s e a portfolio development a longitudinal ......uq business school university of queensland...

SPINOFF’S EARLY ALLIANCE PORTFOLIO DEVELOPMENT: A LONGITUDINAL STUDY IN

AN ALLIANCE-INTENSIVE INDUSTRY

Forough Zarea Fazlelahi M.Sc. Financial Engineering, University of Economic Sciences

B.Sc. Industrial Engineering, Tabriz University

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Australian Centre for Entrepreneurship Research

QUT Business School

Queensland University of Technology

2019

Supervisory Panel

Principal Supervisor

Professor Martin Obschonka


QUT Business School


Associate Supervisor

Professor Per Davidsson


QUT Business School


External Supervisor

Dr Henri Burgers

UQ Business School

University of Queensland

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry i

Keywords

Absorptive capacity, Australian mining industry, Conditional multiple mediation model, Entrepreneurship, Imprinting theory, Knowledge transfer, Longitudinal data analysis, Multiple mediation analysis, Network growth, Network imprinting, Network status, Network research, New firm, Organisational learning, Parent firm, Parent–spinoff context, Quantitative research, Spinoff firms, Spinoff performance, Strategic alliances, Strategic management

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry ii

Abstract

The mining industry in Australia and elsewhere is known for the capital-intensive

nature of its projects that can cost up to hundreds of millions of dollars. Progression of

a mining project from early feasibility tests to an operational mine has low success

rates and it could take many years. The Minerals Council of Australia estimates only

one in a thousand exploration projects successfully leads to a new mine site. Due to

high costs and high failure rates of mining projects, companies frequently tend to enter

strategic alliances to share risks and pool resources. In mining, as an alliance-intensive

industry, not only is it important for established mining firms to forge new alliances,

but it is also even more pronounced for new mining firms. This is due to resource

constraints that new firms face at the time of their founding.

Prior studies show that alliance networks are an important way for new firms to gain

access to the necessary resources. Despite the importance of this topic, it is an under-

researched area in entrepreneurship literature. Most prior network-based research in

entrepreneurship has focused on social networks of entrepreneurs, and not on the firm-

level strategic alliances. In particular, the share of research on the alliance network

growth of spinoffs is small, although they constitute a major group of entrants into

various industries. Spinoffs are firms started by ex-employees of incumbent firms in

the same industry as their parent firm, which gives them an initial advantage over other

types of new firms. According to The Register of Australian Mining database, in a ten-

year period over 70% of new entrants into the industry had an intra-industry founder

on their board. Studies show that contextual knowledge inherited from pre-entry

experiences can lead to the successful performance of new firms. However, the relation

between knowledge inheritance and post-spinoff outcomes is an overlooked area in

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry iii

the network-based research in entrepreneurship. Therefore, there is clear scope for

additional research to better understand the early alliance portfolio establishment in

mining spinoff firms.

This thesis investigates the antecedents, underlying mechanisms, and outcomes of

early alliance network growth of spinoff firms in three longitudinal studies. I draw

upon network imprinting, organisational learning, knowledge transfer and social

categorisation lenses to find answers for research questions. In Study I, I perform an

extension of Milanov and Fernhaber (2009) on determinants of spinoff alliance

network growth. Specifically, I test the positive imprinting effect of initial partners as

well as the parent firm’s network size versus centrality on the spinoff firm’s alliance

network growth. Informed by the findings of this study, I test a multiple mediation

model in Study II, where spinoff absorptive capacity and spinoff network status

mediate the relationship between parental network centrality and spinoff network

growth. I also test for the moderating effect of knowledge overlap between parent and

spinoff on the mediated relationships. Finally, in Study III, I examine the influence of

spinoff alliance network growth on the spinoff’s performance. I also consider whether

this relationship is spurious, and if it is still driven by parental resources by testing the

effect of parent’s network characteristic on spinoff performance.

This thesis benefits from using secondary data by a synthesis of multiple datasets.

Having access to a comprehensive dataset containing multiple levels of data (i.e., all

firms, all companies, and all directors in the Australian mining industry) for a period

of 10 years, provided a unique opportunity to study the main phenomenon of the thesis.

Further, gathering and combining data from several other datasets yielded a strong

platform for analysis. Data was mainly collected from The Register of Australian

Mining database annually for the period from 2002 to 2011. Additional data was

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry iv

collected from Morningstar Premium, Australian Securities Exchange, Dun &

Bradstreet Hoovers Business Browser, Australian Bureau of Statistics, Bloomberg,

Osiris, and Orbis. Network analysis is performed on 3370 strategic alliances on a

sample of 248 spinoff firms. Together the three studies enhance our understanding of

the phenomenon of alliance network growth in newly founded spinoff firms.

This thesis extends network-based research in entrepreneurship by shedding light on

the influence of parent firm network features on spinoff alliance network growth. It

further extends the literature by suggesting new theoretical explanations for underlying

mechanisms of network imprinting and empirically testing them. Further, it

contributes to the existing literature by providing evidence that the relationship

between spinoff alliance network growth and its early performance is U-shaped for

upstream alliances. This thesis, thus, provides important new avenues for future

research in network-based research in general, and parent–spinoff research in

particular.

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry v

Table of Contents Keywords .................................................................................................................................. i

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

List of Figures ....................................................................................................................... viii

List of Tables .......................................................................................................................... ix

Statement of Original Authorship ........................................................................................... xi

Acknowledgements ................................................................................................................ xii

Chapter 1: Introduction ...................................................................................... 1

1.1 Setting the Scene: An Alliance-Intensive Industry .........................................................1

1.2 Research Background .....................................................................................................3

1.3 Research Aim .................................................................................................................6

1.4 Research Design .............................................................................................................7 1.4.1 Study I- Predictors of Spinoff Alliance Network Growth: The Role of

Centrality versus Size of Parent Firm’s Network .................................................9 1.4.2 Study II- Parental Network Imprinting in Spinoffs: Understanding the

Underlying Mechanism ......................................................................................11 1.4.3 Study III- Coming Out of the Parent’s Shadow: The Role of Spinoff’s

Early Alliance Network Growth .........................................................................13

1.5 Thesis Outline ...............................................................................................................15

Chapter 2: Literature Review ........................................................................... 17

2.1 Spinoff firms: The Who, Why and How.......................................................................17

2.2 Network Structures .......................................................................................................20

2.3 Spinoff Network Growth Antecedents .........................................................................23

2.4 Strategic Alliances and Spinoffs ...................................................................................25 2.4.1 Network Status ...................................................................................................26 2.4.2 Absorptive Capacity ...........................................................................................27 2.4.3 Knowledge Relatedness with Parent Firm..........................................................29

2.5 Spinoff Performance .....................................................................................................31

2.6 Conclusion ....................................................................................................................34

Chapter 3: Research Methodology ................................................................... 37

3.1 Methodological Fit........................................................................................................37

3.2 Research Design ...........................................................................................................39 3.2.1 Secondary Data ...................................................................................................39 3.2.2 Longitudinal Research Design ...........................................................................41

3.3 Sample and Data ...........................................................................................................42 3.3.1 Research Setting .................................................................................................42 3.3.2 Data Sources .......................................................................................................44 3.3.3 Sample and Data Collection ...............................................................................46 3.3.4 Selection Bias .....................................................................................................50 3.3.5 Measures .............................................................................................................51

3.4 The Significance of Replication ...................................................................................51

3.5 The Analysis of Underlying Mechanisms and Contingencies Using PROCESS .........53

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry vi

Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network ................................................. 57

4.1 Introduction .................................................................................................................. 57

4.2 Summary and Discussion of Milanov and Fernhaber (2009) ...................................... 60

4.3 The Present Study ........................................................................................................ 62

4.4 Theory and Hypotheses ................................................................................................ 65 4.4.1 Imprinting Effect of Network Size .................................................................... 67 4.4.2 Imprinting Effect of Network Centrality ........................................................... 69

4.5 Data and Methods ........................................................................................................ 71 4.5.1 Industry Setting .................................................................................................. 71 4.5.2 Sample ............................................................................................................... 73 4.5.3 Measures ............................................................................................................ 74 4.5.4 Model Specification ........................................................................................... 79

4.6 Analysis and Results .................................................................................................... 80 4.6.1 Supplementary Analysis .................................................................................... 85 4.6.2 Robustness Checks ............................................................................................ 86

4.7 Discussion .................................................................................................................... 87

4.8 Appendix A: Full Description of the Replication of Milanov and Fernhaber (2009) in the Non-spinoff Context ......................................................................................................... 92

4.9 Appendix B: Robustness Results with THREE-year Moving Window for Calculating Dependent Variable ................................................................................................................ 94

Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms .......................................................................................... 97

5.1 Introduction .................................................................................................................. 97

5.2 Theoretical Background and Hypotheses ................................................................... 100 5.2.1 Parental Network Imprinting ........................................................................... 100 5.2.2 A Multiple Mediation Model of Spinoff Network Growth: Indirect Effect

of Parent Network Centrality through Spinoff Absorptive Capacity and Spinoff Network Status .................................................................................... 102

5.2.3 A Moderated Multiple Mediated Model of Spinoff Network Growth: Conditional Indirect Effect of Parent Network Centrality through Spinoff Absorptive Capacity and Spinoff Network Status with Knowledge Relatedness between Parent and Spinoff as Moderator ................................... 106

5.3 Methods ...................................................................................................................... 108 5.3.1 Data and Sample .............................................................................................. 108 5.3.2 Measures .......................................................................................................... 110 5.3.3 Model Specification ......................................................................................... 116

5.4 Results and Findings .................................................................................................. 117

5.5 Robustness Checks ..................................................................................................... 123

5.6 Discussion .................................................................................................................. 123

5.7 Limitations and Implications for Future Research ..................................................... 126

5.8 Appendix C: Analysis Results for Regression Analysis ............................................ 128

5.9 Appendix D: Robustness Check Results using Spinoff Network Size as the Dependent Variable ................................................................................................................................ 132

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry vii

Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth ..................................................................................... 135

6.1 Introduction ................................................................................................................135

6.2 Theoretical Background and Hypotheses ...................................................................138 6.2.1 Spinoff Alliance Network Growth and its Performance ..................................141 6.2.2 Parent Network Characteristics and Spinoff Performance ...............................143

6.3 Research Methods .......................................................................................................145 6.3.1 Data and Sample ...............................................................................................145 6.3.2 Measures ...........................................................................................................146

6.4 Analysis and Results ...................................................................................................151 6.4.1 Supplementary Analysis ...................................................................................153 6.4.2 Robustness Checks ...........................................................................................153

6.5 Discussion ...................................................................................................................159

6.6 Limitations and Implications for Future Research .....................................................161

6.7 Appendix E: Robustness Results with 2-year and 3-year Time Lags between Dependent Variable and other Variables ..............................................................................163

Chapter 7: Discussion and Conclusions ......................................................... 165

7.1 Overview of the Main Findings ..................................................................................165

7.2 Theoretical Contributions ...........................................................................................167 7.2.1 Contributions to Network-based Research in Entrepreneurship ......................167 7.2.2 Contributions to Spinoff Research Literature ...................................................168 7.2.3 Contributions to Imprinting Literature .............................................................170

7.3 Practical Implications for Management ......................................................................171 7.3.1 Implications for Spinoff Managers ...................................................................171 7.3.2 Implications for Strategic Alliance Managers ..................................................172

7.4 Limitations and Future Research Directions ..............................................................173

7.5 Conclusion ..................................................................................................................175

Bibliography ........................................................................................................... 177

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry viii

List of Figures

Figure 1-1 Research framework ................................................................................... 8

Figure 1-2 Thesis structure ......................................................................................... 15

Figure 3-1 Number of existing listed firms in each year in The Register dataset ...... 47

Figure 3-2 Number of new firms established from 2002 to 2011 separated by type ............................................................................................................... 48

Figure 3-3 Number of directors listed in The Register .............................................. 49

Figure 3-4 A multiple mediator model (panel A) and three conditional process models (panels B, C, and D) (adapted from Hayes, Montoya, and Rockwood (2017)) ....................................................................................... 54

Figure 4-1 Difference between initial partner and parent firm’s network centrality coefficients, with 95% confidence intervals ................................ 83

Figure 5-1 Relationship between parent network centrality and spinoff network growth ........................................................................................................ 108

Figure 5-2 Betweenness and eigenvector centrality measures versus network size ............................................................................................................. 111

Figure 5-3 Moderating effect of market relatedness on the relationship between parent network centrality and spinoff network status ................................ 123

Figure 6-1 Conceptual model ................................................................................... 145

Figure 6-2 Estimated effect of founding alliances on spinoff performance ............. 157

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry ix

List of Tables

Table 1-1 Distribution of new firms based on intra-industry founders in the Australian mining industry (period: 2002-2011; source: data extracted from The Register of Australian mining database) ........................................ 3

Table 3-1 Distribution of projects based on partnerships in the Australian mining industry (period: 2002-2011) ........................................................... 44

Table 3-2 Key measures used in studies I, II and III ................................................. 51

Table 3-3 Dimensions of replication (adapted from Bettis, Helfat, et al. (2016)) ..... 52

Table 4-1 Means, standard deviations and correlation for spinoff firms ................... 81

Table 4-2 Random-effects Poisson regression results (dependent variable: spinoff network growth) .............................................................................. 82

Table 4-3 Summary of effect sizes (incident rate ratios) for independent variables in Models 2 to 7 ............................................................................ 83

Table 4-4 Random-effects Poisson regression results (dependent variable: non-spinoff network growth) .............................................................................. 93

Table 4-5 Summary of effect sizes (incident rate ratios) for independent variables in Models 2 and 3 ......................................................................... 93

Table 4-6 Robustness results for random-effects Poisson regression with three-year moving window (dependent variable: spinoff network growth) .......... 94

Table 4-7 Summary of effect sizes for robustness check (incident rate ratios) for independent variables in Models 2 to 7 .................................................. 95

Table 5-1 Means, standard deviations and correlation for spinoff firms ................. 119

Table 5-2 Multiple mediation results (boot=5000) ................................................. 120

Table 5-3 Moderated multiple mediation results (boot=5000) ................................ 121

Table 5-4 Parent network centrality as a predictor of spinoff network status ......... 128

Table 5-5 Parent network centrality as a predictor of spinoff absorptive capacity (ability to value knowledge) ........................................................ 129

Table 5-6 Parent network centrality as a predictor of spinoff absorptive capacity (ability to apply knowledge) ........................................................ 130

Table 5-7 Analysis results for spinoff network growth predictors .......................... 131

Table 5-8 Multiple mediation results (boot=5000) .................................................. 132

Table 5-9 Moderated multiple mediation results (boot=5000) ................................ 133

Table 6-1 Means, standard deviations and correlation............................................. 155

Table 6-2 Random-effects regression results (Dependent Variable: Spinoff Revenue (t+1)) ........................................................................................... 156

Table 6-3 Mediation analysis results for testing direct and indirect effects of parent network centrality on spinoff performance ..................................... 158

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry x

Table 6-4 Random-effects regression results (Dependent variable: spinoff revenue (t+2)) ............................................................................................ 163

Table 6-5 Random-effects regression results (Dependent variable: spinoff revenue (t+3)) ............................................................................................ 164

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry xi

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: _________________________ 15/10/2019

QUT Verified Signature

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry xii

Acknowledgements

I knew doing a PhD was not supposed to be easy. But little did I know about

the extent of efforts and attempts it demanded for a long period of time. It is only now

that after looking back over the last four years, I realise how challenging it has been.

However, I am proud that I did it and I consider it as one of my most important

accomplishments in life. This would not have been possible without the support and

guidance of a number of individuals to whom I would like to express my sincere

appreciation and gratitude.

First and foremost, I would like to thank my supervisors Martin Obschonka,

Henri Burgers and Per Davidsson. Martin, thank you for accepting to supervise my

PhD in my mid-candidature. Thanks for always being available for helpful advice and

promoting new thoughts on my research. Thanks for pushing me to do research that is

more challenging. Henri, thank you for always being my toughest reviewer and

challenging my thought process. I appreciate your continuous support both as principal

and external supervisor. I still cannot thank you enough for giving me a scholarship in

my first year. I am forever grateful for the time, energy and resources you have spent

on training me in the last four years. Per, I have learned so much from you starting

with the contemporary issues in entrepreneurship research unit and continuing with

being my associate supervisor throughout my candidature. Your passion for doing

cutting-edge research is truly inspiring. Thank you for the time and effort you were

willing to put into reading my papers and thesis drafts, and for the productive high-

quality feedback you frequently provided me with. It has been a great privilege to work

with you all. Thank you for your guidance, support, and encouragement throughout

my candidature. I have really enjoyed working with you and I hope that will continue.

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry xiii

In addition, thank you for providing me with the opportunity to present at many

domestic and international conferences. I am grateful for your introducing me to

Australian Centre for Entrepreneurship Research (ACE) and involving me in many

impactful activities such as annual paper development bootcamp at Tangalooma, and

research seminars by ACE’s visiting scholars.

I warmly thank members of ACE and scholars who provided feedback on

earlier versions of studies in this thesis. Special thanks to Karen Taylor for organising

all the events in our centre and being such a good friend. Many thanks to Professor

Paul Steffens, Dr Char-lee Moyle, Dr Frederik von Briel, Dr Ozgur Dedehayir, Dr

Jaehu Shim, Dr Colin Jones, Professor Peter O’Connor, and Associate Professor Rene

Bakker for their advice and support. I would like to thank Associate Professor Erik

Lundmark and Dr Anna Jenkins for organising ACERE doctoral consortiums in the

best way. Special thanks to Professor Dean Shepherd for his constructive keynote

speeches and workshops in ACERE and ACE bootcamps over the years. I would also

like to thank Dean for his encouraging and helpful comments on my papers. Many

thanks to Professor Mike Wright for giving feedback on my thesis. My sincere thanks

to Professor Hana Milanov for reviewing my papers while she was a visiting scholar

at ACE. Many thanks to Dr Collette Kirwan for organising a warm and friendly

doctoral consortium in Babson conference in Waterford, Ireland. I would like to thank

everyone at QUT research support office, especially Jeremy Campbell, Dennis

O’Connell, and Dr Jonathan Bader. I would also like to acknowledge the support

provided by the Queensland University of Technology for awarding me a scholarship.

Generous funding for my extended scholarship from the Australian Research Council

linkage grant in mining is greatly acknowledged.

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry xiv

I would also like to express my thanks to my fellow PhD students – many of

them doctors now – who provided a great escape from stressful and tough times. It was

great having stimulating dialogues about various research topics in the university or in

conferences and seminars. I am grateful for receiving support through the ups and

downs of the journey. I look forward to keeping in touch with you all and continuing

to be research colleagues in the future.

Finally, I want to express my deepest gratitude to my family. I could not have

completed this thesis without your unconditional love and support. Dad, thank you for

teaching me to aim high and be persistent in achieving my goals. Mom, thanks for

being a role model as a mother of three who studied to get a higher degree while having

a full-time job as a top manager. I cannot thank both of you enough for raising me to

be who I am today. Farid and Faraz, thank you for always cheering me up and giving

me positive energy. Above all, thank you, my beloved husband, Hojat. I feel so lucky

and blessed to have you in my life. Thanks for constantly encouraging me to look

forward and never give up.

Forough

June 2019

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry xv

List of Publications and Conference Presentations Based on This Doctoral Research

Book chapter:

Zarea Fazlelahi, F., & Burgers, H. (2018). Natural imprinting and vertical integration in the extractive industries. In G. George & S. J. D. Schillebeeckx (Eds.), Managing Natural Resources: Organizational Strategy, Behaviour and Dynamics (pp. 138-162): Edward Elgar Publishing. (Published on 26/1/2018) https://www.elgaronline.com/view/9781786435712.00016.xml

Peer-reviewed Conferences:

Zarea Fazlelahi, F., Obschonka, M., Burgers, H., Davidsson, P. (2020). Alliance network growth and young spinoffs' performance in an alliance-intensive industry. ACERE Conference, 2020, Adelaide, Australia. (Accepted for presentation)

Zarea Fazlelahi, F., Obschonka, M., Davidsson, P., Burgers, H. (2019). Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms. Academy of Management Conference, 2019, Boston, USA. (Presented in ENT division)

Zarea Fazlelahi, F., Obschonka, M. (2019). Parental Network Imprinting Influence on New Venture Alliance Network Growth: A Study of Mining Spinoffs. ACERE Conference, 2019, Sydney, Australia.

Zarea Fazlelahi, F., Burgers, H., Davidsson, P. (2018). Meet the Parents: An Empirical Study of Spin-off Network Development. Academy of Management Conference, 2018, Chicago, USA. (Presented in ENT division)

Zarea Fazlelahi, F., Burgers, H., Davidsson, P. (2018). The impact of early imprinting on the spin-off network development. ACERE Conference, 2018, Brisbane, Australia.

Zarea Fazlelahi, F., Burgers, H., Davidsson, P. (2017). Vertical Integration: An Imprinting Perspective. ACERE Conference, 2017, Melbourne, Australia.

Spinoff’s Early Alliance Portfolio Development: A Longitudinal Study in an Alliance-Intensive Industry xvi

Research Presentations:

Zarea Fazlelahi F., Poster presentation of PhD research in Doctoral Consortium, Babson College Entrepreneurship Research Conference, Waterford, Ireland, 2018.

Zarea Fazlelahi F., Artistic presentation of PhD research in Gallery of Management Research Conference, Queensland University of Technology, Brisbane, Australia, 2019.

Chapter 1: Introduction 1

Chapter 1: Introduction

1.1 SETTING THE SCENE: AN ALLIANCE-INTENSIVE INDUSTRY

Mining industry figures prominently as a source of Australia’s wealth by

employing over a quarter-million people and being a major contributor to Australia’s

GDP1. Historically, the mining sector has been known for the capital-intensive nature

of its projects, where companies have to invest significant amounts of resources

compared to other settings (Beamish, 1987; Hartman & Mutmansky, 2002). Mining

projects are generally undertaken in several stages over the course of many years,

including prospecting, development and exploitation (Bakker & Shepherd, 2017).

Only a small proportion of mining projects progress from site investigation to new

mine sites (Bakker & Shepherd, 2017). The Minerals Council of Australia estimates

only one in a thousand exploration projects successfully leads to a new mine2. Despite

being highly risky, when reached to exploitation stage mining projects can potentially

yield large returns3.

Due to high risks associated with mining projects, strategic alliances are very

commonplace in this sector to share risks and resources (Bakker, 2016). Strategic

alliances4 are defined as ‘any independently initiated link that involves exchange,

sharing, or co-development’ (Gulati, 1995a, p.86). Mining firms contribute to alliances

by pooling their resources such as geological and technical expertise, know-how and

contacts, and sometimes a geological or proprietary database to draw upon (Khaled,

1 Source: Australian Bureau of Statistics (ABS) www.abs.gov.au 2 The rate of discovery is higher. However, not every discovered ore deposit can be feasibly developed into an operating mine site. (Source: www.minerals.org.au) 3 Source: IBISWorld industry reports www.ibisworld.com.au 4 Also known as interfirm alliances (Das & Teng, 1996) or interorganisational alliances (Stuart, 2000).


2013). The mining industry in Australia and globally is often characterised by the

existence of a mix of large companies and junior miners (usually new firms) (Knoben

& Bakker, 2019). Junior miners are often involved in exploration projects5. Due to

high costs (Harrigan, 1986) and high failure rates of exploration projects (Bakker &

Shepherd, 2017), new firms need to partner with larger companies.

It is not only important for junior miners to enter alliances with large companies

but also for large companies to get involved in such alliances. This is because

established mines are subject to depletion, or access to deeper ore bodies might become

more difficult and require more sophisticated techniques and equipment over the

years6. Additionally, the rate of significant new mineral discoveries is not always

stable. Therefore, the mining industry is an alliance-intensive context.

Another prominent feature of firms in the Australian mining industry is that new

firms are often started by intra-industry founders or spinoffs. Spinoffs are new firms

founded by ex-employees of incumbent firms in the same industry as their former

employer, without support or sponsorship from the parent firm (Klepper, 2001, 2009).

Table 1-1 shows the proportion of new firms started by a team, where at least one of

the founders was working within the mining industry immediately one year before the

establishment of the new firm. Over 70% of all new firms have an intra-industry

founder on board (Table 1-1), which shows the high frequency of spinoffs as a way of

starting new firms in the mining industry of Australia.

5 Source: IBISWorld industry reports 6 Source: IBISWorld industry reports


Table 1-1 Distribution of new firms based on intra-industry founders in the Australian mining industry (period: 2002-2011; source: data extracted from The Register of Australian mining database)

Number Proportion

New firms started by at least one intra-industry founder 408 72.21%

New firms started without any intra-industry founders 157 27.79%

Total number of new firms 565

Thus, a focus on alliance building in an alliance-intensive industry and in a

spinoff context is an important and worthwhile topic. We need to know more about

the drivers and potential outcomes.

1.2 RESEARCH BACKGROUND

Antecedents and outcomes of alliance network growth have increasingly

received attention from research scholars in entrepreneurship (Hoang & Antoncic,

2003; Hoang & Yi, 2015). This is particularly important for newly founded firms since

they have limited access to resources (Hite & Hesterly, 2001). One class of new firms

that has gained special attention in recent years is spinoffs (also called spawns,

progeny, spinouts (also sometimes with a hyphen)). Spinoffs play a critical role in

economic and employment growth, and diffusion of knowledge and innovation in the

markets (Dahl & Sorenson, 2013; Garvin, 1983). Spinoffs differ from other new firms

because of their prior links to a parent firm, which is considered to be a source of initial

advantage for them over other types of new firms (Adams, Fontana, & Malerba, 2019;

Agarwal, Echambadi, Franco, & Sarkar, 2004; Bruneel, Van de Velde, & Clarysse,

2013; Eriksson & Kuhn, 2006). However, parent firm’s influence on the network

growth of spinoffs has mostly been explored regarding social networks of spinoff

entrepreneurs, and not on the firm-level networks (i.e., strategic alliances) (cf. Aldrich

& Reese, 1993; Baum & Silverman, 2004; Stam & Elfring, 2008).


The importance of alliance networks for the founding and growth of spinoffs

is acknowledged in a growing body of studies (Hoang & Antoncic, 2003; Slotte Kock

& Coviello, 2010). Establishing alliance networks has been shown to have a positive

influence on spinoffs’ performance (Walter, Auer, & Ritter, 2006), survival rates

(Perez & Sánchez, 2003), product innovation (Löfsten & Lindelöf, 2005), and

innovative output (George, Zahra, & Wood, 2002). However, determinants of alliance

network growth in spinoffs have been investigated less (Hoang & Antoncic, 2003).

Moreover, despite the emphasis on the parent’s role in development and growth

trajectory of spinoffs by previous research, the influence of the parent firm’s network

characteristics has not been widely and empirically scrutinised.

The majority of the theorising aiming to explain alliance network formation

and growth has been done in the strategic management research field, such as resource

dependence theory (Pfeffer & Salancik, 1978), social embeddedness (Granovetter,

1985), structural homophily (Gulati & Gargiulo, 1999), and resource-based view

(Eisenhardt & Schoonhoven, 1996), tested on samples of established firms. Loaning

these theoretical explanations and applying them in the context of spinoff firms will

not be straightforward and needs further investigation. One main reason is that these

theories assume firms have a portfolio of strategic alliances at the start of their study

time, based on which researchers theorise how they can develop this portfolio. This

could be problematic when theorising for spinoff firms at founding because they, like

all new firms, do not start with an existing network of alliances. Therefore, there seems

to be a need for further theorising and development of new network theories that can

be applied to such groups of firms.

One important theoretical explanation for networks formations in newly

founded firms is drawn upon imprinting theory. Marquis and Tilcsik (2013) define


imprinting as ‘… a process whereby, during a brief period of susceptibility, a focal

entity develops characteristics that reflect prominent features of the environment, and

these characteristics continue to persist despite significant environmental changes in

subsequent periods.’ (p.199). Previous studies have used this theory in the network-

based research in spinoff firms’ context, sometimes referred to as network imprinting

theory (Marquis & Tilcsik, 2013). Based on this theory, founding period characteristics

such as social technology available (Marquis, 2003), social contexts of markets

(Sedaitis, 1998), innovation aspirations (Elfring & Hulsink, 2007), and top

management team (Eisenhardt & Schoonhoven, 1996) set an important influence on

spinoff’s profile of future alliances. However, despite the emphasis on the importance

of the parent firm as an imprinting source on spinoff’s growth trajectory (Klepper &

Sleeper, 2005), parent firm’s network imprinting influence on the subsequent alliance

network growth of spinoffs is largely untested. Overall, this under-researched area in

the spinoff firms’ context provides a clear scope for additional research to better

understand the determinants of alliance network growth in spinoff firms.

Although research in the spinoff context has long noted the persistent impacts

of parent firms on the organisational behaviour and outcomes of spinoffs (Klepper &

Sleeper, 2005), underlying mechanisms of parental imprinting influence have mostly

been theorised rather than rigorously and empirically being tested. This gap can also

be observed in general imprinting research. As noted by Simsek, Fox, and Heavey

(2015), imprinting researchers have often tested the genesis of imprinting as a black

box. Therefore, there is a need for more empirical research studies to open the black

box and inform imprinting scholars of how to hypothesise and test the underlying

mechanisms of parental imprinting effect.


In addition to antecedents and dynamics of spinoff alliance network growth,

there is a need to understand what this means for spinoff performance. However,

spinoff performance cannot be properly assessed without considering how these firms

develop, grow and perform over time (Mathisen & Rasmussen, 2019). On the one

hand, there are studies that suggest the importance of the parent firm’s financial

resources and knowledge bases for spinoff performance (Fackler, Schnabel, &

Schmucker, 2016). On the other hand, there are studies that suggest spinoff firms need

to establish strategic alliance right from the start to respond to their need for diverse

resources based on their needs in each stage of their early development (Hite &

Hesterly, 2001). The pool of studies that have explored the strategic choices of the

spinoff on its performance is limited. Therefore, examining the outcomes of spinoff

alliance network growth can be an important step towards extending this line of

research.

1.3 RESEARCH AIM

The aim of this thesis is to develop a more in-depth overall understanding of

the antecedents, underlying mechanisms, and outcomes of spinoff alliance network

growth. Specifically, this thesis seeks to provide a better understanding of the influence

of parent firm’s network features on the network growth trajectory of spinoffs on a

longitudinal sample of young spinoffs in the mining industry of Australia. The

implications of building a larger alliance network are also examined and discussed.

To this end, the overall research aim that will guide this thesis is as follows:

To enhance our understanding of the influence of long arm of the parent firm on

antecedents, underlying mechanisms, and outcomes of spinoff alliance network

growth.


1.4 RESEARCH DESIGN

This thesis employs a quantitative method design using the firm-level unit of

analysis in order to gain clearer insights into the alliance network growth of spinoffs

in the early stage. Specifically, research design includes three different longitudinal

research projects, in which quantitative approaches are applied on the firm level to

examine the underlying network growth dynamics and early performance of spinoffs.

Each study addresses a specific research question that contributes to the overall

research aim (see Figure 1-1). The core construct that appears in all three studies is the

spinoff alliance network growth. Study I investigates the predictors of spinoff network

growth at the time of the founding. Informed by the findings of Study I, Study II studies

the underlying mechanism of parental network imprinting effect on the spinoff

network growth through a (conditional) multiple mediation model. In Study III, the

performance outcomes of spinoff network growth are investigated in the early years

of establishment of spinoffs.

This thesis benefits from using secondary data by a synthesis of multiple

datasets. Having access to a comprehensive dataset with data on multiple levels (i.e.,

all firms, all companies and all directors in the Australian mining industry) for a period

of 10 years, provided a unique opportunity to study the main phenomenon of the thesis.

Further, gathering and combining data from several other datasets yielded a strong

platform for analysis7. The remainder of this section provides further elaboration of

the three empirical studies.

