The rise of AI technology has greatly transformed the traditional PMS and improved its effectiveness and reliance on data in HRM. This study mainly aims to identify the essential variables determining the implementation of AI in PMS, including the benefits, ethics, challenges, and impacts. The descriptive research methodology was used by means of analyzing secondary literature available on Scopus, Google Scholar, ScienceDirect, Springer, and ResearchGate. Peer-reviewed articles published in the period from 2019 to 2024 were selected by applying the PRISMA approach. Initially, there were 454 articles, and after the eligibility criteria, 40 articles were chosen for thematic analysis. According to the findings, the application of AI in PMS makes organizations more productive owing to automation, appraisal, predictions, customization, and performance management. Based on the statistical data obtained from the articles examined, the top four advantages associated with the use of AI in PMS include improved efficiency (90%), real-time feedback (85%), need for human supervision (82%), and minimized biases (80%). As for the challenges, ethical considerations (75%), privacy (70%), and user acceptance (68%) were identified as the primary barriers. Further, technology readiness, organizational readiness, environmental readiness, ethics of AI, and employee trust become important determinants in ensuring successful AI implementation within PMS. According to the authors, implementation of AI within organizations is associated with great potentials in improving the efficiency of processes within the organization, involvement of employees, and the decision-making process under ethical considerations. For this to happen, it is crucial to ensure proper governance, organizational readiness, acceptance of the technology by employees, and collaboration between humans and the system.
Aguinis H, Beltran JR, Cope A. How to use generative AI as a human resource management assistant. Organizational Dynamics. 2024;53(1):101029.
2.
Rashed Alrumi A. HARNESSING THE POWER OF ARTIFICIAL INTELLIGENCE TO IMPROVE MANAGEMENT INFORMATION SYSTEMS. International Journal for Quality Research. 2024;18(1):115–28.
3.
Chowdhury S, Budhwar P, Dey PK, Joel-Edgar S, Abadie A. AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework. Journal of Business Research. 2022;144:31–49.
4.
Chowdhury S, Dey P, Joel-Edgar S, Bhattacharya S, Rodriguez-Espindola O, Abadie A, et al. Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review. 2023;33(1):100899.
5.
E. J. C, K. E. D. Strategic Role of Artificial Intelligence (AI) on Human Resource Management (HR) Employee Performance Evaluation Function. International Journal of Entrepreneurship and Business Innovation. 2024;7(2):269–82.
6.
Cubric M. Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society. 2020;62:101257.
7.
Cuéllar MF, Larsen B, Lee YS, Webb M. Does Information About AI Regulation Change Manager Evaluation of Ethical Concerns and Intent to Adopt AI? The Journal of Law, Economics, and Organization. 2022;40(1):34–75.
8.
Dabbous A, Aoun Barakat K, Merhej Sayegh M. Enabling organizational use of artificial intelligence: an employee perspective. Journal of Asia Business Studies. 2021;16(2):245–66.
9.
Fenwick A, Molnar G, Frangos P. Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace. Frontiers in Artificial Intelligence. 2024;6.
10.
Garengo P, Sardi A, Nudurupati SS. Human resource management (HRM) in the performance measurement and management (PMM) domain: a bibliometric review. International Journal of Productivity and Performance Management. 2021;71(7):3056–77.
11.
Graßmann C, Schermuly CC. Coaching With Artificial Intelligence: Concepts and Capabilities. Human Resource Development Review. 2020;20(1):106–26.
12.
Grover P, Kar AK, Dwivedi YK. Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Annals of Operations Research. 2020;308(1–2):177–213.
13.
Gupta S, Ghardallou W, Pandey DK, Sahu GP. Artificial intelligence adoption in the insurance industry: Evidence using the technology–organization–environment framework. Research in International Business and Finance. 2022;63:101757.
14.
Haenlein M, Kaplan A. A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review. 2019;61(4):5–14.
15.
Hangl J, Behrens VJ, Krause S. Barriers, Drivers, and Social Considerations for AI Adoption in Supply Chain Management: A Tertiary Study. Logistics. 2022;6(3):63.
16.
Hmoud BI, Várallyai L. Artificial Intelligence in Human Resources Information Systems: Investigating its Trust and Adoption Determinants. International Journal of Engineering and Management Sciences. 2020;5(1):749–65.
17.
