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
Australian Centre for Entrepreneurship Research
QUT Business School
Queensland University of Technology
Associate Supervisor
Professor Per Davidsson
Australian Centre for Entrepreneurship Research
QUT Business School
Queensland University of Technology
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).
Chapter 1: Introduction 2
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
Chapter 1: Introduction 3
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).
Chapter 1: Introduction 4
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
Chapter 1: Introduction 5
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.
Chapter 1: Introduction 6
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.
Chapter 1: Introduction 7
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.
7 This has been comprehensively discussed in Chapter 3.
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Chapter 1: Introduction 9
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:
Chapter 1: Introduction 10
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 &
Chapter 1: Introduction 11
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-
Chapter 1: Introduction 12
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,
Chapter 1: Introduction 13
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:
Chapter 1: Introduction 14
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.
Chapter 1: Introduction 15
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
Chapter 2: Literature Review 18
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
Chapter 2: Literature Review 19
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
Chapter 2: Literature Review 20
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 &
Chapter 2: Literature Review 21
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
Chapter 2: Literature Review 22
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.
Chapter 2: Literature Review 23
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
Chapter 2: Literature Review 24
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.
Chapter 2: Literature Review 25
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
Chapter 2: Literature Review 26
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
Chapter 2: Literature Review 27
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 &
Chapter 2: Literature Review 28
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
Chapter 2: Literature Review 29
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).
Chapter 2: Literature Review 30
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
Chapter 2: Literature Review 31
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
Chapter 2: Literature Review 32
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.
Chapter 2: Literature Review 33
(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
Chapter 2: Literature Review 34
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
Chapter 2: Literature Review 35
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, &
Chapter 3: Research Methodology 38
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.
Chapter 3: Research Methodology 39
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
Chapter 3: Research Methodology 40
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.
Chapter 3: Research Methodology 41
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
Chapter 3: Research Methodology 42
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
Chapter 3: Research Methodology 43
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.
Chapter 3: Research Methodology 44
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
Chapter 3: Research Methodology 45
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
Chapter 3: Research Methodology 46
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.
Chapter 3: Research Methodology 47
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
Chapter 3: Research Methodology 48
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
Chapter 3: Research Methodology 49
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
Chapter 3: Research Methodology 50
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.
Chapter 3: Research Methodology 51
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.,
Chapter 3: Research Methodology 52
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
Chapter 3: Research Methodology 53
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
Chapter 3: Research Methodology 54
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
Chapter 3: Research Methodology 55
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’.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 58
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.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 59
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.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 60
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 61
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.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 62
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-
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 63
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.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 64
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).
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 65
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,
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 66
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 67
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,
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 68
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 69
(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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 70
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 71
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 72
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.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 73
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 74
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 75
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 76
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).
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 77
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 78
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.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 79
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 80
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
pter
4: P
redi
ctor
s of S
pino
ff A
llian
ce N
etw
ork
Gro
wth
: The
Rol
e of
Cen
tralit
y ve
rsus
Siz
e of
Par
ent F
irm’s
Net
wor
k 81
Tabl
e 4-
1 M
eans
, sta
ndar
d de
viat
ions
and
cor
rela
tion
for s
pino
ff fi
rms
S.E.
0.
107
0.10
0 0.
058
0.02
0 0.
015
0.11
7 0.
029
0.01
7 0.
047
0.15
6 0.
200
0.18
0 0.
014
Mea
n 3.
053
2.46
8 1.
853
0.64
6 0.
252
-5.8
48
2.37
0 0.
647
-4.6
20
-2.0
35
-8.3
88
-6.0
75
0.18
6
Var
iabl
es
1 2
3 4
5 6
7 8
9 10
11
12
13
1. S
pino
ff n
etw
ork
grow
th
1.00
0
2.
Spi
noff
net
wor
k gr
owth
(la
gged
) 0.
919*
1.
000
3.
Tim
e si
nce
esta
blish
men
t 0.
282*
0.
391*
1.
000
4. C
omm
odity
a 0.
433*
0.
429*
0.
189*
1.
000
5.
Spi
noff
pro
fit st
atus
-0
.063
-0
.071
* -0
.235
* -0
.103
* 1.
000
6. S
pino
ff si
ze
-0.0
54
-0.1
07*
-0.4
44*
-0.0
91*
0.68
2*
1.00
0
7. In
dustr
y ne
twor
k de
nsity
b 0.
