The effect of experience quality on behavioral intention to an island destination: The mediating role of perceived value and happiness
Bibliographic record
Abstract
The purpose of this study was to determine the effect of experience quality, perceived value and happiness on behavioral intention. The study also examines the mediating role of perceived value and happiness on the effect of experience quality on behavioral intention. The samples of the study were 220 Indonesian tourists who have traveled to Dodola island or those who are currently travelling in Dodola Island Destination. This study used purposive sampling techniques using the following criteria: (1) have traveled to Dodola Island; (2). are currently travelling in Dodola Island; (3). Have a minimum age of 18 years old. The data analysis technique used was Partial Least square (PLS) with SmartPLS 3.0. The results showed that the experience quality has a positive and significant effect on behavioral intention, perceived value and happiness. Then the perception of value and happiness had a positive and significant effect on intention to behave. The results of this study also indicate that the perception of value and happiness can act as a partial mediation of the influence of the 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".