Understanding Cross-Boundary Information Sharing in Emergency Management: Insights from Public Alert and Warning Messages in US Local Governments

Abstract

Cross-boundary information sharing has a decisive influence on managing natural or human-made disasters. Existing literature provides valuable insights into the actors engaged in information sharing, the factors that influence their willingness to share information, the specific content they share, and the main results of information sharing in the context of emergency management. However, despite these contributions, our current knowledge about this topic is still limited in several ways, including overgeneralizing the involved actors, frequently ignoring the interdependence of multiple information-sharing flows, and a lack of empirical research that assesses information-sharing activities from a holistic point of view. Consequently, this study intends to extend the current discussion by empirically exploring the actors in detail and analyzing the multiple-flow information sharing dynamics. A case study of public alerts and warnings in US local governments was conducted to investigate the distinctive actors and features of information sharing and the factors that affect the information-sharing dynamics. Our interviews with eighteen alerting authorities indicate that both government and nongovernment actors can be subdivided into more specific categories with varying information needs, information resources, and levels of authority. We also find that the success of public alerts and warnings depends largely on the activities in two closely connected stages of information sharing: government-to-government and government-to-citizen, each of which faces unique challenges and has specific enablers. These findings reveal that only through a detailed analysis of various actors and information sharing flows can we have a holistic understanding of cross-boundary information sharing for emergency management and, from a practice perspective, provide a more accurate problem diagnosis for future improvement.