006
-0.0
87*
-0.5
11*
-0.0
48
0.12
2*
0.31
7*
1.00
0
8.
Spi
noff
loca
tion
0.08
8*
0.08
4*
-0.0
16
0.05
6 0.
030
0.03
7 -0
.053
1.
000
9.
Initi
al p
artn
er's
netw
ork
size
a 0.
060
0.07
2*
0.09
2*
-0.0
56
-0.0
73*
-0.0
73*
0.11
3*
-0.0
93*
1.00
0
10
. Ini
tial p
artn
er's
netw
ork
cent
ralit
y a
-0.0
09
0.00
9 0.
085*
-0
.084
* -0
.078
* -0
.082
* 0.
099*
-0
.048
0.
820*
1.
000
11
. Par
ent's
net
wor
k si
ze a
0.13
0*
0.12
3*
0.03
8 -0
.007
-0
.040
0.
044
0.07
7*
-0.0
72*
-0.1
09*
-0.0
78*
1.00
0
12
. Par
ent's
net
wor
k ce
ntra
lity
a 0.
179*
0.
176*
0.
045
0.04
5 -0
.021
0.
046
0.09
5*
-0.1
04*
0.00
8 0.
062*
0.
675*
1.
000
13
. Spi
noff
ow
ners
hip
stat
us
-0.0
67
-0.0
38
0.08
4*
-0.0
16
-0.1
12*
-0.1
03*
-0.0
59
-0.0
05
0.18
4*
0.21
3*
0.02
7 0.
070*
1.
000
*P<0
.05
(n=1
045)
a v
aria
bles
hav
e be
en tr
ansf
orm
ed
b var
iabl
e ha
s bee
n sc
aled
by
1000
Cha
pter
4: P
redi
ctor
s of S
pino
ff A
llian
ce N
etw
ork
Gro
wth
: The
Rol
e of
Cen
tralit
y ve
rsus
Siz
e of
Par
ent F
irm’s
Net
wor
k 82
Tabl
e 4-
2 R
ando
m-e
ffec
ts P
oiss
on re
gres
sion
resu
lts (d
epen
dent
var
iabl
e: sp
inof
f net
wor
k gr
owth
)
Mod
el 1
Mod
el 2
Mod
el 3
Mod
el 4
Mod
el 5
Mod
el 6
Mod
el 7
C
oeffi
cien
t S.
E.
Coe
ffici
ent
S.E.
C
oeffi
cien
t S.
E.
Coe
ffici
ent
S.E.
C
oeffi
cien
t S.
E.
Coe
ffici
ent
S.E.
C
oeffi
cien
t S.
E.
Con
trol
var
iabl
es:
Spin
off n
etw
ork
grow
th (l
agge
d)
0.15
2***
(0
.009
) 0.
153*
**
(0.0
09)
0.15
1***
(0
.009
) 0.
153*
**
(0.0
09)
0.15
0***
(0
.009
) 0.
152*
**
(0.0
09)
0.15
1***
(0
.009
)
Indu
stry
net
wor
k de
nsity
0.
093*
* (0
.035
) 0.
099*
* (0
.036
) 0.
090*
(0
.036
) 0.
100*
* (0
.036
) 0.
084*
(0
.036
) 0.
096*
* (0
.036
) 0.
090*
(0
.036
)
Spin
off o
wne
rshi
p sta
tus
-0.1
34†
(0.0
75)
-0.1
17
(0.0
76)
-0.1
36†
(0.0
75)
-0.1
12
(0.0
76)
-0.1
48*
(0.0
75)
-0.1
20
(0.0
76)
-0.1
25
(0.0
76)
Tim
e si
nce
initi
al p
artn
er
-0.0
49*
(0.0
22)
-0.0
48*
(0.0
22)
-0.0
51*
(0.0
22)
-0.0
47*
(0.0
22)
-0.0
55*
(0.0
22)
-0.0
49*
(0.0
22)
-0.0
53*
(0.0
22)
Com
mod
ity
0.18
0***
(0
.049
) 0.
172*
**
(0.0
49)
0.18
3***
(0
.049
) 0.
170*
**
(0.0
49)
0.18
6***
(0
.049
) 0.
