The role of AI integration and governance standards: Enhancing financial reporting quality in Islamic banking
Bibliographic record
Abstract
The objective of this research is to investigate the impact of Artificial Intelligence (AI) on improving the quality of financial reporting in the Islamic banking industry. The study is conducted within the theoretical framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). The study utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the data collected from a sample of 364 professionals working in the field of Islamic banking. The results of our study suggest that Performance Expectancy, Effort Expectancy, and Social Influence are important factors in predicting individuals' Behavioural Intention to use Artificial Intelligence (AI). Additionally, the presence of Facilitating Conditions further enhances the impact of these factors on individuals' actual Use Behaviour. Significantly, it was shown that Use Behaviour played a significant role in determining the perceived quality of financial reporting. Nevertheless, the study did not find empirical evidence to demonstrate the direct influence of Behavioural Intention on Financial Reporting Quality. This implies that the actual implementation of Artificial Intelligence is required to fully realize its advantages. The use of artificial intelligence (AI) into governance frameworks presents a potentially advantageous pathway for Islamic banks to uphold Shariah principles, while concurrently bolstering accountability and fostering ethical banking practices.
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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 | 0.012 | 0.037 |
| 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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".