Two-Stage Model for Jointly Revealing Determinants of Noncompensatory Conjunctive Choice Set Formation and Compensatory Choice

Understanding the distribution and the determinants of search criteria thresholds helps in representing choice set formation and choice behavior. The development and estimation are presented of a two-stage model that jointly represents search criteria thresholds, within a noncompensatory choice set formation stage, and choice behavior, within a compensatory stage. Data were collected by using a custom-designed web-based choice experiment that seamlessly tracked the entire choice process by recording choice protocols. The model is applied to off-campus rental apartment choice by students as an example of a complex choice situation. The model combines three correlated ordered response models for the search criteria with a multinomial logit model for the choice among a realistically large realm of 200 alternatives. Estimates demonstrate that (a) the thresholds are correlated and their selection can be explained by individual characteristics, preferences, and perceptions and (b) the suggested methodology alleviates the computational complexity embedded in two-stage models by reducing the number of theoretically possible choice sets to the actually chosen ones.

[1]  C. Manski.  The structure of random utility models , 1977 .

[2]  D. Gensch.  A Two-Stage Disaggregate Attribute Choice Model , 1987 .

[3]  Richard G. Netemeyer,et al.  Price Perceptions and Consumer Shopping Behavior: A Field Study , 1993 .

[4]  Rabikar Chatterjee,et al.  Reservation Price as a Range: An Incentive-Compatible Measurement Approach , 2007 .

[5]  Jessica Y Guo,et al.  Destination Choice Model Incorporating Choice Set Formation , 2008 .

[6]  Kent B. Monroe,et al.  Measuring Price Thresholds by Psychophysics and Latitudes of Acceptance , 1971 .

[7]  W. Greene,et al.  Recent Progress on Endogeneity in Choice Modeling , 2005 .

[8]  J. Ortúzar,et al.  A semi-compensatory discrete choice model with explicit attribute thresholds of perception , 2005 .

[9]  John Aitchison,et al.  THE GENERALIZATION OF PROBIT ANALYSIS TO THE CASE OF MULTIPLE RESPONSES , 1957 .

[10]  Akshay R. Rao,et al.  The Effect of Prior Knowledge on Price Acceptability and the Type of Information Examined , 1992 .

[11]  S. Srinivasan,et al.  A multidimensional mixed ordered-response model for analyzing weekend activity participation , 2005 .

[12]  Allan D. Shocker,et al.  Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions , 1991 .

[13]  Chandra R. Bhat,et al.  A parameterized consideration set model for airport choice: an application to the San Francisco Bay Area , 2004 .

[14]  Valerie J. Trifts,et al.  Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .

[15]  Moshe Ben-Akiva,et al.  Incorporating random constraints in discrete models of choice set generation , 1987 .

[16]  M. Ben-Akiva,et al.  Discrete choice models with latent choice sets , 1995 .

[17]  John W. Payne,et al.  Task complexity and contingent processing in decision making: An information search and protocol analysis☆ , 1976 .

[18]  Dennis H. Gensch,et al.  A maximum likelihood hierarchical disaggregate model for predicting choices of individuals , 1984 .

[19]  P. Mokhtarian,et al.  Self-Selection in the Relationship between the Built Environment and Walking: Empirical Evidence from Northern California , 2006 .

[20]  Benjamin Heydecker,et al.  A discrete choice model incorporating thresholds forperception in attribute values , 2006 .

[21]  Joffre Swait,et al.  A NON-COMPENSATORY CHOICE MODEL INCORPORATING ATTRIBUTE CUTOFFS , 2001 .