The effect of e-WOM model mediation of marketing mix and destination image on tourist revisit intention
Bibliographic record
Abstract
Word-of-mouth (WOM) has been recognized as one of the most influential forms of information transmission. Advances in information technology and the emergence of online social networking sites have changed the manner in which data is delivered. This phenomenon has an impact on consumers because readily accessible information can significantly influence consumption-related decisions. The purpose of this paper is to examine the role of e-WOM in mediating the influence of the marketing mix and destination image on the intention of tourists to re-visit destinations. The primary data was derived from 190 tourist respondents and collected by means of questionnaires distributed via Google forms. The resulting data was analyzed using AMOS-SEM, while evaluating the role of e-WOM as a model involving a Sobel test. The results of the analysis indicated that e-WOM plays a highly significant role in piquing individual's' interest in re-visiting tourist sites. Contributory factors such as the marketing mix, destination image, and e-WOM all support this research hypothesis.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.001 |
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".