Most of the articles that formed the corpus for this research were based on the definitions provided by Spigel (2017) and Stam and Van de Ven (2021) ( Table 2). 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. Nevertheless, differences persists regarding in the specific components included in each framework. While 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), other focus on simplified aspects like the presence of skilled individuals and available resources ( Fomishyna et al., 2023; Kshetri, 2014; Shen et al., 2023; Zhang et al., 2024). From an evolutionary perspective, Sternberg et al. (2019) and Stam and Van de Ven (2021) argue that the EE concept has moved beyond earlier notions like industrial clusters, broadening the scope to include intermediaries and political institutions that specifically enable productive entrepreneurship within a territory.

The development of metrics to assess the performance of entrepreneurial ecosystems is crucial for several reasons. First, it enables the identification of factors that contribute to value generation and their influence on entrepreneurial activity ( Corrente et al., 2019). Second, 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); Third, 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 current 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 indicators related to the performance of entrepreneurial ecosystems.

This study focuses on general-purpose EEs applicable in diverse contexts, focusing on indicators that reflect interactions within a specific territory. To ensure coherence, digital and university entrepreneurial ecosystems were excluded due to characteristics that significantly differentiate them from territorially rooted systems. Digital ecosystems, for instance, are characterized by globalization and digital scalability, operating in virtual environments that are not strictly dependent on a geographic context ( Bejjani et al., 2023; Pigola et al., 2024; Wibisono, 2023). Consequently, their metrics do not necessarily reflect the performance of territorially grounded ecosystems. Similarly, university-based ecosystems follow an institutional logic tied to academic spin-offs and technology transfer, using specific indicators like patents and research funds that are not representative of broader entrepreneurial dynamics outside the academic sphere ( Ayala-Gaytán et al., 2024; Kobylińska & Lavios, 2020; Wang et al., 2024).

While existing systematic reviews have successfully catalogued the conceptual boundaries and general components of entrepreneurial ecosystems ( Stam & Van de Ven, 2021), a significant analytical void persists regarding the critical synthesis of performance metrics specifically calibrated for territorially rooted systems ( Feldman et al., 2022). Prior research has predominantly focused on descriptive inventories, often failing to address the problem of endogeneity and the subsequent causal ambiguity where ecosystem conditions (inputs) and entrepreneurial outcomes are treated as discrete, static categories without acknowledging their recursive nature ( Nicotra et al., 2018). Furthermore, the prevailing literature frequently conflates the measurement logics of digital, university-led, and traditional territorial ecosystems, thereby diluting the precision of policy interventions ( Leendertse et al., 2022). This study addresses these gaps by providing a novel taxonomical refinement that isolates general-purpose territorial ecosystems. By problematizing the interplay between structural inputs and systemic outputs, this review moves beyond mere descriptive cataloging to offer a theoretically grounded framework that exposes the limitations of current metrics in capturing temporal evolution and causal complexity.

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 ( Pranckutė, 2021; Torres-Salinas & Arroyo-Machado, 2026). 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 ( Ciule et al., 2025; Pranckutė, 2021; Torres-Salinas & Arroyo-Machado, 2026). Therefore, these databases ensure the inclusion of high-impact scientific literature, thereby strengthening the validity and relevance of the findings obtained.

To structure the search equations, a keyword adherence test was conducted to assess the relevance and pertinence of the terms, aiming to eliminate those not representative of the research area ( Ruthes and da Silva., 2015). Furthermore, the search strategy was systematically restricted to the title field to ensure the highest degree of thematic alignment between the retrieved literature and the primary research objective. This methodological boundary was established to prioritize precision over recall, as broader searches encompassing titles, abstracts, and keywords frequently return a substantial volume of peripheral studies where entrepreneurial ecosystems are discussed only as a contextual element rather than as the core focus of the investigation. While this conservative approach represents a potential limitation, as it may exclude relevant contributions that address performance metrics within the body of the text or abstract without reflecting these specific terms in the title, this trade-off between exhaustiveness and relevance was considered necessary to maintain a rigorous and manageable scope focused on territorial entrepreneurial dynamics. The parameters used for this initial keyword adherence test are summarized in Table 3.

