The Moderating Role of SSB Conflicts of Interest and Audit Committee Independence in Good Corporate Governance and Islamic Bank Performance in Indonesia
Bibliographic record
Abstract
The Sharia Supervisory Board (SSB) and the Audit Committee (AC) are crucial components of Good Corporate Governance (GCG) in Islamic banks. This study investigates the moderating role of SSB conflicts of interest arising from cross-membership in various Islamic Financial Institutions (IFIs) and AC members’ independence in the relationship between GCG and Islamic bank performance in Indonesia. Using a sample of ten full-fledged Islamic banks from 2014 to 2023, a Moderated Regression Analysis (MRA) was employed to test three hypotheses. The key findings indicate a significant positive relationship between GCG and Islamic bank financial performance. However, no significant moderating effects of SSB conflicts of interest on the GCG–performance relationship were found. Conversely, a significant positive moderating effect of AC independence was identified. These results have important implications for practitioners, regulators, and stakeholders of the Islamic banking industry. Islamic banks should prioritize the establishment of independent audit committees to strengthen their governance framework. While SSB cross-membership may not necessarily harm performance, banks should implement appropriate oversight mechanisms to manage potential conflicts of interest. The Indonesian Financial Services Authority (OJK) and similar regulatory bodies should continue to emphasize the importance of audit committee independence in their governance guidelines.
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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 | 0.007 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".