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This book series reports valuable research results and progress in scientific and related areas. Mainly contributed by the distinguished professors of the East China Normal University, it will cover a number of research areas in pure mathematics, financial mathematics, applied physics, computer science, environmental science, geography, estuarine and coastal science, education information technology, etc.
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Vol. 17 Geography of Technology Transfer in China: A Glocal Network Approach by Chengliang Liu (East China Normal University, China)
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Vol. 16 Forty Years of Algebraic Groups, Algebraic Geometry, and Representation Theory in China: In Memory of the Centenary Year of Xihua Cao’s Birth edited by Jie Du (University of New South Wales, Australia), Jianpan Wang (East China Normal University, China) and Lei Lin (East China Normal University, China)
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East China Normal University Scientific Reports — Vol. 17
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PREFACE
When we used to glance over the research on innovation diffusion and knowledge flow, there has already been a great volume of outcomes toward different innovation actors in various regions. Academics in China and abroad have focused on the technology transfer studies on core innovation actors such as multinational corporations, universities, and research institutes, along with industry–university–research communities. However, the majority of results are derived from economics and management science, and there are relatively few studies that examine the spatial concerns of technology transfer and national innovation issues highlighted by it from a geographical perspective. Therefore, our edition of Geography of Technology Transfer in China primarily aims at discovering the geographical character of technology transfer and its determinants from the interaction of global and local scales, which involves the study of innovation geography, economic geography, urban, and regional economics.
In essence, our choice of China is due to its role of innovation growth pole under the current background which is a suitable objective to depict a classic increasing model of emerging economies. In the era of the knowledge-based economy, China’s economic development has entered a new economic mode from factor-driven growth to innovation-driven development, and innovation has emerged as the dominant driver to promote national and regional economic growth. To accelerate the transfer and transformation of innovative technologies, the State Council has made the clarity that
vi Geography of Technology Transfer in China: A Glocal Network Approach
China must urgently strengthen the distribution and construction of a national technology transfer system in accordance with the laws of technological innovation, technology transfer, and industrial development. With the help of national innovation policies and local innovation milieu, China has shown a great increase in both technology generation and transfer since 2000s. Therefore, the empirical research on China’s technology transfer has precious value in indicating a successful innovation system and innovation spillover pattern to the developing countries that have a serious locked-in effect or locate in lower reaches of global innovation chain.
The chapters selected in this book create a clear structure of a series of studies. The contents are organized from easy to complicated, as well as from theoretical to empirical. The research content begins by constructing an analytic framework for urban technology transfer network. It abstracts the study target from innovation elements to cities of technology transfer, and develops an initial conceptual framework for urban technology transfer network. In addition, it further puts forward the dual-pipeline framework to analyze the mechanism of internal technologies and external technologies on local innovation performance (Wang et al., 2023b), which enriches the perspective of current innovation geography research and opens a new field of study. In the course of examining the urban system of technology transfer in China, the research scale has been refined with the interaction of both the global and local (named as glocal network); in this way, the following empirical studies depict the spatial evolution by integrating international, national, and regional scales at first. Then this book deeply analyzes the spillover effect of the technology transfer network and also its determinants, which can be regarded as a bidirectional study. On the other hand, the research methodology emphasizes not only the combination of geographical visualization and econometric analysis but also the combination of big data and small data mining analysis, as well as bibliometric analysis and traditional literature review.
Finally, we hope the outcomes in this book will raise more debates on the geography of science and technology (S&T) innovation between scholars. Due to the context of S&T sovereignty, the
competition among countries has targeted S&T which is endorsed to be the necessity for nations and regions to cope with external constraints to achieve long-term sustainable development. Therefore, the voice on innovation development may forge ahead with new ideas and understanding for especially developing countries to achieve a breakthrough when faced with a locked-in predicament. We would also be very grateful to anyone who provides further ideas and research on the geography of technology transfer in different regions, especially in those late-developing countries led by technology catch up.
In the publishing process, this book has been jointly supported by the National Social Science Fund of China (NSSFC) (No. 21ZDA011) and the Shanghai Shuguang Talent Program (No. 19SG22).
I would like to thank my research group who have helped me to prepare this book efficiently. Junxian PIAO, a Ph.D. candidate and former Master’s student from the Bartlett School of Planning in the University College of London, took the effort of translating the book into English. Ms. Shanshan YAN, Ms. Caicheng Niu, and Mr. Mingming Guan, who have received their Master’s degrees, participated in most of the data collection, analysis, and content writing. My four Ph.D. candidates and three Master’s undergraduate students enhanced the theoretical and empirical analysis. Specifically, Mr. Weisheng MAO oversaw the theoretical background and framework. Mr. Yuan LI designed the figures on the transnational technology transfer. Ms. Bangjuan WANG renewed the models of hubs and their hinterworlds and helped in designing figures in various chapters of the book. Mr. Jiange LIN revised the models on the determinants of technology transfer network. Mr. Xiangjie Liu was responsible for the research methods design, the section on domestic technology transfer network as well as designing its figures. Ms. Xue Luo collected the data material on the transportation network section.
