The Moderating Role of Board Characteristics in the Relationship Between CSR and Bank Stability: Evidence from MENA Banks
Bibliographic record
Abstract
This study uses a dataset of conventional banks from 2010 to 2022 to investigate the moderating effect of board characteristics (BC) on the relationship between corporate social responsibility (CSR) and bank stability in the MENA region. Bank stability is measured using the Z-ROA index, which captures a bank’s ability to withstand financial shocks. The study addresses endogeneity and heterogeneity concerns using the system generalized method of moments (SGMM), with diagnostic tests confirming the validity of instruments and the absence of second-order autocorrelation. Three main conclusions are presented. First, CSR has a major detrimental impact on bank stability, indicating that when poorly managed or misaligned with strategic objectives, CSR initiatives may weaken financial resilience. Second, board attributes such as independence, diversity, and experience have a positive impact on bank stability, highlighting the importance of sound governance in ensuring prudent financial management. Third, the interaction between CSR and board characteristics exerts a positive and significant influence on bank stability, suggesting that well-structured boards can enhance the strategic value of CSR initiatives. As a robustness check, the study re-estimates the model using non-performing loans (NPLs) as an alternative measure of bank stability. The results remain consistent with the baseline findings, confirming the robustness and credibility of the conclusions. CSR continues to show a positive association with NPLs, while board characteristics and their interaction with CSR maintain negative and significant effects. These findings reinforce that effective board governance can transform CSR practices into stability-enhancing strategies. For policymakers and banking executives seeking to integrate sustainability into governance frameworks, the results underscore the crucial role of corporate governance in translating CSR efforts into tangible stability outcomes. The study calls for greater regulatory focus on board structures to maximize the stability benefits of CSR in the banking sector, contributing to the growing body of research on CSR and financial stability in developing economies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".