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Factors Affecting Chinese Young Adults’ Acceptance of Connected Health

Author

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  • Lin Jia

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
    Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China)

  • Yuting Tan

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

  • Feiyu Han

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

  • Yi Zhou

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

  • Chu Zhang

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

  • Yufei Zhang

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

Abstract

The global health care industry faces several challenges, such as an aging population, insufficient medical resources, and uneven allocation of high-quality medical resources. These challenges impede the development of a sustainable medical care system. Connected health aims to relieve these challenges by deploying information technology in healthcare. However, there is a lack of research on adoption of connected health and as a result, its acceptance rate is still low. This study summarized 25 potential factors that may affect its acceptance, and ranked their importance by performing a best–worst scaling experiment. Fifteen important factors were distinguished, which included nine technological factors, five individual factors, and one environmental factor. To explore how these factors affect individuals’ acceptance of connected health, this study conducted a qualitative study based on grounded theory. We coded the contents collected in a semi-structural interview by applying open coding, axial coding, and selective coding techniques. Finally, nine core categories were distinguished, and a conceptual model was proposed to explain how these core categories affect individuals’ acceptance of connected health. This study deepens our understanding of factors affecting the acceptance of connected health and helps build a sustainable medical care system.

Suggested Citation

  • Lin Jia & Yuting Tan & Feiyu Han & Yi Zhou & Chu Zhang & Yufei Zhang, 2019. "Factors Affecting Chinese Young Adults’ Acceptance of Connected Health," Sustainability, MDPI, vol. 11(8), pages 1-22, April.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2376-:d:224813
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    References listed on IDEAS

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    1. Loureiro, Maria L. & Dominguez Arcos, Fernando, 2012. "Applying Best–Worst Scaling in a stated preference analysis of forest management programs," Journal of Forest Economics, Elsevier, vol. 18(4), pages 381-394.
    2. Andrea Carugati & Walter Fernández & Lapo Mola & Cecilia Rossignoli, 2018. "My choice, your problem? Mandating IT use in large organisational networks," Post-Print hal-01927380, HAL.
    3. Louviere, Jordan & Lings, Ian & Islam, Towhidul & Gudergan, Siegfried & Flynn, Terry, 2013. "An introduction to the application of (case 1) best–worst scaling in marketing research," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 292-303.
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    Cited by:

    1. Sonia Chien-I Chen & Chenglian Liu & Ridong Hu, 2020. "Fad or Trend? Rethinking the Sustainability of Connected Health," Sustainability, MDPI, vol. 12(5), pages 1-22, February.
    2. Chenming Peng & Hong Zhao & Sha Zhang, 2021. "Determinants and Cross-National Moderators of Wearable Health Tracker Adoption: A Meta-Analysis," Sustainability, MDPI, vol. 13(23), pages 1-16, December.

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