Factors Determining Holiday Intentions of Serbian Travelers during COVID-19
Bibliographic record
Abstract
This research aims to examine tourist behavior changes that occurred during the Covid-19 pandemic. We witnessed that there were more frequent negative decisions about tourist trips as well as changes in the desired tourism types and products due to pandemic effects. Some of the factors which may influence the decision-making process of tourists are the psychological impact of Covid-19, risk perception and finally the economic impact of Covid-19. Empirical research was conducted in the first quarter of 2022 on a sample of 188 residents of the Republic of Serbia using Smart-PLS software. Findings indicate that tourist Risk perception measured through Travel risk, Destination risk, and Hospitality risk have a positive statistically significant influence on tourist Holiday intention during a period of Covid-19 as making a negative decision about travel. Furthermore, the Psychological and Economic impact of Covid-19 did not have a statistically significant influence on tourist Holiday intention. We outline potential improvements for tourism management to face up to this situation like adding information on epidemic situations and prevention measures at the tourist destination to increase the knowledge of potential tourists and reduce risk perceptions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".