Additionally, I would also like to express my appreciation to other undergraduates such as Shuqi Sun, Tong Liu, Xue Lan, and Qixiang Li, who helped me in redrawing figures and data collection.
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ABOUT THE AUTHOR
Chengliang Liu is a professor and supervisor of economic geography, distinguished professor (class B) of geography, the vice director of the Institute for Global Innovation and Development at East China Normal University (ECNU), Shanghai, China. He earned his Ph.D. in human geography from Central China Normal University and was a visiting scholar at Louisiana State University in the US. He has won five subnationallevel research awards and four government-funded talent projects. His research interests focus on geographical complexity of glocal innovation network and transport network by integrating Global Innovation System (GIS), spatial econometrics, along with methods of complex network and other complexity science. His work has been supported by seven national grants including a major project of the National Social Science Fund of China (NSSFC) and two general programs of the National Natural Science Foundation of China (NSFC). He is also on the editorial board of several academic journals including Chinese Geographical Science, World Regional Studies, and Regional Sustainability. He has published four monographs and over 100 refereed articles signed first or corresponding author. These works are distributed in famous geography journals (such as Geoforum, Journal of Transport Geography,
x Geography of Technology Transfer in China: A Glocal Network Approach
Computers Environment and Urban Systems, Environment and Planning A, etc.) indexed by SSCI, SCI, and CSSCI. Moreover, he is devoted to teaching world regional geography and serves as a teacher in charge of several national-level courses. He has been honored as the Teaching Master in Curriculum-based Values Education by the Ministry of Education of China.
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LIST OF FIGURES
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Figure 1.2 Spatial disparity of China’s intercity technology transfer network from 2009 to 2018
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List of Figures
Figure 2.46 Commercialization model of technology transfer process
Figure 2.47 Determinants of technology transfer process
Figure 2.48 STI model of technology transfer process
Figure 2.49 DUI model of technology transfer network
Figure 2.50 The interaction of transfer actors in TTN
Figure 2.51 Network agents of TTN
Figure 2.52 Brokerage roles in TTN
Figure 2.53 Modes of governance in TTN
Figure 2.54 Glocalization of leading firm in TTN
Figure 2.55 Reverse transfer through subcontracting linkages in TTN
Figure 2.56 Brokerage organization of intermediaries in TTN
Figure 2.57 The iceberg metaphor of knowledge
Figure 2.58 Basic model of TTN
Figure 2.59 Recombination of global pipelines through tradeshow
Figure 2.60 Different buzzs of cluster glocalization
Figure 2.61 Recombination of local buzz within cluster
Figure 2.62 Recombination of glocal pipelines in glocalization
Figure 2.63 Dual-pipeline framework of “global–local” interaction in TTN
Figure 2.64 Spatial disaggregation: Configurations, dynamics, and hierarchies
Figure 2.65 “Ternary spaces” of TTN
Figure 2.66 Spatial types of TTN based on its glocalization
Figure 2.67 Spatial patterns of TTN based on its organizational mechanism
Figure 2.68 Topological structure of TTN based on its motif
Figure 2.69 Topological structure of TTN based on the strong and weak ties
Figure 2.70 Network organizations with hierarchies
Figure 2.71 Network hierarchies of TTN
Figure 2.72 Scalar hierarchies of TTN
xviii Geography of Technology Transfer in China: A Glocal Network Approach
Figure 2.73 Dynamic structure of TTN
Figure 2.74 Life-cycle law of TTN
Figure 2.75 Diversification and networking process of TTN
Figure 2.76 Critical factors of path dependence and creation of TTN
Figure 2.77 Path dependence and creation of TTN
Figure 2.78 Structural dynamics of actors in TTN
Figure 2.79 Multiple evolutionary mechanisms of TTN
Figure 2.80 Multiple proximities mechanism of TTN evolution
Figure 2.81 Buzz-and-pipelines effect on TTN
Figure 2.82 Territorial dynamics of innovative milieu
Figure 2.83 Territorial types of innovative milieu
Figure 2.84 Effect of innovative milieu on TTN
Figure 2.85 Effect of transport infrastructure on TTN
Figure 2.86 Subjects’ barriers of TTN
Figure 3.1 Flowchart of research methods
Figure 3.2 Construction of TTN
Figure 3.3 Graph representations of complex networks
Figure 3.4 The head/tail breaks rule
Figure 3.5 Three organizational properties of dominant relations
Figure 4.1 Dynamics of China’s transnational technology transfer intensity
Figure 4.2 Top 30 cities of technology transfer-in intensity
Figure 4.3 Top 30 cities of technology transfer-out intensity
