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

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 close this research gap by comparing perceptions of AI and human feedback based on construal level theory and the feedback process model. Using these theories, our objective was also to investigate the moderating role of feedback valence and the mediating effect of social distance. A 2 × 2 between-subjects design was applied to manipulate feedback source (human vs. AI) and valence (negative vs. positive) via vignettes. In a preregistered experimental study (S1) and subsequent direct replication (S2), responses from N S1 = 263 and N S2 = 449 participants were studied who completed a German online questionnaire asking for feedback acceptance, performance motivation, social distance, acceptance of the feedback source itself, and intention to seek further feedback. 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.

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Publication Details

Journal
Frontiers in Organizational Psychology
Published
2024-12-23
DOI
https://doi.org/10.3389/forgp.2024.1468907
Citations
6
Primary Topic
Behavioral Health and Interventions
Type
article
Field-Weighted Citation Impact
2.46

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article

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

Eva Lermer, Julia Cecil, Ilka Hein
6 citations
Frontiers in Organizational Psychology
Behavioral Health and Interventions
2.46
article

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

Eva Lermer, Julia Cecil, Ilka Hein
article en
6 citations

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 close this research gap by comparing perceptions of AI and human feedback based on construal level theory and the feedback process model. Using these theories, our objective was also to investigate the moderating role of feedback valence and the mediating effect of social distance. A 2 × 2 between-subjects design was applied to manipulate feedback source (human vs. AI) and valence (negative vs. positive) via vignettes. In a preregistered experimental study (S1) and subsequent direct replication (S2), responses from N S1 = 263 and N S2 = 449 participants were studied who completed a German online questionnaire asking for feedback acceptance, performance motivation, social distance, acceptance of the feedback source itself, and intention to seek further feedback. 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.

Frontiers in Organizational PsychologyVol. 2
Technische Hochschule Augsburg (DE), Ludwig-Maximilians-Universität München (DE)
Volkswagen Foundation, Ludwig-Maximilians-Universität München
Reduced inequalities
Openalex Percentile: Top 10%
Behavioral Health and Interventions
2.46
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