To structure the search equations, a keyword adherence test was conducted to assess the relevance and pertinence of the terms, aiming to eliminate those not representative of the research area. A comparison between ‘entrepreneurial ecosystem’ and ‘entrepreneurship ecosystem’ revealed that the former is preferred as the standard term within the discipline. Building on this foundation, the selection of the complementary terms “index,” “indicator,” “metrics,” and “performance” was based on their prevalence in the foundational and most recent literature regarding the evaluation of entrepreneurial ecosystems. These terms represent the standardized technical constructs used to operationalize ecosystem success across the discipline. Specifically, the focus on “metrics” and “performance” aligns with the measurement frameworks proposed by Leendertse et al. (2022) and Nicotra et al. (2018), who emphasize the need for quantifiable indicators to capture systemic dynamics. Similarly, the terms “index” and “indicator” were included due to their widespread application in high-impact benchmarking studies, such as those utilizing the Global Entrepreneurship Index (GEI) and the Regional Entrepreneurship and Development Index (REDI) developed by Acs et al. (2017) and Szerb et al. (2019). Furthermore, the inclusion of “performance” as a central search term is supported by the process-oriented and systemic analyses of Stam and Van de Ven (2021) and Feldman et al. (2022), who identify it as the primary dependent variable for understanding ecosystem health.

By adopting these terms, the search strategy ensures alignment with the established academic lexicon, facilitating the retrieval of studies that are central to the current scholarly debate. The subsequent evaluation confirmed that the combination of “entrepreneurial ecosystem” with these validated terms yielded a significantly higher volume of indexed articles in both Scopus and Web of Science (WOS) compared to other variations. Consequently, to maintain the methodological rigor of the study and ensure a robust search framework aligned with contemporary research trends, this specific combination was adopted. The parameters used for the initial keyword adherence test are summarized in Table 3.

Complementing the initial keyword selection, several auxiliary terms (specifically “index,” “indicator,” “metrics,” and “performance”) were evaluated to target concepts directly related to the performance of entrepreneurial ecosystems. This evaluation indicated that the combination of “entrepreneurial ecosystem” with these terms yielded a significantly higher volume of indexed articles in both Scopus and Web of Science (WOS) compared to “entrepreneurship ecosystem”. Therefore, to ensure a robust search framework aligned with contemporary research trends, this specific combination was adopted, optimizing the retrieval of relevant literature while maintaining the methodological rigor of the study.

The subsequent bibliographic search was executed without restrictions regarding the year of publication to capture the most extensive corpus of documents available up to January 25, 2025. To maintain data quality and accessibility, the scope was restricted to open-access and full-text articles, whereas abstracts and conference papers were systematically excluded. The specific search equations formulated for each database, reflecting these parameters and the final selection of keywords, are detailed in Table 4.

The search strategy was systematically restricted to the title field to ensure the highest degree of thematic alignment between the retrieved literature and the primary research objective. This methodological boundary was established to prioritize precision over recall, as broader searches encompassing titles, abstracts, and keywords frequently return a substantial volume of peripheral studies where entrepreneurial ecosystems are discussed only as a contextual element rather than as the core focus of the investigation.

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.

Following the identification phase, the retrieved records were imported into the Rayyan platform ( Ouzzani et al., 2016) for systematic screening. Rayyan was selected for its capacity to facilitate a blind review process through its specialized management of bibliographic data. To ensure procedural reliability and mitigate selection bias, the document screening phase followed an independent dual-review protocol. Utilizing the ‘blind mode’ feature, the initial title and abstract screening was performed without visibility of individual decisions, thereby eliminating potential inter-coder influence. Discrepancies or conflicting entries were systematically resolved through a consensus-based deliberation process to achieve high inter-rater reliability. This rigorous screening was further safeguarded by the application of predefined inclusion and exclusion criteria, ensuring a consistent evaluation across all records.

