Entrepreneurial ecosystems linked to outputs
| Main Arguments | Focus* | Main Findings | Methodology | Selected Empirical Studies |
|---|---|---|---|---|
| Different ecosystem configurations lead to different outputs | Multiple elements | Different ecosystem configurations can support knowledge-intensive entrepreneurship, leading to different types of productive outputs. | Mixed: fsQCA (CAGED, SEADE, IBGE, PIPE-FAPESP) | Cherubini Alves et al. (2021) |
| Different entrepreneurial ecosystem configurations lead to differences in ecosystem performance and behavior. | Quantitative: seemingly unrelated regression (GEM, OECD, World Bank, Google trends) | Yan and Guan (2019) | ||
| Efficiency at the entrepreneurial ecosystem level can be obtained through different configurations, but efficiency also does not correlate with the Index of Dynamic Entrepreneurship (IDE). | Quantitative: DEA (IDE report) | Dionisio et al. (2021) | ||
| Entrepreneurial ecosystems are place-sensitive and complex, where different configurations can lead to desirable (high-growth) and non-desirable (low-growth) outputs simultaneously. | Mixed: fsQCA (GEM, Amorós et al., 2019) | Muñoz et al. (2020) | ||
| Family businesses and start-ups, two ends of the entrepreneurship continuum, are embedded in different kinds of entrepreneurial ecosystems. | Quantitative: descriptive statistics (Destatis, Bureau van Diik) | Wolff et al. (2022) | ||
| Multiple and equally effective entrepreneurial ecosystem configurations can lead to both high-quantity and high-quality entrepreneurship. | Mixed: fsQCA (China City Statistical Yearbook, Hurun Global Unicorn List, Wind Financial Terminal) | Xie et al. (2021) | ||
| Small town entrepreneurial activities are the result of forces that differ from those identified in ecosystems in large urban areas. | Qualitative: comparative case study (370 archival documents) | Roundy (2019) | ||
| Entrepreneurial ecosystem outputs can arise from different configurations and strong national systems and institutions (high GEI rankings) are not necessarily efficient. | Quantitative: indicator/index creation | Inacio Junior et al. (2021) | ||
| Marginal changes in the initial configurations of entrepreneurial ecosystems can lead to unexpected, disproportionate changes in the outputs. | Quantitative: Pointwise D2, Brock-Dechert-Scheinkman test and Local Largest Lyapunov Exponents | Haarhaus et al. (2020) | ||
| Universities | Entrepreneurial ecosystem configuration is linked to university spin-outs’ growth (employment and sales) in Spain but not in Italy; there are specific mechanisms to support successful entrepreneurial activity beyond locational factors. | Quantitative: Multilevel modelling (516 Spanish and 904 Italian USOs) | Prencipe et al. (2020) | |
| (Informal) institutions and policy | Policy makers can use formal institutions to foster high-growth and social entrepreneurship, even in nations whose cultural conditions do not seem to be supportive of entrepreneurship. | Quantitative: OLS regression (Gelfand et al., 2011; GEM, OECD, World Bank) | Harms and Groen (2017) | |
| Four distinct institutional settings enable different types of entrepreneurship (e.g., high/medium/low-tech ventures). | Quantitative: PCA and OLS regression (Eurostat, OECD, World Bank) | Dilli et al. (2018) | ||
| Nested subsystems or clusters with ecosystems can produce different outputs | Multiple elements | Ecosystems are host to a variety of subsystems and clusters based on organizational- and individual-level factors. | Mixed: social network analysis; interviews (45 each for two regions) | Neumeyer and Santos (2018) |
| Subsystems within the same entrepreneurial ecosystem can produce different outputs. | Qualitative: 43 semi-structured interviews and participant observation | Scheidgen (2021) | ||
| Sub-systems within a regional ecosystem support different output (here: worker cooperatives). | Qualitative: comparative case study (22 semi-structured interviews, document analysis of 19 organisations) | Spicer and Zhong (2022) | ||
| Subsystems of the wider entrepreneurial ecosystem support internationalization efforts of companies. | Qualitative: 20 semi-structured interviews | Theodoraki and Catanzaro (2022) | ||
| Social and human capital | Even very advanced ecosystems remain nested, with few cross-over points between different communities, yet general managerial/entrepreneurial know-how is still important across all subsystems for high-growth firms. | Quantitative: descriptive statistics (1,570 individuals in 380 British FinTechs) | Spigel (2022) | |
| Digitali-zation | Digital entrepreneurial ecosystems (as a subsystem of the wider entrepreneurial ecosystem) is linked to higher share of small IGT firms. | Quantitative: PCA, composite indicator, GWR (INSEE, TechOnMap) | Cornet et al. (2022) | |
| Networks | There are social clusters within entrepreneurial ecosystems that focus on particular types of entrepreneurship. | Mixed: Social network analysis; 300 interviews | Neumeyer et al. (2019) | |
