Entrepreneurship Ecosystems in Action: Regional Models for Innovation and Economic Transformation
Bibliographic record
Abstract
Entrepreneurial ecosystems have emerged as a critical framework for explaining how regions foster innovation and achieve long-term economic transformation. While prior research highlights the importance of ecosystem elements such as finance, networks, and institutions, the pathways through which these systems generate macroeconomic outcomes remain contested, particularly in relation to institutional and policy contexts. This study develops and tests a moderated mediation model linking entrepreneurial ecosystems, innovation capacity, and regional economic transformation, with regional models, policies, and clusters as moderators. Data was collected from 200 ecosystem stakeholders, including entrepreneurs, incubator managers, executives, and policymakers, and analyzed using structural equation modeling with bootstrapped moderated mediation techniques. Results indicate that ecosystem quality significantly enhances innovation capacity and directly contributes to regional transformation. However, the mediating role of innovation capacity was weaker than anticipated and became significant only under high levels of supportive policy and cluster frameworks. These findings advance theory by demonstrating that ecosystems exert both direct effects on regional development and conditional indirect effects through innovation, thereby integrating ecosystem, innovation, and institutional perspectives. Practically, the study underscores the need for policymakers to invest not only in ecosystem infrastructure and networks but also in inclusive and coherent policy frameworks that enable innovation to translate into economic impact. The paper concludes by identifying limitations of the cross-sectional design and outlining avenues for future research on longitudinal ecosystem dynamics and contextual heterogeneity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".