A Meta-Analysis of Entrepreneurial Bricolage: Determinants and its Comparison with EO
Bibliographic record
Abstract
This study represents the first effort to meta-analytically summarize and aggregate the results of quantitative research on entrepreneurial bricolage, with a focus on its impact on firm performance. We also employ meta-analysis in the service of conceptual development, suggesting that there are potential problems of discriminant validity between entrepreneurial bricolage, opportunity pursuit, and entrepreneurial orientation. These three variables were relatively highly correlated. We found bricolage to be a significant determinant of performance, although its effect size diminishes when we control for opportunity pursuit or entrepreneurial orientation. When we control for both, the effect of entrepreneurial bricolage effectively vanishes completely while both opportunity pursuit and entrepreneurial orientation remain as significant determinants of performance.
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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 | 0.078 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.031 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".