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1.4.1 Study I- Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network

Prior research has long noted the importance of alliance networks for spinoffs

by a growing body of literature (Hagedoorn, Lokshin, & Zobel, 2018; Mohr, Garnsey,

& Theyel, 2013). However, understanding the emergence and growth of strategic

alliances in spinoffs is still an overlooked area, especially in the entrepreneurial

context (Ahuja, Soda, & Zaheer, 2012; Hoang & Antoncic, 2003). The majority of the

theoretical explanations for alliance formation in spinoffs have been borrowed from

the network-based research of strategic management that often use samples of

established firms that have already experienced cooperation with external firms.

Spinoffs, like all other new ventures, have to forge ties and gain trust to obtain access

to necessary resources. This is commonly known as the liability of newness

(Stinchcombe, 1965), which could be problematic when applying network theories to

the early-stage spinoff context. For instance, Gulati and Gargiulo (1999) model the

formation of networks as a dynamic process that is driven by exogenous resource

dependencies and endogenous network embeddedness mechanisms. In their view, new

alliances that are formed become increasingly embedded in the networks that shaped

them in the first place, orienting the choice of new partners in future. This could be

problematic when theorising for the spinoff’s context for two reasons. First, spinoffs,

as new firms, do not have an existing alliance network upon which to start growing

their network. Based on resource dependence theory, this theory can explain why

spinoffs are drawn to form partnerships with other firms to obtain access to their

resources (Pfeffer & Nowak, 1976). However, it cannot explain why other firms are

attracted to newly founded spinoffs. Second, it does not consider the prior links of

spinoffs to their parent firm, which could be effectively influential in their subsequent

alliance network growth trajectory. Hence, the question that is still on the table is:


RQ1: What predicts a spinoff’s alliance network formation and expansion in its

early years of initiation?

In order to fill this gap in the literature, I draw from Milanov and Fernhaber’s

longitudinal study [Milanov, H., & Fernhaber, S. A. 2009. The impact of early

imprinting on the evolution of new venture networks. Journal of Business Venturing,

24(1): 46-61.], which found a positive link between initial partner’s network

characteristics and new venture’s subsequent network growth. Drawing on

organisational learning in imprinting literature and utilising longitudinal data of 237

spinoff firms, I expand Milanov and Fernhaber’s model to the parent spinoff context.

I test the positive imprinting effect of initial partners as well as the parent firm’s

network size versus centrality on the spinoff firm’s alliance network growth.

Secondary data is used that is a panel data which is collected annually during the period

2002-2011 from a comprehensive dataset that contains information about all firms

(including both private and public firms), and all alliances in the mining industry of

Australia. My findings suggest parent firm’s greater network centrality is a positive

predictor of spinoff’s subsequent network growth. This study provides insight into the

field of alliance network growth in spinoffs in several important ways. First, it provides

new insights into the effect of the parent firm’s network characteristics on the spinoff’s

networks at the founding. Previous studies of parental influence have mostly focused

on the individual networks level, not on the firm level. Second, I assess two different

sources of network imprinting on the spinoff’s network growth: initial partner and

parent’s network characteristics. Based on my findings, I rule out the importance of

the initial partner’s network imprinting influence in the spinoff context. Third, I

address the call in the entrepreneurship network literature that seeks to elaborate on

‘who’ drives the changes in the process of network development (Slotte Kock &


Coviello, 2010). I suggest that entrepreneurs who are coming from incumbent firms

have an influential role in managing the changes in networks of new ventures affected

by their parent firm. Finally, this study addresses calls for creating more cumulative

research in management by undertaking a replication analysis (Bettis, Helfat, &

Shaver, 2016; Ethiraj, Gambardella, & Helfat, 2016).

1.4.2 Study II- Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanism

Informed by findings of Study I, Study II delves deeper into the parental

network imprinting effect. An increasing number of studies have provided evidence

that the growth of alliance networks are beneficial for new firms (Hoang & Antoncic,

2003; Hoang & Yi, 2015). However, there is little known about the underlying

mechanisms of network growth (Slotte Kock & Coviello, 2010). Findings of Study I

suggest that parent firm’s higher network centrality in the industry networks has a

positive imprinting effect on the subsequent network growth of spinoffs. I, and other

network imprinting scholars, have only theorised the explanation of how this effect

unfolds based on organisational learning perspective. However, there is a gap in our

understanding of how parent’s network features translate into offspring network

growth through imprinting. Thus, an important research question is:

RQ2: What is the underlying mechanism of the effect of parent firm’s network

imprinting on the spinoff’s subsequent network growth?

In the search for plausible explanations of the network imprinting process, I

identified two leading approaches in the prior empirical studies. The first lens is

through knowledge transfer and organisational learning that focuses on the richness of

learning opportunities as an imprinting founding condition. McEvily, Jaffee, and

Tortoriello (2012) show that legal firms started by lawyers that were trained by late-


career lawyers in previous companies will have greater growth rates in terms of adding

associates. While they use an organisational learning perspective to explain and test

the network dynamics, their focus is on social networks of lawyers, not on the firm

level. In this study, building on the organisational learning theory of Cohen and

Levinthal (1990), I suggest that the parental network imprinting theory can be

explained through the increased absorptive capacity of spinoffs on the firm level. A

firm’s absorptive capacity is defined as its ability to value, assimilate and apply

knowledge, which is a critical requirement for learning from experiences (Cohen &

Levinthal, 1990). The second lens suggested for studying network imprinting

dynamics is through social categorisation theory (Ashby & Maddox, 2005). A firm’s

network status refers to how centrally the position of a firm is relative to others in the

industry network (Benjamin & Podolny, 1999). Network status of a newcomer to a

network has been shown to be imprinted by its first venture capital’s partner reputation

through social categorisation mechanism (Milanov & Shepherd, 2013).

Knowledge overlap between the knowledge bases of sender and receiver has

been discussed to influence the absorptive capacity of the receiver. There, I examine

the knowledge overlap between parent and spinoff as a moderator between parent

network centrality and spinoff network growth on the mediated path through spinoff

absorptive capacity, and additionally the path through spinoff network status. My aim

is to present a finer-grained perspective of the network imprinting dynamics by

considering the boundary conditions. Using the same sample as the first study, I only

add data on the mediators and moderators.

My main contribution is to the network imprinting theory in entrepreneurship.

While there has been an ongoing conversation about the role of network status in the

tie formation processes (Ahuja, Polidoro, & Mitchell, 2009; Milanov & Shepherd,


2013), I suggest incorporating learning arguments through absorptive capacity also

explains additional variance in the spinoff’s network growth beyond the outcome

dependencies arising from the improved network status arguments. My second

contribution is providing evidence that the benefits parents with higher network

centrality have for spinoff network growth may not be fully realised unless there is a

knowledge overlap between parent and spinoff. This could also be a step towards

extending imprinting theory arguments in regard to the existence of boundary

conditions that can facilitate the process of imprinting. I, for the first time, suggest

consideration of imprinting moderators during the genesis phase of Simsek et al.’s

(2015) imprinting model. Finally, I not only respond to calls for more longitudinal

studies in the network research in entrepreneurship (Hoang & Antoncic, 2003) but also

use a state-of-the-art conditional multiple mediated model design to test the underlying

mechanisms of network formation dynamics.

1.4.3 Study III- Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth

Study III investigates the performance outcomes of spinoff alliance network

growth. Newly founded spinoffs vary considerably in their access to resources and

stable relationships, which could lead to a difference in their early performance

(Bruneel et al., 2013). Spinoff literature suggests that spinoffs can tap into their

parent’s resources (Parhankangas & Arenius, 2003). However, it is not clear whether

this is as important for the type of spinoff firms where parents have no role in their

initiation and there is no obligation of an ongoing linkage post-spinoff. From

entrepreneurship and strategic management perspectives, studies have long noted the

importance of alliance networks for young firms to obtain access to necessary

resources (Baum, Calabrese, & Silverman, 2000). The question in the spinoff context

is:


RQ3: Is spinoff’s early performance fostered by its previous access to its parent

firms’ network resources or driven by its ability to establish an alliance network

right from the start? Are these two effects independent or does parental influence

indirectly influence spinoff performance through spinoff alliance network

growth?

In order to find answers to these questions, Study III links theories on knowledge

transfer and learning perspectives of the firm, and spinoffs to investigate the early

drivers of spinoff performance. Since most partnerships that new mining firms forge

are in the upstream, I suggest that early alliance network growth of spinoffs will

potentially have a negative effect on spinoff’s early performance at first, but over time

this relationship will become positive. This also addresses a significant gap in the

previous empirical literature on this relationship. Most prior studies’ findings are

inconsistent in this regard. While some of the inconsistency might have resulted from

design and measurement variations, my study suggests that the relationship between

upstream alliances network growth and spinoff performance is nonlinear and U-

shaped. Specifically, from a network theory perspective, it is predicted that this

relationship is positive (Baum et al., 2000). However, considering knowledge transfer

arguments would predict results without necessarily assuming linearity.

This study adds to work by developing a theoretical connection between

upstream alliance network growth and spinoff early performance. It contributes to

knowledge transfer literature (Kogut & Zander, 1992). By dissecting the drivers of

spinoff early performance, it offers a more detailed evaluation of parent knowledge

transfer to spinoffs.


1.5 THESIS OUTLINE

Chapters of this thesis are mapped in Figure 1-2. Chapter 2 starts with a

theoretical overview of spinoffs and their network growth antecedents, dynamics and

consequence in the entrepreneurship and strategic management literature. Chapter 3

describes the methodologies used in this research and chapters 4 to 6 each focus on

one of the studies mentioned above. Chapter 7 highlights the main conclusions of this

research and provides a reflection of the findings and approaches taken.

Figure 1-2 Thesis structure

Chapter 1

Introduction

Chapter 2

Literature review

Chapter 3

Methodology

Chapter 4

Study I: Predictors of Spinoff Alliance

Network Growth: The Role of Centrality

versus Size of Parent Firm’s Network

Chapter 5

Study II: Parental Network Imprinting in

Spinoffs: Understanding the

Underlying Mechanism

Chapter 6

Study III: Coming Out of the Parent’s

Shadow: The role of Spinoff’s Early

Alliance Network Growth

Chapter 7

Discussion and Conclusions

Chapter 2: Literature Review 17

Chapter 2: Literature Review

This chapter commences with an introduction of spinoff firms (Section 2.1),

followed by an overview of the network-based research in entrepreneurship (Section

2.2), and spinoff network growth (Section 2.3). In Section 2.4, research on network

growth theorising about the dynamics and underlying mechanisms are discussed.

Finally, Section 2.5 reviews previous studies on spinoff performance.

2.1 SPINOFF FIRMS: THE WHO, WHY AND HOW

There has been special attention to spinoff firms in the entrepreneurship

literature. The spinoff phenomenon has been studied from a diverse range of interests

in the literature, such as strategy (Ito & Rose, 1994), technology transfer (O'Shea,

Allen, Chevalier, & Roche, 2005), regional developments (Asheim, Boschma, &

Cooke, 2011), finance (Semadeni & Cannella, 2011) and economies (Wenting, 2008).

As such, spinoffs are important to economic growth, technology diffusion and

development, and commercialisation. However, spinoffs are not a uniform group of

firms (Fryges & Wright, 2014). The main classifications for spinoff phenomenon in

the previous research have been based on the sources and origins of spinoffs. Two

main groups discussed in prior research are: university spinoffs, and corporate

spinoffs (Fryges & Wright, 2014; Parhankangas & Arenius, 2003). University spinoffs

(also known as academic or alumni spinoffs) are new firms that originate from the

university context, whereas corporate spinoffs originate from incumbent firms. What

the two groups have in common is the formation of a new firm ‘based on business

ideas developed within the parent firm being taken into a self-standing firm’

(Parhankangas & Arenius, 2003, p.464). Since my research focus is related to


corporate spinoffs, I limit my literature review to this type of spinoff. I encourage

interested readers to visit Fryges and Wright (2014) for a review of university spinoffs.

Previous spinoff literature considers two subcategories of corporate spinoffs;

namely, divestment spinoffs (i.e., spinoff initiated by the parent firm itself by divesting

a subsidiary or a department), and employee spinoffs (i.e., employees of parent firms

who make the decision to leave their parent firm and start a business of their own)

(Parhankangas & Arenius, 2003). In the former, there is a transfer of the majority of

voting power to a new legal entity, while in the latter, there is no formal transfer of

ownership rights.

Regardless of being either type, the formation of corporate spinoffs involves

the movement of entrepreneurs from the parent firm to a newly founded venture. This

movement is usually accompanied by the movement of other employees who also

worked in the parent firm together with entrepreneurs. In quantitative research,

employee spinoffs are typically operationalised by consideration of a percentage of the

employees in the new firm to be from the same parent firm. For instance, Eriksson and

Kuhn (2006) consider a 50% cut-off rate; or Muendler, Rauch, and Tocoian (2012)

define a 25% share of the workforce. In this research, my focus is on the employee

spinoffs, considering a 25% cut-off rate following Muendler et al. (2012).

Spinoffs have played a key role in formation and growth of well-known

industries, including semiconductors (Cheyre, Klepper, & Veloso, 2014), disk drives

(Chesbrough, 1999), combinatorial chemistry field (Hagedoorn, Lokshin, & Malo,

2018). In 2010, 85% of all startups in Germany were started by employee spinoffs

(Gude et al., 2010). Also, 49% of mining new ventures were initiated by employee

spinoff activities over the mining boom decade to 2012 in Australia (Figure 3-2).

Klepper (2009) suggests that the best-performing spinoffs are initiated by employees


in the same industry as their parents, which are called intra-industry or horizontal

spinoffs (Muendler et al., 2012).

Spinoffs are hardly all alike and they differ according to their parent firm

(Klepper, 2009). A major part of the difference among spinoffs is from the transfer of

knowledge from their parent firm. In the spinoff literature, genealogical terms such as

heredity, parental heritage, and transmission of genes have been metaphorically used

to describe the process of transmission of knowledge (Ellis, Aharonson, Drori, &

Shapira, 2017; Klepper & Sleeper, 2005). Klepper and Sleeper (2005) suggest that

spinoffs inherit technical and market-related knowledge from their parent firm that

shapes them at birth. Sapienza, Parhankangas, and Autio (2004) examine the transfer

of three types of knowledge from the parent firm that can give spinoffs a competitive

advantage at founding: production, technology, and marketing knowledge. Ellis et al.

(2017) show the transfer of knowledge happens through explicit as well as tacit

knowledge.

Spinoff literature traces the formation of corporate spinoffs as triggered based

on different occasions. Bruneel et al. (2013) propose a categorisation based on the

event that triggers the formation of a spinoff (i.e., opportunity or adverse development)

and the actor (i.e., parent firm or the employee). Entrepreneurship research suggests

that new businesses could potentially originate from external enablers, new venture

ideas and opportunity confidence (Davidsson, 2015). The literature of corporate

entrepreneurship emphasises that new businesses such as spinoffs are developed based

on ideas and discoveries in the incumbent organisation (Burgers, Jansen, Van den

Bosch, & Volberda, 2009; Phan, Wright, Ucbasaran, & Tan, 2009). The main reasons

for a parent firm to start a corporate spinoff that is suggested by prior spinoff literature

include misfit between a new technological discovery and current core activities in the


parent firm (Chesbrough & Rosenbloom, 2002), or agency problems and information

asymmetry between managers and investors (Krishnaswami & Subramaniam, 1999).

These spinoffs are accounted as important sources of future growth of the parent firm.

In contrast to spinoffs started by parent firms, spinoffs that are started by employees

are based on the accumulated knowledge by their founders when they were still in the

parent firm (Agarwal et al., 2004). The main motives that prior spinoff literature has

identified for starting spinoff by employees comprise: frustration with the parent firm

for supporting new ideas for launching new activities (Hellmann, 2007), or intentions

to pursue opportunities independently (Klepper, 2001), or adverse advancements in

the parent firm such as being acquired, or being bankrupted (Eriksson & Kuhn, 2006).

Post-spinoff links with the parent firm and their consequences for spinoffs has

also been the centre of attention in the previous research. Spinoffs may benefit from

relationships with their parent firm in several ways. Resource sharing and access to

complementary assets have been identified by previous research as a source of

competitive advantage that can influence spinoff’s performance and growth (Clarysse,

Wright, & Van de Velde, 2011; Parhankangas & Arenius, 2003; Sapienza et al., 2004).

Collaboration with parent and working as a supplier have been linked to faster learning

processes in spinoffs (Uzunca, 2018). Spinoffs can also benefit from social and

alliance networks of their parent firm (Elfring & Hulsink, 2007; Stam & Elfring, 2008;

Zarea Fazlelahi, Burgers, & Davidsson, 2018). Governance tie to the parent firm can

have a positive influence on the performance trajectory of spinoffs (Semadeni &

Cannella, 2011).

2.2 NETWORK STRUCTURES

The importance of networks for the founding and growth of newly founded

firms is acknowledged in a growing body of research (Birley, 1986; Hoang &


Antoncic, 2003; Slotte Kock & Coviello, 2010). The establishment of links with

various agents is a critical success factor for the survival and growth of a spinoff firm

in its early years of initiation. It is highly unlikely for new firms to stay isolated and

achieve higher growth rates. Previous research suggests that links must be established

very early on, that is as soon as the spinoff is created (Perez & Sánchez, 2003). Despite

the importance of early network growth in entrepreneurial firms, there are few

empirical studies which have studied its antecedents in new firms (Hoang & Antoncic,

2003). Special contexts like spinoffs have received a small share of these studies,

although they are a prevalent way of starting new firms. Here, I will start with a

summary of the network constructs.

The network structure is defined as a ‘pattern of direct and indirect ties between

actors.’ (Hoang & Yi, 2015, p.11). From a network theory perspective, the positioning

of actors within a network can influence its flow of resources. Since newly founded

firms have limited access to resources in their initial years, this network position can

arguably affect their outcomes (Hoang & Rothaermel, 2005). Several measures have

been used in the literature to characterise the network positions of individuals or firms

in the network.

Network size has been widely used in the network-based studies, that is defined

as the number of direct ties between a focal entity and other entities (Hoang &

Antoncic, 2003). At the individual level, network size is usually the size of social

networks of entrepreneurs (Elfring & Hulsink, 2007). At the firm level, network size

has been equalled with a firm’s alliance networks (Milanov & Fernhaber, 2009).

Measuring network characteristics merely based on size can only reveal a part

of the network. It would be an atomistic view that only focuses on the direct ties

between contacts to the entrepreneur or the new firm. For obtaining a perspective on


the overall network of entities and their relationships, one must consider indirect ties

that are not in immediate relationship with the focal entity. An important measure of

network position that gives credit to indirect ties is centrality. Several measures of

centrality have been applied by prior network research that are different in their

concepts, where two of the most important ones include: betweenness centrality

(Freeman, 1978), and eigenvector centrality (Bonacich, 1987). Betweenness centrality

includes ‘the ability to access (or control) resources through indirect as well as direct

links.’(Hoang & Yi, 2015, p.12). This measure characterises the ‘reach’ of entities to

their network through intermediaries. Eigenvector centrality captures the position or

role of the entity in the networks (Podolny, 1993). According to this measure, the most

central entities are those having ties with many other entities, which in turn are linked

to several others (Podolny, 2010). Centrality measures have been studied in the prior

research to a lesser extent compared to network size (Hoang & Antoncic, 2003; Hoang

& Yi, 2015). This is due to the difficulty of accurately and efficiently gathering

networking information about all actors in the network. I will focus on firm-level

measures since it is the primary interest in this thesis.

In recent years, there has been a growing interest in networks in

entrepreneurship research. In a review of research on networks, Hoang and Antoncic

(2003) categorise prior studies as either focusing on: (1) what is the impact of networks

on the entrepreneurial process; or (2) what is the impact of the entrepreneurial process

on network development. In other words, in Category 1, they study networks as

independent variables, and in Category 2, they focus on them as dependent variables.

I also continue my review of the literature in networks research in a spinoff context by

first exploring the antecedents of spinoff network growth, and then by investigating

the outcomes of spinoff network growth in the previous studies.


2.3 SPINOFF NETWORK GROWTH ANTECEDENTS

In the entrepreneurship literature, new ties formed by new firms are of utmost

interest since they are assumed to provide access to critical resources (Hoang & Yi,

2015). Additionally, understanding the network change and development has been

emphasised in this literature (Hoang & Antoncic, 2003; Slotte Kock & Coviello,

2010). In the strategic management literature, some of the theoretical explanations for

tie formation between firms have been transaction costs, improving competitive

advantage, and quest for organisational learning (Kogut, 1988). These motivations

could broadly apply to the new firms, as well. However, there are differences between

the contexts of established firms in strategic management and newly founded firms in

the entrepreneurship literature, such as liabilities of newness and smallness in new

ventures (Aldrich & Auster, 1986; Stinchcombe, 1965). While these motivations can

explain why new firms are drawn to forming ties with other players in the market, they

are not efficient in fully explaining the motivations of other firms to form ties with

them (Ahuja et al., 2009; Milanov & Fernhaber, 2009). For instance, based on resource

dependence theory, organisations enter partnerships when they see a critical strategic

interdependence with other organisations (Pfeffer & Salancik, 1978). From this view,

firms get involved in a resource exchange relationship, where one organisation has

resources and capabilities beneficial but not possessed by the other one. This

perspective has been used in the spinoff literature to explain the motivation of post-

spinoff relationships between a parent and their spinoff firms (Parhankangas &

Arenius, 2003). However, from a broader perspective, this theory cannot explain why

and how spinoffs enter inter-firm relationships with other firms in the industry

network. Interdependence relationships cannot explain how other firms know about

the opportunity of working with spinoff firms, and how they can trust these new firms


as a partner without the fear of opportunistic behaviour. An alternative or

complementary perspective to interdependence theory has been offered by

sociologists, who suggest actors address concerns of opportunism by embedding

transactions in a social context (Granovetter, 1985).

Studies suggest that embeddedness of firms plays an important role in their

alliance behaviour (Gulati, 1998). Studies show that firms that had more prior alliances

occupied more central positions in the network of firms, and were more likely to enter

alliances (Eisenhardt & Schoonhoven, 1996; Gulati, 1998; Kogut, Shan, & Walker,

1992; Powell, Koput, & Smith-Doerr, 1996). Firms that had partnerships in the past

are more likely to repeat their partnerships (Gulati, 1995b). Gulati and Gargiulo (1999)

show how newly built alliances become embedded in the network of firms, which lead

to shape future partnerships of firms. They suggest that formation of alliance network

is a longitudinal process, in which ‘the network structure that results from the

accumulation of those ties increasingly becomes a repository of information on

potential partners, helping organizations decide with whom to form new alliances.’

(Gulati & Gargiulo, 1999, p.1475). In the spinoff literature, Elfring and Hulsink (2007)

theorise, but do not test empirically, that spinoff entrepreneurs shape their initial

networks with a different pattern compared to other start-ups, relying on their socially

embedded positions in their parent firms’ networks and aspiration for innovation.

While studies in this literature have considered embeddedness of spinoff entrepreneurs

in social networks of their parents, pre and post-spinoff, there has been lesser attention

paid to the firm-level networks. This is important since spinoffs, like all new firms, do

not start with a portfolio of strategic alliances. They need to build these partnerships

on the firm level as they grow. The question that is still on the table is what determines

these initial developments of alliance networks and their subsequent growth trajectory.


There is a crucial need for rigorous quantitative studies that can add to our knowledge

in this direction.

2.4 STRATEGIC ALLIANCES AND SPINOFFS

The importance of the founding period in determining the subsequent

development and growth of newly founded spinoffs has been corroborated by multiple

prior studies. The theoretical lens that has often been drawn upon in this field is the

imprinting theory. Based on imprinting theory, founding period characteristics such as

social technology available (Marquis, 2003), exchange markets’ social contexts

(Sedaitis, 1998), innovation aspirations (Elfring & Hulsink, 2007), and top

management team (Eisenhardt & Schoonhoven, 1996) set important influence on the

spinoff’s profile of future alliances. In an explorative case study, Elfring and Hulsink

(2007) focus on the development of networks in 32 IT start-ups in the Netherlands.

They show how founding conditions and post-founding entrepreneurial processes

influence tie-formation processes in spinoffs. Drawing upon the resource-based view,

Eisenhardt and Schoonhoven (1996) show the strong social positions of a top

management team provides entrepreneurial firms with social opportunities that

facilitate their alliance formation. Overall, despite the traces of the parent’s role in all

the spinoff literature, parent’s network imprinting influence on the network growth

trajectory of spinoffs has not been directly and empirically tested in the prior literature.

Another important observation, as Slotte Kock and Coviello (2010) mention

in their comprehensive network research review in entrepreneurship, is that above all

is a need for greater understanding of these network development processes. Their

review suggests that empirical efforts to study how a network develops are relatively

rare in entrepreneurship. The current level of research in networks research in

entrepreneurship ‘…does not capture the actions and explanations underlying tie


dissolution and network change,…’ (Slotte Kock & Coviello, 2010, p.43). This gap

coincides within the imprinting literature, where in their comprehensive review of

imprinting studies Simsek et al. (2015) point to insufficient attention to underlying

mechanisms that form imprints. Thus, in addition to understanding whether a parent’s

network characteristics at founding have an imprinting effect on the network growth

of spinoffs, we need to investigate what underlying mechanisms can explain this

phenomenon. In my search for plausible explanations of the network imprinting

process, I identified three constructs as mediators and moderators, which I will review

in the following section: network status, absorptive capacity and knowledge

relatedness between parent and spinoff firms.

2.4.1 Network Status

Network status refers to a relative position of an entity in a given network based

on its direct and indirect ties when compared with other entities’ positions based on

their own direct and indirect ties (Burt, 1982). Status is potentially a valuable resource

in the networks for new firms since it can be used as a reference by other players in

the market about the value of forming ties with the focal firm (Podolny, 2001). A

similar concept in the network research is reputation that is also used to make

judgements about potential partnerships with the focal entity. Reputation is a

‘perpetual representation of a company’s past actions and future prospects that

describes the firm’s overall appeal to all its key constituents when compared to other

leading rivals’ (Fombrun, 1995, p.72). Both status and reputation can facilitate access

to resources (Benjamin & Podolny, 1999). However, reputation is an economic

concept that is based on firm’s past performance, while status is a sociological concept

based on affiliations with other firms (Jensen, 2003; Milanov & Shepherd, 2013).

While spinoff firms, like any other newly founded firms, do not have a performance


track record at the founding, their network status would be an important basis for

making initial judgements about them and classifying them in different social

categories for partnership considerations (Milanov & Shepherd, 2013).

Previous studies suggest that firms often show homophilous relationships in

terms of network status (Gulati & Gargiulo, 1999). Based on the definition of status,

when firms enter into ties with other firms, they also enter into new status positions

considering the surrounding firms (Gulati, 1995b; Podolny, 1993). This also implies

to the dynamic nature of network formation, where new ties influence the formation

of subsequent ties with other firms based on varying social identities firms achieve

(Gulati & Gargiulo, 1999). The principal of homophily is not always held in networks.

High-status firms might tend to enter into alliances with poorly embedded firms

depending on obtaining better terms of trade and alliance governance (Ahuja et al.,

2009).

Despite the growing attention paid to the influence of network homophily or

heterophily of status on tie formation process, there has been less focus on how an

organisation establishes its initial network position (Hallen, 2008; Milanov &

Fernhaber, 2009). Hallen (2008) is one of the few studies that tests 92 internet security

ventures forming ties with venture capitalists in a longitudinal study. He finds that new

ventures obtain their initial network positions through their founders’ ties and human

capital. What is not investigated in this literature is the influence of parent’s network

position on spinoff’s initial status formation through founders who move from the

parent to start their own firm.

2.4.2 Absorptive Capacity

Absorptive capacity refers to a firm’s ability to recognise the value of new,

external knowledge, assimilate, and apply it from external sources (Cohen &


Levinthal, 1990). Cohen and Levinthal (1990) suggest that absorptive capacity is a

function of a firm’s level of prior knowledge. In their paper, Cohen and Levinthal

(1990) argue that the development of absorptive capacity is a path-dependent

phenomenon. From a cognitive aspect, they argue that an individual’s ability to

assimilate information is a function of their knowledge. While learning is cumulative,

it is greatest when it is closer to what is already learned. From an organisational aspect,

Cohen and Levinthal (1990) argue that an organisation’s absorptive capacity depends

on the absorptive capacities of its individual members. Therefore, ‘the development of

an organization’s absorptive capacity will build on prior investment in the

development of its constituent, individual absorptive capacities, and, like individual’s

absorptive capacity, organizational absorptive capacity will tend to develop

cumulatively.’ (Cohen & Levinthal, 1990, p.131).

Zahra and George (2002) offer a reconceptualisation of absorptive capacity

construct, where they suggest absorptive capacity is a dynamic capability that affects

a firm’s competitive advantage. They suggest absorptive capacity consists of potential

and realised absorptive capacities. Potential capacity consists of knowledge

acquisition and assimilation capabilities and realised capacity includes knowledge

transformation and exploitation. Previous studies have often operationalised a firm’s

realised capacity, while potential capacity has been less scrutinised empirically (Zahra

& George, 2002).

A wide range of research applies the discussed two lenses for explaining

various phenomena. In a study of alliance portfolio implications for high technology

firms’ performance, George, Zahra, Wheatley, and Khan (2001) show that absorptive

capacity mediates this relationship. This study considers two dimensions of absorptive

capacity based on Cohen and Levinthal’s (1990) definition: the ability to value


knowledge, and ability to apply knowledge. These two dimensions are similar to the

classification of potential versus realised absorptive capacity in Zahra and George

(2002). However, many alliance network studies use absorptive capacity concept to

explain the underlying mechanisms of their target phenomena, rather than

operationalising it and investigating its role empirically, such as entry of firms into

alliances (Gulati, 1999), alliance management capability (Rothaermel & Deeds, 2006),

learning mechanisms in alliance portfolios (Heimeriks & Duysters, 2007), and firm’s

exploratory innovation (Phelps, 2010). There is still a need for research that explicitly

shows the influence of absorptive capacity in investigating the alliance network

studies, specifically in the entrepreneurship field. This is because the ability to value

and apply knowledge from external resources is very critical in the initial years of new

firms. In particular, studying absorptive capacity in specific contexts such as spinoffs

provides a broader view of how absorptive capacity is developed throughout the firm’s

growth trajectory. Since spinoffs consist of a group of individuals who leave an

incumbent firm, studying the absorptive capacity of the firm they subsequently

establish together helps us understand how new firms build on their prior knowledge

and assimilate external knowledge into their current routines and structures.

2.4.3 Knowledge Relatedness with Parent Firm

Learning theories suggest that relatedness of prior knowledge is critical for a

firm’s absorptive capacity in terms of ability to value and apply external knowledge

(Cohen & Levinthal, 1990; Grant, 1996). Prior research also suggests that a firm’s

learning ability is improved in the vicinity of their existing knowledge bases

(Levinthal, 1997). Shared language, codes and symbols can facilitate the transfer of

knowledge from one organisation to another due to improved communication and less

resistance (Grant, 1996).


In the spinoff literature, knowledge overlap with the parent firm has been

scrutinised by several studies to be related to spinoff’s subsequent performance in

terms of sales growth. Sapienza et al. (2004) suggest that learning from the parent firm

is maximised for intermediate levels of knowledge relatedness. That is, too much or

too low levels of knowledge relatedness between parent and spinoff would limit

spinoff’s learning process. They investigated the effect of three types of knowledge

relatedness between parent and spinoff: market, production and technological. They

found that production and technological knowledge relatedness were related to spinoff

growth in a curvilinear manner.

Clarysse et al. (2011) demonstrate that technological knowledge relatedness

with the parent organisation will be negatively associated with both university and

corporate spinoffs’ growth in terms of sales and number of employees. They argue that

being too similar to the parent department spinoffs come from actually hinders their

growth in terms of identifying opportunities outside of their adopted knowledge and

technology systems. Additionally, spinoffs need to be able to differentiate themselves

from their parent firm in order to succeed (Klepper & Sleeper, 2005). However,

Clarysse et al. (2011) find no curvilinear relationship between relatedness and growth.