| Outputs of ecosystems evolve over time | Multiple elements | Entrepreneurial and intrapreneurial activities as entrepreneurial ecosystem outputs fluctuate over time. | Quantitative: OLS panel regression (Mannheim Enterprise Panel, OECD, German Statistics Office) | Buratti et al. (2022) |
| Ecosystems foster productive entrepreneurship (e.g., scale-ups) | Multiple elements | Ecosystem elements are interrelated at the national level with a penalty for bottlenecks among elements. | Quantitative: Weighted index-development (based on e.g., GEM, WEF, World Bank) | Acs et al. (2014) |
| Ecosystems provide the basis for high-tech entrepreneurship. | Qualitative: 20 interviews, survey to develop genealogical model (184 responses) | Neck et al. (2004) | ||
| The most relevant entrepreneurial ecosystem factors enabling the birth and activity of high-growth start-ups can be identified in cultural and social norms, government programs, and internal market dynamics. | Quantitative: stochastic multicriteria acceptability analysis (GEM, Eurostat EIP) | Corrente et al. (2019) | ||
| Entrepreneurial ecosystems lead to high-growth firms (persistent in the short- and medium-term). | Quantitative: “within-between” random effects model (UK ONS, APS, HEBCI, NOMIS) | 427 | ||
| Entrepreneurial ecosystems have a positive effect on levels of regional innovation capital and high-growth firms. | Quantitative: OLS, bootstrap and robust estimation | Mikic et al. (2021) | ||
| Entrepreneurial ecosystems support the growth of VC-backed start-ups, but the regional resource dependencies dynamically shift as start-ups mature. | Mixed: fsQCA | Vedula and Fitza (2019) | ||
| Favorable aspects of the local entrepreneurial ecosystem enable entrepreneurs to more effectively translate their personal resources into firm performance. | Quantitative: descriptive statistics, bivariate correlations and reliability coefficients (based on 223 survey responses) | Lux et al. (2020) | ||
| Overall quality of an ecosystem is positively related to entrepreneurial output. | Quantitative: PCA, linear regression model (Quality of Government, CBS, EU RCI, Nat Assoc of Private Equity, Birch) | Stam and Van de Ven (2021) | ||
| Entrepreneurial ecosystem performance is linked to productive entrepreneurship. | Quantitative: correlation, regression (QOG, GEM World Bank, ESS, RIS, RCI, EIB, CORDIS, Crunchbase, Eurostat, CB Insights, Dealroom) | Leendertse et al. (2022) | ||
| Larger and more innovative start-ups appear to rely more on their local entrepreneurial ecosystem. | Quantitative: regression (163 start-ups) | Gueguen et al. (2021) | ||
| A well-developed entrepreneurial ecosystem is a prerequisite to (smart specialization) industry prioritization because the latter with fail without the entrepreneurial ecosystem being able to nurture high-growth ventures. | Quantitative: index creation, penalty of bottleneck (REDI) | Szerb et al. (2020) | ||
| Seven propositions, which open new avenues for understanding entrepreneurial ecosystems, global value chains, and their interplay in emerging high-tech industries. | Qualitative: case study (eight semi-structured interviews, document analysis) | Reis et al. (2022) | ||
| Entrepreneurial ecosystems that allow immigrant entrepreneurs to rapidly build a network, get reputational benefits from being located in this ecosystem, and provide access to a market for experimentation are attractive to immigrant entrepreneurs and conductive to their performance. | Mixed: QCA (54 semi-structured, in-depth interviews plus follow-up interviews five years later) | March-Chordà et al. (2021) | ||
| Entrepreneurial ecosystems mitigate obstacles for innovative start-ups. | Quantitative: general LMM (based on 911 innovative start-ups) | Noelia and Rosalia (2020) | ||
| (Informal) Institutions | Institutional trust within regional entrepreneurial ecosystems affects productive entrepreneurship in challenging institutional environments. | Mixed: OLS estimation (657 survey respondents) and 51 semi-structured interviews | Khlystova et al. (2022) | |
| (Informal) Institutions | Institutions (economic freedom) at the regional level enable Schumpeterian entrepreneurship. | Quantitative: Panel data econometric methods (US Census Bureau Business Dynamism Statistics) | Bennett (2021a) | |
| Digitali-zation | Digital entrepreneurial ecosystems might be more useful to explain high-quality entrepreneurship (e.g., unicorns) than new business creation, although not all elements are equally important. | Quantitative: necessary condition analysis and fsQCA (World Bank, CB Insights, EIDES) | Torres and Godinho (2022) | |
| Institutions and human capital | In developing economies, human capital and institutions are crucial to support knowledge spillovers for high-tech start-ups. | Quantitative: hierarchical linear modelling (SII, INE, CBC, INAPI, Global Data Lab, CASEN Survey) | Mahn and Poblete (2022) | |
