Factors Influencing Internal Shariah Audit Effectiveness: Evidence From Bahrain
Bibliographic record
Abstract
It is this research’s objective to analyze factors that cause an effective internal Shariah audit among the Islamic Financial Institutions (IFIs) in Bahrain. The questionnaire method has been employed to examine the said topic. The questionnaire’s primary source of information was the Accounting and Auditing Organizations for Islamic Financial Institutions (AAOIFI) Governance Standards. Meanwhile, the respondents are consisted of 52 IFIs’ head of internal Shariah audit department. These IFIs are registered with the Central Bank of Bahrain. To analyze those relationships the structural equation method (SEM) via SmarPLS3.0 has been adopted. The study has found that the effective execution of internal Shariah audit is positively linked with the competency and performance of internal Shariah audit. Meanwhile, the other two variables, i.e. being independent and Shariah supervisory board have been discovered to be positively related with internal Shariah audit effectiveness. Nonetheless, there is no significant contribution. Overall, all the variables contribute 63.2% to IFIs’ internal Shariah audit effectiveness. The regulatory and professional bodies may benefit from this study in their assessment of factors that result in a successful and meaningful internal auditing of Shariah matters.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".