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Factors that influence users’ adoption intention of mobile health: a structural equation modeling approach

Author

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  • Rui Miao
  • Qi Wu
  • Zheng Wang
  • Xilin Zhang
  • Yuqin Song
  • Hui Zhang
  • Qingfang Sun
  • Zhibin Jiang

Abstract

Mobile health represents the future trend of health care due to its great potential in improving health care efficiency, accessibility and quality. It is particularly beneficial for chronic disease patients who require long-term and regular services. To improve the products, mobile health developers need to understand patient needs, values and preferences, and assess the key factors that influence their mobile health adoption intentions. This study focuses on identifying the influential factors of patients’ adoption intention of m-Health. A structural equation model is constructed, and the confirmatory factor analysis and standard path coefficient are used to explore the key factors in chronic disease patients’ adoption process of m-Health. The results show that perceived usefulness and perceived ease of use have the strongest positive effect on patients’ adoption intention. Meanwhile, subjective norm, existing degree of satisfaction, network effect, and cost factor also influence adoption intention. Finally, the House of Quality method is used to examine the relative importance of various properties of m-Health. The Teoriya Resheniya Izobreatatelskikh Zadatch method is applied to resolve the contradictions between these properties. Our study offers important insights for mobile health developers on how to optimally design a product, thereby increasing users’ adoption intention and overall satisfaction.

Suggested Citation

  • Rui Miao & Qi Wu & Zheng Wang & Xilin Zhang & Yuqin Song & Hui Zhang & Qingfang Sun & Zhibin Jiang, 2017. "Factors that influence users’ adoption intention of mobile health: a structural equation modeling approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5801-5815, October.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:19:p:5801-5815
    DOI: 10.1080/00207543.2017.1336681
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    Cited by:

    1. Manish Mohan Baral & Amitabh Verma, 2021. "Cloud Computing Adoption for Healthcare: An Empirical Study Using SEM Approach," FIIB Business Review, , vol. 10(3), pages 255-275, September.
    2. Chauhan, Ankur & Jakhar, Suresh Kumar & Jabbour, Charbel Jose Chiappetta, 2022. "Implications for sustainable healthcare operations in embracing telemedicine services during a pandemic," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    3. Tian , Xiaoguang & Prybutok, Victor & Mirzaei, Fouad & Dinulescu, Catalin C., 2020. "Millennials Acceptance of Insurance Telematics: An Integrative Empirical Study," American Business Review, Pompea College of Business, University of New Haven, vol. 23(1), pages 156-181, May.
    4. Tushar Gahlaut & Gourav Dwivedi, 2024. "A Comprehensive Study for Multi-Criteria Comparison of EV, ICEV, and HEV," Papers 2404.11705, arXiv.org, revised Jun 2024.
    5. Md. Abdul Kaium & Yukun Bao & Mohammad Zahedul Alam & Najmul Hasan & Md. Rakibul Hoque, 2019. "Understanding the insight of factors affecting mHealth adoption: A systematic review," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 8(6), pages 181-200, October.

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