| Government and policy | Entrepreneurial ecosystems amplify the effectiveness of public and social services by regional governments for supporting opportunity entrepreneurship. | Quantitative: fixed effect model (CEIC China Premium Database, Yearbook of Industry and Commerce Administration of China, Finance Yearbooks of China, NERI, China Statistical Yearbook, China Education Statistical Yearbook, Science and Technology Statistics Compilation of Higher Education Institutions, China Civil Affair Statistical Yearbook) | Wei (2022) | |
| The gap between productive and unproductive entrepreneurship in emerging economies is mainly caused by the market uncertainty and the perception of political entrepreneurship and corruption. | Mixed: 18 in-depth interviews; index generation, OLS regression (218 survey responses and secondary data) | Belitski et al. (2021) | ||
| Universities | Different entrepreneurial ecosystem configurations lead to higher spin-out retention (in lower urbanization and localization economies) and attraction rates (in higher localization economies and innovation resources). | Quantitative: regression (universities’ websites, HEFCE, SFC, HEFCW, Department for the Economy NI) | Rossi et al. (2021) | |
| Social capital and support organizations Universities and finance | Dense ecosystems do not automatically lead to more interactions, but those entrepreneurs who do, have a higher rate of survival (especially high-tech start-ups). | Quantitative: Cox non-parametric proportional hazards model (Kauffman Firm Survey) | Bandera and Thomas (2019) | |
| Universities and finance | Local presence of research-oriented universities, access to capital, and business concentration are correlated to the emergence of know ledge-intens i ve entrepreneurship. | Quantitative: descriptive statistics with year-to-year variations with Heckit correction (1196 proposals to FAPESP) | Fischer et al. (2018) | |
| High information asymmetries impede high-tech entrepreneurial ideas based on university knowledge to attract external finance. In provinces where residents tend to behave opportunistically, the relative presence of cooperative banks magnifies the positive effect of university knowledge on high-tech entrepreneurship. Conversely, this effect is negligible in provinces with less opportunistic residents. | Quantitative: zero-inflated negative binomial regression (Movimprese, Bank of Italy) | Ghio et al. (2019) | ||
| Ecosystems foster entrepreneurial activity in general (start-ups) | Multiple elements | Ecosystems (including internet access and connectivity) are linked to start-up rates in cities. | Quantitative: exploratory factor analysis, SEM (Eurostat, REDI) | Audretsch and Belitski (2017) |
| Entrepreneurial ecosystems support start-up creation. | Quantitative: panel regression (Annual Survey of Industrial Firms of China, National Enterprise Credit Information Publicity System of China, China Statistical Yearbook, National Intellectual Property Administration of China, NASA, Chinese Academy of Sciences) | Long et al. (2022) | ||
| Even a high local knowledge base does not guarantee knowledge spillovers and start-ups if there is not an entrepreneurial ecosystem that fosters collaboration. | Quantitative: bibliometrics (WoS, USPTO, Traxn) | Cetindamar et al. (2020) | ||
| Entrepreneurial ecosystems facilitate collisions of diverse actors which can lead to higher levels of diverse start-ups. | Quantitative: panel regression (CrunchBase, UN) | Nylund and Cohen (2017) | ||
| Ventures in high-performance ecosystems perform better, higher survival chances (less important for serial entrepreneurs). | Quantitative: index development, semi-parametric Cox hazard regression (variety of public and private secondary sources, Kauffman Firm Survey) | Vedula and Kim (2019) | ||
| Networks | Inter-organizational ties among actors make entrepreneurial ecosystems in low-income countries more conducive to entrepreneurial dynamics. | Mixed: quantitative graph theory, web scraping, fsQCA | Guéneau et al. (2022) | |
| Universities | Despite their prominence, university spin-offs are mostly not high-growth businesses and do not drive an ecosystem but depend on it in their development. | Mixed: Case study and descriptive statistics (HEFCE) | Harrison and Leitch (2010) | |
| Descriptive evidence of how academic spin-offs depend on entrepreneurial ecosystem conditions in Norway. | Quantitative: descriptive statistics (FORNY, BRREG, Retriever) | Abootorabi et al. (2021) | ||
| Human connectedness to the physical environment, including urban design, buildings, and infrastructure, can affect entrepreneurial activity. | Qualitative: two case studies (34 interviews, document analysis) | Johnson et al. (2019) | ||
| Government | Ecosystems require stakeholder alignment and a holistic approach to create a fertile environment for entrepreneurial activity. | Qualitative: Q-Methodology (44 statements based on semi-structured interviews) | Jung et al. (2017) | |
