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Relational risk assessment and management: investigating capacity in wildfire response networks

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Failures in effective communication and coordination within the network of responding organizations and agencies during a wildfire can lead to problematic or dangerous outcomes. Although risk assessment and management concepts are usually understood with regards to biophysical attributes in the wildfire context, these concepts can be extended to understanding risk for problematic communication and coordination embedded within social and organizational relationships. In this research, we propose leveraging existing network and social coordination theory to investigate how pre-fire relationships and capacities affect both preparedness before a wildfire and inter-agency communication and coordination during a wildfire. This research will not only advance the science of incident management but also provide the empirical foundation for the development of a new set of concepts and rapid assessment tools that we call: Relational Risk Assessment and Management (RRAM). While existing social theory and research from other disaster contexts offers a starting point in the development of RRAM, there is much left to understand about the factors that facilitate effective inter-agency coordination during wildfires. For RRAM to be empirically-based, theoretically-sound, and contextually-grounded in the realities of fire management, applied social science is needed. We propose to utilize a quasi-experimental mixed-methods research design and advanced statistical and social network analysis techniques to address the following questions: 1)How do key characteristics of responder networks contribute to greater preparedness and more effective fire response? 2)How do these network characteristics interact with incident complexity to heighten or minimize relational risk? 3)What are best practices of relational risk management for IMTs given different risk profiles?

B. Nowell

Nowell B. Relational risk assessment and management: investigating capacity in wildfire response networks. (McCaffrey SM).; 2017.

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