Horani OM, Al-Adwan AS, Yaseen H, Hmoud H, Al-Rahmi WM, Alkhalifah A. The critical determinants impacting artificial intelligence adoption at the organizational level. Information Development. 2023;41(3):1055–79.
18.
Jain R, Garg N, Khera SN. Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model. Frontiers in Psychology. 2022;13.
19.
Kelley S. Employee Perceptions of the Effective Adoption of AI Principles. Journal of Business Ethics. 2022;178(4):871–93.
20.
Kelly S, Kaye SA, Oviedo-Trespalacios O. What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics. 2023;77:101925.
21.
Khair MA, Mahadasa R, Tuli FA, Ande JRPK. Beyond Human Judgment: Exploring the Impact of Artificial Intelligence on HR Decision-Making Efficiency and Fairness. Global Disclosure of Economics and Business. 2020;9(2):163–76.
22.
Kshetri N. The Role of Artificial Intelligence in Promoting Financial Inclusion in Developing Countries. Journal of Global Information Technology Management. 2021;24(1):1–6.
23.
Maley JF, Dabic M, Moeller M. Employee performance management: charting the field from 1998 to 2018. International Journal of Manpower. 2020;42(1):131–49.
24.
Malik A, Budhwar P, Kazmi BA. Artificial intelligence (AI)-assisted HRM: Towards an extended strategic framework. Human Resource Management Review. 2023;33(1):100940.
25.
Nayal K, Raut R, Priyadarshinee P, Narkhede BE, Kazancoglu Y, Narwane V. Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. The International Journal of Logistics Management. 2021;33(3):744–72.
26.
Omrani N, Rivieccio G, Fiore U, Schiavone F, Agreda SG. To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts. Technological Forecasting and Social Change. 2022;181:121763.
27.
Owolabi OS, Uche PC, Adeniken NT, Ihejirika C, Islam RB, Chhetri BJT. Ethical Implication of Artificial Intelligence (AI) Adoption in Financial Decision Making. Computer and Information Science. 2024;17(1):49.
28.
Oyekunle D, Boohene D. DIGITAL TRANSFORMATION POTENTIAL: THE ROLE OF ARTIFICIAL INTELLIGENCE IN BUSINESS. International Journal of Professional Business Review. 2024;9(3):e04499.
29.
Pan Y, Froese FJ. An interdisciplinary review of AI and HRM: Challenges and future directions. Human Resource Management Review. 2023;33(1):100924.
30.
Pathak A, Bansal V. Factors Influencing the Readiness for Artificial Intelligence Adoption in Indian Insurance Organizations. IFIP Advances in Information and Communication Technology. Springer Nature Switzerland; 2023. p. 43–55.
31.
Phuoc NV. The Critical Factors Impacting Artificial Intelligence Applications Adoption in Vietnam: A Structural Equation Modeling Analysis. Economies. 2022;10(6):129.
32.
Pillai R, Sivathanu B, Mariani M, Rana NP, Yang B, Dwivedi YK. Adoption of AI-empowered industrial robots in auto component manufacturing companies. Production Planning & Control. 2021;33(16):1517–33.
33.
Qin S (Marco), Jia N, Luo X, Liao C, Huang Z. Perceived Fairness of Human Managers Compared with Artificial Intelligence in Employee Performance Evaluation. Journal of Management Information Systems. 2023;40(4):1039–70.
34.
Sardi A, Sorano E, Ferraris A, Garengo P. Evolutionary paths of performance measurement and management system: the longitudinal case study of a leading SME. Measuring Business Excellence. 2020;24(4):495–510.
35.
Solaimani S, Swaak L. Critical Success Factors in a Multi-Stage Adoption of Artificial Intelligence: A Necessary Condition Analysis. Elsevier BV; 2022.
36.
Stone DL, Lukaszewski KM, Johnson RD. Will artificial intelligence radically change human resource management processes? Organizational Dynamics. 2024;53(1):101034.
37.
Tjebane MM, Musonda I, Okoro C. Organisational Factors of Artificial Intelligence Adoption in the South African Construction Industry. Frontiers in Built Environment. 2022;8.
38.
Tong S, Jia N, Luo X, Fang Z. The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal. 2021;42(9):1600–31.
39.
Uren V, Edwards JS. Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management. 2023;68:102588.
40.
Varma A, Pereira V, Patel P. Artificial intelligence and performance management. Organizational Dynamics. 2024;53(1):101037.
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