粤港澳大湾区技术转移网络的时空演变及影响因素OA

The Guangdong-Hong Kong-Macao Greater Bay Area(GBA)has been positioned in the national strategy as an international science and technology innovation center.As innovation resources are unevenly distributed across the 11 cities,technology transfer has become a key channel through which cities acquire external technology,break path dependence and technological lock-in,and upgrade their industrial structures.However,existing studies have paid more attention to the volume of technology transfer than to its direction;thus,the spatiotemporal evolution and driving mechanisms of the directed technology transfer network in GBA remain insufficiently understood.To fill this gap,this study constructed a directed,weighted technology transfer network for the eleven GBA cities from the perspective of the"space of flows."Based on invention patent transfer records from 2005 to 2024,together with statistical data from provincial and municipal yearbooks,this study employed social network analysis(network density,hierarchical stratification,degree centrality,and betweenness centrality)and stage-specific negative binomial regression to characterize the spatiotemporal evolution of the network across four stages(2005-2009,2010-2014,2015-2019,and 2020-2024)and identify the mechanisms underlying its influencing factors.At the network-wide level,network density increased steadily,and technology transfer conformed to a significant rank-size rule,with transfers preferentially occurring among core cities.The network also exhibited a pronounced Matthew effect,whereby a small number of high-value city pairs accounted for a large share of the total transfer volume.At the channel level,both the number and scale of transfer channels grew continuously.Shenzhen and Guangzhou acted as radiation(outflow)centers;Huizhou,Jiangmen,Zhaoqing,and Zhongshan served as convergence(inflow)centers;Hong Kong shifted from a convergence to a radiation role;and Foshan and Zhuhai shifted from radiation to convergence.The direction of transfer shifted from mutual exchanges among core cities toward diffusion to surrounding cities,and the diffusion mode evolved from hierarchical diffusion to a mixed mode combining hierarchical,contact,and jump diffusion.In terms of advantageous flows,Guangzhou and Shenzhen exhibited both agglomeration and spillover effects.Shenzhen-Dongguan and Guangzhou-Foshan constituted the largest mutual flow pairs,and Hong Kong's advantageous flow pointed stably toward Shenzhen.At the node level,the network exhibited a polycentric structure:Shenzhen functioned as the first-tier core,with Guangzhou,Zhongshan,Foshan,and Dongguan as secondary centers,and the network evolved from a Guangzhou-Shenzhen"dual core"toward a multi-center pattern;Shenzhen,Guangzhou,and Zhongshan remained the key nodes,while Dongguan,Shenzhen,Guangzhou,and Hong Kong maintained relatively high betweenness centrality.Negative binomial regression showed that geographic distance,industrial similarity,economic development level,R&D personnel,imports,and technological absorption capacity significantly influenced technology transfer,whereas R&D expenditure and FDI were significant only at specific stages.Among them,geographic distance and economic development level exerted negative effects,confirming their constraining roles,while industrial similarity exerted a robustly positive but gradually declining effect because within the GBA,similar industrial structures reflect collaborative division of labor along the same industrial chain rather than homogeneous competition.This study constructed a directed technology transfer network from a"space of flows"perspective and revealed the spatiotemporal evolution and multi-factor driving mechanisms of technology transfer in the GBA,uncovering its evolutionary patterns across multiple scales,namely the overall network,transfer channels,and node functions.By incorporating both dyadic and node-attribute variables,this study further revealed the dynamic evolution of influencing factor effects through four stage-specific regressions.The findings provide empirical evidence and policy implications for optimizing regional technology transfer and advancing the construction of the GBA as an international science and innovation center.