Structural Characteristics and Spatial Patterns of the Technology Transfer Network in the Guangdong–Hong Kong–Macao Greater Bay Area

Abstract

Recently, the Chinese government released the Outline of the Development Plan for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), raising the development of the GBA urban agglomeration to a national strategy. An efficient technology transfer network is conducive to promoting the integrated and coordinated development and enhancing the scientific and technological innovation capabilities of the GBA urban agglomeration. Therefore, this study uses the patent transaction data for three years (2010, 2014, and 2018), integrates data mining, and uses complex network analysis, based on full-flow and net-flow networks, from the overall characteristics, network node strength, network association, network node importance, and network communities to reveal the structural characteristics and spatial patterns of the technology transfer network in the GBA. The results revealed that: (1) Technology transfer networks (full-flow and net-flow) in the GBA show heterogeneity. (2) Full-flow network presents a clear hierarchy within the GBA, showing a “two poles and two strong” pattern, and technology transfer has the same city preference; outside the GBA, there are close technology transfer regions that have technical and geographical proximity characteristics; the net-flow network presents a “one pole, two strong” pattern, and Guangzhou has become the core region of the net-flow network. (3) Connection objects of the technology transfer network have path dependence and spatial preference. Coexistence of concentration and decentralization characterizes the spatial flow. (4) Spatial distribution of the hub and authority of the technology transfer network is heterogeneous and hierarchical. Each city in the GBA has its own technological advantages. (5) Spatial clustering characteristics of the community within the technology transfer network are obvious. (6) The GBA is dominated by the transfer of patented technology in the high-tech industry, while the transfer of patented technology in the traditional manufacturing industry also plays an important role.

Research on the Correlation and Influencing Factors of Digital Technology Innovation in the Guangdong–Hong Kong–Macao Greater Bay Area

“…Current research on innovation linkages in the Guangdong-Hong Kong-Macao Greater Bay Area is mainly based on linkage data such as thesis collaboration [14,15,27,29,30], patent collaboration [21,22], patent rights transfer [20,31], or attribute data such as R&D personnel and R&D funding [32]. Because of the limited availability of attribute data, only a smaller number of model variables can be chosen, compromising estimation accuracy.…”

“…Some researchers argue that promoting the flow of digital technology as an innovation factor and facilitating digital technology linkage and cooperation among cities can improve innovation efficiency and strengthen the Guangdong-Hong Kong-Macao Greater Bay Area's innovation capacity [11,13]. However, most scholars have focused their research perspectives on innovation production factors such as scientific research knowledge, technological infrastructure, technological innovation talents, and industries in existing studies on innovation linkages in the Guangdong-Hong Kong-Macao Greater Bay Area [14][15][16][17][18][19][20][21][22], with reflections on digital technology innovation linkages being rare. According to some scholars, digital technology also suffers from the problem of gathering but not linking and flowing, which will be the most challenging barrier to building a linked innovation system in the Greater Bay Area [23].…”

Spatial Network Structures of Urban Agglomeration Based on the Improved Gravity Model: A Case Study in China’s Two Urban Agglomerations

“…Based on previous studies in [29][30][31][32][37][38][39], this paper establishes a city center function evaluation index system with 38 indicators (as shown in Table 1) considering the aspects of population size, economic aggregation, and residents' quality of life. Using principal components analysis, six principal components are extracted, and the principal component scores of each index are substituted into the city center function intensity model (as shown in formula (2)) to obtain the center function intensity index of each city to more accurately and comprehensively characterize that city's quality.where K i is the central functional intensity index of city i; and K Inf i are the central function indexes of population, ecology, society, economy, transportation, and infrastructure of city i, respectively.…”

“…Community structure can be used to characterize the spatial organization mode of urban agglomerations, and this index reflects the relative intensity of intercity interaction. e community structure is an essential reflection of the mesoscale network [2], and it is also an important way to discover the network structure and function of the entire urban agglomeration. Identifying these communities is essential for discovering unknown functional modules, such as topics in information networks or urban groups in urban agglomerations.…”

“…e coordinated development of China's core hub cities and surrounding areas has become an important form for promoting new urbanization. To improve the quality of urbanization development and promote the benign and sustainable future development of urban agglomerations, it is particularly necessary to explore and study the evolution of urban agglomerations from the perspective of spatial structure [2]. As the main manifestation and material carrier of urban agglomeration and urbanization development, the spatial structure of urban agglomerations can reflect not only the interaction and relationship between cities within the urban agglomeration but also the development stage and level of the urban agglomeration.…”