Factors Influencing Visitor Satisfaction and Revisit Intention in Lombok Tourism: The Role of Holistic Experience, Experience Quality, and Vivid Memory
Bibliographic record
Abstract
Lombok Tourism, known for its natural panoramas, protected forests, white sandy beaches, and green rice fields, offers substantial potential as a natural tourist destination.This study aims to examine the influence of Holistic Experience, Experience Quality, Vivid Memory, Visitor Satisfaction, and "Wow" Tourism as mediating factors for revisit intention in Lombok's tourism villages.This research employs a quantitative approach with a survey method.The sample consisted of 200 respondents, selected using purposive sampling, with the criteria being tourists who have visited Lombok at least once in the past year.Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM).The results revealed that Holistic Experience and Experience Quality positively and significantly influence Visitor Satisfaction and "wow" tourism, serving as mediators for revisit intention.However, one of the hypotheses -that Vivid Memory significantly impacts Visitor Satisfaction -was not supported.This suggests that further research is necessary to identify the elements influencing tourist satisfaction and revisit intention in the context of tourism.The findings from this survey can be utilized by hotel authorities, travel agencies, and the Lombok tourism department to enhance customer satisfaction and provide remarkable experiences for travelers.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".