Incorporating Context Effects in the Multidimensional Scaling of 'Pick Any/N' Choice Data

This paper presents a multidimensional scaling model that is estimated on pick any/N choice data, and accommodates a broad range of context effects. The methodology estimates a set of parameters capturing the direction and magnitude of the context effects, as well as the locations of brands and consumers' ideal points in a joint multidimensional space. Numerical simulation suggests that our approach can recover the true configuration of brands and ideal points in the presence of context effects with greater accuracy than a `baseline' model that does not incorporate any context effects. To illustrate the methodology and its implications, we provide an empirical application based on choice of supermarkets, from a consumer study conducted in the Netherlands.

[1]  H. Bozdogan.  Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions , 1987 .

[2]  B. Wernerfelt,et al.  An Evaluation Cost Model of Consideration Sets , 1990 .

[3]  Ajay K. Manrai,et al.  A New Multidimensional Scaling Methodology for the Analysis of Asymmetric Proximity Data in Marketing Research , 1992 .

[4]  K. Lancaster.  A New Approach to Consumer Theory , 1966, Journal of Political Economy.

[5]  J. Farley,et al.  Impact of Different Comparison Sets on Evaluation of a New Subcompact Car Brand , 1978 .

[6]  Marcel Corstjens,et al.  Formal Choice Models in Marketing , 1983 .

[7]  A. Tversky.  Elimination by aspects: A theory of choice. , 1972 .

[8]  John W. Payne,et al.  Contingent decision behavior. , 1982 .

[9]  William L. Moore,et al.  Experiments in Constrained Choice , 1987 .

[10]  John G. Lynch,et al.  A Framework For Exploring Context Effects on Consumer Judgment and Choice , 1983 .

[11]  W. DeSarbo,et al.  A stochastic multidimensional scaling procedure for the spatial representation of three-mode, three-way pick any/J data , 1991 .

[12]  Sanjoy Ghose,et al.  When Choice Models Fail: Compensatory Models in Negatively Correlated Environments , 1989 .

[13]  Eric J. Johnson,et al.  Compensatory Choice Models of Noncompensatory Processes: The Effect of Varying Context , 1984 .

[14]  Barbara E. Kahn,et al.  Measuring Variety-Seeking and Reinforcement Behaviors Using Panel Data , 1986 .

[15]  Joel Huber.  The Effect of Set Composition on Item Choice: Separating Attraction, Edge Aversion, and Substitution Effects , 1983 .

[16]  Jaewun Cho,et al.  A stochastic multidimensional scaling vector threshold model for the spatial representation of “pick any/n” data , 1989 .

[17]  V. Rao,et al.  GENFOLD2: A set of models and algorithms for the general UnFOLDing analysis of preference/dominance data , 1984 .

[18]  T. S. Robertson,et al.  Handbook of Consumer Behavior , 1990 .

[19]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[20]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[21]  John G. Lynch,et al.  Contrast Effects in Consumer Judgments: Changes in Mental Representations or in the Anchoring of Rating Scales? , 1991 .

[22]  Joel Huber,et al.  Market Boundaries and Product Choice: Illustrating Attraction and Substitution Effects , 1983 .

[23]  Christopher P. Puto,et al.  Adding Asymmetrically Dominated Alternatives: Violations of Regularity & the Similarity Hypothesis. , 1981 .

[24]  Ajay K. Manrai,et al.  Elimination-By-Cutoffs , 1989 .

[25]  Marcel G. Dagenais,et al.  The dogit model , 1979 .

[26]  A. Tversky,et al.  Context-dependent preferences , 1993 .

[27]  Donna L. Hoffman,et al.  Constructing MDS Joint Spaces from Binary Choice Data: A Multidimensional Unfolding Threshold Model for Marketing Research , 1987 .

[28]  Terry Elrod,et al.  Choice Map: Inferring a Product-Market Map from Panel Data , 1988 .

[29]  G. Zinkhan,et al.  Changes in consumer choice: Further investigation of similarity and attraction effects , 1987 .

[30]  J. Hauser,et al.  The Value Priority Hypotheses for Consumer Budget Plans , 1986 .

[31]  Wayne S. DeSarbo,et al.  A nonspatial methodology for the analysis of two-way proximity data incorporating the distance-density hypothesis , 1990 .

[32]  C. Krumhansl.  Concerning the applicability of geometric models to similarity data: The interrelationship between similarity and spatial density. , 1978 .

[33]  John Roberts,et al.  Development and Testing of a Model of Consideration Set Composition , 1991 .

[34]  C. Krumhansl,et al.  Density versus feature weights as predictors of visual identifications: comment on Appelman and Mayzner. , 1982, Journal of experimental psychology. General.

[35]  K. Lancaster,et al.  Consumer Demand. A New Approach. , 1972 .

[36]  Joel Huber,et al.  Adapting Cutoffs to the Choice Environment: The Effects of Attribute Correlation and Reliability , 1991 .

[37]  Russell S. Winer,et al.  Constructing Joint Spaces from Pick-Any Data: A New Tool for Consumer Analysis , 1982 .

[38]  Berend Wierenga,et al.  Empirical test of the Lancaster characteristics model , 1984 .

[39]  I. Simonson,et al.  Choice Based on Reasons: The Case of Attraction and Compromise Effects , 1989 .

[40]  Allan D. Shocker,et al.  Toward understanding the attraction effect: The implications of product stimulus meaningfulness and familiarity. , 1987 .

[41]  H. Akaike.  A new look at the statistical model identification , 1974 .

[42]  Wayne S. DeSarbo,et al.  Simple and Weighted Unfolding Threshold Models for the Spatial Representation of Binary Choice Data , 1986 .