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Social media-enabled healthcare: A conceptual model of social media affordances, online social support, and health behaviors and outcomes

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  • Lin, Xiaolin
  • Kishore, Rajiv

Abstract

Due to the popularity of social media, patients are increasingly using social media for the social support exchange to improve health outcomes. To understand this phenomenon of social media-enabled healthcare, we propose a conceptual framework by integrating literature from three areas—social media affordance, online social support, and health behaviors and outcomes—with the goal of developing propositions on how social media can help patients improve self-management of chronic diseases. We identified and distinguished three social media affordances: affordance for community co-creation, affordance for social learning, and affordance for social relationships. We also distinguish three kinds of social support and tailored them to the healthcare domain and the social media context: informational support, emotional support, and experiential support. We posit the following: (a) social media affordances foster social support, where co-creation facilitates informational support, user interaction facilitates emotional support, and social learning facilitates experiential support; (b) informational support and experiential support are both linked to self-care; and (c) informational support and emotional support are both linked to psychological health. Our work advances the literature in the area of social media-enabled healthcare.

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  • Lin, Xiaolin & Kishore, Rajiv, 2021. "Social media-enabled healthcare: A conceptual model of social media affordances, online social support, and health behaviors and outcomes," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:tefoso:v:166:y:2021:i:c:s0040162521000068
    DOI: 10.1016/j.techfore.2021.120574
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    Cited by:

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    2. Shan, Wei & Wang, Jiaxuan & Shi, Xiaoxiao & David Evans, Richard, 2024. "The impact of electronic word-of-mouth on patients’ choices in online health communities: A cross-media perspective," Journal of Business Research, Elsevier, vol. 173(C).
    3. Guo, Wenshan & Luo, Qiangqiang, 2023. "Investigating the impact of intelligent personal assistants on the purchase intentions of Generation Z consumers: The moderating role of brand credibility," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    4. Tseng, Hsiao-Ting & Ibrahim, Fahad & Hajli, Nick & Nisar, Tahir M. & Shabbir, Haseeb, 2022. "Effect of privacy concerns and engagement on social support behaviour in online health community platforms," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    5. Nanath, Krishnadas & Balasubramanian, Sreejith & Shukla, Vinaya & Islam, Nazrul & Kaitheri, Supriya, 2022. "Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    6. Bernardi, Roberta & Wu, Philip F., 2022. "Online health communities and the patient-doctor relationship: An institutional logics perspective," Social Science & Medicine, Elsevier, vol. 314(C).
    7. Cenamor, Javier, 2022. "Use of health self-management platform features: The case of a specialist ehealth app," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    8. Lamrhari, Soumaya & Ghazi, Hamid El & Oubrich, Mourad & Faker, Abdellatif El, 2022. "A social CRM analytic framework for improving customer retention, acquisition, and conversion," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    9. Huarng, Kun-Huang & Lee, Cheng-Fang & Yu, Tiffany Hui-Kuang, 2023. "Case study of a healthcare virtual community model," Technological Forecasting and Social Change, Elsevier, vol. 188(C).

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