Service Co-creation Behaviour in Actor-to-Actor Co ... · Gratiﬁcation theory (UGT) to the...

Service Co-creation Behaviour in Actor-to-Actor Co-creationSystems: from Service Dominant Logic to Socio-Service

Dominant Logic

A THESIS SUBMITTED TO

THE SCIENCE AND ENGINEERING FACULTY

OF QUEENSLAND UNIVERSITY OF TECHNOLOGY

IN FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Reihaneh Bidar

Science and Engineering Faculty

Queensland University of Technology

2018

Copyright in Relation to This Thesis

c© Copyright 2018 by Reihaneh Bidar. All rights reserved.

Statement of Original Authorship

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

award at this or any other higher education institution. To the best of my knowledge and belief,

the thesis contains no material previously published or written by another person except where

due reference is made.

Signature:

Date:

i

08.06.2018

QUT Verified Signature

ii

To my family

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Publications From This Thesis

Bidar, R., Watson, J., & Barros, A. (2017). Classification of

service co-creation systems: An integrative approach. In

19th international conference on Advanced Communication

Technology (IEEE ICACT)(pp. 333-340).

Bidar, R., Watson, J., & Barros, A. P. (2016). Literature

review to determine environmental and cognitive factors

underlying user value cocreation behaviour. In 20th Pacific

Asia Conference on Information Systems (PACIS)(p.327).

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Abstract

Organisations’ business models are evolving to leverage customer networks to orchestrate

service creation and delivery, called co-creation, to grow competitive scale, reduce cost and

increase revenue growth. More and more online platforms are using customers’ collective

intelligence to leverage customer skills, ideas and knowledge for self-service service creation

(e.g., PatientsLikeMe, StackExchange) or service transaction and delivery (e.g., AirBnB,

GoGet, Uber). These platforms are transforming the divide traditionally present between

consumers and providers to engage consumers in the process of service creation and delivery.

Based on the shift in organisations’ co-creation models from participation to collaboration, this

research investigated why actor collaboration plays a role in the service co-creation context.

Current attempts to investigate factors to characterise actor service co-creation fall short in that

they have not adequately explored service co-creation behaviour in actor-to-actor co-creation

systems.

The study applies qualitative case study, adopting the semi-structured interview method.

Two case studies of service co-creation platforms, StackOverflow and GitHub, were selected

to provide empirical insights into how actors’ collaboration contributes to service co-creation

behaviour. Semi-structured interviews were conducted with 36 users who were collaborating in

co-creation activities on StackOverflow (19 participants) and GitHub (17 participants). The data

was analysed using an inductive thematic analysis approach. Following the analysis of the two

cases, both sets of results (15 StackOverflow themes and 17 GitHub themes) are compared to

create an integrated theoretical model based on the Stimulus-Organism-Response (SOR) model.

This research proposes a model of service co-creation behaviour (SCB) that represents why

actors’ value perceptions are environmentally influenced a nd r esult i n c ollaborative service

co-creation activities. The findings r evealed s even t hemes i ncluding P latform Capabilities,

Relational Capital and Actor Competencies as the key environmental stimuli in the co-creation

v

ecosystem, and which influence the two actor value perceptions of Purposive value and

Network value (individual and service level), which all combine to lead actors to collaborative

and citizenship behaviours (i.e., SCB). Purposive value consists of Learning, Utilitarian,

Hedonic, and Economic values. Network value in the individual level represents actors’ value

perceptions on Social Position, Belongingness, and Collaborative Effort, while Network value

in the service level includes the values of Quality and Support.

The major theoretical contributions include the presented SCB model, using the SOR

model. The research contributes to how the SOR model can be used effectively in the

co-creation context. By updating four Uses and Gratification benefits introduced by Katz et al.

[1973] and examined by Nambisan and Baron [2009], this research extended Uses and

Gratification theory (UGT) to the actor-to-actor service co-creation context to enhance current

understanding of actor value perception. Further, this research updated Yi and Gong’s (2013)

conceptualisation of value co-creation behaviour to include collaboration in the service

co-creation context. This research contributed to the elaboration of service-dominant (SD)

logic [Vargo and Lusch, 2016] using the identified SCB model, with a focus on service

network models and many-to-many interactions. This research elaborated two of the five SD

logic axioms introduced by Vargo and Lusch [2016], and further added three extra axioms in

the actor-to-actor service co-creation context.

From the practical perspective, the developed SCB model helps practitioners to increase

collaboration through understanding their co-creators’ behaviour. Also, practitioners need to

understand both Purposive and Network values from the co-creators’ perspective and support

their value perceptions by improving the platform design and implementing social influence

strategies to achieve their desired end result. Practitioners as facilitators of service exchange

can provide a healthy interactive environment to reduce destructive behaviours that decrease

potential value outcome and manage collaborations.

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Keywords

Co-creation Systems, Service Co-creation, Value Co-creation, Co-creation Behaviour,

Collaborative Platforms, Actor-to-Actor Interaction, Service Science, Participation Behaviour,

Citizenship Behaviour, Service Networks, Service Ecosystems, Collaboration,

Service-Dominant (SD) logic.

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viii

Acknowledgments

I am delighted to express my appreciation and acknowledgement to people who assisted me

to succeed in my PhD journey. Friends and colleagues who helped me with ideas, comments

support, encouragement and my family who lived the journey and made it worthwhile.

My sincere gratitude goes to my supervisors, Dr Jason Watson and Professor Alistair Barros,

for their support, guidance, encouragement and navigating my research so expertly during this

journey. Thank you, Jason and Alistair, for being a great teacher and mentor, and for keeping

me motivated. Working with you inspired me to continue my career as an academic.

A special thanks to my good friends Nick Dyriw, Dr Fahame Emamjome, Dr Elham Abdi,

Nazli Safavi, Ehsan Tabatabaee, Jaleh Hosseinzadeh, Adel Bakhtiyari, Michael Hermano and

Mojtaba Aliakbarzadeh, who were part of this challenging yet rewarding experience. I have

been fortunate to be surrounded by friends who believed I could do this and shared their

experiences and highs and lows of doing research.

With warm thanks to Dr Asif Gill, and Dr Edwina Luck for their enthusiasm in my

research, constructive feedback, insight, and remarks. I should thank Dr Christin Long from

QUT Academic Language and Learning Services for being an amazing person and helping me

out to improve my academic writing from the start to the end of my PhD.

I would especially like to thank my husband, Mani, for his patience, constant support and

love throughout this journey. My parents, Ahmad and Shahin, for their unconditional love and

teaching me to believe in myself and encouraging me to pursue higher education. My beautiful

sisters, Massomeh and Hannaneh, for always being there, encouraging me to keep going, and

to make me smile.

Thank you all for your support.

Reihaneh Bidar

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x

Abbreviations

A2A Actor-to-Actor

AC Actor Competencies

B2C Business-to-Customer

C2C Customer-to-Customer

CB Citizenship Behaviour

CC Co-creation

COB Collaborative Behaviour

CS1 Cooperative Co-creation System

CS2 Coordinative Co-creation System

CS3 Collaborative Co-creation System

FP foundational Premises

GH GitHUb

IVF Interactive Value Formation

PB Participation Behaviour

PC Platform Capabilities

RC Relational Capital

SCB Service Co-creation Behaviour

SD logic Service-Dominant Logic

SI Social Influence

SO StackOverflow

Socio-SD logic Socio-Service-Dominant Logic

xi

SOR Stimulus-Organism-Response

UGT Uses and Gratification Theory

UI User Interface

VC Virtual Community

xii

Table of Contents

Abstract v

Keywords vii

Acknowledgments ix

List of Figures xix

List of Tables xxii

1 Introduction 1

1.1 Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Need for a Clear Understanding of service Co-creation Systems . . . . 3

1.1.2 Research Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.2 Overview of Research Questions and Research Design . . . . . . . . . . . . . 8

1.2.1 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.2.2 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3 Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.4 Summary of Contribution to the Theory and Practice . . . . . . . . . . . . . . 10

1.5 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2 Literature Review 13

2.1 Service-Dominant (SD) Logic in Service Science . . . . . . . . . . . . . . . . 15

2.1.1 Co-creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

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2.1.2 Resource Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.1.3 Customer as Co-creator . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.1.4 Co-creation Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2 Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.2.1 Method and Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.2.2 Summary of Systematic Literature Review . . . . . . . . . . . . . . . 37

2.3 Theoretical Background and Conceptualization . . . . . . . . . . . . . . . . . 38

2.3.1 Stimulus-Organism-Response (SOR) Model . . . . . . . . . . . . . . . 38

2.3.2 Uses and Gratification Theory (UGT) . . . . . . . . . . . . . . . . . . 40

2.3.3 Co-creation Behaviour Model . . . . . . . . . . . . . . . . . . . . . . 41

2.3.4 Section Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3 Research Design and Methodology 57

3.1 Philosophical Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.2 Qualitative Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

3.3 Research Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.3.1 Case Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.3.2 Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3.3.3 Sampling and Recruitment . . . . . . . . . . . . . . . . . . . . . . . . 74

3.3.4 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.3.5 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

3.4 Ethical Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

3.5 Trustworthiness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

3.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4 Findings of Case Study 1: StackOverflow (SO) 83

4.1 Themes of Environmental Stimulus (S) . . . . . . . . . . . . . . . . . . . . . . 84

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4.1.1 Theme One: Accessibility . . . . . . . . . . . . . . . . . . . . . . . . 85

4.1.2 Theme Two: Quality Control Mechanism . . . . . . . . . . . . . . . . 86

4.1.3 Theme Three: Social Influence (SI) . . . . . . . . . . . . . . . . . . . 87

4.1.4 Theme Four: Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.1.5 Theme Five: Actor Competencies . . . . . . . . . . . . . . . . . . . . 91

4.2 Themes of Actor Value Perception (O) . . . . . . . . . . . . . . . . . . . . . . 92

4.2.1 Theme Six: Learning value . . . . . . . . . . . . . . . . . . . . . . . . 92

4.2.2 Theme Seven: Utilitarian Value . . . . . . . . . . . . . . . . . . . . . 94

4.2.3 Theme Eight: Hedonic Value . . . . . . . . . . . . . . . . . . . . . . . 95

4.2.4 Theme Nine: Potential Engagement . . . . . . . . . . . . . . . . . . . 96

4.2.5 Theme Ten: Social Status . . . . . . . . . . . . . . . . . . . . . . . . 98

4.2.6 Theme Eleven: Social Role . . . . . . . . . . . . . . . . . . . . . . . . 99

4.2.7 Theme Twelve: Belongingness . . . . . . . . . . . . . . . . . . . . . . 100

4.2.8 Theme Thirteen: Quality . . . . . . . . . . . . . . . . . . . . . . . . . 101

4.2.9 Theme Fourteen: Support . . . . . . . . . . . . . . . . . . . . . . . . 102

4.3 Theme of Response(R) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

4.3.1 Theme Fifteen: Service Co-creation Behaviour (SCB) . . . . . . . . . 104

4.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

5 Findings of Case Study 2: GitHub (GH) 109

5.1 Themes of Environmental Stimulus (S) . . . . . . . . . . . . . . . . . . . . . . 110

5.1.1 Theme One: Platform Feature . . . . . . . . . . . . . . . . . . . . . . 111

5.1.2 Theme Two: User Interface (UI) . . . . . . . . . . . . . . . . . . . . . 113

5.1.3 Theme Three: Social Influence (SI) . . . . . . . . . . . . . . . . . . . 114

5.1.4 Theme Four: Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

5.1.5 Theme Five: Actor Competencies . . . . . . . . . . . . . . . . . . . . 120

5.2 Themes of Actor Value Perception (O) . . . . . . . . . . . . . . . . . . . . . . 120

5.2.1 Theme Six: Learning Value . . . . . . . . . . . . . . . . . . . . . . . 122

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5.2.2 Theme Seven: Utilitarian Value . . . . . . . . . . . . . . . . . . . . . 123

5.2.3 Theme Eight: Hedonic Value . . . . . . . . . . . . . . . . . . . . . . . 125

5.2.4 Theme Nine: Potential Engagement . . . . . . . . . . . . . . . . . . . 126

5.2.5 Theme Ten: Project Marketing . . . . . . . . . . . . . . . . . . . . . . 127

5.2.6 Theme Eleven: Belongingness . . . . . . . . . . . . . . . . . . . . . . 128

5.2.7 Theme Twelve: Collaborative Effort . . . . . . . . . . . . . . . . . . . 130

5.2.8 Theme Thirteen: Social Status . . . . . . . . . . . . . . . . . . . . . . 131

5.2.9 Theme Fourteen: Role . . . . . . . . . . . . . . . . . . . . . . . . . . 132

5.2.10 Theme Fifteen: Quality . . . . . . . . . . . . . . . . . . . . . . . . . . 134

5.2.11 Theme Sixteen: Support . . . . . . . . . . . . . . . . . . . . . . . . . 135

5.3 Theme of Response (R) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

5.3.1 Theme Seventeen: Service Co-creation Behaviour (SCB) . . . . . . . . 136

5.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

6 Discussion 141

6.1 Environmental Stimuli in Service Co-creation System (S) . . . . . . . . . . . . 143

6.1.1 Platform Capabilities (PC) . . . . . . . . . . . . . . . . . . . . . . . . 146

6.1.2 Relational Capital (RC) . . . . . . . . . . . . . . . . . . . . . . . . . 149

6.1.3 Actor Competencies . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

6.2 Actor Value Perception (O) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

6.2.1 Purposive Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

6.2.2 Network Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

6.3 Service Co-creation Behaviour (SCB) . . . . . . . . . . . . . . . . . . . . . . 173

6.3.1 Collaborative Behaviour (COB) . . . . . . . . . . . . . . . . . . . . . 174

6.3.2 Citizenship Behaviour (CB) . . . . . . . . . . . . . . . . . . . . . . . 175

6.3.3 Creative and Destructive forces in COB and CB . . . . . . . . . . . . . 176

6.4 From SD Logic to Socio-SD Logic . . . . . . . . . . . . . . . . . . . . . . . . 178

6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

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7 Conclusions 183

7.1 Contribution to the Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184

7.2 Contribution to the Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

7.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

7.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

References 225

A Participant Recruitment Flyer 227

B Pilot Interview Topic Guide 229

C Main Interview Topic Guide 233

D Coding Example 235

E Initial Code list: StackOverflow 237

F Initial Code list: GitHub 241

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List of Figures

2.1 Literature review process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2 Focus of research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.3 Stages of article selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.4 Comparison of different types of service co-creation systems. . . . . . . . . . . 31

2.5 Value co-creation behaviour model . . . . . . . . . . . . . . . . . . . . . . . . 43

3.1 Philosophical perspective of research. . . . . . . . . . . . . . . . . . . . . . . 59

3.2 Research strategy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.3 SO co-creation process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.4 GH co-creation process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.5 Innovation stages of SO and GH. . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.6 Role vs. level of contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.1 Example of trust model in SO. . . . . . . . . . . . . . . . . . . . . . . . . . . 91

6.1 Service co-creation behaviour (SCB) model . . . . . . . . . . . . . . . . . . . 142

6.2 Hedonic dimensions in service co-creation system. . . . . . . . . . . . . . . . 161

6.3 Quality value attributes in service co-creation system. . . . . . . . . . . . . . . 171

6.4 Resource integration process. . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

6.5 Structure of SD logic axioms in the SCB model . . . . . . . . . . . . . . . . . 180

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List of Tables

1.1 Stages of innovation process. . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1 The SD logic axioms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.2 Database search details. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.3 Cooperative service co-creation system (CS1). . . . . . . . . . . . . . . . . . . 33

2.4 Coordinative service co-creation system (CS2). . . . . . . . . . . . . . . . . . 34

2.5 Collaborative service co-creation system (CS3). . . . . . . . . . . . . . . . . . 36

2.6 Environmental and cognitive factors from service ecosystem and co-creation. . 42

3.1 SO demographic information. . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.2 GH demographic information. . . . . . . . . . . . . . . . . . . . . . . . . . . 73

3.3 Research trustworthiness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

4.1 Frequency of SO themes in SCB. . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.2 Characteristics of environmental stimuli themes in SO. . . . . . . . . . . . . . 85

4.3 Characteristics of primary value. . . . . . . . . . . . . . . . . . . . . . . . . . 93

4.4 Characteristics of response themes in GH. . . . . . . . . . . . . . . . . . . . . 104

5.1 Frequency of GH themes in SCB. . . . . . . . . . . . . . . . . . . . . . . . . 110

5.2 Characteristics of environmental stimuli themes in GH. . . . . . . . . . . . . . 111

5.3 Characteristics of actor value perception in GH. . . . . . . . . . . . . . . . . . 121

5.4 Characteristics of response themes in GH. . . . . . . . . . . . . . . . . . . . . 136

6.1 Service co-creation environment characteristics . . . . . . . . . . . . . . . . . 145

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6.2 Actor value perception characteristic. . . . . . . . . . . . . . . . . . . . . . . 156

6.3 Matrix intersection between environmental stimuli and purposive values. . . . . 157

6.4 Matrix intersection between environmental stimuli and network value. . . . . . 164

6.5 Matrix intersection between environmental stimuli network value. . . . . . . . 168

6.6 SD logic axioms based on the SCB model . . . . . . . . . . . . . . . . . . . . 181

6.7 SD logic axioms based on the SCB model. . . . . . . . . . . . . . . . . . . . . 182

7.1 The new findings of Platform Capabilities. . . . . . . . . . . . . . . . . . . . . 191

7.2 The new findings of Actor Competencies. . . . . . . . . . . . . . . . . . . . . 191

7.3 The new findings of Relational Capital. . . . . . . . . . . . . . . . . . . . . . 192

7.4 The new findings of Purposive value . . . . . . . . . . . . . . . . . . . . . . . 193

7.5 The new findings of Network value (individual-level) . . . . . . . . . . . . . . 194

7.6 The new findings of Network value (service-level) . . . . . . . . . . . . . . . . 195

7.7 The new findings of service co-creation behaviour . . . . . . . . . . . . . . . . 196

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Chapter 1

Introduction

This chapter presents an overview of the current research. It introduces the background to the

research and presents the research problem. This is followed by a description of the objectives

and contribution of the research. The chapter concludes by providing the thesis layout.

1.1 Research Background

In 2004, Lego company went through a negative trend that forced them to go through a

dramatic change and transform into one of the most powerful brands in the world. The reason

for the success of Lego was the cultural shift and looking outward to collaborate with

customers rather than looking inward [Libert et al., 2016]. As the result, by 2005, Lego had

120 staff designers while they potentially had 120,000 volunteer designers and by 2012 over

100,000 active designers outside the company who shared ideas and design through “Lego

digital designer software” [Antorini et al., 2012, Libert et al., 2016]. This example manifests

the growing, active role of customers in organisations’ core service, where co-creation has

become a strategic choice for organisations, regardless of size and location [Prahalad and

Ramaswamy, 2004]. Ideas offered by potential customers were found to be significantly more

novel than ideas created by employees and marketers, which made customers a key to effective

innovation [Wang et al., 2016].

The following shifts are important in the investigation of the current research: the evolution

of service systems to include the co-creation strategy, the transformation of service co-creation

1

2 CHAPTER 1. INTRODUCTION

business models to be community orchestration models, and the critical role of actors 1 and their

value perception in the success of new business models.

A service system is the configuration of resources (e.g., people and ,technologies) that

“interact with other service systems to create mutual value” [Maglio and Spohrer, 2008, p. 1].

Service systems have become increasingly more complex by incorporating sophisticated

interactions between supplier and customer [Pinho et al., 2014], where advances in IT

technologies facilitate flexible interactions and information exchange through digital

platforms. This technical shift has been reflected in the practical strategies being used for

business development and consequently customer/provider interaction behaviours. Traditional

service production and delivery systems have influenced technical business strategies such as

crowdsourcing and co-creation with the aim of increasing efficiency and shared value, through

characterising roles and shared responsibilities with stakeholders (provider, customer, third

parties), and strengthening networking relationships [Bidar et al., 2017].

In service-based industries, understanding and applying co-creation as a novel strategy is

becoming as important as understanding business profits. John Chambers (Cisco chairman)

predicted that within ten years, 40% of companies will fail because they are behind digital

networks [Libert et al., 2016]. For example, the success of Facebook is not because they created

a great product but because they allow other people to do the work, and make games and provide

different quality time sinks. However, Google+ failed because their approach predicted what

people want and delivered it instead of allowing people to enter the process [Libert et al., 2016].

These examples illustrate customer participation in a new business phenomenon, co-creation,

where customers increasingly take part in stages of organisations’ service creation and delivery

processes. Customer-organisation collaboration depends on the objectives of the organisation,

where the customers are assets and essential for the effectiveness and success of the business.

Co-creation systems are becoming an integral part of service ecosystems with the aim of

encouraging people to engage in value co-creation and collaborative innovation. Terwiesch

and Ulrich [2009] reported that approximately 25% of innovation opportunities are generated

through customer-provider interactions. More and more organisations are using customers’

collective intelligence to leverage customer skills, ideas and knowledge for customer generated

service creation (e.g., PatientsLikeMe, Stack Exchange) or service transaction and delivery

1Following Vargo and Lusch [2016] actors (individuals or organisations) are defined as participant’scontribution in resource integration and value co-creation.

1.1. RESEARCH BACKGROUND 3

(e.g., AirBnB, GoGet, Uber). These platforms are transforming the divide traditionally present

between consumers and providers by engaging consumers in the process of creation and

delivery (which has conventionally been controlled by providers) by utilizing interactions and

customer connections through online platforms [Bidar et al., 2016]. Therefore, organisations

are gradually realizing that the cultural shift to more co-creation oriented systems and

empowering customers to contribute to their core service, is imperative for organisations to

maintain and advance their innovation competencies.

Innovation can occur as the result of customers’ contribution in co-creation activities and

value formation. Service co-creation activities facilitate a venue for innovation through a

collaborative process in an actor-to-actor (A2A) network [Lusch and Nambisan, 2015].

Customer contribution to product creation can be structured through different stages of the

innovation process including ideation, development and marketing [Schweisfurth and Herstatt,

2016]. Another study revealed that inter-organizational collaborative, cooperative and

citizenship behaviour occurs through three innovation phases of ideation, invention, and

exploitation (similar to ideation, development and marketing capabilities) [Gerke et al., 2017].

Therefore, customers’ collaboration in creative value formation and innovative outcome occurs

through three different stages of the innovation process, depending on the type of co-creation

business model. Table 1.1 illustrates the stages of the innovation process and the capabilities in

each stage.

1.1.1 Need for a Clear Understanding of service Co-creation Systems

The development of service co-creation platforms with different types of relationships,

purposes and outcomes to reduce cost and to increase efficiency brings the focus to understand

the current models and how they function to extract value and approach service integration

[Barros et al., 2000]. It is argued that a clear understanding of the attributes of various

co-creation models, is needed to realize how the current mechanism matches with the practical

purpose of organisations. This understanding contributes to the success of business and

problem solving for future potential platforms. This research conducted a systematic literature

review to investigate service co-creation in the A2A context by classifying the different types

of current service co-creation systems (see Section 2.2 for details), revealing three

classifications for service co-creation systems: cooperative co-creation (CS1), coordinative


Table 1.1: Stages of innovation process.Source: [Gerke et al., 2017, p. 59] and [Schweisfurth and Herstatt, 2016, p. 115]

Ideation

“The generation of a thoughtor suggestion as to possiblecourses of action”[Gerke et al., 2017].

Idea generation, external informationabsorption, and competitive intelligence[Schweisfurth and Herstatt, 2016]

Idea generation, evaluation, and selection[Gerke et al., 2017]

Developmentor Invention

“The first realization and testof an existing idea for a newproduct or process”[Gerke et al., 2017].

Specification setting and testing[Schweisfurth and Herstatt, 2016]

Prototype development, testing, andrefinement [Gerke et al., 2017]

Marketing orExploitation

“To large-scale production andthe commercial exploitation ofthe invention in the marketplace”[Gerke et al., 2017].

Company representation and opinionleadership[Schweisfurth and Herstatt, 2016]

Commercial exploitation[Gerke et al., 2017]

co-creation (CS2) and collaborative co-creation (CS3) service systems. A key finding was that

service co-creation systems vary considerably in actor affordances in service production and

delivery, and in how value is distributed between stakeholders. In CS1 and CS2, customers

co-create value with the organisation, but the final production and delivery of the service is by

the organisation. However, in CS3s, the organisation is a facilitator of service exchange and

members of the community as actors provide and deliver the service to each other (A2A

relationship).

This research focuses on the CS3 with an A2A service creation and delivery. The reason is

the rise of these collaborative systems as the new business model in the daily life service

system (e.g., transportation- Uber and GoGet, hotel- Airbnb, software projects development-

GitHub) which is not empirically investigated by previous studies from the service co-creation

behaviour (SCB) perspective. The Libert et al. [2016, p. 8] investigation of different business

models revealed that compared to the previous service system models, “network orchestrator

grew revenues faster, generated higher profit margins, and used assets more efficiently” than

traditional business models, resulting in significantly higher enterprise values comparing to

revenues. They examined different business models in terms of price (market value) to revenue

ratio (i.e., companies’ multiplier) and revealed that on average a network orchestrator model is


approximately four times more productive compared with asset builder (e.g., Ford) and service

provider (e.g., Humana) models. Libert et al. [2016] found that a network orchestrator

(community-orchestration in this research) model has a low asset cost and builds 50% more

value over time than an asset builder model [Libert et al., 2016]. However, only 2% of

companies used a network orchestrator model in 2014. This small amount of network

orchestration shows that business leaders do not understand network orchestration yet [Libert

et al., 2016] and additional research must be conducted to generate more scientific evidence to

support practice.

The success of CS3 models is highly related to the actors’ collaboration (e.g., customer,

provider) in the co-creation process, including the service creation, delivery and support

phases. Value co-creation entails ”the activities that underlie resource integration and the

implied actor roles” to create mutual value [Lusch and Vargo, 2014, p. 168] and mutually

beneficial relations with the company or other customers. Co-creation is a higher level of

customer participation in various activities [Damkuviene et al., 2012] such as co-production

(i.e., shared innovative knowledge), customization and co-design [Lusch and Vargo, 2006],

and co-delivery of product/service. The nature of participation, as the central part of

co-creation, varies based on different types of service [Xie et al., 2008] and platform

architecture [Lusch and Nambisan, 2015]. Research to date has investigated customer

participation in co-creation [Fuller et al., 2009, Lorenzo-Romero et al., 2014, Zhang et al.,

2015]. However, little attention has been paid to the role of actors in CS3 platforms and factors

influencing actors to collaborate in service co-creation.

The evolution towards more community orchestrated models and the shift in the role of

customers to be the provider of the service has been changing the focus of research to the

context of community-orchestrator (CS3) platforms. Instead of companies creating services and

involving customers to the co-creation process to enhance value, the model is moving towards

the company as a facilitator, with actors using the network to create the service and deliver

it to each other. As CS3s are the central focus of this research, a detailed literature review

was undertaken to better understand the nature of CS3s and how they differ from other types

of service co-creation platforms. Also, the extension of service creation and delivery through

actor communities has profound yet insufficiently understood implications for businesses and

communities, through emerging actor affordances. Hence, it is critical to investigate service

co-creation systems and specifically the drivers of actor collaboration in those communities that


develop SCB.

1.1.2 Research Problem

According to service-dominant (SD) logic, the customer is always the co-creator of value

[Payne et al., 2008, Vargo and Lusch, 2008]. Customers actively collaborate with the

organisation or other customers practicing their skills and knowledge to improve new product

development [O’Hern and Rindfleisch, 2010, Zhang et al., 2015] to create a service offering

[Vargo and Lusch, 2004] and to share their experiences [Prahalad and Ramaswamy, 2004,

Rowley et al., 2007, Svensson and Gronroos, 2008]. Value is always created as customers

interact to integrate resources by way of knowledge, skills and tangible artefacts [Lusch and

Vargo, 2006]. Since customers are active players in the co-creation process, it is essential to

focus on their behaviour patterns [Xie et al., 2008] and how they collaborate.

Previous studies have investigated the co-creation phenomenon in customer settings. Most

of the existing work on customer co-creation has been conducted in the business-to-business

(B2B) and business-to-customer (B2C) context and a few in the customer-to-customer (C2C)

context. However, the focus of this research is on the A2A context which is more complex

than the other contexts. The complexity of A2A service co-creation systems is because of the

A2A environment that is characterised by community-oriented initiatives and a less structured

organisational framework, where resource integration can be more difficult to achieve. The key

difference between C2C and A2A is that C2C co-creation platforms are organisation–centric

where the organisation is the main beneficiary and grounded on one-way transaction and service

delivery. However, in A2A approach the notion of provider and consumer disappears and value

transforms from value-in-use only to a more contextual and personal value (value–in-context),

with shared power. Also, organisations instead of being a provider of service play the role of

facilitating service exchange using actors’ experience.

The core of customer participation is co-creation behaviour that facilitates mutually

beneficial relationships among actors [Laud et al., 2015]. From a B2C value co-creation

perspective, customer value co-creation is comprised of two types of behaviour introduced by

Yi and Gong [2008], reflecting the customer’s contribution in different value creation

activities. Firstly, customer participation behaviour (PB) refers to customer engagement in the

development of a service/product that is necessary for useful value co-creation and the


completion of service delivery (in-role behaviour). Secondly, customer citizenship behaviour

(CB) is a voluntary (extra-role) behaviour where customers provide extra value to the firm by

giving feedback and helping others [Yi et al., 2011, Yi and Gong, 2013]. However, Romero

and Molina [2011] argue that value co-creation behaviour relates to the customer’s

involvement in: new product design and development; mass-customization; customer

feedback; value and knowledge influenced by individual experiences; and open community

ideation. Frow et al. [2011] and Alexander et al. [2012] describe different styles of value

co-creation as co-conception, co-promotion, co-pricing, co-disposal, presumption and

co-production.

The importance of the concept of value co-creation behaviour is to develop a better

understanding of how customers interact and collaborate with other actors to achieve a desired

value and enable an effective co-creation process [Libert et al., 2016]. Most of the literature on

SD logic discusses co-creation of value [e.g., Prahalad and Ramaswamy, 2004, Payne et al.,

2008, Vargo and Lusch, 2004] and some focuses on value co-creation behaviour [e.g., Yi et al.,

2011]. However, this research concentrates on service co-creation which has a very limited

focus on the literature [e.g., Finsterwalder, 2016, Gill et al., 2011, Hilton et al., 2012]. On the

other hand, no study in the literature investigates SCB in an A2A context.

Previous studies have attempted to reveal the drivers of customer co-creation.

Experimental value and social influences were found to be two dimensions influencing

customer value co-creation behaviour in retail with a C2C perspective [Shamim and Ghazali,

2014]. Customers’ future participation in co-creating product marketing was found to be

influenced by co-creation experiences (learning value, social integrative value and hedonic

value) and environmental stimuli (perceived task relevant and affection-relevant cues).

Although extensive research has been conducted on SD logic in the customer co-creation

context, only some aspects of co-creation have been realized to date, and clear investigation of

the relational and network aspect of co-creation in SD logic is needed [Achrol and Kotler,

2012]. Further, Vargo and Lusch [2017, p. 47] suggest that “for SD logic to move forward over

the next decade, it needs more midrange theory development, as well as evidence-based

research”. Therefore, the following research provides evidence in regards to actors’

collaborative interactions as an integral part of service co-creation, and how it leads to the

value formation. Specifically, three new axioms for SD logic in the A2A service co-creation

context were proposed, as socio-SD logic. This research argues that operant resources do not


directly drive actors, but it is the value that initiates and drives actors, and by extension

initiates and drives resource integration. The findings reveal key factors that affect SCB, and

leads to a proposed model of SCB.

The shared contributions of value perceptions, environmental stimuli and SCB enhance our

understanding of how to improve collaboration and creative value outcomes. While value

outcomes and environmental drivers have been proposed as important in B2C value

co-creation, there is no empirical validation for this assumption in the A2A context,

specifically in the service co-creation context. Hence, this research conducted an empirical

investigation to better understand the environmental stimuli and actor value perceptions that

shape SCB.

1.2 Overview of Research Questions and Research Design

Previous studies in SD logic have explored customer co-creation phenomenon in B2C [e.g., Yi

and Gong, 2013], C2C [e.g., Shamim and Ghazali, 2014] and A2A [e.g., Vargo and Lusch,

2017] contexts. Nonetheless, the gaps of this research is to investigate SCB and how

collaboration plays out in relation to the actor value perceptions and environmental stimuli, a

combination that does not appear to exist in the current literature. Accordingly, the aim of this

research is to explore actor SCB in an A2A context. To address the main research question

four objectives and three associated questions were formulated.

Objective 1: To explore the nature of service co-creation in an actor-to-actor (A2A) context.

Objective 2: To explore environmental and cognitive stimuli that lead to service co-creation

behaviour (SCB).

Objective 3: To develop a conceptual model to better understand the problem under

investigation (i.e., actors’ collaboration in service co-creation).

Objective 4: To develop a theoretical model representing how environmental stimuli and

value perception influence actor service co-creation behaviour (SCB).

1.3. KEY FINDINGS 9

1.2.1 Research Questions

Main RQ: Why do actors collaborate in service co-creation?

The developed associated questions are:

RQ1: How are service co-creation systems classified based on the different dimensions in a

co-creation context?

RQ2: How do environmental stimuli influence actors’ service co-creation behaviour?

RQ3: How does value perception influence actors’ service co-creation behaviour?

1.2.2 Research Design

The aim of this research was to investigate SCB and how collaboration plays out in service

co-creation systems. To achieve this aim, a systematic literature review was conducted to

explore the nature of A2A service co-creation systems compared to the other co-creation

business models.

Due to the exploratory nature of this research, an interpretive paradigm and a qualitative

case study with two cases were adopted to address the second and third research questions.

Two cases of StackOverflow (SO) and GitHub (GH) were adopted to collect the data through

conducting semi-structured interviews. The unit of analysis was determined as co-creation.

A total of 36 participants were interviewed using purposive and snowball sampling

techniques. Thematic analysis was used following an inductive approach. After the coding and

theme identification steps, a new model of SCB was developed based on the

stimulus-organism-response (SOR) model. A detailed discussion of the research design is

provided in Chapter 3.

1.3 Key Findings

This research showed that environmental stimuli (i.e., operant resources) in the co-creation

system influence actor value perception and lead to actor service co-creation behaviour (SCB).

SCB comprises collaborative and citizenship behaviour (COB and CB) that results in creative

or destructive value formation.

Thematic analysis revealed 15 established themes from the SO and 17 from the GH


studies. The two sets of outputs were compared and a theoretical model of SCB was

developed. The theoretical model was developed based on the SOR model to present

environmental stimuli (addressed RQ2) and value perception (addressed RQ3) that influence

actors’ service co-creation behaviour.

The SCB model consists of seven final constructs. The identified environmental stimuli (S)

consist of Platform Capabilities, Relational Capital, Actor Competencies that influence two

actor value perceptions of Purposive and Network value and lead to COB and CB. Purposive

value includes Learning, Utilitarian, Hedonic and Economic values that capture informational,

functional, experimental and financial-related aspects of actors’ value perceptions. However,

Network value was found to be the in-process value perceptions that are created through

actors’ reciprocal interactions and through the network effect. Network value is comprised of

two levels, individual and service. Individual level values (i.e., ego values) involve Social

Positioning, Belongingness and Collaborative Effort while service level values (i.e collective

benefit) include Quality and Support.

1.4 Summary of Contribution to the Theory and Practice

The key theoretical contributions is the development of an SCB model, using an SOR model.

The research contributed to how the SOR model can be used effectively in the co-creation

context. This research extended Uses and Gratification theory (UGT) to an A2A co-creation

context by updating the four UG benefits introduced by Katz et al. [1973], and examined by

Nambisan and Baron [2009], to enhance current understanding of actor value perception.

Further, the research updated Yi and Gong’s (2013) conceptualization of value co-creation

behaviour to include collaboration in the co-creation context. The research proposes a new

midrange theory of SCB (i.e., collaboration-related), using the meta theoretical level of SD

logic.

Practitioners as facilitators need to provide a healthy interactive environment to reduce

destructive outcomes and manage collaborations. Also, practitioners need to understand both

Purposive and Network values from the co-creators’ perspective, and support their value

perceptions through improving design and implementing social influence strategies to get to

their desired result. As for the practical implications, the research suggests that by

1.5. STRUCTURE OF THE THESIS 11

understanding co-creators’ behaviour, the developed SCB model will help practitioners to

increase collaboration and innovation.

1.5 Structure of the Thesis

The reminder of this thesis is structured as follows:

Chapter 2 presents the current literature in three key sections. First, value co-creation and

customer participation is discussed from the SD logic point of view. Second, a systematic

literature review is conducted to investigate the nature of A2A service co-creation systems.

Third, the Stimulus-Organism-Response (SOR) model, and Uses and Gratification theory

(UGT) are reviewed, and a conceptual model is developed.

Chapter 3 details the research design and methodology conducted in this research. This

chapter discusses the rationale for choosing an interpretive philosophy and qualitative case study

approach. The process of conducting the data collection and data analysis (semi-structured

interview and thematic analysis) is then discussed.

Chapters 4 and 5 provide the main findings of the StackOverflow (SO) and GitHub (GH)

case studies. Using both data sets, this research conducted an inductive thematic analysis and

presented the themes that emerged from each case study.

Chapter 6, the Discussion chapter, integrates the findings of the SO and GH studies to

propose a theoretical model that represents actors’ service co-creation behaviour (SCB). The

chapter provides a discussion of each identified construct from the developed theoretical

model. Finally, Chapter 7 concludes the thesis by discussing the contribution of the research,

the research’s limitations and future work.


Chapter 2

Literature Review

The purpose of this chapter is to present the related literature on co-creation from a

service-dominant (SD) logic perspective, actor collaboration in co-creation, and actor

co-creation behaviour. This chapter is presented based on the three primary sections of

discussion of co-creation (Section 2.1), systematic literature review (Section 2.2), and

theoretical background and model conceptualisation (Section 2.3) (see Figure 2.1).

The first section presents the two approaches of SD Logic [Vargo and Lusch, 2004, 2008,

2016] and Service Science [Spohrer and Maglio, 2008] to explore the co-creation concept.

This section discusses on an integrative viewpoint of value co-creation, resource integration

and service exchange provided by SD logic [Vargo and Lusch, 2008, 2016]. Specifically, with

the shift toward dynamic and complex relationships, and the customer-orchestrator nature of

the interactions, the researchers’ attention moved toward co-creation behaviour and a link

between SD logic and how actors play a role in the co-creation context. First, an introduction

to SD logic in service science is presented in Section 2.1 that includes a discussion of service,

value, co-creation and resource integration. Then, a review of the customer as co-creator, and

co-creation behaviour, is conducted in Sections 2.2 and 2.3. According to the discussion of this

section and the identified gaps, the developed research questions are:

Main RQ: Why do actors collaborate in service co-creation?



RQ2: How do environmental stimuli influence actors’ service co-creation behaviour?


13

14 CHAPTER 2. LITERATURE REVIEWFigure

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2.1. SERVICE-DOMINANT (SD) LOGIC IN SERVICE SCIENCE 15

The second section reports a systematic literature review to investigate current service

co-creation systems, gain a better understanding of the nature of A2A service co-creation

systems and address the lack of consistency in the application of co-creation business models.

Three classifications were found for service co-creation systems, namely cooperative

co-creation (CS1), coordinative co-creation (CS2) and collaborative co-creation (CS3) service

systems. These service co-creation systems were varied across a set of seven dimensions:

Network Focus, Service, Resources, Value, Roles, Interaction Mode, and Engagement Mode.

A key finding is that service co-creation systems vary considerably in user engagement and in

how value is distributed between stakeholders, ranging from CS1 where the business asks

users to complete tasks through to CS3, where actors provide services to each other, and the

business only facilitates the communication. The literature review develops the understanding

of A2A service co-creation systems and how they are different from the early co-creation

models. The literature review is published in The 19th IEEE International Conference on

Advanced Communications Technology [Bidar et al., 2017].

The third section represents the developed conceptual model for co-creation behaviour using

the Stimulus-Organism-Response (SOR) model, and Uses and Gratification Theory (UGT). The

chapter discusses each identified construct and the theoretical background of the research (the

SOR model and UGT). Although the analysis of this research is based on an inductive approach,

the developed conceptual model enabled the researcher to clearly identify the problem under

investigation and develop the second and third research questions based on the first and fifth

gaps. The developed conceptual model revealed that there is no service co-creation behaviour

model in the SD logic literature, and also co-creation behaviour needs to be investigated from

the environmental and value perception aspects of the institutions in the service system. The

conceptual model is published in The 20th Pacific Asia Conference on Information Systems

[Bidar et al., 2016].

2.1 Service-Dominant (SD) Logic in Service Science

Service science, as an interdisciplinary approach, is the study of service systems that centres on

participants, processes, performance, and resources. The focus of service science is to create

value and improve relationships and innovation rates in service systems [Barile and Polese,

2010, Vargo and Lusch, 2008]. Specifically, service science is the study of complex service

16 CHAPTER 2. LITERATURE REVIEW

systems, “which are dynamic value co-creation configurations of resources (people, technology,

organisations, and shared information)” [Maglio and Spohrer, 2008]. Service systems also

include approaches that help the clarification and understanding of the value co-creation context

for both academia and practice [Spohrer and Maglio, 2010].

SD logic [Vargo and Lusch, 2004] is fundamental to service science and to value creation

research in service systems [e.g., Maglio and Spohrer, 2008, Spohrer and Maglio, 2008, Vargo

et al., 2008, Barile and Polese, 2010]. SD Logic has been studied as a theoretical proposal

[Achrol and Kotler, 2012, Sweeney, 2007], representing a paradigm shift from goods-dominant

(GD) logic to SD logic. However, Lusch and Vargo [2006], Vargo and Lusch [2008] emphasised

SD logic as a mindset to better understand the service (as the process) rather than goods and

services (plural) as the unit of outcome. Yet, SD logic can be used to generate new theoretical

perspectives in service systems [Osborne et al., 2013]. The growth of service science depends

on SD logic conceptualisations, such as value co-creation and resource integration [Maglio and

Spohrer, 2008, Spohrer et al., 2007]. To achieve this, service science requires the application

of different theories from other disciplines such as Marketing, Psychology and Information

Systems [Peters et al., 2016].

SD logic is presented as a framework of eight foundational premises (FPs) [Vargo and

Lusch, 2004], which were revised to eleven premises [Vargo and Lusch, 2008]. Later Vargo

and Lusch [2016] revised the same premises, based on the nature of service systems and

customer interactions, to five key axioms. Table 2.1 shows the five SD logic axioms that are

fundamental to this research.

Table 2.1: The SD logic axioms.Source: [Vargo and Lusch, 2016, p. 18]

Axiom 1: Service is the fundamental basis of exchange.Axiom 2: Value is co-created by multiple actors, always including

the beneficiary.Axiom 3: All social and economic actors are resource integrators.Axiom 4: Value is always uniquely and phenomenologically determined

by the beneficiary.Axiom 5: Value co-creation is coordinated through actor-generated institutions

and institutional arrangements.

In the new SD logic axioms, Vargo and Lusch [2016] argue the importance of institutions


(e.g., rules, norms, meanings) as the foundational facilitators of value co-creation in which

actors coordinate themselves through institutional arrangements. Vargo and Akaka [2012] and

Vargo and Lusch [2016] argue that the focus of SD logic has changed from a service system to

a service ecosystem (Marketing perspective) with a focus more on the role of institutions, and

how institutions influence the interactions between actors and actors’ actions [Vargo and

Akaka, 2012, Vargo and Lusch, 2016]. However, Peters et al. [2016] adopting a service

science perspective, defends the idea that service systems also depend on institutions and

governing the behaviour of actors in the social community, with socially created norms and

regulations. Peters et al. [2016] emphasise that the focus should be to investigate service

innovation in human-centric systems (i.e., service ecosystems, service systems, or

human-centred service systems) through understanding the way people build complex

institutional structures that allow them to create value at the different levels of service systems

(i.e., micro, meso and, macro). Indeed, value creation goes beyond individual systems, to a

dynamic and ongoing process within service systems [Vargo and Akaka, 2009]. Axiom 5/FP11

proposes that “value co-creation is coordinated through actor-generated institutions and

institutional arrangements” [Vargo and Lusch, 2016].

The first and main critical distinction between GD logic and SD logic is the

conceptualisation of service, and a shift to a process and service-centric logic which centres in

value co-creation [Vargo and Lusch, 2008]. Service is the application of competencies (e.g.,

skill and knowledge) to benefit themselves and others [Vargo and Lusch, 2004]. Service is

seen as a process of doing something for another party, rather than the unit of output (i.e.,

services) [Vargo and Lusch, 2008]. “Services are acts performed for others, including the

provision of resources that others will use” [Alter, 2013, p. 3]. Service can be provided

directly or through a good, as the intermediary, for social and economic exchange [Vargo and

Akaka, 2009]. In the SD logic approach, value creation is a process of a service-for-service

exchange perspective, and is fundamental to service science and service systems development

[Maglio and Spohrer, 2008, Vargo et al., 2008]. Vargo and Akaka [2012, p. 207] describe the

co-creation process as the complex and dynamic process in social systems in which “service is

provided, resources are integrated, and value is co-created”. To understand service, an

investigation of the nature of interaction to seek co-created value is suggested by Spohrer et al.

[2008], and Spohrer and Maglio [2008]. This discussion refers to the Axiom 1/FP1 in which

“service is the fundamental basis of exchange” [Vargo and Lusch, 2008, 2016].


2.1.1 Co-creation

From an SD logic perspective, the locus of value co-creation moved from the firm’s output

(value-in-exchange) to value-in-use, and then value-in-context [Vargo et al., 2008]. Value

co-creation is the core concept and refers to a collaborative effort in which different actors

jointly and reciprocally participate in creating value [Lusch and Vargo, 2006]. Value

co-creation suggests that service systems go through a process of recourse integration and the

application of competencies to create value for themselves and others [Vargo et al., 2008]. The

primary part of research on value co-creation has been addressed conceptually, and recently

empirical research has begun to emerge in this area [Hakanen, 2014]. Therefore, more

empirical research is needed in the co-creation context with the shared institutional logic to

contribute to the development of service science research.

