Assessment of possible sustained success of
the entrepreneurial ecosystem of Amsterdam
Master’s Thesis
Dennis Haschke (11782420)
MSc. In Business Administration: Entrepreneurship & Innovation Supervisor: dhr. dr. G.T. (Tsvi) Vinig
Second reader: dr. M. (Michele) Piazzai
II
Statement of originality
This document is written by Dennis Haschke 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 entrepreneurship-related literature the entrepreneurial ecosystem has gained an increasingly significant role. The entrepreneurial ecosystem constitutes the specific circumstances and regional conditions within which a new venture is formed. Based on this previous scientific work, I assess the entrepreneurial ecosystem of Amsterdam by comparing it to the entrepreneurial ecosystem of Berlin. To do so, I use the notion of stem cell ecosystems which is a new approach in the ecosystem literature.
As literature about stem cell ecosystems is limited and there is no definition and consistent description available, I derive a definition and the characteristics from the biological term of stem cells. Accordingly, within this thesis I consider stem cell ecosystems to be characterised by the capability to foster fast growth, by the flexibility to move fast into new industries and by the ability to use existing resources from within the entrepreneurial ecosystem.
As these abilities are difficult to measure and would only give limited information about the specific manifestation of the stem cell-like characteristics of the ecosystems, I also analyse potential indicators for stem cell ecosystems. I do so by combining ecosystem literature with signaling theory. Accordingly, the indicators for stem cell ecosystems are the ability to signal attractiveness to outsiders on the one hand and a high responsiveness to signals that display an opportunity or thread on the other hand.
Both assessed ecosystems have already proven their stem cell-like characteristics in the past as they both managed to quickly develop a significant fintech scene as a response to a composition of various signals. However, these stem cell-characteristics take shape in partly different ways. While the relation between universities and the ecosystem plays an important role in Amsterdam, there is a stronger focus on the relation between incumbents and the new ventures in Berlin. As the usage of knowledge from existing business and start-ups is one of the three characteristics of stem cell ecosystems, policy makers in Amsterdam have to catch up on this aspect in order to maintain the flexibility of the entrepreneurial ecosystem.
IV
Table of content
Statement of originality ... II Abstract ...III Table of content ... IV Table of figures... VI Introduction ... 1 Literature review ... 3Entrepreneurial ecosystems – an overview ... 3
Creation and development of entrepreneurial ecosystems ... 5
Components and influencing factors of entrepreneurial ecosystems ... 5
Governance in entrepreneurial ecosystems ... 8
Methodology ...10
Stem cell ecosystems ...12
Indicators for stem cell ecosystems ...14
Signaling theory in entrepreneurial ecosystems ...14
Signals as triggers in entrepreneurial ecosystems ...17
Regional context for entrepreneurship ...19
Knowledge, learning and resource acquisition ...22
Funding, ownership and remuneration ...23
Founders and founding conditions ...23
Innovation and product development ...24
Product Architecture ...25
Marketing ...26
V
Start-up strategy ...27
Exit, failure, restart ...28
Characteristics and indicators model ...28
Assessing the entrepreneurial ecosystems of Amsterdam and Berlin ...31
The entrepreneurial ecosystem of Amsterdam – an overview ...31
Assessing the entrepreneurial ecosystem of Amsterdam ...33
Signaling attractiveness in the entrepreneurial ecosystem of Amsterdam...33
Signals triggering change in the entrepreneurial ecosystem of Amsterdam ...37
The entrepreneurial ecosystem of Berlin – an overview ...42
Assessing the entrepreneurial ecosystem of Berlin ...44
Signaling attractiveness in the entrepreneurial ecosystem of Berlin...44
Signals triggering change in the entrepreneurial ecosystem of Berlin ...45
Comparison of the two entrepreneurial ecosystems ...48
Signaling attractiveness ...49 Responsiveness to signals ...50 Discussion ...51 Conclusion ...54 Literature ...56 Appendix ...66
VI
Table of figures
Figure 1: Domains of the Entrepreneurship Ecosystem ... 7 Figure 2: A model of the entrepreneurial ecosystem ... 8 Figure 3: Model of characteristics and indicators for stem cell ecosystems with signal categories ...30 Figure 4: Proportion of new fintech start-ups compared to all new start-ups per year ...41 Figure 5: Comparison of the impact of signals within the two entrepreneurial ecosystems ...49
1
Introduction
In a world of constant flux, progressive globalisation and international competition along with the implied continuous need for structural change, the crucial way for a society to sustain and enhance prosperity is to foster entrepreneurship. Economic studies show that high rates of entrepreneurship facilitate the fast growth variety which is accompanied by rapid creation of jobs, GDP growth as well as an increase of long-term productivity (Isenberg, How to start an entrepreneurial revolution, 2010). According to Porter, entrepreneurship and innovation are the heart of a society’s competitive advantage (Porter, 1990). Entrepreneurship as new venture creation and also in the context of corporations is not a process or act by the entrepreneur isolated from the environment, but happens within a certain surrounding, the entrepreneurial ecosystem. The entire entrepreneurial process, containing all acts from identifying an opportunity to starting the venture is interdependent with the system it occurs in (Neck, Meyer, Cohen, & Corbett, 2004). Characteristics and circumstances of a certain environment affect the success of entrepreneurial activities. Porter’s diamond framework to assess a nation’s competitive advantage can help identifying determinants of a society’s entrepreneurial success. National factor creation mechanisms influence the available talent and knowledge as well as the supplier industries add critical help or constitute potential new entrants. Existing new ventures can create an incubation environment through domestic rivalry. Lastly, through positive feedback mechanisms and learning processes, entrepreneurship itself can contribute to the improvement of factor conditions of its ecosystem (Porter, 1990).
The emergence and development of such an ecosystem as well as the co-dependency of all inherit processes and players is complex and does not emerge through few discrete events. Au contraire, according to Van den Ven, entrepreneurship “consists of an accretion of numerous institutional, resource, and proprietary events involving many actors who transcend boundaries of many public and private sector organizations” (Van de Ven, 1993, p. 218). The most famous and also successful entrepreneurial ecosystem is the Silicon Valley in California
2 (Cohen, 2006). By creating such successful ecosystems, policy makers can contribute to nurturing and sustaining entrepreneurship.
The goal of this thesis is to further expand the understanding of entrepreneurial ecosystems by creating measures that indicate high performance potential and flexibility and therefore sustained success of ecosystems. Since the environment and especially the available technology is subject to continuous change, sustained success can only be achieved by creating an ecosystem that is flexible and that can adapt to new developments in technology or markets. In order to sustain success, an entrepreneurial ecosystem, just like stem cells, has to be able to reinvent or transform itself and to enter new industries through innovative start-ups. For that reason these ecosystems are called stem cell ecosystems. Literature about factors that facilitate such flexibility while still maintaining high performance as well as literature about measurements that indicate these is scarce. For that reason I will derive indicators for stem cell ecosystems from existing literature about entrepreneurial ecosystems as well as the biological term of stem cells. Indicators for stem cell ecosystems would not guarantee the success of an ecosystem, but the absence of these factors would be a strong indicator for the volatility of the ecosystem’s success.
