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The Economic Effect of Networks in the Norwegian Startup Scene

Julian H. Hjallum

Department of Informatics

UNIVERSITY OF OSLO

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Foreword

This thesis was written in spring 2020 under the supervision of Professor Truls Erikson at the University of Oslo. Having been part of the Norwegian startup scene since my undergraduate studies I have experienced, first-hand, the important effects of networks. Diving into this research project I wanted to find out if there was any quantifiable impact of networks, and whether it actually can be a predictor for success. Thus, this research project has led me down a path of gaining a deeper understanding of the complexities of networks and their effects. This journey has not only taught me a lot about networks but about science and thinking critically.

This project would not have been possible had it not been for Arne-Petter Omholt, and Svein Parnas of T-Rank. As a true example of the strengths of networks, the T-Rank team generously shared their vast database with me, in order to help me in my research. Their willingness and trust are deeply appreciated. I would also like to thank Dennis Gan for his insights and feedback throughout the project.

Finally, I would like to extend my gratitude to my supervisor Truls Erikson, who has been a part of every step of the project. As a sparring partner, he has helped push and shape the project to something I can be proud of.

Thank you.

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Table of Contents

FOREWORD ...2

ABSTRACT ...5

1.0 INTRODUCTION ...6

1.1MOTIVATION ...6

1.2OBJECTIVES AND RESEARCH QUESTION...7

1.3LAYOUT OF THE THESIS ...7

2.0 LITERATURE REVIEW ...9

2.1BOARD COMPOSITION ...10

2.2BOARD INTERLOCKS ...12

2.3STRATEGIC ALLIANCES...15

2.4NETWORK EFFECTS:THE CONCLUSION ...17

3.0 METHODOLOGY ...19

3.1RESEARCH PHILOSOPHY, APPROACH, AND STRATEGY ...20

3.2RESEARCH DESIGN ...20

3.2.1 Quality of research: reliability ...22

3.2.2 Quality of research: validity ...23

3.3DATA COLLECTION ...23

3.4DATA ANALYSIS ...24

4. 0 DATA ANALYSIS ...27

4.1SUMMARIZING THE DATA ...27

4.1.1 Coding ...28

4.1.2 Variables...28

4.2DESCRIBING THE DATA ...29

4.3ANALYZING THE DATA ...30

4.4BINARY LOGISTIC REGRESSION ANALYSIS ...32

5.0 CONCLUSION AND DISCUSSION...35

5.1LIMITATIONS ...36

5.2FUTURE RESEARCH ...36

5.3IMPLICATIONS ...37

5.4RECOMMENDATIONS ...38

BIBLIOGRAPHY ...39

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Abstract

Startup companies often lack financial- and human-capital, making the importance of their social capital, such as networks, greater than that of established businesses. There is an abundance of literature dedicated to the research of such networks and their effects. There is, however, little consistency in this literature regarding the actual financial effect networks have on startups. This thesis takes on this objective and focuses on finding an economic effect driven by networks for high-tech Norwegian startups. Using a vast database with publicly registered data, the data analysis takes on network effect measured through the board of directors. The study makes use of board interlocks and board composition in a logistical regression analysis, with financial milestones as a measurement for success. There was found to be a significant positive effect driven by board interlocks and board composition, on companies’ abilities to achieve financial milestones. These significant effects provide evidence for how networks play a role in the financial success of Norwegian high-tech startups. Thus, it is recommended that founders take the network reach and effect of their boards into consideration during board formation, as it does positively predict financial success.

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1.0 Introduction

1.1 Motivation

Already as an undergraduate, my interest in entrepreneurship had sparked. I joined my first startup during the second year of my bachelor´s and immediately fell in love with the entrepreneurial culture. The company was a health-tech startup, looking to revolutionize personalized medicine. The team comprised of two serial entrepreneurs, both working on sales and business development and two medical doctors specialized within the field of the venture.

The startup scene was new to me at this point, and I had to learn to adapt to a rapidly changing work-environment. The startup’s growth was grounded in the founder’s ability to sell its product, and I remember wondering how this small company managed to get in touch will some of Norway’s biggest corporations.

Whilst working for this MedTech company I got to spend a lot of time with the founders. I specifically remember one time I was asked to take part in one of their board meetings, as a minute taker. Having worked in the company for some months I was surprised to find that the board comprised of total strangers. Some of these strangers were executives in big companies, while others were healthcare experts. There was a clear agenda throughout the meeting, mostly regarding strategic measures. Smalltalk would emerge, and high-quality discussions took place. As a minute taker, I was struggling to note down as much as possible. I did not understand it at the time, but that meeting sparked a new connection, leading the company to its biggest customer to the current date.

I have since learned that using boards as a way of enhancing networks is a common strategy for startups. The board of directors is one of the strategic ways founders can enhance their network reach, and I feel privileged to have experienced this effect firsthand. The dynamics of board networks and their effect on potential financial success is something that has stuck with me. Thus, I have dedicated my master’s thesis to researching the effect of entrepreneurial network effects. This motivation has been pivotal for the formation of the research question.

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1.2 Objectives and Research Question

With this thesis, I will seek to discover the actual effects of social networks in the Norwegian high-tech startup scene. Because network effects are intangible, it is not easily measured. Even a combination of methods for measurement will only capture part of the essence that is network effects. Given that the dataset used in this research covers the greater population, using a sample size of all of Norway’s high-tech startups, there should be measurable findings, given that network effects has an impact on economic success. Using board interlocks and board composition as a measure for network effects, the objective of the thesis is to find evidence for a positive impact on financial success, answering the research question;

How does the network of the board of directors impact a startup’s economic success in Norway?

To best answer this research question, hypotheses have been formulated. These hypotheses help split the research question into more measurable means and will be key for the objective of the thesis. The research takes on two hypotheses, formulated though the existing theory on network effects.

1.3 Layout of the Thesis

The layout of the thesis follows a traditional way of applying theory, as presented by Wilson (2014). The structure reflects the journey that I as a newcomer to business research has had over the last six months. Starting with the literature review, digging into the existing theory of network effects. This chapter establishes a chain of arguments that theorizes the positive economic effect of networks; Through board composition and interlocks to strategic alliances leading to economic success. The literature review includes the theory referred to throughout the thesis and helps explain the grounding for the hypotheses, thus functioning as a backbone for the thesis. The following chapter defines the methodology of the research done in this thesis.

