Investigating situational effects in wine consumption: a means-end approach

Abstract

This study groups consumer’s means-end chains according to the consumption situation, rather than by consumer characteristics. It relies on a predictive clustering technique, learning vector quantization (LVQ), to form well differentiated clusters which could be used by marketers to position their product for different usage situations. 648 different means-end chains, corresponding to 356 different occasions, were collected from 223 respondents. Using LVQ, an initial 8-cluster solution was found which fit the data well. However, a better predictivity was obtained by increasing the number of clusters to 14. The implications of these results are discussed in the conclusion of this paper along with directions for future research.