The Effect of Corporate Governance Characteristics and Gender Diversity on Dividends Decision: Does ESG Matter?
Bibliographic record
Abstract
Purpose – The current study investigates the effects of several corporate governance characteristics and gender diversity facets on the dividends decision. Moreover, the moderating effect of ESG has been scrutinized on the relationship between board characteristics and gender diversity with dividends decision. Design/methodology/approach –Logistic regression model has been utilized on a sample of 120 EGX listed companies between 2012 and 2019. The non-board data that has been collected from DataStream, the board information has been gathered based on the financial statements and websites of the companies. Findings – The results of the study demonstrated a substantial positive relationship between gender diversity and dividends decision. Additionally, the association between gender diversity and dividend decision is moderated by ESG. Furthermore, the decision to declare a dividend is significantly positively correlated with the board's size, but negatively correlated with the board's independence. Moreover, neither the relationship between board size nor board independence on dividends decision is significantly moderated by ESG. Originality/value – Results hold up well to determine the imperative role of board characteristics and more specifically the gender diversity in dividend decision-making and on ESG. They thus offer compelling evidence in favor of the significance of sustainability in dividends decision.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".