Understanding how information use contributes to uncertainties surrounding evacuation decisions is crucial during disasters. While literature increasingly establishes that people consult multiple information sources in disaster situations, little is known about the patterns in which multiple media and personal network sources are combined simultaneously and sequentially across decision‐making phases. We address this gap using survey data collected from households in Jacksonville, Florida affected by 2016's Hurricane Matthew. Results direct attention to perceived consistency of information as a key predictor of uncertainty regarding hurricane impact and evacuation logistics. Frequently utilizing National Weather Service, national and local TV channels, and personal network contacts contributed to higher perceived consistency of information, while the use of other local and online sources was associated with lower perceived consistency. Furthermore, combining a larger number of media and official sources predicted higher levels of perceived information consistency. One's perception of information amount did not significantly explain uncertainty. This study contributes to the theorizing of individuals' information environment from the perspective of media and network multiplexity and provides practical implications regarding the need of information coordination for improved evacuation decision‐making.
An abundance of unstructured and loosely structured data on disasters exists and can be analyzed using network methods. This paper overviews the use of qualitative data in quantitative social network analysis in disaster research. We discuss two types of networks, each with a relevant major topic in disaster research (i.e., whole network approaches to emergency management networks and personal network approaches to the social support of survivors) and four usable forms of qualitative data. We explain five opportunities afforded by these approaches revolving around their flexibility and ability to account for complex network structures. Next, we present an empirical illustration that extends our previous work examining the sources and types of support and barrier experienced by households during long‐term recovery from Superstorm Sandy, wherein we utilized quantitative social network analysis on two qualitative datasets (Lee et al., 2020). We discuss three challenges for these approaches related to the samples, coding, and bias.
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