Visit Intention Via Mobile App Usage in Pandemic Alleviation: Influences of Regulatory Focus and Risk
Bibliographic record
Abstract
This study examines the relationship between pandemic alleviation and tourists’ intention to visit tourist and hospitality sites. It identifies that tourists’ regulatory focus affects their risk perception in visiting tourist and hospitality sites via mobile app usage during the stage of pandemic alleviation. Mixed methods that combine quantitative modeling and experiment were adopted. A total of six sets of panel data of mobile app usage in South Korea during and after the first wave of the pandemic was analyzed, supported by an experimental study to test the causal effect. Results reveal an increasing intention to engage in on-site hospitality and tourism activities in the alleviating period of a pandemic wave. This tendency is stronger for promotion-focused (vs. prevention-focused) tourists. The experimental study also confirms that risk perception mediates the relationship between pandemic alleviation and visit intention. This research provides insights for facilitating the recovery of the travel and hospitality industry.HighlightsMobile app use records reflect visit intention to tourist sites in the pandemic.Perceived risk mediates the effect of pandemic alleviation on visit intention.Promotion-focused people show higher visit intention when pandemic waves abate.A mixed-method approach was adopted to examine the aforementioned effects.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".