It is structured according to Wilson´s “Honeycomb of Research Methodology” (2014). This chapter explains and argues for all of the methodical choices that have been made in order to best answer the research question. It takes on the research design, the data collection process, and the means for data analysis. The actual data analysis is structured into its own chapter, explaining the methods for analysis as well as their immediate findings. These analyses have

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the relevant tables showcasing their data and the findings within them. The chapter starts with a summary, and description of the data, including a correlation matrix for the variables. Lastly, the analyses take on the binary logistic regression used to try and answer the hypotheses and research question. The final chapter discusses the findings from the data analysis. Here, the theory from the literature review is linked with the findings in an effort to give an answer to the research question. The discussion includes limitations to the current research, future research, and final recommendations based on the findings.

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2.0 Literature review

To explore the current state of research on the topic of network effects, a literature review was conducted. This research was done in two phases; The first phase included the initial research that helped narrow down the scope of the study, mapping out what and where to focus the research. The second phase of the literature review was dedicated to digging deeper into the phenomenon’s that were discovered in the first phase. To try to answer the research question, a chain of arguments was found, that theorizes how network effects indirectly can impact the financial success of startups, and how it can be quantitatively measured.

Startups are in their nature small and lean companies, often with high ambitions. They do however suffer from “liability of newness”, often manifesting itself as a severe lack of resources. These limited resources make it important for startups to maximize the value of their intangible-resources, such as social capital. One of Sarah Saravsathy´s (2009) entrepreneurial principles are built on this notion and is called the “Bird in Hand”-principle. This principle says that as an entrepreneur you have to start with what you have, what you know, and whom you know. One of the most important factors for a startup is its network; who do the founders of the company know? Entrepreneurs with valuable social networks are more likely to find themselves with more options (McGrath, 1996). Such options might manifest themselves as discovering opportunities, securing resources, or gaining legitimacy (Elfring & Hulsink, 2003).

A common way to increase the social capital of startups is through the board of directors.

As compared to mature companies, the board of directors plays a different role in early-stage companies. Traditionally, a company's board of directors is a formal mechanism that connects people through the company's best interest. Mature company boards’ role is to make strategic decisions to try to ensure future success, satisfying the shareholders. Board meetings provide an opportunity for leaders to exchange information and learn from their peers (Gulati &

Westphal, 1999). The role of the board of directors in startups is different because the founders typically are the majority shareholders (Borch & Huse, 1993). Thus, the board has less “legal power”, making the board members role more of an advisor, rather than a decision-maker.

Startups may use their boards to expand their network and to fill knowledge gaps, putting on influential and knowledgeable people. To measure the network capacity of a company, researchers tend to use board interlocks (Beckman, Schoonhoven, Rottner, & Kim, 2014; Non

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& Franses, 2007; Knoben & Bakker, 2019). A board member of a startup is interlocked if they have a board position in another company. Such board interlocks are found to increase the chances of strategic alliances happening between the two interlocked companies, which in return could impact the financial success of the startup.

The literature review is structured to cover the different aspects of the network effects of boards, as well as arguments as to how it may impact financial success in startups. Starting with explaining how board composition impacts network-reach. Further, the review will go into detail regarding board interlocks and how they are used to measure network size and effect.

Lastly, the literature will be used to make a linkage between network effects and the formation of strategic alliances. Board composition, board interlocks, and strategic alliances are concepts that are thoroughly researched. There are also examples in the literature that links two or more of the concepts together to make an argument for network effects. Throughout the review, it will be clarified what disagreements are found within the literature, and how this might impact the theory. Summarized this literature review will cover the entire chain of arguments explaining how the network effects of boards may lead to economic success in startups.

2.1 Board Composition

Board composition concerns the nature of the board; its members as well as its features. It includes overall board size, the relationships each board member has to the founders (family, friends, etc.), the number of board members having business relations with the company, the prior experience of the board members, the number of employee board members and the number of board members holding stock in the company (Borch & Huse, 1993).

A board of directors is elected by the shareholders to make strategic decisions in the best interest of the company. The board includes a chairman of the board, who has additional power as well as legal responsibilities. Boards also typically have a co-chairman who must fill the chairman's duties, should he/she not be present. In Norway, publicly registered companies are obliged to have at least one board meeting every year. For mature companies and corporations, the board of directors are chosen to represent the shareholders, and thus holds substantial legal power over the direction and strategy of the company (Gulati & Westphal, 1999). In startup companies, however, the founders of the company typically hold the majority of the shares, leaving them all the “real” legal power. Therefore, startup boards tend to be more focused on

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strategic discussions rather than decision making. It is also normal for founders and early employees to be part of the board. It is not unusual for startups to form “advisory boards”, in addition to their legal board. Advisory boards are an informal mechanism without the legalities of a board of directors. Such an advisory board is also used to gain resources and network which the founders themselves do not employ. These people may end up gaining a position at the board of directors. Because advisory boards are informal they are not registered in public databases and thus receive less scientific attention.

The board composition affects the effectiveness of the board. Startups should strive to form optimal boards, filling the founder’s own knowledge gaps, and increasing their network reach.

Resource Dependency Theory (RDT) tells us that to gain resources a company has to connect with other companies or institutions. Arguing that the composition of a board is favored to include outsiders with connections towards these external resources. A board outsider is defined as members of the board that is not employed in the company. These members will typically function as connections to outside resources. There is near consensus in the literature that boards that are comprised of more outsiders more effectively connect the company to outside resources. This effect is again strengthened by agency theory which speaks to managerial separation of ownership and control. Owner-managers tend to make biased decisions in their own interest, while outsiders tend to see things more objectively, thus often making better strategic decisions (Dalton, Daily, Ellstrand, & Johnson, 1998).

