Probabilistic Subset-Conjunctive Models for Heterogeneous Consumers

Abstract

The authors propose two generalizations of conjunctive and disjunctive screening rules. First, they relax the requirement that an acceptable alternative must be satisfactory on one criterion (disjunctive) or on all criteria (conjunctive). Second, they relax the assumption that consumers make deterministic judgments when evaluating alternatives. They combine the two generalizations into a probabilistic subset-conjunctive rule, which allows consumers to use any number or subset of decision criteria when screening alternatives and permits them to be uncertain about the acceptability of attribute levels. These two features allow for a screening process that is uncertain and more flexible than the deterministic conjunctive and disjunctive rules currently described in the literature. The authors describe a latent-class method for the estimation of the subset-conjunctive rules and the attribute-level consideration probabilities using either consideration or choice data. Applications using both types of data suggest that the proposed models predict as well as linear models do; can make different predictions of consideration, choice, and market shares; and provide insights into consumer decision processes that are different from those obtained with linear models.