Using the conceptualized measurements that indicate sustained success of an ecosystem, I will assess the start-up ecosystems of Amsterdam and of Berlin through semi-structured interviews as well as secondary sources and compare the results. By doing so, I intend to not only make statements about the current state and future opportunities of the Amsterdam entrepreneurial ecosystem, but also to test the developed stem cell ecosystem indicators on theri applicability in a real ecosystem. The result of the assessment can be of interest for policy makers and help them shape entrepreneurial ecosystems in general and the ecosystem of Amsterdam in particular. As incubators, accelerators and co-working spaces can be seen as entrepreneurial ecosystems on a miso level, the model with the stem cells and also the application with its results can contribute valuable insights for providers or operators of such institutions in order to improve their specific ecosystems.
3 Thus, there are two research questions which I intend to elaborate on and answer. The first question targets the derivation of stem cell indicators in an entrepreneurial ecosystem and reads: what are the indicators for sustained and long-term success of an entrepreneurial ecosystem? The second question puts the indicators for stem cell ecosystems into use and reads: how take these indicators shape in Amsterdam in comparison to the ecosystem of Berlin?
The value of such work seems obvious. Since start-ups and entrepreneurial behaviour are critical factors for growth, contributing to an effective entrepreneurial ecosystem is an important societal instrument to increase prosperity and living standards. Benchmarking other ecosystems can indicate strengths or flaws of an ecosystem that have to be promoted or resolved.
Literature review
Entrepreneurial ecosystems – an overview
To fully understand the concept of entrepreneurial ecosystems I would like to approach the term from its origin, from population biology. According to Monga et al, an ecosystem is an ecological complex that is formed by coexisting organisms that not only interact with each other but also with the physical features of the habitat. Its characteristics are determined by the interplay of the existing organisms and the environment. Each ecosystem is different and the complexity of its structure is dictated by the diversity of its species. Above that, all ecosystems, as the major ecological units, are not only at least indirectly connected with each other but are also all part of bigger ecosystems themselves (Monga, Radhika, & Sharma, 2017). Odum describes a shift in biological literature from separately describing parts of an ecosystem, as if they were isolated from their environment, to a more holistic and interconnected view (Odum, 1971). Equally, literature about entrepreneurship shifts from an isolated view towards a perspective which also takes the whole environment into account.
An entrepreneurial ecosystem can be compared metaphorically to the biological term. It is the interplay of all interdependent entrepreneurial actors with each other and with their
4 environment (Cohen, 2006). In this sense the pivotal players in an entrepreneurial ecosystem are the entrepreneurs themselves. According to Schumpeter’s popular definition, an entrepreneur is a person that “owns and directs an independent firm that innovatively and creatively destroys existing market structures” (Wennekers & Thurik, 1999, p. 48). This definition is in line with Baumol’s term of productive entrepreneurship which is characterised by the entrepreneur’s aim for fast growth and maximized value-adding (Baumol, 1990). Therefore, Schumpeter’s definition inherently excludes two types of entrepreneurship. Firstly, since it states that the entrepreneur directs a firm it does not consider any kind of entrepreneurship that happens within established organizations and is started by non-managerial employees. Additionally, this definition excludes all kinds of entrepreneurship that is only manifested in a way of business ownership for the sake of pursuing self-fulfilment. Correspondingly, entrepreneurship is the act of “perceiving and creating new economic opportunities” (Wennekers & Thurik, 1999, p. 46) as well as the introduction of new ideas “in the market, in the face of uncertainty and other obstacles, by making decisions on location, form and the use of resources and institutions” (Wennekers & Thurik, 1999, p. 46 f.).
As the focus of this thesis is set on entrepreneurial ecosystems in the sense of ecosystems for new venture creation, substantial innovation and significant growth, this definition is suitable for the given context.
According to the given definitions of the ecosystem from a biological perspective, of the entrepreneur and of entrepreneurship, as per Acs, Autio and Szerb, an entrepreneurial ecosystem is a “dynamic, institutionally embedded interaction between entrepreneurial attitudes, abilities, and aspirations, by individuals which drives the allocation of resources through the creation and operation of new ventures” (Acs, Autio, & Szerb, 2014, p. 479). Stam stays closer to the biological term and defines the term entrepreneurial ecosystem as “a set of interdependent actors and factors coordinated in such a way that they enable productive entrepreneurship within a particular territory” (Stam & Spigel, Entrepreneurial Ecosystems, 2016, p. 1).
5 Since exact manifestation of the aforementioned territory is not defined further, every closely interacting society or community can be seen as an entrepreneurial ecosystem. That would furthermore imply that every entrepreneurial ecosystem which is not entirely independent or isolated from other ecosystems, is part of a bigger entrepreneurial ecosystem. That means that every description of an ecosystem has also to define very precisely its extent and borders (Neck, Meyer, Cohen, & Corbett, 2004).
Creation and development of entrepreneurial ecosystems
As mentioned, the emergence of entrepreneurial ecosystems is a process of its interplaying components. Therefore, the development of the infrastructure and the growth of the entrepreneurial system is limited by the ability of the environment to foster entrepreneurial activities and especially the creation of new ventures. The evolution of an ecosystem is a result of the complex interaction between its components. Not only new ventures have to develop in close cooperation with each other and with the given environment in order to grow an entrepreneurial ecosystem, but also the infrastructure, public institutions and existing firms have to interact with each other to build advanced production systems (Neck, Meyer, Cohen, & Corbett, 2004).
The emergence of entrepreneurial ecosystems is a process of intelligent evolution according to Isenberg. This intelligent evolution consists of a combination of opportunity in the market and the support of public authority. After its emergence and over the time, need for government involvement within the entrepreneurial ecosystem decreases continuously to the point where the entrepreneurial ecosystem is relatively self-sustainable. Hence, the role of the government transforms from a central player that facilitates change and makes resources available to one that sustains investments (Isenberg, The entrepreneurship ecosystem strategy as a new paradigm for economy policy: principles for cultivating entrepreneurship, 2011).
Components and influencing factors of entrepreneurial ecosystems
Neck et al provide an analysis of the components of the entrepreneurial ecosystem of Boulder County and have identified four major components. The first components are the incubators,
6 which they define as “the organization where the entrepreneur was employed before starting his or her new venture” (Neck, Meyer, Cohen, & Corbett, 2004, S. 193). Secondly, formal and informal networks contribute to the likelihood of the creation of new ventures within an ecosystem. While informal networks are constituted by the unofficial social environment, like friends and family of the entrepreneur, the formal network contains all official institutions that the entrepreneur can interact with. These include universities, government, professional services, capital sources, talent pool and large corporations. Above that, the physical infrastructure as well as the predominant culture play an important role in the context of new venture creation within the boundaries of the entrepreneurial ecosystem (Neck, Meyer, Cohen, & Corbett, 2004).
