Subjects: Library Science，Information Science >> Library Science submitted time 2023-08-27 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] Under the big data environment, government services gradually shift from information services to knowledge services. In order to better improve the quality of e-government knowledge services, it is necessary to understand the factors that affect the quality of knowledge services of the government and micro-channel public platforms.[Method/process] Mainly on the influencing factors of refined knowledge service quality, using survey questionnaires to collect relevant data, using Spss tools and exploratory factor analysis methods, an influencing factor model of knowledge servicequality of government and micro-channel public accounts was constructed.[Result/conclusion] According to the result of the rotation component, the influencing factors of the quality of service of government affairs and WeChat public number are mainly divided into three levels:service process and system operation, knowledge quality and public characteristics and sensory psychological experience, and service type comprehensiveness and service security privacy. The influencing factor index lays the foundation for the development of the government-based WeChat public number knowledge service business.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] The emerging interactive media represented by live-streaming is profoundly changing people's living habits and the spiritual and cultural needs. Analysis of the influencing factors of the behavior of live-streaming APP users can enable the live-streaming platform to better understand the adoption characteristics of live-streaming users. The adoption characteristics of live-streaming users help platform operators to provide better services.[Method/process] Based on the TAM and UTAUT models, a questionnaire and a structural equation model were used to construct a conceptual model of the influencing factors of the use behavior of online live APP users, and an empirical analysis was conducted on the influencing factors model of typical groups.[Result/conclusion] The results of data analysis show that the most influential effect of online live APP user is perceived interactivity, followed by user perceived value; perceived risk has a negative impact on the willingness of users of live-streaming users and social factors have no effect on their willingness to use.
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