Downward causation and path dependency
| Main Arguments | Focus* | Main Findings | Methodology | Selected Empirical Studies |
|---|---|---|---|---|
| Path dependence and Matthew effects in regions | Multiple elements | Path dependence in the evolution of entrepreneurial ecosystems as entrepreneurial output feeds back into the regional ecosystem. | Quantitative: PCA, linear regression model (Quality of Government, CBS, EU RCI, Nat Assoc of Private Equity) | Stam and Van de Ven (2021) |
| Entrepreneurial agents, especially individual (regional) entrepreneurs, drive the evolution and resource dynamics of regional ecosystems. | Qualitative: 51 semi-structured interviews, site visits, focus group, and secondary data | Shi and Shi (2021) | ||
| Entrepreneurial ecosystems are shaped by economic development of the country and high-growth firms have greater impact on entrepreneurial ecosystems than new ventures in general. | Quantitative: SEM (GEM, NES data) | Martínez-Fierro et al. (2019) | ||
| Institutions | Regional entrepreneurial activity positively affects objective institutional performance and also negatively affects subjective performance. | Quantitative: mixed-effects regression (USCMP, StatsAmerica, CMS, AHA) | Meek and Tietz (2022) | |
| Anchor firms and institutions | Large anchor firms can stimulate the development and an entrepreneurial ecosystem, but this can also lead to dependencies and the entrepreneurial ecosystem not maturing without the anchor firm (particularly problematic in case of the anchor firm going out of business). | Qualitative: comparative case study (process tracing from 102 interviews) | Ornston and Camargo (2022) | |
| Local start-ups develop capabilities slower than MNE entrants, but cause higher spillovers of skills and knowledge and higher engagement/support for the local Entrepreneurial ecosystem. | Qualitative: comparative case study (19 interviews, secondary data) | Lorenzen (2019) | ||
| Ecosystems enable entrepreneurial “recycling” | Institutions | Local institutional structures support recycling and mobility within entrepreneurial ecosystems after shocks. | Quantitative: descriptive statistics (data from career-based social media platform for 782 individuals) | Spigel and Vinodrai (2021) |
| Informal institutions and social capital | Entrepreneurial ecosystems with strong informal institutions (particularly de-stigmatizing failure) and networks are more supportive of entrepreneurs who want to start a new venture after a previous business failure. | Quantitative: fixed-effect dynamic GMM estimation of panel data (GEM, World Bank, WEF, IMF) | Espinoza-Benavides et al. (2021) | |
| Multiple elements | The framework conditions of entrepreneurial ecosystems have different influences on the reentry decisions of males and females who experience business failure. | Quantitative: hierarchical linear modeling (GEM, WDI, Flash EB Nos. 192, 283, and 354) | Simmons et al. (2019) | |
| Entrepreneurial ecosystems facilitate the quality and speed of the re-entry of failed entrepreneurs. | Qualitative: 20 semi-structured interviews, secondary data | Guerrero and Espinoza-Benavides (2021) | ||
| Women and men benefit in different ways from ecosystems and their elements | Multiple elements | Globally, women benefit more from many of the ecosystem factors than men, but depending on the phase of economic development men might benefit more. | Quantitative: regression, GMM estimator (World Bank, GEM APS & NES) | Hechavarría and Ingram (2019) |
| Regional entrepreneurial ecosystems are not generic and do not affect all entrepreneurs equally; peer support, learning opportunities, and visible female role models can support women entrepreneurs. | Qualitative: participatory action research | Birdthistle et al. (2022) | ||
| Chosen start-up strategies are a reflection of the perceived support from the ecosystem, the entrepreneurs’ current life situation, and the intended goals. Women tend to mobilize more resources than men in order to overcome support constraints, men are more confident of their capabilities. | Mixed: fsQCA (PSED II) | Sperber and Linder (2019) | ||
| State of the ecosystem affects individual entrepreneurs’ behaviors and the influence of top-down policy interventions | Multiple elements | State of the ecosystem impacts whether entrepreneurs come/stay to start a new tech venture. | Mixed: logistic regression and 45 semi-structured, in-depth interviews | Stephens et al. (2019) |
| Local/regional ecosystem characteristics are crucial for effectiveness of systemic innovation policy. | Qualitative: Longitudinal case study (secondary sources; 44 interviews at three points in time over 10 years) | Brown et al. (2016) | ||
| Descriptive evidence that in countries characterized by an underdeveloped VC market and with a limited number of large Arms, innovative start-ups often locate in entrepreneurial ecosystems with SMEs clustered in industrial districts. | Quantitative: descriptive statistics (ISTAT) | Cavallo et al. (2021) | ||