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 extracted data was used to compile a RIS file containing the final set of documents. For the bibliometric analysis and the visualization of the conceptual structure of the field, this study employed VOSviewer ( Van Eck & Waltman, 2010). This software was chosen over other analytical tools due to its robust algorithmic capacity to construct and visualize complex bibliometric networks. The RIS file was uploaded into the software to perform a precise keyword co-occurrence analysis, which is essential for identifying emerging thematic clusters and objectively mapping the intellectual structure of the literature on entrepreneurial ecosystem performance metrics.

To ensure the replicability and technical rigor of the bibliometric mapping, the keyword co-occurrence analysis was executed using a specific set of parameters. Specifically, a customized thesaurus file was applied to standardize the data, which involved unifying plural forms and synonymous terms such as consolidating ‘entrepreneurship ecosystem’ and ‘entrepreneurial ecosystem (ee)’ into the single label ‘entrepreneurial ecosystems’ and filtering out non-substantive geographical or methodological terms that did not contribute to the conceptual clarity of the performance metrics. Complementing this process, a minimum threshold of three occurrences per keyword was established to identify the most salient research themes while excluding idiosyncratic or peripheral terminology. The thematic clusters were generated automatically by the software’s VOS clustering algorithm, utilizing the association strength as the normalization method to determine the relatedness between terms. While the grouping of keywords was algorithmic, the subsequent interpretation and naming of each cluster were performed manually based on a detailed content analysis of the documents within each thematic group.

The results of the process for selecting relevant literature are presented in Figure 1.

While this systematic review adheres to rigorous protocols, certain methodological boundaries must be acknowledged. First, the search strategy was intentionally restricted to the title field to ensure high thematic granularity and conceptual centrality. This approach prioritizes precision over recall, focusing exclusively on studies where the performance of entrepreneurial ecosystems is the primary unit of analysis rather than a secondary contextual variable. Although this strategic prioritization may have excluded peripheral studies where metrics are discussed only within the body text, it was necessary to consolidate a high-density corpus of specialized indicators. Furthermore, the exclusion of digital and university-based ecosystems, while narrowing the scope to territorially rooted dynamics, represents an opportunity for future research. Subsequent studies should adapt the taxonomical framework developed here to these specialized niches to determine if their scalability and institutional logics require fundamentally different performance metrics.

Regarding potential methodological bias, this study implemented several procedural safeguards consistent with the PRISMA 2020 guidelines to ensure transparency and replicability. Selection bias was mitigated through a dual-blind screening process using the Rayyan platform, where two independent researchers evaluated records without visibility of individual decisions. Inter-rater reliability was further strengthened by resolving discrepancies through a consensus-based deliberation protocol, minimizing subjective interpretation. Additionally, while the reliance on the Scopus and Web of Science databases and the restriction to English-language publications may introduce a geographic or linguistic bias, these sources were selected due to their status as the primary repositories of high-impact scientific literature in the field of management and regional economics. Future research efforts should aim for broader inclusion by incorporating emerging databases and non-English literature to validate the cross-cultural applicability of the identified metrics.

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. •

Cluster 1 (Red): Regional Development of Entrepreneurship

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. •

Cluster 2 (Green): Global Measurement of Entrepreneurship

Cluster 2 focuses on measuring entrepreneurial ecosystems at a global level, with a particular emphasis on the interplay between innovation, social networks, and startups. The prominent role of the “Global Entrepreneurship Monitor” (GEM) indicates the use of international indicators to assess the quality of entrepreneurship across different nations. This perspective is complemented by the term “social media,” reflecting a research interest in digital platforms as facilitators of visibility, funding, and growth for new ventures. Studies Kshetri (2014), Sitaridis and Kitsios (2020) and Yan and Guan (2019) highlight the GEM as a foundational tool for analyzing ecosystem dynamics and economic impact. Furthermore, evidence suggests that more developed ecosystems exhibit strong interactions among entrepreneurs, investors, and support networks, particularly when driven by innovation and digital connectivity. •

Cluster 3 (Blue): Networks, Policies, and Regional Dynamics of Entrepreneurship

This thematic grouping centers on the role of collaborative networks and public policies in shaping the performance of entrepreneurial ecosystems. The co-occurrence of “networks” and “policy” reflects a scholarly interest in explaining how interactions among entrepreneurs, governments, and organizations impact regional entrepreneurial activity. Authors such as González-Serrano et al. (2021) and Sternberg et al. (2019) underscore the importance of these networks as vital conduits for the flow of knowledge, investment, and resources within the ecosystem. Conversely, Sitaridis and Kitsios (2020) argue that public policies play a dual role, noting that while they can foster dynamic environments, poorly designed incentives may inadvertently act as hurdles that hinder business growth rather than supporting it. •

