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Performance, entrepreneur, entrepreneurship, metrics, entrepreneurial ecosystems
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Chaves-Ladino R and Jiménez-Hernández CN. How to Measure the Performance of Entrepreneurial Ecosystems: A Systematic Literature Review [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:1307 (https://doi.org/10.12688/f1000research.172389.1)
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Systematic Review
How to Measure the Performance of Entrepreneurial Ecosystems: A Systematic Literature Review
[version 1; peer review: awaiting peer review]
PUBLISHED 25 Nov 2025
OPEN PEER REVIEW
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Abstract
Corresponding author:
Rodrigo Chaves-Ladino
Competing interests:
No competing interests were disclosed.
Grant information:
This research was funded by the “Convocatoria para la Formación de Capital Humano de Alto Nivel para los Departamentos de Amazonas, Arauca, Boyacá, Caquetá, Casanare, Guaviare, Nariño, Putumayo, San Andrés y Vichada”, under the framework of Convocatoria 15 del Sistema General de Regalías (SGR), for the development of doctoral studies.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright:
© 2025 Chaves-Ladino R and Jiménez-Hernández CN.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Chaves-Ladino R and Jiménez-Hernández CN. How to Measure the Performance of Entrepreneurial Ecosystems: A Systematic Literature Review [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:1307 (https://doi.org/10.12688/f1000research.172389.1)
First published: 25 Nov 2025, 14:1307 (https://doi.org/10.12688/f1000research.172389.1)
Latest published: 25 Nov 2025, 14:1307 (https://doi.org/10.12688/f1000research.172389.1)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Introduction
The concept of entrepreneurial ecosystems (EEs) has emerged in response to the need to give greater prominence to the entrepreneur, complementing earlier studies on the clustering of economic activity and socio-territorial entities (Maroufkhani et al., 2018). This concept differs from other approaches in that the entrepreneur is considered the fundamental unit, rather than the firm, as previously assumed, underscoring the importance of the social and economic context in which entrepreneurship is embedded, as well as the policy agenda that promotes entrepreneurial processes (De Brito & Leitão, 2021; Maroufkhani et al., 2018; Mukiza et al., 2020). By placing the entrepreneur at the center, the role of other actors shifts—for instance, the role of government changes from being a leader and coordinator to that of a guarantor of a favorable socioeconomic environment for sustainable entrepreneurial activities.
With regard to the environment, Alam and Bhowmick (2023) emphasize that a genuinely entrepreneurial context emerges through the everyday interaction of society and entrepreneurial activities over time—for example, successful entrepreneurs can act as mentors and role models for emerging and growing entrepreneurs (Mukiza et al., 2020). For this reason, several authors (Isenberg, 2010; Meshram & Rawani, 2018; Qian, 2018) view EEs as evolutionary entities that develop in different ways depending on their context, meaning no two EEs are alike.
Research on entrepreneurial ecosystems remains scarce and fragmented (Guimarães et al., 2023; Meshram & Rawani, 2018; Ribeiro et al., 2024), and there is still no generalized consensus on what constitutes an entrepreneurial ecosystem, the challenges of its conceptualization, or its research agenda (Carayannis et al., 2022; Fernandes & Ferreira, 2022; Mukiza et al., 2020). Entrepreneurial ecosystems have emerged as a key conceptual framework for understanding the dynamics that facilitate the emergence, development, and consolidation of new ventures within a given territory (Stam, 2015). This approach acknowledges that the success of entrepreneurial initiatives depends not only on the individual characteristics of entrepreneurs, but also on the interaction among multiple actors and institutions that constitute the environment in which they operate (Spigel, 2017). Despite growing academic and policy interest in strengthening these ecosystems, a fundamental challenge persists: the lack of consensus on how to objectively and comparably measure their performance (Corrente et al., 2019; Huang-Saad et al., 2017).
The development of metrics to assess the performance of entrepreneurial ecosystems is crucial for several reasons: (1) it enables the identification of factors that contribute to value generation within the ecosystem and how these influence entrepreneurial activity (Corrente et al., 2019); (2) it supports decision-making by policymakers, investors, and other stakeholders interested in designing intervention strategies to foster more dynamic and sustainable ecosystems (Leendertse et al., 2022); and (3) it provides an empirical basis for comparing different ecosystems at regional, national, and international levels, thereby advancing the understanding of their functioning and evolution (Acs et al., 2017; Lafuente et al., 2022).
