Transparency, Algorithmic Fairness, and Employee Performance in AI-Based HR: The Moderating Role of Trust in Management
DOI:
https://doi.org/10.59431/ijer.v6i1.729Keywords:
Algorithmic Fairness, AI-Based HRM, Transparency, Trust In Management, Employee Performance, PLS-SEMAbstract
This study examines how perceived transparency and algorithmic fairness in AI-based human resource (HR) practices relate to employee performance, and whether trust in management moderates these relationships. Data were collected through a quantitative cross-sectional survey of 152 employees from Indonesian service organizations using AI-enabled tools in HR decision processes, and analyzed using partial least squares structural equation modeling (PLS-SEM) with bootstrapping. The results show that perceived algorithmic fairness and trust in management have positive and significant relationships with employee performance, whereas perceived transparency does not have a significant direct effect. Trust in management also strengthens the positive relationship between perceived algorithmic fairness and employee performance, but does not significantly moderate the relationship between perceived transparency and employee performance. These findings suggest that organizations seeking performance benefits from AI-based HR should prioritize fairness governance and trust-building managerial practices, while designing transparency as meaningful, employee-relevant explanations rather than merely technical disclosure.
Downloads
References
Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186–3191.
Burhanuddin, B., Narinawati, U., Kurniawan, B., Riyanto, A., & Santy, R. D. (2026). Pengaruh Human Resource Information System (HRIS) Terhadap Kinerja Sumber Daya Manusia Dengan Efektivitas Kerja Sebagai Variabel Intervening. Syntax Literate: Jurnal Ilmiah Indonesia, 11(2), 1125–1137. https://doi.org/10.36418/syntax-literate.v11i2.63828
Cohen-Charash, Y., & Spector, P. E. (2001). The role of justice in organizations: A meta-analysis. Organizational Behavior and Human Decision Processes, 86(2), 278–321. https://doi.org/10.1006/obhd.2001.2958
Colquitt, J. A. (2001). On the dimensionality of organizational justice: A construct validation of a measure. Journal of Applied Psychology, 86(3), 386–400. https://doi.org/10.1037/0021-9010.86.3.386
Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31(6), 874–900. https://doi.org/10.1177/0149206305279602
Dirks, K. T., & Ferrin, D. L. (2002). Trust in leadership: Meta-analytic findings and implications for research and practice. Journal of Applied Psychology, 87(4), 611–628. https://doi.org/10.1037/0021-9010.87.4.611
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns. Science and Engineering Ethics, 26(6), 3333–3361. https://doi.org/10.1007/s11948-020-00276-4
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.2307/3150980
Hair Jr., 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
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Jabagi, N., Croteau, J., Audebrand, C.-H., & Marsan, J. (2025). Do algorithms play fair? Analysing the perceived fairness of HR-decisions made by algorithms and their impact on job satisfaction and perceived organisational support. The International Journal of Human Resource Management, 36(2), 235–274. https://doi.org/10.1080/09585192.2024.2441448
Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data & Society, 8, 1–14. https://doi.org/10.1177/20539517211020332
Kochling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: A systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, 13(3), 795–848. https://doi.org/10.1007/s40685-020-00134-w
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/IJeC.2015100101
Koopmans, L., Bernaards, C., Hildebrandt, V., Schaufeli, W. B., De Vet, H. C. W., & Van der Beek, A. J. (2014). Measuring individual work performance: Identifying and selecting indicators. Work, 48(2), 229–238. https://doi.org/10.3233/WOR-131659
Koopmans, L., Bernaards, C., Hildebrandt, V., van Buuren, S., van der Beek, A. J., & de Vet, H. C. W. (2013). Development of an individual work performance questionnaire. International Journal of Productivity and Performance Management, 62(1), 6–28. https://doi.org/10.1108/17410401311285273
Langer, M., & König, C. J. (2023). Introducing a multi-stakeholder perspective on algorithm-based human resource management: A review and agenda for future research. Human Resource Management Review, 33(1), 100881. https://doi.org/10.1016/j.hrmr.2021.100881
Mahadianto, M. Y., Muhammad Siddiq, D., Mamat, M., Abu Bakar, H., & Hartono, E. (2025). How procedural and informational justice moderated by organizational collectivism predict employee organizational commitment. International Research Journal of Business Studies, 18(2), 159–173. https://doi.org/10.21632/irjbs.18.2.159-173
Mayer, R. C., & Davis, J. H. (1999). The effect of the performance appraisal system on trust for management: A field quasi-experiment. Journal of Applied Psychology, 84(1), 123–136. https://doi.org/10.1037/0021-9010.84.1.123
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 115. https://doi.org/10.1145/3457607
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 115. https://doi.org/10.1145/3457607
Meijerink, J., Boons, M., Keegan, A., & Marler, J. (2021). Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM. The International Journal of Human Resource Management, 32(12), 2545–2562. https://doi.org/10.1080/09585192.2021.1925326
Mirbabaie, M., Langer, M., Rieskamp, J., & Hofeditz, L. (2025). Transparency fallacy: Perceived fairness in algorithmic management. Business & Information Systems Engineering. https://doi.org/10.1007/s12599-025-00963-1
Newman, D. T., Fast, N. J., & Harmon, D. J. (2020). When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes, 160, 149–167. https://doi.org/10.1016/j.obhdp.2020.03.008
Nirwana, N. K. A., Makduani, R., Marhum, U., Rahmayanti, R., & Widaningsih, R. R. A. (2023). Dampak implementasi HRIS (Human Resource Information System) terhadap kinerja karyawan. Jurnal Darma Agung, 31(6), 393–400. https://doi.org/10.46930/ojsuda.v31i6.3685
Ochmann, J., Michels, L., Tiefenbeck, V., & Maier, C. (2024). Perceived algorithmic fairness: An empirical study of transparency and anthropomorphism in algorithmic recruiting. Information Systems Journal, 34(2), 384–414. https://doi.org/10.1111/isj.12482
Ötting, S. K., & Maier, G. W. (2018). The importance of procedural justice in human–machine interactions: Intelligent systems as new decision agents in organizations. Computers in Human Behavior, 89, 27–39. https://doi.org/10.1016/j.chb.2018.07.022
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Savitri, T. A., Buchori, I., & Supratikta, H. (2024). Exploring the role of Human Resources Information System in employee performance management: A systematic literature review. Indonesian Development of Economics and Administration Journal, 3(1), 55–64. https://doi.org/10.70001/idea.v3i1.211
Sumaryono, S. (2023). Transformasi e-HRM (Electronic Human Resource Management) dalam Pengembangan Kualitas SDM (Sumber Daya Manusia) Berbasis Teknologi Informasi Terhadap Kinerja Karyawan. Jurnal Ekonomi Manajemen Sistem Informasi, 5(1), 50–63. https://doi.org/10.31933/jemsi.v5i1.1680
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s Alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600–1631. https://doi.org/10.1002/smj.3322
Widyastuti, T., Ramdani, Z., Alim, S., & Harlianty, R. A. (2024). The Indonesian version of Individual Work Performance Questionnaire (IWPQ): Structure factor and criterion validity. Psychological Research and Intervention, 7(1), 1–16. https://doi.org/10.21831/pri.v7i1.76457
Yu, L., & Li, Y. (2022). Artificial intelligence decision-making transparency and employees’ trust: The parallel multiple mediating effect of effectiveness and discomfort. Behavioral Sciences, 12(5), 127. https://doi.org/10.3390/bs12050127
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Muhammad Irvan, Bachrudin Syahroni, Mahadianto
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