175*
**
(0.0
49)
0.17
5***
(0
.049
)
Spin
off s
ize
0.00
6 (0
.011
) 0.
006
(0.0
11)
0.00
5 (0
.011
) 0.
006
(0.0
11)
0.00
2 (0
.012
) 0.
005
(0.0
11)
0.00
2 (0
.011
)
Spin
off l
ocat
ion
0.12
3*
(0.0
62)
0.11
4†
(0.0
62)
0.12
7*
(0.0
62)
0.11
7†
(0.0
62)
0.13
1*
(0.0
62)
0.11
8†
(0.0
62)
0.12
4*
(0.0
61)
Spin
off p
rofit
stat
us
-0.0
72
(0.0
74)
-0.0
75
(0.0
74)
-0.0
68
(0.0
74)
-0.0
78
(0.0
74)
-0.0
63
(0.0
74)
-0.0
71
(0.0
74)
-0.0
70
(0.0
74)
Inde
pend
ent v
aria
bles
:
In
itial
par
tner
's ne
twor
k si
ze
-0.0
26
(0.0
24)
-0.0
23
(0.0
24)
Pare
nt's
netw
ork
size
0.
005
(0.0
05)
0.00
4 (0
.005
)
In
itial
par
tner
's ne
twor
k ce
ntra
lity
-0.0
09
(0.0
07)
-0.0
09
(0.0
07)
Pare
nt's
netw
ork
cent
ralit
y
0.
014*
(0
.006
)
0.
014*
(0
.006
)
Con
stan
t 0.
341*
(0
.147
) 0.
214
(0.1
88)
0.38
3*
(0.1
53)
0.31
2*
(0.1
48)
0.42
6**
(0.1
51)
0.26
4 (0
.197
) 0.
397*
* (0
.151
)
Ln a
lpha
C
onst
ant
-2.9
83**
* (0
.290
) -3
.040
***
(0.3
04)
-2.9
81**
* (0
.289
) -3
.043
***
(0.3
04)
-2.9
98**
* (0
.287
) -3
.033
***
(0.3
03)
-3.0
69**
* (0
.304
)
Log
likel
ihoo
d -1
147.
600
-1
147.
100
-1
147.
200
-1
146.
800
-1
144.
900
-1
146.
700
-1
144.
000
W
ald
chi-s
quar
e 48
5.34
0
502.
15
48
6.45
503.
98
49
5.86
*
501.
460
51
7.34
*
Cha
nge
in li
kelih
ood
ratio
chi
-squ
are
16.8
10
1.
110
18
.640
10.5
20*
16
.120
32.0
00*
† p<
0.1,
* p
<0.0
5, *
* p<
0.01
, ***
p<0
.001
stan
dard
erro
rs a
re in
par
enth
esis
num
ber o
f obs
erva
tions
= 10
45
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 83
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 84
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.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 85
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 86
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.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 87
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 88
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).
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 89
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 90
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
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 91
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.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 92
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.
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 93
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
redi
ctor
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ff A
llian
ce N
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Gro
wth
: The
Rol
e of
Cen
tralit
y ve
rsus
Siz
e of
Par
ent F
irm’s
Net
wor
k 94
4.9
APP
EN
DIX
B: R
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NE
SS R
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LT
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TH
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stry
net
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* (0
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* (0
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* (0
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* (0
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0981
* (0
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off o
wne
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ity
0.16
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7
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ize
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4
Chapter 4: Predictors of Spinoff Alliance Network Growth: The Role of Centrality versus Size of Parent Firm’s Network 95
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
initial partner network centrality 0.9914 0.0074 -1.1700 0.2430 0.9771 1.0059 4
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
initial partner network centrality 0.9909 0.0073 -1.2500 0.2130 0.9767 1.0053 7
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.
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 98
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 99
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 100
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 101
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 102
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 103
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 104
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 105
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 106
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 107
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 108
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 109
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 110
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.
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 111
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 112
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:
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 113
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 114
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 115
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 116
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.
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 117
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 118
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
.061
3*
0.03
7 1
14.
Mar
ket
rela
tedn
ess
0.31
21*
0.28
45*
0.21
23*
0.06
21*
0.19
11*
0.20
95*
0.02
17
0.01
8 0.
5683
* 0.
2385
* -0
.065
4*
0.11
84*
-0.1
113*
1
15.