However, Harrison et al disagree with the importance of the incubator as the last local organization employing the entrepreneur. They argue that the significance of the incubator in the context of new venture creation is overemphasised by the current literature because of several reasons. Harrison et al claim that the concept of incubator organizations is misleading in a reality where most new ventures are started by multiple founders. Above that, since most entrepreneurs have experienced various environments in their careers in the context of modern national and international markets, characterising and emphasising entrepreneurs as “local” is problematic. Lastly, not only the incubator organization provides entrepreneurial learning, nor is the incubator organization necessarily the only organization that employed the entrepreneur prior to starting the venture. The authors conclude that incubators have a certain influence on the entrepreneurial activity through providing a network and know-how but the complexity of moderating factors is too high to just assume a high positive impact from incubator organizations on the entrepreneurial activity (Harrison, Cooper, & Mason, 2004). Isenberg proposes a model that adds several new components and considers more influencing factors in the entrepreneurial ecosystem, than Neck et al do. He additionally includes supports and finance on the factor market side and markets on the consumer side as shown in Figure
7 1 (Isenberg, The entrepreneurship ecosystem strategy as a new paradigm for economy policy: principles for cultivating entrepreneurship, 2011).
Figure 1: Domains of the Entrepreneurship Ecosystem (Isenberg, The entrepreneurship ecosystem strategy as a new paradigm for economy policy: principles for cultivating entrepreneurship, 2011, p. 7)
According to Isenberg’s model, markets, policy, finance, culture, supports and human capital jointly create an entrepreneurial ecosystem which has a certain performance, determined by the level of entrepreneurship that these components facilitate. As this model splits the concept of entrepreneurial ecosystems in several components which are further represented in various sub-components, the model also constitutes a fundament to assessing entrepreneurial ecosystems and helps generating statements about their sustained success (Isenberg, The entrepreneurship ecosystem strategy as a new paradigm for economy policy: principles for cultivating entrepreneurship, 2011).
Erik Stam additionally creates his own model of domains or elements which largely overlaps with the provided models by Neck et al (2004) and Isenberg (2011). However, he differentiates between framework conditions and systematic conditions. Framework conditions are
8 manifested in formal institutions, culture, physical infrastructure and demand. Networks, leadership, finance, talent, knowledge and support services constitute the systematic conditions (Stam, Entrepreneurial Ecosystems and Regional Policy: A Sympathetic Critique, 2015). The whole model is shown in Figure 2.
Figure 2: A model of the entrepreneurial ecosystem (Stam, Entrepreneurial Ecosystems and Regional Policy: A Sympathetic Critique, 2015, p. 1765)
In addition to these models describing domains of entrepreneurial ecosystems, literature provides several statements that describe the impact of different factors or manifestations of components. Glaeser, Kerr and Ponzetto provide influencing factors that positively impact the creation and success of new ventures. According to their studies, a large proportion of small organizations within an ecosystem significantly increases the rate of entrepreneurship in the given region. As an explanation, the author proposes the lower cost of entrepreneurship in this region. Since large corporations constitute high fixed costs for an ecosystem, a high proportion of smaller firms decreases the economic cost for an ecosystem to create new ventures (Glaeser, Kerr, & Ponzetto, 2010).
Governance in entrepreneurial ecosystems
The nature of the interaction between and among the components determine the development of entrepreneurial ecosystems. An example for this is the fact that systems or geographical regions with high level of entrepreneurship will likely also facilitate more cases of
9 entrepreneurship. Additionally, the sociological theory of mimetic isomorphism implies that organizations tend to imitate each other. Thus, the spawning effect, which describes the phenomena that new ventures will rather appear within the surrounding of similar ventures, leads to increasingly homogenous entrepreneurial ecosystems throughout their development (Neck, Meyer, Cohen, & Corbett, 2004).
Deliberately intervening into the process of creation and development of entrepreneurial ecosystems with the goal to facilitate and accelerate performance is challenging, since a one-fits-it-all solution does not exist. The predominant focus in current literature is, contrarily, creating a more essential theory which describes a blueprint of an entrepreneurial ecosystem and which can be adjusted to the local environment. Above that, reproducing a specific ecosystem does not provide success either, as the context specific-circumstances significantly determines the composition and interplay of the components needed for success of an ecosystem (Hospers, Desrochers, & Sautet, 2009). For that reason there is rather a need for a universal and flexible tool set that can be used according to the given context than an ideal type which every ecosystem should work towards.
Therefore, Isenberg additionally provides a guideline for policy makers on how to promote entrepreneurship and help the entrepreneurial ecosystem to prosper. He suggests to not just imitate the most famous ecosystem, the Silicon Valley, but to independently shape an ecosystem around local conditions as well as an own, unique vision in cooperation with the private sector. Isenberg also suggests for policy makers to focus on high potentials rather than spread budgets among many small or slowly growing ventures, as success stories and big buy-outs have a high positive impact on the entrepreneurial motivation within the ecosystem. Above that, policy makers should dare to try to create and foster an entrepreneurial culture in the ecosystem, even if it seems like an abstract and difficult initiative. Lastly, Isenberg argues that an ecosystem should dare to expose new ventures to the market as early as possible instead of financing high-potential entrepreneurs for too long (Isenberg, How to start an entrepreneurial revolution, 2010).
10 Also Auerswald elaborates on the influence of the circumstances on entrepreneurship in a region and the responsibility of governments to foster ecosystems by following several maxims. He gives five suggestions, which he deduced from the current state of literature, to help policymakers cultivate a prosperous entrepreneurial ecosystem. Auerswald suggests to focus less on incumbents but to dismantle entry barriers through reducing restrictive and over-complex regulations. Additionally, policy makers should actively and personally engage entrepreneurs in order to develop policies that enable innovation through the encouragement of dynamism. A strong starting point for such activities and for developing entrepreneurship-incentivising strategies is an ecosystem map where participants of the ecosystem and their relations are captured and which is validated by members of the entrepreneurial ecosystem (Auerswald, 2015). Above that, Auerswald suggests to “[t]hink big, start small, move fast” (Auerswald, 2015, p. 1). He thereby encourages policy makers and all players of the ecosystem to internalize the dynamic and the entrepreneurial logic of effectuation into their interventions. Also, policy makers should understand the multidimensional complexity of each player in the entrepreneurial ecosystem and include this awareness into their actions. Above, participants should be expected to play multiple roles and entrepreneurship-related activities can not only be found in new ventures but also in various contexts, inter alia, in large corporations or advisories (Auerswald, 2015). Lastly, Auerswald suggests to “[p]repare to capitalize on crises” (Auerswald, 2015, p. 2). Policy makers should therefore focus on building resilient entrepreneurial ecosystem with the ability to turn technological threads into disruptive opportunities (Auerswald, 2015). Even though Auerswald’s suggestions seem to be quite broad and superficial to some degree, he manages to create an outline for how policy makers can positively impact an entrepreneurial ecosystem.
