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Factors affecting the intention to adopt wearable devices for health monitoring among white-collar workers in Malaysia

In: Proceedings of the 13th International Conference on Business, Accounting, Finance and Economics (BAFE 2025)

Author

Listed:
  • Seetha Munisamy

    (Manipal University College Malaysia (MUCM), Faculty of Medicine)

  • Lai Ka Fei

    (Universiti Tunku Abdul Rahman, Teh Hong Piow Faculty of Business and Finance (THP FBF)
    Universiti Tunku Abdul Rahman, Centre for Business Management (CBM), Teh Hong Piow Faculty of Business and Finance)

  • Chang Ke Jun

    (Universiti Tunku Abdul Rahman, Teh Hong Piow Faculty of Business and Finance (THP FBF))

  • Lim En Gee

    (Universiti Tunku Abdul Rahman, Teh Hong Piow Faculty of Business and Finance (THP FBF))

  • Yip Ning

    (Universiti Tunku Abdul Rahman, Teh Hong Piow Faculty of Business and Finance (THP FBF))

  • Hemaniswarri Dewi Dewadas

    (Universiti Tunku Abdul Rahman, Teh Hong Piow Faculty of Business and Finance (THP FBF)
    Universiti Tunku Abdul Rahman, Centre for Business Management (CBM), Teh Hong Piow Faculty of Business and Finance
    Universiti Tunku Abdul Rahman, Centre for Biomedical and Nutrition Research (CBNR), Faculty of Science)

Abstract

Wearable health technology gained significant attention for its potential to improve personal health management and reduce the burden on healthcare systems. Yet, despite its benefits, adoption among white-collar workers in Malaysia remains relatively low, highlighting the need to better understand the determinants of intention to adopt. This study examines the key factors influencing their intention to adopt wearable devices for health monitoring, focusing on perceived usefulness (PU), perceived ease of use (PEOU), facilitating conditions (FC), and social influence (SI). This study employed a quantitative approach, gathering responses from 408 white-collar workers across various industries and states in Malaysia. A structured questionnaire was distributed via Google Forms, and non-probability convenience sampling was used to select participants. The collected data were analyzed using SPSS, incorporating descriptive statistics, reliability testing, and inferential analysis. The results revealed that PU emerged as the strongest predictor of adoption intention (β = 0.405, p

Suggested Citation

  • Seetha Munisamy & Lai Ka Fei & Chang Ke Jun & Lim En Gee & Yip Ning & Hemaniswarri Dewi Dewadas, 2025. "Factors affecting the intention to adopt wearable devices for health monitoring among white-collar workers in Malaysia," Advances in Economics, Business and Management Research, in: Thurai Murugan Nathan & Abdelhak Senadjki & Hemaniswarri Dewi Dewadas & Siti Nur Amira Othman & Ravi (ed.), Proceedings of the 13th International Conference on Business, Accounting, Finance and Economics (BAFE 2025), pages 440-459, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-968-1_30
    DOI: 10.2991/978-94-6463-968-1_30
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