What determines sustainable food consumers? Application of a support vector machine model

Abstract

While the importance of the big data available within companies is well recognised, there is a lack of academic research on how to effectively utilise this data to contribute to scholarly knowledge. This study addresses this gap by analysing large-scale survey data on food choices during package tours distributed using the database of a German travel agency. This study makes two contributions: understanding sustainable food consumption and offering methodological advancements by demonstrating the utility of machine learning (specifically a support vector machine model) in tourism research. The study found that preferences for organic and fair-trade products, along with positive attitudes toward the quantity and variety of food offerings, are key factors influencing sustainable food consumption. To attract sustainability-conscious consumers, hotels and restaurants should provide a diverse selection of food in adequate portions that meet organic and fair-trade standards. Methodologically, it was demonstrated that a support vector machine model not only allows for the prediction of consumption choices, but also for the identification and analysis of their determinants.