Food product development

Abstract

PurposeThis 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/approachA 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.FindingsClear linkages were ascertained between four respondent and four preference clusters for the two key product dimensions taste and ingredients of the hamburger.Research limitations/implicationsClusters expressed were in close conformity to the conception of the standard hamburger. Only one student sample (N=100) was used.Practical implicationsA 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/valueProduct dimensions were integrated in a meaningful way into distinct preference clusters that could be used to segment consumers when innovating new food products.