecosystems based on

39

ecosystem-level output and terminological emphasis: innovation ecosystems, entrepreneurial

ecosystems and knowledge ecosystems. As such, this dissertation will use these three

ecosystem types as the respective units of analysis.

In addition, Clarysse et al. (2014) and Hayter (2018) state that network theory is well suited

for studying interactions within and between ecosystems, while Audretsch et al. (2019) and

Jacobides et al. (2018) assert that the dynamism of the interrelationships and interdependencies

on complementary resources, poses yet unanswered questions in terms of how advantage is

attained within and across ecosystems for cross-comparative analysis. Better insight into the

competitiveness, both within and between these ecosystems, would thus further serve to

cumulatively shed light on the phenomenon as a vehicle for competitive advantage within

industrial marketing. To address the overall research problem and add to the theory and

literature on ecosystems in industrial marketing, the formulated research questions are

presented next.

2.6. Formulation of Research Questions

The delineation of the two main theoretical approaches and the respective ecosystem types

enable the formulation of four distinct research questions, flowing from the overarching

research problem previously articulated. These research questions are as follows:

RQ1: What are the drivers of competitiveness using a network analysis approach to

ecosystems?




Figure 4 provides an illustrative overview of how these four respective research questions

address the articulated research problem. The dimensional considerations regarding the

theoretical approach, as well as the ecosystem type and dynamics of competitive advantage –

either within or between ecosystems – are also indicated.

40

Figure 4: Overview of research questions

The four research questions are discussed in more detail below, including a brief overview of

each accompanying paper that forms the critical path of this body of research.

2.6.1. Research Question 1 is addressed by Research Paper 1

Research question 1 addresses the research problem through the lens of network theory. The

research question probes the underlying idea that in ecosystems, firms do not just rely on their

own resources, knowledge and capabilities, or compete through stand-alone strategies, to

achieve advantage over rivals (Audrestch et al., 2019). Instead, the research question seeks to

analyze the strategic and competitive advantage of relying on network effects and externalities

offered through knowledge spillovers, shared resources, and public sector support (Lehmann

& Menter, 2018), which reach beyond firm-specific approaches to competitive advantage

(Porter, 1990). A nuanced approach like this would serve to enrich the extant literature

regarding the close competitive environment and causal relationships present within

ecosystems (Kuratko et al., 2017).

Entrepreneurial ecosystems, in particular, create macro-level competitive advantage and value

for ecosystems and individual firms and sectors by flexing network-related links, thus

contributing to the shaping of regional innovation outcomes (Audretsch et al., 2019;

Cunningham et al., 2017). Described as geographically-bound social networks of institutions

and cultural values that stimulate and sustain entrepreneurial activity, entrepreneurial

ecosystems foster regional competitiveness through economic growth and heightened

41

innovativeness (Roundy, 2016; Roundy et al., 2018; Spigel, 2016). Despite evolving and inter-

disciplinary discussions on entrepreneurial ecosystems, a comprehensive understanding of the

drivers of competitive advantage, and accompanying research directions or latest developments

in the field, are elusive. More analyses are needed to streamline the proliferated discussion on

entrepreneurial ecosystems from a wide range of disciplines, which would serve to provide an

underpinning understanding regarding the impact of ecosystems on regional competitiveness

and strategies (Audretsch et al., 2019). To achieve this, a clear understanding of the current lay

of the land is necessary, which would uncover the current theoretical understanding of this

ecosystem type from an industrial marketing point of view. To contribute to the discourse and

explore this area in more depth, the following research question and research paper are

proposed:

RQ1: What are the drivers of competitiveness using a network analysis approach to

ecosystems?

Paper 1: Entrepreneurial Ecosystems and the Public Sector: A Bibliographic Analysis

In the entrepreneurial environment various potential and existing interconnected

entrepreneurial actors need to be interlinked to reach a common performance goal. Central to

the success of entrepreneurial ecosystems is the notion that business performance depends on

the quality and quantity of interactions between internal firm factors, e.g., investment in

innovation, marketing and internationalization strategies, as well as external stakeholders,

including public sector organizations, universities and research institutions (Audretsch et al.,

2019).

The aim of this paper is to provide an overview of the origins of the entrepreneurial ecosystems

concept in literature, to offer insight into key concepts that have emerged in research over the

past twenty-five-years (1995 to 2019). The paper employs bibliographic techniques to track

knowledge, identify trends, and highlight the primary emerging patterns and conceptual

clusters. Using the visualization of similarities software tool, VOSviewer, a comparative

overview of the diverse representation of entrepreneurial ecosystems developments across

disciplines, countries, institutional clusters, networks and teams will be presented. The analysis

offers a map of the covered territory and facilitates the identification of gaps and under-

researched areas in the field, as well as driving themes that have contributed to our better

understanding of entrepreneurial ecosystems as a vehicle to drive regional competitiveness.

Despite the evolving discussions on entrepreneurial ecosystems in a wide range of disciplines,

few studies have conducted a critical analysis of the entrepreneurial ecosystems literature, with

a surge in recent calls to do so (Audretsch et al., 2019; Cavallo et al., 2018; Mack & Mayer,

2016). In addition, little is actually known about the networks of people, institutions or

countries that have shaped the development of the field. It is important to identify the most

influential scholars, as these individuals are the thought leaders who have contributed to the

conceptual development and will further advance the research domain in literature. Identifying

the most important universities is of equal importance, as these are the institutions that have to

42

date been most successful in developing and disseminating new knowledge, and are training

future thought leaders. From a policy perspective, identifying the countries that are most

productive in producing research in this area and have shown to have dynamic involvement

from a public-sector viewpoint, assists us in comparing and contrasting the trajectory of the

phenomenon as an important component of competitive regions.

Through bibliographic analysis, the paper thus aims to contribute to entrepreneurial ecosystems

research, by identifying influential and prolific individuals, institutions and countries between

1995 and 2019. This will provide an overview of the origins of the entrepreneurial ecosystems

concept and assist scholars to better track knowledge and identify trends that have developed

in literature over this period. The purpose of reviewing the extant literature through the lens of

a network analysis approach is four-fold. First, it provides insight into key concepts that have

emerged in entrepreneurial ecosystems research over the past quarter-century, with regional

competitiveness being the main ecosystem-level output. Second, it provides clarity regarding

the key concepts and research directions which have contributed to a better understanding of

what the drivers of the competitiveness of regions are, using an entrepreneurial ecosystems

approach. Third, it offers a map of the covered territory and facilitates the identification of gaps

or under-researched areas in the field. From a policy perspective, it finally offers a comparative

overview of the diverse representation of entrepreneurial ecosystems developments across

countries – with particular reference to public sector contributions and its impact on regional

competitiveness.


Research question 2 follows on from the previous in the sense that the identified drivers of

competitiveness in entrepreneurial ecosystems, as per research question 1, are further probed

to see its applicability in an innovation ecosystem context. Furthermore, areas identified as

under-researched or showing potential for further examination, are scoped for potential

assessment. As previously identified in the literature and per the theoretical perspectives

proposed for this dissertation, an exploration of the role of social networks within ecosystems

and the leveraging of social capital among the various network members could also present

timely insights in times of limited resources and great uncertainty.

As the aggregate product of embedded resources derived from a network of structural,

relational and cognitive connections, literature has shown that social capital plays a

complementary role in organizational knowledge transfer (de Wit-de Vries et al., 2018; Kang

& Hau, 2014; Leposky et al., 2017). Using a social capital approach, research question 2 seeks

to understand the role that social capital plays in attaining competitive advantage within an

innovation ecosystem, specifically within university and industry-related innovation

ecosystems, as these have shown, in paper 1, to be pertinent drivers of regional competitiveness

within entrepreneurial ecosystems. The research question guiding this inquiry and addressed

by paper 2, is as follows:


43

Paper 2: Leveraging Social Capital in University-Industry Knowledge Transfer Strategies: A

Comparative Positioning Framework

The purpose of paper 2 is to present a framework that would comparatively assess how the

presence of social capital in knowledge transfer strategies between university-industry

innovation ecosystems would impact the transformation of knowledge into products and

processes for commercial exploitation. Structural, relational and cognitive social capital

dimensions are mapped against the knowledge transfer strategy that the university-industry

partnership employs: leveraging existing knowledge or appropriating new knowledge. A clear

differentiation is made between creating new knowledge, i.e. disruptive innovation – a

knowledge appropriating strategy; or using existing knowledge for the purpose of incremental

innovation or development – a knowledge leveraging strategy. By establishing a link between

social capital and the knowledge transfer strategy employed, industrial marketers can gauge

how it impacts the competitive positioning of the innovation ecosystem.

University-industry partnerships in three different countries, all from regions at varying stages

of development, are compared using the proposed framework. These include a developed

region (Canada), a transition region (Malta), and a developing region (South Africa). The main

objective of the paper is to present a social capital university-industry knowledge transfer

framework to guide ecosystem actors to better align their knowledge strategy with their

respective competitive imperative. Competitiveness, in this context, would be based on the

comparative position of the region, according to their respective knowledge transfer strategy

and leveraging of social capital between ecosystem actors, as a means to achieve strategic

advantage. The premise is that the knowledge spillover from university to industry would

promote accelerated regional learning and alignment, facilitating innovation by virtue of the

provision of new ideas, building on the notions as expressed in paper 1. In turn, this would

enhance market performance (Riege, 2005) as a domino-effect of the development of better

products or processes, faster go-to-market, the commercialization of research, as well as skilled

human capital (Etzkowitz et al., 2012). As a strategic imperative, university-industry

knowledge transfer thus provides bidirectional access to institutional knowledge (Inkpen &

Dinur, 1998), with a high level of social capital among ecosystem actors, thus operating as a

driver for competitive advantage. This would result in all parties standing to benefit from the

collaboration (Barbolla & Corredera, 2009), as per the characteristics of an innovation

ecosystem (Bramwell et al., 2012).


Building on the findings of research question 2 and paper 2, research question 3 will address

the overarching research problem by operationalizing dynamic capabilities in the context of

knowledge-related resources, and quantitatively analyzing its impact on the innovation

performance of innovation ecosystems across 129 countries at different stages of economic

development. Both questions 2 and 3, as well as their accompanying papers, assess competitive

advantage between ecosystems. Expanding on performance-oriented measures of competitive

44

advantage, as employed in paper 2, the guiding question and accompanying paper for research

question 3 is as follows:


Paper 3: Innovation Performance: The Effect of Knowledge Based Dynamic Capabilities in

Cross-Country Innovation Ecosystems

Literature asserts that a firm’s ability to achieve market success is dependent on the efforts of

other innovators in its environment (Aarikka-Stenroos & Ritala, 2017). This environment acts

as an innovation ecosystem consisting of inter- and codependent relationships between the

members of the ecosystem (Moore, 1993). The innovation ecosystem underscores the dynamic

nature of innovation to achieve innovation outcomes (Bacon et al., 2020) or innovation

performance. Innovation ecosystems vary in terms of knowledge configurations, entity

specializations, innovative capacity and spatial distribution ─ with no panacea for success

(Kamaşak & Bulutlar, 2010; Manniche et al. 2017). Apart from idiosyncrasies regarding

context, characteristics and conditions within innovation ecosystems are of equal importance

to ensure sustained success (Autio et al. 2014; Penrose, 1959).

Despite the burgeoning interest in innovation performance in the context of innovation

ecosystems, little is known about its drivers (Adner & Kapoor, 2010; Autio & Thomas, 2014;

Maurer et al. 2011; Oh et al. 2016; Zahra & Nambisan, 2011). Research, however, indicates

that capabilities to innovate faster, better and smarter, and to transform and adapt to new

contexts through managing knowledge, provide competitive advantage (Peris-Ortiz et al.

2019). Since the emergence of the RBV (Barney, 1991) many theoretical perspectives have

emphasized knowledge and knowledge-related practices as fundamental to positive business

and innovation performance (Kazadi et al. 2016), especially where interconnected and

interdependent networks are at play (Galati & Bigliardi, 2017; Martín-de Castro, 2015).

Knowledge does not exist in a vacuum (Paavola et al. 2004) and similarly, innovation seldom

exists in isolation (Rybnicek & Königsgruber, 2019). Both knowledge and innovation are

embedded within a bigger innovation context with evolving, recursive interactions between

multi-level network members contained within the ecosystem (Acemoglu et al. 2016; De

Vasconcelos et al. 2018; Valkokari, 2015). In this respect, Knowledge-Based Dynamic

Capabilities (KBDC) are said to enable the exploration of organizational abilities to generate,

combine, and acquire knowledge resources to deal with environmental dynamics for innovative

market success (Beuter et al. 2019; Denford, 2013). However, few studies have empirically

examined the link between KBDC and innovation performance (Beuter et al. 2019; Cheng et

al. 2016; Zheng et al. 2011), and there are mounting calls to better understand how KBDC

impact innovation performance in the context of innovation ecosystems (Andreeva & Kianto,

2011; Malerba & McKelvey, 2020; Nunn, 2019). In view of these gaps, a review of the role of

KBDC in innovation performance, particularly within a broader innovation ecosystem, is

warranted.

45

From an industrial marketing perspective, a more comprehensive understanding of the KBDC

drivers of innovation performance within innovation ecosystems is important for the following

four considerations:

1. An understanding of the links between the various building blocks of KBDC and

innovation performance within an innovation ecosystem would facilitate and

expedite learning between actors within the ecosystem to accelerate the innovation

process.

2. Leveraging the expertise of actors and network members across the ecosystem

could improve the overall know-how and flow of information within the innovation

ecosystem.

3. Awareness of the diverse and complementary knowledge capabilities within the

interconnected and co-dependent members of the innovation ecosystem, may

heighten the potential of ecosystem members to sense and shape new innovation

opportunities.

4. Closely linked to the previous, an appreciation of the knowledge dynamics in an

ecosystem may stimulate the proliferation of new product, service, technology,

platform or process developments.


To expand on the previous research question and its assessment of the impact of dynamic

capabilities on the competitive advantage of ecosystems, focusing on innovation ecosystems

in particular, the fourth and final question focuses on how resource- and capability-based

theories can explain competition from a knowledge ecosystem perspective. Where innovation

ecosystems prioritize the joint exploitation of knowledge bases and knowledge transfer

between ecosystem members to materialize a focal value proposition, knowledge ecosystems

focus on the exploration of knowledge to create a shared knowledge base. Using a theory

elaboration approach, resource- and capability-based theories will be used to gain a better

understanding of how competition works in knowledge ecosystems. Knowledge ecosystems

have been said to operate in a pre-competitive state, yet little is known about what that entails

and if (or how) they attain competitive advantage. Knowledge ecosystems are regarded as a

regional community of hierarchically independent, yet interdependent heterogeneous

participants who advance the translation of primary knowledge into the development of new

products and services.

All ecosystem actors knowingly and willingly form part of the same ecosystem to, most often,

drive economic or market growth. Research institutes situated on university campuses are

viewed as knowledge-intensive entities that support economic transformation (Tödtling &

Grillitsch 2015). They enable actors from outside the university to access its resources, while

allowing the actors in this multi-stakeholder group to learn from and lean into a wider stock of

knowledge (Powell & Grodal 2005). Using a single in-depth case study, a university-based

keystone actor of a knowledge ecosystem is analyzed as the focal ecosystem for inquiry. The

research question and research paper is titled as follows:

46


Research Paper 4: Competition in Knowledge Ecosystems: A Theory Elaboration Approach

using a Case Study

Knowledge ecosystems do not address defined audiences to the extent that market choice

would be relevant for their survival (Thomas & Autio, 2020). In addition, they often consist of

a heterogeneous resource base which increases opportunities to gain access to complementary

resources and capabilities necessary to explore or expose knowledge (Naudé & Sutton-Brady,

2019). It would also contribute to the accumulation of a knowledge repository over time due

to the exchange of information (Ahuja, 2000), potentially increasing actor interdependence

(Teece & Pisano, 2003). Industrial marketing scholars would benefit from a more nuanced

understanding of the strategic and competitive dynamics present within these ecosystems, as it

offers insights on effectively engaging in collaborative initiatives to build a shared knowledge

base for exploitation and innovation-related outcomes. Furthermore, it also provides deeper

insight into knowledge transfer strategies, knowledge flow, and resources regarded as

knowledge stock, which, viewed cumulatively, could be leveraged to attain competitive

advantage for all ecosystem partners involved.

Following the discussion on the respective research questions and an overview of each research

question’s accompanying paper, Figure 5 provides an illustrative representation of the

overarching research problem, and a schema of how it will be addressed.

47

Figure 5: Schema of research problem, research questions and papers

48

2.7. Delineation: Construct, Context and Units of Analysis

With the theoretical and ecosystem focus of this dissertation reviewed, the definitional clarity

and ecosystem typology, as per the previous section, enable a more concise delineation of the

various areas that the research problem will address. In addition, the respective research

questions that aim to address the articulated research problem further provide distinct

parameters within which this research will operate. To ensure clarity in delineation, Figure 6

provides an overview of the construct, context and units of analyses that encompass this

research.

Figure 6: Delineation of construct, contexts and unit of analysis

As illustrated in Figure 6, the focal theoretical construct of this study focuses on how

competitive advantage is achieved through an ecosystem approach in industrial marketing.

The theoretical context of the overarching research problem will be assessed using the network

and social capital, as well as RBV and dynamic capabilities theories. These theories also form

the basis for the development of the four respective research questions, with two papers being

grounded in each of these theories. The various units of analysis comprise the three identified

main types of ecosystems, namely innovation, knowledge and entrepreneurial ecosystems.

Each paper, however, has a specific focus on an embedded unit of analysis, which forms part

of the particular ecosystem under inquiry.

For paper 1 the analysis of networks presents the key areas of research pertaining to the drivers

of regional competitiveness through an entrepreneurial ecosystems approach. In paper 2 the

university-industry partnerships of three particular countries, all at different stages of

development, form the embedded units of analysis for innovation ecosystems. For paper 3 the

embedded unit that is used for analysis is cross-country innovation ecosystems, as per each

respective country’s stage of economic development. Paper 4 entails an in-depth case study

exploration of a strategic marketing institute as the keystone actor in a knowledge ecosystem.

49


Chapter 2 sought to review the extant ecosystem literature to track the evolution and

development of the ecosystem concept. The conceptualization of the ecosystem construct in

the management and marketing literature provided an overview of ecosystem-linked research

themes in industrial marketing literature in particular. An ecosystem typology was presented,

which led to the identification of three overarching ecosystem types: innovation ecosystems,

entrepreneurial ecosystems, and knowledge ecosystems. The Chapter reviewed two streams of

theoretical thought, specifically focusing on theories which may provide a framework for

answering the research problem, namely, how is competitive advantage achieved through an

ecosystem approach in industrial marketing? These two streams are the RBV theory and

dynamic capabilities framework, as well as network and social capital theories. The Chapter

finally presented an explanation regarding the development of the four research questions as

well as provided an overview of the four papers that form the core of this dissertation. The

delineation of the constructs, context and units of analysis was also provided. Next, the

methodology of the four individual papers are presented, including the overall research

approach, strategy and design.

50

CHAPTER 3: METHODOLOGY

3.1. Introduction

This Chapter entails an overview of the research methods to be collectively employed in this

study. The specific procedures or techniques used to identify, select, process and analyze the

information are presented in more detail. The research approach, research design, and the

research strategy followed, are elaborated on to provide a clear demarcation of the

methodology employed.

The overarching research problem with the associated research questions are illustrated in

Figure 7.

Figure 7: Overview of research questions

As per Figure 7, the dissertation is guided by an overarching research problem, which is

investigated by means of four respective research questions, each examined through an

independent research paper. The respective research papers and their structure are also further

explicated in this chapter to provide more clarity on how the research questions were

approached and answered from a methodological perspective.

3.2. Research Approach

The research approach refers to the research plans and procedures that would be most suitable

to use for a particular study topic (Creswell, 2015). The selection of the research approach is

51

based on the nature of the research problem or issue being addressed, the researchers’ personal

experiences, as well as the audience for the study (Mohajan, 2018). Broadly speaking, an

unstructured or structured approach can be taken (Dawson, 2019). An unstructured approach

is most often classified as qualitative research, with this approach allowing flexibility in all

aspects of the research process. This approach is most appropriate when exploring the nature

of a problem, issue or phenomenon without quantifying it (Mohajan, 2018). The main objective

with an unstructured approach would be to describe the variation in a phenomenon, situation

or attitude (Dawson, 2019). The structured approach is mostly classified as quantitative

research, in which case the objectives, design, sample and research questions, are

predetermined (Dawson, 2019). This mode of inquiry is more appropriate to determine the

extent of a problem, issue, or phenomenon by quantifying the variation (Mohajan, 2018).

Creswell (2015) proposes that “qualitative and quantitative approaches should not be viewed

as rigid, distinct categories, polar opposites, or dichotomies” (p.4). Instead, Newman et al.

(1998) suggest that they are representative of different ends on a continuum. In other words, a

study tends to either be more suited to a qualitative than quantitative research approach, or vice

versa. Both of these approaches have inherent strengths and weaknesses, and often studies

employ both approaches in order to sufficiently address a research problem, by using a mixed

methods approach. Mixed methods research resides in the middle of this continuum because it

incorporates elements of both qualitative and quantitative approaches.

A qualitative research approach entails exploring, and involves emerging questions and

procedures, which are mostly a form of inductive inquiry into a research problem (Creswell et

al., 2008). Quantitative research is employed when testing objective theories in order to

examine the relationships among variables (Mohajan, 2018). The variables are measurable,

typically on instruments which would allow the analyses of the data through statistical

procedures and techniques (Creswell, 2015). Qualitative research is mostly engaged in the

deductive inquiry of theories in order to generalize and replicate the findings. Mixed methods

research involves the collection of both quantitative and qualitative data, which then integrates

both forms of data for use in distinct designs that could involve philosophical assumptions

(Creswell, 2015) and theoretical frameworks (Dawson, 2019).

It is assumed that a combination of qualitative and quantitative approaches provides a more

comprehensive understanding of a research problem than using either approach on its own. A

quantitative research approach is often associated with deductive approaches (based on logic),

while qualitative research methods are usually associated with inductive approaches (based on

empirical evidence) (Kothari, 2004). Similarly, deductive-quantitative designs are usually

more structured than inductive-qualitative designs (Kothari, 2004). As a starting point towards

developing the research strategy for this study, previous research approaches, designs and

methods employed in peer-reviewed academic marketing journals over the past five years

(2015 to 2020), which studied competitive advantage, competitiveness or competitive

strategies and ecosystems, are briefly reviewed and discussed next.

The first paper reviewed is that of Mackalski and Belisle (2015), who used a quantitative

52

research approach, with a quasi-experimental research design, to measure the short-term

spillover impact of product recall on a brand ecosystem. To assess the disclosure or

transformative behavior of a consumer ‘tribe’ within a consumer ecosystem, Healy and

Beverland (2016) employed an exploratory research design, with the research method of

netnography as part of a conceptual research approach in their study. To study service

ecosystems, Vargo and Lusch (2016) applied deductive theorizing to introduce an additional

institutional axiom to their service-dominant logic framework within service ecosystems. The

research approach of this study was conceptual, with a descriptive research design (Vargo &

Lusch, 2016). A paper by Cheng et al. (2018) studied the difference in consumer online

response rates between individual and aggregator service systems, to compare the

competitiveness of their online search ads within service ecosystems. To do this, the authors

conducted empirical research, making use of a quantitative research approach and an

experimental research design, using logistic regression (Cheng et al., 2018).