Given the heterogeneity of approaches and the lack of a clear consensus on such metrics (Guimarães et al., 2023; Meshram & Rawani, 2018), this research proposes a systematic literature review aimed at identifying and analyzing existing metrics related to the performance of entrepreneurial ecosystems.
This study focuses on general-purpose EEs applicable in diverse contexts, a delimitation that enables the analysis to concentrate on indicators that reflect the functioning of ecosystems based on entrepreneurial, institutional, and governmental interactions within a specific territory. To ensure the coherence and relevance of the analysis, digital and university entrepreneurial ecosystems were excluded due to their characteristics, which significantly differentiate them from traditional and territorially rooted entrepreneurial ecosystems. In the former case, digital ecosystems exhibit distinct dynamics characterized by globalization, digital scalability, and the predominance of technological platforms as core elements of their operation (Bejjani et al., 2023). As such, digital ecosystems are not strictly dependent on a specific geographic context but operate in virtual environments and are often based on transnational networks (Bejjani et al., 2023; Pigola et al., 2024; Wibisono, 2023). The metrics used to evaluate them typically focus on factors such as user traction, technological scalability, and digital venture capital investment, which do not necessarily reflect the performance of more territorially grounded entrepreneurial ecosystems (Bejjani et al., 2023; Pigola et al., 2024; Wibisono, 2023).
On the other hand, university-based entrepreneurial ecosystems are closely tied to academic institutions, research centers, and incubation programs managed by universities. Their development and performance are measured using specific indicators, such as the number of university spin-offs and patents generated, the research funds obtained, and the levels of technology transfer. These metrics follow an institutional logic that is not representative of broader entrepreneurial ecosystems not exclusively linked to the academic sphere (Ayala-Gaytán et al., 2024; Kobylińska & Lavios, 2020; Wang et al., 2024).
Based on this review, the aim is to offer a structured synthesis of the main indicators used to evaluate EEs. In doing so, the study seeks to contribute to the development of more robust analytical frameworks and to the generation of knowledge that is useful to researchers, policymakers, and other stakeholders interested in measuring and improving entrepreneurial ecosystems.
Methods
This systematic literature review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology (Page et al., 2021).
Information sources
The literature search was carried out using the Scopus and Web of Science (WOS) databases, due to their recognition as leading sources for indexing high-quality scientific publications. Both databases offer broad and multidisciplinary coverage, ensuring access to relevant and up-to-date research across various academic fields; moreover, they provide robust tools for bibliometric analysis, such as impact indicators, citation metrics, and tracking of research trends. Their ability to filter documents according to rigorous quality criteria and their advanced search functionalities enable the identification of studies that meet the methodological standards required for a reliable and replicable systematic review. Therefore, these databases ensure the inclusion of high-impact scientific literature, thereby strengthening the validity and relevance of the findings obtained.
Selection of keywords for the systematic literature review
To structure the search equations, a keyword adherence test was conducted. According to Ruthes and da Silva (2015) this strategy is used to assess the relevance of the keywords employed in the research and to quantitatively evaluate the pertinence of each term, the objective being to eliminate nonadherent keywords—that is, those that are not representative of the research area.
Accordingly, to identify combinations that generate a greater number of relevant results in scientific databases, a comparison was made between the terms “entrepreneurial ecosystem” and “entrepreneurship ecosystem,” which revealed that the former is more commonly used in the academic literature, suggesting that it is preferred as the standard term within the discipline ( Table 1).
Table 1. Keyword adherence test for the structuring of search equations.
In addition, several complementary terms were evaluated within the second group of keywords—specifically “index,” “indicator,” “metrics,” and “performance”—as these concepts are directly related to the performance of entrepreneurial ecosystems. The results showed that the combination of “entrepreneurial ecosystem” with these terms yielded a greater number of indexed articles in both Scopus and Web of Science (WOS), compared to “entrepreneurship ecosystem.”
Therefore, to ensure a robust search framework aligned with research trends in the field, the combination of “entrepreneurial ecosystems” with the terms “index,” “indicator,” “metrics,” and “performance” was used. This decision optimizes the retrieval of relevant literature while maintaining the methodological rigor of the study.
The search process for relevant publications was carried out without restrictions regarding the year of publication in order to include the largest possible number of documents collected up to January 25, 2025. However, only open-access and full-text articles were considered, with abstracts and conference papers being excluded. Table 2 presents the search equations used in each of the consulted databases.