Methodology
In order to develop a model that describes which measurements indicate the sustained success of an ecosystem, I carried out desk research and assessed the provided factors by existing sources. From the biological term, perspective and description of stem cells, I derived
11 which characteristics are inherent to stem cell ecosystems that are flexible, have the ability to adjust and also the ability to create change.
Further, I investigated on signals that trigger change in an entrepreneurial ecosystem’s general nature. The underlying assumption is that an entrepreneurial ecosystem can proof its ability to change and to exploit new opportunities or avoid threats, by reacting to certain signals. These signals, internal as external, are expressed through a change in one or in the composition of several factors that shape an entrepreneurial ecosystem. By combining these factors with signaling theory, I assessed the strength as well as the usefulness of each kind of signal within the categories given by the categorisation of the factors. From the analysis of the signals that can trigger change, I derived a model, which includes the characteristics of stem cell ecosystems on the one hand and indicators for stem cell ecosystems in form of the ability to signals attractiveness and the responsiveness to signals on the other hand.
To verify the applicability of the model and in order to be able to make statements about possible sustained success of the Amsterdam ecosystem, I assessed and compared the entrepreneurial ecosystems of Amsterdam and Berlin. These two ecosystems seem suitable for the comparison as they both are located in cities with very similar cultures and environments which minimizes external factors that could influence the results.
Hence, to get a better insight into both entrepreneurial ecosystems, to verify the proposed model and to assess the ecosystems, I conducted semi-structured interviews. The question these interviews are intended to answer is, how different players perceive different signals, to which signals the players are most likely to respond to and in which ways the players normally respond to such signals. Above verifying the model, the interviews gave a starting point for the evaluation of the specific entrepreneurial ecosystems of Amsterdam and Berlin. This is to be achieved by asking the interviewees about past signals and past transformations of the respective entrepreneurial ecosystems.
The interviews were conducted with four or respectively five entrepreneurs and executives of incubators and co-working spaces in Amsterdam and in Berlin. The players in entrepreneurial
12 ecosystems are diverse and only interviewing members of one of these groups would not only decrease the relevance of the sample, but also the credibility. Thus, the population sample is intended to be as diverse as the population of the entrepreneurial ecosystem itself. If statements were repeated by players from different groups or players from different groups have the same opinion on a certain indicator, the liability and the importance of that statement would be increased significantly. An overview of the interviewees, the interview structure and a transcription of one interview can be found in the appendix.
In addition to the primary data of the interviews, I collected secondary data through desk research. This data granted further insight on how the two entrepreneurial ecosystems reacted to opportunity or threat indicating signals in the past. The combination of the two methods, direct insights from the players of entrepreneurial ecosystems and historical data regarding transformation of the ecosystems, is intended to create high quality output which is comprehensive to a high degree. This enables an assessment of the ability of the two ecosystems to possibly sustain success founded on a deep understanding of the two entrepreneurial ecosystems.
Stem cell ecosystems
In addition to these explanations, this thesis will be based on the assumption that the sustainability of the success of an ecosystem in an ever and fast changing world is only guaranteed if the ecosystem manages to continuously reinvent itself and therefore to adapt to new developments in markets, technology and policy. Only ecosystems that continue to be able to penetrate entirely new markets and generate new industries will be able to sustain their success. The requirements for maintaining this high and radical flexibility while still providing strong performance are the ultimate long-term success factors for a start-up ecosystem. To adequately develop the metaphorical stem cell ecosystem and its indicators, I will approach this term again from its biological origin. In a biological sense, stem cells “are a population of undifferentiated cells characterized by the ability to extensively proliferate (self-renewal),
13 usually arise from a single cell (clonal), and differentiate into different types of cells and tissue (potent)” (Kolios & Moodley, 2013).
Some of these characteristics can be transferred to stem cell ecosystems. For doing so, entrepreneurial ecosystems have to be seen as complex organisms that are formed through the interplay of its components and that are created through initial factors. Firstly, stem cells have the ability to facilitate quick growth. Accordingly, stem cell ecosystems have to have the ability to facilitate fast scale-up for highly innovative new ventures. Stem cell ecosystems react quickly to the need of the current pivotal players of the ecosystem and are able to foster high performance rates.
Secondly, stem cells can transition themselves into any other natural cell. They have the ability to specialize on a certain task in the organism. The same also applies for the metaphorical stem cell ecosystems. These need to have the capacity not only to quickly react to changes in technology, the user market or the factor market, but also to proactively and assertively facilitate change through the creation and facilitation of new value-adding ventures. That means stem cell ecosystems would not necessarily constantly create new industries through radical innovation but these ecosystems would rather be able to morph into other specializations and allow its players to enter new industries as a reaction to certain signals or changes. This ability will remain a crucial asset of the ecosystem throughout its lifetime. The flexibility of a stem cell ecosystem through reinvention and transformation would be enabled through the interplay of its components as a reaction to advance of technology or as a proactive act of innovation.
Furthermore, stem cells, emerging from a single origin cell, have the ability to duplicate themselves. Accordingly, the last requirement which can be conducted from the biological definition, besides enabling fast growth and flexibility, is that a stem cell ecosystem can transform its success in one industry or one certain technology into other industries or technologies. This can for example happen through knowledge spill-overs. The crucial part that this requirement adds to the specialisation requirement is that stem cell ecosystems are
14 not only able to transform themselves in order be flexible but also use resources like talent or knowledge that is already existing in the ecosystem and which emerged in the context of the development of the entrepreneurial ecosystem.
Therefore, components that create the stem cell-like characteristics of an ecosystem constitute crude and pure potential. Thus, we can assume that every entrepreneurial ecosystem, which is successful over a longer period of time, contains these components in a certain structure or composition. As entrepreneurial ecosystems are complex, multidimensional structures, we cannot assume that the presence of a single factor promotes or facilitates the performance of the ecosystem, but rather that the combination of certain given characteristics can be seen as the metaphorical stem cell.
To conclude and to summarize the derived requirements in a definition, stem cell ecosystems are entrepreneurial ecosystems that achieve sustained performance and success through the ability to foster fast growth (potent), through perennially reinventing themselves by specialising on new markets or technologies (self-renewal) and through using existing resources in the new specialisation (clonal).
Indicators for stem cell ecosystems
Signaling theory in entrepreneurial ecosystems
In this chapter I will develop measurements that indicate whether an ecosystem is a stem cell ecosystem with its described characteristics. These indicators will refer to an ecosystem’s past and provide information on how an ecosystem has integrated stem cell capacities.