| Finance | Angel investments have a positive impact on Arm growth, performance, survival, and follow-on fund raising, which is independent of the level of venture activity and entrepreneur-friendliness in the country; but in less mature ecosystems only more mature start-ups seek angel investment. | Quantitative: regression discontinuity (self-reported data from angel groups) | Lerner et al. (2018) | |
| Government and finance | Nature and prevalence of Anance changed due to changes in formal institutions and the resulting regulatory changes; path development of the ecosystem is strongly shaped by endogenous initiatives of foremost public authorities. | Qualitative: case study (22 semi-structured interviews supported by secondary data) | Radinger-Peter et al. (2018) | |
| Government | Ecosystems represent higher-level system in which, e.g., clusters are embedded; policy making needs to account for current state of the ecosystem and interventions have different effects on involved clusters/industries. | Quantitative: case study with descriptive statistics (e.g., ACS, Ine 5000, NSF, USPTO, WoS) | Auerswald and Dani (2017) | |
| Effective entrepreneurial ecosystem policy should use a policy mix by combining different instruments and adapted to local contexts (including the growth characteristics of start-ups and the current state of the ecosystem). | Mixed: fsQCA (1351 survey responses) | Wang et al. (2022) | ||
| Particularly in emerging economies, dedicated entrepreneurship policies need to be complemented by and harmonised with taxation, social, and educational policies, among others. | Quantitative: cluster analysis (World Bank, WEF, IMF, GEM, GCI, GII, UNIDO, World Value Survey, UNDP, ILO, UNESCO, G. Hofstede Database) | Kantis et al. (2020) | ||
| National economies can be categorized by their entrepreneurial ecosystem framework conditions; improving their performance and competitiveness requires tailored interventions based on the current state of the entrepreneurial ecosvstem. | Quantitative: factorial and cluster analysis (GEM) | Farinha et al. (2020) | ||
| In formal institutions and human capital | Entrepreneurial readiness is a more valid representation of individual-level characteristics than other individual traits and is also influenced by several dimensions of the national environment, forming a reinforcing loop. | Quantitative: EFA, PLS-based CFA, multilevel logistic regression (GEM, World Bank, GCI) | Schillo et al. (2016) | |
| Universities | Students’ perception of the entrepreneurial ecosystem is positively related to their entrepreneurial intention. | Quantitative: PLS-SEM (259 survey respondents) | Elnadi and Gheith (2021) | |
| Entrepreneurial universities in post-conflict entrepreneurial ecosystems are orientated toward rebuilding human capital in the region, before being able to focus on knowledge exchange. | Qualitative: case study (secondary data, field notes) | Nkusi et al. (2020) |
| Main Arguments | Focus | Main Findings | Methodology | Selected Empirical Studies |
|---|---|---|---|---|
| Path dependence and Matthew effects in regions | Multiple elements | Path dependence in the evolution of entrepreneurial ecosystems as entrepreneurial output feeds back into the regional ecosystem. | Quantitative: PCA, linear regression model (Quality of Government, CBS, EU RCI, Nat Assoc of Private Equity) | |
| Entrepreneurial agents, especially individual (regional) entrepreneurs, drive the evolution and resource dynamics of regional ecosystems. | Qualitative: 51 semi-structured interviews, site visits, focus group, and secondary data | |||
| Entrepreneurial ecosystems are shaped by economic development of the country and high-growth firms have greater impact on entrepreneurial ecosystems than new ventures in general. | Quantitative: SEM (GEM, NES data) | |||
| Institutions | Regional entrepreneurial activity positively affects objective institutional performance and also negatively affects subjective performance. | Quantitative: mixed-effects regression (USCMP, StatsAmerica, CMS, AHA) | ||
| Anchor firms and institutions | Large anchor firms can stimulate the development and an entrepreneurial ecosystem, but this can also lead to dependencies and the entrepreneurial ecosystem not maturing without the anchor firm (particularly problematic in case of the anchor firm going out of business). | Qualitative: comparative case study (process tracing from 102 interviews) | ||
| Local start-ups develop capabilities slower than MNE entrants, but cause higher spillovers of skills and knowledge and higher engagement/support for the local Entrepreneurial ecosystem. | Qualitative: comparative case study (19 interviews, secondary data) | |||
| Ecosystems enable entrepreneurial “recycling” | Institutions | Local institutional structures support recycling and mobility within entrepreneurial ecosystems after shocks. | Quantitative: descriptive statistics (data from career-based social media platform for 782 individuals) | |
| Informal institutions and social capital | Entrepreneurial ecosystems with strong informal institutions (particularly de-stigmatizing failure) and networks are more supportive of entrepreneurs who want to start a new venture after a previous business failure. | Quantitative: fixed-effect dynamic GMM estimation of panel data (GEM, World Bank, WEF, IMF) | ||