A paper by Hartmann et al., (2018) that examined service ecosystems, which was published in

the Journal of Marketing, developed a theoretical perspective of selling within service

ecosystems, from an institutional and service-dominant logic perspective. This paper was

conceptual in its approach, with a descriptive research design, employing deductive theorizing

(Hartmann et al., 2018). Guillemot and Privat (2019) is the only study that employed a

qualitative research approach to identify collaborative consumer communities’ relationship to

digital tools within a service ecosystem. The study used a phenomenological design, with 23

in-depth interviews being conducted to determine the role of technology in collaborative

consumer communities (Guillemot & Privat, 2019). In a recent study regarding health-care

referrals in a service ecosystem, O’Connor and Cook (2020) assessed patient referral leakage

and connectivity within a hospital network, by conducting empirical research, using a

quantitative approach with a descriptive research design. The last study that was assessed, was

that of Bacon et al. (2020), which explores the conditions for knowledge transfer success,

examining how knowledge transfer differs in coopetitive versus non-competitive ecosystem

partnerships. This empirical study made use of a causal-comparative research design,

employing fuzzy-set qualitative comparative analysis (fsQCA), to study coopetition in

innovation ecosystems (Bacon et al., 2020).

Based on the review of research methodology on the focal topic, which has been published

specifically in marketing journals over the past five years, it seems that although not that many

studies have been conducted, a variety of research approaches, designs and methods have been

employed. Both empirical and a number of conceptual studies have been conducted, which

indicated that there is still much to explore regarding the ecosystem concept from a marketing

and specifically an industrial marketing perspective. Using the theoretical frameworks

previously identified and discussed, while seeking to fill the literature-identified gaps in

ecosystem theory and literature within the industrial marketing milieu, the research strategy

and design of this dissertation is discussed in more detail next.

53

3.3. Research Strategy and Design

This section provides more information regarding the research strategy of the study, including

research aspects that relate to the data sources, analysis of data and overall research methods

of the four papers. For ease of reference Table 5 provides an overview of the research strategy.

The rest of this section will expound on the various research aspects represented in the table.

Table 5: Summary of the research strategy of this dissertation

Paper Title Research

Approach

Research

Design

Research Methods

1 Entrepreneurial

Ecosystems and the Public

Sector: A Bibliographic

Analysis

Quantitative Exploratory Bibliographic

analysis, using

secondary data

2 Leveraging Social Capital

in University-Industry

Knowledge Transfer

Strategies: A Comparative

Positioning Framework

Quantitative Descriptive Comparative

dimensional

scoring, using

secondary data

3 Innovation Performance:

The Effect of Knowledge-

Based Dynamic

Capabilities in Cross-

Country Innovation

Ecosystems

Quantitative Descriptive Partial least squares

structural equation

modeling, using

secondary data

4 Competition in Knowledge

Ecosystems: A Theory

Elaboration Approach

Using a Case Study

Qualitative Exploratory Single in-depth

case study, using

primary data

As summarily presented in Table 5, the research strategy of this dissertation entails a

combination of quantitative and qualitative research approaches, with two papers employing a

descriptive research design and two papers employing an exploratory research design. The

research methods used include a bibliographic analysis, comparative dimensional scoring, as

well as partial least squares structural equation modeling (PLS-SEM), all making use of

secondary data. For the final paper, a single in-depth case study was employed, which made

use of primary data.

In developing this research strategy the central research problem of this study, the literature-

identified gaps, calls for more research, as well as previous research relating to this issue was

considered. Firstly, the research strategy addresses one of the identified gaps in the ecosystem

literature, which calls for more empirical research regarding the central constructs of

competitive advantage within ecosystems, particularly in the industrial marketing stream.

54

Secondly, subsequent to reviewing previous research and research strategies examining this

issue from different perspectives within marketing, it seems that limited empirical research has

been conducted to distinctly elucidate our understanding regarding competitiveness and

competitive advantage through an ecosystem approach in industrial marketing specifically.

Consequently, the research strategy of this dissertation is predominantly empirical in nature.

Next, as a plan for the study, the research design provides the overall framework for collecting

the data, subjects, and research sites necessary to answer the research questions. The research

design provides the strategic framework of steps to take, bridging the research question and

execution, or to implement the research strategy (Durrheim, 2004). Research design refers to

the type of inquiry within qualitative, quantitative, or mixed method approach, which provides

focused direction for the procedures to be followed in a research design (Creswell, 2015). As

such, the research design can be defined as the “procedural plan that is adopted by the

researcher to answer the questions validly, objectively, accurately and economically” (Kumar,

2019, p.95). Denzin and Lincoln (1994) refer to the research design as the strategies of inquiry.

In light of the above, the section to follow describes the research design and research method

used for each of the papers in more detail.

3.3.1. Addressing Research Question 1


3.3.1.1. Research Design

The dataset for this analysis was retrieved through the Web of Science Core Collection

database. Web of Science is regarded as the foremost scientific citation platform for

comprehensive data-intensive research studies (Li et al., 2018). Web of Science enables the

researcher to specify the desired search term by using Boolean logic, which then includes or

excludes search terms, and specific journals. Previous research by Malecki (2018) explored the

prevalence and dominance of the entrepreneurial ecosystem environment in October 2017. The

author conducted searches in Web of Science (WoS) and Scopus of the full range of sources in

two databases. Based on this study, it was found that the term “entrepreneurial ecosystems”,

which emerged in the 2000s, has become the dominant term. Alvedalen and Boschma (2017)

similarly made use of the search term “entrep* ecosystem” to compare the relevant

entrepreneurial ecosystem literature with the “entrepreneurial system” literature. The authors

concluded that “entrep* ecosystem” saw a considerable increase in academic articles over the

past decade.

For the sake of consistency and in the light of its dominance as a term to use when referring to

this domain of research, the search term “entrepreneurial ecosystem*” was used as the search

query in October of 2019 to construct the dataset. Additionally, the scope of this research was

specific in presenting a snapshot representation of the current state of literature in this specific

field. The search query was restricted to only include articles, thus eliminating entries of any

other type, and with that, only relevant articles in English. Conference papers (16) were

55

removed to avoid duplication of information. Articles thus had to have used the terms

“entrepreneurial” and “ecosystem”, or variations of the word in the title, abstract or keywords

of the research in order to be eligible for analysis. The search was further restricted to the period

1995 to 2019, thus, a twenty-five-year period. The search resulted in 431 publications which

were used as data for further analysis.

Using the bibliographic analysis tool, VOSviewer, the paper also graphically mapped the

bibliographic material. As a means through which to visually represent citation networks, co-

citation, co-authorship, and the co-occurrence of keywords, VOSviewer has become an

influential tool (Van Eck & Waltman, 2014). Citation analysis facilitates the identification of

how documents cite each other, by counting the number of times that A cites B and vice versa

(Merigó et al., 2018). Co-citation takes place when two documents receive a citation from the

same third source (Shugan, 2006). Co-authorship represents the number of documents that are

co-authored by more than one author, institution or country, and shows how these are

connected. The co-occurrence of author keywords points out which keywords are most

frequently used, as well as the keywords that more frequently appear in the same documents.

As free, distance-based mapping software, VOSviewer is compatible with several online

databases to create visual bibliographic networks. Distance-based mapping represents the level

of closeness (e.g., authors, keywords) between two entities. The closer the entities are to each

other, the more closely they are related to each other. The maps are created following three

distinct steps. First, a similarity matrix is calculated based on the co-occurrence matrix. Second,

the VOS mapping technique is applied to the similarity matrix to create a map. And third, the

map is translated, rotated, and reflected to ensure consistent results (Van Eck et al., 2010).

3.3.1.2. Research Method

Paper 1 primarily made use of bibliometric methods and bibliographic analysis. Bibliometrics

is defined as a research area of information and library sciences that quantitatively analyzes

bibliographic data as derived from scientific publications (Broadus, 1987; Verbeek et al.,

2002). The purpose of bibliometrics is to highlight the nature and development of a research

domain (Pritchard, 1969), in order to classify and provide a representative overview of a set of

bibliographic documents (Merigó et al., 2018). The bibliographic analysis allows researchers

to explore knowledge diffusion and influence on a particular topic, within a particular domain

of interest, focusing on the networks that have been established within a scientific field (Kraus

et al., 2012). This method of analysis has increasingly gained popularity among academe due

to its "systematic, objective and replicable" nature (Most et al., 2018, p.231), which eliminates

potential subjectivity and researcher bias.

The bibliographic analysis incorporates an examination of citation, co-citation and co-

occurrence analysis, using established citation metrics to evaluate the scientific contribution of

different actors in the research domain (Aparicio et al., 2019). Different items serve as units of

analysis, such as keywords, scholars, journals, institutions, or countries (Merigó et al., 2016).

Citation, co-citation, and co-occurrence analysis are established and beneficial tools to explore

the knowledge structure of a particular domain (Servantie et al., 2016), with the two main

56

perspectives being productivity and influence (Podsakoff et al., 2008). Productivity is typically

measured by the number of publications, whereas number of citations is usually used as a

metric to gauge influence (Merigó et al., 2018). Journals are regarded as equal, with the number

of citations used as a proxy for journal quality (Rentschler & Kirchner, 2012). The influence

of specific journals can be assessed by examining the knowledge structures (Samiee &

Chabowski, 2012), respective authors (González et al., 2018), and the scientific influence of

specific articles (Most et al., 2018). Of specific interest was a closer examination of keywords

used when researching entrepreneurial ecosystems as vehicles for regional competitiveness,

which provided insight into areas of interest among researchers in the entrepreneurial

ecosystem research domain.


Paper 2: Leveraging Social Capital in University-Industry Knowledge Transfer Strategies:

A Comparative Positioning Framework


The research question guiding this paper focused on assessing the impact of social capital on

the knowledge transfer strategies employed within three different university-industry

partnerships, as examples of innovation ecosystems. To address this question, the objectives of

this paper were threefold. First, the respective social capital dimensions that are leveraged

during the process of university-industry knowledge transfer were examined across three

different regions: a developed region (Canada), a transition region (Malta), and a developing

region (South Africa). Three social capital dimensions and their accompanying sub-

dimensions, as originally proposed by Nahapiet and Ghoshal (1998) and refined by Inkpen and

Tsang (2005) were then used as a guideline for this purpose. Second, the intent of the

knowledge transfer activity was explained to better understand the strategic imperative: either

gaining new knowledge (appropriating strategy), or leveraging existing knowledge (leveraging

strategy). Finally, a social capital university-industry knowledge transfer framework was

developed to guide university-industry partners and similar innovation ecosystems, to better

align their knowledge strategy with their respective competitive and innovation imperative.

3.3.2.2. Research Method

The study utilized the principles of secondary data analysis. The researcher analyzed existing

studies on university-industry knowledge transfer from each identified region, to explore

whether it contained the variables needed to address the current research question. Nine

applicable studies were found (three studies from each respective region), which were used as

secondary data sources for the analysis. The studies consisted of both peer-reviewed academic

research articles as well as publicly available data collected by the Science-to-Business

Marketing Research Centre of Germany as part of a 2011 European Commission project and a

research report on Canadian university-industry collaboration, by The Board of Trade of

57

Metropolitan Montreal (2011). Operational definitions of the variables in each of the studies

were first established to ensure that these were aligned to the objectives of this study.

The three different countries were chosen as they are representative of regions that are currently

at different stages of development (OECD, 2017) and are thus faced with different challenges

relating to knowledge absorption capacity, capabilities, market stability, and cultural values

(Geuna & Muscio, 2009; Schofield, 2013). These are all aspects that would influence both

social capital dimensions as well as knowledge transfer collaborations. These three countries

also share commonalities. All three are ranked in the top 30% of most innovative countries in

the world on the Global Innovation Index 2018 (Canada 18th; Malta 26th; South Africa 58th),

and in the top 30% of most competitive countries in the world on the Competitive Industrial

Performance Index 2016 (Canada, 18th; South Africa, 45th; Malta, 65th).

To address the first objective of the study, the researcher assessed the level of social capital

dimensions present in each of the various country-specific university-industry knowledge

transfer partnerships. A score of one was assigned to each social capital dimension which

positively impacts knowledge transfer. This was then coded to denote a high level of social

capital in that particular dimension. If a particular social capital dimension negatively impacted

the transfer of knowledge, no score was assigned to that dimension, which was then coded as

a low level of social capital in that particular dimension. In addition to distinguishing between

the various levels of social capital dimensions present, the study secondly set out to assess the

strategic intent of the knowledge transfer activity, based on the knowledge transfer strategy

typology by von Krogh et al. (2001). To do this, a differentiation was made between creating

new knowledge, i.e. disruptive innovation – a knowledge appropriating strategy; or using

existing knowledge for the purpose of incremental innovation or development – a knowledge

leveraging strategy.


Paper 3: Innovation Performance: The Effect of Knowledge Based Dynamic Capabilities in



Paper 3 commences by first reviewing the extant literature pertaining to innovation

performance and the role of knowledge and knowledge based dynamic capabilities (KBDC).

It proceeds to conceptualize KBDC as consisting of four elements and proposes hypotheses

linking these to innovation performance in an innovation ecosystem context. To test the

research hypotheses and proposed model, a descriptive research design is employed using

quantitative secondary data analysis. Secondary data pertains to any data that has been

previously collected by a researcher for another purpose and is freely available (Kothari, 2004).

The benefits of using secondary data include a significantly lower level of resources required

to obtain the data (Malhotra, 2010). However, a potential limitation to using secondary data,

may relate to its usefulness to address a specific research problem that the data was not

58

originally collected to address. Therefore, the data necessitated an evaluation of its reliability

(Malhotra, 2010).

3.3.3.2. Data and Sample

The data source for this paper was the publicly available Global Innovation Index 2019 dataset.

The Global Innovation Index is a collaborative research effort between Cornell University,

INSEAD and the World Intellectual Property Organization. The reasons for selecting the

Global Innovation Index 2019 as the primary data source for the paper were two-fold. First,

the magnitude of the dataset offers a near complete examination of countries across the globe

representing a significant proportion of the global population. In fact, the dataset spans 129

countries, representing 91.8% of the world’s population, accounting for 96.8% of the world’s

GDP (Dutta et al., 2019). Second, the Global Innovation Index employs a broad

operationalization of innovation that allows for the data to reflect improvements made to

outcomes (Dutta et al., 2019).

3.3.3.3. Measures

Effective economies are those that succeed in translating innovation inputs into innovation

outputs, thus yielding a higher innovation performance score (Dutta et al., 2019). The Global

Innovation Index provides innovation input and output sub-indices together with innovation

performance scores. The innovation input sub-index comprises areas within national

economies that enable innovation activities in a country. The innovation output sub-index

encompasses information about outputs as a result of innovative activities within economies.

The innovation performance is the average of both the input and output sub-indices for the

particular country. The Global Innovation Index dataset also provides a country-level

innovation efficiency score that captures innovation performance, based on the output and

input sub-index scores.

Given the focus of the research model on the role of KBDC in driving innovation performance,

the components of the Global Innovation Index dataset relating to the conceptualized KBDC

constructs were identified for use as data. These KBDC are knowledge creation, knowledge

absorption, knowledge diffusion and knowledge impact. Knowledge creation, diffusion, and

impact are all regarded as output indicators of innovation performance. Operationally,

knowledge creation is a result of both inventive and innovative activities, while knowledge

diffusion reflects the outputs achieved, based on the knowledge that has been absorbed.

Knowledge impact represents elements that reflect the impact of innovative activities at both a

micro- and macro-economic level post market implementation. On the other hand, knowledge

absorption is regarded as an input indicator of innovation performance, and encompasses

elements that are connected to economic sectors with high-tech content or those that are key to

innovation-directed activities. All data for the KBDC constructs consisted of composite

variables that range from 0-100.

59

3.3.3.4. Data Analysis

A variance-based structural equation modelling (SEM) technique, using the partial least

squares (PLS) approach (Ringle et al., 2015) was used. SmartPLS was selected as the most

appropriate software for this analysis for three reasons. First, as a predictive technique, PLS

does not require as large a sample size as covariance-based SEM (Anaza et al., 2015; Hair et

al., 2019). Second, when working with secondary data, Hair et al. (2019) note that PLS-SEM

is especially suitable in offering flexible interaction between theory and data. Third, PLS-SEM

allows the research focus to move from strictly confirmatory to predictive and causal-predictive

modeling (Hair et al., 2019).

PLS data analysis necessitates assessing the measurement model as well as the structural model

(Chin, 1998; Hair et al., 2014). First, the psychometric properties of the measurement model

were analyzed and tested for common method bias, followed by the evaluation of the structural

model. To test relationships by economic level of development, the dataset was split according

to the 2019 United Nations published country classification of economic development (United

Nations Department of Economic and Social Affairs, 2019), grouped as either a developed,

transition or developing economy.


Paper 4: Competition in Knowledge Ecosystems: A Theory Elaboration Approach Using a

Case Study

3.3.4.1. Research Design and Research Method

Little research has been conducted in the area of strategic and competitive dynamics within

knowledge ecosystems, and as such, a theory elaboration approach is taken, which first entails

conceptualization of constructs and then conducting empirical research (Fisher & Aguinis,

2017). This dual process facilitates discovery and exploration rather than validation (Van

Maanen Sørensen, & Mitchell, 2007). For the empirical component of the study, qualitative

data was collected. The analysis of qualitative data allows a naturalistic, interpretative approach

to explore the phenomena in-depth (Flick, 2013). It also allows the researcher to take the

perspectives and accounts of the research participants as a starting point for further exploration

(Ritchie et al., 2013).

The research design entailed a single case study to collect rich empirical evidence from this

particular contextual knowledge ecosystem, organized around the joint search for knowledge

for shared exploration. A single case study is most appropriate when the research requires an

in-depth, qualitative understanding to provide rich insights into a substantive topic (Jones et

al., 2009). As a qualitative form of inquiry, case study methodology focuses on a detailed

investigation of a particular entity to provide an analysis of both the context and the processes

involved in the topic being studied (Henry et al., 2020). Due to the lack of relevant existing

data as well as the complexity of the variables involved, a detailed case study offered the best

60

method to explore the questions relating to context-specific strategic decision-making and

competition (Teece, 2020). Previous studies have also pointed to the need for more in-depth

case studies at the ecosystem and keystone actor level, to contextualize the strengthening,

sustaining, or undermining of competitive advantage (Van der Borgh et al., 2012).

To explore how competition works, the case study focuses on the specific knowledge

ecosystem context of a university-based marketing research institute, the University of Cape

Town Liberty Institute of Strategic Marketing (UCT Liberty ISM or Institute for short), based

in Cape Town, South Africa. As the keystone actor in its knowledge ecosystem, the UCT

Liberty ISM was selected as an ideal case. It fully aligns with the definition of a knowledge

ecosystem, consisting of hierarchically independent, yet interdependent heterogeneous

participants who advance the translation of research knowledge. Also, characteristically of a

knowledge ecosystem, the UCT Liberty ISM is a university-based organization, with most of

its ecosystem actors consisting of public and private sector partners, brands, government, and

other research institutes in and around the same geographic area. The fact that the knowledge

ecosystem focuses on strategic marketing, further contributes to a more nuanced understanding

from an industrial marketing perspective.

3.3.4.2. Data Collection

From a data collection perspective, the following procedures were followed. Research

interviews were scheduled both with the UCT Liberty ISM head of projects, Dr James

Lappeman, as well as the founding Institute director, Professor John Simpson. Prior to the

scheduled interviews, both research participants were sent short research primers via email,

relating to the objectives and the main constructs that would be discussed during the interviews.

Both participants sent back written replies to the research primers to highlight particular areas

which they felt they could best contribute towards during the interviews. These responses were

used as opening questions for the interviews. Face-to-face interviews were initially scheduled,

but, due to lockdown regulations during the COVID-19 pandemic, the interviews were

facilitated online via the Zoom platform. Both participants were individually interviewed

twice, with the interviews being video and audio recorded, as well as fully transcribed to aid

the analysis. The first round of interviews lasted approximately 90 minutes each. The second

round of interviews lasted approximately one hour each, serving to clarify any potential

misunderstandings following the first interview, and providing an opportunity to add any

additional information and context. The second round of interviews are indicative of the “linear

but iterative process” of case study research (Yin, 2017, p. 25).

As it is preferable that multiple sources of evidence are used throughout the case study method

(Yin, 2017), the research participants also shared additional archival documentation, research

reports and some secondary data, which further aided in assessing the phenomenon in a way

that the video and audio recordings would not have been able to do. Once all recordings were

transcribed, the researcher shared the full transcripts and initial analysis with the participants

for final clarification of any particular points. For the purposes of this study, a theory

elaboration approach was adopted to extend the theory in cases where "pre-existing conceptual

61

ideas or a preliminary model drives the study design" (Lee et al., 1999, p.164) and hence

increase internal validity (Gibbert et al., 2008).

3.4. Quality Criteria

To ensure that the research in the various papers is characterized by evidence that is

trustworthy, applicable to multiple practical settings, consistent and neutral or unbiased

(Frambach et al., 2013), the various quality criteria or standards for good evidence, are

discussed next.

In paper 1, Entrepreneurial Ecosystems and the Public Sector: A Bibliographic Analysis, the

research analysis encompassed the results from a very specific search query. Although the use

of more and broader search terms would yield different results, a recent study by Malecki

(2018) indicated that the term ‘entrepreneurial ecosystems’ has become the dominant term in

this field of research. In terms of the documents being used for analysis, only English peer-

reviewed research articles formed part of the scope of study, and thus the inclusion of other

research material, such as book chapters or other publication types, might provide different

insights. As this paper only sought to include articles that had undergone a rigorous review

process, it is believed that this is not a quality limiting factor. The citations included only

represent articles published during a particular period of time and citation count for an article

is also dependent on how long it has been since the article has been published. As such, it

would be necessary to keep pace with the literature and to update reviews such as these, to

ensure that relevance and dependability is maintained.

Paper 2, Leveraging Social Capital in University Industry Knowledge Transfer Strategies: A

Comparative Positioning Framework, only offers a snapshot representation of the university-

industry partnerships present in the three regions identified, due to the cross-sectional design.

As such, findings relate to a particular context at a particular point in time and the researcher

does not deny the influence of additional macro-contextual factors on the interpretation of the

results. The design did, however, allow the researcher to measure the prevalence of all factors

under investigation (Kumar, 2019), allowing for multiple outcomes to be studied (Creswell,

2015). In addition, the researcher and co-authors analyzed existing studies on university-

industry knowledge transfer from each identified region, and iteratively analyzed these studies

to explore whether it contained the variables needed to address the central research question of

this paper.