Table 2. Search equations and number of results obtained.
Eligibility criteria
The inclusion criteria for this review were as follows: (1) the article develops or proposes metrics for the performance of entrepreneurial ecosystems; and (2) articles written in English with full-text availability. Studies were excluded if: (1) they focused on metrics related to digital or university-based entrepreneurial ecosystems, as these are not aligned with the objective of the present research; or (2) they were literature review articles, since this study seeks to directly analyze articles that propose or adapt metrics.
Data extraction
Records from the Scopus and WOS databases were downloaded in RIS (Research Information Systems) format and then uploaded into the Rayyan software for duplicate removal and the selection of relevant documents based on title and abstract.
Data processing
The following information was extracted from the included articles: title, author name(s), year of publication, article objective, and the list of proposed or used metrics. This information was used to compile a RIS file containing the final set of documents, which was then uploaded into the VosViewer software to perform a cluster analysis based on the co-occurrence of keywords.
Results
The results of the process for selecting relevant literature are presented in Figure 1.
Figure 1. Stages of the PRISMA systematic review process.
Legend: This figure illustrates the sequential stages of the PRISMA protocol applied in the study, including identification, screening, eligibility, and inclusion of the final set of articles used to analyze the metrics for entrepreneurial ecosystems.
Source: Authors’ elaboration.
Figure 2 shows the distribution of publications selected for this literature review over the years. Although publications on metrics in entrepreneurial ecosystems are still limited in number, there has been a modest increase, with 2022 being the year with the highest number of publications on the topic. It is also worth noting that the topic has only begun to develop over the past ten years.
Figure 2. Publications per year on entrepreneurial ecosystem metrics.
Legend: This figure shows the temporal distribution of the selected publications, evidencing an increasing research trend over the last decade and identifying 2022 as the year with the highest number of studies on ecosystem performance metrics.
Source: Authors’ elaboration.
Concept of entrepreneurial ecosystem
Most of the articles that formed the corpus for this research were based on the definitions provided by Acs et al. (2017), Spigel (2017) and Stam and Van de Ven (2021). A common feature across multiple definitions is the existence of interrelated actors (including entrepreneurs, institutions, and organizations) that interact within a specific environment to facilitate the emergence and development of productive ventures. Similarly, entrepreneurial ecosystems are characterized by interactive dynamics, in which institutional, social, and economic aspects influence the entrepreneurial capacity of a given territory (Acs et al., 2017; Audretsch & Belitski, 2017). Accordingly, it can be affirmed that there is a consensus in recognizing these ecosystems as interdependent sets of actors, factors, and coordinated processes that support entrepreneurial activity.
However, there are differences in the specific components included in each definition. Some authors emphasize elements such as leadership, entrepreneurial culture, capital markets, and receptive customers as being essential for the effective functioning of an EE (Andrews et al., 2022; Cowell et al., 2018; Kansheba et al., 2023; Komlósi et al., 2024), while other studies focus on more simplified aspects, such as the presence of skilled individuals, business opportunities, and available resources (Fomishyna et al., 2023; Kshetri, 2014; Shen et al., 2023; Zhang et al., 2024).
From another perspective, Sternberg et al. (2019) emphasize that an EE comprises actors, organizations, and factors that specifically enable productive entrepreneurship within a given territory. Furthermore, authors such as Stam and Van de Ven (2021) argue that the EE concept has evolved beyond earlier notions such as industrial clusters, broadening the scope of relevant actors to include intermediaries and political institutions.
While most definitions align in identifying interdependent elements that benefit entrepreneurial dynamics, they differ in the breadth and specificity with which they address factors such as leadership, culture, types of entrepreneurships, and the complexity of actor interactions.
Key thematic areas
Figure 3, which was generated using VosViewer software, illustrates the relationships between key terms that co-occur across the analyzed document set. The colors identify groups or clusters of terms that tend to be thematically related.
Figure 3. Keyword co-occurrence network of entrepreneurial ecosystem metrics.
Legend: The figure displays a network map generated in VosViewer based on co-occurrence analysis of keywords from Scopus and Web of Science. Clusters represent thematic areas of research—such as regional development, global measurement, and policy analysis—indicated by different colors.
Source: Analysis based on data from Scopus and Web of Science processed in Rayyan and VosViewer. Search date: January 25, 2025.
