Comparing Entrepreneurial Ecosystems
Amsterdam versus Shanghai
Sven Verleun Master Thesis, June 2018
Master Thesis
S.L.J.M. Verleun Student Nr.: 11929588
June 6th, 2018
MSc Entrepreneurship Joint Degree UvA & VU
Amsterdam Business School University of Amsterdam
Supervisor: Dr. R.C.W. Van der Voort Second Reader: Dr. G.T. Vinig
Preface
Before you lies a Master Thesis comparing the entrepreneurial ecosystems of Amsterdam and Shanghai. It has been written to meet the graduation requirements of the MSc Entrepreneurship curriculum jointly educated by the University of Amsterdam and the Vrije Universiteit Amsterdam.
The process of choosing a thesis topic I have found to be very difficult in these interesting entrepreneurial times. However, together with my thesis supervisor Dr. Roel van der Voort, the groundwork for a study on entrepreneurial ecosystems was prepared. One could say that we are currently living in an era of startups. We are witnessing a revolution of starting businesses that are often build upon innovative technologies. While most of the attention is directed towards the actual businesses, the framework conditions are regularly taken for granted. During the course of this research, I have experienced that these conditions are rarely easy to transform into tangible observations.
Nevertheless, I feel satisfied with the product that has come out of it. It provides an interesting glimpse into the entrepreneurial ecosystems of Amsterdam and Shanghai. I would therefore like to thank my supervisor Dr. Van der Voort for his adequate support and academic guidance throughout the process.
Starting the 11th of June, I will experience the Shanghai ecosystem firsthand by working and living in it. Although this was planned only after starting this thesis project, performing research on the city its ecosystem has also led to useful by-products in preparation of this two-month business trip. I look forward to indulge myself in the 'Paris of the East' but am also very confident that 'our own' Amsterdam will further excel as a leading entrepreneurial ecosystem in the upcoming years.
Sincerely,
Sven Verleun
Statement of Originality
This document is written by S.L.J.M. Verleun, who declares to take full responsibility for the contents of this document, including possible aberrations.
The author declares 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 Universiteit van Amsterdam and Vrije Universiteit are merely responsible for the supervision of the work and not for the contents. Therefore, both universities cannot be hold accountable for the content produced by the author of this thesis.
Abstract
The goal of this paper was to enhance mutual understanding between the entrepreneurial ecosystems of Amsterdam and Shanghai. In order to do so, the overall research question formed was: what are the main differences between the entrepreneurial ecosystems of Amsterdam and Shanghai?
The eleven pillars of an entrepreneurial ecosystem as stated by Isenberg (2011) were compared to relevant leading literature to identify the most important pillars: Government, Financial Capital, Educational Institutions, and Networks. Specific indicators were adopted and developed for each of these four pillars. The government pillar was examined in terms of the regulatory framework and tax environment in place. Additionally, specific policy instruments by both local and national governments were studied. Financial capital was studied by looking at the number, type, and focus of investments made within both ecosystems. Educational institutions were studied by examining their level of entrepreneurship in terms of knowledge output and patent applications. Networks was studied in terms of embeddedness and the role of networking within both ecosystems.
The findings of this study show the major differences between both ecosystems for all four pillars. First, Amsterdam has a better regulatory framework and a more friendly tax environment than Shanghai. However, Shanghai has certain incentive-focused policy instruments in place that positively influence entrepreneurship. Second, the state of financial capital seems to be more mature in Shanghai compared to Amsterdam. Next to this, the amounts of funding within Shanghai exceed Amsterdam dramatically. The focus of investments is similar within both ecosystems. Third, educational institutions within Shanghai apply for dramatically more patents than institutions in Amsterdam. The same trend accounts for knowledge output. However, this study also shows that the quality of patent applications in China is currently under debate due to certain subsidy programs stimulating the quantity of applications. Fourth, the entrepreneurial ecosystem of Amsterdam is more embedded than that of Shanghai. Next to this, this study found that the role of networking in Shanghai is different to its role in Amsterdam. An entrepreneur in Shanghai is more likely to start a business from within his or hers core circle while an Amsterdam entrepreneur will immediately utilize his or hers full networking potential.
Table of Contents Preface 3 Statement of Originality 4 Abstract 5 1. Introduction 11 2. Background 14 2.1. Defining Entrepreneurship 14 2.2. Entrepreneurial Ecosystems 15
2.2.1. Extant Theory & Models 15
2.3. Comparing Entrepreneurial Ecosystems 19
2.3.1. Determining Focus Pillars 19
2.3.2. Government 21
2.3.3. Financial Capital 22
2.3.4. Educational Institutions 22
2.3.5. Networks 23
3. Methods 25
3.1. Nature of this Research 25
3.1.1. Research Philosophy 25
3.2. Strategy & Design 26
3.2.1. Government 26 3.2.2. Financial Capital 27 3.2.3. Educational Institutions 28 3.2.4. Networks 29 3.3. Data Collection 30 3.3.1. Databases 30
3.3.1.1. The World Bank: Doing Business 30
3.3.1.2. Crunchbase 30
3.3.1.3. State Intellectual Property Office (SIPO) 30
4. Findings 33
4.1. Government 33
4.1.1. Ease of Doing Business 33
4.1.2. Policy Instruments 34
4.2. Financial Capital 35
4.2.1. Type of Investments 36
4.2.2. Focus of Investments 38
4.3. Educational Institutions 39
4.3.1. Research & Patents 39
4.4. Networks 41
4.4.1. Collision Density 41
4.4.2. The role of Networking 42
5. Conclusions & Discussion 45
5.1. Answering the Research Questions 45
5.2. Analysis of the Findings 46
5.3. Limitations 48
5.4. Future Research 49
5.5. Theoretical and Practical Implications 50
References 52 Appendices 62 Appendix A 62 Appendix B 63 Appendix C 64 Appendix D 65 Appendix E 70 Appendix F 71 Appendix G 72
List of Tables
Table 1. Nine Prescriptions for Creating an Entrepreneurial Ecosystem 16 Table 2. Pillars of an entrepreneurial ecosystem based upon leading literature 20
Table 3. Regulatory framework and tax indicators 27
Table 4. Indicators for financial capital 27
Table 5. Indicators for (entrepreneurial) educational institutions 28
Table 6. Indicators for networks 29
Table 7. Search strings that were used, divided by studied pillar 31
Table 8. Regulatory framework and taxes 33
Table 9. Most common types of funding within the entrepreneurial ecosystems 36 Table 10. The average amount of funding in Euros within the ecosystems 37 Table 11. The ten foremost company categories that received funding 38 Table 12. Research output and patent applications by educational institutions
within the entrepreneurial ecosystem of Shanghai 39 Table 13. Research output and patent applications by educational institutions
within the entrepreneurial ecosystem of Amsterdam 40 Table 14. Academic Staff within the educational institutions studied
in Amsterdam 40
Table 15. Research output (RO) by educational institutions within the
entrepreneurial ecosystems divided by academic staff 41 Table 16. Collision Density within the entrepreneurial ecosystems 42 Table 17. Importance of networking when starting a business in The Netherlands 43
List of Figures
Figure 1. Entrepreneurial Ecosystem 17
Figure 2. Key elements, outputs and outcomes of the entrepreneurial ecosystem 18 Figure 3. Domains of the Entrepreneurship Ecosystem 19 Figure 4. A stage model of guanxi network development in the