According to Axiom Two (A2), Vargo and Lusch [2016] discuss the nature of co-creation

and propose that “value is co-created by multiple actors, always including the beneficiary”. A2

emphasises the multi-actor nature of value co-creation. Value creation does not occur through

an individual actor contribution or between an organisation and customers, but it happens

through the collaboration of the actors’ network, and the value is the result of integration of

resources, provided by different actors [Vargo and Lusch, 2016]. This multi-actor interaction

determined as the main characteristic of the value-in-context which distinguishes it from the

value-in-use concept [Kuzgun and Asugman, 2015, Vargo et al., 2008]. Despite extensive

literature on value as being co-created, Hilton et al. [2012] believe that value is the judgement

outcome of service from an individual perspective. SD logic literature considers the

co-creation as value being created rather than value as the outcome of service co-creation.

Therefore, there is a gap regarding the service co-creation phenomenon to be understood in

relation to SD logic (further discussion in Section 2.1.2).

Value in a multi-actor relationship is conceptualized as value-in-context. Context refers to

“a set of unique actors with unique reciprocal links among them” [Chandler and Vargo, 2011,

p. 40]. Kuzgun and Asugman [2015] state that value-in-exchange and value-in-use (i.e., added

value as the unit of output (GD logic)) might be identified and actualized as the “function of

value-in-context”. Therefore, rather than embedded value being the product/service,

value-in-use should be captured through a customer experience of co-creating the

product/service [Woodruff and Flint, 2006]. The co-created value outcome includes subjective


benefits for customers rather than static benefits and can be perceived in positive/negative ways

in a particular context [e.g., Lobler and Hahn, 2013]. Limited empirical studies in the SD logic

context discussed value-in-context as the outcome of customer participation in resource

integration [Laud, 2015]. For example, Lobler and Hahn [2013] identified value-in-context in

terms of situational factors in the co-creation process including the aspects of object-oriented

(nontangible resources), self-oriented (self-expressions) or social-oriented (engagements)

processes. The contextual nature of value and the integrative role of actors are represented in

Axiom Four (A4) by Vargo and Lusch [2016]: “Value is always uniquely and

phenomenologically determined by the beneficiary”.

The above discussion on value as the context reflects the complex nature of value as a

perception [Ballantyne et al., 2011]. Value perception is defined as the evaluation of

consumers on the utility of a supplier’s offering, including the cost and benefits [Zeithaml,

1988]. Salem Khalifa [2004] described value perception as weights that customers give to

different benefits when assessing a product/service. Benefits can include tangible and

intangible attributes [Gale and Wood, 1994] while costs include monetary or non-monetary

factors such as time and effort [Gronroos, 1997]. From this perspective, value is a function of

actors’ preferences and perceptions [Echeverri and Skalen, 2011]. Actors in service systems

always determine value [Vargo and Lusch, 2004]. Value is determined by a perception and is

evaluated through using or experiencing service outcomes [Vargo and Lusch, 2008].

Therefore, value can be multi-faceted and can change through experience [Vafeas et al., 2016].

Previous researchers verified customer value perception as a cognitive concept that directly

impacts buying behaviour [Dodds et al., 1991, Eggert and Ulaga, 2002]. Eggert and Ulaga

[2002] suggest that value can be a predictor of behavioural outcome or customer satisfaction.

Therefore, value is conceptualized in terms of context and is realized through the subjective

judgement of actors in co-creation systems. This research defines value as the

“value-in-context” in A2A interactions [Vargo and Lusch, 2016], and as realization over time

[Hilton et al., 2012, Vafeas et al., 2016]. Accordingly, this research is in line with Hilton et al.

[2012, p.1509] where “service is co-created, while value is realised by the individual as an

evaluative judgement of the benefit or worth against criteria derived from personal values

(plural)”.

Gap 1: SD logic needs to develop actor value perception in the service co-creation


context reflecting the value-in-context initiatives.

2.1.2 Resource Integration

SD logic views co-created value as the result of a complex relational network and outcome of

the resource integration process [Kleinaltenkamp et al., 2012]. Resource is a carrier of

capabilities and is built when it is used in integration activities [Lobler and Hahn, 2013].

According to SD logic, operant resources are a “fundamental source of strategic benefit”

[Vargo and Lusch, 2016, p. 8]. However, resources have potential value that should initiate

through resource integration [Edvardsson et al., 2011]. Operant resources include knowledge,

experience and competency which are important to develop innovative performance and create

greater service quality [Hasan and Rahman, 2016]. Resource integration is the process of

performing a series of activities by actors [Payne et al., 2008, p. 86]. The service offered by a

customer is the subset of resources that should be integrated to create value [Vargo and Akaka,

2009] and the service is only provided when the resources are integrated [Lobler and Hahn,

2013].

SD logic sees all actors as resource integrators which presents the idea of actors as

co-creators [Vargo and Lusch, 2016], proposed as Axiom Three (A3). Most of researchers in

SD logic focus on resource integration in organisation-customer value co-creation [e.g.,

Jaakkola and Alexander, 2014, Nambisan and Baron, 2007]. Only a few studies regard

resource integration as collaboration among actors with institutions [e.g., Edvardsson et al.,

2014, Kleinaltenkamp et al., 2012]. Kleinaltenkamp et al. [2012] define resource integration as

a process of collaboration through actors co-creating value-in-context. However, Hilton et al.

[2012] propose resource integration is identical to the co-production that results in service

co-creation. In line with Edvardsson et al. [2014, p. 297], resource integration consists of

“collaborative processes between actors, leading to experiential outcomes and outputs, as well

as mutual behavioural outcomes for all actors involved”.

The overall discussion on SD logic and value co-creation reveals that there are very few

studies [e.g., Hilton et al., 2012] that discuss resource integration from a service co-creation

perspective and value as the outcome of service co-creation. The current literature on SD logic

dominates the context of customer participation in value creation and little attention is on

service co-creation from a collaborative perspective with institutional norms. The importance


of determining institutions in SD logic is that institutions reflect the rules of resource

integration by “actors that constrain and coordinate themselves” to make joint value

co-creation possible [Vargo and Lusch, 2016, p. 6]. Also, the institutions shared by actors

result in a network effect which increases actors’ service exchange and value co-creation

[Vargo and Lusch, 2016].

Gap 2: Very limited studies on SD logic reflect service co-creation as the result of

actors’ resource integration process.

The literature on value co-creation considers resource integration as an interactive value

formation (IVF) where value is co-created through a provider-customer interaction [Prahalad

and Ramaswamy, 2004, Vargo and Lusch, 2004]. For example, Lusch and Vargo [2006] stated

that value is always created as customers interact to integrate resources by way of knowledge,

skills and tangible artefacts. However, this research, in line with Hilton et al. [2012] and

Echeverri and Skalen [2011], defines resource integration as a process of collaborative action

of actors that results in service co-creation and leads to value formation. Value formation in

co-creation systems ”is co-created, realised, and assessed in the social context of the

simultaneous production and consumption process” rather than provided by an organisation

through a product/service [Echeverri and Skalen, 2011, p. 353].

Aside from extensive literature on the positive side of value formation, few studies believe

resource integration has only positive value outcomes (value co-creation), rather they also think

there are negative outcomes (value co-destruction) [e.g., Echeverri and Skalen, 2011, Ple and

Chumpitaz Caceres, 2010]. While value co-creation refers to value-in-context and collaborative

value creation, value co-destruction refers to a decline in the well-being of one of the interactive

actors [Ple and Chumpitaz Caceres, 2010], and collaborative destruction or diminishment of

value [Echeverri and Skalen, 2011]. Although value co-destruction is conceptualised in a few

studies in the B2C context, little attention has been paid to the A2A context. Specifically, more

empirical studies from an A2A perspective are required to explore value formation in SD logic.

Few studies empirically have been conducted into co-destruction in the shared practice

context. Echeverri and Skalen [2011] found informing, greeting, delivering, charging and

helping as dimensions of co-destruction in the B2C context of transportation services.

M. Smith [2013] investigated co-destruction from the customer perspective in the B2C context


and found value co-destruction as a failure in the resource integration process that results in

unexpected resource loss (i.e., material, social, or energy), and perceived misuse and decline in

customer well-being which impacts their emotions and behaviour. Camilleri and Neuhofer’s

(2017) study characterise co-destruction in the Airbnb sharing economy setting to include

welcoming, expressing feelings, evaluating location and accommodation, helping and

interacting, recommending and thanking. However, the notions of value co-destruction lack

empirical study, particularly in the A2A co-creation context.

2.1.3 Customer as Co-creator

The customer plays a central role in the co-creation process. Co-creation refers to an

organisational process to “partition some of the work done by the firm and pass it on to their

customers” [Prahalad and Ramaswamy, 2004, p. 8]. Co-creation serves as the main function,

and value as the main purpose of the relationships between members of the network. Value of

a service is created by the mutual engagement of firm and customer [Prahalad and

Ramaswamy, 2004], for the development of a service [Edvardsson et al., 2011, Payne et al.,

2008], personalization of experiences [Harwood and Garry, 2010, Ramaswamy, 2008,

Shamim and Ghazali, 2014], mutual beneficial collaboration [Frow et al., 2011], collective

creativity [Fuller et al., 2011, Lorenzo-Romero et al., 2014], and fulfilling customers’ needs

[Durugbo and Pawar, 2014]. Therefore, co-creation includes all forms of customer

participation and collaboration practices [Jouny-Rivier et al., 2017]. A co-creation process can

be used as a learning strategy that enables organisations to enhance the design of customer

experiences and develop co-creation with customers [Payne et al., 2008]. Therefore, the main

output of the co-creation process is value creation and gained experiences [Prahalad and

Ramaswamy, 2004].

The co-creation function involves customer participation, and the knowledge, techniques,

and existing values used for fulfilling the customer’s need, and interactions in each activity to

co-create a new value [Durugbo and Pawar, 2014]. The aim of activities is to create value

collaboratively [Durugbo and Pawar, 2014]. Customers can contribute to problem-solving and

providing network solutions [Durugbo and Pawar, 2014, Prahalad and Ramaswamy, 2004,

Jaakkola and Hakanen, 2013], and idea sharing and evaluation [Geiger et al., 2011a,b].

Further, actors collaborate in content design [Doan et al., 2011, Hassan and Toland, 2013,


Zwass, 2010], co-design [Durugbo and Pawar, 2014, Fuller et al., 2011], product promotion

[O’Hern and Rindfleisch, 2010, Tuunanen et al., 2010, Zhang et al., 2015, Zwass, 2010],

constructing and personalizing experiences [Durugbo and Pawar, 2014, Gronroos and Voima,

2013, Payne et al., 2008, Prahalad and Ramaswamy, 2004, Ramaswamy, 2008], developing

innovation [Nambisan and Baron, 2009], and enhancing the well-being of the service system

[Spohrer et al., 2008, Vargo et al., 2008]. Customer participation provides mutual value to both

customers and firms [Dong et al., 2008, Chan et al., 2010, Yim et al., 2012]. Therefore,

co-creation from creating value between an organisation and their customers has moved

toward a dynamic contribution of customers in the development and distribution of a service,

where collaboration happens between multiple actors.

Customer participation in co-creating new a product and service has been discussed in the

literature from the B2C perspective [Payne et al., 2008] or B2B setting [Joshi and

Chebbiyyam, 2011, Jouny-Rivier et al., 2017]. However, this research adopts the view that all

actors that collaborate in a service exchange are resource integrators, based on Vargo and

Lusch’s (2016) Axiom Three (A3). Although customer participation has been discussed in the

value co-creation and new product development literature, little attention has being paid to

how collaboration plays out in the service co-creation context, specifically in the A2A context.

Gap 3: SD logic has not fully revealed how collaboration plays out in the service

co-creation context.

2.1.4 Co-creation Behaviour

To facilitate co-creation it is important to investigate different levels of actors’ engagement

behaviour during interactions. Co-creation is the result of customer engagement behaviour

[Qiao and Zhang, 2011]. The interactive co-creation process requires actors’ involvement in

service exchange and is influenced by their psychological state and behaviour [Finsterwalder,

2016, Kleinaltenkamp et al., 2012]. Co-creation manifests the way actors interact, behave, and

experience within their social construction [Lusch and Vargo, 2006, Prahalad and

Ramaswamy, 2004, Ranjan and Read, 2016]. Since customers are the active player in the

co-creation process, it is critical to focus on the behaviour they exhibit [Xie et al., 2008].

Central to customer engagement and participation is co-creation behaviour that facilitates


valuable relationships among actors [Laud, 2015, p. 70]. Due to the direct relationship

between customer participation and co-creation behaviour, the following reviews the literature

on co-creation behaviour.

The focus of co-creation behaviour, so far, has been on its contribution to value co-creation

processes [e.g., Chan et al., 2010, Yi and Gong, 2013, Yi et al., 2011]. Value co-creation

behaviour refers to the realisation of how co-creators communicate and interact to exchange

resources in a service system [Laud and Karpen, 2017]. Current investigations of co-creation

behaviour have identified two types of customer value co-creation behaviour, customer

participation behaviour (PB) and customer citizenship behaviour (CB) [Yi and Gong, 2013, Yi

et al., 2011]. PB is the behaviour that is necessary for useful value co-creation and the

completion of service delivery, i.e., in-role behaviour. On the other hand, CB refers to a

voluntary (or extra-role) behaviour which provides additional value [Yi and Gong, 2013, Yi

et al., 2011]. Researchers should treat PB and CB separately and use separate scales for

assessing them [Yi and Gong, 2013]. These two types of behaviours capture different aspects

of how customers interact to exchange services, resulting in resource integration.

PB and CB include different types of activities in the co-creation process. PB represents

customers’ engagement in information seeking (to clarify service requirements), information

sharing (i.e., providing resources for co-creation process), responsible behaviour (recognise

roles and duties), and personal interaction (the interpersonal relationship between customer

and provider) [Yi and Gong, 2013]. CB, on the other hand, includes exhibited behaviours such

as feedback (to help the firm improve service creation), advocacy (recommending the business

to others), helping (assist other customers), and tolerance (showing patience when expectations

are not met) [Yi and Gong, 2013]. These in-role and extra-role behaviours exhibit different

patterns of behaviour and different antecedents [Yi and Gong, 2008, Yi et al., 2011]. Romero

and Molina [2011] argue that value co-creation behaviour relates to a customer’s involvement

in new product design and development, mass-customisation, customer feedback, value and

knowledge influenced by individual experiences, and open community ideation. Alexander

et al. [2012], and Frow et al. [2011] describe different styles of value co-creation as

co-conception, co-promotion, co-pricing, co-disposal, presumption, and co-production.

Therefore, different types of co-creation (e.g., co-design and ,co-delivery) as business

objectives distinguish required types or combinations of activities and behaviours in the

service system.


Although co-creation behaviour was found fundamental in actor participation, few studies

explored value co-creation behaviour [e.g., Yi and Gong, 2013, Groth, 2005] and no study

explored service co-creation behaviour, to the best of our knowledge. Also, Laud and Karpen

[2017] suggest the need and necessity of research on preconditions that facilitate co-creation

behaviour among customers, by increasing the importance of co-creation processes. To

understand actors’ co-creating behaviour, this research indicates the importance of

understanding the structure of the social and individual state of actors that form their

collaboration in the co-creation process.

Overall, to investigate co-creation behaviour this research needs to consider the

behavioural pattern of actors, including actors themselves, their resources and interactions

within the system based on institutional agreements. Investigating actors’ co-creation

behaviour within institutions is important to capture the norms and their beliefs which lead

them to action.

Gap 4: SD logic needs to extend actor service co-creation behaviour to recognise

how actors collaborate in service co-creation.

Gap 5: SD logic needs to consider preconditions to collaborate in service

co-creation reflecting actor service co-creation behaviour.

The five gaps identified in SD logic literature (Figure 2.2) are further filled in this research

investigating “Why do actors collaborate in service co-creation?” For this purpose, this

research firstly conducted a systematic literature review to better understand the nature of the

A2A service co-creation system (Section 2), and then presented a conceptual model of value

co-creation behaviour (Section 3) to investigate a better understanding of the current literature

on the co-creation behaviour phenomenon. For the purpose of this research, the focus is on

A2A relationships on the network level rather than on a dyadic level.

To broaden the understanding of co-creation behaviour that is central in actor

collaboration, this research presented a conceptual model of current literature on value

co-creation behaviour. The conceptual model helps to develop our understanding of the

co-creation behaviour context, and develop further a SCB model, theoretically, as the main

focus of this research. The conceptual model is presented in Section 3.


Figure 2.2: Focus of research.

SCC: service co-creation

2.2 Systematic Literature Review

The aim of this section is to classify different types of service co-creation systems and

investigate how they vary based on principle dimensions in the service ecosystem and

co-creation contexts. This section identifies a lack in the nature and application of different

co-creation business models. The following research question is being addressed in this

section: “How are service co-creation systems classified based on different dimensions in the

co-creation context?” For this purpose, different strategic perspectives of actor collaboration

(i.e., crowdsourcing and co-creation from various disciplines) have been considered rather than

a single viewpoint. Then, the integration of these strategies with different types of network

(i.e., 3Cs) introduced by Keast et al. [2007] has been determined.

Different mechanisms have been identified in the organisational literature as a necessary

task to establish a better relationship between a service provider and their customer, to achieve

service integration and to create a comprehensive service delivery system [Keast et al., 2007].

Fine et al. [2001], Brown and Keast [2003], and Mandell and Steelman [2003] followed a

continuum of organisational relationships in which the “3Cs” or “Ns” are located along a

loosely structured and fragmented system to a fully integrated system: cooperative (Micro

level), coordinative (Meso level), and collaborative (Macro level) networks. A cooperative

network represents a voluntary activity, short-term relations with a loose linkage among

stockholders, usually involves lower-level actors and entails application of few resources

2.2. SYSTEMATIC LITERATURE REVIEW 27

[Cigler, 2001, Keast et al., 2007, Lawson, 2002]. In a coordinative network, the aim is to better

coordinate existing services with a predetermined goal with medium-term relations; the level

of relationships needs a higher level of effort and commitment and results in shared benefits

[Cigler, 2001]. In a collaborative network, participants are independent with strong and

longer-term relationships, shared goals and a holistic perspective [Cigler, 2001, Keast et al.,

2007]. This research develops these three types of service integration mechanisms in online

service co-creation systems.

2.2.1 Method and Result

This research conducted a systematic literature review of 36 of the most significant scientific

articles. The identified publications were found on the online co-creation and crowdsourcing

domains. The scope of literature was limited to studies published between 2002 and 2015. The

studies were taken from marketing, business, management areas because of the wide variety of

co-creation-related publications in this area. The identified articles related to crowdsourcing

were published in the Information Systems discipline. The selected articles were extracted

from the “Scopus” database as a comprehensive source of scientific publications [Falagas

et al., 2008] with indexed articles from “Elsevier”, “Emerald” and “Springer”. The Association

of Information Systems electronic Library (AISeL) was used as a major database in the field of

information systems (Table 2.2).

Table 2.2: Database search details.

Database Initial Search Final PoolScopus 351 21AISeL 309 9Others - 6Total 661 36

The data derived from the result of searching the main terms of (“value co-creation” +

system),“value co-creation process”, and (“crowdsourcing systems” + service). Initially, 661

articles were found and were reviewed by title, abstract and then by full text respectively. A

number of criteria was set including articles published 1) in English; 2) between 2002 and

2015; 3) with a focus on online co-creation and crowdsourcing strategies in service platforms;


and 4) co-creation and crowdsourcing studies with the aim of understanding collaboration with

customers. In each phase of review, those papers that did not satisfy the inclusion criteria were

rejected [Meade and Richardson, 1997], which led to the final list of 36 principal articles for

further analysis (see Figure 2.3).

Figure 2.3: Stages of article selection.

Thematic synthesis was used to analyse the data where each article was treated as a single

case. The analysis followed an inductive approach, and all texts from findings, discussion, and

conclusion were read and extracted from each paper. To better understand each case, a

summaries of aims, methods and goals were mapped from each study. Two phases were

followed in analysing the data. Firstly, the researcher identified main dimensions in the

co-creation context and then classified co-creation systems based on the identified dimensions.

2.2.1.1 Identifying Dimensions of Service Co-creation Systems

The content of each article was coded, using NVivo. A cross-case thematic analysis was

conducted to find the main themes which emerged from the data that were essential when

considering the co-creation process. After grouping, the seven final themes are: Network

Focus, Service, Resource, Value, Roles, Interaction Mode and Engagement Mode.


Network Focus represents the centrality of the customer network in the service systems.

Customer networks are playing an increasingly greater active role as organisations transition

from being organisation-centric, to customer-organisation centric, and ultimately to

customer-network-centric.

Service is defined differently in the crowdsourcing and co-creation contexts. In

crowdsourcing, service refers to a completed task that is provided by users (as provider) to

reach a business goal. Crowd services enable organisations to identify a specific category of

tasks that are aggregated by the crowd [Geiger et al., 2011a]. In SD logic, service refers to the

application of competencies (knowledge and skills) to benefit another party [Lusch and Vargo,

2006] and demonstrates the exchange [Vargo and Lusch, 2004]. Service is the purpose of

co-creation to fulfil customers’ need [Durugbo and Pawar, 2014]. Moving toward

customer-centric and community-orchestration platforms, the service perspective is to

distribute benefits mutually or collectively.

Resources are categorized into two types of operand (physical materials) and operant

resources (human, organisational, informational and relational) [Edvardsson et al., 2011, Hunt

and Derozier, 2004]. Any knowledge, shared information, technology, people and

organisations within the network are defined as resources [Maglio and Spohrer, 2008]. Pinho

et al. [2014] added that customer position, role and interaction in a social system can be

considered as resources. The amount and type of resources that actors can access varies in the

network [Pinho et al., 2014]. Resources need to be combined to be useful [Hammervoll, 2014].

The result of integration of resources through interactions (dyadic or many-to-many) between

actors in the network is innovation and value [Pinho et al., 2014, Hammervoll, 2014]. The

created value from the exchange of resources [Hassan and Toland, 2013] emerges from the

physical, mental or possession use of resources [Gronroos and Voima, 2013].

Value was found as the strongest component of co-creation in the literature. Value is the

“comparative appreciation of reciprocal skills or services that are exchanged to obtain utility”

[Vargo and Lusch, 2004, p. 7]. Value is the main outcome of the co-creation process [Pinho

et al., 2014] and the result of integrated resources (physical or mental)[Frow et al., 2011, Pinho

et al., 2014, Gronroos and Voima, 2013]. Value in co-creation considers customer experience

of value-in-use [Edvardsson et al., 2011, Payne et al., 2008, Prahalad and Ramaswamy, 2004,

Gronroos and Ravald, 2011, Gronroos and Voima, 2013, Ramaswamy, 2008] which derives


from the use of a service to improve the process of identifying customers’ needs beforehand

[Lusch and Vargo, 2006, Vargo and Lusch, 2004]. This includes any perceived or actual

benefits from the service for the customer [Lorenzo-Romero et al., 2014, Durugbo and Pawar,

2014, Shamim and Ghazali, 2014], and innovation benefits for the organisation [Fuller et al.,

2011]. The benefits were classified as financial or non-financial [Hassan and Toland, 2013],

and utilitarian or hedonic [Tuunanen et al., 2010, Hassan and Toland, 2013]. However, Spiteri

and Dion [2004] regard value as the proper combination of quality, service and cost.

Interaction Mode refers to a dialogical process [Ballantyne, 2004, Decker et al., 2008] in

which “the interacting parties are involved in each other’s practices” [Gronroos, 2011a,

p. 289]. Interaction among actors was found an important component in co-creation because

information is shared and knowledge is generated [Berthon and John, 2006]. Interactions

include relationships between customer and provider [Durugbo and Pawar, 2014, Edvardsson

et al., 2011, Gronroos and Voima, 2013, Payne et al., 2008, Shamim and Ghazali, 2014] or

more than two actors in C2C relationships [Payne et al., 2008, Hassan and Toland, 2013].

Interactions can be directed through an active dialogical process with the firm [Gronroos,

2011b, Lorenzo-Romero et al., 2014] or indirect through outcome and resource of a firm’s

process [Gronroos and Voima, 2013]. Interactions are platforms for co-creation and result in

value formation [Gronroos, 2011b].

Engagement Mode and Role was found to include how customers contribute in a

co-creation system. From one perspective, the “customer is always a co-creator of value”

[Vargo and Lusch, 2008, p. 8] and the firm facilitates value by providing resources and

supporting the customer in the co-creation process [Payne et al., 2008, Vargo and Lusch, 2004,

2008]. However, value can also be the result of a direct customer-provider collaboration

[Prahalad and Ramaswamy, 2004, Durugbo and Pawar, 2014, Gronroos and Ravald, 2011,

Shamim and Ghazali, 2014]. The organisation as the main provider of service allows customer

involvement in the production process to influence product and share experiences [Harwood

and Garry, 2010]. Co-creation emphasises the customers’ active role in service creation and

delivery rather than a passive role in simply receiving the value propositions offered by

organisations [Durugbo and Pawar, 2014].


2.2.1.2 Identifying Classification of Service Co-creation Systems

In the second phase, the classification represented three service integration network models

following the organisation context introduced by Keast et al. [2007]. The seven identified

themes (Section I) compared different examples of platforms such as “Netflix prize”, “LEGO”

and “Airbnb”. Three classifications for co-creation platforms were identified (Figure 2.4) that

differ based on the role and responsibilities of the customer and provider, and the level of

customer engagement in service delivery and value distribution. First, in a cooperative service

co-creation system (CS1), customers contribute in activities requested by the organisation to

complete a task, and the organisation will aggregate the contributions. Second, in coordinative

service co-creation systems (CS2) there is a higher level of customer engagement in which

customers create value along with the provider (organisation). Third, collaborative service

co-creation systems (CS3) includes an A2A service co-creation where actors collaborate in the

creation of collective value (community-orchestration).

Figure 2.4: Comparison of different types of service co-creation systems.

Source: [Bidar et al., 2017, p. 338]


I) Cooperative Service Co-creation Systems (CS1)

CS1 is organisation-centric and refers to the service systems in which organisations use

crowds’ collective intelligence [Malone et al., 2010], with the aim of harnessing the potential

input of a large number of people [Geiger et al., 2011a] for business motivation. The main aim

of the organisation (primary provider (PP)) is to gain benefits from crowds’ capabilities and

insights [Malone et al., 2010], save costs and access outsiders’ capabilities [Rouse, 2010],

problem solve and host idea competitions [Leimeister et al., 2009, Jeppesen and Lakhani,

2010]. Organisations as a seeker assign a task with a specific objective to target workers or an

undefined crowd of anonymous individuals [Rouse, 2010], to achieve an explicit goal [Geiger

et al., 2011b]. Contributors play the role of workers to accomplish the requested task in large

quantities where the aggregation of contributions matters rather than individual contributions

[Geiger et al., 2011a]. Aggregations can be done by integrative or selective approaches in

which contributions develop equal outcome or values are distinct [Geiger et al., 2011a]. The

goal is achieved by a process of sourcing and aggregating contributions from the crowd

[Geiger et al., 2011b] in short-term relations. Table 2.3 illustrates the summary of CS1

characteristics.

CS1 includes one-to-many relationships where the task is distributed to many contributors

with few beneficiaries [Rouse, 2010]. The highest value is for the organisation and contributors

may have a financial or altruism value for their participation. The focus of this type of service

system is on tasks and projects which are targeted by the organisation and value is provided

to the business by the crowd by the aggregation of contributions. Netflix and Huffington Post

are two examples of such a co-creation model. In 2009, Netflix set a prize competition to

develop better algorithms for movie recommendations. They used the crowd to improve the

accuracy of predictions based on ones’ movie preferences. Huffington Post aggregates news

from individuals by asking them to pitch posts to their blog editors. However, there are some

difficulties in the CS1 model, including selecting contributors, organizing outsourcing to be

sure about a satisfactory outcome, providing incentives for active contributors, and assessing

the process and product [Zwass, 2010]. Responses to these difficulties lead user collaboration

to a higher level of engagement and a deeper relationship with the customer.


Table 2.3: Cooperative service co-creation system (CS1).Source: [Bidar et al., 2017, p. 336]

Dimensions Cooperative Co-creation (CS1)

Network Focus Organisation-centric (Main power with organisation)

Service Information and functional value (e.g., Idea, tasks)

Resources Individual impact lowOrganisation as resource integratorShared resources

Value Beneficiaries in relation to service under organisationcontrol and delivery.Potential value for community.

Roles Two different areas for provider and customer.Fixed provider role and diversified user role beyondcustomer crowdsourced value provider.

Interaction Mode C2B transaction contributionB2C service deliveryControlled orchestrationTrust is vested by provider side

Engagement Mode Organisation recruits contributors for problem solvingand innovative ideas.Service system is providing platform, Platform advertisestasks and tasks visibility is to community.Risks vested by provider.Tasks have a contribution of financial or altruism.

Example Netflix prize/ Huffington Post

II) Coordinative Service Co-creation Systems (CS2)

The focus in CS2 is on the customer-organisation interaction as the locus of value creation

[Prahalad and Ramaswamy, 2004]. This type of co-creation, departing from harnessing users’

ideas as downloadable information to benefit the organisation [Roser et al., 2009], is a process

of customers engaging with the organisation to expand value together [Gronroos and Voima,

2013]. Thus, the aim of companies changes from the firm-centric perspective to a personalized

customer experience [Prahalad and Ramaswamy, 2004, Vargo and Lusch, 2004]. CS2 creates a

more engaged process that goes beyond one-to-many relationships (Engagement Mode

dimension) that eliminates some of the obstacles in CS1.


The focus of CS2 is on services that are provided by the joint collaboration of the

organisation as the primary provider and the customer (Focus dimension). Customers

influence future products/services but not in a direct way. The customer (Secondary provider

(SP)) can create their own unique, personalized consumption experience [Prahalad and

Ramaswamy, 2004]. The organisation (PP) needs to understand their customers’ desire to

improve their service and their customers’ satisfaction. Customers are actively creating value

rather than passively using the value (Role dimension) [Zwass, 2010]. Therefore, value derived

from the gained experiences and service use for both organisation and customer is a two-way

relationship. Table 2.4 shows a summary of CS2 characteristics.

Table 2.4: Coordinative service co-creation system (CS2).Source: [Bidar et al., 2017, p. 337]

Dimensions Coordinative Co-creation (CS2)

Network Focus Organisation-customer centric(Main power with organisation)

Service Information and functional value (e.g., Idea, Design)

Resources Customer main resourceIntegration of resourcesDiverse set of resourcesShared resources

Value Value-in-use/ value-in-experienceValue to customer and providerPotential value for community.

Roles Two different areas for provider and customer.Engage in a joint area and mutually co-create value.Organisation is primary provider (PP)Customer can be secondary provider (SP) and end-user.

Interaction Mode C2B transaction contribution/ B2C service deliveryTwo-way relationship between customer and provider (reciprocal)Controlled orchestrationTrust is vested by provider side and expanded to customer

Engagement Mode Engagements are controlled by organisation.Customer co-construct the service experience and personalizethe service to develop product/service.Risks vested by provider.Engagements have a contribution of financial or altruismto fulfil customer’s need.

Example Nike/ LEGO


The LEGO the company evolved from listening to the adult LEGO communities of

practice LUGNET (LEGO user group network), to creating forums to build the relationship

with customers. Today, LEGO offers participation in virtual design and then buying the

manufactured version [Roser et al., 2009]. Nike offers a software tool for soccer teams and

professional leagues to customize their soccer shoes, to tap the collective creativity and

engaged community to build unique brands [Ramaswamy, 2008]. MyStarbucksIdeas.com

allows customers to engage with the organisation’s internal preference market to improve their

service and products.

III) Collaborative Service Co-Creation Systems (CS3)

In CS3 customers are part of the value co-creation system [Prahalad and Ramaswamy, 2002]

and expect a 360-degree view of the experience [Prahalad and Ramaswamy, 2004]. In this

type of service system, from a value network perspective, “all actors collaborate and integrate

resources to create value for themselves and others” [Pinho et al., 2014] and value emerges

from their collaborative interaction [Vargo and Lusch, 2008]. The outcome of co-creation is a

collective value that benefits whole networks (Value dimension).

In CS3, customers, as actors, are instrumental in creating and delivering the service to each

other (role dimension). The types of exchanged service include knowledge sharing or

delivering particular assets which have been created and delivered by the customer network.

The idea underlying co-creation through service networks with multiple connectivities is that

all actors who play a role will get value at all times. A high level of interaction between actors

is required in this model (engagement dimension) with dyadic or many-to-many interactions to

create jointly beneficial relationships [Pinho et al., 2014]. Both community and individuals

gain value from the interactions while the organisation gains value financially and builds brand

loyalty (interaction dimension). Value in this type of service system is a combination of

utilitarian and hedonic outcome (e.g., quality, service and price) that leads customers to engage

in co-production and co-delivery of the service.

Using Frow et al. [2011]’s definition of co-creation, this study defines co-creation within

CS3 as: the active contribution of two or more actors with different roles, the integration of

unlimited resources that bring beneficial value to the whole network, a willingness to interact

and co-create the service, co-production and co-delivery of the service and co-construction of


experiences within the actors’ network independent of the firm. Therefore, this research

defines A2A service co-creation as a function of interaction to integrate resources within a

shared value network, facilitated through an integrated platform with micro-level

organisational involvement. Table 2.5 shows the summary of CS3 characteristics.

Table 2.5: Collaborative service co-creation system (CS3).Source: [Bidar et al., 2017, p. 338]

Dimensions Collaborative Co-creation (CS3)

Network Focus Customer-centric (shared power)

Service Information and functional value (e.g., Idea, design)/transactional

Resources Customer main resourceIntegration of resourcesDiverse set of resourcesCollective resources

Value Value-in-use/ value-in-experienceValue to customerValue to providerPotential value for community

Roles One integrated area for different roles (actors)Organisation is only facilitator of service between customersUsers can be PP and customer

Interaction Mode C2C service co-creation, co-deliveryTwo-way/multiple interactions between membersCommunity orchestrationService process happens in the C2C networkTrust is vested throughout community

Engagement Mode High level of customer engagementUsers contributes in co-production and co-delivery of serviceand construct.the service experience with each otherRisks vested through communityEngagements have a contribution of financial or altruism tofulfil stockholders’ need

Example Airbnb / Uber/ Stack Exchange

Examples of CS3 platforms with transactional service delivery are Uber and Airbnb. Uber,

a car ride-sharing company, connects riders and drivers together. Airbnb enables people to

discover and book accommodation in other members’ homes globally. Examples of


informational service platforms are StackExchange and PatientsLikeMe. StackExchange, is a

Q & A community to provide users a better and smarter solution, from experts to different

contexts of programming, health and science. PatientsLikeMe, a healthcare network, enables

people to monitor their health, connect to patients similar to them, help others by sharing their

experiences and insight into different symptoms/treatments, and support them to improve their

condition. The generated data about the real world nature of disease helps researchers, health

providers and health companies to develop more effective care services. The role of the

organisation as the provider evolved into acting as a medium to connect actors. However,

actors use platforms provided by an organisation that benefits economically from their work

[Zwass, 2010].

2.2.2 Summary of Systematic Literature Review

This section presented a classification of service co-creation systems using co-creation from

SD logic, crowdsourcing from an open innovation paradigm, and 3Cs from a service

integration continuum. Three types of service co-creation systems were identified, namely

cooperative co-creation (CS1), coordinative co-creation (CS2) and collaborative co-creation

(CS3) service systems. These three classifications were based on the seven dimensions that

form characteristics for each kind of service system: Network Focus, Service, Resource,

Value, Roles, and Interaction Mode and Engagement Mode.

These service co-creation systems demonstrate how customers become an integral part and

focal point in the success of service co-creation systems. The focus of service systems changes

from organisation-centric to customer-centric. Facilitating co-creation networks and

experience environments became a priority for organisations [Prahalad and Ramaswamy,

2004] by assigning more responsibility for the creation and delivery of the service to the user.

The willingness, motivation and skills of participants contributes to value formation

[Gronroos, 2011b]. The consequence of this transformation is a higher chance of value

extraction for customers. The outcome of the co-creation process is the driver for future

engagement of co-creation processes [Payne et al., 2008, Hassan and Toland, 2013].

This review contributes to a better understanding of service co-creation systems, and in

particular to the clarification of the nature of the A2A service co-creation model and how it

differs from the previous co-creation models. Practitioners can consider the different levels of


customer involvement in their businesses to assess risk, quality of service and performance.

They gain insight to choose appropriate strategies to collaborate with customers by better

understanding the communication and service system environment.

2.3 Theoretical Background and Conceptualization

In this section, a model is developed to investigate the concept of co-creation behaviour. Based

on the first and the fifth gap, there is a necessity for research on actor value perception and

preconditions that facilitate co-creation behaviour. Thus, this research used the SOR model

[Mehrabian and Russell, 1974], with the integration of UGT [Katz et al., 1973] as the

Organism (O) aspect of the SOR model, to investigate co-creation behaviour and how it is

influenced by actor value perception (O), and drivers in the service ecosystem as preconditions

of actor co-creation behaviour. Although the focus of this research is on the service co-creation

behaviour, because of the lack of research in this context, the conceptual model is presented

through the current studies on value co-creation behaviour which includes service co-creation

as the subset of value co-creation.

Section 2.3.1 and 2.3.2 discuss the theoretical background of the research, the SOR model

and UGT. Section 2.3.3 continue discussion of co-creation behaviour (from Section 2.1.4), and

the result of the conceptual model is presented in Section 2.1.5. The conceptual model is

comprised of environmental stimuli (network structure, service platform capabilities, roles and

social influence), actor value perception (cognitive, social and personal integrative and hedonic

values) and value co-creation behaviour (participation and citizenship behaviour).

2.3.1 Stimulus-Organism-Response (SOR) Model

The SOR model proposed by Mehrabian and Russell [1974] used the theoretical framework to

explain the concept of consumer behaviour and the consumer decision making process [e.g.,

Eroglu et al., 2003]. According to Mehrabian and Russell [1974], the SOR model represents

how environmental stimuli affect individuals’ cognitive and affective (i.e., utilitarian and

hedonic) reactions and lead to some behaviours. The environment in which decision making

occurs is determined as the stimulus to the decision maker and can carry positive or negative

outcomes. The environment then induces internal states that influence behaviour. The

2.3. THEORETICAL BACKGROUND AND CONCEPTUALIZATION 39

organism’s intermediary states or processes mediate the relationship, the environmental

stimulus and the behaviour response.

The SOR model has been mostly used to investigate online shopping behaviour [Eroglu

et al., 2003, Sheng and Joginapelly, 2012, Jiang et al., 2010] to explain the major impact of the

service system environment on consumer behaviour. The SOR model suggests that the effects

of technological environmental stimuli on customer behaviour are mediated through customer

experiences [Animesh et al., 2011]. Stimuli (S) refers to external or environmental cues that

are related to objects or social-psychological characteristics surrounding the purchase [Arora,

1982], such as design features of sales websites [Eroglu et al., 2003], the web environment

[Wang et al., 2011] and the retail environment [Mehrabian and Russell, 1974, Eroglu et al.,

2001]. Organisms (O) refers to internal cues based on the individual’s experience, perceptions

and beliefs [Jiang et al., 2010], such as hedonic and utilitarian shopping values [e.g.,

?McKinney, 2004]. Response (R) represents the behaviour that is shaped by external cues and

internal cognitions.

This research applies the SOR model as the primary framework for three main reasons.

First, this model is appropriate to focus on different dimensions that stimulate actors to

co-create service and value, which is important to enrich collaboration in co-creation activities.

The model helps us to better understand co-creation behaviour through the effect of

environmental factors in the service ecosystem and the cognitive perspectives of actors.

Second, although this model has been used in the online shopping environment [e.g., Jiang

et al., 2010], only one research project [Zhang et al., 2015] has used this model in the

co-creation context. Zhang et al. [2015] used the SOR model to understand the customer

intention of future participation in value co-creation with organisations through the use of

social media sites. However, this research differs from the current research in significant ways:

This research focused on A2A service creation, delivery and, support by actors to each other

than product marketing and using user experience to improve a product (i.e., B2C, C2C). In

addition, the focus of value co-creation in this research occurs through service creation and

bringing innovation to product/service and problem-solving (new ideas/solutions), through

specialized knowledge rather than a mechanism for information diffusion. Third, the SOR

model has been applied extensively in exploring customer behaviour in the online shopping

and e-commerce context that confirms it is a pertinent model in investigating co-creation

behaviour as the response to the environmental stimuli and actors’ internal perceptions.


The service co-creation system provides an interactive environment for actors and includes

their roles, beliefs, norms and institutional agreements, based on Axiom Five (see discussion

in Section 2.1). This includes the environmental characteristics that facilitate and support

collaborations, and actors’ value perceptions that serve as the primary concept in co-creation

for the evaluation of benefit from their collaboration. Therefore, in this research, the stimuli

are the service co-creation environment characteristics, and the organism value perception (i.e.,

internal processes-using UGT), mediating external stimuli to the actors’ reactions and

behaviour (co-creation behaviour). Also, the positive and negative outcomes of interactions

loaded by the environment suggested in the SOR model, is in line with the value formation in

the SD logic.

2.3.2 Uses and Gratification Theory (UGT)

Uses and Gratifications theory (UGT) [Katz et al., 1973] helps us to understand the

psychological needs which form people’s reason to engage and use a particular form of mass

communication, and their motivation to engage in certain behaviours to meet specific needs

[Rubin, 2002]. UGT presumes that individuals are aware of their needs, act in a goal-oriented

manner and are able to evaluate value judgements [Katz et al., 1973]. UGT describes four

types of benefits including cognitive, social integrative, personal integrative, and hedonic

which manifest the nature of benefits customers expect to gain from their participation in

virtual communities (VCs)[Nambisan and Baron, 2007, 2009].

UGT has been used extensively as a grounding theory in the social media/ social network

[e.g., Malik et al., 2016] and communication literature, to explore uses and practices or

obtained gratifications and understand user behaviour. In the co-creation context, researchers

have used UGT to explain different motives and benefits derived from customer engagement in

online co-creation [Nambisan and Baron, 2007, Nambisan and Nambisan, 2008, Katz et al.,

1999]. Nambisan and Nambisan [2008] discuss these benefits - pragmatic, sociability,

usability, and hedonic - as four experience dimensions to fulfil customers’ needs in virtual

co-creation systems. They found that these gained benefits significantly influenced customers’

participation in online communities, determined their actual continued participation

[Nambisan and Baron, 2007] and predicted future participation in co-creation [Zhang et al.,

2015]. Indeed, in co-creation activities value can be determined by perceived or actual benefits


from a service for the customer [Durugbo and Pawar, 2014, Shamim and Ghazali, 2014,

Lorenzo-Romero et al., 2014].

This research applies UGT as the organism aspect of the SOR model, manifesting actor

value perception. Referring to the first gap which explains the lack of value perception in the

co-creation studies, UGT represents different aspects of actor value perception and the benefit

actors expect to gain from their collaboration.

2.3.3 Co-creation Behaviour Model

Following the discussion of co-creation behaviour (Section 2.1.4), this section argues that the

previous co-creation studies failed to investigate the environmental and cognitive drivers in the

service ecosystem that influence actors’ co-creation behaviour. Shamim and Ghazali [2014]

identified experimental value and social influences as two dimensions affecting customer

participation behaviour and customer citizenship behaviour (introduced by Yin, 2013) in retail.

Neghina et al. [2015] conceptualized value co-creation by determining six dimensions

(individuating, ethical, developmental, relating, joint actions and empowering) and nine

antecedents (labelled as communicating, relating, and knowing factors). Tommasetti et al.

[2015] identified eight dimensions for value co-creation behaviour, each divided into

sub-dimensions: cerebral activities, cooperation, searching and sorting information, changing

habits, co-production, co-learning and connecting). Zhang et al. [2015] studied how to

improve customer co-creation experiences on social media sites that impact intention to

participate in the future. The results revealed that a customer’s future participation in product

marketing is influenced by co-creation experiences (learning value, social integrative value and

hedonic value) and environmental stimulus (perceived task relevant and affection-relevant

cues). However, all these studies investigated co-creation behaviour from a B2C perspective.

The conceptual model in this current research is different from the literature in two important

ways. Firstly, the model includes factors that affect actor collaboration in value co-creation

considering both the co-production and delivery phases (participation behaviour) as well as

the supportive phase ( citizenship behaviour). In contrast, prior studies have focused on these

phases in isolation without considering their mutual influences. For example, co-production and

co-delivery studies include design and development [Nambisan and Nambisan, 2008, Nambisan

and Baron, 2007, Hoyer et al., 2010, Zhang et al., 2015, Fuller et al., 2009] while the support


phase study includes Yi and Gong [2013]. There is one study that conceptualised PB and CB

together [Shamim and Ghazali, 2014] but is focused on C2C in retail not A2A. Second, although

current studies have examined value co-creation behaviour, no work has been conducted to

investigate and categorize the environmental and cognitive factors underlying actor participation

and citizenship behaviour in the A2A service co-creation context.

The result of reviewing the literature showed that five main concepts are significant in the

occurrence of co-creation activities within the service ecosystem that can be categorised as

environmental and cognitive factors (Table 2.6). The model is categorised to three sections

based on the SOR model and UGT is used as the O aspect of the model representing value

perception. First, environmental stimuli (external stimuli) includes the four concepts of

network structure, service platform capabilities, roles and social influences. Second, actor

value perception (as cognitive stimuli) includes cognitive, social integrative, personal

integrative and hedonic values. Third, value co-creation behaviour as response includes

participation behaviour (PB) and citizenship behaviour (CB). Accordingly, this section

investigates whether environmental stimuli affecting actors’ perceived value are influential in

actors’ participation and citizenship behaviour. The derived co-creation behaviour model is

presented in Figure 2.5.

