Protection of Prior Learning in Complex Consumer Learning Environments

Juliano Laran, University of Florida
Marcus Cunha, Jr., University of Washington
Chris Janiszewski, University of Florida
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.
[ to cite ]:
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.