List of Appendix Tables & Figures
Tables
Table A1. Nine Attributes of a successful start-up community 62 Table D1. Pillars of an entrepreneurial ecosystem by Isenberg (2011)
compared to Feld (2012a 65
Table D2. Pillars of an entrepreneurial ecosystem by Isenberg (2011)
compared to Stam (2015) 67
Table D3. Pillars of an entrepreneurial ecosystem by Isenberg (2011)
compared to WEF (2013) 68
Table E1. List of Funding Types as identified in the database of Crunchbase 70
Table F1. Scopus Query Strings for Research Output 71
Table G1. Overview of search engines and (online) libraries accessed 72
Figures
Figure B1. Components of Entrepreneurial Ecosystem Pillars 63 Figure C1. Domains of the Entrepreneurship Ecosystem 64
1. Introduction
If one has to think of a Mecca for entrepreneurship, most people would not hesitate to name Silicon Valley as the text-book example. The area has been the starting point of some of the world its most innovative companies and is often considered to be the birth place of what we now call 'the internet' (Business Insider, 2017). Its emergence dating back to at least halfway through the 20th century (Klepper, 2010), the making of Silicon Valley did not happen overnight. In the meantime, numerous scholars (Saxenian, 1994; Lee, 2000;Sturgeon, 2000; Kenney & Patton, 2005) have sought to understand how this area developed itself into the technological powerhouse it is today. However, up until now no formula has been able to interpret what happened in 'The Valley'. As stated by Isenberg (2010), 'even Silicon Valley could not become itself today if it tried' (p. 3). In other words, the circumstances were so unique that a replication of the process that led to its existence is highly unlikely. Silicon Valley seems to however have ignited a growing interest in the concept of an 'entrepreneurial ecosystem' (Malecki, 2018). An entrepreneurial ecosystem is described as a 'set of interdependent actors and factors coordinated in such a way that they enable productive entrepreneurship' (p. 1765, Stam, 2015).
A lack of understanding in how to build the perfect entrepreneurial ecosystem has not made policymakers around the world recoil from attempting to achieve precisely that (Hospers, Desrochers, & Sautet, 2009). Regional governments are spending billions to create the new Silicon Valley while actual success often remains absent (Wadhwa, 2013). If successful, it is unclear what factors caused which outcomes and what the role of policy was in this (Mazzarol, 2014). Despite these setbacks, the governmental focus on entrepreneurship remains in place. This is mainly because of its proven positive influence on economic growth patterns (Urbano & Aparicio, 2016). This accounts for high-growth entrepreneurship in particular (Wong, Ho, & Autio, 2005). In combination with a growing interest in, and need for, entrepreneurship as an occupation (EY, 2015; Forbes, 2017; Huffington Post, 2016), it is of great importance for governmental organizations to facilitate entrepreneurship in the best ways possible. A recent study performed by Acs, Estrin, Mickiewicz and Szerb (2018) found evidence for entrepreneurial ecosystems to be one of the missing links in explaining cross-country differences in economic growth rates.
It seems evident that understanding the dynamics of an entrepreneurial ecosystem is of great value to both the academic and professional world. Thus far, an
understanding of these dynamics seems to be absent (Alvedalen & Boschma, 2017; Stam & Spigel, 2016). One of the five specific shortcomings causing this, as stated by Alvedalen and Boschma (2017), is the lack of a comparative perspective.
This study therefore acts upon Alvedalen and Boschma (2017) their recommendation by comparing two successfully growing ecosystems. By doing so, the aim is to make a contribution to the understanding of how elements within entrepreneurial ecosystems are connected, and what factors cause which effects. The two ecosystems that will be compared are Amsterdam (The Netherlands) and Shanghai (The People's Republic of China). It is interesting to study these two specific ecosystems in comparison to each other for a number of reasons. First, previous studies have focused on comparing entrepreneurial ecosystems on a national level. However, this research approach opposes extant literature which shows that local conditions and context are most relevant to an entrepreneurial ecosystem (Autio, Kenney, Mustar, Siegel, Wright, 2014; Isenberg, 2010; Spigel, 2017). Therefore, this study will examine the ecosystems of two cities, Amsterdam and Shanghai. Both cities their ecosystems are rapidly growing and increasingly show up in relevant rankings. Shanghai is listed in the Global Startup Ecosystem Report (Startup Genome, 2017) only for the second year but already strongly holds a 9th place. As a newcomer to the global ranking, Amsterdam debuted on a 19th place (Startup Genome, 2017).
Second, these two countries differ greatly when it comes to entrepreneurship on a national level. This year its edition of the Global Entrepreneurship Monitor (2018) report lists The Netherlands as an innovation-driven economy while China is marked an efficiency-driven economy. The fact that China is transitioning into an innovation-driven economy is undisputed but the country is not quite there yet (World Economic Forum, 2018). Interesting enough, China holds the 29th place whereas The Netherlands holds the 37th place in the report its innovation ranking. It will be interesting to see how two of the countries their leading entrepreneurial ecosystems compare to each other.
Third, and perhaps most interesting, is the fact that both Amsterdam and Shanghai are known for their focus on the Financial Technology (FinTech) sector. This year its version of the Global Startup Ecosystem Report (Startup Genome, 2018) zoomed in on key sub-sectors strengths per ecosystem. For both Amsterdam and Shanghai, the report finds FinTech to be the strongest sub-sector within the ecosystem
and Shanghai are going to compete directly with each other in the world of FinTech. Although Amsterdam is most likely never going to be comparable to Shanghai in terms of size, Forbes (2013) already pointed out that the Dutch city its small size could turn out to be a major advantage. Either way, it is not unlikely that these cities will end up in the midst of a competitive battle in the nearby future.
Summarizing, both entrepreneurial ecosystems are on the rise and it is interesting to examine what drives their success. Based on existing knowledge, it is expected that these ecosystems will differ from one another. Therefore, the following main research question is formed:
RQM: What are the main differences between the
entrepreneurial ecosystems of Amsterdam and Shanghai?
By answering this research question, this paper contributes to the extant literature in various ways. First, this paper compares two growing entrepreneurial ecosystems, Amsterdam and Shanghai. Existing research has focused mainly on defining an entrepreneurial ecosystem and studying the independent variables within it. Although previous studies (Hechavarria & Ingram, 2014; Khan, 2013; Sheriff & Muffatto, 2015; Stam, 2014) have compared ecosystems within countries or geographical regions, no prior research has studied these two particular ecosystems in comparison to each other. The aim of this comparison is however not one to produce a verdict on which one is superior but more one to gain insight into both ecosystems separately by studying its inner processes. By doing so, the goal is to enhance mutual understanding between both entrepreneurial ecosystems. Therefore, answering the aforementioned research question could be of great practical relevance to Dutch and Chinese entrepreneurs looking to understand what is happening in these ecosystems. Additionally, the outcomes of this study are expected to benefit policymakers of governmental institutions in both The Netherlands and China.
