图书情报知识 ›› 2018, Vol. 0 ›› Issue (5): 105-113.doi: 10.13366/j.dik.2018.05.105

• 情报、信息与共享 • 上一篇    下一篇

用户视角下的学术社交网络信息质量影响因素研究——基于扎根理论方法

张宁,袁勤俭   

  • 出版日期:2018-09-10 发布日期:2018-09-10

The Influence Factors of Information Quality in Academic Social Networks from User’ Perspective Based on Grounded Theory

  • Online:2018-09-10 Published:2018-09-10

摘要:

用户是信息质量的最终评价者,立足用户视角,研究学术社交网络信息质量的前置影响因素并未获得应有的关注。利用扎根理论的方法,对126份学术社交网络信息质量用户评价及研判质性资料,通过开放式编码、主轴编码、选择性编码和理论饱和度检验等多个步骤,归纳了影响学术社交网络用户感知信息质量的120个初始概念和28个范畴,并发展成为9个主范畴,最终获得一条影响学术社交网络信息质量的故事线,在此基础上构建学术社交网络用户感知的信息质量影响因素CPUC模型。结果表明学术社交网络用户感知的信息质量受到社区、平台、用户和内容4个方面的影响,其中,内容因素直接影响学术社交网络的用户感知信息质量,用户因素既是直接变量,又是学术社交网络社区因素及平台因素之间的中介变量,由此析出未来研究的命题和方向。

关键词: 信息质量, 影响因素, 学术社交网络, 用户感知, 扎根理论

Abstract:

Users are the final evaluators of information quality. There has not much attention been paid to the study of factors that affect the information quality of academic social networks from users' perspective. By using grounded theory 126 information quality evaluations of academic social networks' data have been analyzed according to several steps including open coding, spindle coding, selective coding, and theoretical saturation test. 120 initial concepts and 28 categories of information quality in academic social networks have been achieved, which have been summarized into 9 main categories. Eventually, a story line that influence the information quality of academic social networks was developed. On this basis, a model of the information quality influencing factors in academic social networks was constructed. The results show that the community, platform, user and content can affect the information quality of academic social networks. Among the four factors, content has direct impact on information quality of the academic social networks; user is not only a direct variable, but also an intermediary variable between the factors of community and platform. According to the findings, proposition and direction of the future research have been put forward.

Keywords: Information quality, Influence factors, Academic social networks, User perception, Grounded theory