Unravelling Smart HRM 4.0: A Narrative Review of Progressive 4.0 Technology Integration in Human Resource Management
Keywords:
Industry 4.0, 4.0 technologies, Smart HRM 4.0, Ethical implications, Corporate digital responsibility.
Abstract
The integration of progressive 4.0 technology into human resource management (HRM) represents a significant shift in how organizations optimize and enhance workforce capabilities. This review explores the origins, applications, benefits, challenges and future prospects surrounding smart HRM 4.0. By reviewing existing literature, this paper examines the utilization of Artificial Intelligence (AI), Big Data analytics, machine learning (ML) and the Internet of Things (IoT) in HRM, specifically in talent acquisition, training, performance management, and rewards. Additionally, it addresses the implementation challenges including data quality assurance, skill shortages, and cultural resistance. The paper also emphasizes the importance of ethical considerations. In terms of future research, it highlights the necessity for ethically deploying Industry 4.0 and establishing robust AI governance frameworks. By combining technological innovation with ethical values, organizations can navigate the complexities of this integration, leading to a future characterized by workplace efficiency, accountability, and fairness.
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References
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Flores, E., Xu, X., & Lu, Y. (2020). Human Capital 4.0: A workforce competence typology for Industry 4.0. Journal of Manufacturing Technology Management, 31(4), 687–703.
Gal, U., Jensen, T. B., & Stein, M.K. (2020). Breaking the vicious cycle of algorithmic management: A Virtue ethics approach to people analytics. Information and Organization, 30(2), 100301. https://doi.org/10.1016/j.infoandorg.2020.100301
Ganatra, N. J. & Pandaya, J. D. (2023). The transformative impact of artificial intelligence on hr practices and employee experience: A review. Journal of Management Research and Analysis, 10(2), 106-111.
Ghobakhloo, M., Vilkas, M., Stefanini, A., Grybauskas, Marcinkevicius, G., Petraite, M. & Sarvari, P. A. (2023). Developing capabilities underlying Industry 4.0 design principles within the manufacturing context. Journal of Manufacturing Technology Management, 34(7), 1183-1207.
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Hartley, N., Kunz, W. & Tabit, J. (2024). The corporate digital responsibility (CDR) calculus: How and why organizations reconcile digital and ethical trade-offs for growth. Organizational Dynamics, 53(2), 101056. https://doi.org/10.1016/j.orgdyn.2024.101056.
Heidemann, A., Hulter, S. M. & Tekieli, M. (2024). Machine learning with real-world HR data: mitigating the trade-off between predictive performance and transparency. The International Journal of Human Resource Management, 35(14), 2343-2366
Hickman, L., Saef, R., Ng, V., Woo, S. E., Tay, L., & Bosch, N. (2021). Developing and evaluating language-based machine learning algorithms for inferring applicant personality in video interviews. Human Resource Management Journal, 34(2), 255-274. https://doi. org/10.1111/1748-8583.12356
Hmoud, B. (2021). Assessing HR Leader’s attitude toward the adoption of artificial intelligence in recruitment. Journal of EcoAgriTourism, 17(1), 20-32
Hoffmann Souza, M.L., da Costa, C.A., de Oliveira R. G. & da Rosa R. R. (2020). A survey on decision-making based on system reliability in the context of Industry 4.0. Journal of Manufacturing Systems, 56, 133-156.
Hunkenschroer, A.L. & Luetge, C. (2022). Ethics of AI-enabled recruiting and selection: a review and research agenda. Journal of Business Ethics, 178(4), 977-1007
James, A.T., Kumar, G., Tayal, P., Chauhan, A., Wadhawa, C. & Panchal, J. (2022). Analysis of human resource management challenges in the implementation of Industry 4.0 in the Indian automobile industry. Technological Forecasting and Social Change, 176, 121483
Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt Industry 4.0 in the Indian manufacturing industry. Computers in Industry, 101, 107-119. https://doi.org/10.1016/j. compind.2018.06.004
Lobschat, L., Mueller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M., & Wirtz, J. J. J. (2021). Corporate digital responsibility. Journal of Business Research, 122, 875–888.
Maamari, B.E. & Osta, A. (2021). The effect of HRIS implementation success on job involvement, job satisfaction and work engagement in SMEs. International Journal of Organizational Analysis, 29(5), 1269-1286.
Marcon, E., Soliman, M., Gerstlberger, W. & Frank, A.G. (2022). Sociotechnical factors and Industry 4.0: an integrative perspective for the adoption of smart manufacturing technologies. Journal of Manufacturing Technology Management, 33(2), 259-286.
Marler, J.H. & Boudreau, J.W. (2017). An evidence-based review of HR Analytics. International Journal of Human Resource Management, 28(1), 3-26.
Matt, D. T., Orzes, G., Rauch, E., & Dallasega, P. (2020). Urban production – A socially sustainable factory concept to overcome shortcomings of qualified workers in smart SMEs. Computers and Industrial Engineering, 139. https://doi.org/10.1016/j. cie.2018.08.035
McIver, D., Lengnick-Hall, M. A., & Lengnick-Hall, C. A. (2018). A strategic approach to workforce analytics. Business Horizons, 61(3), 397-407.
Meddour, H., Abdussalaam, I. I., & Abdul Majid, A. H. (2020). Withstanding the effect of Industry 4.0: The role of high-performance work system and management philosophy. International Journal of Supply Chain Management, 9(1), 511–519.
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.
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Published
2024-10-05
How to Cite
Rusdi, S. D., Ismail, I. R., & Isa, R. M. (2024). Unravelling Smart HRM 4.0: A Narrative Review of Progressive 4.0 Technology Integration in Human Resource Management. Information Management and Business Review, 16(3(I)S), 415-423. https://doi.org/10.22610/imbr.v16i3(I)S.4070
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Copyright (c) 2024 Syezreen Dalina Rusdi, Ida Rosnita Ismail, Rosmah Mat Isa
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