Prod
uctio
n re
late
dnes
s -0
.049
2 -0
.033
6 0.
1131
* 0.
0374
-0
.010
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
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
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
ed m
ultip
le m
edia
tion
resu
lts (b
oot=
5000
) M
odel
3
M
odel
4
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
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
Effe
ct
S.E.
t
p LL
CI
ULC
I Ef
fect
S.
E.
t p
LLC
I U
LCI
0.00
94
0.00
89
1.04
71
0.29
55
-0.0
082
0.02
69
0.00
94
0.00
89
1.04
71
0.29
55
-0.0
082
0.02
69
Con
ditio
nal e
ffec
t of p
aren
t net
wor
k ce
ntra
lity
on s
pino
ff n
etw
ork
grow
th
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
In
dire
ct e
ffect
:
In
dire
ct e
ffect
:
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
15
0.00
55
0.00
07
0.02
24
Prod
uctio
n re
late
dnes
s -0
.012
1 0.
0072
-0
.027
2 0.
0007
Indi
rect
effe
ct:
Indi
rect
effe
ct:
pare
nt
netw
ork
cent
ralit
y ->
Spin
off
AC
AP
(abi
lity
to
appl
y kn
owle
dge)
->
spin
off
netw
ork
grow
th
pare
nt
netw
ork
cent
ralit
y ->
Spin
off
AC
AP
(abi
lity
to
appl
y 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
09
0.00
11
-0.0
012
0.00
34
Prod
uctio
n re
late
dnes
s 0.
0009
0.
0016
-0
.001
9 0.
0046
(Lev
el o
f the
con
fiden
ce in
terv
al o
f all
outp
uts=
95%
)
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 123
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 124
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 125
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.,
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 126
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 127
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.
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 128
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
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 129
Table 5-5 Parent network centrality as a predictor of spinoff absorptive capacity (ability to value knowledge) Dependent variable: Ability to value knowledge
Model 1 Model 2 Model 3 Coeff. S.E. Coeff. S.E. Coeff. S.E.
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:
Parent’s network centrality
-0.0078 0.0116 -0.0289† 0.0154 -0.0567*** 0.0165
Moderators:
Market relatedness
0.1351† 0.0779
Production relatedness
0.6706*** 0.1575
Interaction terms:
Parent network centrality X Market relatedness
0.0238* 0.0119
Parent network centrality X Production relatedness
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
† p<0.1, * p<0.05, ** p<0.01, *** p<0.001 Number of observations=1045
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 130
Table 5-6 Parent network centrality as a predictor of spinoff absorptive capacity (ability to apply knowledge) Dependent variable: Ability to apply knowledge
Model 1 Model 2 Model 3 Coeff. S.E. Coeff. S.E. Coeff. S.E.
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:
Parent’s network centrality
0.1533*** 0.0309 0.0248 0.0334 0.0870* 0.0384
Moderators:
Market relatedness
2.3000*** 0.1743
Production relatedness
3.0637*** 0.3763 Interaction terms:
Parent network centrality X Market relatedness
0.0279 0.0272
Parent network centrality X Production relatedness
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
† p<0.1, * p<0.05, ** p<0.01, *** p<0.001 Number of observations=1045
Chapter 5: Parental Network Imprinting in Spinoffs: Understanding the Underlying Mechanisms 131
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
† p<0.1, * p<0.05, ** p<0.01, *** p<0.001 Number of observations=1045
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 13
2
5.9
APP
EN
DIX
D: R
OB
UST
NE
SS C
HE
CK
RE
SUL
TS
USI
NG
SPI
NO
FF N
ET
WO
RK
SIZ
E A
S T
HE
DE
PEN
DE
NT
VA
RIA
BL
E
Tabl
e 5-
8 M
ultip
le m
edia
tion
resu
lts (b
oot=
5000
)
Mod
el 1
Mod
el 2
The
dir
ect e
ffec
t of P
aren
t net
wor
k ce
ntra
lity
on S
pino
ff n
etw
ork
size
T
he d
irec
t eff
ect o
f Par
ent n
etw
ork
cent
ralit
y on
Spi
noff
net
wor
k si
ze
Effe
ct
S.E.
t
p LL
CI
ULC
I Ef
fect
S.
E.
t p
LLC
I U
LCI
0.09
6 0.
0251
3.
8187
0.
0001
0.
0466
0.
