Corporate governance and firm risk-taking: the moderating role of board gender diversity
Bibliographic record
Abstract
Purpose This study aims to explore the moderating role of board gender diversity (BGD) in the relationship between corporate governance mechanisms (i.e. board size, board independence, chief executive officer (CEO) gender, CEO duality and ownership concentration) and firm risk-taking. Design/methodology/approach Using a sample of 192 non-financial publicly traded Italian firms over 2014–2018, this study tests the proposed research hypotheses and assess the moderating effect of BGD. Findings Drawing on agency theory and resource dependence theory, this study finds a significant relationship between corporate governance mechanisms and firm risk-taking, which is significantly moderated by BGD. BGD accentuates the negative effect of board size, independent directors, CEO gender and CEO duality on firm systematic risk and attenuates the positive impact of CEO duality on firm unsystematic risk. The results, which are consistent with the risk-reduction effect of BGD, are robust to the use of alternative measures of firm risk-taking. Practical implications Women’s presence on corporate boards plays a critical role in the board’s involvement in risk-taking. Hence, investors and stakeholders should consider women on corporate boards as a crucial risk-mitigating factor. Originality/value This paper contributes to our knowledge on risk management by demonstrating the moderating role of BGD while relating corporate governance mechanisms and firm risk-taking.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".