A new paradigm of personnel evaluation: from HR metrics to AI and empathy-led leadership
DOI:
https://doi.org/10.31558/2307-2318.2026.2.8Keywords:
personnel evaluation; HR Metrics; artificial intelligence in HRM; empathy-led leadership; performance management; socio-technical systems; algorithmic fairness; psychological safetyAbstract
The article examines the limitations of traditional personnel evaluation systems in the context of contemporary organisations characterised by dynamic work environments, digitalisation, and increasing reliance on data-driven decision-making. Conventional appraisal approaches are critically assessed in terms of subjectivity, episodic nature, and insufficient developmental orientation, which reduce their effectiveness and credibility. The study aims to develop and theoretically justify an Integrated Personnel Evaluation Model (IPEM) that combines HR metrics, AI-driven People Analytics, and empathy-led leadership within a unified socio-technical framework. The research is based on an integrative literature review, comparative analysis, and conceptual modelling. The proposed model consists of three interrelated layers: HR metrics and goal architecture, an AI-based analytics engine, and empathy-led leadership. The model also incorporates a governance layer ensuring fairness, transparency, and regulatory compliance. The findings demonstrate that effective evaluation systems should evolve toward continuous, data-informed, and development-oriented processes. The study concludes that the integration of analytical precision with human-centred leadership is essential for building valid, trustworthy, and strategically relevant personnel evaluation systems in the digital economy.
References
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/10.1177/0008125619867
van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quarterly, 45(3), 1557–1582. https://doi.org/10.25300/MISQ/2021/16559
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 ACM Conference on Fairness, Accountability, and Transparency, 469–481. https://doi.org/10.1145/3351095.3372828
Wachter, S., Mittelstadt, B., & Russell, C. (2021). Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI. Computer Law & Security Review, 41, 105567. https://doi.org/10.1016/j.clsr.2021.105567
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–132. https://doi.org/10.1111/1748-8583.12258
Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71–79. https://images.template.net/wp-content/uploads/2016/04/11122659/balanced-scorecard-key-performance-indicators.pdf
Locke, E. A., & Latham, G. P. (2002).Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717. https://doi.org/10.1037/0003-066X.57.9.705
Cappelli, P., & Tavis, A. (2016). The performance management revolution. Harvard Business Review, 94(10), 58–67.
Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11. https://doi.org/10.1111/1748-8583.12090
Edmondson, A. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, 44(2), 350-383. https://doi.org/10.2307/2666999
Frazier, M. L., Fainshmidt, S., Klinger, R. L., Pezeshkan, A., & Vracheva, V. (2017). Psychological safety: A meta-analytic review and extension. Personnel Psychology, 70(1), 113–165. https://doi.org/10.1111/peps.12183
De Neve, J.-E., Kaats, M., & Ward, G. (2024). Workplace wellbeing and firm performance (Wellbeing Research Centre Working Paper 2304). University of Oxford. https://doi.org/10.5287/ora-bpkbjayvk
Scullen, S. E., Mount, M. K., & Goff, M. (2000). Understanding the latent structure of job performance ratings. Journal of Applied Psychology, 85(6), 956–970. https://doi.org/10.1037/0021-9010.85.6.956
Murphy, K. R., & Cleveland, J. N. (1995). Understanding performance appraisal: Social, organizational, and goal-based perspectives. Sage Publications.
Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), 100838. https://doi.org/10.1016/j.hrmr.2021.100838
Doerr, J. (2018). Measure What Matters: OKRs: The Simple Idea that Drives 10x Growth. Portfolio. https://sunbytes.io/app/uploads/2022/04/Measure-What-Matter-John-Doerr.pdf
Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699
Pulakos, E. D., Hanson, R. M., Arad, S., & Moye, N. (2015). Performance management can be fixed: An on-the-job experiential learning approach for complex behavior change. Industrial and Organizational Psychology: Perspectives on Science and Practice, 8(1), 51–76. https://doi.org/10.1017/iop.2014.2
Latham, G. P., & Locke, E. A. (2007). New developments in and directions for goal-setting research. European Psychologist, 12(4), 290–300. https://doi.org/10.1027/1016-9040.12.4.290
Bankins, S., Formosa, P., Griep, Y. et al. (2022). AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context. Inf Syst Front 24, 857–875 https://doi.org/10.1007/s10796-021-10223-8
Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159–182. https://doi.org/10.1002/job.2735
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI literacy: An integrative framework and research agenda. International Journal of Human Resource Management, 36(2), 267–314. DOI: 10.1016/j.hrmr.2022.100899
Rigamonti E, Gastaldi L, Corso M (2024), Measuring HR analytics maturity: supporting the development of a roadmap for data-driven human resources management. Management Decision, Vol. 62 No. 13 pp. 243–282, doi: https://doi.org/10.1108/MD-11-2023-2087
Madad, S., Maumy-Bertrand, M., Bertrand, F., & Valero, Y. (2024,). NLP approach to ground employee performance evaluation. In Proceedings of the International Conference on Connected Innovation and Technology (IC-CITX.O) & The Smart Healthcare International Conference (SHeIC), Danang, Vietnam. https://hal.science/hal-05555901v1/document
Kniffin, K. M., Narayanan, J., Anseel, F., Antonakis, J., Ashford, S. P., Bakker, A. B., Bamberger, P., Bapuji, H., Bhave, D. P., Choi, V. K., Creary, S. J., Demerouti, E., Flynn, F. J., Gelfand, M. J., Greer, L. L., Johns, G., Lemoine, G. J., Probst, T. M., Putnam, L. L., van Vugt, M. (2021). COVID-19 and the workplace: Implications, issues, and insights for future research and action. American Psychologist, 76(1), 63–77. https://doi.org/10.1037/amp0000716
Dewe, P., & Cooper, C. L. (2021). Work and stress: A research overview. Routledge/Taylor & Francis Group.
Rousseau, D.M. and Barends, E.G.R. (2011), Becoming an evidence-based HR practitioner. Human Resource Management Journal, 21: 221-235. https://doi.org/10.1111/j.1748-8583.2011.00173.x
