Customer loyalty development in online shopping: An integration of e-service quality model and commitment-trust theory
Bibliographic record
Abstract
The aim of this study is to explore the determinants of cognitive loyalty in an online shopping environment. The study established a theoretical model by incorporating both e-service quality model and commitment-trust theory. A total of 937 responses were collected form Indian online shoppers by using the mail survey method. We assessed measurement model and structural model by using SPSS and AMOS. Study outcomes confirm that customer satisfaction, e-trust, commitment, and cognitive loyalty were strongly influenced by e-service quality and perceived value. Further, satisfaction had direct and positive influence on both e-trust and commitment but not on cognitive loyalty. E-trust had a positive impact on e-commitment and cognitive loyalty. Lastly e-commitment had a positive influence on cognitive loyalty. Based on the existing literature, there was a dearth of theoretical understanding of cognitive loyalty in an emerging economy perspective. Thus, the current study accomplished the critical theoretical gap by encompassing previous investigations. We examined the phenomenon of customer loyalty by integrating e-service quality model and commitment-trust theory in business to consumer e-commerce environment while considering e-satisfaction as a mediator, highlighting the originality and contribution of the current research to the online consumer loyalty literature.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".