Table 2.6: Environmental and cognitive factors from service ecosystem and co-creation.Concepts Resources

Network structure e.g., Edvardsson et al. [2011]; Kane et al. [2014];(Environmental) Lusch and Nambisan [2015] ;Lusch et al. [2010];

Romero and Molina [2011].

Service platform e.g., Fuller and Matzler [2007]; Fuller et al. [2009];capabilities ; Kohler et al. [2011];Lusch and Nambisan [2015];(Environmental) Ramaswamy [2006]; Romero and Molina [2009, 2011].

Roles e.g., Edvardsson et al. [2011]; Fuller et al. [2009];(Environmental) Lusch et al. [2010]; Nambisan [2002];Zwass [2010]

Romero and Molina [2011]; Vargo and Lusch [2008];Hoyer et al. [2010];Nambisan and Baron [2009].

Social influence e.g., Lusch and Nambisan [2015]; Shamim and Ghazali [2014].(Environmental)

Value e.g., Edvardsson et al. [2011]; Hoyer et al. [2010];(Cognitive) Lorenzo-Romero et al. [2014]; Nambisan and Nambisan [2008];

Nambisan and Baron [2007]; Ramaswamy [2008];Katz et al. [1999];Zhang et al. [2015].


Figure 2.5: Value co-creation behaviour model

Source: [Bidar et al., 2016]

2.3.4 Environmental Stimuli in Co-creation (S)

This research considers service ecosystems as an A2A connected network in which social and

economic actors are connected by mutual value creation through service exchange and

interactions [Lusch and Nambisan, 2015, Lusch et al., 2010]. Each service ecosystem is

comprised of three different aspects that lead the community toward service exchange and

co-creation behaviour: environmental characteristics, platform characteristics and value

exchange [Lusch and Nambisan, 2015]. This research follows the overall view of Lusch and

Nambisan [2015], regarding how service innovation (that includes co-creation) happens in the

network environment. According to Lusch and Nambisan [2015] actors act within a structure

of social rules and collective meanings that enable them to collectively create and shape their

environment with an A2A relationship, and provide value for themselves and others. Each

actor seeks the best combination of resources to enhance their viability within the network.

However, the efficiency of service exchange and interactions among actors largely depends on

the applied service platform characteristics that facilitates easy access to appropriate service

bundles. Finally, the integration of resources brings the focus to value co-creation. Co-creation

behaviour occurs as actors track a better density, and service platform characteristics facilitate

value/service exchange. Hence, the co-created value extracted from the delivered service is

highly influenced by the characteristics of the service co-creation environment.

Network Structure

Network structure is the way social and economic actors are connected within the network.

Actors create the structure with others based on shared competences, relationships, and


information resources [Vargo and Lusch, 2004, 2008] and, as such, the expected value

propositions build their connections [Lusch and Nambisan, 2015]. Lusch and Nambisan

[2015] explain the importance of actor’s structure and digital infrastructure within the service

ecosystem by determining different ways of organizing actors to reach innovation

opportunities (structural flexibility). They believe that understanding and designing the nature

of ties (or relationships), and the structure of participants influence diverse actors to engage in

a network (structural integrity), and represent how shared rules and institutional logics of a

system cause users to engage in a service exchange. Thus, individuals within the system are

influenced by the structure which carries rules and resources, and leads to interaction and

service provision among provider and customer [Edvardsson et al., 2011].

Researchers have looked at the properties of networks using structural measurements,

models and algorithms to find out new forms of behaviours in online social networks [e.g.,

Mislove et al., 2007, Liben-Nowell et al., 2005]. The type of connectivity (proximities,

relations, interactions, flows) and ties characteristics (degree, affect, strength, symmetry) that

form these structures affect network formation, with implications for a platform’s design

which consequently influences the behaviour and dynamic of the network [Kane et al., 2014].

It is important to understand how the structure of the network and pattern of ties leads to

performance variation among actors, and how these features affect actors’ networking

behaviour that shapes the formation and characteristics of the network [Kane et al., 2014].

Understanding the structure of networks leads to algorithms that can detect trusted or

influential actors [Mislove et al., 2007], identifying similarities and differences in behaviours

[Haythornthwaite, 1996] and helps in the prediction of valuable and active areas and influential

co-creators within the network.

Features of a social network structure which result from different interaction ties can be the

primary source of benefit in the network [Kane et al., 2014]. In service co-creation, system

interactions are built based on the finding of the proper resources, improving the value of

connecting users to others which presents the importance of structural integrity or connectivity

of nodes in the system [Lusch and Nambisan, 2015]. Indeed, customer’s interactions in the

co-creation process are a major source of value [Prahalad and Ramaswamy, 2002] and value

propositions should develop through the interactions [Romero and Molina, 2011]. Edvardsson

et al. [2011] argue that actors’ value perception is dependent on a user’s position within the

social context which is itself influenced by the size of the network. Also, actors’ value


perception dimensions (learning, social integrative and hedonic) are influenced by the extent of

their interactivity in the network [Romero and Molina, 2011].

Overall, it can be argued that the following are significant in understanding of the value

co-creation behaviour: how users are connected through ties and the similarity of their

behaviour based on their position in the network and connectivity of nodes, the strength of

their interaction and the relational pattern of their co-creation activities. Thus, it can be

expected that network structure could influence actor value perception and leads to

participation and citizenship behaviour which eventually leads to co-creating value.

Hypothesis 1: The environmental stimulus, Network Structure, contributes to

Participation and Citizenship Behaviour by affecting actors’ value perception,

which eventually leads to co-creating value.

Service Platform Capabilities

Service platforms act as mediators among networked parties to find an appropriate resource

match and to exchange/deliver a service. Lusch and Nambisan [2015] define a service platform

as a modular structure that facilitates the interaction of actors and resources. Other scholars also

recognize the significance of the service platform in the service ecosystem. They believe that

the technological tools (including service platforms) facilitate information exchange [Burgoon

et al., 1999, Meyronin, 2004], enhance overall communications with the customer [Barnes et al.,

2005] and lead to co-creation by facilitating interactions among users [Grace et al., 2008].

The nature of the service platform that is provided for interaction directly affects the service

innovation [Lusch and Nambisan, 2015] in the co-creation process.

The features of the platforms are related to different users’ performance due to the network

structure [Kane et al., 2014] and lead them to a particular behaviour. Lusch and Nambisan

[2015] discuss that a layered architecture of service platform is associated with a different

design hierarchy and product variety, and a modular architecture is associated with a single

design hierarchy and a fixed product boundary. Such architectures enable service platforms to

exchange different services within functional or multiple design hierarchies and lead to

variation in value propositions [Lusch and Nambisan, 2015]. This implies that different

features in different service platforms indicate how people need to interact and what specific


skills and knowledge are required for their interactions. Control over the design of platform

and how to apply nodes and ties characteristics can homogenize user behaviour on the service

platform and influence the formation and outcome of the networks [Kane et al., 2014]. For

example, the relational ties in Facebook are characterised as “friends” in which the connection

is made by one sided requests. However, in Twitter ties are embodied by “followers” in which

both relational parties must confirm the tie [Kane et al., 2014].

Co-creation in virtual communities is required to be leveraged by the representational

richness of the medium to inspire and stimulate co-creators [Kohler et al., 2011]. Since

customers’ interactions are essential in the value co-creation process [Romero and Molina,

2009], building and managing an effective co-creation platform that regulates co-creation

interactions needs to be considered. In building co-creation channels, designers need to design

each experience gateway based on the DART building blocks (dialogue, access, risk,

transparency). In addition, they need to ensure service quality throughout the interaction

channels and co-creators, and consider multiple choices and simple transaction processes for

the co-creation experience [Romero and Molina, 2011]. Co-creation platforms enhance a fast

and an easy way for consumers to participate in co-creation experiences [Ramaswamy, 2006].

Indeed, customer experiences of interaction/co-creation on a site are influenced by the

characteristics of the site [Zhang et al., 2015]. Therefore, the design of the environment should

ensure that co-creators feel as they are participating in something which is real [Kohler et al.,

2011].

The designed value co-creation system must align with customers’ expectations and value

perception. Co-creation platforms allow collaborative networking that leads to value

co-creation and satisfying customer’s specific needs in an efficient and quick way [Romero and

Molina, 2009]. An effective interaction tool must provide functions that allow product

understanding, articulation of ideas, enhance consumers’ creativity and enable customers to

actively engage in virtual co-creation [Fuller et al., 2009]. Based on Kohler et al. [2011], to

encourage the acquisition of domain knowledge (pragmatic dimension), designers need to

develop interactive objects of the service platform and follow features (such as incorporated

animation and video) that fulfil user informational goals. To increase the sociability

dimension, platform designers need to consider features that encourage user collaboration and

engage in conversations through avatar-to-avatar or avatar-to-company interactions [Kohler

et al., 2009]. Regarding usability and hedonic cues, design of a clean, technical, easy to use


interface and nurturing, entertaining activities are highlighted [Kohler et al., 2009]. Interaction

tools have a positive effect on enjoyment and customer empowerment [Fuller et al., 2009,

Fuller and Matzler, 2007]. Co-creation platforms should consider empowering tools for

customers with low levels of qualification to make valuable contributions [Fuller et al., 2009].

The feeling of empowerment by customers results in their involvement, enjoyment of

community and gives them a sense of control and self-determination [Fuller et al., 2009].

Co-creation tools enable customers to problem solve an assigned task and create innovative

products [Fuller et al., 2009]. Customers tend to participate more when they find the content

useful [Koh et al., 2007]. Clearly, the quality of the medium and its features moderate the

relationship between community drivers and the level of community activity. Therefore, the

design of suitable interaction tools is critical for the success of co-creation projects [Fuller

et al., 2009]. According to the above discussion, it can be argued that the platform’s

capabilities significantly control actors’ value perception and lead to value co-creation

behaviour by engaging them in both participation and citizenship behaviour.

Hypothesis 2: The environmental stimulus, Service Platform Capabilities,

contributes to the Participation and Citizenship Behaviour by affecting actors’

value perception which eventually leads to co-creating value.

Role

Engaging in co-creation activities depends on the role of actors to deliver the service and

co-create the value. The role of actor refers to “socially defined expectations of individuals’

behaviours, in particular, social positions” [Edvardsson et al., 2011, p. 331]. According to SD

logic, all economic and social actors adopt the role of resource integrators [Vargo and Lusch,

2008, FP9] rather than individual users. Value co-creation occurs through provider-beneficiary

interactions, as the result of resource integration [Vargo and Lusch, 2008]. Service co-creation

systems often have pre-defined roles built directly into their service delivery model e.g., an

Uber driver with an Uber traveller, or in PatientsLikeMe they can act as patient, caregiver,

clinician and researcher.

Consumers vary in their capability and interest to participate in co-creation activities

[Hoyer et al., 2010]. The integration of customers’ capabilities results in the co-creation of

value. Researchers categorized co-creator roles as: innovators [Romero and Molina, 2011,


Hoyer et al., 2010], lead users [Romero and Molina, 2011, Hoyer et al., 2010, Fuller et al.,

2009], emergent consumers [Hoyer et al., 2010], market mavens [Hoyer et al., 2010] and

co-designers [Romero and Molina, 2011]. Innovators are prior customers that adopt new

products and provide their own product and service using toolkits (e.g., modelling,

prototyping) [Romero and Molina, 2011]. Lead users are users that actively seek innovation,

face needs before others in the marketplace and are experts on the forefront of product

development [Von Hippel, 1986, Romero and Molina, 2011]. They need to articulate their

innovation skills and love to feel a sense of mastery [Fuller et al., 2009] which represents their

perception toward a personal integrative approach. Emergent consumers “are capable to

improve product concepts that mainstream consumers will find appealing and useful”

[Hoffman and Bateson, 2010]. Marketers have a high level of information about

products/services, and have a high potentiality to initiate discussions and respond other users’

requests for information [Feick and Price, 1987], help the spread of reputation and support

others by sharing experiences [Romero and Molina, 2011]. This represents the sociability

capability and marketers’ tendency to citizenship behaviour. Co-designers exhibit participation

behaviour by engaging in product development including idea-generation, design and testing

[Nambisan, 2002]. Different roles jointly participate in co-creation activities to bring value for

themselves and others. These roles engage in co-creation activities depending on their

competencies and behaviours in the co-creation process [Romero and Molina, 2011].

Co-creators’ engagement in in-role or extra-role activities depends on their expectations

and motivations. Hoyer et al. [2010] explain that co-creators are motivated by financial rewards

directly by monetary prizes or indirectly by intellectual property they may receive. They may

expect social benefits from the title, status and social esteem, or good citizenship such as “top

100 reviewer in Amazon” [Hoyer et al., 2010, Nambisan and Baron, 2009]. Co-creators may

also expect to gain knowledge about the product/ service or environment which is related to the

cognitive benefit of information acquisition [Hoyer et al., 2010, Nambisan and Baron, 2009].

They may desire to enhance their sense of self-improvement and enjoyment [Fuller et al., 2009].

Therefore, the role actors play in the co-creation activities affects their value perception and

their engagement in the activities.

Hypothesis 3: The environmental stimulus, the actors’ Role, contributes to their

Participation and Citizenship Behaviour through affecting actors’ value perception


which eventually leads to co-creating value.

Social Influence

Social influence refers to process conformity, and change in attitudes and behaviours to be

similar to others [Vaughan and Hogg, 2008, La Fond and Neville, 2010], and people not

always being aware of the influence on their behaviour [Klobas and Clyde, 2001]. Influence

can occur based on the acceptance of others’ information as evidence about reality

(informational influence) or can be based on the need to conform to others’ behaviour in a

group (normative influence) which represents the need for approval (social rewards) without

internalised change in an individual’s attitudes [Bartle, 2011, p. 42].

Any communication (both offline and online) contains some element of social influence

that causes both sides to experience behaviour change based on the nature of their interaction

[Guadagno et al., 2013]. Social influence is recognised as a way to determine behaviours in

networks [Anagnostopoulos et al., 2008], as a strong factor motivating human behaviour

[Ajzen and Fishbein, 1980] and as a main factor in adoption of information technology [Li,

2011]. Klobas and Clyde [2001] suggested that social influences have considerable impact on

peoples’ perceptions about the Internet, its value, and the ability to use it. Social influence can

exert an effect directly through cohesion of the structure of ones’ beliefs or indirectly through

structural equivalence (occurs when two nodes are connected to the same user) in a network

[Burt, 1987]. In this situation, actors have similar patterns of relations to other individuals in

the group. Therefore, actors are located in the same social environment and can be affected by

each other easily [Giuffre, 2013]. As such, the dynamics of a network (i.e., changes in the

network topology over time) depend on social influences that happen through that network

[Nguyen et al., 2013] . Social influence in networks cause epidemic distribution of ideas,

modes of behaviour, or new technologies [Anagnostopoulos et al., 2008].

Within a social system (the same as in service systems) actors form “mental models” of

each other’s behaviours, that results into reciprocal roles in relation to each other [Edvardsson

et al., 2011]. Similarly, within a service network customers can impact others both directly and

indirectly, through specific interpersonal encounters and by being part of the same environment

[Huang et al., 2010]. They are influenced by social norms and values that they produce during

their interactions [Giddens, 1984]. The roles are significant in terms of “how people perceive


the norms, values of the system and social reality, including their thinking and behaviour with

respect to the co-creation of value” [Edvardsson et al., 2011, p. 328].

Although studies have examined social influence on participation within virtual

communities, little attention has been paid to social influence in co-creation platforms.

Bagozzi and Dholakia [2002] found internalization (group norm) and identification (social

identity) as significant predictors of participation in virtual communities (VCs), whereas

compliance (subjective norm) was not significant enough. Similarly, Tsai and Bagozzi [2014]

found that internalization (group norm) and identification processes play relatively more

important roles than compliance. Li [2011] proposed a TRA (the theory of reasoned action)

model involving determinants of sociability and status (i.e., two interpersonal motives),

perceived enjoyment (hedonic motive) and three social influence processes (compliance,

identification and internalization) to measure intention to use social networks. They found that

internalization is weaker than identification and compliance. In addition, social influence

affected intention directly through the compliance process. Although the existence of social

influence in online networks is confirmed, little attention has been paid to the importance of

social influence in the co-creation process. Regarding social influence in co-creation, Shamim

and Ghazali [2014] found social influence has a moderating function in the relationship

between experimental value and customer value co-creation behaviours. This research

emphasizes the importance of social influence on actors’ value perception and consequently on

their participation and citizenship behaviour.

Hypothesis 4: The environmental stimulus, Social Influence, contributes to the

user’s Participation and Citizenship Behaviour by affecting actors’ value

perception, which eventually leads to co-creating value.

2.3.5 Actor Value Perception (O)

Actors might have different beliefs and perceptions to engage in value co-creation activities.

Actor’s value perception as organism in this research shows customers’ beliefs and expectations

related to the potential value that will derive from their participation. Since Actors have an

active role in co-creation, their perception about outcome value is critical. In co-creation, value

is considered as providing either financial or non-financial benefits [Hassan and Toland, 2013]

and utilitarian or hedonic benefits [Hassan and Toland, 2013, Tuunanen et al., 2010]. Hassan


and Toland [2013] present different types of value in the C2C social commerce environment: 1)

utilitarian, extrinsic, practical, and functional value, 2) hedonic, emotional and intrinsic value,

3) systematic and logical value, 4) self-expressed value, and 5) social value. However, Spiteri

and Dion [2004] define value as the proper combination of quality, service and cost. Value can

be considered as financial benefits, customer satisfaction and utility value (intrinsic or extrinsic).

Therefore, out of these different types of benefits, value can be categorised into utilitarian,

hedonic, social and personal beliefs that drive customers to engage in co-creation.

Cognitive Value

Cognitive benefits refers to information acquisition and enhancement in environmental

understanding [Kohler et al., 2011, Katz et al., 1999]. Cognitive value refers to the desire to

gain knowledge about a product/service, underlying technologies and usage [Hoyer et al.,

2010, Nambisan and Baron, 2009] and the perception of the information acquisition quality

process [Kohler et al., 2011]. Researchers have found that the customer learning value (i.e.,

similar to cognitive value) predicts future participation [Zhang et al., 2015], enables customers

to use the product more efficiently and leads to continued participation [Nambisan and Baron,

2007, 2009]. Also, the greater involvement of customers, the higher the product-related

learning benefits [Nambisan and Baron, 2007]. Indeed, the greater the perceived customer

learning benefits, the higher the actual participation [Nambisan and Baron, 2009]. Kohler et al.

[2011] emphasised the significant role of this value on pragmatic design components, to

stimulate co-creators.

Social Integrative Value

Social integrative benefits are related to strengthening relationships with others [Kohler et al.,

2011, Katz et al., 1999]. Social Integrative refers to “the benefits deriving from the social and

relational ties that develop over time among the participating entities in the virtual community

environment” [Nambisan and Baron, 2009, p. 391]. The more in-depth interaction between

peers in the community, the higher perceived value of their relationships would be [Wasko and

Faraj, 2000] and better mutual understanding of problems [Algesheimer et al., 2005].

Nambisan and Baron [2007] also found customers’ beliefs associated with social integrative

benefits positively impacted customers’ future/continous participation in product support in the


VC. Similarly, Zhang et al. [2015] found social integrative value can predict future

participation and Nambisan and Baron [2009] found the greater the perceived customer social

integrative benefits, the higher the actual participation. Social benefits of co-creation results in

the increase of status, social esteem, “good citizenship,” and strengthens the bond with relevant

others. It is shaped by interaction of different users within the community [Nambisan and

Baron, 2009]. Because of the interaction among customers it may increase a sense of

belonging and social identity [Hoyer et al., 2010, Nambisan and Baron, 2009, Kollock, 1999].

Kohler et al. [2011] considered the social integrative aspect of designing co-creation

experience platforms and found it enhances social interaction and encourages actors to

collaborate and engage with each other and the company.

Personal Integrative Value

Personal integrative benefits are related to the credibility, self-efficacy and status of the users

[Kohler et al., 2011, Katz et al., 1999]. Personal Integrative refers to the desire to gain status

and reputation, and to gain a feeling of self-efficiency [Katz et al., 1999, Nambisan and Baron,

2009]. Virtual communities provide customers a venue to exhibit their knowledge, and

enhance their status and reputation with the firm and other customers [Nambisan and Baron,

2009]. Personal integrative value predicts future participation [Zhang et al., 2015]. Similarly,

based on the Nambisan and Baron [2009], the greater the perceived customer personal

integrative benefits, the higher the actual participation. Beliefs associated with achieving

personal integrative benefits intensify customer participation in the VC [Nambisan and Baron,

2007]. Also, within the co-creation process, customers intend to enhance intrinsic value such

as a sense of pride and self-expression [Etgar, 2008].

Hedonic Value

Hedonic benefits are related to aesthetic or pleasurable experiences [Kohler et al., 2011, Katz

et al., 1999]. Nambisan and Baron [2009] found that the greater the customer’s perceived

hedonic value, the higher the actual participation in virtual communities. In online co-creation,

participants’ interactions can be a source of entertainment, and enjoyment through mentally

stimulating activities [Nambisan and Nambisan, 2008]. Based on Kohler et al. [2011], the

nurture of playfulness and providing challenging tasks in the design of a platform have a


significant influence on customers’ perceived hedonic value and lead to participation in

co-creation activities. Zhang et al. [2015] also found that the hedonic value plays a greater role

in participation intention than other values.

Following previous findings, this research argues that the four dimensions of cognitive,

social, personal and hedonic value, considered as an actor’s value perception, will influence

the actor’s participation and citizenship behaviour. An actor’s value perceptions of a higher

level of benefits increases the level of their engagement and improves their level of interactivity

[Damkuviene et al., 2012]. Therefore, customers’ belief regarding the type of benefits and

value they derive from the interactions significantly influences their future participation in a

virtual customer environment [Nambisan and Baron, 2009]. Following the above discussion,

hypotheses five and six are:

Hypothesis 5: the actor’s cognitive, social and personal integrative and hedonic

value perception, strongly contributes to actors’ Participation Behaviour and leads

to co-creating value.

Hypothesis 6: the actor’s cognitive, social and personal integrative and hedonic

value perception, strongly contributes to actors’ Citizenship Behaviour and leads

to co-creating value.

2.3.4 Section Summary

This section investigated the critical factors that influence actor value, co-creation behaviour

and their collaboration in co-creation activities. Using the literature, this research hypothesised

that environmental factors including the network structure, service platform capabilities, and

role and social influence, lead actors to value co-creation behaviour through influencing their

cognitive, social, personal and hedonic value perception. In the following sections, further

research will be undertaken to develop the theoretical model of service co-creation behaviour

by contributing aspects which are missing in the SD logic literature.

This section concludes that practitioners need to be aware of environmental and cognitive

influences on actors, to increase value co-creation behaviour and enhance the success of service

co-creation platforms. Theoretically, the presented model provides evidence that environmental

and cognitive factors are critical in actors’ value co-creation behaviour. As such, this section


provides a better understanding of value co-creation behaviour in the context of SD logic and

helps the development of the two following research questions focusing on service co-creation

behaviour:

RQ2: How do environmental stimului influence actors’ service co-creation

behaviour?


2.4 Chapter Summary

This chapter is a review of the literature related to value co-creation and service co-creation in

the SD logic perspective. Value perception, A2A service co-creation behaviour and

preconditions to collaborate in service co-creation systems identified the main problem as ”

Why do actors collaborate in service co-creation?” The following three research questions are:

RQ1: How are service co-creation systems classified based on the different

dimensions in a co-creation context?

RQ2: How do environmental stimului influence actors’ service co-creation

behaviour?


The first research question was addressed through conducting a systematic literature review,

presented in Section 2.2. Three service co-creation platforms were found, namely cooperative,

coordinative and collaborative co-creation service systems (CS2, CS2, CS3). A key finding

is that the identified service co-creation systems vary considerably in user engagement and

value distribution between stakeholders ranging from CS1 where the business asks crowd to

complete tasks through to CS3, where actors provide services to each other, and the business

only facilitates the communication.

Then a conceptual model of value co-creation behaviour was presented to gain a better

understanding of co-creation behaviour in the current SD logic literature (Section 2.3). This

research used the SOR model [Mehrabian and Russell, 1974], with the integration of UGT

[Katz et al., 1973] to investigate service. The researcher hypothesised that environmental factors

2.4. CHAPTER SUMMARY 55

including the network structure, service platform capabilities, and role and social influence, lead

actors to value co-creation behaviour through influencing their cognitive, social, personal and

hedonic value perception.

This research investigated RQ2 and RQ3 through a qualitative case study to present a

theoretical model for service co-creation behaviour (presented in the following chapters).


Chapter 3

Research Design and Methodology

This chapter explains the proposed research plan to address the research questions. This chapter

outlines the interpretive paradigm as philosophical assumption, justifies qualitative case study

as the research method, discusses case selection and the population and sampling strategies,

and discusses the data collection procedure (semi-structured interview) and the data analysis

method (thematic analysis) in detail. Finally, ethical considerations and the trustworthiness of

the research are presented.

3.1 Philosophical Perspective

A paradigm is the researchers’ standpoint about their way of conducting the research. There

are three fundamental research paradigms which explain how research should be conducted:

positivism, interpretivism and critical study [Orlikowski and Baroudi, 1991, Neuman, 2007].

A survey of Australian universities exploring the state of information systems (IS) research

showed a balance between positivist and interpretive research; survey was the most frequently

used research method (71% of schools), followed by case study (54%) Gregor2008. Generally

in Australia, positivism is the dominant paradigm, with growing popularity in interpretivist

approaches and little use of critical theory [Gregor et al., 2008].

Positivists believe in a “reality that can be measured and observed in a rigorous and

semantic way to develop objective knowledge (facts)” [Petty et al., 2012, p. 270] that are

independent from the observer [Neuman, 2007]. Positivist studies are appropriate for an

explanatory design that prioritizes quantitative data. The criteria for considering the study as

57

58 CHAPTER 3. RESEARCH DESIGN AND METHODOLOGY

positivist are, ”Evidence of formal propositions, quantifiable measures of variables, hypothesis

testing, and drawing of inferences about a phenomenon to increase predictive understanding of

phenomena” [Orlikowski and Baroudi, 1991, p. 5]. In contrast, interpretivist studies seek the

meaning of a social action or an explanation for a certain phenomenon [Crotty, 1998].

Interpretivists believe in the existence of a multiple, constructed and holistic reality [Pickard,

2013]. Interpretivism is appropriate for an exploratory design that prioritizes qualitative data.

Reviewing these paradigms alongside the purpose of this research indicates that the current

study is an interpretive study.

This research seeks to understand why actors exhibit service co-creation behaviour within

A2A co-creation networks. This research looks at the behaviours, experiences, and attitudes

of members of two co-creation networks (i.e., the social life), with a focus on interactions, as a

productive social world. So, meaning is constructed through an interaction between actors in the

network (i.e., subject) via the platform as the technology being used (i.e., object) [Crotty, 1998].

Following the interpretivist approach, interpretation of individuals’ constructed meanings and

how they create their social world is concerned with the behaviour they exhibit [Neuman, 2007].

Lincoln and Guba [1985], identified paradigm differences in terms of epistemology

(relationship between subject and researcher), ontology (the nature of reality), and

methodology (the process of research). Epistemologically, this research takes a subjectivist

stance in which the relationship between investigator and the subject of the study influences

the findings [Guba and Lincoln, 1994]. The researcher attempts to explore the subjective

opinions and experiences of the actors in co-creation activities, and to understand different

interpretations and meanings of service co-creation behaviour and its stimulus. Ontologically

this research follows a relativist position in which realities are multiple, intangible, and

socially and experientially-based [Guba and Lincoln, 1994]. In relativism, society is not

considered as a real entity with objects but rather as the result of people’s engagement with

each other in a social context. It is consistent with social practices and interactive explanations

of how people exist and live in the world [King and Horrocks, 2010]. In the case of this

research, the nature of service co-creation behaviour and the existence of A2A co-creation

networks are dependent on actors’ collaboration in the co-creation process. Based on the

ontological and epistemological assumptions, the aim of this research is to understand the

experiences of actors of a specific co-creation network (as a social context) about mutual

service/value creation and delivery.

3.2. QUALITATIVE CASE STUDY 59

The interpretivist perspective entails a researcher investigating the subjects’ experience and

ideas, and interpreting their world view [Cassell et al., 2006]. The interpretation is dependent

on the researcher as observer and leads to building a theory inductively, from users’ experiences

rather than by testing hypotheses. Particularly, social behaviour (here co-creation behaviour) is

a difficult phenomenon to quantify and needs the depth of insight offered by qualitative research

[Alasuutari, 2010]. Consequently, the most appropriate methodology is a qualitative case study.

Figure 3.1 represents the philosophical view of the study and why case study is relevant to this

study.

Figure 3.1: Philosophical perspective of research.

CC: co-creation

3.2 Qualitative Case Study

Case studies usually investigate a particular organisation, individual or group, project and event

in the real-life context that needs to be analysed. Case studies are an in-depth exploration of a


bounded system (e.g., process, individual) over a period of time through detailed data collection

of one or more cases and multiple rich sources of information [Creswell and Creswell, 2007,

p. 73]. A qualitative case study examines ”the qualitative nature of experience” in a particular

socio-cultural context [Simons, 2009, p. 5]. The qualitative case study explores ”in-depth a

program, an event, an activity and a process of one or more individuals” [Creswell, 2003, p. 15].

Stake and Savolainen [1995] perceives case study as an interpretive approach in which the

researcher should be responsive to the sort of data to be collected and the developing ideas. The

case study methodology allows direct observation and collects “data in natural settings rather

than relying on derived data” [Bromley, 1986, p. 23]. The findings of qualitative case study

enhance understanding and finding a solution to a particular problem.

This research relies primarily on definitions and the main components offered by Yin [2013],

and Stake and Savolainen [1995]. The following are the strengths of employing case study as a

research strategy.

1- The appropriate research questions for a qualitative case study are ”how” and ”why”.

This research explores actor co-creation behaviour within A2A service co-creation networks.

More specifically, in RQ1 and RQ2 the research investigates, ”How do environmental stimuli

influence actors’ service co-creation behaviour?” and ”How does value perception influence

actors’ service co-creation behaviour?”

2- The case study method can be applied when the research addresses either a descriptive,

an explanatory or an exploratory question to “produce a first-hand understanding of people and

events” [Yin, 2004, p. 3]. This study aims for an exploratory outcome. The focus is on ”how”

to explore what is happening. In exploratory studies, data collection occurs prior to theory

formulation [Yin, 2013]. So, the case study facilitates building a theory, ensuring that issues

of context are understood, contributing to the knowledge base and ensuring that findings are

generalizable [Maimbo, 2004].

Yin [2013] recommends using hypotheses and propositions as an analytic lens for the

researcher to clearly focus on issues. ”Each proposition directs attention to something that

should be examined within the scope of study” [Yin, 2003, p. 21]. The researcher found 6

hypotheses (see Chapter 2, Section 2.3) retrieved from the conceptual model that enabled the

researcher to clearly state the problem and which facilitated the development of the interview

instrument. However, the strategy in this research is not to test a hypotheses. The analysis of

3.2. QUALITATIVE CASE STUDY 61

the data was based on an inductive approach to find themes retrieved from the collected data

but the conceptual model helped the development of the instrument. Furthermore, the

hypotheses helped to better understand the problem under investigation and to find more

evidence. According to Yin [2013] ”how” and ”why” questions point to what the research is

going to answer, while some hypotheses or propositions are needed to move the research to the

right direction.

3- The third component is related to defining the case (unit of analysis) and bounding the

case. The selection of a proper unit of analysis occurs after specifying an accurate research

question [Yin, 2003]. Although service networks are the fundamental area of investigation in

this research, they are not the research focus. The primary focus of this research is the

co-creation that occurs through service networks. The focus of this research is on actor

perception and the experience of collaboration in the service co-creation process, which is a

contemporary phenomenon in a real-life context. So, the unit of analysis is A2A co-creation.

This research focuses on a particular online service platform with the nature of co-creation as a

real-life context. The phenomenon under investigation is actors’ engagement in the co-creation

process (actors’ service co-creation behaviour).

Case study boundaries help to determine the scope of data collection and the subject of the

case [Yin, 2013]. Case study boundaries can be clarified by “settings, participants, time, space”

[Creswell, 1998, p. 61] and screening goal [Yin, 2004]. According to Yin (2004), a useful

screening criteria is identifying key persons to participate in the study. This research includes

users of two service co-creation platforms who are actively contributing to co-creation activities.

The type of platform should be an A2A co-creation platform (collaborative co-creation system

– CS3) because of the lack of empirical studies and the novelty of the A2A co-creation context

(see Chapter 2). More detailed boundaries are included in the case selection criteria (see Section

3.3.1) and criteria for choosing participants (see Section 3.3.2).

This research follows the holistic and collective case study approach to increase the

generalizability of our findings, considering the limitation of the case study that one cannot

generalize from a single case [Yin, 2013]. However, Flyvbjerg [2011] believes this is one of

the greatest misunderstandings of case study research. This research aims to strengthen the

findings by comparisons of two case studies.


4- Criteria for interpreting research findings in qualitative research are dependent on the

researcher [Yin, 2003]. The analysis in this research was executed on two levels. An important

strategy for findings interpretation is to address and identify rival explanations of findings [Yin,

2013]. First, for the research conducted a within-case analysis and for each case a complete and

separate report was prepared that includes a case analysis and the theoretical model retrieved

from the data. Secondly, cross-case analysis was undertaken to integrate the findings, determine

themes across the two platforms and further explore the nature of co-creation in A2A service

co-creation systems (CS3). The following section presents the research strategy for this research

project.

3.3 Research Strategy

Figure 3.2 shows the developed case method research strategy. After determining qualitative

case study as the research methodology, the criteria for case selection was proposed. The target

cases were identified and the interview protocols were developed, based partly on the developed

conceptual model (see Chapter 2, Section 2.3) and also following an inductive open approach.

Data collection and analysis for the first case (StackOverflow) and the second case (GitHub)

were conducted consecutively. The model development for each case formed separately and

were merged together in the final step.

3.3.1 Case Selection

Two service networks, StackOverflow (SO) and GitHub (GH), were selected as the service

co-creation systems to analyse and compare actors’ service co-creation behaviour. The content

of the two networks is semantically close, with the focus on actors’ collaboration in

programming knowledge co-creation platforms. However, the contribution of actors in the

co-creation process is different in the two cases, which enhances generalizability and builds

strong cross service domain recommendations.

3.3. RESEARCH STRATEGY 63

Figure 3.2: Research strategy.

I) StackOverflow (SO)

SO is a sub-community site of the StackExchange network with 40 million visitors each

month. The number of members on SO was only 53,000 after its first year, but increased to 1.3

million by 2012 [Asaduzzaman et al., 2013] and in 2015 it reached 4 million registered users 1.

SO’s model is based on the co-creation model that entails actors collaborating to create

expert information and knowledge and peer-reviewed answers (i.e., service). SO helps

programmers to learn coding, share their knowledge/code, advance their career, and helps to

build an archive of questions and answers. In practice, developers ask questions based on tags

1https://stackoverflow.com


(e.g., #java), provide answers and help each other to solve particular problems they face in

programming and technical issues, and learn new skills. More than 92% of the questions in SO

are answered within 11 minutes [Ponzanelli et al., 2013], which shows the high level of

participation and collaboration among actors.

SO uses a different method to motivate actors with enough incentive to contribute [Singh

and Shadbolt, 2013], for example by making it a requirement for actors to collaborate by

increasing their reputation score and achieving budgets, actors are able to perform quality

control by voting (up-votes/down-votes) on questions, answering and editing. An actor’s

reputation score indicates their level of contribution, level of expertness in the particular

language and level of trustworthiness in the community. The main SO building blocks (or

constituents) include voting (for control quality), tags (to easily find subject areas), badges (to

encourage Participation), Bounties (to draw attention to your question), monthly Data Dump

(to encourage doing creative work), documentation (collaborative authoring) and careers 2.0

(providing job opportunities) 2.

Non-code contributions on SO are under a creative commons license (cc-wiki) that allows

users to share (i.e., copy and distribute) and adapt each other’s work under the following

conditions (CC BY-NC-SA): by attribution that is giving credit to author (BY),

non-commercial purposes (NC), and share alike (SA). However, Code contributions are under

the terms of the MIT License, that means using the name of the copyright holder.

From SO’s perspective, the service for users is providing a platform as a medium and

interface to connect actors to exchange service and share resources. The service being

exchanged on SO is programming knowledge with the purpose of building a strong

community of professional software developers who create a repository of knowledge for

current and future users. The survival and success of the business rests on active content

co-creators who are willing to contribute to the network free of charge, which leads to building

brand. SO gains economic value by selling advertisement, and connecting developers who

need a job with companies who are looking to hire developers.

SO defines different roles for actors (i.e., participants) including new users, voters, editors

and moderators. Depending on the level of contribution, actors’ reputations increase and they

unlock new privileges, such as having access to vote, comment, and edit posts in order to

2https://stackoverflow.com


clarify or fix mistakes. At the highest level of their contribution, actors are able to moderate

the community. The goal of contributions is to provide quality answers and keep the

community helpful. For the goal of this study, actors are divided into three groups: passive

users (consumers), co-creators and active co-creators. Consumers are developers who only

Google their questions to find suitable answers and they mostly are not aware of being SO

users. The second group are co-creators who ask questions, provide answers, and vote. The

third group are the most active co-creators (i.e., moderators) who go beyond answering and

questioning, to moderating others’ contributions and controlling the quality of codes and

answers. The focus of this research is only on co-creators/active co-creators for the data

sample because of their level of engagement in collaborative activities. Figure 3.3 shows the

co-creation process within SO.

Figure 3.3: SO co-creation process.

Source:Designed


II) GitHub (GH)

GitHub is a social coding service network and software hosting website which in 2016

had over 5.8 million active users, 331,000 active organisations (e.g., Facebook and Microsoft)

and over 19.4 million active repositories (GH website). GitHub allows collaborative creation,

sharing code, and permits third parties and external developers to apply changes, improve or

reuse codes to expand their project/business scale and innovate remotely.

The co-creation process starts when an actor creates a branch (i.e., ongoing features or ideas)

to provide an innovative environment and to experiment with new ideas. The owner can then

open a pull request 3 and ask other actors to review and give suggestions on any steps of the

co-creation process (development process). Opening a pull request brings some discussions and

comments over the opened task or commits 4 within the community. Co-creators also contribute

in other activities, such as data sharing and text documents. Meanwhile, the main owner of the

repository can add commits that are the history of applied changes and keep track of the progress

by using version control and social alert features [Mergel, 2015]. The co-creation process will

be closed by deploying changes and merging changes to the master branch (guides.github.com).

The co-creation process in GitHub is shown in Figure 3.4.

Each project has a related community, with some default roles such as the owner of the

repository, maintainers who monitor contributions, collaborators who have a pull request

merged into the project, and other community members who participate in the discussions and

resolution of issues.

From GitHub’s perspective, the service is the platform as a hub for actors to manage

repositories via Git (i.e., distributed version control system) and collaborate on different

projects. The exchanged service is coding to collaborate in the development, delivery and

support of products/services. Reaching the outcome of service delivery is subject to delivery

constraints like pricing and copyright [Barros and Oberle, 2012]. Owners of the projects can

choose an open source licensing which allows others to use, change, and distribute the

software freely and to inform others of the limitations and the actual status the source code

usage. The absence of a license means the source code is protected by copyright laws and

3“Pull requests are proposed changes to a repository submitted by a user and accepted or rejected by arepository’s collaborators” (https://help.github.com/articles/github-glossary/).

4“A commit, or ”revision”, is an individual change to a file (or set of files). It’s like when you save a file, exceptwith Git, every time you save it creates a unique ID (a.k.a. the ”SHA” or ”hash”) that allows you to keep record ofwhat changes were made when and by who” (https://help.github.com/articles/github-glossary/).


Figure 3.4: GH co-creation process.

Source:Designed

actors can contribute to sharing codes and ideas by keeping ownership of the source code. In

terms of economic value, GitHub is an open source project that is free of charge for public use,

but also provides paid plans for private repositories for individuals and business, and offers

GitHub Enterprise.

III) Case selection justification

The focus of this research is the orchestration of service creation and delivery by a network

of actors. Both SO and GH are good examples of a community orchestration platform in

which customers (i.e., actors) are empowered by the platform to contribute in the co-creation

and exchange of service. The SO case study represents the co-creation of knowledge type of

platform which is required as the embedded part of all co-creation models. However, GH

complements the co-creation of knowledge in SO by application of more transactional services

and the co-creation of projects. The two co-creation platforms of SO and GH were selected

first, because every service co-creation platform needs the co-creation of knowledge aspect.

According to the SD logic, the knowledge sharing aspect in co-creation platforms is the

foundation of the innovation process [Gronroos et al., 2015, Shamim et al., 2017]. Second,

both studies consist of potential future transactional exchanges.


On the other hand, both platforms are innovation-centric service systems. Gerke et al.

[2017, p. 59] introduced three scopes for innovation processes, including ideation, invention

and exploitation (see Table 1.1, p.3). Ideation refers to “The generation of a thought or

suggestion as to possible courses of action” that includes idea generation, evaluation, and

selection. Invention refers to “the first realization and test of an existing idea for a new product

or process” that consists of prototype development, testing, and refinement. Exploitation

consists of “the transfer to large-scale production and the commercial exploitation of the

invention in the marketplace”. While SO is placed within the scope of ideation, GH is centred

on ideation and invention that represent their suitability and centrality on service co-creation

and innovation opportunities (Figure 3.5).

Figure 3.5: Innovation stages of SO and GH.

SO and GH provide services in two ways: from the larger platform perspective the service

is a knowledge sharing opportunity. From the A2A view, SO actors collaborate in

problem-solving and sharing innovative ideas. The target service of value in SO is social

learning of programming technologies and service credential for job applications. In GH,

actors collaborate in the development and co-creation of projects (i.e., software, apps,

libraries). The target service of value in GH is real world, potentially commercial projects,

third-party integration, learning and open-source innovation, and evolving current resources.

Both platforms are large in size and famous platforms in the programming context in

which their users are actively collaborating in co-creation activities (i.e., problem solving and


code sharing). This high level of co-creation interactivity was one of the major principles in

the selection of SO and GH, with the creation and delivery of service happening within the

community domain. The importance of the activeness of the platform is to find subjects who

have greater practical co-creation experience.

3.3.2 Population

The population of the research included actors of two co-creation platforms, SO and GH,

collaborating in the programming problem solving and co-creation of projects, respectively.

The general criteria for the selection of subjects were the level of their contribution, their

membership period, and the type of activities in which they were involved. The level of

contribution shows how active participants were, which helped the researcher to target more

experienced co-creators, to provide rich data. The duration of membership was considered as

one of the main criteria that showed the level of the actor’s familiarity with the co-creation

process. Finally, the type of activities participants were involved in helped to identify them as

proper subjects of study.

The researcher observed each case for a short period of time to get familiar with the culture

of each platform and find suitable subjects. More detailed information regarding the

recruitment criteria for the selection of participants in each case is presented below.

I) SO participants

Three criteria were set to select SO participants: a) to be an active co-creator (not passive).

Co-creators ask questions, answer and vote, or go beyond answering and questioning and

moderate others’ contributions and collaborate in documentation. This research does not

consider lurkers (or consumers) as co-creators, because they do not actively engage in

activities, however they may passively create value. b) actors with more than a 50-reputation

score, which can be considered as the basic type of co-creators. At this level, actors gain the

privilege to comment on other’s posts and can participate in discussions. However, the ideal is

to target the actors with a higher reputation who can answer questions (over 2000 rep) and

moderate discussions (over 10,000 rep 5). c) actors with more than a year membership, which

5“Reputation is a rough measurement of how much the community trusts you. It is earned by convincing yourpeers that you know what you’re talking about. The more reputation you earn, the more privileges you gain”


shows greater experience and familiarity with the platform.

Demographic information of the participants in StackOverflow

Nineteen SO users out of 125 contacted users were interviewed to investigate how they

participate in co-creating service. The interview process continued until nothing new emerged

from the data. The participant demographic information is shown in Table 3.1. All participants

were male and their overall reputation number, role and graph of activities were considered to

evaluate their level of collaboration.

Table 3.1: SO demographic information.Participants Gender Country Reputation Level of Role

Contribution

P1 Male Australia 50-2000 low CommenterP2 Male Malaysia 20,000-50,000 Medium Moderator/AnswererP3 Male Bulgaria >100,000 High Moderator/AnswererP4 Male Turkey 50-2000 Low AskerP5 Male Germany >100,000 High Moderator/AnswererP6 Male Germany 2000-10,000 Low AnswererP7 Male Australia 10,000-20,000 Medium AnswererP8 Male US 2000-10,000 Low AnswererP9 Male Singapore 2000-10,000 Low AskerP10 Male US >100,000 High Moderator/AnswererP11 Male US >100,000 High Moderator/AnswererP12 Male Belgium 20,000-50,000 Medium Moderator/AnswererP13 Male UK 10,000-20,000 Low Moderator/AnswererP14 Male Netherlands 10,000-20,000 Medium Moderator/AnswererP15 Male Bangladesh 2000-10,000 Medium Asker/AnswererP16 Male India 2000-10,000 Medium Asker/AnswererP17 Male France >100,000 High Moderator/AnswererP18 Male Belgium 2000-10,000 Medium Asker/AnswererP19 Male India 2000-10,000 Low Asker/Answerer

Actor’s reputation - An actor’s reputation was considered as the first criteria of an actor’s

level of collaboration within the community. Most participants were active co-creators with a

reputation of 2000-10,000 (36.84%) and more than 100,000 (26.32%).

Role attribute - A role attribute is the role actors play in the co-creation process. Role was

(https://meta.stackexchange.com/questions/40353/stack-exchange-glossary-dictionary-of-commonly-used-terms).


set based on the type of activities actors were more engaged in the co-creation process. The

role assigned to each participant was identified by observation of their profile and then through

the interview. The number of questions and answers and other activities in an actor’s profile

were checked. If there was a significant difference between the number of answered and asked

questions, actors could have the role of asker or answerer depending on which outweighed the

other. In case an actor was an answerer and high rep user, they chose to moderate activities at

the same level of answering questions. Most participants play the role of answerer/moderator

with 47.37% of nodes, and 21% Asker/moderator,15.79% answerer, 10.53% asker and 5.26%

more of commentator (Figure 3.6).

