AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Complex System Modeling and Simulation Article
PDF (11.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Research on Influencing Factors of Innovation Network Performance of Industrial Enterprises above Designated Size in Nanjing Based on System Dynamics

School of Economics and Management, Nanjing Tech University, Nanjing 211816, China
Show Author Information

Abstract

Based on the method of system dynamics, this paper simulates the cycle of collaborative innovation network, involving enterprises, government, research institutions, and science and technology intermediaries from the perspective of capital flow. The results of model analysis show that among the influencing factors, the number of innovation achievements is the most complex variable, and the influence of patent elimination rate is the most significant. The number of Research and Development (R&D) projects is most sensitive to the positive impact of the number of technology intermediaries, and with the passage of time, this kind of effect gradually increases. At the same time, the total industrial assets are positively affected by the intensity of enterprise innovation investment, but there is about three years delay in this effect.

Keywords

system dynamicsinnovation networkcollaborative innovationinnovation performance

References

[1]
C. X. Sheng, Thinking and countermeasures of promoting the conversion of new and old kinetic energy in the 14th Five-Year Plan period, (in Chinese), Reform, no. 2, pp. 5-19, 2020.
Google Scholar
[2]
Y. B. He, The theoretical model of I-U-R collaborative innovation, (in Chinese), Stud. Sci. Sci., vol. 30, no. 2, pp. 165-174, 2012.
Google Scholar
[3]
M. X. Qiao and Y. M. Li, How does cooperative willingness affect innovation achievements transformation of university-industry collaboration, in Proc. 2015 Int. Conf. Management Science and Management Innovation, Guilin, China, 2015, pp. 524-529.
Crossref
[4]
V. E. Dike, Leadership and management in the 21st century organizations: A practical approach, World J. Soc. Sci. Res., vol. 2, no. 2, pp. 139-159, 2015.
Crossref Google Scholar
[5]
D. C. Pai, C. Y. Tseng, and C. H. Liou, Collaborative innovation in emerging economies: Case of India and China, Innovation, vol. 14, no. 3, pp. 467-476, 2012.
Crossref Google Scholar
[6]
D. C. Zhu, C. Liu, and X. X. Li, A study on the organizational model of major scientific and technological projects under whole-nation system in the new period, (in Chinese), J. China Acad. Electron. Inf. Technol., vol. 15, no. 4, pp. 299-305, 2020.
Crossref Google Scholar
[7]
J. J. Guo and F. J. Xie, An empirical study on the determinants of collaborative innovation network formation: Based on the ERGM, (in Chinese), Chin. J. Manag., vol. 18, no. 1, pp. 91-98, 2021.
Google Scholar
[8]
Z. S. Luo, How to build an effective university collaborative innovation network system — Based on the analysis of Nanjing University of traditional Chinese Medicine, (in Chinese), Chin. Univ. Sci. Technol., no. 10, pp. 69-72, 2020.
Google Scholar
[9]
Y. F. Wei, W. L. Shi, and P. N. Ruan, Research on synergy behavior and promotion strategy of enterprise innovation network based on evolutionary game theory, (in Chinese), Math. Pract. Theory, vol. 49, no. 23, pp. 18-30, 2019.
Google Scholar
[10]
Q. Sun, R. Y. Hou, and G. S. Hailekiros, The effect of dual network embeddedness on enterprise innovation performance: An empirical study from structural perspective, in Proc. of the 6th Int. Conf. Industrial Technology and Management, Cambridge, UK, 2017, pp. 47-54.
[11]
M. A. H. Npdp, Swarm creativity: Competitive advantage through collaborative innovation networks by Peter A. Gloor, J. Prod. Innov. Manag., vol. 24, no. 4, pp. 407-408, 2007.
Crossref Google Scholar
[12]
F. Feranita, J. Kotlar, and A. De Massis, Collaborative innovation in family firms: Past research, current debates and agenda for future research, J. Fam. Bus. Strategy, vol. 8, no. 3, pp, 137-156, 2017.
Crossref Google Scholar
[13]