In order to build upon existing literature in this field, chapter two will provide a theoretical background and introduces a conceptual model. Following from this literature review, relevant sub-questions will be introduced as well. The third chapter explains the methodology used while performing this study, after which the fourth chapter discusses the results that were derived from these methods. Discussion and conclusions together form the fifth and final chapter of this paper.
2. Background
This chapter contains a theoretical framework which will serve as a basis for this study. Additionally, the framework will also provide guidance during the course of research if necessary. Starting with choosing a definition of entrepreneurship, the following paragraphs include existing background literature on entrepreneurial ecosystems, measuring entrepreneurial ecosystems, while also incorporating its relevance to the cities of Amsterdam and Shanghai.
2.1. Defining Entrepreneurship
This paper its goal is by no means to engage in the long lasting debate on the definition of entrepreneurship. However, it is important to clarify what type of entrepreneurship should be stimulated within entrepreneurial ecosystems. In their study on entrepreneurial ecosystems, the World Economic Forum (2013) described entrepreneurs as 'key drivers of economic and social progress. Rapidly growing entrepreneurial enterprises are often viewed as important sources of innovation, productivity growth and employment' (p. 5). Mason and Brown (2014) agree with this view and state that there is a need to focus on high-growth startups within entrepreneurial ecosystems.
Although it is difficult to define and execute such a focus in practice, it is clear that a Schumpeterian view of entrepreneurship is leading in this context. Schumpeter (1934) saw entrepreneurs as innovators who caused change within the markets. This entrepreneurial change has five manifestations according to Schumpeter (1934). First, the introduction of a new (or improved) good; second, the introduction of a new method of production; third, the opening of a new market; fourth, the exploitation of a new source of supply; fifth, the re-engineering/organization of business management processes (Schumpeter, 1934). Summarized, people who come up with ideas and turn those into high-growth companies (The Economist, 2014). A recent study performed by Henrekson and Sanandaji (2014) corresponds with this view and shows that small business activity does not measure actual entrepreneurship. Instead, a Schumpeterian view should be leading when studying entrepreneurship.
Concluding, in the context of entrepreneurial ecosystems an entrepreneur is seen as an innovator. This is the type of entrepreneur that should be enabled by a successful entrepreneurial ecosystem. To determine how this relates to other
definitions used in the extant literature, see a discourse analysis of entrepreneurship definitions and theories by Raimi (2015).
2.2. Entrepreneurial Ecosystems
The usage of the term 'ecosystem' in relationship to business and entrepreneurship traces back to work of Moore (1993) which mentioned an ecosystem as a firm its external environment. Moore (1993) did not go any further than merely using it as a metaphor though. It was Cohen (2006) who first introduced the term 'entrepreneurial ecosystem' in his exploration of sustainable valley entrepreneurial ecosystems. However, a few years earlier Neck, Meyer, Cohen, and Corbett (2004) had already addressed a similar concept but defined it as an entrepreneurial system in their study of Boulder County, Colorado.
The introduction of 'entrepreneurial ecosystem' as a discourse can be seen as part of a broader movement in entrepreneurship research throughout the past decades. Instead of viewing the entrepreneur as the superior individualist, academics started to take into account the importance of his or hers surroundings and place in society (Granovetter, 1985; Low & MacMillan, 1988; Eckhardt & Shane, 2003; Nijkamp, 2003; Steyaert & Katz, 2004; Zahra, 2007). Dodd and Anderson (2007) stated that 'to conceive the entrepreneur as an atomistic and isolated agent of change is to ignore the milieu that supports, drives, produces and receives the entrepreneurial process' (p. 342). More recently, Welter (2011) proposed a contextualized view of entrepreneurship and argued that context is important if one wants to understand when, how, and why entrepreneurship happens. This increased interest in entrepreneurial context (environment, ecosystem, infrastructure, system) is well visualized by Malecki (2018) who shows that the concept of an 'entrepreneurial ecosystem' has become dominant in the area of contextual entrepreneurship research.
2.2.1. Extant Theory & Models
An often-cited article by Isenberg (2010) published in Harvard Business Review can be seen as a catalyst for this growing interest. Isenberg (2010) begins by stating that governments around the world have approached the concept of an entrepreneurial ecosystem as a holy grail. According to Isenberg (2010), governments have been taking a misguided approach to building ecosystems. Most importantly, they have been pursuing ideals that do not suit their local conditions. It is therefore that Isenberg
(2010) stresses the fact that there is no exact formula for creating an entrepreneurial ecosystem. He does however proposes nine key principles for leaders to follow, as shown in Table 1. Acknowledging the never ending aspect of experimenting with enhancing entrepreneurial ecosystems, Isenberg (2010) concludes that his prescriptions 'will help governments move the needle of entrepreneurship in the right direction' (p. 11).
Table 1: Nine Prescriptions for Creating an Entrepreneurial Ecosystem as stated by
Isenberg (2010)
Nr. Prescription
1 Stop Emulating Silicon Valley
2 Shape the Ecosystem Around Local Conditions 3 Engage the Private Sector from the Start 4 Favor the High Potentials
5 Get a Big Win on the Board 6 Tackle Cultural Change Head-On 7 Stress the Roots
8 Don’t Overengineer Clusters
9 Reform Legal, Bureaucratic, and Regulatory Frameworks
Following Isenberg (2010), Feld (2012a) his bestseller 'Startup communities: Building an Entrepreneurial Ecosystem in Your City' further popularized the idea that an entrepreneurial ecosystem is able to influence the entrepreneurial activity of actors within the ecosystem. His work describes the rise of the entrepreneurial ecosystem of Boulder (Colorado). Based upon this best practice, Feld (2012a) proposes nine attributes that belong to a successful entrepreneurial ecosystem. These attributes are Leadership, Intermediaries, Network density, Government, Talent, Support services, Engagement, Companies, and Capital (p.186-187, Feld, 2012a). The corresponding descriptions for these attributes can be found in Appendix A. Furthermore, Feld (2012b) stresses the crucial role of entrepreneurs within an ecosystem: 'Unless the entrepreneurs lead, the startup community won't be sustainable' (p.1). However, Feld (2012a) also notes the importance of interaction between the different actors within an ecosystem.
In order to study these interactions within an entrepreneurial ecosystem, several models have been developed. First, a model developed by The World Economic Forum (2013) will be discussed. The latter organization asked entrepreneurs which pillars of an entrepreneurial ecosystem they viewed as most important to the growth or success of their companies. Figure 1 shows the model derived from the entrepreneurs their responses. The World Economic Forum (2013) formed their model with direct relationships to the ecosystem. Eight pillars together form the core of this: Accessible Markets, Human Capital Workforce, Funding and Finance, Support Systems, Regulatory Framework and Infrastructure, Education and Training, Major Universities as Catalysts, and Cultural Support. Descriptions of all components belonging to these pillars can be found in Appendix B.
