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User adoption of physician's replies in an online health community: An empirical study

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  • Yanli Zhang
  • Xinmiao Li
  • Weiguo Fan

Abstract

Online health question‐and‐answer consultation with physicians is becoming a common phenomenon. However, it is unclear how users identify the most satisfying reply. Based on the dual‐process theory of knowledge adoption, we developed a conceptual model and empirical method to study which factors influence adoption of a reply. We extracted 6 variables for argument quality (Ease of understanding, Relevance, Completeness, Objectivity, Timeliness, Structure) and 4 for source credibility (Physician's online experience, Physician's offline expertise, Hospital location, Hospital level). The empirical results indicate that both central and peripheral routes affect user's adoption of a response. Physician's offline expertise negatively affects user's adoption decision, while physician's online experience positively affects it; this effect is positively moderated by user involvement.

Suggested Citation

  • Yanli Zhang & Xinmiao Li & Weiguo Fan, 2020. "User adoption of physician's replies in an online health community: An empirical study," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(10), pages 1179-1191, October.
    • Handle: RePEc:bla:jinfst:v:71:y:2020:i:10:p:1179-1191
    DOI: 10.1002/asi.24319
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    References listed on IDEAS

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    1. Linlin Jing & Wei Shan & Richard David Evans & Xiaoxiao Shi, 2024. "Getting to know my disease better: The influence of linguistic features of patients’ self-disclosure on physicians’ social support in online health consultation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-24, December.
    2. Qingfeng Zeng & Wei Zhuang & Qian Guo & Weiguo Fan, 2022. "What factors influence grassroots knowledge supplier performance in online knowledge platforms? Evidence from a paid Q&A service," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2507-2523, December.
    3. Han Zheng & Dion Hoe‐Lian Goh & Edmund Wei Jian Lee & Chei Sian Lee & Yin‐Leng Theng, 2022. "Understanding the effects of message cues on COVID‐19 information sharing on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(6), pages 847-862, June.
    4. Zhaohua Deng & Zihao Deng & Guorui Fan & Bin Wang & Weiguo (Patrick) Fan & Shan Liu, 2023. "More is better? Understanding the effects of online interactions on patients health anxiety," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(11), pages 1243-1264, November.
    5. Yifan He & Min Zhang & Tianshi Wu & Xitong Guo & Doug Vogel, 2025. "The influence of similarity heuristics on evaluation deviation: Evidence from an online crowdsourced Q&A healthcare platform," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-21, December.

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