Feature-based elimination: Model and empirical comparison

Experimental and verbal protocol research suggests that consumers appear to use noncompensatory screening strategies to remove alternatives and simplify complex choice situations prior to making a choice. Existing multi-phased choice models assume that the consumer initially evaluates each alternative to determine whether it should pass the first-stage screen and enter the choice set. The feature-based elimination model proposed in this study allows the consumer to avoid processing information for each alternative when forming the choice set. The consumer is assumed to apply a sequence of noncompensatory screens, similar to the elimination-by-aspects strategy, to form the choice set. An empirical application of the model demonstrates that cross-sectional heterogeneity in screening strategies can also be accommodated. One finding from this application is that heterogeneity in screening strategies may be at least as prevalent as heterogeneity in preferences. A comprehensive empirical comparison of the proposed model with existing two-stage models for scanner panel data shows that the model performs at least as well as all existing models and substantially better than most. The empirical performance of the model, coupled with its theoretical appeal and consistency with actual accounts of decision making in complex situations, make the proposed model an appealing alternative to existing multi-phased choice models.