The Impact of Board of Directors’ Characteristics on the Financial Performance of the Banking Sector in Gulf Cooperation Council (GCC) Countries: The Moderating Role of Bank Size
Bibliographic record
Abstract
This study investigates the impact of corporate governance characteristics on bank financial performance in Gulf Cooperation Council countries. The board characteristics include board size, board independence, board gender diversity, and CEO duality (CEO is also Board Chair), with bank size as the moderating variable. Sixty-six commercial banks from six Gulf Cooperation Council countries—Saudi Arabia, United Arab Emirates, Kuwait, Bahrain, Oman, and Qatar—are examined from 2019 to 2023 using two-stage least squares and generalized method of moments econometric methods. Board size, board independence, and board gender diversity significantly increase return on assets and return on equity. The impact of CEO duality is mixed. The empirical findings show that CEO duality increases return on equity, with a non-significant impact on return on assets. Finally, results show that bank size moderates the impacts of board size, board independence, and gender diversity in boards on the financial performance of banks. Large banks significantly increase return on assets and return on equity due to the board characteristics examined, to a greater extent than small banks. Bank leaders should expand board membership, and add independent directors and women, to improve financial 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 | 0.002 | 0.006 |
| 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) | 0.002 | 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".