The Effect of Algorithmic Performance Appraisal on Employee Trust in Digital and Technology-Based Companies
DOI:
https://doi.org/10.62504/jimr1491Keywords:
Algorithmic Performance Appraisal, Employee Trust, Procedural Fairness, HR Technology, Organizational Justice TheoryAbstract
This study examines how employees perceive and trust Algorithmic Performance Appraisal (APA) in digital-native and technology-driven companies. Adopting Organizational Justice Theory, the Trust in Technology Framework, and Cognitive Appraisal Theory, the research explores both the direct and indirect effects of APA on employee trust, with Perceived Procedural Fairness (PPF) as a mediating variable. The study uses a quantitative, cross-sectional approach, collecting data from 200 employees in technology-based organizations and analyzing the data with Partial Least Squares Structural Equation Modeling (PLS-SEM). Results show that APA significantly enhances both procedural fairness and employee trust, with PPF playing a partial mediating role in this relationship. These findings underscore the importance of transparency, procedural legitimacy, and avenues for employee voice in cultivating trust in algorithmic systems. The study’s theoretical contribution lies in its integration of multiple perspectives on trust and fairness within algorithmic HR management. The practical implication calls for the careful design and implementation of APA systems that employees perceive as fair and trustworthy. Future research should investigate these relationships in longitudinal and multi-contextual settings to deepen the understanding of trust dynamics in evolving AI-mediated work environments.
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Almatrodi, I., Li, F., & Alojail, M. (2023). Organizational Resistance to Automation Success: How Status Quo Bias Influences Organizational Resistance to an Automated Workflow System in a Public Organization. Systems, 11(4), 191. https://doi.org/10.3390/systems11040191
Arrieta, A. B., Díaz-Rodríguez, N., Ser, J. D., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Bandara, R., Biswas, K., Akter, S., Shafique, S., & Rahman, M. (2025). Addressing Algorithmic Bias in AI‐Driven HRM Systems: Implications for Strategic HRM Effectiveness. Human Resource Management Journal, 35(4), 1047–1063. https://doi.org/10.1111/1748-8583.12609
Bigman, Y., Wilson, D., Arnestad, M. N., Waytz, A., & Gray, K. (2023). Algorithmic Discrimination Causes Less Moral Outrage Than Human Discrimination. Journal of Experimental Psychology General, 152(1), 4–27. https://doi.org/10.1037/xge0001250
Braganza, A., Chen, W., Canhoto, A. I., & Sap, S. (2021). Productive Employment and Decent Work: The Impact of AI Adoption on Psychological Contracts, Job Engagement and Employee Trust. Journal of Business Research, 131, 485–494. https://doi.org/10.1016/j.jbusres.2020.08.018
Deng, J., Hao, X., & Yang, T. (2022). The Increase of Counterproductive Work Behaviour From Organizational and Individual Level Due to Workplace Conflict: A Sequential Moderated Mediation Model. International Journal of Conflict Management, 34(2), 213–233. https://doi.org/10.1108/ijcma-04-2022-0079
Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2021). A Review of Machine Learning Applications in Human Resource Management. International Journal of Productivity and Performance Management, 71(5), 1590–1610. https://doi.org/10.1108/ijppm-08-2020-0427
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., & Tomas M Hult Christian M Ringle Marko Sarstedt, J. R. G. (2022). PARTIAL LEAST SQUARES STRUCTURAL EQUATION MODELING [PLS-SEM] THIRD EDITION (3rd ed.).