Figure 3.6: Role vs. level of contribution

Reputations gained per day - The decision for their level of collaboration was based on the

amount of activities they contributed in the last two months before their profile was observed

(Dec 2016 ) and their activity per day in the month observed. Three levels of contributions

were identified. First, a low level of contribution was determined for actors who were mostly


consumers and commentators, and usually did not participate in questioning and answering

activities. Their contribution to activities was on a monthly basis (36.84% of participants).

Second, medium level of contribution was considered for actors who were contributing on a

weekly basis or not necessarily contributing to activities daily. Finally, high level contribution

was related to actors contributing to activities on a daily basis, who were mostly moderators

and answerers (26.32% of participants).

Figure 3.5 shows the roles versus level of contribution. Users with high levels of

contribution played the role of answerer and moderator. The level of contribution is not related

to the quality of completing a task, but can be influenced by other factors like time and level of

activity in a specific period of time. However, actors with high levels of contribution were

routinely active.

II) GH participants

To select participants for the GH case, three criteria were set: a) Two most starred (i.e.,

most popular) repositories in GH had been selected: facebook/react (a JavaScript library for

building user interfaces for Facebook) and Oh-my-zsh (a community-driven framework for

managing Z shell configuration ). b) number of repository contributors higher than 500 had

been considered to focus on the most active projects: react (999) and Oh-my-zsh (1054) . C)

among contributors in the selected repositories, this research started from the ones who made

the most commits and participated in discussions in issues. By checking the profile of

contributors, the researcher limited the sample to those with more than one-year membership,

higher contribution level than 250 per year and those who had provided contact information in

their profile. Because similar to SO, GH did not provide the direct messaging feature.

Demographic information of the participants in GitHub

Seventeen GH users from 247 contacted users were interviewed in the second case study.

The demographic information of participants is shown in Table 3.2. All the participants were

male. Fifteen participants from the Oh-my-Zsh project and 2 from the Facebook/react project

were interviewed. The participant’s graph of contribution and type of activity were considered

to evaluate their level of contribution and suitability for the research.


Table 3.2: GH demographic information.Participants Gender Country contribution Level of Project

number Contribution

P1 Male US 887 Medium Facebook/reactP2 Male France 355 Medium oh-my-zshP3 Male Korea 310 Medium oh-my-zshP4 Male Brazil 1110 High oh-my-zshP5 Male US 252 Low oh-my-zshP6 Male Canada 600 Medium oh-my-zshP7 Male Germany 1451 High oh-my-zshP8 Male US 320 Medium Facebook/reactP9 Male US 3573 High oh-my-zshP10 Male India 305 Medium oh-my-zshP11 Male US 308 Medium oh-my-zshP12 Male Spain 284 Low oh-my-zshP13 Male US 258 Low oh-my-zshP14 Male Spain 1069 High oh-my-zshP15 Male US 759 Medium oh-my-zshP16 Male US 96 low oh-my-zshP17 Male China 432 Medium oh-my-zsh

The number of contributions in the last year - the actor’s number of contributions observed

from their profile and considered was the first criteria of their level of activity. All participants

had a higher number of contribution than 250, except one participant with a 96 contribution

number. The reason to interview one participant lower than the considered criteria was that the

subject was the owner of the opensource project and a suitable, rich subject for the research.

Four participants had a low level of collaboration with a contribution number less than 300, 9

participants with a contribution number between 300 and 1000, and 4 high-level collaborators

with a contribution number of more than 1000 per year.

Type of activities - the subjects who had a high level of contribution in the open repositories

were selected, and the ones with the higher level of activity in the private repositories were

removed from the pool. The type of actor’s activities was observed to see if they are engaging

in the co-creative activities, such as code sharing, problem solving and documentation. The

subjects who had greater engagement in the collaborative tasks were selected.


3.3.3 Sampling and Recruitment

Purposive-snowball sampling was chosen as the technique for the selection of participants.

Purposive selection defined as ”The selection of participants or sources of data to be used in a

study, based on their anticipated richness and relevance of information in relation to the study’s

research questions” [Yin, 2011, p. 311]. Purposive selection is a beneficial technique in case

study design as it helps to gain pertinent and plentiful data and draw themes from a small

number of participants (Yin 2014). Considering purposive sampling, this research looks for

suitable participants on the platform who meet the prescribed participant criteria for each case,

due to the reason that participants needed to fulfil a specific set of prerequisites to participate

in the study [Bryman, 2015]. The snowball technique is defined as ”referral from one source to

another” [Yin, 2011, p. 312]. In each interview, the researcher asked purposive subjects to

introduce other subjects who they believed were eligible for the study. Among the potential

participants introduced, the ones who meet the criteria were selected.

Target subjects were contacted directly by a preliminary email or direct message through

the provided contact information on the platform. The purpose of the research was clarified and

they were invited to participate in the study (refer to Appendix A). Although both purposive

and snowball sampling was used in SO, GH’s study only used purposive sampling.

A sample size for qualitative case study is suggested to be between 15 and 30 participants

[Guest et al., 2006]. However, an observation of sample size in the case study approach was

found to be between a single participant to 95 in Mason’s (2010) study. Glesne and Peshkin

[1992] suggested that the sample size for a qualitative study can be determined by reaching

saturation point when nothing new emerged by looking at the data during data gathering.

Saturation can be achieved at any point [Mason, 2010]. The estimated sample size for this

research was at least 15 subjects to reach the saturation threshold.

3.3.4 Data Collection

Data collection began after case selection, observation of the cases to choose suitable

participants and contacting potential participants. Qualitative data collection primarily

includes observing, interviewing, and analysing documents [Creswell, 2009]. Interviews are

considered as the main source of evidence in qualitative case studies [Yin, 2011]. Adopting a


qualitative case study design, semi-structured interviews were chosen as the data collection

approach.

The advantages of selecting semi-structured interviews over other approaches are

collection of in-depth information with greater understanding because of the nature of

open-end questions, and the researcher has more control over the questions. Investigating

service co-creation behaviour of actors is a complex phenomenon that requires in-depth

investigation which can be achieved by semi-structured interviews eliciting the participants’

(SO and GH users’) own voice and viewpoint rather than structured interviews and

questionnaires.

I) Interview instruments

Developing an interview instrument is an essential part of setting rules to guide the

implementation of an interview. A semi structured interview guide was formulated from the

developed conceptual model (see Chapter 2), as open-ended questions and follow up probes.

After doing two interviews on SO we refined the interview protocols. Pilot and main interview

guides (i.e., the version after a pilot study) are presented in Appendices B and C. The final

interview protocol divided to five sections:

1- Introduction: the interview started with welcoming the participants, giving a brief

description of the purpose of the study, and the benefits of the participant’s involvement.

Participants were reminded about the recording of the interview, identification of

participants in the thesis and their right to withdraw (mentioned in the ethics clearance).

2- Co-creation activity: this group of warm-up questions targeted the nature of co-creation,

activities that users are involved in the co-creation process, and the nature of the platform

itself.

3- Value perception: the value perception question directly responded to the third research

question, the actors’ value perceptions that lead to collaborating with others and exhibiting

service co-creation behaviour.

4-Environmental stimuli: this group of questions were intended to respond to the second

research question. The primary source of these questions were the concepts found from

the developed conceptual model. Through the literature, we expected that service platform

capability, role of users, and social influences lead users to co-creation behaviour. A series


of probing questions explored participants’ perspectives and experiences on environmental

factors. However, other factors such as environmental trust were found through the follow

up questions.

5- End questions: the researcher closed the interview by thanking the participants, asking

them to suggest five other users as project participants for the purpose of snowball sampling.

II) Conducting semi-structured interviews

The interview is the main source of evidence in qualitative case studies (Yin 2011).

Individual interviews were used in this study to explore the in-depth experiences and views of

individuals [Robson, 2011]. Semi-structured interviews might be carried out face to face, by

telephone or via the Internet [Robson, 2011]. In this research, one interview was conducted

face-to-face and the remainder by Skype because of geographical distribution of the subjects.

Each interview lasted 30-60 minutes. All the interviews were audio recorded by MP3 Skype

Recorder and transcribed manually into Word documents by the researcher, which enabled

rigorous coding and analysis [Rubin and Rubin, 2011].

Pilot Study - A pilot study was conducted after the development of the interview protocols

and prior to the implementation of the main study. The main goal of the Pilot study was to refine

the questions and ensure they would address the research questions. Also, the pilot interviews

helped the researcher to prepare for the main interviews.

Two pilot interviews were conducted in July 2016 from the SO case. The pilot interviews

were eventually included in the final data set since there was not a major change in the primary

questions of the main interviews. The secondary questions were changed to improve the

wording, to enhance clarity and remove extra and repeated questions (refer to Appendices B

and C for the pilot and main interview topic guides). The main changes in the questions were:

- During the pilot study, some participants problems understanding some questions, so the

researcher changed the structure of the questions or added support questions to make them

clearer.

- In the co-creation related questions, three questions were removed to reduce the number

of warm-up questions. Some questions were found to have similar answers.

- A few social influence questions were changed or removed, such as a question related to the

subjective norm and 2 questions have been added. The main reason for these changes was


that in service co-creation networks the focus is on the main task or service being delivered

rather than the actors and it was hard for subjects to answer the questions. The researcher

decided to add a higher-level question about social influence to widen the discussion on

how actors are socially influenced and by whom, rather than limit interviewees to the social

influence questions followed in the literature on participation in online communities.

Main Data Collection - The data collection consisted of two cases, SO and GH, were

conducted sequentially after the pilot study. The primary data collection for the SO study was

conducted after revision of the interview instrument in August and September 2016. The data

collection of the GitHub study was in February and March 2017. From 125 contacted SO users

and 216 GH users a total of 19 interviews per case were conducted. However, from 19 GH

interviews only 17 interviews were analysed. The obtained data from two GH interviews were

not included due to insufficient information provided by one subject and ineligibility of one

subject (participants had to be above 18 according to the Ethics Approval). After the completion

of the data collection in each case, the analysis of the related cases was conducted.

3.3.5 Data Analysis

The analysis of the data was mainly based on an inductive approach to find themes retrieved

from the collected data, however the final emerged themes were influenced by the applied

theories for developing conceptual models. Qualitative thematic analysis was conducted to

analyse the data. In this research two steps were taken to analyse the data - data coding and

theme identification. The researcher read the transcribes several times and developed an initial

open coding from the material by focusing on both in-vivo (the words used by participants)

and latent code (underlying ideas, meaning and assumptions) [Braun and Clarke, 2006, Flick,

2014].

Step 1) Data coding

This study used an inductive approach to finding themes emerged from the data. The

unit of analysis was considered as a sentence or paragraph, depending on the content. An

initial open coding was developed from the data material by focusing on both in-vivo (the

words used by participants) and latent codes (underlying ideas, meaning and assumptions)

[Braun and Clarke, 2006, Flick, 2014] (see Appendix D for coding example).

Initially 116 codes were retrieved from the SO data and 101 from the GH data. The


process of coding was monitored by experts’ consultation in qualitative research to

confirm the accuracy of initially emerged themes. The initial coding process was

reviewed by a supervisory team where minor changes were advised and lead to a

modification. The inter-code agreement included revisiting codes in case of

disagreement to ensure alignment between coders and reviewer. After the first

comparison, further sessions were planned to monitor successful completion of the

coding exercise. Then we grouped similar content and codes to a higher order category.

For this process, the computer software NVivo (V.11) was used to organize/manage the

stored data and facilitate efficient code retrieval (refer to Appendix E and F for initial

coding of two studies).

Step 2) Theme identification

Qualitative thematic analysis was used to analyse the data. Thematic analysis presents

the data in in-depth detail considering diverse subjects through interpretations [Boyatzis,

1998]. Thematic analysis moves “beyond counting explicit words or phrases and focuses

on identifying and describing both implicit and explicit ideas” [Namey et al., 2008,

p. 138]. The themes were identified by reviewing the codes and taking decisions on key

themes. The emerged theme is the outcome of coding and categorization [Saldana,

2015]. The thematic analysis showed 15 final themes in the SO study and 17 emerged

themes in the GH study. The relationships between the concepts identified and the

replicated data were compared.

3.4 Ethical Consideration

Prior to the conduct of the research, the researcher submitted an ethics application to the QUT

Human Research Ethics Committee (Approved Number: 1500000502).

This research was considered “negligible risk research”, as there was no risk of harm or

discomfort. Participation did not involve any physical, legal, social, psychological, or other

risks and strategies were implemented to maintain confidentiality. To minimise potential risks

such as inconvenience experienced by interviewees, the researcher ensured that the interview

sessions were conducted in a friendly environment and manner. Also, the interview could be

stopped at any time and participants could withdraw at any stage of the study without comment

or penalty. Since all processes in the project occurred with the awareness and acceptance of

3.5. TRUSTWORTHINESS 79

participants, the risk of this research was extremely low. The researcher maintained

confidentiality in the following ways:

1) The researcher ensured participant privacy by using the following methods: All gathered

data were accessible only by the research team. Participants’ identities were kept private

and maintained as non-identifiable by coding the data. Files containing transcripts and

reports of the participants were labelled using codes. There was no requirement to focus

on any name in the transcripts or in the reporting of the results. The identifiable data were

stored securely and separately from the non-identifiable and coded data. The identity

of participants was not published at any stage of the study. The identity of members

communicating with the participants was kept confidential.

2) Participants were informed that all the gathered data from the observations would be

collected from their public interactions with others, that are accessible to everyone in the

platform and the researcher would not include any personal information.

3) Participants were assured of the safe storage of the gathered data. All the gathered data

and the recorded interviews were kept in locked storage at QUT that was only accessible

by the research team. Identifiable data and coded data were kept separately. A file linking

participants’ identities to the codes was kept on a password-protected computer, separate

from the transcripts of the interview. Audio files were stored in a password-protected file

on a secure server at QUT. Only the research team had access to the materials. Also,

organisations’ data were securely stored on a secure computer and the QUT H drive.

4) Participation was voluntary and participants could withdraw from the study at any time

they desired. Participants were informed that involvement in the study was completely

voluntary.

3.5 Trustworthiness

Trustworthiness in quantitative studies is determined as validity and reliability. However,

different criteria are recommended for ensuring rigour in qualitative studies. This is because

validity and reliability cannot be addressed as in quantitative research by using measures and

metrics. The alternative concepts for qualitative researchers to ensure a trustworthy study are


credibility, transferability, confirmability, and dependability [Lincoln and Guba, 1985].

Shenton [2004] introduced strategies to meet these four concepts to present a convincing case

by qualitative researchers. This study followed strategies introduced by Lincoln and Guba

[1985], and Shenton [2004] to establish trustworthiness of the research outcome (see Table

3.3).

Table 3.3: Research trustworthiness.Quality criteria Trustworthiness strategiesCredibility X Adoption of appropriate research methods

X Development of familiarity with culture of organisationsX Random sampling of individuals serving as informantsX Triangulation via use of different methods/informants/sitesX Tactics to help ensure honesty in informantsX Iterative questioning in data collection dialoguesX Negative case analysisX Debriefing sessions between researcher and superiorsX Peer scrutiny of projectX Use of reflective commentaryX Description of experience of the researcherX Member checksX Thick description of phenomenon under scrutinyX Examination of previous research to frame finding

Transferability X Provision of background data to establish context of studyand detailed description of phenomenon in question to allowcomparisons to be made

Dependability X Employment of overlapping methodsX In-depth methodological description to allow study to be repeated

Confirmability X Triangulation to reduce effect of investigator biasX Admission of researcher biasX Recognition of shortcomings in study’s methodsX In-depth methodological description to allow integrity of researchX Use of diagrams to demonstrate “audit trail”

Source:Shenton (2004, p.73)

Credibility (Internal Validity) is the most important factor in qualitative research. The

researcher increases the credibility of their research by:

First, in the interest of “prolong engagement”, researchers aim to develop of familiarity

with the participants and the culture of the platform [Lincoln and Guba, 1985, Shenton, 2004].

3.5. TRUSTWORTHINESS 81

Participants’ profiles and their activities were observed and some discussions took place by

email prior to the interviews to establish trust between the researcher and the participants.

However, the aim of communication prior to the interview was to reach a reciprocal

understanding that nevertheless did not influence the professional judgement of the researcher

[Shenton, 2004]. For the researcher, the aim was to gain a better understanding of the

participant’s suitability for the research and to ensure the participant was comfortable. For the

subject, the aim was to gain more information about the research process and the intended

results of the study.

Second, pursuing “Tactics to help ensure honesty in in formant” [Shenton, 2004, p. 66]

entailed informing the participants that their involvement in the study was completely voluntary

and they could withdraw from the study at any time they desire.

Third, using probes and “iterative questioning”, the researcher aimed to gain more detailed

data. Fourth, “member checking” by sending the transcripts of interviews to the participants

was not used in this study because the participants did not consider they had the time. However,

the researcher verified emerging themes with the participants at the end of their interview.

Fifth, “peer scrutiny of research project” and “Peer debriefing” were sought by having

frequent sessions with the supervisory team, to receive feedback. The involvement of the

supervisory team in various steps of the research such as reviewing codes, and discussions

about the emerged themes and model was to establish agreement. Also, when participating in

various conferences and presentations, the researcher aimed to receive feedback about the

research process (the research was presented at ACIS 2015, PACIS 2016, IEEEICACT2017,

ISS-DC 2015,2016).

Finally, examining previous research findings helped the researcher to consider the

feasibility and congruence of the research findings with previous studies.

Transferability (external validity) is related to the generalizability of the findings. In

qualitative research because of the small sample size compared with quantitative research it is

hard to show that the findings are appropriate for other situations [Shenton, 2004]. To enhance

generalizability of the findings, this research investigated two case studies. Also, the researcher

tried to collect enough contextual information [Lincoln and Guba, 1985] and provide sufficient

rich description of the problem under investigation [Shenton, 2004] to maintain transferability

(as provided in the findings chapter).


Dependability (reliability) is addressed through “In-depth methodological description to

allow study to be repeated” and a detailed research report [Shenton, 2004]. The detailed process

of data collection and data analysis is provided to increase clarity and enhance readability, for

the use of future potential researchers. This includes in-depth information about raw data,

recording, transcribing, and developing instrument.

Confirmability (objectivity) in qualitative research is concerned with ensuring that the

findings are the result of participants’ experience and ideas rather than the researchers’

preferences [Shenton, 2004]. In order to maintain confirmability, in-depth description of the

methodology and analysis are provided. To reduce bias, the process of data collection and

analysis were reviewed several times by the researcher and the supervisory team.

3.6 Chapter Summary

This chapter presented the research plan and methodology for the research. The chapter started

with a discussion of interpretivism as a philosophical view of research. The discussion covers

relativist, subjectivist and qualitative case study as the ontological, epistemological and

methodological views adopted in this research. After a justification of qualitative case study, a

detailed discussion of the selection of cases and case boundaries was presented.

StackOverflow (SO) and GitHub (GH), the two selected case studies, were discussed in detail.

Data collection was conducted after developing the instruments. For each case, the data was

collected using semi-structured interviews to gain the actors’ experience and ideas about their

contribution in the co-creation process. This was followed by a discussion about the thematic

analysis of the data. The chapter concluded with a discussion of the way the researcher ensured

the trustworthiness of the research. The next two chapters discuss the findings of the two case

studies (SO and GH).

Chapter 4

Findings of Case Study 1: StackOverflow (SO)

This chapter presents the results of the within-case analysis of StackOverflow (SO). The chapter

includes coding information to categorise environmental and cognitive themes, and the themes

themselves.

The initial 116 inductive codes retrieved from the data were reduced by combining the initial

codes to 35 codes which resulted in a final 15 themes. The identified 15 themes were based on

the Stimulus-Organism-Response (SOR) model including five environmental stimulus themes

(S), nine value Perception themes (O), and one theme as the Response (R).

Table 4.1 lists the themes and their frequency in the SO study. Each theme is indicated by

“references” and “resources” that represent the total number of references which were coded,

and the total number of sources that the nodes referred to. The frequency of the themes/codes

was the indicator for the degree of their strength and density and helped to categorize and find

the final themes.

The findings are structured based on the SOR model to demonstrate the effect of the service

systems environment and value perception on actors service co-creation behaviour. Section

4.1 presents the categorized themes for environmental stimulus (S) and addresses the second

research question. Section 4.2 presents the categorized themes for actor value perceptions (O)

and addresses the third research question. Section 4.3 shows the related behaviours (R) of

service co-creation behaviour (SCB).

83

84 CHAPTER 4. FINDINGS OF CASE STUDY 1: STACKOVERFLOW (SO)

Table 4.1: Frequency of SO themes in SCB.SOR Model Themes Sources References

Accessibility 15 59Environmental Quality Control Mechanism 20 68

Stimulus Social Influence 19 108(S) Trust 14 50

Actor Competencies 11 23

Learning Value 13 64Utilitarian Value 17 48Hedonic Value 15 41

Actor Potential Engagement 13 31Value Perception Social Status 18 36

(O) Social Role 17 60Belongingness 14 41Quality 17 65Support 9 16

Response Service Co-creation Behaviour 19 145(R) (SCB)

4.1 Themes of Environmental Stimulus (S)

The aim of this section is to present the identified themes relating to the environmental stimulus

(S) part of the SOR model. A service environment refers to the infrastructure of a service

co-creation system that includes physical and virtual resources, and social-psychological and

cognitive characteristics surrounded by co-creators’ interactions such as the service platform

features. Table 4.2 shows the frequency of codes in the identified environmental themes.

The identified environmental stimuli in the actor-to-actor (A2A) service co-creation system

investigated in this study are Accessibility, Quality Control Mechanism, Social Influence,

Trust, and Actor Competencies. Regular participants in service co-creation activities claimed

that open access to the technical documents and technical support made collaboration handy

and useful and found it the easiest and fastest way to access other developers directly and

indirectly and collaborate with them. Most participants found the platform model

straightforward and effective, making collaborations easy. A Quality Control Mechanism such

as a voting system provided a gaming model to motivate actors to collaborate and improve

performance. Social Influence was a key stimulus in co-creators’ collaboration. Integrated

decision-making on the quality of the offered service built trust in the community. Co-creators

4.1. THEMES OF ENVIRONMENTAL STIMULUS (S) 85

were required to demonstrate a sufficient level of knowledge to be able to collaborate and

provide quality service.

Table 4.2: Characteristics of environmental stimuli themes in SO.Themes Related Codes Sources References

Accessibility Access to resources 9 20Intuitive UI 12 19

Quality Control Mechanism Ranking system 11 20Voting System 8 13Badges 4 5Other 14 30

Social Influence Normative SI 16 44(SI) Informational SI 7 14

Other 19 50

Trust Subjective Trust 8 12Objective Trust 10 11Other 14 28


4.1.1 Theme One: Accessibility

Accessibility mattered in two ways for many co-creators. First, it was important to provide an

intuitive User Interface (UI) that made the service platform available for as high a scale of

users as possible and increases collaboration. Most participants mentioned that the simple

platform model made contributing fast and effective, and fosters future collaboration.

Participants believed that the service platform should include straightforward and need-specific

functionality options to improve collaboration and meet their needs faster. The following

participant statement illustrates the importance of an Intuitive UI for collaboration:

It’s fast, I don’t muck around, I get a simple lay out, actually a minimum of CSS. I

don’t need all fancy things I just need a button and a link on it works, that’s it. So,

I’m a software engineer I’m not going there for one animated pictures. I’m going

there for getting my jobs done. (P7) 1

The second benefit of accessibility was that co-creators could easily access different types of1P: participant (e.g. P7: participant number 7)


resources such as an archive of provided information and experts as a “free of charge service”

(P3,P16). Actors could access the archive and retrieve data from previous work even if they

were not SO members. This meant they could access a greater variety of resources which

provided resource efficiency that enhanced overall collaboration. Co-creators believed easy

access to resources helped them to save time, claiming: “it’s very time consuming and no

trouble to get access to those information” (P1).

4.1.2 Theme Two: Quality Control Mechanism

A Quality Control Mechanism is a series of features provided by a service platform to regulate

activities, improve performance and encourage collaborators. A quality control mechanism was

found to be essential in regulating the co-creation environment when service was provided by a

large number of actors. The main aim of this regulation was to improve service quality, enhance

trust of the provided service quality, and to encourage actors to follow a certain behaviour.

Regulating SO service quality could happen through a voting system. Participant 12 presented

how platform features influence their collaboration:

I think up-vote and down-vote is definitely good idea because you have a peer

reviewed system and usually the best answer are indeed ranked higher so you don’t

need to read every answer. (P12)

The quality control and design features of co-creation platform could be applied as a

mechanism to enhance collaboration by providing a gamified environment. The majority of

SO participants found the gamification model one of the main reasons for their collaboration.

Application of reputation systems, voting system and badges in SO had significant results in

getting actors involved in the game flow. Harnessing the hedonic aspect of gamification, a

platform provides a competitive environment by rewarding actors to develop collaboration in

service co-creation activities. The following example presents views of how the gamification

mechanism influenced collaboration:

The whole thing is gamified in a sense that you gain points when you solve a

problem so you have a sense of achievement that you are getting something. (P16)


I think having a gamification system is crucial. Gamification strategy which is

probably most unique feature very careful thought about and intensively

brainstormed. And you know whole sort of ranking levels. Which I think is

probably what’s accelerated its growth over the competition. (P7)

These examples confirmed most SO participants agreed that different features such as a

voting/score system, and badges enhanced their level of collaboration and the quality of service.

Success of a co-creation system depends on the higher level of engagement that is fuelled by

successful implementation of gamification and an incentivized model.

4.1.3 Theme Three: Social Influence (SI)

The majority of SO participants were significantly influenced by other actors and learned from

them. Social Influence in this study refers to how co-creators were socially influenced by other

actors to get approval or compete and compare themselves which consequently enhanced their

collaboration. Although actors’ SCB was mostly influenced by significant others, for some

actors influence was centred on the quality and the depth of the solution provided.

The influence by other actors was found to occur in different ways, such as: “quality of

provided answers” (P1), “ Someone’s knowledge in particular subject is high” (P11), “positive

or negative behaviour of others and tense of the spectrum” (P13), “someone is guru in the

specific technology” (P5). However, one participant believed that the level of another actor’s

participation had no direct effect on his contribution, but he may subconsciously get involved

in the flow of the game and get points.

This research revealed the importance of social influence on actors’ SCB. When participants

were asked how their contribution was influenced by others, most answered they were inspired

by top actors or by gaining external confirmation about their capability. Some said they followed

people who provided good quality information and by the style of coding of others. These

answers represent the existence of two types of social influence that are important in actors

collaboration in the co-creation process: Normative and Informational.

Normative SI: Normative influence is based on the need to conform to others’ behaviour

in the group and the need for acceptance and approval (social rewards) (Bartle, 2011, p. 42). In

this research, Significant Others and Social Approval were found to be the two main normative


social influences that enhanced the desire to collaborate, resulting in SCB through actors’

value perception. According to the majority of participants, actors were influenced more to

collaborate in co-creation activities by the “level of contribution of other actors and higher

rank actors” (P1). Experts and seniors in the subject were role models and inspired others to

be similar to them, since “I believe people like X[name], one of the top ranking users in SO

was quite an inspiration.” (P17). Significant others then became one significant form of

Normative SI that enhanced the level of collaboration in service co-creation through

influencing actors’ value perception.

On the other hand, getting approval from other co-creators was found to be another form of

normative SI. Getting external confirmation on an actor’s knowledge created a sense of value

and confidence about their collaboration. Getting approval in SO was mostly supported by

platform activities through a voting system and through comments. The following examples of

participant statements show how social approval influenced their collaboration:

You answer a question and you immediately get 10 to 15 votes. Then you say OK

actually I’m certified to be good at that. Yeah, this external confirmation for your

knowledge is important... (P3)

When you get up-vote you feel that wow that’s great someone is using your solution

and there is a good feeling. If you get a down-vote you say oh no again, a negative

feeling and say why I’m getting get down-vote I can just improve that.(P15)

Informational SI: Informational influence is based on the acceptance of others’ information

as evidence about reality [Bartle, 2011, p. 42]. Informational influence is defined in the literature

as conforming to others’ information because of the desire to be correct. However, in this

research Informational SI, also considered as conformity, occurred because of others’ previous

performance and the quality of information they provided. The focus was on the quality rather

than the subject and individuals.

Half the SO participants believed they were influenced by good quality information. Actors

started following answers and other co-creators who provided critical answers because they

felt they were producing high quality material. In this type of social influence actors were not

necessarily influenced by the rank of other actors or because they knew them, but for the good

standard of information provided that solved their problem. One participant said:


I can get influenced when I see other people answering a question and the quality

of their answers are amazing or they evidently spend several hours crafting a single

answer and it feels like they are producing very high quality material ... the quality

of material they produce is so good, it encourages participants such as myself.

(P10)

4.1.4 Theme Four: Trust

Trust was found as an environmental-physiological factor helping decision making and was

critical as an assurance for service quality. This study revealed that trust existed in the

co-creation environment to enable interactions. This meant there was a basic level of trust

among collaborators that made co-creation happen. However, trust was critical for

decision-making in service quality, and increased interaction and exchange among actors.

Participants expressed the importance of trust in their decision making, highlighting how

challenging it would be to distinguish between an incorrect suggestion or whether the solution

was worth implementing. They believed “you should get a second opinion because solutions

could be quite tricky sometimes” (P1).

Trust in co-creation systems is more of a social concept than interpersonal relationships.

Some SO participants believed they trusted the quality of information provided because of the

position and status of the provider. Most participants said that trust of quality depended

significantly on the collective agreement of a solution and together represented two types of

trust - Subjective and Objective.

Subjective Trust: Subjective trust was found mean to trust the quality of information

provided by experts or people actors knew: “you know their answers are usually good”(P1).

Here, Trust was based on an actor’s psychological state on a subjective norm and their

subjective opinion of actors.

I started following answers and I kind of had couple of people in the mind which I

always respect them. By respecting them I mean I started following them and I had

a high rely for them. (P10)

Objective Trust: Objective trust meant trusting a collective agreement on the quality of

a service provided. This type of trust depended on the capabilities provided by the platform


to support actor perception and decision making about quality. For example in SO, the voting

system helped actors’ decision making on the best idea or answer by providing “an overall

agreed upon as the best solution” (P1). SO participants expressed the importance of peer

support and assistance to optimise the recommended solutions. To enhance objective trust actors

engaged in voting and peer review activities to “make sure that the best answer rises to the top”

(P18) and to aggregate collective ideas.

Actors tended to have a positive assumption about higher ranked actor knowledge, as

noted: “I treat a high reputation as yeah they probably know what they are talking about”

(P8). Trust could be gained over the time as the result of Informational SI: “Trust based on

previous performance of peers”(P1).

If I saw someone with high ranking or very experience in say Java programming

and answering my Java program, I’d be more likely to trust him or her just because

he is more experience. (P16)

Overall actors trusted any potential provider who was interested in collaborating. However,

an actors’ level of trust on the quality of a provided service depended on the ranking and

expertness of the provider, and also the collective agreement of the network. Figure 4.1 shows

an example of Trust in SO:

Q: asker of the question

A1, A2, A3: answerers to the question with different reputation scores.

+/-: Votes

The edges between Q and A1, A2, A3 illustrate the suggested solution for the question that

can be assigned by +/- votes through the network. Actors’ perspective of trust is based on

a) the aggregation of votes which shows the best solution and trustworthiness of the provided

information, and is an indicator of high quality information. b) The second type of trust is

based on actor profile and their social status. When the solution provided by A1 and A3 have a

close number of votes, the answer provided by A3 is considered more reliable because of their

reputation score.


Figure 4.1: Example of trust model in SO.

Source: Designed

4.1.5 Theme Five: Actor Competencies

Actor Competencies refers to the capability and level of expertise of actors that enable them

to collaborate in the service co-creation process. This research revealed Actor Competencies

as the primary operant resource in A2A co-creation networks. To co-create the service actors

needed the required skills, and creativity to enter the innovation process of service co-creation.

Consequently, an actor’s collaboration in service co-creation developed their skill. The majority

of participants agreed that their collaboration in problem solving, answering and supporting

others directly depended on their skill and knowledge:

You answer mostly when you are knowledgeable enough in that area. (P19)

When I feel like I know enough to participate or contribute I’ll definitely put back

as well. (P1)

StackOverflow (SO) participants believed the success of co-created service that resulted

in value depended on the actor’s effort in the co-creation process, and their familiarity with

the platform. The longer the duration of actors’ collaboration, the higher their experience and

chance of co-created quality service through their co-creation performance:

It is important how to properly answer the question. For example, you do not ask

a single line question, you do have to explain a bit of your context. Similarly you


do not answer just with just a link stating that go there and you will see the right

way to do it all. You have to import what is relevant and significant from that link

into you answer and to something more elaborate and those kind of best practices

have been evolving about years. I have been answering daily for the past 8 years

but someone who is coming new to the site will get a quite different experience and

possibly a harsher experience because of the lack of the knowledge of feature and

best practices. (P17)

Overall, the higher an actor’s expertise in the related technology, the more potential

collaboration occurred in the service co-creation process. The greater the familiarity and level

of effort with the activities, the greater the effectiveness and value they gained from their

collaboration in co-creation activities.

4.2 Themes of Actor Value Perception (O)

The second set of findings relate to Actor Value Perception that represents the interpretation of

the Organism (O) part of the SOR model. Actor value perception was found as the perceived

benefits available in relation to collaboration in service co-creation. Table 4.3 presents a

summary of Value Perception themes and related codes.

4.2.1 Theme Six: Learning value

Learning was found as one of the main values participants perceived from their collaboration in

problem solving. Actors collaborating with others in service creation could benefit subsequently

from self-improvement and by evaluating their knowledge. Observation and contribution in

co-creation activities such as discussions, tags, and answering were part of the learning process

for the SO actors. When collaborating in co-creation activities to learn, the service co-creation

system could enhance the individual’s and consequently the system’s knowledge.

One of the reasons why you keep contributing is because you learn a lot through

these years, I mean that’s per say per question or per answer you learn a lot. (P12)

4.2. THEMES OF ACTOR VALUE PERCEPTION (O) 93

Table 4.3: Characteristics of primary value.Sub-themes Related Codes Sources References

Learning Learning through engagement 13 42Value Personal development 8 11

Exploration 6 6Self-evaluation 3 5

Utilitarian Delivery speed 7 11Value Fulfil needs 6 9

Usefulness 5 8Speed of operation 6 7Self-presentation 8 13

Hedonic Fun and game 10 12Value Competition and achievement 11 21

Puzzle solving 4 8

Potential Economic Future career 11 20Value Professional engagement 8 11

Social Status Credibility 4 5Reputation 6 7Other 13 24

Social Helper, teacher 6 8Role Consumer 6 9

Leader 4 12Trusted user 7 16

Belongingness 14 42

Quality 17 67

Support Reciprocity 12 28Altruism 15 43

The majority of SO participants said they learned programming through collaborating with

others in SO. Other reasons given for continuous contribution were discovering new topics and

tools, new styles of coding and strategies, and daily practice.

...now the most voted tags on my profile is Git (the version system control tool) use

for open source, and that’s the tool I didn’t know at all when I started to participate

in SO eight years ago and I’m very much expert in that tool just because I have

answered more than 7 or 8 thousand questions on it... So, for me is very much a

daily practice, it’s a way to get daily training. (P19)

Collaboration in co-creation activities for some actors was not just learning about a specific


technology, but learning “how to tackle a certain problem in general, in working environment

or real life”(P1,P12), how to improve the way they “interact with others” (P13), and enhance

cultural attitude (citizenship behaviour), as noted : “provide good answers but also remain

humble” (P12).

The findings revealed that co-creators wanted to evaluate their level of knowledge with

others as part of their learning process. Self-evaluation through co-creation activities became

an integral practice for actors to judge the level and quality of their knowledge and expertise.

Actors relied on peer feedback as the primary source of self-validation. The following examples

illustrate participant ideas on evaluation of their learning and skill:

Sometimes I participate to check my learning and how much I have learned really.

to see if i am able to answer others questions ... (P19)

If I have free time, then I just want to judge myself that’s why I answer some

questions to see what are really my skill sets. (P15)

Overall, actors’ collaboration in co-creation activities provided a daily practice on the

technology they desired to learn and increase the chance of learning from experts in the related

field. Actors collaborated in a learning process of acquiring new resources and skills

development that enriched their co-creation behaviour. Such collaboration in service creation

established an environment for mutual learning.

4.2.2 Theme Seven: Utilitarian Value

Utilitarian value is an “assessment of functional benefits” which is relevant to a task-specific

use of a system [Hoffman and Novak, 1996]. This research determined Utilitarian value as the

specific usage co-creators expected to attain from their contribution. The expected utilities by

SO participants were to improve efficiency of task operation, prompt service delivery,

self-presentation, and reach expected services or resources.

Participants said that their collaboration helped them “to do their job better and faster”

(P18), and it “dramatically accelerated writing code and accelerated the quality of code” (P7),

thus saving time for actors.


Co-creator’s Utilitarian value was influenced by service platform capabilities. They

mentioned that free resources, easy-to-use UI and features such as tags and voting systems

made the system quite efficient, helping them “to get the answer straight away”(P12), and “to

collaborate more and better” (P17).

Some participants’ answers to why they contributed in problem solving included to

showcase their skills, capabilities and knowledge to others. Actors’ self-presentation of

knowledge and skills found to occur as part of interactions in completing co-creation activities

for getting reputation, impressing peers, and getting credential and job opportunities.

Self-presentation in co-creation systems happened due to the high level of resource flow

integration, and task-oriented activities that engaged actors to compete on delivery of service.

Although by self-presentation actors emphasised their ego value, the power of positioning in

the social units and belongingness to the community triggered the action. The two examples

below illustrate participant statements about self-presentation of their skill:

...Specially I have never had a formal education, I never went to the university, so I

can show my knowledge by doing stuff like that. (P14)

One is you get the chance to not quite show off but show that you know about a

particular topic or set of topics, you get the reputation feedback as I mentioned.

(P11)

4.2.3 Theme Eight: Hedonic Value

Hedonic value was related to experiential benefits such as entertainment and fun. The nature of

playfulness and providing a challenging task were suggested as the major drivers for

participation in the virtual co-creation system [Kohler et al., 2011]. The majority of SO

participants reported that part of their collaboration was for entertainment and fun: “when you

want to take a break, that break maybe going for a coffee or checking Facebook but maybe I

open SO and contribute to something that I already know” (P3).

According to the participants, being involved in challenging tasks and puzzle solving, the

competition aspect of problem solving, and the gamified model of collaborations were part

of pleasure seeking facet of their collaboration. Participants believed the environment was

gamified in a sense that they “gain points when they solve a problem and this brings a sense of


achievement” (P16). The gamification aspect was supported by game design features such as a

reputation system and badges to enhance SCB. By being part of a game, co-creators expected to

gain some incentives that proved they were “valuable members of the community” (P19). The

following example presents participant 10’s perspective about engaging in the game flow:

When I say the flow of gaming I mean like constantly thinking about it all the time,

have some competitor to compete against and yeah that was mostly the thing all

the time in the mind. (P10)

For some participants being involved in the game was not just to gain points but for the

competitive nature of environment. In this case, competition helped actors to validate their

knowledge as a practice for “programming contest in real life” (P12), to prove their

“proficiency in specific topic (P17)”, or to “solve the problem faster than the others” (P7).

The effect of the sense of competition among co-creators resulted in a greater level of problem

solving and prompt SCB.

On the other hand, some participants expressed the aspect of puzzle solving directed their

collaboration. They believed the challenge of finding an interesting question, doing their own

innovation, solving the problem and coming up with new ideas made the co-creation

environment more fun and enjoyable for co-creators.

I think it’s the challenge to think through a problem that someone has, and I can

understand it and fix it if I saw it through... it makes a lot of fun. (P6)

These examples illustrate that providing a competitive and gamified environment made

co-creators more dedicated to the activities and contribute to SCB. Actors’ Hedonic Value was

socially influenced by other actors’ activity and through service platform features that enabled

them to experience the experimental nature of the environment.

4.2.4 Theme Nine: Potential Engagement

Actors’ collaboration in the service co-creation process and formation of value became the

main source of prospective economic value formation. In this research, Potential Engagement

was actors’ expectation of gaining economic profits from their collaboration. Although


co-creators’ collaboration was not based on monetary transactions, they were willing to

enhance their contribution in order to invest in future economic values.

Prospective economic principals for SO participants were categorized as future career and

professional engagement. Many SO participants reported the main part of their collaboration

was to build a portfolio (i.e., building their CV) to represent their talent and skills, and make

professional connections with other developers and companies. They believed in their

collaboration as a scheme for potential employers to evaluate the level and quality of their

professional Competencies.

If I am looking for a change in another company they will look at my SO profile and

how I contribute and of course they will get impressed that I’m contributing on a

society at large. (P16)

Co-creators believed that the SO profile was a more reliable reference for assessing IT

professionals rather than the traditional resume. Potential employers evaluate candidates based

on their SO accomplishments and their critical thinking and strategies in solving problems in a

group. Participants 5 and 13 presented the use of SO profile as a reference:

It’s almost like LinkedIn where people see the resource for their career or get

involve in the projects based on their profile. (P13)

They put on their resumes to show here is my SO profile look at my reputation here

etc. So, this is becoming significant references in job market. (P5)

Some participants said that their collaboration in problem solving and co-creation activities

went beyond collaboration on the platform. Actors had been approached by other developers

to work on a projects off-platform. Their relationship evolved from online collaborators to

colleagues with working relationships:

I’ve actually been approached by employers and other developers because of my SO

profile to work on different projects and that’s definitely something that’s interesting

to me. (P6)


I actually have quite a few Facebook followers who contacted me from SO and with

some of them that has actually evolved to friendships and working relationship.

(P5)

Overall, collaboration in service co-creation activities played an important role in attracting

job opportunities and professional connections by virtue of building the actor’s profile. By

initiating a reference showing actor’s level of technical competency in the related technology,

ability to understand and solve problems and communicate solutions clearly, SO participants

could later extend their participation in the projects.

4.2.5 Theme Ten: Social Status

Social Status was found to be one of the main value perceptions leading actors to the SCB.

Social Status referred to co-creators’ professional identity though gaining reputation and

credibility. Actors’ status was empowered by their active collaboration and support of the

platform. A higher social status created more reliability in the community and a rich

environment for professional relationships and job offer.

Participants mentioned they engaged in co-creation activities to achieve an online status

that enabled them to gain reputation and recognition through other actors in the network.

Recognition through the network was required to provide useful contributions and gain points.

Social status was mentioned frequently in relation to reputation. Participants believed they

”earned peoples’ respect on by answering questions well enough” (p11), ”inspire others to

write better answers by writing incredibly, absolutely detailed answers” (P17), and ” learning

from users with high quality answers” (P10). One participant mentioned:

Reputation system for some users is like their online status, a lot of contributors are

quite obsessed by better reputation ranking. I have heard that even some people use

it in their CVs to get a new job. (P1)

The index factors for social status in the community were identified as credibility and

reputation. Credibility could be established based on the actor’s SCB and building online

reputation. Credibility referred to the level of trustworthiness in the community. Actors tended

to rely on people with higher social status in the community. Co-creators believed that higher


credibility facilitated a positive perception though the community and brought a higher

position in the community. As an example, one participant claimed: “... because of the

ranking system you slowly go up in the rank and it gives you credibility” (P1). Another

example shows participants believed credibility benefited their career: “you get the point and

also when I was looking for a new job it also gives you credibility, I print out what’s my points

are on SO, it gives you kind of credibility when you go for a job hunt” (P18).

Social status largely depended on the technological functionality and strategic interaction

among actors built by the platform. As such, a ranking system empowered actors by giving

them a reputation number and more privileges within the system. This reputation number was

part of their online identity and level of expertise that raised the level of respect among peers.

The greater the actor collaboration in service co-creation activities, the greater network value

they could gain. Participants believed “reputation is one of the main drivers” (P3), and having

a certain reputation score represents “how much the community trusts you” (P13).

Co-creators’ perception that their contribution enhanced their social status and reputation as

part of their professional identity, drove them to collaborate more in co-creation activities.

4.2.6 Theme Eleven: Social Role

By increasing the active role of customers in service co-creation networks, companies often set

some pre-defined roles built directly into their service delivery model e.g., in StackOverflow,

answerer, asker, and moderator. These pre-defined roles are set based on the actor’s capability

and interest in the co-creation activities. This study revealed that roles could be socially

constructed as the effect of the network. The participants mentioned they played the role of

helper, teacher, adviser, and leader while contributing in the activities. These types of

perceived roles gave actors a sense of responsibility and guided how they behaved.

I certainly willing to answer questions that might be regarded as more

complicated.I have to put myself forward, it’s about teaching people who are less

experienced. (P13)

Although most participants perceived their role as “contributors for a greater cause” (P9),

some established co-creators in the community said they played the role of “‘old expert” and

influencer that showed their level of trustworthiness in the community. When contributors


reached a certain level of reputation, the “community trust them” (P13), because their

reputation was an indicator of their knowledge and skill and the position they had gained

through the community. A co-creator’s role may change in relation to their status. The higher

the status gained by co-creators, the stronger role they perceived. The following quotes

illustrate how participants saw their role as a leader:

The area which I know about, I’m a leader. If it’s necessary, I will insert myself to

the discussion where I ask in comments and people respond to them and clean up

the whatever messes I think has been made. (P11)

I have some influence on that regard because of my position I can have a wide

range of moderation facility other than that I do not feel any special because of my

current ranking. (P17)

Overall, an actor’s perceived role was socially constructed through their interactions with

other co-creators over time. The perceived role described a set of expectations and

responsibilities for co-creators that contributed to greater SCB.

4.2.7 Theme Twelve: Belongingness

Belongingness in this study was a cognitive response reflecting the feeling of attachment and

bond to the community that contributed to a higher level of SCB. Belongingness was found

strongly associated with network interactions. Regular collaboration in co-creation activities

and constructive communication was found to enhance a sense of belongingness and

responsibility through community which was essential for continuous collaboration.

