What Drives Memorable Rural Tourism Experience: Evidence from Indonesian Travelers
Bibliographic record
Abstract
This study aimed to examine the effect of Arousal on Memorable rural Tourism Experiences and social media trip sharing experiences that can moderate memorable rural tourism experiences and tourist behavioral intention. This research is quantitative research with SEM-PLS software. The technique of data collection is done by the survey method. The instrument for data collection is in the form of a questionnaire distributed online to 470 tourists who have visited tourist villages in Indonesia at least once a year. The results showed that all hypotheses could be accepted. The arousal variable significantly and positively affects the Memorable Tourism Experience. Authenticity, Entertainment, and Escapism variables significantly and positively affect Arousal. Memorable rural tourism experience variables significantly and positively affect Tourist Behavioral Intention, memorable rural tourism experience, and behavioral intention moderated by social media trip sharing experiences. The results of the study have implications for consideration for increasing sustainable tourism. As for the planning and development strategy of the domestic tourism industry, we can create sensational experiences and introduce sustainable travel themes to stimulate the desire for in-depth travel and to live in tourist destinations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".