Figure 1. Entrepreneurial Ecosystem. Reprinted from “Entrepreneurial ecosystems around the globe and company growth dynamics.” by Author World Economic Forum, 2013, p. 6. Copyright [2013] by
World Economic Forum.
Another influential model, developed by Stam (2015), defines key elements, outputs and outcomes of an entrepreneurial ecosystem as shown in Figure 2. Whereas the World Economic Forum (2013) lists eight pillars in direct relationship to an entrepreneurial ecosystem, Stam (2015) his model can be seen as a more detailed bottom-up approach. Elements of the ecosystem are divided into two types of conditions: framework conditions and systematic conditions. 'The presence of these elements and the interaction between them predominantly determine the success of the ecosystem' (p.1766, Stam, 2015). Together, these conditions create certain outputs which Stam (2015) measures by innovative start-ups, high-growth start-ups, and entrepreneurial employee activity. The outcome is labeled as value creation at the top of the model.
Figure 2. Key elements, outputs and outcomes of the entrepreneurial ecosystem. Reprinted from “The
Dutch entrepreneurial ecosystem.” by Author E. Stam, 2014, p. 6. Copyright [2014] by E. Stam.
Furthermore, Stam (2015) proposes to measure outcomes by productivity, income, employment, and well-being. Although this model uses different labels and integrates outputs and outcomes, the framework and systematic conditions can be considered as in agreement with the pillars introduced by the World Economic Forum (2013).
Up until now, extant literature views a model introduced by Isenberg (2011) as the most extensive model yet. Isenberg (2011) proposes the 'entrepreneurship ecosystem strategy as a new paradigm for economy policy' (p.1). Isenberg (2011) his model consists out of six ecosystem domains: Markets, Policy, Finance, Culture, Supports, and Human Capital. A total of eleven pillars are all linked to one of these domains. As seen in Figure 3, under every pillar Isenberg (2011) listed some key descriptions to define its role within an entrepreneurial ecosystem. This paper will use the model developed by Isenberg (2011) as conceptual framework for its study.
This model (Figure 3, full-page version Appendix C) by Isenberg (2011) shows multiple similarities with the models and factors listed by Feld (2012a), Stam (2014), and the World Economic Forum (2013). All four theories will be examined more closely in relationship to each other in subparagraph 2.3.1.
Figure 3. Domains of the Entrepreneurship Ecosystem. Reprinted from “The entrepreneurship
ecosystem strategy as a new paradigm for economy policy: principles for cultivating entrepreneurship” by Author D. J. Isenberg, 2011, Babson Entrepreneurship Ecosystem Project, Babson College, Babson Park: MA, p. 11. Copyright [2011] by Daniel Isenberg.
2.3. Comparing Ecosystems
Due to a constraint in both time and space, this paper is unable to address all pillars within an entrepreneurial ecosystem as portrayed by Isenberg (2011) while comparing Amsterdam and Shanghai. As incorporated in the central research question, this study aims to find the main differences between the two ecosystems. Therefore, extant literature has been studied to examine which pillars are considered to be crucial in enabling an entrepreneurial ecosystem. This subchapter discusses these factors and assesses whether they are relevant in the case of Amsterdam and Shanghai. Relevant research questions are introduced under every selected pillar.
2.3.1. Determining Focus Pillars
Isenberg (2011) his model views all its pillars as equally important. Although this paper also stresses the fact that dynamics between factors within an ecosystem lead to its potential success, a distinction between all pillars had to be made. In order to do
so, the pillars formulated by Isenberg (2011) were compared to the variables stated by Feld (2012a), Stam (2015) and the World Economic Forum (2013). Considering every pillar in the model of Isenberg (2011), the other scholars were examined to see whether a corresponding variable was present. The results of the cross-referenced pillars can be found in Table 2.
A full overview of the similar characterizations by Feld (2012a), Stam (2015) and The World Economic Forum (2013) can be found in Appendix D.
Table 2: Pillars of an entrepreneurial ecosystem based upon leading literature by
Feld (2012a), Stam (2015), and the World Economic Forum (2013). (1= an identical variable is present in the model, 2= variable is mentioned in the model, X= variable is absent in the model)
Pillar by Isenberg (2011) Feld (2012a) Stam (2015) WEF (2013)
Leadership 1 1 X Government 1 1 1 Financial Capital 1 1 1 Success Stories 2 2 2 Societal Norms 2 1 1 Non-Governmental Institutions 2 X X Support Professions 1 1 X Infrastructure X 1 2 Educational Institutions X 1 1 Labor 2 2 1 Networks 1 1 2 Early Customers X 1 2
Note: The underlined pillars are the ones that will be studied
Based upon this comparison, all pillars with two or more identical variables were considered to be included in this study. The pillars that will be studied are Government, Financial Capital, Educational Institutions, and Networks. The choices for these pillars are justified in the following four subparagraphs. Next to this, their relevance in light of this study will be discussed. Corresponding research questions will be introduced and listed at the end of every subparagraph.
2.3.2. Government
This pillar was selected because of the expected differences between the two ecosystems. Mainly due to the fact that The Netherlands and The People's Republic of China have different political and economic systems in place. Although originally a socialist system, the correct label for China its current economic system is subject to discussion (Naughton, 2017). At the end of the country its market transition, the term 'socialist market economy' was introduced by President Deng Xiaoping to describe its economic system (Qian, 2000). Wei (2007) described the result of the Chinese economic reform as a 'hybrid product of Western capitalism and Confucian parental governance' (p. 26). On the contrary, The Netherlands is home to a long established free-market economy and can be characterized as a capitalist country. It is therefore interesting to examine what role the local and national government plays within the respective ecosystems.
In 2013, China launched the Shanghai Free Trade Zone (FTZ) program (Business Insider, 2013). Wan, Zhang, Wang and Chen (2014) expected this program to 'simplify regulatory procedures, reduce business costs, and develop as a large tariff-free zone' (p.3). According to Isenberg (2010), 'the right legal and regulatory frameworks are critical to thriving entrepreneurship' (p.9). It is therefore interesting to see how the regulatory framework of Shanghai its ecosystem relates to that of Amsterdam. As listed below, three research questions are designed to study this regulatory framework and actual concrete policy instruments. The first two specific research questions address the administrative hurdles on has to take before being able to start a business or legally register a property. Finally, a research question is formed to explore the specific policies in place to stimulate entrepreneurial activity.
RQ1: What administrative steps is an entrepreneur required to take before
being able to start a business within the ecosystem?
RQ2: What administrative steps is an entrepreneur required to take before
being able to legally register a property?