However, the influence of knowledge overlap with a parent has been less

scrutinised on other spinoff’s growth aspects, such as alliance networks (Sapienza et

al., 2004). If parent and spinoff have less overlap in their activities, markets and

technologies this might not relate them much to their parent in the eyes of potential

players in the market. Too much similarity can also make them look too much like

their parent and could limit them to diversify their partners. It is also possible that this

could work as a boundary condition or mediator. Thus, there might be alternative


explanations and arguments around the knowledge relatedness construct in the spinoff

literature that need to be addressed.

2.5 SPINOFF PERFORMANCE

In the spinoff literature, studies have investigated spinoff performance from

two perspectives: either (a) they have looked at spinoff activity’s outcomes, explored

spinoffs’ growth, and survival rates in comparison with other startups; or (b) they have

explored the factors that lead to higher performance rates among spinoffs.

As an example of the first category, Fackler et al. (2016) analyse a sample of

German startups and find that spinoffs are generally less likely to exit than other

startups. They also show that survival rates of spinoffs coming from parents that

continue to operate after they are founded are higher than spinoffs where their parent

stops operations. In a similar study, Dahl and Reichstein (2007) using a comprehensive

dataset of Danish startups find the same results.

My study is placed in the second category of spinoff performance studies.

Spinoffs are a specific but major group of entrepreneurial firms, which are started due

to different motivations of their founders and in various founding conditions. Prior

studies have looked at these diverse initial conditions and distinguished among

different types of spinoffs to study differences in their performance and growth rates.

For instance, Bruneel et al. (2013) differentiate between three types of corporate

spinoffs based on their founding conditions and relationships with their parents pre-

spinoff: incumbent-backed, opportunity and necessity spinoffs. They define

incumbent-backed spinoffs as a group that are started due to the discretion of an

incumbent firm to pursue an opportunity outside of their main activities. They define

opportunity spinoffs as startups established by employees of an incumbent firm to

pursue a potential commercial project. Moreover, necessity spinoffs are triggered due


to the involuntary exit of the parent firm. They use data on 46 spinoffs in Flanders to

test their hypotheses. Their findings show that opportunity spinoffs outperform the

other two groups in terms of employee and revenue growth. As can be seen throughout

the spinoff literature, parental influence pre and post-spinoff event is a main point of

interest since it is considered as the unique advantage of spinoffs over other types of

entrepreneurial firms (Klepper, 2009). Walter, Heinrichs, and Walter (2014) in an

empirical study of technology spinoffs show that parent’s hostility towards the spinoff

firm after the establishment has a negative effect on spinoff’s performance in terms of

time to break even8. They also find that developing a network help spinoffs to alleviate

this negative effect on their break even point. Semadeni and Cannella (2011) show that

spinoffs can benefit from links to their parents post-spinoff to some extent. However,

too many links seem to hinder their performance in terms of shareholder returns.

An overview of this literature reveals that previous studies have measured

spinoff performance in diverse ways, which makes it difficult to make an overall

generalisation of parent and spinoff relationship implications for spinoffs. Thus, there

is a need for studies that consider the multi-facets of spinoff performance.

Additionally, little empirical research has explored the impact of network properties

and development on spinoff performance. This might be partly because of the fact that

in this literature studying the influence of network growth, specifically strategic

alliances, has been overshadowed by the focus on the parent–spinoff relationship. Prior

literature on spinoffs often emphasises the various types of resources that spinoffs

have access to from their parent in their founding. However, spinoffs like all other new

ventures need to obtain various resources depending on their stage of development

8 Time to break even is defined as the number of full months from a firm’s incorporation time before its costs equal its earnings.


(Hite & Hesterly, 2001). Their parent firm cannot single-handedly provide all their

necessary needs. Spinoffs need to develop their own network of collaborative

relationships to overcome their initial liabilities. Thus, there is a need for studies that

both look at the effect of different types of resource availabilities at founding on the

one hand, and the effect of them on different performance indexes of spinoffs on the

other hand.

Network constructs have been used to explain important entrepreneurial

processes and outcomes, such as developing a business model, founding of a new

venture, gaining access to resources and customers, acquiring financial capital, and

collaborating to foster innovation. Thus, this line of research has important

implications for entrepreneurs and practitioners.

A diverse body of work links networks and performance. Prior literature has

shown strategies for creating strategic alliances to be important for start-up

performance (Hite & Hesterly, 2001; Stuart & Sorenson, 2007). Walter et al. (2014)

show that network development negatively moderates the positive relationship

between parent hostility and spinoff’s time to break even. Walter et al. (2006) using

empirical information about university spinoffs find that network capability moderates

the relationship between entrepreneurial orientation and organisational performance.

However, they do not find any direct effect of network capability on spinoff

performance.

Many studies investigate the parent–spinoff relationship implications for

spinoff performance and growth, however, the specific influence of parent and spinoff

collaboration in a strategic alliance has not been widely and rigorously explored. As

encouraged by Slotte Kock and Coviello (2010) entrepreneurship research‘…would

be aided by integrating the SN -Social Networks- approach by first assessing how


interactions lead to network structure and then linking structural changes in (for

example) network density or actor centrality to organizational performance.’ (p.48).

2.6 CONCLUSION

In this chapter, I reviewed the entrepreneurship literature on spinoff firms,

networks research, antecedents and outcomes of spinoff network growth. The literature

reveals that many studies have focused on the network growth of spinoffs regarding

its antecedents and outcomes. However, many of these studies have been qualitative

based on small samples, or on the social networks of spinoff entrepreneurs rather than

on the firm-level alliance networks. Despite the emphasis on the parental influence on

the subsequent development and growth of spinoffs, parent’s network imprinting

effect on the network growth trajectory of spinoffs has not been directly and

empirically tested. The literature also lacks an understanding of the underlying

mechanisms of network growth. This gap also coincides with imprinting literature

where there has been insufficient attention paid to the dynamics that form the imprints.

Research shows that network status is important for tie formation, but the influence of

the parent firm on the establishment of spinoffs is not discussed. Additionally,

literature discusses the importance of parental inheritance of knowledge, but it has not

been investigated how spinoffs build on the knowledge they are bringing from the

parent firm and how they assimilate and apply it to their new spinoff firm’s

management systems. There are still inconsistencies regarding knowledge overlap

with the parent on the growth trajectory of spinoffs. Spinoff’s performance from the

perspective of their strategic choices independent of the parent has so far been

overlooked in the prior literature. As such, Study I suggests initial partner’s and parent

firm’s network characteristics as predictors of spinoff network growth as founding.

Study II examines the underlying mechanisms of spinoff network growth through


spinoff network status and absorptive capacity. I also explore the role of knowledge

relatedness with the parent as a moderator. Finally, Study III examines the outcomes

of spinoff network growth on its performance. Together, these studies provide a richer

understanding of the antecedents, underlying mechanism and outcomes of spinoff

network growth by addressing some of the missing links in the previous research.

Chapter 3: Research Methodology 37

Chapter 3: Research Methodology

I commence this chapter with a discussion of methodological fit by applying

the framework proposed by Edmondson and McManus (2007) along with further

comments by Davidsson (2004) (Section 3.1). A discussion of research design then

follows, where I elaborate on the choice of secondary data and longitudinal research

design (Section 3.2). Then, I explain sampling and my quantitative approaches

(Section 3.3). The last two sections of the chapter provide further elaboration on

replications studies (Section 3.4) and use of PROCESS (Section 3.5).

3.1 METHODOLOGICAL FIT

Edmondson and McManus (2007) define methodological fit as ‘internal

consistency among elements of a research project’ (p.1155). These elements consist of

a research question, prior work, research design and contribution to literature

(Edmondson & McManus, 2007). They associate qualitative data with exploratory

research of phenomena. In their framework, quantitative data are typically more

associated with explanatory or theory-testing research such as testing theory-driven

hypotheses about the sign and magnitude of direct and moderated/mediated effects of

an independent variable on a dependent variable (Davidsson, 2004). Edmondson and

McManus (2007) suggest there is an advancement in the state of knowledge over time.

In a typology, they classify three categories for the state of prior literature: nascent,

intermediate and mature. This progression is paralleled by conducting qualitative to

mixed methods to quantitative approaches.

Poor fit between research design and prior stage of literature in a field of study

can diminish the effectiveness of research studies (Edmondson & McManus, 2007).

There are examples of using qualitative studies in mature fields (Arino, LeBaron, &


Milliken, 2016). However, Edmondson and McManus (2007) suggest that often

singular reliance on qualitative approaches would not move the field forward.

As illustrated in Chapter 2, research on strategic alliance networks is well

established, specifically in the strategic management domain. Most of our knowledge

in this field comes from quantitative studies that have been performed on large

longitudinal samples over two decades ago (cf. Eisenhardt & Schoonhoven, 1996;

Gulati & Gargiulo, 1999; Milanov & Fernhaber, 2009; Rothaermel & Deeds, 2006).

Most studies are done by hypothesis testing and they involve existing constructs and

measures, as suggested by Gulati (1998) in his comprehensive literature review. For

instance, alliance type has been used by Rothaermel and Deeds (2006) to investigate

new product development in high-technology firms in a large sample of 325 firms.

George et al. (2001) also test the effect of alliance types on 149 biotechnology firms’

absorptive capacity and performance. Another indication of maturity is that developed

constructs such as embeddedness (Gulati & Gargiulo, 1999), top management team

competencies (Eisenhardt & Schoonhoven, 1996), and status (Jensen, 2003) have been

extensively investigated in this literature from several theoretical views. However, in

the entrepreneurship domain in general and spinoffs’ literature in particular, there is a

need for theory-testing research in the study of strategic alliances. Specifically, since

the rules of the game are different (e.g., different assumptions about firms’ initial

resources and liabilities), challenging already established theories and testing new

theories will substantially add to our knowledge. In addition, considering the research

questions that aim to build on the existing literature and extending the prior

accumulated knowledge on the strategic alliances, use of quantitative method design

seems to be a good fit.


However, there are some critics of quantitative design who point the

consideration of heterogeneity. For instance, if the analysis is performed on a small

sample of a heterogeneous population, the results might be either weak or only true on

average but not for most individual cases (Davidsson, 2004). But using a narrowly

defined sample is expected to deal with heterogeneity to a great extent (Davidsson,

2004). I chose to study the whole population in the mining industry in Australia that is

theoretically relevant. Therefore, choosing one industry in one country (known for

being a homogeneous context in Australia) enables me to reduce the risks of

unobserved heterogeneity and causal heterogeneity.

3.2 RESEARCH DESIGN

3.2.1 Secondary Data

I conducted my three studies using secondary data (also known as archival

data) from several datasets. Secondary data provide a number of unique benefits for

conducting this research. First, I got historical and longitudinal data that were collected

at the time. Second, it provided me with access to the entire population of mining firms,

their directors and projects in the Australian mining industry. This led to having a

relatively large sample for analysis.

Additionally, choosing to work with secondary data seemed inevitable for the

purpose of this dissertation. This is because the focal phenomenon of interest is spinoff

alliance network growth. Due to the causal nature of the hypotheses proposed, I needed

to have access to longitudinal data that were collected over a long period of time.

Specifically, it was crucial to have access to information on firm-level alliances that

all firms formed in the entire industry over time. For instance, measuring global

network variables such as centrality and status requires access to the whole network

of alliances in the industry. Not only would it have cost a lot of money to keep track


of all firms in the industry on multiple levels of data (i.e., firms, directors and projects)

over years, it would have required a large group of research assistants and scholars to

execute this. Also, three to four years for a PhD timeline is an insufficient amount of

time.

Fortunately, I had access to a dataset that gave me this unique opportunity. This

dataset had been first (and only) used by Rene Bakker and Dean Shepherd by the time

I started my PhD. They had published a number of high-quality papers using this

dataset in the Strategic Management Journal and Academy of Management Journal

(cf. Bakker, 2016; Bakker & Shepherd, 2017). Except for one common variable (i.e.,

firm size in terms of assets and liabilities), I have developed and prepared my own

variables for analysis. Their research focus is on totally different aspects of strategic

alliances (such as decision-making speed, and alliance reconfiguration outcomes), and

not in the parent–spinoff or new firm contexts. For this thesis, I conducted all the

coding for firms, alliances, and directors myself.

Working with archival data has its own challenges. This is because ‘they are

put there for general purposes or for other purposes than yours. They do illuminate

some area but they do not necessarily cast light on the issues you are interested in.’

(Davidsson, 2004, p.141). The main dataset that I have used for my analysis is The

Register of Australian Mining9. I had some challenges working with this database.

First, this dataset is not survey-based. Although it is a very comprehensive dataset,

data are collected for purposes other than specifically for scholarly research.

Therefore, it took a bit of hard work and a substantial amount of the PhD timeline

(about six months) to make the dataset analysable and useful. Second, since it was

9 I will elaborate on the dataset comprehensively in the next sections.


gathered by a third party, I needed more than one additional dataset for collecting

further data for measuring various constructs. Therefore, I gathered more data for each

firm from several other datasets and combined them (for an additional three to four

months). It required a good understanding of computer science and different analytical

software to be able to store, retrieve and combine large amounts of data. On the bright

side, synthesis of multiple datasets was a chance to use triangulation techniques and

increase the reliability of my analysis. Finally, this dataset has been published in

handbooks annually since 1980. It is only available in digital format from 2002 to

2011. Each annual handbook is between 300 to over 1000 pages. Transformation of

handbooks from the hard to soft format for additional years required substantial

programming skills, time and budget. For this PhD, there were insufficient funds to

cover these costs. Nevertheless, the period from 2002 to 2011 is an important period

in the Australian mining industry (Downes, Hanslow, & Tulip, 2015; Tulip, 2014).

There was an increase in allying activities (Bakker, 2016; Bakker & Shepherd, 2017)

and the number of new firms. Figure 3-2 shows the number of new firms established

in the Australian mining industry in the observation period. Not only is it a large

sample, but also almost half of the newly founded firms are spinoffs. This gave me a

large sample of newly founded ventures and spinoffs that created enough variance to

test my hypotheses.

3.2.2 Longitudinal Research Design

A longitudinal study (or panel study) is a research design that involves repeated

observations of the same variables (e.g., people, firms) over short or long periods of

time. A longitudinal design occurs in all the studies of this thesis since longitudinal

designs provide rich insights into the causal relationships between network constructs

(Hoang & Antoncic, 2003). Making causal claims based on cross-sectional design are


not recommended. This is because a causal relationship exists if (1) the cause preceded

the effect, (2) the cause was related to the effect, and (3) there is no other plausible

explanation for the effect other than the cause (Shadish, Cook, & Campbell, 2002).

Longitudinal research designs allow a temporal separation between cause and effect

that is not possible in cross-sectional designs. Longitudinal research was considered

particularly relevant for this research for two main reasons. First, quantitative studies

typically focus on independent variables that statistically explain the dependent

variable (Van de Ven, 2007). In all three studies, statistical techniques are used, in

which variables are defined, sampled and measured over time enabling an ability to

show the variance of measures or change over time. In the first two studies, I am

interested in studying spinoff alliance network growth, and in the third study, I

investigate the predictors of spinoff early performance over time. Second, longitudinal

studies provide a stronger basis for causal claims since they can view the cause

(independent variable) before effect (dependent variable) (Aldrich & Martinez, 2003;

Davidsson & Wiklund, 2006).

The xt series of commands in Stata provide a rich variety of panel analytical

procedures. I used Stata v.15 to manage data and perform my analysis with

longitudinal data. I further used MATLAB software to deal with parts of the data

management that would have been more time-consuming with other available

software. All the analyses in the studies are on the firm level.

3.3 SAMPLE AND DATA

3.3.1 Research Setting

I chose Australian mining as the setting for my research. Australian mining

dates back to early 1850s when gold was discovered in the colonies of New South

Wales and Victoria. People from all over the world came to these colonies to try their


luck at making a fortune, mostly using primitive methods. Much has changed since

then. Nowadays mining is a globally developed industry using sophisticated and

advanced technology and equipment. According to Australian National Accounts

documented in the Australian Bureau of Statistics mining was the second major

contributor to Australia’s GDP in December 201810.

Mining projects are defined in two main categories: offshore/onshore oil and

gas exploration, and mineral mining11. Mining projects cost up to millions of dollars

(Hartman & Mutmansky, 2002). The average time for mining projects to develop from

prospecting stage to exploitation is about five years (Bakker & Shepherd, 2017, p.140),

after which it needs further developing with heavy investments to be profitable.

Therefore, strategic alliances are very commonplace in this industry. Table 3-1 shows

the number of projects managed by mining companies from 2002 to 2011. Overall, out

of 8396 projects, 3370 of projects were conducted by more than one firm in strategic

alliances. This consists of about 40% of all projects. Mining companies pool their

resources to explore mining sites when forming strategic alliances (Bakker, 2016). The

exploration companies contribute to the alliance their geological and technical

expertise, local operational experience, know-how and contacts, and often have a

geological or proprietary database to draw upon (Khaled, 2013).

10 http://www.abs.gov.au/ 11 The focus of this dissertation and data are about the mineral mining. I have used the term mining to refer to mineral mining in the entire thesis.


Table 3-1 Distribution of projects based on partnerships in the Australian mining industry (period: 2002-

2011)

Number Percent

Projects managed by only one firm 5026 59.86%

Projects managed by more than one firm 3370 40.14%

Total number of projects 8396

There were a significant number of new firms started in the observation period,

as shown in Figure 3-2. Figure 3-2 also separates the number of new firms started as

spinoffs and non-spinoffs. As can be seen in Figure 3-2, intra-industry employee

spinoffs are a prevalent way of starting firms in the mining industry in Australia.

Almost half of the new firm establishments were companies started by ex-employees

of incumbent mining firms. This gave me a large sample on which to test my

hypotheses.

3.3.2 Data Sources

As discussed earlier in this chapter, I synthesised multiple datasets to gather

data for my analysis; including The Register of Australian Mining (hereafter The

Register), Morningstar DatAnalysis Premium, Australian Security Exchange (ASX),

D&B Business Browser12, Orbis, Osiris, and Bloomberg.

The Register was my primary source for annual data on public and private

(mineral) mining companies, their directors and all the mining projects undertaken in

Australia. The Register has been published annually since 1980. The booklets are

publicly available. The Register provides an accurate and comprehensive snapshot of

the Australian mining industry for each year. Information is derived from the RIU13

12 Dun & Bradstreet Hoovers Business Browser 13 Resource Information Unit


database, which stores and collates resource information from stock exchange

announcements from ASX and London Stock Exchange (LSE), business media outlets

such as Bloomberg, MiningNews.net, Creamer Media and Mining; and a wide range

of other sources including Sedars, Morningstar, Read Corporate, Marketwire, and

MBendi; as well as from government and company websites and the email alerts and

annual and quarterly reports of companies (Bakker, 2016). The information for each

mine includes location, ownership information, commodity and further comments

about the progress of the project over the years. For each company, there is information

about their main activity, registered office location, senior management, profit/loss,

asset/liability, background and further comments about the history of the firm and its

activities over time. Finally, there is a separate section for directors that lists directors’

names, their backgrounds, and companies they are working for. In digital format, the

data is available from 2002 to 2011. Despite being a very comprehensive dataset, the

gathered data was not tailored for my specific research purpose. Therefore, I used some

other data sources as well.

Morningstar DatAanalysis Premium is a website managed by Morningstar

Incorporation. I used this dataset to extract more information about all directors listed

in the Register dataset, since Morningstar documents all the previous and current

affiliations of directors, their positions in the companies, and dates appointed and

resigned. I also drew information about companies’ financial data from annual reports.

I used the D&B Global Business Browser to crosscheck the incorporation date

of the newly founded firms identified from The Register dataset.

The Australian Securities Exchange (ASX) is Australia’s primary securities

exchange. I used ASX to track all the name changes across the years. This is because

some of the new firms which appeared in The Register were due to name changes, and


not a result of the establishment of a new entity. This data let me code different names

of the same company under one identity code.

I further used Osiris, Orbis and Bloomberg dataset for cross-checking data and

following information about ownership information for firms. Osiris is a widely used

database that is available online and provides data about publicly-listed companies

worldwide. Orbis is very similar to Osiris. The difference is that Orbis provides data

about private firms globally. Bloomberg is a popular website that delivers data

services, and news to financial companies and organisations for investment purposes.

3.3.3 Sample and Data Collection

The main sample was identified from The Register dataset. For doing so, I first

had to code all the firm names that were listed in the digital format of the database

available in spreadsheets from 2002 to 2011. I checked ASX and extracted all the name

changes of firms that were documented in my observation period in spreadsheets for

each year. Then, I merged the two datasets and assigned the same ID to different names

used by the same firm over the years. Figure 3-1 shows the number of firms that I

coded for each year. As can be observed from this figure, there has been a substantial

increase in the number of firms listed from 2002 to 2011. Overall, I coded 2295 firms

that existed in the dataset across the years.


Figure 3-1 Number of existing listed firms in each year in The Register dataset

For identifying new firms, I tracked company new name appearances in The

Register dataset from 2003 to 2011. I crosschecked the incorporation date for each

identified firm from D&B Global Business Browser to make sure they were founded

within the ten-year period. I did not have the 2001 list to see what firms were new in

2002 list. So, I checked the incorporation date of all firms listed in 2002 to identify the

new firms that were founded in this year. I initially identified 565 new entries in the

dataset over the ten-year period. However, due to missing data, I ended up with 527

new firms that were founded between 2002 to 2011 (Figure 3-2).

0

200

400

600

800

1000

1200

1400

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

709 700 696 723678

9421046 1051

1111

1213


Figure 3-2 Number of new firms established from 2002 to 2011 separated by type

For separating spinoff firms from non-spinoffs, I worked on the directors’ data.

As already explained, The Register has a list of all directors across years. For assigning

IDs to directors, I first had to check any differences in names entered for the same

person throughout the years. Sometimes a nickname had been used in one year and a

full name in another for the same person. For instance, ‘Anthony Sage’ in the year

2003 and ‘Tony Sage’ in 2006, both referred to the same person. In such cases, I

checked background as well as company affiliations in the previous year to make sure

this is the same person. Additionally, I had to check for middle names. In some years,

directors’ names were entered without their middle names, in other years with

abbreviations of middle names, and full names in the rest. For instance, ‘Christopher

Rawlings’, ‘Christopher L. Rawlings’, ‘Christopher Leo Rawlings’ were entered for

the same person in different years. I had to make sure the ID that I assigned for these

0

20

40

60

80

100

120

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

23 19 26 28 27

5135

2315 22

119

2028 36

59

49

17

1316

non-spinoff spinoff


different names was the same. This had to be done for each name listed in the dataset

across years. Figure 3-3 depicts the number of directors identified and coded for each

year. Overall, I coded 7668 directors in the dataset across the years.

Figure 3-3 Number of directors listed in The Register

The next step was to merge the data on new firms and directors’ lists to

determine the founding team for each new establishment. After that, I determined

previous employment of the founding team (if any) immediately one year before

initiation of their new firm. I checked for each firm separately to determine the

percentage of the directors that were coming from the same firm one year before. In

quantitative research, employee spinoffs are typically operationalised by consideration

of an arbitrary percentage of the employees in the new firm to be from the same parent

firm. For instance, Eriksson and Kuhn (2006) consider a 50% cut-off rate; or Muendler

et al. (2012) define a 25% share of the workforce. In this research, my focus is on the

employee spinoffs, considering a 25% cut-off rate following Muendler et al. (2012).

Muendler et al. (2012) have restricted their sample to new firms with at least five or

more employees. The 25% cut-off would ensure that at least two people were coming

0

500

1000

1500

2000

2500

3000

3500

4000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

1391 15141689 1809

1522

2401

2720 2807 2910

3907


from the same prior employer. The founding team size in our sample ranged from one

to 10 people where about 75% of firms had a founding team size of fewer than five

members. Choosing 25% cut-off allowed me to include smaller size new firms where

at least one founder was from a prior employer. Accordingly, the criteria paralleled

with Muendler et al. (2012)’s for larger founding teams of more than five founders.

Additionally, among the sampled new firms as spinoffs, the percentage of employees

coming from the same parent firm was about 45% which is higher than the 25% cut-

off rate and suggests the strength of the spinoff phenomenon studied in the sample.

After assigning a type for each new firm (e.g., spinoff or non-spinoff) based on the

cut-off rate, I assigned the previous employment in common as the parent firm for

spinoffs.

3.3.4 Selection Bias

Selection bias arises when the sample is not obtained through proper

randomisation and therefore it is not a good representative of the population intended

to be analysed (Heckman, 1979). Notably, by incorporating information on all new

firms since my sample is the entire population, my research design avoids the common

sample selection problem of overrepresenting currently successfully founded new

firms that can undermine inferences about factors producing organisational outcomes

and success (Berk, 1983; Davidsson & Honig, 2003).

I also considered survivor bias where the focus is on only the successful cases,

overlooking the failed ones. This could lead to biased results (Davidsson, 2005). This

did not seem to be an issue with my analysis. It is because out of 248 newly founded

spinoffs in the sample, only 14 cases, regardless of their incorporation time during the

ten-year period, were terminated before 2011. Therefore, survivor bias did not seem

to be a problem.


3.3.5 Measures

Table 3-2 provides an overview of the key measures, including the independent

variables, moderators, mediators and dependent variables used in each study. For most

variables such as spinoff network growth, I used established measures that had been

previously used in the literature. I also used measures that were more appropriate for

the specific mining context and explained the validity based on literature. Further

specifics around the measures are provided comprehensively in each study.

To compute the network measures, I utilised data on all strategic alliances

between firms to build adjacency matrices for each year. Using adjacency matrices is

a common way of representing the relationships in network studies (cf. Gulati &

Gargiulo, 1999; Milanov & Fernhaber, 2009; Milanov & Shepherd, 2013). Adjacency

matrices have dichotomous values for each element. It is 1 if there is a relationship

between the two firms, and 0 otherwise. I used Ucinet 6 for constructing all network-

related measures (Borgatti, Everett, & Freeman, 2002).

Table 3-2 Key measures used in studies I, II and III

Study Independent Moderator Mediator Dependent I - Parent network size

- Parent network centrality - Initial partner network size - Initial partner network centrality

Spinoff network growth

II Parent firm network centrality

Knowledge relatedness between parent and spinoff

- Absorptive capacity - Network Status

Spinoff network growth

III - Spinoff network growth - Parent network size - Parent network centrality

Spinoff performance

3.4 THE SIGNIFICANCE OF REPLICATION

In recent years, there has been a growing recognition of the importance of

replication studies of statistical results in various fields of study (Bettis, Helfat, et al.,


2016). A substantial percentage of articles in highly cited journals cannot be replicated.

This could be due to over-emphasis of journals on publishing results only with

significant coefficients. This could cause problems. First, statistical results only apply

to a particular sample, which can merely partially make conclusions about a

population. Results from other samples in different time periods might be different due

to sample variation. Second, the use of significant levels (e.g., 0.05 or 0.01) for finding

significant results has been extensively criticised due to the lack of scientific basis

(Cumming, 2013). Therefore, there is a need for replication studies in the management

discipline to close the theory–practice gap (Block & Kuckertz, 2018).

There are several ways of performing replication studies. Bettis, Helfat, et al.

(2016) propose a framework for classifying replication studies based on two

dimensions: similarity of the data and empirical setting to the original study, and

similarity of research design between replication and original studies (see Table 3-3).

Table 3-3 Dimensions of replication (adapted from Bettis, Helfat, et al. (2016))

Same Research Design Different Research Design

Same Data and Sample I-Checking for errors and/ or falsification of results

IV-Robustness to different measures, methods, and models

Same Population (Same Context) with Different

Sample

II-Reliability and representativeness of data

V-Robustness to different measures, methods, and models

Different Population (Different Context)

III-Generalise to new population (subjects, industry, time period, etc.)

VI-Generalise to new population and assess the robustness

According to Table 3-3, the narrowest form of replication is square I, where

the replication study uses exactly the same data and research design as the original

study. While this type of replication study can inform us if there was an error in the

results of the original study, it adds little to what is previously known. Often

researchers would like to know if the findings of a particular study are the same in a

different context or by using different research design. This can add to our knowledge


of how well the results of the original study are generalisable to other settings (Bettis,

Helfat, et al., 2016).

I have conducted a replication and extension of Milanov and Fernhaber (2009)

in my first study of the thesis. As will be comprehensively discussed in the first study,

Milanov and Fernhaber (2009) published in the Journal of Business Venturing, where

authors examine the connection between network characteristics of new venture’s

initial partner at the time of founding on new venture’s subsequent network growth.

Their study uses a sample of 209 biotechnology new ventures that were established

between 1991 and 2000 in the United States.

According to Table 3-3, my first study is positioned in square VI. I am using a

sample of 237 spinoffs and 244 non-spinoff firms to test the possible connections

between network characteristics of these new firms’ initial partner as well as parent

firms on their network growth trajectory. The sample of firms that I use is active in the

mining industry of Australia. Due to the difference in industries, I could not use all the

measures applied in the original study and had to develop my own measures,

accordingly. Thus, not only the data and sample are different but also the research

design is slightly different from the original study. This suggests the novelty and

originality of this research that can offer valuable contributions to literature.

3.5 THE ANALYSIS OF UNDERLYING MECHANISMS AND CONTINGENCIES USING PROCESS

In Study II, I test a (conditional) multiple mediation model to study the

underlying mechanisms of the relationship between parent network centrality and

spinoff network growth. Mediation analysis is a statistical procedure for testing

hypotheses about the mechanisms by which a causal effect operates (Preacher, 2015).

A mediation model contains at least one mediator variable that is causally between

independent and dependent variables, such that independent variable’s effect on the


dependent variable is transmitted through the joint causal effect of the independent

variable on a mediator, which in turn affects the dependent variable. Such models are

commonplace in empirical studies. Figure 3-4, panel A depicts a mediation model with

two mediators.

A growing body of empirical studies is using mediation models that allow for

the moderation of a mechanism, that is called moderated mediation models or what

Hayes (2013) calls a conditional process model. Figure 3-4, panels B, C, and D depict

a few conditional models.

Figure 3-4 A multiple mediator model (panel A) and three conditional process models (panels B, C,

and D) (adapted from Hayes, Montoya, and Rockwood (2017))

The models depicted in Figure 3-4, for most researchers, bring to mind

structural equation modelling (SEM) as an analytical strategy, since it looks like a path

diagram with unidirectional arrows. Yet, most methodologists have offered to test the

contingencies of the mechanisms using ordinary regression-based path analysis (cf.

Fairchild & MacKinnon, 2009; MacKinnon, 2012; Preacher, 2015). As an analytical


tool based on regression analysis, the PROCESS macro of SPSS introduced by Hayes

(2013) has become popular, especially in management studies. However, the question

that comes in mind is the difference between what PROCESS does and what an SEM

program does.

PROCESS uses regression to estimate the parameters of each of the equations,

a common practice in observed path analysis (Hayes et al., 2017). For example, in

Figure 3-4, panel A, the model requires three equations (i.e., one for each mediator M1

and M2, and one for Y). PROCESS estimates each equation separately. But this is not

what PROCESS is needed for.

In mediation and conditional process analysis, many important statistics

useful for testing hypotheses, such as conditional indirect effects and the

index of moderated mediation, require the combination of parameter

estimates across two or more equations in the model. Furthermore,

inference about these statistics is based on bootstrapping methods, given

that many of these statistics have irregular sampling distributions…

(Hayes et al., 2017, p.77)

All of this is done by PROCESS by inserting one line of SPSS or SAS code

that would otherwise require considerable effort in programming to implement. SEM

program can do path analysis as PROCESS, but it requires more coding and it cannot

generate all of the statistics PROCESS calculates, or SEM cannot implement

bootstrapping in a way that facilitates inference using those statistics (For more

detailed discussions, I encourage tapping into Hayes et al. (2017), and Hayes (2013).)

Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 57

Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network

4.1 INTRODUCTION

Establishing and expanding strategic alliances14 is shown to help new firms to

overcome challenges and constraints at founding in the entrepreneurship literature.

This is corroborated by multiple studies that suggest alliance networks can help new

firms to alleviate liabilities of newness (Stinchcombe, 1965) and liabilities of

smallness (Aldrich & Auster, 1986) by providing access to complementary resources

and external legitimacy (Baum et al., 2000; Baum & Oliver, 1991; Dacin, Oliver, &

Roy, 2007; Hagedoorn, Lokshin, & Malo, 2018; Mohr et al., 2013; Pisano, 1990).

Despite being important, emergence and growth of alliance networks in new firms is

an under-researched area in the network-based research literature, especially spinoffs

as a specific group of new firms (Ahuja et al., 2012; Brass, Galaskiewicz, Greve, &

Tsai, 2004; Hoang & Antoncic, 2003; Marquis, 2003; Stuart & Sorenson, 2007). My

focus in this paper is on the growth of alliance networks in spinoffs.

Considering this overlooked area, spinoffs that are started by ex-employees of

incumbent firms (Klepper, 2001, 2009) are worthwhile of study. These firms are called

by a variety of names in the literature (e.g., progeny, spin-out, spawn, spin-off), but

regardless of what they are called, they are characterised by their prior links to a parent

firm. This is demonstrated in prior studies to give them an initial advantage over other

14 Gulati (1995a, p.86) defines strategic alliances or joint ventures as ‘any independently initiated interfirm link that involves exchange, sharing, or co-development’.


types of new firms (Agarwal et al., 2004; Bruneel et al., 2013; Chatterji, 2009; Elfring

& Hulsink, 2007; Mohr et al., 2013; Phillips, 2002). Spinoffs play a critical role in

economic and employment growth, and diffusion of knowledge and innovation in the

markets (Clarysse et al., 2011; Dahl & Sorenson, 2013). Early emergence of the

semiconductor industry in Silicon Valley is significantly in debt of entries by spinoffs

(Cheyre et al., 2014; Cheyre, Kowalski, & Veloso, 2015). Also, much of the

combinatorial chemistry field was originated in the academic laboratories and

launched by academic spinoffs (Hagedoorn, Lokshin, & Malo, 2018). In the Australian

mining industry, over 49% of the new firms were initiated by spinoff activities over

the mining boom decade to 2012, which have been a major contributor to increased

employment rates in Australia (Downes et al., 2015; Tulip, 2014).

Given the importance of spinoffs’ outcomes, it is a well-researched topic in the

prior literature mostly in terms of their superior performance (Woolley, 2017).

However, the antecedents that lead to the formation of initial advantages in spinoffs

have so far been overlooked in the literature. For instance, since the centre of

discussion in the spinoff literature is that the majority of the initial endowments are

coming from their parent firms (Bruneel et al., 2013), it is surprising to see how

heterogeneity in parent firm’s attributes and characteristics have been underexplored

as a source in subsequent spinoffs’ different outcomes. This is important because

although spinoffs might have higher results in comparison to other startups, they still

achieve heterogeneous outcomes as an independent group of firms depending on the

different founding conditions (e.g., coming from parents with different attributes)

(Greve & Salaff, 2003; Hite & Hesterly, 2001). In the present study, I focus on the

alliance network growth of spinoffs and explore its link to the influential role of the

parent firm’s network characteristics.


Despite acknowledging the importance of alliance networks for spinoffs by a

growing body of literature (Hagedoorn, Lokshin, & Zobel, 2018; Mohr et al., 2013),

understanding the emergence and growth of strategic alliances in spinoffs is still an

overlooked area, especially in entrepreneurship research (Ahuja et al., 2012; Hoang &

Antoncic, 2003). The majority of the theoretical explanations for alliance formation in

spinoffs have been borrowed from the new firm’s alliance formation literature. This is

while the new firm’s alliance formation literature is itself relying on the network-based

research of strategic management that has focused on established firms, that have

already experienced cooperation with external firms. For instance, Gulati and Gargiulo

(1999) model the emergence of networks as a dynamic process that is driven by

exogenous resource dependences and endogenous network embeddedness

mechanisms. In their view, new alliances that are formed become increasingly

embedded in the networks that shaped them in the first place, orienting the choice of

new partners in the future. This could be problematic when theorising for newly

founded firms and spinoffs context, where new firms do not have an existing alliance

network to start growing their networks upon. The closest networks research in

strategic management has reached that could be applied to the new firms and spinoffs

context is by Ahuja et al. (2009), where they seek to answer how poorly embedded

firms manage to form alliances. While their findings have important theoretical

implications, their sample consists of 97 established leading firms in the global

chemical industry, opting out small or new firms. Hence, the question that is still under

consideration is what predicts a spinoff’s alliance networks formation and expansion

in its early years of initiation? And what is the effect of different founding conditions?

This fascinating question calls for studies that focus on theorising the establishment

and growth of alliance networks in new firms in general, and spinoffs in particular.


4.2 SUMMARY AND DISCUSSION OF MILANOV AND FERNHABER (2009)

One existing study, published in the Journal of Business Venturing, examined

a possible connection between network characteristics of new firm’s initial partner at

the time of founding and new firm’s subsequent network growth (Milanov &

Fernhaber, 2009). In this study, the authors analysed a sample of 209 biotechnology

new firms that were established between 1991 and 2000 in the United States and tested

whether initial partner firm’s network characteristics at founding would predict

subsequent new firm alliance network growth.

The authors hypothesised a positive relationship, thereby drawing upon

imprinting theory (Stinchcombe, 1965), which argues that organisational outcomes

have a history, which has had an enduring impact over the course of time. Milanov

and Fernhaber (2009, p.49) argue that since new firms’ development trajectory is

imprinted by the conditions and circumstances surrounding their early years (Boeker,

1989), the first alliance’s network characteristics may be an important predictor of

their network trajectory ‘ … in terms of the structural location, or entrance into the

industry network’. They consider two aspects of the initial partner’s network, namely

size, and centrality, to be positively associated with the subsequent network growth of

the new firm. Further, drawing on structural homophily principle (Gulati & Gargiulo,

1999), Milanov and Fernhaber (2009) develop their network imprinting theory. This

principle assumes that firms in central positions in a network tend to seek central

players to add to their own attractiveness (Gulati & Gargiulo, 1999). Accordingly,

firms in central positions may not see an incentive to partner with peripheral players

unless they need something that peripheral firms have, such as new technology (Shane

& Stuart, 2002). In this way, Milanov and Fernhaber (2009) argue that the initial

alliance’s larger network size and higher network centrality will imprint the new firm’s


network growth ‘… through more subtle dimensions related to the development of its

collaborative capabilities, recognition of partnering opportunities, its legitimacy and,

correspondingly, its attractiveness as a partner’ (p.49).

However, Milanov and Fernhaber (2009) study also has some limitations, as

also stressed by the authors themselves. First, in their assumption, they do not consider

embeddedness and involvement of founders in the social networks of their parents.

While some new firms might be started from a clean slate, there are new firms that are

founded by entrepreneurs who are coming from incumbent firms (namely, spinoffs)

and they have had experience in strategic alliances in the previous workplace. This can

potentially make a difference in terms of their initial network endowments.

Second, they exclude all new firms that are a result of a corporate spinoff15

from their sample to eliminate the confounding effect of being imprinted in other ways

than by making an initial alliance by the new venture. However, many new firms are

started by founders that are not always from outside of industry but insiders. There is

hard evidence of genealogical knowledge links between parent and spinoff firms in the

prior literature indicating the important role of parent firms in survival and growth of

spinoffs (Agarwal et al., 2004).

Third, they do not consider the role of entrepreneurs in driving the changes in

the formation and evolution of new firms at the time of the founding. This is despite

the arguments of imprinting literature that suggests imprinting takes place at the

individual level (Johnson, 2007; Tilcsik, 2014) and individuals are the carriers of

environmental stamps at the organisational level (Ellis et al., 2017).

15 Prior literature sometimes makes a distinction between corporate and employee spinoffs. They both refer to separate legal entities that are centred around activities originally developed in an incumbent firm (Van de Velde & Clarysse, 2006). The difference is corporate spinoff is started by the incumbent firm and employee spinoff is started by the ex-employees of the incumbent firm (Bruneel et al., 2013). However, in both cases a significant proportion of employees are coming from the incumbent firm.


4.3 THE PRESENT STUDY

Inspired by Milanov and Fernhaber (2009)’s model, the present study was set

up as replication with extension to the parent–spinoff context. While Milanov and

Fernhaber (2009) focus is on initial partner’s network structural characteristics, in the

spinoff context one other possible predictor, considering the demonstration of parent

firm’s important role in the development trajectory of spinoffs in the literature (Fackler

et al., 2016; Parhankangas & Arenius, 2003), may be the network imprinting role of

their parent firms. Prior research has seldom made a quantitative and longitudinal

attempt to explore and provide evidence for the parent firm’s attributes role in the

alliance formation of spinoffs. Thus, an interesting research question is: What is the

role of the parent firm’s network structural characteristics in the subsequent network

growth of spinoffs?

Drawing on the organisational learning in the imprinting literature, I begin by

replicating the Milanov and Fernhaber (2009) model in the parent–spinoff context by

testing the imprinting effect of initial partner’s network characteristics (i.e., size and

centrality). Then, I go beyond replication and test something new by taking into

account the parent’s network imprinting effect. I test the positive effect of the parent’s

network size versus centrality on the spinoff’s subsequent network growth.

I use a panel of 237 spinoff firms in the mining industry from 2002 to 2011 to

examine the impact of initial founding conditions on the subsequent alliance network

growth of spinoff firms. My secondary data is collected annually for 10 years from the

Register of Australian mining dataset that contains comprehensive annual data for all

firms (including private as well as public firms), all projects and all founding

teams/directors in the mining industry of Australia. I have chosen a golden period in

Australian mining during which the investment spent in the mining sector over a ten-


year period to 2012 increased the GDP from 2 to 8 per cent (Tulip, 2014). Also, the

number of mining projects increased by 130% from 1504 to 3468 projects during this

time. My data provide a unique opportunity for studying strategic alliances in a context

other than knowledge-intensive industries such as high-technology, biotechnology, or

pharmaceutical industries that have been extensively utilised in prior studies of

network-based research. Unlike knowledge-intensive industries where competition for

learning the latest technology and accessing innovative resources drive firms to get

involved in strategic alliances (Powell et al., 1996), mining projects are characterised

by capital intensity where firms form alliances mainly to ‘share risk and pool

resources’ (Bakker, 2016, p.1920). This is due to the fact the end product in mining is

the same for all firms, meaning that gold is gold (Zarea Fazlelahi & Burgers, 2018).

So, basically, the more financial resources firms have, the more projects they can be

involved in. Thus, the importance of participating in alliances for new ventures in

capital intensive industries is even more pronounced since they often start with limited

financial resources and they face a greater need to be considered as potential partners

in more mining projects.

My main contribution is to the network-based research in the entrepreneurship

spinoff research. I add to prior studies by investigating the parent firm’s role in the

spinoff network growth trajectory (Elfring & Hulsink, 2007). Doing that, my research

attempts to theorise not just the benefits and endowments spinoffs receive from their

parent firm, but also explore the heterogeneity in the parent firm’s attributes, and how

they affect growth outcomes in spinoffs. I conceptualise that the impact of the parent

firm’s initial endowments to the spinoff firm differs according to the different

characteristics of the parent firm’s network structure.


Second, I also advance theoretical and empirical knowledge of the

heterogeneity of founding conditions of spinoffs (Elfring & Hulsink, 2007) by

explaining network endowments from two different sources (i.e., initial partner and

parent firm) and elaborating and testing in what different ways they affect the alliance

network growth of spinoffs. This is an interesting contribution to the entrepreneurship

spinoff literature because it provides a basis for comparing the relative importance of

parent firm versus other sources of influence in spinoffs’ founding.

This study also attempts to expand the line of research that seeks to explain

how poorly embedded firms manage to form alliances or become a more connected

part of the network. While existing research shows that more central firms enter

heterophilies relationships with poorly embedded firms due to higher negotiating

power and secure more favourable terms of trade (Ahuja et al., 2009), I propose that

parent firm’s position in the network can also add to the attractiveness of spinoffs as a

partner.

I also contribute to the entrepreneurial network literature that seeks to elaborate

on ‘who’ drives the changes in the process of network development (Slotte Kock &

Coviello, 2010). I suggest that entrepreneurs who are coming from incumbent firms

have an influential role in managing the changes in networks of new venture affected

by their parent firms.

Further, to ensure the veracity of my findings, I also test my hypotheses on a

sample of 244 non-spinoff firms in a replication of Milanov and Fernhaber (2009) to

address calls for creating more cumulative research in management (Bettis, Helfat, et

al., 2016; Ethiraj et al., 2016).


4.4 THEORY AND HYPOTHESES

Imprinting has been established by Stinchcombe (1965) as a conceptual insight

that positions the formation and evolution of new firms within the framework of

founding mechanisms. In this view, new firms are subject to ‘liabilities of newness’ in

time of their initiation. Kale and Arditi (1998) argue that liability of newness is

associated with establishing a firm’s external processes such as forging new stable ties

with outside organisations and acquiring access to resources in the firm’s environment;

and its internal processes such as learning and defining new roles and developing

internal competencies (Stinchcombe, 1965). Therefore, firms are more susceptible to

their environment in the first few years. The decisions made at this initial stage can

affect a firm’s subsequent decision making, organisational structures, learning,

performance, and survival in their life cycles (Marquis & Tilcsik, 2013).

Stinchcombe (1965) defines a firm’s environment as consisting of ‘groups,

institutions, laws, population characteristics, and sets of social relations …’ (p.142).

In addition to confirming the firm’s environment role as a major imprinter, many

studies have considered the role of founders as conduits of environmental conditions

on their firm’s culture, knowledge and strategies (Boeker, 1988; Eisenhardt &

Schoonhoven, 1996; Ellis et al., 2017; Johnson, 2007; Parhankangas & Arenius, 2003;

Zarea Fazlelahi & Burgers, 2018). So, this gives rise to the possibility that imprinting

elements are transmitted to new firms by prospective entrepreneurs who leave their

parent organisation and establish their own ventures.

The notion of ‘network imprinting’ was first coined by Marquis (2003) for

extending the imprinting literature to the field of network research. Network

imprinting explores ‘… the lingering influence of past network structures and

positions’ on firm’s (or focal entity’s) present outcomes’ (Marquis, Davis, & Glynn,


2013, p.231). The role of the first alliance’s network as a source of imprinting on the

new firm has been established by Milanov and Fernhaber (2009). In extending the

network imprinting theory for spinoff’s network growth, I also focus on their parent

company’s networks and how the genealogical relationships imprint the subsequent

spinoff’s network growth. Since ‘… the window of “imprintability” is only open

during restricted periods of time, and when it is shut, the environment is less likely to

have a lasting impact …’ (Marquis et al., 2013, p.199), I will focus on the early years

of spinoff firms.

There have been several explanations for how imprinting works. Early studies

mostly focused on passive mechanisms such as inertial forces and path dependence

(Boeker, 1988, 1989; Hannan & Freeman, 1984). Recently, researchers have adopted

a more active approach to the imprinting process and emphasised the role of ‘social

agents’ (Johnson, 2007). Specifically, more research has drawn upon organisational

learning and the role of knowledge dissemination in explaining the imprinting process

(Ellis et al., 2017; McEvily et al., 2012; Uzunca, 2018).

Prior studies in strategic alliance research emphasise that firms need to learn

how to form and manage alliances in order to develop larger portfolios of alliances

(Heimeriks & Duysters, 2007; Hoang & Rothaermel, 2005; Rothaermel & Deeds,

2006). Accordingly, Rothaermel and Deeds (2006) define alliance management

capability as ‘a firm’s ability to effectively manage multiple alliances’ (p.431).

Alliance management capabilities can be perceived as a firm-level competitive

advantage since they cannot be perfectly imitated and are heterogeneously distributed

across firms (Barney, 1991). Developing alliance management capabilities may play a

key role in spinoffs subsequent network growth, given the importance of resource

access for new firms (Alvarez & Barney, 2002). Alliance capabilities have been


discussed as the underlying mechanism that links the alliance experience to alliance

performance (Heimeriks & Duysters, 2007). Therefore, I describe my model in which

initial partner and parent firm’s network characteristics, learning mechanisms, alliance

capabilities, and spinoff firm-level competitive advantages are linked. Drawing on

organisational learning literature (Levitt & March, 1988) and imprinting through

knowledge dissemination (Ellis et al., 2017), I argue there are two mechanisms (i.e.,

experiential learning and congenital learning) that drive the formation of alliance

networks in spinoffs influenced by an initial partner and parent firm’s network

structures, respectively. I explain the transfer of knowledge from initial partner to

spinoffs through experiential learning because spinoffs are involved in collaborative

activities with initial partners in their early years and can learn by doing (Levitt &

March, 1988). Congenital learning mechanism is suggested for the transfer of

knowledge from parent firm to spinoffs. This is because a substantial part of spinoff

entrepreneurs’ learning has happened pre-spinoff while they were still working in the

parent firm.

4.4.1 Imprinting Effect of Network Size

Network size is the number of firms to which the focal firm is connected. Prior

research in strategic alliance stream often used network size as a proxy for general

alliance experience (Deeds & Hill, 1996; Hoang & Rothaermel, 2005; Kale, Dyer, &

Singh, 2002; Zollo, Reuer, & Singh, 2002). Larger network size of an initial partner or

parent firm suggests more engagements in different strategic alliances with other firms.

Since organisational learning theory suggests that firms learn by doing (Levitt &

March, 1988), these repeated alliance engagements over time contribute to developing

alliance management capabilities through creating codified routines, policies and

procedures (i.e., explicit knowledge) as well as tacit knowledge (Rothaermel & Deeds,


2006). These management capabilities consist of partner selection (Lee, Hoetker, &

Qualls, 2015), and forming and managing alliances (Kale et al., 2002). In other words,

‘it enhances the firm’s ability to manage effectively a large number of alliances’

(Rothaermel & Deeds, 2006, p.433). Thus, initial partner or parent firms with larger

alliance network size should have acquired higher capabilities in managing a larger

number of alliances effectively through developing organisational routines (Nelson &

Sidney, 2005). Studies suggest that higher alliance experience also allows firms to

analyse a critical process and manage conflicts more effectively as well as spotting

better potential partners (Heimeriks & Duysters, 2007; Mohr & Spekman, 1994).

Spinoff entrepreneurs that are involved in their first collaboration with their

initial alliance are receivers of two types of knowledge from initial partner’s alliance

management capabilities: explicit and tacit. It is through this first experience that they

start to develop their own alliance management capabilities through building up norms,

codified routines, and written procedures as well as interpersonal interactions. This

gradual accumulation of knowledge is referred to as experiential learning (Levitt &

March, 1988). As spinoff engages in collaborative activities and develops capabilities,

its absorptive capacity increases and that facilitates its future learning. This can help

further their collaborations with other firms and involve them in more complex

alliances. Thus, how developed their initial partner is in terms of alliance management

capabilities helps spinoffs orient their alliance formation activities and procedures in

a better starting direction in the overall networks. Therefore,

Hypothesis 1a: Network size of a spinoff’s initial partner will have a positive imprinting effect on the subsequent network growth of the spinoff.

On the other hand, spinoff founders have also been involved in the alliance

formation activities of their parent pre-spinoff. By moving from parent firm to their

own venture, founders transfer their developed knowledge through congenital learning


(Bruneel, Yli Renko, & Clarysse, 2010). Congenital learning, coined by Huber

(1991), refers to ‘… the body of knowledge that the founder originally brought to the

firm along with new knowledge that she has developed as she has been involved with

managing the firm’ (Cegarra-Navarro & Wensley, 2009, p.534). Previous experiences

as a result of direct involvement and face-to-face interactions with previous colleagues

are retained in their mindset and affect their future decision making (Ellis et al., 2017;

Kim, 1993). Such congenital learning should impact a spinoff’s alliance formation

activities in two ways. First, by transferring more alliance management capabilities

from their parents as a result of the higher managerial capabilities of their parents.

Therefore, spinoff founders are more alert to the collaboration opportunities in the

whole network and they can better assess and spot the potential partners. Second, they

can potentially be a better allying choice for outside organisations when approaching

them or being approached by them. This is because spinoff founders, based on their

congenital learning, can better manage and build relationships with potential partners.

Therefore,

Hypothesis 1b: Network size of a spinoff’s parent will have a positive imprinting effect on the subsequent network growth of the spinoff.

4.4.2 Imprinting Effect of Network Centrality

Studies show that higher levels of alliance experience do not necessarily imply

higher levels of capability (Helfat & Peteraf, 2003). While network size emphasises

the number of connected alliances to the focal firm, network centrality focuses on the

position of the focal firm in the overall network among other players. Technically,

network centrality refers to the ability of the focal firm to reach to indirect as well as

direct ties (Milanov & Fernhaber, 2009). It is because the more central position a firm

occupies in the network, the more times it appears in the shortest path between two

other firms (Freeman, 1978). This provides the central firm with wider and faster


access to different information and resources in the network (Powell et al., 1996). This

can also be interpreted as ‘power’ and ‘status’ (Milanov & Shepherd, 2013; Podolny,

1993).

Initial partner or parent firms that are in more network central positions have

access to a broader range of collaborative efforts which provides greater opportunity

for them to refine their organisational routines for cooperation and makes them more

versatile (Powell et al., 1996). As a result, they can develop higher levels of alliance

management capabilities due to their information-rich positions.

I argue that the founders of spinoffs that are partnering with first alliances with

higher network centrality may develop more advanced routines and collaborative

capabilities, which makes partnering occur more readily with less effort for them.

Therefore,

Hypothesis 2a: Network centrality of a spinoff’s initial partner will have a positive imprinting effect on the subsequent network growth of the spinoff.

As noted by Powell et al. (1996, p.119):

The development of cooperative routines goes beyond simply learning

how to maintain a large number of ties. Firms must learn how to transfer

knowledge across alliances and locate themselves in those network

positions that enable them to keep pace with the most promising scientific

or technological developments.

Strategic alliances are more than just contractual and formal agreements. Every

alliance consists of several informal relationships (Powell et al., 1996). Entrepreneurs

that are coming from a parent firm that has a well-established position in the whole

network may have acquired more knowledge through interaction with a diverse range

of contacts involved in the alliance network of their parents. This may have shaped


their reputation in social networks and increased their ability to attract potential

partners when they start their own firm. This enables them to transfer more valuable

knowledge from their parent firm to their own collaborative routines and procedures.

Therefore,

Hypothesis 2b: Network centrality of a spinoff’s parent will have a positive imprinting effect on the subsequent network growth of the spinoff.

4.5 DATA AND METHODS

4.5.1 Industry Setting

To test the above set of hypotheses, I study a sample of 481 new firms of

Australian mining ventures over the 2002 to 2011 period. I observed that about 49%

of this sample consists of spinoff firms which emphasise the importance of spinoff

activities in the growth of this industry section in Australia. There has been an increase

in allying activities in the last decade (Bakker, 2016; Bakker & Shepherd, 2017). Due

to the mining boom in the period leading to 2012 (Tulip, 2014), the number of projects

increased by 130%. The number of alliance projects had an increase from 1504 to 3468

from 2002 to 201216. Besides, mining is a very important industry in Australia in terms

of employment and economic growth. Mineral mining is a major contributor to

Australia’s GDP (Tulip, 2014). Australia’s revenue only from iron ore and black coal

mining was 122819 million dollars in 201817.

The mineral mining industry in Australia and elsewhere is a capital-intensive

industry and mining projects can cost up to billions of dollars to establish (Goldstein,

Pinaud, & Reisen, 2006; Sadorsky, 2001). It is also a highly project-based industry

where the main activities include exploration of minerals, exploitation and extraction

of deposits and services to firms involved in such activities (Bakker & Shepherd,

16 Source: The Register of Australian Mining dataset 17 IBIS industry reports, 2018


2017). Mining projects are often started by multi-parties and alliances are very

commonplace (Stuckey, 1983).

There are several reasons why the Australian mining industry is a suitable

setting for testing my hypotheses. For one, unlike most of the literature that uses

samples of firms in knowledge-intensive industries (cf. Ahuja, 2000; Ahuja et al.,

2009; Eisenhardt & Schoonhoven, 1996; Milanov & Fernhaber, 2009; Powell et al.,

1996), mineral mining is a capital-intensive industry. The nature of alliances is often

‘production-oriented rather than knowledge-oriented’ (Bakker, 2016, p.1926). Due to

the high scale of required investments and resources, it is even more important for new

firms in these industries to develop ties with other players. Previous studies have

discussed that the tendency of central organisations to partner with peripheral firms

increases when they control something that larger organisations need, such as new

technology or innovation (Shane & Stuart, 2002). However, due to the high costs of

mining projects, it is very unlikely that new firms possess such technologies. And since

the resource is the same as the product in mineral mining (Zarea Fazlelahi & Burgers,

2018), it leaves even less superior advantage for a new firm in competition for

partnerships. However, their winning advantage may be offering better deals for

cooperation (Ahuja et al., 2009; Bakker, 2016), which makes it really vital for them to

acquire knowledge and develop management capabilities in forming alliances.

Also, the period I have chosen from 2002 to 2011 has witnessed a mining boom

in Australia (Downes et al., 2015; Tulip, 2014). As a result, there has been an increase

in the number of new ventures. This gave me a large sample of spinoffs that created

enough variance to test my hypotheses.


Due to the mining boom, there was also an increased number of alliance

activities from 2002 to 2011, where the number of projects increased by 130%. So it,

even more, emphasises the importance of strategic alliances in the mining context.

Additionally, alliances that are established consist of diverse portfolios of

mining firms. Sometimes they only involve large companies, or only smaller ones, and

also a mix of large and small ones (Bakker, 2016). Partners involved in alliances often

make the decisions together and arm’s length relationships are mostly avoided or kept

to a minimum (Bakker, 2016; Stuckey, 1983). Therefore, new firms that are involved

in projects have to work closely with their partners, which puts emphasis on the role

of knowledge transfer and management capabilities as facilitating collaborations

among them.

I gathered data from several sources. The main source for this study is the

Register of Australian Mining database, which provides data on the mining companies

in Australia since 1980. This is a publicly available archive of reference books

containing annual data on companies, projects and directors in this sector (Bakker &

Shepherd, 2017). Other supplementary datasets include Sirca, Morningstar

DatAnalysis Premium, Bloomberg, Osiris, Australian Bureau of Statistics and

Australian Securities Exchange website.

4.5.2 Sample

My sample consists of new mining firms that were 10 years old or less as of

the year 2011. I identified new firms based on their first appearance in the Register

dataset from 2002 to 2011. For each firm, I also checked other additional datasets

(namely, Morningstar DatAnalysis Premium, Bloomberg and Osiris) to make sure this

first appearance was not due to name changes of companies. I found 481 new firms

that were established in this period. I also limited the sample to include only new firms


that entered into an alliance within their first three years of founding. The three years

is suggested by Milanov and Fernhaber (2009) to satisfy the notion of ‘sensitive years’

when new firms are most susceptible to being imprinted by their environment due to

their liability of newness (Stinchcombe, 1965). To be identified as a spinoff, firms had

to be new companies where at least 25% of their employees were coming from the

same mining company immediately one year before initiation (Muendler et al., 2012).

I identified this mutual firm as the parent firm for that spinoff. Among my initial 481

new firms, 237 firms were identified as spinoff firms. The rest of the 244 new firms

were categorised as non-spinoff firms.

For some spinoffs, first collaboration was in multi-party alliances. For

identifying an initial partner in such partnerships, I followed some criteria. First, since

the Register dataset provided detailed annual information about each alliance, I chose

the firms that were the main operators of the project as spinoff’s initial alliance partner.

Then, if that was equal between two partners, I chose the firm that had the highest

ownership stake in the project.

4.5.3 Measures

I used Ucinet 6 (Borgatti et al., 2002) to construct the network-related data.

This software has been utilised in many prior network analysis studies (cf. Ahuja et

al., 2009; Gulati & Gargiulo, 1999). The Register dataset reports all the mineral

projects in Australia on an annual basis, which contains detailed information about

their ownership stake in these alliance partnerships. Following Milanov and Fernhaber

(2009) and to deal with common method bias (Podsakoff, MacKenzie, Lee, &

Podsakoff, 2003), I collected my dependent variable in year t+1 to allow for a time-

lag between control and dependent variables. While both control and dependent

variables are updated yearly, the independent variables are time-invariant and have


been measured only at the year of first alliance (the same as Milanov and Fernhaber

(2009)).

I developed adjacency matrices by using all the alliance relationships among

all firms in the whole industry network for each year between 2002 and 2011. All

adjacency matrices in each year have dichotomous values; 1 if there is an alliance

between two firms, and 0 if there is no relationship. Following Milanov and Fernhaber

(2009) and prior network literature, I considered alliances as active links in a five-year

period and considered a five-year moving window to construct my network related

measures (cf. Gulati & Gargiulo, 1999; Rothaermel & Deeds, 2006; Soda, Usai, &

Zaheer, 2004). Use of a moving window of five-years is based on Kogut (1988), that

suggests a normal lifespan of no more than five years for most alliances.

Dependent variable

Spinoff firm network growth: Consistent with the original study, the network

growth for each spinoff firm is measured as a count of the total number of alliance

partners. I utilised the five-year moving window industry network matrices to calculate

my dependent variable. Hence, a spinoff firm’s network size in year t+1 would count

all of the new alliance partners that the spinoff firm formed alliances within the five-

year period preceding year t+1 (Wasserman & Faust, 1994). The measure is updated

yearly for each firm.

Independent variables

Initial partner’s network size/ Parent firm’s network size: Following Milanov

and Fernhaber (2009), I measure the network size of the initial partner as the count of

the number of alliance partners that a new venture’s first partner had in the year of

their alliance. Similarly, for parent companies, as the count of the number of partners

that the parent company had in the year that spinoff happens. I normalise this variable


by dividing the number of firms in the entire network for each respective year. This

enables me to compare measures across years (Borgatti et al., 2002; Wasserman &

Faust, 1994). Then, I will transform the variable by taking the natural logarithm due

to lack of linearity. This is a time-invariant covariate.

Initial partner’s network centrality/ Parent firm’s network centrality:

Following the original study, I use the Freeman (1978)’s centrality measurement that

gives the expected value of the number of times a firm is in the shortest path connecting

two other firms. In other words, it considers the probability of a central point

controlling the communication between pairs of other network points:

Where,

= network centrality of point

= number of geodesics18 linking point and point in the network

= the number of geodesics linking point and point that contain point

.

This variable computes a measure for the position of each firm in the whole

network. The higher centrality measure for an organisation shows it is linked to many

organisations, which are in turn linked to many other firms. To normalise network

centrality across years, I divide each network centrality score by the maximum

possible centrality score in the respective year. Then I take the natural logarithm to

address lack of linearity (Cohen, Cohen, West, & Aiken, 2003). This is also a time-

invariant covariate.

18 The shortest path linking a given pair of points (Freeman, 1978).


Control variables

Spinoff firm size: I controlled for the size of the new firm, which may affect

its network growth (Bakker, 2016). Financial resources of a firm can potentially affect

the propensity of other firms to collaborate with them (Ahuja et al., 2009). I control

for size by obtaining the financial assets and liabilities of the spinoff firms in

Australian dollars and then taking the natural log of company size in each year. Using

financial assets as a proxy for firm size has been used by previous networks studies as

a control variable (cf. Ahuja et al., 2009; Bakker, 2016).

Spinoff firm network growth (lagged): Like Milanov and Fernhaber (2009), I

include a lagged value of the dependent variable as an explanatory variable to the

model. This lagged dependent variable captures any alliance capabilities and relational

history that the spinoff captures owing to its current alliance experience (at time t), and

which may have helped it in forming new alliances in the following year (t+1).

