The entrepreneurial ecosystem of Amsterdam : a study on start-ups participating in accelator programs

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University of Amsterdam

The Entrepreneurial Ecosystem of

Amsterdam:

A Study on Start-ups Participating in Accelerator Programs.

Author: Supervisor:

Zuhal Hayat Dhr. dr. G.T. Vinig

10183590

Second Reader:

Dhr. B. Szatmari MSc

A thesis submitted in partial fulfilment of the

requirements for the degree of master of Business Administration at the Faculty of Economics and Business

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Acknowledgments

This master thesis is written to fulfil the partial graduation requirements of the study Business Administration at the University of Amsterdam. The research described here was conducted under the supervision of Tsvi Vinig. The master thesis has been guided at the Faculty of Economics and Business, during the period of January 2017 to June 2017.

I would like to express my appreciation to the following persons without whom this master thesis would never have been possible.

First of all, I would like to thank my thesis supervisor Tsvi Vinig. Throughout the year, he has provided interesting lectures and discussions that enthused and motivated me to write my master thesis, within the exciting topic of entrepreneurship, focused on accelerators and start-ups in the entrepreneurial ecosystem of Amsterdam. I am appreciative for all the insightful meetings that provided me with valuable feedback, support, and guidance during this process of writing my thesis.

Secondly, I wish to thank all the participants that contributed to my research, and who willingly shared their precious time during the process of interviewing and filling out questionnaires. I have learned a lot from the participants sharing their experience and knowledge with me, and providing the data necessary to complete this research.

It has been a pleasant journey to fulfil the study Business Administration at the University of Amsterdam.

Alkmaar, June 23, 2017 Zuhal Hayat

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Statement of Originality

This document is written by Zuhal Hayat, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

In literature, the concept of entrepreneurship in relation to studying the determinants of start-up performance is well known. However, despite the abundance of the literature relating to this subject, little is known about start-ups participating in accelerators. Especially, in the entrepreneurial ecosystem of Amsterdam, the Netherlands. This means that there is a clear need for research and, therefore, the following research question has been formulated: “How do accelerators affect start-up performance in the entrepreneurial ecosystem of Amsterdam?”

In total four propositions were established. The study found evidence for three. The following findings are found: accelerators provide resources that often influence start-ups positively. Additionally, the accelerator’s network positively influences the start-up performance and is often considered to be the most important resource. However, entrepreneurs do not consider accelerator programs to be vital for start-up success. Furthermore, the mentoring resources provided by the accelerator play a central role in influencing start-ups. On the other hand, the direct financial resources provided by the accelerator seem to be irrelevant, whereas the chance to get funding from investors increases through participation.

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2.7 Conclusion ...33 3. Methodology...35 3.1 Research Design ...35 3.2 Unit of Analysis ...36 3.2.1.Accelerators ...36 3.2.2 Start-ups ...37 3.3 Sample Selection ...38 3.4 Data Collection ...38 3.4.1.Interviews ...38 3.4.2 Memos ...39 3.4.3 Questionnaire ...39 3.5 Research Procedure ...39 3.5.1 Interviews ...39 3.5.2 Questionnaire ...41 3.6 Data Analysis ...43 3.6.1 Interviews ...43

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3.6.2 Questionnaire ...44

3.7 Quality of the Research ...45

3.7.1 Interviews ...45 3.7.2 Questionnaire ...46 3.8 Ethical Issues ...48 3.8.1.Informed Consent ...48 3.8.2 Confidentiality ...48 4. Research Results ...49

4.1 The Entrepreneurial Ecosystem of Amsterdam ...49

4.2 Accelerator Resources ...50 4.2.1.Networking Opportunities ...50 4.2.2.Mentoring ...50 4.2.3.Financial Resources ...51 4.2.4.Acceleration ...51 4.2.5.Knowledge ...52 4.3 Start-ups ...53 4.3.1.Reasons to Participate ...53 4.3.2.Different Start-ups ...54 4.3.3.Start-up Success ...55

4.4 Influence on Start-up Performance ...56

4.4.1.Networking Opportunities ...56

4.4.2.Mentoring ...57

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4.4.4.Acceleration ...58

4.4.5.Knowledge ...59

4.4.6.Successful Graduation ...59

5. Discussion ...61

5.1 Analysis of the Findings ...61

5.2 Theoretical and Practical Implications ...66

5.3 Limitations ...67

6. Conclusion ...69

6.1 Overview of the Findings ...69

6.2 Future Research ...70

References ...71

Appendices ...83

Appendix A Topic List for Both Start-ups and Accelerators ...83

Appendix B Memos ...87

Appendix C Interview Transcripts Start-ups ...89

Appendix D Interview Transcripts Accelerators ...203

Appendix E Short Explanation ...240

Appendix F Questionnaire Questions ...241

Appendix G Word Frequency Query ...252

Appendix H Coding Process ...253

Appendix I Additional Questionnaire Pilot Participants ...265

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LIST OF FIGURES

Figure 1. Structure of the Paper ...12

Figure 2. Six Domains Isenberg ...17

Figure 3. Lifecycle of Start-ups ...19

Figure 4. The S-curve Model ...20

Figure 5. The Research Framework ...34

Figure 6. Start-ups Participating in Accelerators in Amsterdam, the Netherlands ...44

Figure 7. Start-up Industries ...45

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LIST OF TABLES

Table 1. Definitions of Entrepreneurship ...14

Table 2. Key Differences between Incubators and Accelerators ...24

Table 3. The Formulated Propositions ...33

Table 4. Accelerators in the Entrepreneurial Ecosystem of Amsterdam ...37

Table 5. Functions of the Accelerator Representatives ...37

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1. Introduction

The concept of ‘entrepreneurial ecosystems’ is relatively new amongst scholars, and is rapidly increasing in interest (Fuerlinger, Fandl, & Funke, 2015). It is defined as a set of interconnected entrepreneurial actors (both existing and potential), entrepreneurial organisations (e.g. start-ups, business angels, venture capitalists, incubators, accelerators), institutions (e.g. universities, public sector agencies), and entrepreneurial processes (e.g. the number of high growth firms) which together have the potential to connect and improve the performance within the local entrepreneurial environment (Mason & Brown, 2014). More specifically, Bluestein and Barrett (2010) emphasise the importance of early, high-quality mentorship for start-ups in an entrepreneurial environment. This refers to start-ups participating in accelerators.

Accelerators are (groups of) experienced and successful entrepreneurs who provide guidance, expertise, office space, and networks to help start-ups succeed in the early stages of a venture (Radojevich-Kelly & Hoffman, 2012). According to Bluestein and Barrett (2010), start-ups participating in accelerators have a significant longer breath and, therefore, have a higher probability of becoming successful (Swamidass, 2013). However, this finding is not universal (Hathaway, 2016). For instance, Hallen, Bingham and Cohen (2014) did not find a significant accelerator effect on start-up performance.