| Digitali-zation | Level of digital technology, especially when complemented by a strong entrepreneurial ecosystem, is positively associated with start-up rates at the national level. | Quantitative: fixed effect panel data model (GEM APS data) | Zhang et al. (2022) | |
| (Informal) Institutions | Ecosystem development is important for growing “entrepreneurial spirit” and support programs can lower the fear of failure. | Quantitative: regression with moderator analysis (GEM, Turkish Chamber of Commerce) | Oner and Kunday (2016) | |
| Institutional transparency positively moderates the relationship between entrepreneurial ecosystems and start-up rates. | Quantitative: regression (PORDATA, Transparência e Integridade) | Riaz et al. (2022) | ||
| Subculture rather than mainstream culture plays a key role in entrepreneurial ecosystems for fostering new venture creation in the ICT sector. | Quantitative: EFA (Census data 2011 combined with e.g., Grunderszene.de, Urban audit, Eurostat) | Audretsch et al. (2019) | ||
| Different regional institutions (the multiple dimensions of economic freedom) affect regional entrepreneurship rates in different ways. | Quantitative: Panel data econometric methods (US Census Bureau Business Dynamism Statistics) | Bennett (2021b) | ||
| Human capital | Entrepreneurial absorptive capacity drives knowledge-based entrepreneurial activity; high technology and cultural diversity contribute to the vibrancy of ecosystems. | Quantitative: SEM (Business Information Tracking System, Integrated Postsecondary Data Set, Milken Institute, US Census. USPTO) | Qian et al. (2013) | |
| Quality of life | Quality of life as an additional aspect of entrepreneurial ecosystems, which together support entrepreneurial activities in tourism. | Qualitative: case study (20 semi-structured interviews) | Bichler et al. (2020) | |
| Smart cities | Smart city policies promote entrepreneurship through fostering the ecosystem. | Quantitative: multiple linear regression (INE, DIRCE, Eiirnstat) | Barba-Sánchez et al. (2019) | |
| Ecosystems foster social and sustainability-oriented entrepreneurship | Multiple elements | Different regional entrepreneurial ecosystem configurations are required to support the emerging needs of nonprofit-oriented innovators and social entrepreneurs. | Qualitative: exploratory case study (28 semi-structured interviews and secondary data) | Audretsch et al. (2022) |
| Entrepreneurial ecosystems with high GDP and either (1) high shares of female founders of start-ups or (2) high shares of non-religious people in the population lead to relatively high levels of sustainability enterprises. | Mixed: generative probabilistic topic model and fsQCA | Tiba et al. (2021) | ||
| Networks | Entrepreneurial ecosystems support social enterprises, particularly networks and entrepreneurial resources (across industries). | Quantitative: regression (Statistics Korea, KS EPA) | Woo and Jung (2022) | |
| Ecosystems support female entrepreneurship | Multiple elements | Regional rather than national entrepreneurial ecosystem configurations have a greater impact on women tech entrepreneurs. | Mixed: fsQCA (StartupGenome, UNDP) | Berger and Kuckertz (2016) |
| Social capital and demand | Family moral support, social network support, and exposure to local markets at start-up affect of the success of women-owned businesses in Indian entrepreneurial ecosystems. | Quantitative: Ordinal logistic regression (based on 25S survey responses) | Welsh et al. (2023) | |
| Ecosystems foster frugal and informal entrepreneurship | Multiple elements | Entrepreneurial ecosystems are linked to frugal innovation and informal entrepreneurship. | Qualitative: 10 interviews, 2 focus groups (5 and 7 participants) | Igwe et al. (2020) |
| Ecosystems foster entrepreneurship in the creative industries | Social capital | Entrepreneurs from under represented groups help promote each other within the wider entrepreneurial ecosystem and support the formation of creative businesses. | Qualitative: case study (55 in-depth interviews, field observations, and archival documentation) | Wang and Richardson (2021) |
| Ecosystems foster the creation of knowledge intensive business services | Multiple elements | Quality of the ecosystem positively influences KIBS formation rates and positively moderates the relationship between manufacturing specialization and the rate of new KIBS; a healthy entrepreneurial ecosystem seems essential for an effective territorial servitization. | Quantitative: Spatial Durbin cross-section models (Eurostat, GEM, REDI) | Horváth and Rabetino (2019) |
| Ecosystems or at least many of their elements do not impact or even hinder entrepreneurial activity | Multiple elements | Several national level ecosystem aspects have no significant impact on rates of male or female entrepreneurial engagement. | Quantitative: regression, GMM estimator (World Bank, GEM APS & NES) | Hechavarría and Ingram (2019) |
| Inadequate entrepreneurial ecosystems hinder the development of “transformative entrepreneurship” (sustainable businesses with societal impact). | Quantitative: multiple linear regression (based on 576 survey responses) | Egere et al. (2022) | ||
