SNA of educational economics cooperation network in China: a scinetometrics study from 1980 to 2009

2012 ◽  
Vol 3 (1) ◽  
pp. 48
Author(s):  
Huang Wei ◽  
Chen Yong
Keyword(s):  
Educational Economics ◽  
Cooperation Network

scholarly journals How Does Inter-Organizational Cooperation Impact Organizations’ Scientific Knowledge Generation? Evidence from the Biomass Energy Field

2020 ◽  
Vol 13 (1) ◽  
pp. 191
Author(s):  
Liu Li ◽  
Chaoying Tang
Keyword(s):  
Scientific Knowledge ◽  
Biomass Energy ◽  
Knowledge Generation ◽  
Degree Centrality ◽  
Technical Knowledge ◽  
Technical Cooperation ◽  
Scientific Cooperation ◽  
Structural Holes ◽  
Cooperation Network ◽  
Positive Effect

Previous studies have demonstrated that accessing external knowledge is important for organizations’ knowledge generation. The main purpose of this study is to investigate how the diversity and amount of organizations’ external scientific knowledge influence their scientific knowledge generation. We also consider the moderating effect of the redundant industrial scientific knowledge and the amount of technical knowledge from external technical cooperators. The social network analysis method is used to establish both ego- and industrial-scientific cooperation network, and ego-technical cooperation network in order to analyze the external scientific knowledge and technical knowledge. The empirical analysis is based on patent and article data of 106 organizations in the biomass energy industry (including firms, universities and research institutes), and the results show that organizations’ structural holes and degree centrality of scientific cooperation network have positive effects on their scientific knowledge generation. In addition, organizations’ degree centrality of technical cooperation network positively moderates the relationship between their degree centrality of scientific cooperation network and scientific knowledge generation. Furthermore, density of industrial scientific cooperation network decreases the positive effect of organizations’ structural holes on their scientific knowledge generation, while it strengthens the positive effect of degree centrality of scientific cooperation network on their scientific knowledge generation. Academic contributions and practical suggestions are discussed.

scholarly journals How Intellectual Property Management Capability and Network Strategy Affect Open Technological Innovation in the Korean New Information Communications Technology Industry

2018 ◽  
Vol 10 (8) ◽  
pp. 2600 ◽  
Author(s):  
So Kim ◽  
Eungdo Kim
Keyword(s):  
Intellectual Property ◽  
Technological Innovation ◽  
Open Innovation ◽  
Communications Technology ◽  
Management Capability ◽  
New Information ◽  
Cooperation Network ◽  
Ict Industry ◽  
The Impact ◽  
Ip Management

This paper analyses factors in open innovation activity in the Korean new information and communications technology (ICT) industry, with a focus on cooperation network strategy and intellectual property (IP) management capability, by applying multiple regression models with data collected from 300 companies within the industry. The results of this analysis suggested that the intensity and variation of a company’s technological cooperation with a new ICT company has a statistically meaningful impact on its innovation. In particular, the impact depended on the type of cooperation network. Though IP management capability was also shown to have an important influence on a new ICT company’s innovation, the impact of specific actions for IP management varied by the specific type of innovation results. This study suggests that new ICT companies need to construct technological innovation networks using multiple external sources and enhance their IP management capability in order to increase their technological innovation performance. The factors influencing technological innovation are elements of open innovation, indicating the open technological innovativeness of the new ICT Industry.

scholarly journals Cooperative Evolution of China’s Excellent Innovative Research Groups from the Perspective of Innovation Ecosystem: Taking an “Environmental Biogeochemistry” Research Innovation Group as a Case Study

2021 ◽  
Vol 18 (23) ◽  
pp. 12584
Author(s):  
Jie Gao ◽  
Shu Liu ◽  
Zhijian Li
Keyword(s):  
Complex Systems ◽  
Technological Innovation ◽  
Research Group ◽  
Innovation Ecosystem ◽  
Team Cooperation ◽  
Innovative Research ◽  
Global Innovation ◽  
Peking University ◽  
Cooperation Network ◽  
External Governance

Research, understanding, and prediction of complex systems is an important starting point for human beings to tackle major problems and emergencies such as global warming and COVID-19. Research on innovation ecosystem is an important part of research on complex systems. With the rapid development of sophisticated industries, the rise of innovative countries, and the newly developed innovation theory, innovation ecosystem has become a new explanation and new paradigm for adapting to today’s global innovation cooperation network and the scientific development of complex systems, which is also in line with China’s concept of building an innovative country and promoting comprehensive innovation and international cooperation with scientific and technological innovation as the core. The Innovative Research Group at Peking University is the most representative scientific and technological innovation team in the frontier field of basic research in China. The characteristics of its organization mechanism and dynamic evolution connotation are consistent with the characteristics and evolution of innovation ecosystem. An excellent innovative research group is regarded as a small innovation ecosystem. We selected the “Environmental Biogeochemistry” Innovation Research Group at Peking University as a typical case in order to understand and analyze the evolution of cooperation among scientific and technological innovation teams, improve the healthy development as well as internal and external governance of this special small innovation ecosystem, promote the expansion of an innovation team cooperation network and the improvement of cooperation quality, promote the linkage supports of funding and management departments, and improve their scientific and technological governance abilities. Through scientometrics, visual analysis of knowledge maps, and an exploratory case study, we study the evolution process and development law of team cooperation. It is found that the main node authors of the cooperation network maintain strong cooperation frequency and centrality, and gradually strengthen with the expansion of the cooperation network and the evolution of time. Driven by the internal cooperative governance of the team and the external governance of the funding and management departments, this group has gradually formed a healthy, orderly, open, and cooperative special innovation ecosystem, which is conducive to the stability and sustainable development of the national innovation ecosystem and the global innovation ecosystem.

