The role of e-satisfaction, e-word of mouth and e-trust on repurchase intention of online shop
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it