The Role of Digital Transformation Capabilities in Improving Banking Performance in Jordanian Commercial Banks
Bibliographic record
Abstract
In today’s competitive business environment, digital transformation is crucial for organizational success. The Jordanian banking sector faces the challenges of adapting to rapid digital advancements, evolving customer expectations, and intense competition. This study investigated the impact of digital transformation capabilities—technological adaptation, strategic positioning, and competitive positioning—on perceived performance among 129 bank managers from 16 Jordanian commercial banks. Data were collected via a web-based survey that included a 29-item perceptual scale using a 5-point Likert scale. Multiple linear regression analysis revealed a significant positive relationship between these capabilities and perceived performance, explaining 68% of the variance. Specifically, technological adaptation (β = 0.310), strategic positioning (β = 0.260), and competitive positioning (β = 0.360) all significantly predicted perceived performance. Harman’s single-factor test indicated minimal common method bias, and strong positive correlations were found among all study variables. This research underscores the importance of a holistic digital transformation strategy for Jordanian banks, emphasizing the need for strategic investments in technology, competitive differentiation, and alignment with business objectives. Future research should explore additional factors such as organizational culture and regulatory frameworks and incorporate objective performance measures to provide a more comprehensive understanding of the impact of digital transformation. This study offers valuable insights for practitioners, policymakers, and researchers seeking to navigate digital disruption and foster business growth.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".