This cluster groups terms related to access to financial, human, and social capital in the context of entrepreneurship and its impact on regional development. The inclusion of terms such as “regional development” and “regional planning” suggests a focus on the territorial planning of entrepreneurship, highlighting the importance of public policies and infrastructure in strengthening the ecosystem (González-Serrano et al., 2021; Sternberg et al., 2019; Szerb et al., 2019). The presence of the keyword fsQCA (Fuzzy-set Qualitative Comparative Analysis) indicates the use of comparative methodologies to analyze the factors that determine the success of entrepreneurial ecosystems, recognizing them as complex systems requiring both qualitative and quantitative approaches. fsQCA is useful for studying EEs because multiple factors interact nonlinearly and may lead to different pathways to ecosystem success or failure (González-Serrano et al., 2021; Komlósi et al., 2022; Zhang et al., 2024).
In the reviewed literature, studies such as those by Komlósi et al. (2024) and Szerb et al. (2019) emphasize the importance of financing and regional policies in enhancing the competitiveness of entrepreneurial ecosystems in Europe.
This cluster groups studies focused on measuring entrepreneurial ecosystems at a global level, with emphasis on the relationship between innovation, social networks, and startups. The presence of the term “Global Entrepreneurship Monitor” (GEM) indicates the use of international indicators to assess the quality of entrepreneurship across different countries. The combination with the term “social media” reflects a focus on digital platforms as facilitators of visibility, funding, and growth for new ventures.
Studies such as those by Kshetri (2014), Sitaridis & Kitsios (2020) and Yan and Guan (2019) highlight the role of the GEM as a key tool for analyzing the dynamics of entrepreneurial ecosystems and their impact on the economy. Similarly, Kshetri (2014) and Sitaridis and Kitsios (2020) emphasize the influence of innovation and digital connectivity on the performance of startups, showing that more developed ecosystems exhibit strong interactions among entrepreneurs, investors, and support networks.
This grouping of terms focuses on the role of collaborative networks and public policies in shaping and influencing the performance of entrepreneurial ecosystems. The presence of the keywords “networks” and “policy” reflects researchers’ interest in explaining how interactions among entrepreneurs, governments, and organizations impact entrepreneurial activity in different regions (Auerswald & Dani, 2017; Iacobucci & Perugini, 2021; Rocha et al., 2022).
Studies by González-Serrano et al. (2021) and Sternberg et al. (2019) underscore the importance of networks as facilitators of knowledge, investment, and resource flows within the ecosystem. On the other hand, Sitaridis and Kitsios (2020) argue that public policies play a critical role in fostering dynamic entrepreneurial environments, noting that poorly designed incentives can hinder business growth rather than support it.
This cluster focuses on the comparative evaluation of entrepreneurial ecosystems through benchmarking and systems analysis. Its main objective is to measure and compare the impact of public policies on entrepreneurial development, identifying best practices at national and regional levels Balawi and Ayoub (2022) and Stam and Van de Ven (2021).
The study by Balawi and Ayoub (2022) proposes a methodology for assessing the effectiveness of entrepreneurial ecosystems using a systems-based approach, highlighting benchmarking as a powerful tool for identifying strengths and weaknesses across Nordic countries.
This cluster centers on measuring the economic impact of entrepreneurial ecosystems, including financial, market, and business growth metrics. The combination of the terms “economics” and “metrics” indicates that these studies analyze how entrepreneurship contributes to economic development through quantifiable indicators.
Research by Leendertse et al. (2022), for example, has developed models to evaluate EE performance in terms of job creation, R&D investment, and GDP growth. Similarly, Balawi and Ayoub (2022) propose the Global Entrepreneurship Index as a key metric for comparing the quality of entrepreneurial ecosystems internationally, emphasizing the importance of factors such as access to finance, education, and digital infrastructure in business success.
Discussion
Methodologies for measuring entrepreneurial ecosystems
Several methodologies have been employed to measure entrepreneurial ecosystems, using both quantitative and qualitative approaches (Sternberg et al., 2019), and these methodologies are aimed at capturing the complexity of ecosystems and their impact on entrepreneurship (Rocha et al., 2022). For instance, composite indexes use various variables and methods to evaluate entrepreneurial ecosystems, often drawing from multiple data sources to generate a single index (Corrente et al., 2019), and some of these indexes also incorporate data on the quality of entrepreneurs (Andrews et al., 2022).
