Alternative Models for Capturing the Compromise Effect

The compromise effect denotes the finding that brands gain share when they become the intermediate rather than extreme option in a choice set. Despite the robustness and importance of this phenomenon, choice modelers have neglected to incorporate the compromise effect in formal choice models and to test whether such models outperform the standard value maximization model. In this article, the authors suggest four context-dependent choice models that can conceptually capture the compromise effect. Although the models are motivated by theory from economics and behavioral decision research, they differ with respect to the particular mechanism that underlies the compromise effect (e.g., contextual concavity versus loss aversion). Using two empirical applications, the authors (1) contrast the alternative models and show that incorporating the compromise effect by modeling the local choice context leads to superior predictions and fit compared with the traditional value maximization model and a stronger (naive) model that adjusts for possible biases in utility measurement, (2) generalize the compromise effect by demonstrating that it systematically affects choice in larger sets of products and attributes than has been previously shown, (3) show the theoretical and empirical equivalence of loss aversion and local (contextual) concavity and (4) demonstrate the superiority of models that use a single reference point over “tournament models” in which each option serves as a reference point. They discuss the theoretical and practical implications of this research as well as the ability of the proposed models to predict other behavioral context effects.