Risk Seeking Preferences Lead Consumers to Reject Algorithms in Uncertain Domains
We propose that when the amount of irreducible uncertainty in a forecasting task is high, making a near perfect forecast improbable for even the best possible algorithm, consumers reject any algorithm and turn instead towards more uncertain options like human judgment. We find support for this theory in five studies.
Berkeley Jay Dietvorst and Soaham Bharti (2019) ,"Risk Seeking Preferences Lead Consumers to Reject Algorithms in Uncertain Domains", in NA - Advances in Consumer Research Volume 47, eds. Rajesh Bagchi, Lauren Block, and Leonard Lee, Duluth, MN : Association for Consumer Research, Pages: 78-81.
Berkeley Jay Dietvorst, University of Chicago, USA
Soaham Bharti, University of Chicago, USA
NA - Advances in Consumer Research Volume 47 | 2019
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