RQ3: What are the foremost local or national government policy instruments
2.3.3. Financial Capital
The availability of financial capital is found to be crucial to growing young innovative firms (Block, Colombo, Cumming, & Vismara, 2018). The World Economic Forum (2013) found that entrepreneurs view funding and finance as being of pivotal importance to an ecosystem. Therefore, it is interesting to examine how this pillar functions within the ecosystems of Amsterdam and Shanghai. Studying the types of finance driving the ecosystem is found to be an interesting perspective. Currently, venture capital is often linked to high-growth companies (Colombo & Grilli, 2010; Lerner, 2010). However, extant literature seems to be indefinite whether this is the main type of financing in the case of high-growth companies in daily practices. Mason and Brown (2014) even speak of a 'a general tendency to overstate the importance of venture capital in entrepreneurial ecosystems' (p.16) and refer to a study performed by Brown and Lee (2014). They found venture capital to be involved in less than five percent of high-growth companies in the United Kingdom.
This paper will contribute to existing literature by examining the main form of financing within the respective ecosystems of Amsterdam and Shanghai. Additionally, this study will examine which sort of companies receive investments. In order to do so, the following research questions are formed.
RQ4: What is the main form of financing within the ecosystem?
RQ5: What sort of companies receive most of the investments made within
the ecosystem?
2.3.4. Educational Institutions
The pillar of educational institutions is defined by Isenberg (2011) in terms of 'general degrees' and 'specific entrepreneurship training'. Previous studies have focused on these two aspects of educational institutions and show the effects of entrepreneurship education on entrepreneurial intention within both ecosystems (Wu & Wu, 2008; Oosterbeek, Van Praag & IJsselstein, 2010; Zhang, Duysters & Cloodt, 2014). However, literature considering the level of entrepreneurship of the educational institutions themselves is found to be limited for both entrepreneurial ecosystems. Therefore, this study finds it more interesting to examine how entrepreneurial these educational institutions are.
Instead of examining the role of educational institutions within an ecosystem in terms of degrees and training, this paper therefore proposes to measure the entrepreneurial activity of educational institutions in terms of patent applications and knowledge output. A number of previously performed studies (Audretsch & Lehmann, 2005; Tijssen, 2006; Vinig & Lips, 2015) have shown that this is an interesting way to examine how entrepreneurial an educational institution is. Audretsch and Lehmann (2005) even show that the knowledge output of a university positively influences the number of young firms located around it. Additionally, a study performed by Tijssen (2006) shows that 'universities that apply for patents are more likely to exhibit entrepreneurial activities . . . as compared to those universities without patents' (p.1579).
The most recent study examining Shanghai in terms of patent applications and research dates back to Wu (2007a). In order to examine how entrepreneurial educational institutions within the ecosystems of Amsterdam and Shanghai are today, the following research questions addressing this pillar are formed.
RQ6: How entrepreneurial are educational institutions within the ecosystem
in terms of research output?
RQ7: How entrepreneurial are educational institutions within the ecosystem
in terms of patent applications?
2.3.5. Networks
Isenberg (2011) describes this pillar in the relatively general terms of 'Entrepreneur's networks', 'diaspora networks', and 'multinational corporations'. Therefore, this paper looks at the other influential models to further build upon. Feld (2012a) places an emphasis on the interaction between various actors within an entrepreneurial ecosystem. One of three characteristics of this, according to Feld (2012a), is high network density. This study therefore aims to further examine this in terms of embeddedness. This paper builds upon a definition by Yli-Renko and Autio (1998), and explains embeddedness as the extent to which a firm and its environment are intertwined. Several studies have found that the more embedded an overlaying network is, the more significant and positive its influence on the entrepreneurial activity within it (Jack & Anderson, 2002; Johannisson, Ramírez-Pasillas & Karlsson, 2002; Simsek, Lubatkin & Floyd, 2003; Pitelis, 2012). As described by Nosella and
Petroni (2007), network embeddedness can be an opportunity as it is able to 'provide a firm with access to information, resources, markets and technologies with advantages from learning, scale, and scope economies and allow firms to achieve strategic objectives, such as sharing risks and outsourcing value chain stages and organizational functions' (p.179). This study will examine the presence of linkages within the entrepreneurial networks of both entrepreneurial ecosystems.
While Shanghai is a mega city with over 24 million inhabitants (National Bureau of Statistics of China, 2017), Amsterdam counts only close to 850 thousand inhabitants (IAmsterdam, 2018a). A similar major difference accounts for the physical size of both ecosystems. Whereas Shanghai covers around 6.340 square kilometers, Amsterdam only covers 219 square kilometers (Google Map Data, 2018). It is therefore interesting to examine how both ecosystem compare to each other in terms of embeddedness. In order to do so, the following research question is formed.
RQ8: How embedded is the ecosystem?
Furthermore, this study examines the role of networking within both entrepreneurial ecosystems. It is expected that, due to cultural differences, the role of networking is different for both ecosystems. Intercultural research by Hofstede (1980) introduced individualism(-collectivism) as one of four dimensions of national culture. Hofstede (2018) summarizes this dimension as follows. He describes that individualism can be defined as 'a preference for a loosely-knit social framework in which individuals are expected to take care of only themselves' (p.1, Hofstede, 2018). The other side of this dimension, referred to as collectivism, can be described as 'a preference for a tightly-knit framework in society in which individuals can expect their relatives or members of a particular ingroup to look after them in exchange for unquestioning loyalty' (p.1, Hofstede, 2018).
According to Hofstede (2018), The Netherlands ranks 80 out of 100 points on this dimension while China ranks only 20 points out of 100, thus China is considered to be more collectivistic. It is therefore interesting to examine in what way these cultural differences affect the role of networking within the entrepreneurial ecosystems. In order to do so, the following research question is formed.
3. Methods
This chapter consists out of the methods used to perform this study. At first, the nature, type, and design of this study are described. Furthermore, this chapter will discuss relevant databases and data analysis methods used to answer the in the second chapter formulated research questions.
3.1. Nature of this Research
The study described in this paper is one departing from an explorative nature. The open and broad research question proposed in chapter 1 reflects this. Results of this study do not intend to provide conclusive answers to the research problems as described in the first chapter of this paper. Instead, this study aims to enhance the understanding of the dynamics within an entrepreneurial ecosystem. Correspondingly, a deductive approach was applied during the course of research. As stated by Wilson (2014), a deductive approach 'begins with and applies well-known theory' (p. 12). In general, this approach is taken to confirm or reject specific hypotheses based on existing theory. However, this study departed from the Isenberg (2011) model as well-known theory and then aimed to deduct the dynamics of its pillars inner workings on which limited relevant theory is available.
3.1.1. Research Philosophy
This subparagraph introduces the research philosophy that functioned as a departing point for this research. It is included in this chapter to explain the underlying reasoning behind certain methodological decisions made in the design of this study.