Composite indexes
A significant portion of the studies analyzed in this review employed the Global Entrepreneurship Index (GEI) (e.g., Balawi & Ayoub, 2022; Calispa-Aguilar, 2021; Lafuente et al., 2022), which considers both individual and institutional aspects of entrepreneurship in assessing the state of an entrepreneurial ecosystem within a given country (Balawi & Ayoub, 2022; Calispa-Aguilar, 2021). The GEI relies on expert surveys and national statistics to measure entrepreneurial attitudes, skills, aspirations, and contextual conditions (Calispa-Aguilar, 2021; Sitaridis & Kitsios, 2020); however, composite indexes are often criticized for oversimplifying ecosystem complexity, difficulties in variable selection and weighting, and their limited guidance on how to improve specific ecosystem components (Corrente et al., 2019; Rocha et al., 2022).
Network analysis
Another increasingly adopted approach is network analysis, which uses social network metrics to analyze the relationships and structure of entrepreneurial ecosystems in order to quantify structural elements (Ancona et al., 2023). Network analysis enables the evaluation of strength and collaboration among actors and is argued to be a more suitable method for capturing the complexity of entrepreneurial ecosystems than traditional metrics (Auerswald & Dani, 2017; Huang-Saad et al., 2017).
Multicriteria analysis
This method is used to compare entrepreneurial ecosystems while considering the variability of weights that can be assigned to different factors, producing a more reliable probabilistic ranking than single-score classifications. For example, stochastic multicriteria acceptability analysis (SMAA) is a derived methodology that identifies relationships between ecosystem factors and growth-oriented startups (Corrente et al., 2019). Nonparametric methods such as the “benefit of the doubt” approach have also been applied to assess the relative efficiency of entrepreneurial ecosystems and determine which components should be prioritized for improvement (Lafuente et al., 2022).
Qualitative methods
These studies provide detailed descriptions of specific entrepreneurial ecosystems and enable a deep understanding of ecosystem dynamics. Qualitative methods may include interviews with key actors and document analysis (Stam & Van de Ven, 2021). Qualitative comparative analysis (QCA) explores combinations of factors leading to a specific outcome and can uncover the causal complexity of these phenomena (Komlósi et al., 2024; Zhang et al., 2024).
Mixed methods
The combination of quantitative and qualitative methods can offer a more comprehensive understanding of entrepreneurial ecosystems (Sternberg et al., 2019). This approach allows quantitative data to measure outcomes and qualitative data to uncover the underlying processes (Cowell et al., 2018). Several authors (Aliabadi et al., 2022; Corrente et al., 2019; Sternberg et al., 2019) argue that measuring entrepreneurial ecosystems requires a mix of methods to capture the complexity of these systems, although the choice of methodology depends on the specific goals of the research.
Metrics for evaluating entrepreneurial ecosystems
No single metric is capable of fully capturing the complexity of an entrepreneurial ecosystem (La Rovere et al., 2021; Rocha et al., 2022). The metrics used to evaluate ecosystem performance identified in this systematic review can be grouped into several categories, each with a specific focus that captures different aspects of these environments ( Table 3).
Table 3. Classification of metrics for entrepreneurial ecosystems.