Time since initial alliance: Milanov and Fernhaber (2009) suggest that it is

important to control for the time since the initial alliance. It is because older spinoffs

have had a longer time to build up their network. The time since the initial alliance is

updated yearly and measured as the number of years from the initial alliance until the

respective year in the study.

Spinoff firm ownership status: Milanov and Fernhaber (2009) control for the

ownership status by a binary variable; that is 1 if they have had an IPO19, and 0

otherwise from 2001 to 2011. It is due to the fact that going public can either improve

a new firm’s legitimacy (having a positive effect on the dependent variable) or signal

its independence and less need for external resources (having a negative effect of the

dependent variable).

19 Initial Public Offering


Industry network density: Milanov and Fernhaber (2009) consider this

variable to be important in influencing the firms’ propensity in forming alliances if it

is perceived as a norm in the industry (Gulati & Gargiulo, 1999). Sedaitis (1998) also

reports a network density influence on spinoff networking activities. It is calculated by

taking the total number of partnerships in a given year divided by the total number of

possible partnerships among all organisations within the industry. Milanov and

Fernhaber (2009) then scaled this variable by 1000 to make its regression coefficient

comparable to the other variables in the model.

Spinoff firm profit status: Firms in mining are not always profitable. During

their exploration phase, their profit is negative. It is only in the exploitation phase that

they can make a profit. So, this might potentially affect their ability to form alliances.

Therefore, I control for this by a dummy variable that is 1 if the profit is positive and

0 otherwise.

Spinoff firm location: Suggested by the original study, the new firm’s location

could potentially influence the dependent variable because alliance partners might

want to tap into locally available resources. In more condensed areas for mining

activities, these partnerships become more useful and possible. To control for the

geographic location effects, I employed the ABS20 reports to identify the major mining

cities in Australia. A dummy variable was then developed to indicate whether the new

firm’s headquartered location was in one of the eleven cities identified; a firm is

assigned 1 if the headquarters location is in a concentrated mining area, and 0

otherwise.

20 Australian Bureau of Statistics 2011 reports (mining cities: Perth, Brisbane, Adelaide, Mackay, Melbourne, Kalgoorlie-Boulder, Mount Isa, Newcastle, Sydney, Wollongong, Townsville). Australia's urban centres are ranked according to the number of permanent residents employed in the mining industry.


Commodity: Milanov and Fernhaber (2009) define a control variable for the

number of patents that new firms hold in each year. This is because the number of

patents can improve a new firm’s visibility and attractiveness as a partner by signalling

its innovativeness and knowledge (Powell et al., 1996). Similarly, I considered the

number of commodities that mining firms are involved in as potential influence on

their number of alliance partners. This variable was transformed by taking a natural

logarithm.

4.5.4 Model Specification

I used a longitudinal research design for testing my hypotheses. I employed a

panel data method because my data traces 481 new ventures (237 spinoffs) over a ten-

year period. Since my dependent variable is a non-negative count variable using a

simple regression is not advised (Hsiao, 2014; Wooldridge, 2015). This is because

utilisation of a linear regression model could result in inefficient, inconsistent and

biased regression modes (Blevins, Tsang, & Spain, 2015; Long, Long, & Freese,

2006). Like Milanov and Fernhaber (2009), I used the Poisson model instead

(Hausman, Hall, & Griliches, 1984). I applied the following model to test my

hypotheses:

,

where is the spinoff network growth for firm i and time t. is the

vector of regressors containing the independent and control variables described above.

And is the firm-specific unobserved heterogeneity that does not vary across time.

Since my independent variables are time-invariant, I apply a random-effects model in

my Poisson panel-data regression (Cameron & Trivedi, 2013). Therefore, the

condition on the expected value of must be satisfied. Otherwise, my model will be


a fixed-effects model that allows to be an unknown parameter (Cameron & Trivedi,

2013). Maximum-likelihood estimators are used to obtaining coefficients and alpha.

One of the assumptions of Poisson models is equidispersion where the mean

and variance of the distribution are equal. However, prior studies discuss that these

assumptions are ‘unreasonable’ and the variance of the number of occurrences usually

exceeds the expected number of occurrences (Kennedy, 2003). This problem is

referred to as ‘overdispersion’ which could lead to biased results (Blevins et al., 2015).

Overdispersion can arise due to different reasons ranging from sampling problems to

excessive zeros (Blevins et al., 2015). Hoetker and Agarwal (2007) suggest that when

the ratio of the standard deviation exceeds 130% of the mean, overdispersion is likely

to be a problem. Since this ratio is about 90% in my regression, overdispersion is not

very likely to have affected the results. I use Stata v.15 for all statistical analysis.

4.6 ANALYSIS AND RESULTS

I replicated the Milanov and Fernhaber (2009) model by using the data on non-

spinoff firms (see Appendix A for a detailed analysis). Overall, the results did not

confirm the positive effect of greater initial partner’s network size and centrality on

the network growth of non-spinoff firms.

Next, I examined the extended model. Table 4-1 represents means, standard

deviations, and bivariate correlations. The average age of spinoff firms was 2.5 and

ranged from 1 to 10. As evident from the correlation table, the network size and

centrality of the initial alliance partner are again significantly correlated at 0.82

(p<0.05). Similarly, the network size and centrality of parent firms are also

significantly correlated at 0.68 (p<0.05). Therefore, I entered the initial partner’s

network size and centrality separately.

Cha

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e 4-

2 R

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ffec

ts P

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on re

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lts (d

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ent

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t S.

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ffici

ent

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t S.

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trol

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0.15

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(0

.009

) 0.

153*

**

(0.0

09)

0.15

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(0

.009

) 0.

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152*

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)

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k de

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pend

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tner

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tner

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ork

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ralit

y

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stan

t 0.

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(0

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) 0.

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(0.1

88)

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(0.1

53)

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(0.1

48)

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(0.1

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4 (0

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) 0.

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onst

ant

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) -3

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***

(0.3

04)

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* (0

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) -3

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***

(0.3

04)

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* (0

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) -3

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***

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03)

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69**

* (0

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e 48

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45


Table 4-3 Summary of effect sizes (incident rate ratios) for independent variables in Models 2 to 7

spinoff network growth IRR Std. Err. z P>|z| [95% Conf.

Interval] alpha Model

initial partner network size 0.9747 0.0231 -

1.08 0.28

1 0.9304 1.0211 0.0478 2

parent network size 1.005 0.0052 0.97 0.331 0.9949 1.0152 0.050

7 3

initial partner network centrality

0.9914 0.0067 -

1.28 0.20

1 0.9785 1.0046 0.0477 4

parent network centrality 1.0138 0.0059 2.34 0.01

9 1.0022 1.0254 0.0499 5

initial partner network size 0.9773 0.0235 -

0.96 0.33

9 0.9324 1.0244 0.0481 6

parent network size 1.0043 0.0052 0.84 0.40

3 0.9942 1.0146 0.0481 6

initial partner network centrality

0.9909 0.0066 -

1.36 0.17

4 0.978 1.004 0.0465 7

parent network centrality 1.0139 0.0058 2.39 0.01

7 1.0025 1.0254 0.0465 7

Figure 4-1 Difference between initial partner and parent firm’s network centrality coefficients, with 95% confidence intervals

-.02

-.01

0.0

1.0

2.0

3

-.02

-.01

0.0

1.0

2.0

3be

ta

initial partner parent

initial partner centrality vs parent firm centrality95% confidence interval for coefficients of IVs


Table 4-2 reports results of the random-effects Poisson regressions of the

spinoff network growth on 237 mining spinoff firms over the 2002-2011 period. To

evaluate my hypotheses, I consider both statistical significance and effect sizes. To

obtain an interpretation of effect size, I used incident rate ratio (IRR) to estimate the

percentage change in the dependent variable as a function of increases in distinct

values for independent variable on the basis of its coefficient, as suggested by Long

et al. (2006).

The coefficients in Table 4-2 are the results of my analysis. Model 1 is the

baseline model and includes the control variables only. Models 2 and 3 test hypotheses

1a and 1b, where I introduced initial partner and parent firm’s network size as

predictors of spinoff network growth, respectively. I found no support for hypotheses

1a and 1b. Specifically, the directionality of Hypothesis 1a is not supported by my

findings. Table 4-3 shows a summary of incident rate ratios for independent variables

in Models 2 to 7. I have added the column for alpha21 that was calculated for each

model to use for interpreting the effect sizes. Table 4-3 illustrates if there is a one-unit

change in the initial partner’s network size, the network growth of its spinoff is

expected to decrease by a factor of 0.9747 while holding all other variables in the

Model 2 constant. It also shows that the dependent variable is expected to increase by

a factor of 1.005 with a unit change in a spinoff’s parent network size in Model 3. The

Wald test in each model tests the null hypothesis that the coefficient of an independent

variable is equal to zero22. If the test fails to reject the null hypothesis, this suggests

21 Random-effects Poisson regression coefficients are interpreted as the difference between the log of expected dependent variable for every one-unit change in the independent variable. Since difference of log is equal to the log of their quotient, I could also interpret the parameter estimates as the log of the ratio of expected dependent variable, which explains the ‘ratio’ in the incident rate ratios term. Incident rate ratios are obtained by exponentiating the Poisson regression coefficients. 22 Wald test can also be used to test multiple parameters simultaneously.


that removing the variables from the model will not substantially harm the fit of that

model, since a predictor with a coefficient that is very small relative to its standard

error is generally not doing much to help predict the dependent variable (Dinardo,

Johnston, & Johnston, 1997; Fox, 1997). As evidenced in Models 2 and 3 the Wald

chi-square is not significant for either the network size of the initial partner or the

network size of the parent firm. This means these variables do not significantly

improve the overall fit of the model.

Hypotheses 2a and 2b consider the possibility that initial partner and parent

firm’s network centrality are positively linked to the subsequent network growth of

spinoff firms. Upon introducing network centrality of the initial partner in Model 3,

the fit of the model is not significantly improved compared according to Wald test

results. Therefore, I find no support for Hypothesis 2a. In contrast, as evidenced in

Model 4, there is a positive effect significant at 5% level between the parent firm’s

network centrality and spinoff network growth. Also, Wald test results show an

improvement in the overall fit of the model compared to when I had not entered this

independent variable. So, the coefficients support Hypothesis 2b. As depicted in Table

4-3, considering effect sizes, if there is a one-unit change in the parent’s network

centrality, the network growth of its spinoff is expected to increase by a factor of

1.0138 while holding all other variables in the Model 5 constant.

4.6.1 Supplementary Analysis

In addition to testing hypotheses and in further consideration and comparison

of the role of initial partner and parent firms, I also explored whether spinoffs benefited

more from a greater initial partner network or parent firm’s structural characteristics

in Models 6 and 7. In Model 6, I entered initial partner and parent’s network size as

independent variables together. And in Model 7, I entered initial partner and parent


firms’ network centrality. Again Model 6 does not provide statistical significance for

either of the independent variables. However, in Model 7 I find evidence for the

stronger effect of parent firm’s network centrality effect as it is significant on the 5%

level. I further developed Figure 4-1 to graphically compare and analyse the difference

between the initial partner versus the parent firm’s network characteristics. Figure 4-1

depicts a significant difference between the initial partner and parent firm’s network

centrality. As can be seen, there is little overlap between the confidence interval of

coefficients of two variables, with network centrality of parents being higher. The

initial rate ratios are also the same as Models 2 to 5.

4.6.2 Robustness Checks

To test the robustness of my findings, I estimated a number of alternative

specifications of my model. I explored the models with an alternative dependent

variable, calculated using a three-year window, to examine a potential influence of

shorter lags on spinoff’s network growth (this analysis is attached in Appendix B). The

results are largely in line with those obtained from the reported models using a five-

year moving window. One exception is that the coefficient of parent firm’s network

centrality that was statistically significant on a 5% level in the initial analysis, became

statistically significant on 1% level. This fortifies my discussion that the parent firm’s

effect is a dominant force at the founding of spinoffs, and it is more pronounced by

considering a shorter time for founding.

I also tested for curvilinear effects of my independent variables by including

their squared terms in the models. However, none of these squared terms was

statistically significant. Hence, I can rule out curvilinearity.


4.7 DISCUSSION

This article contributes to the rapidly growing literature on firms’ alliance

formation and collaboration strategies in newly founded spinoffs. I examined Milanov

and Fernhaber (2009) model in the parent–spinoff context. I attempted to provide

empirical quantitative evidence and explored the initial partner and parent firm’s

network characteristics at the time of founding and their effect on spinoffs’ outcomes.

Based on a sample of mining firms in Australia, I found support for the positive

imprinting effect of parent’s network centrality on spinoff network growth. However,

I found support neither for the positive effect of the parent firm’s network size nor for

the initial partner’s network size and centrality.

The specific implication that emerged from my findings confirms and

contributes to the existing theories in network-based research in spinoff

entrepreneurship and strategy. My findings support prior research that emphasises the

need to move beyond a focus on dyadic relationships to the whole networks in which

they are embedded (Slotte Kock & Coviello, 2010). Since I only found support for

the positive effect of parent firm’s network centrality, it suggests that spinoff firms

that are coming from a parent that is actively involved in the whole network of firms

and possess information-rich positions can act as better partnering choices in the long-

term.

I also advance theoretical and empirical knowledge of the heterogeneity of the

founding conditions of spinoffs (Elfring & Hulsink, 2007; Hite & Hesterly, 2001).

Most studies have focused on only one aspect of the founding condition as the

predictor of spinoffs’ organisational outcomes. This is why I have considered two

different sources suggested by the network research literature (namely, initial partner

and parent firms), which has provided a unique opportunity to compare the relative


importance of them. I show that the parent firm has a more influential role in the

development trajectory in the alliance networks of spinoffs.

I provide insight into the line of research that seeks to explain why highly

embedded firms become involved with poorly embedded firms (such as new firms) in

the networks. Existing research show heterophilies relationships happen due to higher

negotiating power and securing more favourable terms of trade for the firms in higher

central positions (Ahuja et al., 2009). I suggest these relationships can happen because

of where the new firms are coming from. While Milanov and Fernhaber (2009) have

demonstrated the initial partner’s influence for heterophilies interfirm relationships to

happen for new ventures, I extend this strand of research by proposing the imprinting

role of parent firm’s status and central position in spinoffs.

Additionally, I contribute to the entrepreneurial network literature that seeks to

elaborate on ‘who’ drives the changes in the process of network development (Slotte

Kock & Coviello, 2010). While the approach of previous research has been slightly

passive, limited to the firm-level drivers, I provided finer-grained explanations of the

driving forces of alliance network establishment centred around the focal role of

founders. And this also confirms the results reported in the imprinting literature that

discusses the pivotal role of entrepreneurs in initiating organisational outcomes (Ellis

et al., 2017; Favero, Finotto, & Moretti, 2016; Johnson, 2007).

Further, to ensure the veracity of my findings, I also tested Milanov and

Fernhaber (2009)’s hypotheses on a sample of 244 non-spinoff firms (discussed in

Appendix A). I found no support for the original hypotheses in the setting of non-

spinoff firms in my sample. These results further highlight the calls for more

cumulative research in management research (Bettis, Ethiraj, Gambardella, Helfat, &

Mitchell, 2016; Bettis, Helfat, et al., 2016).


There are several promising opportunities to further extend research in this

area. My results show that the coefficient sign for the initial partner’s network size and

centrality are negative, which is in the opposite direction of predictions I made based

on Milanov and Fernhaber (2009) model. It may be that the firm size of the initial

partners has an effect in obtaining this result. The findings of one recently published

paper in spinoff alliance literature by Hagedoorn, Lokshin, and Malo (2018) show that

spinoffs benefit from a positive effect on their innovation performance through

partnering with large partners. Interestingly, their results show this effect is negative

for partnering with small and medium-sized firms. This could be an explanation for

the negative sign of the influence of the initial partner’s network characteristics on my

dependent variable. Since I did not have information about the firm size of initial

partners in terms of their employee numbers, I encourage future studies to explore this

effect.

One of the specific implications that emerged from my findings contributes to

imprinting literature in ‘… failed imprinting (“failure to imprint”) – instances where

entities do not incorporate or resemble the features of their environment, industries, or

networks.’ (Simsek et al., 2015, p.306) under the same assumptions. As one of the

overlooked topics in the imprinting literature, I know little about how, why and when

entities differ in their responses to imprinting forces (Simsek et al., 2015). Relying on

network imprinting literature, I predicted that the initial partner and parent firm’s

network structure could have an imprinting effect on the network growth of spinoff

firms. Since I only found evidence for the positive imprinting effect of parent’s

network centrality, my study adds to the prior research on imprinting literature that

imprinting forces are sensitive to contextual and temporal changes, and they can be

dominated by other sources of influence. This may be a good starting point for further


research into the imprinting failure in imprinting literature and questioning the other

imprinting sources that are identified in this literature.

One other direction for future research is investigating the boundary conditions

in the relationship between the parent firm’s attributes and spinoff’s organisational

outcomes. Sapienza et al. (2004) show that there is a curvilinear relationship between

the knowledge overlap (namely: technology, production, and market relatedness) with

parent firm and spinoffs’ performance post-spinoff. This suggests too small overlap or

too large overlap both can be detrimental to spinoff’s performance. However, Clarysse

et al. (2011) find no support for the curvilinear relationship between the technological

knowledge overlap with parent firm and spinoff’s performance. This necessitates the

need to delve further into this matter. Since I discussed organisational learning as an

underlying mechanism that links the parent firm’s initial advantages to spinoff’s

organisational outcomes, it is worth considering the moderating role of knowledge

relatedness to expand my model and derive finer-grained information regarding the

contingency relationships.

I acknowledge that my study has a number of limitations. First, my sample of

a single industry could limit the generalisability of my findings. While one of the

advantages of my replication and extension study is testing the extant alliance network

model in a different industry compared to the majority of the literature, there are other

capital-intensive industries as well. One of them is the oil and gas industry, which is

very similar to the mining industry, but the scope and scale of projects could be larger

compared to the average mineral mining sector.

Second, in order to identify an initial partner for the spinoff and non-spinoff

firms in my dataset, I had to choose among several partners for some firms since their

first involvement was in a multiparty project. It could be worth considering a portfolio


of initial partners and tracking their collective effect on the subsequent spinoff’s

organisational outcome. This necessitates doing multilevel analysis which is an

interesting way of expanding my understanding of the group dynamics in alliancing

activities.

Third, for considering the size of the firms as a control variable I only had

assets and liabilities of firms in each year. Since firm size in terms of employees has

been mostly used in the spinoff literature, it is worth replicating the analysis

considering this variable in future studies.


4.8 APPENDIX A: FULL DESCRIPTION OF THE REPLICATION OF MILANOV AND FERNHABER (2009) IN THE NON-SPINOFF CONTEXT

This additional text gives a detailed view of the replication results of the original study

in the sample of non-spinoff firms. As before, I utilised a random-effects Poisson regression in

Stata v.15. I used a sample of 244 non-spinoff firms. Using the same variables consistent with

my analysis, I developed three models in Table 4-4. I also calculated effect sizes for predictors

in Models 2 and 3 in Table 4-5 to summarise them. In Model 1, I entered the control variables

only. In Model 2, I entered initial partner’s network size as the predictor of non-spinoff firm’s

network growth. My results are not different from the results obtained for the spinoff sample.


Table 4-4 Random-effects Poisson regression results (dependent variable: non-spinoff network growth) Model 1 Model 2 Model 3 Coefficient S.E. Coefficient S.E. Coefficient S.E.

Control variables: Non-spinoff network growth (lagged) 0.161*** (0.009) 0.161*** (0.009) 0.161*** (0.009)

Industry network density -0.012 (0.031) -0.007 (0.032) -0.013 (0.031) Spinoff ownership status 0.062 (0.069) 0.062 (0.068) 0.060 (0.068) Time since initial partner -0.043* (0.019) -0.041* (0.019) -0.044* (0.019)

Commodity 0.148** (0.048) 0.148** (0.048) 0.148** (0.048) Non-spinoff size 0.011 (0.012) 0.010 (0.012) 0.010 (0.012)

Non-spinoff location 0.037 (0.054) 0.043 (0.055) 0.039 (0.054) Non-spinoff profit status -0.017 (0.084) -0.016 (0.084) -0.017 (0.084) Independent variables:

Initial partner's network size -0.027 (0.023) Initial partner's network centrality -0.006 (0.006)

Constant 0.623*** (0.130) 0.477** (0.181) 0.610*** (0.131) Ln alpha Constant -3.910*** (0.463) -3.941*** (0.475) -3.932*** (0.475)

Log-likelihood -1098.000 -1097.300 -1097.600 Wald chi-square 558.920 556.770 564.570

† p<0.1, * p<0.05, ** p<0.01, *** p<0.001 standard errors are in parenthesis number of observations= 652

Table 4-5 Summary of effect sizes (incident rate ratios) for independent variables in Models 2 and 3

Non-spinoff network growth IRR Std. Err. z P>|z| [95% Conf. Interval] model initial partner network size 0.9738 0.0221 -1.1700 0.2420 0.9313 1.0181 2

initial partner network centrality 0.9944 0.0061 -0.9200 0.3600 0.9824 1.0065 3

Cha

pter

4: P

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Gro

wth

: The

Rol

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tralit

y ve

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e of

Par

ent F

irm’s

Net

wor

k 94

4.9

APP

EN

DIX

B: R

OB

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Table 4-7 Summary of effect sizes for robustness check (incident rate ratios) for independent variables in Models 2 to 7

spinoff network growth IRR Std. Err. z P>|z| [95% Conf. Interval] Model

initial partner network size 0.9761 0.0258 -0.9100 0.3610 0.9268 1.0281 2

parent network size 1.0060 0.0058 1.0400 0.2990 0.9947 1.0175 3


parent network centrality 1.0169 0.0066 2.5900 0.0100 1.0041 1.0300 5

initial partner network size 0.9790 0.0261 -0.8000 0.4260 0.9291 1.0315 6

parent network size 1.0055 0.0058 0.9400 0.3480 0.9941 1.0170 6


parent network centrality 1.0171 0.0065 2.6300 0.0090 1.0043 1.0300 7

Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 97

Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms

5.1 INTRODUCTION

Despite the mounting evidence substantiating the growth of alliance networks

benefits for newly founded firms (e.g., Baum et al. (2000); Hoang and Antoncic

(2003)), we still know relatively little about the dynamics of the network growth in

newly founded firms (Slotte Kock & Coviello, 2010). One class of new firms that has

gained special attention in recent years is spinoffs that are identified as important

vehicles of employment growth and economic change (Dahl & Sorenson, 2013).

Spinoffs are new firms founded by employees of incumbent firms, which are

commonly referred to as parent firms (Klepper (2009); for a typology of spinoffs, see

Bruneel et al. (2013); Fryges and Wright (2014)). Spinoffs differ from other firm

entries in that they may benefit from their prior links to their parent firm. Previous

research has shown parent firm’s influence on organisational outcomes of spinoffs

such as survival rates (Adams, Fontana, & Malerba, 2015; Fackler et al., 2016),

employment and revenue growth (Bruneel et al., 2013), and sales growth (Sapienza et

al., 2004). In the previous chapter, I showed that the parent firm’s higher network

centrality in the industry networks has a positive imprinting effect on the subsequent

network growth of spinoffs. I, among other network imprinting scholars, only

theorised the explanation of how this effect unfolds based on organiszational learning

perspective. Therefore, there is a gap in our understanding of how parent’s network

features translate into spinoff network growth through imprinting.


Network imprinting perspective suggests that initial conditions within which

networks are established have a critical influence on their formation, which will persist

over time (Marquis, 2003; Stinchcombe, 1965). In my search for plausible

explanations of the network imprinting dynamics, I identified two leading approaches

that have been used in prior empirical studies to explain how the antecedents at

founding lead to network imprinting outcomes in new firms. The first lens is through

knowledge transfer and organisational learning that focuses on the richness of learning

opportunities as an imprinting founding condition. McEvily et al. (2012), in a study of

lawyers in Nashville, focus on the learning potential of apprenticeship23 relationships

as a network imprinting source. They show that new legal firms started by lawyers that

were trained by late-career lawyers in previous companies will have greater growth

rates in terms of adding associates. While they use an organisational learning

perspective to explain and test the network dynamics, their focus is on the social

networks of lawyers, and not on the firm level. In Study I, I applied organisational

learning theory and used the development of the alliance management capability

concept to establish my parental network imprinting hypotheses. In this study, building

on Cohen and Levinthal (1990) theory on organisational learning, I suggest parental

network imprinting mechanisms can be explained through the increased absorptive

capacity of spinoffs on the firm level. A firm’s absorptive capacity, defined as the

ability to value, assimilate, and apply knowledge, is a critical requirement for learning

from experiences (Levitt & March, 1988). The second lens suggested for studying

network imprinting dynamics is through network status and social categorisation

literature (Ashby & Maddox, 2005). A firm’s network status refers to how centrally

23 Relationships between early career lawyers and experienced partners


the position of a firm is relative to others in the industry (Benjamin & Podolny, 1999).

Milanov and Shepherd (2013), in a context of venture capital networks, suggest that

the network status of a newcomer to a network is imprinted by its first venture capital

partner’s reputation through social categorisation mechanisms. I use this second lens

and develop a multiple mediation model to test the two competing theoretical

explanations for network imprinting effect in the context of parent–spinoff firms.

Lane and Lubatkin (1998) suggest that the similarity between the knowledge

bases of the sender and receiver of knowledge influences the absorptive capacity of

the receiver. Therefore, I consider knowledge overlap between parent and spinoff as a

moderator between parent’s network centrality and spinoff absorptive capacity. I also

suggest that knowledge overlap may moderate the second mediated path in my model.

In this way, I can present a finer-grained perspective of the network imprinting

dynamics by considering the boundary conditions.

Using secondary data from the Register of Australian Mining, MorningStar

Premium, D&B Business Browser, and Zephyr datasets, I conduct my analysis based

on 3370 strategic alliances entered into by 237 mining new ventures in the ten-year

period between 2002 and 2011.

My results suggest that a spinoff’s absorptive capacity (in terms of ability to

apply knowledge) and spinoff network status mediate the relationship between

parental network centrality and spinoff network growth. Overall, my results show a

full mediation through the obtained significant paths. I also find significant results for

the moderated mediation effect of market knowledge relatedness between spinoff and

its parent on the obtained significant mediated path through spinoff network status.

My main contribution is to the network imprinting theory in entrepreneurship.

I suggest and empirically demonstrate that parent firm’s network centrality at the


founding of spinoff is a key factor that shapes spinoff network growth through two

underlying mechanisms simultaneously: increased absorptive capacity and network

status of spinoffs. While there has been an ongoing conversation about the role of

status in tie formation processes (cf. Ahuja et al., 2009; Eisenhardt & Schoonhoven,

1996), incorporating learning arguments through absorptive capacity also explains

additional variance in a spinoff’s network growth beyond the outcome dependencies

arising from the improved network status arguments.

My findings provide evidence that the benefits parents with higher network

centrality have on a spinoff’s future network growth may not be fully realised unless

there is knowledge overlap between parent and spinoff. While imprinting framework

suggests persistent impact from imprinting sources during founding or sensitive

periods on the focal entity (Simsek et al., 2015), the existence of boundary conditions

that facilitate the process of imprinting has not so far been theorised, to the best of my

knowledge. In this paper, I, for the first time, consider imprinting moderators during

the genesis phase of Simsek et al.’s (2015) imprinting model.

My paper is not only a response to call for doing more longitudinal studies in

the network research in entrepreneurship (Hoang & Antoncic, 2003), but it also uses a

state-of-the-art (conditional) multiple mediation model design that can test two

competing theories for simultaneously testing and explaining the change in the alliance

network growth of spinoffs.

5.2 THEORETICAL BACKGROUND AND HYPOTHESES

5.2.1 Parental Network Imprinting

Research in the parent–spinoff context has long noted that parent firms have

persistent impacts on the organisational behaviour and outcomes of spinoff firms

(Klepper & Sleeper, 2005). Parent firms have been shown to leave imprints on spinoff


firms’ product market entry strategy (Boeker, 1997), exploitative or explorative

behaviour, and organisational ambidexterity (Beckman, 2006), organisational identity

(Ashforth, Harrison, & Corley, 2008), collaborative behaviour (Uzunca, 2018), and so

on. Parental network imprinting effect has also been explored in prior research, but to

a limited extent and mostly on the social networks of spinoff founders. In a study of

law firms, McEvily et al. (2012) focus on the imprinting effect of apprenticeship

relationship as a key feature of network and demonstrate that imprinted ties of early-

stage lawyers in terms of bridging the gaps in the network have a greater impact on a

law firm’s growth rate. In a case-based study, Elfring and Hulsink (2003) suggest that

heterogeneous initial founding conditions regarding the relationship with a parent firm

can lead to particular development patterns of tie formation processes. Regarding the

alliance networks on the firm level, Eisenhardt and Schoonhoven (1996) show that

prior ranks of the top management team in their previous jobs have an effect on the

rate of alliance formation in new firms. Sedaitis (1998) suggests that social structures

between spinoff and parents at the time of founding shape spinoff firm’s future strategy

of alliance networks. I demonstrated a positive imprinting effect of parent network

centrality in the whole network on the subsequent network growth of spinoff firms in

Study I. I suggested that spinoffs coming from parents with such network

characteristics subsequently build a larger alliance network due to developing greater

alliance management capabilities. Thus, learning to organise and manage multiple

alliances may be the key to developing a larger network in the future for spinoffs

through cultivating and exploiting the knowledge that spinoff entrepreneurs transfer

from their parent. However, what is less undertaken is rigorous empirical testing of the

underlying mechanisms of the parental network imprinting that can let us know how


this effect unfolds. And whether there are other plausible explanations that can explain

the dynamics of network growth in parallel with learning arguments.

This gap can also be observed in the main imprinting literature. The core of

imprinting theory is that the initial founding conditions and environments can have

persistent impacts on a firm’s behaviour and outcomes (Marquis & Tilcsik, 2013;

Simsek et al., 2015; Stinchcombe, 1965). There have been major efforts in prior

research to explain the underlying mechanisms of imprinting. While the earlier

approach in explaining imprinting has been through inertia and institutionalisation of

established routines (cf. Hannan & Freeman, 1984), recently there has been a growing

interest in unravelling the imprinting through cognitive mindsets of entrepreneurs (cf.

Favero et al., 2016; Zarea Fazlelahi & Burgers, 2018) and organisational learning and

knowledge transfer processes (cf. Ellis et al., 2017). However, as Simsek et al. (2015)

note ‘genesis of imprinting remains a “black box” and continues to be taken for granted

by most researchers…’ (p.305). There is a need for more empirical research to open

the black box and inform imprinting scholars on how to hypothesise and test the

underlying mechanisms of imprinting.

5.2.2 A Multiple Mediation Model of Spinoff Network Growth: Indirect Effect of Parent Network Centrality through Spinoff Absorptive Capacity and Spinoff Network Status

Absorptive capacity is a set of firm capabilities such as the ability to value,

assimilate, transform and exploit knowledge (Zahra & George, 2002). In terms of

growing alliance networks, this set of capabilities refers to a set of routines and

procedures that firms use to efficiently manage a portfolio of alliances, that is referred

to as ‘alliance management capability’ (Rothaermel & Deeds, 2006). Being able to

develop higher capabilities of alliance management based on experiences spinoff

founders had in the parent firm, enables them to establish routines and procedures that


help them effectively manage a larger portfolio of alliances. These capabilities could

be manuals, databases, and other diverse tools (Hoang & Rothaermel, 2005). Initiating

and establishing such procedures happens earlier in spinoffs with higher absorptive

capacity at the founding. Firms with higher absorptive capacity in terms of valuing

knowledge are better able to identify potential partnership opportunities in the first

place. In addition to spotting the right partner, firms that possess higher capabilities of

alliance management can move faster toward exploiting these relationships and

maintaining efficient collaborations (Rothaermel & Deeds, 2006).