Cluster 4 (Yellow): Benchmarking and Policy Analysis of Entrepreneurial Ecosystems

The fourth cluster is dedicated to the comparative evaluation of entrepreneurial ecosystems through benchmarking and systems analysis. The primary objective within this area is to measure and compare the impact of public policies on entrepreneurial development to identify best practices at both national and regional levels, as discussed by Balawi and Ayoub (2022) and Stam and Van de Ven (2021). Specifically, the methodology proposed by Balawi and Ayoub (2022) for assessing ecosystem effectiveness highlights benchmarking as a powerful diagnostic tool for identifying structural strengths and weaknesses, a concept exemplified in their comparative study of Nordic countries. •

Cluster 5 (Purple): Metrics on the Economic Impact of Entrepreneurship

Cluster 5 focuses on measuring the economic impact of ecosystems through financial, market, and business growth metrics. The combination of “economics” and “metrics” indicates that these studies analyze how entrepreneurship contributes to broader development through quantifiable indicators.

For instance, research by Leendertse et al. (2022) has developed models to evaluate performance in terms of job creation, R&D investment, and GDP growth. Additionally, Balawi and Ayoub (2022) emphasize the Global Entrepreneurship Index (GEI) as a key metric for international comparison, highlighting the fundamental importance of access to finance, education, and digital infrastructure in the success of entrepreneurial ventures.

The thematic clusters identified through the co-occurrence analysis ( Figure 3) directly correspond to the functional categories of metrics synthesized in this review. Specifically, Cluster 1 (Regional Development) and Cluster 3 (Networks) align with Ecosystem Condition Metrics (Inputs), focusing on structural requirements such as infrastructure, financing, and network density. Conversely, Cluster 5 (Economic Impact) and Cluster 2 (Global Measurement) provide the empirical foundation for Output Metrics, utilizing indicators such as GDP growth, job creation, and the Total Early-Stage Entrepreneurial Activity (TEA) index to assess systemic results. Finally, Cluster 4 (Benchmarking) is intrinsically linked to Composite Indexes and policy evaluation frameworks, which facilitate standardized comparisons of ecosystem performance across territories.

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).

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).

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).

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).

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).

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.

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 5).

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.

The transition from a descriptive inventory of metrics to an interpretive and critical synthesis requires a holistic perspective that acknowledges the multidimensional and complex nature of entrepreneurial phenomena ( Batista-Canino et al., 2024). This study moves beyond the mere cataloging of indicators to address the underlying conceptual tensions that characterize territorially rooted systems. By adopting this integrative lens, the following sections deconstruct the primary challenges identified in the literature, specifically focusing on causal ambiguity, temporal sequencing, and the inherent endogeneity of ecosystem performance measurement.

Metrics as dual functional agents

A critical finding of this synthesis is the inherent causal ambiguity within existing metrics. While the literature often categorizes indicators into discrete groups such as ‘systemic conditions’ (inputs) and ‘entrepreneurial outputs’ ( Stam & Van de Ven, 2021), this analysis reveals that many metrics function as dual functional agents. This functional duality exposes a fundamental limitation in the traditional ‘input-process-output’ models used to evaluate EE performance. While quantitative indicators—such as the density of startups or venture capital investment levels—are typically recorded as systemic outputs, they recursively nourish the ecosystem’s structural pillars over time.

For example, the presence of successful entrepreneurial exits is often categorized as a terminal performance result; however, such events simultaneously serve as critical inputs by increasing the regional pool of ‘recycled’ human and financial capital, thereby enhancing the human capital and finance domains ( Stam & Van de Ven, 2021; Spigel, 2017). This circularity creates a profound measurement challenge: the performance of an ecosystem is not merely a terminal score, but rather the velocity and efficiency of these internal feedback loops ( Nicotra et al., 2018). Consequently, current metrics must be re-evaluated as dynamic drivers of systemic evolution rather than as isolated end-points of a linear process.

