Constructing MDS Joint Spaces from Binary Choice Data: A Multidimensional Unfolding Threshold Model for Marketing Research

1987 ◽  
Vol 24 (1) ◽  
pp. 40 ◽  
Author(s):  
Wayne S. DeSarbo ◽  
Donna L. Hoffman
Keyword(s):  
Threshold Model ◽  
Marketing Research ◽  
Binary Choice ◽  
Choice Data ◽  
Multidimensional Unfolding

Constructing MDS Joint Spaces from Binary Choice Data: A Multidimensional Unfolding Threshold Model for Marketing Research

1987 ◽  
Vol 24 (1) ◽  
pp. 40-54 ◽  
Author(s):  
Wayne S. Desarbo ◽  
Donna L. Hoffman
Keyword(s):  
Choice Model ◽  
Threshold Model ◽  
Marketing Research ◽  
Joint Space ◽  
Space Representation ◽  
Individual Choice ◽  
Choice Data ◽  
Spatial Choice ◽  
Multidimensional Unfolding ◽  
Dichotomous Variables

The authors present a new multidimensional unfolding methodology that can analyze various types of individual choice data. The model represents choice data, defined by dichotomous variables that indicate whether a particular brand was chosen or not, in terms of a joint space of consumers and brands. Explicit treatment of marketing and subject background variables is allowed through optional model reparameterizations of consumers and brands. Together with the joint space representation of both consumers and brands, these optional reparameterizations can provide information on appropriate market segmentation bases and respective product positioning strategies. The authors apply this spatial choice model to data on consumer (intended) choices for 12 residential communications devices and demonstrate how the results can be used for optimal positioning decisions.

scholarly journals Simple and Weighted Unfolding Threshold Models for the Spatial Representation of Binary Choice Data

1986 ◽  
Vol 10 (3) ◽  
pp. 247-264 ◽  
Author(s):  
Wayne S. DeSarbo ◽  
Donna L. Hoffman
Keyword(s):  
Spatial Representation ◽  
Binary Choice ◽  
Threshold Models ◽  
Choice Data

Relative advantage maximisation as a model of context dependence for binary choice data

2014 ◽  
Vol 11 ◽  
pp. 30-42 ◽  
Author(s):  
Waiyan Leong ◽  
David A. Hensher
Keyword(s):  
Context Dependence ◽  
Relative Advantage ◽  
Binary Choice ◽  
Choice Data

A Parametric Multidimensional Unfolding Procedure for Incomplete Nonmetric Preference/Choice Set Data in Marketing Research

1997 ◽  
Vol 34 (4) ◽  
pp. 499 ◽  
Author(s):  
Wayne S. DeSarbo ◽  
Martin R. Young ◽  
Arvind Rangaswamy
Keyword(s):  
Marketing Research ◽  
Multidimensional Unfolding ◽  
Choice Set

Appendix to Non-Parametric Unfolding of Binary Choice Data

1999 ◽  
Author(s):  
Keith T. Poole
Keyword(s):  
Binary Choice ◽  
Choice Data ◽  
Non Parametric

scholarly journals An Integrated Approach to the Analysis of Binary Choice Data

1982 ◽  
Vol 6 (1) ◽  
pp. 31-40
Author(s):  
W. Cermak ◽  
J. Lieberman ◽  
Harold P. Benson
Keyword(s):  
Integrated Approach ◽  
Binary Choice ◽  
Choice Data

A new stochastic multidimensional unfolding model for the investigation of paired comparison consumer preference/choice data

1987 ◽  
Vol 8 (3) ◽  
pp. 357-384 ◽  
Author(s):  
Wayne S. DeSarbo ◽  
Geert De Soete ◽  
Jehoshua Eliashberg
Keyword(s):  
Paired Comparison ◽  
Consumer Preference ◽  
Choice Data ◽  
Multidimensional Unfolding ◽  
Unfolding Model

Exploring Product Solution Differences Due to Choice Model Selection in the Presence of Noncompensatory Decisions With Conjunctive Screening Rules

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Jaekwan Shin ◽  
Scott Ferguson
Keyword(s):  
Product Design ◽  
Optimization Problems ◽  
Choice Model ◽  
Marketing Research ◽  
Research Literature ◽  
Product Line ◽  
Additive Models ◽  
Choice Data ◽  
Compensatory Model ◽  
Line Design

Research in market-based product design has often used compensatory preference models that assume an additive part-worth rule. These additive models have a simple, usable form and their parameters can be estimated using existing software packages. However, marketing research literature has demonstrated that consumers sometimes use noncompensatory-derived heuristics to simplify their choice decisions. This paper explores the quality of optimal solution obtained to a product line design search when using a compensatory model in the presence of noncompensatory choices and a noncompensatory model with conjunctive screening rules. Motivation for this work comes from the challenges posed by Bayesian-based noncompensatory models: the need for screening rule assumptions, probabilistic representations of noncompensatory choices, and discontinuous choice probability functions. This paper demonstrates how respondents making noncompensatory choices with conjunctive rules can lead to compensatory model estimations with distinct respondent segmentation and relative, large absolute part-worth values. Results from a product design problem suggest that using a compensatory model can provide benefits of smaller design errors and reduced computational costs. Product design optimization problems using real choice data confirm that the compensatory model and the noncompensatory model with conjunctive rules provide comparable solutions that have similar likelihoods of not being screened out when using a consideration set verifier. While many different noncompensatory heuristic rules exist, the presented study is limited to conjunctive screening rules.

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