The role of customer e-trust, customer e-service quality and customer e-satisfaction on customer e-loyalty
Bibliographic record
Abstract
This study aims to determine the effect of e-service quality on customer e-satisfaction, the effect of e-trust on customer e-satisfaction, the effect of e-service quality on customer e-loyalty, the effect of e-trust on customer e-loyalty and finally, the effect of e-satisfaction on online shop customer e-loyalty. The type of research used in this research is correlational research with a quantitative approach and testing the research hypothesis was carried out by using the Structural Equation Model (SEM) approach based on Partial Least Square (PLS). The sample or respondent used in this study is 432 consumers of online shops in Banten, Indonesia. The results show that E-Service Quality had positive but not significant effect on E-Satisfaction, E-Trust had a positive but not significant effect to E-Satisfaction, E-Service Quality had a positive but not significant effect towards E-Loyalty, E-Trust had a negative but not significant effect to E-Satisfaction and finally E-Satisfaction had a positive but not significant effect on E-Loyalty.
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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 | 0.002 | 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".