1454
0.
0798
0.
0241
3.
3125
0.
001
0.03
25
0.12
71
The
indi
rect
eff
ect o
f Par
ent n
etw
ork
cent
ralit
y on
Spi
noff
net
wor
k si
ze v
ia
spin
off n
etw
ork
stat
us a
nd A
CA
P (a
bilit
y to
val
ue k
now
ledg
e)
The
indi
rect
eff
ect o
f Par
ent n
etw
ork
cent
ralit
y on
Spi
noff
net
wor
k si
ze v
ia
spin
off n
etw
ork
stat
us a
nd A
CA
P (a
bilit
y to
app
ly k
now
ledg
e)
Ef
fect
B
ootS
E B
ootL
LCI
Boo
tULC
I
Ef
fect
B
ootS
E B
ootL
LCI
Boo
tULC
I
TOTA
L 0.
0572
0.
022
0.01
65
0.10
32
TO
TAL
0.08
72
0.02
3 0.
0452
0.
1345
spin
off
netw
ork
stat
us
0.05
78
0.02
19
0.01
75
0.10
38
sp
inof
f net
wor
k st
atus
0.
0651
0.
0187
0.
031
0.10
41
AC
AP
(abi
lity
to v
alue
kn
owle
dge)
-0
.000
6 0.
0013
-0
.003
8 0.
0015
AC
AP
(abi
lity
to
appl
y kn
owle
dge)
0.
0221
0.
0106
0.
004
0.04
55
(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 13
3
Tabl
e 5-
9 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 si
ze
The
dir
ect e
ffec
t of p
aren
t net
wor
k ce
ntra
lity
on sp
inof
f net
wor
k si
ze
Effe
ct
S.E.
t
p LL
CI
ULC
I Ef
fect
S.
E.
t p
LLC
I U
LCI
0.09
6 0.
0251
3.
8187
0.
0001
0.
0466
0.
1454
0.
096
0.02
51
3.81
87
0.00
01
0.04
66
0.14
54
Con
ditio
nal e
ffec
t of p
aren
t net
wor
k ce
ntra
lity
on sp
inof
f net
wor
k si
ze
Con
ditio
nal e
ffec
t of p
aren
t net
wor
k ce
ntra
lity
on sp
inof
f net
wor
k si
ze
Indi
rect
effe
ct:
Indi
rect
effe
ct:
pa
rent
ne
twor
k ce
ntra
lity
->
Spin
off
netw
ork
stat
us
->
spin
off
netw
ork
size
pare
nt
netw
ork
cent
ralit
y ->
Sp
inof
f ne
twor
k st
atus
->
sp
inof
f ne
twor
k si
ze
Inde
x of
mod
erat
ed m
edia
tion
Inde
x of
mod
erat
ed m
edia
tion
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
0.07
77
0.02
62
0.02
53
0.12
77
Pr
oduc
tion
rela
tedn
ess
-0.0
315
0.03
2 -0
.094
6 0.
0299
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
size
pare
nt
netw
ork
cent
ralit
y ->
Spin
off A
CA
P (a
bilit
y to
va
lue
know
ledg
e)
->
spin
off
netw
ork
size
Inde
x of
mod
erat
ed m
edia
tion
Inde
x of
mod
erat
ed m
edia
tion
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
18
0.00
21
-0.0
013
0.00
7
Prod
uctio
n re
late
dnes
s 0.
0055
0.
0054
-0
.004
3 0.
0171
(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 13
4
Tabl
e 5-
9 (c
ontin
ued)
- Mod
erat
ed m
ultip
le m
edia
tion
resu
lts (b
oot=
5000
) M
odel
3
M
odel
4
The
dir
ect e
ffec
t of p
aren
t net
wor
k ce
ntra
lity
on sp
inof
f net
wor
k si
ze
The
dir
ect e
ffec
t of p
aren
t net
wor
k ce
ntra
lity
on sp
inof
f net
wor
k si
ze
Effe
ct
se
t p
LLC
I U
LCI
Effe
ct
se
t p
LLC
I U
LCI
0.07
98
0.02
41
3.31
25
0.00
1 0.
0325
0.
1271
0.
0798
0.
0241
3.
3125
0.
