The Effect of Perceived Risk on Repurchase Intention and Word – of – Mouth in the Mobile Telecommunication Market: A Case Study from Vietnam
Bibliographic record
Abstract
This study aims at examining the effect of perceived risk on repurchase intention and positive word-of- mouth (WOM). An online survey method was used to collect data, and 268 questionnaires were properly completed and submitted by subscribers in the Vietnam’s mobile telecommunication market. First-order and second-order CFA were established to analyze and confirm dimensions of perceived risk and its construct by using AMOS software. Then, the structure equation model (SEM) was used to test hypotheses. The results show that perceived risk has a significantly negative impact on repurchase intention. The findings also indicate that there is a negative relationship between perceived risk and word - of - mouth. Based on scientific proof as well as practical evidence, it suggests that the mobile service providers in Vietnam need to prevent any cause that may lead to an increase in terms of consumer’s risky perception in order to retain existing subscribers and attract more potential customers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".