| Type of metric | Metric description |
|---|---|
| Output Metrics | Number of Startups or Emerging Enterprises: This is a fundamental metric that measures the number of new businesses created in a specific area (Huang-Saad et al., 2017; Iacobucci & Perugini, 2021; Rocha et al., 2022). It is often used as a key indicator of entrepreneurial activity in a region (Corrente et al., 2019; Rocha et al., 2022). |
| Total Early-Stage Entrepreneurial Activity (TEA): This metric, used by the Global Entrepreneurship Monitor (GEM), measures the percentage of the adult population involved in starting new businesses (Calispa-Aguilar, 2021; Szerb et al., 2019; Yan & Guan, 2019). It is widely used to compare levels of entrepreneurial activity across regions and countries (Yan & Guan, 2019). | |
| Business Survival Rate: Indicates the proportion of businesses that remain operational after a specific period, reflecting the ecosystem’s resilience (Iacobucci & Perugini, 2021; Leendertse et al., 2022; Nicotra et al., 2018). | |
| High-Growth Firms: This metric identifies the number of firms experiencing rapid growth in terms of revenue or employment (Corrente et al., 2019; Iacobucci & Perugini, 2021). These companies are considered key drivers of job creation and wealth (Iacobucci & Perugini, 2021). | |
| Ecosystem Condition Metrics (Inputs) | Financing: The availability of funding is crucial for the development of new ventures. Common metrics include access to venture capital, business loans, and entrepreneurship subsidies (Corrente et al., 2019; Huang-Saad et al., 2017; Iacobucci & Perugini, 2021; Rocha et al., 2022). |
| Human Capital and Knowledge: These metrics assess the availability and quality of talent and knowledge within the ecosystem. They include R&D expenditure, the quality of human capital, higher education attainment, and the number of researchers (Guerrero & Siegel, 2024; Huang-Saad et al., 2017; Iacobucci & Perugini, 2021; Leendertse et al., 2022). | |
| Infrastructure: The availability and quality of physical (transport, communications) and technological infrastructure are critical. Metrics include access to physical infrastructure, hardware and software facilities, and R&D centers (Guerrero & Siegel, 2024; Huang-Saad et al., 2017; Iacobucci & Perugini, 2021; Leendertse et al., 2022). | |
| Institutions: These evaluate the regulatory framework and institutional support for entrepreneurship, including government programs, institutional transparency, institutional quality, and entrepreneurship policy support (Corrente et al., 2019; Fomishyna et al., 2023; Iacobucci & Perugini, 2021; La Rovere et al., 2021; Yan & Guan, 2019). | |
| Education: Assesses the availability and quality of entrepreneurship education programs at all levels (Huang-Saad et al., 2017; Sharma et al., 2024). | |
| Culture: The presence of social and cultural norms that promote entrepreneurship is essential. This can be measured through expert surveys evaluating social and cultural support for entrepreneurship (Corrente et al., 2019; Huang-Saad et al., 2017; Sharma et al., 2024). | |
| Networks: Entrepreneurial connectivity and network density are crucial for collaboration, knowledge exchange, and access to resources. Metrics include the number of connections between entrepreneurs and other ecosystem actors (Balawi & Ayoub, 2022; Corrente et al., 2019). | |
| Market: Metrics include market dynamism, sophistication, and scale. A dynamic and competitive market is important for the development of new businesses and openness to new ideas and technologies (Corrente et al., 2019; Nicotra et al., 2018; Riaz et al., 2022; Yan & Guan, 2019). | |
| Composite Indexes | Entrepreneurial Ecosystem Index (EE): This index combines multiple variables to assess the overall quality of the ecosystem, allowing comparisons across regions (Iacobucci & Perugini, 2021; Leendertse et al., 2022; Riaz et al., 2022; Sharma et al., 2024; Stam & Van de Ven, 2021). |
| Global Entrepreneurship Index (GEI): The GEI evaluates the state of an entrepreneurial ecosystem using 14 pillars (Lafuente et al., 2022). | |
| Regional Entrepreneurship and Development Index (REDI): This index assesses regional development and entrepreneurship by combining various ecosystem-related factors (Komlósi et al., 2024). | |
| Subjective and Perceptual Metrics | Perception of the Environment: Surveys are used to capture how entrepreneurs perceive the supportiveness of their environment and its contribution to the region (Fomishyna et al., 2023; Guerrero & Siegel, 2024; Iacobucci & Perugini, 2021). |
| Entrepreneurial Attitude: Scales and surveys are used to measure the attitudes and perceptions of entrepreneurs (Sethar et al., 2022). | |
| Other Metrics | Online Attention: Measures the level of attention entrepreneurship receives online, particularly on social media, and can reflect public interest and the ecosystem’s dynamism (Yan & Guan, 2019). |
First, output metrics include indicators such as the number of startups created in a region, the total early-stage entrepreneurial activity (TEA) rate, business survival rates, and the number of high-growth firms. These metrics assess the direct impact of the ecosystem on the creation and sustainability of new ventures and are commonly used in studies evaluating the effectiveness of public policies and entrepreneurship support programs.
In contrast, input metrics focus on the conditions that foster venture creation and growth. Within this category, access to financing is a key element, as assessed through indicators like venture capital investment and startup loans. Human capital and knowledge are measured via variables such as R&D expenditure, the educational level of the population, and the presence of researchers in the region. Infrastructure—both physical and technological—is another crucial component, including access to transportation networks, digital connectivity, and the availability of innovation hubs. Institutions also play a critical role in the ecosystem, as their regulations and support can either facilitate or hinder entrepreneurial activity. This includes evaluating the quality of government policies and entrepreneurship programs. Lastly, entrepreneurial culture and collaboration networks are also considered under this category, as they promote knowledge exchange and opportunity creation for new entrepreneurs.
