E3S Web Conf.
Volume 235, 20212020 International Conference on New Energy Technology and Industrial Development (NETID 2020)
|Number of page(s)||9|
|Section||Industrial Technology Development and Industrial Structure Adjustment and Upgrading|
|Published online||03 February 2021|
Research on innovation Mode Selection of high-tech Industry from the perspective of knowledge potential difference
Wuhan university of technology, Wuhan, China
2 South-Central University for Nationalities, Wuhan, China
* Corresponding author. Hu kai. email: firstname.lastname@example.org
This paper puts the supply and demand of external technology into the scope of knowledge potential difference and discusses the choice of innovation mode in high-tech industry from the perspective of knowledge potential difference. And based on high technology industry in 2009-2016 provincial panel data, the relative difference between the human capital of universities and the human capital of high-tech industries is used to measure the regional knowledge potential difference. The threshold regression method is used to empirically test the nonlinear effect of innovation mode on innovation performance of high-tech industry under different knowledge potential differences and identify the “Inverted U” mechanism of knowledge potential difference on external technology transfer of high-tech industry. The results show that there are two thresholds for knowledge potential difference in Chinese provinces, and with the increase of knowledge potential difference, the efficiency of independent R&D in high-tech industries decreases. When the knowledge potential difference is moderate, the innovation mode of domestic technology transformation can significantly promote the high-tech industry. According to the two threshold values, the knowledge potential difference of each jurisdiction presents typical three-stage regular changes. The conclusion of this paper provides theoretical support for the innovation mode selection of high-tech industry.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.