E-Service Quality and Perceived Value as Predictors of Customer Loyalty towards Online Supermarkets
Bibliographic record
Abstract
Service quality is progressively acknowledged as a significant characteristic of electronic commerce. As online comparison of the technical features of products is largely costless, viable, and easier than comparison of merchandises through traditional networks, service quality is perceived as the strategic element of customer loyalty in electronic commerce spectrum. A conceptual model explaining the relationship among e‐service quality dimensions, customer’s perceived value and loyalty towards online super markets is proposed and discussed in the present study. The research design adopted for the current study was descriptive in nature. The research approach followed was field study, by administering a structured questionnaire. Online survey method was adopted for data collection, so the data source is online shoppers in India. The study pinpointed that the four aspects of e‐service quality, namely, fulfilment, system availability, efficiency, and privacy positively influence the perceived value. It also disclosed that perceived value positively contributed to customer loyalty. The study results brought about the catalytic role of e-service quality and perceived value in online shopping framework. While designing websites of online supermarkets, the four pillars of electronic service quality needs to be focused on, considering their potential to promote customer loyalty to the store.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".