Information and Management
Research Note---Estimating Heterogeneous Price Thresholds
Marketing Science
Estimating Heterogeneous EBA and Economic Screening Rule Choice Models
Marketing Science
When Do Price Thresholds Matter in Retail Categories?
Marketing Science
Structural Modeling in Marketing: Review and Assessment
Marketing Science
Research on Innovation: A Review and Agenda for Marketing Science
Marketing Science
Prediction in Marketing Using the Support Vector Machine
Marketing Science
Research Note---Estimating Heterogeneous Price Thresholds
Marketing Science
Estimating Heterogeneous EBA and Economic Screening Rule Choice Models
Marketing Science
Search Strategies in Shopping Engines: An Experimental Investigation
International Journal of Electronic Commerce
Barter Markets for Conjoint Analysis
Management Science
Information Systems Frontiers
The Effect of Media Advertising on Brand Consideration and Choice
Marketing Science
Efficient Choice Designs for a Consider-Then-Choose Model
Marketing Science
Multiple-Constraint Choice Models with Corner and Interior Solutions
Marketing Science
Active Machine Learning for Consideration Heuristics
Marketing Science
Noncompensatory Dyadic Choices
Marketing Science
A decision support tool for evaluating customer intentions
Expert Systems with Applications: An International Journal
Two New Prediction-Driven Approaches to Discrete Choice Prediction
ACM Transactions on Management Information Systems (TMIS)
Marketing Science
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Many theories of consumer behavior involve thresholds and discontinuities. In this paper, we investigate consumers' use of screening rules as part of a discrete-choice model. Alternatives that pass the screen are evaluated in a manner consistent with random utility theory; alternatives that do not pass the screen have a zero probability of being chosen. The proposed model accommodates conjunctive, disjunctive, and compensatory screening rules. We estimate a model that reflects a discontinuous decision process by employing the Bayesian technique of data augmentation and using Markov-chain Monte Carlo methods to integrate over the parameter space. The approach has minimal information requirements and can handle a large number of choice alternatives. The method is illustrated using a conjoint study of cameras. The results indicate that 92% of respondents screen alternatives on one or more attributes.