We introduce a multi-attribute model that generalizes the multinomial logit model by introducing one additional parameter. It captures framing and context effects, and allows violations of regularity, independence of irrelevant alternatives and order independence. Conceptually, the model considers the value of an alternative to be a generalized mean of the importance values associated with its attributes. A single parameter determines the type of mean. Its value can change across decision frames and choice sets. A positive/negative parameter value corresponds to a positive/negative evaluation frame, and a more negative (positive) parameter value reflects greater aversion to (tendency towards) choosing extreme outcomes. Limiting cases of the model correspond to lexicographic rules by which the value of an alternative is determined solely by its best or worst attribute. We use data from published studies to illustrate how the model captures the effects of context and framing on choice. We describe an application concerning digital cameras and discuss the implications of the model for product design, product positioning and demand forecasting.
Boughanmi, Khaled, Kamel Jedidi, and Rajeev Kohli. "Framing, Context and Value Averaging." Columbia Business School, 2018.
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