Board Gender Diversity and Banks Profitability for Business Viability: Evidence from Serbia
Bibliographic record
Abstract
As an important topic in the field of corporate governance, the influence of the board of directors’ characteristics on the profitability of corporations is examined here. This paper examines the influence of the board of directors’ and chief executive officers’ (CEO) characteristics on the profitability of banks in Serbia. In this study, the characteristics of boards of directors were examined in terms of size and the participation of women, and the characteristics of CEOs were examined similarly in terms of women’s participation. The research was conducted on a sample of 23 commercial banks from Serbia in the period from 2017 to 2021. Profitability was measured by the rate of return on operating assets (ROA) and the rate of return on equity (ROE). The results of the panel regression analysis indicate that the size of the board of directors had a positive impact on bank profitability during the COVID-19 pandemic period, while this impact was not statistically significant before the pandemic. The participation of women on the board of directors did not have a statistically significant impact on bank profitability before or during the COVID-19 pandemic. It has been found that the participation of women as CEOs had a negative impact on bank profitability before and during the COVID-19 pandemic.
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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 | 0.001 | 0.003 |
| 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) | 0.003 | 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".