Which recommendation system do you trust the most? Exploring the impact of perceived anthropomorphism on recommendation system trust, choice confidence, and information disclosure

Bibliographic Data

ID6443583
AuthorsYanyun Wang (0000-0002-2100-1366, University of Colorado Boulder, corresponding author), Weizi Liu (0000-0003-2071-1603, University of Illinois Urbana-Champaign), Min Yao (0000-0002-6429-0233, University of Illinois Urbana-Champaign)
Year2024
Volume27
Issue6
Pages3264-3292
Publication date2024-01-23
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueNew Media & Society (JOURNAL)
Journal identifiersISSN: 1461-4448 • E-ISSN: 1461-7315
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/14614448231223517
OpenAlexW4391145630
LanguageEN
Citations received9
References cited79

Recommendation systems (RSs) leverage data and algorithms to generate a set of suggestions to reduce consumers’ efforts and assist their decisions. In this study, we examine how different framings of recommendations trigger people’s anthropomorphic perceptions of RSs and therefore affect users’ attitudes in an online experiment. Participants used and evaluated one of four versions of a web-based wine RS with different source framings (i.e. “recommendation by an algorithm,” “recommendation by an AI assistant,” “recommendation by knowledge generated from similar people,” no description). Results showed that different source framings generated different levels of perceived anthropomorphism. Participants indicated greater trust in the recommendations and greater confidence in making choices based on the recommendations when they perceived an RS as highly anthropomorphic; however, higher perceived anthropomorphism of an RS led to a lower willingness to disclose personal information to the RS

Business · Internet privacy · Recommender system · World Wide Web · AI in Service Interactions · Behavioral Health and Interventions · Computer Science · Digital Marketing and Social Media · Psychology · Social Psychology

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