The effect of experience quality, perceived value, happiness and tourist satisfaction on behavioral intention
Bibliographic record
Abstract
This research aims at examining and determining the effect of experience quality on tourists’ behavioral intention either directly or by perceived value, happiness, and tourist satisfaction. The sample in this research includes 227 tourists visiting Dodola Island using purposive sampling technique. The analytical method to test the hypothesis in this research is SEM-PLS. The results show that Experience Quality, Tourist Satisfaction, and Happiness had positive and significant effects on Tourists’ Behavior Intention. Meanwhile, Perceived value did not have any significant effect on Tourist Behavioral Intention as Perceived Value was not able to act as a mediator on the effect of Experience Quality on Behavioral Intention. On the other hand, Perceived Value variable had a positive and significant effect on Tourist Satisfaction. Therefore, the increase in Tourist Satisfaction sourced from Perceived Value could affect behavioral intention. The results of further research also show that Tourist Satisfaction and Happiness could partially mediate the effect of Experience Quality on Behavioral 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".