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Simultaneous multidimensional unfolding and cluster analysis: an investigation of strategic groups

Author: Cool, Karel O. ; Jedidi, Kamel ; Schendel, Dan ; DeSarbo, Wayne S.INSEAD Area: StrategyIn: Marketing Letters, vol. 2, no. 2, April 1991 Language: EnglishDescription: p. 129-146.Type of document: INSEAD ArticleNote: Please ask the Library for this articleAbstract: This paper develops a maximum likelihood based methodology for simultaneously performing multidimensional unfolding and cluster analysis on two-way dominance or profile data. This new procedure utilises mixtures of multivariate conditional normal distributions to estimate a joint space of stimulus coordinates and K ideal points, one for each cluster or group, in a T-dimensional space. The conditional mixture, maximum likelihood methodology is introduced together with an E-M algorithm used for parameter estimation. A Marketing strategy application is provided with an analysis of the PIMS data for a set of firms drawn from the same competitive industry in order to determine strategic groups while simultaneously depicting strategy-performance relationships
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This paper develops a maximum likelihood based methodology for simultaneously performing multidimensional unfolding and cluster analysis on two-way dominance or profile data. This new procedure utilises mixtures of multivariate conditional normal distributions to estimate a joint space of stimulus coordinates and K ideal points, one for each cluster or group, in a T-dimensional space. The conditional mixture, maximum likelihood methodology is introduced together with an E-M algorithm used for parameter estimation. A Marketing strategy application is provided with an analysis of the PIMS data for a set of firms drawn from the same competitive industry in order to determine strategic groups while simultaneously depicting strategy-performance relationships

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