Examining the cohesiveness and nestedness entrepreneurial ecosystems: evidence from British FinTechs

Building strong entrepreneurial ecosystems — the localized actors and factors that support high-growth entrepreneurship — has emerged as a popular regional economic development strategy (Spigel, 2020; Wurth et al., 2021). While researchers have discussed different definitions of ecosystems (e.g., Audretsch & Belitski, 2016; Cao & Shi, 2020), these works all stress the importance of common factors such as active investment scenes; leadership from entrepreneurs; dense networks connecting founders, advisors, mentors, and employees; knowledge spillovers from universities and major corporations; and a culture that supports innovation and risk-taking. These place-based factors provide the economic and social support needed to encourage ambitious, growth-oriented entrepreneurship. But given the rapid growth of the entrepreneurial ecosystem literature, there are numerous research gaps. One of the most important unanswered questions is if regions have a single, cohesive ecosystem through which entrepreneurial resources and support circulate or if regions contain several “nested” ecosystems centered around individual industries. While scholars such as Brown and Mason (2017) and Auerswald and Dani (2017) have suggested that ecosystems have multiple nested communities, others suggest that ecosystems are cohesive, with sharing of knowledge and resources between firms in different sectors (Mack & Mayer, 2016; Motoyama & Knowlton, 2016).

Determining if ecosystems are cohesive or nested is more than a conceptual quibble: it speaks to the heart what entrepreneurial ecosystems are, how they operate, and how they contribute to firm competitiveness. Recent developments in technologies such as digital platforms (Ewens et al., 2018) and the spread of entrepreneurial methodologies like Lean Startup (Contigiani & Levinthal, 2019) have arguably made the skills required for scaling up entrepreneurial ventures more generic. This means that entrepreneurs in different sectors benefit from similar knowledge bases, such as how to pitch to investors or use advertising platforms. If these generic types of entrepreneurial know-how are the most essential part of the scale-up process, we would expect regions to have a single, cohesive entrepreneurial ecosystem. On the other hand, if industry-specific knowledge and know-how are more important to scaling, we would expect to see regions to develop nested ecosystems where entrepreneurs in similar sectors benefit from interacting with one another rather than outsiders.

The FinTech sector is a useful case to study the role of generic and specific knowledge and resources in ecosystems. FinTech firms use digital technologies to solve problems and reduce costs in the financial services sector. Successful FinTech entrepreneurship requires insight into both the finance industry in order to identify and evaluate potential opportunities and technological knowledge to develop new products and services that provide value and lower costs (Harris, 2021). In theory, FinTech firms should benefit from an integrated ecosystem that allows technical and business knowledge to flow between the finance and technology sectors. However, the financial industry is notoriously opaque, which makes it difficult for outsiders to draw on the insights of those in the industry to find and exploit emergent entrepreneurial opportunities. It is difficult to say if growth in the FinTech space is driven by the concentration of specialized knowledge that allows entrepreneurs to find and exploit emergent opportunities or if it is driven by more generic forms of entrepreneurial knowledge around how to develop and scale products.

The extent to which FinTech ecosystems are cohesive or nested speaks to the underlying processes through which ecosystems develop and support the competitive advantage of high-growth ventures. Individual choices by ecosystem actors such as who they meet and choose to partner with are made within the broader context of ecosystem norms and networks, which structure the potential options available to individuals at any given time. The configurations of ecosystems provide a way to understand the often-invisible mechanism which connect ecosystem structure with entrepreneurial action. This does not mean that ecosystems are deterministic nor that regions cannot have multiple configurations. Rather, examining the extent to which entrepreneurial ecosystems have a cohesive or nested structure allows us to better understand the underlying pressures affecting how ecosystems work.

To investigate this tension, this paper examines the backgrounds of the founding and management teams of British FinTech scale-ups. The paper uses a novel analytical methodology to classify the backgrounds of the founders, senior managers, and advisors of high-growth FinTech firms in the City of London, East London, Leeds, and Edinburgh. These regions have the highest concentration of FinTech firms in the United Kingdom. In total, data was gathered on 1,570 founders, top managers, and advisors at 380 high-growth FinTech firms. The nature of founding and management teams are examined because the skills, knowledge bases, and networks of these individuals are a crucial determinant of firm success (Barringer et al., 2005; Hasan & Koning, 2019). In particular, the paper examines these actors’ knowledge base: if they have work experience in the finance industry, digital technology, or other sectors. Their work experience and backgrounds potentially give founders and managers key insights into markets and new technologies that could spark FinTech entrepreneurial innovation. These data are used to understand the extent of overlap between people whose backgrounds are in technology, finance in order to understand the cohesiveness or nestedness of different FinTech ecosystems. This provides a quantitative measure of ecosystem cohesiveness which sheds new light on their structure and level of development.

