Proceedings of the 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019)

Analysis on the Propensity Score Matching Model of the Industry–University–Research Collaboration and Enterprise Innovation: Based on Radical Innovation and Incremental Innovation Perspective

Authors
Jing-jing Huang*
Corresponding Author
Jing-jing Huang*
Available Online 20 December 2019.
DOI
https://doi.org/10.2991/aebmr.k.191217.011How to use a DOI?
Keywords
Industry-university-research collaboration, Radical innovation, Incremental innovation, Propensity score matching
Abstract
Based on the data from enterprise economic information database which based on the research and evaluation of the technical ability of enterprises in Liaoning Province, this paper researches the impact of industry-university-research collaboration on different types of enterprises’ innovation and its influence paths by using propensity score matching method to control the endogeneity. It is found that industry-university-research collaboration has a significantly positive effect on the radical innovation performance, but does not have a significant effect on the incremental innovation. “R&D investment way” is an important path for industry-university-research collaboration to influence radical innovation, which means industry-university-research collaboration will significantly improve the marginal contribution rate of the enterprise’s R&D investment.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Jing-jing Huang*
PY  - 2019
DA  - 2019/12/20
TI  - Analysis on the Propensity Score Matching Model of the Industry–University–Research Collaboration and Enterprise Innovation: Based on Radical Innovation and Incremental Innovation Perspective
BT  - Proceedings of the 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019)
PB  - Atlantis Press
SP  - 67
EP  - 71
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.191217.011
DO  - https://doi.org/10.2991/aebmr.k.191217.011
ID  - Huang*2019
ER  -