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Ecosystems of entrepreneurship: configurations and critical dimensions

Abstract

Entrepreneurial ecosystems research has largely focused on the profile of a handful of successful locations. This has prevented a deeper understanding of the mechanisms that shape entrepreneurial activity across the geographical space. Our goals in this research are (1) to identify the critical dimensions of entrepreneurial ecosystems, and (2) to assess whether successful ecosystems rely on heterogeneous configurations. Through fuzzy-set qualitative comparative analysis, we address this issue with data from the State of São Paulo, Brazil. Findings generate a typological hierarchy of attributes, where the range of critical dimensions seems to be much more restricted than previously argued, and alternative configurations appear to lead to similar outcomes. A first pivotal path toward establishing a thriving ecosystem is fundamentally based on the conditions of the knowledge Infrastructure. A second approach combines elements of the socioeconomic system with the knowledge environment. Although some elements are ubiquitous, contributing attributes differ across distinct configurations, suggesting some level of heterogeneity in the dominant dimensions of entrepreneurial ecosystems. Such evidence contributes to the debate on entrepreneurial ecosystems’ dimensions and elements, offering exploratory insights on alternative ways to promote an environment conducive to knowledge-intensive ventures.

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Fig. 1

Source Adapted from Isenberg (2010), Mason and Brown (2014) and Mazzarol (2014)

Fig. 2

Source: Authors (based on research data)

Notes

  1. While our analytical framework does not include an express mention to policy for local-level ecosystems, manifestations of institutional initiatives can be perceived in a distributed manner in most components of the model that are related both to the knowledge infrastructure and the socioeconomic system.

  2. To complement this perspective, we plot cities in a map (Sect. 6) allowing to infer potential spillovers associated with EE.

  3. This follows the notion that innovative entrepreneurial activity is not necessarily connected to newly founded firms (e.g., Spigel 2017; Henrekson and Sanandaji 2019; Kirzner 1997; Baumol 1996), but rather on the Schumpeterian tradition defining entrepreneurs as those who “exploit market opportunity through technical and/or organizational innovation” (Schumpeter 1965).

  4. Ultimately, the definition of inputs and outputs will be arbitrary to some extent, as the relationships in ecosystems are complex and involve high degrees of endogeneity (Spigel 2017).

  5. For both universities and habitats, as we deal with a pooled sample, there is the risk of a given university, incubator and/or science park springing up or closing down throughout the period. For these cases, our analysis dealt with the percentage of observed years in which the municipality had an active entrepreneurial habitat or research university.

  6. The complete truth table is available from the authors upon request.

  7. “A counterfactual case is a substantively relevant combination of causal conditions that nevertheless does not exist empirically” (Ragin 2008, p.9). Counterfactuals involve all possible combinations that could lead to an outcome to which data are unavailable or are redundant as an explanatory assertation. ‘Easy’ counterfactuals are those cases where available data exist, but are redundant as a causal explanation. ‘Difficult’ counterfactuals come from those cases that cannot be observed given the lack of available data.

  8. It should be pointed out, however, that the statistical associations between research universities and other explanatory variables are moderate (Habitats and Tertiary Enrollment) or weak (Knowledge-Intensive Jobs, Diversity)—see "Appendix 3" for the detailed correlation matrix. Hence, deterministic perceptions on the role of these academic institutions as potential policy solutions to engender entrepreneurial ecosystems are not recommended.

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Acknowledgements

The authors acknowledge support by the São Paulo Research Foundation (FAPESP) in connection to the São Paulo Excellence Chair “Innovation Systems, Strategy and Policy” (InSySPo) at the University of Campinas (UNICAMP) (Grant 2013/50524-6). Fischer also acknowledges Grant n. 2016/17801-4. Vonortas acknowledges the infrastructural support by UNICAMP’s Department of Science and Technology Policy and by the Institute for International Science and Technology Policy at the George Washington University. Fischer and Vonortas also acknowledge support from the Basic Research Program at the National Research University Higher School of Economics within the framework of the subsidy to the HSE by the Russian Academic Excellence Project ‘5–100’. None of these organizations is responsible for the contents of this paper. Valuable contributions from the Editor, Martin Andersson, and three anonymous reviewers were essential for this article. Remaining mistakes and misconceptions are solely the responsibility of the authors.

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Correspondence to Bruno Brandão Fischer.

Appendices

Appendix 1

See Table

Table 6 Crossover points and thresholds for full membership and full non-membership

6.

Appendix 2: Frame of reference for knowledge-intensive activities (NACE Rev. 2)

Manufacture of chemicals and chemical products (20), Manufacture of rubber and plastic products (22), Manufacture of computer, electronic and optical products (26), Manufacture of motor vehicles, trailers and semi-trailers (29), Repair and installation of machinery and equipment (33), Computer programming, consultancy and related activities (62), Information service activities (63), Activities auxiliary to financial services and insurance activities (66), Legal and accounting activities (69), Activities of head offices; management consultancy activities (70), Architectural and engineering activities; technical testing and analysis (71), Scientific research and development (72), Advertising and market research (73), Other professional, scientific and technical activities (74).

Appendix 3

See Table

Table 7 Correlation matrix for analytical variables (original values)

7.

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Cherubini Alves, A., Fischer, B.B. & Vonortas, N.S. Ecosystems of entrepreneurship: configurations and critical dimensions. Ann Reg Sci 67, 73–106 (2021). https://doi.org/10.1007/s00168-020-01041-y

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  • DOI: https://doi.org/10.1007/s00168-020-01041-y

JEL Classification

  • L26
  • M21
  • R12
  • R58