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Where to go when there is no snow? Ski tourists’ preferences for a holiday destination

Robert STEIGER, Department of Public Finance, University of Innsbruck, Austria, robert.steiger@uibk.ac.at

Eva POSCH, Institute of Geography, University of Innsbruck, Austria

Gottfried TAPPEINER, Department of Economics, University of Innsbruck, Austria

Janette WALDE, Department of Statistics, University of Innsbruck, Austria


<insert-authors> (2019). <insert-abstract-title>. AIRTH 2019 Conference: Innovation and Entrepreneurship for Sustainable Success; 2019 Sep 12 - 14; Innsbruck, Austria. Retrieved: <insert-date>, from http://www.airth.global


Mountain regions are disadvantaged in terms of economic location factors and thus often are structurally weak. The use of these landscapes for tourism purposes provides one of few opportunities for economic activity. Alpine skiing tourism is one such example for economic success in rural areas where topography and climate serve as locational advantages. Farming villages have developed into tourism hotspots with urban lifestyle and architecture and many of them are highly dependent on skiing tourism (Bätzing, 2015). Thus the future prosperity of these areas is linked to its attractiveness as a winter tourism destination.

The aim of this paper is to investigate skiers’ preferences for ski destination characteristics and the impact of marginal snow conditions on destination choice. A survey was conducted which included questions on important factors for destination choice, measured with Likert scales, and also included a choice-based experiment. With choice-based conjoint analysis we identified the importance of certain destination attributes and also respondents’ preferences for certain attribute levels.

Snow conditions turned out to be the most important factor for destination choice. Nevertheless, the CBC revealed a large variance in almost all factors pointing to the fact that e.g. also less snow sensitive segments exist within the sample. At average, marginal snow conditions can be compensated with lower prices or by avoiding crowding situations in the ski area. Additional non-snow activities turned out to be less effective as compensation measure.

In a scenario where 50% of ski slopes are closed due to a lack of snow 18% of respondents would come nevertheless and another 49% were unsure. Price discounts would appeal to 34% of these undecided respondents. Therefore, if price discounts are granted in such a situation, 35% of skiers could be attracted to the ski area despite marginal snow conditions, which would mean a loss of 65% of customers compared to a normal situation. But, this number needs to be put in relation to seasonal distribution of demand and the likelihood of occurrence of such snow conditions. If this is considered and assuming that such snow conditions only occur in the beginning (until Christmas) and the end (April) of the season, the overall season loss would be 8%. If the Christmas holidays would be affected as well, 27% of winter demand could be lost. This shows that it is very important to consider seasonality of demand when assessing climate change impacts on demand.

For winter tourism regions, these results show that marginal snow conditions can be tackled with price discounts, if the quality of the tourism product is sufficient to achieve high prices in normal snow situations. To date pricing is mainly based on the seasonality of demand, most pronounced in the accommodation sector. Price structuring of ski lift tickets, at least in Europe, is usually not very differentiated (Pellinen, 2003) throughout the season and very rarely dependent on snow conditions. A more dynamic pricing would enable ski areas and other tourism service providers to increase occupancy rates (Malasevska & Haugom, 2018), which is very important in sectors with high proportion of fixed costs, as e.g. ski areas and accommodation. On the other hand, higher prices on peak days might provide opportunities to increase revenue and potentially avoid crowding on the ski slopes and lifts. This kind of yield management has been introduced in the last years in a few ski areas in Switzerland (e.g. St. Moritz, Andermatt; Pröbstl‐Haider & Flaig, 2019). Demand models including e.g. calendar and weather/snow effects for better yield management could not only be used by ski area operators, but also by accommodation owners. This would allow the tourism sector to react more systematically to these changing conditions.


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