Board members should ask critical questions and push the management of a company to improve. An American study showcases just how important the board composition is, by focusing on the member’s technical competence; In 2020 making good strategic decisions requires an understanding of digital transformation and the possibilities that come with it. Non- digital-savvy board members, typically from traditional industries have more traditional views of strategy. Digital savvy board members however typically have a leaner approach to strategy, and push management to rapidly test and learn, rather than plan and execute. Weill et al. (2019) state that company boards must have at least three digital-savvy board members for their competence to impact the decision making of the board. Companies whose boards are digitally savvy score higher on performance KPIs such as; revenue growth, return on assets, and market cap.

The size of a startups board may also impact its effectiveness; A few numbers of active directors are found to be more effective than bigger inactive boards. This speaks to the quality

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and motivation of each independent board member, instead of having a plethora of passive board members. Smaller boards also have a bigger sense of “team spirit”, which increases the likeliness that the board members will produce beneficial output (Borch & Huse, 1993).

The board composition also has an impact on the effectiveness of the relation between the multiple board members. We summarize the relationships between board members using the terms heterogeneity, multiplexity, and asymmetry. These relations are shown to have an effect on the board’s ability to form external network relations. Heterogeneity speaks to how different the board members are. Multiplexity speaks to whether the board members have some other role in the company (investor, founder, employee, etc.). Both heterogeneity and multiplexity have a positive effect on the diversity of the board’s network relations; The more heterogeneous and multiplex a board composition is, the more diverse their network becomes. Asymmetry speaks to the power imbalance between the board members and is negatively associated with diverse network formation. Startups should, therefore, strive to form heterogeneous and multiplex boards with low power asymmetry, to increase their chances of forming strong and diverse network relations (Beckman, Schoonhoven, Rottner, & Kim, 2014).

Even though the literature is consistent in regard to board compositions’ effect on decision making and network formation, there is conflicting evidence in regard to its effect on financial performance. Weill et al. (2019) found that there are strong economic effects of corporate board members’ technical competence. Whereas Dalton et al. (1998) states that there is no consistent linkage between board composition and financial performance. I would argue that the positive network effect of board composition is stronger for startups, seeing as they are more reliant on their network effects than mature companies. This theory is also strengthened by the following literature regarding board interlocks and alliance formation. Hence I state my first hypothesis:

H1: Startup companies with more board outsiders form more network relations.

2.2 Board interlocks

A board interlock is defined as the network relation of a member of the board of directors who also has a board position in another company. The board member of a company A will be interlocked if he/she also has a board position in company B. In this case, companies A and B share a board interlock, through this board-member. Interlocks have been a field of vast

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research throughout the years because it provides a way for researchers to quantitatively measure a company’s network-reach through its board.

Board members will by design attend collective board meetings where strategy will be discussed, among other things. The general notion is that this strategic discussion may be altered because of the interlocks of the different members. The insights and responsibility obtained through sitting on multiple boards may be shared with the current board, and new relations may be formed. The board members should try to the best of their skill and knowledge to make good strategic decisions, and if the opportunity presents itself it feels only natural to connect their two relations. Customer, partner, or alliance relationships are but some of the outputs that may come from interlocking board members strategizing on behalf of the company. The economic effect of interlocks is heavily debated in the literature, presenting evidence for both negative and positive effects. The network effects, however, are undeniably real and may provide real benefits to the company, if used in the right way.

Except for creating strong network effects, interlocks can be a source of gained legitimacy for a startup. Imagine a small fintech (Financial-Technologies) startup having a board member of an established bank on their board. This will inevitably increase the startup’s legitimacy in the industry, given that the interlocked member does not have a bad reputation (Connelly & Van Slyke, 2012). For a startup, such an increase in legitimacy can be the difference between someone wanting to partner, or not. This phenomenon of legitimacy happens frequently in the startup scene and is reason alone for founders trying to get influential people as members of their board.

The effect of board interlocks on strategic alliance formation has been documented in multiple studies (Connelly & Van Slyke, 2012; Knoben & Bakker, 2019; Non & Franses, 2007). These articles propose that the board interlocks do indeed work as a connector between potential alliance partners, and their existence does increase the chances for a partnership happening. If you as a startup company employ a board member of a corporation to your board of directors, the chances are increased that your startup will end in an alliance with the interlocked corporation. This makes sense from a networking point of view and is a strategic tool that startups can use to try and land crucial alliance partnerships with fitting companies.

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Interlocks may also be a resource, and act as a safety net in periods of environmental hostility.

As markets experience high levels of uncertainty and competition, network ties become more valuable, helping make sure customer- and partner-relationships are maintained. Multiple studies showcase how companies tend to tie up with one another in times of uncertainty, to increase the chances of firm survival (George, Wood Jr, & Khan, 2001; Beckman, Haunschild,

& Phillips, Friends or Strangers? Firm-Specific Uncertainty, Market Uncertainty, and Network Partner Selection, 2004). As the environmental hostility goes up, management changes its strategy towards increasing the company’s network relations, trimming down the number of board members while increasing the number of interlocks. This phenomenon has been documented by Boyd (1990) and is explained as a managerial answer to high levels of uncertainty. By having a leaner and more well-connected board, companies are more likely to endure the hostile environment. This effect is particularly strong for successful companies.

There are also negative effects associated with board interlocks, in addition to there being little measured consistency in the relationship between interlocks and financial performance (Dalton, Daily, Ellstrand, & Johnson, 1998). First of all; board members with multiple board positions might struggle with information overload, lacking the resources to properly fulfill their role as board members. This risk increases as the number of interlocks go up (Connelly

& Van Slyke, 2012; Non & Franses, 2007). Additionally, board members with key interlocks, highly important for the firm, may gain unfavorably levels of control. This asymmetric power balance can, as discussed in the former section, end up being disadvantageous for the board composition, and thus the positive effect from the board of the company (Connelly & Van Slyke, 2012). It is therefore important for founders to not “put all their eggs in one basket”, meaning that they should strive to minimize the power imbalance by having multiple individuals with important interlocks on the board if any. Lastly, it is hypothesized that board members with many interlocks share many of the same traits, seeing that they both have a tendency to end up on multiple boards. This means that putting on multiple board members with many interlocks can cause homogeneity within the board. This phenomenon is called the upper-class cohesion hypothesis and can be a cause of negative side effects from interlocked board members (Non & Franses, 2007).

