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Surprising Robustness of the Self-Explicated Approach to Customer Preference Structure Measurement

V. Srinivasan and Chan Su Park
Journal of Marketing Research
Vol. 34, No. 2 (May, 1997), pp. 286-291
DOI: 10.2307/3151865
Stable URL: http://www.jstor.org/stable/3151865
Page Count: 6
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Surprising Robustness of the Self-Explicated Approach to Customer Preference Structure Measurement
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Abstract

The authors introduce customized conjoint analysis, which combines self-explicated preference structure measurement with full-profile conjoint analysis. The more important attributes for each respondent are identified first using the self-explicated approach. Full-profile conjoint analysis customized to the respondent's most important attributes then is administered. The conjoint utility function on the limited set of attributes then is combined with the self-explicated utility function on the full set of attributes. Surprisingly, the authors find that the self-explicated approach by itself yields a slightly (but not statistically significantly) higher predictive validity than does the combined approach.

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