Research on the Characteristics of the Industry-University-Research Cooperation Innovation Network Structure of the Pharmaceutical Manufacturing Industry in China’s Four Economic Zones

Our country’s equipment manufacturing industry ranks among the best in all developing countries, but compared with developed countries, there is still a long way to go. It is not only the backwardness of various technologies, but also the interference of other countries. Although our country's equipment manufacturing industry is not as advanced as the advanced technology of developed countries, we still have to stick to our original aspirations, do not underestimate ourselves, and be good at absorbing and learning from the strengths of others to make up for our own weaknesses. While not working behind closed doors and while absorbing technology from other countries, we can make use of our strengths to make up for our weaknesses and develop our own industrial technology. This paper studies the evolution trend of innovation network structure and at the same time studies the evolution mechanism of advanced equipment manufacturing innovation network structure from the perspective of complex systems. The explained variable in this article is green total factor productivity. The variable adopts the Malmquist–Luenberger global super-efficiency index model. There are two main explanatory variables. One is the heterogeneity that affects the efficiency of industrial evolution, including factor heterogeneity, structural heterogeneity, and environmental heterogeneity, and the other is the interaction term of equipment manufacturing specialization agglomeration degree dummy variable multiplied by factor heterogeneity. The regional economic development level is added to the model as a control variable. In the selection of measurement indicators, the per capita GDP is used as the control variable. The experimental results show that each sample is tested in pairs, and the standard error level of the mean is 0.018, which is less than 0.05, indicating that the efficiency of the equipment manufacturing industry’s economic correlation spatial network has a significant impact on the overall economic development level of the industry. The reduction in spur helps to increase economic output.

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Research on the Influence of Industry-University-Research Cooperation Innovation Network Characteristics on Subject Innovation Performance

Mathematical Problems in Engineering ◽

10.1155/2021/4771113 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Mingxing Li ◽

Mengjuan Zhang ◽

Fredrick Oteng Agyeman ◽

Hira Salah ud din Khan

Keyword(s):

Automobile Industry ◽

Innovation Performance ◽

University Research ◽

Innovation Activity ◽

Innovation Network ◽

Research Cooperation ◽

Network Characteristics ◽

Structural Hole ◽

The Subject ◽

Per Capita

The automobile industry serves as the primary industry for national development and also enhances manufacturing development strategies. Its source of power comes from the maturity and operational efficiency of the technology innovation network within the industry. Firstly, this paper takes the automobile industry of the Yangtze River Economic Belt as the research object and depicts the topological structure of the 1985–2015 Industry-University-Research Cooperation Innovation Network (IURCIN) from the perspective of space-time evolution. Then, based on the 2015 industrial network characteristic data, the paper analyses the impact of individual network characteristics such as intermediate centrality, structural hole limitation, and cooperation intensity on the innovation performance of the respective network. At the same time, it adds innovation activity, per capita gross regional product (GRP), and research and development (R&D) fund input intensity as control variables in the quantitative analysis. The results show that the centrality and structural hole limitation significantly and positively affect the subject innovation performance. The cooperation intensity and the subject innovation performance have an inverted U-shaped relationship. The innovation activity has a positive effect on the subject’s innovation performance. Furthermore, per capita GRP and R&D expenditure intensity on the main innovation performance is not significant. Finally, the countermeasures and suggestions are put forward to promote the innovation performance of each subject in the IURCIN.

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Modeling and Simulation of Knowledge Innovation Diffusion in Weighted Scale-free Industry-University-Research Cooperative Innovation Network

10.21203/rs.3.rs-107986/v1 ◽

2020 ◽

Author(s):

Xia Cao ◽

Chuanyun Li ◽

Jinqiu Li ◽

Yunchang Li

Keyword(s):

