THE IMPACT OF NON-PHARMACEUTICAL INTERVENTIONS FOR \nCOVID-19 ON DOMESTIC TRAVEL INTENTION: THE EXTENDED \nTHEORY OF PLANNED BEHAVIOR
Bibliographic record
Abstract
Since its first outbreak in the late 2019 in Wuhan, China, the spreading of COVID-19 has never been showing an end. The pandemic viciously spread to more than 210 countries, including Indonesia, in less than a quarter of 2020 and has reached the third semester. This crisis has been infecting multi-dimension of economic sectors in a worldwide scale, and the most of it is tourism. The international tourism in all regions has sunk to 70% due to travel restrictions. With the set-up of non-pharmaceutical interventions regulations within the country during the COVID-19 pandemic, this study aims to predict travelers’ behavioral intentions using the extended theory of planned behavior by adding non-pharmaceutical interventions as another determining variable. This study applied quantitative exploratory with online survey technique. In this study, the questionnaire was administered to 277 young adult travelers. The indicators were employed to address how all four indicators influence behavior intention in the new normal phase during COVID-19 pandemic. This study found that subjective norm and perceived behavioral intention have positive influence towards domestic travel intention during the COVID-19 pandemic. Meanwhile, attitude and non-pharmaceutical interventions have negative influence towards domestic travel intention during the COVID-19 pandemic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".