Influence of category attitudes on the relationship between SERVQUAL and satisfaction in Islamic banks; the role of disruptive societal-level events
Bibliographic record
Abstract
Purpose The purpose of this study is to modify the service quality scale (SERVQUAL) for measuring the performance of Islamic banks after modifying and validating it to fit the Islamic banking context. Furthermore, it provides insights for the limitations good service delivery has on maintaining favorable provider’s satisfaction in Islamic banks under conditions when category attitudes have been disrupted and made salient by major disruptive events. Design/methodology/approach Focus groups (managers of Islamic banks) were conducted to develop context-specific service quality measures. Exploratory factor analysis and CFA have been conducted to test the validity of scales developed and questionnaires were then sent to Islamic banking customers of major Islamic banks in Egypt for testing the relationships between the research variables and hypotheses developed. Findings The findings of this study are two-fold; identifying a service quality dimension not previously identified in extant service quality measures (SERVQUAL, CARTER), devotion to customers and alerts brand managers that harm to category attitudes caused by societal-level disruptive events can make it difficult to gain high satisfaction even with superior customer service. In this disruptive event context, although high service quality is associated with brand satisfaction, brand satisfaction seems unexpectedly dampened by category attitudes, perhaps, due to the general malaise felt toward the category following the disruptive events. Practical implications Islamic institutions are alerted to the heightened need for providing superior service, as even superior service may no longer assure high satisfaction. Extrapolating to an even bigger societal disruptor, in the soon-to-come post-COVID-19 world, category attitudes toward walk-in banking services will be in turmoil and highly salient. So, brand managers will need to develop creative ways to strengthen not just brand attitudes but also to re-stabilize category attitudes if the category is to recover. Originality/value This is considered a pioneer study in using mixed methods to re-think the famous SERVQUAL scale in a unique context and relating findings to impacts of disruptive events on attitude toward Islamic banking.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".