The data suggest that ecosystems are generally nested, with founding teams generally coming from one type of background (e.g., technology). Where there is overlap, it tends to be between people with either a background in finance or technology with someone from a managerial background. This suggests that FinTech entrepreneurship does not require an overlap of specialized finance and technology knowledge, but rather requires similar, generic forms of scale-up know-how. The ecosystem is useful for connecting founders with similar backgrounds to advisors and top managers with more diverse knowledge bases. These findings make three contributions to the entrepreneurial ecosystems literature. First, it argues that ecosystems remain nested, even in highly developed economies such as in London. This helps resolve long-standing questions about ecosystems structure. Second, it develops a new, repeatable methodology that can measure the cohesiveness of different entrepreneurial ecosystems across nations and over time. This responds to calls for better use of new data sources from digital platforms and social media (Feldman, 2014; Feldman & Lowe, 2015). Such an approach allows for more robust empirical comparisons within and between ecosystems. Third and most importantly, it explores the importance of generic industry-specific knowledge in entrepreneurial ecosystems and the importance of these different types of knowledge in the modern scale-up entrepreneurship process.

To better understand the nature of cohesive and nested entrepreneurial ecosystems, this paper examines the backgrounds of founders, senior managers, and advisors at FinTech scale-up firms in four British ecosystems. By examining the professional backgrounds of founders and managers at these firms, we can explore if and how entrepreneurs combine insights from within and outside of their ecosystems to identify new opportunities and develop new product and services. If founders and top managers tend to come from a single type of background (e.g., they are all financial professionals or programmers), this would suggest that they are operating in nested ecosystems that make it hard for founders to find people of other backgrounds that they trust enough to engage in business with. If founding and top management teams have a variety of overlapping backgrounds, this indicates a more cohesive ecosystem that is rich in linkages between different communities.

A major challenge in studying the background of founding and management teams is the lack of individual-level data on their skills, work histories, and education. Such issues have led to calls for researchers to draw on more varied sources of data, such as corporate databases and social media data (Feldman & Lowe, 2015). To address this challenge, the paper employs two such data sources. First, it uses data from Beauhurst Business Intelligence to identify high-growth FinTech firms along with their founders and senior management teams. Beauhurst identifies high-growth firms based on their product offerings, levels of investment, and use of scale-up programs such as accelerator programs or public R&D support grants. After identifying firms, Beauhurst gathers data on the founders and top management team, including their gender and job titles, and links to their social media pages if they exist. In mid-2017, data was collected on the 1,315 FinTech firms in the UK which Beauhurst classified as FinTech. Most FinTech firms are clustered in London: 769 (58%) are located within the Greater London Region compared to 546 (42%) in the rest of the country. This paper looks at the two largest FinTech ecosystems in London — The City of London and East London (made up of the Hackney and Islington council areas) — as well as the two largest FinTech ecosystems outside of London, Edinburgh, and Leeds.

As shown in Table 1, the City of London and East London are both home to more than one hundred FinTech scale-up firms each, with relatively fewer in Edinburgh and Leeds. After removing firms from the analysis if they were misclassified as FinTech (e.g., they were a traditional investment firm rather than having a technology product) or which were founded before 2005 and therefore could not be considered new ventures, 380 firms were included in the final dataset: 193 firms in the City of London, 137 in East London, 31 in Edinburgh, and 19 in Leeds.

The City of London has long been the center of financial services in the United Kingdom, including the London Stock Exchange, the Bank of England, and the headquarters of major firms such as Lloyds Banking Group and Aviv (Cook et al., 2007). It is one of the world’s largest financial clusters and it has seen the development of a large cluster of FinTech firms looking to sell new technologies to the globally leading banks and investment firms located there. East London — sometimes referred to as Tech City or Silicon Roundabout — is a technology and software cluster, with concentrations of technical talent, scale-up support programs, and venture capital firms (Nathan, 2020). The ecosystem is known as being center of creative, design-based technology work, with a great deal of socializing between firms and high levels of worker mobility. Though these regions are proximate to each other in London, they can be thought of as having separate ecosystems. The City of London has a unique, inward-facing culture focused on the finance industry while East London has developed a very separate culture around technology innovation and entrepreneurship. Consequently, the two ecosystems have developed their own support programs and networks based around their different orientations.

