Customer Satisfaction Index Model for Indian Banking Industry: A Qualitative Study
Bibliographic record
Abstract
The conventional financial measures have always dominated the business performance evaluation in India. There is a need to augment the current approaches to evaluate the financial health of individual firms and industries. Customer Satisfaction Index (CSI) is one of the best solutions which is a customer-based satisfaction benchmarking system and serves as a standard metric, widely implemented in the United States and Europe. However, there is no such index in India and there is a need for a non-financial, customer-based satisfaction metric. This study is a pilot attempt to develop a Customer Satisfaction Index (CSI) model, specifically for the Indian banking industry. To achieve this, the focus group technique was employed to find the key determinants of customer satisfaction in the banking industry. The comprehensive thematic analysis revealed a total of six themes and nine sub-themes which have been proposed as the antecedents of customer satisfaction in the CSI model for the Indian banking industry. The future research intends to develop instrumentation based on the focus group results and validate the hypothesized CSI model proposed in this study.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".