Accommodating the effects of brand unfamiliarity in the multidimensional scaling of preference data

This paper presents a multidimensional scaling (MDS) methodology (vector model) for the spatial analysis of preference data that explicitly models the effects of unfamiliarity on evoked preferences. Our objective is to derive a joint space map of brand locations and consumer preference vectors that is free from potential distortion resulting from the analysis of preference data confounded with the effects of consumer-specific brand unfamiliarity. An application based on preference and familiarity ratings for ten luxury car models collected from 240 consumers who intended to buy a luxury car within a designated time frame is presented. The results are compared with those obtained from MDPREF, a popular metric vector MDS model used for the scaling of preference data. In particular, we find that the consumer preference vectors obtained from the proposed methodology are substantially different in orientation from those estimated by the MDPREF model. The implications of the methodology are discussed.

[1]  F. Bessière,et al.  Optimization and Suboptimization: The Method of Extended Models in the Non-Linear Case , 1968 .

[2]  M. Sujan,et al.  Consumer Knowledge: Effects on Evaluation Strategies Mediating Consumer Judgments , 1985 .

[3]  Hubert Gatignon,et al.  Toward a Methodology for Measuring Advertising Copy Effects , 1984 .

[4]  Robert J. Meyer,et al.  A Multiattribute Model of Consumer Choice During Product Learning , 1985 .

[5]  J. Ramsay.  Maximum likelihood estimation in multidimensional scaling , 1977 .

[6]  David B. MacKay,et al.  Probabilistic multidimensional scaling: An anisotropic model for distance judgments , 1989 .

[7]  Rakesh K. Sarin,et al.  A Procedure for Measuring and Estimating Consumer Preferences under Uncertainty , 1983 .

[8]  Barton A. Weitz,et al.  Time horizon effects on product evaluation strategies. , 1977 .

[9]  Barton A. Weitz,et al.  Time Horizon Effects on Product Evaluation Strategies , 1977 .

[10]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  J. Frank Yates,et al.  Evaluation of Partially Described Multiattribute Options , 1978 .

[12]  Probabilistic Multidimensional Scaling Models for Analyzing Consumer Choice , 1987 .

[13]  G. Urban,et al.  You have printed the following article : Modeling Multiattribute Utility , Risk , and Belief Dynamics for New Consumer Durable Brand Choice , 2007 .

[14]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[15]  N. Anderson.  Foundations of information integration theory , 1981 .

[16]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[17]  R. Meyer.  A Model of Multiattribute Judgments under Attribute Uncertainty and Informational Constraint , 1981 .

[18]  Geert De Soete,et al.  The Wandering Ideal Point Model: A Probabilistic Multidimensional Unfolding Model for Paired Comparisons Data , 1986 .

[19]  Joseph L. Zinnes,et al.  Probabilistic multidimensional scaling: Complete and incomplete data , 1983 .

[20]  Geert De Soete,et al.  A maximum likelihood method for fitting the wandering vector model , 1983 .

[21]  Y. Takane.  A MAXIMUM LIKELIHOOD METHOD FOR NONMETRIC MULTIDIMENSIONAL SCALING , 1978 .

[22]  J. Pratt.  RISK AVERSION IN THE SMALL AND IN THE LARGE11This research was supported by the National Science Foundation (grant NSF-G24035). Reproduction in whole or in part is permitted for any purpose of the United States Government. , 1964 .

[23]  Wayne S. DeSarbo,et al.  A Constrained Unfolding Methodology for Product Positioning , 1986 .

[24]  Robert A. Pollak,et al.  THE RISK INDEPENDENCE AXIOM , 1973 .

[25]  :I. 観測された非類似性の対の順序関係がすべて独立のとき-理論 , 1978 .

[26]  Rabikar Chatterjee,et al.  The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach , 1990 .

[27]  John R. Hauser,et al.  A Measurement Error Approach for Modeling Consumer Risk Preference , 1985 .

[28]  J. Pratt.  RISK AVERSION IN THE SMALL AND IN THE LARGE11This research was supported by the National Science Foundation (grant NSF-G24035). Reproduction in whole or in part is permitted for any purpose of the United States Government. , 1964 .

[29]  Joseph L. Zinnes,et al.  A Probabilistic Model for the Multidimensional Scaling of Proximity and Preference Data , 1986 .

[30]  J. W. Hutchinson,et al.  Dimensions of Consumer Expertise , 1987 .

[31]  G. De Soete,et al.  Probabilistic multidimensional analysis of preference ratio judgments , 1989 .

[32]  Paul Slovic,et al.  Dimensional Commensurability and Cue Utilization in Comparative Judgment. , 1974 .

[33]  F. B. Vernadat,et al.  Decisions with Multiple Objectives: Preferences and Value Tradeoffs , 1994 .

[34]  J. Bettman.  Perceived Risk and Its Components: A Model and Empirical Test , 1973 .

[35]  Paul E. Green,et al.  Multidimensional scaling and related techniques in marketing analysis , 1971 .