Furthermore, the systematic conflation of causes and consequences contributes to a significant degree of endogeneity in ecosystem assessment. When a metric such as ‘regional innovation capacity’ is treated as an exogenous input that triggers business creation, it often ignores that this capacity is itself an endogenous outcome of prior entrepreneurial activity and long-term institutional learning ( Leendertse et al., 2022).

Failing to account for this recursive interaction leads to a theoretical ‘blind spot’ regarding the direction of causality, making it difficult for researchers and policymakers to isolate the specific drivers of success. To overcome this causal ambiguity, a more sophisticated taxonomical approach is required, one that distinguishes between ‘static stocks’ (the available resources) and ‘dynamic flows’ (the rate at which those resources are utilized and replenished). Only by acknowledging this interconnectedness can performance frameworks accurately reflect the resilience and growth potential of territorially rooted ecosystems, moving beyond static snapshots toward an evolutionary understanding of systemic health ( Feldman et al., 2022).

Temporal sequencing and the limitation of static snapshots

The analysis further reveals a significant deficit regarding temporal sequencing, as current metrics predominantly rely on cross-sectional data that provides only static ‘snapshots’ of systemic health. This approach obfuscates the role of path dependency, where contemporary performance is inextricably linked to historical institutional configurations and prior resource accumulation ( Leendertse et al., 2022). Consequently, there is a pervasive theoretical tension in benchmarking: metrics signaling maturity in established systems—such as high exit rates—may be erroneously interpreted in emerging ecosystems where process-oriented and connectivity indicators are more representative of potential ( Stam, 2015). Without calibrating indicators to the specific stages of the ecosystem life cycle, comparative evaluations remain analytically limited, often failing to distinguish between structural readiness and terminal outcomes.

Moreover, the prevailing literature struggles to account for the lead-lag relationships and inherent latency between policy interventions and measurable entrepreneurial outputs. As noted by Feldman et al. (2022), the time-horizon required for infrastructural supports to translate into high-impact productive entrepreneurship often exceeds the typical duration of cross-sectional studies. This temporal gap leads to a risk of premature policy evaluation or the misattribution of success to short-term drivers. To transition from descriptive inventories to substantive theoretical integration, future performance frameworks must adopt longitudinal methodologies. Such an evolution is necessary to model the dynamic flows and maturation of social and financial capital, ensuring that performance metrics reflect the longitudinal resilience and evolutionary trajectory of territorially rooted systems.

Addressing the endogeneity challenge in ecosystem performance

This review problematizes the endogeneity inherent in ecosystem variables. Structural conditions, such as specialized infrastructure or regional policy, are not exogenous shocks but are often endogenous outcomes of prior entrepreneurial success. This creates a ‘circularity’ in measurement that complicates the isolation of specific drivers of performance. By failing to grapple with this endogeneity, current metrics may overstate the impact of policy interventions while underestimating the organic, bottom-up momentum of the system. Consequently, a more substantive theoretical integration requires a shift toward configurational approaches—such as Qualitative Comparative Analysis (QCA)—that evaluate how ‘bundles’ of metrics interact rather than analyzing them in isolation.

This structural circularity fundamentally compromises the validity of traditional linear regression models, which often assume that ecosystem components operate as independent and exogenous variables. In reality, the ‘health’ of a territorially rooted ecosystem is an emergent property resulting from the non-linear interaction between institutional support and entrepreneurial agency ( Nicotra et al., 2018). Consequently, the endogeneity challenge suggests that performance should not be evaluated through isolated indicators, but rather through configurational patterns. These patterns identify how specific ‘bundles’ of elements—such as the alignment between regional culture and the availability of specialized venture capital—interact to produce systemic outcomes ( Leendertse et al., 2022). Shifting the analytical focus toward these combinations of metrics allows for a more nuanced understanding of how ecosystems achieve self-sustainability and overcomes the theoretical limitations of individual variable analysis, as proposed in recent advancements in configurational theory ( Feldman et al., 2022).