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Record W4406524941 · doi:10.3390/jrfm18010040

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

2025· article· en· W4406524941 on OpenAlexvenueno aff
Zouhour El Abiad, Rebecca Abraham, Hani El-Chaarani, Ruaa Binsaddig

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessFinancial systemAccountingFinancial sectorFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.197
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2025
Admission routes1
Has abstractyes

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