The Effect of Tourist Destination Image (TDI) on Intention to Visit through Tourism Risk Perception (TRP) of COVID-19 in the Tourism Industry in the New Normal Era in Indonesia: Case Study in East Java
Bibliographic record
Abstract
The travel industry was the first and most affected by the pandemic. Different countries took action to limit the spread of the coronavirus disease, including total or partial lockdowns and strict restrictions on gatherings of people in public. They closed public and private places, limited the free mobility of residents, and restricted the implementation of services. This study aims to identify and analyze tourists’ behavioral intentions due to COVID-19. It is very difficult to predict the behavior of tourism consumers after the crisis. Therefore, an empirical study was carried out to obtain information from tourists to identify potential changes in their tourism consumption due to COVID-19. This study proves that tourist destination image (TDI) through tourism risk perception (TRP) positively and significantly affects the intention to visit. Therefore, it is recommended that tourism destination managers pay attention to the risk factors perceived by potential tourists who were tested in this study. Future research is also advised to examine factors that cannot be controlled by tourism destination managers, namely government policies regarding the management of tourist destinations in the new normal era.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".