Research On The Impact Of Science And Technology Cooperation Networks Cross-country Knowledge Flow And Digital Economy Scale

With the advancement of science and technology and the deepening of globalisation,cross-country cooperation in science and technology and the dissemination of knowledge have become increasingly important.The development of technology plays an important role in the economy and society,and has become the key for countries to maintain their competitive advantage.In his report to the 20th Party Congress,General Secretary Xi Jinping emphasised the importance of achieving a high level of scientific and technological self-reliance,proposing to take national strategic needs as the guiding principle,to gather forces for original and leading scientific and technological research,and to resolutely win the battle against key core technologies.However,despite the critical role played by technological cooperation networks in knowledge flow and competition among nations,our understanding of their specific mechanisms and influencing factors remains limited.How can the diffusion and evolution of transnational knowledge flows on a global scale be understood and characterised?How do networks of science and technology cooperation shape the pathways and innovation trajectories of transnational knowledge flows?What are the transnational diffusion pathways of innovative S&T knowledge?What characterises S&T development across countries?What is the impact of transnational scientific cooperation on the size of the digital economy?How can the impact of transnational knowledge flows on the size of the digital economy be predicted?Therefore,This doctoral thesis focuses on the scientific and technological cooperation network,transnational knowledge flow and the scale of digital economy,and the studies progress each other.The former study provides the foundation and inspiration for the latter study,and constitutes a gradually in-depth research path.It takes social network analysis,bibliometrics,patentometrics and combines spatial measurement model and machine learning model as the basis of theoretical model,to deepen the research on knowledge diffusion and digital economy scale in cross-country based on complex network of technological co-operation,which involves the following four aspects.Firstly,the study of cross-country knowledge flows from a social network perspective is exemplified by the COVID-19 Scientific Collaborative Network.In previous studies of knowledge flow based on scientific literature,they are mainly limited to statistics or mapping of networks,but this study combines complex network theory and further employs different concepts of centrality to determine the importance and value of individual nodes(countries,institutions,schools,journals,topics)in the network.Aiming at the hotspots of current affairs,the study comprehensively analyses the current status of research related to the new coronavirus from the perspective of knowledge flow,which is of guiding significance for global public health cooperation;it provides reference for researchers to screen the literature,select journals,hotspot areas,and carry out collaborative research,and it is of great significance for policymakers of various countries to explore the paths of cooperation in the field of global public health,and to improve the public health system.Secondly,the study on transnational knowledge flow and innovation trajectory identification based on science and technology cooperation networks,with an example of patent citation network for 5G technology.This study makes four main contributions.Firstly,employing social network analysis methods,it reveals the macro-level development trajectories among nations and the micro-level evolutionary trajectories of technical knowledge flows.It summarizes the patterns of national competitive development trajectories and knowledge flow trajectories and analyzes the relationship between the two.Secondly,it innovatively proposes an assessment framework for evaluating the innovation capabilities of 5G technology.By analyzing the social network of patent citations in different countries,it evaluates the influence of each country’s 5G development and provides comprehensive evaluation and classification of their development pathways,offering reference paths for countries at different stages of development.Thirdly,it constructs co-occurrence networks of IPC classification codes for 5G patents globally,as well as in China,the United States,Japan,South Korea,and Europe,identifying key technologies with broad and strong connectivity.This provides guidance for future research and development focuses.Fourthly,by applying K-core decomposition to the 5 G technology co-occurrence network based on time and network scale,it categorizes technologies into peripheral,related,and core technologies.It identifies the diffusion trajectories of core technologies and the core and important patent clusters in different periods,depicting the evolution of patents in different categories.These research findings contribute to a deeper understanding of the patterns of knowledge flow and innovation pathways among nations.Thirdly,the study of the impact of cross-country scientific cooperation on the digital economy scale:an analysis based on social networks and spatial econometric modelling.This study takes into account the spatial spillover effects that scientific collaboration may generate,constructs a network of scientific collaboration among nations,and applies spatial econometric analysis methods to explore the factors influencing