Preventing algorithm aversion: People are willing to use algorithms with a learning label

Simple item page
dc.contributor.authorChacon, Alvaro
dc.contributor.authorKausel, Edgar E.
dc.contributor.authorReyes, Tomas
dc.contributor.authorTrautmann, Stefan
dc.date.accessioned2025-01-21T00:03:49Z
dc.date.available2025-01-21T00:03:49Z
dc.date.issued2025
dc.description.abstractAs algorithms often outperform humans in prediction, algorithm aversion is economically harmful. To enhance algorithm utilization, we suggest emphasizing their learning capabilities, i.e., their increasing predictive precision over time, through the explicit addition of a "learning" label. We conducted five incentivized studies in which 1,167 participants may prefer algorithms or take up algorithmic advice in a financial or healthcare related task. Our results suggest that people use algorithms with a learning label to a greater extent than algorithms without such a label. As the accuracy of advice improves beyond a threshold, the use of algorithms with a learning label increases more than algorithms without a label. Thus, we show that a salient learning attribute can positively affect algorithm use in both the financial and health domain.
dc.description.funderFondecyt
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.jbusres.2024.115032
dc.identifier.eissn1873-7978
dc.identifier.issn0148-2963
dc.identifier.urihttps://doi.org/10.1016/j.jbusres.2024.115032
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/95480
dc.identifier.wosidWOS:001359158600001
dc.language.isoen
dc.revistaJournal of business research
dc.rightsacceso restringido
dc.subjectAdvice
dc.subjectAlgorithm aversion
dc.subjectAlgorithm appreciation
dc.subjectAlgorithm use
dc.subjectLearning algorithms
dc.titlePreventing algorithm aversion: People are willing to use algorithms with a learning label
dc.typeartículo
dc.volumen187
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
Files
Collections
Artículos de revistas