The effect of emerging technologies on the performance and engagement of human resources (HR) professionals: evidence of existing studies

Anos Chitamba, L. Z. Ngidi and S. Bangura
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Anos Chitamba: Durban University of Technology
L. Z. Ngidi: Durban University of Technology
S. Bangura: Durban University of Technology

International Journal of Business Ecosystem & Strategy (2687-2293), 2025, vol. 7, issue 3, 233-240

Abstract: The swift advancement of emerging technologies has profoundly changed the workplace, especially within human resource management (HRM) practices and applications. Innovations such as data analytics, artificial intelligence (AI), and employee self-service (ESS) systems are pivotal to this transformation. While these technologies offer considerable advantages, they also present challenges that organizations need to address for effective integration into their operations. This study aims to examine the impact of emerging technologies on the performance and engagement of HR professionals, drawing on existing research. To achieve this, a comprehensive literature review was conducted. The findings indicate that emerging technologies have significantly transformed the workplace and human resource management, enhancing engagement within these areas. Furthermore, their implementation has notably advanced the modern workplace and the HR profession. Despite these benefits, challenges such as job security, potential skill displacement, and concerns about skill replacement exist. However, the research suggests that these challenges can be mitigated through the implementation of training programs, leadership endorsement initiatives, and technical support. The theoretical implications of these findings contribute valuable insights regarding the role of emerging technologies in HRM practices. Practically, the findings provide a framework for managers and organizational stakeholders to effectively leverage emerging technologies in human resource management applications. Key Words:Artificial Intelligence, Data Analytics, Employee Self-Service, Technology Acceptance Model

Date: 2025
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