Balancing entrepreneurial and learning orientations: A meta-analytic approach to understanding performance variability
Bibliographic record
Abstract
The entrepreneurial orientation (EO)-performance correlation varies across firms, traditionally attributed to external moderators. This study introduces a novel perspective, examining the EO and learning orientation (LO) correlation as an internal moderator on the EO-performance relationship. Our meta-analysis of 418 samples from 400 studies and a total of 129,695 firms, reveals a strong positive association between EO and LO, indicating their synergistic potential. The combined effects of EO and LO on performance were found to be significantly greater than their individual impacts. Furthermore, the correlation between EO and LO significantly influences the EO-performance relationship, suggesting that firms with high levels of both EO and LO exhibit higher performance variability. With the right balance between EO and LO, the EO-performance relationship can be almost doubled, providing a strategic lever for managers to enhance firm 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 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.027 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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".