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| Resilience Evolution and Driving Mechanism of Collaborative Innovation Network in the Yangtze River Delta |
| Xu Weixiang1,Zhou Jianping1,Zhou Mengyao1,Zheng Jinhui1,Liu Chengjun2 |
| (1.College of Economics, Zhejiang University of Technology, Hangzhou 310023, China;2.Zhijiang College of Zhejiang University of Technology, Shaoxing 312030, China) |
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Abstract As the Yangtze River Delta regional integration strategy is elevated to a national strategy, the development of the Yangtze River Delta is becoming increasingly important. As a concentration area of innovation resources and high-tech industries in China, the Yangtze River Delta needs to play a leading role in unblocking the new double-circulation pattern, bridging national innovation factors and promoting higher levels of collaborative and original innovation. Against this background, this study aims to investigate the spatial evolution of innovation network resilience and the driving mechanism of the Yangtze River Delta city cluster.#br#This study identifies domestic and international research on network resilience, and finds that the existing research on network resilience has been relatively comprehensive and in-depth, but the research on innovation network resilience is still insufficient with a lack of a comprehensive and systematic way to measure innovation network resilience, and there is relatively little analysis of the driving mechanism of innovation network resilience. Therefore, based on the patent cooperation data among cities in the Yangtze River Delta from 2010 to 2018, this study firstly constructs an innovation network node resilience measurement system from four perspectives: resistance and recovery ability, adaptation and adjustment ability, control and transformation ability, and local knowledge base. Secondly, this study uses network modal to analyse the structural resilience of innovation network resilience. Then, this study employs the random walk algorithm to analyse the innovation network resilience cluster resilience. Finally, this study uses the ERGM model to analyse the driving mechanism of innovation network resilience formation.#br#The main conclusions of this paper are as follows. (1) The resilience pattern of innovative network nodes presents dynamic evolution characteristics. In 2010, the resilience pattern of network nodes was formed with “Shanghai-Nanjing” as the core. By 2014, the resilience pattern of network nodes “Shanghai-Suzhou-Wuxi” is the core of strength and resilience. By 2018, the resilience pattern of network nodes was formed with “Shanghai-Nanjing-Suzhou-Hangzhou” as the core. (2) In the innovation network, the structure formed by the radiation of core nodes and the strong interaction structure is the key to the formation of the structural resilience of the innovation network. Specifically, the structure of the phantom with a strong interaction relationship presents a fluctuating area, and the network connection of balance and coordination continues to increase. On the other hand, the overall structural toughness of the innovation network shows a tortuous upward trend, and the strengthening of the structural toughness improves the resilience of the innovative network. (3) The innovation network community is gradually declining and the innovation network aggregation index continues to rise. This shows that the degree of integration of innovation in the Yangtze River Delta continues to deepen, and the ability to withstand external shocks is strengthened. (4) The self-organization effect of the network shows that the reciprocity and interactivity of innovation cooperation have gradually become the driving factors for strengthening the resilience of innovation networks. Among the effects of individual attributes, the driving role of industrial structure and opening to the outside world is more obvious. The role played by government supply has become more prominent over time. However, technological services and education investment have not shown a positive effect on the resilience of innovation networks, which suggests the conversion from positive drive to negative drive. In the exogenous network effect, the role of geographic distance is weakened, but the role of spatial adjacency in improving innovation resilience is gradually increasing, and the resilience of innovation networks is strongly dependent on the information distance network.#br#The innovations in this paper are as follows. (1) This study considers that if the role of inter-regional linkages is neglected, the measurement of regional resilience will be biased and it is difficult to understand the formation mechanism of regional resilience in depth, therefore, the theory of relational economic geography is introduced into resilience research to explain the variability of regional resilience in a network perspective, and in the context of the transformation of economic geography research from a "local view" to a "network view", a shift in the research perspective of resilience is achieved. In the context of the shift from the "local view" to the "network view" of economic geography research, the research perspective of resilience has been transformed. (2) The study enriches the study of innovation networks from the perspective of resilience, and constructs an innovation network resilience evaluation system based on node resilience, structural resilience and community resilience, which can capture the evolutionary patterns of different elements in innovation networks, and has important theoretical significance for the accurate development of innovation network resilience analysis. (3) This study introduces node toughness into innovation networks, constructs dependent variables for innovation network toughness analysis, and explores the influence of network self-organization effect, individual attribute effect and exogenous network effect on innovation network toughness based on ERGM model. This study enriches the research content of toughness driving mechanism exploration and expands the application field of ERGM model, which provides an important reference for subsequent studies.#br#
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Received: 12 March 2021
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