TY - JOUR
T1 - Thumbs Up or Down
T2 - Consumer Reactions to Decisions by Algorithms Versus Humans
AU - Yalcin, Gizem
AU - Lim, Sarah
AU - Puntoni, Stefano
AU - van Osselaer, Stijn M.J.
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This is funded by the Erasmus Research Institute of Management
Publisher Copyright:
© The Author(s) 2022.
PY - 2022
Y1 - 2022
N2 - Although companies increasingly are adopting algorithms for consumer-facing tasks (e.g., application evaluations), little research has compared consumers’ reactions to favorable decisions (e.g., acceptances) versus unfavorable decisions (e.g., rejections) about themselves that are made by an algorithm versus a human. Ten studies reveal that, in contrast to managers’ predictions, consumers react less positively when a favorable decision is made by an algorithmic (vs. a human) decision maker, whereas this difference is mitigated for an unfavorable decision. The effect is driven by distinct attribution processes: it is easier for consumers to internalize a favorable decision outcome that is rendered by a human than by an algorithm, but it is easy to externalize an unfavorable decision outcome regardless of the decision maker type. The authors conclude by advising managers on how to limit the likelihood of less positive reactions toward algorithmic (vs. human) acceptances.
AB - Although companies increasingly are adopting algorithms for consumer-facing tasks (e.g., application evaluations), little research has compared consumers’ reactions to favorable decisions (e.g., acceptances) versus unfavorable decisions (e.g., rejections) about themselves that are made by an algorithm versus a human. Ten studies reveal that, in contrast to managers’ predictions, consumers react less positively when a favorable decision is made by an algorithmic (vs. a human) decision maker, whereas this difference is mitigated for an unfavorable decision. The effect is driven by distinct attribution processes: it is easier for consumers to internalize a favorable decision outcome that is rendered by a human than by an algorithm, but it is easy to externalize an unfavorable decision outcome regardless of the decision maker type. The authors conclude by advising managers on how to limit the likelihood of less positive reactions toward algorithmic (vs. human) acceptances.
KW - algorithms
KW - attribution theory
KW - decision making
KW - decision outcome favorability
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U2 - 10.1177/00222437211070016
DO - 10.1177/00222437211070016
M3 - Article
AN - SCOPUS:85126534585
JO - Journal of Marketing Research
JF - Journal of Marketing Research
SN - 0022-2437
ER -