4.6 Article

The emergence of complex adaptive response networks in China: A case study of four disasters

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WILEY
DOI: 10.1111/risa.14121

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complex adaptive system; exponent random graph model; interorganizational networks; network analysis; network formation

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This study aims to identify the driving forces behind interorganizational networks in China following disasters. Using the theory of complex adaptive systems, the self-organization process of disaster response is identified as the network formation process. Interorganizational networks that emerged in response to natural hazards and technical disasters are analyzed using an exponential random model. The findings suggest that bonding structures and sector/jurisdiction-based homophily effects play crucial roles in network formation for disaster response. Five propositions describing the network formation process in China's disaster response are proposed based on the research findings. This theoretical model is essential for advancing research and practice in complex disaster network management.
This study aims to identify the driving forces behind interorganizational networks in China following disasters. Using the theory of complex adaptive systems, we identified the self-organization process of disaster response as the network formation process. We identified interorganizational networks that emerged in response to two natural hazards and two technical disasters by collecting data from multiple sources. The exponential random model analysis is performed to analyze the effects of structures and organizational attributes on network formation. In structuring networks for disaster response, findings demonstrate that bonding structures take precedence over bridging structures for organizations. Meanwhile, the sector and jurisdiction-based homophily effects facilitate network formation in disaster response. On the basis of research findings, five propositions describing the network formation process in China's disaster response are proposed. Such a theoretical model is essential for advancing research and practice in complex disaster network management.

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