Citation

Subbarao, Anusuyah and Rizwan, Syeda Hafsa and Khan @ Thandar Oo, Nasreen and Ahmed Khan, Farrukh and Fazlur Rahman, Shaikh and Aziz, Nimra and Azhar Shah, Syed Muntazir (2026) Factors Affecting the Adoption of Artificial Intelligence-Driven Wearable Technology in the Malaysian Healthcare Sector: A Conceptual Framework. International Journal of Management Finance and Accounting, 7 (1). p. 193. ISSN 2735-1009

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Abstract

This conceptual paper examines the adoption of Artificial Intelligence (AI)-driven wearable technology, including smartwatches, fitness trackers, wearable ECGs, glucose monitors, and pain management devices, in transforming healthcare in Malaysia. Despite the extreme potential to enhance patient care and healthcare monitoring, the adoption of AI-driven wearable technology remains limited due to several persistent barriers. The purpose of this study is to examine these challenges and propose a framework to improve the integration of AI-driven wearable technology in Malaysia’s healthcare system. Grounded in the Diffusion of Innovation (DOI) theory, this study examines how five key innovation attributes —relative advantage, compatibility, complexity, trialability, and observability—impact patient trust and the adoption of wearable technologies. A quantitative research design will be employed, utilizing structured surveys to collect data and analyze the relationships among the DOI factors, patient trust, and technology adoption. The expected outcome is a validated conceptual framework that identifies the barriers to adoption and provides empirical insights for strengthening digital healthcare initiatives. This research aligns with Malaysia’s Shared Prosperity Vision 2030 and contributes to both academic literature and policy, ultimately offering actionable recommendations to enhance trust, accessibility, and the successful implementation of digital health technologies across Malaysia.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence (AI), Wearable Technology, Healthcare, Technology Adoption, Diffusion of Innovation
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 Management (FOM)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	08 Jul 2026 07:06
Last Modified:	08 Jul 2026 07:06
URII:	http://shdl.mmu.edu.my/id/eprint/16261

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