Protection of Prior Learning in Complex Consumer Learning Environments

Firms often attempt to introduce new benefits that existing product features can provide (i.e., new uses for a product). Associative-learning theories disagree about the extent to which new learning will lead to the updating of associations between product features and product benefits. An efficient-learning hypothesis proposes that consumers will use features that have been relevant before to predict new benefits. A protected-learning hypothesis proposes that consumers will protect learning about features that have been relevant before and will not use these features to predict new benefits. Three experiments support the efficient-learning hypothesis.



Citation:

Juliano Laran, Marcus Cunha, Jr., and Chris Janiszewski (2008) ,"Protection of Prior Learning in Complex Consumer Learning Environments", in NA - Advances in Consumer Research Volume 35, eds. Angela Y. Lee and Dilip Soman, Duluth, MN : Association for Consumer Research, Pages: 865-866.

Authors

Juliano Laran, University of Florida
Marcus Cunha, Jr., University of Washington
Chris Janiszewski, University of Florida



Volume

NA - Advances in Consumer Research Volume 35 | 2008



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