Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time

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dc.contributor.authorLeffrang, Dirk
dc.contributor.authorBösch, Kevin
dc.contributor.authorMüller, Oliver
dc.date.accessioned2022-12-27T19:11:15Z
dc.date.available2022-12-27T19:11:15Z
dc.date.issued2023-01-03
dc.description.abstractOptimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.491
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.othera2df1cc5-4271-406b-9a8d-e0cef51ff18f
dc.identifier.urihttps://hdl.handle.net/10125/103122
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectHuman-centricity in a Sustainable Digital Economy
dc.subjectadvice taking
dc.subjectalgorithm aversion
dc.subjectdecision making
dc.subjectforecasting
dc.subjecttime series
dc.titleDo People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time
dc.type.dcmitext
prism.startingpage4016

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