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The local government’s continuous support is critical for the well-being of a community during disaster events. E-Government systems that establish and maintain ongoing connections with the community thus play a vital role in supporting crisis response and recovery. Such systems’ ability to adapt to the crisis circumstances and to address emergent needs helps them continue their fundamental functions during disasters. Considering various services might require different amounts and types of resources, prioritization strategies are helpful in determining the processing order of requests. This paper discusses the role of prioritizing services within an e-Government system, to better understand how such a system can be managed to best utilize available resources. The study examines how a well-functioning e-Government system, the Orange County, Florida 311 non-emergency service system, responded to the COVID-19 pandemic and how the changes in service operations requirements can affect service provision, specifically with respect to assigning or re-assigning priority levels. 

In addition to their normal task of supporting community participation, engagement, and improved information access, information technology-based public service systems are also essential for maintaining critical services and providing effective communication with citizens before, during, and after emergencies. This study focuses on the impacts of disaster events on the operational performance of such service systems and discusses opportunities for managing service efficiency by rearranging and reallocating resources during emergencies. To the best of our knowledge, this is the first attempt to provide a practical method for improving the relative efficiency of public service systems in such a context. We suggest a Data Envelopment Analysis (DEA) approach for quantifying the relative efficiencies associated with service requests from an input-output-based standpoint, and discuss the Orange County (Florida) 311 non-emergency service system, in the context of the COVID-19 pandemic, as an example of how such operational efficiency can be managed during a disruption. 

As the third hurricane the U.S. experienced in 2017, Hurricane María generated impacts that resulted in both short term and long term suffering in Puerto Rico. In this study, we aim to quantify the vulnerability of Puerto Ricans by taking region and society specific characteristics of the island into account. To do this, we follow Cutter et al.’s social vulnerability calculation, which is an inductive approach that aims to represent a society based on its characteristics. We adapted the Social Vulnerability Index (SoVI) for Puerto Rico by using data obtained from the U.S. Census Bureau. We analyzed the newly calculated SoVI for Puerto Rico and compared it with the existing deductive approach developed by the Center for Disease Control (CDC). Our findings show that the new index is able to capture some characteristics that the existing vulnerability index is unable to do.