Repurchase intention of e-commerce customers in Indonesia: An overview of the effect of e-service quality, e-word of mouth, customer trust, and customer satisfaction mediation
Bibliographic record
Abstract
The rapid development of e-commerce in Indonesia makes the competition in this business increasingly fierce. This study aims to determine and analyze the effect of e-service quality, e-word of mouth (e-WOM), customer trust on customer satisfaction on e-commerce customers in Indonesia, then the study aims to determine and analyze the effect of e-service quality, e-word of mouth (e-WOM), customer trust and customer satisfaction on the repurchase intention of e-commerce customers in Indonesia. The study also aims to determine and analyze the mediating role of customer satisfaction on the relationship between e-service quality, e-word of mouth (e-WOM), and customer trust in repurchase intentions. The research is quantitative by distributing questionnaires to respondents; the sample collection method is purposive sampling. The number of samples used was 344 e-commerce consumers from Shopee, Tokopedia, Lazada, and Bukalapak throughout Indonesia. Data processing is applied by using the SmartPLS 3 Structural Equation Modelling (SEM) method. The results of this study indicate that there was a positive and significant effect of e-service quality on customer satisfaction, there was a positive and significant effect of e-WOM on customer satisfaction, customer trust had a positive and significant impact on customer satisfaction, e-service quality had no significant effect on purchase intention, e-WOM had a positive and significant effect on repurchase intention, customer trust had no significant effect on repurchase intention, e-service quality had a positive and significant effect on repurchase intention through customer satisfaction, e-WOM had a positive and significant effect on repurchase intention through customer satisfaction, customers trust had a positive and significant impact on repurchase intention through customer satisfaction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".