Food product development

Abstract

Purpose This paper aims to illustrate a new method to cluster consumer attribute preferences and to transform spontaneously written texts by consumers about a certain favourite food product (hamburger) into distinct preference clusters of attributes. Design/methodology/approach A new way of finding significant clusters of consumer attribute preferences is developed by means of a new text analytical approach (Pertex) and a multi‐step two‐sided cluster analysis procedure. Findings Clear linkages were ascertained between four respondent and four preference clusters for the two key product dimensions taste and ingredients of the hamburger. Research limitations/implications Clusters expressed were in close conformity to the conception of the standard hamburger. Only one student sample ( N =100) was used. Practical implications A new and practical method to transform written text into distinct consumer preferences (segments) was tested using a multi‐step cluster analysis to support food innovation in the food industry. Originality/value Product dimensions were integrated in a meaningful way into distinct preference clusters that could be used to segment consumers when innovating new food products.