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2.3 Strategic alliances

A strategic alliance is an arrangement between two parties to gain mutual benefits. Such benefits can be access to resources, market shares, or legitimacy. Since the 1980s both the quantity and quality of strategic alliances have gone up, as businesses started to incorporate them as part of their strategy (Keil, 2000). Over the last 5 years, its relevance has only grown as more and more corporations see to Open Innovation as a substitute for their traditional forms of R&D. For startups, strategic alliances can be a key to unlocking market trust, market share, or access to distribution or manufacturing. For corporates or other established firms, partnering with startups serves as access to technology, people, and ideas that they are not in a position to pursue themselves.

Strategic alliances are a thoroughly researched topic, and researchers have learned to differentiate them based on their nature. An alliance is typically between two parties, and the power-relation between the two alliance partners will define what kind of alliance they form, symmetric or asymmetric. We also differ between equity and non-equity alliances. For equity alliances, one or both of the parties gain a stake in the partnering company, which in the view of Resource Dependence Theory is the greatest form of control and incentive-alignment (Gudergan, Devinney, Richter, & Ellis, 2012). In non-equity alliances, the parties have to strive to align their interests to be best suited to draw synergies from one another. These different types of alliances and their set success criteria drastically change their recommended execution strategy. Each type of alliance should be treated differently to increase the chances of success.

Symmetric alliances are the most traditional form of alliance. Here, two similar companies form an alliance to draw synergies from each other. They are called symmetric alliances because the size and power of the included parties are similar. For example, both companies could be in the same stage of their life cycle, same size, and even same customer segment. For traditional symmetric alliances, it is recommended to strive for similarities in goal alignment.

Both parties should be looking for the same output from the alliance. Such output could be increased market share or technological advancements.

Asymmetric alliances are when two companies in totally different stages (ex. a startup and a corporation) form a strategic alliance, drawing different values from one another. Asymmetric

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alliance parties should strive for complementary value appropriation, meaning that each of the partners is looking for a different kind of value, instead of similar value output (such as is recommended in symmetric alliances). As an example, the corporation may get help with optimizing a part of their value chain, while the startup gets to properly test their product as well as the trust related to working with a larger partner. An asymmetric partner’s commercial goals should not be overlapping (Pérez, Florin, & Whitelock, 2012).

Asymmetric strategic alliances are one of the ways startups can find a source of economic success through its network. Interlocks have been found as a predictor for strategic alliances happening. From a networking point of view, it seems only natural that interlocked companies would have a greater chance of collaborating. Through researching Australian mining startups Knoben and Bakker (2019) found that interlocks, as part of relational pluralism, increase both the chances of strategic alliances happening and their chances for success. Relational pluralism is defined as a company having multiple (plural) relations with another company; a board- interlock and a strategic alliance relation. Such a relationship is a strong network effect, with multiple positive consequences.

Companies that share a common board member have a better chance of extorting value from their strategic alliance. This is due to the relational pluralism giving the two parties more leverage in negotiations, as they have a shared connection and thus both have more to lose. The common board member will typically be a frontrunner in the partnering company, as they themselves feel more involved in the alliance. A common problem in strategic alliances is a lack of insights into what the priorities and goals of the partnering company are. Through a board interlock, the companies have an arena to share such insights, paving way for good decisions to be made for the better of the alliance.

Not only does relational pluralism increase the chances of successful strategic alliances, but they also increase the chances of them even existing. Companies that share a common board member has an increased chance of ending up in a strategic alliance with one another (Knoben

& Bakker, 2019). When forming multiple alliances, diversity is key. Being in more than one alliance, with similar benefits gains little value, but provides all the same costs. The extent to which relational pluralism leads to a diverse alliance portfolio is moderated by three differentiators of the board’s composition: Heterogeneity, how different the individuals on the

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board are; Multiplexity, whether the board members have any additional roles in the company (insiders); and asymmetry, the power difference between the members of the board. Boards with high heterogeneity and multiplexity and low asymmetry have an increased chance of relational pluralism resulting in diverse strategic alliances (Beckman, Schoonhoven, Rottner,

& Kim, 2014).

The temporal sequencing of relational pluralism also plays a role in the success of the strategic alliance. If the board interlock is formed as a product of the strategic alliance, most of the leverage from the startup’s side is lost. In these cases, the corporation will typically ask for a board seat, as a means of controlling from their side. However, if the board interlock happened before the strategic alliance was formed, the positive effect of relational pluralism is heightened. For both parties, such an alliance would seem more natural to occur, given that they share a board member (Knoben & Bakker, 2019).

To capitalize on the potential value gained from asymmetric strategic alliances, startups should strive to build boards with interlocks to valuable partner-companies. Such an alliance can unlock key resources for the startup, as well as give them the proper level of market trust that they would need to reach financial success. It is through alliances that the tie from network effects and financial success is made. Better connected companies are theorized to have a better chance of ending up in strategic alliances, and these alliances often put the startups in a better position than they were. Hence I state my second and final hypothesis:

H2: Startup companies with more interlocks have a better probability of reaching economic success

2.4 Network effects: The Conclusion

Startups have limited resources and should, therefore, capitalize on their social capital to ensure growth and increase their chances of reaching economic success. Their network is one of their major assets, and many startups use their boards as a means to increase their reach and legitimacy. Interlocks within the board can increase the chances of strategic alliances happening, even though there is conflicting evidence on interlocks’ actual economic effect.

Interlocks are being used as a measurement of a company’s network reach and are often seen in relation to strategic alliances. Strategic alliances can be a valid strategy for startups seeking

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market share, trust, or access to a value chain. Relational pluralism has a measured positive effect on startups’ economic growth and occurs when a startup has multiple relations with an alliance partner, notably an interlock and an alliance relation.

Using the current literature, a chain of arguments that can be used to explain how the network effects of companies impact the economic success of a startup company has been mapped out.

This chain of arguments is useful because it helps to further design the research, by presenting a measurable way of analyzing the network effects and their impact on financial growth for startups. The chain of effect starts with board composition. The notion is that bigger boards have an increased chance of having the board being comprised of more outsiders, people who are not employed in the company. These outsiders have a greater chance of connecting with other companies through their boards, measured through board interlocks. Thus, the proportion of board outsiders have a measurable effect on the number of board interlocks. There has been found evidence that interlocks can be a predictor for strategic alliances happening, and also the success of these alliances. This is where the linkage to economic success is made, as successful strategic alliances could have a positive economic impact on the company. This chain of events is grounded in Resource Dependency Theory, which tells us that to gain resources a company has to connect with other companies or institutions, often through their own network. The next chapter will explain how this theory is used in the research design to try and find an answer to the research question through the hypotheses.