Edinburgh is the UK’s second-largest finance center outside of London, hosting the headquarters of major banks and breakage houses. It is also a successful technology entrepreneurial ecosystem, with a dense network of scale-up support programs and high-growth entrepreneurs. However, there are still significant weaknesses in terms of coordination between government and non-government actors and a fragmented investment community (Miao & Maclennan, 2019). Finally, Leeds is one of the fastest-growing British economies outside of London and has become a center for back-office services for the global financial industry, with regional headquarters for major British and European banks (Gonzalez & Oosterlynck, 2014). This includes both major multinational banks and the secondary office of the Bank of England.

The second source of data was the LinkedIn profiles of the founders, top management team, and advisors of these FinTech firms. LinkedIn is a professional social networking site in which users list their personal and professional experience to both connect with others and as a tool for job hunting. This means that users have an incentive to keep their details about their job histories, employers, and educational attainment accurate and up to date. LinkedIn is increasingly used by researchers as a data source because it provides individual-level data on knowledge workers and professionals that is otherwise not available through more traditional sources (Jiang et al., 2017; Spigel & Vinodrai, 2020).

The LinkedIn profiles of up to seven founders and top managers, as identified by Beauhurst, for all FinTech firms in the four study ecosystems were collected in late 2017. Of these, 524 (33.3%) were classified as founders, with the remaining 1046 (66.6%) being senior managers or advisors. The data collected included self-written descriptions, their current employers, and job titles those for their previous six jobs, up to two of their listed higher education degrees, and up to six self-reported skills. Not every individual provided all the data, but most users had profiles that contained usable information.

These data were used to create two classifications of each individual in order to provide a robust way of understanding each person’s experience and knowledge base: their job background and sector of their prior employers. First, the background classification refers to the skills and expertise they have developed in their jobs and through their education. Individuals’ backgrounds were classified into one of six categories based on their current and previous job titles, job descriptions, educational history, and self-description. These backgrounds were finance background (e.g., investment manager, risk analyst, or equity researcher), technology background (e.g., developer and software engineer), management background (e.g., marketing or sales director), other background (anything else, often a creative or legal background), mixed background (an even mixture of two of these backgrounds), or unknown/unclassifiable (not enough data in their profile or their profile was not available).

To perform this classification, a guide was first developed based on a pilot analysis of the data, which was then applied to the full dataset by two PhD students acting as coders, who worked independently and were blinded to individuals’ names and genders to avoid potential bias. They agreed on 69.7% of all profiles (Cronbach’s α = 0.78), representing overall acceptable levels of inter-rater reliability. It is important to note that backgrounds are based on job titles and self-described skills rather than who their employers were: a human resources professional at an investment bank would be classified as having a management background rather than a financial one. The overall classification distribution is shown in Table 2.

However, job backgrounds and skills are not the only determinant of entrepreneurs’ ability to observe opportunities and understand the FinTech market. The nature of their prior employers can create the potential to observe new problems or developments that can be used as a basis for new entrepreneurial ventures. To cover this possibility, a second classification scheme was developed to classify individuals based on the sectors of their prior employers. Data on the last six listed employers on each individual profile was gathered from those employers’ LinkedIn pages. In total 4,073 different employers in the dataset. Of these, data were gathered on the 2,460 (60.4%) firms which had company LinkedIn pages. These pages have a combination of data provided by the firm and imputed by LinkedIn and include information such as a text description of the firm, number of employees with LinkedIn profiles, and founding date. Data on firms could not be acquired if they were too small to have a LinkedIn page or if the individual put in a non-firm name such as “travelling” or “volunteering” to demarcate gaps in their job histories.