Only two studies (Stangler & Bell-Masterson, 2015; Taich, Piazza, Carter, & Wilcox, 2016) have yet introduced and used comprehensive methods to measure an entrepreneurial ecosystem. The results of a recent study performed by Lock (2017) have shown that these methods are difficult to apply in the case of China. Main reason for this is that the country is not included in large economic databases such as the one of the Organization for Economic Cooperation and Development (OECD). A lot of this and other relevant data does exists for developed countries such as The Netherlands but is absent for China. This creates a major challenge for researchers who want to examine China in relationship to certain economic concepts such as entrepreneurial ecosystems. A preliminary finding throughout existing literature that
has led to the following philosophy being the methodological foundation of this research.
Pragmatism argues that 'the most important determinant of the research philosophy adopted is the research question - one approach may be ‘better’ than the other for answering particular questions' (p.109, Saunders, Lewis & Thornhill, 2009). Feilzer (2010) summarizes this and states that pragmatism 'brushes aside the quantitative/qualitative divide' (p.9). This by suggesting that the most important question is whether a research has helped to find out what the research wants to know. By departing from this philosophy, aim of the later described research strategy was to anticipate the existing methodological problems when it comes to studying China. This to preclude a situation in which results for the entrepreneurial ecosystem of Shanghai would be absent. Therefore, this philosophy was leading in this research and has subsequently led to the strategy and design discussed in the following paragraph.
3.2. Strategy & Design
This study was designed as a secondary research, in other terms described as desk research. This involves the collection and analysis of secondary data. The latter is described as 'data collected by someone else' and 'available to you from books, libraries and on the web' (p. 117, Adams, Khan, Raeside & White, 2007). In doing so, both qualitative as quantitative sources were utilized in order to answer the research questions proposed in the second chapter of this paper.
The aforementioned philosophy and this variety of data sources has led to different approaches for different research questions. Therefore, this paragraph explains how the pillars listed in paragraph 2.3 were studied. For every pillar, the specific methods used are described and explained.
3.2.1. Government
The 'government' pillar as stated by Isenberg (2011) was studied by examining both quantitative and qualitative data available. A description of the method applied for the qualitative part can be found in 3.3.2. In order to quantitatively examine the regulatory and tax framework in place, a number of indicators were studied. Table 3 describes the indicators taken into account for this pillar.
Table 3: Regulatory framework and tax indicators
Indicator Description
Business Procedure The total number of procedures required to register a firm Procedure Time The total number of days required to register a firm
Procedure Costs The amount that the entrepreneur needs to deposit in a bank or with a notary before registration
Property Procedure The total number of procedures legally required to register property
Procedure Time The total number of days required to register property Procedural Costs Procedural costs as a percentage of the property value Tax and Contribution
Rate (% of profit)
Amount of taxes and mandatory contributions payable by the business in the second year of operation, expressed as a share of commercial profits
Postfiling Index (0-100) Time to comply with VAT or GST refund, time to obtain VAT or GST refund, time to comply with corporate income tax audit and time to complete a corporate income tax audit
Note: These indicators were adopted from The World Bank (2017).
3.2.2. Financial Capital
The 'financial capital' pillar as stated by Isenberg (2011) was studied by examining quantitative data available. Additional qualitative data was examined by applying the method described in paragraph 3.3.2. Table 4 summarizes the indicators used to study the financial environments relevant to an entrepreneurial ecosystem. Appendix E includes all possible types of funding.
Table 4: Indicators for financial capital
Indicator Description
Number of investments Total number of investments made in companies within the ecosystem
Value of investments Total value of investments made within the ecosystem Type of investment Most common type of investment indicated
Additionally, databases were utilized to examine information about the type of companies investors are investing in within both entrepreneurial ecosystems. This particular information was included in the later discussed results as well.
3.2.3. Educational Institutions
Both quantitative and qualitative data was examined to answer the research questions related to the 'educational institutions' pillar as stated by Isenberg (2011). The qualitative research method is further described in 3.3.2. In order to study the role of educational institutions in entrepreneurial ecosystems, the indicators as listed in Table 5 were used. These indicators were selected based upon existing literature. One of the most used indicators to measure knowledge output is the number of publications per university (Audretsch & Lehmann, 2005). Based upon a study by Tijssen (2006), the indicator 'patents' was included as well to measure the extent to which an educational institution can be considered entrepreneurial.
Table 5: Indicators for (entrepreneurial) educational institutions
Indicator Description
Publications The total number of research publications by the universities within the entrepreneurial ecosystem
Patents The total number of patents applications filed by the universities within the entrepreneurial ecosystem
The indicators as listed in Table 5 were used to examine the two leading educational institutions per entrepreneurial ecosystem. These universities were selected based upon two leading worldwide rankings: the QS World University Rankings by Quacquarelli Symonds (2017) and the Academic Ranking of World Universities (2017). Following these rankings, the universities examined were: Fudan University (Shanghai), Jiao Tong University (Shanghai), University of Amsterdam (Amsterdam), and the Vrije Universiteit (Amsterdam). Their respective medical university centres were included in this study as well based on their expected impact on research and invention patents. Patent data was extracted from annual reports in the cases of the educational institutions within the entrepreneurial ecosystem of Amsterdam. For
educational institutions in Shanghai, the State Intellectual Property Office their database and annual reports were studied in order to find patent data.
A previous study performed by Vinig and Lips (2015) measured knowledge output for Dutch universities by means of data provided by the universities in their annual reports. This study found recent data on publications in the annual reports of the educational institutions studied in Amsterdam. However, the two institutions in Shanghai do not disclose numbers on their publications. Therefore, a study on these universities their research output by Wu (2007a) was examined in terms of methodology. This study then decided to adopt her approach of measuring research output and used a citation database (Scopus) to measure research output. By doing so, the possibility of distorted results due to various ways of representing the number of publications in annual reports was prevented. A full overview of query strings used to find the research output per educational institution can be found in Appendix F.
3.2.4. Networks
The 'networks' pillar as stated by Isenberg (2011) was studied by examining both quantitative and qualitative data available. A description of the method applied for the qualitative part can be found in 3.3.2. In order to quantitatively examine this pillar within both entrepreneurial ecosystems in terms of embeddedness, a new indicator introduced by Nylund and Cohen (2017) was used. They use the concept of 'collision density' to explain the dynamic aspect of growing entrepreneurial ecosystems and define collision density as 'the potential frequency of interdisciplinary interactions' (p.1, Nylund & Cohen, 2017). Cohen (2016) explains that if the number of cross-disciplinary interactions increases, the more potential there is for break-through innovations.
Table 6: Indicators for networks
Indicator Description
Collision density The potential frequency of interdisciplinary interactions
Note: This indicator was adopted from Nylund & Cohen (2017) and the respective entrepreneurial
3.3. Data Collection
In order to generate results and answer the research questions of this study, various sources of information were consumed. This paragraph discusses the collection of these sources and explains the processes through which they were utilized.
3.3.1. Databases
A number of databases were used to perform this study. These databases and their corresponding methodology will be discussed in the upcoming paragraphs.