Figure 4.4 Degree and cumulative distributions of transnational TTN
Figure 4.5 Breadth of domestic terminal cities involved in transnational technology transfer-in
Figure 4.6 Breadth of foreign original cities involved in transnational technology transfer-in 199
Figure 4.7 Linkage intensity of domestic terminal cities involved in transnational technology transfer-in
Figure 4.8 The linkage intensity of foreign original cities during transnational technology transfer-in 206
Figure 4.9 Linkage breadth of domestic original cities involved in transnational technology transfer-out 209
Figure 4.10 Linkage breadth of foreign terminal cities involved in transnational technology transfer-out 211
Figure 4.11 Linkage intensity of domestic original cities involved in transnational technology transfer-out 215
Figure 4.12 Linkage intensity of foreign terminal cities involved in transnational technology transfer-out 216
Figure 4.13 Spatial dynamics of transnational technology import pipelines 218
Figure 4.14 Spatial dynamics of transnational technology export pipelines
Figure 4.15 Subcategories of China’s imported technologies
Figure 4.16 Categories of China’s exporting technology
Figure 4.17 Terminal distribution of China’s importing technology sectors
Figure 4.18 Original distribution of China’s importing technology sectors
Figure 4.19 Original distribution of China’s exporting technology sectors 234
Figure 4.20 Terminal distribution of China’s exporting technology sectors
Figure 4.21 Spatial distribution of importing technology diversity
Figure 4.22 Spatial distribution of exporting technology diversity
Figure 4.23 Spatial disparity of importing technology relatedness
Figure 4.24 Spatial disparity of importing technology similarity
Figure 5.1 Dynamics of national technology transfer intensity (2001–2018)
Figure 5.2 Rank–size distribution of national technology transfer-in (2001–2018)
Figure 5.3 Degree and cumulative degree distributions of national technology transfer-in (2001–2018)
xx Geography of Technology Transfer in China: A Glocal Network Approach
Figure 5.4 Rank–size distribution of national technology transfer-out (2001–2018)
Figure 5.5 Degree and cumulative degree distributions of national technology transfer-out (2001–2018)
Figure 5.6 Spatial evolution of domestic technology transfer network (2001–2018)
Figure 5.7 Spatial evolution of hubs’ hierarchy in national technology transfer network (2014–2018)
Figure 5.8 Dominant flow dynamics of national technology transfer network (2004–2018)
Figure 5.9 Technology diversity dynamics of China’s technology transfer hubs (2004–2018)
Figure 5.10 Functional evolution of China’s technology transfer hubs (2004–2018)
Figure 5.11 Dominant sectoral evolution of China’s technology transfer hubs (2004–2018)
Figure 5.12 Evolutionary stages and modes of technology transfer hubs
Figure 5.13 Hinterworld dynamics of Beijing, Shanghai, Shenzhen, and Guangzhou (2004–2018)
Figure 5.14 Spatial evolution of the hinterworlds of Beijing, Shanghai, Shenzhen, and Guangzhou (2004–2018)
Figure 5.15 Categories of China’s transferred technology (2001–2018)
Figure 5.16 Top 30 cities of imported technology categories (2001–2018)
Figure 5.17 The distribution of imported technology sectors among the four hubs (2001–2018)
Figure 5.18 Top 30 cities of exported technology categories (2001–2018)
Figure 5.19 Spatial disparity of domestic importing technology diversity (2001–2018)
Figure 5.20 Spatial disparity of domestic importing technology relatedness
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Figure 5.21 Spatial disparity of domestic importing technology similarity
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Figure 5.22 Evolutionary path of domestic technology transfer cities 314
Figure 6.1 Degree and weight degree centralities of cities within the three urban agglomerations in 2008, 2012, and 2015 325
Figure 6.2 Intercity technology flows in the three urban agglomerations from 2008 to 2015 333
Figure 6.3 Intercity technology transfer network for the three urban agglomerations from 2008 to 2015 336
Figure 6.4 Core–peripheral structure of urban weight degree in the TTN between the northeast and the whole country in (a) 2005, (b) 2010, and (c) 2015
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Figure 6.5 Spatial dynamics of urban degree and weight degree in the TTN between the northeast and the whole country 341
Figure 6.6 Spatial dynamics of urban connectivity in the TTN between the northeast and the whole country 345
Figure 6.7 Spatial dynamics of urban connectivity in the northeast TTN 345
Figure 6.8 Chord diagram of dominant technology flows in the northeast interurban technology transfer network in 2005, 2010, and 2015 351
Figure 7.1 The dual-pipeline structure of TTN 358
Figure 7.2 Spatial disparity of urban patent application in China from 2001 to 2018 365
Figure 9.1 The research framework of this chapter 411
