Enhancing cyber governance in Islamic banks: The influence of artificial intelligence and the moderating effect of Covid-19 pandemic
Bibliographic record
Abstract
The aim of this study was to examine how the implications of the Covid-19 pandemic moderate the impact of Artificial Intelligence (AI) on the effective application of Cyber Governance (CG) in Islamic banks. A total of 93 questionnaires from branch heads of Islamic banks were used in this study, and the data were analyzed using the Statistical Package for Social Sciences (SPSS) through descriptive-analytical methods. The findings indicated that AI has a significant influence on the effective application of CG in Islamic banks. The study also revealed that the Covid-19 Pandemic positively moderates the influence of AI on the effective application of CG in Islamic banks. The results of this study have implications for regulators and decision-makers in proposing new legislation to effectively apply CG in the Islamic banking sector, which can help protect public funds and limit cyber-attacks. This study is the first to investigate the moderating effect of the Covid-19 pandemic on the influence of AI on the effective application of CG in Islamic banks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".