In literature, there is an abundance of research relating to entrepreneurship and the determinants of start-up performance. However, there is a paucity of research on how accelerators influence start-start-ups, especially in the context of entrepreneurial ecosystems in Europe. Academics argue that Europe lacks entrepreneurial activity and needs more research, in order to compete globally (Fuerlinger et al., 2015). Therefore, this paper focuses on the Netherlands and, in particular, on Amsterdam. Since, Ollongren, first deputy mayor and alderman of Amsterdam, states that academics need to examine the entrepreneurial ecosystem of Amsterdam as it has the potential to become the next best hub in Europe. However, Amsterdam has some difficulty keeping up with other start-up ecosystems in Europe, such as Berlin or London (I Amsterdam, 2017). According to the Dutch government, one of the possible reasons underlying this problem is that the Dutch entrepreneurial ecosystem may not be sufficiently geared

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11 towards business growth (2014). Thus, it needs to be explored whether accelerators affect start-up performance. Consequently, the following research question has been formulated: “How do accelerators affect start-up performance in the entrepreneurial ecosystem of Amsterdam?”

Multiple studies imply that early-stage start-ups do not survive their first year in business, and even less survive the first five years. The Organisation for Economic Co-operation and Development (OECD) shows that only 56 percent of the Dutch enterprises survived the first year in business, in 2012. For this reason, the purpose of this study is to investigate how accelerator resources might influence the successful survival or growth of start-ups. Accordingly, the author explores accelerator practices in supporting start-ups and increasing survival probability in their performance, with the main objective of comparing results of preceding studies and combine this with new data to provide new insights. This is important as there is little rigorous evidence on whether business accelerators support new ventures, so whether accelerators influence start-up survival or even success, by decreasing the probability of failure. This issue is particularly relevant given the significance of new ventures for economic growth. More specifically, increasing the success rate of start-ups can have a profound effect on employment creation, economic development, and poverty reduction. Besides, many local governments would be able to use accelerators to transform their local economies through the establishment of successful start-up clusters.

The remainder of this research is structured as follows: in section two, the literature review, the obtained literature will be discussed extensively. In the third section, the methodology is presented. Fourthly, the research results are presented. In the fifth section, the discussion provides a critical analysis of the findings. It also provides an overview of the implications and limitations of this research. In the last section, the conclusion, the research question will be answered and recommendations for future research will be provided. The structure of the paper is presented in more detail in Figure 1.

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Input

---

Section

---

Output

Background of the Study Research Question

Research Gap Research Relevance

Structure of the Study

Current state of literature Theoretical &

Conceptual Framework

Research Methodology Research Design Unit of Analysis Sample Selection Data Collection Procedure Data Analysis Quality of Research Ethical Issues

Data sourced from interviews, Results from Data memos and questionnaire

Analysis of the Results Theoretical & Practical

implications, limitations

Overview Answering the RQ

Recommendations

Figure 1: Structure of the Paper

Section 1: Introduction Section 2: Literature Review Section 3: Methodology Section 4: Research Results Section 5: Discussion Section 6: Conclusion

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2. Literature Review

The literature review is performed to collect fundamental definitions and theories that are necessary to establish propositions, and eventually answer the research question. The literature review consists of seven sections.

2.1 Entrepreneurship

In the first section, the concept of entrepreneurship is elaborated to understand the research topic in more depth.

2.1.1 Defining Entrepreneurship

Currently, the literature on entrepreneurship holds diverse opinions on the role of the entrepreneur (Hébert & Link, 1989). In general, there are two opposing views. One group focuses on the characteristics of entrepreneurship (e.g. innovation), while the other group focuses on the outcomes of entrepreneurship (e.g. value creation) (Gartner, 1990).

One of the most cited and influential academics in entrepreneurship is Joseph Schumpeter, who belongs to the first group. He claims that entrepreneurs cause economic development (Hébert & Link, 1989). He defines entrepreneurs as innovators, who take advantage of change. More precisely, Schumpeter connects entrepreneurship with innovation. Thus, entrepreneurs identify market opportunities and use innovative approaches to exploit them (Ahmad & Seymour, 2008). Here, the crucial aspect of entrepreneurship is the process of “doing” rather than “owning” (Hébert & Link, 1989).

In contrast, Turoff states that the definition of entrepreneurship is more behavioural (e.g. new venture development) and not based on personal characteristics (Gartner, 1990). Here, the term ‘entrepreneur’ is been applied to a person who starts a new business where there was none before (Cunningham & Lischeron, 1991). This description is based on the ownership status of the individual, and not based on the evaluation of one’s actions (Audretsch, Kuratko, & Link, 2016). Similarly, Lumpkin and Dess (1996) argue that the fundamental act of entrepreneurship is the act of launching a new venture. Moreover, Bygrave and Hofer (1991) view the entrepreneur as the initiator, with firm creation as the

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14 actual mechanism of exploitation. The following table provides a summary of the contrasting definitions, see Table 1.

Table 1. Definitions of Entrepreneurship

Author Definitions

Cantillon (1734) Self-employment with an uncertain return.

Schumpeter (1934) An entrepreneur is a person who carries out new combinations, causing discontinuity. The carrying out of a new combination can include a new good or quality of a good, a new method of production, opening of a new market, conquest of a new source of raw materials or the reorganisation of any industry.

Cole (1968) Entrepreneurship is an activity dedicated to initiation, maintenance and development of a profit-oriented business.

Leibenstein (1968) An entrepreneur is one who marshals all resources necessary to produce and market a product that answers a market deficiency.

Drucker (1985) The capacity is an innovation act who presupposes the endowment of the existing resources with the capacity of producing wealth.

Gartner (1985) Entrepreneurship is the creation of new organisations.

Kirzner (1985) An entrepreneur is one who perceives profit opportunities and initiates action to fill currently unsatisfied needs.

Bygrave and An entrepreneur is one who perceives an opportunity and creates an Hofer (1991) organisation to pursue it.

Herron and Entrepreneurship is a set of behaviours which initiate and manage the re- Robinson (1993) allotment of economic resources and whose purpose is the creation of value by

these means. (Source: Misra & Kumar, 2000)

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2.1.2 Theories Related to Entrepreneurship

There are numerous dominant theories related to entrepreneurship. Schumpeter states that entrepreneurs carry out new combinations of existing ideas or come up with breakthrough ideas, which contribute to the manifestation of discontinuous change, as the core of economic growth (1912). Somewhat differently, Naudé (2013) argues that entrepreneurship is about individuals utilising opportunities by creating and growing new businesses. While Shane and Venkataraman (2000) define opportunities as goods being sold at profit, Naudé (2013) claims that this is insufficient as it suggests that utility from entrepreneurship solely depends on monetary gains. He believes that opportunities should include situations where persons can create new firms. Similarly, Shane (2003) defines an entrepreneurial opportunity as a situation in which a person can create a new means-end framework, for recombining resources that the entrepreneur believes will yield a profit. Here, the main difference between an entrepreneurial opportunity and many other situations in which people seek profit is that an entrepreneurial opportunity requires the creation of a new means-ends framework (i.e. new venture) rather than just optimising within an old framework.

