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| Identification Method of Innovation Driving Relationships among Science, Technology, and Industry from the Perspective of the Knowledge Network |
| Wang Chao1, Xu Haiyun1, Qi Yancui2, Wu Huawei3 |
1.Business School, Shandong University of Technology, Zibo 255000
2.Shandong Normal University Library, Jinan 250014
3.Archives of Northwestern Normal University, Lanzhou 730070 |
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Abstract Research on the relationship between innovation drivers in science, technology, and industry is crucial for achieving deep and effective integration and development of scientific discovery, technological innovation, and industry. This study investigates innovation-driven relationships between science, technology, and industry through the lens of knowledge networks. First, a temporal knowledge network is constructed to capture the structural characteristics of the knowledge network using the concept of structural entropy in the community network. Subsequently, this study explores the relationship between the three systems of science, technology, and industry in the innovation process by analyzing the transfer entropy between the characteristic information of the network structure. Furthermore, the changing characteristics of the knowledge transfer path under the main driving relationship are examined using mutual information. Finally, empirical research focuses on the field of regenerative medicine (stem cells) to investigate how the mutual driving relationships among science, technology, and industry vary at different stages. This study also reveals different inheritance patterns of knowledge content on the knowledge transmission path among the three systems. In science-driven technology processes, the inheritance characteristics of the knowledge transfer path are consistently strengthened. However, in technology-driven science, the inheritance of the knowledge-transfer path is unstable and exhibits discontinuous characteristics. Moreover, this study demonstrates the scientific validity of the proposed method through various means and suggests its applicability in identifying innovation-driven relationships among science, technology, and industry in other innovation fields.
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Received: 11 January 2023
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