Many participants stated that parts of their contribution occurred because they learned a lot

from the community and they were part of the community. They said they spent time daily to

find interesting questions to answer, so “it’s part of their working life” (P1).

This study revealed belongingness developed over time. The more actors engaged in

co-creation activities, the greater their level of belongingness to the community.

Belongingness was reported as an important factor by participants who were more established

in the community and had a higher position:


You have a lot of people who work around pascal or work around Prolog, etc and

those people know each other quite well at least people who contribute often. After

a while there is a bit of feeling of being part of it, like look I know that person is

working on that sub community. (P12)

As you get more reputation you get more privileges you can do more things and it

helps people feel they belong and gives them a sense of duty to help, clean up, look

after the site make sure people are behaving etc. (P11)

Belongingness was the result of an actor being respected and valued for their collaboration

which brought the feeling of both responsibility and commitment.

4.2.8 Theme Thirteen: Quality

Quality was found as a key shared goal among co-creators. Quality in this study emphasized

fundamental and supplemental aspects of quality which were to provide service to fulfil a

requested service or to provide an additional service solution for future use. Whilst a good

quality solution was principally to encourage collaboration, the lack of quality was found as a

barrier for future collaboration and to creating value. Constructive feedback and effective

communication was found to enable co-creators to deliver and support a higher quality service.

Study participants claimed maintaining the quality of offered solutions was fundamental for

all co-creators. The majority of participants believed that high quality questions and answers

encouraged their participation while poor quality questions put them off. For example, one

participant stated:

If people are posting really good questions then we feel that we are co-creation a

resource of high quality and that can encourage collaboration. I think the convert is

also true, if one feels that a lot of low quality material being produced, for example

a lot of duplicate questions being asked or a lot of lazy questions being asked that

are subsequently closed. Or there are lot of people who ask questions just once and

then don’t have to reply to the answers that given. This creates low quality material

and that can put people off from participating. (P13)


Although all co-creators felt responsible to improve quality, the perception of quality for

provider and consumer could be different. For most of the providers, answers should be detailed

and accurate enough to not only answer the asker’s question, but to support future reference.

However, for the asker receiving an answer that solves their problem is principal. The following

examples show participant ideas on the importance of service quality:

I’m just making sure that I answer the best possible not just technically but the best

possible illustration of how you would like to answer, detail answer with the links

with the nice formatting, with the little extra details. So, it will be some kind of best

practice example, because I’m not the only one doing that. And that would be it a

kind of model in order to improve the participation, the quality of the participation

but not much else. (P17)

The main thing that we all agree on is the knowledge sharing platform is that we

are trying to provide answers for the future not just for the guy who is asking now,

but also as a future reference. (P5)

These examples highlight that the quality of the offered service from co-creators’

perspective was functional or supplemental. The functional aspect provided a service to satisfy

need and provide an answer that addresses the actor’s problem. The supplemental aspect of

quality supported the service with additional parts for future preferences.

4.2.9 Theme Fourteen: Support

All participants mentioned helping-out as one of the main values they expected in their

collaboration. Being willing to help others to provide a quality information and build a

repository of knowledge were recalled as important drivers for their collaboration in service

co-creation activities. Co-creators supported others based on altruistic or reciprocal support.

The reason for the tendency to be altruistic was to promote the network as a whole by

benefiting other co-creators as well as themselves implicitly. The implicit benefit was to gain

satisfaction and happiness from helping others. On the other hand, some participants felt

obligated to help other actors to create a win-win situation because they themselves were

gaining help from the community (reciprocity). This study confirmed helping out as a shared

4.3. THEME OF RESPONSE(R) 103

goal and supportive facet of service (problem solving and learning) that encouraged others to

collaborate:

The clear goal of the community is helping the fellow programmer in a precise

way, not just give them the answer and vague description but now a clear specific

answer, that is one of the goals I think and I am totally aligned with. (P10)

Study participants highlighted two reasons for supporting others. The majority of

participants mentioned they had learned a lot from the community and they wanted to return

the favour. Others believed they enjoyed helping others without any self-interest. However,

they gained a sense of satisfaction and joy by teaching others and providing a solution that

solved others’ problems. The following examples represent participant reasons to support

others with problem solving and learning.

I want to return the favour that I’ve gotten from the other people and I don’t want

other people to suffer from the same problem that I have encountered. So this is

what drove me to participate. (P1)

Emotional feeling that you get at the end of the day that yes you helped 5 people

and you don’t know them and you haven’t seen their faces but yes the feeling is

awesome to help someone. And it not just that user that benefit it, thousands of

users will visit that question and get benefit out of it without you even knowing it.

(P16)

Overall, Support and guidance in co-creation systems facilitated an effective service

interaction and encouraged future collaboration. Co-creators supported others through

feedback, constructive communication and offering solutions. Lack of support is a barrier that

hindered the actors’ collaboration and consequently their development in productivity and

service quality.

4.3 Theme of Response(R)

This section represents the Response section (R) of the SOR model with results based on the

social/environmental stimulus and state of actors’ value perception. The Response theme is


service co-creation behaviour (SCB) that includes two types of behaviour - Collaborative and

Citizenship behaviour (COB and CB), which can be creative or destructive. Table 4.4

summarises the codes associated with the Response themes.

Table 4.4: Characteristics of response themes in GH.Theme Related Codes Sources References

Service Co-creation Collaborative Behaviour (COB) 15 46Behaviour (SCB) Citizenship Behaviour (CB) 13 31

Creative/Destructive forces 16 38

4.3.1 Theme Fifteen: Service Co-creation Behaviour (SCB)

Service co-creation behaviour was found to include two types of behaviour. First, Collaborative

Behaviour (COB) that referred to actors’ collaboration in offering solutions/codes/ideas and

debugging of technical issues. Second, Citizenship behaviour (CB) that was the supportive

behaviour of actors to enhance collaboration and outcome value. Citizenship behaviour was

the core component driving commitment to collaborative practice. The creation of destructive

forces in COB and CB lead to an increased or reduced value outcome.

Collaborative Behaviour (COB) was found essential for the viability of service

co-creation system performance. COB in this study refers to the integration of all activities an

actor contributes to create and deliver the service. The system’s progress and growth highly

depends on the actor’s COB. The type of activities an actor contributed to collaborate in

service co-creation were analysing and finding solutions to the problems which entailed a

particular level of technical/educational programming knowledge:

You need to solve problem. Normally when you employed with some employer of

course you have to solve some problems that you work on, like you work on some

idea, it could be a project, it could be a product. You get to solve some problems.

but the problem you are solving might not be in the entire horizon of the technology

you are working on.(P16)

Participants believed that SO provided “a collaborative environment, so even if you are the

first person to post your answer others can edit your answer and update them” (P5). They

4.3. THEME OF RESPONSE(R) 105

claimed that the co-creation process was a community process in which everyone was

responsible to make sure“ the content within the site is high quality and correct” (P1). One

common scenario from the participants’ viewpoint was that they posted a link to some

documentation, but the structure changes and then other actors took responsibility and

improved/updated the documentation or answers. Although for some co-creators the level of

collaboration was proportional to their activeness in a real-life job, for others it was depended

“ on the workload and how you view the contribution” (P3).

You will find yourself more active when you are more active on your job. if your job

becomes less active or in a sense that your job is about less coding then you will

find yourself less answering on SO. Because it’s likely relational. So, the more you

are coding in your real life the more you will be answering on SO. (P18)

Participants stated that through collaboration practices they built evolutionary software.

Co-creators brought peer reviewed answers and explored innovative possibilities to the

evolutionary software. These two came together and created an acceleration in knowledge and

innovative projects. The community shared different compelling ideas that helped the

community’s knowledge enhancement, with faster and more efficient results for their projects:

We can rapidly built evolutionary software is consistently changing which is

amazing and a major revolution. So within that revolution we bring about this

peer reviewed answers revolution. The two come together and create an

acceleration in knowledge, how we assemble things and how we build things how

we critic things, how we fix things, how we learn things and then thousands and

thousands of projects work together and engage in the community as well. (P7)

Overall, actors’ COB was an essential behaviour in the survival and success of co-creation

systems. Environmental and social stimuli in the co-creation system such as technology

influenced an actor’s Value Perception and triggered continuous contribution in the service

co-creation process that resulted in a change in the actor’s attitude and triggered COB. In

return, COB enhanced the actor value perception and reinforced COB and the co-creator’s

responsibility and commitment.

Citizenship Behaviour (CB) This study found two main concepts for citizenship

behaviour in A2A co-creation systems, consisting of feedback and moderation. Feedback was


referred to as the communication between actors to as evaluate the offered service (i.e.,

problem solving/sharing innovative ideas), and represented actors’ reactions to other actors’

task performance. Feedback in A2A co-creation was shown as the degree of supporting and

encouraging others through communication (i.e., through comments).

Moderation by co-creators was essential in order to maintain a healthy community, through

different activities such as flagging and voting. Moderation was discussed in organisational

citizenship behaviour as policing behaviour to observe other customers’ behaviour and ensure

that the appropriate behaviour occur [Bettencourt, 1997, Gruen, 1995].

According to the findings of the SO study, feedback and moderation were important in

A2A co-creation systems in order to enforce roles and develop group norms. Co-creators were

encouraged though CB to collaborate more in co-creation activities. Below is an example of a

participant’s citizenship behaviour:

I’m responsible to motivate other people, for example if somebody answer the

question and its good I would just motivate them by appointing them. Also

generalizing the communication and making sure everything is good when I’m

present. Just to make sure everything is in the fairness. (P10)

Creative and Destructive forces in COB and CB

The findings of this study identified that the outcome of creative or destructive forces in

SCB not only resulted in value formation in the connected programmers’ community, but may

lead to the reduction or destruction of any potential value. Value formation happened at the

point of better resource integration and higher compatibility of resources. Value reduction

occured when there was low compatibility of resource integration and negative interaction

among collaborators. Ple and Chumpitaz Caceres [2010] propose the existence of implicit

value co-destruction due to the decline of one of the parties’ well-being, and the destruction of

value by actors or resources. Resources can be misused when actors have failed to apply

available operant/operand resources in an appropriate or expected manner.

The resulting value from creative forces was identified as a quality of reciprocal

information, building a repository of knowledge for current and future users, and higher

efficiency in real-life workplaces. SO Participants mentioned that the quality of questions and

answers through co-creation activities and the reaction of other actors through moderation

4.4. CHAPTER SUMMARY 107

activities could improve their future contribution or lower (diminish) their interest in future

participation in co-creation activities.

If people are posting really good questions then we feel that we are co-creation a

resource of high quality and that can encourage participation. I think the convert

is also true, if one feels or he community feels that a lot of low quality material

being produced, for example a lot of duplicate questions being asked or a lot of

lazy questions being asked that are subsequently closed. Or there are lot of people

who ask questions just once and then don’t have to reply to the answers that given.

This creates low quality material and that can put people off from participating.

(P13)

One participant mentioned that one reason for reduced interest to help others was “help

vampirism” (P13). This happened when someone made a lower contribution than standard and

expected others to solve their problem irrespective of others’ time and effort. These types of

actors came to a community and virtually wasted the energy and effort of others.

While actors’ effort in providing high quality and detailed information, providing

constructive feedback and motivating others through effective communication all increase the

outcome value, giving poor quality information and wrong answers, misbehaving and

discouraging others though ineffective communication all reduce the chance of co-creation of

value and may destroy value.

4.4 Chapter Summary

This chapter presented the findings of environmental stimuli and value perceptions that

triggered actor service co-creation behaviour in the SO study. Fifteen final themes were

presented based on the SOR model. The Five identified environmental themes were

Accessibility, Quality Control Mechanism, Social Influence, Trust and Actor Competencies.

Actors’ value perception included nine themes of Learning, Utilitarian, Hedonic, Potential

Economic Value, Social Status, Social Role, Belongingness, Quality and Support. Finally, the

theme presented as the interpretation of response (R) in the SOR model was SCB. Table 4.1

lists the summary of the total identified themes and their frequency in the StackOverflow study.


Chapter 5

Findings of Case Study 2: GitHub (GH)

This chapter presents the results of the within-case analysis of GitHub (GH). The chapter

includes coding information to categorise environmental and cognitive-related themes, and the

themes themselves.

The initial 101 inductive codes retrieved from the GH data were reduced by combining the

initial codes to 41 codes which resulted in a final 17 themes. Stimulus-Organism-Response

(SOR) model was used to present the 18 established themes in GH. The themes represent five

themes related to the environmental stimulus (S), eleven themes related to actor value perception

(O) and one theme as the response (R).

Table 5.1 shows the list of themes and their frequency in the GH study. Each

theme/sub-theme is indicated by ”references” that represent the total number of references

which were coded and ”sources” which represents the total number of sources that the nodes

referred to. The frequency of the codes was the indicator for the degree of their strength and

density and helped in the categorization and finding of final themes.

The final analysis of GH revealed whilst 13 themes are similar to themes identified in the

StackOverflow (SO) study, they differ in the way they express themselves and their order of

importance. The similarities are in a way that related-codes were confirmed (e.g., Social

Approval in Social Influence theme) or completed (e.g., Utilitarian value) the top themes in the

SO study. For example, the related-codes that reflect Utilitarian value in GH are completely

different from the SO study. The importance of Social Approval was very low (2

participants-10%) compared with the SO study (50% of participants). Collaborative Effort and

Project Marketing themes are new findings to the actor value perception (i.e., represents O in

109

110 CHAPTER 5. FINDINGS OF CASE STUDY 2: GITHUB (GH)

the SOR model).

Table 5.1: Frequency of GH themes in SCB.

SOR Model Themes Sources References

Platform Feature 14 38Environmental User-Interface (UI) 6 8

Stimulus Social Influence 15 39(S) Trust 10 42


Learning Value 12 31Utilitarian Value 13 34Hedonic Value 9 19

Actor Potential engagement 14 30Value Perception Project Marketing 3 5

(O) Belongingness 17 26Collaborative Effort 13 40Social Status 8 12Role 16 39Quality 16 46Support 11 23

Response Service Co-creation Behaviour 17 160(R) (SCB)

The findings are structured based on the SOR model, representing environmental and actor

value perceptions as the lead to service co-creation behaviour (SCB). Section 5.1 represents

the result of the categorized themes for the environmental stimulus part of the model (S),

addressing Research Question 2. Section 5.2 represents the results of the categorized themes

for the value perceptions that is the interpretation of Organism in the model (O), addressing

Research Question 3. Section 5.3 represents behaviours that reflect service co-creation

behaviour (R).

5.1 Themes of Environmental Stimulus (S)

This section presents the identified themes related to the environmental stimulus (S) part of the

SOR model, addressing Research Question 2. Environmental stimuli consist of the resources

and social psychological characteristics that surrounded the SCB. The GH co-creation system

provides a centralized environment that removes location constraints in daily life, enables


discovery and sharing, and improves collaboration by influencing each other’s work. The

identified environmental stimulus investigated in this study are Platform Feature,

User-Interface (UI), Social Influence, Trust and Actor Competencies. Table 5.2 lists the

identified environmental themes and codes associated with each of the themes.

Table 5.2: Characteristics of environmental stimuli themes in GH.

Sub-themes Related Codes Sources References

Platform Feature Collaboration graph 8 9Star 7 9Pull request 6 6Follow 4 4Trending 3 3Add-on tools 1 2Other 4 5

User-Interface (UI) 6 8

Social Influence Normative SI 16 33(SI) Informational SI 5 6

Trust Subjective Trust 4 5Objective Trust 9 22Other 8 15


5.1.1 Theme One: Platform Feature

Platform Feature was found to be an important environmental stimuli influencing actors’

collaboration in SCB. In this study, Platform feature refers to the functionalities and services

provided by the platform to encourage people to practice. Features such as a Collaboration

Graph (i.e., a calendar representing actors’ contribution per day) and Star (i.e., showing the

popularity and rank of projects) have some reputation component and were found to reward

actors who collaborate more. Co-creators were influenced by these features to trust or judge

the project quality or their co-creators’ trustworthiness, and to enhance their position and

reputation in the community. Although these features were not an accurate method of quality

evaluation, they intensified social influence in the community and enhanced collaboration

behaviour. Other features, such as Pull-request (enabling push code and applying changes in

projects), Trending (updating of top projects) and Follow, were found to make the


collaboration easier. While these features were found to enhance actor collaboration though

social influence and learning, allowing integration of external services provided a greater

utility, work efficiency and reliability in coding.

Collaboration Graph: Half the participants said that the GH collaboration graph make

them “feel like be more active” (P8). Co-creators used this graph as a timeline of

accomplished activities. The graph represents a reward system that motivates actors to see

what they contributed, and what activities others are involved in. Participants believed that the

graph is designed to “keep you going like doing it every day”(P13). Participant 2 commented

on the influence of seeing other actors’ contribution to their work and their future

collaboration:

We have what we call GH map (graph). When you have contribution every day,

there is a square hat gets greener and greener. When you go to the home page

of the person when it’s as green as possible it means you have contributed a lot.

Unconsciously we want to see more green colour in the graph to show others we

are hard worker and how much we have contributed. (P2)

Actors found the gamification of the contribution graph helpful. Most participants claimed

they judged others based on the actor’s profile and “how green is their contribution graph”

(P5). They believed “there is a correlation between how much someone contributed and how

good they are” (P5). However, one participant did not believe the GH graph to be an accurate

method to evaluate contributions, they noted: “ I don’t think it’s an indicator of the quality of

the developer. Like it’s a very poor one. They should update and represent the graph to more

accurately measure users’ contribution” (P6).

Star: Star was another feature that was stated as a reputation component and shows the

popularity of a project. Star was important for the owner of the project to show “how

successful is the project”(P2) because actors can determine the value and see the community’s

level of interest in collaborating in the project. For co-creators, Star was a factor to “trust how

valuable is the project” (P18) and was crucial to show if “the repository or project is

updated” (P14) and active.


Other features found to make collaboration easy were Pull-request, Follow and Trending.

Actors claimed they used Pull request to send changes to other actors’ code and follow

discussions on the potential changes with other co-creators, “this makes collaboration easier”

(P1,P10) and is “one of the reasons that platform is successful” (P8). The Follow and

Trending features keep actors up-to-date about “what’s popular cross the world” (P10), “to

check on the new projects and collaborate” (P1) and “to learn a certain language” (P9).

Finally, GH enables the integration of external services that help developers to use other

tools such as troubleshooting tools. One participant stated that they employ integrated external

tools for different use cases, such as running tests and making sure the code works. These tools

helped developers to work more efficiently and ensure the reliability of codes, as P4 noted:

We can add other services, when you submit a code you can add other external

service like Travis CI, that can realize and make some test in our code

automatically, these types of software motivate collaboration because I have more

alternatives to work with.... I have access to the code of other people, I can see

their code is good or not. (P4)

5.1.2 Theme Two: User Interface (UI)

Platform design and User Interface (UI) was found to influence co-creators’ collaboration levels.

A straightforward service ecosystem that developed a flow of collaboration was important for

co-creators in GH.

The learning curve in Git (which is a version control system built under the GH

collaborative platform) was found to be steep. A few participants claimed that the Git

ecosystem was complex, which reduced collaboration, as noted: “With GH you don’t really

see many contributors because it takes a while to get introduced with Git ecosystem. Specially

it’s not easy for beginners to get involved” (P11). However, the GH UI is easy to use and

encouraging to contribute. Other participants believed that although working with Git can be

complex, “once we have good knowledge in this tool, using GH is very very easy” (P2). P12

confirmed that:

Git is very powerful but it’s learning curve is very steep. So, things that are related

to Git are complex sometimes but GH UI is easy. (P12)


Although the GH UI is intuitive and interactive, the background service (Git) is complex

and can hinder or decelerate collaboration.

5.1.3 Theme Three: Social Influence (SI)

Social Influence was found as a key environmental stimulus that influenced co-creators’

collaboration and created a greater and continuous SCB. Social Influence in this study refers to

the influence of co-creators’ on each others’ value perception in the social interaction process

that consequently guided their social actions and resulted in SCB. The findings showed that

co-creators’ quality perception was influenced by the work quality and innovative ideas of

other co-creators. Co-creators were socially influenced by authority through constructive

feedback and social support to get validation, improve their skills and guide their collaboration

in activities. The study revealed that authority can be a positive influence and play a supportive

role that provides a rich environment for learning experiences and interactive engagement.

This influence consequently allowed actors to compete with others and gain a greater position

and reliability in the network, and display greater collaborative behaviour.

Sixteen out of seventeen GH participants believed they were socially influenced by other

actors to collaborate in co-creation activities. Social influence occurred through directing other

co-creators to learn, to be similar to others, or to prove the quality of the submitted code and

their knowledge. Participants believed “there is a social pressure to collaborate more and be

more active” (P8).

This research revealed that social influence had a powerful effect on actor’s SCB on GH.

Most participants were influenced by experts’ contribution or people they knew. Some

participants were influenced by the information provided by others. This represented two types

of social influence that were important in actors’ SCB: Normative and Informational social

influence. The difference between these two types of SI is that the first is conformity to be

similar to others with the focus on actors, (i.e subject) and the second is conformity to follow

the correct information and actors are not the main target.

Normative SI: Normative influence is based on the need to conform to others’ behaviour

in the group and the need for acceptance and approval (social rewards) [Bartle, 2011]. Similar

to SO, GH participants showed normative SI in two ways: significant others such as a

maintainer of the project or experts, and social approval. GH participants were mostly


influenced by significant others and only two participants mentioned the need for approval.

Actors’ level of contribution was influenced by other experts on the platform. According to

the majority of participants, actors were influenced to collaborate more in service co-creation

activities by “the level of effort and contribution of other actors and experts” (P11) or if they

saw other actors “have done a great job in building a library or a framework” (P10). Actors

followed a particular co-creator as a role model because they believed they are “leader in their

language” (P14), “have a greater and interesting ideas” (P10), they are “ committed to their

work” (P11) which influenced their learning and “the way of dealing with the project” (P12).

Participant 12 presented how he was socially influenced by experts:

I am specially influenced by highly reputation people , X[name] for example. She

influences a lot because she did a lot of work and she was so passionate about being

a maintainer. So I looked at her way of doing things. (P12)

Participants claimed that people with a formal role in a project could significantly influence

their work. An actor with a formal role in the project such as “the maintainer who is supportive

and very inclusive” (P1) could lead future collaboration of actors in that project. Good feedback

from the project owner to the pull-request “can improve the way of thinking and has more

influence than other actors” (P14). They believed following the actors with formal roles was

important “ to improve the quality of their work” (P14) because they were “more familiar with

the goal of the project and can help more specific” (P2). These examples show that actors were

influenced by authority and actors with the official roles and experts with higher positions in the

community rather than other co-creators.

According to the GH participants, platform provided more visibility to the shared projects

and accomplishments. When actors saw others build a good project and gain recognition from

that particular software or library, “this gives motivation that if someone could do it, so maybe

you can do it too, so you become more discoverable” (P10). Observing other co-creators’

success created a sense of competition that resulted in a greater collaboration, as noted: “I’m a

little competitive and if I see others and specially experts contribution I’ll be overshown”

(P15). Participant 14 confirmed this statement, saying “ I definitely can tell there is like a

sense of envy or competing” (P14) in collaborating on a good project.


Two participants emphasised the social pressure of work colleagues on their collaboration.

They believed when they saw their colleagues’ (off-platform) level of contribution on GH

projects, there was a social pressure for them to be more active.

I definitely get influence from others. For example, one of the guys a co-worker of

mine X[name], he created a popular JavaScript library. He does something at least

once a day for four years. There is a bit of social pressure for me to do stuff more.

(P8)

On the other hand, for two participants getting approval from other co-creators was

important. They believed actors collaborated to share their code and get validation from others.

Receiving feedback from an external actor to “look at your code and admire your code” (P8)

was a “motivation and give a sense of bond to the community” (P2).

Informational SI: Actors’ conformity was because of quality information and “quality

projects” (P18) where “the idea and method” (P18) were more important than who provided

the information. In this type of social influence actors sought information and were inspired by

‘quality code and style of coding of other people to improve mine” (P15). In Informational SI,

it did not matter if providers “are experts, but it is important if they are interested and provides

good solutions or good code” (P4). The following example represents participant 7’s viewpoint

on Informational SI:

When I see some clean logical, readable code it motivates me to be very precise on

what I write and not leaving trash, functions flying around and just narrow it down

to core what is needed. So seeing their code motives me. (P7)

Overall, social influence was found to be a key stimulus in different stages of the service

co-creation process to alter actors’ behaviour. Co-creation systems created a social pressure

environment for actors that resulted in their SCB through Normative and Informational

influence from others. Informational SI was found to be less influential than Normative SI on

actors’ collaboration, that represents the dominant influence of authority and higher position

actors in the service co-creation system.


5.1.4 Theme Four: Trust

Trust in the GH co-creation environment system was found to be important from two

viewpoints: to recruit a co-creator for an official role (e.g., as the main team member) in the

project, or to ensure the quality of contributed code by co-creators. The first point of view was

based on the trustworthiness and suitability of the co-creator for an official position in the

project. The second view was based on trust in the quality of offered code that could be

evaluated with different parameters, which is discussed below. The parameters were mostly

objective such as testing the quality of code with different tools, but sometimes subjective, as

in checking the co-creator’s profile.

From a project owner perspective, to recruit someone for a team maintainer role to manage

the project was mentioned as risky. Depending on the nature of the project, the margin of trust

could be minimized for potential co-creators for the role of maintainers. For example, if the

project had to be installed on the co-creator’s computer, the reliability of the main members

of the project was crucial . The owner of the project felt responsible to assure the security of

co-creators, as noted:

I always wanted more people to help out at the maintainer level of the project, but

I was a little nervous about someone getting in but making few contributions and

potentially sneaking in some sort of code that could be really harmful on peoples’

computers. (P16)

From the second point of view, GH participants expressed that trust was important for the

quality assurance of provided code, and not for collaboration and offering code. They believed

“trust is always there but the quality of what you are contributing is the main thing we look at”

(P12). Most participants said that trust depended significantly on the collective agreement on a

solution, testing the quality of provided code, the quality of project, and some said that the

co-creators’ profile also mattered. These represented the existence of two types of trust that

were important to an actor’s decision making on the trustworthiness of the provided code:

Objective and Subjective trust. Although actors considered the individual reputations and

profile as a parameter of trust in their offered service, the main elements of trust were based on

objectively measured quality:


There is like an objective way to decide whether the one thing is better than the

other one by performing what you can do. In GH you rely a lot less on reputation

and these stuff and more on an objective whether there is a better solution. (P5)

Objective Trust

Objective trust was found to occur when an actor evaluates the quality of provided code

with objective measurements. In this study, objective trust refers to the collective agreement

(through platform aggregator) or using tools to measure the quality of offered code. Collective

agreement was claimed by half of participants as an important criteria for trust where actors

seek “more mind on code and project” (P15).

Collective agreement was found to occur through service platform features such as Star

and Follow, discussions and Thumbs-up. Collective agreement was found to be important for

individual code quality and the quality of a project, as P16 noted: “It’s like everybody is in

agreement in something, I usually see a bunch of thumbs up or stars or something like that.

There is usually lots of validations and thank yous” (P16).

From the co-creators’ stand point, to choose reliable and quality projects to collaborate in,

the Star feature and the number of project collaborators were important, as noted: “ I trust

projects that has more stars and more contributors that’s the things that generics to me”

(P15). Star feature is a reputation component for the project and represents the popularity and

success of the project. Star represents “how many people had expressed interest on it” (P1).

Level of project collaborators was another measurement for project quality mentioned by GH

participants. Number of collaborators also represents “how many people have actively

committed their code in the repository, give us sort of ideas that how good the quality of

project is” (P10). The number of collaborators also shows if “the project is being updated”

(P12) frequently or “how many users impact any change I do” (P14). So, a higher the number

of Stars and collaborators in the project represents a greater project quality. The following

examples show how participants evaluate quality projects to collaborate in:

I look at GH starts to see how many people had expressed interest on this. that’s

the way of showing how good it is. You know if it has 150 stars then maybe this

either hasn’t ran very long or maybe isn’t very good quality or if has tones of starts

then I say oh OK a lot of people told this was a cool idea. (P1)


The number of contributors could be another important thing that means that

people use it and have high level of contribution, so it is updated and shows that’s

a good project. (P14)

Testing the code by automated code analysis and quality tools was stated as another

method to evaluate the code’s quality. Participants claimed that there are “methods or tools to

test the project quality, every pull request will be tested” (P18), using tools from the platform

marketplace. Co-creators use external tools to automatically test codes, track errors, and

analyse application in order to improve code quality and ensure that code works correctly.

Subjective Trust

Subjective trust was found to occur when actors evaluated quality based on the

trustworthiness of the co-creator as an individual. Subjective trust was considered by

co-creators when they wanted to know the provider of the code, to help decide on one offered

code over another, as noted: “The code is kind of speaks for itself to a degree, I feel I think

more about the code and then realize and know that a person associated to that” (P16).

Subjective trust in GH was based more on the profile and background of the provider of the

project. Co-creators preferred to collaborate with a project maintained by “knowing companies

such as Facebook and Google because they are more reliable than personal projects” (P18).

Participants emphasised that one element of trust was an actor’s profile, including their profile

picture, their history and contribution, and their followers, which represented their reputation

and credibility. They believed they trusted more in an actor who had a profile picture rather

than remain anonymous. If a provider of the code had no profile picture and a low number

of contributions, they were judged not trustworthy. Another important factor in evaluating an

actors’ profile was their Followers and actors who had a relationship with them. The following

examples represent the participant statements related to subjective trust:

If they have no profile picture and just 5 contributions last year I don’t trust them,

But someone who has very popular repositories, and good code, contributes a lot,

you know you trust. (P13)

Generally I look at their profile, look at other projects they are involved with and

their connections are mostly more important than say like the reputation. (P11)


Overall, trust in the provided code was essential to have a successful service delivery.

Evaluation of the quality of offered service was based on objective trust through collective

agreement and tools. However, trustworthiness of the provider was another factor that helped

the reliability of the provided code and decision making (i.e., subjective trust).

5.1.5 Theme Five: Actor Competencies

Actor Competencies was found as the capability of actors that enabled them to collaborate in

service co-creation activities. The findings revealed that actors were required to have some

level of knowledge and skills to be part of a service co-creation process. Unfamiliarity with the

technology used in the project was a barrier for co-creators to collaborate. To create a quality

service bundle, co-creators with different expertise and skills, such as design and coding, were

needed in different parts of the application.

Since GH is a developer’s code sharing platform, all actors were required to have

specialized programming knowledge to be able to collaborate with other co-creators.

collaborating in the project “ required a developer to know a number of things before they

could use and contribute in the project” (P16). Participants believed that most actors they

interact with knew the language and had the required skill, so they “can directly view all the

changes and see what code they have contributed and then you generally can get some level of

quality from that” (P11).

Co-creators were assigned to aspects of a project and worked in different parts of the

application with different expertise, whether building code or design. They had “different

drivers but share similar passion and goal” (P15). Participants believed they “don’t share

similar skill but similar interest” (P4) and “similar goal to grow the project and make it

better” (P7). To have higher quality projects actors needed to work collaboratively in different

parts of the application, “because things are become so sophisticated these days that

one-person can’t actually do everything by themselves” (P10).

5.2 Themes of Actor Value Perception (O)

This section represents actor value perceptions, which are the interpretation of the Organism

(O) part of the SOR model, addressing Research Question 3. The actors’ expected value was


found as the key driver toward service co-creation behaviour (SCB). Actor value perception

refers to the benefit co-creators expect to attain from their collaboration in service co-creation.

The categorization of value perception in the GH study is similar to the SO study. However,

the themes of Collaborative Effort and Project Marketing are new findings to the Actor Value

Perception section. Although the nine value perception themes are categorized the same as the

SO study, the themes were evidenced by different numbers of associated codes and represent

different viewpoints. Each theme clarifies new points to complete the identified theme in SO or

confirm the themes with better and more examples. Table 5.3 lists the identified value perception

themes and the associated codes in GH.

Table 5.3: Characteristics of actor value perception in GH.

Themes Related Codes Sources References

Learning Learning through collaboration 10 22Personal development 8 9

Utilitarian Being up-to-date 7 12Self-presentation 12 22

Hedonic Fun 6 11Puzzle solving 4 5Competition and achievement 3 3

Potential Engagements Job seeking 11 16Professional engagement 9 14

Project Marketing 3 5

Belongingness 17 26

Collaborative Effort 13 40

Social status Credibility 6 7Reputation 4 5

Role 16 39

Quality Project quality 8 10Quality as shared goal 16 24Other 8 12

Support Reciprocity 6 11Help to benefit others 4 5Other 5 7


5.2.1 Theme Six: Learning Value

Learning was found as one of the main values actors expect from their collaboration in service

co-creation activities. In this study, Learning refers to collaborative learning through observing

and interacting with other co-creators that impact co-creators’ collaborative behaviour. Actors

learned by looking at other co-creators’ code, collaborating in other projects and allowing others

to collaborate on their project and imitate to the gained knowledge to their practice. Feedback

found to be an effective way to transfer knowledge and improve critical thinking that resulted

in personal enhancement and a greater collaborative practice. The more they collaborated in

service co-creation activities, the greater level of learning and self-improvement they gained

from their collaboration.

Learning through collaboration was claimed as a key value for actors who wanted to be

“at the cutting edge” (P10). Through learning they could build “future technologies such as

libraries or software through coding, design or documentation” (P10).

Participants believed that service co-creation systems connected people who were

“intellectually curious to learn the way world works and what’s the possibilities out there”

(P10). They were able to experience learning through collaboration in a very large scale of

resources, which was close to the direct experience of face-to-face collaborations, at least

intellectually, as claimed by participants 10 and 13.

Learning through collaboration in the projects and communities was emphasised as what

drove actors to learn different ways of programming and coding, new ideas and methods of

high quality people and projects such as test driven development. They followed and learned

the coding style of programmers they admired over time. The other way of learning stated by

participants was ‘to let other people poke at your project and make it better” (P16). Co-creators

also could “improve more possibility for work” (P14) through collaboration and learning. The

following example represents participant 15’s idea about learning from collaboration:

I’ve learned a lot from contributions things in the projects that I never thought of,

like ways of programming, codes, files. There is a lot to get from other people on

GH. (P15)


Two participants claimed that they did not have a formal computer science background

from university and had learned programming through looking though others’ collaborations

and collaborating on opensource software, as noted:

I don’t have a formal programming background, I dropped out in high school when

I was a teenager. I taught myself how to find answers and how to program through

searching for code and how to read the code behind the documentation on the

projects, and seeing how other people did it. Because I was able to kind of lean on

previous developers, code that they shared, I was able to learn enough so that I can

program and so I thought it only fair that I paid that to continue contribute to the

community and so that’s something I have been doing since I could start sharing,

and through sharing I was able to enhance other aspects and learn more. (P16)

Receiving feedback and communication from other co-creators was claimed as a learning

process for co-creators. Participants stated that when they shared their code in a project the

feedback they received from experts to change their code helped them to “improve the way of

thinking and learning” (P14). They believed even if the result of their collaboration in a project

was the rejection of the submitted code, it could help them to “improve the code until it gets

accepted” (P11). Through communication with others they could not only learn coding, but

also “learn a number of things like how to manage open source code, how to be kind of code

manager, like a manager of people” (P8).

Overall, effective co-creation environment provided collaborative learning through social

and transactional interactions and observation of others’ collaboration in the projects which

resulted in SCB. The greater SCB enhanced co-creators’ skills and produced higher value and

quality.

5.2.2 Theme Seven: Utilitarian Value

Utilitarian value was found important when co-creators perceived to gain practical and utility

values from their collaboration. The experienced utilities by GH participants were being

up-to-date, self-presentation, improving the quality of their code and helping job-related

problems. This research revealed that keeping actors up-to-date was facilitated mainly by

platform features to develop the social influence of quality people and quality projects, and to


encourage collaboration. Self-presentation was important for actors to represent their

capabilities and expertise to others by sharing their projects and improving their profile for

potential professional relationships. The more collaboration in co-creation activities and

feedback influenced their code quality, solved their job-related problems and increased

efficiency at their work.

Incorporating the latest information was found to be one of the utility values contributing to

actor collaboration, as reported by half the participants. The service platform provided features,

such as Follow and Trending, to keep actors up-to-date about trending codes/projects, and other

actors to guide their collaboration. The platform provided a glimpse of what other actors were

interested in, what technologies and projects they were working on, and actors “want to be on

top of that” (P10). The “Trending” feature was emphasised as an effective feature in actors’

collaboration because “It gives you an idea of where the technologies are heading” (P10) and

the goal of projects. What was important about Trending was that it showsed “quality and

density” (P13) of the projects and repositories. Actors were looking for “what is great to

implement, and new and innovative ideas” (P4). Participant 1 presented how a service platform

kept them updated about new projects and ideas and guided their collaboration:

Trending informs you about the top 10 projects that have become popular recently

and got most stars the day before. And that has definitely let me to check on the new

projects and in some cases, collaborate if its suitable for me and I have enough and

related knowledge to collaborate in that specific project. (P1)

Self-presentation was identified as another utility value by the majority of participants.

Self-presentation was found to be a way for actors to communicate their skills and capabilities

to other actors, as noted: “ GH provides an environment to show off your projects and other

use and collaborate” (P7). Actors believed they are able to display their technical expertise to

show they were a “suitable candidate for work as a software engineer” (P2) and “set

themselves apart from other programmers” (P15).

Building a strong professional portfolio to represent their expertise was claimed as a reason

for actor collaboration in GH where “part of contribution can be making profile or future

opportunities” (P5). Actors collaborated in open-source projects to build a history of their

contributions and collaborative projects as a credential for future opportunities. Other


developers and companies evaluated actors based on their profile to provide the opportunity

“to engage with their project in the future” (P13). P3 presented the importance of profile

building on actors’ collaboration:

My collaboration can be as my portfolio, it’s the first thing we are willing to

contribute in OS project. When I get high rank in the platform at some point over

my activates, it’s an evidence that I’m a professional in this domain. (P3)

Some Participants claimed their collaboration in creating software improved their code

quality and efficiency in their work. They believed “I definitely want the quality of my work to

be quite high” (P10) for other actors to see value in it and use or collaborate into it.

Co-creator’s also found that their collaboration influenced their efficiency and the quality of

their work in their job. Collaboration helped co-creators “to do my work faster and more

efficient” (P12) and “cooperate better with colleagues and manage things better” (P5).

Overall, Utilitarian value is a key value that stimulated SCB to gain greater tangible benefits

such as improvement in code quality, efficiency and speed of work, and presentation of their

work practice (i.e., business or projects).

5.2.3 Theme Eight: Hedonic Value

The Hedonic aspect of collaborations was found as a value that represented actors’ expectation

of entertainment and fun. Although Hedonic value was found not as significant as other values

in the GH co-creation system, some participants were influenced by its competitive and puzzle

nature which enhanced their collaboration to some extent.

Half the participants enjoyed collaborating with other actors because it was fun, competitive,

and gave them a sense of achievement by solving problems. Co-creators believed they “enjoy

making things better” (P3). They wanted to improve the projects mainly because they wanted

to “solve the problem in that moment” (P14) and “it’s fun to collaborate with other people and

GH is the best place to do that” (P15).

Some participants claimed that the nature of puzzle solving and competition was fun in their

collaboration. Fixing problems and bugs in the program gave them a sense of achievement,

encouraging them to “fix everything when they found some error” (P3). When co-creator’s


code was accepted in a valuable project they “feel very awarded because I participated in a

very complex project” (P2). The effect of fixing others problem on their collaboration:

is mainly for my self-steem that at least I have contributed to some project and I

feel glad when I see a bug that I fixed. And if the patch is accepted it’s very good

for me, so its fun and personal achievement. (P2)

Two participants described how competing with others influenced their collaboration. They

followed co-creators who worked on quality projects and would like to collaborate on that

project to compete with them (P11, P15). Participant 15 presented how a competitive

environment influenced his collaboration:

Well for me personally I’m a little competitive and if I see people doing things I’ll

be overshown I guess by anybody. That’s a way of driving. (P15)

5.2.4 Theme Nine: Potential Engagement

Potential engagement was found as one of the most important valued in actors’ expectations of

their collaboration. Potential engagement in this study refers to the extension of co-creator’s

professional relationship beyond the platform connectivity to enrich and maximise

professional identities. Potential engagement in GH study represented job seeking and

professional engagement. The study revealed that the higher co-creators collaborated the

service co-creation activities, the greater the status and chance of them continuing potential

engagement off-platform. Co-creators were connected with other co-creators and employers

for a potential job or were building professional collaboration on other projects, because of

their collaboration on GH projects.

The majority of GH participants believed that employees evaluated the suitability of

candidates based on their profile and their collaboration in the projects. Collaborating in the

high-profile projects was claimed as a valuable status in the community that results in job

offers, as one noted:

Your profile shows not just that you know how to code and or do a job beyond

that you do as a hobby, that you know what’s happening in the real world, so that


employers can see you are more legitimate and kind of more serious about it, that

you contributed in GH. (P13)

Collaborating in projects was found to be important for professional engagement with other

co-creators and engagement in real-life projects. Participants believed that GH’s co-creation

environment aggregated professionals and “like a bridge connects you to other developers and

the potential projects out there” (P3). They believed their collaboration on open-source

projects could be extended to “external collaboration and real working relationships” (P2).

P1 presented the extension of their working relationship beyond collaboration on the projects:

There is also value in just being part of the community, you know getting to know

people, I’ve gotten to know several cool and quality people just because of my

contribution on GH and contributing to projects and then they gave me like oh hey

do you want to join this project we are working on, and I had contract work with

them to complete that project, and other similar things. (P1)

I also work on open source and collaborate with people on GH to work on projects

either private or public projects. That’s the useful case for me, just to showcase

my work, so other developers may ask me to collaborate on their private projects.

(P10)

Overall, potential engagement was one of the critical delivery values that actors expected

to gain from their collaboration in service co-creation activities. The proposed further

collaboration with other stakeholders was a confirmation of their valuable status in the

network.

5.2.5 Theme Ten: Project Marketing

Project Marketing was found to be a critical value for the project owner. Project Marketing

in this study referred to the owner’s attempts to use different ways to advertise and popularize

the project to get remuneration. For successful marketing and sale of the project, the end goal

of project should be that people benefited from using it. Different ways of advertising were

through social media channels such as a Facebook group and Youtube, though word-of-mouth


and building community, participating in different meet-ups and conferences and presentation

of the project to other professionals. The successful advertisement and popularity of project

were dependent on the owner’s active collaboration in service co-creation activities, moderating

activities, and also supporting co-creators and novice developers.

The goal of the project owner was to build project reputation “to advertise their products and

services in different ways such as conferences” (P8). When it came to marketing the project,

owners did so by advertising and communication with their collaborators in social media. The

strategy was to find competitive products and alternatives, and they identified how to position

themselves among them. Owner effort to market the project enhanced a number of co-creators

and resulted in project success, as P16 noted:

I’m really interested into the marketing of the project and to expand the usage of it.

Projects doesn’t find users automatically, I think some of the success of the project

is because I have been able to experiment with marketing ideas over the years and

how to promote opensource project. (P16)

Overall, project marketing to offer a project and service was an important target for a

project owner. Successful marketing and creating competitive advantage resulted from an

active collaboration and the support of the owner was required. Owners are responsible to

clarify the long-term goal of the project, bringing innovative ideas and encouraging

collaborators to develop ideas and interest to raise the project position and outperform

competitors.

5.2.6 Theme Eleven: Belongingness

Actors’ service co-creation behaviour (SCB) was significantly associated with their

belongingness to the community and the project. Belongingness in GH study occurs when

actors feel more responsible to collaborate and feel part of the community, so that they solve

problems together, and create a better and more powerful project as a community. This feeling

among actors was found to increase individuals’ attachment to social responsibility and

consider the benefit of project, which resulted in a greater reliability or the project and a

service outcome. In the GH study, social responsibility was associated with obligation toward

a greater collaboration in service co-creation activities, monitoring and supporting others


through feedback and communication. Although for most co-creators Belongingness was

subject to their position or duration of collaboration within the project, for some actors

Belongingness was not limited to their position or the amount of contributed code.

Belongingness was found to be related to the duration of actor collaboration. Participants

believed that “the more actors collaborate in the project, the more personal attachment” (P2)

they felt because they “spent a lot of time on the project and got familiar with other maintainers,

and the actual authors” (P9). Specially, co-creators felt a greater belonging to the community

or project where they submitted “a reasonable trace of code” (P6). Participant 13 presented

the relationship between their collaboration and feeling of belongingness:

At the beginning, there is like a period you don’t know how things work. As you get

the hang of it, you start just realizing that it’s just people behind the screen, so you

feel more open in such contribution and get more attracted to do more. (P13)

For some participants belongingness was related to the role and position they had in the

project. When co-creators had the role of maintainer they felt “more responsible and attached

to the community” (P14). The project owner could be more strongly bonded to the community

of co-creators when “people email photos of them wearing t-shirts and stickers with the name

of projects” (P16). Participant 12 presented the relationship between his role as a maintainer

and attachment to the community:

Right now I have very highly demanding role which is organizing project X and

deciding what gets to the project and what doesn’t get to the project. I have a lot of

stress because the project is not at the best of its state and I feel very emotionally

attached to the project so if the project is getting worst and worst every day I feel

more stress, I feel the pain of the project not being tidy. (P12)

For other participants, their feeling of belongingness was not related to their position in the

projects. They felt “very strongly attached” (P12) to the community and project when they

contributed code because they were “the owner of code activity” (P15). Participants also stated

that communication with other developers and owners, through comments, made them feel a

part of the community, as P11 noted:


Once you contributed once to a project no matter how small, you kind of start

feeling that it belongs to you in a way. (P11)

5.2.7 Theme Twelve: Collaborative Effort

Actor service co-creation behaviour was found to be stimulated by the Collaborative Effort of

various co-creators. Being part of a large scale of co-creators with similar interests and shared

goal was found to be a key objective for actors’ collaboration. Collaborative effort signifies

sharing and responsibility distribution among co-creators that develop innovation toward an

effective project.

