by : Pierre VALETTE-FLORENCE, Jean-Marc FERRANDI, Dwight MERUNKA, and Daniel BACHELET
(ESA, University Pierre Mendes-France)
Abstract
The study of cognitive structures has recently received increasing attention. Within that field, means-end chain analysis and the laddering method proposed by Reynolds and Gutman (1988) seem to be of prime importance. Their methodology, which is qualitative, belongs to the micro perspective and hence differentiates much more from the macro approach, quantitative in essence, aimed to segment individuals into groups defined by a priori specified value orientations. New developments relying on the optimal scaling features of non linear generalized canonical analysis (NGCA) have been recently proposed (Valette-Florence, 1998). These improvements not only allow the researcher to identify consumer's segments with specific means-end orientations, but also give the probability of the associations between the meansend solutions and any prespecified criterion, such as the cars owned by the respondents.
Key words: Means-end chain, segmentation, non linear generalized canonical analysis
Key words: Means-end chain, segmentation, non linear generalized canonical analysis
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