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Understanding Use Intention of mHealth Applications Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) Model in China

Author

Listed:
  • Yancong Zhu

    (School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
    Faculty of Psychology, Beijing Normal University, Beijing 100875, China)

  • Zhenhong Zhao

    (Faculty of Psychology, Beijing Normal University, Beijing 100875, China)

  • Jingxian Guo

    (Faculty of Psychology, Beijing Normal University, Beijing 100875, China)

  • Yanna Wang

    (Faculty of Psychology, Beijing Normal University, Beijing 100875, China)

  • Chengwen Zhang

    (Faculty of Psychology, Beijing Normal University, Beijing 100875, China)

  • Jiayu Zheng

    (Faculty of Psychology, Beijing Normal University, Beijing 100875, China)

  • Zheng Zou

    (Stanford Center at Peking University, Stanford University, Beijing 100871, China)

  • Wei Liu

    (Faculty of Psychology, Beijing Normal University, Beijing 100875, China)

Abstract

The COVID-19 pandemic has significantly impacted the healthcare industry, especially public health resources and resource allocation. With the change in people’s lifestyles and increased demand for medical and health care in the post-pandemic era, the Internet and home healthcare have rapidly developed. As an essential part of Internet healthcare, mobile health (mHealth) applications help to fundamentally address the lack of medical resources and meet people’s healthcare needs. In this mixed-method study, we conducted in-depth interviews with 20 users in China (mean age = 26.13, SD = 2.80, all born in China) during the pandemic, based on the unified theory of acceptance and use of technology 2 (UTAUT-2) mode, and identified four dimensions of user needs in mHealth scenarios: convenience, control, trust, and emotionality. Based on the interview results, we adjusted the independent variables, deleted the hedonic motivation and the habit, and added the perceived trust and perceived risk as the variables. Using a structural equation model (SEM), we designed the questionnaire according to the qualitative results and collected data from 371 participants (above 18 years old, 43.9% male) online to examine the interrelationships these variables. The results show that performance expectancy (β = 0.40, p < 0.001), effort expectancy (β = 0.40, p < 0.001), social influence (β = 0.14, p < 0.05), facilitating condition (β = 0.15, p < 0.001), and perceived trust (β = 0.31, p < 0.001) had positive effects on use intention. Perceived risk (β = −0.31, p < 0.001) harmed use intention, and price value (β = 0.10, p > 0.5) had no significant effects on use intention. Finally, we discussed design and development guidelines that can enhance user experience of mHealth applications. This research combines the actual needs and the main factors affecting the use intention of users, solves the problems of low satisfaction of user experience, and provides better strategic suggestions for developing mHealth applications in the future.

Suggested Citation

  • Yancong Zhu & Zhenhong Zhao & Jingxian Guo & Yanna Wang & Chengwen Zhang & Jiayu Zheng & Zheng Zou & Wei Liu, 2023. "Understanding Use Intention of mHealth Applications Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) Model in China," IJERPH, MDPI, vol. 20(4), pages 1-18, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3139-:d:1064605
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    References listed on IDEAS

    as
    1. Rajak, Manindra & Shaw, Krishnendu, 2021. "An extension of technology acceptance model for mHealth user adoption," Technology in Society, Elsevier, vol. 67(C).
    2. Kraus, Sascha & Schiavone, Francesco & Pluzhnikova, Anna & Invernizzi, Anna Chiara, 2021. "Digital transformation in healthcare: Analyzing the current state-of-research," Journal of Business Research, Elsevier, vol. 123(C), pages 557-567.
    3. Pernille Ravn Jakobsen & Anne Pernille Hermann & Jens Søndergaard & Uffe Kock Wiil & Jane Clemensen, 2018. "Development of an mHealth Application for Women Newly Diagnosed with Osteoporosis without Preceding Fractures: A Participatory Design Approach," IJERPH, MDPI, vol. 15(2), pages 1-16, February.
    4. Liu, Junqiang & Zhou, Hui & Liu, Lingrui & Wang, Chunxiao, 2020. "The weakness of the strong: Examining the squeaky-wheel effect of hospital violence in China," Social Science & Medicine, Elsevier, vol. 245(C).
    5. Likoebe M. Maruping & Hillol Bala & Viswanath Venkatesh & Susan A. Brown, 2017. "Going beyond intention: Integrating behavioral expectation into the unified theory of acceptance and use of technology," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(3), pages 623-637, March.
    6. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    7. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, August.
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