Exploring the role of amenities, accessibility, and attractions on tourists' inclination to revisit a destination area
Bibliographic record
Abstract
This study endeavors to explore the influence of amenities, accessibility, and attractions on tourists' inclination to revisit a destination area. Additionally, it aims to delve into the role of safety, comfort, and uniqueness as factors enhancing the relationship between amenities, accessibility, and attractions in fostering intention to revisit. This research holds significant promise in addressing pertinent issues within the tourism landscape of Indonesia, particularly within the captivating realm of Aceh Province. The data collection method employed a questionnaire technique, wherein questionnaires were distributed to a sample of 150 tourists through the online platform Google Form. The data analysis utilized Structural Equation Modeling (SEM) analysis, employing Partial Least Squares (PLS) statistical software techniques. The findings from the data analysis indicate that amenities, accessibility, and attractions are key factors in shaping perceptions of safety and comfort. Interestingly, while amenities and accessibility do not seem to affect perceived uniqueness, attractions were found to have a notable influence in this regard. Moreover, the analysis revealed that accessibility directly impacts revisit intention. It is surprising, however, that perceived safety and comfort do not mediate the effects of facilities, accessibility, and attractions on revisit intention. Similarly, perceived uniqueness does not appear to mediate this relationship either. Research underscores the importance of amenities, accessibility, and attractions in shaping perceptions of safety and comfort. While amenities and accessibility may not impact perceptions of uniqueness, it is evident that attractions significantly influence this aspect. Overall, the research underscores the importance of amenities, accessibility, and attractions in shaping perceptions, with attractions notably influencing uniqueness perception.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".