Innovation Diffusion ◽

Network Structure ◽

Knowledge Diffusion ◽

University Research ◽

Innovation Network ◽

Knowledge Level ◽

Growth Trend ◽

Scale Free ◽

Knowledge Innovation ◽

Cooperative Innovation

Abstract With the weighted scale-free cooperative innovation network of industry-university-research established by the weighted evolutionary BBV model as the research object, based on the interaction between knowledge innovation diffusion and network structure, a corresponding model of knowledge innovation diffusion is constructed. By using complex network theory and simulation analysis methods, the evolution law of knowledge innovation diffusion in the industry-university-research cooperative innovation network is analyzed. The results show that the overall knowledge level of the industry-university-research cooperative innovation network shows a rapid growth trend, and the growth rate of knowledge shows a changing trend of slowing first and then increasing abruptly; the greater the degree of the innovator, the higher its knowledge level; the more stable the cooperative relationships between innovator, and the stronger its knowledge diffusion ability; the knowledge diffusion mode and network structure are the reasons for the emergence of sudden changes in the network; the knowledge diffusion constraints and network structure are the keys to knowledge innovation and diffusion; with the passage of time, the knowledge level differentiation among innovators gradually increases, and the role of hub institutions in knowledge innovation diffusion becomes increasingly prominent.

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The Convergence Management of Strategic Emerging Industries: Sustainable Design Analysis for Facilitating the Improvement of Innovation Networks

Sustainability ◽

10.3390/su12030900 ◽

2020 ◽

Vol 12 (3) ◽

pp. 900 ◽

Cited By ~ 1

Author(s):

Xue Wang ◽

Baizhou Li ◽

Shi Yin

Keyword(s):

Network Structure ◽

Manufacturing Industry ◽

Spillover Effect ◽

Development Trend ◽

Innovation Network ◽

Information Technology Industry ◽

Emerging Industries ◽

Technology Industry ◽

Linkage Effect ◽

Innovation Output

The overall high-end of strategic emerging industries can be effectively promoted by strengthening the inter-industry innovation linkage effect. This paper constructs a strategic emerging industry innovation network based on industry correlation. This paper studies the development trend of the whole network structure and individual network characteristics and analyzes the influence of network structure characteristics on the attribute performance of each industry. The research results are as follows: (i) The innovation network as a whole was in a slight decline trend of low density during the sample investigation period, and the correlation level had great room for improvement. (ii) The high-end equipment manufacturing industry, the new materials industry, and the new-generation information technology industry occupy the center of the network, but their spillover effect on other industries is poor, and some industries even get more innovation benefits. (iii) The innovation network of China’s strategic emerging industries has the characteristics of cross-industry clustering, and the linkage effect between plates is insufficient, showing the characteristics of the “reflexivity” aggregation subgroup. (iv) The unreasonable connection mode of strategic emerging industries’ innovation network leads to the improvement of the centrality degree, which is not conducive to the innovation output of the industry. However, innovation output can be positively affected, to a certain extent, by improving the betweenness and closeness of the industries in the network.

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Research on university Risk Evaluation in industry-University-Research Cooperation Project based on improved FMEA

2020 2nd International Conference on Economic Management and Model Engineering (ICEMME) ◽

10.1109/icemme51517.2020.00164 ◽

2020 ◽

Author(s):

Hanhan Tao ◽

Tielu Gao ◽

Shuai Yue ◽

Xiao Chen

Keyword(s):

Risk Evaluation ◽

University Research ◽

Research Cooperation ◽

Cooperation Project

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Research on Innovation Efficiency and Technology Gap in China Economic Development

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595917500051 ◽

2017 ◽

Vol 34 (02) ◽

pp. 1750005 ◽

Cited By ~ 4

Author(s):

Jian-Wen Fang ◽

Yung-ho Chiu

Keyword(s):

Economic Development ◽

Fixed Effect Model ◽

University Research ◽

Quadrant Analysis ◽

Eastern Region ◽

Network Dea ◽

Research Cooperation ◽

Effect Model ◽

Technology Gaps ◽

Technology Level

In this paper, we use the meta-frontier network DEA approach to evaluate the innovation efficiency of 30 provinces in China from 2009 to 2011. These provinces have been classified into two groups based on their levels of economic development. The first group comprises provinces in the Eastern region, while the second group comprises provinces in the Central and Western regions. First, we use the meta-frontier network DEA method to estimate the technology gaps of innovation efficiency between different operating types. Second, the quadrant analysis method explores the reasons for efficiency losses. Finally, we take the fixed effect model to examine whether industry–university–research cooperation influences technology efficiency. The empirical results indicate (i) the Eastern region has significantly higher innovation efficiency than the Central and Western regions. (ii) Some Eastern provinces have a high technology level, yet their resource allocation capabilities still need to be improved. (iii) Industry–university–research cooperation is an effective way to improve innovation performance.

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