Vol. 10, Issue 4, Part A (2024)
A systematic review of artificial intelligence techniques in HRM: An assessment of performance evaluation and employee engagement
A systematic review of artificial intelligence techniques in HRM: An assessment of performance evaluation and employee engagement
Author(s)
Dr. Ajit Kaur
Abstract
This research paper delves into the transformative impact of Artificial Intelligence (AI) on Human Resource Management (HRM), specifically focusing on performance evaluation and employee engagement. The primary objective of this study is to explore how AI technologies, such as machine learning and natural language processing, can enhance the accuracy and fairness of performance evaluations, and how they can be leveraged to improve employee engagement within organizations. Through a systematic literature review, this study synthesizes findings from recent research papers and case studies to assess the effectiveness of AI tools in HRM practices. The methodology employed involves a comprehensive analysis of peer-reviewed articles, conference proceedings, and empirical studies published between 2015 and 2023. Key findings indicate that AI significantly contributes to performance evaluation by providing data-driven insights that ensure fairness and objectivity. Furthermore, AI-driven tools are found to be instrumental in enhancing employee engagement by facilitating real-time feedback and personalized engagement strategies. However, the integration of AI in HRM also presents challenges, including concerns related to privacy, bias, and the need for human oversight. The paper concludes with practical recommendations for HR professionals aiming to implement AI in HRM practices, emphasizing the importance of ethical considerations and the human element in technology adoption. This study contributes to HRM theory by highlighting the role of technology in evolving HR practices and offers insights into the future of work in the AI era.
How to cite this article:
Dr. Ajit Kaur. A systematic review of artificial intelligence techniques in HRM: An assessment of performance evaluation and employee engagement. Int J Appl Res 2024;10(4):01-05.