| Multiple elements | The framework conditions of entrepreneurial ecosystems have different influences on the reentry decisions of males and females who experience business failure. | Quantitative: hierarchical linear modeling (GEM, WDI, Flash EB Nos. 192, 283, and 354) | ||
| Entrepreneurial ecosystems facilitate the quality and speed of the re-entry of failed entrepreneurs. | Qualitative: 20 semi-structured interviews, secondary data | |||
| Women and men benefit in different ways from ecosystems and their elements | Multiple elements | Globally, women benefit more from many of the ecosystem factors than men, but depending on the phase of economic development men might benefit more. | Quantitative: regression, GMM estimator (World Bank, GEM APS & NES) | |
| Regional entrepreneurial ecosystems are not generic and do not affect all entrepreneurs equally; peer support, learning opportunities, and visible female role models can support women entrepreneurs. | Qualitative: participatory action research | |||
| Chosen start-up strategies are a reflection of the perceived support from the ecosystem, the entrepreneurs’ current life situation, and the intended goals. Women tend to mobilize more resources than men in order to overcome support constraints, men are more confident of their capabilities. | Mixed: fsQCA (PSED II) | |||
| State of the ecosystem affects individual entrepreneurs’ behaviors and the influence of top-down policy interventions | Multiple elements | State of the ecosystem impacts whether entrepreneurs come/stay to start a new tech venture. | Mixed: logistic regression and 45 semi-structured, in-depth interviews | |
| Local/regional ecosystem characteristics are crucial for effectiveness of systemic innovation policy. | Qualitative: Longitudinal case study (secondary sources; 44 interviews at three points in time over 10 years) | |||
| Descriptive evidence that in countries characterized by an underdeveloped VC market and with a limited number of large Arms, innovative start-ups often locate in entrepreneurial ecosystems with SMEs clustered in industrial districts. | Quantitative: descriptive statistics (ISTAT) | |||
| Finance | Angel investments have a positive impact on Arm growth, performance, survival, and follow-on fund raising, which is independent of the level of venture activity and entrepreneur-friendliness in the country; but in less mature ecosystems only more mature start-ups seek angel investment. | Quantitative: regression discontinuity (self-reported data from angel groups) | ||
| Government and finance | Nature and prevalence of Anance changed due to changes in formal institutions and the resulting regulatory changes; path development of the ecosystem is strongly shaped by endogenous initiatives of foremost public authorities. | Qualitative: case study (22 semi-structured interviews supported by secondary data) | ||
| Government | Ecosystems represent higher-level system in which, e.g., clusters are embedded; policy making needs to account for current state of the ecosystem and interventions have different effects on involved clusters/industries. | Quantitative: case study with descriptive statistics (e.g., ACS, Ine 5000, NSF, USPTO, WoS) | ||
| Effective entrepreneurial ecosystem policy should use a policy mix by combining different instruments and adapted to local contexts (including the growth characteristics of start-ups and the current state of the ecosystem). | Mixed: fsQCA (1351 survey responses) | |||
| Particularly in emerging economies, dedicated entrepreneurship policies need to be complemented by and harmonised with taxation, social, and educational policies, among others. | Quantitative: cluster analysis (World Bank, WEF, IMF, GEM, GCI, GII, UNIDO, World Value Survey, UNDP, ILO, UNESCO, G. Hofstede Database) | |||
| National economies can be categorized by their entrepreneurial ecosystem framework conditions; improving their performance and competitiveness requires tailored interventions based on the current state of the entrepreneurial ecosvstem. | Quantitative: factorial and cluster analysis (GEM) | |||
| In formal institutions and human capital | Entrepreneurial readiness is a more valid representation of individual-level characteristics than other individual traits and is also influenced by several dimensions of the national environment, forming a reinforcing loop. | Quantitative: EFA, PLS-based CFA, multilevel logistic regression (GEM, World Bank, GCI) | ||
| Universities | Students’ perception of the entrepreneurial ecosystem is positively related to their entrepreneurial intention. | Quantitative: PLS-SEM (259 survey respondents) | ||
| Entrepreneurial universities in post-conflict entrepreneurial ecosystems are orientated toward rebuilding human capital in the region, before being able to focus on knowledge exchange. | Qualitative: case study (secondary data, field notes) |
Note: *All studies in this list include a variety of ecosystem elements, but some emphasize the role of particular element(s) as indicated in this column.
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