Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network

Open Access. Powered by Scholars. Published by Universities.®

Old Dominion University

Information Technology & Decision Sciences Faculty Publications

Series

2021

Information science

Articles 1 - 2 of 2

Full-Text Articles in Entire DC Network

An Empirical Study On Innovation Ecosystem, Technological Trajectory Transition, And Innovation Performance, Yu Sun, Ling Li, Yong Chen, Mikhail Yu Kataev Jan 2021

An Empirical Study On Innovation Ecosystem, Technological Trajectory Transition, And Innovation Performance, Yu Sun, Ling Li, Yong Chen, Mikhail Yu Kataev

Information Technology & Decision Sciences Faculty Publications

This paper explores technological trajectory transition in the perspective of innovation ecosystem and their effect on innovation performance of latecomers in market. A structural equation model is developed and tested with data collected from 366 firms in China. In specific, this paper categories technological trajectory transition creative accumulative technological trajectory transition (CCT) and creative disruptive technological trajectory transition (CDT). The results indicate that firms' organizational learning ability positively affect their technological trajectory transition and innovation performance. Firms' network relationship strength negatively affects their technological trajectory transition and positively affect their innovation performance. Governments' environmental concerns positively affect firms' technological trajectory …


Ranking Influential Nodes Of Fake News Spreading On Mobile Social Networks, Yunfei Xing, Xiwei Wang, Fang-Kwei Wang, Yang Shi, Wu He, Haowu Chang Jan 2021

Ranking Influential Nodes Of Fake News Spreading On Mobile Social Networks, Yunfei Xing, Xiwei Wang, Fang-Kwei Wang, Yang Shi, Wu He, Haowu Chang

Information Technology & Decision Sciences Faculty Publications

Online fake news can generate a negative impact on both users and society. Due to the concerns with spread of fake news and misinformation, assessing the network influence of online users has become an important issue. This study quantifies the influence of nodes by proposing an algorithm based on information entropy theory. Dynamic process of influence of nodes is characterized on mobile social networks (MSNs). Weibo (i.e., the Chinese version of microblogging) users are chosen to build the real network and quantified influence of them is analyzed according to the model proposed in this paper. MATLAB is employed to simulate …