For paper 3, Innovation Performance: The Effect of Knowledge Based Dynamic Capabilities

in Cross-Country Innovation Ecosystems, an evaluation of the usefulness of the secondary data

was of prime importance. The reliability of the data, in particular examining the recency of the

data, the method of collection and the possible presence of bias (Kothari, 2004) was chiefly

assessed. The Global Innovation Index 2019 dataset is the most recent global innovation dataset

with a record of dependable publication. Furthermore, the dataset was subject to a number of

consistency and reliability assessments, including an examination of conceptual consistency,

62

data checks, statistical coherence and a qualitative review (Dutta et al., 2019). As such, “the

Global Innovation Index 2019 ranks are reliable and for most economies the simulated 90%

confidence intervals are narrow enough for meaningful inferences to be drawn” (Dutta et al.

2019, p. 381).

To address the quality criteria in paper 4, Competition in Knowledge Ecosystems: A Theory

Elaboration Approach Using a Case Study, the credibility of the data was assessed by

referencing multiple sources of data (i.e., data triangulation), and theories (i.e., theory

triangulation), as well as asking the research participants for their feedback on the interpretation

of the data (i.e., member checking) (Frambach et al., 2013). The transferability of the data was

also addressed by describing the context of the case study in as much detail as possible (i.e.,

thick description), serving to make the findings as transferable or applicable as possible to other

contexts and settings. Finally, the confirmability of the data was reviewed by searching the

data and literature for evidence that would disconfirm the findings, which, by doing so, basing

findings on the study participants’ feedback and not the researcher’s biases (Frambach et al.,

2013).

3.5. Structure of Individual Papers

An overview and description of the four papers that comprise this dissertation is presented in

this section. As per the previous section, each respective paper is based on a research question,

which has been derived from the overarching research problem of this dissertation: How is

competitive advantage achieved through an ecosystem approach in industrial marketing?

Based on this research problem, four research questions were formulated as follows:

1. Research Question 1: What are the drivers of competitiveness using a network

analysis approach to ecosystems?

2. Research Question 2: How does social capital impact the competitive advantage of

ecosystems?

3. Research Question 3: How do dynamic capabilities impact the competitive advantage

of ecosystems?

4. Research Question 4: How do resource- and capability-based theories explain

competition in ecosystems?

3.5.1. Paper 1

Robertson, J., Pitt, L., & Ferreira, C. (2020). Entrepreneurial Ecosystems and the Public

Sector: A Bibliographic Analysis. Socio-Economic Planning Sciences, 100862.

Journal: Socio-Economic Planning Sciences

Impact factor: 4.149 (2019)

Status: Published

Paper 1 answers Research Question 1: What are the drivers of competitiveness using a network

analysis approach to ecosystems?

63

As a starting point to address the research problem, the first paper employs bibliographic

analysis to construct and visualize bibliometric networks from a body of scientific literature.

The text from research articles relating to entrepreneurial ecosystems as a vehicle to foster

regional competitiveness, were mined to gain both a comprehensive understanding of the

overall research directions, as well as identify the latest developments in the field, spanning the

period 1995 to 2019. The analysis of networks, through the use of bibliographic techniques,

also provided an opportunity to track knowledge, identify trends, and highlight conceptual

clusters and primary patterns in the literature.

As a still-establishing field, a set definition for entrepreneurial ecosystems is still elusive

(Roundy & Fayard, 2019), and as such, the paper started by providing a consolidated overview

of the conceptual development of the entrepreneurial ecosystem term to also clarify its

definition. To conceptually define entrepreneurial ecosystems, the constituent parts of the term

were first outlined. Based on a synthesis of the literature, the following is proposed:

entrepreneurship refers to the social and relationally-embedded process of creating new goods

and services by exploring, evaluating and exploiting opportunities (Granovetter, 1985; Shane

& Venkataraman, 2000); and ‘ecosystem’ can be defined as the structural alignment of a set of

multilateral partners who interact for the materialization of a focal value proposition, mostly

for the purposes of gaining competitive advantage (Adner, 2017; Moore, 1993). Following on

from this definition it was clear that to reach a common performance goal, various

interconnected entrepreneurial actors needed to be interlinked (Mason & Brown, 2014).

Addressing the interactive nature of entrepreneurial ecosystems, an overview of the interplay

between the different actors within the entrepreneurial environment, focusing in particular on

public sector involvement, was also provided. Audretsch et al. (2019) underlines that the

entrepreneurial ecosystem is directly affected by network externalities, governmental support,

knowledge spillovers and the turbulent competitive environment in which it exists. Changes in

government policy, for example, can have an irrevocable impact on the developmental

trajectory of an ecosystem (Brown & Mason, 2017). The ecosystem comprises clusters with

various firms, universities, science parks, and governmental agents and agencies, which

collaboratively form the structure within which the entrepreneur should navigate their path. As

an example, public sector incubators often involve universities, as connections to universities

are considered to provide new knowledge and opportunities for innovation which firms could

then access and exploit (Mason & Brown, 2017). The Triple Helix model (Etzkowitz &

Leydesdorff, 2000) is an example of an interaction cluster between university, industry and

government which represents the embeddedness of actors in an ecosystem.

Following on from the conceptual clarification of entrepreneurial ecosystems and its interactive

and multi-network nature, the paper set out to conduct a bibliographic review of the

entrepreneurial ecosystems literature to date. As far as could be ascertained, this paper was the

first to conduct a bibliographic study on entrepreneurial ecosystems as the research domain,

and the first to employ the network and citation analysis mapping tool, VOSviewer, to

graphically visualize these networks. The bibliographic network analysis reported on the

following aspects of the entrepreneurial ecosystems literature: first, the growth, breadth, and

64

depth of entrepreneurial ecosystems literature was reviewed and analyzed; second, the leading

authors, institutions and countries were presented; third, the predominant research trends were

graphically mapped and analyzed; and finally, a network map of the most prominent and

frequently co-occurring keywords were illustratively presented, clearly indicating keyword

network clusters that serve as associated determinants of entrepreneurial ecosystem drivers of

competitiveness.

3.5.2. Paper 2

Robertson, J., McCarthy, I. P., & Pitt, L. (2019). Leveraging Social Capital in University-

Industry Knowledge Transfer Strategies: A Comparative Positioning Framework. Knowledge

Management Research & Practice, 17(4), 461-472.

Journal: Knowledge Management Research & Practice


Status: Published

Paper 2 answers Research Question 2: How does social capital impact the competitive

advantage of ecosystems?

The findings from paper 1 highlighted that further exploration of the role of social networks

within an entrepreneurial ecosystem and the leveraging of social capital among the various

network members would present timely insights. Based on the findings from paper 1, it was

highlighted that this would be particularly relevant in times of limited resources and great

uncertainty, especially when a high degree of heterogeneity is present among the members

within the ecosystem. In addition, public sector involvement and interaction clusters

comprising universities and industry, was highlighted as pertinent drivers of new knowledge

creation and innovative outcomes ─ all identified as drivers of competitiveness in

entrepreneurial ecosystems.

To further explore these newly identified or under researched areas, and address the underlying

research problem of how competitive advantage is achieved through an ecosystem approach in

industrial marketing, paper 2 focused on the knowledge transfer between university-industry

actors as a representation of innovation ecosystems (Cai et al., 2019; Heaton et al., 2019).

Coupled with this, the paper additionally aimed to heed recent calls for more strategic analysis

of how social capital is leveraged across regions with organizational differences and goals (de

Wit-de Vries et al., 2018; Gulati et al., 2000) – especially in the context of university-industry

knowledge transfer (Bruneel et al., 2010; Maietta, 2015; Perkmann et al., 2013). The region as

an institutional environment for strategic advantage is well documented (Ferreira et al., 2013),

as is the strategic leveraging of knowledge in university-industry partnerships (Carayannis et

al., 2000). However, the impact of social capital on knowledge transfer, which serves as a

strategic driver of competitive advantage within university-industry partnerships as innovation

ecosystems, is still uncharted territory.

65

The structure of the paper comprised three phases. The first phase entailed using three social

capital dimensions and their accompanying sub-dimensions, as originally proposed by

Nahapiet and Ghoshal (1998) and refined by Inkpen and Tsang (2005), as guideline to

distinguish between the various social capital dimensions that are leveraged during the process

of university-industry knowledge transfer across three regions. Each of these regions represent

countries at different stages of development: a developed region (Canada), a transition region

(Malta), and a developing region (South Africa). The intent of the knowledge transfer activity

was then probed and elucidated in more detail in the second phase, to create clarity regarding

its competitive and strategic imperative ─ either gaining new knowledge (appropriating

strategy), or leveraging existing knowledge (leveraging strategy). Consequently, the final

phase encompassed the development of a social capital university-industry knowledge transfer

framework to guide actors that form part of this innovation ecosystem type to better align their

knowledge strategy with their respective competitive imperative.

3.5.3. Paper 3

Robertson, J., Caruana, A., & Ferreira, C. Innovation Performance: The Effect of Knowledge-

Based Dynamic Capabilities in Cross-Country Innovation Ecosystems

Journal: International Business Review


Status: Under review

Paper 3 answers Research Question 3: How do dynamic capabilities impact the competitive

advantage of ecosystems?

Paper 2 highlighted that social capital provides a foundation for describing and measuring the

cooperative reciprocity of associations in ecosystems. The findings of paper 2 furthermore also

stressed the organizational importance and competitive advantage inherent in the knowledge

sharing capabilities embedded within these interdependent organizational hyper-networks. The

member interaction facilitates the creation and maintenance of embedded assets, as it is “the

aggregate of the actual or potential resources which are linked to possession of a durable

network of more or less institutionalized relationships of mutual acquaintance and recognition”

(Bourdieu, 1986, p.248), that leads to competitive advantage.

Following this notion, paper 3 was built on the premise that organizational hyper-networks’

ability to achieve market success is dependent on the efforts of other innovators in its

environment (Aarikka-Stenroos & Ritala, 2017). This environment acts as an innovation

ecosystem consisting of inter- and codependent relationships between the members of the

ecosystem (Moore, 1993). Knowledge Based Dynamic Capabilities (KBDC) are said to enable

the exploration of organizational abilities to generate, combine, and acquire knowledge

resources to deal with environmental dynamics for innovative market success (Beuter et al.,

2019; Denford, 2013). However, few studies have empirically examined the link between

KBDC and innovation performance (Beuter et al., 2019; Cheng et al., 2016; Han & Li, 2015;

Zheng et al., 2011), and there are mounting calls to better understand how KBDC impact

66

innovation performance in the context of innovation ecosystems (Andreeva & Kianto, 2011;

Malerba & McKelvey, 2020; Nunn, 2019).

To address these gaps, paper 3 first reviewed the extant literature pertaining to innovation

performance and the role of knowledge and KBDC. Although not yet extensively researched,

a Web of Science database search provided 11 research articles that have been published with

KBDC as the focal topic. This allowed for a review of the extant literature to more clearly

conceptualize KBDC and to identify gaps in the theory and literature. In essence, KBDC

expands on the role of knowledge as a unique source of competitive advantage and promotes

a knowledge-based perspective of dynamic capabilities (Zheng et al., 2011). This facilitated

the conceptualization of KBDC as consisting of four elements, namely knowledge creation,

knowledge diffusion, knowledge absorption, and knowledge impact. Hypotheses were then

developed that linked these four elements to innovation performance in an innovation

ecosystem context. From a theoretical perspective, RBV and dynamic capabilities were then

discussed as the underpinning theoretical framework for the study. Secondary data, collected

from the Global Innovation Index 2019 was then collected and analyzed, using SmartPLS. The

paper concludes with the development of an innovation ecosystem framework, centered around

a knowledge based dynamic capabilities’ approach. The framework employed the ecosystem

categorization approach for B2B-research, as proposed by Aarikka-Stenroos and Ritala (2017),

which considers the interaction between actors in the ecosystem, as well as the structural

dynamics of the ecosystem ─ either encouraging change and renewal for market disruption, or

creating stability and symbiosis through institutionalization of the ecosystem.

3.5.4. Paper 4

Robertson, J. Competition in Knowledge Ecosystems: A Theory Elaboration Approach Using

a Case Study

Journal: Sustainability


Status: Published

Paper 4 answers Research Question 4: How do resource- and capability-based theories explain

competition in ecosystems?

Paper 3 showed that knowledge creation relates to the development of new solutions and

capabilities within the innovation ecosystem, which allows for the transformation of

knowledge into innovation outcomes or processes for commercial gain. As such, it is regarded

as a strategic and dynamic resource capability, that is closely linked to competitive advantage.

Knowledge creation also has the strongest impact on innovation performance across all KBDC

and is also the strongest predictor of innovation performance, as per paper 3. This raises some

questions, though – where does the knowledge originate from and what contributes to the

competitive creation of primary knowledge?

67

As a starting point, it is proposed that a dynamic, hypercompetitive, global economy,

technological advances, unpredictable customers and competitors, and blurring industry

boundaries (Hunt & Madhavaram, 2020; Velu, 2015), are compelling scholars and practitioners

to take stock and reevaluate strategic imperatives. This strategic reassessment coincides with a

changing perspective on the dynamics of competition. As per paper 3, knowledge is deemed a

central and strategic asset in developing a competitive edge (Penrose, 2009; Prahalad & Hamel,

1990; Yu et al., 2017), and knowledge ecosystems underscore the participatory process among

ecosystem actors to create, explore, and use a shared knowledge base for the benefit of all

actors (Järvi et al., 2018). Participation in the ecosystem also enables actors to purpose the

primary acquired knowledge into new knowledge for commercialization of products or

services, or as a means to discover new business models or processes which they would not

have been able to do if relying on individual competences only (Clarysse et al., 2014).

Researchers have called for a deepened theoretical understanding of the strategic orientation in

these ecosystems to assess its pertinence to marketing and sustainable enterprise development.

These calls consider whether ecosystems follow an externally-focused or internally-focused

strategic approach (Velu, 2015), and what the relative importance of dynamic, responsive or

adaptive capabilities are in converting knowledge-related insights into value-creating

advantage (Whitler & Puto, 2020). With a marked reprioritization of marketing strategy on the

academic agenda (Hunt & Madhavaran, 2020), and the emergence of knowledge ecosystems

as vehicles for knowledge-creating advantage with which to navigate a complex and

competitive marketplace (Järvi et al., 2018), this paper explores how competition works in

knowledge ecosystems.

Very little research has been conducted in this area to date (Thomas & Autio, 2020). As such,

the paper follows a theory elaboration approach, which entails “specifying constructs, relations,

and processes at the conceptual level and assessing the fit of those relations empirically” (Van

Maanen et al., 2007, p. 1146). The objectives of this paper are threefold. First, three streams of

strategic thought that grapple with the fast-changing contemporary competitive landscape are

reviewed. These are the resource-advantage theory (Hunt & Morgan, 1995), the dynamic

capabilities framework (Teece et al., 1997), and the adaptive marketing capabilities perspective

(Day, 2011; 2014). These streams of strategy converge around the notion that “in today's

dynamic, hypercompetitive, global economy, strategy must focus on firms’ constantly

renewing themselves in the marketplace” (Hunt & Madhavaram, 2020, p. 129). The purpose

of the review is to assess and compare how these strategy streams and its associated

foundational perspectives can explain the dynamics of competition in knowledge ecosystems.

Second, the concept and characteristics of knowledge ecosystems are conceptualized. Finally,

a case study of a knowledge ecosystem is presented, to empirically assess how competition

works in knowledge ecosystems, focusing on the perspective of a keystone actor.


This Chapter addressed the overarching research methodology and specific procedures

followed to address the research problem and accompanying research questions of this

68

dissertation. The Chapter highlighted previous research strategies that have been employed to

study competitive advantage and ecosystems in peer-reviewed academic marketing journals

over the past five years. A predominantly empirical research strategy is followed, consisting of

three papers with a quantitative, descriptive research design, and one paper with a qualitative,

exploratory research design.

Paper 1 makes use of an empirical, quantitative research approach, employing an exploratory

research design to conduct a bibliographic analysis, using secondary data. Paper 2 also employs

an empirical, quantitative research approach, but with a descriptive research design, which

makes use of comparative dimensional scoring of secondary data, to address the research

question. Similar to paper 2, paper 3 uses an empirical, quantitative research approach, with a

descriptive research design, analyzing secondary data using PLS-SEM as the research method.

Finally, paper 4 is qualitative in its research approach, using an exploratory research design by

means of a single in-depth case study, using primary data.

Finally, an overview and description of the structure of the four research questions and

accompanying papers that comprise this dissertation were presented. Each respective paper

addresses a particular research question, which has been derived from the overarching research

problem of this dissertation: How is competitive advantage achieved through an ecosystem

approach in industrial marketing? A summary of the findings is presented in the next Chapter.

69

CHAPTER 4: FINDINGS

4.1. Introduction

Based on the central research problem of this dissertation: How is competitive advantage

achieved through an ecosystem approach in industrial marketing? this Chapter provides an

overview of the findings, as well as key theoretical contributions and managerial implications

emanating from the research questions. The findings from the four respective research

questions, as well as the accompanying papers, are presented. In addition, the limitations of the

research are noted and future suggested areas of research are provided.

4.2. Findings: Research Question 1

The first research question addressed in this dissertation sought to outline what the drivers of

competitiveness are, using a network analysis approach to ecosystems. The findings from this

research question, addressed in paper 1, are grouped into three distinct categories. First, the

growth, breadth, and depth of entrepreneurial ecosystems literature was reviewed and analyzed.

Based on the results of the literature review and, in comparison to its overall development, the

number of articles published in the field of entrepreneurial ecosystems show significant growth

over the past five years (Figure 8). This is corroborated with the recent increase in interest in

the research domain in recent years, as well as the concomitant diffusion of the concept to other

disciplines with associated interests (Ács et al. 2017).

Figure 8: Number of annual published articles on entrepreneurial ecosystems between 1995 to

2019

70

In total, 431 published articles were cited 3,089 times, with an average of 7.17 citations per

article, averaging a total of 123.56 citations for the research domain per year. In total, these

431 published articles comprised 974 different authors, from 622 distinct institutions,

representing 63 different countries.

Based on the Web of Science categories (Figure 9), the top represented fields that contribute

to the literature on entrepreneurial ecosystems were Management, Business, and Economics.

In addition, Table 10 shows the ten journals in which most of the papers on the search term

appeared. As evidenced by the chart, the five journals in which most papers on the topic of

entrepreneurial ecosystems have been published was Small Business Economics (30 articles),

Journal of Technology Transfer (23 articles), European Planning Studies (13 articles),

Technological Forecasting and Social Change (11 articles), and Journal of Enterprising

Communities: People and Places in the Global Economy (10 articles). These five journals

accounted for 20,2% of the total publications, with the remainder of the list comprising a

relatively diverse list of journals. As can be seen from Figure 8, entrepreneurial ecosystems are

regarded as a multi-disciplinary phenomenon which reaches and resonates beyond

entrepreneurship and economics, to include industrial engineering, geography, as well as

education research.

Figure 9: Top ten Web of Science categories in which published articles on entrepreneurial

ecosystems appear between 1995-2019

71

Table 6: Journals with most published articles on entrepreneurial ecosystems (n=431)

Journal Record Count Percentage of Total

Small Business Economics 30 6,96%

Journal of Technology Transfer 23 5,34%

European Planning Studies 13 3,02%

Technological Forecasting and Social Change 11 2,55%

Journal of Enterprising Communities: People and

Places in the Global Economy

10 2,32%

Entrepreneurship and Regional Development 9 2,09%

Strategic Entrepreneurship Journal 8 1,86%

International Journal of Entrepreneurial Behavior

Research

7 1,62%

Industry and Higher Education 6 1,39%

Research Policy 6 1,39%

The most cited article was published in Research Policy in 2014, by authors Autio, Kenney,

Mustar, Siegel, and Wright, titled “Entrepreneurial innovation: the importance of context”. The

three most cited articles were all published in the last five years and jointly represent 16,5% of

the total citations on the search term on the Web of Science (Table 7). It is interesting to note

that there is a bias towards articles published in business journals, with four of the top ten cited

articles published in journals relating to the business research domain.

Table 7: Ten most cited research articles between 1995 and 2019 using “entrepreneurial

ecosystem” as search term on Web of Science (n=431)

Rank Research Article Web of

Science

Citations

Google

Scholar

Citations

1 Autio, E., Kenney, M., Mustar, P., Siegel, D. and Wright,

M., 2014. Entrepreneurial innovation: The importance of

context. Research Policy, 43(7), pp.1097-1108.

207 560

2 Stam, E., 2015. Entrepreneurial ecosystems and regional

policy: a sympathetic critique. European Planning

Studies, 23(9), pp.1759-1769.

162 503

72

3 Spigel, B., 2017. The relational organization of

entrepreneurial ecosystems. Entrepreneurship Theory and

Practice, 41(1), pp.49-72.

142 449

4 Andreev, S., Galinina, O., Pyattaev, A., Gerasimenko, M.,

Tirronen, T., Torsner, J., Sachs, J., Dohler, M. and

Koucheryavy, Y., 2015. Understanding the IoT

connectivity landscape: a contemporary M2M radio

technology roadmap. IEEE Communications Magazine,

53(9), pp.32-40.

101 183

5 Zahra, S.A. and Nambisan, S., 2012. Entrepreneurship

and strategic thinking in business ecosystems. Business

Horizons, 55(3), pp.219-229.

76 259

6 Letaifa, S.B. and Rabeau, Y., 2013. Too close to

collaborate? How geographic proximity could impede

entrepreneurship and innovation. Journal of Business

Research, 66(10), pp.2071-2078.

71 167

7 Zander, I., McDougall-Covin, P. and Rose, E.L., 2015.

Born globals and international business: Evolution of a

field of research. Journal of International Business

Studies, 46(1), pp.27-35.

68 142

8 Pitelis, C., 2012. Clusters, entrepreneurial ecosystem co-

creation, and appropriability: a conceptual framework.

Industrial and Corporate Change, 21(6), pp.1359-1388.

61 153

9 Habbershon, T.G., 2006. Commentary: A framework for

managing the familiness and agency advantages in family

firms. Entrepreneurship Theory and Practice, 30(6),

pp.879-886.

57 161

10 Mack, E. and Mayer, H., 2016. The evolutionary

dynamics of entrepreneurial ecosystems. Urban Studies,

53(10), pp.2118-2133.

52 141

Second, the leading authors, institutions and countries were analyzed using the bibliographic

analysis tool, VOSviewer, to graphically map the networked relationships, as present in the

extant entrepreneurial ecosystems literature. Of the 974 authors in the dataset, Roundy, with

nine articles, was the most productive (accounting for 2% of all published articles), followed

by Carayannis and Audretsch, respectively, with five articles each. The authors’ geographical

distribution, as per their affiliated institutions and countries was also analyzed. The largest

number of articles were authored by researchers from the University of North Carolina,

University of Cambridge, and Indiana University (with 16, 13, and 13 associated articles,

respectively), indicating that most articles were authored by researchers with institution

affiliations in the USA (162 associated articles). Closer examination indicated that institutions

based in the USA and UK (England and Scotland) were dominant in terms of their influence

73

in the research domain of entrepreneurial ecosystems and related determinants, using citation

as a proxy. This signals that findings should be contextualized within the institutional and

regional scope of the representative environments, with particular emphasis on issues relating

to policy development and governmental initiatives.

