| At present,the world is experiencing the fourth wave of industrial change,and the Central Economic Work Conference clearly pointed out: "We must promote industrial innovation with scientific and technological innovation,especially with disruptive technologies and cutting-edge technologies to generate new industries,new models,new momentum,and develop new quality productivity".As an important focus of China’s industrial structure transformation,high-tech industry has become the main driving force of China’s economic and social development.In 2011,the total output value of China’s high-tech manufacturing industry above designated size has reached 9.2 trillion yuan,ranking second in the world,but there are significant differences between regions.How to accurately evaluate the technological innovation efficiency of China’s high-tech industry,find out the differences between different regions,explain the influencing mechanism behind it and give reasonable suggestions?To solve these problems,the academic community has carried out a lot of research and made some achievements,but there are still some limitations and shortcomings:previous literature regards the technological innovation process as an unknown "black box",ignores the internal operating mechanism and process of the innovation system,and lacks the classification and evaluation of different stages of the technological innovation process;When establishing the evaluation system of technological innovation efficiency,the problem of undesirable output such as environmental pollution emission was not taken into account.When analyzing the regional differences of technological innovation efficiency,most studies mainly focus on the differences in time dimension,and rarely consider the difference structure in space dimension,or the comprehensive analysis of spatial differences is insufficient,and only consider the spatial differences,ignoring the correlation characteristics and dynamic evolution trend of space.When studying the causes of the difference of technological innovation efficiency,the author mainly focuses on the influence analysis of a few factors on the high-tech industry,and lacks the comprehensive induction and analysis of the factors affecting the technological innovation efficiency of the high-tech industry.In the mechanism analysis of influencing factors,the obtained action path is often relatively simple,and the research on complex action paths under multiple influencing factors is not deep enough,and the weight assignment mainly relies on subjective methods such as expert scoring,and there is a lack of objective research methods.In view of this,based on theoretical analysis and literature review,this paper constructs a measurement system of technological innovation efficiency of high-tech industries on the basis of concept definition.Based on the panel data of 30 provinces in China from 2000 to 2020,the Super-SBM model containing non-expected output is used to measure the overall efficiency of technological innovation of high-tech industries and the efficiency of each sub-stage.Then,the paper analyzes the spatio-temporal differentiation and convergence of technological innovation efficiency in high-tech industries.Finally,BP-DEMATEL model is used to identify the key influencing factors of technological innovation efficiency in high-tech industries.On this basis,ISM structural interpretation model is used to analyze the mechanism theoretically and empirically.The innovations of this study can be summarized as follows:A new paradigm for the measurement of technological innovation efficiency in high-tech industries has been established.The non-expected output is included in the evaluation system of technological innovation efficiency in high-tech industries.Through literature review,it is found that the study of technological innovation efficiency in high-tech industries often lacks the consideration of environmental constraints,that is,the problem of non-expected output.In addition,the problems of "slack" and "crowding" of input factors are usually ignored in the research.Different from previous studies,this paper refers to green innovation theory and integrates energy consumption and ecological impact into the conventional efficiency measurement model.At the same time,in view of the non-effective innovation factors such as unsuccessful research and development and insufficient transformation of results,a Super-SBM model including non-expected outputs(energy use,environmental impact and innovation inefficiency)is constructed.The selection basis of the measurement method is not only theoretically demonstrated in detail,but also based on the innovation value chain theory.The technological innovation is divided into different stages(technology research and development,product development,market transformation)to analyze and evaluate the efficiency,and the differentiated characteristics of technological innovation efficiency in high-tech industry are discussed in stages,which can be closer to the real situation of technological innovation in high-tech industry.Interdisciplinary reference is made to exploratory spatial data analysis technology in geoinformatics.By introducing spatial processing tools such as Arc GIS and GEODA,it not only breaks through the limitation of relying on time series analysis,but also reveals spatial differences,maps spatial heterogeneity,and introduces spatial correlation perspectives.By using Moran scatter plot,LISA aggregation plot and spatial hotspot pattern map,this paper verifies the spatial difference characteristics,spatial correlation characteristics and evolutionary characteristics of technological innovation efficiency in multiple dimensions,and interprets and analyzes it layer by layer,so as to more intuitively show the regional differences in technological innovation efficiency of high-tech industries.This interdisciplinary research method caters to the modern and future innovation of scientific research paradigm,and is an innovative exploration across different fields and disciplines.The traditional DEMATEL method is improved.By introducing BP neural network,the improved BP-De MATel model is proposed,which not only solves the one-sidedness problem that previous studies only analyze a few influencing factors,but also realizes the comprehensive evaluation of a large number of influencing factors.Moreover,by integrating the objective data processing characteristics of neural network,The subjective bias of factor induction and weight assignment is overcome.Existing studies usually focus on single or multiple specific variables affecting the innovation efficiency of high-tech industries,lack a comprehensive and systematic exploration of all key factors,and often rely on subjective data sources such as questionnaires and expert evaluations,which may lead to lower credibility in dealing with complex influencing factors.This paper builds an improved model of BP-DEMATEL,which can comprehensively and systematically identify the influencing factors of technological innovation efficiency in high-tech industries under the premise of ensuring objectivity,and identify the key influencing factors of technological innovation in high-tech industries as a whole and each sub-stage according to the cause degree and the centrality degree,thus enhancing the objectivity and accuracy of the results.When exploring the action mechanism of technological innovation efficiency in high-tech industries,it no longer relies on subjective analysis and is limited to a single action path,but introduces the ISM model of system engineering to reveal a composite hierarchical action mechanism with multiple influencing factors and multiple conduction paths,and successfully identifies 10 action paths.The current research on the mechanism of action usually only studies the single mechanism path of several influencing factors,and mainly relies on subjective construction of the mechanism of action,and then verifies it with traditional linear analysis methods such as single or multiple regression analysis.But in many cases,socioeconomic phenomena are generated by multiple factors interacting in complex ways,and linear models often fail to adequately capture these complex interactions.By introducing the ISM interpretation structure model in system engineering,this paper clarifies the hierarchical structure among complex influencing factors,clarifies the role and influence degree of each factor in different action paths,and successfully reveals multiple action paths.Through the hierarchical analysis framework built by ISM model,it not only highlights the key driving factors and their impact at the level,but also reveals the complex causal relationship and interaction among factors,so as to more accurately analyze how different factors work together on the efficiency of technological innovation in high-tech industries,and provide a new perspective and depth for formulating more effective strategies and policies. |
PDF Full Text Request