001
0.03
25
0.12
71
Con
ditio
nal e
ffec
t of p
aren
t net
wor
k ce
ntra
lity
on sp
inof
f net
wor
k si
ze
Con
ditio
nal e
ffec
t of p
aren
t net
wor
k ce
ntra
lity
on sp
inof
f net
wor
k si
ze
Indi
rect
effe
ct:
Indi
rect
effe
ct:
pa
rent
ne
twor
k ce
ntra
lity
->
Spin
off
netw
ork
stat
us
->
spin
off
netw
ork
size
pare
nt
netw
ork
cent
ralit
y ->
Sp
inof
f ne
twor
k st
atus
->
sp
inof
f ne
twor
k si
ze
Inde
x of
mod
erat
ed m
edia
tion
Inde
x of
mod
erat
ed m
edia
tion
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
0.05
08
0.02
25
0.00
39
0.09
34
Pr
oduc
tion
rela
tedn
ess
-0.0
509
0.02
86
-0.1
088
0.00
45
Indi
rect
effe
ct:
Indi
rect
effe
ct:
pare
nt
netw
ork
cent
ralit
y ->
Spin
off
AC
AP
(abi
lity
to
appl
y kn
owle
dge)
->
spin
off
netw
ork
size
pare
nt
netw
ork
cent
ralit
y ->
Spin
off A
CA
P (a
bilit
y to
ap
ply
know
ledg
e)
->
spin
off
netw
ork
size
Inde
x of
mod
erat
ed m
edia
tion
Inde
x of
mod
erat
ed m
edia
tion
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
4 0.
0051
-0
.004
0.
0162
Prod
uctio
n re
late
dnes
s 0.
004
0.00
73
-0.0
077
0.02
2
(Lev
el o
f the
con
fiden
ce in
terv
al o
f all
outp
uts =
95%
)
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.
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 136
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 137
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 138
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 139
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)(Rothaermel & Deeds, 2006)(Rothaermel & Deeds, 2006)(Rothaermel & Deeds,
2006)(Rothaermel & Deeds, 2006)(Rothaermel & Deeds, 2006)(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).
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 140
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 141
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 142
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.
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 143
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 144
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.
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 145
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 Network Growth
Spinoff’s Performance
Parent Network
Characteristic
H1
H2a, H2b
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 146
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.
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 147
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.
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 148
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).
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 149
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.
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 150
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:
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 151
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 152
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 153
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 154
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
Sp
inof
f Rev
enue
a 12
.828
1.
880
1 Sp
inof
f Rev
enue
(lag
ged)
a 12
.581
1.
843
0.73
2*
1
Spin
off N
etw
ork
Gro
wth
2.
177
3.21
0 0.
018
0.08
9*
1
Pare
nt N
etw
ork
Size
a 4.
413
5.14
3 0.
019
0.01
9 0.
242*
1
Pare
nt N
etw
ork
Cen
tralit
y a
0.24
9 0.
443
0.04
6 0.
044
0.17
8*
0.83
8*
1
Tim
e Si
nce
Esta
blis
hmen
t 3.
628
1.97
6 0.
184*
0.
292*
0.
261*
0.
044
0.00
3 1
Pare
nt R
OA
-3
2.99
2 11
9.27
8 -0
.029
-0
.035
0.
074
0.07
3*
0.05
3*
-0.0
04
1
Tie
with
Par
ent d
umm
y 0.
217
0.41
2 0.
014
0.02
9 0.
124*
0.
138*
0.
154*
0.
011
-0.0
74*
1
Pare
nt O
wne
rshi
p du
mm
y 0.
256
0.43
7 0.
019
0.02
7 0.
133*
0.
187*
0.
210*
0.
036
0.08
9*
0.33
8*
1
No.
still
in P
aren
t 1.
204
1.02
1 -0
.081
* -0
.038
0.
086
0.06
7*
0.10
7*
0.02
8 0.
094*
0.
280*
0.
346*
1
PhD
exp
erie
nce
0.18
3 0.
387
0.02
6 0.
036
0.08
7*
-0.0
35
-0.0
05
-0.0
06
0.06
0*
0.04
2 0.
080*
0.
256*
1
*p<0
.05
(n=1
144)
a V
aria
bles
hav
e be
en tr
ansf
orm
ed
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
6
Tabl
e 6-
2 R
ando
m-e
ffect
s reg
ress
ion
resu
lts (D
epen
dent
Var
iabl
e: S
pino
ff R
even
ue (t
+1)
)
Mod
el 1
Mod
el 2
Mod
el 3
Mod
el 4
Mod
el 5
Mod
el 6
C
oeff
. S.