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3.0 Methodology

This chapter describes the methodological choices that I have taken and used through the thesis.

These choices were guided by the research question; How does the network of the board of directors impact startups’ economic success in Norway? The motivation for this thesis stems from my own experiences and my interest in the Norwegian startup community. Networks play an important part in startup companies’ upbringing, and I was interested in how these networks actually impact the economic success of the startups. The Norwegian startup community is also quite new, making this study unique. Most of the research in the literature review revolves around mature companies or startups in mature communities.

An abundance of research has been done on networks, using both interlocks and board composition. There is however no consensus in the literature regarding the actual measurable economic effect of networks. Seeing as I am from Norway, and plan to act out bigger parts of my career in its startup scene I have a strong motivation to explore this area. If I am to find applicable measures in my thesis I will apply them to my own startup to enhance the chances of long-term survival, driven by economic success.

While conducting the literature review it became apparent that sales as an entrepreneur are very different from merely selling a product or a service. Startups lack the trust that often enables a

“normal” sales process. You are not only selling your company’s product, but you are selling yourself as a trustworthy person and company. Why should any potential partner or customer want to do business with you, if they do not know who you are? Through my initial research, the importance of networks became clear, and thus the research question started formulating.

This following chapter is introduced by explaining the overarching research philosophy that has been in place for this thesis. Following up by further explaining the research approach and strategy. Going into the research design, clarifying the quality of the research, as well as giving an explanation of the data collection process. Lastly, this chapter will delve into the variables and statistical frameworks that have been used for the data analysis.

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3.1 Research philosophy, approach, and strategy

This study was conducted following a positivist research philosophy. Positivism is characterized by independence between the researcher and the research material (Wilson, 2014). For this thesis, I am using a vast database including financial- and board-data for all of Norway’s companies. Thus, I have not interacted with any research participants to gather primary data. The focus of this study is to find some objective truth, applicable to the population of high-technology startups by analyzing this database. Personally, I tend to find interest in hard facts, functionality, and efficiency. I acknowledge that most phenomena’s in social science are nuanced and relative, but it was apparent to me that I better fit under the positivist umbrella, as compared to the more nuanced philosophy of interpretivism. I have let this positivist approach guide me through my research design, in everything from the choice of research strategy, data collection, and the data analysis.

The research approach taken in this thesis is deductive, as I apply the theory presented in the literature review to my data. The aim is to use this theory in combination with my data to find evidence of a similar network phenomena in the Norwegian population. The research strategy is quantitative, to best pursue an answer to my research question; “How does the network of the board of directors impact startups’ economic success in Norway?”. Networks are an important asset for startups and using board interlocks and size as a measure for networks I will seek to find relations between network effects and economic success. Based on the literature review I theorize that strategic alliances are one of the core ways in which startups leverage their professional network. By taking such a quantitative approach I believe that one should be able to measure the economic effect of startups networks.

3.2 Research Design

Norway has a small, but growing startup community. With my research question, I am trying to assess the same phenomenon found by multiple researchers (Knoben & Bakker, 2019;

George, Wood Jr, & Khan, 2001) regarding the positive effects of networks, measured using board interlocks, in a growing startup community such as Norway. Using a national database provided to me by T-rank AS, I have access to the number of board interlocks, board

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composition, and financial-data for the total population of Norwegian companies. This thesis, therefore, provides a snapshot of the current effect and situation of interlock-network effects in the Norwegian startup scene, best described as a cross-sectional study. A cross-sectional study is characterized by being restricted on duration, meaning that the data analyzed is typically single datapoints over a set timeframe, as opposed to longitudinal studies that analyze the changes in a given dataset over a long duration. The dataset used in this study is a snapshot of the board- and interlock-data from 2018.

To further explain my methodological choices, we will be taking a look back at the research question and hypotheses:

Research Question:

How does the network of the board of directors impact startups’ economic success in Norway?

Hypotheses:

H1: Startup companies with more board outsiders form more network relations.

H2: Startup companies with more interlocks have a better probability of economic success The research question is formulated as a “how”-question, which consequently makes the research explanatory. To pursue this causal research question, this study will make use of binary logistic regression. This method will be applied to test hypotheses 1 and 2. Binary logistic regression was chosen as the means of analyzing the data because it best fits the dataset.

When describing the relationship between a dependent variable and multiple independent variables, linear regression is most commonly used. However, similar to Beckman et al. (2014), this study uses financial milestones as a measurement of economic success. Hence, the dependent variable used in the analysis is categorical, dummy, variables. Such a dichotomous variable fits perfectly into a logistic regression model (Hosmer, Lemeshow, & Sturidvant, 2013). Additionally, as compared to what is needed in linear regression, the independent variables that are being used are not distributed normally. Both interlocks and the number of board members are heavily skewed towards the lower end of the distribution. As seen in Figure 1, the total interlock count is specifically skewed towards zero, because far from all companies have even one interlock within their board of directors. To make these variables fit under such a linear regression model, heavy recoding would have been necessary, potentially cluttering the data and decreasing the validity of the entire study. Instead, by using the binary logistic

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regression model, which does not require the independent variables to be normally distributed, the hypotheses can be thoroughly tested without recoding or grouping the variables. Binary logistic regression outputs the odds for the independent variables to belong to the group of the dichotomous dependent variable (Hosmer, Lemeshow, & Sturidvant, 2013). Thus, the odds of economic success with and without network effects, measured using interlocks and board size

will be used to test the hypotheses.

Figure 1: The distribution of interlock frequency in the dataset

3.2.1 Quality of research: reliability

Wilsons (2014) description of reliability is to what extent the measurement of a phenomenon provides stable and consistent results. In case this cross-sectional study was to be replicated, I argue that the results would be the exact same. Using only secondary data, a database of publicly registered data, there is no subjectiveness in the data-material. Because the dataset includes all Norwegian companies founded in the last 5 years it is also representative of the entire population of Norwegian startups. Because of the nature of the research design and data collection I argue that the reliability of this study is very high. There are however subjective measures taken into the coding and filtering of the data. All of these measures are accounted

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for in the next chapter and have been crucial for the execution of this research.

