Robo-Advising: Algorithm Appreciation

Counter to the widespread conclusion of algorithm aversion, our results suggest that people are willing to rely on algorithmic advice under circumstances that apply to many decisions. They suggest moderators to algorithm aversion and contribute to “theory of machine,” which examines lay beliefs about how algorithmic and human judgment differ.



Citation:

Jennifer Logg, Julia Minson, and Don Moore (2018) ,"Robo-Advising: Algorithm Appreciation", in NA - Advances in Consumer Research Volume 46, eds. Andrew Gershoff, Robert Kozinets, and Tiffany White, Duluth, MN : Association for Consumer Research, Pages: 63-67.

Authors

Jennifer Logg, Harvard Business School, USA
Julia Minson, Harvard Business School, USA
Don Moore, University of California Berkeley, USA



Volume

NA - Advances in Consumer Research Volume 46 | 2018



Share Proceeding

Featured papers

See More

Featured

Data... the 'Hard' & 'Soft' of it: Impact of Embodied Metaphors on Attitude Strength

Sunaina Shrivastava, University of Iowa, USA
Gaurav Jain, Rensselaer Polytechnic Institute
JaeHwan Kwon, Baylor University
Dhananjay Nayakankuppam, University of Iowa, USA

Read More

Featured

Secret Consumption in Close Relationships

Kelley Gullo, Duke University, USA
Danielle J Brick, University of New Hampshire
Gavan Fitzsimons, Duke University, USA

Read More

Featured

Human or Robot? The Uncanny Valley in Consumer Robots

Noah Castelo, Columbia University, USA
Bernd Schmitt, Columbia University, USA
Miklos Sarvary, Columbia University, USA

Read More

Engage with Us

Becoming an Association for Consumer Research member is simple. Membership in ACR is relatively inexpensive, but brings significant benefits to its members.