However, recognising such opportunities and, subsequently, creating new ventures is not easy. Starting a business involves risk, which goes hand in hand with high rates of failure (Kale & Singh, 2007). This relates to the theory of Knight (1921), who states that entrepreneurship by definition requires making investments without precisely knowing the returns. In order to mitigate these risks, some individuals are able to secure resources from resource controllers, whereby considerable risk is shifted (Venkataraman, 1997). This involves integrating and gathering resources (Gartner, 1990), which could be linked to the social capital theory. This theory assumes that new ventures need a suitable network of contacts to progress successfully (Häuberer, 2011). Likewise, Seidel, Packalen, and O’Mahony (2016) state that most entrepreneurs are not capable of launching a successful venture completely on their own. For this reason, entrepreneurs seek mentoring and resources from various connections, in order to acquire resources and increase their probability of survival.

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16 Entrepreneurship is a complex concept, as it has different meanings attached to it. In this paper, an entrepreneur is defined as an individual responsible for the process of creating a new venture. This means that without the individual, the new value would not be created. Here, we focus on start-ups participating in accelerators, therefore entrepreneurs are defined as founders of start-ups.

2.2 Entrepreneurial Ecosystems

In this second section, the meaning of an entrepreneurial ecosystem is explained and the main theories are discussed. Furthermore, the entrepreneurial background of Amsterdam is provided.

2.2.1 Defining Entrepreneurial Ecosystems

The term ‘entrepreneurial ecosystem’ is mostly linked to the concept of stimulating entrepreneurship, which is related to economic growth (Habon, 2015). Therefore, recent publications devote their attention towards explaining the definition of an entrepreneurial ecosystem, as it is clear that there is an ecosystem around the entrepreneur that facilitates entrepreneurship (Habon, 2005). In this paper, the definition of Mason and Brown (2014) is adopted. They define an entrepreneurial ecosystem as a set of interconnected entrepreneurial actors, organisations, institutions and processes, which together have the potential to enhance the performance within the local entrepreneurial environment.

2.2.2 Theories Related to Entrepreneurial Ecosystems

The first set of theories relates to Kirzner (1973), who states that the environment in which the potential entrepreneur finds himself contains factors that might obstruct or improve entrepreneurial vigour. Similarly, Valdez (1988) examines the environmental factors that impact the decision to create a new venture, by analysing the relationship between entrepreneurs and their environment. He uses a framework, consisting of the would-be entrepreneur and the entrepreneurial environment, to gain insights into one dimension of entrepreneurship, the start-up. In his framework, the would-be entrepreneur is responsive to opportunities and views the entrepreneurial environment as either an opportunity or obstacle (Valdez, 1988). In contrast, a second set of theories groups the ecosystem into six domains, and states that an ecosystem is only complete and development will only occur if these domains are handled simultaneously (see Figure 2) (Isenberg, 2011). Therefore, the creation of a strong

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17 entrepreneurial ecosystem is based on a holistic approach in which all factors should be integrated into a system, in order to create a fertile ground for entrepreneurship (Isenberg, 2014). However, a commonality is that a favourable market situation combined with founder-friendly characteristics will boost entrepreneurship in a certain region. This means that an entrepreneurial ecosystem consists of interconnected entrepreneurial actors that altogether facilitate and stimulate entrepreneurial activity, such as starting new ventures. Therefore, an entrepreneurial ecosystem could be described as a garden that needs fertile soils, seeds, and ingredients to make things grow (Kanter, 2012).

Figure 2: Six Domains Isenberg (source: Isenberg, 2011)

2.2.3 Entrepreneurial Background of Amsterdam

In 2016, the Netherlands reached a record number of 1,777,183 million companies ("Recordaantal bedrijven in Nederland", 2017). According to estimates by the Chamber of Commerce, entrepreneurs in Amsterdam account for five to ten percent of these businesses (Damen, 2016). Similarly, StartupAmsterdam, a hyper-connected centre point for start-ups, shows that in September 2016, the stand for start-ups was 1,153 (Amsterdam Economic Board, 2017). Interestingly, the region of Amsterdam, with regard to entrepreneurial activity, keeps growing while other regions are shrinking (Damen, 2016). One possible explanation for this success of Amsterdam is that it has a young and educated population (Storck, 2015). According to the municipality of Amsterdam, with 52 percent of

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18 the workforce being highly educated, human capital is one of the main sources of innovation (Gemeente Amsterdam, 2017).

Consequently, I Amsterdam (2017) states that the entrepreneurial ecosystem of Amsterdam has the potential of becoming one of the most striking locations for start-ups worldwide. In the European Digital City Index (2016), Amsterdam ranked 3rd_{of 60 European cities for both start-ups and scale-ups.}

Amsterdam scored highly on access to capital (5/60), entrepreneurial culture (11/60), and mentoring (6/60). However, the overall business environment (41/60) and the market (30/60) could be improved. Moreover, Amsterdam was named the European Capital of Innovation of 2016-2017 by the European Innovation Commission, and ranked 19th_{in 2016 by the Global start-up Ecosystems in terms of}

performance, funding, talent, market research, and start-up experience (I Amsterdam, 2017). In addition, Amsterdam is known for its excellent infrastructure and English language skills, proximity to European markets, and generous tax regime (“European Digital City Index”, 2016).

2.3 Start-ups

In this third section, a definition of the term ‘start-up’ is provided. Next, we will discuss the lifecycle theory of start-ups, followed by the various challenges start-ups face. This is important as it displays whether the hurdles start-up face matches the solutions accelerators provide.

2.3.1 Defining Start-ups

In the literature, there is agreement on the fact that start-ups are newly launched businesses, found by one or more entrepreneurs (Giardino, Unterkalmsteiner, Paternoster, Gorschek, & Abrahamsson, 2014; Dwivedi, 2016). Another commonality amongst scholars is the recognised ability of start-ups having high growth potential (Robehmed, 2013). However, there is disagreement on when a start-up is not considered a start-up anymore. For instance, Paul Graham states: “A company [of] five years old can still be a start-up. Ten [years old] would start to be stretch”. Likewise, Dwivedi (2016) states that a venture is considered to be a start-up up to five years from the date of incorporation. Others argue that after about three to four years in business, most start-ups cease being start-ups (Robehmed, 2013).

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19 Start-ups go through multiple stages during their development (Salamzadeh & Kawamorita Kesim, 2015). Distinguishing between the different start-up phases is important, as start-ups require different needs in each stage (Clarysse & Bruneel, 2007). Roberts (1991) uses the terms: pre-seed, the seed, and the follow-up phase. Likewise, Salamzadeh and Kawamorita Kesim (2015) have developed a framework that consists of three parts, see Figure 3.