3.2.2 Quality of research: validity

Validity concerns whether the study actually measures what it is designed to measure (Wilson, 2014). The dataset used in this thesis includes thorough and accurate data for board sizes, number of interlocks, and financial performance and growth. It does not include data on the number of outside board members, nor the number of alliances and their success. Using proxies for these are not optimal but provides the best insights producible with the dataset at hand.

Solely researching the direct correlations between the variables in the dataset; board size, interlocks and economic success opens for an abundance of error. It is hard to say if companies reach economic success because of their board size, or if successful companies attract more board members. Controlling for such error using control variables are critical for the research validity. This is being done by using company age as a control variable. To further account for validity the research uses an approved method for measuring economic success as the dependent variable. Using financial milestones, similarly to how Beckman et al. (2014) tested for economic success in startups makes use of a previously tested indicator, increasing the validity of the study capturing economic success.

3.3 Data collection

The data used for this study was taken from a national database of all Norwegian companies.

Through my personal network, I contacted T-rank, a company working within the field of anti- money laundering. As a showcase of the effectiveness of network effects, with their help, I got access to a vast database including financial data as well as general information about all Norwegian company boards, employees, and their industry. T-rank uses this data to help their customers, mainly banks and other financial institutions, find hidden ownerships in complex company ownership structures. The T-rank-software visualizes these complex networks and thus needs a vast amount of ownership data, provided to them by Tieto EVRY.

The extent of T-ranks data far out scaled what I needed for this study, in regard to both the number of variables and data points year over year. To collect the exact data needed for this study, a set of variables were identified. T-rank pulled these variables from their database to a readable text file. The dataset used in this thesis includes an abundance of variables for all companies incorporated between 2015 and 2020. These 200.000 Norwegian companies all

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have associated interlock- and financial data. The dataset does however not include data on alliances, nor a number of outside board members. These variables are not publicly registered, and thus harder to get a hold of. The data analysis is therefore designed to not include these variables. Instead, using the total number of board members as a proxy for a number of outside board members. Assuming that the larger the board, the bigger the chances are for an outside board member existing within that board.

Because the data in the database comes from publicly registered financial statements it includes financial numbers year over year, for as many years as necessary. It does however not include the same year over year data for the board of directors. Thus, all the data used in this thesis in regard to the board of directors, such as interlocks and board members are numbers from 2018.

All financial data is taken from its respective year.

Before performing the analysis, the dataset has been properly filtered to exclude as much error as possible. The methods used to filter the dataset is declared in the following chapter. This database and T-ranks help has given me access to a top-of-the-line dataset, based on nationally registered numbers, used by grand financial institutions in regard to quality and quantity. Never before in Norway have there been done research on interlocks and their financial effect, with such substantial dataset. I feel grateful for their trust, and the opportunity to be able to apply research to such a high-quality dataset.

3.4 Data analysis

The data analysis process was conducted using SPSS (Statistical Package for the Social Sciences). This is a statistical framework capable of doing all the analysis needed for this research. Throughout the next chapter, a combination of methods of measurements will be used to present and analyze the data, and all of these analyses will be done using SPSS. The following analyses will be included in the next chapter; Frequency tables, Pearson correlation matrix, and binary logistic regression.

The following section will present and explain all the variables used for measurement in the data analysis, found in the coming chapter. The analysis includes two iterations of the dependent variable, both trying to capture the same construct; economic success. Because

“economic success” is a broad term, with many ways of measuring this study takes inspiration from Beckman et al. (2014), and uses revenue milestones as the way of measuring startup

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economic success. Startups are different in nature compared to established companies, which can fundamentally change their financial statements, making traditional forms of success measurement obsolete. Financial milestones work as an indicator for success, and potentially a predictor for exits (Beckman, Schoonhoven, Rottner, & Kim, 2014). The independent variables are all chosen based on the literature and hypotheses. Trying to argue for and find evidence for the chain of arguments listed in the literature review.

3.4.1 Measurements

The number of chairman interlocks - c_ilocks

The chairman of the board is in a position of greater power than the rest of the board members.

He or she is responsible for the execution of the board tasks, and how well the board functions.

Typically, the chairman will be the one opening up the board meetings, while maintaining the lead of the conversation. This greater responsibility comes with the potential for a greater impact on the company. The chairman could have a greater say in board discussions, and I theorize that his or her network connections have a higher value on the average board than those of the other board members. This variable represents the total amount of chairman interlocks and is a measure for how well connected the chairman is on the different companies. The chairman’s number of interlocks were chosen as an independent variable over the total amount of board interlocks, because it is not affected by the board size.

The total number of board members in the company - brdmem

This variable is the total count of board members on the company board, based on self-reported numbers saved in the T-rank database. All Norwegian companies must register all their board members in public databases, giving this data a very high accuracy. The number of board members is the first step in the chain of arguments explaining the impact of network effect on economic performance. In this thesis, it is also being used as a proxy variable for a number of outside board members. Assuming that bigger boards have an increased chance to inhabit more board outsiders.

Revenue milestones: 1MNOK and 5MNOK

To measure economic success in a startup is different than in mature companies. Startups have not yet had the time to properly maximize their market potential, which often can be read from

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their financial statements. Thus, traditional forms of success measures are not sufficient. Based on the study done by Beckman et al. (2014) on relational pluralisms in newly founded companies, this study also takes advantage of startups’ financial milestones as an indicator of economic success. Beckman et al. state that professional investors claim that big revenue milestones, and when they are reached is a strong indicator for startup success, and possibly a predictor for an exit. Similarly, two binary dependent variables have been coded for whether revenue milestones have been reached, measured over the last three years of operation. These two variables, 1 million milestone, and 5 million milestone, represent whether the company has reached the respective milestone. As compared to Beckman et al. the analysis does not include the timing for when the companies reach the milestone. Because all the board data in the data material stems from 2018, we cannot say whether or how the boards have changed over the respective period. Hence, the dependent variables in the analysis strictly measure whether a company has reached the financial milestone, and not when.