Because not enough individuals included their length of employment at each employer, it is not possible to rigorously classify the impact of each employer on their industry knowledge base. Individuals were therefore classified based on the number of prior employers they had in different sectors (see Table 3). For example, if out of seven total jobs listed in the database, 3 were in finance, 2 were technology, and one was other, they would be classified as having a finance employer background. In 33 cases, there were an equal number of finance and other employers and in 56 cases, there were an equal number of technology and other employers. Because the focus is on exposure to finance and technology sectors, these cases with equal distributions were assigned as finance and technology, respectively. In 351 cases (22.4% of dataset), classification was not possible because either profile information on the individual was not available or they did not have any classifiable employers in their job history.

For both classification schemes, conflicts between assistants were resolved by the author, who examined the individual profiles in questions and compared the data to the coding guideline to make a final classification decision. In many cases, disagreements were caused by the challenges of classifying people with complex work histories, such as people who started their career at technology firms or in technology roles or roles but moved into more managerial roles as their careers progressed. While this method does create the potential for mis-categorization, it is designed to reduce the impact of individual biases and create a robust method for assigning people into different career groups based on unstructured, messy data.

As shown in Tables 4 and 5, there are significant differences between the four ecosystems when it comes to the backgrounds of founders and top management teams. East London has the highest proportion of senior managers and founders with technology backgrounds. Interestingly, the City of London does not have the highest rate of founders with finance backgrounds, with Leeds exceeding it. However, when looking at the broader top management team (which includes founders), the City of London has the highest rate of people with finance backgrounds.

But while the raw numbers of founders and senior managers job backgrounds across the four ecosystems are important, the overlap of different founder experience bases within companies is the best way to understand the structure of entrepreneurial ecosystems. As shown in Fig. 1, there is substantial variation in the configurations of job backgrounds between ecosystems.¹ Overall, of the 188 firms with more than one listed founder, just 23 (7.1%) had at least one founder whose background was in technology and one whose background was in finance. East London had the highest concentration of these overlapping finance and technology founding teams (9.0%) while Edinburgh had the lowest with 3.7%. In all the ecosystems, the most common background configuration was only management founders (136 firms, 42.1% of sample), followed by only finance (56 firms, 17.3% of sample) and then by an overlap of finance and management backgrounds (23, 7.1% of sample). When examining at the backgrounds of the entire top management and advisory team, we see that both London ecosystems had relatively high rates of teams with backgrounds encompassing both finance and technology (see Fig. 2). There are a smaller number of such firms in Edinburgh and Leeds.

Similar patterns are apparent when looking at individuals’ prior employer backgrounds. As shown in Fig. 3, about 10.6% of all firms in the dataset had at least one founder classified as technology and at least one with a founder classified as finance. There was a diverse array of configurations between the four ecosystems, with Edinburgh having the highest rate of this crossover (15.4%) while Leeds had no firms whose founding team combined finance and technology backgrounds.

The employer backgrounds of the top management team showed a general increase in the overlap between people with experience in both the finance and technology industries (see Fig. 4). Nearly 30% of all firms has this overlap, but Leeds still showed substantially lower levels of overlap in terms of top management teams’ backgrounds, with only 18.8% of firms exhibiting this characteristic, almost 10% less than the other three ecosystems.

The goal of the paper is to determine if entrepreneurial ecosystems tend to be cohesive, in which entrepreneurial actors draw on a mixture of resources from a wide variety of different sectors, or nested in which entrepreneurs operate within close-knit communities centered around particular sectors or technological vertices. Largely cohesive ecosystems would indicate that ecosystems support entrepreneurial growth by allowing firms to access a broad knowledge base that allows founders to overcome common growth challenges such as access to finance or team building. Nested ecosystems would suggest that ecosystems support growth by helping entrepreneurs access niche technical knowledge and build legitimacy within sectoral niches.

These data indicate that the four ecosystems studied are nested rather than cohesive. The low levels of direct overlap between founders and top managers with finance and technology backgrounds suggest that these two communities are separate, with few opportunities for overlap. While the degree of nestedness differs — for example, there is only one firm in Leeds who has founders from more than one background — mono-background is the modal configuration for FinTech founders in all four locations. This homogeneity is lowered when looking at the broader top management team, but we still observe relatively small levels overlaps of these management teams of those who have experience in technology and finance. To be sure, the London-based ecosystems had higher rates of this overlap than Edinburgh or Leeds, but even then, this accounts for only a quarter of firms.

