Consumer Learning of New Binary Attribute Importance Accounting for Priors, Bias, and Order Effects
This paper develops and calibrates a simple yet comprehensive set of models for the evolution of binary attribute importance weights, based on a cue-goal association framework. We argue that the utility a consumer ascribes to an attribute comes from its association with the achievement of a goal. We investigate how associations may be represented and then track back the relationship of these associations to the utility function. We explain why we believe this to be an important problem before...[Show more]
|Collections||ANU Research Publications|
|Source:||Marketing Science: the marketing journal of INFORMS|
|01_Chylinski_Consumer_Learning_of_New_2012.pdf||274.56 kB||Adobe PDF||Request a copy|
|02_Chylinski_Consumer_Learning_of_New_2012.pdf||226.97 kB||Adobe PDF||Request a copy|
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