Control variable – firmage

The age of the companies is used as a control variable in the analysis. Given that all the board and interlock data in the dataset is a snapshot of how it looked in 2018, it is important for the validity of the study to control for the age of the companies. A company of two years is less likely to have reached their financial milestone in their first two years of operations. Whereas a company of five years is more likely to have reached the same milestone over their last two years of operation. This variable functions as a control variable in the analysis, accounting for these age-differences between the companies. This is a measure taken specifically to increase the validity of the research, by taking into accounts the limitations of the snapshot of the dataset. I acknowledge that the nature of this data is biased towards the age of the companies, where this control variable tries to alleviate some of this bias, to the best of my ability. The bias toward company age will probably still impact the analysis and needs to be taken into consideration when analyzing the results.

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4. 0 Data analysis

This chapter will look at the results of the data analysis, as well as discuss the implications and findings that were made. The first part will be an overview of the data material, as well as descriptions of what filtering mechanism was used. To show the overall trend in the data this part will also feature descriptive statistics. Getting into the analysis we will look at tables of correlations, to gain insights into the connection between the variables before going into the main analysis. Finally, the findings from doing the binary logistic regression will be presented.

This chapter tries to dig into the impact of the variables, discussing the findings up against the research question and hypotheses.

4.1 Summarizing the data

In such a comprehensive database such as the one that T-rank possesses, there are also a lot of

“shell” companies, that do not extort value themselves. This includes properties and investment companies mostly in the form of holding companies. To best filter out these shell companies, NACE-codes were used. NACE is an international standard for categorizing legal companies into their respective industries. They have a strict format, making it perfect for filtering. Using a list of over 100 Norwegian startups and their NACE codes a list of NACE-codes that were frequently used to categorize high technology startups was made. This list consisted of six NACE-codes for mostly high-technology industries: 66.190, 73.110, 62.010, 63.120, 62.030, and 72.190.

Lastly, a bundle of the remaining companies had reported unsubstantial earnings over multiple years. To combat freshly incorporated holding companies with high-technology NACE-codes, the decision was made to filter out all companies where the sum of the revenue over the last five years was zero. This could in theory color the data material to only include “successful”

companies but seeing as the analysis is done on financial numbers; revenue is essential.

After filtering the entire database on incorporation date and NACE-codes and revenue the population of companies was down to 2245, from over 200 000. As a final quality check of the population, multiple samples of the filtered database were taken. This was done to test if the database actually captured startups. These sample-tests gave assuring results, crosschecked against a list of Norwegian startups, concluding the efforts to filter the T-rank database.

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4.1.1 Coding

The raw data provided to me by T-rank included an abundance of both financial data and company details. A lot of which was unnecessary for my planned analysis. Thus, I chose to filter out the rows that were not going to be included in the analysis. This included financial data regarding company assets, number om employees, and whether the CEO of the company also functions as the chairman of the board. Apart from filtering and deleting rows of data, I also had to recode data to be able to fit into my analysis.

The rows of data that I ended up using for my analysis in SPSS after both filtering and recoding were the following; the Chairman’s number of interlocks, the number of board members, the incorporation date, and Revenue for 2016 to 2018. These financial metrics were used to code for the financial milestones which are used for dependent variables.

4.1.2 Variables

Dependent Description

MILE1M 1M milestone - Last three years combined revenue is more than 1M MILE5M 5M milestone - Last three years combined revenue is more than 5M Independent

firmage The age of the company in years c_ilocks The number of chairman interlocks brdmem The total number of board members

Table 1: The variables used in the analysis

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4.2 Describing the data

Table 2: Descriptive statistics for the variables used in the analysis

Looking at the frequency table (2) for the variables used in the analysis we find an overview of what their values look like. First of all, both the two dependent variables, MILE1M and MILE5M have binary values, as seen by their maximum and minimum values. A little under half, 42.6% of the companies in the dataset reached the milestone for 1MNOK over the last three years of operations. While under 20% of the companies reached the 5MNOK milestone.

The companies age in years is normally distributed with a mean of 2.5, where 1 is the minimum and 4 is the maximum. The standard deviation is high for the chairman’s interlock because an abundance of the companies in the dataset reported zero interlocks (as seen in Figure 1, Chapter 2). This causes the variable to be highly skewed to the right, lowering the mean and heightening the standard deviation. This is one of the reasons for the analytical choice that was made regarding using binary logistic regression, as compared to linear regression.

We can also see that the maximum for the number of chairman interlocks seems abnormally high. This can be explained by some chairmen sitting on a plethora of holding company boards.

These holding networks can be highly complex, and if a board member sits on multiple company boards with complex ownership structures, the interlock count will get incredibly high. Most holding companies were filtered out from the dataset, but companies that produce value can still be owned by a complex holding structure. These are however rare cases in the dataset. As for board members, the distributions seem normal. Norwegian companies are required to have at least one chairman of the board, and the biggest percentage of the companies

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in the dataset fits under that bracket. The mean lies at around three board members, which seems healthy for startups. The maximum number of board members on any board in the dataset is just 11, most likely because of conventions forcing companies to minimize the legal structure connected to the board of directors.

4.3 Analyzing the data

Table 3 below shows the pairwise correlation between all the variables, both dependent and independent, used in the analysis. Because the sample size is so high, with over 2000 companies, the correlations are statistically significant at the 0.01 level for almost every entry.

The tests use Pearson’s correlation, hence all the coefficients lie between 1 and -1, where both ends of the scale signal high correlation. The tests shown in the matrix are 2-tailed correlations.

Table 3: Correlation matrix of the variables used in the dataset

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The correlation between 1 and 2 is high as all companies that have managed to reach the 5MNOK milestone also have reached the 1MNOK milestone. It is interesting however to note that there is a weak correlation between 3: chairman’s Interlocks and the financial milestones.

Because we are measuring the correlation of over 2000 high-tech startups, the correlation coefficient is low. Network effects are just one potential factor of many in the equation in what makes startups grow and hit revenue milestones, and this variable only captures part of the effect. Nonetheless, there is a notable positive correlation between the number of interlocks of the chairman, and the company’s economic performance. This supports hypothesis 2.