Another group of metrics relates to dynamic capabilities, which are focused on the ability of the ecosystem to adapt and respond to environmental changes. Indicators here include knowledge absorption capacity, flexibility in adopting innovations, and entrepreneurs’ responsiveness to economic and technological challenges.
Also included in the classification are composite indexes, whose aim is to provide an integrated view of the entrepreneurial ecosystem by combining multiple variables into a single metric. Widely used examples include the Entrepreneurial Ecosystem Index (EE), the Global Entrepreneurship Index (GEI), and the Regional Entrepreneurship and Development Index (REDI). These indexes facilitate comparisons across regions or countries and support the design of policies aimed at strengthening entrepreneurial ecosystems.
In addition to quantitative metrics, subjective and perceptual metrics rely on surveys and perception studies to capture entrepreneurs’ impressions of their operating environment. These include assessments of the entrepreneurial climate and entrepreneurial attitudes, often measured through scales gauging confidence in business opportunities and the willingness to take risks.
Finally, some emerging metrics have appeared in recent years, such as online attention, which measures the visibility of entrepreneurial ecosystems on social media and digital platforms. These metrics reflect the ecosystem’s dynamism and its ability to attract investors and talent.
Collectively, these metrics allow for a multifaceted analysis of entrepreneurial ecosystems, offering valuable tools for researchers and policymakers interested in fostering more conducive environments for innovation and business development. Using a combination of these metrics—rather than relying on a single indicator—enables a more complete and accurate assessment of ecosystem performance.
Conclusion
This systematic review of the metrics used to assess entrepreneurial ecosystems reveals the existing conceptual and methodological fragmentation in the literature. The diversity of approaches, both quantitative and qualitative, highlights the inherent complexity of these systems, where multiple factors interact in a nonlinear manner to influence entrepreneurial dynamics.
The analysis made it possible to identify five major categories of metrics: (i) output metrics, which measure the ecosystem’s impact on the creation and consolidation of firms; (ii) ecosystem condition metrics, which assess the availability of essential resources such as financing, human capital, infrastructure, and institutions; (iii) composite indexes, which integrate multiple dimensions to allow comparisons among ecosystems at regional, national, and international levels; (iv) subjective and perceptual metrics, which capture actors’ perceptions of the ecosystem and its conditions; and (v) emerging metrics, which are focused on currently relevant factors such as the visibility of ecosystems on social media platforms.
The findings of this study highlight the adoption of mixed methodologies to evaluate the performance of entrepreneurial ecosystems, combining quantitative and qualitative approaches in order to capture their complexity. While composite indexes such as the Global Entrepreneurship Index (GEI) and the Regional Entrepreneurship and Development Index (REDI) have enabled standardized comparisons, their applicability in specific contexts remains a challenge due to the heterogeneity of entrepreneurial ecosystems and the difficulty of capturing emerging dynamics.
One of the main gaps identified in the literature is the lack of metrics that measure the temporal evolution of ecosystems and their ability to adapt to technological, economic, or regulatory changes. Most of the reviewed studies have adopted static approaches, without accounting for the dynamic nature of entrepreneurial ecosystems. In this regard, further efforts are needed to develop tools that enable longitudinal analysis and the identification of evolutionary patterns in these systems.
The limitations of this study are related to the selection of only two databases, Scopus and WOS, and articles published in English and with full text only, which could exclude relevant papers and contribute to a potential methodological bias. Therefore, broader and more inclusive research is needed in future studies.
In theoretical terms, this study contributes to the consolidation of the research field of entrepreneurial ecosystems by providing a structured classification of the metrics used in the academic literature. It also underscores the need to move toward integrative approaches that combine structural, systemic, and outcome-based measurements for a more accurate evaluation of ecosystem performance.
From a practical perspective, the review offers a useful reference framework for policymakers and other stakeholders interested in measuring the performance of entrepreneurial ecosystems. Identifying key metrics and their applicability in various contexts will support the formulation of more effective strategies aimed at fostering innovation and competitiveness within these environments.
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Chaves-Ladino R and Jiménez-Hernández CN. How to Measure the Performance of Entrepreneurial Ecosystems: A Systematic Literature Review [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:1307 (https://doi.org/10.12688/f1000research.172389.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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