学术视界 | 战略性新兴产业创新网络形成机理研究（附下载）
战略性新兴产业创新网络形成机理研究
李柏洲1，王雪1，薛璐绮2，苏屹1
（1.哈尔滨工程大学经济管理学院，黑龙江 哈尔滨150001；
2.航天信息股份有限公司，北京100195）
摘要：本文基于刺激-反应模型，采用回归分析方法，探究知识流动视角下战略性新兴产业创新网络形成机理，在此基础上，运用Agent建模仿真方法，分析市场机制和政府调控下创新主体、政府及环境之间的知识动态交互过程。回归结果表明：当主体间交互时，知识接收方的知识创新行为受到自身知识吸收能力、知识共享意愿、信息刺激强度以及知识发出方知识权力的显著正向影响；当主体与政府交互时，知识接收方的知识创新行为还受到政府支持力度的显著正向影响，且信息刺激强度在政府支持力度与知识创新行为间具有中介作用。仿真结果表明：创新网络中的知识交互式创新主体数量始终多于知识集成式创新，成为主流趋势，尤其是知识权力较大的主体更倾向于选择知识交互式创新；相比于市场机制，政府调控下的创新网络知识集成式创新主体数量未出现下降趋势，且退出者比例更小，说明政府调控能够有效引导主体知识创新行为。
关键词：创新网络；战略性新兴产业；知识流动；Agent仿真；刺激-反应
主要研究结论：本文基于刺激-反应模型，在知识流动视角下，采用静态回归方法，研究战略性新兴产业创新网络形成机理，在此基础上，运用NetLogo多Agent仿真模型分析市场机制和政府调控下创新主体、政府及环境之间的知识动态交互过程，分析战略性新兴产业创新网络形成的动态演进机理。主要研究结论如下：
（1）知识创新主体间交互规则为：知识接收方的知识创新行为受到自身知识吸收能力、知识共享意愿、信息刺激强度及知识发出方知识权力的显著正向影响，知识发出方的知识存量对接收方的创新行为虽然有正向影响，但并不显著。（2）政府与知识创新主体的交互规则为：知识接收方的知识创新行为除了受到自身属性特征及信息刺激强度的显著正向影响外，还受到政府支持力度的显著正向影响，且信息刺激强度在政府支持力度与知识创新行为间具有中介效应。另外，政府支持力度对信息刺激强度具有显著正向影响。（3）知识交互式创新的主体数量始终远高于知识集成式创新，尤其是知识权力较大的知识创新主体更倾向于选择知识交互式创新。（4）市场机制与政府调控相比而言，市场机制下的创新网络存在选择知识集成式创新的主体转而选择知识交互式创新或退出的情况，但政府调控下的知识集成式创新主体数量始终保持稳定增长趋势，且退出者的比例小，说明通过政府政策的驱动，能够有效引导主体的知识创新行为，对提高创新网络形成的质量具有重要作用。
Research on the formation mechanism of the innovation network of strategic emerging industries
Li Baizhou1, Wang Xue1, Xue Luqi2, Su Yi1
(1. School of Economics and Management, Harbin Engineering University, Harbin 150001, Heilongjiang, China;
2. Aisino Corporation Inc., Beijing 100195, China)
Abstract:Based on the realistic background that strategic emerging industries (SEIs) are in the early stage of development, the resource advantages of various subjects, which is conducive to conquering key technologies can be integrated and concentrated by building a high-level innovation network. Thus, it is necessary to explore the formation mechanism of innovation network and fully grasp the rules of interaction of all elements.
Previous studies have concluded that the process of innovation network formation is accompanied by knowledge flow and that the government macro-regulation has an important impact on the quality of innovation network formation. However, it still has the following shortcomings. First of all, previous studies have not examined the influence of knowledge innovation subject attribute on the formation of innovation network in terms of knowledge complementarity and symbiosis of SEIs. Secondly, previous studies lack the research on the influence of interaction rules among knowledge innovation subjects, environment and the government on the formation of innovation network. Finally, the research on analyzing the dynamic interaction process of the SEIs innovation network based on its static formation mechanism needs to be enriched.
According to the above analysis, the formation mechanism of the innovation network of SEIs was studied by static regression based on the stimulus-response model in this paper. On this basis, the agent simulation model was used to analyze the dynamic interaction process among the innovation subject, the government and the environment under the market mechanism and government regulation, which is to explore the dynamic evolution mechanism. The contribution of this paper is to enrich the theoretical framework of research related to innovation networks in SEIs, and provides a theoretical basis for enterprise decision making and government policy formulation. On the one hand, the attributes of knowledge sender and receiver are considered comprehensively. On the other hand, the interaction rules and dynamic interaction process among innovation subjects, environment and the government are fully explored. The mechanism of SEIs innovation network formation is deeply revealed.
The results shown that in the process of interaction between knowledge innovation subject, the knowledge innovation behavior of the knowledge receiver is positively affected by its own knowledge absorption capacity, knowledge sharing willingness, information stimulus intensity and knowledge power of the knowledge sender. In the process of interaction between the government and knowledge innovation subject, the knowledge innovation behavior of knowledge receiver positively affected by the government support intensity. At the same time, the perceived information stimulus intensity of knowledge receiver has a mediating effect on the relationship between the government support and knowledge innovation behavior. After the formation and stabilization of the innovation network, the number of knowledge-interactive innovation subjects is more than that of knowledge-integrated innovation. Compared with the market mechanism, the number of knowledge-integrated innovation subjects in the government-driven innovation network has always maintained a stable growth trend and the proportion of quitters is small.
Based on the conclusions, this paper draws the following enlightenment. First of all, the subject of knowledge innovation should strengthen the accuracy of the prediction about opportunity, timeliness and ambiguity of opportunity information. As the sender of knowledge, it is necessary to enhance the expertise, irreplaceability and inimitability of its knowledge resources to increase its knowledge power. As the receiver of knowledge, it is necessary to continuously strengthen its own knowledge absorption ability and willingness to share knowledge. The knowledge resources of the innovation network can be used more effectively and maintain the stable operation of the innovation network. Secondly, the government should be able to quickly formulate more guiding policies and improve the policy implementation. Through the joint efforts of enterprises and the government, a high-level innovation network of SEIs will eventually be formed. Thus, a solid foundation will be laid for driving the innovation of traditional industries.
Key words:innovative network; strategic emerging industry; knowledge flow; Agent simulation; stimulus-response
引用本文：李柏洲,王雪,薛璐绮,苏屹.战略性新兴产业创新网络形成机理研究[J].科研管理,2022,43(3):173-182.
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