Figure 3: Lifecycle of Start-ups (source: Salamzadeh & Kawamorita Kesim, 2015)

In the first part of the model, the bootstrapping stage (i.e. pre-seed stage) refers to the very early-stage of a start-up. Here, the entrepreneur wishes to transmit the ideas into a new viable venture. Additionally, this stage is characterised by entrepreneurs asking friends and family members to give their opinion on the entrepreneur’s invention (Salamzadeh & Kawamorita Kesim, 2015).

The second stage is the seed-stage, characterised by the establishment of the venture. However, this does not imply that the company is selling products in the marketplace. This stage is more about developing prototypes, teamwork, and seeking for support mechanisms (e.g. accelerators) that will help the start-up grow. However, most start-ups do not survive this stage, as they are not able to get support. Therefore, the conditions under which a venture is planned, and the process followed in its initial development phase, have significant consequences for the performance in a later stage (Salamzadeh & Kawamorita Kesim, 2015). In addition, they argue that those who succeed in receiving support will have a higher chance of becoming a profitable company.

The final stage, the creation stage, takes place when the company sells its product to customers and starts hiring employees (Salamzadeh & Kawamorita Kesim, 2015). Other researchers refer to this stage

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20 as the follow-up stage (Roberts, 1991; Clarysse & Bruneel, 2007). In addition, some scholars believe that entrepreneurship stops when the creation stage is ended (Ogorelc, 1999).

Furthermore, the start-up life cycle is a subset of the S-curve model (Abraham & Knight, 2001), see Figure 4. It is used to describe and predict the performance of a venture over a period of time. According to Abraham and Knight (2001), organisations and technologies go through three phases of growth before they must either jump to a new level of sophistication or die. Thus, the S-curve signals the need for the start of the creativity phase (Rundi & Voehl, 2016). An organisation must repeat this pattern time and again if it wants to survive and grow in a competitive environment (Abraham & Knight, 2001). The pattern begins with the starting phase of searching for a business idea that will produce growth and profitability. Once the idea is actualised, the replicating and improving phase takes over. When the limits of this phase are reached, the organisation moves to the stabilising phase. Here, the organisation milks the business idea for as long as possible, but as time passes, the returns begin to deteriorate and the original idea loses viability. Therefore, in the last phase, founders need to find breakthrough change and leap to a new S-curve with the potential for future growth and profitability. If this is not found, the business goes into a downward spiral and dies (Abraham & Knight, 2001).

Figure 4. The S-curve Model (Source: Abraham & Knight, 2001)

2.3.3 Start-up Challenges

Launching a new venture is commonly characterised by trial and error (Johannisson, 1988). There are several potential pitfalls associated with starting a business. According to Low and Macmillan (1988),

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21 the main problem is that start-ups lack resources. Hite and Hesterly (2001) add to this by stating that newly created firms suffer from liabilities of both newness and smallness, often resulting in the lack of critical resources to ensure the successful survival of the firm. Additionally, Wallin, Larsson, Isaksson, and Larsson (2011) demonstrate a link between firm growth, knowledge acquisition and knowledge application. This could be associated with the Resource Based View (hereafter RBV), in which Penrose (1959) states that firm growth is dependent on entrepreneurial knowledge.

Start-ups often have the need to gain access to external resources and know-how that cannot be produced internally (Hite & Hesterly, 2001). Therefore, the time needed to accumulate experience can be reduced by contacting mentors and other entrepreneurs (Johannisson, 1988). Especially, since the lack of experience results in lower degrees of legitimacy (Katz & Gartner, 1988). Likewise, Wallin, Still, and Komi (2015) state that start-ups require mentoring and guidance provided by support mechanisms, such as accelerators, which will assist start-ups with formulating a game plan. However, in order to get such support mechanisms, the entrepreneurial environment needs to stimulate entrepreneurial activity. More precisely, a supportive environment has the ability to generate successful start-ups, while a maleficent one could result in failure (van Gelderen, Thurik, & Bosma, 2005).

Furthermore, start-ups participating with potential partners and investors could speed up the growth of a start-up (Torkkeli, Kuivalainen, Saarenketo, & Puumalainen, 2016). However, often start-ups lack connectedness to resource networks, due to their newness (Battistella, De Toni, & Pessot, 2017). Consequently, scholars tend to believe that the shortage of such connections will slow down the progress of a start-up. Likewise, Clarysse and Bruneel (2007) claim that start-ups need access to networks of excellence, where they can utilise external expertise, experience and know-how. Besides, the lack of financial capital is problematic too. The absence of money can cause start-ups to fail at the seed-stage. Torkkeli et al (2016) state that financial resources are easier to acquire in the case of having relationships, such as between firms and investors. On the other hand, some scholars believe that there is no connection between financing and its influence on start-up acceleration. For instance, Hecchavarría, Matthews, and Reynolds (2016) reveal no relationship between funding and start-up survival or growth.

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22 To conclude, this section highlights challenges that might occur when launching a new business into the marketplace. However, fortunately, there are support mechanisms, known as accelerators that are designed to guide start-ups in their process of survival and growth (Malek, Maine, & McCarthy 2014). To illustrate, Battistella et al (2017) mention that accelerator programs act as focal points for providing resources, such as presenting and building new network ties between founders, investors and other stakeholders. In the following section, the concept of accelerators will be explained in detail. Hereby, it is important to analyse to what extent accelerators are able to solve these start-up challenges.

2.4 Accelerators

Deficiencies of former incubation models, which focus on slow growth and start-up survival, led to the emergence of accelerators (Cohen, 2013; Cohen & Hochberg, 2014; Pauwels, Clarysse, Wright, & Van Hove, 2016). Currently, accelerators are evolving from the US to Europe (Battistella et al., 2017). Since, the first accelerator, Y Combinator, was found in 2005 by Paul Graham in Massachusetts, and is considered to be the most successful seed accelerator program (Christiansen, 2009). An accelerator program is a process of intense, rapid, and immersive education aimed at accelerating the life cycle of young innovative ventures, compressing years’ worth of learning-by-doing into just a few months (Hathaway, 2016). They want growth that leads to positive exists, as they often take an equity stake in start-ups. Additionally, accelerators offer intense mentorship, while incubator tenants rarely take full advantage of available advice (Cohen, 2013). Thus, there are fundamental differences between incubators and accelerators, even though, scholars often state that both focus on supporting start-ups (Konczal, 2012). Consequently, misperceptions exist between incubators and accelerators (Cohen, 2013), which cause the definition of an accelerator to remain disputatious (Konczal, 2012). Therefore, the following section is devoted to elucidating the definition of an accelerator.