Collaborative effort was reported by the majority of GH participants to be an important

driver of collaboration in service creation. Teamwork and sharing ideas were important in

co-creators collaboration, because they believed “through engage we can solve and build a

stronger software or product” (P15). Most actors were inclined to collaborate on a larger scale

in either private or public projects, and any kind of applications and future technologies. They

believed collaboration made working “much smoother compare to old days where people using

sending mails to bunch of people to share a document or something, it just happens naturally

at the central repository in the distributed manner” (P10). Co-creators were able to implement

integrated ideas of different professionals with different skills to improve their project quality.

There was a “synergistic effect” (P6, P7) between collaborators that created a greater project

and fulfilled each-others required feature, as P4 noted:

I just like to be part of it, don’t want to be in a competition or better than others.

Just to share my ideas, to receive other ideas and interact. I think my contribution

is just to change projects, ideas. For me at least. (P4)

They believed that the owner was not the only one responsible for the project, but everyone

who is collaborating in developing the owner’s idea is responsible (P4). The project success

depended on co-creators that followed projects and collaborate. For some projects, the direction

of a project was changed by the thoughts and innovative ideas of collaborators. Therefore, the

collaborative nature of service creation with a shared goal of improving the project was one of

the main values for collaborators, as P16 noted:


The project started with a small idea. I didn’t have any big long-term vision for

the project and seeing it’s grown, it’s clearly because other people were coming

up with the ideas and suggestions and helping me now. Over thousand people

were contributed to the project and I’ve learned a lot about the underlying

technology and the coding patterns. It’s funny, earlier today I was looking through

the parts of the project code-base and I don’t really understand how parts of it

worked. And I think that’s kind of awesome to know that people can make it better,

and understand the complexity at the level that I can’t and as a community we

collectively built this thing that is rather easy for people to use but underneath it

could be so more complicated level that I have never would have understood if I

hadn’t sort of sharing something in the first place. (P16)

Overall, collaboratively working on different segments of the project and sharing

responsibilities and ideas among different parties created a more stimulating environment for

co-creators to collaborate in service co-creation activities that resulted in stronger software or

application.

5.2.8 Theme Thirteen: Social Status

Social Status was found to be important regarding gaining recognition and reputation through

collaboration. Building a reputation or getting recognition from other co-creators were the

purpose of most actors in collaborating in service co-creation activities. This study revealed

that a higher status provided a greater chance of Potential Engagement and was an evidence of

being professional in the domain.

Reputation in GH was mostly based on project Star or actors’ levels of contribution in

projects. Reputation was an indicator for actor trustworthiness in the community or quality

projects. Some participants claimed they “judge actors based on their reputation” (P5). Getting

involved in higher quality projects elevated an actor’s status in the community and could result

in job offers and professional relationships.

There was a couple of times when I’m talking to somebody and I look at their GH

and they’ve got a project with several thousand stars and I say ok this person has

done something to the world and vice versa some people can get to my GH and say


oh this is what they have done and the number of people that have been interested

in that project that I have done. And some kind of personal reputations stand point

that’s definitely an aspect. (P1)

I can truly judge like the correlation between how much someone contributes and

how good they are. (P5)

For people who run the projects it was important to present projects that are well respected

in the community and had a high number of collaborators to receive recognition, as noted:

You know when you go to a meet up conference, it’s cool when other people well

say oh that’s the guy who wrote this, that’s the guy who wrote that. That sort of

having a good reputation of course that some other people like. (P8)

Overall, Social Status encouraged actor collaboration in service creation when they placed

a high value on professional engagement and job seeking. On the other hand, higher status and

position in the community was an incentive for co-creators to pursue future collaboration.

5.2.9 Theme Fourteen: Role

The Role actors play in the co-creation process was found to influence their level of

collaborations and responsibility toward other actors well-being and support. Although

responsibilities were shared through co-creators of the project, the owner and maintainer had

an authority role with more power and influence. The responsibility of moderating activities

through communication was higher when an actor had a authority role in the project. Also, the

higher the level of actors’ collaboration, the greater possibility of achieving official roles in the

project.

A co-creator’s role in GH was divided into two types, of managing a team and

collaborators. The project owner and maintainers were responsible for moderating and

managing codes contributed by collaborators. Although the managing team rule initial

guidelines about the project goal, projects implemented “pretty much based on intellectual

manner not just by virtue of owning” (P10). However, there is a relationship between having

an official role in the project and a responsibility to be responsive to collaborators, and


enhance project quality and collaborations. Project success depended on providing a

well-maintained project, developing a clear project vision, and advertising and marketing the

project. P16 presented how the role of maintainer in managing project influenced a greater

level of collaboration:

I suppose I see myself as the evangelist for the projects or the voice of it when it

comes to marketing the project... I would say I still Own the vision of the project,

X[maintainer] and I started to have video chats once a month to talk about some

short term goals of the project and how we can get it there together and I see myself

as a person to work on and trying to head up recruiting more maintainers and think

about the brand of opensource project from realizing that it is kind of the product

in the community there is a need to be some thought behind not just in the technical

level but about the future of the project. (P16)

This research revealed that collaborator roles could be promoted from a regular

collaborator to a maintainer with an official role in the project. Co-creators who had a high

level of collaboration or were collaborating for a long time, were being asked to be part of the

main team, because they were more familiar with the framework and project goals. However,

selecting collaborators as part of the main team required the project owner to consider the

security of collaborators and trustworthiness of candidates. The following examples show how

collaboration enabled participants to become a part of the project team management:

There is a point in the project where they collaborate more or work on the project

harder and they can be selected as an official position like maintainer or the main

team of the project. (P18)

I’m working for a project related to the Indesign scripting library four years ago.

And just a year ago I have started pushing some code and work more and more

with people in this project. Also I’m using it to teach programming to my students.

Now I’m part of the team there, developing these projects. (P7)

Overall, the actor’s Role represents the expected pattern of their behaviour. Project owner

and maintainers have critical role in the success of project through facilitating an effective


communication and moderation. The owner is the decision maker to which codes should be

contributed and added to the project, and is responsible to keep co-creators encouraged to

provide creative solutions and ideas. The specific type of activities that an owner or maintainer

must demonstrate are strengthen collaborations and encourage co-creators.

5.2.10 Theme Fifteen: Quality

Improving the quality of a project was found as a shared value among all participants that

stimulate their SCB. But the meaning of project quality was interpreted from various points of

view: improving design, functionality and performance. The integrated community goal was to

create a maintainable service that facilitated the possibility of future improvement, and increase

the usefulness and efficiency of service. The objective was to increase the practical implication

for co-creators involved in the project and create a software that met more market needs.

The majority of participants believed projects needed “some level of quality assurance and

future maintainability for future people to build on top of that” (P11). That is motivation for

co-creators who are collaborating to improve projects “ to keep on top of technologies out there

and build something that is going to last long” (P10).

From the owner’s perspective, it was critical that “good code to be merged and not poor

quality codes” (P6). Participants claimed because most actors were familiar with the

language,“generally quality level codes are being provided” (P11). So, the focus was to

collaborate in “improving the quality of code” (P14) to “prove higher quality software or

app” (P13).

For some participants quality was “to make the project grow and better” (P11). This

research revealed 3 dimensions of quality emerging from GH participant viewpoints: design,

functionality, and performance. Co-creator viewpoints on improving the project and quality

were to make an easy-to-use and user-friendly UI, make the program more powerful and

effective, and add more functions and features to the project to empower the user. Another

important characteristic claimed for improving quality was providing clear, logical, readable

and precise code. Participant 7 presented the collaborators’ goal on improving a project:

The goal is to make the project better. Of course there are different aims for

projects, some want to make it more user friendly, some want to add more


functions but on the bottom-line it’s to make it work better, and building something

greater. There is no end to it. Of course we can stop at version 2.0 but at some

point somebody will come and say, I found another bug and this feature would be

great or we all say we want to change it again. (P7)

Overall, the theme of quality argues that co-creators collaborate in creating a higher service

quality as the outcome to meet their objectives and perceived values. Service quality can be

established through different expertise (e.g., design) to achieve different dimensions of quality.

Quality can be achieved by making the project function properly, adding more features to the

service, improving the UI or achieving higher performance.

5.2.11 Theme Sixteen: Support

Support of other co-creators and the project was found as a key reason to collaborate in service

co-creation activities. For most participants Support was established through sharing their

projects and ideas, or problem solving and providing code to benefit others. Actors supported

others because of a moral obligation to give back the value they had gained from the

community (Reciprocal support) or feel valuable when helping others. To support a project,

actors were required to determine an effective way of communication and provide constructive

feedback that encouraged others to enhance collaborative behaviour and the project quality.

When collaborating in service co-creation activities, actors fulfilled their needs and shared

with others who had a similar feature request, as noted: “ by building a software we solve

everyone’s problems that was just yours once” (P9). For some actors, need was presented as

a technical problem and codes, while for others it was satisfaction from helping others. They

believed supporting others made them “feel good” (P1) because they provided value for others

“for no monetary gain” (P6). By solving problems and providing code that had value for

others, they “feel happy and proud” (P4). They believed helping others and sharing “add their

knowledge up by receiving instant collaboration feedback” (P10). The following examples

illustrate participant ideas regarding supporting other actors:

I think underlying goal there is mainly just to share and give back because I have

relied so much on other peoples’ sharing so its more of giving back a little bit

considering that I’ve taken a lot from another people in the past. (P16)


I can’t give everybody who comes in this subway and ask me for a Euro, I can’t

give everybody a Euro, but I can give something back this way. (P7)

Overall, to provide a higher quality project and higher outcome value requires community

support. Support can be provided in various ways, monitoring and moderating the project,

contributing codes, providing feedback, and encouraging others’ collaboration. This signifies

the necessity of both collaborative and citizenship behaviour to provide effective support that

results in created value.

5.3 Theme of Response (R)

This section presents the response section of the SOR model. The response to the environmental

and value perceptions were two types of behaviour, collaborative and Citizenship Behaviour,

that reflect actor’s SCB. Table 5.4 summarises the codes associated with the Response themes.

Table 5.4: Characteristics of response themes in GH.

Theme Related Codes Sources References

Service Co-creation Collaborative Behaviour (COB) 17 87Behaviour (SCB) Citizenship Behaviour (CB) 12 26

Creative/Destructive forces 17 47

5.3.1 Theme Seventeen: Service Co-creation Behaviour (SCB)

Two types of identified behaviour form SCB: Collaborative Behaviour (COB) and Citizenship

Behaviour (CB). Collaboration Behaviour was found to occur from actors’ collaboration in

building code, design and any kind of application or documentation. The project owner was

responsible to maintain and update the project. Actors contributed directly into the application

and shared innovative ideas and suggestions to build future technologies. On the other hand,

actor’s CB enhanced COB and the potential value outcome, through feedback and effective

communication. The creative or destructive forces in COB and CB can increase or reduce the

value outcome.

Collaborative Behaviour (COB) was found as the core behaviour in SCB that made

co-creation happen. In this study, COB indicated actors’ contribution in the main activities to

5.3. THEME OF RESPONSE (R) 137

create part of the service and collaborate in resource integration. Main activities that reflected

actors’ collaboration were contributing codes and project maintenance that were required to

co-create the service (i.e., software or project in the GH study). COB in GH was mostly

influenced by actor’s value perceptions and then through strong Citizenship Behaviour (CB)

and support. COB was found to be stronger when actors felt more responsibility and

belongingness in the project.

Actor’s collaboration was different based on the personal or public status of the projects;

personal project (owner, maintainer) or public projects (external collaborator). This study

revealed that the role actors played in the project included different responsibilities. External

collaboration was mostly responsible to contribute code, and report/solve issues in a specific

feature. The owner and maintainer were more responsible for reviewing codes, reframing and

maintenance of the project. The following examples represent actor collaboration in software

creation:

I use GH definitely every day, in some sense it’s just searching GH or posting code

on GH, you know uploading code, reviewing code, you know it’s every day. I use a

lot of enterprise features now because of my job. I work at Microsoft so we use GH

frequently to look at different things and review things and then I’m also involved

in different open source projects so I post code via that. Some part of I just need to

host it somewhere that other people can have access to and GH is a place where

most people are. (P8)

We collaborate on software and GH provides like all the necessities to do the

project management, so you have got issues and pull request and obviously code

hosting, so you can accept codes, you can manage contributions from anyone else

in the world easily. (P9)

Citizenship Behaviour (CB) was found to be critical behaviour in the service co-creation

process. Communication and feedback were found as two key components that reflected CB

and significantly influenced collaboration and the outcome of SCB. Communication in GH

represents the dialogue and the way actors interacted through comments while Feedback

represents actors’ responsiveness to the provider of code. Both components played an

important role in encouraging collaboration and improving the value outcome, as noted:


Encouragement and responsiveness means everything. if there is lack of any of two,

it is not likely that a first-time contributor will stick around. (P4)

Most participants claimed that other actors directed their collaborative actions through

feedback and comments. They believed “that’s the biggest motivation and helps improve

myself” (P10). When the owner responded to requests and kept the project active,it “make you

to even work harder on it because environment is very important for professional people (P3).

Participants stated that through positive interactions co-creators could “solve and build a

stronger software” (P5).

Overall, a GH service co-creation system facilitated actors’ collaboration in code sharing

and collaborating in the projects (i.e., software, apps) to co-create the service. Whilst COB was

the active collaboration of actors in service co-creation, a good CB provided a more effective

environment that improved COB, resulted in project success and developed delivered value.

Creative and Destructive forces in COB and CB

As the consequence of actor SCB, value was found to be created or reduced through creative

and destructive forces. Although actor SCB mainly resulted in co-created value, there were

ways to reduce the possibility of value outcome. This research revealed that while constructive

feedback and an effective way of communication enhanced collaboration and value outcome

(forming relationships), any form of destructive communication (e.g., rejection of code without

feedback) reduced collaboration and overall outcome value. Unmerged pull-requests, rejected

code without explanation, and a lack of communication were found to reduce collaboration and

resulted in reduced value. Value could be destroyed when the code did not work or it was not

maintainable. Value destruction in GH was mostly related to developer’s time spent and effort,

which was rarely translated as a value. Therefore, the value of destruction was temporary in

nature.

According to the GH participants, poor project maintenance was one reason for reduced

collaborative behaviour. Unmerged pull-requests and refused feedback were the greatest

problems actors had in their collaborations. Participants believed they were less likely to

collaborate in a project when they suggested a change to a project and the code was rejected

without any feedback from the maintainer. Alternatively, when the maintainer was supportive

and gave constructive feedback to improve the code, actors were encouraged to collaborate

more and work harder. If the main developer ignored the submitted code “external

5.3. THEME OF RESPONSE (R) 139

contribution becomes very difficult to pursue to continue” (P2). Receiving feedback was

claimed as the greatest motivation for co-creators regardless of whether it merged into the

project or not. They believed feedback improved their way of thinking and learning “even if

the code needs to be changed and submitted again” (P14). “ Some kind of reward system and

incentive” (P4) suggested as a way that could motivate maintainers to respond to

collaborations.

From the owners’ point of view, large projects had an extensive volume of offered code and

could be responsive only to the significant changes. However, they felt they were responsible

to respond to questions and pull-requests when they pushed a project. They believed the way

a maintainer handled the project was important to clearly establish the project goals and its

priority, so collaborators could follow directions. On the other hand, collaborators believed that

when the response took a while (e.g., 6 months with no response), the project was being poorly

managed. Feedback did influence collaboration as:

There have been times when I have suggested a change to a project and the

maintainer totally shut me down and it was like “No don’t care about this” and

this made me much less likely to contribute on that project in the future or involve

in that project. And then on the flip side, there are maintainers that are very

supportive and very inclusive and ready for new people and when I make a change

maybe it’s pretty good but it’s got some problems but they worked with me on it

and say “Oh you know I haven’t expect these things that looks very good but just

need some changes.” (P1)

I am a main developer for one of projects and I receive many contributions from

external people and I try to give at least an answer and the reason why I won’t

accept some changes and I accept another. At least I give a reason. And when

I don’t get it when I do contribute on external projects I feel frustrated. So the

feedback is very important for us. (P2)

Misbehaving was claimed as a way of destroying and discouraging communication and so

reduced collaboration. Participants believed that as in any other social networks, “trolls” (P8)

made the collaboration environment less useful especially in large projects. It was important to

“communicate with respect” (P14). Actors tended to encourage others with “more up-votes


than down-votes, because they don’t wanna make people feel bad about their suggestion”

(P16). However, some actors were not friendly “with new developers and have a lot of strong

arguments” (P16) that discouraged their collaboration. They believed that GH lacks some

communication tools and “there are not any kind of moderation tool to control bad behaviours

so it can get nasty and offtopic” (P1).

According to most participants, there might be some sort of value co-destruction but they

believed benefits were much better than any destruction (P10). Some believed if the code was

not worth getting into the project because it “makes the project messy” (P12) or because codes

were not “good quality and may not be maintainable” (P11), the value was being reduced.

Another way to destroy value was mentioned as “when code doesn’t work” (P16), “the spent

time” (P9), and “abandon projects and not getting reply” (P1). One believed that even if code

quality was low there was still in some cases value in it because it is maintainable and “other

people can refine it and refactor it” (P16), but if it did not work or was not maintainable it was

a waste of time and effort and value.

5.4 Chapter Summary

The findings presented in this chapter investigated the environmental and value perceptions

that influenced actors’ service co-creation behaviour in GitHub. The GitHub data analysis

revealed 17 themes based on the SOR model. Five associated themes to the service

environment were Platform Feature, User-Interface, Social Influence, Trust and Actor

Competencies. Eleven identified Actor Value Perception themes were Learning, Utilitarian,

Hedonic, Potential Engagements, Project Marketing, Belongingness, Role, Collaborative

Effort, Social Status, Quality and Support. The theme of service co-creation behaviour

represented the Response section of the SOR model. Table 5.1 summaries the list of the

themes in the GitHub study.

Chapter 6

Discussion

This research used the Stimulus-Organism-Response (SOR) model to represent how

collaboration plays out in the service co-creation context. This research used the Uses and

Gratification Theory (UGT) as part of the interpretation of the Organism in the SOR model

representing value perception and extended UGT to the service co-creation context. The

Service-Dominant (SD) logic mindset was adopted to address the overall research question:

“why do actors collaborate in service co-creation?” A systematic literature review was

conducted (see Chapter 2) to explore the nature of service co-creation in the actor-to-actor

(A2A) context to address the first research question: “How are service co-creation systems

classified based on different dimensions in a co-creation context?” Following the main

research question, two studies of StackOverflow (SO) and GitHub (GH) were conducted to

address how environmental stimulus and value perception influence actors’ service co-creation

behaviour (SCB) (RQ2,RQ3).

This chapter integrates the findings of the SO and GH studies to propose a model that

represents actors’ SCB. Following the analysis of the two cases, both sets of outputs (15 SO

themes and 17 GH themes) are compared to create an integrated theoretical model based on

the SOR model (Figure 6.1). The SOR model represents how environmental stimulus in the

service co-creation system influences actors’ value perception and influences SCB. In the

environmental stimulus (S) section, the aggregation of the Quality Control Mechanism and

Accessibility themes from the SO study, and the Platform Features and User-Interface themes

from the GH study created the first construct, Platform Capabilities. Second, a combination of

Social Influence and Trust led to Relational Capital. The third construct represents Actor

141

142 CHAPTER 6. DISCUSSION

Competencies, which is the same in both the SO and GH study. The identified environmental

stimuli in the co-creation system are considered to be the source of innovative performance

and COB through influencing actors’ value perception. Actor’s value perception (i.e., value

in-context) deviates from the way actors influenced through service co-creation environment

and exchange resources. The expectations of collaboration occur through the way relationships

can dynamically impact others belief.

Figure 6.1: Service co-creation behaviour (SCB) model

Source: Designed

Under Actor Value Perception (O), the Potential Engagement and Project Marketing

themes led to Economic Value and together with Learning, Utilitarian, and Hedonic values

represent Purposive Value. Second, a combination of Role and Social Status created Social

Position and together with Belongingness and Collaborative Effort represent the

individual-level Network Value. Third, the themes of Quality and Support represent the

service-level Network Value. In the response (R) section of the SOR model, the SCB

comprises Collaborative and Citizenship Behaviour (COB and CB) that through creative or

destructive forces influence value outcome. Purposive values formed the basis of actors’

collaboration in the service co-creation process and capture actors ongoing needs including

informational, functional, experimental and financial-related aspects of actors’ value

6.1. ENVIRONMENTAL STIMULI IN SERVICE CO-CREATION SYSTEM (S) 143

perception. Network value develops over time when the Purposive value is addressed. The

co-creation environment supports the interaction among actors and enhances the level of

perceived Network value. Network values extend the density of actor collaboration in

co-creation activities, leading to a greater SCB.

The final six themes include Platform Capabilities, Relational Capital and Actor

Competencies as key factors in the co-creation environment, and influence the two actor value

perceptions of Purposive value and Network value (individual and service level), which all

combine to influence SCB. The model illustrates how environmental stimulus (resources) leads

to SCB by affecting actor value perception.

The NVivo Matrix Intersection Query was used to develop the relationship between the

concepts. A NEAR Matrix Intersection was applied to present the interrelationship between

the coded content and its significance, along with the narrative way to enhance the reliability

of analysis. Each section includes a table that summarises the strength of the relationship

between the concepts. A higher number represents more code overlap and a stronger

relationship between the concepts.

This chapter follows the SOR model to present the discussion of the outcome model. In

Section 6.1, a detailed discussion of the environmental stimuli found in the service co-creation

environment is presented. Section 6.2 discusses the categorised themes for the value perceptions

and how they are influenced by service co-creation environment stimuli. Section 6.3 discusses

how SCB results from actors’ interaction in the service co-creation system. To achieve the

purposes of this chapter, each concept is supported by current literature on the co-creation

context from the disciplines of Service Science, Marketing, and Management, which is further

explored for new meaning and understanding using the empirical case study data from this

research.

6.1 Environmental Stimuli in Service Co-creation System (S)

A service co-creation environment in this research was identified as the infrastructure of a

service co-creation system that includes operant resources (e.g., Actor Competencies) and

social-psychological and cognitive characteristics surrounded by co-creators’ interactions

(e.g., Social Influence). A service co-creation system mobilises dynamic co-creation initiatives


for service innovation and value-oriented outcomes. According to this research, the collective

relationship among actors through sharing and exchange of resources, effective

communication, and intensity of relational capital strengthens the co-creation environment.

Actors exchange service - application of resources to benefit themselves and others [Lusch

and Nambisan, 2015] within the service co-creation system to integrate resources and

co-create services. This research indicates that the interaction between actors is centred on

their value perception and how they are socially influenced by other actors. Actors contribute

firstly based on their level of competency and the value they expect from their collaboration.

Second, actors’ collaboration largely depends on the support of service platform capabilities

and how actors collectively influence each other’s contribution, forming their Collaborative

and Citizenship behaviours (COB, CB). Therefore, in an actor’s value perception, SCB and

value outcome are influenced by the characteristic of a service co-creation environment and its

environmental stimuli. Supporting the finding of this research, Lusch and Nambisan [2015]

indicated that a service ecosystem is collectively created by actors that form their environment.

Value co-creation and innovation happen with an efficient service platform, an effective service

exchange and a higher resource density among actors. The purpose of the network in

service-integration activities is individual survival/well-being, as a partial function of

collective well-being [Lusch and Nambisan, 2015].

The identified environmental stimuli in the co-creation system are the operant resources of

Platform Capability, Relational Capital and Actor Competencies, all of which are considered

to be the source of innovative performance of actors and their COB. Vargo and Lusch [2004]

argue that operant resources are key to value co-creation. To integrate a customer’s expertise to

co-create value it is necessary to have a “trustable cognitive, normative and affective

collaborative environment based on dialogue and common values” [Romero and Molina, 2009,

p. 406] .

All actors in the service co-creation system are resource integrators, applying the

application of resources and competencies through service exchange [Vargo and Lusch, 2017].

Integration requires actors to implement resources in the process of interactions, and undertake

a number of activities [Hibbert et al., 2012] to form collaboration and benefit not only for

oneself and another party, but for the whole network. Resource integration occurs in the

process of ongoing combination of resources [Frow et al., 2015] and actors’ interaction with


the use of resources [Laud et al., 2015] to create intended value [Skalen et al., 2015].

Following Edvardsson et al. [2014], this research defines resource integration as a set of

collaborative processes among actors, resulting in mutual behavioural and experiential

outcomes. Specifically, this research regards resource integration as the application of actors’

specialized competencies and service exchange in the process of collaborative interactions, to

combine and match with the proper service bundle leading to co-created services and

value-oriented outcomes. So, service co-creation is determined as the ultimate integrated

resource. The process of resource integration was found to be influenced by other operant

resources in the service system including relational influence and platform characteristics. The

collective behavioural response of actors to the interactions is the outcome of SCB.

This research conceptualises the operant resources (i.e., environmental stimuli) in the A2A

service co-creation system as Platform Capabilities, Relational Capital, and Actor

Competencies. In this research, Platform Capabilities are related to the features of the service

platform and abilities that are virtually designed to support the interactions. Relational Capital

refers to the cognitive relation of entities and the influence of actors on each other’s

interactions. Actor competencies refer to an actor’s skill and knowledge to successfully deliver

the service or complete the task. Depending on the nature of co-creation interactions and

service exchange, operant resources can be applied directly or through assets as the operand

resources. Table 6.1 lists the environmental concepts and related sub-themes. The following

sections discuss each concept and their influence on actors’ value perception that form SCB.

Table 6.1: Service co-creation environment characteristics

Environmental Stimuli Description Sub-themes

Platform Capabilities PC relates to the features provided by Platform Features(PC) service platform to foster contribution UI

and use. Accessibility

Relational Capital RC related to the cognitive relation of Social Influence(RC) actors and their influence on others’ beliefs Trust

Actor Competencies AC refers to the Level of actors’ capability(AC) and expertise to deliver the service


6.1.1 Platform Capabilities (PC)

Platform Capabilities in this study refers to the design features and abilities provided by the

service platform to support service co-creation and foster collaboration and use. A service

platform is defined as a modular structure that facilitates the interaction of actors and resources

- service exchange - to improve resource density and result in “innovative, scalable solutions”

[Lusch and Nambisan, 2015]. Lusch and Nambisan [2015] suggest that to have a higher

resource density (i.e best combination of resources) the service platform requires a

layered-Modular structure and granularity. The layered-Modular structure facilitates

components within and across functional design hierarchies to lead to different types of value

propositions. The granularity provides diversity to service exchange.

As presented in the literature, the design of an effective co-creation platform has been

regarded as essential in regulating actors’ interactions and user experience [Kohler et al., 2011,

Romero and Molina, 2011, Fuller et al., 2009] and a successful co-creation process. The goal

of an effective design is to build an engagement platform to enable efficient co-creation [Frow

et al., 2015]. Consistent with the literature, this study indicates the importance of the design

and capability of a service co-creation platform in making collaboration and engagements easy

and improves the success of the co-creation process.

According to the SO study, Accessibility and Quality Control Mechanism were associated

with actors’ SCB through influencing co-creators’ value perception. This research confirms

the findings of Lusch and Nambisan’s (2015) study that access to diverse resources shapes

resource integration and service innovation. The co-creation platform UI should be intuitive to

support straightforward and need-specific functionality. Easy flow of co-creation activities and

the process provides access to a wider variety of resources and information seeking, which is

the first step to evaluate the process and decision making on continuous collaboration.

On the other hand, Quality Control Features, such as a voting/score system and badges,

were also found essential for an A2A co-creation community to supply system regulation and

influence actors’ behaviour. Quality features improve performance and encourage

collaborators through harnessing the hedonic aspect of gamification, and providing a

competitive environment by rewarding actors. Quality control is significantly related to actors’

aspiration towards higher position and status in the community, and gaining higher utility from

their contributions. Kohler et al. [2009] found that the co-creation platform must meet


Hedonic, Usability, Sociability and Learning experiences needs of users. They suggested

developing interactive objects such as Follow to gain an informational goal, a technical and

easy to use interface, and entertaining activities to enhance creativity and participation. The

findings of the SO study confirmed that the platform design needs to be aligned with the stated

values. However, this study also found that an A2A service co-creation platform must provide

a quality control mechanism to moderate activities, track the quality of services provided by

actors and support service exchange to enhance the quality and quantity of collaborations.

Additionally, it must apply a quality mechanism to improve actors’ decision making and trust

in the provided service. Therefore, in the context of a less structured co-creation platform

(community-orchestration co-creation system) in which actors are the main players of service

creation and delivery, strategic quality control (i.e., reliable and accurate) is required.

In the GH study, UI and features that encourage collaboration were found to be essential in

the process of service co-creation. The different key characteristics found in the design of GH

include features to encourage actors to practice and enhance collaboration (e.g., Pull-request),

to represent reputation components (e.g., Star), and to evaluate the validity of the project and the

trustworthiness of the actors. GH features were more focused on the Utilitarian value for actors

which is called Usability in Kohler et al.’s (2009) study. However, this study also found the

importance of providing social features that increase actors’ status and reputation to improve

co-creators’ COB.

Furthermore, enhancing the visibility of the process flow of co-creation activities for the

actors will enhance collaboration and efficiency. All phases of the co-creation process are

critical for the success of service co-creation and value outcome. Therefore, the visualization

of activities through a process map helps track the transactions and activities, and maintain

collaborations and competitive edge.

In both studies a simple and intuitive UI was important to meet actors’ Utilitarian

perceptions. Similar to the findings of Kohler et al. [2011], the platform should provide an

easy-to-use design with intuitive navigation to reduce user effort. So, a straightforward

platform model makes contributions fast and effective, and helps the flow of collaboration.

Although in SO a high level of engagement, as the principle for the success of co-creation

is fuelled by the successful implementation of gamification and an incentivized model to meet

actors’ Hedonic value, GH is not based on a game-design-oriented model. Previous studies


suggested the importance of providing entertaining and enjoyable experiences by the company

[Nambisan and Nambisan, 2008, Kohler et al., 2011]. This research confirms that the gamified

feature of the platform not only influences the competitive and puzzle-solving drive of actors

through challenging tasks [Kohler et al., 2011], but is also fundamental for profile-building

using a variety of techniques such as a reputation system.

This research also claims that the success of A2A co-creation systems not only depends on

design based on intangible incentives such as a point system but also requires tangible rewards.

Although financial rewards for co-creation activities are discussed in the literature [e.g.,

Zwass, 2010], this study suggests the implementation of tangible reward modules depending

on the level of collaboration. Co-creators who reach a certain degree of reputation or rank in

the community and are regarded as trustworthy are not motived by a point system alone.

Organisations need to target super users with tangible rewards to optimise their productivity.

Tangible rewards such as the privilege of Virtual Money can be a solution to maintain and

support these super users’ collaboration. Practitioners should consider an accurate

gamification method to measure contributions to avoid producing low-quality services and

poor conceptualization of the process. Therefore, the objectives of implementing gamified

models, such as types of behaviour and levels of actors’ engagement, should be taken into

account to design a successful gamified model.

The objectives and types of related activities for the process of creating and delivering the

service should be identified. Strategies to apply functionalities to enrich social influence and

support trust should be determined to enrich collaboration. GH provides functionalities such as

create/merge pull-request and an Issues feature to collaborate on code while features such as a

Collaboration graph, Star and Follow are used to keep track of activities, also as a mechanism for

opinion formation and to influence others’ SCB. On the other hand, in the SO study moderating

features play a significant role in regulating processes and behaviours which are missing in GH.

Frow et al. [2015] specify the importance of understanding the nature of co-creation activities

which can provide potential opportunities for co-creation.

This research indicates that to implement activities in the A2A service co-creation process,

the service platform needs to provide intellectual and technical capabilities to link co-creators’

resources into an integrated service bundle along with evaluating how the process map is shaped

by social influence and trust-building techniques. Applying a collective agreement mechanism


as a platform feature (e.g., voting system) was found to be strongly related to trust and decision

making. Implementing quality control mechanisms (e.g., Voting system) helps practitioners to

apply the rules and goals related to governing service quality. Overall, this research emphasised

the importance of the type, context and level of co-creation in the design of the co-creation

platform, as well as the target co-creators.

6.1.2 Relational Capital (RC)

Relationships in co-creation systems were found to be value relationships in which the link is

based on mutual value. Trust and Social Influence were conceptualised as Relational Capital in

which the link among actors depends on the way they trust each other or influence each other.

This research uses “Relational Capital” as the cognitive relation of entities (i.e., actors) and

how their relationship can dynamically impact their beliefs in the network. Actors’

relationships are considered to be capital, not because of the structure of their relationships but

for the way they influence each other’s beliefs and behaviours, resulting in value creation. This

research indicates that the co-creation system environment facilitated by effective social

influence and trust strategies will contribute to service exchange performance and an efficient

resource integration leading to value creation.

Social Influence (SI) is defined as “a process by which attitudes and behaviours are

influenced by real or implied presence of others” [Vaughan and Hogg, 2005]. The social

influence process causes individuals to conform and change their attitudes to be more similar

to others [La Fond and Neville, 2010]. In an evolutionary perspective, social influence helps

individuals to communicate effectively based on prosocial motives within social groups. The

ability to influence others within a group using influence techniques helps today’s managers,

marketers and organisations to navigate a social world similar to older societies [Sundie et al.,

2012].

The impact of social influence on human behaviour has been observed in previous studies.

Klobas and Clyde [2001] believe social influences are pervasive, although people are not

always aware of their influence. Social influence is recognised as a strong factor to motivate

human behaviour [Ajzen and Fishbein, 1980] and an important factor in the adoption of

information technology [Li, 2011]. Klobas and Clyde [2001] suggested that social influences

have a considerable impact on peoples’ perceptions of the Internet, its value, and their ability


to use it. Social influence can directly affect behaviour through cohesion to the structure of

ones’ beliefs or indirectly through structural equivalence in a social network [Burt, 1987].

Structural equivalence can be used to understand the attitude and actions of the actors in a

network. Structural equivalence occurs when two nodes are connected to the same actors. In

this situation, actors have similar patterns of relations to other individuals in the group.

Therefore, they are located in the same social environment and can be easily affected by each

other [Giuffre, 2013].

In this research, social influence was found to be one of the important stimuli leading

actors to SCB by influencing their value perceptions. Despite the extensive literature about

social influence in online participation in social networks, the role of social influence in the

service co-creation environment has not been sufficiently discussed. In SD logic the operant

and operand resources are embedded in the social system where actors are influenced by

societal norms and values, through interactions. Shamim and Ghazali [2014] found social

influence (identification, internalisation and compliance) to be a moderating function in the

relationship of experimental value and customer value co-creation behaviour in retail. The key

social influence variables discussed in the literature are normative social influences including

social identity, group norm and subjective norm. However, the findings of the SO and GH

studies revealed the strength of both normative (subjective-oriented) and informational

(quality-oriented) social influences on actors’ co-creation action and SCB, through their value

perception.

In particular, this research identified two types of behavioural influence for Normative

Social Influence: (i) Social Approval, induced by need for approval and acceptance of the

delivered service; and (ii) Significant Others, induced by the presence of high ranking actors

and experts or people culturally rooted as role models in the community or peers. Another

important dimension of social influence that was identified was Informational SI, which is

conformity to follow correct and quality information and service offers.

In the SO study, the influence was to compete with others in higher status, gaining points

and competing in puzzle solving (i.e Hedonic and Social Status values), and learning from

significant others to enhance the quality of their performance. However, in the GH study

co-creators’ collaborations were less based on Hedonic and Status values and more on learning

and self-improvement. In the GH study, normative SI through significant others was stronger


and social approval was low. In the SO study actors tended to follow and get advice from

actors with more power and experts, and GH actors’ performance and collaborative behaviour

were also significantly influenced by authority and people who had an official role in the

project, such as the owner. This finding is supported by Cialdini and Goldstein’s (2004) social

psychology study that found people have a greater tendency to accept recommendations and

opinions from agents and experts on a topic.

This research revealed that the influence of authority through constructive feedback and

social support is stronger toward SCB and creates higher value than other co-creators. However,

the influence is not always beneficial and may reduce collaborative behaviour. For example,

when the offered service by an actor is completely ignored without any feedback or destructive

communication, this can hinder continuous contribution in the co-creation activities. Another

new finding in this research is that some co-creators show more collaborative behaviour because

of the quality, accurate source/information and efficiency of a service rather than the individuals.

Social influence in both studies was found to be supported by platform features such as the

Quality control mechanism in SO, and Star/Collaboration graph in GH, that shape an actor’s

value perception and consequently lead to SCB. Since actors’ SCB and a greater chance of

collaboration in service innovation were identified as strongly related to the actors’ opinion

sharing and social influence on others, the presence of elements that develop mechanisms of

social influence need to be determined by practitioners. This research recommends that the

pattern of Social Influence in the less structured community-orchestrator (e.g., GH and SO)

should be extended and applied to the more structured A2A co-creation systems such as

DHLMyways. The challenge is to evaluate how social influence strategies could be

implemented through platform features that reduce the chance of negative social influence and

maintain a positive service co-creation environment.

Trust is defined as a customer’s reliance on the organisation to provide satisfactory service

[Morgan and Hunt, 1994]. Trust plays a critical role in different economic and social

transactions in the online service context [Pavlou, 2003] and leads to greater buyer-provider

information sharing [Agarwal et al., 2007]. The constructs of trust are mostly characterised as

benevolence, credibility, and integrity [Morgan and Hunt, 1994]. Active participation by the

customer depends on engagement in mutual decision-making processes [Chan et al., 2010] that

rely on the quality of the relationship and constitute a position of trust among the actors.


Trust has been addressed in the co-creation literature as a major factor driving effective

value co-creation. The relationship between customer and organisation should be based on

trust and cohesive bonds to offer benefits, and lead to co-operation and value creation [Hajli,

2014, Gronroos, 2007]. A high level of trust is required for a cooperative process to reach a

common goal [Romero and Molina, 2011] and construct unique value [Ramaswamy, 2006],

and for customers to participate in the value creation of a service/product [Abela and Murphy,

2008]. Prahalad and Ramaswamy [2004] introduced Dialogue, Access, Risk-Assessment and

Transparency (DART) as a customer risk-benefit assessment to contribute to an action or

organisation’s decision-making. Despite the extensive literature about the importance of trust

in customer-provider interactions to co-create value, how trust is manifested in different ways

in relation to actor value perception and their SCB in A2A co-creation systems needs more

attention.

This research focuses on environmental trust, and conceptualises trust as the reliability and

decision-making on the accuracy of service quality, based on the relational norms and collective

action. This research revealed that there is a basic level of trust among co-creators to enable

service exchange and offer a value proposition in the A2A environment and make co-creation

happen. However, trust was found to be essential for decision-making on service quality and the

trustworthiness of the provider, and is fundamental to maintain relationships that shape further

service exchange.

In A2A service co-creation systems, trust was found to be more of a social concept than an

interpersonal relationship. This research revealed two types of trust emerging from both SO

and GH studies, (i) Objective and (ii) Subjective Trust, that helps decision-making on the

quality of an offer and the trustworthiness of an actor (i.e., service offer supplier), respectively.

Objective trust was defined as the evaluation of actors of the quality of an offered service by

objective measurements such as a platform aggregator or integration of external tools. The

“collective agreement” was found as the most important constituent of Objective Trust.

Collective agreement in GH was through the application of features such as Star and Follow,

and in the SO study through a Voting/reputation system. Therefore, the main part of trust

should be created through service co-creation platform functionalities. On the other hand,

Subjective Trust was found to be based on the actors’ subjective opinion of another interactive

actor’s profile or popularity. For example, in SO actors rely more on the solution provided by

an actor they know or an expert. However, in GH subjective trust was a more profile-based


evaluation. An actor with a complete profile was found to be more reliable compared to an

anonymous actor. Although trust in co-creation systems was mostly found to be based on

objective measurement, the subjectivity of trust can build stronger relational value with the

service supplier and reduce uncertainty.

Subjective Trust in computer science refers to an estimation of ”a specific character or

specific behaviour level of trust objects, namely people” [Wang et al., 2008, p. 44] . In

economic terms, subjective trust is defined as an evaluation of having a ”desirable action

performed by trustee” and having had an opposite and reciprocal relationship with a perceived

risk that is an evaluation of a desirable outcome [Delbufalo, 2015]. Subjectivity and objectivity

of trust mostly have been explored to test the effectiveness of trust models and algorithms.

However, in this research Objective and Subjective Trust is discussed from the co-creators’

decision-making viewpoint on the offered service and other parties’ performance in the

co-creation process. This research claims the importance of subjective and objective evaluation

preferences in assessing the quality and shaping trust relations in the service co-creation

environment that enhance value relationships and collaborative behaviour.

6.1.3 Actor Competencies

Competency was found to be a key operant resource along with Relational Capital and

facilitated platform characteristics to establish a value service co-creation system (i.e., a value

network). Actor Competencies in this research refers to the capabilities and specialized

expertise of actors that enable them to collaborate in service co-creation activities. Actors’

competencies include dynamic capabilities that facilitate innovation and service development

[Sharma, 2016]. Service co-creation systems’ performance depends on the actor’s capability

and resources. When a co-creation system is rich in resources such as actors’ skills and

knowledge, the co-creation environment is equipped for potential service co-creation and

creating value.

Previous studies in SD logic have determined competencies as the essential component in

the co-creation process. According to Echeverri and Skalen [2011], competencies are primary

for customers’ activities and interactions that result in value-in-context. The nature of

information sharing relies on the actor’s competencies and their connectivity based on value

propositions [Maglio and Spohrer, 2008]. To create value the application of provider’s


resources should be integrated with the competencies and resources of the beneficiary [Vargo

et al., 2008]. Co-creative innovation allows actors to apply their skills and operant resources to

the design of experiences [Ramaswamy, 2010]. Actors develop their capabilities through

collaborating in service co-creation activities and resource integration and learn from their

performance with parties’ interactions to survive and grow in the network [Sharma, 2016].

Barile and Polese [2009] determined competencies as the principal element for markets,

management and business strategies within network systems.

The findings of this research from the both SO and GH studies are consistent with the

literature that supports Actor Competencies as the key element in the process of value

co-creation and system survival. Also, actors are required to have a standard level of expertise

to be part of a creative and valuable service co-creation process and to be able to support and

moderate others. The result of the GH study revealed that actors should be segmented in

different parts of the service co-creation process with different competencies in more complex

service co-creation systems to have a more efficient service system with a higher quality

outcome. According to the SO study, social approval and support from others helps actors to

be confident to interact with others and apply their competencies. The more actors collaborate

in service co-creation activities, the more they broaden their experiences and skills through

integrative learning and normative relations that lead to the development of new value

propositions.

6.2 Actor Value Perception (O)

This section presents how actors’ value perceptions are influenced by environmental stimuli

that influence their SCB. Actor value perception represents the “O” section of the SOR model,

reflecting the Uses and Gratification theory (UGT). Although customer value has been

discussed in the co-creation literature as value experience to explore the results of their

interactions with the co-creation system [Shamim and Ghazali, 2014, Kohler et al., 2011,

Zhang et al., 2015], to the best of our knowledge no study has been conducted to understand

actors’ value perception as a basis of resource integration and shaping SCB in the A2A

context. Also, previous studies focused on the product development and design experience of

the organisation while this research investigates integrated services from the service science

perspective in an A2A context. Damkuviene et al. [2012] investigated the customer perceived

6.2. ACTOR VALUE PERCEPTION (O) 155

co-creation value on their value co-creation behaviour. However, they investigated the B2C

context and the findings are not supported with empirical data. Edvardsson et al. [2011, p. 334]

suggest the necessity of empirical studies on ”how value is perceived by different customers in

different service contexts ... as a basis for developing and managing value propositions and

resource configuration”. This research empirically investigated the actor value perception in

the service co-creation context.

Previous studies investigated customer experience with the system as value after the

co-creation process. Value experience evaluates their interest and value gained from their

experimental services [Vera and Trujillo, 2013]. This research, however, focuses on perceived

value which is based on the value of a service perceived by customers as a trade-off and the

exchange between what is given and what is obtained” [Zeithaml, 1988]. According to this

research, value is not only related to evaluating outcome but evaluating potential value prior,

in-process and after co-creating service and the resource integration. An actor’s value

perception is different in different types of co-creation and changes over time, based on

different levels of SCB and actors’ roles. This outcome confirms that the experience of service

exchange gradually influences service value expectations [Edvardsson et al., 2011]. So, this

research does not reject customers’ engagement in creative activities to gain experience [Dahl

and Moreau, 2007] but strongly believes that actors’ collaboration and SCB is centred on

perceived value. Value here is the perception of tangible/intangible benefits [Nambisan, 2002]

and is dependent on the objective-subjective interaction within a particular situation (Holbrook

2006). Therefore, the actors’ perceived value shapes the pattern of actors’ SCB through

service exchange and resource integration.

A consumer’s perceived value is defined as experiential consumption (value-in-use) in

which the consumer personally experiences the value co-creation process [Gronroos, 2011b].

Value-in-use is the customer’s objective and outcome that is gained through a service

[Macdonald et al., 2011, p. 1]. While value-in-use was the focus of SD logic in early studies

[e.g., Vargo and Lusch, 2004, Prahalad and Ramaswamy, 2004], transforming to a more

network-oriented service system and change in the role of service, the concept has changed to

value-in-context [Vargo and Lusch, 2004, 2008]. Context refers to resource integration

activities performed in a specific co-creation context. Therefore, the value co-creation process

is context-dependent [Lobler, 2017]. This research confirms the actors’ perceived

value-in-context as the benefit co-creators expect from their collaboration in service


co-creation activities. Table 6.2 lists the actor value perception concepts and the related

themes.

This research conceptualizes an actor’s value perception to include two types of Purposive

value and Network value (Individual level and Service level). Purposive values (first-order) are

considered by actors as managing their ongoing needs. These drivers are permanent primary

drivers for actors leading to SCB. Network values are in-process drivers that are latent in the

early stages of collaboration and appear with more engagement in the co-creation activities.

