Paper

Journal of Targeting, Measurement and Analysis for Marketing (2006) 15, 12–20. doi:10.1057/palgrave.jt.5750028

Analysis of means-end chain data in marketing research

Eugene Kaciak1 and Carman W Cullen2

Correspondence: Eugene Kaciak, Faculty of Business, Brock University, St Catherines, Ontario L2S 3A1 Canada. Tel: +1 905 688 5550 ext 3902; Fax: +1 905 688 9779; Email: ekaciak@brocku.ca

1PhD, is Associate Professor of Management at Brock University in St Catharines, Ontario, Canada. He earned both his MSc in Economics (1973) in the field of Management Science and his PhD in the field of Econometrics (1977) at Warsaw School of Economics (Poland). He taught for more than 30 years at various business schools in Poland (Warsaw School of Economics), Algeria (University of Algiers) and Canada (University of Alberta, Laurentian University and Brock University). He published articles in a number of scientific journals, including Journal of Marketing Research, Journal of International Consumer Marketing and Revue Internationale PME.

2PhD, Associate Professor of Marketing, is a three-time winner of the Faculty of Excellence award in the Faculty of Business at Brock University. He has an extensive background in consumer research and retailing, both as practitioner and as a consultant. He has offered numerous seminars and conducted research throughout North America and Europe with a most recent focus on wine marketing.

Received 25 September 2006; Revised 25 September 2006.

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Abstract

Means-End Chain Theory has been developed in order to understand how consumers link attributes (A) of products with particular consequences (C), and how these consequences satisfy their personal values (V). The associations in the mind of the consumer between A's, C's and V's are labelled means-end chains (MEC). These chains are often seen as a representation of the basic drive that motivates consumer behaviour. Numerous studies have shown that techniques using MEC are suitable for a wide range of marketing applications. But there is no agreement among researchers as to the way MEC observations should be analysed. In this paper, we review methods of analysis of such observations and suggest the most appropriate procedures. Data from a study of smokers' perceptions of cigarettes in a European city are used for illustration purposes.

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Henry Stewart