| Perceptions of a weaker entrepreneurial ecosystem in remote/peripheral regions mitigate (potential) entrepreneurs’ ambitions and actions and opportunities to scale. | Quantitative: Mann-Whitney U-test (595 completed surveys) | Freitas and Kitson (201S) | ||
| Government | Context makes innovative entrepreneurship difficult despite substantial government support. | Qualitative: 40 in-depth, semi-structured interviews, document analysis and observation | Biru et al. (2020) | |
| Universities and human capital | Regional scientific knowledge and talent has a limited effect on the internationalization of academic spin-offs, regional demand growth has a negative effect. | Quantitative: regression, DiD, PSM (156S innovative Italian start-ups) | Civera et al. (2019) |
| Main Arguments | Focus | Main Findings | Methodology | Selected Empirical Studies |
|---|---|---|---|---|
| Different ecosystem configurations lead to different outputs | Multiple elements | Different ecosystem configurations can support knowledge-intensive entrepreneurship, leading to different types of productive outputs. | Mixed: fsQCA (CAGED, SEADE, IBGE, PIPE-FAPESP) | |
| Different entrepreneurial ecosystem configurations lead to differences in ecosystem performance and behavior. | Quantitative: seemingly unrelated regression (GEM, OECD, World Bank, Google trends) | |||
| Efficiency at the entrepreneurial ecosystem level can be obtained through different configurations, but efficiency also does not correlate with the Index of Dynamic Entrepreneurship (IDE). | Quantitative: DEA (IDE report) | |||
| Entrepreneurial ecosystems are place-sensitive and complex, where different configurations can lead to desirable (high-growth) and non-desirable (low-growth) outputs simultaneously. | Mixed: fsQCA (GEM, | |||
| Family businesses and start-ups, two ends of the entrepreneurship continuum, are embedded in different kinds of entrepreneurial ecosystems. | Quantitative: descriptive statistics (Destatis, Bureau van Diik) | |||
| Multiple and equally effective entrepreneurial ecosystem configurations can lead to both high-quantity and high-quality entrepreneurship. | Mixed: fsQCA (China City Statistical Yearbook, Hurun Global Unicorn List, Wind Financial Terminal) | |||
| Small town entrepreneurial activities are the result of forces that differ from those identified in ecosystems in large urban areas. | Qualitative: comparative case study (370 archival documents) | |||
| Entrepreneurial ecosystem outputs can arise from different configurations and strong national systems and institutions (high GEI rankings) are not necessarily efficient. | Quantitative: indicator/index creation | |||
| Marginal changes in the initial configurations of entrepreneurial ecosystems can lead to unexpected, disproportionate changes in the outputs. | Quantitative: Pointwise D2, Brock-Dechert-Scheinkman test and Local Largest Lyapunov Exponents | |||
| Universities | Entrepreneurial ecosystem configuration is linked to university spin-outs’ growth (employment and sales) in Spain but not in Italy; there are specific mechanisms to support successful entrepreneurial activity beyond locational factors. | Quantitative: Multilevel modelling (516 Spanish and 904 Italian USOs) | ||
| (Informal) institutions and policy | Policy makers can use formal institutions to foster high-growth and social entrepreneurship, even in nations whose cultural conditions do not seem to be supportive of entrepreneurship. | Quantitative: OLS regression ( | ||
| Four distinct institutional settings enable different types of entrepreneurship (e.g., high/medium/low-tech ventures). | Quantitative: PCA and OLS regression (Eurostat, OECD, World Bank) | |||
| Nested subsystems or clusters with ecosystems can produce different outputs | Multiple elements | Ecosystems are host to a variety of subsystems and clusters based on organizational- and individual-level factors. | Mixed: social network analysis; interviews (45 each for two regions) | |
| Subsystems within the same entrepreneurial ecosystem can produce different outputs. | Qualitative: 43 semi-structured interviews and participant observation | |||
| Sub-systems within a regional ecosystem support different output (here: worker cooperatives). | Qualitative: comparative case study (22 semi-structured interviews, document analysis of 19 organisations) | |||
| Subsystems of the wider entrepreneurial ecosystem support internationalization efforts of companies. | Qualitative: 20 semi-structured interviews | |||
| Social and human capital | Even very advanced ecosystems remain nested, with few cross-over points between different communities, yet general managerial/entrepreneurial know-how is still important across all subsystems for high-growth firms. | Quantitative: descriptive statistics (1,570 individuals in 380 British FinTechs) | ||
| Digitali-zation | Digital entrepreneurial ecosystems (as a subsystem of the wider entrepreneurial ecosystem) is linked to higher share of small IGT firms. | Quantitative: PCA, composite indicator, GWR (INSEE, TechOnMap) | ||