2.4.1 Defining Accelerators

Throughout the literature, one group of scholars focus on the tangible services an accelerator provides, such as the office space, to minimise the cost for the start-up and to provide it with necessary support. The other group defines accelerators by focusing on the intangible resources the accelerator offers. For

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23 instance, the acceleration (Grimaldi & Grandi, 2005). However, in the first formal definition, accelerators are defined as a cohort-based program that includes mentorship and educational mechanisms, that finish with a public pitch event known as ‘demo day’ (Cohen, 2013; Cohen & Hochberg, 2014; Hochberg, 2016). The purpose of an accelerator is to speed start-ups into successful venture formation by offering various tools. It provides start-ups the opportunity to target the right market segments, and increase the probability of survival and growth (Drobysheva, 2016). These resources are provided during an intensive program of limited duration, which usually lasts three to six months (Cohen & Hochberg, 2014). Similarly, Radojevich-Kelly and Hoffman (2012) explain accelerators as a group of experienced and prosperous entrepreneurs, who focus on the provision of intangible services (e.g. advice, network), to help start-ups succeed in their early-life stages (Cohen & Hochberg, 2014).

2.4.2 Types of Accelerators

Academics often separate non-profit accelerators (e.g. university accelerators) from for-profit accelerators (e.g. corporate accelerators), while other academics do not make a distinction at all. According to Dempwolf, Auer, and D’Ippolito (2014), non-profit accelerators deliver the same collection of services (e.g. facilitating resources such as mentoring, networking, and facilities), compared to the commercialised accelerator. However, for-profit accelerators offer these services in exchange for equity, while non-profit accelerators follow a free program participation (Dempwolf et al., 2014).

2.4.3 Theories Related to Accelerators

Currently, the state of literature on accelerators is based on little peer-reviewed articles and secondary media sources. The lack of peer-reviewed literature is due to both the novelty of accelerators and the fact that accelerators are generally privately held and funded, meaning that it is not mandatory for them to share information about their programs (Dempwolf et al., 2014). Besides the dearth of literature, there is also a lot of misunderstanding about accelerators and incubators amongst scholars.

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24 The first confusion is that these terms are used interchangeably in primary and secondary sources (Chang, 2013). The main difference between accelerators and incubators is that incubators tend to help start-ups by protecting them from the environment and give them space to grow. Accelerators, on the other hand, speed up market interaction for ups to adapt quickly (Cohen, 2013). In relation, ups in accelerators enter in batches and are active for a period of three to six months. In contrast, start-ups stay in incubators for a period of three to five years (Chang, 2013). Consequently, accelerators accelerate start-ups, resulting in either a quick start or failure, while incubators focus on the survival of start-ups (Cohen, 2013). In the table below, a detailed overview is provided of the differences.

Table 2. Key Differences between Incubators and Accelerators

Incubator Accelerator

Duration 1 – 5 years 3 – 6 months

Cohort No Yes

Business Model Rent; non-profit Investment; non-profit

Selection Frequency Non-competitive Competitive; cyclical

Venture Stage Early; late Early

Education On request, HR/legal/accounting Seminars; workshops

Venture Location On-site Usually on-site

Mentorship Minimal, and for a remuneration Intense, by self and others

(Source: Cohen, 2013)

Additionally, Dempwolf et al (2014) stress the significance of clearly distinguishing between these two concepts and, therefore, developed a framework for accelerators. The framework consists of five parts: 1) customer market, 2) activities, 3) rewards, 4) value experience, and 5) differentiation. The first, customer market, relates to accelerators targeting early-stage start-ups, with the focus on converting inventions into commercial products. Hereby, accelerators offer various activities to start-ups, including public pitch event, mentoring services, and a combination of cash and in-kind contributions. Additionally, the accelerator’s rewards are described as start-ups gaining specialised knowledge, obtaining additional seed capital to proceed to the next stage development or continuous validation from the accelerator program. Besides, the cohort effect of the accelerator program increases the value

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25 experience. Finally, Dempwolf et al (2014) state that accelerators are differentiated as they specialise in specific industries and offer a unique combination of brokerage, mentoring, and funding activities.

Finally, in addition to the lack of empirical evidence regarding the effectiveness of accelerators on start-up performance, there are also scholars who are sceptical about this relationship. For example, Dempwolf et al (2014) argue that there is no consensus on the effectiveness of accelerators improving start-ups’ rate of survival and success. More importantly, they claim that some scholars have distorted statistical measures to display an overly positive assessment of early accelerator results. Therefore, they state that research on accelerators needs to be directed towards measuring the effectiveness of accelerators on improving start-up survival or growth.

2.4.4 Working Definition

This paper follows the formal definition presented by Cohen (2013) and Cohen and Hochberg (2014), in which an accelerator is a cohort-based program that provides several services to help early-stage start-ups to survive and grow. Moreover, this paper wants to contribute to the theoretical knowledge on accelerators. Therefore, a clear distinction is made between accelerators and incubators. Moreover, this research does not focus solely on one specific accelerator type.

2.5 Resources Provided by Accelerators

“Why do firms determinedly outperform others?” Several scholars believe that the answer to this question lies within a firm’s idiosyncratic set of competencies. The dominant theory that explains these inter-firm performance differences is known as the RBV, which was introduced by Barney in 1991 (Ireland, Hitt, & Sirmon, 2003). Barney defines resources as: “assets, capabilities, processes, information and knowledge controlled by the firm, enabling it to select and use strategies that enhance organisational efficiency and effectiveness” (1991). Hereby, he claims that firm resource heterogeneity can only lead to competitive advantage and entrepreneurial success, when resources are valuable, rare, inimitable, and non-substitutable (Barney, 1991). In a like manner, Mesquita, Anand, and Brush (2008) state that the successful creation of new ventures relies on the firm’s ability to both accumulate and systematise resources.

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26 However, creating and acquiring valuable and rare resources, and building fences to their mobility and inimitably is challenging (Barney & Arikan, 2001). New ventures typically do not have all the resources required for success (Peters, Rice, & Sundararajan, 2004), as they lack organisational history, a loyal customer base, and cannot point to its reputation for performance (McGrath, 1999). This caused accelerators to step in and provide resources to help emerging start-ups (Miller & Bound, 2011; Malek et al., 2014; Eveleens, van Rijnsoever, & Niesten, 2016). More precisely, resources are the starting point of what accelerators provide to ups, as accelerators provide resources based on the needs of start-ups (Miller & Bound, 2011).

Cohen and Hochberg argue that research needs to focus on the different dimensions of accelerator programs offering support for start-ups (2014). Therefore, this study focuses both on identifying the resources offered by accelerators to start-ups, and whether these resources affect the relationship between the accelerators and start-up performance. Consequently, resources are seen as a moderator between accelerators and start-up performance. This means that this relationship is stronger for higher values of resources. Therefore, the following proposition is formulated:

Proposition 1: The positive relationship between Accelerators and Start-up Performance is

moderated positively by Resources.

