A new clustering methodology for the analysis of sorted or categorized stimuli

Wayne S. Desarbo; Kamel Jedidi; Michael D. Johnson

doi:10.1007/BF02404077

A new clustering methodology for the analysis of sorted or categorized stimuli

Wayne S. Desarbo, Kamel Jedidi, Michael D. Johnson

Marketing

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed.

Original language	English (US)
Pages (from-to)	267-279
Number of pages	13
Journal	Marketing Letters: A Journal of Research in Marketing
Volume	2
Issue number	3
DOIs	https://doi.org/10.1007/BF02404077
State	Published - Aug 1991

All Science Journal Classification (ASJC) codes

Business and International Management
Economics and Econometrics
Marketing

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10.1007/BF02404077

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title = "A new clustering methodology for the analysis of sorted or categorized stimuli",

abstract = "This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed.",

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year = "1991",

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AB - This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed.

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A new clustering methodology for the analysis of sorted or categorized stimuli

Abstract

All Science Journal Classification (ASJC) codes

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