O. Neumann, C. Matt, B. S. Hitz-Gamper, L. Schmidthuber, and M. Stürmer, Joining forces for public value creation? Exploring collaborative innovation in smart city initiatives, Gov. Inf. Quart., vol. 36, no. 4, p. 101411, 2019.
Crossref Google Scholar
[14]
X. Cao and C. Y. Li, Evolutionary game simulation of knowledge transfer in industry-university-research cooperative innovation network under different network scales, Sci. Rep., vol. 10, no. 1, p. 4027, 2020.
Crossref Google Scholar
[15]
L. F. Wang and Y. Hu, Does venture capital improve innovation efficiency? Evidence from China’s firm-level analysis, (in Chinese), J. Financ. Res., no. 1, pp. 177-190, 2017.
Google Scholar
[16]
J. Y. Li and Y. Mu, Innovation performance of Chinese public companies based on DEA method, (in Chinese), Sci. of Sci. Manag. of S. & T., vol. 36, no. 2, pp. 111-121, 2015.
Google Scholar
[17]
H. B. Cong, D. L. Zou, and F. X. Wu, Influence mechanism of multi-network embeddedness to enterprises innovation performance based on knowledge management perspective, Cluster Comput., vol. 20, no. 1, pp. 93-108, 2017.
Crossref Google Scholar
[18]
S. Y. Liu, Impact mechanism of technological innovation policy on regional innovation performance: A case study of Henan province, (in Chinese), Areal Res. Dev., vol. 39, no. 5, pp. 30-35, 2020.
Google Scholar
[19]
D. M. You, Z. P. Li, and X. H. Yang, The influence path of high-tech enterprises innovation network capability on innovation network performance, (in Chinese), Sci. of Sci. Manag. of S. & T., vol. 36, no. 2, pp. 70-82, 2015.
Google Scholar
[20]
Y. D. Qiu, Research on innovation incentive effect of industry-university-research cooperation-Analysis based on different adjustment mechanisms, (in Chinese), Reform Econ. Syst., no. 5, pp. 107-112, 2020.
Google Scholar
[21]
Y. Song, W. Elsner, Z. Y. Zhang, and R. Berger, Collaborative innovation and policy support: The emergence of trilateral networks, Appl. Econ., vol. 52, no. 34, pp. 3651-3668, 2020.
Crossref Google Scholar
[22]
S. G. Liao and W. D. Xie, Research on the construction of China’s science and technology innovation service platform based on the perspective of collaborative innovation, Int. J. Bus. Econ. Res., vol. 8, no. 4, pp. 161-169, 2019.
Crossref Google Scholar
[23]
Y. K. Jiang and Q. Mei, Empirical research on impact of social capital of scientific and technological intermediary on knowledge transfer-Taking the science and technology park of Nanjing University as an example, SHS Web Conf., vol. 24, p. 01001, 2016.
Crossref Google Scholar
[24]
F. Yin, P. Zhang, Y. X. Lan, Y. X. Xia, Q. Zhang, and Z. Li, Research on internet rumors about emergencies based on the system dynamics model, (in Chinese), Inf. Sci., vol. 36, no. 4, pp. 57-63, 2018.
Google Scholar
[25]
B. Zhu and W. Q. Ou, Study on enterprise mainstream and new-stream innovation dynamic evolution based on system dynamic, (in Chinese), Sci. Technol. Prog. Policy, vol. 34, no. 1, pp. 66-74, 2017.
Google Scholar
[26]
M. J. Chi and L. Qiao, A skeletal review of the Sino-U.S. “trade war”: Contentious issues, trade multilateralism and policy recommendations, Can. Foreign Policy J., vol. 26, no. 1, pp. 99-107, 2020.
Crossref Google Scholar
[27]
S. H. Xi, F. Wang, and X. Y. Li, First-and second-order local and global sensitivity analyses on ignition delay times of four typical fuels, (in Chinese), Acta Phys.-Chim. Sin., vol. 35, no. 2, pp. 167-181, 2019.
Crossref Google Scholar
[28]
Y. Yang and H. Xue, Study on regional diversity of industrial technology independent innovation capability, (in Chinese), China Industrial Economics, no. 11, pp. 68-76, 2010.
Google Scholar
Complex System Modeling and Simulation
Volume 1 Issue 1,
March 2021
Pages 65-76
DOI: 10.23919/CSMS.2021.0005
Cite this article:
Lu Y, Fang Y. Research on Influencing Factors of Innovation Network Performance of Industrial Enterprises above Designated Size in Nanjing Based on System Dynamics. Complex System Modeling and Simulation, 2021, 1(1): 65-76. https://doi.org/10.23919/CSMS.2021.0005

938

Views

67

Downloads

1

Crossref

1

Scopus

Altmetrics
Received: 13 December 2020
Revised: 16 March 2021
Accepted: 09 April 2021
Published: 30 April 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return