scholarly journals Detecting and Visualizing the Communities of Innovation in Beijing-Tianjin-Hebei Urban Agglomeration Based on the Patent Cooperation Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Fang Zhou ◽  
Bo Zhang
Keyword(s):  
Deep Understanding ◽  
Urban Agglomeration ◽  
Geographical Information ◽  
Single Center ◽  
Power Construction ◽  
Flat Structures ◽  
Collaborative Innovation ◽  
The Hierarchical Structure ◽  
Cooperation Network ◽  
Communities Of Innovation

For a deep understanding of Beijing-Tianjin-Hebei (BTH) collaborative innovation, we detected and visualized the communities of innovation in BTH Urban Agglomeration based on the patent cooperation network. China Patent Database was connected with Business Registration Database and the Tianyan Check to achieve the geographical information of organizational innovators. Spinglass algorithm was applied and ultimately 12 communities of innovation were detected. Based on the different structure characteristics, we further clustered the 12 communities into four typical structures that are hierarchical, single-center, polycentric, and flat structures. The hierarchical structure is usually large in scale and the cooperative intensity is relatively high. Single-center structure has a center with a high proportion of centrality and the cooperative intensity is relatively low. Polycentric structure has multiple centers with similar proportions of centrality. Flat structure is usually small in scale and has no obvious network center. In the patent cooperative network of BTH Urban Agglomeration, universities and state-owned enterprises occupied the centers and acted important roles to connect other organizations. Some communities of innovation showed significant industry characteristics, mainly involving six industry fields that are electric power, construction, petroleum, metallurgy and materials, municipal transportation, and railway. From the geographical perspective, some communities manifested local attributes and some demonstrated cooperation between regions. Beijing was the center of the Beijing-Tianjin-Hebei patent cooperation network. Compared with the pair of Beijing-Tianjin and the pair of Beijing-Hebei, Tianjin and Hebei were not closely connected. In the future, Beijing-Tianjin-Hebei collaborative innovation should strengthen cooperation between Tianjin and Hebei.

scholarly journals Eastern Borderland of the European Union in the Process of Networking, as Exemplified by the Polish-Belarusian Cross-Border Cooperation

2018 ◽  
Vol 11 (1) ◽  
pp. 34-50
Author(s):  
Sławomir Partycki ◽  
Dawid Błaszczak
Keyword(s):  
European Union ◽  
Network Analysis ◽  
International Relations ◽  
The Other ◽  
The European Union ◽  
Cross Border ◽  
Cooperation Network ◽  
State Projects ◽  
Cross Border Cooperation ◽  
Key Nodes

Abstract Summary Subject and purpose of work: The purpose of the study is to analyse the structure and the relation of the Polish-Belarusian cross-border cooperation network, to identify the key nodes in the network, to analyse the dynamics of connections between the actors, and to identify the most important changes in the structure of the network. Materials and methods: The article quotes the results of analyses of cross-border projects from 2004- 2017. The analysis includes projects completed, applicants, and partners of projects. The network analysis was carried out using Ucinet and NetDraw software. Results: The structure of the Polish-Belarusian cooperation is dominated by several large nodes, on the other hand there are many micronetworks - of three or four nodes, which are connected with each other. Conclusions: The Polish-Belarusian cooperation is of great importance for the international relations of the Polish state. Projects carried out by entities located at the border strengthen the cooperation, bringing a number of measurable benefits (hard and soft), depending on nature of the projects, as well as frequency and scale of the undertaken activities.

Research on Network Coding Based on Convolutional Codes

2013 ◽  
Vol 694-697 ◽  
pp. 2270-2273
Author(s):  
Hai Ping Liu ◽  
Shi Bin Su
Keyword(s):  
Outage Probability ◽  
Network Coding ◽  
Convolutional Code ◽  
Convolutional Codes ◽  
Probability Analysis ◽  
System Model ◽  
Code Scheme ◽  
Cooperation Network

For the system with two sources, three relays and one destination, cooperation network convolutional code scheme is presented to solve the relay sharing problem. The scheme searches out convolutional code corresponding to system model, and then decides relay coding structure. Outage Probability analysis and BER simulation show that 3-order diversity is achieved.

Sign in / Sign up

Export Citation Format

Share Document