Abstract
Ski touring is a winter sport activity that enjoys increasing popularity. Recreationists practice it exclusively without using ski lifts in the backcountry, where conditions continuously and rapidly change, and avalanche danger exists. Ski tourers can increase their own and others' avalanche survival chances, among others, by carrying standard avalanche safety equipment (i.e., transceiver, probe, and shovel). Recent studies among backcountry recreationists identify various aspects to influence the decision to 'carry or not' this equipment by testing each factor individually for its statistical significance for the decision. This explorative study, in contrast, applies a new methodological approach and considers 'carry or not' as a decision process. The analysis bases on the behavioral decision theory and uses the machine learning algorithm decision tree to illustrate the decision process and examine the relative importance of each influencing feature. Therefore, we conduct a researcher-administered survey (n = 359) among ski tourers in a German touring region. According to their carrying behavior, this study classifies ski tourers into three different types: weather-oriented, complex, and conformist. Conformists always carry the avalanche equipment and are known in research. Weather-oriented ski tourers, who predominantly base their decision on environmental conditions (i.e., avalanche danger level and weather), are as new as the complex type, which relies on various features. In contrast to previous findings, personal traits play a subordinate role in the decision process of any type. Furthermore, we interpret environmental aspects in decision-making as decision heuristics that awareness-raising measures and education programs need to address.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Fakultät: | Geowissenschaften > Department für Geographie |
| Themengebiete: | 500 Naturwissenschaften und Mathematik > 550 Geowissenschaften, Geologie |
| ISSN: | 0925-7535 |
| Sprache: | Englisch |
| Dokumenten ID: | 103120 |
| Datum der Veröffentlichung auf Open Access LMU: | 05. Jun. 2023, 15:42 |
| Letzte Änderungen: | 05. Jun. 2023, 15:42 |
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