Digital transformation: The role of AI, social dynamics, and political support on the quality of strategic decisions and their implications for the progress of Islamic banking in Malaysia and Indonesia
Bibliographic record
Abstract
The research design uses a quantitative approach, especially correlational, verification or hypothesis testing based on empirical data in the field. The population of this research is all Islamic banking customers, totaling around 33,600,000 people. The sample size for this study was 178 people, which was determined using the Joseph F. Hair formula. The research sample was selected randomly using stratified random sampling techniques to represent the relevant population. Data was collected using a questionnaire which was distributed to selected samples using a Google form. Primary data was analyzed using SMART PLS. The results indicate that the role of AI has an impact on the Quality of Strategic Decisions; Social Dynamics have an impact on the Quality of Strategic Decisions; Political Support has an impact on the Quality of Strategic Decisions; The role of AI has an impact on the Progress of Islamic Banking, Social Dynamics has an impact on the Progress of Islamic Banking; Political Support has an impact on the Progress of Islamic Banking; The quality of strategic decisions has an impact on the progress of Islamic banking; Quality of Strategic Decisions mediates the relationship between the Role of AI and the Progress of Islamic Banking; Quality of Strategic Decisions mediates the relationship between Social Dynamics and Islamic Banking Progress; and finally, Quality of Strategic Decisions mediates the relationship between Political Support and Islamic Banking Progress.
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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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".