The sections below discuss each concept and how the influence of the co-creation environment

leads to their SCB.

Table 6.2: Actor value perception characteristic.

Value Perceptions Description Themes

Purposive Value Values related to the ongoing needs Learning(Personal values) Hedonic

UtilitarianEconomic

Network Value Values that are the result of network Social Position(Individual Level) effect Belongingness

Collaborative Effort(Service Level) Values related to the community goal Quality

Support

6.2.1 Purposive Value

Purposive value represents the importance of personal values and values of high self-importance

in actors’ collaboration in the service co-creation process. Purposive values are found to be

critical for co-creators for managing their ongoing needs. Purposive values occur prior to the

collaboration and service exchange, and will continue after forming the SCB and actual use.

Purposive values formed the basis of actors’ collaboration in the service co-creation process

and include Learning, Utilitarian, Hedonic and Economic values that capture information, and

the functional, experimental and financial-related aspects of actors’ value perception.

Table 6.3 represents the relationships between the identified environmental stimuli and

Purposive value to illustrate how Purposive value perceptions are influenced by the co-creation

environment. Although Platform Capabilities strongly influenced the Utilitarian, Hedonic and

Economic values in the SO study, in the GH study they mostly influenced Utilitarian value.


While Relational Capital in both studies influenced all four Purposive values, Actor

Competencies was found to have low influence on Purposive values.

The findings of this research show that although in the first study (SO), Utilitarian and

Hedonic values were preferred by co-creators rather than Learning and Economic values. In the

second study (GH) Utilitarian and Economic values were found to be more significant, followed

by Learning and Hedonic values. Both studies confirmed the importance of collaboration to

obtain utility and measurable value from their collaboration. However, SO is more based on the

gamified and hedonic model and GH is utility-oriented. A likely explanation is that the nature

of co-creation plays out differently in the different service co-creation systems.

Table 6.3: Matrix intersection between environmental stimuli and purposive values.Note: the higher the value and more significant the connection.

Study Environmental Stimuli Learning Utilitarian Hedonic Economic

SO Platform Capabilities 1 13 10 6Relational Capital 6 3 2 1

Actor Competencies 1 0 1 0

GH Platform Capability 1 5 0 1Relational Capital 7 2 4 2

Actor Competencies 1 0 0 0

Learning Value

Learning has been identified in the literature as one of the benefits users can derive from virtual

communities, to gain better understanding and knowledge acquisition of the products (Katz et

al., 1999). Previous studies on co-creation have suggested that customers’ engagement in the

learning process is associated with their interactions with the organization [Payne et al., 2008,

Nambisan and Baron, 2009], which has an impact on customers’ future engagement in value

co-creation activities with the supplier [Payne et al., 2008].

Learning value has been discussed in the co-creation literature as a key component of the

customer experience in product development [Nambisan and Baron, 2009] and a moderator for

their future participation [Zhang et al., 2015]. Learning value has been discussed mostly as the

desire to gain information about the service/product [e.g., Nambisan and Baron, 2007, 2009,

Hoyer et al., 2010], learn a new skill [Ramaswamy, 2010] and develop an understanding of

the environment to enhance effective product usage [Nambisan and Baron, 2009]. Edvardsson


et al. [2011] argued that actors’ learning value is influenced by the extent of their interactivity

in the network. Consistent with the literature, this research found Learning value as a source of

gaining knowledge about the core service, value propositions, cultural attitude and group norms

to become familiar with the environment, and improvement of skills and self-evaluation that

lead to SCB rather than product development. Learning value reflects the information-related

aspect of actors’ value perception. In addition, this study found the important role of learning

through supporting others, which can occur through service delivery or service support such

as feedback. The findings of both the SO and GH studies show an interactive learning among

actors that contributes to innovation and service co-creation.

Actors’ learning value was affected by normative or informational social influence, and

collective agreement (Relational Capital) to enhance their knowledge and performance in

service co-creation activities. This research revealed that actor’s learning was generated mostly

through Following ”significant others” as role models, and trusting the quality of information

provided through the collective agreement of a greater number of co-creators. On the other

hand, the greater the potential interactive learning through SCB constantly develops an actor’s

competencies leading to a greater collaborative practice. Learning was enhanced mostly

through observation of interactions, collaboration in discussions, communication and

feedback. Payne et al. [2008] supports the finding of this research by describing co-creation as

a reversive process that influences the learning experience and improves future co-creation

activities. Therefore, the greater a SCB, a greater experience and knowledge that reforms their

cognitive patterns leading to a more effective collaborative application.

Overall, this research revealed that in an A2A co-creation system actors are increasingly

interacting and engaging in reciprocal learning through co-creation activities. The reciprocal

learning emerges via collective contributions and the intensity of shared quality resources

among co-creators. Through co-learning which is socially constructed, actors are able to

enhance their professional performance and level of competencies to broaden their SCB. The

role of co-creation systems is to provide a dynamic and interactive environment to link actors’

resource integration to their learning outcome as a service being requested. Therefore, actors

co-create and re-co-create the service in the co-creation system that develops the individual’s

and the system’s knowledge.


Utilitarian Value

Utilitarian value is the result of an efficient service/product acquisition that causes a customer

to participate in competitive behaviour [Babin et al., 1994]. Most literature on Utilitarian value

is related to online shopping and retail as the outcome of functional consumption experiences

[Hwang and Griffiths, 2017]. From the consumption perspective, Utilitarian value is associated

with cost saving and utility [Mohlmann, 2015], functional and economic benefits [Hwang and

Griffiths, 2017], opportunity for greater efficiency in exchange [Babin and Attaway, 2000], and

valuable and useful experiences for the customer [Nambisan and Watt, 2011]. From a service

viewpoint, Utilitarian value reflects the customer’s perception of the efficiency and usefulness

of service participation [e.g., Rodie and Kleine, 2000, Park, 2016], and functional value

representing the speed and convenience of performing activities (Moh-Any 2014). Park [2016]

found an increase of Utilitarian value in the customer-company collaboration in the case of a

successful service recovery co-creation.

Consistent with the co-creation literature, the current research indicates Utilitarian value as

a functional aspect of an actor’s value perception where co-creators are perceived to gain

practical and utility value from their collaboration. While the findings of the SO and GH

studies revealed that Utilitarian value was associated with the efficiency of task operation,

prompt service delivery, and accessibility/presentation in large scale resources, the GH study

also captured effective collaboration and fulfilled job related problems.

This research represents Utilitarian value as a tangible and measurable unit such as quality,

efficiency, delivery speed and performance. This value is derived from the direct outcome that

actors achieve to fulfil their needs through COB. Although the relationship between

Collaborative Behaviour (COB) and Utilitarian value in both studies was found to be reversive,

there was no relationship between Utilitarian value and CB. A new finding is that when the

outcome of actors’ COB is consistent with the expected Utilitarian value, co-creators are more

likely to support others to develop the overall quality. The higher performance and efficiency

through a Utilitarian value enhance the chance of a stronger professional relationship and the

possibility of gaining economic value.


Hedonic Value

Hedonic value has been discussed extensively in the e-shopping context as the overall

assessment of experiential benefits that result in more fun rather than task completion [Babin

et al., 1994]. Similarly, in the co-creation context, Hedonic value has been discussed as a

source of pleasure and fun that customers experience in the co-creation process [Quiggin,

2006, Roser et al., 2009] in which interactions are mentally stimulating or entertaining

[Nambisan and Nambisan, 2008]. Kohler et al. [2011] suggest nurturing playfulness by

providing challenging tasks in the design of virtual co-creation systems. This study confirms

the findings of previous studies on the importance of entertainment and fun through interactive

functionalities, the design of challenging tasks and nurturing a puzzle solving experience.

Despite extensive literature on the role of Hedonic value on customers’ interactions, few

studies suggested game-oriented elements to absorb users in the activity [Zwass, 2010, Kohler

et al., 2011]. The findings of this research revealed the power of a gamification model to shape

collaborations’ culture and drive progress in the co-creation platforms by harnessing the

pleasure seeking facet. In the SO study, the game dynamics (i.e., interactions and emotions

based on the game mechanism) are more explicit than in the GH study. Although in the SO

study gamified elements such as points and badges were a preference for actors to enhance

their collaboration through fulfilling their hedonic needs, the findings of GH revealed that

creating a competitive environment is far more important for the service exchange and

resource integration outcome. According to the findings of both studies, the competitive nature

of the environment nurtured actors’ proficiency and problem-solving capability as a practice in

real life where creating innovative ideas is part of the fun. However, harnessing the hedonic

value by proving rewards such as a point-based system may reduce the quality of

collaboration.

This research indicates that the utility aspect of service co-creation systems should

outweigh the hedonic aspect, to enhance actors’ SCB. The hedonic elements should be

considered to bridge utility and creativity with a smooth flow of interactions among

co-creators. The service co-creation model requires effective gamified solutions to direct the

flow of interactions, encouraging collaboration through natural competitive logic. Therefore,

this research suggests that practitioners should capitalize on the Hedonic perception of

co-creators by providing a more effective and logical design thinking approach to drive the


four dimensions of innovation, quality, positive culture, and value actor’s efforts, that are

critical for the success of collaboration initiatives (Figure 6.2).

Figure 6.2: Hedonic dimensions in service co-creation system.

Source: Designed

Economic Value

Economic value was suggested as the functional value in the consumption value theory which

is related to the utilitarian purpose of the product [Sheth et al., 1991]. However, in this study

Economic value was determined differently from the Utilitarian value. Although both values

are related to measurable usefulness benefits from an actor’s collaboration, the Utilitarian

value is function-based and the Economic value is related to potential financial value. Several

co-creation studies discussed financial rewards and monetary prizes [Fuller and Bilgram, 2017,

Quiggin, 2006, Zwass, 2010, Deborah et al., 2014] or extrinsic motives [Pera et al., 2016,

Martınez-Canas et al., 2016] to encourage participation in the co-creation activities. Other

co-creation studies regard co-creation of Economic value as offering a greater service quality,

customised service and higher control over quality through customer participation [Chen and

Wang, 2016, Yim et al., 2012]. However, in this research Economic value is the ability to

create income through proposed opportunities and professional connections.

Zwass [2010] acknowledged the requirement of more research on the distribution of

economic value in the different types of co-creation activities. According to the findings of

both the SO and GH studies, co-creators were willing to invest in future economic values by


building a stronger profile for potential job opportunities and professional collaborations on/off

the platform. Also, the result of the GH study revealed Project Marketing as a critical value for

suppliers. A likely explanation for this new finding in the GH study is the nature of service

co-creation, which is based on projects in which the role of economic actors is more explicit

than in the SO study.

Although the literature indicated career advancement as a key motivation for a customer’s

co-creation with a firm [Zwass, 2010, Fuller, 2006, Deborah et al., 2014, Martınez-Canas

et al., 2016], the findings of this research differ in the following ways: The first finding is that

co-creators’ economic perception was not limited to collaboration on the platform. Actors’

SCB was highly related to extending their collaboration to real world service co-creation. The

higher Economic benefits in the A2A co-creation systems were found to be related to the

virtue of an actor’s status and profile on the platform. Also, marketing was necessary for the

actor-as-supplier. In this case, the Economic value was not only perceived based on the

financial outcomes, but also the growth of the services and project. Actors with the role of

suppliers attempted to advertise and popularise the project to get remuneration and build a

reputation.

6.2.2 Network Value

Adding the so-called network effect through the co-creation communication system develops a

second type of value for co-creators, Network value. This research presents Network value as

the in-process value perception that is constructed through actors’ connectivity and reciprocal

interaction within the network of co-creators. Network values reflect the reaction of an actor’s

collaboration in service co-creation activities. This type of value relies on the actors’

interactions with the others while co-creating a service, and subjective opinions of others in the

network. This research refers to the Network value as the perceived value that develops over

time (longevity), depends on the degree of collaboration (i.e., outgoing value or degree of

benefiting others) and occurs when the Purposive value is addressed. This research claims that

Perceived Network value is more important than actual Network value in enhancing actors’

COB:

Network value= Longevity + Purposive Value+ Outgoing value


This research divides Network value into the two levels of individual and service.

Individual level values are strongly related to the actors’ self-importance and ego values,

including Social Positioning, Belongingness and Collaborative Effort. However, service level

values are associated with collective importance and higher community benefits, including

Quality and Support. Network values are related to the values that matter after a certain level

of contribution in co-creation activities. When the co-creation environment supports the

interaction among actors and enhances the level of perceived Network value, Network values

extend the density of actor collaboration in co-creation activities, leading to a greater SCB.

Edvardsson et al. [2011] stated that the exchange of service over time affects actor value

perception and their practice in value co-creation. This research confirms this idea and

proposes Network values as value perceptions that develop after constant collaboration is

established.

I) Individual level

Individual-level Network values were found as intangible value perceptions that benefited

individual actors but were dependent on the network (social interactions). Network value at the

individual level includes three value perceptions: Social Positioning, Belongingness that

emerged from the both studies, and Collaborative Effort that was a new finding from the GH

study. Social positioning is conceptualized in terms of an actor’s perception of their social

status and social role. Social status was one of the main value perceptions for SO participants

but was not as important in the GH study. While Role was important as a social position in the

community constructed through social status in the SO study, in the GH study the importance

of role was found as achieving formal position in the project but not in socially constructed

roles. Belongingness in both studies was significant and a value that made co-creators more

responsible toward service co-creation activities. Collaborative Effort was important in

initiating a stimulating co-creation environment, by facilitating the aspect of teamwork and

shared responsibilities among actors.

Table 6.4 represents the relationship between the identified environmental stimuli and

Network values (individual-level). While all three identified environmental stimuli

significantly influence actors’ Social Position value perception, their influence is less on

Belongingness and Collaborative Effort.


Table 6.4: Matrix intersection between environmental stimuli and network value.(individual-level)

Note: the higher the value and more significant the connection.

Study Environmental Stimuli Social Position Belongingness Collaborative Effort

SO Platform Capability 14 2 -Relational Capital 12 3 -

Actor Competencies 2 1 -

GH Platform Capability 7 0 0Relational Capital 6 1 0

Actor Competencies 1 0 2

Social Position

This research defines Social Position as a position assigned to an actor in the community based

on their efforts and expertise. The actor’s position in the community is structured by their social

status and role, and represents their proffered type of activities, responsibilities and behaviour

expectations. Social position is dynamic by nature and changes through interactions over time.

Actors’ social status and role are highly related and together represent the co-creators’ level

of trustworthiness in the community. The actors’ status reflects the network’s impression of

actors’ level and quality of collaboration in service co-creation activities. The reputation actors

gain from their collaboration defines their roles and obligations within the service co-creation

system.

Status has been addressed in the value co-creation literature as a social benefit and

motivation for customer participation [e.g., Nambisan and Baron, 2009] and is related to

reputation, credibility and recognition. In this research, social status is defined as the

co-creators’ professional identity through gaining reputation and credibility from the result of

their outgoing values.

This research confirms the findings of previous studies in which actors expected to achieve

a higher status and to establish credibility and reputation. In co-creation activities, participants

expect a higher social status to secure a good title in the community [Hasan and Rahman,

2016] and to engage in non-transactional behaviours [Fuller, 2010, Nambisan and Baron,

2009]. Reputation enhancement specifically leads actors to share with the community to attain

credibility [Pera et al., 2016]. However, this research believes that the social status value is not

only perceived for non-transactional behaviours but is associated with the transactional context


as well. Because of the nature of the community-orchestrator service co-creation systems with

A2A service creation and delivery (e.g., DHL MyWays with co-delivery services), actors need

to establish trust by enhancing their status in the competitive environment.

The new finding this research revealed was that although social status leads to SCB, it may

not always have a positive outcome. In reputation-based co-creation systems the gaining point

becomes the centre of attention in preference to the service growth, poor quality outcomes and

ineffective transactions may be a challenge and a cost rather than benefit. This research suggests

that practitioners should determine a balance between incentivising actors and quality control.

Since actors’ status is empowered by their active collaboration and support of the platform,

well-positioned gaming elements that meet the needs of business and quality momentum are

necessary.

The role of the customer is one of the fundamental dimensions in SD logic [Vargo and

Lusch, 2008], as customers actively engage in value co-creation with the company [Prahalad

and Ramaswamy, 2004]. The result of service transactions depends on the customers’ role

during service production [Damkuviene and Balciunas, 2010]. Recent studies shifted from the

customer and focused on actors that are consumers and resource providers within the service

ecosystem, and each actor may play two or more roles [Fu et al., 2017]. The role of actors has

been determined to be to integrate resources, contribute to service production or co-create

value [Vargo and Lusch, 2016]. Lusch and Nambisan [2015] identified three roles of ideator,

designer, and intermediary depending on the nature of the resource integration. All roles

provide the opportunity for service innovation through the integration of knowledge resources

with other actors. This defines actors as co-creators of service and value through the

integration of resources.

Service co-creation platforms clarify the role of actors based on the actor’s capability

directly into their service delivery model (e.g., moderator). Customer’s role clarity is critical in

the value co-creation process [Bharti et al., 2015]. The new findings from the SO study

indicate that apart from the predefined roles (i.e., actual role), actors perceive (visualize) some

roles (e.g., Helper, Adviser, Leader, Influencer) are the expression of their responsibilities.

Actors’ perceived role is created from a bundle of activities and the reaction of others through

status and reputation. Therefore, a co-creator’s role changes in relation to their status.

However, perceived role was not significant in the GH study, but promotion to an authority role


(e.g., maintainer of the project) was found important as it offered more control over the

co-creation procedure. The importance of the authority role was also found in the SO study,

where actors perceived to have roles such as influencer, leader, moderator or expert matter. By

moving to a more community-orchestrator platform, actors expect more power over the

co-creation process such as evaluating quality that leads them to collaborate more in the

co-creation activities.

Belongingness

The sense of belongingness refers to “the experience of personal involvement in a system or

environment so that persons feel themselves to be an integral part of that system or

environment” [Hagerty et al., 1992] and the feeling of membership and identification to the

VC [Zhao et al., 2012]. The sense of belongingness or relatedness need [Juvonen, 2006] is

established through relationships and interactions among actors of a community (e.g., social

norm) [Rovai, 2002, Pera et al., 2016]. Therefore, “by strengthening ties among actors, a

deeper feeling of belonging and loyalty towards the ecosystem develops” [Pera et al., 2016].

This research refers to Belongingness as a cognitive response that reflects the sense of

attachment and bond to the community that contributes to a higher level of SCB.

Although Belongingness was discussed extensively in the online community literature, few

studies discussed Belongingness in the co-creation context. Belongingness creates a strong

feeling of contribution [Hasan and Rahman, 2016], a meaningful involvement and behavioural

response [Hagerty et al., 1992] in online communities. Fu et al. [2017] suggested a service

platform with an integral reward process creates a stronger sense of belongingness and

engagement. In the co-creation context, Belongingness was found to be a social benefit that

positively influenced customers’ active participation [Nambisan and Baron, 2009, Zhang et al.,

2015] and increased meaningful innovation outcomes [Butler et al., 2002]. According to

Zwass [2010], co-creators show a sense of identity (i.e., the sense of belongingness) in the VC

[Wang et al., 2016] from co-creating in the community.

Consistent with the view that a sense of belonging is a moderator between social capital

factors and participation behaviours in a VC [Zhao et al., 2012], this research found that

actors’ environment (i.e., social capital of structure and norm) influences their belongingness

value perception and leads to COB in the co-creation context. However, the finding of this


research revealed that belongingness not only influences actors’ COB but also enhances their

CB. According to studies of both SO and GH, regular collaboration in co-creation activities

enhances belongingness perception and increases social responsibility and obligation to

monitor and support others through constructive feedback and communication. Similarly,

Deborah et al. [2014] supports the idea that actors feel obligated to support others as the result

of a sense of belonging in the gamer community. Belongingness can create an effective

outcome through constructive resource integration and support of others. This outcome

suggests that Belongingness value perception depends on an effective collaborative

relationship among actors.

According to the findings of the SO and GH studies, Relational Capital (Social Influence

and Trust) influences actors’ Belongingness value perception. Although the influence of trust

on belongingness was found in the VC literature [Blanchard and Markus, 2004, Lin, 2008], no

research in the co-creation context has described the influence of trust and social influence on

belongingness. Despite the similarity of network structure between VC and A2A co-creation

systems, the importance of trust is different in co-creation systems because of the nature of

transactions in service businesses, which can be challenging in terms of money, timing, and

accuracy. A collaborative relationship is a fundamental component in the A2A service

co-creation systems that facilitate trust and social influence among actors. The established

reliability through collaborative relationship in the service co-creation process enhances the

sense of being part of the community. Belongingness was mostly subject to the co-creators’

position in the system. This suggests that Belongingness is the result of actors being respected

and valued for their collaboration, which makes them trustworthy and a role model for others.

Collaborative Effort

Service co-creation is a collaborative process in which innovation and new services are created

within a community of collaborators. Co-creation is defined in the literature as a ”collaborative

work” or ”collaborative value” [e.g., Russo-Spena et al., 2016, Hassan and Toland, 2013] that

links the customer to the provider. Despite extensive literature on the collaborative aspect of the

co-creation environment, Collaborative Effort has not yet been addressed as a value perception

for actors. The findings of the GH study found that Collaborative Effort as a value perception

constructed from co-creators’ viewpoints leads to COB.


This research proposes Collaborative Effort as an actor’s desire to develop effective

teamwork and collaborate in a large-scale co-creation network where all actors play an

important role in improving service quality. This research determines Collaborative Effort as a

social-related value in which actors expect to develop innovation through sharing and

responsibility distribution. Actors’ collaboration in service co-creation activities through

Collaborative Effort was found not only because of their belongingness to the community but

because of their commitment to engage and make a stronger and higher quality service

collectively.

II) Service Level

This research conceptualizes Service-level Network values as the actors’ shared goal to

provide better service outcomes where the network benefits are concerned. Service-level

Network value includes two value perceptions of Quality and Support that emerged from both

SO and GH studies. Both the Quality and Support aspects of service were found to be strong

value perceptions leading to SCB (both COB and CB) and are significant in maximizing the

outcome value. Table 6.5 presents the relationship between the identified Environmental

Stimuli and Service-level Network Values.

Table 6.5: Matrix intersection between environmental stimuli network value.(service-level)

Note: the higher the value and more significant the connection.

Study Environmental Stimuli Quality Support

SO Platform Capability 2 6Relational Capital 7 3


GH Platform Capability 1 0Relational Capital 4 1


In both studies, the three identified environmental stimuli influence the Quality Value. On

the other hand, Support is influenced by environmental stimuli in the SO study, but no significant

influence was found between environmental stimuli and Support Value in the GH study.


Quality

Quality is defined as a product’s perceived excellence or superiority [Zeithaml, 1988]. Quality

as a service value relates to the expectation of the “performance, durability, reliability” of the

service [Okdinawati et al., 2015]. Service quality refers to how well the core services are

performed compared to the expected service performance [Roberts and Merrilees, 2007].

Service quality is the foundation of all economic exchange [Vargo and Lusch, 2008], an

essential factor to build customer loyalty [Chao, 2008], and crucial for business success and

sustainability [Vargo and Lusch, 2004].

Co-created value is assessed by how actors perceive the quality of their own and others’

resources as well as the joint resource integration process in a specific context [Macdonald

et al., 2016]. Customers not only evaluate the quality of service outcome but also determine

quality perception through the process [Gronroos, 1983]. Nambisan and Baron [2007] argue

that customers’ perception is that the firm is responsible for the overall quality of the

environment. However, this research identified that in the community-orchestrator service

co-creation systems, actors are mostly responsible for improving the overall service quality.

Quality has been interpreted in various ways in the service quality literature. Gronroos

[1983] conceptualised quality into two attributes from the customer perception: Technical and

Functional service quality. Technical quality refers to what type of services are delivered (i.e.,

outcome) to the customer and the Functional quality refers to how services are delivered to the

customer (i.e., process). Chao [2008] proposed service quality is made up of personnel

(competencies), operational (fulfilling customer need), physical (excellence of physical

appearance) and merchandise (superiority of wholesalers’ merchandising performance such as

availability). Gronroos’s (1983) quality conceptualization reflects the Utilitarian value in this

research (Section 6.2.1). Utilitarian value perception is determined by actors’ expectation of

service delivery and when actors’ expectation is met through their COB, Utilitarian value

represents the outcome of service delivery.

Quality has been discussed in the co-creation literature regarding its importance in

information sharing quality [e.g., Yi et al., 2011], interaction quality [e.g., Yi et al., 2011,

Kelley et al., 1990], improving product quality (Fuller 2011) and mostly as the benefits of a

product or service [Ulaga, 2003]. In most services, the nature of service quality as client value

is poorly understood [White and Badinelli, 2012], and more research on how quality is judged


is needed [Macdonald et al., 2016]. However, to the best of our knowledge no study has been

conducted on the relationship between the Quality value and co-creation behaviour. Also, the

attributes of quality have not been discussed in the service co-creation context from both the

individual and collective perspective.

In this research Quality value is identified as a service level value (i.e., collective view

and shared goal) in which co-creators collaborate to improve the overall service quality. The

SO study confirms Macdonald et al.’s (2016) findings on the importance of assessing value

based on the quality of actors’ own/others resources and joint resource integration. The SO

and GH studies revealed that lack of quality related to both individual resources (e.g., Actor

Competencies) and joint process resource integration, creating a barrier to future collaboration

and reducing actor’s COB.

A new finding of this research indicates that the perception of quality differs between the

actor-as-provider and actor-as-customer view. From the actor-as-provider view, quality refers

to accuracy, specification and support for future reference, however, from the

actor-as-customer viewpoint quality is the satisfied need. This research proposes two types of

quality perception that are crucial for the success of the co-creation process and the

improvement of SCB: a) Fundamental to satisfy a current need, and b) Supplemental to

facilitate future preference and use. On the other hand, the GH study revealed different quality

viewpoints including Design (i.e., representation and experimental), Functionality (i.e.,

powerful and effective) and Performance (implementation and empowerment). Figure 6.3

represents how the quality value perception in the service co-creation system is a combination

of Fundamental and Supplemental level perceptions with Design, Functionality and

Performance attributes of service qualities. These attributes are determined by co-creators at

the collective level to collaborate in service co-creation activities.

As shown in Table 6.5, actors’ Quality value perception is highly influenced by Relational

Capital and social norm. Actors’ Quality value toward their SCB is shaped through the

collective view in the service co-creation system. The findings of both SO and GH studies

revealed that actors’ Quality value perception is influenced by Individual-level Network values

and Purposive Values. Network values of Social Position in the SO study and Collective Effort

in the GH study were found to have a significant influence on Quality value. The reason is that

social status and role (i.e., Social Position) in the SO study is more featured than in the GH


Figure 6.3: Quality value attributes in service co-creation system.

Source: Designed

study. In the GH study because of the nature of the service, which was project-based, the result

of effort in the system was more distinct.

Overall, the Quality value is the actor’s collective perception toward improving the service

quality (e.g., implementation and efficiency) and co-creating maintainable services that enable

the possibility of future improvement and facilitate future innovation to meet market needs.

Support

Traditionally, service support was delivered by the firm to help customers and enhance their

learning and decision-making about a product/service-use. Collaborative innovation is

effective when companies use external support [Gianiodis et al., 2010]. By growing more

community-orchestration models, service support is provided less by the organisation and

more through the network. Community support through interactions impacts diffusion of

innovation [Cai et al., 2017]. Specifically, in the A2A service co-creation systems, innovators

require the assistance of others to develop innovations.

Helping has been identified in the literature as one of the main antecedents of (CB). The

current literature identified helping in different forms of social support [Pera and Viglia, 2015,

Fuller, 2010, Nambisan and Baron, 2009], empathy [Hwang and Griffiths, 2017] and to assist

other customers to co-create [Yi and Gong, 2013]. The findings of both SO and GH studies

confirm the key role of Support in both the COB and CB. The findings revealed that Support

through feedback, constructive communication and monitoring others activities facilitates an

effective service interaction and encourages future collaboration.


The finding of the SO study revealed two types of altruistic and reciprocal support while

support in GH was based on reciprocal action. Literature discusses Altruism-Community

Support regarding its influence on customer engagement in new product development [Fuller,

2006, Hoyer et al., 2010] and as a potential desire to contribute in co-creation [Zwass, 2010].

This research argues altruistic action as an objective to benefit the network without considering

self-benefits, however Zwass [2010] determines customer altruistic desire as an “expression of

personal values ideological beliefs, or deeply felt needs”. The finding of this research reveals

that although actors may gain some emotional benefit such as a sense of being useful and

valuable through their COB and CB, their intention is toward supporting the community and

improving services as a whole. Reciprocity, on the other hand, is associated with the exchange

theory in which actors feel they are obligated to engage in reciprocal actions to create a

win-win benefit for the community.

The new findings of this research show that the higher social position and belongingness

in the community, the more Support value would be perceived by co-creators as leading to a

greater COB and CB. This relationship can be related to the leadership role perception of actors

which entails an actor’s engagement in support of others and social influence in performing

service co-creation activities.

Hwang and Griffiths [2017] found that Utilitarian and Hedonic value perceptions influence

actors’ empathy (i.e., support) toward their collaborative consumption. However, the finding of

this research revealed that Purposive values (i.e., Utilitarian, Hedonic, Usefulness, and

Economic) do not influence Support, but Network values significantly lead actors to SCB

through support.

Another finding of this research is that support value perception highly enhances service

quality and actors’ learning value. Dholakia et al. [2009] supported the idea that provision of

service support enhances customer learning because of the diverse and story-driven approach

of customer-generated support that is more effective in a complex service setting. Providing

effective support is critical to reducing users’ cognitive costs [Kohler et al., 2011]. Therefore,

Support value Perception enhances actors’ learning and service quality through COB and CB.

However, lack of support is a barrier that hinders actors’ collaboration, and consequently

development in productivity and service quality.

6.3. SERVICE CO-CREATION BEHAVIOUR (SCB) 173

6.3 Service Co-creation Behaviour (SCB)

This section presents the Response aspect of the SOR model build on the SD logic. Most of the

literature on SD logic discusses the value co-creation context [e.g., Prahalad and Ramaswamy,

2004, 2002, Vargo and Lusch, 2004, Payne et al., 2008]. There is little discussion of value

co-creation behaviour [e.g., Yi et al., 2011, Yi and Gong, 2013, Shamim et al., 2017, Tsai et al.,

2017]. This research is centred on service co-creation which had very limited focus in the

literature [e.g., Gill et al., 2011, Hilton et al., 2012, Finsterwalder, 2016].

Co-creation is a function of interaction [Gronroos and Voima, 2013] and the result of

resource integration in a shared value network. Interactions reflect actors’ contribution to the

activities that result from their cognitive and behavioural performance and collaboration with

other users [McColl-Kennedy et al., 2012]. This research confirms Hilton et al.’s (2012)

definition of service co-creation in which actors intend to realise a value proposition through a

“planned resource integration behaviour”. As stated in Section 6.1, this research defines

resource integration as “application of actors’ competencies and service exchange in the

process of collaborative interaction to combine and match the proper service bundle that leads

to the co-created service”. So, service co-creation is the ultimate integrated service that results

from resource integration and value is the outcome of this co-creation process.

The findings of this research revealed that actors’ collaboration in the resource integration

resulted in SCB. The findings of both SO and GH studies revealed two types of behaviours,

Collaborative and Citizenship Behaviour (COB and CB). The two identified behaviours are

proposed as an extension of the value co-creation behaviour (Participation and Citizenship)

introduced by Yi et al. [2011] to the service co-creation context and A2A setting. Participation

is extended to collaboration where actors are not just a part of creating value with the

organisation, but actors’ resources are essential as the input for integration and service

creation. Similar to Yi et al.’s (2011) study, Citizenship behaviour is a voluntary engagement

in co-creation activities. However, this research revealed that the success of SCB is

coordinated by actors’ CB to create value as the outcome. Therefore, CB plays a critical role in

creating value formation and directing COB.


6.3.1 Collaborative Behaviour (COB)

Similar to participation behaviour (referred to as ”in-role” behaviour) by Yi and Gong [2013],

Collaborative Behaviour (COB) is actors’ contribution to core activities and task performance

for service exchange. Similar to Tsai et al.’s (2017) study, in-role behaviour is mandatory

behaviour for successful service delivery. However, the new finding of this research proposing

the difference between participation and COB comes from the level of actors’ involvement in

the co-creation process. When participation behaviour is presented as the opportunity for

customers to get involved in the organisations’ value creation, COB represents the actors’

practice in creating the core service together with the network of co-creators. Therefore,

Participation Behaviour is located in the lower level of actors’ co-creation involvement

continuum, while COB represents the highest level where actors are empowered to practice in

a joint service innovation effort.

From a behavioural perspective, collaboration occurs “when a group of autonomous

stockholders of a problem domain engage in an interactive process, using shared rules, norms

and structures to act or decide on issues related to that domain” [Wood and Gray, 1991,

p. 147]. However, for Mariano and Awazu [2017] customer collaboration refers to

contributions in sharing practices and co-creation of artifacts. This research defines COB as

the joint effort of actors in different phases of the service creation process integrating resources

and capabilities to achieve a desired outcome. This research found COB as the actors’

collaboration in providing (i.e., creation and delivery) a requested service (or need) by

completing various activities. Collaboration in service creation and delivery is the core of

co-creation process. Although Zhang and Chan [2017] defined PB in interactive and

non-interactive modes (active and passive participation), this research found collaboration

behaviour as only active contribution in co-creation activities (e.g., knowledge sharing rather

than reading and using content).

According to the conceptualization of COB in this research, actors collaborate in an

intellectual effort to exchange a service and integrate the qualified resource match. The

findings of this research propose that to establish such collaboration, individual actors’

perceived value will condition the success of the service exchange and resource integration.

These findings revealed that both Purposive and Network values have a reflexive influence on

COB.


6.3.2 Citizenship Behaviour (CB)

Citizenship Behaviour (CB) refers to the voluntary and extra-role behaviours that customers

participate in, during or after the service delivery, to a benefited organisation [Groth, 2005].

CB is based on social exchange theory, to develop and maintain relationships and provide

mutual benefits over time [A. Anaza and Zhao, 2013]. CB in retail requires customers to

participate in extra effort activities in service delivery to provide additional value for the

organisation [Tat Keh and Wei Teo, 2001, Yi and Gong, 2013]. This research confirms the

voluntary and supportive role of CB in the co-creation process. However, this research also

reveals that CB is an essential behaviour in improving performance and collaboration to

exhibit SCB in community-orchestration models. Despite the finding of Tsai et al. [2017]

showing a strong influence of mandatory behaviour (or PB) on voluntary behaviour (CB) in

sharing economy co-creation, this research reveals a significant influence of CB on actors’

performance and COB.

This research claims that the key role of CB in relationship building is not only between

organisation and customer but more importantly among different actors in the service

co-creation environment. This research revealed that actor CB in the service co-creation

context is not only an extra effort to add extra value to the organisation but is an essential

behaviour for service support and building a stronger COB. According to Keast et al. [2007],

CB is a stronger inter-organizational behaviour than collaboration, which in this research

supports the equal importance of CB in the A2A co-creation systems, to facilitate COB.

Dimensions of CB have been discussed in the service context as providing

recommendations and feedback to the organisation, helping other customers, and spreading

positive word-of-mouth [Bove et al., 2009, Groth, 2005, Revilla-Camacho et al., 2015, Yi and

Gong, 2013, Shamim and Ghazali, 2014, Zhang and Chan, 2017]. The findings of this research

are consistent with the service context literature and confirm the importance of Feedback and

different ways of constructive communication in the co-creation environment. This research

also reveals the importance of moderation as a new dimension for actor CB in the service

co-creation environment. Although Policing has been discussed in Organisational Citizenship

behaviour to control inappropriate behaviours [Bove et al., 2009, p. 699], this research extends

this dimension in the context of the service co-creation environment as “Moderation” of other

actors’ activities through different communication methods such as voting or feedback.


Moderation was found as a direct collaborative action toward other actors’ performance to

improve actors’ collaboration and service quality.

CB has been found to have a positive impact on employee performance and commitment

in the organisational context [Yi et al., 2011]. However, this research identifies actor CB as

the result of commitment and belonging to the community. Actor CB is mostly displayed by

high-rank co-creators and actors with a higher social position in the community because of the

stronger sense of commitment and belonging to the system that is achieved during the time in

and as the effect of the network. Consistent with the finding of [Liu et al., 2014], CB directly

and indirectly impacts the co-creation experience of others. Therefore, actor CB happens as the

result of the responsibility and commitment of actors in the service co-creation system which

constantly influences other actors’ performance and creates a dynamic co-creation environment.

Overall, actor CB in an A2A service co-creation environment is critical to the success of future

collaboration and creation of a sustainable value outcome, due to the absence of one particular

provider and the complexity of service integration.

6.3.3 Creative and Destructive forces in COB and CB

According to SD logic, co-creation is an interactive process of resource integration to benefit

others [Vargo and Lusch, 2008, Chandler and Vargo, 2011] which leads to Interactive Value

Formation (IVF) [Echeverri and Skalen, 2011]. Value co-creation has been discussed in the

literature as a positive and unproblematic process [Ple and Chumpitaz Caceres, 2010, Lefebvre

and Ple, 2011, Echeverri and Skalen, 2011, Zhang and Chan, 2017] that improves system

well-being [Vargo and Lusch, 2008]. However, few researchers argue for the potential negative

consequences and destructive ways of the co-creation process [Dong et al., 2008, Echeverri

and Skalen, 2011, Ple and Chumpitaz Caceres, 2010]. Little empirical work has been

conducted on the co-destruction context [e.g., Echeverri and Skalen, 2011, M. Smith, 2013].

Consistent with the argument of IVF introduced by Echeverri and Skalen [2011], this research

presents the result of SCB and resource integration as IVF. Also, the empirical findings of this

research reveal that the result of creative or destructive SCB generates not only positive

outcomes and increase in value but also negative outcomes and reduction of value.

Value co-creation is the outcome of congruent expectations of resource integration through

interactions [Ple and Chumpitaz Caceres, 2010]. Value co-creation happens at a higher level of


resource integration and compatibility of resources. According to the findings of the SO and

GH studies, the co-creative value is the outcome of actors’ SCB when actors’ individual and

collective value perception is obtained. On the individual level, actors may gain any perceived

Purposive and Network values. At the collective level, value is mostly related to service quality

and growth of the network.

Consistent with the literature, interactive value formation is not only linked to positive

outcomes, but the value can also be collectively destroyed or diminished during the co-creation

interaction process [Echeverri and Skalen, 2011]. Makkonen and Olkkonen [2017] also

claimed that value could be co-creative, no-creative, and co-destructive. Ple and

Chumpitaz Caceres [2010, p. 431] defined value co-destruction as unsuccessful resource

integration in an expected manner which “results in a decline in at least one of the systems’

well-being”. As it has not been thoroughly discussed in SD logic [Ple and Chumpitaz Caceres,

2010, Echeverri and Skalen, 2011], it is important to clarify value co-destruction to reduce

cost, customer loss and negative word of mouth [M. Smith, 2013]. According to the findings of

this research, although actors’ SCB mainly results in value formation, destructive forces

reduce or destroy the value outcome.

Value co-destruction results from the misuse of resources, negative experiences [Ple and

Chumpitaz Caceres, 2010], value imbalance between provider and customer (Woodruff 2006),

resource loss [Echeverri and Skalen, 2011, Lintula et al., 2017], non-integrated resources, and

insufficient perceived value [Lintula et al., 2017]. However, despite these findings, there is a

lack of empirical studies on how the co-creation process results in value co-destruction [e.g.,

Echeverri and Skalen, 2011, M. Smith, 2013]. The empirical findings of both the SO and GH

studies reveal that destructive forces include destructive communication (e.g., rejection of code

without feedback), poor maintenance, and poor or faultily-integrated service bundles. These

findings illustrate the direct influence of creative or destructive forces in COB and CB on the

value outcome (i.e., IVF). Constructive feedback, effective communication and motivating

others through CB, delivering good quality service and matching high-quality resources

through COB increase value formation. On the other hand, lack of communication,

misbehaviour, refusing feedback, and poor/non-maintainable service integrated bundles reduce

collaboration and result in reduce of value.


6.4 From SD Logic to Socio-SD Logic

This section presents how SD logic aligns with the identified SCB model. This research

justifies the concept of service co-creation behaviour with a focus on service network models

and many-to-many interactions by following the five axioms introduced by Vargo and Lusch

[2016].

Vargo and Lusch [2016] modified the 11 foundational premises of SD logic to five axioms

which focus on “service” (singular) as the basis of exchange rather than “services” (plural) as the

unit of output (A1/FP1), on the co-creative nature of value (A2/FP6) and resource integration

(A3 /FP9) by multiple actors and in a network-to-network view. A4 (FP10) introduces the

experimental and contextual nature of value by beneficiary. Finally, A5 (FP11) explains that

value co-creation depends on institutional agreement and mutual understanding. This research

follows Vargo and Lusch’s (2016) axioms with the adoption of an A2A service co-creation

perspective in the service system. Also, this research modifies Vargo and Lusch’s (2016) A2

and extends A2, A3 and A4 by considering SCB and value perception perspectives to create

A6, A7, and A8. Table 6.6 (page 181) and Table 6.7 (page 182) illustrate how SD logic as the

meta-theory could be presented more effectively in the midrange-theory of service co-creation

behaviour. Table 6.6 represents the elaboration of SD logic axioms [Vargo and Lusch, 2016]

based on the understanding of this research. Table 6.7 represents the added axioms based on

the findings of this research.

The cross-case study analysis of this research reveals that actors exchange their

programming knowledge and skill as operant resources, and as the offered service to solve

others’ technical problems or to improve offered projects (A1). Therefore, the first aspect of

SD logic is the importance of Actor Competencies as the fundamental basis of exchange and

the essential factor for resource integration and service co-creation. The platform integrates

resources offered from different actors (A3) as the integrated service. This research adds that

resource integration is developed through actors’ value perceptions facilitated by operant

resources. Further, resource integration is built on the service offers from different actors. A

bundle of integrated resources shape the co-created service (service co-creation). Value

formation is the result of a bundle of integrated resources, developed as Axiom 8. Figure 6.4

shows the resource integration process.

6.4. FROM SD LOGIC TO SOCIO-SD LOGIC 179

Integrated service is constructed through the orchestration and maintenance of a pool of

collective resources by multiple actors (A2). Value can be created or destroyed by multiple

actors through SCB. Therefore, this research elaborates Vargo and Lusch’s (2016) Axiom 2

and presents it as “service is always co-created by multiple actors, and may result value

formation or reduction through creative and destructive forces”. Co-created value as one of the

two forms of value is rooted in the actors’ SCB which this research develops as A6. SCB

relates to actors’ performance (A6) in accordance with the service systems’ institutional

agreements (A5). SCB is fundamental to developing creative value (A6). Axiom 6 extends

Axioms 2 and 5, which represent service as always co-created by multiple actors that may

result in formation or reduction of value in accordance with institutional agreements in the

service system.Figure 6.4: Resource integration process.

Source: Designed

Axiom 4 by Vargo and Lusch [2016] explains the nature of value-in-context. This research

revealed that value realisation is based on the psychological state of the actor-as-customer.

Also, valuation depends on the subjective value perceptions of individual actors because of

their diverse experiences. Therefore, this research develops an additional axiom, A7, to

represent the importance of actor value perception in the A2A service co-creation context. A7

proposes that “actors’ validation of actual value (profitability) is centred on actors’ value

perception” (Extension of A4), including both Purposive and Network values. Finally, this

research confirms that the whole co-creation process occurs through an institutional

arrangement (A5). This research proposes that the institutional arrangement is shaped by

actors’ effective citizenship behaviour and collective action. Therefore, the effectiveness of the

institutional agreement is critical in the formation of creative value. Figure 6.5 represents the


structure of SD logic in this research.

Figure 6.5: Structure of SD logic axioms in the SCB model

Source: Designed

6.5 Summary

This chapter revisited the findings of the SO and GH studies in relation to the objectives of

the research and the research questions. An integrated theoretical model was proposed based

on the SOR model. Each concept of the model was discussed separately with reference to the

literature. Also, the research presented new meanings of each concept in the context of service

co-creation with new characteristics and relationships. This research differs from the existing

literature in that it proposes a new model for SCB in the A2A context.

The SCB model consists of seven final concepts, based on the SOR model. Platform

Capabilities, Relational Capital and Actor Competencies are key factors in the co-creation

environment, and influence the two actor value perceptions of Purposive Value and Network

Value (individual and service level). These five combine to influence SCB and Outcome. The

model illustrates how environmental stimulus affects Actor Value Perception which leads to

SCB, which in turn leads to value co-creation/co-destruction.

Finally, this chapter discussed how SD logic is aligned with the identified theoretical SCB

model. The research elaborated on A2 and A3 introduced by Vargo and Lusch [2016] based

on the new understanding gained (Table 6.6). Further, this research added three extra axioms,

developed from A2, A3, A4 based on its findings (Table 6.7).

6.5. SUMMARY 181

Tabl

e6.

6:SD

logi

cax

iom

sba

sed

onth

eSC

Bm

odel

Sour

ce:

Des

igne

dfo

llow

ing

Varg

oan

dLu

sch

(201

6)

(Var

go&

Lus

ch,2

016)

Ext

ensi

onba

sed

onth

ere

sear

chfin

ding

sA

2Ase

rvic

eco

-cre

atio

nsy

stem

s(Fi

ndin

gsof

this

stud

y)

A1:

Serv

ice

isfu

ndam

enta

lba

sis

ofex

chan

ge.

Con

firm

ed

-Im

port

ance

ofA

ctor

Com

pete

ncie

sas

the

fund

amen

talb

asis

ofex

chan

ge.

-Act

orC

ompe

tenc

ies

isth

ees

sent

ialf

acto

rfor

reso

urce

inte

grat

ion

and

serv

ice

co-c

reat

ion.

-Soc

iala

ppro

vala

ndco

mm

unity

-sup

port

optim

ize

the

appl

icat

ion

ofop

eran

tres

ourc

es.

