Identifying Determinants of Sustainable Food Travel Product Choices – A Support Vector Machine Approach

Considering the variety of factors that can influence sustainable consumption choices and their complex interaction, it is likely that sustainable consumption is a high-dimensional problem lacking a simple linear relationship to its various antecedents. Therefore, in this study, the support vector machine method is applied to classify consumers according to their choice behaviour of tourism products with specific sustainable food qualities. The results show that the developed support vector machine is able to correctly classify sustainable and less sustainable consumers in the great majority of cases. From the analysis of the importance of single features for predicting sustainable consumption behaviour, it can be concluded that characteristics of the last trip, certain attitudes towards ‘sustainable food on holidays’, and vegan orientation are most important for the choice of sustainable food travel products.