The antecedents of behavioural intention for island tourism across traveller generations: a case of Bali
Bibliographic record
Abstract
Island tourism presents the natural beauty and the authenticity of local culture that provides a unique experience for foreign tourists. If not being managed properly, tourist abundance at the island destination can threaten the destination quality which can actually harm tourists and reduce trust. The main objective of this study is to construct an understanding regarding the effect of destination quality on trust and behavioural intention. The sample in this research was 450 international tourists visiting Bali, Indonesia. Bali is a natural-based and cultural-based tourism destination which offers natural beauty, the authenticity of local culture, and unique cultural attractions. WarpPLS was used to analyze the effect of destination quality, trust, word-of-mouth intention, and revisit intention. The findings show that destination quality has a significant effect on trust. Trust is proven to have a significant effect on word-of-mouth intention and revisit intention. In addition, the results of this study also show that destination quality has a direct effect on word-of-mouth intention and revisit intention. More in-depth results show that in generation Y, destination quality has no significant effect on word-of-mouth intention and revisit intention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".