The signaling theory constitutes a starting point for approaching indicators for stem cell ecosystems. The flexibility and the performance of abstract structures like entrepreneurial ecosystems are highly complex concepts. Single players like entrepreneurs or bank representatives can hardly make reliable statements about the stem cell-like characteristics of an ecosystem as they only constitute small components of the overall complex system. Therefore, we can determine a significant information asymmetry between the entrepreneurial
15 ecosystems, as insiders on the one hand, and any outsider to the ecosystem on the other hand. However, since entrepreneurial ecosystems are abstract concepts which are the result of the interplay of entrepreneurial actors, they cannot communicate their attractiveness to outsiders directly. For that reason, indicators for stem cell ecosystems will be derived from signaling theory in order to overcome this information asymmetry.
According to Conelly et al, the main contribution of signaling theory is the reduction of information asymmetry between two parties (Connelly, Certo, Ireland, & Reutzel, 2011). Analysing multiple types of signals and their respective situations constitutes the core of signaling theory (Spence, 2002). In the context of this thesis the focus will not be on strategic signaling, which refers to elaborate actions which a signaller can take in order to influence behaviours of the receiver (Zmud, Croes, Shaft, & Zheng, 2010), but on signals that an entrepreneurial ecosystem as an abstract system sends to indicate attractiveness to any new possible entrant. The underlying concept of signaling theory is the signaling timeline. In this timeline a signaler who is an insider with an underlying quality, is sending a signal to a receiver in order to gain his trust and overcome the knowledge asymmetry. The receiver interprets the signal, choses a certain person, product or firm and gives feedback to the signaler accordingly (Connelly, Certo, Ireland, & Reutzel, 2011).
In order to develop and foster high performance and flexibility, an entrepreneurial ecosystem has to signal to outsiders and consisting players that they offer interesting opportunities and a beneficial environment. Every new player that is persuaded to enter the ecosystem and becomes active there, provides new opportunities to the ecosystem for example in form of knowledge, new ideas, capital or as a new potential customer. This means that every new player is offering the possibility for higher performance for example by offering more affordable capital or more sophisticated knowledge, as well as the possibility to transform the ecosystem’s nature and character for example by implementing an idea for a product in a new industry. A new entrance that does not provide any of these forms of added value to the entrepreneurial ecosystem, for example because the added contribution in form of knowledge, skills, ideas or
16 any other value is not needed in its specific quality and price, will be rinsed out of the ecosystem by the Darwinian nature of the market. For that reason, in average, every new entrance to an ecosystem increases its ability for quick growth as well as its flexibility and therefore increases its stem cell-like characteristic. Thus, we can assume that the better an entrepreneurial ecosystem is able to signal its attractiveness to outsiders as well as consisting players and to gain their trust, the more likely the entrepreneurial ecosystem is a stem cell ecosystem. Hence, in the context of this thesis I will assess how entrepreneurial ecosystems can signal their attractiveness and therefore attract new entrants and current players in order to determine if an entrepreneurial ecosystem is a stem cell ecosystem or not.
Signals that the ecosystem can send out in order to demonstrate legitimacy and attractiveness, are diverse. According to Gulati and Higgins the quality of a signal differs corresponding to the ease the receiver can detect it. The higher the visibility or observability of a signal the stronger it is. Hence, a strong signal is easier to detect than a weak signal (Gulati & Higgins, 2003). How useful the signal is, depends on the one hand on the degree of accordance of the signal to demanded quality of the signaler or short, signal fit. This means that, in order to determine the usefulness of a signal, first the seeked-for qualities by the players of the ecosystem have to be established. In the case of entrepreneurial ecosystems this would be for example the availability of talent, of consumer markets or the attractiveness of legislation and taxation. On the other hand the usefulness of a signal depends on the signal honesty (Davila, Foster, & Gupta, 2003). According to Connelly et al signal honesty is defined as “the extent to which the signaler actually has the underlying quality associated with the signal” (Connelly, Certo, Ireland, & Reutzel, 2011, p. 46). The combination of these two aspects is the signal credibility which constitutes the determinant of the usefulness of a signal (Davila, Foster, & Gupta, 2003). Since an entrepreneurial ecosystem is made up through the uncoordinated interplay of self-interested players, we can assume that none of the signals are send deliberately to falsely convince a new player to enter. Hence, the signal honesty can be assumed to be high and not determinant to the signal usefulness in case of entrepreneurial ecosystems.
17 Above that, we can differentiate between internal and external signals. Internal signals would be signals in form of inside-out projections. They are coming from within the ecosystem and project a certain image. Examples for that are changes in legislation or the prices in co-working spaces. Contrarily, external signals come from a third party and represent the outside-in reflection (Mavlanova, Benbunan-Fich, & Lang, 2016). External signals could be articles in magazines about bigger buy-outs or scientific papers about the specific ecosystem.
To summarize, stem cell ecosystems are able to signal their attractiveness and legitimacy to outsiders which constitutes the basis for their high performance and radical flexibility. The strength of these signals depends on the visibility in the sense of ease of detection and above that the usefulness depends on signal fit and signal honesty.
Aaltonen describes ten factors that shape entrepreneurial ecosystems (Aaltonen, 2018). Signals that display attractiveness to outsiders can be categorised according to these ten factors. These ten categories are described in the end of the following chapter.
Signals as triggers in entrepreneurial ecosystems
In addition to signals that the ecosystems sends out to outsiders to communicate attractiveness, signals that the ecosystem receives, can also trigger the ecosystem’s transformation into a new industry and thereby attract new key players for this specific industry. That means that an ecosystem changes as a reaction to one or several signals and moves into a new technological focus. Flexibility of an entrepreneurial ecosystem is the critical requirement to respond to such triggers through change. Above that, an entrepreneurial ecosystem needs the ability to identify signals that display new opportunities and threats in some way, in order to adequately react to these triggers. Since entrepreneurial ecosystems are also competing with each other at consumer and factor markets, they need the ability to effectively react to signals in order to sustain their success in the long term. Hence, an entrepreneurial ecosystem that changes quickly and efficiently to such triggers and thereby exploits new opportunities as well as avoids threats can be assumed to incorporate stem cell-like characteristics.
18 Signals like that could be for example changes in regulation or new developments in technologies. Also a buy-in by a bigger company that is known for a certain kind of technology could trigger the change. Above that, success stories of certain start-ups or statements of an influential person can be seen as triggers. A specific example for that could be Mark Cuban assuming that the world’s first trillionaire would be an artificial intelligence entrepreneur and thereby promoting the creation of AI start-ups (Clifford, 2017).