3.3.1.1. The World Bank: Doing Business
This database provides measures of business regulations and their enforcement across 190 economies. Data is provided on a subnational and regional level. It looks at domestic small and medium-size companies and measures the regulation that are in place and apply to them throughout their existence. This ranges from starting a business to enforcing a legal contract. The most recent edition of the report (The World Bank, 2017) contains a number of eleven indicators. Precise methodologies of The World Bank (2017) its indicators used in this study can be found in The World Bank (2018).
3.3.1.2 Crunchbase
Crunchbase is an online open source database containing information about companies all over the world. The database is operated by TechCrunch (2018), a news company publishing about the technology industry. Its focus lies on startups and their funding processes. Therefore, the database contains extensive information on financing of early-stage companies in various ecosystems.
3.3.1.3. State Intellectual Property Office (SIPO)
The State Intellectual Property Office (SIPO) of the People's Republic of China is the patent office for the handling of patent related issues throughout the country. Activities include patent administration, policy making, and coordinating intellectual property rights work nationwide. Their database can be accessed online and includes all patent applications filed by Chinese individuals or organizations.
3.3.2. Books and Journal articles
Next to the aforementioned databases, a number of qualitative sources were examined as well. Qualitative sources such as news articles, books and journal articles were searched for in various search engines and (online) libraries. An overview of the search engines used and (online) libraries accessed can be found in Appendix G.
In order to search for relevant literature that could (partly) answer the proposed research questions in this study, a number of search strings were used. These search strings can be found in Table 7 and were entered into search functions of the intermediaries listed in Appendix G.
Table 7: Search strings that were used, divided by studied pillar
Ecosystem Pillar
Search strings
Government Government AND <country> OR <city> AND entrepreneurship Municipality AND <country> OR <city> AND entrepreneurship Regulation OR 'regulatory framework' AND <country> OR <city> AND entrepreneurship
Legislation AND <country> OR <city> AND entrepreneurship Policy AND <country> OR <city> AND entrepreneurship
'Policy instruments' AND <country> OR <city> AND entrepreneurship
Financial Capital
Finance OR financing AND <country> OR <city> AND entrepreneurship OR entrepreneurial
Capital OR 'Financial capital' AND <country> OR <city> AND entrepreneurship OR entrepreneurial
Investment OR investor(s) AND <country> OR <city> AND entrepreneurship OR entrepreneurial
Venture Capital AND <country> OR <city> AND entrepreneurship OR entrepreneurial
Educational Institutions
Education AND <country> OR <city> AND entrepreneurship OR entrepreneurial
Ecosystem Pillar
Search strings
Training AND <country> OR <city> AND entrepreneurship OR entrepreneurial OR entrepreneur
Universities OR university AND <country> OR <city> AND entrepreneurship OR entrepreneurial
Curriculum OR courses AND <country> OR <city> AND entrepreneurship OR entrepreneurial
Networks Network(s) OR networking AND <country> OR <city> AND entrepreneurship OR entrepreneurial OR entrepreneur
'Social network(s)' AND <country> OR <city> AND entrepreneurship OR entrepreneurial OR entrepreneur
Cooperation OR connection AND <country> OR <city> AND entrepreneurship OR entrepreneurial OR entrepreneur
Cooperation OR connection AND <country> OR <city> AND entrepreneurship OR entrepreneurial OR entrepreneur
Hubs or clusters AND <country> OR <city> AND entrepreneurship OR entrepreneurial OR entrepreneur
Note: The <city> term represents both 'Amsterdam' and 'Shanghai', this search string was used twice
with both city names in separate search strings. The <country> term represents both 'The Netherlands' and 'The People's Republic of China' OR 'China'.
3.4. Data Analysis
If retrievable, the quantitative data from the databases (as listed in 3.3.1) was downloaded and adapted for use in IBM SPSS Statistical Software (IBM, 2018). The database of The World Bank (2018) could not be exported to statistical software and could only be accessed online on a corresponding analysis platform. IBM Statistical Software was utilized to analyze the other databases and export descriptive statistics relevant to the results section of this paper.
4. Findings
This chapter discussed the results that followed from the study as described in the previous chapters. Every paragraph covers one of the selected pillars of an entrepreneurial ecosystem as formulated by Isenberg (2011). Subsequent subparagraphs further describe the quantitative and, if present, qualitative results relevant to that pillar.
4.1. Government
Results relevant for examining the government pillar are discussed in this paragraph. First, the ease of doing business in both entrepreneurial ecosystems will be discussed on the basis of data that covers starting a business, registering property, and the tax environment. Second, specific policy instruments executed in both entrepreneurial ecosystems will be discussed..
4.1.1. Ease of Doing Business
This subparagraph discusses the ease doing business in both entrepreneurial ecosystems. In particular, the processes of starting a business and registering property. Furthermore, the tax environment was included in this study as well. Table 8 shows the regulatory framework and tax policy in place in both Amsterdam and Shanghai.
Table 8: Regulatory framework and taxes
Indicator Amsterdam Shanghai
Business Procedure 4 steps 7 steps
Procedure Time 3.5 days 22 days
Paid Min. Capital 0.0% 0.0%
Property Procedure 5 steps 4 steps
Procedure Time 2.5 days 28 days
Cost (% of value) 6.1% 3.6%
Tax and Contribution Rate (% of profit)
40.7% 67.1%
Postfiling Index (0-100) 91.95 49.08
The results described in Table 8 show that when it comes to starting a business, the entrepreneurial ecosystem of Amsterdam is more friendly than Shanghai. Starting a business in Amsterdam requires four instead of seven procedural steps. Additionally, the time involved in taking these steps is considerably lower with only 3.5 calendar days in Amsterdam while 22 calendar days in Shanghai. In both Amsterdam and Shanghai, an entrepreneur is not required to make a capital deposit before being able to start a business.
The results regarding the registration of property are considered to be mixed. First, the number of procedures required is higher in Amsterdam than in Shanghai, five versus four. However, the time these procedures take is considerably lower for the entrepreneurial ecosystem of Amsterdam in comparison to Shanghai. Registering a property will take an entrepreneur 2.5 calendar days in Amsterdam while 28 days will keep an entrepreneur occupied in Shanghai. The registration process does cost an Amsterdam entrepreneur more than a Shanghai one, 6.1 percent of the property value versus 3.6 percent in Shanghai.
Concluding, the tax environment can be considered more friendly in Amsterdam than in Shanghai. The tax and contribution rate in Amsterdam is 40.7 percent in the second year of operating a business, while an entrepreneur in a similar situation will pay 67.1 percent in Shanghai. Lastly, the postfiling index shows that it is more efficient for a local medium-size company to comply with postfiling processes in Amsterdam than in Shanghai.
4.1.2. Policy Instruments
This subparagraph discusses specific policy instruments that were found to positively influence entrepreneurship in both the entrepreneurial ecosystems of Amsterdam and Shanghai. These findings do not to provide an inclusive overview of all policy instruments across both ecosystems but give an indication of influential policy instruments that have a major effect on the two ecosystems in place.
