Citation

Hossain, Md Sohag and Yeow, Jian Ai and Kuppusamy, Jayanty A. P. (2026) Readiness for Artificial Intelligence Adoption in Human Resource Management: A Conceptual Framework for Malaysia’s Manufacturing Industry. Studies in Big Data, 192. pp. 487-496. ISSN 2197-6503

Text Readiness for Artificial Intelligence Adoption in Human Resource Management_ A Conceptual Framework for Malaysia’s Manufacturing Industry _ Springer Nature Link.pdf - Published Version Restricted to Repository staff only Download (690kB)

Abstract

Human Resource Management (HRM) is undergoing a significant transformation driven by technological advancements. In Malaysia’s multinational manufacturing sector, the application of intelligent technologies offers new opportunities to enhance recruitment, performance management, and employee engagement. However, adoption remains limited due to various readiness challenges. This conceptual paper proposes a framework grounded in the Technology-Organization-Environment (TOE) model to examine the key readiness dimensions which includes technological, organizational, and environmental that influence the implementation of advanced tools in HRM. Additionally, the framework introduces a mediating factor focused on internal preparedness. Drawing from recent literature, this study provides insights into how internal capabilities, structural support, and external influences shape the successful integration of emerging digital solutions in HR functions. The proposed framework aims to guide industry practitioners and policymakers in strengthening foundational readiness for digital transformation within HRM.

Item Type:	Article
Uncontrolled Keywords:	AI, HRM, Manufacturing industry
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Business (FOB)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Oct 2026 00:32
Last Modified:	30 Oct 2026 00:32
URII:	http://shdl.mmu.edu.my/id/eprint/17010

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