According to Aaltonen, there are ten generic factors that shape entrepreneurship within an ecosystem which he observes across a number of studies in 252 articles. Signals that are the initial cause for an entrepreneurial ecosystem to significantly change and move into new industries will inherently signal a change in one or several of these ten factors that form the entrepreneurial ecosystem’s nature. That means, a change causing signal always shows a change in the composition of the factors that shape entrepreneurship in an ecosystem. For that reason we can categorize the signals that trigger substantial change according to the ten factors proposed by Aaltonen. Every signal, internal or external, will fall into one of these categories. The categories also determine the visibility of the signal to a certain degree. The ten factors that shape an entrepreneurial ecosystem are the following (Aaltonen, 2018, p. 13): Regional context for entrepreneurship
Knowledge, learning and resource acquisition Funding, ownership and remuneration
Founders and founding conditions Innovation and product development Product architecture
Marketing
Intellectual property Start-up strategy Exit, failure and restart
19 A change in these factors can be expressed through very specific events in the entrepreneurial ecosystem with diverse visibility and usefulness. For that reason, within the context of this thesis, the investigation of the signals is limited to the categories or respectively the factors that shape an entrepreneurial ecosystem.
Further, I will elaborate on each of the ten factors and on how strong of a signal a change in each one of the factors is.
Regional context for entrepreneurship
One factor that shapes the nature of entrepreneurship within an ecosystem is the regional context. As this factor can be divided into three categories, the signals that display change in this specific factor can be divided accordingly. They can signal a change in cultural, institutional and demographic characteristics, but also a change in geographical proximity, industrial clusters and agglomeration and a change in government and public interventions (Anokhin & Wincent, 2012).
Culture, institutional and demographic characteristics
Each of these three sub-components have a significant influence on the nature of a specific entrepreneurial ecosystem. For example a change in the cultural dimension of individualism and collectivism can influence the type and probability of innovation within start-ups. A high degree of individualism as well as certain types of collectivism can increase innovativeness within entrepreneurial ecosystems (Taylor & Wilson, 2012) while a high degree of societal collectivism decreases the number of new firms created (Autio, Pathak, & Wennberg, 2013). However, as culture is a complex and slowly changing construct, the signaling effect of changes is limited. There are two reasons for that. Firstly, the strength of a signal is dependent on the visibility of the signal. Slow changes in complex systems are difficult to capture, therefore the visibility and thereby the strength of the signal is low. Secondly, besides honesty, the usefulness of a signal is dependent on the degree of accordance of the signal to demanded quality of the signaler (signal fit). Societal culture within an entrepreneurial ecosystem is complex enough that players can be assumed to not have a very specific preferred
20 manifestation of culture. Hence, as cultural changes only send weak signals with an unspecified signal fit, they will not likely trigger radical change of the entrepreneurial ecosystem’s nature.
The institutional landscape which is formed by, inter alia, universities or incubators, further shapes entrepreneurial ecosystems since they provide new market opportunities in form of technological know-how or physical space for offices or production facilities (Radosevic & Yoruk, 2013). The combination of regional human capital and suitable innovation infrastructure is a key innovation driver (Sleuwaegen & Boiardi, 2014). Universities raise the educational level including entrepreneurial and technological competences in a region and provide the entrepreneurial ecosystem with new venturesome entrepreneurs that acquired the capabilities of learning new skills, which is positively associated with new venture creation (Ouimet & Zarutskie, 2014). Changes in institutional circumstances in an entrepreneurial ecosystem have a high visibility. For example, technological and educational success of universities can be easily accessed through certain public rankings or journals. Also the suitability and convenience of the existing infrastructure is highly visible for instance through the average rentals or available bandwidths of internet connections in the specific area. Therefore, changes in the institutional landscape are strong signals and a possible trigger for radical change of the overall ecosystem.
Above that, demographic characteristics shape entrepreneurial ecosystems. For instance, firms of founders who have lived longer in a specific region tend to perform better (Dahl & Sorenson, 2012). Also welfare indicators like the GDP per capita, tax rates or the unemployment rate impact the level of entrepreneurship of an ecosystem (Arin, Huang, Minniti, Nandialath, & Reich, 2015). Changes in demographic characteristics are slow and therefore constitute a weak signal. Furthermore, entrepreneurs are not likely to have strong and specific demands regarding the demographic composition within the entrepreneurial ecosystem which hence does not indicate a strong signal fit and thereby limited usefulness of the signal. Thus,
21 changes in the demographic characteristic of an ecosystem are unlikely to trigger change on its nature.
Geographical proximity, industrial clusters and agglomeration
All three factors benefit the entrepreneurial performance within an ecosystem in some way. Geographical proximity drives knowledge acquisition through collaboration within networks especially in the early stages of a venture while a company later may have the resources and ability to invest in research and development (de Jong & Freel, 2010). Above that, the existence of technology clusters significantly drives entrepreneurship through knowledge spill-overs (Delgado, Porter, & Stern, 2010). For which firms the cluster is beneficial, depends on the maturity stage of the cluster. An emerging cluster is rather beneficial for new ventures while a mature cluster increases firm survival (Wang, Madhok, & Li, 2014). Additionally, agglomeration is also a factor which impacts entrepreneurship. Urban areas provide market opportunities and a significant labour market (Jansson, 2011) and resident international companies attract knowledge-intensive businesses (Jacobs, Koster, & van Oort, 2014). Change in geographical proximity and change in agglomeration are both not strong signals for a change in the entrepreneurial ecosystem’s nature. Both change slowly and have therefore a low visibility. Changes in industrial clusters on the other hand can be strong signals. A new bigger enterprise that founds a branch in a certain area and that needs suppliers in that area can be a strong signal as the visibility of such developments is very high. Also a popular accelerator with a certain technological focus can constitute such a signal.
Government and public interventions
One factor that shapes an entrepreneurial ecosystem is the established regulation and significantly influences the level of innovation within the ecosystem. Besides regulation, public authorities can impact entrepreneurship through direct interventions, like the offer of funding or other subsidies (Dau & Cuervo-Cazurra, 2014). Also regulation which protects innovation for instance through patents, are beneficial for entrepreneurship within an ecosystem (Pinkse, Bohnsack, & Kolk, 2014).
22 Changes in government or public involvement are utterly strong signals. The reason for that is its visibility since the changes in regulations and instruments of the public sector to promote entrepreneurship in the community not only happen relatively fast but are also easily publicly apparent. Above that, accessibility of funding, deregulation of technology and protection of developments of start-ups are in line with the entrepreneurs’ needs. Thus, players within an entrepreneurial ecosystem and especially the entrepreneurs themselves can be assumed to have very specific demands regarding these interventions. The signal fit and thereby the usefulness of the signal in form of changes of these interventions is therefore also high.
Knowledge, learning and resource acquisition
The creation and development of new ventures require practical as well as analytical and creative intelligence (Baum & Bird, 2010) to maintain high chances of success. After the initial stages these forms of intelligence have to be sourced through other means than the entrepreneur himself (Friesl, 2012). Therefore, the entrepreneurial ecosystem has to not only provide a founder with these features of intelligence, but also have the capacity to provide intelligent personnel.
