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Research On Technological Innovation Efficiency Characteristics And Driving Factors Of China’s ICT Manufacturing Industry

Posted on:2023-02-15Degree:MasterType:Thesis
Country:ChinaCandidate:Y H ZhuFull Text:PDF
GTID:2539307091987579Subject:Applied Economics
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As an important component of high-tech industry,technological innovation in Information and Communication Technology(ICT)manufacturing industry provides the main technology driver for China’s industrial digital transformation and move toward a manufacturing powerhouse.In recent years,the U.S.has frequently cracked down on China’s ICT manufacturing industry,and a series of sanctions have seriously hampered the industry ’s development.China’s high technology manufacturing is “big but not strong”,and phenomenon of key technologies being restricted by others has become prominent.Technological innovation efficiency is an important indicator of technological innovation resource allocation and its operational capability,which can effectively reflect technological innovation capability.Based on this,how to improve the technological innovation efficiency of ICT manufacturing industry has important theoretical value and practical significance to promote the high-quality development of technological innovation in this industry.Based on the analysis of current status of technological innovation development in China’s ICT manufacturing industry,study of technological innovation efficiency characteristics and driving factors is carried out.Firstly,two-stage SBM method is applied to measure technological innovation efficiency and sub-stage efficiency of this industry in 30 provinces,and the evolution characteristics are analyzed comparatively.Dagum Gini coefficient and decomposition method is used to examine the spatial difference characteristics.Conclusions are as follow.Efficiency of the whole process and sub-stage of technological innovation are both at a low to medium level at present,and the rising trend is obvious.Knowledge production stage efficiency is greater than that of commercialization stage,indicating that commercialization stage is the short link that restricts high-quality development of technological innovation.The degree of spatial variation in total efficiency and sub-stage efficiency between provinces is large due to hyper-variable density,intra-regional variation and net inter-regional variation,respectively.Secongly,Tobit regression model is constructed to identify the key factors driving the evolution of technological innovation efficiency in ICT manufacturing.Conclusions are as follow.Firm size has a significant negative driving effect on technology innovation efficiency.Government subsidies also show a significant negative driving effect.Government governance capability has a significant positive driving effect on technology innovation efficiency.In the external development environment,per capita GDP and industrial structure,which reflect the level of economic development,have significant positive driving effects on efficiency.Property rights protection,investment in science and technology infrastructure and the number of research institutions,which reflect the level of science and technology development,show significant positive driving effects.Level of opening to the outside world has a significant positive driving effect on technological innovation efficiency.Both financial development and educational development show a significant negative effect.The regression model results are significantly heterogeneous in terms of sub-stage and sub-region samples.According to the research conclusions,countermeasure suggestions for accelerating efficiency improvement of ICT manufacturing technology innovation are proposed from three perspectives: promoting the transformation of inno vation achievements,optimizing the spatial layout and the innovation policy environment,so as to provide support for China’s industrial digital transformation and the construction of an innovation power.
Keywords/Search Tags:ICT manufacturing industry, technological innovation efficiency, two-stage SBM method, Tobit regression model
PDF Full Text Request
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