Leisure travel intention following a period of COVID-19 crisis: a case study of the Dutch market.
Bibliographic record
Abstract
Purpose: This study aims to analyse what drives and limits the Dutch population during COVID-19 in their intention to travel for leisure once travel restrictions have been lifted, to gain an insight in the psychological travel barriers following a period of crisis. Design/methodology/approach: The research process involved an online self-administrated method created with one of the leading research and web-based survey tools called Qualtrics. The questionnaire was filled by 402 respondents. Findings: The findings indicate that the impact of COVID-19 on cutting down travel plans, certain personal values and structural constraints have a positive relationship with the leisure travel intention to various destinations. Moreover, risk perceptions and intrapersonal constraints have a positive relationship with domestic leisure travel intentions. However, these factors have a negative connection with the leisure travel intention to some international destinations. Further, decreased perceptions of risks have a negative relationship with the domestic leisure travel intentions. Research limitations/implications: Using questionnaires in the form of online, self-administrated surveys made it impossible to get an insight in and have control over who responded to the questionnaire. Gaining an insight into the factors impacting the leisure travel intentions following a period of crisis will make it possible for the tourism industry to respond adequately to future crises and will make it easier for destination marketers and managers to attract new tourists during the recovery process. Originality/value: To the best of the author’s knowledge, no analysis has been so far published with a focus on the impact of COVID-19 on the Dutch population and their intention to travel. It is crucial for gaining an insight into leisure travel intention and the factors impacting this intention following a period of crisis since travel intention is an under-researched topic of academic tourism literature. This study closes the existing gap in literature.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".