Furthermore, specific knowledge and the process of learning that knowledge are relevant. This knowledge and learning concerns, inter alia, strategic decisions within uncertainty and fast change (Fernhaber & Patel, 2012), growth into new markets (Banerjee, Prabhu, & Chandy, 2015) and development as well as implementation of new products (Chuang, E., & Robson, 2015). This expertise can not only come from within the start-up (internal knowledge sources), but also external sources of knowledge play a significant role. That means that the entrepreneurial ecosystem has to provide the possibilities to acquire knowledge for example through strong or wide networks ties (Ganotakis & Love, 2012) or local and foreign connections to grow fast into new markets (Patel, Fernhaber, McDougall-Covin, & Van der Have, 2014) Changes in the accessibility of intelligence and knowledge or opportunities for learning can be strong signals for change of the entrepreneurial ecosystem’s nature. For example the start of a new study-program regarding a special technological topic like artificial intelligence or data
23 science can signal new availability of a certain kind of knowledge and lead the ecosystem to move into that technological specialisation. Also current success of established ventures can signal the accessibility of experience and opportunity to learn and can thereby trigger the ecosystem to move into that specific industry.
Funding, ownership and remuneration
Availability of funding drives entrepreneurship within an ecosystem. There are four different types of funding. There is independent funding, corporate funding, angel investments as well as public government funding and each of these four types has a distinct nature and differently impacts the entrepreneurial activities (Aaltonen, 2018). For instance, independent venture capital often goes along with a coaching function and therefore promotes the growth in two ways (Bertoni, Colombo, & Grilli, 2011). Public government funding is especially beneficial for venture survival if combined with the latter (Dushnitsky & Shapira, 2010).
As these types of funding vary in their implied effects for the entrepreneurial activities, the overall availability of funding as well as its composition impacts the nature of an entrepreneurial ecosystem. Above that, focus of the funding on a specific venture type or technology, e.g. self-driving cars, significantly shapes entrepreneurial ecosystems. To founders who seek funding, changes in the availability, composition and focus of the funding are highly visible as on the one hand, they usually deliberately approach investors and on the other hand big funding rounds are often portrayed in specialized magazines and news articles. As venture capital managers can decide which start-ups get the means needed for scaling up, this significantly influences the entrepreneurial ecosystem. Changes in the funding landscape of an ecosystem can visibly impact the growth of different specific start-ups and are thereby strong signals, especially due to their strong impact and importance for new ventures.
Founders and founding conditions
Additionally, an entrepreneurial ecosystem is shaped by the availability, traits and composition of potential and established founders. For instance, existing entrepreneurial experience is strongly correlated with the founder’s chances to succeed (Gompers, Kovner, Lerner, &
24 Scharfstein, 2010) and also with the risk-aversion of the founding team (Dencker & Gruber, 2015). Above that, the gained experienced for working in a corporate environment and opportunity confidence both increase the chances of new venture creation (Dimov, 2010). Hence, the given and developing founding conditions within prospective founders and also within founding teams that already exist, significantly shape an entrepreneurial ecosystem. Changes of the situation of founding conditions can be signalled through the success of a high risk venture or a new focus of educational institutions on entrepreneurship. However, these changes are either small or slow. For that reason there is not a high visibility and therefore only a weak signal. Thus, the chances that such a change will trigger a holistic change in nature of the entrepreneurial ecosystem are rather negligible.
Innovation and product development
Even though the innovativeness of a new venture is negatively correlated with its chances of survival (Hyytinen, Pajarinen, & Rouvinen, 2015), innovation and technology plays a critical role for the performance of entrepreneurial ecosystems. Inter alia, customer understanding, delayed adoption of new technology in established corporations or university research can be origin of new innovations in an entrepreneurial context. Thus, technological vison, the ability to identify opportunities created through new inventions, can be the source of a competitive advantage. Beside the sole technological vision, the success of innovativeness is determined by other factors like the ability to adjust basic inventions to the market (Di Stefano, Gambardella, & Verona) and to use collaborations in order to acquire critical knowledge (Sullivan & Marvel, 2011).
From the signal perspective this means, that a positive change in technological vision within an entrepreneurial ecosystem is expressed through the identification and exploitation of an opportunity provided by a new technology for example through the emergence of a new start-up. The identification and use of such a technology can be quite visible since start-ups are tend to be open about their purpose and vision. That means the changes in innovation and
25 product development through the identification of promising technology are strong signals and can critically impact the nature of an entrepreneurial ecosystem.
Product Architecture
The predominant product architecture also impacts an entrepreneurial ecosystem. This predominant product architecture significantly impacts the complementaries and interdependencies between products. For instance, it can take shape in form of the creation of platforms in contrast to standalone products (Lee, Venkatraman, Tanriverdi, & Iyer, 2010). This interdependence of products is manifested in the product architecture in two related ways: modularity and platform. The modular product architecture enables the usage of certain components or aspects across a broader range of products (Magnusson & Pasche, 2014). A platform, controlled by a platform owner, provides “the foundation upon which outside firms […] can develop their own complementary products, technologies, or services” (Gawer & Cusumano, 2014, p. 418). Hence, the occurrence and interplay between these two aspects of product architecture partly shape an entrepreneurial ecosystem since new products and services have to be adjusted to these. However, the influence of this factor is limited by the fact that especially virtual products like software and online services can be transferred across the globe very cheaply and are hardly dependent on one geographical location.
For that reason, the strength of the signal in form of a change in the predominant product architecture is limited as well. Software is not a physical product and the compatibility of new software products can be established worldwide. For instance, even though Amazon as the largest mail order company managed to establish a powerful platform to broaden their product range, the company is not reliant on the physical proximity of their sub-dealers. That means, that even if the product architecture in an entrepreneurial ecosystem significantly changes, for example through the entry of a large incumbent or fast growth of a start-up using modular or platform product architectures, the rest of the entrepreneurial ecosystem does not have to adjust their product architectures simply due to their physical proximity. Thus, a change in the product architecture in an entrepreneurial ecosystem is a signal with only mediocre strength.
26
Marketing
According to Aaltonen, also marketing activities as well as the ability to present and advertise products shape an entrepreneurial ecosystem since these activities and their success determine the accessibility and coverage of markets and are often the first contact point between start-ups and customers (Aaltonen, 2018). Especially the creation of markets for highly innovative products takes more time and is more costly than for products with only a low degree of innovativeness (O’Connor & Rice, 2013). Above that, new ventures often only have a small product portfolio and addressing the right target group with their limited means is crucial for a new venture’s success (Aaltonen, 2018). Therefore, the marketing activities of a new venture are a determinant of success for a firm and the overall marketing activities are a determinant for the success of the entrepreneurial ecosystem.