Third, a network map of the most prominent and frequently co-occurring keywords was

graphically generated using VOSviewer, clearly indicating keyword network clusters that serve

as associated determinants of entrepreneurial ecosystem drivers of competitiveness. In

interpreting the VOSviewer map, the lines between the nodes indicate networked links between

the keywords, with the default setting being to show the 500 strongest links in the dataset

(Colavizza et al., 2018). The distance between the respective nodes is an indication of how

related they are. In other words, the closer two nodes are, the closer related they are in terms

of citation links (Van Eck & Waltman, 2018). To be included in the map, a word had to appear

a minimum of twenty times in all the documents combined. Of the 2015 keywords that

appeared in the 431 research articles, twenty-three words met the cut-off. A network map of

these terms and their co-occurrence and interaction in academic journals appears in Figure 10.

Figure 10: Most commonly occurring keywords on entrepreneurial ecosystems (1995 to 2019)

Perhaps not surprising, the word innovation was shown to be the most commonly-appearing

word, with the words entrepreneurship, performance, knowledge and entrepreneurial

74

ecosystems rounding out the top five most common words in the research articles literature.

The network map shows that there are three differentiable keyword clusters: the green cluster,

which relates to innovation, strategy, technology, industry, university, and associated

determinants within the literature; the blue cluster, which encompasses words like

entrepreneurship, business, and management; and finally, the red cluster which links nodes

that are connected through words like entrepreneurial ecosystems, knowledge, policy,

networks, and growth. Key themes in this area thus seem to revolve around three main streams:

innovation (linked to performance, technology, university and industry), entrepreneurship

(with a focus on an ecosystem, management and business), and knowledge (with an emphasis

on growth, policy and networks).


Research question number 2 sought to address how social capital impacts the competitive

advantage of ecosystems. This research question was addressed in paper 2. Following the

keyword links and nodes identified as networked clusters acting as drivers of competitiveness

in entrepreneurial ecosystems in paper 1, paper 2 focused on the one cluster encompassing

innovation, strategy, industry, and university interaction, to gauge how this impacts the

competitive advantage of ecosystems. Focused on three particular objectives, the paper

proposed a link between social capital and knowledge transfer strategies, by illustrating how it

impacts the competitive positioning of university-industry partnerships as representative of an

innovation ecosystem.

The first objective of the paper sought to assess the respective social capital dimensions

leveraged during the process of university-industry knowledge transfer. As a starting point, the

knowledge strategy typology by von Krogh et al. (2001) was used to determine which

knowledge transfer strategy the university-industry partnerships employed. The typology

delineates knowledge transfer based on two structural categories: knowledge domain (existing

knowledge or new knowledge), and knowledge process (knowledge transfer or knowledge

creation). Within the process of knowledge transfer, the transfer of existing knowledge is

termed a leveraging strategy, while the transfer of new knowledge is described as the

appropriating strategy. Figure 11 provides a visual representation of the two knowledge

transfer strategy delineations. The leveraging strategy is set forward from existing knowledge

domains and focuses on transferring knowledge to the various actors within an ecosystem to

allow for faster innovation, achieving efficiency and flexing resource capabilities. The

appropriating strategy is predominantly externally oriented, as the key challenge is to construct

a new knowledge domain by channeling knowledge transfer from outside of the ecosystem and

appropriate it to a new internal domain.

75

Figure 11: Knowledge transfer strategy delineation, as adapted from von Krogh et al. (2001)

Using the knowledge strategy typology as proposed by von Krogh et al. (2001), existing studies

relating to university-industry partnerships from the previously identified three regions were

iteratively analyzed to explore whether they contained the variables needed to address the

research question. Secondary data, as found in previous studies that related to each region, was

used as data sources. Operational definitions of the variables in each of the studies were first

established to ensure that these were aligned with the objectives of this study. The secondary

data were analyzed and grouped to assess the knowledge transfer strategy which these

university-industry partnerships employed. Next, the level of social capital dimensions present

in each of the various country-specific university-industry knowledge transfer partnerships was

also analyzed. Three social capital dimensions and their accompanying sub-dimensions, as

originally proposed by Nahapiet and Ghoshal (1998) and refined by Inkpen and Tsang (2005)

were used as a guideline for this purpose. Table 8 provides an overview of the results.

76

Table 8: An overview of social capital dimensions present

Social Capital

Dimensions

Developed Region

(Canada)

Transition Region

(Malta)

Developing Region

(South Africa)

Structural

Network ties

Strong network ties,

both formal and

informal.

Strong and closely-

knit network ties, due

to interconnectivity

of network members

to formal University

Research Parks.

Inter-member ties are

weak and interaction

is relatively low and

disconnected.

Network

configuration

Systems- and

structure focused.

Hierarchical and

bureaucratic, runs the

risk of high density,

which may inhibit the

ease of knowledge

transfer.

Bureaucratic,

impedes and restricts

knowledge transfer.

Network

stability

High rate of network

stability.


stability.


instability, due to

high level of political

uncertainty.

Relational

Trust

Trust relationship

managed through

clearly defined roles

with accompanying

accountabilities.

Strong trust

relationships.

High level of trust in

established

relationships, but

reluctant to form new

trust-based

relationships based

on perceived threat

of opportunism.

Cognitive

Shared goals

Goals are clearly

defined and aligned,

but often not

enforced.

Goals are wide-

ranging and run the

risk of

incompatibility.

Goals are both

compatible and

mostly mutually

shared.

Shared culture Culture of

compatibility.

High level of cultural

compatibility.

Mostly cultural

tolerance, but

declining research

culture.

Overall Social

Capital Score High Medium to High Medium to Low

77

From a social capital dimension perspective, as can be seen in Table 8, university-industry

knowledge transfer partnerships in Canada and Malta consist of strong and close-knit network

ties, with Canada showing evidence of a systems and structure focus in their network

configuration. Both Malta and South Africa show hierarchical and bureaucratic network

configurations, which may restrict and inhibit the ease of knowledge transfer. Canada and

Malta possess a high rate of network stability, with South Africa, in contrast, exhibiting a high

rate of network instability, ascribed to the uncertain political climate. The relational dimension

was found to mostly be built on strong trust relationships for all three countries, with university-

industry knowledge transfer partnerships in South Africa at times being under threat due to the

perception of potential opportunism from one of the partners in the relationship. The cognitive

sub-dimensions of shared goals and culture show varying results, with university-industry

partnerships in Canada displaying alignment, yet the goals are not always accomplished or

realized. University-industry knowledge transfer partnerships in Malta have wide-ranging and

at times incompatible goals, with South Africa having mostly compatible and mutually shared

goals. Cultural compatibility is shared in both Canada and Malta, with South Africa exhibiting

a tolerant culture, yet weakening research culture.

The second objective centered on the establishing intent of the knowledge transfer activity to

better understand the strategic imperative: either gaining new knowledge (appropriating

strategy), or leveraging existing knowledge (leveraging strategy). Linked to this objective, the

third objective related to the development of a social capital university-industry knowledge

transfer framework. This had the aim of guiding innovation ecosystem actors to better align

their knowledge strategy with their respective competitive imperative. To address both

objective two and three, the developed social capital university-industry knowledge transfer

framework (Figure 12), plots the respective regional countries’ knowledge transfer strategies,

as well as their social capital dimension score (as per Table 8) on a two by two matrix.

Figure 12: Social capital university-industry knowledge transfer framework

78

The plot positioning on the horizontal line represents the predominant knowledge transfer

strategy employed, while the vertical positioning denotes the level of social capital present

within the university- industry knowledge transfer partnerships. The results, as illustrated in

Figure 12, indicate that university-industry partnerships in Canada, situated in a developed

region, predominantly employ an appropriating knowledge transfer strategy for the purpose of

creating and sharing new knowledge. In terms of the level of social capital present within the

university-industry knowledge transfer activities, Canada shows to utilize a high level of social

capital within these processes of exchange. Canada is thus situated in the first quadrant of the

matrix. As a country situated in a region in transition, Malta is plotted in the second quadrant

of the matrix, with the results indicating that university-industry knowledge transfer

partnerships in Malta most often rely on a leveraging strategy. With relatively low levels of

social capital displayed in university-industry knowledge transfer activities in South Africa,

the results show that the partnerships most often centre around the appropriation of new

knowledge, with the country being plotted in the fourth quadrant.


The third research question asked how dynamic capabilities impact the competitive advantage

of ecosystems? This question was addressed in paper 3 and specifically focused on competitive

advantage between innovation ecosystems. Drawing from the findings as per research question

2, knowledge-related factors were explored to assess their effect on innovation performance

between innovation ecosystems. Using a RBV view, flexing the lens of dynamic capabilities

in particular, two research objectives guided the inquiry. The first objective sought to identify

the knowledge-related constructs that encompass KBDC in an innovation ecosystem, while the

second objective centered on determining the role the identified KBDC constructs play as

drivers of innovation performance across diverse economic markets.

To address the first objective, a comprehensive review of the literature was conducted, which

led to the conceptualization of KBDC to encompass the components of knowledge creation,

knowledge diffusion, knowledge absorption, and knowledge impact. Knowledge creation is

widely acknowledged as a key construct of KBDC (Faccin et al., 2019), with close ties to

innovative performance outcomes. Andreeva and Kianto (2011, p. 1010) define knowledge

creation as the “ability to develop new and useful ideas and solutions”, relating to

organizational activities, new products or services, technological processes and managerial

procedures (Nonaka, 1991; Un & Cuervo-Cazurra, 2004). As a focal part of the innovation

process (Nonaka et al., 2014; Quintane et al., 2011), thriving innovation ecosystems are

characterized by knowledge creation (Bramwell et al., 2012).

Within an innovation ecosystem, a clear understanding of the diffusion of knowledge is

fundamentally important from an economic perspective, as the ease with which diffusion

occurs directly affects economic growth (Grossman & Helpman, 1991). Knowledge diffusion

also holds implications for firm, regional and national technology strategies, technology

transfer policies, as well as incoming and outgoing investment (Singh, 2008). The diffusion of

knowledge is not a homogeneously distributed process across all potential adopters (Klarl,

79

2014). Research shows that induced knowledge spillovers in certain specialized sectors,

markets and countries lead to enhanced capabilities for knowledge diffusion in those

environments (Boschma & Frenken, 2011; Lundvall, 2007). Capello and Varga (2013) assert

that proximity-related advantages between innovating partners, as can often be found in

innovation ecosystems, would contribute to the increased creation and diffusion of knowledge,

and lead to enhanced innovation performance.

Closely-linked with knowledge diffusion, knowledge absorption is viewed as a KBDC in that

it recognizes the importance of new knowledge and its assimilation and application for

commercial purposes, to increase the capacity for innovation (Cohen & Levinthal, 1990; Faccin

et al., 2019). In the innovation ecosystem, knowledge absorption entails the process of

managing the acquisition and exploitation of external knowledge for internal knowledge

application (Zheng et al., 2011). For knowledge to have an impact, it has to go through a

process of integration, synthesis, refinement, management, and importantly ─ market

implementation. Knowledge impact represents this process of implementation, which

necessitates collaborative effort and inclusion of entities both internal and external to an

organization (Nonaka et al., 2014).

To determine what role the identified KBDC constructs play as drivers of innovation

performance across diverse economic markets as per the second objective, the identified

KBDC components were first operationalized. Thereafter, hypotheses were developed,

grounded in the RBV and dynamic capabilities frameworks.

4.4.1. Knowledge Creation and Innovation Performance

As a dynamic capability, knowledge creation competencies promote new thinking and

capabilities within networked environments (Nonaka et al., 2000), including innovation

ecosystems (Kazadi et al., 2016). Furthermore, knowledge creation has been shown to be

closely linked with an organization’s competitive advantage (Gupta et al., 2016), and is

regarded as an output indicator of innovation performance (Andreeva & Kianto, 2011). In light

of the above, it is hypothesized:

H1: There is a positive relationship between knowledge creation and innovation

performance in an innovation ecosystem.

4.4.2. Knowledge Diffusion and Innovation Performance

In line with the diffusion of innovations theory (Rogers, 1962), knowledge diffusion refers to

the rate at which newly created technological content and intellectual property spreads for

eventual adoption (Klarl, 2014). Innovation performance is thus regarded as an outcome of

knowledge diffusion. Therefore:

H2: There is a positive relationship between knowledge diffusion and innovation


80

4.4.3. Knowledge Absorption and Innovation Performance

Knowledge absorption often leads to new knowledge creation, which in turn improves the

ability to gain and sustain competitive advantage (Zahra & George, 2002) of innovation

performance. Knowledge absorption is dependent on the ability of innovation ecosystem

members to acquire, absorb, and apply often external knowledge from outside the boundaries

of its own entities. These activities make it possible for the firm to redeploy these resources as

new products, services, processes or systems. In light of the above, it is hypothesized:

H3: There is a positive relationship between knowledge absorption and innovation


4.4.4. Knowledge Impact and Innovation Performance

Knowledge impact represents the effect that the integration and combination of knowledge-

based innovation activities has at both a micro- and a macroeconomic level. As an output

indicator, the relationship between knowledge impact and innovation performance seems

intuitively connected. From an innovation ecosystem perspective, the accurate measurement

of knowledge impact is a budding area of research which the academic literature has identified

as not yet sufficiently investigated (Faccin et al., 2019; Santoro et al., 2018). Therefore:

H4: There is a positive relationship between knowledge impact and innovation


The impact of knowledge is inextricably reliant on new knowledge being created (Santoro et

al., 2018) and the different types of knowledge are known to be interlinked. Therefore, we

further hypothesize the following mediated relationships:

H5a: The relationship between knowledge diffusion and innovation performance is

mediated by knowledge creation.

H5b: The relationship between knowledge absorption and innovation performance is

mediated by knowledge diffusion.

H5c: The relationship between knowledge impact and innovation performance is

mediated by knowledge creation.

The hypotheses described above are represented in the research model in Figure 13.

81

Figure 13: Research model and hypotheses

Based on partial least squares analysis, using SmartPLS, Table 9 provides an overview of the

significance of the hypothesized paths.

Table 9: Results of PLS analysis

Hypothesis Path β t-statistic Significance Results

H1 Knowledge creation🡪

Innovation performance 0.54 11.65 0.00** Supported

H2 Knowledge diffusion 🡪


H3 Knowledge absorption 🡪

Innovation performance 0.13 1.57 0.12ns Not supported

H4 Knowledge impact 🡪


H5a Knowledge diffusion 🡪

Knowledge creation 0.55 7.02 0.00** Supported

H5b Knowledge absorption 🡪

Knowledge diffusion 0.83 29.20 0.00** Supported

H5c Knowledge impact 🡪

Knowledge creation 0.28 4.49 0.00** Supported

** Significant at 1% level of significance.

ns = not significant.

With the exception of H3, all path coefficients yielded statistically significant results (p < 0.00)

– Table 9. The results suggest that three of the KBDC, namely knowledge creation, knowledge

diffusion and knowledge impact, are significant drivers of innovation performance. In terms of

mediating relationships, knowledge diffusion fully mediates the relationship between

knowledge absorption and innovation performance, while knowledge creation is found to be a

82

partial mediator of the relationship between knowledge diffusion and innovation performance,

as well as knowledge impact and innovation performance. An examination of the standardized

coefficients provides insight into the strongest driver of innovation performance allowing for

the indicators to be ranked. Knowledge creation is the strongest driver of innovation

performance, followed by knowledge diffusion and knowledge impact, which are of equal

importance.

Eisenhardt and Martin (2000) assert that although there are some commonalities in how

dynamic capabilities are flexed across different organizations, they are mostly idiosyncratically

developed and deployed. Zheng et al. (2011) also propose that since the level and form of

dynamic capabilities can be quite different across different environments, it would be prudent

to consider how this may lead to distinctively differing innovation performances. It is thus

posited that this would be similar across different innovation ecosystems. Therefore, the

relationships were also comparatively tested across developed, transition and developing

economies. Accordingly, the assessment sought to examine which of the four capabilities

would be the most important driver of innovation performance within the respective market

economy innovation ecosystems.

The results of the PLS path analysis suggest that in developed economies, knowledge creation

is the strongest predictor of innovation performance, while in transition economies, knowledge

absorption is the strongest predictor of innovation performance, followed by knowledge

impact. In developing economies, knowledge creation is the strongest predictor of innovation

performance, followed by knowledge diffusion and knowledge impact.

In summary, four knowledge-related capabilities, namely knowledge creation, knowledge

diffusion, knowledge absorption, and knowledge impact, are respectively identified as input-

output indicators of innovation performance in innovation ecosystems. Aligned to the extant

understanding of RBV and dynamic capabilities, these components represent knowledge-based

capabilities which would serve to enable innovation activities, provide competitive advantage,

and lead to enhanced innovation performance when leveraged within innovation ecosystems.

Operationally, the four components comprise knowledge dimensions that span both discrete

innovation outcomes (e.g., product, service, scientific publication), as well as process-based

innovative capabilities (e.g., improved high-tech or operational processes) that lead to

economic market success. Innovation ecosystems are presumed to possess heterogeneous

knowledge bases and differ by context (Autio & Thomas, 2014), thereby necessitating

comparative analysis.

In terms of the comparative importance of KBDC for innovation performance between

innovation ecosystems, the ecosystem categorization approach for B2B-research (Aarikka-

Stenroos & Ritala, 2017) was used as reference, which proposes that KBDC drives innovation

performance and competitive advantage goals by aligning to a particular focused approach. An

interaction focus concentrates on interactions between customers, stakeholders and other actors

in the ecosystem. These are fundamental components that facilitate market structure and

organizing for value creation through innovation. A system dynamics focus is concerned with

83

the structural dynamics of the ecosystem to either encourage change and renewal for market

disruption, or create stability and symbiosis through a process of institutionalization. Figure 14

provides an illustrative representation of how this applies to KBDC in an innovation context,

highlighting its pertinence to innovation performance and competitive advantage.

Figure 14: Innovation ecosystem framework centered around a knowledge-based dynamic

capabilities’ approach

Knowledge creation relates to the development of new solutions and capabilities within the

innovation ecosystem that allows the transformation of knowledge into innovation outcomes

or processes for commercial gain. It is seen as a strategic and dynamic resource capability, that

is closely linked to competitive advantage. Based on the results of this research, knowledge

creation has the strongest impact on innovation performance across all KBDC. Knowledge

creation is also the strongest predictor of innovation performance in developed and developing

economies. In relation to all components of KBDC, knowledge creation also acts as a partial

mediator and conduit to facilitate the diffusion and impact of knowledge for overall innovation

performance in the innovation ecosystem.

As inferred from the literature, knowledge diffusion is markedly connected with the pursuit of

economic growth, as evidenced by the fact that it is only a strong predictor of innovation

performance in developing economies. From a dynamic capabilities viewpoint, it refers to the

rate at which newly created knowledge disperses through the greater ecosystem for adoption

and is seen as a significant indicator of innovation performance. In an innovation ecosystem,

knowledge diffusion would facilitate the flow and absorption of knowledge and knowledge

spillovers, which advances innovation exports and affects competitive advantage.

84

The capability to sense, seize and transform knowledge for competitive advantage is

represented by knowledge absorption. It necessitates a high level of interaction and networked

engagement within an innovation ecosystem, as it entails the assimilation of both internal and

external knowledge. Although the results indicate that knowledge absorption is not a

significant indicator of innovation performance, it indirectly determines innovation

performance through knowledge diffusion in a fully mediated relationship. It is the strongest

predictor of innovation performance in transition economies, which may indicate that these

economies dynamically require external input in order to transform content into innovation

outcomes.

Knowledge impact symbolizes the ripple effect of utilized knowledge for innovation activities

in the micro- and macro-economic environment. As a KBDC it has been found to be a

significant indicator of innovation performance. It is surprising that the results indicate that

knowledge impact does not act as a strong driver of innovation performance in developed

economies. However, although not the strongest driver, knowledge impact does show to be an

important driver of innovation performance in transition and developing economies.


The final research question to address the overall research problem focused on exploring how

resource- and capability-based theories can explain competition in ecosystems, focusing on

knowledge ecosystems in particular. To explore how competition works within a knowledge

ecosystem, a case study of a university-based Institute of Strategic Marketing (UCT Liberty

ISM), the keystone actor in a knowledge ecosystem, was conducted. First, the literature-

identified organizational factors of an ecosystem are used as a structure to present the results.

These factors relate to the ecosystem actors, the nature of their activities, the organizational

alignment of the knowledge ecosystem, as well as ecosystem-level artifacts or output.

Thereafter, the findings are further laid bare by contextually relating it to extant theoretical

perspectives on strategic choice and competition.

85

4.5.1. Findings Based on Ecosystem Factors

Table 10: Overview of case study findings, based on ecosystem factors and knowledge

ecosystem characteristics as strategic determinants of how competition works

Factor Characteristic Strategic Determinants

Actors

Network oriented

Diverse

Connected

● Multiple networked actors encompassing two main

categories: contributors (exchange, explore, build

central knowledge base) and benefit members (exploit

knowledge base for further innovation or commercial

purposes)

● Actors are specialized, representing heterogeneous

knowledge bases, which contributes to sustained

knowledge exploration with potential for broad

application

● Actors are often embedded in other, different

ecosystems, e.g., business, innovation, or

entrepreneurial ecosystems, which can expedite flow

and spillover of knowledge for value-adding

advantage

Activities

Externally focused

Interdependency

Cooperative and

coopetitive

● Primarily focused on external knowledge exploration

over a 12 to 18-month period for the purposes of

commercial knowledge exploitation

● Vulnerabilities that relate to continuous reliance on

external funding and extensive time resources

required to fulfill value proposition

● University-affiliation key interdependency to access

resources

● Keystone actor activities are mainly cooperative

among marketing fraternity that they serve; actor

activities are at times coopetitive to benefit whole

ecosystem

Alignment

Dynamic

Emergent

Influence based

● Ecosystem relationships and capacities are coevolving

and dynamic, although it takes time and intentionality

● Keystone actor mostly determines direction of the

knowledge ecosystem—emergent realignment to

environmental changes regarded as important but not

mandatory for advantage

● University-affiliation signals and affirms legitimacy

Artifact Knowledge

exploration

● Explored knowledge provides broad and general

knowledge repository for all ecosystem members to

adapt, modify, and exploit for own contexts and

purposes—vital for competitiveness

86

4.5.1.1. Actors

The actors that encompass the knowledge ecosystem structure and organization of the UCT

Liberty ISM, can be divided into two categories. The first category relates to the entities,

organizations and individuals that contribute to the exchange, exploration and building of the

central knowledge base for shared use (contributors). The second refers to members of the

ecosystem who primarily belong to the ecosystem for the purposes of using the shared

knowledge base for further innovation, market or technological development (benefit

members). The two categories are not necessarily mutually exclusive and contributors can

become benefit members and vice versa. It is important to differentiate between the two

categories, as each uniquely contributes to the ecosystem. The actors also vary in terms of the

roles that they need to fulfil, depending on the research request or the project that the Institute

is working on.

