LAUSR

This study investigates the importance rating of influencing factors in driving wine consumption under four specific situations, that is, gift, banquet, party, and self-drinking, and thus achieves consumer segmentation. The affecting factors containing wine quality and socio-demographic variables are measured on a national representative sample (N=609) in China. Lasso method is used to select the factors, and a binary classifier v-twin support vector machine (v-TSVM) is extended to a multi-classification case by using a “one-versus-one” approach, which predicts the purchasing behavior of consumers. The monthly income, occupation, and knowledge of a consumer toward wine, the origin of wine, the vintage, and advertisement, are critical factors in driving consumption. Wine color and packing emerge as leading factors when consumer purchase wine for gift and banquet. Promotion significantly contributes to wine price selection for banquet, party, and self-drinking. Results show that the importance ranking of determinants varies under different purchasing motivations. In addition, the recognition accuracy can be considerably increased with prior knowledge of the consumption purpose. The nonlinear classifier is recommended for application because this classifier performs better than the linear one. This paper offers a fresh perspective on wine consumption behavior in China by applying two machine learning methods to identify and quantify determinants in specific situations. The results significantly assist wine managers to provide informed decisions with regard to wine production and marketing.