Science Research Management ›› 2021, Vol. 42 ›› Issue (10): 97-103.DOI: 10.19571/j.cnki.1000-2995.2021.10.011

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Impact of CAS-industry collaboration on the transformation of scientific and technological achievements

Guo Ying1, Lian Xiangpeng2, Xi Xiaowen3   

  1. 1. School of Business,China University of Political Science and Law, Beijing 100088,China;
    2. School of Public Policy & Management, Tsinghua University, Beijing 100084,China;
    3. Archives of Chinese Academy of Sciences, Beijing 100190,China
  • Received:2018-10-18 Revised:2019-04-26 Online:2021-10-20 Published:2021-10-18

Abstract:     How to promote the transformation of scientific and technological achievements (TS&TA) has become a hot research topic in China. Some researches discuss about the way of enhancing the efficiency of technology transfer, such as taking technology achievements as investment into incorporated companies for shares, starting university spin-offs, establishing technology transfer offices, etc. Though these methods provide universities some insights to commercialize their technologies, the rate of technology transfer is still not high in China. Recently, it is noticed that the Industry-University-Research Institutes Cooperation (I-U-RIC) which helps fill the information barriers between research organizations and companies may become a new method to promote the TS&TA. I-U-RIC could give deeply insights for the companies on whether a technology match the market demand or not. Many studies propose different viewpoints about how the I-U-RIC influence the TS&TA, but there are few empirical studies. The Chinese Academy of Sciences (CAS) is the national academy for the natural sciences of China, and it plays a vital role in the innovation system of China. However, there are few studies pay attention to the CAS′s transformation of scientific and technological achievements. To fill the gap, this paper endeavors to explore the impact of CAS-Industry Collaboration (CAS-IC) on the TS&TA. The complex network analysis is widely used in the analysis of I-U-RIC, and its various statistical indicators have good capability of description as well as prediction. In this paper, we use the complex network analysis to describe the CAS-IC network. Combining with regression analysis, we test how the small-world property of I-RIC network influence the total technology transfer of CAS from the macro-perspective. Furthermore, we analyze the impact of centrality and structural holes of single research institute on its own technology transfer.Our empirical study shows that the CAS-IC is helpful to promote the TS&TA for CAS. Firstly, the small world property of whole CAS-IC network has a significant positive effect on the TS&TA. Small world refers to the network who has high clustering coefficient and short average path length at same time. In other words, on the one hand, each node in the network connects closely with nodes surrounding, and on the other hand, there would be short path for each node to arrive at other nodes not around it. Small world network is better than regular network in terms of information flow and resources integration. Due to the high clustering coefficient, the connections among nodes are close so lead to improve the efficiency and accuracy of information communication. Moreover, due to the small average path length, it is convenient for nodes to obtain fresh and non-redundant information from remote nodes. When the small world property of I-RIC network enhances, the information communication between the whole CAS and companies becomes closely and efficient. Companies could understand which research institute has the proper technologies they need through this close collaboration, and research institutes could get more information about what is the real technical demand of companies and adjust their research direction to meet company′s demand. Secondly, for local network, the centrality of one research institute in the CAS-IC network demonstrates a positive and statistically significant influence on its TS&TA. Lack of trust to the research organizations is one barrier for TS&TA. Furthermore, company cannot accurately evaluate the market value of one technology merely depending on the instruction from CAS within a short time. Moreover, the R&D goal of companies and CAS is totally different which would make it more difficult to use new technologies in the process of production directly. In the I-RIC network, research institutes with high centrality indicates that they have formed a stable and close relationship with companies, which can bring more understanding and trust to both sides. In addition, companies and CAS′s research institutes can accumulate technological base through collaboration, and companies don′t need to invest too much capital and people for a second innovation. In this way, companies can transform the new technologies into production with low cost.Thirdly, the structural holes of research institute do not have significant impact on the TS&TA. Different with centrality, structural holes represent the diversity of information. Previous studies propose that high structural holes help research organizations gain various information from partners in different fields. But in this paper, we find companies almost are in the same fields with CAS′s research institutes they purchase technologies from. Thus, what CAS′s TS&TA need is the stable information from companies in specific fields.In summary, this article finds that the industry and research institutes collaboration is an efficient way to promote the TS&TA in the case of CAS. We believe the collaboration can break the information barriers between industry and academic circles. Not only technology information but also technology base and trust between companies and research institutes can be established through collaboration. Policy maker should introduce a series of measures to encourage research institutes to directly participate in industry and research institutes collaboration.

Key words: CAS-industry collaboration, transformation of scientific and technological achievements, complex network