Firm-level absorptive capacity depends on the absorptive capacity of its

members (Cohen & Levinthal, 1990). Cohen and Levinthal (1990) emphasise that the

development of individual absorptive capacity depends on not only the accumulation

of related knowledge but also on the richness and diversity of knowledge acquired

from external sources. I argue that the absorptive capacity of the spinoff firm will be

greater when its founders are coming from parents that have a more central position in

the industry network. This is because network centrality makes a parent firm an

obligatory point for passage of information, resource exchange, and communication in

the industry network (Freeman, 1978). As suggested by prior research, the inflow of

external knowledge is the input of absorptive capacity (Zahra & George, 2002). The

exposure of parent firms to critical information in the network promotes its level of

experiential learning accumulated to manage and generate value from outside

knowledge. For instance, mining parent firms in such a central position will be in a

better position to readily identify the value of relationships with other firms, since they

are more aware of mining technological advancements in use, which firms have access

to these technologies and who other firms would be more likely to work with.

Additionally, being in a central position between various pairs of firms enables parent


firms to access diverse information that might not be essentially a part of their main

activities but makes them aware of broader knowledge bases. Therefore, network

centrality of parent firms will increase their incentive to build absorptive capacity.

Consequently, spinoff founders that are moving from parent to the spinoff firm, will

possess broader levels of absorptive capacity that will add to the spinoff firm’s

absorptive capacity. This is because while they are starting an intra-industry firm and

they have cumulated related knowledge of the past, they are also able to tap into a

more diverse knowledge associated with their experiences in the parent firm.

Absorptive capacity refers not only to the valuation of information but also to

application and exploitation of information by organisations (George et al., 2001). The

knowledge acquired through spinoff founders must also transfer across and within the

spinoff firm, which depends on better communication systems within units of a firm.

Parents with more network centrality are likely to have developed better

communication systems in the organisation. Since the majority of the founding team

is coming from the same parent firm, it is easier for them to assimilate and develop

similar knowledge bases as their parent in terms of manuals, databases, and routines,

that will allow them to develop better communication systems. This, in turn, helps to

increase absorptive capacity in terms of ability to apply knowledge, which will lead to

growing a larger network of alliances.

Spinoff status is likely to have a positive effect on its subsequent network

growth because status can function as a signal that firms can use to make inferences

about the future unobservable quality of a spinoff’s partnership activities (Jensen,

2003). Due to the absence of a track record and high uncertainty, it is very difficult for

an external audience to assess the quality of a new firm and its effective functioning.

Therefore, they need to rely on observable attributes that work as a foundation of their


judgement about a new firm’s quality as a partner. Network status is a potentially

valuable resource for spinoffs because it can function as an observable characteristic

that the potential partners can use. Therefore, it can affect their future tie formation.

The higher the initial status of a spinoff, the more attractive it becomes in the network

as a potential partner because it can reflect their future performance (Gulati, 1995b).

Thus, it increases their chances of tie formation in the network of firms.

New firms that enter an industry are subject to an initial assessment period by

other players in the network that can make up their future network status. Unlike de

novo firms that start with zero status, spinoff founders start with prior affiliations to a

parent firm that can influence their status and the way an industry network audience

perceives and evaluates them. These initial evaluations can have a persistent effect on

a spinoff’s status in the long term, according to social categorisation theory (Macrae

& Bodenhausen, 2000). External audience categorises new firms based on their readily

identifiable characteristics (Milanov & Shepherd, 2013), which can affect their

decisions about establishing partnerships and positions with new firms in the industry

(Kim & Higgins, 2007). In the absence of a track record, the prior affiliation of the

spinoff founding team can influence the judgement of the outside network about the

spinoff’s status. Network status is potentially transferable from parent firm to spinoff

through inheritance.

Network centrality has been considered as a key factor that reflects a firm’s

position in the whole network of firms. It refers to the extent to which a firm is engaged

in important ties through direct as well as indirect links (Hoang & Antoncic, 2003;

Madhavan, Koka, & Prescott, 1998). In other words, network centrality gauges a

firm’s position and the extent to which it has an influential role in its networks

(Podolny, 2010). Firms in highly central positions link between pairs of others, which


shows their association with a higher power to control these communications

(Podolny, 1993). This can influence their visibility and attractiveness for other

organisations throughout the network, even if they are not directly or indirectly tied to

them (Gulati & Gargiulo, 1999). Thus, the higher centrality network characteristic of

a spinoff’s parent firm can give the spinoff an advantage of legitimacy and give it a

good start in the network in terms of initial status. Therefore, I suggest that status can

mediate the relationship between parent network centrality and spinoff network

growth.

Hypothesis 1: The indirect effect of parent network centrality on spinoff network growth is mediated through spinoff absorptive capacity and spinoff network status

5.2.3 A Moderated Multiple Mediated Model of Spinoff Network Growth: Conditional Indirect Effect of Parent Network Centrality through Spinoff Absorptive Capacity and Spinoff Network Status with Knowledge Relatedness between Parent and Spinoff as Moderator

Learning theories suggest that absorptive capacity depends highly on

knowledge held in common with the external source of knowledge (Powell et al.,

1996). I discussed that parental higher network centrality can lead to higher levels of

absorptive capacity in spinoff firms, that in turn, leads to growing a larger alliance

network in spinoffs. This happens through the improved absorptive capacity of spinoff

founders that are moving to the new spinoff firm. Here, I argue that the higher levels

of knowledge overlap between parent and spinoff will moderate this effect. The ability

of the spinoff firm to value and apply external knowledge depends on the cumulated

body of knowledge that has been brought in by its founders from the parent firm

(Cohen & Levinthal, 1990). The closer this knowledge is to the spinoff’s current

activities, the easier for them to leverage on that. Firms learn better in the vicinity of

their knowledge bases (Autio, Sapienza, & Almeida, 2000). Additionally, Cohen and

Levinthal (1990) mention that it is more difficult to learn in novel domains. Spinoffs


that are established in domains that are not close to their parents will have to spend

longer to develop the capabilities needed, since learning from experiences is

incremental. Therefore, leveraging on prior knowledge bases of their parent will not

be as helpful for them in the valuation of new external knowledge and assimilating it

within their firm.

While parent’s network centrality facilitates categorisation by improving

external audience’s initial assessments of the spinoff firm’s quality and abilities,

another important aspect of improving their judgement might be how related the

spinoff firm is to its parent in terms of knowledge bases and activities. More

specifically, the positive impact of parent network centrality on spinoff network status

is likely to be enhanced when their knowledge overlap is higher. It is because when

assessing a spinoff, potential partners also want to know whether the spinoff has an

understanding of the industry, including production and technology. In other words, it

is important for them to weigh the legitimacy of a new venture (Zimmerman & Zeitz,

2002). Legitimacy refers to a social judgement of acceptance, desirability, and

appropriateness (Zimmerman & Zeitz, 2002). Legitimacy is not assumed as another

resource, but rather, ‘a condition reflecting cultural alignment, normative support, or

consonance with relevant rules or laws’ (Scott, 1995, p.45). Having a greater overlap

with the parent in terms of market activities and production can potentially improve a

parent firm’s network centrality effect because interaction of knowledge relatedness

and parent firm network centrality aspects together can signal a higher legitimacy of a

spinoff. Thus, it can improve a spinoff’s attraction and trustworthiness in the network

(Zimmerman & Zeitz, 2002), that can lead to tie formation with the firm possessing a

higher network status.

Hypothesis 2: The conditional indirect effect of parent network centrality on spinoff network growth via spinoff absorptive capacity


and spinoff network status is moderated by spinoff knowledge relatedness with parent firm

The conceptual model is depicted in Figure 5-1.

Figure 5-1 Relationship between parent network centrality and spinoff network growth

5.3 METHODS

5.3.1 Data and Sample

To test the hypotheses relating parent network centrality to spinoff network

growth through moderated multiple mediation paths, I chose the Australian mining

industry as the research setting. Due to the capital-intensiveness nature of the mining

industry, mining projects can cost up to billions of dollars to establish (Goldstein et

al., 2006; Sadorsky, 2001). The main activities in mining projects are the exploration

of minerals, development of sites for extraction, exploitation and providing services

for firms involved in these activities (for a detailed explanation about mining stages

visit Bakker and Shepherd (2017)). Mining is a very project-based industry, where

most projects are started and implemented by multi parties (Bakker & Shepherd,

Parent Network Centrality

Spinoff Network Growth

Spinoff Knowledge Relatedness with Parent Firm

-Market knowledge relatedness -Production market relatedness

Spinoff Network Status

Spinoff Absorptive Capacity

-Ability to value knowledge -Ability to apply knowledge


2017). Therefore, strategic alliances are a very commonplace strategy for companies

(Stuckey, 1983). This industry seems particularly suitable to test the notion of parental

imprinting effect since the majority of spinoffs are intra-industry and it is a common

way of starting new firms. Additionally, there has been an unprecedented growth in

the strategic alliances and various forms of collaboration in the mining industry in the

last decade (Bakker, 2016; Bakker & Shepherd, 2017). In particular, the number of

strategic alliances in the Australian mining sector has more than doubled, due to the

boom in the industry in the period leading to 2012 (Tulip, 2014).

I gathered data from several sources. I collected the main data for strategic

alliances, partnerships and new firm information from The Register of Australian

Mining database. Additionally, I used Morningstar DatAnalysis Premium to identify

spinoff founders and their previous affiliations. I also gathered data about R&D

expenditure and accounting performance measures and indexes from this database

along with Osiris and Orbis. I gathered information about incorporation dates of

companies from the D&B Business Browser.

My sample consists of new mining firms that were 10 years old or younger as

of the year 2011. I identified new firms based on their first appearance in the Register

dataset from 2002 to 2011. Then, I searched the incorporation date of each firm in the

list from the D&B Business Browser to confirm that they incorporated after 2002. I

also checked for name changes to make sure this first appearance is not because of

that. To be identified as a spinoff, firms had to be incorporated during or after 2002,

where at least 25% of the founding team was coming from the same mining firm (i.e.,

Parent firm) immediately one year before incorporation (Muendler et al., 2012). Using

a cut-off rate has been used in prior research using similar datasets where an explicit


note of new firm type is not available (cf. Andersson & Klepper, 2013; Eriksson &

Kuhn, 2006). I identified 237 spinoffs.

5.3.2 Measures

The Register database annually documents all the mining projects with details

of their ownership stake and parties involved. I used Ucinet6 (Borgatti et al., 2002) to

construct dependent and independent variables, which is a commonly used software in

many prior network analysis studies (Ahuja et al., 2009). I developed adjacency

matrices in Ucinet6 by using all the alliance relationships in the whole industry

network for each year between 2002 and 2011. All adjacency matrices have

dichotomous values; 1 if there is an alliance between two firms, and 0 if there is no

relationship. I considered alliances as an active link in a five-year period and

considered a five-year moving window to construct my dependent variable. Using a

five-year period is suggested by prior network studies, who propose a normal lifespan

of no more than five years for most alliances (Gulati & Gargiulo, 1999). This approach

has been extensively used in prior literature in network-based research (cf. Gulati &

Gargiulo, 1999; Milanov & Fernhaber, 2009; Rothaermel & Deeds, 2006; Soda et al.,

2004).

Dependent variable

Spinoff network growth (t+2): My dependent variable is calculated in the same

way as in Study I. Network growth for each spinoff firm is measured as a count of the

total accumulated number of alliance partners (Ahuja, 2000). I calculated the

dependent variable using the five-year moving window for network matrices. This

variable is calculated at time t+2 that would count all the new alliance partners that

the spinoff firm formed ties with in the five-year period preceding year t+2.


Independent variable

Parent firm network centrality (t): As in Study I, I measure parent network

centrality based on Freeman (1978)’s betweenness centrality measurement. This

measurement considers the probability of a central point controlling the

communication between pairs of other network points as shown in Figure 5-2.

Figure 5-2 Betweenness and eigenvector centrality measures versus network size

Mediators

A firm’s absorptive capacity is its ability to value and apply information

(Cohen & Levinthal, 1990). Following this definition and as suggested by George et

al. (2001), I consider two components of absorptive capacity (namely; ability to value,

and ability to apply knowledge), that are measured at time t+1, as below.

Spinoff absorptive capacity (t+1): To measure the spinoff’s ability to value

knowledge, I utilise R&D expenditure as a key measure that was used by George et al.

(2001). This measure has been extensively used in prior studies as a proxy for

absorptive capacity. In this study, I use this proxy by measuring mining firms’

Highest Betweenness Centrality

Highest Network Size

Highest Eigenvector Centrality


exploration and development expenditure that is documented in their annual financial

reports in statements of cash flows. Since mining projects are capital intensive, the

investments that firms make in exploration and development allow them to identify

the potential opportunities (Bakker & Shepherd, 2017). It also makes them a better

candidate as a potential alliance. This is because mining strategic alliances are equity-

based, and each firm is expected to contribute to the project costs proportionately.

I used the number of mines as a proxy to measure spinoff’s ability to apply

knowledge. This is while prior studies in high-tech industries have mostly used the

number of patents to measure a firm’s ability to apply or exploit knowledge (George

et al., 2001; Zahra & George, 2002). Mining firms also go through several stages of

opportunity recognition stages (Bakker & Shepherd, 2017). At the early stage, they go

through previous reports of land analysis data, run tests based on a limited number of

drillings and evaluate the potential minerals composition and value in the ground.

Mostly after obtaining satisfactory results from the early stages, they are more likely

to enter strategic alliances to get involved in more exploration and developmental

activities. Therefore, the number of mines in the mining industry shows a firm’s

evolving and the breadth of its efforts in exploiting opportunities.

Spinoff network status (t+1): Spinoff network status was defined in terms of

spinoff’s status position in the networks of mining firm strategic alliances in the

Australian mining industry. I used Bonacich (1987) eigenvector measure that has been

used in prior research as a measure of firm status (e.g., Milanov and Shepherd (2013);

Jensen (2003); Podolny (1993)). Based on this measure, a spinoff’s status is its

summed connections to other firms, weighed by their status, which is also contingent

on their partners’ status and so on (Bonacich, 1987). Mathematically, it is calculated

through the below formula:


Where, is a matrix of relationships where each element shows the number

of times firm i and j have been involved in strategic alliance together, is a scaling

factor that normalises the measure, and is a weighing factor that represents the

degree to which the status of firm i is a function of the status of other firms at the

network. The - parameter shows the emphasis put on the status of the actors the focal

firm is related to; larger positive values give more weight to being connected to high-

status actors, whereas larger negative values increase the weight given to being

connected to a low-status actor. I set as the reciprocal (inverse) of the largest

eigenvalue of R, as suggested by prior studies (cf. Borgatti et al., 2002; Milanov &

Shepherd, 2013). For ease of comparison of this measure over time for each firm, I

normalised them by the maximum status score in each year.

Moderators

Knowledge relatedness between the spinoff firm and the parent firm was

operationalised in this study from two dimensions relating to the similarity of market

knowledge and production knowledge (Sapienza et al., 2004). Technology knowledge

similarities have also been explored by other studies (cf. Clarysse et al., 2011; Sapienza

et al., 2004). My data for measuring technology relatedness in a mining firm is not

complete and I could not find an alternative database that possessed it. However, I

believe these two measures can represent the construct to a great extent.

Spinoff market knowledge relatedness with parent firm (t): For this measure,

I focused on the commodity markets in which both spinoff firm and parent were

engaged (Sapienza et al., 2004). I first counted the number of commodities that a

spinoff explored or extracted in its mining projects for each year. To measure the extent

to which there is a market relatedness between spinoff firm and parent, I counted the


number of similar commodities between spinoff’s market measure over time and

parent firm’s market measure at the time of the founding. Then, I calculated a ratio by

dividing this number to the overall number of market activities of the spinoff.

Spinoff production knowledge relatedness with parent firm (t): For

measuring this dimension, I focused on the extent to which there is an overlap between

production knowledge of spinoff firm and its parent in terms of their activities. I coded

different activities throughout the mining value chain based on the nature of activities

(Kaplinsky & Morris, 2000). Then, I counted the number of similar activities between

spinoff’s activities through the time period and the parent firm’s production activities

at the time of the founding. Finally, I calculated a ratio by dividing this number to the

overall number of production activities of the spinoff.

Control variables

Spinoff firm network growth (lagged): I include a lagged value of the

dependent variable as an explanatory variable to the model. This lagged dependent

variable captures any alliance capabilities and relational history that the spinoff

captures owing to its current alliance experience (at time t+1), and which may have

helped it in forming new alliances in the following year (t+2).

Spinoff age: It is important to control for spinoff firm’s age. This is because

older spinoffs have had a longer time to build up their network. This variable measured

as the number of years from establishment until the respective year in the study. It is

updated for each year.

Spinoff firm ownership status: I control for the ownership status by a binary

variable; that is 1 if they have had an IPO, and 0 otherwise from 2001 to 2011 (Milanov

& Fernhaber, 2009). It is because going public can either improve a new firm’s

legitimacy (having a positive effect on the dependent variable) or signal its


independence and less need for external resources (having a negative effect on the

dependent variable).

Industry network density: I controlled for network density because denser

networks are known to promote trust and curb opportunism, which could affect the

partnering activities in the network (Coleman, 1988; Zaheer & Soda, 2009). Sedaitis

(1998) also reports a network density influence on spinoff networking activities. It is

calculated by taking the total number of partnerships in a given year divided by the

total number of possible partnerships among all organisations within the industry. I

then scaled this variable by 1000 to make its regression coefficient comparable to the

other variables in the model.

Spinoff firm profit status: Firms in mining are not always profitable. During

their exploration phase, their profit is negative. It is only in the exploitation phase that

they can make a profit. So, this might potentially affect their ability to form alliances.

Therefore, I control for this by a dummy variable that is 1 if the profit is positive and

0 otherwise.

Spinoff firm size: I controlled for the size of the new firm, which may affect

its network development (Bakker, 2016). Financial resources of a firm can potentially

affect the propensity of other firms to collaborate with them (Ahuja et al., 2009). I

control for size by obtaining the financial assets and liabilities of the spinoff firms in

Australian dollars and then taking the natural log of company size in each year. Using

financial assets as a proxy for firm size has been used by previous networks’ studies

as a control variable (cf. Ahuja et al., 2009; Bakker, 2016).

Spinoff firm location: The new firm’s location could potentially influence the

dependent variable because alliance partners might want to tap into locally available

resources. In more condensed areas for mining activities, these partnerships become


more useful and possible. To control for the geographic location effects, I employed

the ABS24 reports to identify the major mining cities in Australia. A dummy variable

was then developed to indicate whether the new venture’s headquartered location was

in one of the eleven cities identified; a firm is assigned 1 if the headquarters location

is in a concentrated mining area, and 0 otherwise.

Commodity: I considered the number of commodities that mining firms are

involved in as potential influence on their number of alliance partners. This variable

was transformed by taking a natural logarithm.

5.3.3 Model Specification

Since I am using a multiple mediator model to draw inferences about

underlying mechanisms of a causal process, there should be a time lag between a cause

and its associated effect to allow for the effect to unfold (Preacher, 2015). While

longitudinal mediation designs strengthen causal inferences, they help to build

evidence for a particular causal ordering of variables and grant the ability to study

whether change itself plays a role in the mediation process (Preacher, 2015). Among

major types of longitudinal mediation models that are commonly used, I utilise

methods based on cross-lagged panel model (CLPM) (Bentley, 2011; MacKinnon,

2012; Preacher, 2015). CLPM is based on structural equation modelling (SEM) for

repeated measures of an independent variable (X), mediators (M) and dependent

variable (Y) in sequential process design (i.e., Xt → Mt+1 → Yt+2). Accordingly, I

consider this time lags between variables.

24 Australian Bureau of Statistics 2011 reports (mining cities: Perth, Brisbane, Adelaide, Mackay, Melbourne, Kalgoorlie-Boulder, Mount Isa, Newcastle, Sydney, Wollongong, Townsville). Australia's urban centres are ranked according to the number of permanent residents employed in the mining industry.


I performed my analysis using PROCESS which is a freely-available

computational tool available for SPSS to perform advanced moderation-mediation and

multiple-mediation analysis (Hayes, 2017). PROCESS implements a regression-based

method that differs from SEM (Hayes et al., 2017). Differences between PROCESS

and SEM have been comprehensively discussed in the methodology chapter of this

thesis. The regression-based nature of PROCESS estimation allowed us to look at each

piece of the model separately as well as the overall model of the moderated mediation

mechanisms. For making inferences, I used the PROCESS results for my model as a

whole rather than just its pieces. I also reported the PROCESS regression results for

separate pieces of the model in Appendix C.

5.4 RESULTS AND FINDINGS

Table 5-1 presents the summary statistics and correlations among the variables.

Table 5-2 presents multiple mediation estimates for the direct and indirect effects of

parent network centrality on spinoff network growth through spinoff absorptive

capacity and spinoff network status. Model 1 considers spinoff network status and

absorptive capacity in terms of ability to value knowledge, while Model 2 represents

results when mediators are spinoff network status and spinoff absorptive capacity in

terms of ability to apply knowledge. Table 5-3 reports estimation results of

bootstrapping for direct and conditional indirect effects of parent network centrality

on spinoff network growth via spinoff network status and absorptive capacity

moderated by spinoff knowledge relatedness with the parent firm. Models 1 and 3

report results when the moderator is market relatedness, and Models 2 and 4 report the

results for production relatedness.

Hypothesis 1 proposed that the indirect effect of parent network centrality on

spinoff network growth is mediated through spinoff absorptive capacity and spinoff


network status. The indirect effect via spinoff network status was significant in Models

1 and 2, Table 5-2 (Model 1: indirect effect= 0.0107, S.E.=0.0045, %95CI: 0.0028 to

0.0203; Model 2: indirect effect= 0.0152, S.E.=0.0050, %95CI: 0.0065 to 0.0257).

The indirect effect through absorptive capacity in terms of ability to value knowledge

is not significant. The indirect effect via absorptive capacity in terms of ability to apply

knowledge is significant, Table 5-2, Model 2 (indirect effect= 0.0049, S.E.=0.0037,

%95CI: 0.0000 to 0.0107). The direct effect of parent network centrality on spinoff

network growth did not remain significant in both models, which suggests full

mediation. The ratio of indirect effect to direct effect in Model 2 is 2.1382

(=0.0201/0.0094), which is more than double. Therefore, Hypothesis 1 was supported.

Hypothesis 2 suggests that the conditional indirect effect of parent network

centrality on spinoff network growth via spinoff absorptive capacity and spinoff

network status is moderated by spinoff knowledge relatedness with the parent firm. In

Table 5-3, Models 1 and 3 show spinoff market relatedness with parent significantly

moderates the mediated path via spinoff network status (Model 1: index of conditional

effect= 0.0148, S.E.= 0.0056, %95CI: 0.0044 to 0.0267; Model 3: index of conditional

effect= 0.0115, S.E.= 0.0055, %95CI: 0.0007 to 0.0224). Figure 5-3 illustrates these

results. As shown in Table 5-3, market knowledge relatedness does not moderate the

paths through any dimensions of absorptive capacity. Models 2 and 4 show that

production relatedness does not moderate any of the mediated paths. Overall,

Hypothesis 2 is supported for the path through spinoff network status.

Cha

pter

5: P

aren

tal N

etw

ork

Impr

intin

g in

Spi

noffs

: Und

erst

andi

ng th

e U

nder

lyin

g M

echa

nism

s 11

9

Tabl

e 5-

1 M

eans

, sta

ndar

d de

viat

ions

and

cor

rela

tion

for s

pino

ff fir

ms

Mea

n 3.

4817

3.

0532

-6

.206

0 2.

2220

0.

6527

0.

9120

2.

1480

14

.367

9 3.

4208

0.

0037

0.

0852

0.

6459

-6

.102

5 0.

7273

0.

8119

std.

dev

. 3.

2738

3.

0487

5.

1175

1.

8720

0.

5725

0.

2835

0.

8831

1.

4252

4.

0439

0.

0049

0.

2793

0.

4785

3.

3935

0.

9823

0.

5499

Var

iabl

es

1 2

3 4

5 6

7 8

9 10

11

12

13

14

15

1.

Spin

off

netw

ork

grow

th

1

2.

Spin

off

netw

ork

grow

th (l

agge

d)

0.92

31*

1

3.

Pare

nt’s

ne

twor

k ce

ntra

lity

a 0.

1804

* 0.

1788

* 1

4.

Tim

e si

nce

esta

blish

men

t 0.

1884

* 0.

2824

* 0.

0449

1

5.

Com

mod

ity

0.43

76*

0.43

26*

0.04

51

0.18

90*

1

6.

Spin

off

owne

rshi

p sta

tus

0.01

32

0.02

65

0.02

1 0.

0946

* 0.

0046

1

7.

Net

wor

k de

nsity

a b

0.08

81*

0.00

59

0.09

49*

-0.5

112*

-0

.048

3 0.

0963

* 1

8.

Abi

lity

to v

alue

kn

owle

dge

a 0.

1459

* 0.

1549

* -0

.026

5 0.

2180

* 0.

1141

* -0

.033

7 -0

.129

7*

1

9.

Abi

lity

to a

pply

kn

owle

dge

0.41

39*

0.37

73*

0.14

76*

0.03

35

0.25

19*

0.23

14*

0.00

89

0.16

96*

1

10.

Spin

off

netw

ork

stat

us

0.78

39*

0.72

25*

0.17

73*

-0.0

352

0.32

21*

-0.0

044

0.24

47*

0.10

68*

0.36

69*

1

11.

Spin

off p

rofit

st

atus

0.

0485

0.

0840

* -0

.039

5 0.

1287

* 0.

0278

0.

0464

-0

.009

7 0.

0401

-0

.065

8 0.

0551

1

12.

Spin

off

Loca

tion

0.08

88*

0.08

77*

-0.1

035*

-0

.015

9 0.

0558

-0

.081

7*

-0.0

525

-0.0

179

0.13

84*

0.01

99

-0.0

178

1

13.

Spin

off f

irm

size

a -0

.027

5 -0

.054

1 0.

0458

-0

.444

2*

-0.0

907*

-0

.475

3*

0.31

67*

0.01

64

-0.0

874*

0.

1187

* -0

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3*

0.03

7 1

14.

Mar

ket

rela

tedn

ess

0.31

21*

0.28

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0.06

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0.19

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95*

0.02

17

0.01

8 0.

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* 0.

2385

* -0

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4*

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113*

1

15.

Prod

uctio

n re

late

dnes

s -0

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2 -0

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6 0.

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* 0.

0374

-0

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9 0.

0124

0.

0616

0.

0685

0.

0125

0.

0151

-0

.076

2*

-0.2

461*

0.

0224

0.

1754

* 1

*P<0

.05

(n=1

045)

a va

riabl

e is

tran

sfor

med

b v

aria

ble

has b

een

scal

ed b

y 10

00

Cha

pter

5: P

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tal N

etw

ork

Impr

intin

g in

Spi

noffs

: Und

erst

andi

ng th

e U

nder

lyin

g M

echa

nism

s 12

0

Tabl

e 5-

2 M

ultip

le m

edia

tion

resu

lts (

boot

=500

0)

Mod

el 1

M

odel

2

T

he d

irec

t eff

ect o

f Par

ent n

etw

ork

cent

ralit

y on

Spi

noff

net

wor

k gr

owth

T

he d

irec

t eff

ect o

f Par

ent n

etw

ork

cent

ralit

y on

Spi

noff

ne

twor

k gr

owth

Effe

ct

S.E.

t

p LL

CI

ULC

I

Effe

ct

S.E.

t

p LL

CI

ULC

I 0.

0114

0.

009

1.26

91

0.20

5 -0

.006

3 0.

0291

0.00

94

0.00

89

1.04

71

0.29

55

-0.0

082

0.02

69

The

indi

rect

eff

ect o

f Par

ent n

etw

ork

cent

ralit

y on

Spi

noff

net

wor

k gr

owth

via

sp

inof

f net

wor

k st

atus

and

AC

APa (

abili

ty to

val

ue k

now

ledg

e)

T

he in

dire

ct e

ffec

t of P

aren

t net

wor

k ce

ntra

lity

on S

pino

ff n

etw

ork

grow

th v

ia sp

inof

f net

wor

k st

atus

and

AC

AP

(abi

lity

to a

pply

kno

wle

dge)

Ef

fect

B

ootS

E B

ootL

LCI

Boo

tULC

I

Effe

ct

Boo

tSE

Boo

tLLC

I B

ootU

LCI

TO

TAL

0.01

06

0.00

46

0.00

26

0.02

03

TOTA

L 0.

0201

0.

0058

0.

0096

0.

0323

spin

off n

etw

ork

stat

us

0.01

07

0.00

45

0.00

28

0.02

03

spin

off n

etw

ork

stat

us

0.01

52

0.00

5 0.

0065

0.

0257

AC

AP

(abi

lity

to v

alue

kn

owle

dge)

-0

.000

1 0.

0005

-0

.001

5 0.

0007

AC

AP

(abi

lity

to

appl

y kn

owle

dge)

0.

0049

0.

0027

0.

0000

0.

0107

(L

evel

of t

he c

onfid

ence

inte

rval

of a

ll ou

tput

s= %

95)

a Abs

orpt

ive

Cap

acity

Cha

pter

5: P

aren

tal N

etw

ork

Impr

intin

g in

Spi

noffs

: Und

erst

andi

ng th

e U

nder

lyin

g M

echa

nism

s 12

1

Tabl

e 5-

3 M

oder

ated

mul

tiple

med

iatio

n re

sults

(boo

t=50

00)

Mod

el 1

Mod

el 2

The

dir

ect e

ffec

t of p

aren

t net

wor

k ce

ntra

lity

on sp

inof

f net

wor

k gr

owth

T

he d

irec

t eff

ect o

f par

ent n

etw

ork

cent

ralit

y on

spin

off n

etw

ork

grow

th

Effe

ct

S.E.

t

p LL

CI

ULC

I Ef

fect

S.

E.

t p

LLC

I U

LCI

0.01

14

0.00

9 1.

2691

0.

205

-0.0

063

0.02

91

0.01

14

0.00

9 1.

2691

0.

205

-0.0

063

0.02

91

Con

ditio

nal e

ffec

t of p

aren

t net

wor

k ce

ntra

lity

on sp

inof

f net

wor

k gr

owth

C

ondi

tiona

l eff

ect o

f par

ent n

etw

ork

cent

ralit

y on

spin

off n

etw

ork

grow

th

Indi

rect

effe

ct:

Indi

rect

effe

ct:

pare

nt

netw

ork

cent

ralit

y ->

Sp

inof

f ne

twor

k st

atus

->

sp

inof

f ne

twor

k gr

owth

pare

nt

netw

ork

cent

ralit

y ->

Sp

inof

f ne

twor

k st

atus

->

sp

inof

f ne

twor

k gr

owth

Inde

x of

mod

erat

ed m

edia

tion

In

dex

of m

oder

ated

med

iatio

n

In

dex

Boo

tSE

Boo

tLLC

I B

ootU

LCI

Inde

x B

ootS

E B

ootL

LCI

Boo

tULC

I

Mar

ket

rela

tedn

ess

0.01

48

0.00

56

0.00

44

0.02

67

Prod

uctio

n re

late

dnes

s -0

.005

3 0.

0064

-0

.019

2 0.

0063

Indi

rect

effe

ct:

Indi

rect

effe

ct:

pare

nt

netw

ork

cent

ralit

y ->

Spin

off

AC

AP

(abi

lity

to

valu

e kn

owle

dge)

->

spin

off

netw

ork

grow

th

pare

nt

netw

ork

cent

ralit

y ->

Spin

off

AC

AP

(abi

lity

to

valu

e kn

owle

dge)

->

spin

off

netw

ork

grow

th

Inde

x of

mod

erat

ed m

edia

tion

In

dex

of m

oder

ated

med

iatio

n

In

dex

Boo

tSE

Boo

tLLC

I B

ootU

LCI

Inde

x B

ootS

E B

ootL

LCI

Boo

tULC

I

Mar

ket

rela

tedn

ess

0.00

03

0.00

09

-0.0

014

0.00

24

Prod

uctio

n re

late

dnes

s 0.

