Pan, Y., Froese, F.J. and Xue, S. (2025) The Role of AI in Performance Appraisal: A Mixed‐Method Study of Employee Experience Through a Relational Lens. Human Resource Management. ISSN: 0090-4848
Abstract
As artificial intelligence (AI) becomes more common in human resource management (HRM), especially in performance appraisals, questions arise about how employees respond to AI involvement in these processes. While existing research often treats AI as a technical tool, this study also views AI as a social actor that interacts with employees. Using sociomateriality and attribution theory, we examine how AI's characteristics and its role in appraisal procedures influence employee satisfaction with performance appraisals. Across three scenario-based experiments with 1002 participants and one survey with 321 respondents, we find that certain characteristics of the AI rater and the decision-making power distribution in AI appraisal procedures have a significant impact on employees' performance appraisal satisfaction. These results highlight the importance of considering the technical and social dimensions of AI in HRM practices. Our findings offer practical guidance for organizations implementing AI in performance appraisals and contribute to a deeper understanding of AI's impact on the employee experience.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | algorithmic HRM; artificial intelligence; performance appraisal satisfaction; sociomateriality |
| Dates: |
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| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Work and Employment Relation Division (Leeds) |
| Date Deposited: | 09 Apr 2026 13:00 |
| Last Modified: | 09 Apr 2026 13:00 |
| Status: | Published online |
| Publisher: | Wiley |
| Identification Number: | 10.1002/hrm.70049 |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:239520 |
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