How do gender diversity and CEO profile impact dividend policy in banking? Evidence from Islamic and conventional banks
Bibliographic record
Abstract
Purpose This paper aims to examine the determinants of the dividend distribution policy in a banking setting. Design/methodology/approach Using a sample of 48 Islamic banks and 94 conventional banks from 15 Islamic countries over a period spanning from 2012 to 2019, we document the effect of board gender diversity, executive director profile and governance mechanisms on dividend payment decisions. We also analyze the moderating effect of Islamic banks on the relationship between gender diversity and dividend policy. Findings We find new evidence on the role of women directors in determining dividend distribution policy and confirm the risk aversion hypothesis, hence contributing to the ongoing debate on gender diversity literature. Our results show that the moderating role of Islamic banks is effective only for small banks. Practical implications Our findings have practical implications for shareholders, managers and financial analysts as they suggest rationalizing dividend distribution strategies. Originality/value Our study contributes to the growing body of knowledge on dividend policy, gender diversity and Islamic banks.
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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 | 0.009 |
| 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 | 0.001 |
| 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".