The role of e-satisfaction, e-word of mouth and e-trust on repurchase intention of online shop
Bibliographic record
Abstract
The purpose of this study was to analyze the relationship between E-Satisfaction, E-Word of Mouth and E-Trust on Repurchase Intention of Online Shop. The approach in the research used is a quantitative approach using PLS-SEM SmartPLS software as a data processing tool. In this study, the data collection technique was carried out using an online questionnaire which was distributed to 150 respondents’ consumers of online shops. Sampling system with snowball sampling method. Based on the results of hypothesis testing, it was found that this study found that satisfaction had a positive and insignificant effect on repurchase intention. This shows that the e-satisfaction of online shop consumers does not significantly affect the repurchase intention of these consumers towards e-commerce online shops. In addition, e-word of mouth has a positive and insignificant effect on repurchase intention. This shows that the higher the e-word of mouth perceived by e-commerce consumers, the less significant customers will repurchase online. E-trust has a positive and significant effect on repurchase intention. This shows that the higher the e-trust perceived by online shop e-commerce consumers, the more customers will repurchase online. The novelty of this research is the new correlation model of e-satisfaction, e-word of mouth and e-Trust on repurchase intention of online shops and the research can be a reference for further research to be applied in other places or countries.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".