Tools for measuring the intention for adapting to climate change by winter tourists: some thoughts on consumer behavior research and an empirical example
Why this work is in the frame
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Bibliographic record
Abstract
Purpose Climate change will lead to new environmental conditions in winter sport destinations. Even if the motivations of the visitors remain the same, climate change will inevitably influence their behavior. At the same time, tourism destinations try to influence visitor behavior by implementing adaptation strategies and offering new products. The purpose of this paper is to discuss the advantages and disadvantages of possible consumer research approaches from a destination's perspective. Design/methodology/approach In order to study the influence of climate change on winter destinations in Austria, the authors adapted an existing behavioral framework to the model for proactive tourist adaptation to climate change, which is helpful to understand the influencing factors and the individual decision‐making process towards adaptation intention. Thereafter they used the results of a choice experiment (=intended behavior) to calibrate a decision support tool (DST) for a cross‐country skiing destination in Austria. Findings The paper presents a DST based on the choice experiment. The DST shows the changing market shares for three segments as a destination and its entrepreneurs attempt to identify the best opportunities for the various adaption strategies they can possibly consider. The authors suggest this as a suitable market research tool for proactive destination management. Research limitations/implications Compared to the theory of planned behavior (TPB), Choice experiments (CE) are less suitable to contribute to the understanding of behavior; at the same time, CEs are well suited to model intended behavior, and to predict the demand for currently non‐existing alternatives when past behavior might be a poor predictor. Practical implications The authors propose a conceptual framework that explicitly combines the modeling of behavior and behavioral intention with relevant concepts of the individual customer's cognitive process. The authors want to ensure that destination managers are able to understand, and eventually direct and influence travel behavior as it relates to their local conditions, which in the context of climate change implies that the destination must lay the foundation for tomorrow's success while competing today. Originality/value The paper focuses on two main challenges related to destination choice in the context of climate change: tourists encounter a rather unique decision context, as their decision to visit is completely voluntary, and predicting visitor reactions to climate change enters uncharted waters as clients have not encountered these situations before.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it