Journal of Human Resource Management Future Studies
Volume 1, Issue 1 (6-2026)
Journal of human resource management future studies 2026, 1(1): 88-107 |
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مدیریت منابع انسانی: روندهای نوظهور و آینده. Journal of human resource management future studies 2026; 1 (1) :88-107
URL: http://jhrmfs.khu.ac.ir/article-1-164-en.html
URL: http://jhrmfs.khu.ac.ir/article-1-164-en.html
Abstract: (160 Views)
Background and Objective: Human resource management (HRM) is undergoing profound transformation driven by technological innovation, changing workforce expectations, and global socio-economic shifts. The fourth industrial revolution, the COVID-19 pandemic, and the rise of a digitally native workforce have collectively reshaped the landscape of HRM. This study aims to identify and analyze the key emerging trends reshaping HRM practices and to project the trajectory of the field through 2030.
Methodology: A systematic literature review was conducted using Scopus, Web of Science, Google Scholar, Emerald, and ProQuest. Studies published between 2015 and 2024 were reviewed. Following a rigorous screening process, 85 peer-reviewed sources were analyzed using thematic synthesis and a four-stage coding procedure.
Findings: Five major trends were identified as central to the future of HRM: (1) artificial intelligence and process automation, (2) remote and hybrid work models, (3) employee experience centrality, (4) data-driven HR decision-making, and (5) diversity, equity, and inclusion (DEI) initiatives. Cross-impact matrix analysis reveals that AI and data analytics are the primary drivers, while employee experience serves as the primary outcome variable.
Conclusion: Organizations that proactively adapt to these emerging trends will achieve competitive advantage in talent attraction, retention, and development. HR professionals must evolve into strategic architects who combine human-centered leadership with data literacy and technological fluency. The implications for both research and practice are substantial, calling for longitudinal studies and context-sensitive implementation frameworks.
Methodology: A systematic literature review was conducted using Scopus, Web of Science, Google Scholar, Emerald, and ProQuest. Studies published between 2015 and 2024 were reviewed. Following a rigorous screening process, 85 peer-reviewed sources were analyzed using thematic synthesis and a four-stage coding procedure.
Findings: Five major trends were identified as central to the future of HRM: (1) artificial intelligence and process automation, (2) remote and hybrid work models, (3) employee experience centrality, (4) data-driven HR decision-making, and (5) diversity, equity, and inclusion (DEI) initiatives. Cross-impact matrix analysis reveals that AI and data analytics are the primary drivers, while employee experience serves as the primary outcome variable.
Conclusion: Organizations that proactively adapt to these emerging trends will achieve competitive advantage in talent attraction, retention, and development. HR professionals must evolve into strategic architects who combine human-centered leadership with data literacy and technological fluency. The implications for both research and practice are substantial, calling for longitudinal studies and context-sensitive implementation frameworks.
Keywords: Human Resource Management, Artificial Intelligence, Remote Work, Digital Transformation, Future of Work, People Analytics, Employee Experience
Type of Study: Research |
Subject:
Special
Received: 2026/04/28 | Accepted: 2026/05/30 | Published: 2026/06/7
Received: 2026/04/28 | Accepted: 2026/05/30 | Published: 2026/06/7
References
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43. Beer, M., Spector, B., Lawrence, P. R., Mills, D. Q., & Walton, R. E. (1984). Managing Human Assets. Free Press.
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45. Boxall, P., Purcell, J., & Wright, P. (2021). The Oxford Handbook of Human Resource Management (2nd ed.). Oxford University Press.
46. Budhwar, P., Malik, A., De Silva, M. T., & Thevisuthan, P. (2022). Artificial intelligence–challenges and opportunities for international HRM. International Journal of Human Resource Management, 33(6), 1065–1097. [DOI:10.1080/09585192.2022.2035161]
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72. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management. California Management Review, 61(4), 15–42. [DOI:10.1177/0008125619867910]
73. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. [DOI:10.1111/1467-8551.00375]
74. Ulrich, D. (1997). Human Resource Champions. Harvard Business School Press.
75. Ulrich, D., & Filler, R. (2023). HR from the Outside In (2nd ed.). McGraw-Hill.
76. 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), 1557–1580. [DOI:10.25300/MISQ/2021/16589]
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78. World Economic Forum. (2023). Future of Jobs Report 2023. World Economic Forum.
79. World Economic Forum. (2025). Future of Jobs Report 2025. World Economic Forum.
80. Yarberry, S., & Sims, C. (2021). The impact of COVID-19-prompted virtual/remote work environments on employees' career development. Advances in Developing Human Resources, 23(3), 237–256. [DOI:10.1177/15234223211017544]
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