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Marketing Letters

Erschienen in:

01.03.2011

Deriving joint space positioning maps from consumer preference ratings

verfasst von: Wayne S. DeSarbo, Joonwook Park, Vithala R. Rao

Erschienen in: Marketing Letters | Ausgabe 1/2011

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Abstract

Joint space multidimensional scaling maps are often utilized for positioning analyses and are estimated on survey samples of consumer preferences, choices, considerations, or intentions so as to provide a concise spatial depiction of the competitive landscape including relevant dimensions or attributes, competing brands, and consumers in the same joint space representation. Care has to be given concerning the underlying scale properties of such survey data so as not to distort the resulting joint space positioning map. We present a new joint space multidimensional scaling procedure for positioning analyses for displaying the structure in such survey data when such common ordered successive category measurement scales such as Likert, Edwards, semantic differential, etc., are employed. We present the technical details of this stochastic ordered preference multidimensional scaling vector model as well as the maximum likelihood estimation-based algorithm devised for parameter estimation. Favorable comparisons are made with several existent multidimensional scaling methods in representing the internal structure for such data in marketing positioning studies. An actual commercial positioning application concerning large sports utility vehicles consideration to buy judgments is presented with predictive validation comparisons with other multidimensional scaling joint space procedures.

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This assumption is possible given the scalar product form of M _ij to be introduced has multiplicative scale indeterminacies (as well as others).

We specify a vector MDS model for two reasons. Unlike many unfolding MDS model solutions, vector model solutions do not typically result in degenerate solutions which are either uninterruptable or useless from a manager’s perspective. Unlike many correspondence analyses solutions, the solutions derived from our vector model allow the user to directly interpret geometric relationships between consumer and brand representations in the derived space.

We ran several simulations with synthetic data to test the performance of the estimation algorithm in recovering joint space configurations, alternative model selection criteria, and various comparisons with a host of alternative two-way joint space methods including MDPREF (Carroll 1980), CATPCA (Gifi 1990), PREFSCAL (Busing et al. 2005), ALSCAL (Takane et al. 1977query), and Correspondence analysis (queryBenzécri 1992). Significant improvement was witnessed with respect to both overall fit and configuration recovery in comparison to these alternative procedures. Interested readers can contact the authors for details.

Actually, the scale measured consideration to buy, lease, or finance the vehicle.

Note, running the data in ALSCAL as non-metric and row conditional failed to produce an unfolding MDS representation given the huge number of ties encountered in the data.

Interested readers can contact the authors for details.

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Titel: Deriving joint space positioning maps from consumer preference ratings
verfasst von: Wayne S. DeSarbo
Joonwook Park
Vithala R. Rao
Publikationsdatum: 01.03.2011
Verlag: Springer US
Erschienen in: Marketing Letters / Ausgabe 1/2011
Print ISSN: 0923-0645
Elektronische ISSN: 1573-059X
DOI: https://doi.org/10.1007/s11002-009-9100-7

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