| Networks | There are social clusters within entrepreneurial ecosystems that focus on particular types of entrepreneurship. | Mixed: Social network analysis; 300 interviews | ||
| Outputs of ecosystems evolve over time | Multiple elements | Entrepreneurial and intrapreneurial activities as entrepreneurial ecosystem outputs fluctuate over time. | Quantitative: OLS panel regression (Mannheim Enterprise Panel, OECD, German Statistics Office) | |
| Ecosystems foster productive entrepreneurship (e.g., scale-ups) | Multiple elements | Ecosystem elements are interrelated at the national level with a penalty for bottlenecks among elements. | Quantitative: Weighted index-development (based on e.g., GEM, WEF, World Bank) | |
| Ecosystems provide the basis for high-tech entrepreneurship. | Qualitative: 20 interviews, survey to develop genealogical model (184 responses) | |||
| The most relevant entrepreneurial ecosystem factors enabling the birth and activity of high-growth start-ups can be identified in cultural and social norms, government programs, and internal market dynamics. | Quantitative: stochastic multicriteria acceptability analysis (GEM, Eurostat EIP) | |||
| Entrepreneurial ecosystems lead to high-growth firms (persistent in the short- and medium-term). | Quantitative: “within-between” random effects model (UK ONS, APS, HEBCI, NOMIS) | 427 | ||
| Entrepreneurial ecosystems have a positive effect on levels of regional innovation capital and high-growth firms. | Quantitative: OLS, bootstrap and robust estimation | |||
| Entrepreneurial ecosystems support the growth of VC-backed start-ups, but the regional resource dependencies dynamically shift as start-ups mature. | Mixed: fsQCA | |||
| Favorable aspects of the local entrepreneurial ecosystem enable entrepreneurs to more effectively translate their personal resources into firm performance. | Quantitative: descriptive statistics, bivariate correlations and reliability coefficients (based on 223 survey responses) | |||
| Overall quality of an ecosystem is positively related to entrepreneurial output. | Quantitative: PCA, linear regression model (Quality of Government, CBS, EU RCI, Nat Assoc of Private Equity, Birch) | |||
| Entrepreneurial ecosystem performance is linked to productive entrepreneurship. | Quantitative: correlation, regression (QOG, GEM World Bank, ESS, RIS, RCI, EIB, CORDIS, Crunchbase, Eurostat, CB Insights, Dealroom) | |||
| Larger and more innovative start-ups appear to rely more on their local entrepreneurial ecosystem. | Quantitative: regression (163 start-ups) | |||
| A well-developed entrepreneurial ecosystem is a prerequisite to (smart specialization) industry prioritization because the latter with fail without the entrepreneurial ecosystem being able to nurture high-growth ventures. | Quantitative: index creation, penalty of bottleneck (REDI) | |||
| Seven propositions, which open new avenues for understanding entrepreneurial ecosystems, global value chains, and their interplay in emerging high-tech industries. | Qualitative: case study (eight semi-structured interviews, document analysis) | |||
| Entrepreneurial ecosystems that allow immigrant entrepreneurs to rapidly build a network, get reputational benefits from being located in this ecosystem, and provide access to a market for experimentation are attractive to immigrant entrepreneurs and conductive to their performance. | Mixed: QCA (54 semi-structured, in-depth interviews plus follow-up interviews five years later) | |||
| Entrepreneurial ecosystems mitigate obstacles for innovative start-ups. | Quantitative: general LMM (based on 911 innovative start-ups) | |||
| (Informal) Institutions | Institutional trust within regional entrepreneurial ecosystems affects productive entrepreneurship in challenging institutional environments. | Mixed: OLS estimation (657 survey respondents) and 51 semi-structured interviews | ||
| (Informal) Institutions | Institutions (economic freedom) at the regional level enable Schumpeterian entrepreneurship. | Quantitative: Panel data econometric methods (US Census Bureau Business Dynamism Statistics) | ||
| Digitali-zation | Digital entrepreneurial ecosystems might be more useful to explain high-quality entrepreneurship (e.g., unicorns) than new business creation, although not all elements are equally important. | Quantitative: necessary condition analysis and fsQCA (World Bank, CB Insights, EIDES) | ||
| Institutions and human capital | In developing economies, human capital and institutions are crucial to support knowledge spillovers for high-tech start-ups. | Quantitative: hierarchical linear modelling (SII, INE, CBC, INAPI, Global Data Lab, CASEN Survey) | ||