digital economic development.The main contributions of this research are as follows:at the academic level,the study expands upon classical econometric models by considering the impact of scientific collaboration on digital economic development.Additionally,it combines bibliometric analysis,social network analysis,and spatial econometric methods,particularly expanding the spatial weight matrix to consider the effect of scientific collaboration distance on economic growth.Empirical analysis results demonstrate significant spillover effects of scientific knowledge flow across borders,and the degree of completeness in the scientific collaboration network significantly promotes national digital economic growth.At the practical level,the research explores the factors influencing digital economic development in different countries,providing guidance for countries to enhance their technological innovation capabilities and promote digital economic development through talent exchange and expanded collaboration.Moreover,by examining the research foundation of China and other countries in the field of digital economy from a global perspective,the study recognizes the relevance,diversity,and interactions of technological knowledge among nations,fundamentally supporting China’s innovation-driven strategy and the research and development activities of enterprises,accelerating the development of the digital economy,and enhancing the country’s core competitiveness.Fourthly,cross-country scientific and trade cooperation for predicting the digital economy scale:based on machine learning and social network analysis.This study addresses the important issue of predicting the scale of the digital economy and proposes a prediction framework based on network analysis and machine learning,utilizing network characteristics among nations and machine learning methods to forecast the scale of the digital economy.The main contributions of this research are threefold.Firstly,from the perspective of complex network analysis,the interdependencies and power dynamics among nations are depicted,and networks of scientific and trade collaborations are constructed,reflecting the relationships and evolution of scientific and trade collaborations among major digital economy countries globally.Secondly,the research fills the gap in variable selection and method selection in digital economy prediction studies.The selected variables have practical significance and provide insights for research on other economic issues.Thirdly,by validating the correlation with relevant indicators of trade and scientific collaboration network properties,the predictive power of the model is significantly improved,providing robust evidence for the proposed prediction framework combining network analysis and machine learning methods.This research comprehensively explores and deeply analyzes the role of complex networks based on technological collaboration in the knowledge flow and and digital economy development among nations,offering significant theoretical and practical implications.Firstly,through in-depth study of the structure and characteristics of technological cooperation networks,we selected two hot topics,namely "COVID-19"and "5G",and based on the analysis of complex networks and social networks,we better understand the paths and mechanisms of transnational knowledge flow from the perspectives of bibliometrics and patentometrics.respectively.Additionally,studying the dynamic evolution of technological collaboration networks allows us to identify key nodes,critical pathways,and evolutionary patterns,providing comprehensive and insightful insights and references for national technology development strategies.The progress of science and technology for the sake of social and economic development,this study focuses on the impact of transnational knowledge flow of science and technology cooperation network on the scale of digital economy of each country,and further predicts the scale of digital economy of each country.By analyzing the impact of technological collaboration networks on national competitiveness,it deeply examines the strengths and weaknesses of countries’ technological innovation capabilities.This contributes to optimizing the implementation of innovation-driven strategies,promoting the enhancement of research and development capabilities in enterprises,and ultimately strengthening the nation’s core competitiveness.In the research process,this paper innovatively proposes some research frameworks while learning from existing research results,such as the assessment of national innovation capacity;broadens the existing research,such as proposing an economic-science cooperation weight matrix;and constructs a brand-new predictive indicator system,such as economic indicators+scientific cooperation network indicators+trade cooperation network indicators.Lastly,the outcomes of this research contribute to promoting international technological cooperation and development.By better understanding effective models and mechanisms of international technological collaboration,identifying high-quality partners and collaboration opportunities,and fostering deepened international technological cooperation through partnership establishment,resource and experience sharing,nations can collectively tackle scientific and technological challenges,advance global technological progress and economic development.The findings of this research provide theoretical and empirical support for national technology innovation strategies and the promotion of digital economic development.