0009

0.

0026

-0

.003

9 0.

0066

(Lev

el o

f the

con

fiden

ce in

terv

al o

f all

outp

uts=

95%

)

Cha

pter

5: P

aren

tal N

etw

ork

Impr

intin

g in

Spi

noffs

: Und

erst

andi

ng th

e U

nder

lyin

g M

echa

nism

s 12

2

Tabl

e 5-

3 (c

ontin

ued)

- Mod

erat

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Figure 5-3 Moderating effect of market relatedness on the relationship between parent network

centrality and spinoff network status

5.5 ROBUSTNESS CHECKS

I further performed robustness checks. For doing this, I used spinoff network

size (measured as network size in each year) instead of network growth. I wanted to

see if not using a moving window could change the results. The results are respectively

shown in Tables 5-8 and 5-9 in Appendix D. I did not find any difference between the

new analysis results and previous findings.

5.6 DISCUSSION

In the previous study, I identified parent network centrality to be linked with

spinoff’s network growth but there is still a black box in terms of mechanisms through

which this effect unfolds in the network imprinting literature. Here, I addressed this

gap by using a set of Australian mining firms and their spinoffs, which provides a

focused test of a parental imprinting perspective. I examined and analysed the

underlying mechanisms of the parental network imprinting influence on the spinoff

network growth through two mediated paths. Organisational learning and knowledge

transfer have been used in prior network studies to explain how founding conditions


lead to network imprinting outcomes, but not tested empirically as the explaining

mechanism. I, too, used this lens in my first study. As a competing theoretical

explanation in the imprinting literature, network status and social categorisation have

also been utilised. I used these two lenses to build a multiple mediation model. Not

only is studying the underlying mechanisms of network imprinting a widely held

premise central to imprinting and networks theories, but it is largely untested.

Furthermore, I tested the contingencies by considering the moderating role of

knowledge relatedness between spinoff and its parent.

I predicted that parental network centrality has an imprinting influence on the

spinoff’s network growth through two competing theoretical paths: inheritance of

network status and organisational learning. Specifically, I examined the mediating role

of absorptive capacity in two ways: the ability to value and the ability to apply

knowledge. Furthermore, I examined the moderating effect of market and production

knowledge relatedness on the mediated paths. As reported in the prior section, I found

that spinoff absorptive capacity in terms of ability to apply knowledge and spinoff

network status mediate the relationship between parent network centrality and spinoff

network growth. I did not find significant results for the direct path in the presence of

mediators, which suggests a fully mediated model. I also found significant results for

the moderated mediation effect of market knowledge relatedness between spinoff and

its parent on the obtained significant mediated effect through spinoff network status.

My results highlight the value of the parent’s network position on spinoff

network growth. My study adds to the network development literature by applying

imprinting theory to predict spinoff’s network growth. I suggested and tested two

competing theoretical explanations to explain the network development process based

on two different theoretical lenses. Much of the literature on spinoff network


development has focused on the social embeddedness of entrepreneurs in their parent’s

networks, paying lesser attention to firm-level networks. I showed that one of the

factors that allows spinoff entrepreneurs to leverage their social networks is how well

their parent firm is located in the network positions of the whole industry network.

My results pinpoint the importance of inheritance and learning processes

between spinoff and its parent firm. I showed that these processes happen

simultaneously in the founding period. Spinoff entrepreneurs coming from parents in

more central positions in the network can benefit from both improving their firm’s

status and enhanced alliance network capabilities. This gives them an initial advantage

over other new firms in the industry network since they can start from a higher social

categorisation and legitimacy position in addition to having the advanced management

capabilities in partnering with other firms.

My results emphasise the value of knowledge relatedness with the parent in the

spinoff growth trajectory. Starting a firm in the markets closer to their parent firm’s

activities is more beneficial for spinoffs in their initial years of establishment in terms

of finding partners in the industry network that are willing to cooperate with them.

Prior literature often stresses the need for differentiation from parent’s activities in

order to establish an independent identity for the spinoff firm. However, I suggest if

the parent firm is performing well in their networks, spinoffs can use this opportunity

to find partners.

In addition, my findings expand the imprinting framework by suggesting

consideration of imprinting moderators as an underlying mechanism during the

genesis phase in Simsek et al.’s (2015) model. This is a step towards opening the black

box of imprinting that has mostly been theorised by previous research (Simsek et al.,


2015). So far, imprinting literature has mostly been focused on identifying the sources

of imprinting rather than the underlying process of imprints formation.

I have also used an advanced statistical design, which tests the whole multiple

mediation model as a whole, in addition to keeping a longitudinal design. Many studies

that test the mediating effects, develop the hypotheses for each piece of the model

separately and test each piece separately. I encourage future research to design and test

multiple mediation and conditional mediation models and test all components of their

models simultaneously in order to obtain a finer-grained perspective of the underlying

causal mechanisms.

The results failed to show any significant effect of multiple mediation through

spinoff absorptive capacity in terms of ability to value knowledge. This is while R&D

expenditure that I used to measure this construct is a widely used measure for

absorptive capacity in the prior literature (George et al., 2001). As may be seen in

Table 5-1, spinoff’s ability to value knowledge is related to commodity; thus, any

effects on growth may be masked by commodity effects. I also did not find significant

results for the moderating effect of production knowledge relatedness on the mediated

paths. Another explanation for nonsignificant mediating effect through spinoff’s

ability to value knowledge is simply that I lacked the power to detect a relationship

that exists.

5.7 LIMITATIONS AND IMPLICATIONS FOR FUTURE RESEARCH

My results may be generalised only with some caution. I performed my

analysis on a sample of mining firms in Australia. My focus on a single industry and

culturally homogeneous country helped to control for unobserved heterogeneity.

However, I cannot check if similar effects would be found in other cultural and

industrial settings. For instance, Anglo-Saxon countries are sometimes claimed to have


more transaction-oriented business cultures than do Scandinavian countries, which are

often seen as relationship-oriented cultures (Hofstede, 1980). I predict that this will

fortify my claims if, for example, tested in a Scandinavian-like culture. Additionally,

the mining industry is often seen as a capital-intensive industry compared to high-tech

industries that are knowledge-intensive associations. I have little reason to believe that

my results will be different in other high-tech industries. In fact, the capital intensity

may put more emphasis on the importance of building a good initial status and showing

higher management capabilities in alliances to attract external partners and investors.

Nevertheless, I suggest future studies to replicate my analysis in other industries and

countries.


5.8 APPENDIX C: ANALYSIS RESULTS FOR REGRESSION ANALYSIS

Table 5-4 Parent network centrality as a predictor of spinoff network status Dependent variable: Spinoff network status

Model 1 Model 2 Model 3 Coeff. S.E. Coeff. S.E. Coeff. S.E.

Control variables:

Constant -0.0007 0.001 -0.0018† 0.0009 -0.0008 0.0011 Network density 0.0012*** 0.0003 0.0012*** 0.0002 0.0012*** 0.0003 Spinoff age 0.0001 0.0002 0.0001 0.0001 0.0001 0.0002 Commodity 0.0024*** 0.0003 0.0019*** 0.0003 0.0025*** 0.0003 Spinoff firm size 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 Location 0.0007*** 0.0004 0.0003 0.0004 0.0008*** 0.0004 Spinoff profit status 0.0004 0.0008 0.0003 0.0007 0.0005 0.0008 Independent variable:

Parent’s network centrality

0.0001*** 0.0000 -0.0001† 0.0000 0.0001* 0.0001

Moderators:

Market relatedness

0.0018*** 0.0002

Production relatedness

0.0001 0.0005 Interaction terms:

Parent network centrality X Market relatedness

0.0001*** 0.000

Parent network centrality X Production relatedness

-0.0001 0.0001

R-sq. 0.1477

0.2325

0.1516

R2-change

0.0243*** 0.0012

F 13.3224

17.0017

0.7379

† p<0.1, * p<0.05, ** p<0.01, *** p<0.001 Number of observations=1045


Table 5-5 Parent network centrality as a predictor of spinoff absorptive capacity (ability to value knowledge) Dependent variable: Ability to value knowledge


Control variables: Constant 14.3541*** 0.3161 14.2605*** 0.3198 13.8562*** 0.3351 Network density -0.0595 0.0836 -0.0672 0.0835 -0.045 0.0825 Spinoff age 0.2166*** 0.0498 0.2119*** 0.0498 0.2112*** 0.0491 Commodity 0.2572 0.1099 0.2296* 0.1121 0.2716* 0.1086 Spinoff firm size 0.0703* 0.0224 0.0707** 0.0224 0.0688** 0.0221 Location -0.0932 0.1243 -0.1111 0.1262 -0.1193 0.1231 Spinoff profit status

-0.1557 0.2468 -0.1724 0.2464 -0.1931 0.2445

Independent variable:


-0.0078 0.0116 -0.0289† 0.0154 -0.0567*** 0.0165

Moderators:

Market relatedness

0.1351† 0.0779


0.6706*** 0.1575

Interaction terms:


0.0238* 0.0119


0.0726*** 0.0192

R-sq. 0.0649

0.0964

0.0724

R2-change

0.0069*** 0.0241*

F 5.3349

4.6518

6.3528



Table 5-6 Parent network centrality as a predictor of spinoff absorptive capacity (ability to apply knowledge) Dependent variable: Ability to apply knowledge


Control variables:

Constant 1.9223* 0.8130 0.8619 0.6867 -0.4311 0.7695 Network density 0.0241 0.2198 -0.0354 0.1839 0.1246 0.1975 Spinoff age -0.152 0.1307 -0.0881 0.1095 -0.0890 0.1175 Commodity 1.7683*** 0.2924 1.0488*** 0.2488 2.0109*** 0.2633 Spinoff firm size -0.1367*** 0.055 -0.0646 0.0463 -0.0811† 0.0497 Location 1.3958*** 0.3342 0.6369* 0.2835 1.6121*** 0.3019 Spinoff profit status -0.9092 0.6367 -0.7782 0.5329 -0.3679 0.5747 Independent variable:


0.1533*** 0.0309 0.0248 0.0334 0.0870* 0.0384

Moderators:

Market relatedness

2.3000*** 0.1743


3.0637*** 0.3763 Interaction terms:


0.0279 0.0272


0.0279 0.0465

R-sq. 0.1305

0.3937

0.3013

R2-change

0.2632

0.1708

F 12.6893

42.5663

28.2647



Table 5-7 Analysis results for spinoff network growth predictors Dependent variable: Spinoff network growth

Model 1 Model 2 Coeff. S.E. Coeff. S.E.

Control variables:

Constant -0.2150 0.5367 0.0741 0.2302 Spinoff network growth (lagged)

0.9018*** 0.0268 0.863*** 0.0256

Network density 0.2108** 0.0657 0.185** 0.0634 Spinoff age -0.2255*** 0.0417 -0.2122*** 0.0399 Commodity 0.3043*** 0.0912 0.2964** 0.09 Spinoff firm size -0.0037 0.0174 0.0112 0.0157 Location 0.0477 0.0967 0.0379 0.0958 Spinoff profit status -0.0844 0.1898 -0.1681 0.1801 Independent variable:

Parent’s network centrality 0.0114 0.009 0.0094 0.0089 Mediators:

Spinoff network status 102.1159*** 16.1299 111.1716*** 14.8295 Ability to value knowledge 0.0125 0.0333

Ability to apply knowledge

0.0318** 0.0123 R-sq. 0.8971

0.8929

F 466.1746

490.9771


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Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 135

Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth

6.1 INTRODUCTION

Employee spinoffs25 occur when employees of an incumbent firm (also known

as parent firm) leave and start a new independent company in the same industry as

their former employer (Klepper, 2001). Newly founded spinoff firms, like all new

entrants, face liabilities of newness (Stinchcombe, 1965) and smallness (Aldrich &

Auster, 1986) in their early years of establishment. These liabilities suggest that newly

founded firms are characterised by a lack of stable relationships and adequate

resources. However, young spinoffs vary considerably in their access to resources and

stable relationships, which could lead to differences in their early performances

(Bruneel et al., 2013). On the one hand, spinoff literature suggests that spinoffs can

tap into their parent’s resources (Parhankangas & Arenius, 2003). However, the

question is whether parental resources are as important for employee spinoffs, where

the parent firm has no role in their initiation and there is no obligation for a post-spinoff

linkage. On the other hand, entrepreneurship and strategy scholars have long noted the

importance of alliance networks for young firms to obtain access to necessary

resources (Hoang & Antoncic, 2003). Early establishment of alliance networks has

been shown to benefit new venture performance (Baum et al., 2000). However, the

question in the parent–spinoff context that is still under consideration is whether

25 Throughout this thesis, ‘spinoff’ term has been used to refer to employee spinoffs that are the focus of this dissertation. Other types of spinoffs have been referred to with their specific type’s name assigned in the literature (such as university spinoffs or government spinoffs), where there was a need to emphasise the difference between spinoffs’ categories.


spinoffs early performance is fostered by their previous access to their parent firms’

network resources or driven by their ability to establish an alliance network right from

the start. Do parent network characteristics and spinoff alliance networks have

independent direct effects on spinoffs early performance? Or is this an indirect effect

of parent network characteristics via influencing spinoff network growth?

Although there has been considerable research acknowledging the value of

strategic alliances for new firms’ early performance and growth (cf. Baum et al., 2000;

McGee & Dowling, 1994; McGee, Dowling, & Megginson, 1995; Stuart, Hoang, &

Hybels, 1999), in the context of newly founded spinoffs, the main focus has been on

the outcomes of the social network, and not on the firm-level alliances (cf. Elfring &

Hulsink, 2003, 2007; McEvily et al., 2012). While prior research suggests both social

networks and alliance networks views could be combined to present a better

understanding of new spinoffs’ alliance formation (Eisenhardt & Schoonhoven, 1996)

and subsequently their performance, there has been little attempt to study the firm-

level alliances in the spinoff literature. Therefore, I investigate the spinoff’s alliance

network effect on its early performance drawing on knowledge-based and learning

views.

Parent firm’s role as a source for initial resources and knowledge inheritance

has been highly recognised by prior research in the spinoff literature (Fryges & Wright,

2014). Klepper and Thompson (2010) note that ‘Better-performing firms have better-

performing intra-industry spinoffs’ (p.528). This assumption has often been tested for

the effect of performance indexes of parents on spinoffs’ survival rates (Fackler et al.,

2016), or on employment and revenue growth of spinoffs (Bruneel et al., 2013). The

parental effect in terms of knowledge transfer has also been tested. Phillips (2002) and

Agarwal et al. (2004) show that the transfer of knowledge from parent firm increases


the survival rates of spinoffs. What is less emphasised and empirically tested is the

effect of the quality of parental knowledge stock on spinoff’s subsequent performance,

such as parent firm network characteristics. This is specifically important for employee

spinoffs since, in the absence of a post-spinoff link, the lessons learned and knowledge

brought in by spinoff founders is their main initial advantage that can be deployed

towards achieving a superior performance (Klepper & Sleeper, 2005). And networks

are knowledge and information gateways for parents’ accumulated knowledge (Ahuja,

2000). Therefore, I test the parents’ network characteristics impact on spinoff early

performance drawing on knowledge transfer perspective.

I test my propositions using a sample of newly founded mining spinoff firms

in Australia. This choice provides a unique opportunity to study spinoff’s early

performance. Spinoffs are a very common way to start a new firm in this industry (as

shown in Figure 3-2). The mining sector is largely characterised as an alliance-

intensive industry (Bakker & Shepherd, 2017). And the use of Australia as a setting

provides a degree of homogeneity (Autio et al., 2000). I draw upon the Register of

Australian mining dataset that provides multilevel data on all mining firms in Australia

since 1980. I test my predictions through a study of 3370 strategic alliances and 248

mining spinoffs founded in Australia during the ten-year period from 2002 to 2011.

For performance data, I collected performance measures in terms of revenue for each

firm from MorningStar Premium dataset. I attempt to control for much of the observed

heterogeneity by including lagged performance in addition to controlling for several

spinoffs’ and parent firms’ characteristics and organisational measures. This enables

me to interpret my results with greater confidence.

I mainly contribute to the literature on spinoff performance. By dissecting the

drivers of spinoff early performance, I offer a more detailed evaluation of parent


knowledge transfer to spinoffs. Prior research has often used spinoff type as a predictor

of its performance, thereby failing to capture performance heterogeneity due to

knowledge transfer. I investigate whether knowledge transferred from parent to spinoff

is directly instrumental for spinoff’s early performance during the founding period.

6.2 THEORETICAL BACKGROUND AND HYPOTHESES

In the case of employee spinoffs that I have analysed and discussed in the two

previous chapters, parent firm does not initiate or sponsor the establishment of these

intra-industry spinoffs (Hunt & Lerner, 2012; Klepper, 2001). Broader spinoff

literature has identified other types of spinoffs, such as corporate spinoffs (that are

initiated and supported by a parent firm) (Parhankangas & Arenius, 2003), university

spinoffs (that are new firms which have been incubated in a university for the purpose

of spinoff) (Huyghe, Knockaert, & Obschonka, 2016), and recently introduced

government spinoffs (that are spinoffs from government facilities) (Woolley, 2017).

What is common among all these categories is the emphasis on the association of

spinoffs with their parent firms that has been shown to give spinoffs an initial

advantage in terms of access to initial resources and knowledge inheritance (Fryges &

Wright, 2014). The knowledge inherited due to the parent’s network characteristics

can be interpreted as the long arm of the parent in this context. Therefore, I consider

the network characteristics of the parent firm to be a predictor of spinoff performance.

I use a knowledge transfer perspective to explain the underlying mechanisms.

Some researchers have found a positive relationship between a new firm’s

alliance network size and its performance (Shan, Walker, & Kogut, 1994). However,

prior research has failed to generalise this relationship to different types of alliances

(Rothaermel & Deeds, 2006). The distinction between different types of alliances is

important from a knowledge-based perspective since different types of knowledge will


be transferred among partners engaged in alliancing activities in various stages of the

industry value chain including upstream and downstream26(Rothaermel & Deeds,

2006)(Rothaermel & Deeds, 2006)(Rothaermel & Deeds, 2006)(Rothaermel & Deeds,




2006) . There are also different managerial capabilities required to manage different

types of alliances (Rothaermel & Deeds, 2006). For instance, Baum et al. (2000)

hypothesised that the size of the alliance network at founding can have a positive effect

on a new biotechnology firm’s early performance. Since new biotechnology firms

might forge ties with different types of partners along the industry value chain, they

empirically tested their hypothesis for different alliance types. While their results

supported their hypothesis for downstream alliances (e.g., with pharmaceutical

companies), they could not detect a linear relationship between upstream alliance

network size (e.g., with research institutes) and new firm revenue. In another study,

Baum and Silverman (2004) found no linear relationship between upstream alliance

network size and new firms’ performance in terms of revenue. As suggested by

Rothaermel and Deeds (2006), the inconsistencies might have arisen from the different

demands that each type of alliancing partnership involves. For instance, Rothaermel

and Deeds (2006) show that upstream alliances demand higher levels of new firms’

alliance management capabilities compared to downstream alliances. They also show

that alliance experience (i.e., the cumulative sum of alliance duration for all

26 Upstream refers to the activities that are close to the exploitation of the natural resources, where the output of these activities is a raw material or commodity (Singer & Donoso, 2008). Downstream activities, on the other hand, are closer to the consumer end, where the final product is manufactured and distributed (Singer & Donoso, 2008).


partnerships of a new firm) plays a critical role in performance outcomes. Following

these arguments, I suggest that prior non-significant results might also be attributable

to the prior assumption that the relationship between upstream alliance network size

and spinoff performance is linear. There is also a gap in considering the cumulative

effect of the alliance network on new venture’s performance. I suggest considering

alliance network growth rather than the size would provide a more realistic modelling

of the real world, especially in the spinoff firms context. This is because, as extensively

discussed in Study I, the spinoff firm brings the inherited knowledge from its parent

firm through congenital learning mechanisms. But this knowledge has to be

assimilated with the new knowledge received from new external sources. Studying this

would only be possible over time and through consideration of accumulated

knowledge; such as through alliance network growth. By relaxing the assumption of

linearity and considering the effect of upstream alliance network growth, I will develop

a hypothesis for a nonlinear U-shaped relationship with spinoff performance based on

knowledge transfer and learning arguments.

My specific focus on the upstream alliances is coming from the fact that the

panel data of network connections that I have all involve upstream mining activities.

Bakker and Shepherd (2017) suggest three stages for mining activities: prospecting,

developing and exploiting. Prospecting stage involves the exploration of potential

fields, assessing the economic feasibility of a mining venture (Rasheed et al., 2012).

Developing stage is about more advances exploration in an attempt to produce more

information about the findings in the prospecting stage (Bakker & Shepherd, 2017).

And exploiting involves full-scale operations to exploit findings in the previous stages

(Bakker & Shepherd, 2017). Each of the alliance projects in the Register dataset falls


into one or more of these categories. Therefore, I would only hypothesise for a specific

type of alliances, that is upstream alliances.

6.2.1 Spinoff Alliance Network Growth and its Performance

Despite the many advantages that alliance networks can bring for new ventures,

especially in their early years of initiation (Baum et al., 2000), effective management

of alliances in order to achieve the desired outcomes is a difficult organisational task.

Alliances often do not proceed as well as expected and most of them fail (Kogut, 1989).

Rothaermel and Deeds (2006) show that upstream alliances are the highest demanding

types of alliances in terms of alliance management capabilities. They argue it is more

demanding since involvement in upstream alliances requires ‘…transfer of tacit,

ambiguous and complex knowledge…’ (Rothaermel & Deeds, 2006, p.437). There are

important similarities in this regard between firms in mineral mining and other

industries. The goal of upstream alliances in mining is to make significant discoveries

of ore bodies in often remote areas using numerous sophisticated equipment. Every

mining site and project can be different, specifically considering special conditions for

exploration and exploitation of a diverse range of commodities (e.g., gold, tin,

platinum, …) together with soil types, accessibility and government regulations (Zarea

Fazlelahi & Burgers, 2018).

For newly founded spinoffs that are coming from a parent firm, an initial

advantage is that they do not start from a clean slate because their founders have

brought in knowledge about the alliance management (for a comprehensive discussion

see Study I findings). However, engaging in alliances will expose them to new external

knowledge that they need to assimilate with their prior related knowledge in order to

effectively manage their alliances (Cohen & Levinthal, 1990) to, therefore, see

performance outcomes. However, this might be a challenging task for a newly founded


spinoff in the early years. Early growth of alliance networks means spinoff has to

overcome the challenges of alliancing activities and starting to build necessary

capabilities by assimilating their inherited knowledge and new knowledge transfer to

be able to see economic outcomes from these partnerships. Therefore, I suggest in the

very early stage spinoff alliance network growth will have an adverse influence on its

performance outcomes.

Learning perspective suggests that repeated engagements in the focal activity

will induce learning through learning-by-doing (Lieberman, 1984). Therefore,

repeated engagements in strategic alliances and repeated practising of assimilation of

knowledge will help spinoff firms to build codified routines and procedures in order

to effectively manage their alliances. Additionally, since the growth in their alliance

network over time will still occur in the upstream domain, there will be a cumulation

of the related knowledge, which would facilitate the transfer of knowledge for

subsequent alliances (Autio et al., 2000). This will improve spinoff’s performance in

subsequent alliances (Dyer & Singh, 1998; Zollo et al., 2002), that can potentially lead

to experiencing higher performance outcomes. Therefore, after hitting a minimum

point with the firm performance, forging ties with new alliances and growth of spinoff

alliance networks will start to pay off and have a positive effect on spinoff’s

performance.

Based on the arguments for downward and upward trends in the spinoff

performance influenced by the spinoff network growth over time, I suggest the

following:

Hypothesis 1: The relationship between a spinoff network growth and its early performance is U-shaped.


6.2.2 Parent Network Characteristics and Spinoff Performance

Accumulated evidence in the prior studies has secured sufficient empirical

support to heredity theory in parent–spinoff context (Klepper, 2009). Klepper (2001)

suggests that ‘…spinoffs benefit from the experience of their founders and the more

diverse those experiences, then the better the performance of spinoffs’ (p.660). This

emphasises the knowledge replication and transfers from the parent firm to spinoffs.

Therefore, employee spinoffs from parents with large stocks of knowledge are

expected to perform better (Agarwal et al., 2004; Klepper, 2009). Agarwal et al. (2004)

also discuss that inherited knowledge from a parent firm is more effectively transferred

to spinoff by its founders rather than knowledge acquired through hired employees.

Therefore, the quality and amount of parent firm’s knowledge are expected to be a

predictor of spinoff’s performance. This should be particularly important in the early

years of spinoff’s establishment, due to limited access to resources and finances, and

reliance on what experience and expertise is brought in by the founders.

One of the ways that the knowledge stock of the parent can be assessed is

potentially through its alliance network features and its positions in the industry

networks. In Study I, I considered two dimensions of parent network characteristics;

namely network size and centrality. Network size refers to the number of firms that a

focal firm is connected to immediately (Ahuja, 2000), which in this study they refer to

as the number of strategic alliances that a focal firm has. Powell et al. (1996) argue

that firms do not merely form inter-organisational collaborations for the purpose of

compensation for lack of resources and internal skills. Alliances should not be looked

at as one-off discrete transactions (Powell et al., 1996). Having a larger network of

alliances suggests beyond the development of cooperative routines and the ability to

maintain a large number of ties. Powell et al. (1996) suggest that a larger alliance

network of a firm shows its ability in transferring knowledge across alliances and being


located in important network positions that enable them to learn from industry

networks. Therefore, the larger alliance network of a parent firm can signal the higher

quality of its knowledge bases. Thus, I suggest that inheritance of knowledge from

such parent firms, in turn, can contribute more to a spinoff’s better early performance.

Therefore,

Hypothesis 2a: Parent’s larger network size at time of spinoff establishment will have a positive effect on spinoff’s early performance.

Network centrality refers to the ability of the focal firm to reach to direct as

well as indirect ties (Wasserman & Faust, 1994). The importance of network centrality

is because it suggests the position of the focal firm in the overall network of firms, and

not just among the immediately connected firms. Central positions in the network not

only reflect a firm’s reputation and visibility but also are information-rich positions

that facilitate the flow of information between two other firms that are not directly

connected (Freeman, 1978). This results in firms developing more divergent

capabilities for benefiting from collaborations (Powell et al., 1996). Therefore, parent

firms in more central positions in the network have potentially developed higher

quality accumulated knowledge. I suggest that employee spinoffs from such parent

firms will benefit more from the transferred knowledge for shaping their early

performance. Therefore,

Hypothesis 2b: Parent’s higher network centrality at time of spinoff establishment will have a positive effect on spinoff’s early performance.


Figure 6-1 Conceptual model

Figure 6-1 depicts the conceptual model of this study. I have not hypothesised

for the relationship between parent network characteristics and spinoff network growth

since Study I comprehensively focuses on this relationship.

6.3 RESEARCH METHODS

6.3.1 Data and Sample

I tested my hypotheses using multilevel longitudinal data on alliances,

organisational characteristics, and performance growth of spinoffs that began

operations in Australia during the ten-year period between 2002 and 2011. I compiled

data on 248 mining spinoffs that were founded during this period. The Register of

Australian Mining is the most comprehensive dataset in the existence of mining firms,

their directors and strategic alliances. This dataset tracks all Australian mining firms

from 1980 and is published annually as handbooks that are publicly available. It is

available in digital format from 2002 to 2011. The Register’s data about strategic

alliances includes all partnerships, the companies involved and their stake in each

project. It also gives a summary regarding the progress of each project in each year. I

crossed-checked information about founding time for each firm with D&B Global

Business dataset. I checked for name changes of firms from the Australian Exchange

website. It is because sometimes the disappearance and appearance of some companies


Spinoff’s Performance

Parent Network

Characteristic

H1

H2a, H2b


in the dataset are just due to name changes, not establishing a new firm. Economic

growth data was gathered from MorningStar Premium dataset for each spinoff.

My sample consists of new spinoff firms that were 10 years old or less as of

the year 2011. To be identified as spinoffs, firms had to be new companies where at

least 25% of their employees were coming from the same mining company

immediately one year before initiation (Muendler et al., 2012). The mining firm that

the majority of the founding team was coming from was identified as the parent firm

for that spinoff firm. Overall, I found 24827 new firms with such characteristics. By

incorporating information on all newly founded spinoffs during my observation

period, my research design avoids the common sample selection problem of

overrepresentation of currently successful firms that can cause a survival bias and

influence the inferences about factors producing organisational behaviour and success

(Baum & Silverman, 2004).

6.3.2 Measures

Dependent variables

A number of indicators of spinoff performance have been used by prior spinoff

studies. Several scholars have argued the inappropriateness of using profit-based

indicators for the early performance of young firms (Shane & Stuart, 2002) since most

new firms may be loss-making in their early stages. Therefore, revenue data is often a

preferred measure of firm growth and financial performance of new ventures (Baum

et al., 2000), because it is relatively accessible and applies to all sorts of firms and it is

relatively insensitive to capital intensity (Delmar, Davidsson, & Gartner, 2003). In the

27 This is a slightly bigger sample of spinoffs compared to the last two studies. It is because I relaxed one of the conditions of the first study, where I conducted replication. This condition was that new firms had to have at least one partner in their three first years after establishment to be included in the sample. Considering such condition was not necessary in this study.


employee spinoff context, employment growth has also been used as a measure of

performance (cf. Bruneel et al., 2013; Muendler et al., 2012). However, I could not

find data for this measure from any of the available datasets. Another widely used

indicator of spinoff firm performance in the literature is survival rates (Adams et al.,

2015; Fackler et al., 2016). However, this was not an appropriate choice for this study

considering the length of the observation period. As already noted in Chapter 3, out of

248 spinoffs founded in different time points during the ten-year period, only 14 firms

were terminated before 2011. This suggests failure is highly unlikely, and therefore it

is not a proper measure for performance in this analysis.

I obtained spinoffs’ revenue from annual reports of mining firms that are

available from Morningstar Premium dataset. To get a less skewed distribution of this

variable, I took a natural logarithm from revenue. I use absolute growth rather than

percentage growth. This preference is because many of the firm values at founding and

in early subsequent years are zeros. So, calculating percentage growth will generate a

lot of missing values.

To test my hypotheses, I estimated changes in these variables using a log-linear

growth model that suits linear methods (Wooldridge, 2015). It is also used by prior

longitudinal studies of performance (cf. Baum et al., 2000):

Where is a time-varying measure of performance, is a parameter that

indicates how current performance depends on prior performance, and is a vector of

parameters for the effects of independent and control variables. Inclusion of the lagged

dependent variable helps account for unobserved heterogeneity, which enables me to

draw causal inference with greater confidence.


I estimated my model with longitudinal data for each spinoff firm. I entered an

observation for each spinoff for every year that I had data. For instance, a spinoff that

has five years of data would contribute five observations to the analysis. The length of

each spinoff’s observations differs due to its founding time or failure during the

observation period.

The dependent variable is measured at time t+1 to let the effect of independent

variables unfold. I measure the early performance rather than long performance. This

choice is deliberate and provides a unique opportunity since I am looking at the long

arm of the parent and the influence of parental heritage. It is because Ferriani, Garnsey,

and Lorenzoni (2012) suggest that spinoffs are subject to reimprinting, that is, a

transformation process of newly started spinoffs. Their model suggests that after the

initial imprinting in the founding period, spinoffs go through a critical revision phase,

where they modify the initial imprinted and inherited traits from their parent to develop

their own idiosyncratic trajectory (i.e., reimprinting). There is also evidence in the

broader imprinting literature that imprints do not last forever, and the imprints are

subject to metamorphosis (e.g., change, evolution, and transformation) (Simsek et al.,

2015). To capture a clearer perspective about the influence of the parental network

characteristics on the spinoff performance, I chose to focus on the early years of

spinoff performance after being founded. That is also the reason I considered a one-

year gap between the dependent variable and other variables to stay within the

founding period, which is corroborated by many entrepreneurship studies to be less

than 10 years (cf. Carpenter, Pollock, & Leary, 2003; Milanov & Fernhaber, 2009;

Robinson, 1999).