These data suggest that highly developed ecosystems provide an opportunity for FinTech entrepreneurs to observe opportunities and then bring on people with technology and finance expertise on to their top management team, helping to address deficiencies in the founders’ experience. Even if founding teams infrequently involve people from different backgrounds, these teams were able to increase the types of skills and knowledge bases they can draw on by bringing on outside advisors and managers with different backgrounds. Such hires are facilitated by the ecosystem, which helps founders build out their networks to find and screen qualified workers. However, this was not the case in the less developed ecosystem of Leeds, where background overlap of management teams was substantially lower than in the other three ecosystems. Thus, even if the more developed and more well-functioning ecosystems of London and Edinburgh do not result in more heterogeneous founding teams, these founders are still able to use their local ecosystems to find advisors and managers with different skill sets to help develop new products and grow the firm.

The most unexpected finding is the low number of firms whose founding teams have an overlap of experience between finance and technology. In terms of their career history and employer background, only 7.4% and 10.7% of the firms had this founding team configuration, respectively. By either measure, this is a small minority. Even by the broadest measure (prior employer backgrounds of the top management teams), less than a quarter of all firms in the dataset had an overlap between finance and technology. In all ecosystems except Leeds, the modal configuration that the founding teams experience is exclusively in the management domain. These low levels of overlap are a consequence of the number of solo founders. One hundred sixty-three firms, or 46.4% of the dataset, had only one founder. These numbers are relatively in line with other studies of founding teams, where single-founder firms are the plurality (Reuf et al., 2003). But even when expanding the analysis to include top managers and advisors who joined the firm after its founding, we see that there is still a minority of firms whose top management teams have both finance and technology backgrounds. Only 24.5% (90) of firms had a senior management team with work experience in both sectors and 29.7% (105) had founders, managers, and advisors whose collective prior employers experience covered both sectors. The most common background of both founding teams and of senior management teams is management. 64.1% of firms (207) have at least one founder whose background was classified as management. This increases to 80.0% (294) firms when looking at senior management teams.

The frequency with the founding and top management teams had individuals with a management background speaks to the importance of more generalized business knowledge in the creation and growth of innovative firms. Forty-three firms in the dataset (11.3%) had an overlap of founders with managerial and technology backgrounds and 8.4% of firms (32) had a combination of founders with management and finance backgrounds. One hundred fifty-two firms (40.0%) had a combination of managerial and technology experience in their top management teams and 37.6% of firms (143) had a combination of management and finance experience. This suggests that business knowledge is just as important in the FinTech entrepreneurship process as more specialized technological or industry experience. That is, digital affordances mean that FinTech firms use similar tools and techniques as firms outside this sector in order to find opportunities, innovate, and grow. The ecosystem allows FinTech founders with deep, specialized knowledge to connect with more generalist managers that can help the company find product-market fit and scale, while simultaneously founders with generalist managerial experience can connect with more specialized finance and technology founders and advisors to help them develop products and penetrate the finance market.

These findings add new dimensions to debates about the role of broad and deep knowledge in firm growth or specialized versus diverse clusters in regional economic development. While there are few direct connections between the technology and finance communities of the four studied ecosystems, founders with specific technical or financial industry experience often partnered with other founders or found top managers who have more managerial backgrounds. The more developed ecosystems in London and Edinburgh appeared to be effective at connecting founding teams with more managers with more general business skills such as operations, marketing, and fund raising, skills that the original founders did not have. This points to the importance of the generic entrepreneurial skills of a top management team in addition to their deep sectoral-specific knowledge that has been previously studied in work on top management teams (Amason et al., 2006; Kor, 2003; Zimmerman, 2008). These generic entrepreneurial skills relate to the ability to evaluate an opportunity and how new technologies enable novel solutions to market gaps but just as importantly the ability to bring together employees and advisors with more specialized knowledge bases to effectively pursue an opportunity. Such managerial skills have received far less attention in the entrepreneurship literature on human capital than technical knowledge, which is easier to proxy through educational background. However, some recent papers such as Siepel et al. (2021) have begun to examine the relationship between diverse founder skills and firm performance. This work tends to show the importance of overlapping skills across a breadth of technical, creative, managerial domains within a firm rather than deep specialization. This organizational structure is supported by diverse ecosystems which contain populations with a variety of talents that can be easily accessed through dense local networks which are built through frequent interactions at formal events and informal meetups.

