The impact of institutions on the entrepreneurial orientation‐performance relationship
Bibliographic record
Abstract
Abstract Research summary In this study, we theorize how regulatory, normative, and cognitive institutions moderate the entrepreneurial orientation (EO)‐performance relationship. We test our hypotheses using data from entrepreneurial ventures in 31 countries. In countries with well‐developed legal and financial institutions and where entrepreneurship is normatively supported, entrepreneurs achieve higher returns from an entrepreneurial strategic posture. Institutions positively moderate this relationship through increased resource access, a critical element for innovative entrepreneurial strategies. The effect of institutions is further moderated by the stage of economic development. We advance our understanding of EO by exploring country‐level institutional boundary conditions to its value and extend institutional theory through evidence of its moderating effects and interactions with economic development. Managerial summary A country's institutional environment influences the extent to which firms benefit from being entrepreneurially orientated (EO). Based on data from 31 countries, we show that entrepreneurs receive higher returns from an entrepreneurial strategic posture in countries where institutions—legal and financial systems, entrepreneurship education, and cultural support for entrepreneurship—are more developed. Well‐developed institutions increase returns from EO indirectly by enhancing the ability of firms to access the resources needed to experiment and generate value from their strategic orientation. The influence of institutions on the EO‐performance relationship further depends upon the stage of economic development of a country, with institutional impact being more pronounced within “efficiency‐driven” economies compared to more developed “innovation‐driven” economies.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".