Jung, J. H., Yoo, J., & Jung, Y. (2021). The Synergistic Effects of LMX and Procedural Justice Climate on Employee Motivation and Customer Loyalty in a Retail Service Context. Journal of Service Theory and Practice, 32(2), 232–257. https://doi.org/10.1108/jstp-04-2021-0079
Kim, S., Khoreva, V., & Vaiman, V. (2024). Strategic Human Resource Management in the Era of Algorithmic Technologies: Key Insights and Future Research Agenda. Human Resource Management, 64(2), 447–464. https://doi.org/10.1002/hrm.22268
Li, C., Xing, W., & Leite, W. L. (2022). Building Socially Responsible Conversational Agents Using Big Data to Support Online Learning: A Case With Algebra Nation. British Journal of Educational Technology, 53(4), 776–803. https://doi.org/10.1111/bjet.13227
Moosa, L., Pearson, H., & Mthombeni, M. (2023). Invoking Team Trust to Facilitate Performance Management in the Context of Virtual Teams. South African Journal of Business Management, 54(1). https://doi.org/10.4102/sajbm.v54i1.3823
Noponen, N., Feshchenko, P., Auvinen, T., Luoma‐aho, V., & Abrahamsson, P. (2023). Taylorism on Steroids or Enabling Autonomy? A Systematic Review of Algorithmic Management. Management Review Quarterly, 74(3), 1695–1721. https://doi.org/10.1007/s11301-023-00345-5
Prem, P. S. (2024). Machine Learning in Employee Performance Evaluation: A HRM Perspective. International Journal of Science and Research Archive, 11(1), 1573–1585. https://doi.org/10.30574/ijsra.2024.11.1.0193
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling. In Handbook of Market Research (Issue July). https://doi.org/10.1007/978-3-319-57413-4_15
Seeber, I., Waizenegger, L., Seidel, S., Morana, S., Benbasat, I., & Lowry, P. B. (2020). Collaborating With Technology-Based Autonomous Agents. Internet Research, 30(1), 1–18. https://doi.org/10.1108/intr-12-2019-0503
Shen, H., Jin, H., Cabrera, Á. A., Perer, A., Zhu, H., & Hong, J. (2020). Designing Alternative Representations of Confusion Matrices to Support Non-Expert Public Understanding of Algorithm Performance. Proceedings of the Acm on Human-Computer Interaction, 4(CSCW2), 1–22. https://doi.org/10.1145/3415224
Shim, D. C., Park, S., & Park, H. H. (2024). Linking Performance Appraisal and Government Employees’ Organizational Citizenship Behavior. Review of Public Personnel Administration. https://doi.org/10.1177/0734371x241237564
Soekiman, S., Suhesti, N., Krisprimandoyo, D. A., Brumadyadisty, G., & Sufa, S. A. (2023). THE ROLE OF ARTIFICIAL INTELLIGENCE IN TALENT ACQUISITION AND HR DECISION-MAKING: A BIBLIOMETRIC REVIEW OF STUDY CASES. In Jurnal Pendidikan Sosial dan Humaniora (Vol. 2). https://publisherqu.com/index.php/pediaqu
Sun, L. (2024). Enterprise Performance Management and Evaluation Incentive Model for Blockchain Technology and Big Data Algorithms. Advances in Computer Signals and Systems, 8(4). https://doi.org/10.23977/acss.2024.080417
Tomsett, R., Preece, A., Braines, D., Cerutti, F., Chakraborty, S., Srivastava, M., Pearson, G., & Kaplan, L. (2020). Rapid Trust Calibration Through Interpretable and Uncertainty-Aware AI. Patterns, 1(4), 100049. https://doi.org/10.1016/j.patter.2020.100049
Wu, B., & Xu, H. (2024). Harmonizing Human-Computer Interaction: Exploring Evolution and Integration in Media and Computing. Applied and Computational Engineering, 106(1), 1–6. https://doi.org/10.54254/2755-2721/106/20240910
Zhu, R. (2023). The Evolution From Information-Based HRM to Big Data HRM. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns.2023.2.00037
Федушко, С., Ustyianovych, T., & Greguš, M. (2020). Real-Time High-Load Infrastructure Transaction Status Output Prediction Using Operational Intelligence and Big Data Technologies. Electronics, 9(4), 668. https://doi.org/10.3390/electronics9040668
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Copyright (c) 2026 Rini Anisyahrini, Winne Wardiani, Azizun Kurnia Ilahi, Anita Asnawi, Mochammad Arfani (Author)

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