Do Sustainable Tourism Development, Psychological Safety, and Halal Friendly Destination Performance Lead to Tourist Electronic Word of Mouth? The Role of Tourist Satisfaction
Bibliographic record
Abstract
This research investigated the antecedents of electronic Word of Mouth (eWoM) for the halal tourism context, including psychological safety, sustainable tourism development, halalfriendly destination performance, and tourist satisfaction. The role of tourist satisfaction in the relationships between psychological safety, sustainable tourism development, Halal-friendly destination performance, and electronic Word of Mouth (eWoM) was also analyzed. Questionnaires were distributed to 310 respondents who visited Halal tourist destinations in West Sumatra, Indonesia, from February to May 2022. Structural Equation Modelling (SEM)-Partial Least Square (PLS) was employed in data analysis, and results showed that psychological safety and halal-friendly destination performance significantly affected tourist satisfaction. However, sustainable tourism development did not significantly influence tourist satisfaction. Moreover, psychological safety, sustainable tourism development, halal-friendly destination performance, and tourist satisfaction also influenced electronic Word of Mouth (eWoM). Tourist satisfaction did not mediate the effect of psychological safety, sustainable tourism development, and halal-friendly destination performance on electronic Word of Mouth (eWoM).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".