Resources can be either tangible or intangible (Barney, 1991). According to Allee (2008), tangible resources are classified as physical resources, such as financial resources. Intangible resources may be classified as ‘skills’ and include human capital, information capital, and organisational capital (Barney & Arikan, 2001; Allee, 2008). Prior research on accelerators indicates that the network resources, mentoring, and financial resources are the most valuable resources an accelerator offers. Therefore, each variable will be defined and explained.

2.5.1 Networking Opportunities

A network is a set of patterned relationships between individuals, groups, and organisations (Dubini & Aldrich, 1991). In general, there is consensus among scholars about networks successfully influencing firm emergence and growth (Hite & Hesterly, 2001; Elfring & Hulsink, 2003; Bøllingtoft, 2012). This

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27 can be proven by the fact that networks are fundamental in gaining access to opportunities and allow entrepreneurs to collect resources outside one’s close circle, to develop a successful venture (Elfring & Hulsink, 2003). Nevertheless, some studies display opposite relationships. For example, Turati (1988) states that networking is a fruitless effort, due to the time-consuming process (Dubini & Aldrich, 1991). Similarly, Aldrich and Reese (1994) conclude that networks involved in start-ups have no effect on subsequent performance. Thus, it is not fully clear what the impact of a network is on the early development of a new venture. Therefore, it is important to take a closer look at whether networks, provided by accelerators, have a positive effect on start-up performance.

Ratinho, Amezcua and Honig (2015) found empirical evidence for the fact that the network of accelerators is essential for the establishment of start-ups, as it empowers new ventures to overcome their lack of resources and accelerate firm growth. Besides, a study in China found that start-ups naturally need to establish relationships with other organisations that can provide networks and, thereby, obtain legitimacy (Peng & Luo, 2000). Entrepreneurs seek legitimacy to decrease the perceived risk by gaining explicit certification from well-regarded individuals and organisations (Hoang & Antoncic, 2003). Furthermore, Steier and Greenwood (2000) state that networks are the final arbiter of competitive success, and that accelerators giving access to networks could provide the difference between survival and failure for a new venture.

In this research, the focus is on acquaintances provided by accelerators and not on one’s idiosyncratic network. Singh, Hills, Hybels, and Lumpkin (1999) found that entrepreneurs in the information technology industry reported a higher number of opportunities identified within a 12-month period, due to their network contacts whom they did not know well. Additionally, Granovetter (1973) describes the extent to which actors can gain access to new information through ties that lie outside an entrepreneur’s immediate cluster of contacts as more relevant (Hoang & Antoncic, 2003). Besides, this research focuses on accelerators and their networking resources. This has little to do with one’s personal network of family and friends. Also, Steier and Greenwood (2000) state that ties between family and friends add little value when an individual is seeking support, new information and resources to develop its start-up. Similarly, Hite and Hesterly (2001) state that entrepreneurs must move beyond their close, cohesive

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28 networks if they are able to enjoy long-term success. Considering the above-mentioned reasons, there are some mixed results in the literature and some factors that need to be taken into account. Consequently, the following proposition is formulated:

moderated positively by Networking Opportunities.

2.5.2 Mentorship

Mentorship is an interactive relationship between a more experienced individual (i.e. the mentor) and a less experienced individual (i.e. the protégé) (Eesley & Wang, 2014). In literature, mentorship is described as the foundation of an accelerator program, as mentors provide start-ups with mentoring during intensive, temporally-compressed programs (Clarysse & Yusubova, 2014). Similarly, Cohen (2013) states that mentorship is regularly cited as a valued aspect of accelerator programs. Since mentors work with start-up founders throughout the duration of the program, to give advice and valuable feedback based on their personal experiences as entrepreneurs (Clarysse & Yusubova, 2014). Furthermore, Bluestein and Barrett (2010) state that early, high-quality mentorship is the key ingredient for a successful start-up. These findings are in line with the study of Sharma, Joshi and Shukla (2014), who state that accelerators rely on their mentoring methodology to develop high-quality start-ups. Since strong mentoring programs help start-ups to make lesser mistakes. Furthermore, they found that mentoring decreases the mortality of the start-ups attending an accelerator program with twenty percent (Sharma et al., 2014).

According to Cohen (2013), mentorship varies among programs. For example, Hallen, Bingham, and Cohen (2016) argue that start-ups receive various forms of learning. On one hand, they receive more formal learning which includes seminars and workshops on topics, such as marketing and legal issues. On the other hand, they receive more informal learning which is the interaction with mentors. Cohen and Hochberg (2014) state that program directors help founders to understand the knowledge they are storing through mentor meetings, seminars, and other means. All in all, Hallen et al (2016) claim that these various forms of mentoring are intended to help ventures more quickly to understand and pursue

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29 promising business opportunities, thereby better adapting to their environment which will lead to better performance results. Therefore, the following proposition has been formulated:

moderated positively by Mentoring.

2.5.3 Financial Resources

In recent years, accelerators have emerged as an alternate source of funding for start-ups (Smith & Hannigan, 2015). According to Smith and Hannigan (2015), accelerators pursue small levels of direct financial capital combined with high levels of engagement with start-ups. Additionally, Andruss (2013) states that the initial financial capital draws entrepreneurs to accelerator programs as it is the potential for securing early capital, and to refine their concept or get their business started. The amount of funding to get started, as well as, the amount of equity the accelerator receives in return, varies across different accelerator programs (Andruss, 2013). Hochberg (2014) claims that most accelerators provide a cash investment between $15,000 and $40,000 in return for a range of 5 to 8 percent equity, with the median offer around 5 percent equity for a $20,000 stipend.

However, in literature, there are mixed results on whether the direct funding is of great relevance for entrepreneurs and their performance. For example, Wauters (2014) states that the exact direct financial investment is strong and relevant enough to be noted as a valuable aspect of accelerator programs (>30% of respondents), but that it is evidently not the main reason for participating in an accelerator. Likewise, in the Seed-DB research, the impact of direct investment was rated significantly lower than the impact of product development support and the impact of accelerator network (4.14 compared to 7.83 on a 10-point scale) (Christiansen, 2009). Somewhat differently, Carvalho (2016) argues that the main advantage of direct funding is that it allows entrepreneurs to focus on their business full-time, and not having to work on the side. Similarly, Christiansen (2009) found in his study that the Y Combinator aims to provide enough financial assistance, so founders can cover living expenses. In other words, funding clearly matters to entrepreneurs as the level of support becomes irrelevant when start-up founders cannot focus on their business sufficiently (Christiansen, 2009).

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30 All in all, accelerators as alternative methods of funding have received little attention in the literature (Yu, 2016), and the differing results do not clarify this. In order to find out whether direct funding is perceived as a factor contributing to start-up performance in the entrepreneurial ecosystem of Amsterdam, the following proposition is formulated:

moderated positively by acquiring Financial Resources from Accelerators.

