The Effect of Network Relational Structure on Knowledge Diffusion Learning: An Empirical Study
Zhang Renping, Wuhan University ; Zheng ShiYong, Guilin university of electronic technology ; Qiu Ming, Guangxi College Of Education ; Rizwan Ali, Wuhan Technology and Business University ; Ubaldo Comite, University Giustino Fortunato
iJET Volume 16, Number 1, ISSN 1863-0383 Publisher: International Journal of Emerging Technology in Learning, Kassel, Germany
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.
Renping, Z., ShiYong, Z., Ming, Q., Ali, R. & Comite, U. (2021). The Effect of Network Relational Structure on Knowledge Diffusion Learning: An Empirical Study. International Journal of Emerging Technologies in Learning (iJET), 16(1), 109-123. Kassel, Germany: International Journal of Emerging Technology in Learning.