The legitimacy and specialization of contributors, as well as their networked connections, are

vital not only for the resources that they contribute toward the sustained exploration of

knowledge, but also for the heterogeneity of the knowledge bases that they contribute. In turn,

the benefit members of the ecosystem are often embedded in other ecosystems as well, be it

business, innovation, or entrepreneurial ecosystems, which means that they have the ability to

bridge the divide between knowledge ‘stock’ and ‘flow’ (Archer-Brown & Kietzmann, 2018),

which requires “new systems and understanding of the way in which [knowledge] can flow

between diverse individuals, teams and organizations” (Archer-Brown & Kietzmann, 2018,

p.1290). From a strategic marketing perspective, it also denotes the development of adaptive,

agile and innovative marketing skills (Erevelles et al., 2007).

In terms of the geographic locality of actors, the Institute historically predominantly consisted

of actors that were in close proximity to the Institute and heavily relied on face to face contact

─ a model that started to change in 2019, and is now quickly accelerating due to COVID-19.

“We need to prioritize relationships based on where the research expertise and the market

demand for projects are, which used to entail a lot of travel. Our main focus has been South

Africa, but we’ve started working with partners outside of our borders in recent years. We are

in constant discussion as to how we evolve our business and research delivery model. We do

not want to make geographic proximity a barrier” (Dr Lappeman).

4.5.1.2. Activities

The ecosystem activities that the UCT Liberty ISM gets involved with, primarily center on the

production of research, which is of benefit to academics, strategic marketers, and researchers

in the commercial and public sector. As the Institute is externally and privately funded, the

research reports and projects that they take on are all externally focused, as Professor Simpson

put it: “Our primary focus and interest group is industry ─ so everything we do needs to align

with that.” The Institute conducts large-scale research projects on broad market segment topics

over a 12 to 18-month period, which would likely be too resource intensive, extensive and

87

expensive for most research firms to conduct if not pertinently being commissioned by a client

to do so.

The research reports and projects that the Institute conduct require considerable funding. As

such, their business model includes a number of ways to secure funding. As per the inception

of the Institute, the Institute has a long-term anchor sponsor, which includes joint naming

rights. The anchor sponsorship further includes access to all research reports and findings, and

they are allowed to include questions to which they alone would see the results. In addition,

they receive access to all public workshops and will get in-house presentations of any research

output. For specific research projects that are initiated by the Institute, based on an identified

market need, potential project partners and supporters are identified, which goes toward

covering the costs of conducting the research and all project-specific related expenses. Other

forms of securing funds include making the research output (new and archived research)

available for purchase, paid attendance of public presentations of new research, and paid in-

house presentations of the research to individual firms. Research output, including reports and

case studies, are made available to other academic organizations free of charge. A key

differentiator that the Institute prides themselves on, is their ability to access and bring a broad

range of actors together for the purposes of joint learning.

Not only does the Institute leverage their association with the university as a form of signaling,

but they also use it to set the agenda in terms of the research output that they deliver. Over the

past three years, the Institute has invested time and financial resources into publishing their

research in peer-reviewed academic journals as well. Although their primary focus is still

industry, they have realized that their university-association affords them the opportunity to

build further brand equity among potential industry partners.

A distinct tension mentioned was the need to reinvent the Institute and finding new ways to

address the changing consumer landscape. Opportunities for reinvention and transformation do

present itself, but, as the keystone actor, the Institute tends not to pursue these opportunities as

they feel it may distract from their core value proposition and their non-biased appeal among

the marketing fraternity.

4.5.1.3. Alignment

The UCT Liberty ISM aligns its actors and their activities based on their dynamic and

coevolving capabilities, as well as with industry demands. The Institute needs to keep pace

with the changes they can sense in their external environment. In terms of the Institute’s

establishment, that was its main mandate ─ to reflect and review a changing consumer market.

Although a sensitivity to these changes is key to the Institute’s long-term sustainability and

development, both research participants acknowledge that this dynamic process takes time and

intentionality to implement.

As the Institute is university-based, but doesn’t independently own any assets, the leveraging

of their university association also comes into play when steering the ecosystem actors into a

88

direction regarding the research reports or projects that they get involved in. It both serves as

a form of indemnity and affirmation of independence, “...people expect that because the

research is coming out of the university, that there are no hidden agendas” (Dr Lappeman).

4.5.1.4. Artifact

Within the ecosystem context, artifacts refer to products and services, inputs and outputs

(including tangible and intangible resources) that are jointly created as an ecosystem-level

output among all actors (Granstrand & Holgersson, 2020). Knowledge ecosystems differ from

other ecosystem types in the sense that their artifacts or ecosystem-level output is generally

research-based knowledge and associated applications. Actors jointly create and explore new

knowledge as a shared resource, with Järvi et al. (2018) stating that knowledge ecosystems

mostly occur in pre-competitive and pre-commercialization settings.

Aligned with most other knowledge ecosystems, the UCT Liberty ISM is focused on the

exploration of knowledge (Clarysse et al., 2014), with their research being broad and general

for firms and other ecosystem member-actors to adapt or modify the primary research into new

knowledge, based on their respective needs. The exploration of knowledge is central to the

sustained existence of the Institute. Dr Lappeman pointed out that “there’s a little bit of

controversy around whether knowledge gets produced or rather just exposed. But that does put

a bit of pressure on us ─ we need to keep producing research that is valuable. The industry

will very quickly pick up whether what we're saying is something that they've heard before or

whether it's new.”

4.5.2. Findings Based on Strategic Determinants of Ecosystems

Following the theory elaboration approach (Lee et al., 1999), four dimensions that relate to

perspectives on competition, as deduced from the three strategy streams of thought reviewed

in the paper, are thereafter discussed as strategic determinants to explore how competition

works. These dimensions are the competitive context, market attentiveness, beliefs regarding

organizational boundaries, and the sustainability of strategic advantage.

4.5.2.1. The Competitive Context

As evidenced by the case, the UCT Liberty ISM is acutely aware of their current and direct

competitor set, as well as their changing environment. From a resource- and capabilities-based

view, their knowledge ecosystem represents a diverse, heterogeneous, and specialized set of

actors, which serves to heighten their competitive advantage and agility to adapt to a fast-

changing and dynamic competitive context. They maintain a dynamic orientation towards their

competitive context, and their ecosystem actors, both contributors and benefit members, reflect

a broad and network-oriented range of resources and capabilities from which to draw on.

Knowledge ecosystem activities within the UCT Liberty ISM are exclusively externally

focused and complementarities in resources and capabilities are sought in addressing

89

ecosystem output in the form of research. In terms of alignment of actors and activities, the

university-association serves as a signaling and influence-leveraging mechanism. Interesting

to note is that although the university does not contribute towards the Institute financially, it

does confer scientific and academic legitimacy on the ecosystem, which does prove to be

beneficial to the ecosystem as a whole. The university association also levels the playing field

in terms of opening up access to collaborations among traditional competing actors, as the joint

goal of knowledge exploration stands to benefit all involved.

4.5.2.2. Market Attentiveness

As the keystone actor, the UCT Liberty ISM manages a fine balance between panarchy and

restoration. Although the participants revealed high levels of market attentiveness by

constantly meeting with all ecosystem actors to assess changes in the market, the intricacies of

all the stakeholders involved in the ecosystem would render it difficult for them to constantly

restructure and reorganize to capitalize on new opportunities. The Institute purposefully does

not pursue all potential opportunities and maintain that their intentional decision to produce

broadly-themed research-based knowledge output secures their survival. This perspective

resembles a Resource-Advantage based approach, wherein the resources of the knowledge

ecosystem only have value in as much as they contribute to enhance performance outcomes,

which, in this case, would be serving existing actors and benefit members of the ecosystem.

A potentially too narrowly-focused strategic approach to service primarily the marketing

research fraternity, could additionally be perceived as representing a static view of their market

and competitive context. As they increasingly start to employ technology to bridge the

geographic boundaries of their actor and market base, one will expect that their reach and the

range of their activities will concomitantly also be broadened. This, in turn, would open up

new segments through time, which would make the strategy more dynamic as well.

Characteristically inherent to knowledge ecosystems, is the fact that their focal ecosystem-level

output, knowledge, takes time to explore or expose. This creates a potential weakness in not

being able to promptly seize disruptive market changes.

Day (2020) asserts that an outside-in approach requires anticipation, adaptation and alignment

to the market. The fact that the Institute’s business model hasn’t changed much over the past

20 years indicates that although there is an attentiveness and anticipation of changes in the

market, the adaptive and dynamic capabilities to transform and align with the identified

opportunities or threats in the markets, is a difficult task to accomplish. The artifact of the

ecosystem is primarily exploratory in nature, which means that the commercialization of the

explored knowledge mostly happens outside of the boundaries of the knowledge ecosystem. In

addition, and perhaps linked, the actor activities, although network-oriented, are not entirely

interdependent in terms of the survival of the ecosystem. As such, barring the small core staff

complement of the Institute, the other ecosystem actors are not overtly incentivized to

contribute to the long-term sustainability and development of the knowledge ecosystem.

90

4.5.2.3 Organizational Boundaries

The knowledge ecosystem implies a hyper-networked context where relationships constitute

the most valuable resources, which is also evidenced in this particular case. Relationships with

ecosystem actors contribute towards resources, capabilities and activities that are mobilized for

knowledge exploitation. As an extension, access to the networks of actors situated in other

ecosystems, contribute to potential resources and capabilities to complement that of the

ecosystem or keystone actor, which can in turn enhance performance (Håkansson & Snehota,

2006). Additionally, both research participants reiterated that the Institute needs to add value

and have a compelling value proposition to constituents, which underscores that they put a

premium on being relevant to all stakeholders, internal and external.

This iterative process of external resource and capability exchange, combined with constant

pursuit of producing research that is valuable, points to a primarily outside-in approach.

Resource exchange is, however, dependent on the relative efficiency of the internal resources

to adapt and extract the necessary insights, and as such, spanning capabilities also play an

important role in this knowledge ecosystem. The Institute, however, has full autonomy over

the strategic direction of the knowledge ecosystem and all the other actors follow their lead in

terms of strategic choices and activities, reaffirming the importance of keystone actors or tenant

firms in knowledge ecosystems.

4.5.2.4. The Sustainability of Strategic Advantage

As the keystone actor, the UCT Liberty ISM’s approach to the sustainability of their knowledge

ecosystem’s strategic advantage, is caught between two tensions. On the one hand, the research

output that they offer, once explored and exposed, is available for all customers and actors to

further exploit, and as such the focal output is by nature transient and not enduring. The brand

equity and brand recognition of the Institute, the intangible assets and by-products of their

knowledge base, does however contribute to a longer term sustainable competitive advantage

over other potential market entrants. As previously stated, the knowledge ecosystem is not fully

able or geared towards continually reconfiguring its structures, resources and capabilities to

renew their advantages from one to the next. The process of knowledge exploration also takes

time, which adds another layer of complexity in the process of achieving rapidly-created

advantages, typical of a transient advantage strategic approach.

4.6. Overview of Overall Findings

In this section, a summary of the findings for each research question are presented. Figure 15

presents an overview of how the findings from each respective research question and paper

address the overarching research problem: How is competitive advantage achieved through an

ecosystem approach in industrial marketing?

91

Figure 15: An overview of the findings of the dissertation, according to the four research

questions and accompanying papers

92

The golden thread in terms of the findings based on the four research questions and the research

problem, relate to two central themes (Figure 15). Firstly, the role that knowledge plays in

attaining competitive advantage through an ecosystems approach in industrial marketing

repeatedly came to the fore. Secondly, the theoretical perspectives highlight that it is the

relationships between the various actors in the ecosystem that drive the knowledge-related

advantages of the whole ecosystem. In a networked context, knowledge drives competitive

advantage, and is vital to achieve other metrics of performance, including, innovation- and

entrepreneurship-related outcomes. For example, when new knowledge is created and there is

a high level of social capital present in an ecosystem, the biggest competitive advantage will

be achieved. Similarly, where knowledge creation is prioritized as a dynamic capability,

innovation performance is higher and competitive advantage is attained. Finally, knowledge

seems to best create competitive advantage when shared – in other words storing knowledge

for exploration, without sharing it for exploitation, doesn’t equate to achieving sustainable

competitive advantage. Thus, pursuing an outside-in strategic approach would also necessitate

interdependence between ecosystem actors to ensure that the primary knowledge explored is

purposed for the growth of the whole ecosystem.

The section to follow discusses the theoretical contributions and managerial implications of

this dissertation.

4.7. Research Contributions

In assessing how competitive advantage is achieved through an ecosystem approach in

industrial marketing one needs to start with an understanding of what the ecosystems concept

encompasses. Extant theories relating to competitive advantage have, at their core, the

ownership or exclusive access to assets and certain resources or capabilities (Barney, 1991;

Porter, 1990; Wernerfelt, 1984). This assumes that businesses operate in a stable environment

where asset ownership and firm-managed resources serve as assured capitalizing sources of

advantage. In an increasingly unpredictable and fast-changing business environment, these

sources of advantage have, however, become less effective at building or sustaining

competitiveness (Möller et al., 2020). No current theory of ecosystems exists, and although it

was not the aim of this dissertation to develop such a theory, empirically, the results of the

research does extend and evolve our current understanding of how competitive advantage plays

out from an ecosystems approach. Focusing on a network theory, RBV, and dynamic

capabilities perspective, three particular areas are highlighted.

First, the research points to the centrality of knowledge as a key component of ecosystem

competitiveness from a network theory perspective. Understanding that a high degree of social

capital may facilitate the appropriation of new knowledge and lead to better innovation and

competitive outcomes, is a first step towards better understanding how interrelationships,

which are key within network theory, contribute towards the transfer of knowledge. Second,

the RBV of the firm posits that competitive advantage is achieved through ownership or control

of assets and resources. The research, however, shows that this is only partially true in an

ecosystems approach. Instead, we see that ecosystems are shaped by partial influence.

93

Birkinshaw (2019) uses the analogy of resources and assets previously being seen as moats

that provide and protect one’s competitive advantage. In ecosystems, however, these moats

should rather be seen as turnstiles, where the larger the ecosystem becomes, the better it can

create and add value to a bigger market (Birkinshaw, 2019). Third, the research extends on the

dynamic capabilities framework, by reinforcing the dynamism inherent in the survival of the

ecosystem. Ecosystems that do not adapt, die. But, at the same time, an ecosystem needs all

elements to be in balance for continued existence. As such, it extends the notion of dynamic

capabilities to be analogous to market attentiveness from an organizational perspective, flexing

the notion of panarchy versus restoration. Panarchy is presented as a theory of change

describing human and ecological interactions as adaptive cycles of destruction and

reorganization, which provides opportunities for restructuring (Holling & Gunderson, 2002).

Adopting this view, organizations with a high level of market attentiveness would encourage

change, build resilience, facilitate sustainability, and encourage diversity. The converse being

that a low level of market attentiveness would discourage change and rather focus on

“ecosystem restoration”, which implies not taking advantage of new opportunities but rather

returning to the original ecosystem state or status quo.

Möller et al. (2020, p.389) point out that “business marketing and marketing in general is losing

its relevance because it views business environments as simplistic “markets” and concentrates

on dyadic business relationships and their management rather than ecosystem analysis and

orchestration.” Addressing this challenge this dissertation emphasizes that an ecosystem

approach is characterized by dynamic and complex activities between actors, that encourage

collaboration across organizational borders. It focuses on engaging in shaping strategies (Hagel

et al., 2008), which aim to influence the other actors in the ecosystem for the shared purpose

of creating more value. The research problem, accompanying research questions, and papers

in this dissertation aim to contribute to our understanding of the strategies that can be employed

to achieve competitive advantage in a complex and disruptive business environment, by using

an ecosystem approach.

At a conceptual level, the dissertation points out that the ecosystem properties that relate to

ecosystem-level output, ecosystem-level activities and ecosystem-level structure, underscore

the respective strategies that are employed to achieve competitive advantage within and across

ecosystems in industrial marketing. Table 11 provides a summary of the research contributions,

which further points to the level of competitiveness inherent in each ecosystem type. As

indicated in Table 11, knowledge ecosystems are shown to be the least competitively focused,

with entrepreneurial ecosystems positioned as the most competitive. Illustratively shown in

Figure 16 the ecosystem properties that relate to ecosystem-level output, ecosystem-based

activities, and ecosystems-as-structure can be used as a frame of reference to unify the research

contributions under the joint conceptual ecosystem umbrella and show that there are

transferable contributions across all ecosystem types, in order to address the overarching

research problem. Detailed specifics that relate to the theoretical contribution of each particular

research question are additionally also contained within each respective paper1.

1 All papers are presented within the Appendices section.

94

Table 11: Summary of research contributions

95

Firstly, the centrality of knowledge as an ecosystem-level output emerged as an important

driver of competitive advantage across all of the studied ecosystems. In particular, the

exploration of a shared knowledge base as ecosystem-level output within knowledge

ecosystems, creates a ‘knowledge foundation’ which other ecosystem types can capitalize on,

by gaining insight into the competitive context, market, or their organizational boundaries.

Akin to both resource- and capabilities-based theories, this relates to environmental sensing or

deep market learning (Hunt & Madhavaram, 2020; Day, 2020), which often act as precursors

to innovation and new product or service development. Knowledge exploration as a source of

competitiveness is, however, constrained by a number of factors. These include the availability

of suitable resources and capabilities within the ecosystem to capitalize on and exploit the

knowledge base, as well as the interest and competence of the actors that comprise the

ecosystem. In addition, the exploration of knowledge is a time-consuming process.

Secondly, newly acquired primary knowledge can be exploited in other ecosystem types,

which, based on the typology presented in Chapter 2, mostly happens within innovation-related

ecosystems, i.e. business, service and platform ecosystems. These innovation ecosystems fulfil

a critical role in actuating ecosystem-level output into ecosystem-based activities, for example

business models or processes, that would yield some form of idiosyncratically defined value

outcome for the whole ecosystem. As per the findings of two of the research questions, the

exploitation and creation of new knowledge domains have the biggest impact on ecosystem

performance and competitiveness. As such, ecosystem-level activities that facilitate the

creation and appropriation of new knowledge, by encouraging actor interaction that influences,

mobilizes and leverages relationships, both within and beyond the ecosystem, would yield the

biggest value-adding advantage. Once the newly acquired knowledge has been exploited and

a common goal has been established for its appropriation, it often requires the collaborative

effort of a heterogeneous set of actors outside the organizational boundaries of the innovation

ecosystem to further diffuse the innovation into a bigger context and broader market.

Thirdly, entrepreneurial ecosystems provide a vehicle with which to connect regional economic

development strategy, entrepreneurial activity and innovative initiatives associated with job

creation, urban revitalization, economic growth and development (Ács et al. 2017; Adner 2017;

Audretsch & Belitski 2017; Isenberg 2016). Characterized by a broad range of actors and

ecosystem members that all converge around the sole purpose of market development and

growth, entrepreneurial ecosystems provide the structural framework with which to actuate

transformation in the broader context and achieve competitive advantage. As explored

knowledge only provides transient advantage, as per the findings of research question 4, the

process of knowledge exploration, exploitation and transformation needs to be ongoing in order

to constantly renew and reinvent the ecosystem and explore or expose new knowledge for

sustained advantage.

96

Figure 16: Illustrative representation of broader ecosystem-level theoretical contributions

4.8. Managerial Implications

From a managerial perspective, this body of work provides a new lens through which to view

the strategic potential of an ecosystems approach to industrial marketing. Various strategies

are presented to achieve and maintain competitive advantage, which can be employed to

compete and add value in a fast-changing and complex business environment. Following on

from the four competitive considerations of ecosystems that were proposed to have an impact

on the evolving nature of strategic competitiveness within industrial marketing in Chapter 1,

several competitive advantage strategies are presented next in this section.

4.8.1. Interorganizational Collaboration: Leveraging Knowledge

First, the importance of cooperation and collaboration between actors both within and across

ecosystems are underscored. This highlights the move away from seeking to establish

superiority over other external competitors, to an evolution towards investing in connections,

including cooperative and competitive relationships, which serves to build the overall

competitiveness of the whole ecosystem (Aarikka-Stenroos & Ritala, 2017). Considering the

interdependency among partners in ecosystems, the leveraging and exploration of new

knowledge provides the biggest strategic competitive advantage in an ecosystem. Ecosystems

with a high level of social capital and which succeed in creating new knowledge, would have

the biggest likelihood of achieving and maintaining competitive advantage.

4.8.2. Dependency on Resources and Capabilities Outside of Direct Control of

One Single Organization: Outside-in Strategic Orientation

Second, the dependency on resources and capabilities outside of the direct control of any one

particular entity in an ecosystem approach, highlights the importance of managing

97

interorganizational and interdependent relationships (Pellikka & Ali-Vehmas, 2016). This

stands in contrast to a positioning approach where competitive advantage is achieved by only

focusing on industry-specific resources and capabilities (Fuller et al., 2019), to a strategic

approach where influence and complementarity reach beyond the control of the individual

organization (Jacobides et al., 2018). An awareness of the competitive context and market

surrounding the ecosystem, specifically relating to the resources and capabilities of the broader

ecosystem actors and members that can be leveraged for enhanced ecosystem performance,

strengthens not only the individual organization, but the ecosystem as a whole. Collaborative

opportunities that seek to build the exploration and exploitation of knowledge, as well as the

transformation of this knowledge into entrepreneurial endeavors, build and create competitive

advantage for all within the ecosystem.

4.8.3. Dynamic Connections: Adaptation, Integration and Reconfiguration

Third, competitiveness is established through dynamic connections. The larger the ecosystem,

the greater the ability to interact with potential complementary actors to create and share value

(Iansiti & Levien, 2004; Möller et al., 2020). This is, in other words, a move away from creating

high barriers to entry – rather, newcomers are welcomed, as long as they can add value and

grow the market. At times, the advantage might be transient in nature, which emphasizes the

importance of diffusing knowledge throughout the ecosystem as best as possible in order to

fully capitalize on its value. In ecosystems with a high level of social capital present, the

leveraging of existing knowledge could serve to reach incremental innovation goals quickly,

albeit that it would not serve to maintain long-term competitive advantage. To be adaptable to

a dynamic environment, ecosystem connections need to be dynamic and adaptability to change

and evolutionary thinking would serve to strengthen the sustainability of competitiveness of

the ecosystem and its members.

4.8.4. Competing Beyond Traditional Industry Boundaries: Advantage for All

Fourth and finally, from a competitive perspective, success within ecosystems spans beyond

traditional industry boundaries and advantage is achieved by creating value for other actors in

the ecosystem as much as seeking to create value for one’s own organization, regardless the

industry or sector (Jacobides, 2019). The stronger the ecosystem as a whole, the more value it

can add to all its actors and members – building the market as a whole, transcending traditional

industry and sector boundaries.

The section to follow notes the limitations of the study and also presents areas for future

research.