E.
Coe
ff.
S.E
. C
oeff
. S.
E.
Coe
ff.
S.E
. C
oeff
. S.
E.
Coe
ff.
S.E
.
Con
trol
Var
iabl
es:
Spin
off R
even
ue (l
agge
d)
0.68
5***
0.
052
0.68
0***
0.
051
0.68
0***
0.
051
0.68
4***
0.
056
0.68
2***
0.
056
0.67
8***
0.
055
Tim
e Si
nce
Esta
blis
hmen
t -0
.019
0.
023
-0.0
02†
0.02
4 0.
008†
0.
024
-0.0
31
0.02
5 -0
.029
0.
024
-0.0
13
0.02
5
Pare
nt R
OA
-0
.001
0.
000
-0.0
01
0.00
0 -0
.001
0.
000
-0.0
01
0.00
1 -0
.001
0.
001
-0.0
01
0.00
1
Tie
with
Par
ent d
umm
y 0.
092
0.16
2 0.
114
0.16
7 0.
157
0.17
0 0.
129
0.17
2 0.
125
0.17
1 0.
139
0.17
5
Pare
nt O
wne
rshi
p du
mm
y 0.
031
0.16
7 0.
046
0.17
2 0.
047
0.16
7 -0
.075
0.
199
-0.0
89
0.20
2 -0
.077
0.
206
No.
still
in P
aren
t -0
.139
* 0.
059
-0.1
40*
0.06
0 -0
.150
* 0.
059
-0.1
59**
0.
061
-0.1
60**
0.
061
-0.1
61**
0.
062
PhD
exp
erie
nce
0.09
0 0.
162
0.10
7 0.
160
0.11
7 0.
157
0.09
7 0.
177
0.09
6 0.
175
0.10
8 0.
173
Inde
pend
ent V
aria
bles
:
Spin
off N
etw
ork
Gro
wth
-0
.027
0.
018
-0.1
00**
0.
035
-0.0
25
0.01
9
(Spi
noff
Net
wor
k G
row
th)2
0.
004*
* 0.
002
Pare
nt N
etw
ork
Size
0.
009
0.01
2
Pare
nt N
etw
ork
Cen
tralit
y
0.
155
0.13
0 0.
177
0.13
4
_con
s 4.
433*
**
0.59
6 4.
490*
**
0.59
1 4.
550*
**
0.58
8 4.
483*
**
0.64
3 4.
511*
**
0.64
5 4.
555*
**
0.63
9
R-s
quar
ed (o
vera
ll)
0.55
0
0.55
0
0.55
4
0.55
7
0.55
8
0.55
8
Chi
-squ
are
277.
480
27
8.02
0
304.
500
16
9.43
0
172.
880
17
4.10
0
df
7
8
9
8
8
9
† p<
0.1,
* p
<0.0
5, *
* p<
0.01
, ***
p<0
.001
N
umbe
r of o
bser
vatio
ns=
1144
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 157
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 158
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)
<-
Parent Network Centrality
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)
<-
Spinoff Network Growth
-0.028 0.022 -1.270 0.204 -0.071 0.015
Parent Network Centrality
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 159
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 160
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.
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 161
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
Chapter 6: Coming Out of the Parent’s Shadow: The Role of Spinoff’s Early Alliance Network Growth 162
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
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NE
SS R
ESU
LT
S W
ITH
2-Y
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e 6-
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ress
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lts (D
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pino
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Cha
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Sha
dow
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Rol
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noff’
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llian
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wth
16
4
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5 R
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ion
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lts (D
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var
iabl
e: sp
inof
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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
Chapter 7: Discussion and Conclusions 166
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.
Chapter 7: Discussion and Conclusions 167
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
Chapter 7: Discussion and Conclusions 168
(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
Chapter 7: Discussion and Conclusions 169
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
Chapter 7: Discussion and Conclusions 170
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
Chapter 7: Discussion and Conclusions 171
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
Chapter 7: Discussion and Conclusions 172
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
Chapter 7: Discussion and Conclusions 173
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
Chapter 7: Discussion and Conclusions 174
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.
Chapter 7: Discussion and Conclusions 175
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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