-Act

orco

mpe

tenc

ies

(res

ourc

e)is

offe

red

asth

ese

rvic

eto

othe

ract

ors.

A2:

Val

ueis

alw

ays

co-c

reat

edby

mul

tiple

acto

rs,a

lway

sin

clud

ing

the

bene

ficia

ry.

Serv

ice

isal

way

sco

-cre

ated

bym

ultip

leac

tors

,and

may

resu

ltva

lue

form

atio

nor

redu

ctio

nth

roug

hcr

eativ

ean

dde

stru

ctiv

efo

rces

-Int

egra

ted

serv

ice

isco

nstr

ucte

dby

orch

estr

atio

nan

dm

aint

enan

ceof

apo

olof

colle

ctiv

ere

sour

ces

perf

orm

edby

mul

tiple

acto

rs.

-Val

ueca

nbe

crea

ted

orde

stro

yed

bym

ultip

leac

tors

thro

ugh

SCB

.-C

o-cr

eate

dva

lue

isro

oted

inac

tors

’ser

vice

co-c

reat

ion

beha

viou

r(SC

B)(

refe

rto

A6)

A3:

All

soci

alan

dec

onom

icac

tors

are

reso

urce

inte

grat

ors.

Res

ourc

ein

tegr

atio

nis

ase

rvic

ein

tegr

ated

proc

ess

that

isde

velo

ped

thro

ugh

acto

rs’v

alue

perc

eptio

nfa

cilit

ated

byop

eran

tre

sour

ces.

-Alth

ough

oper

antr

esou

rces

are

esse

ntia

lfor

reso

urce

inte

grat

ion

toha

ppen

,the

appl

icat

ion

ofre

sour

ces

occu

rsth

roug

hva

lue

perc

eptio

n.-V

alue

perc

eptio

nis

the

key

inre

sour

cein

tegr

atio

nth

roug

hdi

rect

ing

acto

rs’c

olla

bora

tion

inac

tiviti

es.

-Ope

rant

reso

urce

sin

clud

eac

tors

’cap

abili

ties

and

Rel

atio

nalC

apita

l.-R

esou

rce

inte

grat

ion

isin

fluen

ced

byco

-cre

atio

nen

viro

nmen

tand

cont

extt

hrou

ghac

tors

’val

uepe

rcep

tion.

A4:

valu

eis

alw

ays

uniq

uely

and

phen

omen

olog

ical

lyde

term

ined

byth

ebe

nefic

iary

.C

onfir

med

Nat

ure

ofva

lue-

in-c

onte

xt.

-Val

uere

alis

atio

nis

base

don

the

psyc

holo

gica

lsta

teof

acto

r-as

-cus

tom

er.

-Val

uatio

nis

onth

esu

bjec

tive

valu

epe

rcep

tion

ofin

divi

dual

acto

rsbe

caus

eof

thei

rdiv

erse

expe

rien

ce.(

Act

orva

lue

perc

eptio

nA

7)

A5:

Val

ueco

-cre

atio

nis

coor

dina

ted

thro

ugh

acto

r-ge

nera

ted

inst

itutio

nan

din

stitu

tiona

larr

ange

men

t.

Con

firm

ed

-Ins

titut

iona

larr

ange

men

tis

shap

edby

acto

rs’c

itize

nshi

pbe

havi

oura

ndco

llect

ive

actio

n.-D

ynam

icse

tofr

egul

atio

nsin

the

netw

ork

that

are

shap

edby

Act

ors

valu

epe

rcep

tions

and

Rel

atio

nalc

apita

l(so

cial

influ

ence

and

trus

tas

norm

s).

-The

deve

lope

din

stitu

tiona

larr

ange

men

tis

core

ofth

ede

velo

pmen

tofN

etw

ork

valu

essu

chas

belo

ngin

gnes

san

did

entit

y.


Table6.7:SD

logicaxiom

sbased

onthe

SCB

model.

Source:D

esigned

Added

Axiom

sA

2AService

Co-C

reationSystem

s(Findingsofthisstudy)

A6:SC

Bis

fundamentalto

developingcreative

value.SC

Brelates

toactors’perform

ancein

accordancew

iththe

servicesystem

s’institutionalagreements.

(Extension

ofA2,A

5)

-SCB

=C

OB

+CB

SCB

=[Service

systemenvironm

ent+V

alueperception]

SCCprocess

=[

Resou

rces︸︷︷︸

operand(PC

,RC

,AC

) ,Purposiv

evalue

percep

tion︸

︷︷︸

L,H

,U,E

,Netw

orkvalue

percep

tion︸

︷︷︸

SP,B,C

E,Q

,S

]

-Serviceco-creation

behaviour(i.e.,serviceresource

integration)isinduced

byco-creator’s

valueperception

enabledby

operantresources,includingService

PlatformC

apabilities,Relationalcapital,

andA

ctorcompetences

(i.e.,environmentalstim

ulus).-C

ollaborativebehaviour(C

OB

):Co-creators’collaborative

performance

inproviding

(i.e.,creationand

delivery)requested

serviceorneed

throughcom

pletingdifferentactivities.

-Collaborative

performance

inresource

integrationthrough

completing

differentactivitiestow

ardproducing

integratedservices

(unitofoutput)andgain

value(i.e.,com

binationofm

ultipleactors’

resourcestow

ardcreating

services).-C

itizenshipbehaviour(C

B):C

o-creators’responsibilitytow

ardm

aintainingservices

andenvironm

entand

supportotheractorsto

enhancevalue

output.-service

isalw

aysco-created

bym

ultipleactors,and

may

resultvalueform

ationorreduction

throughcreative

anddestructive

forces.A

7:Actors’validation

ofactualvalue(profitability)is

centeredon

actors’value

perception(E

xtensionofA

4)

Purposivevalue

Netw

orkvalue=

Lon

gevity

+Purposiv

evalue+Outgoin

gvalue

A8:A

bundleofintegrated

resourcesconstructs

theintegrated

service(co-created

service).(Extension

ofA3)

Value

formation

(creative,destructive)isthe

resultofabundle

ofintegratedresources.

Chapter 7

Conclusions

The aim of this research was to investigate service co-creation behaviour (SCB) and how

collaboration play out in the service co-creation systems. The main research question was

“why do actors collaborate in service co-creation?” To achieve this research goal and address

the research question, a systematic literature review was first conducted to investigate the

nature of actor-to-actor (A2A) service co-creation systems (i.e., collaborative co-creation

system - CS3) compared to the other types of service co-creation systems. The results revealed

that CS3 follows a community-orchestration model (shared power) where actors create and

deliver the service for each other through resource integration and through collective value

distribution. CS3 was found to place a higher level of actor collaboration along actors’

involvement continuum with more responsibility on co-creation activities. Secondly, a

conceptual model was presented, and RQ2 and RQ3 were developed.



RQ2: How do environmental stimului influence actors’ service co-creation behaviour?


A qualitative case study was employed and 36 semi-structured interviews were conducted

with the members of StackOverflow (SO) and GitHub (GH). Thematic analysis revealed 15

established themes from the SO and 17 from the GH studies. The two sets of outputs were

compared and a theoretical model of service co-creation behaviour was developed. The

theoretical model was presented based on the SOR model where “S” represents environmental

183

184 CHAPTER 7. CONCLUSIONS

stimuli (addressed RQ2) and “O” represents value perception (addressed RQ3). The following

sections outline the contribution to the theory, the implication for practice, and a discussion on

the limitation and future work.

7.1 Contribution to the Theory

This research used the Stimulus-Organism-Response (SOR) model to represent how

collaboration plays out in the co-creation context. This research has used Uses and

Gratification Theory (UGT) as part of the interpretation of Organism in the SOR model and

extended UGT to the service co-creation context. By using service-dominant (SD) logic as the

fundamental logic of this research, and applying the SOR model and UGT, this research makes

six theoretical contributions.

Firstly, this research specifically focused on collaborative service co-creation platforms

(A2A co-creation systems) orchestrated by communities of multiple connected actors, which

has not been empirically investigated by previous studies from the SCB viewpoint, to the best

of our knowledge. Previous investigations in the SD logic perspective determined

business-to-customer (B2C) and customer-to-customer (C2C) service co-creation (named

cooperative and coordinative service co-creation systems) where the final delivery of the

service is by the organisation and the customer is part of value co-creation (e.g., Nike,

Mystarbuckidea.com) [e.g., Nambisan and Nambisan, 2008, Hoyer et al., 2010, Zhang et al.,

2015, Shamim and Ghazali, 2014, Fuller et al., 2009] and less on the A2A context. The

importance of investigating co-creation systems with A2A service creation and delivery is the

rise of these collaborative systems as the new business model for service creation and delivery

systems in daily life (e.g., transportation- Uber, GoGet), and to make a more meaningful

contribution to the co-creation context. The significant differences between B2C/C2C and

A2A co-creation systems were found to be the nature of community-orchestration and shared

power in service co-creation, where service creation and delivery occurs through the actors’

network under the organisation’s facilitation.

Secondly, this research demonstrated that SCB in a service A2A context is a combination

of both collaborative behaviour (COB) and citizenship behaviour (CB), and that these concepts

work together influence co-creation environment. Previous studies, focussing on B2C or C2C,

7.1. CONTRIBUTION TO THE THEORY 185

identified the importance of participation behaviour (PB) and citizenship behaviour, but treated

the concepts separately. One study [Shamim and Ghazali, 2014] conceptualised PB and CB

together, but focussed on C2C in the retail context not A2A. This research updated the

conceptualization of customer value co-creation behaviour [Yi and Gong, 2013] to the SCB

context in three ways:

• Moving from participation to collaboration (PB to COB). The concept moved from being

part of organization’s activities to actors collaborating in the joint intellectual effort and

shared assets to create collective value. COB happens as the result of collaboration in

service creation and delivery to maximize service value rather than service co-creation

with the organisation. Collaboration is the highest level of actor engagement along the

actors’ involvement continuum that is dependent on the actors’ network.

• This research found CB and COB equally important. CB not only provides an extra value

for the organisation, but also is a critical behaviour in maintaining the collaborations

and influencing service quality. CB encourages collaboration and innovation through

constructive communication and creating a pleasant environment.

• Value as the outcome of SCB is not always being co-created but can also be reduced

because of destructive communication and low service quality. The outcome of creative

or destructive SCB results in value formation or reduction of potential value outcome.

Thirdly, this research contributed to how SOR can be used effectively in co-creation

scenarios and further studies can use the SOR model more effectively. Based on the SOR

model, a particular stimulus (S) influences user perceptions or emotions (O) and triggers a

response (R). However, the findings of this research revealed that the relationship between O

and R can be reflexive instead of a one-way relationship. This research employed the SOR

model to understand the characteristics and main concepts of co-creation that entail actor

collaboration in SCB, with A2A interactions. Merging the SOR model and UGT (extended by

Nambisan and Baron [2009]), this research presented a conceptual model for actor value

co-creation behaviour. The presented model provides a scale for the development of the

research instrument to address the identified gap in understanding co-creation behaviour based

on the environmental and cognitive concepts. Further, through an inductive approach this

research presented a theoretical model in which environmental stimulus in collaborative


co-creation systems (A2A) affect SCB by influencing actor value perceptions. Also, the more

SCB presented by actors, the stronger the value perceptions created. Although Zhang et al.

[2015] used an SOR model to understand customer intention of future participation in value

co-creation with organisations through the use of social media sites, our study differs in

significant ways: we focused on service creation, delivery, and support by actors to each other

rather than product marketing and using user experience to improve the product (i.e., B2C,

C2C). In addition, the focus of value co-creation in our study occurs through service creation

and bringing innovation to the product/service and problem-solving (new ideas/solutions)

through specialized knowledge rather than a mechanism for information diffusion.

Fourthly, this research extended UGT to the A2A co-creation context by updating four UG

benefits introduced by Katz et al. [1973], examined by Nambisan and Baron [2009] to enhance

current understanding of actor value perception in the co-creation context. This research

considered four benefits (Learning, Hedonic, Personal and Social integrative) as

individual-based value perception and we divided them to two categories of Purposive value

perceptions (first-order) and Network value (in-process). Purposive value perceptions are

considered by co-creators as regards managing their ongoing needs. Network values are

in-process values that are empowered through the influence of others in the network (network

effect). This research considers learning and hedonic values as Purposive value perceptions,

and adds utilitarian and economic values to this category. This research considers social

position and belongingness as social integrative values, introduced by Katz et al. [1973] and

Nambisan and Baron [2009]. The findings of this research show that apart from

individual-based value perceptions, actors also perceive service-based values (quality and

support) that promote SCB to benefit the whole system and result in the success of the system.

Therefore, to categorise the identified actor value-in-context, six categories were introduced:

informational-related (learning), personal-psychological-related (Hedonic, Utilitarian),

financial-related (Economic value), social-related (Social Position, Belongingness,

Collaborative Effort) and service related (Quality, Support) values.

This research determined that to understand the SCB of actors, it is required to understand

the value they perceive from their collaboration. UGT helped to understand the psychological

needs which shape people’s cognition to engage in a certain behaviour and shows that

individuals engage in different forms of mass communication to fulfil particular needs [Rubin,

2002]. According to Katz et al. [1973], this theory assumes that actors are active and


goal-oriented in their behaviour, which conforms to the nature of engagement in co-creation

activities in this research. UGT describes four types of benefits - cognitive, social integrative,

personal integrative, and hedonic - representing the nature of benefits that customers can gain

from their participation in VCs [Nambisan and Baron, 2007, 2009]. Researchers have used

UGT to explain different motives and benefits derived from customer engagement in online

co-creation [Nambisan and Baron, 2007, Nambisan and Nambisan, 2008, Katz et al., 1999].

Nambisan and Nambisan [2008] discuss these benefits - pragmatic, sociability, usability, and

hedonic - as four experience dimensions to fulfil customers’ needs in virtual co-creation

systems. They found that these gained benefits significantly influence customers’ participation

in online communities, their actual continued participation [Nambisan and Baron, 2007] and

predict future participation in co-creation [Zhang et al., 2015].

Regarding the fifth contribution, given the lack of theory in the literature on service

co-creation behaviour, this research contributes to co-creation research by presenting a

theoretical model of SCB. Actors’ SCB is the result of environmental and cognitive factors

influencing collaboration in the service co-creation process. The environmental stimuli were

found to include Platform Capabilities, Relational Capital and Actor competencies. The

identified factors represent the nature of co-creation environment that includes operant

resources (e.g., specialised skill and knowledge of actors), and the social-psychological and

cognitive characteristics that surround actors’ interactions. Actors’ value perception is

constructed of Purposive values and Network values. Purposive values include Learning,

Hedonic, Utilitarian, and Economic Value. Network values at the individual level consist of

Social Position, Belongingness, and Collaborative Effort, while the service level includes

Quality and Support. While these drivers (e.g., Utilitarian) were studied previously in the

context of co-creation or online communities, some of them differ in this research (A2A

co-creation context) in the following ways (Refer to Table 7.1- 7.7 for more details):

• This research contributes to the concept of value perception as the SD logic construct by

developing value perception to two subsets of Purposive and Network values (discussed

in Section 6.2).

• Social Role: Although the role of customers has been discussed in the co-creation

literature, the findings of this research showed that in addition to some pre-defined roles

that co-creators can play as service provider (e.g., co-designer, co-distributor), there are


some roles that are socially constructed as the effect of the network. In the organisations

that are built on the community orchestration model, actors experience new roles that are

empowered by a platform (e.g., reputation system) through the influence of others. The

perceived role (e.g., teacher, influencer, helper) makes them feel responsible to

collaborate more in service co-creation activities and gives them a sense of

belongingness and sense of duty to facilitate and influence others in the community.

• Social influence (SI) is defined as affecting others’ thoughts and behaviours directly and

indirectly. In the co-creation context, the key SI variables are discussed mostly as a

normative SI, including social identity, group norm and subjective norm. For example,

Shamim and Ghazali [2014] found social influence (identification, internalisation and

compliance) as a moderating factor between a user’s experiential value and customer

value co-creation behaviour in retail. However, this research found both informational and

normative SI as the main drivers influencing SCB through co-creators’ value perception.

Based on the findings of this research, co-creators’ collaboration is socially influenced by

two characteristics: social approval and significant others. Social approval is based on

the goal of gaining external approval. Significant others are based on the desire to obtain

accurate information/source by relying on experience and information provided by expert

actors or competing with higher rank users. Social influence is supported by service

platform capabilities that consequently influence co-creators’ SCB and CB, through their

desire to gain higher status and provide higher quality service to similar significant others,

or learning from significant others. Although normative social influence has been found to

be a moderating factor for the value co-creation context, this research found an additional

type of social influence, informational social influence, that reflects the importance of

quality of service provided by co-creators rather than the co-creator him/herself.

• Collaborative Effort as a new construct refers to the desire to contribute to the team work

and to collaborate in a large-scale co-creation network. Collaborative Effort is a

social-related value in which actors expect to develop innovation through sharing and

responsibility distribution. Actors’ collaboration in service co-creation activities through

Collaborative Effort occurs not only because of their belongingness to the community

but because they are willing to engage and make a stronger and higher quality service

collectively.


• Quality value perception is represented as service-level network values which combine

Fundamental and Supplemental level perceptions with the Design, Functionality, and

Performance attributes of service qualities. These attributes determine the ways

co-creators collaborate in service co-creation activities (refer to Chapter 6, Figure 6.3).

In addition, a new finding related to Quality value perception is the correlation between

Quality value and Relational Capital, Purposive values, and Individual-level Network

values (Section 6.2.2- II).

• The new findings of this research show that the higher the social position and

belongingness in the community, the more Support value would be perceived by

co-creators that would lead to a greater COB and CB. Purposive values (i.e., Learning,

Hedonic, Utilitarian, and Economic) do not influence Support, but Network values

significantly lead actors to SCB through Support.

• Concerning creative and destructive forces in SCB, although this research expected

actors to create value through their interactions, the findings reveal that actors’

interactions may cause co-destruction of value (i.e., reduce or destroy potential value) by

providing low-quality content, having insufficient competencies through COB, and

presenting misbehaviour through their CB. Value co-creation and co-destruction are the

main concepts of interactive value practices, however, interactive value formation is

clearly not only linked to positive outcomes [Echeverri and Skalen, 2011]. Value

co-destruction results when there is low compatibility of resource integration and

negative interaction among collaborators. The assumption of the existence of implicit

value co-destruction which occurs due to the decline of one of the parties’ wellbeing and

the destruction of value by actors or resources is proposed by Ple and

Chumpitaz Caceres [2010]. Resources can be misused when actors have failed to apply

available operant/operand resources in an appropriate or expected manner. In this

research actors’ destructive interactions and behaviours that lead to minimising service

quality, actor contributions and reputation, and consequently reduce business

growth/benefits (i.e., measures of company’s success can be monetary like service

income or non-monetary like increasing the number of co-creators) results in the

destruction of potential value. Value co-destruction has been discussed in the literature,

however, to the best of our knowledge, there are few empirical investigations on service

co-creation platforms about co-destruction. Reduction in potential value formation


happens as the result of actors’ destructive behaviour in the co-creation process and

unsuccessful resource integration. The reasons behind dismissing co-destruction might

be: the proportion of co-created of value is much higher than co-destruction of value.

Also, co-destruction of value might happen more in less structured organisations such as

GitHub and StackOverflow that are community-orchestration platforms (with A2A

interaction), which previous studies have not investigated in detail.

Finally, this research contributed to the elaboration of SD logic [Vargo and Lusch, 2016]

using the identified SCB model with a focus on service network models and many-to-many

interactions. This research elaborated axiom two (A2) and axiom three (A3) introduced by

Vargo and Lusch [2016] (Table 6.6). A2 is elaborated to “service is always co-created by

multiple actors, and may result creative and destructive value”. A3 is elaborated to “resource

integration is a service integrated process that is developed through actors’ value perception

facilitated by operant resources”. Further, this research added three extra axioms, developed

from A2, A3, A4, based on the findings (Table 6.7). Section 6.4 fully discussed the SD logic’s

Axioms in the SCB Model.

A6: SCB is fundamental to developing creative value. SCB relates to actors’

performance in accordance with the service systems’ institutional agreements

(Extension of A2, A5).

A7: Actors’ validation of actual value (profitability) is centred on actors’ value

perception (Extension of A4).

A8: A bundle of integrated resources constructs the integrated service (co-created

service) (Extension of A3).


Table 7.1: The new findings of Platform Capabilities.Construct Existing Factors Knowledge Extension

Plat

form

Cap

abili

ties:

Feat

ures

prov

ided

byse

rvic

epl

atfo

rmto

fost

erco

ntri

butio

nan

dco

llabo

ratio

n

.

Design[Kohler et al., 2011][Romero and Molina, 2011][Fuller et al., 2009][Frow et al., 2015]

Gamified featuresfor competitionand puzzle solving[Kohler et al., 2011]

Financial rewards[Zwass, 2010]

The following are found important in A2A serviceco-creation platforms:

In the context of less structured co-creation platform(community-orchestration) in which organisationis a facilitator, the need for quality control featurein a strategic way (i.e., reliable and accurate) isrequired.

Enabling the process flow of co-creation activitiesto be visible for the actors will enhance collaborationand efficiency. The visualization of activities via aprocess map helps to track the transactions andactivities and maintain collaborations andcompetitive edge.

This research suggests the implementation of tangiblereward modules depending on the level of collaboration.Therefore, the objectives of implementing gamifiedmodels, such as types of behaviour and the level ofactors’ engagement, should be taken into account todesign a successful gamified model.

Applying a collective agreement mechanism as aplatform feature (e.g., voting system) was found tobe strongly related to trust and decision making.

Table 7.2: The new findings of Actor Competencies.Construct Existing Factors Knowledge Extension

Act

orC

ompe

tenc

ies:

refe

rsto

the

Lev

elof

acto

rs’c

apab

ility

and

expe

rtis

eto

deliv

erth

ese

rvic

e.

e.g., [Sharma, 2016]and [Vargo et al., 2008]

Actors should be segmented in different parts of the serviceco-creation process with different competences because ofthe complexity of service co-creation systems to have a moreefficient service system with a greater quality outcome.


Table 7.3: The new findings of Relational Capital.Construct Existing Factors Knowledge Extension

Rel

atio

nalC

apita

l:T

heco

gniti

vere

latio

nof

acto

rsan

dth

eiri

nflue

nce

onot

hers

’bel

iefs

Incl

udin

g:So

cial

Influ

ence

and

Trus

t

Social Influence[Shamim and Ghazali, 2014],[Hajli, 2014],[Gronroos, 2007]

TrustSubjectivity andobjectivity oftrust mostly havebeen exploredto test theeffectiveness oftrust modelsand algorithms.

Social Influence (SI): The strength of two types of normative(subjective-oriented) and informational (quality-oriented)SI on actors’ co-creation action and SCB throughtheir value perception.

Normative SI includes Significant others/Social approval.Influence of authority found to be strong through constructivefeedback and support.

SI is not always positive (e.g., when the offered service iscompletely ignored to be integrated without any feedback ordestructive communication can reduce collaborativebehaviour).

SI is transmitted through platform capabilities.Since actors’ SCB and a greater chance of collaboration inservice innovation were identified as strongly related to theactors’ opinion sharing and SI on others, the presence ofelements that develop mechanisms of SI need to bedetermined by practitioners.This research recommends that the pattern of SI in the lessstructured community-orchestrator (e.g., GH and SO)should be extended and applied to the more structured A2Aco-creation systems such as DHLMyways.The challenge is to evaluate how social influence strategiesshould be implemented through platform features that reducethe chance of negative social influence and maintain a positiveservice co-creation environment.

Trust: identified as Subjective and objective in co-creationcontext.

Trust was found to be more of a social concept rather than aninterpersonal relationship.We conceptualise trust as the reliability of the decision-makingaccuracy on the service quality, based on the social influenceand collective action.

Objective and Subjective Trust is discussed from the actors’decision-making viewpoint on the offered service and otherparties’ performance in the co-creation process.

Subjective and objective evaluation preference found to beimportant in assessing the quality and shaping trust relationin the service co-creation environment to enhances valuerelationships and collaborative behaviour.


Table 7.4: The new findings of Purposive value

Construct Existing Factors Knowledge Extension

Purp

osiv

eVa

lue:

valu

esre

late

dto

the

ongo

ing

need

s.

Learning: confirmede.g., [Payne et al., 2008],[Nambisan and Baron, 2009]

Utilitarian:confirmed in B2Ce.g., [Park, 2016]

Hedonic: confirmedin B2C [e.g., Zwass, 2010]

Career advancementliterature indicatedcareer advancementas motivation forcustomer’s co-creationwith firm.[Zwass, 2010],[Fuller, 2006],[Martınez-Canas et al., 2016].

Conceptualization of value perception in two different typesof purposive and network values with individual and servicelevel (i.e., collective).

Learning: was not limited to service information acquisitionbut to improve CB ( cultural and interaction attitudes).

The greater the SCB, a greater experience and knowledge thatreforms their cognitive patterns leading to a more effectivecollaborative application.

Utilitarian: Although the relationship between COB andutilitarian value in both studies was found reversive, therewas no relationship between utilitarian value and CB.A new finding is that when the outcome of actors’ COB isconsistent with the expected utilitarian value, co-creatorsare more likely to support others to develop the overall quality.Also, the higher performance and efficiency through utilitarianvalue enhance the chance of a stronger professional relationshipand the possibility of gaining economic value.

Hedonic: harnessing the Hedonic value by proving rewardssuch as point-based system may reduce the quality ofcollaboration. This research suggest that practitioners shouldcapitalize on the Hedonic perception of co-creators by providinga more effective and logical design thinking approach to drivefour dimensions of innovation, quality, positive culture, valueactor’s efforts that are critical for success (Figure 6.2).The utility aspect of the service co-creation systems shouldoutweigh the hedonic aspect to enhance actors’ SCB.

Economic: Utilitarian and Economic values are related tothe measurable usefulness benefits from actors’ collaboration.However, the Utilitarian value is function-based and Economicvalue is related to potential financial value. Here, EconomicValue is the ability to create income through proposedopportunities and professional connections.

Co-creators’ economic perception was not limited to theircollaboration on the platform but their SCB was highly relatedto extend their collaboration to the real-world service co-creation.

A higher Economic value in the A2A co-creation systems wasfound to be related to the virtue of actor’s status and profile onthe platform.

Marketing was important for actors-as-supplier. In this caseEconomic value was not only perceived based on the financialoutcomes but also growth of their service and project.Actors with the role of suppliers’ attempt to advertise andpopularize the project/service to get remuneration and buildreputation.


Table 7.5: The new findings of Network value (individual-level)Construct Existing Factors Knowledge Extension

Net

wor

kVa

lue:

(Ind

ivid

ualL

evel

)Val

ues

that

are

the

resu

ltof

netw

ork

effe

ct.

Role: was foundas a fundamentaldimension in SDlogic e.g.,[Vargo et al., 2008]Necessity of roleclarity:[Bharti et al., 2015]

Status: e.g.,[Nambisan and Baron, 2009]

Belongingness:Belongingness wasdiscussed extensivelyin VC literature.However, few studiesbriefly discussed Belong-ingness in the co-creationcontext.Belongingness createsstrong feeling ofcontribution[Hasan and Rahman, 2016].

Perceived values that develop during the time (longevity),depends on the degree of collaboration (i.e., outgoingvalue or degree of benefiting others) and occurs when thepurposive value is met.Network value = Longevity + Purposive value + Outgoing value

Social Position (social status and Role): Social Status isnot only perceived for non-transactional behaviours but itis associated with the transactional context as well.Because of the nature of community-orchestrator serviceco-creation systems with A2A service creation and delivery(e.g., DHLMyWays with co-delivery services), actors need toestablish trust by enhancing their status in the competitiveenvironment.

Social status lead to SCB, it may not always have thepositive outcome.

Apart from the pre-defined roles, actors perceive (visualize)some roles (e.g., Helper, Adviser, Leader, Influencer) thatare the expression of an actor regarding their responsibilities.Perceived role was not significant in the GH, but promotingto an authority role (e.g., maintainer of the project) wasimportant to have more control over the co-creation procedure.

Belongingness: not only influence actors’ COB but alsoenhance their CB.

Belongingness was mostly subject to the co-creators’ positionin the system. This suggests that Belongingness is the resultof actors being respected and valued for their collaboration,which makes them trustworthy and a role model for others.Although the influence of trust on belongingness was foundin the VC literature, no research in the co-creation context hasdescribed the relationship between trust and social influenceon belongingness. Despite the similarity of network structurebetween VC and A2A co-creation systems, the importance oftrust is different in co-creation systems because of the natureof transactions in service businesses, which can bechallenging in terms of money, timing, accuracy.

Collaborative Effort: is a new theme that shows desire toteam work and collaborate in a large-scale co-creation network.

Collaborative Effort as a social-related value in which actorsexpect to develop innovation through sharing andresponsibility distribution.

Actors’ collaboration in service co-creation activities throughCollaborative Effort was found not only because of theirbelongingness to the community but to engage and make astronger and higher quality service collectively.


Table 7.6: The new findings of Network value (service-level)Construct Existing Factors Knowledge Extension

Net

wor

kVa

lue:

(Ser

vice

Lev

el)-

Val

ues

rela

ted

toth

eco

mm

unity

goal

.

Quality: has beendiscussed in theco-creation literatureregarding its importancein information sharingquality [Yi et al., 2011],interaction quality[e.g., Yi et al., 2011] and[Kelley et al., 1990],improving product quality[Fuller et al., 2011]and mostly as the benefitsof a product or service[Ulaga, 2003].

Support:Social support[e.g., Pera and Viglia, 2015]Empathy[Hwang and Griffiths, 2017]Assisting other customers[Yin, 2013]

Quality: The perception of quality is different betweenactor-as-provider (accuracy, specification and supportof future reference) and actor-as-customer view(satisfied need).

Figure 6.3 represents that the quality value perceptionin the service co-creation systems is a combinationof Fundamental and Supplemental level perceptionswith Design, Functionality, and Performance attributesof service qualities. These attributes determine byco-creators to collaborate in service co-creation activities.

This research revealed that the lack of quality relatedto both individual resources (e.g., Actor Competencies)and joint process resource integration create a barrierto future collaboration and reduces actor’s COB.

Relational Capital and social norm highly influencequality value perception.

Individual-level Network values and Purposive valuesinfluence actors’ Quality value perception.

Support: The new findings of this research show thatthe higher social position and belongingness to thecommunity, the more Support value would be perceivedby co-creators that lead to a greater COB and CB.

Purposive values (i.e., Learning, Hedonic, Utilitarian,and Economic) do not influence Support but Networkvalues significantly lead actors to SCB through support.

Strong relationship between leadership role perceptionof actors to support others and influence their co-creationperformance.


Table 7.7: The new findings of service co-creation behaviour

Constructs Existing Factors Knowledge Extension

Serv

ice

Co-

crea

tion

Beh

avio

ur(S

CB

)

Service co-creationhad a very limitedfocus on the literaturee.g., [Gill et al., 2011],[Hilton et al., 2012],and[Finsterwalder, 2016].

COB and CB dimensions extended from Yi and Gong [2013]value co-creation behaviour.

New definition for SCB:SCB= COB+CBSCB = [Service system environment + Value perception]

Direct influence of COB and CB on the value outcome (i.e., IVF).

Creative COB/CB including constructive feedback, effectivecommunication, and motivating others through CB, deliveringgood quality service and matching high-quality resources throughCOB results increase of value.

Destructive COB/CB including lack of communication, misbehave, rejecting to give feedback, and poor/non-maintainable serviceintegrated bundles reduces collaboration and results in valueco-destruction.

Col

labo

rativ

eB

ehav

iour

(CO

B)

Act

ors’

colla

bora

tion

incr

eatio

nan

dde

liver

yre

ques

ted

serv

ice

thro

ugh

com

plet

ing

diff

eren

tact

iviti

es.

Participation behaviourdiscussed in B2C[Yi and Gong, 2013]

COB represents the actors’ practice in creating the core servicetogether with the network of co-creators.

Participation Behaviour is located in the lower level of actors’co-creation involvement continuum while COB representsthe highest level where actors are empowered to practice ina joint service innovation effort.

Citi

zens

hip

Beh

avio

ur(C

B)

Act

ors’

cont

ribu

tion

invo

lunt

ary

co-c

reat

ion

activ

ities

tobe

nefit

netw

ork

Citizenship behaviourdiscussed in B2C[Yi and Gong, 2013].

Moderation-related dimension.

The voluntary and supportive role of CB in the co-creation processis confirmed. However, CB was found as an essential behaviourin improving the performance and collaboration.

CB not only adds extra value to the organisation but is an essentialbehaviour for service support and build a stronger COB.

CB is the result of commitment and belongingness to the community.

CB is mostly displayed by high-rank co-creators and actors witha higher social position.

Critical to the success of future collaboration and creation of asustainable value outcome, due to the absence of one particularprovider and complexity of integration of service.

7.2. CONTRIBUTION TO THE PRACTICE 197

7.2 Contribution to the Practice

Co-creation models are fast becoming the basis of organisations’ strategy in industries such

as transportation, healthcare, and hotel. However, Libert et al. [2016, p. 4] stated 98% of the

organisations are based on non-networked business models and are competing to update their

strategy. In this regard, the outcome of this research can be helpful for the business transition

to maintain a co-creation system or improve an organisations’ co-creation strategy.

The large part of the success of organisations with co-creation systems depends on the

co-creators’ engagement. This research presented a model for actor’s SCB, from the actors’

perspective, that explains how collaboration plays out in the co-creation system.

Understanding the behaviour of actors and improving the business through actors’ experiences

and objectives are key factors to the success of leading companies such as Amazon. Amazon

evaluates its performance based on the five hundred goals, and 80% of these goals are

associated with their customer objectives [Libert et al., 2016, p. 177]. This example represents

the importance of this research to the practice by using the presented model as a tool to

identify and fulfil actors’ objectives to collaborate.

As a practical implication, the findings of this research increase the understanding of

collaboration patterns through the impact of environmental stimuli and value perceptions on

actors’ SCB. The findings help practitioners to enhance the technical aspect of platforms such

as the user interface, and to construct their value propositions to increase collaboration and

utilise the identified constructs as an evaluation index for predicting customer behaviour.

In the design of co-creation platforms, practitioners should consider the identified actor

value perceptions based on context to improve collaboration. Apart from facilitating the

platform to cover the actors’ Purposive value perception, practitioners need to determine the

Network values to support active actors’ needs to achieve a higher level of co-creators’

collaboration and performance.

The model helps practitioners to identify and limit the destructive behaviours that occur

through communication to avoid and reduce destructive outcomes. For example, they may

apply gamification for the design of the platform to diffuse positive behaviours and norms,

concentrate on the principles of interactions, and improve their best practices for managing the

innovative outcome.


The organisation as the facilitator needs to improve the service co-creation environment and

maintain collaborations by using social influence strategies which accordingly enhance trust,

enhance competencies, and increase the quality of collaborations. This will lead to a more

effective competitive advantage, a greater control over service integration through community,

cost saving, and over time the best possible value outcome.

7.3 Limitations

The current research has several limitations that are presented here in the three major parts of

systematic literature review, data collection and context.

Systematic Literature Review:

• Only 36 articles met the inclusion criteria for further investigation. However, this research

compiled a strong analysis of the most influential articles in the context of co-creation,

mostly with high citations.

• The research keywords included (“value co-creation” + system), “value co-creation

process”, and (“crowdsourcing systems” + service). Future research may select

additional search terms such as “collaborative networks” and “value networks” to

broaden the scope of analysis.

• The systematic literature review of this research was to gain a better understanding of the

nature of A2A co-creation systems. The researcher did not empirically test the identified

classification. Further study is suggested to conduct both qualitative and quantitative

data by performing an in-depth interview and survey to test the validity of the proposed

classification. Case studies of two different co-creation platforms could be conducted to

enhance the generalizability of findings.

Data Collection:

• The data for this research was gathered from two non-transactional platforms: SO is a

knowledge-based platform and GH is a project-based platform. This research focused on

these two platforms because 1) every service platform includes the knowledge

co-creation side that is supported by SO, 2) both studies consist of potential future

7.3. LIMITATIONS 199

transactional exchanges (refer to section 6.2.1-Economic value), and 3) Both SO and GH

perfectly reflected the A2A interaction and exchange between actors, that makes this

research different from previous studies. Future research will examine transactional

service co-creation platforms with tangible assets such as DHLMyWays, and GoGet to

enhance the generalizability of current findings in other contexts.

• Another limitation of this research is related to the participants’ recruitment. The selected

platforms do not allow direct messaging. The researcher was limited to contacting users

who provided an email address, URL to their website or link to other social networks. So,

the process of recruitment was time-consuming and it was hard to find the right participant

who was active on the platform and could provide rich data.

• A further limitation was related to conducting interviews, including time zone and Skype

interview difficulties. While people from all over the globe could be interviewed, there

was difficulty in arranging and rearranging times because of the time difference. Also, 35

out of 36 interviews were conducted by Skype without a face-to-face meeting. Missing

non-verbal cues was another disadvantage of Skype interviews.

Context

• This research considered actors as individuals (actor-as-supplier and actor-as-customer)

playing a role in the service co-creation process. However, further research might

consider multiple actors such as other stockholders, to capture other aspects of service

co-creation from different viewpoints.

• The research focus was on a singular context and was restricted to programmers’

viewpoint on co-creation. Further research can employ comparison studies in other

contexts or industries to investigate whether a relationship exists between the

cost-related elements of value creation and company performance in these industries.

• With the potential for transactional service co-creation platforms to evolve and

increasingly leverage the benefits of self-orchestration and open communities and A2A

interactions, this research faced an open research question about how best to integrate

co-creation from risk-mitigated transactional delivery and flexible co-creation of

StackExchange type of platforms. One such example is DHL MyWays, where parcels


are being delivered by individuals to other actors for a small fee. Here the orchestrator

organisation (DHL MyWays) appends its existing service by facilitating co-delivery of

transactional services between their community of existing actor-as-customer.

Future research will use the result of this research to create a co-creation toolkit for

practice. The toolkit will help to analyse the current co-creation system performance and help

the improvement of the system. Also, guidelines will be provided for traditional companies

who target co-creation as their strategy.

Future research will focus on transactional service co-creation systems such as GoGet and

DHL MyWays to find out how collaborations differ compared to the findings of this research.

Also, different types of actors such as other third-parties will be considered rather than only

individuals.

7.4 Conclusion

This chapter provided a review of the aims of the research and research questions. Then, the

theoretical and practical implications of the research were discussed in Sections 7.1 and 7.2.

Finally, limitations and future work were outlined.

The major theoretical contributions were related to the presented SCB model, using SOR

model. The research contributed to how the SOR model can be effectively used in a co-creation

context. This research extended UGT to an A2A co-creation context by updating four UG

benefits introduced by Katz et al. [1973], and examined Nambisan and Baron [2009] to enhance

current understanding of actor value perception. Further, the research updated Yi and Gong’s

(2013) conceptualization of value co-creation behaviour to include collaboration in the service

co-creation context.

As a practical implication, the research suggests that the developed SCB model helps

practitioners to increase collaboration through understanding their co-creators’ behaviour.

Practitioners as the facilitator need to provide a healthy interactive environment to reduce

destructive outcomes and manage collaborations. Also, they need to understand both

Purposive and Network values from the co-creators’ perspective, and support their value

perceptions through improving design and implementing social influence strategies to get to a

desirable end result.

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Appendix A

Participant Recruitment Flyer

227

228 APPENDIX A. EXAMPLE I

PARTICIPATE IN RESEARCH Information for Prospective Participants

The following research activity has been reviewed via QUT arrangements for the conduct of research involving human participation. If you choose to participate, you will be provided with more detailed participant information, including who you can contact if you have any concerns.

A Social Influence Theory of Participation in Service Co-creation through Social Networks

Research team contacts Principal Researcher: Reihaneh Bidar PhD Candidate Associate Researchers: Dr Jason Watson Principal Supervisor Prof Alistair Barros Associate Supervisor School of Information System, Science and Engineering Faculty

Queensland University of Technology

What is the purpose of the research?

The purpose of this research is to better understand how people behave on [community name]. A key objective in our research is to investigate the social influences that drive people to participate in different ways in the community.

Are you looking for people like me?

The research team is looking for active members of [community-name] – who have a role in the process of provisioning and delivery of the service to other members. This obviously includes people involved in sharing their experiences, insight and knowledge, providing tangible services (e.g. accommodation, ride-share) for others and assisting others with decision-making.

What will you ask me to do?

Your participation will involve participation in an approximately 30 to 60 minutes interview to discuss questions such as: Who or what influences you to participate in an activity?

Have you ever participated in an activity because your friends or colleagues were doing the same? Why? What has motivated you to continue contributing to the activity? Whose activities have you mostly followed?

Also, we will ask your consent to monitor your activities and interactions with others (such as comments and feedback) on [community name] for approximately two months.

Are there any risks for me in taking part?

The research team does not believe there are any risks beyond normal day-to-day living associated with your participation in this research.

It should be noted that if you do agree to participate, you can withdraw from participation at any time during the project without comment or penalty.

Are there any benefits for me in taking part?

It is expected that this project will not benefit you directly. However, the main benefit of your involvement is that the knowledge gained from this research could help the development of future social network platforms, and improve understanding of consumers’ behaviour and theoretical understanding within this context.

Will I be compensated for my time?

We would very much appreciate your participation in this research.

To recognise your contribution should you choose to participate, the research team is offering you the chance to win one of two $50 book vouchers. The prize draw will take place 15 March 2016. You must be 18 or older to participate in the interview and the prize draw. If you choose to enter the draw for the gift card you will need to provide some basic contact information. However, your identity will not in any way be connected to other data collected relating to your involvement in the study.

I am interested – what should I do next?

If you would like to participate in this study, please contact the principal researcher for details of the next step. You will be provided with further information to ensure that your decision and consent to participate is fully informed.

Thank You! QUT Ethics Approval Number: 1500000502

Appendix B

Pilot Interview Topic Guide

1. Introduction

• The purpose of the study.

• Recording arrangements; use of recordings, and timing of the study.

• Identification of interviewees in the thesis.

• Right to withdraw. Consent

2. Co-creation Questions

2.1 Can you tell me a little bit about StackOverflow and its goal and structure?

• What kind of needs do users have?

• What is the service being provided?

• What kind of activities and actions users do in stackoverflow? (e.g. answering and

questioning or giving ranks and read)

• How does the user participate in the knowledge production and delivery process?

• What kind of challenges or problems are involved in this process (creating and delivery

of knowledge to each other)?

• What do you think is to do in order to facilitate the success of this process?

229

230 APPENDIX B. EXAMPLE II

2.2 Can you tell me how do you personally use StackOverflow? How often? and how do you

describe your contribution?

3. Value Perception Questions

3.1 Why do you contribute? What are you looking for on StackOverflow and what’s

your reason to participate?

4. Environmental Stimulus Questions

4.1 Service platform capabilities

• Which platform features encourage or assist you with contributing? And How do

they work? why are they affective?

4.2 Role

• How do you see your role in the community?

• Can you tell me about your responsibilities on a platform and in what way it

causes you to contribute in the activities?

4.3 Social Influence

• Can you describe the influence of others on your participation in the activities?

Social Identity:

• Can you describe your feelings of belongingness to the community? And how does it

affect you?

• How close you see your personal identity with the identity of people you are engaging in

the community? How does it encourage your involvement?

Group norm:

• Can you describe how close you see your goals to the community’s goals? How does it

affect your involvement?

231

• Explain if engaging in the StackOverflow with other members of community or friends,

can be considered a goal. Why? And how does it affect you?

Subjective norm:

• Please express how strongly most people who are important to you (like your friends) feel

you should or should not engage in the activities on StackOverflow. How do they affect

your involvement?

5. End Questions

• Can you name five people you have more interaction with?

• Possibility of follow-up interviews or emails.

232 APPENDIX B. EXAMPLE II

Appendix C

Main Interview Topic Guide

1. Introduction

• The purpose of the study.

• Recording arrangements; use of recordings, and timing of the study.

• Identification of interviewees in the thesis.

• Right to withdraw. Consent

2. Co-creation Questions

2.1 Can you tell me a little bit about StackOverflow and its goal and structure?

2.2 What kind of activities and actions users do in StackOverflow?

2.3 Can you tell me how do you personally use StackOverflow? How often? and how

do you describe your contribution?

2.4 What kind of challenges or problems are involved in this process (creating and

delivery of knowledge to each other)?

2.5 Why do you think the platform become successful?

3. Value Perception Questions

3.1 Why do you contribute? What are you looking for on StackOverflow and what’s

your reason to participate?

233

234 APPENDIX C. EXAMPLE III

4. Environmental Stimulus Questions

Social influence

4.1 Can you describe the influence of others on your participation in the activities?

4.2 Do you get more motivated to contribute by other users’ participation such as

high-rep users or experts in your field? How does it affect your participation?

4.3 Can you describe your feelings of belongingness to the community? And how

does it affect you?

4.4 How close do you see your personal identity with the identity of people you are

engaging in the community?

4.5 How do you feel about your position in the community?

4.6 Can you describe how close you see your goals to the community’s goals? How

does it affect your involvement?

4.7 Explain if engaging in the StackOverflow with other members of community or

friends, can be considered a goal. Why?

Service platform capabilities

4.8 Which platform features encourage or assist you with contributing? why are they

affective?

Roles

4.9 How do you see your role in the community?

4.10 Can you tell me about your responsibilities on a platform and in what way it

causes you to contribute in the activities?

5. End Questions

5.1 Can you name five people you have more interaction with? (i.e. contributing in

answering and commenting on each other’s question)

5.2 Possibility of follow-up interviews or emails.

Appendix D

Coding Example

235

236 APPENDIX D. EXAMPLE IV

Appendix E

Initial Code list: StackOverflow

237

238 APPENDIX E. EXAMPLE V

239

240 APPENDIX E. EXAMPLE V

Appendix F

Initial Code list: GitHub

241

242 APPENDIX F. EXAMPLE V

243

244 APPENDIX F. EXAMPLE V

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