Independent variables:

Spinoff network growth: As in Study I and II, network growth for each spinoff

firm is measured as a count of the total accumulated number of alliance partners

(Ahuja, 2000). I computed all network measures with the Social Network Analysis

software package Ucinet 6 (Borgatti et al., 2002). I calculated the dependent variable

using the five-year moving window for network matrices. This variable would count

all of the new alliance partners that the spinoff firm formed ties within the five-year

period preceding year t. As Gulati (2007) explains, this is because past alliances are

likely to have an influence on the current organisational outcomes. I used a moving

window of five years of prior alliances, based on research that suggests normal lifespan

for most alliances is usually no more than five years (Kogut, 1988).

Parent firm’s network size: As in Study I, I measure the network size of the

parent as the count of the number of alliance partners that a new venture’s first partner

had in the year of their alliance. I normalise this variable by dividing the number of

firms in the entire network for each respective year. This enables me to compare

measures across years (Borgatti et al., 2002; Wasserman & Faust, 1994). Then, I

transformed the variable by taking the natural logarithm due to lack of linearity. This

is a time-invariant covariate.

Parent firm’s network centrality: As in Study I, I use Freeman (1978)

centrality measurement that gives the expected value of the number of times a firm is

in the shortest path connecting two other firms. To normalise network centrality across

years, I divide each network centrality score by the maximum possible centrality score

in the respective year. Then I take the natural logarithm to address lack of linearity

(Cohen et al., 2003). This is a time-invariant covariate.


Control variables:

In addition to controlling for lagged dependent variable, I considered many

other factors that may influence the performance of a spinoff according to spinoff

literature. I controlled for a variety of additional spinoff and parent firm characteristics

in four main categories: organisational controls, human capital, parent firm

performance and post-spinoff links with the parent.

Organisational controls:

I also controlled for time since establishment (also known as spinoff firm age)

defined as the number of years since its founding, to ensure that any significant effects

of my theoretical variables were not a spurious result of company aging.

Human capital:

Education of the founding team has also been considered to be influential on

the new venture outcomes (Bosma, Van Praag, Thurik, & De Wit, 2004). I controlled

for PhD experience defined as whether there is a PhD holder in the founding team

(Taheri & van Geenhuizen, 2011).

Parental firm performance:

Parent firm’s financial situation has been considered to have an effect on the

success of the spinoff firm (Franco & Filson, 2006). Spinoffs coming from successful

parent firms and not out of necessity are shown to achieve higher growth rates

compared to spinoffs coming from parents in crisis (cf. Amankwah-Amoah, 2014;

Bruneel et al., 2013). Therefore, I controlled for parent’s profitability at the time of

spinoff founding by obtaining ROA measure (i.e., rerun on assets) for each parent firm.

I obtained parent’s ROA from annual reports of mining firms that are available from

Morningstar Premium dataset. This was a time-invariant variable.

Post-spinoff links with parent:


In order to operationalise this variable, we considered three ways that spinoffs

and parent firm would be connected after the spinoff event. These were: being involved

in a strategic alliance with parent or having a tie with parent (Uzunca, 2018), parent

firm holding ownership stake in the spinoff or parent ownership (Semadeni &

Cannella, 2011), and spinoff founders that continue to also work in the parent firm or

number still in the parent (Chesbrough, 2003). I defined a dummy variable for each

of these variables that was 1 when they were the case at time t, and zero otherwise.

These variables were also time-invariant.

All parent’s related measures are time-invariant and have been measured at

time t. Therefore, I used a random-effects model.

6.4 ANALYSIS AND RESULTS

Bivariate correlations and descriptive statistics are provided in Table 6-1. The

average age of spinoff firms (i.e., the time since establishment) is 3.6 and ranges from

one to 10 years. As evident from the table, there are not very high correlations between

variables, which means there is not a multicollinearity problem. Existence of

multicollinearity could lead to less precise parameter estimates for correlated

variables.

Table 6-2 reports analysis of random-effects regression of spinoff

performance. Model 1 is the baseline model which includes all the control variables. I

employed two-stage hierarchical regressions to test for the hypothesised U-shaped

effect of spinoff alliance network growth on its performance. An examination of

standardised betas in Model 2 would reveal the linear effects (if any) of spinoff

network growth on spinoff revenue growth. Although I did not hypothesise any such

effects, it was important to determine whether simple linear effects were present

(Cohen et al., 2003). Model 2 in Table 6-2 shows that there were no significant linear


effects of spinoff alliance network growth on revenue growth. In the second step

(shown in Model 3) the squared form of the measure of spinoff alliance network

growth was entered. For interpretation purposes, a positive quadratic term would

indicate a U-shaped upward curve, while a negative would indicate an inverted U-

shaped downward relationship (Hair, Anderson, Tatham, & Black, 1995). A

significant positive sign for these variables would thus support Hypothesis 1. Model 3

in Table 6-2 shows that spinoff network growth squared was significantly positively

related to revenue growth (β= 0.004, p<0.01), supporting Hypothesis 1.

To illustrate and interpret the patterns of my significant result, I graphically

presented the overall performance implications of Model 3 in Figure 6-2. Figure 6-2

shows how establishing an alliance in the upstream at founding influences spinoff’s

performance over time across all dimensions. Inspired by Baum et al. (2000), I decided

to use a multiplier index. A multiplier is broadly used in economic research studies

and refers to an economic factor that, when increased or changed, causes increases or

changes in many other related economic variables (Lennman, 2016). As noted by

Baum et al. (2000, p.281): ‘… a multiplier of greater (less) than 1 indicates that the

performance growth rate is increased (decreased) relative to the baseline rate by a

factor equal to the multiplier.’ The figure estimates spinoff performance over the

observed period that is one to 10 years. Following Baum et al. (2000), performance on

each dimension at t1 was set equal to the mean performance of a sample of spinoffs in

their establishment year; performance in years t2 to t10 was then estimated iteratively

using model coefficients from Table 6-2. As the figure shows, establishing alliances

at founding produces a U-shaped trajectory of spinoff performance.

Hypothesis 2a predicted that parent network size will have a positive effect on

spinoff performance. I did not find support for this hypothesis. Hypothesis 2b


suggested that parent network centrality will have a positive effect on spinoff

performance. I did not find support for this hypothesis. Further, I tested the spurious

effects of parent network centrality on the relationship between spinoff network

growth and its performance by entering both independent variables together in Model

6. The results were the same.

6.4.1 Supplementary Analysis

In addition to testing hypotheses, I undertook further investigation of the role

of parent network centrality based on prior findings in Study I. One of the main

findings in Study I was that parent network centrality will have a positive effect on

spinoff network growth. Considering the hypothesis has been developed for this

relationship in Study I, I explored a mediation model where spinoff network growth is

the mediator. This is since parent network characteristics could have an indirect effect

on spinoff performance. Table 6-3 shows the results of mediation analysis where

spinoff network growth mediates the relationship between parent network centrality

and spinoff performance. I considered a one-year time lag between the independent

variable and mediator, and also between the mediator and the dependent variable. All

the control variables were also considered in modelling. The results show that spinoff

network growth does not mediate the relationship between parent network centrality

and spinoff performance.

6.4.2 Robustness Checks

I also conducted robustness checks. To do this, I considered a longer time lag

between independent and dependent variables. I once considered a two-year and then

a three-year time lag. I wanted to see if a longer performance measure could change

the results. I kept all other variables at time t. The results are respectively shown in


Tables 6-4 and 6-5 in Appendix E. I did not find any difference between new results

and previous findings.

Cha

pter

6: C

omin

g O

ut o

f the

Par

ent’s

Sha

dow

: The

Rol

e of

Spi

noff’

s Ear

ly A

llian

ce N

etw

ork

Gro

wth

15

5

Tabl

e 6-

1 M

eans

, sta

ndar

d de

viat

ions

and

cor

rela

tion

Var

iabl

e M

ean

SD

1 2

3 4

5 6

7 8

9 10

11

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inof

f Rev

enue

a 12

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1.

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(lag

ged)

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off N

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t 3.

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a V

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dow

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noff’

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ly A

llian

ce N

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ork

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wth

15

6

Tabl

e 6-

2 R

ando

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ffect

s reg

ress

ion

resu

lts (D

epen

dent

Var

iabl

e: S

pino

ff R

even

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+1)

)

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el 1

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el 2

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N

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Figure 6-2 Estimated effect of founding alliances on spinoff performance

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 9 10

Mul

tiplie

r

Time Since Founding


Table 6-3 Mediation analysis results for testing direct and indirect effects of parent network centrality on spinoff performance

Coeff. Robust Std. Err.

z P>|z| [95% Conf. Interval]

Spinoff Network Growth (t=1)

<-


1.198 0.295 4.060 0.000 0.620 1.775

Time since establishment

0.408 0.081 5.020 0.000 0.249 0.567

Parent ROA 0.005 0.001 3.290 0.001 0.002 0.007 Tie with Parent dummy

0.370 0.383 0.970 0.333 -0.379 1.120

Parent Ownership dummy

0.933 0.536 1.740 0.082 -0.118 1.983

No. still in Parent

-0.042 0.160 -0.260 0.793 -0.356 0.272

PhD experience

0.536 0.317 1.690 0.091 -0.086 1.158

_cons 0.859 0.294 2.920 0.003 0.283 1.435 Spinoff Revenue (t=2)

<-


-0.028 0.022 -1.270 0.204 -0.071 0.015


0.289 0.189 1.530 0.127 -0.082 0.660

Time since establishment

0.262 0.059 4.410 0.000 0.145 0.378

Parent ROA 0.001 0.001 0.460 0.646 -0.002 0.003 Tie with Parent dummy

0.118 0.231 0.510 0.611 -0.335 0.571

Parent Ownership dummy

0.067 0.275 0.240 0.807 -0.471 0.605

No. still in Parent

-0.327 0.091 -3.590 0.000 -0.505 -0.148

PhD experience

0.189 0.253 0.750 0.456 -0.307 0.685

_cons 12.574 0.214 58.770 0.000 12.154 12.993 Var (e.Spinoff network growth (t=1))

11.493 1.623

8.714 15.159

Var (e. Spinoff revenue (t=2))

4.022 0.340

3.408 4.746


6.5 DISCUSSION

My aim in this study was to examine whether establishing an alliance network

at founding has an independent effect on spinoff’s performance beyond the parental

influence and use of complementary resources from the parent firm which is mostly

the case in an employee spinoffs context. My focus was on the early performance

spinoffs, since I, based on prior research (Ferriani et al., 2012), assumed that parental

influence will be more significant and less subject to change by other factors in the

early years of the spinoff after initiation. Prior studies have mostly tested the influence

of parental endowments on spinoff performance (cf. Andersson & Klepper, 2013;

Chatterji, 2009; Dick, Hussinger, Blumberg, & Hagedoorn, 2013). However, the

influence of spinoffs’ independent strategic choices on performance is largely

untested. My study set out to test and push the boundaries of networks theory by

considering examining a nonlinear relationship, i.e., the relationship between spinoff

alliance network growth and its performance. Further, I predicted that parent network

characteristics may be important for spinoffs’ performance in the employee spinoff

context.

Based on the knowledge-based and learning views, I predicted that spinoff

alliance network growth will have a negative effect on spinoff performance at first but

over time this effect will become positive. Specifically, I examined a U-shaped

relationship between spinoff network growth and its performance in terms of revenue.

My findings strongly supported the existence of a U-shaped relationship. Further, I

tested for parental influence through testing the effects of parent network

characteristics on spinoff performance. My results did not support these hypotheses.

One possible explanation is that the knowledge learned due to alliance collaborative

activities in the parent firm is not directly helpful for leading to short term financial


outcomes in the newly founded spinoffs. As shown in Study I, the position of the

parent in the whole network has a positive effect on spinoff’s alliance network

establishment and growth. However, the knowledge inherited by spinoff in this regard

is not the type of managerial knowledge and expertise that can be used for growing

revenue. Another explanation is that these effects might show themselves in a longer

period of time and I was unable to capture it due to the length of my observation period.

My findings offer two main contributions to the literature. First, in highlighting

early alliance network growth importance, I am able to further refine the knowledge

transfer perspective in explaining the new firm’s early performance benefits. By

considering a nonlinear relationship, I help reconcile some of the inconsistencies in

previous findings. Much of the research on strategic alliances had considered a linear

relationship for all types of strategic alliances based on network theories. Emphasis on

such mechanisms has sometimes led to mixed results in findings.

Second, the empirical support for the hypothesis predicting the U-shaped

relationship suggests that the managerial focus and type of alliances that spinoffs form

at founding may exercise a more important influence on their early performance than

hitherto recognised. As conceptualised in the prior research (Ireland, Hitt, &

Vaidyanath, 2002), alliance management can be a source of competitive advantage for

firms. In particular, the ability to manage a larger alliance network over time should

allow new firms to achieve higher performance outcomes. The consistent empirical

support for my hypothesis, therefore, suggests that investment in upstream strategic

alliances will not have an immediate performance outcome, but over time it will pay

off.


6.6 LIMITATIONS AND IMPLICATIONS FOR FUTURE RESEARCH

Performance has been considered as a multi-faceted phenomenon in the

entrepreneurship as well as strategy literature (Davidsson, 2004; Venkatraman &

Ramanujam, 1986) that involves various perspectives (e.g., shareholder vs employee)

(Semadeni & Cannella, 2011), time periods (e.g., long term vs short term) (Steffens,

Terjesen, & Davidsson, 2012), and criteria (e.g., new product vs profit) (George et al.,

2001). I could only measure one aspect of spinoff performance in terms of growth;

namely revenue growth. There is considerable debate in the entrepreneurship literature

regarding the use of different measures for firm growth (see Davidsson, Achtenhagen,

and Naldi (2005)). The other commonly used indicator of growth in the newly started

spinoffs is growth in employment (Bruneel et al., 2013). As mentioned before, I did

not find this data in any of the available datasets for this project. I suggest future

research to use different measures for operationalisation of spinoff performance

considering different aspects of growth.

Although I used longitudinal data to investigate spinoff growth in its early

years and it is one of the advantages of my research (Davidsson et al., 2005), the

limitation of my study was due to insufficient data for testing longer-term spinoff

performance. My data was only available for a ten-year period. It would be worthwhile

to test the short-term versus long-term spinoff performance to see how the effect of

independent variables unfolds in the long run. Specifically, as can be seen in Figure 6-

2 the multiplier never passes one, although it shows some indication of a long-term

benefit of establishing alliances in the founding period. A longer observation period

would have made it possible to test a longer effect.

As already discussed in the methods section, another indicator of performance

in spinoffs is survival rates (Adams et al., 2015; Fackler et al., 2016). However, this


was not an appropriate measure for this study since out of 248 firms founded in

different points in the observation period, only 14 did not survive until the end of the

observation period. A longer period would potentially see a higher mortality rate and

give me enough variance to conduct survival analysis. It would also be worthwhile

investigating parental influence in a longer period. I leave further investigation in this

regard to future research.

Cha

pter

6: C

omin

g O

ut o

f the

Par

ent’s

Sha

dow

: The

Rol

e of

Spi

noff’

s Ear

ly A

llian

ce N

etw

ork

Gro

wth

16

3

6.7

APP

EN

DIX

E: R

OB

UST

NE

SS R

ESU

LT

S W

ITH

2-Y

EA

R A

ND

3-Y

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R T

IME

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N D

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EN

T V

AR

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ND

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R V

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Tabl

e 6-

4 R

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m-e

ffect

s reg

ress

ion

resu

lts (D

epen

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e: s

pino

ff re

venu

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2))

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Chapter 7: Discussion and Conclusions 165

Chapter 7: Discussion and Conclusions

The main objective of this thesis was to provide insight into the early

establishment of alliance portfolio in spinoffs, understanding the underlying

mechanisms of this phenomenon, and trying to understand what this means for spinoff

firm’s early performance. In all three studies, a longitudinal research design has been

applied. A mixture of statistical techniques is utilised, including random-effects

regression, and (moderated) multiple mediation, in order to build towards a model for

understanding the main phenomenon on the firm level. This final chapter summarises

the main findings of the three studies in this thesis and highlights the key theoretical

contributions. Furthermore, I conclude by considering the implications for

practitioners, and suggestions for future research.

7.1 OVERVIEW OF THE MAIN FINDINGS

In the first study, my aim was to identify the predictors of spinoff network

growth. I conducted a replication of Milanov and Fernhaber (2009) and extended it to

the parent–spinoff context. Milanov and Fernhaber (2009) found a positive link

between the new firm’s initial partner network characteristics (including size and

centrality) and its subsequent network growth. In Study I, I utilised longitudinal data

of 237 newly founded spinoffs to expand Milanov and Fernhaber’s (2009) model to

the parent–spinoff context. I tested the positive imprinting effect of initial partner’s as

well as the parent firm’s network size versus centrality on the spinoff alliance network

growth. I identified parent’s greater network centrality to be a positive predictor of

spinoff’s subsequent network growth. My results did not support the positive


imprinting effect of the parent firm’s larger network size, or the initial partner’s

network size and centrality on spinoff network growth.

In Study II, my aim was to enrich our understanding of the parental network

imprinting dynamics. Informed by the findings of Study I, I tested two theoretical

explanations of the network imprinting process to investigate the underlying

mechanisms of the parent’s network centrality on spinoff network growth; namely

organisational learning versus social categorisation. I used the same sample as the first

study to test a multiple mediation model where spinoff absorptive capacity and spinoff

network status mediate the relationship between parent network centrality and spinoff

network growth. I further examined the boundary conditions of these relationships by

investigating the role of knowledge relatedness between the parent firm and spinoff.

My findings suggest that the indirect effect of parent network centrality on spinoff

network growth is mediated through spinoff absorptive capacity (in terms of ability to

apply knowledge) and spinoff network status. I also find that the conditional indirect

effect on parent network centrality on spinoff network growth via spinoff network

status is moderated by spinoff knowledge relatedness with the parent.

In Study III, I intended to find out about the performance implications of the

spinoff alliance network growth. I tested the effects of spinoff network growth and

parent’s network characteristics. Most importantly, I predicted a nonlinear U-shaped

relationship between alliance network growth and spinoff’s early performance. I used

secondary data on a sample of 248 mining spinoffs founded in Australia over the ten-

year period from 2002 to 2011. My results revealed the existence of the U-shaped

relationship and suggested an indication of a long-term positive effect on spinoff

performance.


7.2 THEORETICAL CONTRIBUTIONS

7.2.1 Contributions to Network-based Research in Entrepreneurship

This thesis primarily makes two main contributions to the network-based

research in entrepreneurship.

First, it enriches the literature by providing insights into the early stage network

growth of new firms on the firm level. Since most prior studies in the entrepreneurship

literature were aimed at explaining this phenomenon for social networks of

entrepreneurs on the individual level, there is a gap in our knowledge about the firm-

level network phenomena in the entrepreneurship (Hoang & Antoncic, 2003; Hoang

& Yi, 2015). This thesis complements existing research by focusing on the parent–

spinoff context and identifying the founding conditions that affect the alliance network

growth of newly founded spinoffs. In doing so, I also respond to a more general call

by Aldrich and Martinez (2001) to integrate context in the design of studies in

entrepreneurship research.

Second, alliance networks have often been ‘studied outside entrepreneurship’

(Slotte Kock & Coviello, 2010, p.32), and alliance formation theories have mostly

been tested on established firms. Therefore, the existing theories in the broader

strategic management literature can capture the many advantages of strategic alliances

for established firms based on their needs. However, organisational needs for firms

vary based on their stages of development (Hite & Hesterly, 2001). New firms face the

liability of newness and smallness, which deviates their priorities from firms in later

stages. Surprisingly, a few empirical studies have focused on developing theories for

alliance formation in the new firms’ literature. The findings of this thesis add to this

literature by suggesting and demonstrating two underlying mechanisms that can

explain alliance formations in newly founded firms (i.e., organisational learning, and

status development). Accordingly, I respond to calls by Slotte Kock and Coviello


(2010) and for capturing dynamics of change in the network-based research in

entrepreneurship.

7.2.2 Contributions to Spinoff Research Literature

In parallel to section 7.3.1, the collection of studies also contributes to the

spinoff research literature in general, and employee spinoff research literature in

particular.

This PhD thesis contributes to spinoff research by proposing new aspects of

resource inheritance from parent firms to their spinoffs. So far, the literature has

confirmed the importance of knowledge inheritance through spinoff founders (cf.

Agarwal et al., 2004; Chatterji, 2009). I extend this stream in multiple ways.

Specifically, the first study exceeds the consideration of parent’s technological and

market capability knowledge transfer (Agarwal et al., 2004), and investigates the

network resources of the parent as a source of knowledge inheritance in spinoffs. By

studying the influence of the parent firm’s versus initial partner’s network

characteristics as a knowledge source for spinoffs, I illuminate that a parent firm is

more important for a spinoff firm as a network knowledge source. This demonstration

is interesting since the initial partner has an ongoing network tie with the spinoff firm

in its founding period, but the parent firm may or may not be involved with the

employee spinoff in that period. This is also further evidence of genealogical

knowledge links between parent and spinoff firms (Ellis et al., 2017).

Next, while Study I assumed that spinoff’s merit in terms of network resources

was due to the inheritance of knowledge from its parent firm, Study II tested a

competing explanation for the inheritance from the parent that has been used in the

literature, namely network status. By exploring knowledge transfer versus network

status lenses, I showed that parent network characteristics can also shape spinoff’s


status at founding through inheritance. Accordingly, I also respond to a call by

Zimmerman and Zeitz (2002) for more empirical work about legitimacy as an

important resource for new firms.

The findings of this thesis also contribute to discussions in the spinoff research

about the team composition of newly founded spinoff firms. Current research suggests

that founding teams with common prior company affiliations are likely to engage in

exploitative behaviours (Beckman, 2006). Results from Study II also provide further

evidence for this proposition. This is because I only found evidence for the mediating

role of absorptive capacity in terms of the ability to apply knowledge. Zahra and

George (2002) framework suggests that the ability to apply knowledge centres on

knowledge transmission and exploitation. Therefore, my results suggest that

exploitation strategies can be a good orientation for newly founded spinoffs.

This doctoral thesis adds to research in the knowledge inheritance from parent

firm in the spinoff context. As already discussed in Study III, the assumption of

linearity cannot explain the relationship between alliance network growth and spinoff

performance for all types of alliances. For the specific type of upstream alliance

growth, spinoffs will not experience immediate performance outcomes. Over time,

through the cumulation of knowledge and assimilation with prior knowledge

transferred from the parent, spinoffs would be able to managerial capabilities that

would lead to positive performance outcomes after a minimum point. This further

supports prior findings that engagement in alliance networks helps the firm develop

management capabilities that lead to a competitive advantage (Dyer & Singh, 1998;

Ireland et al., 2002).

Finally, this thesis adds to the literature on spinoff research by testing the early

benefits of knowledge inheritance from the parent firm. This is specifically important


for finding answers to questions in the literature that suggest further investigation of

‘how long-lasting is the effect of being a spinoff on firm development?’ (Fryges &

Wright, 2014, p.255). Specifically, Study III looks at the early performance of spinoffs

in relation to a parent’s networks influence and a spinoff’s alliance networks

development. While I did not find any significant effects of parental network

characteristics on the revenue for the very early years, Study I findings showed the

importance of this effect for spinoff’s strategical outcomes such as alliance portfolio

development. This suggests there is a need for more fine-grained theorising about

knowledge inheritance from a parent to a spinoff. It would also be a response to the

specific call by McEvily et al. (2012) for more analysis of the character and content of

the source of knowledge inheritance.

7.2.3 Contributions to Imprinting Literature

This thesis makes contributions to the broader imprinting research in

entrepreneurship and strategic management.

This doctoral dissertation enriches the imprinting literature by providing insights

into the dynamics of imprinting. Specifically, as noted by Simsek et al. (2015) genesis

of imprinting has remained a black box that has mostly been taken for granted by most

scholars. Study II is one of the few attempts in the imprinting research that aims to

open this black box by delving into the underlying mechanisms. In this way, it extends

imprinting research in two important ways. First, this study suggests multiple

mediation models as a tool for designing and testing plausible explanations of the

genesis of imprints in empirical studies. It also suggests that imprinter’s characteristics

are likely to imprint the focal entity through not just one but several ways.

Accordingly, it is also a confirmation of the notion that transmission of imprints is a

complex process (Ellis et al., 2017). Second, the application of imprinting moderators


is quite a new perspective that has been applied in the second study by considering

knowledge overlap as a boundary condition. It suggests that under certain conditions

the focal entity’s receptivity and/or response to imprinting influences may differ. This

also is a response to a call by Simsek et al. (2015) to study the ‘failed imprinting’

instances and provides some answers for future research by developing the

consideration of boundary conditions on the genesis of imprints.

7.3 PRACTICAL IMPLICATIONS FOR MANAGEMENT

The findings in the three studies allow me to develop grounded prescriptions

for managers and practitioners. In this section, I set out the practical implications of

the dissertation for spinoff managers as well as alliance managers. These implications

are specifically applicable in the mining industry context.

7.3.1 Implications for Spinoff Managers

My findings show that when employees decide to leave their company to start

a new firm of their own, it is beneficial for them to start the new firm with some of

their colleagues from prior and most recent employment. The results of this thesis

indicate this is more pronounced if the parent firm had a better position in the industry

network in terms of centrality and embeddedness. Moreover, starting new firms with

colleagues from such parent firms is a worthwhile investment in terms of having a

good start in the industry with higher management capabilities and network status.

This can lead to better organisational outcomes for newly founded spinoffs.

Specifically, in the mining industry that is a capital-intensive as well as a project-based

sector, it becomes more important to signal quality and legitimacy right from the start

to attract more investment and involvement in larger projects.

The results of the second study suggested that market overlap with the parent

firm has a positive moderating effect on the network imprinting dynamics. This means


if spinoff managers start in a market that is closer to their parent firm, they will

experience better outcomes than starting from a completely new market. The

knowledge overlap with the parent in the new market gives spinoff firms an improved

absorptive capacity. This helps spinoff managers in better exploiting of the knowledge

they have built and brought in from their parent firm.

For spinoff managers, this research suggests that giving more attention at

founding on creating a team with common prior company affiliations is useful. This

means that shared understanding and knowledge should be acknowledged. These

affiliations are important for managers to consider since they can improve the adoption

of best practice that leads to enhanced performance and organisational outcomes of

newly established spinoffs. Additionally, deciding on the exploration or exploitation

orientation of the new firm can also be determined by the composition of the founding

team. In the case of employee spinoffs, exploitative strategies can lead to better results.

7.3.2 Implications for Strategic Alliance Managers

The findings of this doctoral dissertation can be used by strategic alliance

managers to develop guidelines for assessing external companies’ potential and

appropriateness for partnerships on projects. This is especially important when they

want to assess the capabilities of a newly founded firm as a potential future partner.

Since new firms do not have a track record that can suggest their future performance

in multiparty projects, alliance managers can look at their founding team and try to use

the information about their recent prior employment as a basis for their evaluation.

Specifically studying the credentials of the parent firm and the embeddedness of it in

the industry network can hint at the quality of the spinoff firm on some high levels. To

make better assessments, alliance managers can also look at the closeness of market

activities of the spinoff and their parent firm and consider the relatedness as a signal


of quality. Findings of this thesis also suggest managers should not expect an

immediate payoff from alliancing activities but should consider its positive influence

on revenue in the long-term.

7.4 LIMITATIONS AND FUTURE RESEARCH DIRECTIONS

My work has a number of limitations that raise opportunities for future

research. I have comprehensively discussed limitations that are specific to each study

in the respective chapter. Here, I provide some general discussions and offer some

future directions for this research stream.

‘Unfortunately, we cannot study “the whole World.”’ (Davidsson, 2004, p.79).

My results may be generalised only with some caution. I performed my analysis on a

sample of mining firms in Australia. My focus on a single industry and culturally

homogeneous country helped to control for unobserved heterogeneity. This increases

the precision of my model and findings. However, I cannot check if similar effects

would be found in other cultural and industrial settings. For instance, Anglo-Saxon

countries are sometimes claimed to have more transaction-oriented business cultures

than do Scandinavian countries, which are often seen as relationship-oriented cultures

(Hofstede, 1980). I predict that this will actually fortify my claims if, for example,

tested in a Scandinavian-like culture. Additionally, the mining industry is often seen

as a capital-intensive industry compared to high-tech industries that are seen as

knowledge-intensive associations. I have little reason to believe that my results will be

different in other high-tech industries. In fact, the capital intensity may put more

emphasis on the importance of building a good initial status and showing higher

management capabilities in alliances to attract external partners and investors.

Additionally, there are other capital-intensive industries, too. One of them is the oil

and gas industry, which is very similar to the mining industry, but the scope and scale


of projects could be larger compared to the average mineral mining sector.

Nevertheless, I suggest future studies to replicate my analysis in other industries and

countries.

I have performed my analysis based on 10 years of panel data. Application of

a longitudinal research design enabled me to make causal inferences and explore the

underlying mechanisms. My work could be extended even more if I had access to

longer periods of panel data. For instance, it would be possible to study several

generations of spinoffs (Ellis et al., 2017), or find answers to questions like how far

the long arm of the parent will reach. Also, as suggested by Fryges and Wright (2014),

it would be interesting to determine how long the benefits of being a spinoff will last.

Having a longer period of panel data for analysis would also add to the imprinting

research studies. For instance, studying the concept of second-hand imprinting

(Tilcsik, 2012) suggested by Marquis and Tilcsik (2013) would potentially enrich my

understanding of the social transmission of imprints. Additionally, studying longer

periods of panel data can provide insight into the metamorphosis phase of the imprints

that entail dynamics of persistence, amplification, decay, and transformation of

imprints that is an overlooked area in the imprinting research (Simsek et al., 2015). As

already discussed in Chapter 3, expansion of the observation to over 10 years was out

of the scope of a PhD thesis due to limited time and resources. I encourage future

studies to consider the suggested future direction with longer longitudinal data.

Another fruitful direction for this research would be conducting multilevel

analysis. Marquis and Tilcsik (2013) model of imprinting suggests the

multidimensionality of the environment and the resulted imprint that reflects elements

of its environment. One aspect could be the influence of particular individuals or the

economic and institutional conditions that together constitute the stamp of the period.


Studying the interplay of these effects from different levels and how they lead to the

formation of specific imprints would be an interesting way of extending this research.

It would also add to our knowledge about the origins of imprints and how to make a

distinction between historical origins.

7.5 CONCLUSION

Investigating the imprinting effect of parental networks on antecedents,

dynamics, and outcomes of alliance network growth is an important but overlooked

area of research in the parent–spinoff firms’ context. This thesis shed light on the

influence of parent firm features on the spinoff network growth. Through three

longitudinal studies of the Australian mining spinoffs, I created important new insights

into the phenomenon. I showed that parent network centrality has a positive imprinting

effect on the subsequent network growth of spinoff (Study I). Parent network centrality

has an indirect positive effect on spinoff network growth through the mechanisms of

spinoff absorptive capacity (in terms of ability to apply knowledge) and spinoff

network status (Study II). The spinoff’s degree of knowledge relatedness with the

parent firm (in terms of market relatedness) positively moderates the effects of parent

network centrality on spinoff network status, thereby enhancing the indirect positive

effect of parent network centrality on spinoff network growth (Study II). I ruled out

the positive linear effect of spinoff network growth on the early spinoff performance

and detected the existence of a U-shaped relationship (Study III). This provides

important new avenues for future research in network-based research in

entrepreneurship in general, and network imprinting research in particular.

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