Service Gaps of a Banking System: A Case Study on Basic Bank
Bibliographic record
Abstract
Financial liberalization has led to intense competitive pressures and private banks dealing in retail banking are consequently directing their strategies towards increasing service quality level which fosters customer satisfaction and loyalty through improved service quality. This article examines the influence of perceived service quality on customer satisfaction. In this paper, we have used SERVQUAL as a technique to measure service quality and identify gaps in a BASIC Bank. The results of this study showed that there are service quality gaps between customers’ expectations and their perceptions in six dimensions. In this issue paying attention to the effective factors on customers’ expectations and its relationship with services quality is one of the important issues of the evaluation of services quality. For this purpose, the recent research was performed based on gap analysis model with the purpose of investigating the quality of banking services on the level of BASIC Bank. It was concluded after determining the desirable services from the standpoints of the customers (investigating customers’ expectations) and its effective factors and also the examination of the current status of services quality (customers’ understandings) that BASIC Bank responses to customers’ expectations in all of the branches under investigation and the understood services quality has been always more than services quality expected by the customers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".