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Reasearch On The Identification Of Science And Technology Innovation Policies And Its Influencin Factors

Posted on:2021-03-07Degree:MasterType:Thesis
Country:ChinaCandidate:H YangFull Text:PDF
GTID:2439330620465736Subject:Public Management
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In the 70 years since the founding of the People's Republic of China,China has made great achievements with world influence in many areas of science and technology innovation.The country's overall level and strength of science and technology innovation have ranked among the best in the world,and has become an important growth pole for the development of science and technology innovation in the world.At present,a new wave of technological revolution and technological innovation has set off around the world.Accelerating the implementation of innovation-driven development requires further deepening the reform of scientific and technological innovation systems and mechanisms.In recent years,pioneering cities represented by national science centers such as Beijing,Shanghai,Hefei,and Shenzhen have formulated a series of scientific and technological innovation policies,which have played a certain role in promoting the development of the local economy and increasing the level of innovation and innovation.However,there is still a large gap between the formulation and implementation of science and technology innovation policies and the need for technological innovation system and mechanism reform.Based on this,it is particularly important to evaluate subjective identification of policy audiences.Through the measurement of technological innovation policy recognition,we can grasp the main influencing factors that affect the technological innovation policy recognition,and provide a feasible policy reference for improving the efficiency of technological innovation policy implementation and responding to the actual needs of science and technology workers.This paper selects Shanghai,Hefei,and Chengdu as three innovative cities along the east,middle,west,and along the Yangtze River,and uses science and technology innovation policy as the research object.Through research status analysis,theoretical literature discussion,interviews,questionnaire surveys,and ordered logistic regression models,the method The degree of technological innovation policy recognition and its influencing factors in the three places were measured.Seven research hypotheses of policy category,policy dissemination,policy attention,policy stability,policy ease of use,policy support,and policy effectiveness were proposed,and related research hypotheses were verified.Based on the 627 valid questionnaires collected,the data of the questionnaires were analyzed and processed.The research results were obtained through descriptive analysis,difference analysis,reliability and validity analysis,and ordered logistic regression analysis.The study found that: there is a significant positive correlation between policy categories,policy dissemination,policy attention,policy stability,policy supportability,policy effectiveness,and technological innovation policy approval;ease of policy and technological innovation policy approval There is no significant correlation between degrees;there is a significant inverse correlation between the educational level of policy audiences and the degree of technological innovation policy approval.Based on relevant research conclusions,the author puts forward five suggestions for improvement: improving the scientificity of policy settings and practicing the policy of responding to people and events;enriching the channels for spreading science and technology innovation policies and using both the old and new methods;maintaining the stability of science and technology innovation policies To prevent the phenomenon of "change from night to night";improve the accuracy of policy formulation to meet the actual needs of policy audiences;attach importance to the role of policy evaluation and monitoring,and continuously improve the effectiveness of policy implementation...
Keywords/Search Tags:science and technology innovation policy, policy identification, ordered logistic regression model
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