The effects of web quality, perceived benefits, security and data privacy on behavioral intention and e-WOM of online travel agencies
Bibliographic record
Abstract
This study seeks to empirically examine the effect of Perceived web quality (PWQ), Perceived Benefits (PB), Security and Privacy (SP), Behavioral Intention (BI) and electronic Word-of-mouth (e-WOM) among online travel agency users in Indonesia. In this study, the behavioral intention variable is the mediating variable, and e-WOM is the dependent variable. The study was conducted on 150 online shopping users in Indonesia using the PLS analysis tool. The test results show that the variables Perceived web quality (PWQ), Perceived Benefits (PB), Security and Privacy (SP) have a significant influence on Behavioral intention (BI) and on electronic Word-of-mouth (e-WOM). The results of the mediation test showed that Behavioral intention (BI) was able to strengthen the influence of the independent variable on electronic word-of-mouth (e-WOM). This study practically underscores the importance of website quality and security and privacy aspects as factors that influence user intentions of online travel agencies in Indonesia. The push for online service providers and sellers to improve services and shopping security in the digital age is a practical implication of this finding.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".