Journal of Information Resources Management ›› 2024, Vol. 14 ›› Issue (1): 55-67.doi: 10.13365/j.jirm.2024.01.055

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Exploring the Antecedents of Health Information Credibility at Social Media Platforms: The Moderating Role of Prior Knowledge

Wang Xiaopan1,2 Zhang Miao3 Wu Yi3,4 Zhang Xiaofei5   

  1. 1.School of Business Administration, Zhejiang Gongshang University, Hangzhou, 310018; 
    2.Enterprise Digital Intelligence and Business Analysis Research Center, Zhejiang Gongshang University, Hangzhou, 310015; 
    3.College of Management and Economics, Tianjin University, Tianjin, 300072; 
    4.Laboratory of Computation and Analytics of Complex Management Systems(CACMS), Tianjin University, Tianjin, 300072;
    5.Nankai Business School, Nankai University, Tianjin, 300072
  • Online:2024-01-26 Published:2024-02-27

Abstract: Based on the elaboration likelihood model, the current study takes fact-checking interruption as the research object, and explores the impact of two fact-checking mechanisms, source rating and content rating, on users' perceived information credibility and subsequent information participation behavior. The moderating effect of prior knowledge is also investigated. Through an online scenario-based experiment involving 202 subjects, the present study found that both source and content rating mechanisms positively affect perceived credibility. Subsequently, perceived credibility has a positive impact on reading, liking, posting supportive comments and forwarding information, but has had no significant impact on posting rebutted comments. Prior knowledge has a negative moderating effect on the relationship between content rating and perceived credibility, but has no significant moderating effect on the relationship between source rating and perceived credibility. The present study enriches the literature and theories related to the credibility of health information in social media and offers managerial insights for the development of a social media information intervention mechanism.

Key words: Fake health information, Information credibility, Elaboration likelihood model, Source rating, Content rating, Prior knowledge

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