Research on intermittent discontinuation behavior in online health information acquisition among patients with chronic diseases: An analysis based on grounded theory and ISM-MICMAC
Huizheng Chen, Raoshan Zhang, Yue Yu, Qiancheng Zhang, Dalong Shi, Du Li, Yong Meng, Junliang ZhangObjective
This study aims to explore the factors influencing the intermittent discontinuance behavior of health information acquisition on online health platforms among patients with chronic diseases. It constructs a hierarchical model of these factors and clarifies the interaction pathways between them, thereby providing insights for improving online health platforms.
Method
Drawing on grounded theory approach, the influencing factors of intermittent discontinuance behavior during online health information acquisition among chronic disease patients were identified and extracted from interview data. Subsequently, Interpretive Structural Modeling (ISM) was employer to analyze the hierarchical relationships, driving forces, and dependencies among these factors, so as to identify the key contributors to intermittent discontinuance behavior.
Results
Physicians, platforms, information, and users are key factors influencing intermittent discontinuance behavior during online health information acquisition by patients with chronic disease patients. Factors such as the medical information black box and regulatory loopholes, are foundational factors located in the independent region. Factors such as service effectiveness and community environment exert indirect influence and are located in the autonomous region. Additionally, factors including service quality and content quality exert a direct impact on intermittent discontinuance behavior during online health information acquisition among chronic disease patients.
Conclusion
This study combined exploratory coding with the ISM-MICMAC approach to construct a hierarchical model of the factors influencing intermittent discontinuance behavior in online health information acquisition among patients with chronic diseases. It identified the driving and dependent relationships among these factors and provides a structured framework for understanding this behavior.