Customer’s Perceptions of E-SQ in Online Shopping Context: An Empirical Study in Mumbai (India)
Bibliographic record
Abstract
The increasing trend of internet usage in India provides an emerging prospect for online retailers. Online shopping has seen tremendous growth worldwide. In developing countries, online shopping is still in the infancy stage. The advent of electronic commerce has encouraged intensified interest in understanding the customer’s perception about online shopping. E-tailers need to understand the nature of the relationships among service quality, customer satisfaction, and their purchase behavior. If online retailers know the service quality dimensions affecting customer’s behavior then they can develop appropriate marketing strategies to convert browsers into active buyers. In this study various e-service quality dimensions of online shopping as perceived by customers are identified and confirmed their relationship with shopping behavior. The study also confirms is it the perceptions of customers are independent of their gender and marital status. It was discovered that perceptions e-service quality in online shopping context differs with respect to gender but not with respect to marital status. In this study only few e-service quality dimensions were analyzed and tested. As per the study the dimensions reliability, responsiveness, access and efficiency are having positive impact where as flexibility and ease of use are having negative impact with respect to online shopping behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".