An aggregate contextual choice model for estimating demand for new products from a laboratory choice experiment

Abstract An aggregate contextual choice model (ACCM) is presented and 2 alternative error theories are developed that lead to multinomial maximum likelihood and least-squares estimation procedures. The model is used to estimate demand for 2 new product concepts in a reduced design laboratory choice experiment. The results of the comparison between the ACCM and the Luce constant utility model strongly favor the context-sensitive model over the Luce model. Furthermore, it is shown that the context-sensitive model is easily transformed so that the least-squares method produces results that are very close to maximum likelihood estimates.

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