文章基本信息

标题：The Effect of Network Relational Structure on Knowledge Diffusion Learning: An Empirical Study
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作者：Zhang Renping ; Zheng ShiYong ; Qiu Ming 等
期刊名称：International Journal of Emerging Technologies in Learning (iJET)
印刷版ISSN：1863-0383
出版年度：2021
卷号：16
期号：1
页码：109-123
DOI：10.3991/ijet.v16i01.18229
出版社：Kassel University Press
摘要：As social media has been popularized, users have shifted from the receiver of knowledge to the creator and communicator of knowledge. Besides, the relationship between users has become more sophisticated. In two-way and one-way networks, different network relationship structures formed be-tween users have different impacts on the knowledge learning of infor-mation recipients. Some studies highlighted that knowledge, according to the different forms of knowledge generation and expression, can be split in-to explicit and tacit knowledge. Thus, in the network structure with differ-ent levels of relationship intensity, which type of knowledge can be spread and learned better? To answer this question, this study first uses second-hand data analysis. As revealed from the results of empirical research, under Weibo and WeChat, i.e., two different network structures, a variety of knowledge dissemination learning will have different effects. Then, by ana-lyzing questionnaire data, the phenomenon and its internal mechanism are explained in accordance with the theory of regulatory focus.