The Current Academic Theory on HRM and SHRM in a Global Context: The Role of AI

Introduction

Human Resource Management (HRM) and Strategic Human Resource Management (SHRM) have developed significantly in response to globalization, technological advancements, and workforce dynamics. One of the most transformative developments in recent years has been the integration of Artificial Intelligence (AI) in HRM, which has modified recruitment, performance management, employee engagement, and decision-making processes (Stone et al., 2015). This has been critically examining contemporary academic theories on HRM and SHRM within a global context, emphasizing the implications and challenges of AI adoption in HRM.

Theoretical Perspectives on HRM and SHRM

HRM theories have traditionally focused on the alignment of HR practices with organizational goals to uplift employee productivity and engagement. The classical theories, including the Resource-Based View (RBV) (Barney, 1991) and the Harvard Framework (Beer et al., 1984), emphasize the strategic value of human capital. SHRM extends HRM by aligning it with long-term business goals, fostering a proactive and comprehensive approach to workforce management. AI has introduced a new layer to HRM by automating repetitive tasks, improving data-driven decision-making, and enabling predictive analytics (Marler & Boudreau, 2017). However, existing HRM theories do not fully capture the significant impact of AI, necessitating an update to frameworks that account for ethical concerns, employee well-being, and the critical role of human oversight.

AI in HRM: Opportunities and Challenges

1. Recruitment and Selection

AI tools like applicant tracking systems (ATS) and predictive analytics make recruitment easier by automating resume screening and candidate assessments (Johnson et al., 2020). While AI increases efficiency and minimizes bias, concerns exist about the lack of transparency in algorithms and the potential to reinforce biases from biased training data (Raghavan et al., 2020).

2. Performance Management

AI supports continuous performance monitoring by using real-time analytics, sentiment analysis, and automated feedback systems (Guenole & Feinzig, 2018). However, excessive reliance on AI may lead to surveillance concerns and employee dissatisfaction, necessitating a balance between automation and human judgment.

3. Employee Engagement and Experience

AI-driven chatbots and virtual assistants improve employee engagement by providing instant support and personalized learning experiences (Jatobá et al., 2019). Even though there are advantages, the impersonal nature of AI-driven interactions may deteriorate the human-centric approach central to HRM theories.

4. Decision-Making and Workforce Analytics

AI-powered HR analytics provide predictive insights into workforce trends, enabling strategic talent management (Bersin, 2019). However, ethical considerations, such as data privacy and the risk of dehumanizing decision-making processes, must be emphasized to align AI applications with HRM principles (Hickman & Robison, 2020).

Video: Artificial Intelligence in Human Resource Decision Making (IWH Research, 2022)

The Global Context and Cultural Considerations

In a globalized workforce, the adoption of AI in HRM differs by region, influenced by variations in  labor laws, cultural perspectives, and technological preparedness (Farndale et al., 2017). Western countries, particularly the US and Europe, emphasize data-driven HRM practices, whereas developing economies may face challenges related to AI infrastructure and ethical governance. In addition, cultural perceptions of AI's role in decision-making affect its acceptance and implementation in different organizational settings (Huo et al., 2021).

Conclusion

AI has the potential to significantly improve HRM and SHRM worldwide, but it requires revising existing theories to address ethical, legal, and human concerns. Future research should focus on balancing AI's efficiency with human oversight, ensuring it supports rather than replaces strategic HR decisions. Organizations must carefully integrate AI, considering cultural, ethical, and legal factors to maximize the benefits of AI.

References

• Barney, J. (1991) ‘Firm resources and sustained competitive advantage’, Journal of Management, 17(1), pp. 99-120.
• Beer, M., Spector, B., Lawrence, P.R., Mills, D.Q. and Walton, R.E. (1984) Managing Human Assets. New York: Free Press.
• Bersin, J. (2019) ‘AI in HR: A revolution in talent and workforce management’, Deloitte Insights. Available at: [URL].
• Farndale, E., Paauwe, J. and Boselie, P. (2017) ‘HRM and context: How does industry sector influence HRM?’, Human Resource Management Review, 27(1), pp. 175-188.
• Guenole, N. and Feinzig, S. (2018) The business case for AI in HR. IBM Smarter Workforce Institute.
• Hickman, C. and Robison, J. (2020) ‘The role of AI in modern HR decision-making’, Harvard Business Review. Available at: [URL].
• Huo, Y., Boxall, P. and Cheung, G.W. (2021) ‘Human resource management and AI: A cross-cultural analysis’, International Journal of HRM, 32(2), pp. 234-256.
• Jatobá, V.G., Almeida, F.A. and da Silva, F.Q.B. (2019) ‘AI-based chatbots in HR: Trends and challenges’, Journal of Business Research, 98, pp. 156-162.
• Johnson, J., Wildman, J.L. and Colquitt, A.L. (2020) ‘The impact of AI on talent acquisition’, Human Resource Management Journal, 30(2), pp. 129-145.
• Marler, J.H. and Boudreau, J.W. (2017) ‘An evidence-based review of HR analytics’, Human Resource Management Review, 27(1), pp. 1-14.
• Raghavan, M., Barocas, S., Kleinberg, J. and Levy, K. (2020) ‘Mitigating bias in AI-driven hiring’, Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, pp. 469-481.
• Stone, D.L., Deadrick, D.L., Lukaszewski, K.M. and Johnson, R. (2015) ‘The influence of technology on the future of human resource management’, Human Resource Management Review, 25(2), pp. 216-231.
• IWH Research (2022) Challenge 2: Artificial intelligence in human-resource decision-making. YouTube video, added by IWH Research. Available at: https://youtu.be/17V-eFEQVZ4?si=FSBCsJDYVjnvf0E0 [Accessed 20 March 2025].