Marketing

The contribution of the feelings-as-information hypothesis to our understanding of the role of affect in judgment and decision making is discussed. Basic principles and regularities in how affective feelings guide judgments and decisions are then identified. Based on these principles and regularities, it is argued that the role of feelings in judgment and decision making may be more adaptive than has been assumed in most academic circles.

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.

Building on the work of Dhar, Menon, and Maach (2004), this commentary describes how the compromise effect models developed in the work of Kivetz, Netzer, and Srinivasan (2004) can be extended to predict complex (business-to-business) purchase decisions and additional behavioral context effects. The authors clarify their general modeling approach and outline how it applies to choices among solutions (augmented products) and group decision making.

We propose that consumers' investment decisions involve processes of promotion and prevention self-regulation that are managed across separate mental accounts, with different financial products seen as representative of promotion versus prevention.

Preference structures that underlie survey or experimental responses may systematically vary during the administration of such measurement. Maturation, learning, fatigue, and response strategy shifts may all affect the sequential elicitation of respondent preferences at different points in the survey or experiment. The consequence of this phenomenon is that responses and effects can vary systematically within the dataset.