AI-Driven Performance Management Systems and Their Implications for Employee Motivation
Keywords:
Artificial Intelligence, performance management, employee motivation, workplace, technologyAbstract
This study reviewed to examine how AI-driven performance management systems influence employee motivation by enhancing feedback and addressing potential ethical and psychological challenges. AI-driven performance management systems are transforming traditional appraisals into dynamic, data-informed frameworks that enhance real-time feedback, engagement, and organizational agility. The conclusion shows that AI has transformed performance management by making feedback more accurate and focused on growth. Its overall impact, however, depends on how fairly, transparently, and ethically it is used. Future research should examine the long-term effects of AI monitoring on motivation, engagement, and well-being through longitudinal and experimental studies to determine whether AI sustains or diminishes motivation over time. It should also explore cultural and contextual differences in how employees perceive and respond to AI systems, enabling the design of adaptive, inclusive, and context-sensitive performance management solutions
References
Adams, J. S. (1963). Towards an understanding of inequity. The journal of abnormal and social psychology, 67(5), 422. https://psycnet.apa.org/doi/10.1037/h0040968
Adams, J. S., & Freedman, S. (1976). Equity theory revisited: Comments and annotated bibliography. Advances in experimental social psychology, 9, 43-90. https://doi.org/10.1016/S0065-2601(08)60058-1
Ahmed, A., Sivarajah, U., Irani, Z., Mahroof, K., & Charles, V. (2024). Data-driven subjective performance evaluation: An attentive deep neural network model based on a call center case. Annals of Operations Research, 333(2), 939-970. https://doi.org/10.1007/s10479-022-04874-2
Akinrinola, O., Okoye, C. C., Ofodile, O. C., & Ugochukwu, C. E. (2024). Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability. GSC Advanced Research and Reviews, 18(3), 050-058. https://doi.org/10.30574/gscarr.2024.18.3.0088
Attwell, G. (2024, October 16). AI and motivation for learning. AI Pioneers. https://aipioneers.org/ai-and-motivation-for-learning-2/
Bahangulu, J. K., & Owusu-Berko, L. (2025). Algorithmic bias, data ethics, and governance: Ensuring fairness, transparency, and compliance in AI-powered business analytics applications. World J Adv Res Rev, 25(2), 1746-63. https://doi.org/10.30574/wjarr.2025.25.2.0571
Benabou, A., Touhami, F., & Demraoui, L. (2024, May). Artificial intelligence and the future of human resource management. In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-8). IEEE. https://doi.org/10.1109/ISCV60512.2024.10620146
Benbasat, I., & Barki, H. (2007). Quo vadis TAM?. Journal of the Association for Information Systems, 8(4), 7. https://aisel.aisnet.org/jais/vol8/iss4/7/
Chang, J. (2024). Exploring the Effectiveness of AI in Project Risk Management: What Project Managers Do to Facilitate the Changes (Master's thesis, Purdue University). https://search.proquest.com/openview/9af8444d2e150aa3ba9baf45df79a59c/1?pq-origsite=gscholar&cbl=18750&diss=y
Chaudhary, G. (2024). Unveiling the black box: Bringing algorithmic transparency to AI. Masaryk University Journal of Law and Technology, 18(1), 93-122. https://www.ceeol.com/search/article-detail?id=1287954
Cropanzano, R., & Folger, R. (1989). Referent cognitions and task decision autonomy: Beyond equity theory. Journal of Applied Psychology, 74(2), 293. https://psycnet.apa.org/doi/10.1037/0021-9010.74.2.293
Davis Jr, F. D. (1986). A Technology Acceptance Model For Empirically Testing New End-User Information Systems: Theory And Results (Doctoral dissertation, Massachusetts Institute of Technology).
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. https://doi.org/10.2307/249008
De Moya, J. F., & Pallud, J. (2020). From panopticon to heautopticon: A new form of surveillance introduced by quantified‐self practices. Information Systems Journal, 30(6), 940-976. https://doi.org/10.1111/isj.12284
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Perspectives in social psychology.
