Service quality in Islamic banks: The role of PAKSERV model, customer satisfaction and customer loyalty
Bibliographic record
Abstract
In service oriented industry, it is very difficult to set a standard rule to satisfy customers. As customer awareness increases on the service offered by banks, expectation from services quality increases too. Quality of a service in banking industry plays an essential role in measuring the performance of banks. Thus, the present study examines the PAKSERV model to measure customer satisfaction and customer loyalty of Islamic Banks in Palestine. A survey method was adopted where data was collected from 482 respondents through structured questionnaire. Structural equation model (SEM) was applied to check the hypothesis relationship between proposed constructs. Statistical finding revealed that PAKSERV model had significant impact on customer satisfaction and customer loyalty in Islamic banks of Palestine. Results also revealed that in cultural context PAKSERV model was the most appropriate scale and had predictive power of service quality in banking industry of Palestine. The findings of this study will be helpful for managers and policy makers to improve the service quality in Islamic banks of Palestine.
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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 | 0.003 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".