Assessing the Islamic Banks Performance in the Gulf Cooperation Council Countries: An Empirical Study
Bibliographic record
Abstract
This paper aims to ask lots of questions about the effect of various factors on the performance of the Islamic Banking Sector (IBS) in the Gulf Cooperation Council (GCC) countries. Both panel data analysis relating to random effect (RE) regression and generalized method of moments (GMM) in the system are utilized to quantify the relationship between board features and banks performance. The population of this research was 40 Islamic banks in the GCC zone with the perception went from 2005 to 2016. Our outcomes point to show that regression with GMM in system confirms the RE results just for the degree of bank capital, demonstrating a positive and significant connection between this variable and the bank performance at a 5% criticalness level. Notwithstanding, sharia board size and board duality apply a positive and huge hit on bank performance just when RE regression technique is utilized. These discoveries are applicable and valuable contribution for Islamic banks in dealing with their speculations inside their establishments. All the more significantly, Islamic banks in GCC should allow more significance to the degree of the capital bank, the structure and nature of the board, and the board duality to improve their 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".