These findings make three contributions to the entrepreneurial ecosystems literature. First, it uses a novel dataset and methodology to examine the structure of entrepreneurial ecosystems. The method goes inside the firm to look at the backgrounds of founders and top manager to examine the extent to which ecosystem structure can bring together people of different backgrounds and experiences to identify opportunities in the FinTech space. By responding to Feldman and Lowe’s (2015) call for more creative use of different types of data, this paper developed a new perspective on the development paths of entrepreneurial ecosystem. LinkedIn offers a rich way to systematically identify not just key players in an ecosystem but also the types of skills, backgrounds, and insights available. Combining LinkedIn data with other public sources such as Meetup.com (A. Rocha et al., 2021) and Twitter (Hannigan et al., 2021) can provide policymakers with a much clearer understanding of the resources within ecosystems as well as the gaps. Researchers should proactively engage with this and other novel datasets to understand the often-invisible dynamics and resources of entrepreneurial ecosystems.

Second, it finds that ecosystems are substantially nested, even in highly developed ones in central London. Nested ecosystems imply that there are multiple distinctive communities within their own dense internal connections instead of a single cohesive community of entrepreneurial actors. This finding helps address important questions in the entrepreneurial ecosystems literature and highlights the importance of more generic forms of scale-up know-how as opposed to specialized technological or market insight.

Third, the paper finds that even though the ecosystems have nested communities of technology and finance entrepreneurs, the importance of managerial founders and top management teams speaks to the importance of more generic forms of entrepreneurial knowledge in the scale-up process. Indeed, the sheer preponderance of founders, advisors, and top managers with backgrounds in management also suggests that nested FinTech ecosystems are not just specialized finance or technology clusters. Rather, it speaks to the importance of generic entrepreneurial and business know-how in the venture development and scaling process alongside more specialized technological knowledge or industry-specific networks and legitimacy. The ecosystem enables non-specialized founders to quickly identify experienced experts who can join the firm as advisors or senior workers.

When interpreting these conclusions, it is important to note the weaknesses of the method and data. There is substantial missing data about founders and managers and since we do not have information on how long they were in different job roles, some individuals may be misclassified. It is also possible for people to gain the knowledge needed to see an important FinTech opportunity outside their normal job: a programmer might have an active interest in the crypto-currency market that gives her new insights into an important user need. The study is also limited to the British FinTech sector: it is difficult to say if the findings are generalizable to other sectors or national contexts. FinTech is unique both due to its openness on the consumer product side but also due to the highly regulated nature of more complex offerings. Other industries without these characteristics may be more amenable to highly cohesive ecosystems. Other very specific industries like aerospace or biotech might only have nested ecosystems with few connections to those without very deep experience in that niche. Further work is required to see if the overall cohesiveness of the ecosystems in this study is a general trend or if this is a unique consequence of the nature of FinTech or British entrepreneurship.

These findings are also useful to ecosystem builders and policymakers. The data used in this paper shows that sources like LinkedIn can be used The findings are also useful for policymakers in thinking through what actions are more important for improving the quality of the ecosystem. It is clear that the less developed ecosystem of Leeds had less background diversity of its FinTech founding and top management teams. Ensuring that founders can develop their own entrepreneurial and business skills and access senior managers with the business skills that they lack is a crucial aspect of the ecosystem that may need direct support when existing networks within the community are not yet dense enough. These findings also suggest that success in one sector within an ecosystem will not necessarily contribute to the development of other sectors in a large ecosystem.

Building on this work, future research can continue to draw on new, novel data from social media and other sources that allow for more detailed examinations of not just what firms are in entrepreneurial ecosystems, but who is starting and running these firms and what their knowledge bases encompass. This allows for new perspectives not possible with traditional datasets. Ideally, further work would look specifically at the networks between founders and their top managers and advisors to understand how these links were forged in order to study the connections between different communities within a broader ecosystem. This could then reveal, for instance, if different aspects of the ecosystem help or hamper links between groups. More broadly, this work calls for a closer look at the relationships between entrepreneurs and other actors in ecosystems to better understand how these connections developed, if by random happenstance or mediated through other actors in the ecosystem. The goal is to understand the complexity of how these relationships are formed and maintained and how they lead to the recombination of knowledge and know-how that produce innovative entrepreneurship. To what extend does the ecosystem support the development of radical knowledge spillovers between different groups within a community or foster the creation of trust-based bonds between potential co-founders and senior employees. This could provide new evidence for the core processes through which regional entrepreneurial ecosystems contribute to venture innovation and growth.