98

4.9. Limitations and Suggested Areas for Future Research


Inevitably, this study has certain limitations, which will pave the way for future research and

analyses. First, the research analysis only encompassed the results from a very specific search

query. The use of more and broader search terms would yield different results and might

include additional sources and scholars as influential to the development of the field. Second,

in terms of the documents used for analysis, only research articles formed part of the scope of

study, and thus the inclusion of other research material, such as book chapters or other

publication types, might provide different insights. As an area with burgeoning

multidisciplinary interest, another potential avenue to explore is the work of PhD students in

the field. By reviewing the authors and papers that influence these students, as well as the

universities that they are affiliated with, one would potentially get a more in-depth

representation of the depth of influence of certain institutions and scholars. The third and final

limitation is the dynamism of citations and the reality that they constantly change. The citations

reported thus only represent articles that were published during a particular period of time. In

addition to this, one should be cognizant of the fact that citation count for an article is also

dependent on how long it has been since the article has been published.

Future research could contrast the evolution of the concept through time, by dividing the

literature into different quartiles and comparing the development of the field, as well as the

keywords that relate to competitiveness, based on different changes and trends per quartile.

These analyses exemplify a current representation over a particular period of time and should

thus periodically be updated to track how knowledge has further developed and influence has

been disseminated. Research into the heightened competitiveness of a region as a result of

public sector involvement in entrepreneurial ecosystems, is a relatively uncharted area which

warrants further investigation. Further to this, a particular focus on entrepreneurial leadership

development programs and public sector involvement in driving these types of initiatives,

could serve to build a better understanding of how successful initiatives can be implemented

by developing more and better entrepreneurs. Empirical and comparative research in the area

of national or transnational innovation and technology policies, and its effect on regional

development and entrepreneurial initiatives is another area that could further progress the

public sector entrepreneurship literature. A final area for future research is the comparison of

high-growth entrepreneurial ecosystems, to assess how replicable they are in terms of sectors,

technology, geography, and performance.

Paper 2: Leveraging Social Capital in University-Industry Knowledge Transfer Strategies:

A Comparative Positioning Framework

This study only offers a snapshot representation of the university-industry partnerships present

in the three regions identified. As such, findings relate to a particular context at a particular

point in time and the researchers do not deny the influence of additional macro-contextual

factors on the interpretation of the results. Future research could conduct longitudinal studies

99

to track the change in positioning over a period of time or to compare how heightened social

capital over a period of time impacts the output of the university-industry partnership as an

innovation ecosystem. An exploration of the products of university-industry knowledge

transfer can also be strategically mapped and compared. In addition, a more detailed

description of the respective intellectual capital dimensions present in university-industry

partnerships and its impact on knowledge transfer, present fertile ground for future exploration.

Paper 3: Innovation Performance: The Effect of Knowledge-Based Dynamic Capabilities in


Three limitations are noted in this study. First, using secondary data constrains the researcher

to analyze dimensions and variables measured with predetermined items. The Global

Innovation Index dataset does, however, provide measures for consistency and reliability, yet,

future research could in particular seek to validate the internal structure between the knowledge

capability constructs and seek to contextually validate all measurement items. As a KBDC

variable, knowledge impact, in particular, could be further probed and empirically studied.

Second, when operationalizing KBDC, human resources or knowledge workers were not

included as constructs. Although people are central to the creation, diffusion, absorption and

impact of knowledge, the exclusion was driven by a focus that was more nuanced towards the

inherent and diverse knowledge abilities present in the people within the ecosystem. As such,

from a unit of analysis perspective, the particularities of the people within the ecosystem fell

outside the scope of this study. Third, the study provides a snapshot representation within a

particular context, and future research could take an in-depth look at member-specific aspects

within an innovation ecosystem. The results indicate that efficient innovation performance,

based on innovation inputs and outputs, are concentrated in specific market economies,

possibly pointing to an innovation divide between innovation ecosystems. A comparative

longitudinal study would provide a more detailed perspective regarding changes over time, and

how this affects innovation performance. Finally, the use of secondary data meant that the study

adopted a linear approach to measuring innovation performance (input-output relationship).

Future research could seek to provide a more holistic inclusion of the factors, influences and

determinants of innovation performance within an innovation ecosystem context.

Paper 4: Competition in Knowledge Ecosystems: A Theory Elaboration Approach Using a

Case Study

The use of a single case study is not without its limitations, however, the rich insights offered

by this design provided the researcher with the ability to gather information that is exploratory

in nature and which would otherwise not have been possible to elicit through other forms of

data collection. Knowledge ecosystems are admittedly multi-level, and as such, competition

will have many other facets based on the various levels and actors’ perspectives. This is an area

that offers numerous avenues for future research. By presenting the perspective of the keystone

actor, the paper does, however, add to our extant understanding of how competition works, by

acknowledging the role that the focal organizational entity plays in setting the strategic agenda

and orientation of the knowledge ecosystem. Finally, adopting a theory elaboration approach

100

with three particular streams of thought predetermined, implies that other theories or

perspectives on competition and inherent strategic orientations were excluded. Future research

could use the theoretical lenses provided by learning theory and chaos theory to further probe

the inherent processes within knowledge ecosystems. Future research would thus further

contribute to our understanding of the competitive forces inherent to knowledge ecosystems,

by also probing the business models that they employ and comparatively assessing their

interaction with other innovation or entrepreneurial ecosystem actors.


Chapter 4 provided a summary of the research findings, based on the central research problem

of this dissertation: How is competitive advantage achieved through an ecosystem approach in

industrial marketing? The chapter further provided an overview of the key theoretical

contributions and managerial implications, as surmised from the respective research questions

and papers. Of interest to note is the centrality of knowledge, as well as the importance of

relationships and interactivity between actors to materialize a focal value proposition within

ecosystems. The interplay between the different ecosystems also provide fertile ground for

further assessment, as the research points to definite complementarities not only within

ecosystems, but also between different types of ecosystems. The Chapter concluded by noting

the limitations of the research and offering suggested areas for future research.

101

LIST OF REFERENCES

Aarikka-Stenroos, L. & Ritala, P. (2017). Network management in the era of ecosystems:

Systematic review and management framework. Industrial Marketing Management, 67, 23-

36.

Aarikka-Stenroos, L., Sandberg, B., & Lehtimäki, T. (2014). Networks for the

commercialization of innovations: A review of how divergent network actors

contribute. Industrial Marketing Management, 43(3), 365-381.

Acemoglu, D., Akcigit, U., & Kerr, W.R. (2016). Innovation network, Proceedings of the

National Academy of Sciences, 113(41), 11483-11488.

Achrol, R. S. (1996). Changes in the theory of interorganizational relations in marketing:

Toward a network paradigm. Journal of the Academy of Marketing Science, 25(1), 56-71.

Aćs, Z. J., Autio, E., & Szerb, L. (2014). National systems of entrepreneurship: Measurement

issues and policy implications. Research Policy, 43(3), 476-494.

Ács, Z.J., Desai, S., & Hessels, J., (2008). Entrepreneurship, economic development and

institutions. Small Business Economics, 31(3), 219-234.

Ács, Z.J., Stam, E., Audretsch, D.B., & O’Connor, A. (2017). The lineages of the

entrepreneurial ecosystem approach. Small Business Economics, 49(1), 1-10.

Adner, R. (2006). Match your innovation strategy to your innovation ecosystem. Harvard

Business Review, 84(4), 98.

Adner, R. (2017). Ecosystem as structure: An actionable construct for strategy. Journal of

Management, 43(1), 39–58.

Adner, R. & Kapoor, R. (2010). Value creation in innovation ecosystems: How the structure

of technological interdependence affects firm performance in new technology

generations. Strategic Management Journal, 31(3), 306-333.

Adner, R., & Kapoor, R. (2016). Innovation ecosystems and the pace of substitution: Re‐examining technology S‐curves. Strategic Management Journal, 37(4), 625-648.

Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal

study. Administrative Science Quarterly, 45(3), 425-455.

Akaka, M. A., & Vargo, S. L. (2015). Extending the context of service: from encounters to

ecosystems. Journal of Services Marketing, 29(6/7), 453–462.

Akaka, M. A., Vargo, S. L., & Lusch, R. F. (2013). The complexity of context: A service

ecosystems approach for international marketing. Journal of International

Marketing, 21(4), 1-20.

Akter, S., Gunasekaran, A., Wamba, S. F., Babu, M. M., & Hani, U. (2020). Reshaping

competitive advantages with analytics capabilities in service systems. Technological

Forecasting and Social Change, 159, 120180.

Almpanopoulou, A., Ritala, P., & Blomqvist, K. (2019). Innovation ecosystem emergence

barriers: Institutional perspective. In Proceedings of the 52nd Hawaii International

Conference on System Sciences

Alvedalen, J., & Boschma, R. (2017). A critical review of entrepreneurial ecosystems research:

Towards a future research agenda. European Planning Studies, 25(6), 887-903.

Amit, R., & Schoemaker, P. J. (1993). Strategic assets and organizational rent. Strategic

Management Journal, 14(1), 33-46.

Anaza, N.A., Rutherford, B., Rollins, M., & Nickell, D. (2015) Ethical climate and job

satisfaction among organizational buyers: An empirical study. The Journal of Business &

Industrial Marketing, 30(8), 962-972.

Andreev, S., Galinina, O., Pyattaev, A., Gerasimenko, M., Tirronen, T., Torsner, J., ... &

Koucheryavy, Y. (2015). Understanding the IoT connectivity landscape: a contemporary

M2M radio technology roadmap. IEEE Communications Magazine, 53(9), 32-40.

102

Andreeva, T., & Kianto, A. (2011). Knowledge processes, knowledge-intensity and

innovation- a moderated mediation analysis. Journal of Knowledge Management, 15(6),

1016–1034.

Andriani, L. (2013). Social capital: A road map of theoretical frameworks and empirical

limitations (Working papers in management). Birkbeck University, London.

Anggraeni, E., Den Hartigh, E., & Zegveld, M. (2007, October). Business ecosystem as a

perspective for studying the relations between firms and their business networks. In ECCON

2007 Annual meeting (pp. 1-28).

Ansari, S., Garud, R., & Kumaraswamy, A. (2016). The disruptor's dilemma: TiVo and the US

television ecosystem. Strategic Management Journal, 37(9), 1829-1853.

Anthony, S. D., Viguerie, S. P., Schwartz, E. I., & Van Landeghem, J. (2018). 2018

Corporate longevity forecast: Creative destruction is accelerating. INNOSIGHT Holdings,

LLC, Boston, MA, Feb.

Aparicio, G., Iturralde, T., & Maseda, A. (2019). Conceptual structure and perspectives on

Entrepreneurship education research: A bibliometric review. European Research on

Management and Business Economics, 25(3), 105-113.

Archer-Brown, C., & Kietzmann, J. (2018). Strategic knowledge management and enterprise

social media. Journal of Knowledge Management, 22(6), 1288–1309.

Arndt, J. (1985). On making marketing science more scientific: role of orientations, paradigms,

metaphors, and puzzle solving. Journal of Marketing, 49(3), 11-23.

Audretsch, D.B., & Belitski, M. (2017). Entrepreneurial ecosystems in cities: establishing the

framework conditions. The Journal of Technology Transfer, 42(5), 1030-1051.

Audretsch, D.B., Cunningham, J.A., Kuratko, D.F., Lehmann, E.E., & Menter, M. (2019).

Entrepreneurial ecosystems: Economic, technological, and societal impacts. The Journal of

Technology Transfer, 44(2), 313-325.

Autio, E., & Thomas, L. (2014). Innovation ecosystems: Implications for innovation

management”, Dodgson, M., Philips, N. and Gann, D. M. (Eds.), The Oxford handbook of

innovation management, Oxford University Press, Oxford, pp.204–228.

Autio, E., & Thomas, L. (2020). Value co-creation in ecosystems: insights and research

promise from three disciplinary perspectives. In Handbook of Digital Innovation, Edward

Elgar Publishing, pp.107–132

Autio, E., Kenney, M., Mustar, P., Siegel, D., & Wright, M. (2014). Entrepreneurial

innovation: The importance of context. Research Policy, 43(7), 1097-1108.

Azzam, J. E., Ayerbe, C., & Dang, R. (2017). Using patents to orchestrate ecosystem stability:

the case of a French aerospace company. International Journal of Technology

Management, 75(1-4), 97-120.

Bacon, E., Williams, M. D., & Davies, G. (2020). Coopetition in innovation ecosystems: A

comparative analysis of knowledge transfer configurations. Journal of Business Research,

115, 307-316.

Baraldi, E., Ingemansson, M., & Launberg, A. (2014). Controlling the commercialisation of

science across inter-organisational borders: Four cases from two major Swedish

universities. Industrial Marketing Management, 43(3), 382-391.

Barbolla, A. M. B., & Corredera, J. R. C. (2009). Critical factors for success in university–

industry research projects. Technology Analysis & Strategic Management, 21(5), 599-616.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of

Management, 17(1), 99-120.

Batt, P. J. (2008). Building social capital in networks. Industrial Marketing Management,

37(5), 487-491.

103

Bengtsson, M., & Kock, S. (1999). Cooperation and competition in relationships between

competitors in business networks. Journal of Business and Industrial Marketing 14(3), 178–

190.

Benson-Rea, M., Brodie, R. J., & Sima, H. (2013). The plurality of co-existing business

models: Investigating the complexity of value drivers. Industrial Marketing

Management, 42(5), 717-729.

Beuter Júnior, N., Faccin, K., Volkmer Martins, B., & Balestrin, A. (2019). Knowledge-based

dynamic capabilities for sustainable innovation: The case of the green plastic

project. Sustainability, 11(8), 2392.

Birkinshaw, J. (2019, August 8). Business ecosystems are changing the rules of strategy.

Harvard Business Review Digital Articles.

Blakeley, G. (2021). The big tech monopolies and the State. Socialist Register, 57.

Boehm, D. N., & Hogan, T. (2013). Science-to-Business collaborations: A science-to-business

marketing perspective on scientific knowledge commercialization. Industrial Marketing

Management, 42(4), 564-579.

Boschma, R., & Frenken, K. (2011). Technological relatedness and regional branching. In

Bathelt, H., Feldman, M. and Kogler, D. (Eds.), Beyond Territory: Dynamic Geographies

of Knowledge Creation, Diffusion and Innovation (pp.64-81). Abingdon: Routledge.

Boudreau, K. (2010). Open platform strategies and innovation: Granting access vs. devolving

control. Management Science, 56(10), 1849-1872.

Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and

research for the sociology of education (pp. 241–258). New York: Greenwood.

Bramwell, A., Hepburn, N., & Wolfe, D.A. (2012). Growing innovation ecosystems:

University-industry knowledge transfer and regional economic development in Canada.

Final Report to the Social Sciences and Humanities Research Council of Canada, 62.

Broadus, R. (1987). Toward a definition of “bibliometrics”. Scientometrics, 12(5-6), 373-379.

Brown, R., & Mason, C. (2017). Looking inside the spiky bits: A critical review and

conceptualisation of entrepreneurial ecosystems. Small Business Economics, 49(1), 11-30.

Bruneel, J., D’Este, P., & Salter, A. (2010). Investigating the factors that diminish the barriers

to university–Industry collaboration. Research Policy, 39(7), 858–868.

Burt, R. S. (2001). Closure as social capital. Social capital: Theory and research, 31-55.

Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2),

349-399.

Cai, Y., Ramis Ferrer, B., & Luis Martinez Lastra, J. (2019). Building university-industry co-

innovation networks in transnational innovation ecosystems: towards a transdisciplinary

approach of integrating social sciences and artificial intelligence. Sustainability, 11(17),

4633.

Carayannis, E. G., Alexander, J., & Ioannidis, A. (2000). Leveraging knowledge, learning, and

innovation in forming strategic Government-University–Industry (GUI) R&D partnerships

in the US, Germany, and France. Technovation, 20, 477–488.

Carmona-Lavado, A., Cuevas-Rodríguez, G., & Cabello-Medina, C. (2010). Social and

organizational capital: Building the context for innovation. Industrial Marketing

Management, 39(4), 681-690. Cavallo, A., Ghezzi, A., & Balocco, R. (2019). Entrepreneurial ecosystem research: present

debates and future directions. International Entrepreneurship and Management

Journal, 15(4), 1291-1321.

Ceccagnoli, M., Forman, C., Huang, P., & Wu, D. J. (2012). Cocreation of value in a platform

ecosystem! The case of enterprise software. MIS quarterly, 36(1), 263-290.

Cennamo, C. (2019). Competing in digital markets: A platform-based perspective. Academy of

Management Perspectives, in press.

104

Cennamo, C., & Santalo, J. (2013). Platform competition: Strategic trade‐offs in platform

markets. Strategic Management Journal, 34(11), 1331-1350.

Cheng, C.C.J., Yang, C. and Sheu, C. (2016), “Effects of open innovation and knowledge-

based dynamic capabilities on radical innovation: an empirical study”, Journal of

Engineering and Technology, Vol. 41, pp.79-91.

Cheng, M., Anderson, C. K., Zhu, Z., & Choi, S. C. (2018). Service online search ads: from a

consumer journey view. Journal of Services Marketing.

Chin, W.W. (1998). The partial least squares approach to structural equation modelling. In

Marcoulides, G.A. (Ed.), Modern Methods for Business Research, (pp.295-358), Erlbaum:

Mahwah.

Clarysse, B., Wright, M., Bruneel, J., & Mahajan, A. (2014). Creating value in ecosystems:

Crossing the chasm between knowledge and business ecosystems. Research Policy, 43(7),

1164-1176.

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning

and innovation. Administrative Science Quarterly, 35(1), 128-152.

Colavizza, G., Boyack, K.W., van Eck, N.J., & Waltman, L. (2018). The closer the better:

Similarity of publication pairs at different cocitation levels. Journal of the Association for

Information Science and Technology, 69(4), 600-609.

Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of

Sociology, 94, S95–S120.

Colombo, M. G., Dagnino, G. B., Lehmann, E. E., & Salmador, M. (2019). The governance of

entrepreneurial ecosystems. Small Business Economics, 52(2), 419-428.

Cornelissen, J. P. (2003). Metaphor as a method in the domain of marketing. Psychology &

Marketing, 20(3), 209-225.

Creswell, J. W., Plano Clark, V. L., & Garrett, A. L. (2008). Methodological issues in

conducting mixed methods research designs. Advances in Mixed Methods Research, 66-83.

Creswell, J.W. (2015). Revisiting mixed methods and advancing scientific practices,

In SN Hesse-Biber & RB Johnson (Eds.), The Oxford handbook of multi method and mixed

methods research inquiry, Oxford University Press, Oxford.

Creswell, J.W., & Tashakkori, A. (2007). Editorial: developing publishable mixed methods

manuscripts. Journal of Mixed Methods Research, 1(2), 107-111.

Cunningham, J. A. Menter, M., & Young, C. (2017). A review of qualitative case methods

trends and themes used in technology transfer research. Journal of Technology Transfer, 42

(4), 923–956.

Cunningham, J. A., & Link, A. N. (2015). Fostering university-industry R&D collaborations

in European Union countries. International Entrepreneurship and Management Journal,

11(4), 849–860.

Dattée, B., Alexy, O., & Autio, E. (2017). Maneuvering in poor visibility: How firms play

Davenport, S., Davies, J., & Grimes, C. (1998). Collaborative research programmes: Building

trust from difference. Technovation, 19(1), 31–40.

Davey, T, Baaken, T, Galan Muros, V, & Meerman, A. (2011). The state of European

university-business cooperation. part of the DG Education and culture study on the

cooperation between higher education institutions and public and private organisations in

Europe. International Journal of Technology Transfer and Commercialisation, 15(1), 65.

Dawson, C. (2019). Introduction to Research Methods 5th Edition: A Practical Guide for

Anyone Undertaking a Research Project. Robinson.

Day, G. (2020). The yin and yang of outside-in thinking. Industrial Marketing

Management, 88, 84-86.

Day, G. S., & Wensley, R. (1988). Assessing advantage: a framework for diagnosing

competitive superiority. Journal of Marketing, 52(2), 1-20.

105

De Vasconcelos Gomes, L.A., Facin, A.L.F., Salerno, M.S., & Ikenami, R.K. (2018).

Unpacking the innovation ecosystem construct: Evolution, gaps and trends. Technological

Forecasting and Social Change, 136, 30-48.

de Wit-de Vries, E., Dolfsma, W. A., van der Windt, H. J., & Gerkema, M. P. (2019).

Knowledge transfer in university–industry research partnerships: a review. The Journal of


Denford, J. S. (2013). Building knowledge: Developing a knowledge-based dynamic

capabilities typology. Journal of Knowledge Management, 17(2) 175–194.

Denzin, N.K., & Lincoln, Y. S. (Eds). (1994). Handbook of qualitative research. Thousand

Oaks, CA: Sage.

Dhanaraj, C., & Parkhe, A. (2006). Orchestrating innovation networks. Academy of

Management Review, 31(3), 659-669.

Durrheim, K. (2004). Research Design Research in Practice: Applied Methods for the social

sciences. M. Terre Blanche & K. Durrheim, 29, 53.

Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (2019). The Global Innovation Index. Creating

Healthy Lives: The Future of Medical Innovation, 12th ed.

Eisenhardt, K. M., & Galunic, D. C. (2000). Coevolving At last, a way to make synergies

work. Harvard Business Review, 78(1), 91-91.

Eisenhardt, K.M., & Martin, J.A. (2000). Dynamic capabilities: what are they? Strategic

Management Journal, 21(10/11), 1105-1121.

Eisenmann, T., Parker, G., & Van Alstyne, M. (2011). Platform envelopment. Strategic


Ekman, P., Raggio, R. D., & Thompson, S. M. (2016). Service network value co-creation:

Defining the roles of the generic actor. Industrial Marketing Management, 56, 51-62.

Eliashberg, J., & Chatterjee, R. (1985). Analytical models of competition with implications for

marketing: issues, findings, and outlook. Journal of Marketing Research, 22(3), 237-261.

Erevelles, S. U. N. I. L., Horton, V., & Fukawa, N. (2008). Understanding B2C brand alliances

between manufacturers and suppliers. Marketing Management Journal, 18(2), 32-46.

Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From national systems

and “mode 2” to a triple helix of university-industry-government relations. Research Policy,

29(2), 109-123.

Etzkowitz, H., Ranga, M., & Dzisah, J. (2012). Whither the university? The Novum Trivium

and the transition from industrial to knowledge society. Social Science Information, 51(2),

143-164.

Faccin, K., Balestrin, A., Martins, B.V., & Bitencourt, C.C. (2019). Knowledge-based dynamic

capabilities: A joint R&D project in the French semiconductor industry. Journal of

Knowledge Management, 23(3), 439-465.

Ferreira, J., Raposo, M., Rutten, R., & Varga, A. (2013). Cooperation, clusters, and

knowledge transfer – universities and firms towards regional competitiveness, In Advances

in Spacial Science, Springer.

Filieri, R., & Alguezai, S. (2014). Structural social capital and innovation. Is knowledge

transfer the missing link? Journal of Knowledge Management, 18(4), 728–757.