2.6 Start-up Performance

Prior studies measured start-up performance using diverse factors (e.g. survival, successful exit, goal attainment, evaluation of success by the founder) (Eveleens et al., 2016). Even though there are several methods to measure start-up performance, there are difficulties involved. Acquiring data is hard as it is not mandatory for ups to publish their reports to the public. Furthermore, the variations in a start-up’s development are quite unpredictable. Besides, both the different industry features and end-goals of start-ups make it difficult to compare absolute measures (Eveleens et al., 2016). Consequently, Eveleens et al (2016) state that the selection of a start-up performance measure alone can seriously influence the outcome of a scientific study. Therefore, this section focuses on covering the term ‘start-up performance’, in relation to accelerators.

2.6.1 Defining Start-up Performance

Several studies have focused on examining the relationship between accelerators and start-up performance. However, there still is considerable uncertainty regarding the outcomes associated with start-ups participating in accelerator programs, as well as, lack of research on which specific processes within accelerators are associated with success (Dempwolf et al., 2014).

The first measure of start-up performance relates to the ‘goal approach’. Here, start-up performance depends on whether the start-up is able to meet the established objectives, which they formulated together with the accelerator (Vanderstraeten & Matthyssens, 2010). Similarly, Mejia and Gopal (2015) state that success depends on comparing the initial stage of the start-up with the final progress of the start-up (i.e. whether the start-up has achieved the set goals). Moreover, Mejia and Gopal (2015) identify

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31 longer-term entrepreneurial outcomes, namely: the survival of start-ups and the amount of external funding received in a certain period.

Secondly, other academics focus on the start-ups’ status after the accelerator program (e.g. operating or closed), as an indicator of start-up performance. Take the case of Bueren and Oliver (2016), who use the start-up status (i.e. dead, acquired or operating) to measure the performance of both the accelerator and the start-up. Hereby, they also take into account other success indicators, such as the number of jobs created and the total funding raised by the start-up (Bueren & Oliver, 2016).

Thirdly, Yu (2016) claims that performance measures should be contrasted between companies that have participated in accelerators and companies that have not participated in accelerators, to investigate the comparisons in performance. Furthermore, he measures performance based on the funding amount the start-up receives and the amount of time it takes to receive funding (i.e. time-to-funding). In addition, he uses the survival of the venture as a start-up performance measure.

In this research, both the survival of a start-up and the successful exit are used as indicators to measure start-up performance, also known as successful graduation. According to Bueren and Oliver (2016), it is important to consider funding in relation to start-up performance. They conclude that the average operating rate is higher among start-ups that raise more funding rounds. This suggests that more funding rounds are increasing the odds of survival. As a consequence, funding is needed to build and grow a start-up to eventually become profitable. Therefore, the average of total funding per start-up seems to be a good indicator of the success of an accelerator. Besides, it is important to consider the characteristics (e.g. program length, historical connections to investors, team-based start-ups, business model) of an accelerator, as some characteristics may be related to success. Furthermore, using more than one success indicators will lead to a sounder performance analysis and could prove particularly helpful to assess the resilience of accelerated start-ups (Bueren & Oliver, 2016).

2.6.2 Theories Related to Start-up Performance

Prior research regarding the relationship between accelerators and start-up performance presents mixed results. To illustrate, Hallen et al (2014) compares 180 accelerated start-ups with 164 independent

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32 matches and did not find a significant effect of accelerators on start-ups. They examine the accelerator effect in terms of follow-on funding and conclude that successful accelerated start-ups are similar to the best independent comparable start-ups (Bliemel et al., 2016). Additionally, they conclude that accelerated start-ups fail faster, due to the cohort effect.

By way of contrast, Mejia and Gopal (2015) conclude that the access to investments and mentorship services provided by accelerator programs assist teams in shaping and enhancing their business ideas, building feasible and viable prototypes, which leads to revenues in the short term. Hereby, they state that start-ups engaging in these mentoring activities are more likely to have an official product launched and increased sales. Additionally, they claim that start-ups engaging in investor-ties building activities are more likely to survive in the longer-term. Since they provide proof for the benefits of building deep investor ties for start-ups through the accelerator program in the form of long-term firm survival and funding (Mejia & Gopal, 2015).

Furthermore, Gonzalez-Uribe and Leatherbee (2016) argue that start-ups participating in accelerator programs are more likely to raise capital, have a larger scale, and are more likely to survive. More importantly, their main finding suggests that entrepreneurship schooling, bundled with the basic services of providing a cash injection and co-working space, leads to significant increases in venture fundraising and scale. On the other hand, they find no evidence for basic accelerator services of cash and co-working space improving venture performance. In their conclusion, they recommend accelerators to focus their resources towards entrepreneurship schooling rather than providing basic services and more generic training and education.

Moreover, Bliemel et al (2016) contrast angel-funded start-ups with accelerated start-ups, in terms of their performance. Representatives of accelerators believe that angel-funded start-ups often fail as the start-up only receives limited advice from one investor, and misses out on having multiple mentors and like-minded entrepreneurs. However, it is also important to note that it is not unusual for angel-funded start-ups to be successful despite not participating in an accelerator program. Yet, accelerators have higher proportions of follow-on funding compared to post angel group investment, with a difference of 40-50 percent.

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2.6.3 Concluding Start-up Performance

In order to prevent contradicting results, the researcher approached this performance measure with a lot of caution and, therefore, has taught about the endgame of accelerator programs in general. Subsequently, all accelerators focus on start-up survival and successful graduation. In this research, successful graduation is defined as start-ups starting commercial operations in the marketplace and/or raising external funds. This suggests that accelerators should be designed to enhance venture survival and the successful graduation.

2.7 Conclusion

Throughout the literature review, four propositions are established. In this section, a list of these formulated propositions is provided. Following the formulated propositions, the research framework is developed to provide a clear overview of this study.

Table 3: The Formulated Propositions

Research Question:

“How do accelerators affect start-up performance in the entrepreneurial ecosystem of Amsterdam?”

moderated positively by Resources.

moderated positively by Networking Opportunities.

moderated positively by Mentoring.

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34 In the figure below, the research framework is provided. The research framework offers an outline of this study and is derived from the literature. It elucidates the positive relationships between the different variables that are relevant to answer the research question of this study. The research framework consists of multiple variables as almost all article states these variables as relevant to accelerators, in relation to start-up performance. However, until now, these variables have been studied separately, which made it difficult to compare the relative importance in explaining start-up performance. Moreover, it is important to realise that this research framework consists of an important assumption. The author assumes that accelerator programs provide resources, and the question is whether these resources obtained moderate the relationship between accelerators and start-up performance. In addition, the research will test whether which variable, acquired through accelerator programs, has the least and most impact on start-up performance. It will also be tested whether the resources provided by accelerator programs match the requirements of start-ups, in order to survive.