The findings show that both ecosystems are focusing their policies in particular on attracting talent from educational institutions both at home and abroad. Amsterdam does this by means of an in 2015 introduced startup visa (Netherlands Enterprise Agency, 2018). It is officially named the 'residence permit for start-up entrepreneurs' and grants entrepreneurs a one-year access to start and grow their
that supports the expat entrepreneur in making the best of his or hers business in the city. After this year, the entrepreneurs is able to receive a statement from the facilitator which enables an extension of the initial visa (Netherlands Enterprise Agency, 2018). Furthermore, to attract talent there is a 30 percent tax ruling in place for 'highly skilled migrants' (p.1) moving to Amsterdam (IAmsterdam, 2018b). This means that the immigrant is only being taxed based upon the remaining 70 percent of his or hers gross salary in Amsterdam. Immigrants eligible for this ruling can benefit from it up until eight years of living in Amsterdam.
The findings show that Shanghai is focusing on the same aspects, first and foremost attracting new talent to its ecosystem. International students who graduate and want to start a business in Shanghai are eligible to apply for a two-year residence permit (China Daily, 2017). The main criteria for this is that one graduates from a university located in mainland China. By implementing such a policy, the local government seems to be keen on solving the long-lasting problem of lacking knowledge talent as previously described by Wu (2007b).
Apart from policy instruments focusing on attracting new talent, the local government also invest greatly in stimulating the flow of financial capital (Netherlands Enterprise Agency, 2016). In 2016, the Shanghai Science and Technology Committee introduced the 'Provisional Measures on Managing Shanghai Angel Investor Risk Compensation' which compensates investment firms for losses that occur when a startup invested in goes bankrupt (Shanghai Science and Technology Committee, 2016). The policy reimburses investment firms up to 3 million RMB per investment (around 420,000 euros) with a limit of 6 million RMB per investment firm per year. Its aim is to encourage investments in technology startups and further advance the ecosystem its transformation into a global innovation hub.
4.2. Financial Capital
This paragraph discusses the results relevant for examining the financial capital pillar. First, the most common types of funding and their respective average amounts within both entrepreneurial ecosystems are discussed. Second, the focus of these investments is shown by means of the company categories that received most of the funding in both ecosystems. This reflects the entrepreneurial ecosystem its focus and provides a brief overview of the industries that are receiving most financial capital.
4.2.1. Type of Investments
Table 9 shows the most common types of funding within both entrepreneurial ecosystems. The results show that the most common type of funding in Amsterdam is seed funding. Seed funding is considered to be one of the earliest investment stages for a starting business. The most common type of funding in Shanghai is a series A funding. Furthermore, Table 9 suggests that investments in the Shanghai ecosystem are done by major players instead of smaller ones such as, for example, angel investors. The results also suggest that companies in Shanghai that received funding in 2017 are more mature than their counterparts in Amsterdam.
Table 9: Most common types of funding within the entrepreneurial ecosystems of
Amsterdam and Shanghai in terms of percentages and frequency in 2017
Type of Funding Amsterdam Shanghai
Seed 46.23% (n=49) 10.81% (n=12)
Angel 5.66% (n=6) 2.70% (n=3)
Grant 5.66% (n=6) N/A
Equity Crowdfunding 4.72% (n=5) N/A
Private Equity 2.83% (n=3) 1.80% (n=2)
Convertible Note 1.89% (n=2) 2.70% (n=3)
Debt Financing 0.94% (n=1) N/A
Initial Coin Offering N/A 0.90% (n=1)
Post-IPO Equity 0.94% (n=1) N/A
Venture - Series Unknown 16.04% (n=17) 6.31% (n=7)
Series A 9.43% (n=10) 33.33% (n=37) Series B 5.66% (n=6) 21.62% (n=24) Series C N/A 9.91% (n=11) Series D N/A 8.81% (n=9) Series E N/A 1.80% (n=2) Total 100% (n=106) 100% (n=111)
In order to examine the extent to which investors fund companies in both entrepreneurial ecosystems, the average amount of funding per type of funding was studied. Table 10 shows that the average amounts per type of funding per ecosystem.
Table 10: The average amount of funding in Euros within the entrepreneurial
ecosystems of Amsterdam and Shanghai divided by type of funding in 2017, including standard deviations (SD)
Type of Funding Amsterdam Shanghai
Seed 1,015,301 (n=25) 2,423,000 (3,113,354) (n=7) Angel 598,000 (520,115) (n=5) 8,226,000 (7,424,621) (n=2) Grant 929,734 (868,328) (n=6) N/A Equity Crowdfunding 630,373 (620,723) (n=4) N/A
Private Equity N/A 84,429,000
(7,817,863) (n=2)
Convertible Note 1,005,750
(1,406,081) (n=2)
N/A
Debt Financing 15,000.000 (n=1) N/A
Initial Coin Offering N/A 38,211,000 (n=1)
Post-IPO Equity 12,300,000 (n=1) N/A
Venture - Series Unknown 6,025,000
(8,925,408) (n=10) 59,546,000 (29,665,716) (n=4) Series A 10,825,000 (20,013,763) (n=8) 50,463,000 (97,210,761) (n=23) Series B 18,000,000 (4,582,575) (n=3) 35,859,000 (40,622,054) (n=22) Series C N/A 382,479,000 (1,060,040,447) (n=10)
Type of Funding Amsterdam Shanghai Series D N/A 187,130,000 (258,425,592) (n=9) Series E N/A 133,185,000 (0) (n=2) Total (n=65) (n=82)
Note: The amounts for Shanghai were originally listed in RMB within the dataset but converted to
EUR by means of a conversion rate as found on the 20th of May on XE Currency (2018): 1 RMB =0.133185EUR. After conversion, the amounts were rounded to thousands. Standard deviations were rounded to the nearest whole number.
The amounts invested in companies within the Shanghai ecosystem are considerably higher compared to Amsterdam. As shown in Table 10, these results show that the financial capital flowing through Amsterdam is not comparable to the major investments made in Shanghai. Comparing both ecosystems, the average amounts of funding are higher for Shanghai regarding every type of funding.
4.2.2. Focus of Investments
Next to the type and amounts of funding, this study also examined the focus of investments made within both entrepreneurial ecosystems. Table 11 shows the top ten categories which received funding during the course of 2017.
Table 11: The ten foremost company categories that received funding within the
entrepreneurial ecosystems of Amsterdam and Shanghai in 2017
Nr. # Amsterdam (n=91) Shanghai (n=92)
1 Software (n=30) Software (n=30)
2 Information Technology (n=18) Hardware (n=15)
3 Internet Services (n=13) Media and Entertainment (n=14)
4 Hardware (n=12) Health Care (n=14)
5 Health Care (n=12) Commerce and Shopping (n=13)
6 Commerce and Shopping (n=11) Internet Services (n=12) 7 Financial Services (n=11) Financial Services (n=10)
