The effect of board nationality and educational diversity on CSR performance: Empirical evidence from Australian companies
Bibliographic record
Abstract
In this study, the impact of board nationality and educational diversity on corporate social responsibility is investigated, by applying a fixed-effect model, instrumental variable approach (IV-GMM), and dynamic panel model (GMM) estimator to account for endogeneity issues, on a sample of Australian listed firms over 10 years from 2010 to 2020. The study findings reveal a significant positive relationship between board nationality diversity and CSR performance and its-related subdimensions including environmental performance and social performance. Furthermore, we find that educational diversity is positively and significantly related to CSR performance and environmental performance, though not to social performance. The findings remained robust under the instrumental variable approach and dynamic panel model. Additional tests compare between two groups of firms—those from heavily and less regulated sectors—and find that the former group has more educated directors, but a similar level of nationality diversity is found among both sectors. Although diversity in terms of nationality and education is still modest at best, its effect on CSR performance and its related sub-dimensions is more pronounced within heavily regulated sectors only. This result contends that when board nationality diversity and educational diversity are properly incentivized (with regulating CSR activities, for instance), the board tends to improve the social and environmental activities of the firm.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".