Acceptance and motivational effect of AI-driven feedback in the workplace: An experimental study with direct replication

Ilka Hein, Julia Cecil and Eva Lermer
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Julia Cecil: Ludwig-Maximilians-Universität München
Eva Lermer: LMU Munich

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Abstract: Artificial intelligence (AI) is increasingly taking over leadership tasks in companies, including the provision of feedback. However, the effect of AI-driven feedback on employees and its theoretical foundations are poorly understood. We aimed to reduce this research gap by comparing perceptions of AI and human feedback based on construal level theory and the feedback process model. A 2 x 2 between-subjects design with vignettes was applied to manipulate feedback source (human vs. AI) and valence (negative vs. positive). In a preregistered experimental study (S1) and subsequent direct replication (S2), responses from NS1 = 263 and NS2 = 449 participants who completed a German online questionnaire were studied. Regression analyses showed that AI feedback was rated as less accurate and led to lower performance motivation, acceptance of the feedback provider, and intention to seek further feedback. These effects were mediated by perceived social distance. Moreover, for feedback acceptance and performance motivation, the differences were only found for positive but not for negative feedback in the first study. This implies that AI feedback may not inherently be perceived as more negatively than human feedback as it depends on the feedback’s valence. Furthermore, the mediation effects indicate that the shown negative evaluations of the AI can be explained by higher social distance and that increased social closeness to feedback providers may improve appraisals of them and of their feedback. Theoretical contributions of the studies and implications for the use of AI for providing feedback in the workplace are discussed, emphasizing the influence of effects related to construal level theory.

Date: 2024-06-06
New Economics Papers: this item is included in nep-ain, nep-cbe and nep-exp
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:uczaw

DOI: 10.31219/osf.io/uczaw

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