The effect of e-WOM on customer satisfaction through ease of use, perceived usefulness and e-wallet payment
Bibliographic record
Abstract
Currently, streaming applications have been widely used by users to get comfort and pleasure in life. Users communicate with each other on social media related to the activities carried out. Communication is formed online as electronic word of mouth (e-WOM) between one user to another. The data distributed was 1238 respondents using streaming applications and 324 respondents in Indonesia who had used e-wallet payments as members. The analysis data was to answer all research hypotheses using partial least squares. The data processing results show that e-WOM impacts the perceived ease of use of e-wallets by 0.408. E-WOM positively impacts the perceived usefulness of the e-wallet by 0.270. E-WOM has an impact on e-wallet payment intention of 0.190. Perceived ease of use has an effect of 0.175 and perceived usefulness of 0.259 on e-wallet payment intention. Perceived ease of use influences perceived usefulness of 0.395. Perceived ease of use and perceived usefulness impact customer satisfaction in terms of 0.157 and 0.217. Finally, it was found that e-wallet payment intention has an impact of 0.173 on customer satisfaction. The results of this study contribute to e-wallet payment users and managers building two-way and effective communication through social media so that they can quickly and accurately solve user problems. The theoretical contribution is to enrich the theory of marketing behavior and technology acceptance models in electronic commerce.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".