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35

3. Methodology

This section describes the research methods which are used to gather and analyse the data that are required to answer the research question and the propositions. In addition, the research design is explained and followed by the unit of analysis, sample selection, data collection, the research procedure, data analysis, and the quality of the research. Finally, the section concludes with ethical issues regarding this study.

3.1 Research Design

The research design is a general plan of how the researcher will go about answering the research question. However, in order to answer the research question, assumptions are made on how the researcher views the world (Saunders, Lewis, & Thornhill, 2012). This relates to the research philosophy (Boeije, 2010). In this research, an interpretivist philosophy is adopted, as interpretivism is more receptive to capturing meaning in human interaction (i.e. motives, meanings and other subjective experiences which are time and context bound) (Neuman, 2002; Black, 2006). Hereby, the close collaboration between the researcher and participants enables participants to more easily describe their views of reality, and researchers to better understand the participants’ actions (Baxter & Jack, 2008). Besides, Bam (1992) states that interpretivism should be adopted when there is a relatively small sample size.

The research approach is a combination of both deduction and induction, known as abduction (Suddaby, 2006). On one hand, the researcher works from the more general theory to the more specific area in which data is used to test the theory. On the other hand, an inductive approach is used to develop a richer theoretical perspective. This is particularly useful when prevailing theories are inadequate or non-existent (Creswell & Garrett, 2008), and is in line with the exploratory research aim. Since, there is lacking research on how accelerators better the performance of start-ups in the entrepreneurial ecosystem of Amsterdam.

In this research, qualitative methods in the form of interviews and memos will be the basis for data collection. Since, the researcher needs to make sense of the subjective and socially constructed meanings

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36 (Boeije, 2010). Besides, the participants’ assessments of accelerator effects can be done in their own words, freeing respondents from the constraints of researcher-defined categories. In this way, participants are able to express their opinions and experiences. Furthermore, descriptive statistics will be derived from the questionnaire. This will confirm whether or not the data collected from the interviews and memos is accurate, and will function as a support tool. Descriptive statistics is the discipline of quantitatively describing the main features of a collection of information (Carvalho, 2016). This is different from inferential statistics, as here the aim is to summarise a sample rather than using the data to learn about the population that the sample of data is believed to represent (Carvalho, 2016). According to Saunders et al (2012), the combination will result in an accurate profile of events.

Finally, this study incorporates a multiple case study, as Baxter and Jack (2008) state that a case study design commonly relates to a research question that starts with “why” or “how”. A multiple case study explores the phenomenon within a number of real-life contexts (Saunders et al., 2012). The rationale for using multiple cases is to gain a rich understanding of the context of the research, start-ups participating in accelerator programs, the processes being enacted, and how accelerators affect start-up performance. Additionally, the focus is on examining whether findings can be replicated across cases, which will increase the likelihood of new theory building (Eisenhardt, 1989).

3.2 Unit of Analysis

In this paper, the unit of analysis is shaped by three factors: social entities, time, and space (Trochim, 2006). This paper explores accelerators and start-ups. Besides, a cross-sectional study is conducted, due to time constraints. This means that the study is conducted only once and indicates a snapshot of a short period of time (Boeije, 2010). Finally, observations are made within a spatial area, which is Amsterdam, the Netherlands. Hereunder, a more detailed description is provided regarding the social entities: accelerators and start-ups.

3.2.1 Accelerators

According to StartupDelta, there are 56 accelerators of which 25 are based in Amsterdam (2017). However, StartupDelta does not distinguish between accelerators and incubators. Therefore, a detailed

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37 analysis took place. In the end, only 8 of the 25 were considered to be accelerators based in Amsterdam, see Table 4. Additionally, StartupDelta does not cover all the accelerators based in Amsterdam. For instance, the Start-up in Residence Accelerator is not listed. Furthermore, all types of accelerators are included in this research, as all accelerators aim to better the performance of start-ups. Moreover, some accelerators focus on certain industries. For instance, ZorgInc., focuses solely on digital health. In this study, no distinction is made between industries.

Table 4: Accelerators in the Entrepreneurial Ecosystem of Amsterdam. Accelerator Programs in Amsterdam Business Model

ACE Venture Lab Start-up Launch Class Non-Profit Clever Cover Clever Cover Program For-Profit

Impact Hub Accelerate For-Profit

Rockstart Web & Mobile Smart Energy

For-Profit

Startupbootcamp E-Commerce Smart City & Living FinTech & CyberSecurity

For-Profit

Start-up In Residence Start-up In Residence 2.0 Non-Profit

ZorgInc. ZorgInc. Accelerator

Program

For-Profit

512 Concepts 512 Concept Program For-Profit

Table 5. Functions of the Accelerator Representatives.

Accelerator Type Respondent

A1 Private Program Associate

A2 Private Managing Director E-Commerce

A3 Public Start-up Liaison

Program Manager Start-up in Residence

3.2.2 Start-ups

Start-ups are also the central focus in this research. Since, this study focuses on how accelerators affect start-up performance. Therefore, the first criteria for selecting start-ups was the age of the business. Start-ups that are four years old or less are taken into account. Again, the focus is on start-ups based in

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38 Amsterdam, that have either participated or are participating in accelerator programs. The final start-up population consisted of ten start-ups, see Table 6.

Table 6. Start-ups

Start-up Founded Founder Industry Accelerator Time in Accelerator One More S1 2016 X HRM A2 3 months S2 2015 X FinTech A1 5 months S3 2013 X HRM A1 5 months S4 2014 X Bicycle Repair A2 3 months S5 2015 X Virtual Reality A2 3 months S6 2016 X FinTech A2 3 months S7 2014 X FinTech A1 5 months S8 2016 X Plastic Recycling A3 5 months S9 2014 X FinTech A2 3 months S10 2015 X Smart City A3 5 months

3.3 Sample Selection

This study uses criterion sampling which, in contrast to quantitative studies, is not random. The sample contains characteristics that are relevant for answering the research question and propositions. Therefore, participants are selected that closely match the criteria for the study; entrepreneurs and accelerator representatives.

3.4 Data Collection

This study pursues data triangulation by using multiple methods of data collection. Information is gathered through interviews, memos, and a questionnaire. Data triangulation will lead to a higher reliability of the collected data and results in diverse perspectives (Baxter & Jack, 2008).

3.4.1 Interviews

The data is collected by conducting semi-structured interviews with ten (co)-founders of different start-ups that participated in accelerator programs, based in Amsterdam. Semi-structured interviews allow researchers to ask further questions which lead to more comprehensive answers, and enable participants to express their opinion on various topics, without restrictions (Bryman, 2008). Hereby, a topic list with

The entrepreneurial ecosystem of Amsterdam : a study on start-ups participating in accelator programs