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A model for predicting factors affecting health information avoidance on WeChat.

Minghong Chen1, Xiumei Huang1, Yinger Wu1

  • 1School of Information Management, Sun Yat-Sen University, Guangzhou, China.

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Personalized health information avoidance on WeChat is influenced by personal, informational, and social factors. Understanding these drivers is key to improving health decision-making and information services.

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ISM modelWeChatgrounded theoryhealth behaviorhealth information avoidance

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Area of Science:

  • Health Communication
  • Information Science
  • Social Science

Background:

  • WeChat is a primary source of personalized health information.
  • Avoiding this information impacts health decision-making.
  • Understanding avoidance factors is crucial for health literacy.

Purpose of the Study:

  • Identify factors influencing personalized health information avoidance on WeChat.
  • Develop a hierarchical framework of these factors.
  • Analyze the relationships and dependencies between factors.

Main Methods:

  • Hybrid approach combining semi-structured interviews and grounded theory.
  • Interpretive Structural Modeling (ISM) for hierarchical framework development.
  • Matrice d'impacts croisés-multiplication appliqué à un classement (MICMAC) for factor analysis.

Main Results:

  • Twenty predictors categorized into personal, informational, and social factors.
  • A three-tier framework identified: root (health characteristics, cognition), middle (decision-making cognition, informational, social), and top (negative emotions, affective factors).
  • Health characteristics and cognition showed strong driving force; negative emotions and affective factors exhibited high dependence.

Conclusions:

  • Addressed the research gap in personalized health information avoidance.
  • Provided insights into factors influencing avoidance behavior on WeChat.
  • Contributed to enhancing personal health information management and WeChat's health information services.