The impact of the board of directors on corporate social performance: a multivariate approach
Bibliographic record
Abstract
Purpose This study investigates the relation between the board of directors' attributes and corporate social performance. The authors examine three board of directors: characteristics, size, independence and gender diversity, and how they interact with industry to affect corporate social performance. Design/methodology/approach The authors use a multivariate approach to analyze and compare the effects of governance variables on two aspects of corporate social performance, its environmental and social dimensions. Findings Based on a sample of 983 firm-year observations, our main findings indicate that board independence, size and gender diversity each has a different impact on the environmental and social dimensions of performance, but that industrial sector moderates these effects. In particular, our results show that board member independence is positively associated with the environmental dimension of the performance of all the sample industries, but only has a positive association with the social dimension when the firms are in industries other than those that are environmentally sensitive. For these latter industries, board independence is negatively associated with the social dimension. Board size is positively associated with the environmental dimension for environmentally sensitive industries only and with the social dimension for all the industries examined, with a stronger positive effect on the latter in regard to environmentally sensitive industries. Research limitations/implications Women directors appear to raise social and environmental concerns within the board, as evidenced by their positive effect on the firms' social and environmental performance, with a stronger impact on the former. Practical implications Regulators can promote changes to the way Canadian companies select directors for the purpose of achieving sustainable performance while investors will be better informed about the impact of some of the board attributes on the environmental and social dimension of performance. Originality/value This study provides a portrait of the impact of governance attributes on the environmental and social dimension of performance of Canadian companies. Given the increasing interest in gender diversity in recent years, this study provides new evidence on the benefits of female board members for the two non-financial dimensions 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".