Fisher, G., & Aguinis, H. (2017). Using theory elaboration to make theoretical

advancements. Organizational Research Methods, 20(3), 438-464.

Flick, U. (Ed.). (2013). The SAGE handbook of qualitative data analysis. Sage.

Frambach, J. M., van der Vleuten, C. P., & Durning, S. J. (2013). AM last page: Quality criteria

in qualitative and quantitative research. Academic Medicine, 88(4), 552.

Frow, P., McColl-Kennedy, J. R., & Payne, A. (2016). Co-creation practices: Their role in

shaping a health care ecosystem. Industrial Marketing Management, 56, 24–39.

106

Frow, P., McColl-Kennedy, J. R., Payne, A., & Govind, R. (2019). Service ecosystem well-

being: conceptualization and implications for theory and practice. European Journal of

Marketing, 53(12), 2657-2691.

Fuller, J., Jacobides, M. G., & Reeves, M. (2019). The myths and realities of business

ecosystems. MIT Sloan Management Review, 60(3), 1-9.

Galati, F., & Bigliardi, B. (2017). Does different NPD project’s characteristics lead to the

establishment of different NPD networks? A knowledge perspective. Technology Analysis

& Strategic Management, 29(10), 1196-1209.

Gawer, A., & Cusumano, M. A. (2014). Industry platforms and ecosystem innovation. Journal

of Product Innovation Management, 31(3), 417-433.

Georghiou, L, Elvira Uyarra, A, Saliba Scerri, R, Castillo, N, & Cassingena Harper, J. (2014).

Adapting smart specialization to a micro-economy – the case of Malta. European Journal

of Innovation Management, 17(4), 428-447.

Geuna, A, & Muscio, A. (2009). The governance of university knowledge transfer: A critical

review of the literature. Minerva, 47(1), 93-114.

Gibbert, M., Ruigrok, W., & Wicki, B. What passes as a rigorous case study? Strategic


Golley, F. B. (1993). A history of the ecosystem concept in ecology: more than the sum of the

parts. Yale University Press.

González Alcaide, G., & Gorraiz, J. I. (2018). Assessment of researchers through bibliometric

indicators: The area of information and library science in Spain as a case study (2001–

2015). Frontiers in Research Metrics and Analytics, 3, 15.

Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological

Theory, 201-233.

Granovetter, M., 1985. Economic action and social structure: The problem of embeddedness.

American Journal of Sociology, 91(3), pp.481-510.

Granstrand, O., & Holgersson, M. (2020). Innovation ecosystems: A conceptual review and a

new definition. Technovation, 90-91, 102098.

Greeven, M. J., & Yu, H. (2020). In a crisis, ecosystem businesses have a competitive

advantage, Harvard Business Review.

Grossman, G.M., & Helpman, E. (1991). Trade, knowledge spillovers, and growth. European

Economic Review, 35(2/3), 517-526.

Guerrero, M., Urbano, D., & Herrera, F. (2019). Innovation practices in emerging economies:

Do university partnerships matter? The Journal of Technology Transfer, 44(2), 615-646.

Guillemot, S., & Privat, H. (2019). The role of technology in collaborative consumer

communities. Journal of Services Marketing. 33(7), 837-850.

Gulati, R., Nohria, N., & Zaheer, A. (2000). Strategic networks. Strategic Management

Journal, 21(3), 203-215.

Gupta, A.K., Tesluk, P.E. and Taylor, M.S. (2007) Innovation at and across multiple levels of

analysis. Organization Science, 18, 885-897

Gupta, H., & Barua, M. K. (2016). Identifying enablers of technological innovation for Indian

MSMEs using best–worst multi criteria decision making method. Technological

Forecasting and Social Change, 107, 69-79.

Gyrd-Jones, R. I., & Kornum, N. (2013). Managing the co-created brand: Value and cultural

complementarity in online and offline multi‐stakeholder ecosystems. Journal of Business

Research, 66(9), 1484-1493.

Habbershon, T.G. (2006). Commentary: A framework for managing the familiness and agency

advantages in family firms. Entrepreneurship Theory and Practice, 30(6), 879-886.

Hagel, J., Brown, J. S., & Davison, L. (2008). Shaping strategy in a world of constant

disruption. Harvard Business Review, 86(10), 80-89.

107

Hair, J., Black, W., Babin, B., & Anderson, R. (2014). Multivariate Data Analysis (7th ed.),

Essex, England: Pearson Education Limited.

Hair, J.F., Risher, J.J., Sarstedt, M., & Ringle, C.M. (2019). When to use and how to report the

results of PLS-SEM. European Business Review, 31(1), 2-24.

Håkansson, H., & Snehota, I. (1989). No business is an island: The network concept of business

strategy. Scandinavian Journal of Management, 5(3), 187-200.

Hannah, D. P., & Eisenhardt, K. M. (2018). How firms navigate cooperation and competition

in nascent ecosystems. Strategic Management Journal, 39(12), 3163-3192.

Hansen, M. T. (2002). Knowledge networks: Explaining effective knowledge sharing in

multiunit companies. Organization Science, 13(3), 232-248.

Hartmann, N. N., Wieland, H., & Vargo, S. L. (2018). Converging on a new theoretical

foundation for selling. Journal of Marketing, 82(2), 1-18.

Hawley, A. H. (1986). Human ecology: A theoretical essay. University of Chicago Press.

Hayter, C.S., Link, A.N., & Scott, J.T. (2018). Public-sector entrepreneurship. Oxford Review

of Economic Policy, 34(4), 676-694.

Healy, M. J., & Beverland, M. B. (2016). Being sub-culturally authentic and acceptable to the

mainstream: Civilizing practices and self-authentication. Journal of Business Research,

69(1), 224-233.

Heaton, S., Siegel, D. S., & Teece, D. J. (2019). Universities and innovation ecosystems: a

dynamic capabilities perspective. Industrial and Corporate Change, 28(4), 921-939.

Hein, A., Schreieck, M., Riasanow, T., Setzke, D. S., Wiesche, M., Böhm, M., & Krcmar, H.

(2019). Digital platform ecosystems. Electronic Markets, 1-12.

Helfat C. E., & Peteraf, M. A. (2009). Understanding dynamic capabilities: Progress along a

developmental path. Strategic Organization 7(1), 91-102.

Helfat, C. E., & Campo-Rembado, M. A. (2016). Integrative capabilities, vertical integration,

and innovation over successive technology lifecycles. Organization Science, 27(2), 249-

264.

Helfat, C. E., & Raubitschek, R. S. (2018). Dynamic and integrative capabilities for profiting

from innovation in digital platform-based ecosystems. Research Policy, 47(8), 1391-1399.

Henfridsson, O., & Bygstad, B. (2013). The generative mechanisms of digital infrastructure

evolution. MIS Quarterly, 37(3), 907-931.

Henry, C., Smith, H. L., Meschitti, V., Foss, L., & McGowan, P. (2020). Networking, gender

and academia: An ecosystems approach. In Gender, Science and Innovation. Edward Elgar

Publishing.

Holling, C. S., & Gunderson, L. H. (2002). Panarchy: Understanding transformations in

human and natural systems. Washington, DC: Island Press.

Holste, J. S., & Fields, D. (2010). Trust and tacit knowledge sharing and use. Journal of


Hsieh, M. H., & Tsai, K. H. (2007). Technological capability, social capital and the launch

strategy for innovative products. Industrial Marketing Management, 36(4), 493-502. Hunt, S. D. (2000). A general theory of competition: too eclectic or not eclectic enough? too

incremental or not incremental enough? too neoclassical or not neoclassical enough?

Journal of Macromarketing, 20(1), 77-81.

Hunt, S. D., & Madhavaram, S. (2020). Adaptive marketing capabilities, dynamic capabilities,

and renewal competences: the “outside vs. inside” and “static vs. dynamic” controversies in

strategy. Industrial Marketing Management., 89, 129-139.

Hunt, S. D., & Menon, A. (1995). Metaphors and competitive advantage: Evaluating the use

of metaphors in theories of competitive strategy. Journal of Business Research, 33(2), 81-

90.

Iansiti, M., & Levien, R. (2004). Strategy as ecology, Harvard Business Review, 82(3), 68-78.

108

Inkpen, A. C., & Dinur, A. (1998). Knowledge management processes and international joint

ventures. Organization Science, 9, 454–468.

Inkpen, A. C., & Tsang, E. W. K. (2005). Social capital, networks, and knowledge transfer.

The Academy of Management Review, 30(1), 146–165.

Isenberg, D.J. (2010). How to start an entrepreneurial revolution. Harvard Business Review,

88(6), 40-50.

Isenberg, D.J. (2016). Applying the ecosystem metaphor to entrepreneurship: Uses and abuses.

The Antitrust Bulletin, 61(4), 564-573.

Jacobides, M. G. (2019). In the ecosystem economy, what’s your strategy? Harvard Business

Review, 97(5), 128-137.

Jacobides, M. G., Cennamo, C., & Gawer, A. (2018). Towards a theory of ecosystems.

Strategic Management Journal, 39(8), 2255–2276.

Jahanmir, S. F. (2016). Paradoxes or trade-offs of entrepreneurship: exploratory insights from

the Cambridge eco-system. Journal of Business Research, 69(11), 5101-5105.

Janeiro, P., Proença, I., & da Conceição Gonçalves, V. (2013). Open innovation: Factors

explaining universities as service firm innovation sources. Journal of Business

Research, 66(10), 2017-2023.

Järvi, K., Almpanopoulou, A., & Ritala, P. (2018). Organization of knowledge ecosystems:

Prefigurative and partial forms. Research Policy, 47(8), 1523-1537.

Jones, B., Temperley, J., & Lima, A. (2009). Corporate reputation in the era of Web 2.0: the

case of Primark. Journal of Marketing Management, 25(9/10), 927-939.

Kamaşak, R., & Bulutlar, F. (2010). The influence of knowledge sharing on innovation.

European Business Review, 22(3), 306-317.

Kang, M., & Hau, Y. S. (2014). Multi-level analysis of knowledge transfer: A knowledge

recipient ’s perspective. Journal of Knowledge Management, 18(4), 758-776.

Kapoor, R. (2018). Ecosystems: broadening the locus of value creation. Journal of

Organization Design, 7(1), 12.

Kapoor, R., & Lee, J. M. (2013). Coordinating and competing in ecosystems: How

organizational forms shape new technology investments. Strategic Management

Journal, 34(3), 274-296.

Kazadi, K., Lievens, A., &Mahr, D. (2016). Stakeholder co-creation during the innovation

process: Identifying capabilities for knowledge creation among multiple stakeholders.

Journal of Business Research, 69(2), 525-540.

Kelly, E. (2015). Business Ecosystems Come of Age, In Business Trends Industry Report,

Deloitte University Press, pp. 1-17.

Kjellberg, H., Azimont, F., & Reid, E. (2015). Market innovation processes: Balancing stability

and change. Industrial Marketing Management, 44, 4-12.

Klarl, T. (2014). Knowledge diffusion and knowledge transfer revisited: two sides of the

medal. Journal of Evolutionary Economics, 24(4), 737-760.

Kneller, R., Mongeon, M., Cope, J., Garner, C., & Ternouth, P. (2014). Industry-university

collaborations in Canada, Japan, the UK and USA–With emphasis on publication freedom

and managing the intellectual property lock-up problem. PloS one, 9(3), e90302.

Koskela-Huotari, K., Edvardsson, B., Jonas, J. M., Sörhammar, D., & Witell, L. (2016).

Innovation in service ecosystems—Breaking, making, and maintaining institutionalized

rules of resource integration. Journal of Business Research, 69(8), 2964-2971.

Kothari, C. (2004), Research Methodology – Methods and Techniques. New Delhi: New Age

International Limited Publishers.

Kotler, P., & Sarkar, C. (2019). “The ecosystem journey: Getting closer to the customer?” The

Marketing Journal, July 8. Retrieved from http://www.marketingjournal.org/the-

ecosystem-journey-getting-closer-to-the-customer-christian-sarkar-andphilip-kotler/

109

Kraus, S., Filser, M., Eggers, F., Hills, G.E., & Hultman, C.M. (2012). The entrepreneurial

marketing domain: A citation and co-citation analysis. Journal of Research in Marketing

and Entrepreneurship, 14(1), 6-26.

Kruss, G, & Visser, M. (2017). Putting university–industry interaction into perspective: a

differentiated view from inside South African universities. The Journal of Technology

Transfer, 42(4), 884-908.

Kruss, G, Adeoti, J, & Nabudere, D. (2012). Universities and knowledge-based development

in sub-Saharan Africa: Comparing university–firm interaction in Nigeria, Uganda and South

Africa. Journal of Development Studies, 48(4), 516-530.

Kumar, P., Dass, M., & Kumar, S. (2015). From competitive advantage to nodal advantage:

Ecosystem structure and the new five forces that affect prosperity. Business

Horizons, 58(4), 469-481.

Kumar, R. (2019). Research methodology: A step-by-step guide for beginners. Sage

Publications Limited.

Kuratko, D. F., Fisher, G., Bloodgood, J. M., & Hornsby, J. S. (2017). The paradox of new

venture legitimation within an entrepreneurial ecosystem. Small Business

Economics, 49(1), 119-140.

Lampel, J., & Germain, O. (2016). Creative industries as hubs of new organizational and

business practices. Journal of Business Research, 69(7), 2327-2333.

Lee, T. W., Mitchell, T. R., & Sablynski, C. J. (1999). Qualitative research in organizational

and vocational psychology, 1979–1999. Journal of Vocational Behavior, 55(2), 161-187.

Lehmann, E. E., & Menter, M. (2018). Public cluster policy and performance. The Journal of


Leposky, T., Arslan, A., & Kontkanen, M. (2017). Determinants of reverse marketing

knowledge transfer potential from emerging market subsidiaries to multinational

enterprises’ headquarters. Journal of Strategic Marketing, 25(7), 567–580.

Letaifa, S. B., Edvardsson, B., & Tronvoll, B. (2016). The role of social platforms in

transforming service ecosystems. Journal of Business Research, 69(5), 1933-1938.

Letaifa, S.B., & Rabeau, Y. (2013). Too close to collaborate? How geographic proximity could

impede entrepreneurship and innovation. Journal of Business Research, 66(10), 2071-2078.

Leyden, D.P., & Link, A.N. (2015). Public sector entrepreneurship: US technology and

innovation policy. Oxford University Press, USA.

Li, K., Rollins, J., & Yan, E. (2018). Web of Science use in published research and review

papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis.

Scientometrics, 115(1), 1-20.

Lin, N. (2008). A network theory of social capital. The handbook of social capital, 50(1), 69.

Lind, F., Holmen, E., & Pedersen, A. C. (2012). Moving resources across permeable project

boundaries in open network contexts. Journal of Business Research, 65(2), 177-185.

Lundvall, B.Å. (2007). National innovation systems: Analytical concept and development

tool. Industry and Innovation, 14(1), 95-119.

Lusch, R. F., Vargo, S. L., & Gustafsson, A. (2016). Fostering a trans-disciplinary perspectives

of service ecosystems. Journal of Business Research, 69(8), 2957-2963.

Lutz, E., Bender, M., Achleitner, A. K., & Kaserer, C. (2013). Importance of spatial proximity

between venture capital investors and investees in Germany. Journal of Business

Research, 66(11), 2346-2354.

Mack, E., & Mayer, H. (2016). The evolutionary dynamics of entrepreneurial

ecosystems. Urban Studies, 53(10), 2118-2133.

Mackalski, R., & Belisle, J. F. (2015). Measuring the short-term spillover impact of a product

recall on a brand ecosystem. Journal of Brand Management, 22(4), 323-339.

110

Maietta, O. W. (2015). Determinants of university–Firm R&D collaboration and its impact on

innovation: A perspective from a low-tech industry. Research Policy, 44(7), 1341–1359.

Makadok, R. (2001). Toward a synthesis of the resource‐based and dynamic‐capability views

of rent creation. Strategic Management Journal, 22(5), 387-401.

Malecki, E. J. (2018). Entrepreneurship and entrepreneurial ecosystems. Geography

Compass, 12(3), e12359.

Malerba, F., & McKelvey, M. (2020). Knowledge-intensive innovative entrepreneurship

integrating Schumpeter, evolutionary economics, and innovation systems. Small Business

Economics, 54, 503–522.

Malhotra, N. (2010), Marketing research – An applied orientation 6th ed. New Jersey: Pearson

Education.

Manniche, J., Moodysson, J., & Testa, S. (2017). Combinatorial knowledge bases: An

integrative and dynamic approach to innovation studies. Economic Geography, 93(5), 480-

499.

Markard, J., & Truffer, B. (2008). Technological innovation systems and the multi-level

perspective: Towards an integrated framework. Research policy, 37(4), 596-615.

Martín-de Castro, G. (2015). Knowledge management and innovation in knowledge-based and

high-tech industrial markets: The role of openness and absorptive capacity. Industrial

Marketing Management, 47, 143-146.

Mason, C., & Brown, R. (2014). Entrepreneurial ecosystems and growth oriented

entrepreneurship. Final Report to OECD, Paris, 30(1), 77-102.

Maurer, I., Bartsch, V., & Ebers, M. (2011). The value of intra-organizational social capital:

How it fosters knowledge transfer, innovation performance, and growth. Organization

Studies, 32(2), 157-185.

McColl-Kennedy, J. R., Cheung, L., & Coote, L. V. (2020). Tensions and trade-offs in multi-

actor service ecosystems. Journal of Business Research, in press.

McFadyen, M. A., Semadeni, M., & Cannella Jr, A. A. (2009). Value of strong ties to

disconnected others: Examining knowledge creation in biomedicine. Organization

Science, 20(3), 552-564.

Mele, C., & Russo-Spena, T. (2015). Innomediary agency and practices in shaping market

innovation. Industrial Marketing Management, 44, 42-53.

Merigó, J.M., Cancino, C.A., Coronado, F., & Urbano, D. (2016). Academic research in

innovation: a country analysis. Scientometrics, 108(2), 559-593.

Merigó, J.M., Pedrycz, W., Weber, R., & de la Sotta, C. (2018). Fifty years of Information

Sciences: A bibliometric overview. Information Sciences, 432, 245-268.

Meynhardt, T., Chandler, J. D., & Strathoff, P. (2016). Systemic principles of value co-

creation: Synergetics of value and service ecosystems. Journal of Business Research, 69(8),

2981-2989.

Miller, D.J., & Ács, Z. J. (2013). Technology commercialization on campus – Twentieth

century frameworks and twenty-first century blind spots. The Annals of Regional Science,

50(2), 407-423.

Minkiewicz, J., Bridson, K., & Evans, J. (2016). Co-production of service experiences: insights

from the cultural sector. Journal of Services Marketing, 30(7), 749-761.

Mintz, O., & Currim, I. S. (2013). What drives managerial use of marketing and financial

metrics and does metric use affect performance of marketing-mix activities? Journal of

Marketing, 77(2), 17-40.

Mohajan, H. K. (2018). Qualitative research methodology in social sciences and related

subjects. Journal of Economic Development, Environment and People, 7(1), 23-48.

Möller, K. (2013). Theory map of business marketing: Relationships and networks

perspectives. Industrial Marketing Management, 42(3), 324-335.

111

Möller, K., & Halinen, A. (2017). Managing business and innovation networks—From

strategic nets to business fields and ecosystems. Industrial Marketing Management, 67, 5-

22.

Möller, K., Nenonen, S., & Storbacka, K. (2020). Networks, ecosystems, fields, market

systems? Making sense of the business environment. Industrial Marketing

Management, 90, 380-399.

Moore, J. F. (1993). Predators and prey: A new ecology of competition. Harvard Business

Review, 71(3), 75–86.

Moore, J. F. (1996). The death of competition: leadership and strategy in the age of business

ecosystems. HarperCollins.

Moore, J. F. (2013). Shared purpose: A thousand business ecosystems, a connected community,

and the future. Create Space Publishing Platform.

Moorman, C., & Day, G. S. (2016). Organizing for marketing excellence. Journal of

Marketing, 80(6), 6-35.

Morgan, N. A., Slotegraaf, R. J., & Vorhies, D. W. (2009). Linking marketing capabilities with

profit growth. International Journal of Research in Marketing, 26(4), 284-293.

Most, F., Conejo, F. J., & Cunningham, L. F. (2018). Bridging past and present entrepreneurial

marketing research. Journal of Research in Marketing and Entrepreneurship, 20(2), 229-

251.

Mu, J. (2015). Marketing capability, organizational adaptation and new product development

Muzellec, L., Ronteau, S., & Lambkin, M. (2015). Two-sided Internet platforms: A business

model lifecycle perspective. Industrial Marketing Management, 45, 139-150.

Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational

advantage. The Academy of Management Review, 23(2), 242–266.

Narayanan, V. K., Colwell, K., & Douglas, F. L. (2009). Building organizational and scientific

platforms in the pharmaceutical industry: A process perspective on the development of

dynamic capabilities. British Journal of Management, 20, S25-S40.

Nath, P., Nachiappan, S., & Ramanathan, R. (2010). The impact of marketing capability,

operations capability and diversification strategy on performance: A resource-based

view. Industrial Marketing Management, 39(2), 317-329.

Naudé, P., & Sutton-Brady, C. (2019). Relationships and networks as examined in Industrial

Marketing Management. Industrial Marketing Management, 79, 27-35.

Newbert, S. L. (2008). Value, rareness, competitive advantage, and performance: a conceptual‐level empirical investigation of the resource‐based view of the firm. Strategic Management

Journal, 29(7), 745-768.

Newman, I., Benz, C. R., & Ridenour, C. S. (1998). Qualitative-quantitative research

methodology: Exploring the interactive continuum. SIU Press.

Nguyen, B., Yu, X., Melewar, T. C., & Chen, J. (2015). Brand innovation and social media:

Knowledge acquisition from social media, market orientation, and the moderating role of

social media strategic capability. Industrial Marketing Management, 51, 11-25.

Nonaka, I. (1991). Managing the firm as an information creation process. Advances in

Information Processing in Organizations, 4, 239-275.

Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization

Science, 5(1), 14-37.

Nonaka, I., Kodama, M., Hirose, A., & Kohlbacher, F. (2014). Dynamic fractal organizations

for promoting knowledge-based transformation–A new paradigm for organizational

theory. European Management Journal, 32(1), 137-146.

Nonaka, I., Toyama, R., & Konno, N. (2000). SECI, Ba and leadership: A unified model of

dynamic knowledge creation. Long Range Planning, 33(1), 5-34.

112

Nunn, R. (2019). Innovation ecosystem and knowledge management: A practitioners view.

Business Information Review, 36(2), 70-74.

O’Connor, G. E., & Cook, L. A. (2020). Reducing referral leakage: an analysis of health-care

referrals in a service ecosystem. Journal of Services Marketing, in press.

Oh, D. S., Phillips, F., Park, S., & Lee, E. (2016). Innovation ecosystems: A critical

examination. Technovation, 54, 1-6.

