Artificial Intelligence and HR Motivation: A Theoretical Review

  • Yahya ALLAM National School of Business and Management of Casablanca, Hassan II University of Casablanca, Morocco
  • Mohammed HABACHI Faculty of Law, Economics and Social Sciences of Agdal, Mohamed V University, Rabat, Morocco
  • Youssef TABIT National School of Business and Management of Casablanca, Hassan II University of Casablanca, Morocco

Abstract

This article is a literature review focusing on the use of artificial intelligence (AI) in human resource management, with a particular emphasis on employee motivation. In a rapidly evolving technological environment, AI is positioned as a strategic lever for reinventing HR practices. It not only optimizes the efficiency of processes but also personalizes training pathways and enhances recruitment processes, while offering more accessible and tailored solutions to meet employees' needs.

The impact of AI on employee motivation is evident in the more refined management of talent and individual expectations. Through tools such as real-time performance feedback and personalized development recommendations, AI provides customized feedback that strengthens employee engagement. Furthermore, these tools help address specific needs, thereby improving employees' working conditions and their overall experience within the organization.

This literature review highlights how AI is transforming traditional approaches to HR motivation, paving the way for new practices that promote well-being, growth, and talent retention. The integration of AI in human resource management emerges as a key driver for the evolution of HR practices in an increasingly technology-driven world.

 

Keywords: HR Motivation, Artificial Intelligence, Motivation Theories

JEL Classification: O15

Paper Type: Theoretical Research

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Published
2024-12-20
How to Cite
ALLAM, Y., HABACHI, M., & TABIT, Y. (2024). Artificial Intelligence and HR Motivation: A Theoretical Review. International Journal of Accounting, Finance, Auditing, Management and Economics, 5(12), 602-613. https://doi.org/10.5281/zenodo.14538222
Section
Articles