Understanding the influence of meaningfulness on memorable tourism experience (MTE) to invoke revisit intention in tourists with moderating effect of the perceived risk of COVID-19
Bibliographic record
Abstract
This study aims to explore meaningfulness and examine its impact on memorable tourism experience (MTE) and the subsequent effect on the revisit intention. The study also investigates the moderating effect of the perceived risk of COVID-19 on the relationship between MTE and revisit intention. The data were collected from tourists travelling to central India destinations through a survey. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the collected data of 549 tourists. It is also found that MTE directly and positively influences revisit intention in tourists. The result also shows that meaningfulness indirectly affects revisit intention of tourists when the relationship is mediated via MTE. Perceived risk of COVID-19 negatively moderates the relationship between MTE and revisit intention. All these results help destination managers and tourism practitioners make more informed decisions to improve the experience of tourists further and make travel destinations more sustainable.
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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 | 0.007 |
| 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 | 0.002 | 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".