Figure 9.2 Colocational distribution between urban innovation capacity and HSR 425
Figure 9.3 Trends in the number of urban patent applications with and without HSR in China 427
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LIST OF TABLES
Table 2.1 Internationalization characteristics of NIS
Table 2.2 Classic types of RIS
Table 2.3 Structural characteristics of innovation networks
Table 2.4 OIL paradigm of technology transfer
Table 2.5 Indicators of TLC
Table 2.6 The “holy trinity” framework of REG
Table 2.7 The main types of learning field based on its knowledge base
Table 2.8 Frameworks of global–local interactions
Table 2.9 Three approaches of EEG
Table 2.10 Forms of proximity: some features
Table 2.11 Proximity mechanisms of technology transfer
Table 2.12 Brokerage roles in the inter-regional TTN
Table 2.13 Types of the international TTN brokers
Table 2.14 Structural dimensions of TTO in TTN
Table 2.15 Life-cycle characteristics of TTN
Table 3.1 Main variables and their interpretations
Table 3.2 Major variables and their measurement methods
Table 3.3 Main variables of nodal attributes and their illustrations 170
Table 3.4 Main variables of city pair’s dualism and their illustrations
Table 3.5 Main variables of network structure and their illustrations
xxiv Geography of Technology Transfer in China: A Glocal Network Approach
Table 3.6 Main variables and their measurements 179
Table 3.7 Socioeconomic indicators and their data sources 182
Table 3.8 Technology sectors and fields 185
Table 4.1 Complexity characters of China’s transnational TTN 196
Table 4.2 Degree centrality of domestic terminal cities involved in transnational technology transfer-in 200
Table 4.3 Degree centrality of foreign original cities involved in transnational technology transfer-in 201
Table 4.4 Weight degree of domestic terminal cities involved in transnational technology transfer-in 203
Table 4.5 Weight degree of foreign original cities involved in transnational technology transfer-in 207
Table 4.6 Degree centrality of domestic original cities involved in transnational technology transfer-out 210
Table 4.7 Degree centrality of foreign terminal cities involved in transnational technology transfer-out 212
Table 4.8 Weight degree of domestic original cities involved in transnational technology transfer-out 213
Table 4.9 Weight degree of foreign terminal cities involved in transnational technology transfer-out 213
Table 4.10 Main transnational technology import pipelines 219
Table 4.11 Main transnational technology export pipelines 223
Table 4.12 The number and percentage of imported patents in different sectors 226
Table 4.13 The number and percentage of exported patents in different sectors 229
Table 5.1 Top 50 cities of in-degree centrality in national technology transfer-in network 258
Table 5.2 Top 50 cities of out-degree centrality in national technology transfer-out network (2001–2018) 260
Table 5.3 Top 50 cities of weight in-degree centrality in national technology transfer-in network (2001–2018) 262
Table 5.4 Top 50 cities of weight out-degree centrality in national technology transfer-out network (2001–2018) 264
Table 5.5 Top 50 cities of closeness centrality in national technology transfer network (2001–2018)
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Table 5.6 Top 50 cities of betweenness centrality in national technology transfer network (2001–2018) 270
Table 5.7 Top 20 city pairs toward domestic technology transfer 274
Table 5.8 Growth modes of the hinterworlds of technology transfer hubs
Table 6.1 Top five cities with the largest degree centrality and betweenness centrality within the three urban agglomerations in 2008, 2012, and 2015
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Table 6.2 Urban weight degree (WD) and net flow in-degree (NFD) in the three urban agglomerations in 2008, 2012, and 2015
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Table 6.3 Top10 urban degree centrality of interurban transfer network between the northeast and the whole country 340
Table 6.4 Topological statistics of the northeast interurban technology transfer network 342
Table 6.5 Top10 urban degree centrality in the northeast technology transfer network 344
Table 6.6 Top 10 urban pairs of interurban transfer network between the northeast and the whole country 347
Table 7.1 Descriptive statistics and correlation matrix 366
Table 7.2 Results of least squares method
Table 7.3 The results of robustness test 371
Table 8.1 Results of panel negative binomial regression for transnational technology transfer-in 378
Table 8.2 Results of panel negative binomial regression for transnational technology transfer-out 383
Table 8.3 The descriptive statistic of city attribute 386
Table 8.4 The descriptive statistic of city pair’s dualism 387
Table 8.5 ERGM for network of technology transfer during the period 2007–2012, with isotropy of city node properties 387