The number of interlocks for the chairman of the board is a less colored variable than the total amount of interlocks. The total amount of interlocks is highly related to the number of board members, as the more board members the more likely there is to exist interlocks. Board size is a variable that can be impacted by other factors, such as company age. We see here in the correlation matrix that there is a weak, but the existing correlation between company age and board size. This might be because startups tend to incorporate with a low number of board members to reduce the legal structure surrounding the company and rather expand it as they grow.

Similarly, to the chairman interlocks, the board size also has a weak correlation with the financial milestones. This might be colored by the older companies having bigger boards, and thus more interlocks. However, as mentioned the correlation between board size and company age is low. The weak correlation between board members, a proxy for the number outside board members, and the total amount of interlocks also support hypothesis 1.

The correlation between 3: Chairman’s interlocks and 5: board size is interestingly high. At first sight, this might seem odd, as there is no obvious direct relation between the number of board members on the board, and the amount of other boards the chairman is part of. However, glancing back at the board-composition -literature, the proportion of board outsiders has a strong network effect, measurable in the number of total interlocks. Seemingly, the chairman of the board tends to interlock with more companies if he is part of a board with many members.

This strengthens the use of total board members as a proxy for board outsiders, as well as support hypothesis 1. The direction of this effect is unclear, opening up for future research.

This is accounted for in the next and final chapter.

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4.4 Binary Logistic Regression Analysis

With logistic regression, we find the changes in odds for the binary dependent variables, based on the independent variables. The direction of the odds can go both ways, increase or decrease based on the independent variables. If the odds, read in the table as Expectancy (Exp(B)), are equal to one (1) there is no increase or decrease in the odds in relation to the independent variable (Hosmer, Lemeshow, & Sturidvant, 2013). However, if the expectancy is three (3) the odds are three times higher for the dependent variable to be 1, given the independent variable.

The regression weight can be read by the symbol “B”. This weight tells us about the direction of the odds, increase or decrease. If the weight (B) is positive the odds increase, and if the weight is negative the odds decreases based on the independent variable. S.E shows the variance in regression weight (B).

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Table 4: Results from the binary logistic regression analysis

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In the binary logistic regression shown in Table 4, we test the odds increase for our two financial milestones, MILE1M, and MILE5M with our independent variables. The table shows the two logistic regression analyses that were performed, in one table, comparatively up against each other. The p-value in the analysis concludes that there is, in fact, a statistically significant relationship between the variables. Because the sample size is high (n=2285) the p-value is very high in almost all of the cases (p<.0001). In both analyses, the control variable, Firm Age, has the highest increase in odds with a 2.243 increase for MILE1M and a 1.926 increase for MILE5M. This means that for both the milestones there is almost a double chance of reaching the goal if the company is older. This makes sense, as the older companies have had more time to be in the market and make the sales necessary to qualify for the milestones. We control for this variable because the total amount of board members may increase based on the companies age. Thus, the two variables are related and should be controlled.

Total board members also have an increased odds of reaching both the financial milestones.

For the 1MNOK milestone, the odds are 1.26 times greater (26%) if the board size is bigger.

For the 5MNOK milestone, the odds are 1.492 times greater (almost 50%) if the board size is bigger. Because the number of board members functions as a proxy for board outsiders this finding supports hypothesis 1. We see an increase in sales based on the board sizes of the companies, and this effect is probably partly due to network effects. However, it is also fair to assume that some of this effect is measured because of the relation between board size and firm age. Older, and more successful startups might increase their boards as their companies evolve.

From the correlation analysis, we could see that there was a weak correlation between the firm age and board size.

Lastly, we find that there is a weak increase in the odds of reaching financial milestones based on the number of chairman interlocks. For the 1MNOK milestone, the odds increase is 3.7%

and for the 5MNOK milestone, the increase is 2.1%. This finding supports the hypothesis 2 and is evidence that there is a measurably positive network effect of chairman interlocks. Even though the odds increase is fairly low, it is important to note that the chairman interlocks only account for a small part of the entire network effects of a company. The firm age does not have an obvious effect on the number of chairman interlocks. This is controlled in the bivariate correlation matrix, Table 3, showing a very weak correlation between the variables. We, however, know that the chairman’s number of interlocks does increase as the board size goes up, arguing for the chairman connecting with board outsiders through interlocks.

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5.0 Conclusion and Discussion

The research managed to fulfill its objective by attending to the research question and the hypotheses. I accepted hypothesis 1 based on the correlation matrix and the logistical regression analysis. There was in fact a greater chance for companies with bigger boards and more outsiders to form network relations. These network relations were measured through interlocks. The fact that the chairman’s number of interlocks had a medium, and significant correlation with the number of board members speaks to this effect. There seemed to be that chairmen link up with more companies if the board of directors has more members. Using board members as a proxy variable for board outsiders may help explain this phenomenon; If the chairman interlocks with companies in which the board outsiders originate from. This is but a hypothesis for explaining the results and will need further investigation to be confirmed.

I accepted hypothesis 2, as I did find a weak increase in odds for reaching financial milestones for the companies with more network ties. These network ties were also measured through interlocks. The odds increase was around 3% for the 1Million milestone and 2% for the 5Million milestone. Using the chairman’s interlocks for the analysis, I enhanced its validity by excluding margins for error. The chairman’s number of interlocks is a variable independent of external effects, and it does not correlate strongly with firm age. Knoben and Bakker (2019) claims that interlocks work as a predictor for strategic alliance formation, which might partially explain this measured effect. This notion is also strengthened by relational pluralism theory, which states that not only does the chances of alliances increase, but also their success ratio.

However, network effects are a complex phenomenon, and this study only managed to capture a small part of what is actually happening in the Norwegian startup scene. In reality, network effects manifest themselves in so many ways it is next to impossible to measure, as most phenomena’s in social science. This study was aimed at exploring and explaining one of the effects of networks in the Norwegian high-tech startup scene. It managed to find evidence for network relations being impacted by board composition, through hypothesis 1. It also managed to find a positive economic effect driven by interlocks, through hypothesis 2. This, in part, helped provide an answer to the complex research question; “How does the network of the board of directors impact startups’ economic success in Norway?”.

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