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

Author

Listed:
  • Hein, Ilka
  • Cecil, Julia

    (Ludwig-Maximilians-Universität München)

  • Lermer, Eva

    (LMU Munich)

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.

Suggested Citation

  • Hein, Ilka & Cecil, Julia & Lermer, Eva, 2024. "Acceptance and motivational effect of AI-driven feedback in the workplace: An experimental study with direct replication," OSF Preprints uczaw, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:uczaw
    DOI: 10.31219/osf.io/uczaw
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    References listed on IDEAS

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    1. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    2. Aickin, M. & Gensler, H., 1996. "Adjusting for multiple testing when reporting research results: The Bonferroni vs Holm methods," American Journal of Public Health, American Public Health Association, vol. 86(5), pages 726-728.
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    Cited by:

    1. Xuan, Hongzhou & He, Guibing, 2025. "Negative feedback from robots is received better than that from humans: The effect of feedback on human–robot trust and collaboration," Journal of Business Research, Elsevier, vol. 193(C).

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