Paavola, S., Lipponen, L., & Hakkarainen, K. (2004). Models of innovative knowledge

communities and three metaphors of learning, Review of Educational Research, 74(4), 557-

576.

Parker, G., & Alstyne, M. V. (2008). Managing platform ecosystems. ICIS 2008 Proceedings,

53. Parris, D. L., Dapko, J. L., Arnold, R. W., & Arnold, D. (2016). Exploring transparency: a new

framework for responsible business management. Management Decision.

Partha, D., & David, P. A. (1994). Toward a new economics of science. Research Policy, 23(5),

487–521.

Passemard, C., & Calantone, K. (2000). Competitive advantage: Creating and sustaining

superior performance by Michael E. Porter 1980, p. 18.

Pellikka, J., & Ali-Vehmas, T. (2016). Managing innovation ecosystems to create and capture

value in ICT industries. Technology Innovation Management Review, 6(10), 17-24.

Peltier, J. W., Dahl, A. J., & Swan, E. L. (2020). Digital information flows across a B2C/C2C

continuum and technological innovations in service ecosystems: A service-dominant logic

perspective. Journal of Business Research, in press.

Peltoniemi, M. (2004, September). Cluster, value network and business ecosystem: Knowledge

and innovation approach. In Organisations, Innovation and Complexity: New Perspectives

on the Knowledge Economy” conference, September (pp. 9-10).

Penrose, E. T. (1959), The Theory of the Growth of the Firm, Oxford University Press: Oxford,

England.

Penrose, E. T. (2009). The Theory of the Growth of the Firm, Oxford University Press: Oxford,

England.

Pera, R., Occhiocupo, N., & Clarke, J. (2016). Motives and resources for value co-creation in

a multi-stakeholder ecosystem: A managerial perspective. Journal of Business Research,

69, 4033–4041.

performance. Industrial Marketing Management, 49, 151-166.

Peris-Ortiz, M., Ferreira, J.J., & Lindahl, J.M.M. (2019), Knowledge, Innovation and

Sustainable Development in Organizations, Springer, Cham.

Perkmann, M., Tartari, V., Mckelvey, M., Autio, E., Broström, A., D’Este, P., . . . Sobrero, M.

(2013). Academic engagement and commercialisation: A review of the literature on

university–Industry relations. Research Policy, 42(2), 423–442.

Perks, H., Kowalkowski, C., Witell, L., & Gustafsson, A. (2017). Network orchestration for

value platform development. Industrial Marketing Management, 67, 106-121.

Peteraf, M. A. (1993). The cornerstones of competitive advantage: a resource‐based

view. Strategic Management Journal, 14(3), 179-191.

Petersen, I. H., Kruss, G., & Lorentzen, J. (2008). Innovation in sub-Saharan Africa:

competitiveness, capability and achievements in South Africa, Nigeria and Uganda. [Project

Number 103470-009]. Report prepared for the project, Knowledge for Development:

university-industry interaction in Sub-Saharan Africa. HSRC. March. Retrieved from

http://hdl.handle.net/20.500.11910/4902

Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-creation, and appropriability: A

conceptual framework. Industrial and Corporate Change, 21(6),1359-1388.

113

Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Bachrach, D. G. (2008). Scholarly

influence in the field of management: A bibliometric analysis of the determinants of

university and author impact in the management literature in the past quarter

century. Journal of Management, 34(4), 641-720.

Porter, M. E. (1985). Technology and competitive advantage. The Journal of Business

Strategy, 5(3), 60.

Porter, M. E. (1990). The competitive advantage of nations. Harvard Business Review, 68(2),

73-93.

Porter, M.E. (1998). Clusters and the new economics of competition. Harvard Business

Review, 76(6), 77-90.

Powell, W. W., & Grodal, S. (2005). Networks of innovators. The Oxford handbook of

innovation, 78.

Prahalad, Ck. H., & Hamel, G. G. (1990). The Core competence of the corporation. Harvard

Business Review, 68(3), 295-336.

Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of

Documentation, 25(4), 348-349.

Purchase, S., Olaru, D., & Denize, S. (2014). Innovation network trajectories and changes in

resource bundles. Industrial Marketing Management, 43(3), 448-459.

Putnam, R. D. (2000). Bowling alone. New York: Simon & Schuster.

Quero Gervilla, M. J., Díaz-Mendez, M., & Gummesson, E. (2019). Balanced centricity and

triads: strategies to reach ecosystem equilibrium in the arts sector. Journal of Business &

Industrial Marketing, 35(3), 447-456.

Quintane, E., Casselman, R.M., Reiche, B.S., & Nylund, P.A. (2011). Innovation as a

knowledge-based outcome. Journal of Knowledge Management, 15(6), 928-947.

Reagans, R., & McEvily, B. (2003). Network structure and knowledge transfer: The effects of

cohesion and range. Administrative Science Quarterly, 48(2), 240-267.

Rentschler, R., & Kirchner, T.A. (2012). Arts management/marketing journal citation analysis:

assessing external impact. Arts Marketing: An International Journal, 2(1), 6-20.

Riege, A. (2005). Three‐dozen knowledge‐sharing barriers managers must consider. Journal

of Knowledge Management, 9(3), 18-35.

Rindfleisch, A. (1996). Marketing as warfare: Reassessing a dominant metaphor. Business

Horizons, 39(5), 3-10.

Ringle, C., Wende, S. and Becker, J-M. (2015), SmartPLS 3. Bönningstedt: Smartpls, available

at: http://www.smartpls.com (accessed 1 June 2020).

Ritala, P., Agouridas, V., Assimakopoulos, D., & Gies, O. (2013). Value creation and capture

mechanisms in innovation ecosystems: a comparative case study. International Journal of

Technology Management, 63(3-4), 244-267.

Ritala, P., Golnam, A., & Wegmann, A. (2014). Coopetition-based business models: The case

of Amazon. com. Industrial Marketing Management, 43(2), 236-249.

Ritchie, J., Lewis, J., Nicholls, C. M., & Ormston, R. (Eds.). (2013). Qualitative research

practice: A guide for social science students and researchers. Sage.

Robinson, C. J., & Malhotra, M. K. (2005). Defining the concept of supply chain quality

management and its relevance to academic and industrial practice. International Journal of

Production Economics, 96(3), 315-337.

Rochet, J. C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the

European Economic Association, 1(4), 990-1029.

Rogers, E. M. (1962). Diffusion of innovations. New York: Free Press.

Rohrbeck, R., Hölzle, K., & Gemünden, H. G. (2009). Opening up for competitive advantage–

How Deutsche Telekom creates an open innovation ecosystem. R&D Management, 39(4),

420-430.

http://www.smartpls.com/

114

Roshani, M., Lehoux, N., & Frayret, J. M. (2015). University-Industry collaborations and open

innovations: an integrated methodology for mutually beneficial relationships. CIRRELT.

Rothschild, M. (1990). Bionomics: Economy as ecosystem. H. Holt and Company.

Roundy, P.T. (2016). Start-up community narratives: The discursive construction of

entrepreneurial ecosystems. The Journal of Entrepreneurship, 25(2), 232-248.

Roundy, P.T. (2017). Social entrepreneurship and entrepreneurial ecosystems: Complementary

or disjoint phenomena? International Journal of Social Economics, 44(9), 1252-1267.

Roundy, P.T., & Fayard, D. (2019). Dynamic capabilities and entrepreneurial ecosystems: the

micro-foundations of regional entrepreneurship. The Journal of Entrepreneurship, 28(1),

94-120.

Roundy, P.T., Bradshaw, M., & Brockman, B.K. (2018). The emergence of entrepreneurial

ecosystems: a complex adaptive systems approach. Journal of Business Research, 86, 1-10.

Rousseau, D. M., Sitkin, S., Burt, R. S., & Camerer, C. (1998). Not so different after all: A

cross-discipline view of trust. Academy of Management Review, 23, 393–404.

Rybnicek, R., & Königsgruber, R. (2019). What makes industry–university collaboration

succeed? A systematic review of the literature. Journal of Business Economics, 89(2), 221-

250.

Samiee, S., & Chabowski, B.R. (2012). Knowledge structure in international marketing: a

multi-method bibliometric analysis. Journal of the Academy of Marketing Science, 40(2),

364-386.

Sandberg, B., & Aarikka-Stenroos, L. (2014). What makes it so difficult? A systematic review

on barriers to radical innovation. Industrial Marketing Management, 43(8), 1293-1305.

Santoro, G., Vrontis, D., Thrassou, A., & Dezi, L. (2018). The Internet of Things: Building a

knowledge management system for open innovation and knowledge management capacity.

Technological Forecasting and Social Change, 136, 347-354.

Sarkar, C., & Kotler P. (2019). “Ecosystem marketing: The future of competition.” The

Marketing Journal, February 21. Retrieved from

https://www.marketingjournal.org/ecosystem-marketing-the-future-of-competition-

christian-sarkar-and-philip-kotler.

Satell, G. (2019). Innovation is the only true way to create value. Forbes. Letöltés helye:

https://www. forbes. com/sites/gregsatell/2015/11/29/innovation-isthe-only-true-way-to-

create-value/Letöltés ideje.

Schilke, O., Hu, S., & Helfat, C. E. (2018). Quo vadis, dynamic capabilities? A content-analytic

review of the current state of knowledge and recommendations for future research. Academy

of Management Annals, 12(1), 390-439.

Schofield, T. (2013). Critical success factors for knowledge transfer collaborations between

university and industry. Journal of Research Administration, 44(2), 38–56.

Servantie, V., Cabrol, M., Guieu, G., & Boissin, J.P. (2016). Is international entrepreneurship

a field? A bibliometric analysis of the literature (1989–2015). Journal of International

Entrepreneurship, 14(2), 168-212.

Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research.

Academy of Management Review, 25(1), 217-226.

Sheth, J. N., & Sinha, M. (2015). B2B branding in emerging markets: A sustainability

perspective. Industrial Marketing Management, 51, 79-88.

Shipilov, A., & Gawer, A. (2020). Integrating research on interorganizational networks and

ecosystems. Academy of Management Annals, 14(1), 92-121.

Shugan, S. M. (2006). Fifty years of Marketing Science, Marketing Science, 25(6), 551-555.

Simula, H., & Ahola, T. (2014). A network perspective on idea and innovation crowdsourcing

in industrial firms. Industrial Marketing Management, 43(3), 400-408.

115

Singaraju, S. P., Nguyen, Q. A., Niininen, O., & Sullivan-Mort, G. (2016). Social media and

value co-creation in multi-stakeholder systems: A resource integration approach. Industrial

Marketing Management, 54, 44-55.

Singh, S. K. (2008). Role of leadership in knowledge management: a study. Journal of


Smith, T. M. & Smith, R. L. (2015). Elements of Ecology (9th ed). Essex: Pearson Publishers.

Søilen, K. S., Kovacevic, M. A., & Jallouli, R. (2012). Key success factors for Ericsson mobile

platforms using the value grid model. Journal of Business Research, 65(9), 1335-1345.

Spigel, B. (2016). Entrepreneurship, policy and society. In The 8th International Conference

for Entrepreneurship, Innovation and Regional Development. (p. 458).

Spigel, B. (2017). The relational organization of entrepreneurial ecosystems. Entrepreneurship

Theory and Practice, 41(1), 49-72.

Spigel, B., & Harrison, R. (2018). Toward a process theory of entrepreneurial ecosystems.

Strategic Entrepreneurship Journal, 12(1), 151-168.

Stam, E. (2015). Entrepreneurial ecosystems and regional policy: A sympathetic critique.

European Planning Studies, 23(9), 1759-1769.

Storbacka, K., & Nenonen, S. (2011). Scripting markets: From value propositions to market

propositions. Industrial Marketing Management, 40(2), 255-266.

Storbacka, K., & Nenonen, S. (2015). Learning with the market: Facilitating market

innovation. Industrial Marketing Management, 44, 73-82.

Storbacka, K., Brodie, R. J., Böhmann, T., Maglio, P. P., & Nenonen, S. (2016). Actor

engagement as a microfoundation for value co-creation. Journal of Business

Research, 69(8), 3008-3017.

Subramaniam, M. (2020). Digital ecosystems and their implications for competitive strategy.

Journal of Organization Design, 9, 1-10.

Teece, D. J. (2020). Hand in glove: Open innovation and the dynamic capabilities framework.

Strategic Management Review 1(2), 233-253.

Teece, D., & Pisano, G. (2003). The dynamic capabilities of firms. In Handbook on knowledge

management (pp. 195-213). Springer, Berlin, Heidelberg.

Teece, D.J. (2007). Explicating dynamic capabilities: The nature and microfoundations of

(sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319-1350.

Teece, D.J. (2014). A dynamic capabilities-based entrepreneurial theory of the multinational

enterprise. Journal of International Business Studies, 45(1), 8-37.

Teece, D.J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic

management. Strategic Management Journal, 18(7), 509-533.

The Board of Trade of Metropolitan Montreal. (2011). A look at Canadian university-industry

collaboration. Retrieved from

http://www.ccmm.qc.ca/documents/activities_pdf/autres/2010_2011/ccmm_rdvssavoir_20

11_en.pdf

the ecosystem game when uncertainty is high. Academy of Management Journal.

Theodoraki, C., Messeghem, K., & Rice, M. P. (2018). A social capital approach to the

development of sustainable entrepreneurial ecosystems: an explorative study. Small

Business Economics, 51(1), 153-170.

Thomas, L.D.W., & Autio, E. (2020). Innovation Ecosystems in Management: An Organizing

Typology. In Oxford Research Encyclopedia of Business and Management, Oxford

University Press.

Thomas, L.D.W., & Autio, E. (2013, February). Emergent equifinality: an empirical analysis

of ecosystem creation processes. In Proceedings of the 35th DRUID Celebration

Conference, Barcelona, Spain (Vol. 80).

http://www.ccmm.qc.ca/documents/activities_pdf/autres/2010_2011/ccmm_rdvssavoir_2011_en.pdf

http://www.ccmm.qc.ca/documents/activities_pdf/autres/2010_2011/ccmm_rdvssavoir_2011_en.pdf

116

Thomas, L.D.W., & Ritala, P. (2021). Ecosystem legitimacy emergence: A collective action

view. Journal of Management, 1-27.

Thomas, L. D.W., Sharapov, D., & Autio, E. (2018). Linking entrepreneurial and innovation

ecosystems: The case of AppCampus. In Entrepreneurial ecosystems and the diffusion of

startups. Edward Elgar Publishing.

Tödtling, F., & Grillitsch, M. (2015). Does combinatorial knowledge lead to a better innovation

performance of firms? European Planning Studies, 23(9), 1741-1758.

Töytäri, P., Rajala, R., & Alejandro, T. B. (2015). Organizational and institutional barriers to

value-based pricing in industrial relationships. Industrial Marketing Management, 47, 53-

64.

Trischler, J., Johnson, M., & Kristensson, P. (2020). A service ecosystem perspective on the

diffusion of sustainability-oriented user innovations. Journal of Business Research, 116,

552–560

Tsai, W., & Ghoshal, S. (1998). Social capital and value creation: The role of intrafirm

networks. Academy of Management Journal, 41(4), 464–476.

Tsujimoto, M., Kajikawa, Y., Tomita, J., & Matsumoto, Y. (2018). A review of the ecosystem

concept - Towards coherent ecosystem design. Technological Forecasting and Social

Change, 136, 49-58.

Ulaga, W., & Reinartz, W. J. (2011). Hybrid offerings: how manufacturing firms combine

goods and services successfully. Journal of Marketing, 75(6), 5-23.

Un, C.A., & Cuervo‐Cazurra, A. (2004). Strategies for knowledge creation in firms, British

Journal of Management, 15(S1), 27-41.

United Nations Department of Economic and Social Affairs. (2019), “World Economic

Situation and Prospects 2019”: available at:

https://www.un.org/development/desa/dpad/wpcontent/uploads/sites/45/WESP2019_BOO

K-web.pdf (accessed 12 May 2020).

Uzzi, B., & Lancaster, R. (2003). Relational embeddedness and learning: The case of bank loan

managers and their clients. Management Science, 49(4), 383–399.

Valkokari, K. (2015). Business, innovation, and knowledge ecosystems: How they differ and

how to survive and thrive within them. Technology Innovation Management Review, 5(8),

17-27.

Van Bockhaven, W., Matthyssens, P., & Vandenbempt, K. (2015). Empowering the underdog:

Soft power in the development of collective institutional entrepreneurship in business

markets. Industrial Marketing Management, 48, 174-186.

Van der Borgh, M., Cloodt, M., & Romme, A.G.L. (2012). Value creation by knowledge‐based

ecosystems – evidence from a field study. R&D Management, 42(2), 150-169.

Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Measuring

scholarly impact (pp. 285-320). Springer, Cham.

Van Eck, N. J., & Waltman, L. (2018). Manual for VOSviewer version 1.6. 8. CWTS

Meaningful Metrics. Universiteit Leiden.

Van Eck, N. J., Waltman, L., Dekker, R., & van den Berg, J. (2010). A comparison of two

techniques for bibliometric mapping: Multidimensional scaling and VOS. Journal of the

American Society for Information Science and Technology, 61(12), 2405-2416.

Van Maanen, J., Sørensen, J. B., & Mitchell, T. R. (2007). The interplay between theory and

method. Academy of Management Review, 32(4), 1145-1154.

Vargo, S. L., & Akaka, M. A. (2012). Value cocreation and service systems (re)formation: A

service ecosystems view. Service Science, 4(3), 207-217.

Vargo, S. L., & Lusch, R. F. (2016). Institutions and axioms: an extension and update of

service-dominant logic. Journal of the Academy of Marketing Science, 44(1), 5-23.

https://www.un.org/development/desa/dpad/wpcontent/uploads/sites/45/WESP2019_BOOK-web.pdf

https://www.un.org/development/desa/dpad/wpcontent/uploads/sites/45/WESP2019_BOOK-web.pdf

117

Vargo, S. L., & Lusch, R. F. (2017). Service-Dominant Logic 2025. International Journal of

Research in Marketing, 34(1), 46-67.

Vargo, S. L., Akaka, M. A., & Wieland, H. (2020) Rethinking the process of diffusion in

innovation: A service-ecosystems and institutional perspective. Journal of Business

Research, 116, 526–534.

Vargo, S. L., Wieland, H., & Akaka, M. A. (2015). Innovation through institutionalization: A

service ecosystems perspective. Industrial Marketing Management, 44, 63–72.

Velu, C. (2015). Knowledge management capabilities of lead firms in innovation

ecosystems. AMS Review, 5(3-4), 123-141.

Venkatraman, N., & Lee, C. H. (2004). Preferential linkage and network evolution: A

conceptual model and empirical test in the US video game sector. Academy of Management

Journal, 47(6), 876-892.

Verbeek, A., Debackere, K., Luwel, M., & Zimmermann, E. (2002). Measuring progress and

evolution in science and technology: the multiple uses of bibliometric indicators.

International Journal of Management Reviews, 4(2), 179-211.

Verganti, R., & Öberg, Å. (2013). Interpreting and envisioning: A hermeneutic framework to

look at radical innovation of meanings. Industrial Marketing Management, 42(1), 86-95.

von Krogh, G., Nonaka, I., & Aben, M. (2001). Making the most of your company’s

knowledge: A strategic framework. Long Range Planning, 34, 421–439.

Walrave, B., Talmar, M., Podoynitsyna, K. S., Romme, A. G. L., & Verbong, G. P. (2018). A

multi-level perspective on innovation ecosystems for path-breaking

innovation. Technological Forecasting and Social Change, 136, 103-113.

Wernerfelt, B. (1984). A resource‐based view of the firm, Strategic Management Journal, 5

(2), 171-180.

West, D. C., Ford, J., & Ibrahim, E. (2015). Strategic marketing: creating competitive

advantage. Oxford University Press, USA.

Whitler, K. A., & Puto, C. P. (2020). The influence of the board of directors on outside-in

strategy. Industrial Marketing Management, 90, 143-154.

Wieland, H., Vargo, S. L., & Akaka, M. A. (2016). Zooming out and zooming in: Service

ecosystems as venues for collaborative innovation. In Service Innovation (pp. 35-50).

Springer, Tokyo.

Wilkinson, I. F., & Young, L. C. (2013). The past and the future of business marketing

theory. Industrial Marketing Management, 42(3), 394-404.

Wittgenstein, L. (1953). Philosophical investigations, trans. GEM Anscombe, 261, 49.

Wu, H., Chen, J., & Jiao, H. (2016). Dynamic capabilities as a mediator linking international

diversification and innovation performance of firms in an emerging economy. Journal of

Business Research, 69(8), 2678-2686.

Yami, S., & Nemeh, A. (2014). Organizing coopetition for innovation: The case of wireless

telecommunication sector in Europe. Industrial Marketing Management, 43(2), 250-260.

Yang, J., Alejandro, T. G. B., & Boles, J. S. (2011). The role of social capital and knowledge

transfer in selling center performance. Journal of Business & Industrial Marketing, 26(3),

152–161.

Yin, R. K. (2017). Case study research and applications: Design and methods. Sage

publications.

Yu, C., Zhang, Z., Lin, C., & Wu, Y. J. (2017). Knowledge creation process and sustainable

competitive advantage: The role of technological innovation

capabilities. Sustainability, 9(12), 2280.

Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and

extension. Academy of Management Review, 27(2), 185-203.

118

Zahra, S. A., & Nambisan, S. (2011). Entrepreneurship in global innovation ecosystems.

Academy of Marketing Science Review, 1(1),.4-17.

Zahra, S.A., & Nambisan, S. (2012). Entrepreneurship and strategic thinking in business

ecosystems. Business Horizons, 55(3), 219-229.

Zander, I., McDougall-Covin, P., & Rose, E.L. (2015). Born globals and international business:

Evolution of a field of research. Journal of International Business Studies, 46(1), 27-35.

Zheng, S., Zhang, W., Wu, X., & Du, J. (2011). ‘Knowledge-based dynamic capabilities and

innovation in networked environments, Journal of Knowledge Management, 15(8), 1035-

1051.

Zollo, M., Cennamo, C., & Neumann, K. (2013). Beyond what and why: Understanding

organizational evolution towards sustainable enterprise models. Organization &

Environment, 26(3), 241-259.

DOCTORA L T H E S I S

Jeandri Robertson C

ompetitive A

dvantage Strategies in Industrial Marketing

Department of Social Science, Technology and ArtsDivision of Business Administration and Industrial Engineering

ISSN 1402-1544ISBN 978-91-7790-791-6 (print)ISBN 978-91-7790-792-3 (pdf)

Luleå University of Technology 2021

Competitive Advantage Strategies in

Industrial Marketing Using an Ecosystem Approach

Jeandri Robertson

Industrial Marketing

Tryck: Lenanders Grafiska, 135942

135942 LTU_Robertson.indd Alla sidor135942 LTU_Robertson.indd Alla sidor 2021-03-31 09:352021-03-31 09:35

Date post:	07-Mar-2023
Category:	Documents
Upload:	khangminh22
View:	0 times
Download:	0 times

Competitive Advantage Strategies in Industrial Marketing

Documents