Changes in marketing activities can be expressed through, inter alia, a new advertisement or a new product for a specific target group. Marketing instruments are an important tool for ventures to communicate with their environment and advertisements are a way to present a new and innovative product. Changes in these activities are therefore highly visible and represent thereby a strong signal that can trigger a whole entrepreneurial ecosystem to move into a new industry.
Intellectual Property
Intellectual property can be the result of or the starting point for a new venture’s core innovation. A start-up either bases its innovation on new scientific finding and/or tries to protect the commercialisation of their findings through patenting (Aaltonen, 2018). Above the protection of their intellectual property, patents can also signal the viability of the new venture and its innovations to potential investors or buyers (Hsu & Ziedonis, 2013). The overall amount of intellectual property is therefore an indicator for the innovativeness and prosperity of an entrepreneurial ecosystem. Also the type of prevailing intellectual property, for instance if it concerns the automotive or the chemical industry, is an indicator for the focus of the entrepreneurial ecosystem. A change in the overall intellectual property can be expressed
27 through changes in legislation regarding intellectual property, through new patents or the expiration of patents. As legislation regarding intellectual property is always country-specific, intellectual property is strongly connected to the entrepreneurial ecosystems of a specific country, even if the product itself, for example software, is easily transferable.
Changes in intellectual property are highly visible if the start-up discloses their possession of intellectual property through new products or patents. Therefore, changes in intellectual property that are expressed publicly, are strong signals for an entrepreneurial ecosystem and have relatively high chances to trigger the ecosystem to move into a new industry. They not only signal the innovative advance or potential of an ecosystem, but also can be the base of further innovation in the entrepreneurial ecosystem.
Start-up strategy
Also the strategies of the start-ups within an area shape the entrepreneurial ecosystem. Depending on the entrepreneur’s decision whether to enter an existing market or to create a new market through innovation, start-ups have to consider two strategic aspects considering the market: timing and incumbent reactions (Aaltonen, 2018).
Entrepreneurs have the chance to design an entire new market as a first-mover. First-movers have the chance to set an industry standard and other advantages like superior technological space, superior consumer perceptual space (Liebermann & Montgomery, 1998) and have a chance to benefit from increasing return mechanisms (McIntyre, 2011). However, at the same time they have to carry costs for educating the market and risks for betting on a new technology. Also the reactions of incumbents have to be considered. As established firms often have the resources to innovate when they are forced to (Argyres, Bigelow, & Nickerson, 2015), start-ups have to select a strategy that minimizes the risk of triggering radical innovation of incumbents, for instance by hiding their innovation as long as possible (Katila, Chen, & Piezunka, 2012) or by targeting incumbents only marginally (Fan, 2010).
How the existing start-ups strategically act in their specific maturity states therefore shapes an entrepreneurial ecosystem, since bringing major innovation to the market as fast as possible
28 or hiding it until it is developed to a sophisticated level, changes the venture as well as the product landscape. Changes in these strategies can be expressed through new first-movers or products approaching entirely new markets. As products are highly visible, changes in start-up strategies are too. For that reason changes in start-start-up strategies are relative strong signals that can trigger the entrepreneurial ecosystem to move into a new industry.
Exit, failure, restart
The last factor that shapes entrepreneurial ecosystems is the end of entrepreneurial projects which can be expressed in three ways: growth, acquisition or failure (Aaltonen, 2018).
If a new venture survives long enough and manages to create significant growth over a longer period of time, the company becomes an incumbent. For many founders, also acquisition by a large incumbent is a favourable outcome of the entrepreneurial project. There are several reasons for established companies to acquire a new venture, for instance in order to enter new markets or to complete their product range (Lee & Lieberman, 2010). As entrepreneurship and innovation is significantly driven by trial and error, failure is the most probable outcome of an entrepreneurial project. The rehabilitation of entrepreneurs that experience failure of their business often leads to learning and re-emergence of the venture (Cope, 2011).
Signals that count to the category of exit, failure and restart are often highly visible. As a venture grows, signals that display change in this category are often even covered by mainstream media. Especially success stories about strong growth of a new business or an expensive acquisition of a start-up are strong signals that can trigger a change in the whole ecosystem. The reason for that is that strong growth or an acquisition are not only pure signals to other players in the ecosystem but also create a capital flow into the entrepreneurial ecosystem which can foster further performance of the ecosystem.
Characteristics and indicators model
This thesis is not testing an existing model and its moderating or mediating factors as the relation between dependent and independent variables, but is rather explorative in nature. By
29 combining theory about entrepreneurial ecosystems with signaling theory, a model with indicators for stem cell ecosystem can be derived.
We determined earlier, that stem cell ecosystems have the ability to foster fast growth (potent), through perennially reinventing themselves by specialising on new markets or technologies (self-renewal) and through using existing resources in the new specialisation (clonal). As all of these three characteristics are hard to measure directly, indicators for these abilities have to be determined through signaling theory. As described, there are indicators which in combination signify stem cell ecosystems.
Firstly, a stem cell ecosystem needs to signal its attractiveness to outsiders in order to create fast growth and flexibility. Only a continuous flow of new players entering the entrepreneurial ecosystem can guarantee that new ideas, knowledge and resources are available in order to stay potent and in order to maintain the ability of self-renewal. For that reason, the ability of an entrepreneurial ecosystem to signal attractiveness to potential new entrants is an indicator for the stem cell-like characteristic of an entrepreneurial ecosystem. These signals can be categorized according to Aaltonen’s ten factors that shape an entrepreneurial ecosystem described earlier (Aaltonen, 2018).
Secondly, according to the requirements derived from the biological stem cell definition, a stem cell ecosystem has to effectively react to signals that indicate a new opportunity or threat and needs the ability of self-renewal and usage of existing resources in an entire new industry. Therefore, a high responsiveness to signals in form of a holistic transformation of the entrepreneurial ecosystem into a new industry is the second indicator for stem cell ecosystems. As elaborated in the chapter above, these initial signals that trigger the change can also be categorized according to Aaltonen’s factors that shape an entrepreneurial ecosystem (Aaltonen, 2018).
Thus, the model that expresses the indicators for the stem cell nature of an entrepreneurial ecosystem contains the ability of an ecosystem to signal its attractiveness to outsiders and the responsiveness to signals. The model is visualised in the following figure.
30 Figure 3: Model of characteristics and indicators for stem cell ecosystems with signal categories
Even though the ability to signal attractiveness to outsiders and high responsiveness to signals indicating opportunities or threats can be seen as the requirements that eventually lead to the three characteristics that inhabit stem cell ecosystem, they are on the right side of the model. The reason for that is that the purpose of this work is to indirectly measure the stem cell-like nature of the entrepreneurial ecosystems of Amsterdam and Berlin. We assume, that the fulfilment of the two requirements inevitably leads to the entrepreneurial ecosystem being a stem cell ecosystem and vice versa. For that reason, the two requirements constitute also the indicators for stem cell ecosystems.
