Analyzing the Complexity of Public Opinion Evolution on Weibo: A Super Network Model

Li, Ning

doi:10.1007/s13132-024-02059-9

J Knowl Econ

2024

DOI: 10.1007/s13132-024-02059-9

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Analyzing the Complexity of Public Opinion Evolution on Weibo: A Super Network Model

Ning Li

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Analyzing the Impact of Information Features on User Continuance Intent in Recommendation Systems

2024

International Journal on Semantic Web and Information Systems

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Under the protection of recent legislation, users are increasingly opting to disable personalized recommendation features in applications. This study, for the first time from an information perspective, draws on Psychological Reactance Theory and Innovation Resistance Theory to explore the impact of the semantic characteristics of personalized recommendation information on users' intentions to continue using the application. A contextual analysis based on the intensity of social media use is conducted. Empirical evidence is derived from cross-sectional data of Chinese users. The results indicate that information characteristics affect users' perceived freedom risks and threats, inhibiting their intention to continue using the application. The intensity of social media use moderates this inhibition. As one of the earliest studies to explore discontinuing personalized recommendations, the research deepens the understanding of how recommendation systems affect users' behaviors. It provides feasible insights for developers to optimize recommendation systems.

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Analyzing the Impact of Information Features on User Continuance Intent in Recommendation Systems

2024

International Journal on Semantic Web and Information Systems

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Analyzing the Complexity of Public Opinion Evolution on Weibo: A Super Network Model

Cited by 1 publication

References 47 publications

Analyzing the Impact of Information Features on User Continuance Intent in Recommendation Systems

Analyzing the Impact of Information Features on User Continuance Intent in Recommendation Systems

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