A Meta‐Analytical Study of Cultural Dimensions Moderating the Relationship Between Board Diversity and Environmental Performance
Bibliographic record
Abstract
ABSTRACT Inconsistent findings on the relationship between board diversity attributes and environmental performance in the extant literature warrant a comprehensive meta‐analytical assessment. Therefore, this research is targeted to examine the relationship between board diversity attributes—such as age, gender, education, nationality, and ethnicity—and corporate environmental performance through meta‐analysis. Additionally, it sheds light on the moderating influences of cultural dimensions—individualism, masculinity, power distance, uncertainty avoidance, long‐term orientation, and indulgence—on these relationships as national‐level cultural factors were rarely examined in the extant literature on corporate environmental performance. This research relied on 182 effect sizes from 91 studies published in Chartered Association of Business Schools (ABS) ranked journals in the last two decades. Mean correlation coefficients and random‐effect meta‐regressions were used to test the hypothesized relationships. All the board diversity attributes have a significant positive relationship with environmental performance. Concerning moderating effects, cultural dimensions of high power distance and uncertainty avoidance strengthen the positive relationship between board diversity attributes and environmental performance, whereas high individualism and indulgence dimensions weaken them. Masculinity and long‐term orientations yielded mixed results. In general, the results suggest that policies aimed at promoting board diversity can improve corporate environmental performance. However, the effectiveness of board diversity attributes in promoting corporate environmental performance is influenced by cultural factors. Since this research exclusively relies on articles published in English‐language ABS‐ranked journals, it may limit the generalizability of the findings by excluding potentially relevant studies published in other languages in other institutional and cultural contexts.
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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 | 0.026 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.023 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".