| Government and policy | Entrepreneurial ecosystems amplify the effectiveness of public and social services by regional governments for supporting opportunity entrepreneurship. | Quantitative: fixed effect model (CEIC China Premium Database, Yearbook of Industry and Commerce Administration of China, Finance Yearbooks of China, NERI, China Statistical Yearbook, China Education Statistical Yearbook, Science and Technology Statistics Compilation of Higher Education Institutions, China Civil Affair Statistical Yearbook) | ||
| The gap between productive and unproductive entrepreneurship in emerging economies is mainly caused by the market uncertainty and the perception of political entrepreneurship and corruption. | Mixed: 18 in-depth interviews; index generation, OLS regression (218 survey responses and secondary data) | |||
| Universities | Different entrepreneurial ecosystem configurations lead to higher spin-out retention (in lower urbanization and localization economies) and attraction rates (in higher localization economies and innovation resources). | Quantitative: regression (universities’ websites, HEFCE, SFC, HEFCW, Department for the Economy NI) | ||
| Social capital and support organizations Universities and finance | Dense ecosystems do not automatically lead to more interactions, but those entrepreneurs who do, have a higher rate of survival (especially high-tech start-ups). | Quantitative: Cox non-parametric proportional hazards model (Kauffman Firm Survey) | ||
| Universities and finance | Local presence of research-oriented universities, access to capital, and business concentration are correlated to the emergence of know ledge-intens i ve entrepreneurship. | Quantitative: descriptive statistics with year-to-year variations with Heckit correction (1196 proposals to FAPESP) | ||
| High information asymmetries impede high-tech entrepreneurial ideas based on university knowledge to attract external finance. In provinces where residents tend to behave opportunistically, the relative presence of cooperative banks magnifies the positive effect of university knowledge on high-tech entrepreneurship. Conversely, this effect is negligible in provinces with less opportunistic residents. | Quantitative: zero-inflated negative binomial regression (Movimprese, Bank of Italy) | |||
| Ecosystems foster entrepreneurial activity in general (start-ups) | Multiple elements | Ecosystems (including internet access and connectivity) are linked to start-up rates in cities. | Quantitative: exploratory factor analysis, SEM (Eurostat, REDI) | |
| Entrepreneurial ecosystems support start-up creation. | Quantitative: panel regression (Annual Survey of Industrial Firms of China, National Enterprise Credit Information Publicity System of China, China Statistical Yearbook, National Intellectual Property Administration of China, NASA, Chinese Academy of Sciences) | |||
| Even a high local knowledge base does not guarantee knowledge spillovers and start-ups if there is not an entrepreneurial ecosystem that fosters collaboration. | Quantitative: bibliometrics (WoS, USPTO, Traxn) | |||
| Entrepreneurial ecosystems facilitate collisions of diverse actors which can lead to higher levels of diverse start-ups. | Quantitative: panel regression (CrunchBase, UN) | |||
| Ventures in high-performance ecosystems perform better, higher survival chances (less important for serial entrepreneurs). | Quantitative: index development, semi-parametric Cox hazard regression (variety of public and private secondary sources, Kauffman Firm Survey) | |||
| Networks | Inter-organizational ties among actors make entrepreneurial ecosystems in low-income countries more conducive to entrepreneurial dynamics. | Mixed: quantitative graph theory, web scraping, fsQCA | ||
| Universities | Despite their prominence, university spin-offs are mostly not high-growth businesses and do not drive an ecosystem but depend on it in their development. | Mixed: Case study and descriptive statistics (HEFCE) | ||
| Descriptive evidence of how academic spin-offs depend on entrepreneurial ecosystem conditions in Norway. | Quantitative: descriptive statistics (FORNY, BRREG, Retriever) | |||
| Human connectedness to the physical environment, including urban design, buildings, and infrastructure, can affect entrepreneurial activity. | Qualitative: two case studies (34 interviews, document analysis) | |||
| Government | Ecosystems require stakeholder alignment and a holistic approach to create a fertile environment for entrepreneurial activity. | Qualitative: Q-Methodology (44 statements based on semi-structured interviews) | ||
| Digitali-zation | Level of digital technology, especially when complemented by a strong entrepreneurial ecosystem, is positively associated with start-up rates at the national level. | Quantitative: fixed effect panel data model (GEM APS data) | ||
| (Informal) Institutions | Ecosystem development is important for growing “entrepreneurial spirit” and support programs can lower the fear of failure. | Quantitative: regression with moderator analysis (GEM, Turkish Chamber of Commerce) | Oner and Kunday (2016) | |
| Institutional transparency positively moderates the relationship between entrepreneurial ecosystems and start-up rates. | Quantitative: regression (PORDATA, Transparência e Integridade) | |||
