An exploratory study on determinants of customer satisfaction of leading mobile network providers – case of Kolkata, India
Bibliographic record
Abstract
Purpose The study is designed to explore the drivers of customer satisfaction of leading mobile network providers in a high‐growth market like Kolkata a metropolitan city in India. Design/methodology A framework was developed based on earlier study of eminent researchers pertinent to customer satisfaction of mobile network providers in Germany, France, Korea, Canada, the USA and Greece. The construct flexibility was considered as a new determinant for customer satisfaction. For this data were collected from 277 respondents and pertinent analysis were made using multivariate techniques. Findings The study finds that generic requirements, price, and flexibility are major drivers of customer satisfaction of mobile network providers and brand wise relevance of these key determinants. Research limitations/implications The fixed line telephone directory was the sampling frame, and all the respondents considered in the survey had a fixed line but there are situations where customer subscribes only to mobile phones. It is also necessary to study other metropolitan cities of India to validate the results we have obtained for Kolkata. Originality/value – The current research has taken into account new driver of customer satisfaction in a high‐growth market and this is the first study on drivers of customer satisfaction of leading mobile network providers in the city of Kolkata, India.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".