Trends in winter sport tourism: challenges for the future
Bibliographic record
Abstract
Purpose The purpose of this paper is to survey climate change impacts on winter sport tourists' activity and destination choice, to estimate shifts in customer demand and to provide recommendations and decision support for destination management. Design/methodology/approach A total of 540 skiers from Vienna, Austria were surveyed with a standardized online questionnaire. The survey also contained a discrete choice experiment a stated preference method which forces respondents into trade‐off behavior between various possible combinations of destination profiles. Findings The results show a strong preference for destination attributes promising sufficient (natural) snow conditions. In winters that lack snow, resorts in high destinations gain importance and travel distances lose some relevance. A large proportion of skiers would forgo skiing if it becomes more expensive. Snow independent substitutes are accepted as a short time compensation but not for the whole winter holiday. When asked to trade off additional costs and additional travel distances for a snow secure destination, the majority of winter sport tourists are willing to incur some additional cost but the majority reach thresholds at about 10 percent additional cost and 2h additional driving. Originality/value The survey shows, that a discrete choice experiment is a suitable method to cover the complexity of activity and destination choice. Therefore it is an unique individual‐oriented approach to consider customer demand and to evaluate the success of offer setting in tourism management. The sequential presentation of three related choice sets is a novel contribution in the field of choice experiments, and appears to be well suited to simulate climate change‐related 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".