| Subculture rather than mainstream culture plays a key role in entrepreneurial ecosystems for fostering new venture creation in the ICT sector. | Quantitative: EFA (Census data 2011 combined with e.g., Grunderszene.de, Urban audit, Eurostat) | |||
| Different regional institutions (the multiple dimensions of economic freedom) affect regional entrepreneurship rates in different ways. | Quantitative: Panel data econometric methods (US Census Bureau Business Dynamism Statistics) | |||
| Human capital | Entrepreneurial absorptive capacity drives knowledge-based entrepreneurial activity; high technology and cultural diversity contribute to the vibrancy of ecosystems. | Quantitative: SEM (Business Information Tracking System, Integrated Postsecondary Data Set, Milken Institute, US Census. USPTO) | ||
| Quality of life | Quality of life as an additional aspect of entrepreneurial ecosystems, which together support entrepreneurial activities in tourism. | Qualitative: case study (20 semi-structured interviews) | ||
| Smart cities | Smart city policies promote entrepreneurship through fostering the ecosystem. | Quantitative: multiple linear regression (INE, DIRCE, Eiirnstat) | ||
| Ecosystems foster social and sustainability-oriented entrepreneurship | Multiple elements | Different regional entrepreneurial ecosystem configurations are required to support the emerging needs of nonprofit-oriented innovators and social entrepreneurs. | Qualitative: exploratory case study (28 semi-structured interviews and secondary data) | |
| Entrepreneurial ecosystems with high GDP and either (1) high shares of female founders of start-ups or (2) high shares of non-religious people in the population lead to relatively high levels of sustainability enterprises. | Mixed: generative probabilistic topic model and fsQCA | |||
| Networks | Entrepreneurial ecosystems support social enterprises, particularly networks and entrepreneurial resources (across industries). | Quantitative: regression (Statistics Korea, KS EPA) | ||
| Ecosystems support female entrepreneurship | Multiple elements | Regional rather than national entrepreneurial ecosystem configurations have a greater impact on women tech entrepreneurs. | Mixed: fsQCA (StartupGenome, UNDP) | |
| Social capital and demand | Family moral support, social network support, and exposure to local markets at start-up affect of the success of women-owned businesses in Indian entrepreneurial ecosystems. | Quantitative: Ordinal logistic regression (based on 25S survey responses) | ||
| Ecosystems foster frugal and informal entrepreneurship | Multiple elements | Entrepreneurial ecosystems are linked to frugal innovation and informal entrepreneurship. | Qualitative: 10 interviews, 2 focus groups (5 and 7 participants) | |
| Ecosystems foster entrepreneurship in the creative industries | Social capital | Entrepreneurs from under represented groups help promote each other within the wider entrepreneurial ecosystem and support the formation of creative businesses. | Qualitative: case study (55 in-depth interviews, field observations, and archival documentation) | |
| Ecosystems foster the creation of knowledge intensive business services | Multiple elements | Quality of the ecosystem positively influences KIBS formation rates and positively moderates the relationship between manufacturing specialization and the rate of new KIBS; a healthy entrepreneurial ecosystem seems essential for an effective territorial servitization. | Quantitative: Spatial Durbin cross-section models (Eurostat, GEM, REDI) | |
| Ecosystems or at least many of their elements do not impact or even hinder entrepreneurial activity | Multiple elements | Several national level ecosystem aspects have no significant impact on rates of male or female entrepreneurial engagement. | Quantitative: regression, GMM estimator (World Bank, GEM APS & NES) | |
| Inadequate entrepreneurial ecosystems hinder the development of “transformative entrepreneurship” (sustainable businesses with societal impact). | Quantitative: multiple linear regression (based on 576 survey responses) | |||
| Perceptions of a weaker entrepreneurial ecosystem in remote/peripheral regions mitigate (potential) entrepreneurs’ ambitions and actions and opportunities to scale. | Quantitative: Mann-Whitney U-test (595 completed surveys) | Freitas and Kitson (201S) | ||
| Government | Context makes innovative entrepreneurship difficult despite substantial government support. | Qualitative: 40 in-depth, semi-structured interviews, document analysis and observation | ||
| Universities and human capital | Regional scientific knowledge and talent has a limited effect on the internationalization of academic spin-offs, regional demand growth has a negative effect. | Quantitative: regression, DiD, PSM (156S innovative Italian start-ups) |
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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