Evaluation On Policy Efficacy Of Industry-University-Research Cooperative Innovation And Research On Optimize Simulation

The report of the 19 th national congress of China pointed out that to achieve China's goal of entering the forefront of innovative countries by 2035,it is necessary to establish a technological innovation system that takes the enterprise as the main body,the market as the direction,and the deep integration of industry,university,and research.At present,China's scientific and technological innovation capabilities in a variety of industrial fields,including strategic emerging industries,are already at the forefront of the world,providing strong support for the construction of a technologically strong country and a smart society.The establishment of a sound science and technology innovation policy system can further provide a powerful guarantee for improving China's science and technology innovation capabilities,adjusting the economic structure,and building an innovative country,with the in-depth implementation of the innovation-driven development strategy.A large number of science and technology innovation policies have also been formulated at all levels and in various departments and regions in China in order to promote the development of industry-universityresearch(IUR)cooperative innovative activities.However,the current IUR cooperative innovation capability is still not very satisfactory,and comprehensive evaluation and optimization of the policy efficacy of the IUR cooperative innovation are urgently needed.This paper studies from the macro and micro levels: analyzes the effectiveness and effect of policies at the macro level and explores the path of how policy drives innovation cooperation through empirical analysis at the micro level,and then conduct simulations.The research purpose is to improve the IUR collaborative innovation performance.The research uses a combination of normative analysis and empirical analysis,qualitative research and quantitative research to study the relationship between the policy of IUR cooperative innovation and innovation performance from both static and dynamic levels.Not only grasp the performance of China's IUR cooperative innovation policy from a macro perspective,but also explore the mechanism of the driving effect of IUR cooperative innovation policy on innovative behavior,so as to better guide the formulation of policy.It not only analyzes the implementation effect of the policy,but also simulates the implementation process of the policy,and subdivides the path to achieve the policy goals,so as to propose a policy optimization strategy suitable for China's national conditions and innovative development.First,systematically evaluate the effectiveness of innovation policies.Comprehensively collect the IUR collaboration innovation policies at all levels of the country from 2006 to 2019 and divide them into three categories: supply-oriented,demand-oriented,and environment-oriented.Using content analysis to quantify policies to measure effectiveness of policy texts and using policy analysis related econometric models to analyze effectiveness of policy implementation.Research indicates,overall effectiveness of policy increases with W-shaped volatility of the average annual effectiveness,but the number of sub-policies of the three types of policies is unevenly distributed.The effectiveness of policy is affected mainly by accumulation of policies.Policy strengths,objectives,measures,and feedback are used to measure policy effectiveness,scores of measures and objectives are conclusively higher than strengths and feedback.The lag period of policy effects and promotion of innovation performance are different varies by policy type.Supply--oriented policy is conducive to direct output,whereas demand-oriented and environment-oriented policy are conducive to economic output.Demand-oriented policies can promote more innovation performance indicators,but the lag period is longer than the other two categories.Second,explore the driving path of IUR collaboration innovation policy.Based on a literature review of related literatures such as triple helix theory,collaborative innovation theory,policy network model,and complex adaptive system theory,it can be seen that the research on combining IUR collaboration innovation policy with performance is not comprehensive enough.Combining actual investigations,reorganizing and improving related variables and conducting empirical analysis to build a structural equation model.Use Mplus to analyze the driving path of IUR collaboration innovation policy,and test and analyze the path of policy adjustment effect.The results show that,the willingness to cooperate variable indirectly promotes innovation performance by affecting intermediary variables,that is,cooperative behavior.The regulatory effects of policy context variables show that there are many differences in the regulatory effects of different policy instruments.The role of different policy tools in promoting IUR collaboration innovation provides a better basis for subsequent optimization of policies.Finally,simulation and optimization of the IUR collaboration innovation policy were done.On the basis of verifying the role of policy context variables in the promotion of IUR cooperative behaviors.Agent-based computer simulation modeling technology is used to construct simulation models for the driving effects of different policy tools on the cooperation behaviors of enterprises,universities,and research institutes.Through BP artificial neural network,determine the behavior selection function,behavior effect perception function and calculation relationship between related factors of the three parties.The NetLogo simulation platform was used to simulate the effects of different policy tools on the IUR cooperative behavior model.Based on the simulation results,an IUR innovative policy guidance and optimization scheme was designed,and policy suggestions were proposed to guide the three parties in cooperation.The policy analysis framework of IUR cooperative innovation constructed in this study focuses on evaluating the effectiveness and effect of the policy.Through empirical analysis,the driving path of policy to collaborative innovation behavior was explored,and the optimization simulation of policy was carried out.This is not only a new attempt to quantify policy research,but also makes up for the lack of analysis of the role of previous research on the role of innovation policy.No matter from the theoretical analysis level or the actual operation level,it has certain guiding significance for the collaborative innovation of production,teaching and research.The paper contains 43 figures,64 tables,and 149 references.