Deci, E. L., & Ryan, R. M. (2000). The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior. Psychological inquiry, 11(4), 227-268. https://doi.org/10.1207/S15327965PLI1104_01
Deutsch, M. (1975). Equity, equality, and need: What determines which value will be used as the basis of distributive justice?. Journal of Social Issues, 31(3), 137-149. https://doi.org/10.1111/j.1540-4560.1975.tb01000.x
Du, J. (2025). Toward Responsible and Beneficial AI: Comparing Regulatory and Guidance-Based Approaches, Comprehensive Comparative Analysis of Artificial Intelligence Governance Frameworks across the European Union, United States, China, and IEEE. arXiv preprint arXiv:2508.00868. https://doi.org/10.48550/arXiv.2508.00868
Ezzeddine, Y. (2024). Artificial Intelligence in Law Enforcement Surveillance: Citizen Perspectives, Resistance and Counterstrategies (Doctoral dissertation, Sheffield Hallam University (United Kingdom)). https://search.proquest.com/openview/0c13e9a694dbeb0a412c444cdd2977d9/1?pq-origsite=gscholar&cbl=2026366&diss=y
Felix, B., Dourado, D., & Nossa, V. (2023). Algorithmic management, preferences for autonomy/security and gig-workers' wellbeing: A matter of fit?. Frontiers in Psychology, 14, 1088183. https://doi.org/10.3389/fpsyg.2023.1088183
Gagné, M., & Deci, E. L. (2005). Self‐determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331-362. https://doi.org/10.1002/job.322
Gagné, M., Parent-Rocheleau, X., Bujold, A., Gaudet, M. C., & Lirio, P. (2022). How algorithmic management influences worker motivation: A self-determination theory perspective. Canadian Psychology/Psychologie canadienne, 63(2), 247. https://doi.org/10.1037/cap0000324
Galič, M., Timan, T., & Koops, B. J. (2017). Bentham, Deleuze and beyond: An overview of surveillance theories from the panopticon to participation. Philosophy & Technology, 30(1), 9-37. https://doi.org/10.1007/s13347-016-0219-1
Greenberg, J. (1990). Organizational justice: Yesterday, today, and tomorrow. Journal of Management, 16(2), 399-432. https://doi.org/10.1177/014920639001600208
Greenberg, J. (1993). The social side of fairness: Interpersonal and informational classes of organizational justice. https://psycnet.apa.org/record/1993-97521-004
Gupta, R. (2024). Impact of artificial intelligence (AI) on human resource management (HRM). International Journal for Multidisciplinary Research, doi: 10. https://pdfs.semanticscholar.org/777c/20d96221f47f83130be9c5cf6a9986759917.pdf
Iancu, C., & Oprea, S. V. (2025). AI and Human Resources in a Literature-Driven Investigation Into Emerging Trends. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3568338
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(2), 20539517211020332. https://doi.org/10.1177/20539517211020332
Kaggwa, S., Eleogu, T. F., Okonkwo, F., Farayola, O. A., Uwaoma, P. U., & Akinoso, A. (2024). AI in decision making: transforming business strategies. International Journal of Research and Scientific Innovation, 10(12), 423-444. https://doi.org/10.51244/IJRSI.2023.1012032
Lee, Y., Lee, J., & Hwang, Y. (2015). Relating motivation to information and communication technology acceptance: A self-determination theory perspective. Computers in Human Behavior, 51, 418-428. https://doi.org/10.1016/j.chb.2015.05.021
Lieberman, H. (2020, September). Intrinsic and extrinsic motivation in intelligent systems. In International Workshop on Self-Supervised Learning (pp. 62-71). PMLR. https://proceedings.mlr.press/v131/lieberman20a
Marcus, S. (2025). AI in Employee Engagement and Performance Management. The Critical Review of Social Sciences Studies, 3(2), 2428-2435. https://doi.org/10.59075/x61bde56
Matthew, B. (2025). Psychological Effects of Continuous Monitoring. https://www.researchgate.net/profile/Ilesanmi-Michael-2/publication/393374094
Nath, A., Aziz, F., Fuad, M. N., Nipa, M. N., Khan, S. R., & Barsa, N. J. (2025). AI In Performance Management: A Study on Employee Perceptions and Organizational Outcomes. Available at SSRN 5194491. https://dx.doi.org/10.2139/ssrn.5194491
Oladele, O. K. (2024). Performance Management Analytics: Using AI to Analyze Employee Performance Data and Inform Development and Rewards Programs. https://www.researchgate.net/profile/Oluwaseyi-Oladele-3/publication/384932994
Orji, F. A. (2024). Artificial Intelligence and Persuasive Computing Approach for Motivating Students and Enhancing their Learning Experience (Doctoral dissertation). https://hdl.handle.net/10388/16265
Pickren, W. (2019). Psychology and Health: Culture, Place, History. https://doi.org/10.4324/9781003006435
Quan, Y. (2025). The role of AI-driven feedback in fostering growth mindset and engagement: A self-determination theory perspective. Learning and Motivation, 92, 102192. https://doi.org/10.1016/j.lmot.2025.102192
Radanliev, P. (2025). AI ethics: Integrating transparency, fairness, and privacy in AI development. Applied Artificial Intelligence, 39(1), 2463722. https://doi.org/10.1080/08839514.2025.2463722
Roemmich, K., Schaub, F., & Andalibi, N. (2023, April). Emotion AI at work: Implications for workplace surveillance, emotional labor, and emotional privacy. In Proceedings of the 2023 CHI conference on human factors in computing systems (pp. 1-20). https://doi.org/10.1145/3544548.3580950
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. https://psycnet.apa.org/doi/10.1521/978.14625/28806
Scatiggio, V. (2020). Tackling the issue of bias in artificial intelligence to design AI-driven, fair, and inclusive service systems. How human biases are breaching into AI algorithms, with severe impacts on individuals and societies, and what designers can do to face this phenomenon and change for the better. https://www.politesi.polimi.it/handle/10589/186118
Thirunagalingam, A., Addanki, S., Vemula, V. R., & Selvakumar, P. (2025). AI in performance management: Data-driven approaches. In Navigating organizational behavior in the digital age with AI (pp. 101-126). IGI Global Scientific Publishing. https://www.igi-global.com/chapter/ai-in-performance-management/364227
Usmani, U. A., Happonen, A., & Watada, J. (2023, June). Human-centered artificial intelligence: Designing for user empowerment and ethical considerations. In 2023, the 5th international congress on human-computer interaction, optimization, and robotic applications (HORA). IEEE. https://urn.fi/URN:NBN:fi-fe20231101142268
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Waller, S. (2025). AI for Performance Management in HR: A Review of Reported Effects on Evaluation and Development. Authorea Preprints. https://doi.org/10.36227/techrxiv.175744086.61604077/v1
