Digital transformation: An empirical analysis of operational efficiency, customer experience, and competitive advantage in Jordanian Islamic banks
Bibliographic record
Abstract
This research aims to investigate the impact of digital transformation on the operational efficiency, customer experience, competitive advantage, organizational performance, and risk management in Jordanian Islamic banks. A descriptive analytical method was used, collecting primary data from a survey of 68 employees across four Islamic banks. Statistical tools, including linear regression and correlation, were used for data analysis and hypothesis testing. The findings revealed that digital transformation significantly influences the operational efficiency, competitive advantage, customer experience, organizational performance, and risk management of Islamic banks at a significance level of α ≤ 0.05. While digital transformation generally enhanced operational outcomes and customer experience, it also increased exposure to risks such as electronic attacks, fraud, and privacy concerns. The results highlight the importance of integrating digital transformation in Islamic banking while employing robust risk management strategies. These findings provide insights for policymakers, bank managers, and researchers in formulating strategic initiatives for digital transformation in the banking sector. The research contributes to the literature by focusing on the role of digital transformation in Islamic banking, a less-explored area in academic studies. This research also presents valuable implications for practice, specifically for banks and regulators to balance the potential of digital transformation with the associated risks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".