Understanding Tourists’ Behavioral Intention and Destination Support in Post-pandemic Recovery: The Case of the Vietnamese Domestic Market
Bibliographic record
Abstract
Many countries have recently strived to accelerate the tourism recovery process by restarting their tourism industry despite the unprecedented risks of the COVID-19 crisis. Noticeably, several tourism destinations have experienced an impressive revitalization of both domestic and international tourist arrivals right after lifting all social distance restrictions. However, little is known about how a tourist destination may revive from the pandemic and to what extent tourists are willing to support a destination recovery. This study, therefore, aims to examine factors influencing the travel demand of domestic tourism and tourists’ willingness to support a destination recovery in new normal conditions. The Partial Least Square-Structural Equation Modeling was employed to predict the structural model derived from a sample size of 695 valid questionnaires. The results indicate that there is a significant improvement in domestic tourists’ travel intention and their willingness to support the post-pandemic destination revival. It is interesting to learn that the destination health risk image is no longer a critical determinant to tourists’ travel plans, while other factors including attitude, monetary promotion, and social media significantly influence their travel intention and support of tourism destination re-opening in new normal conditions. Theoretically, this study generates important contributions to post-disaster crisis management and predicting tourists’ behavioral intentions that may influence tourism destination recovery prospects. Practically, the study also provides several important implications to rebuild the domestic tourism industry in a more resilient way against future pandemic challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".