Timely assessment of disaster and emergency response networks in the aftermath of superstorm Sandy, 2012. Issue 7 (12th November 2018)

• Record Type: Journal Article
• Authors: Yeo, Jungwon; Comfort, Louise; Jung, Kyujin
• Is Part Of: Online information review. Volume 42:Issue 7(2018)
• Page Start: 1010
• Page End: 1023
• Publication Date: 2018-11-12
• Subjects: Disaster response network analysis -- Manual content analysis -- Rapid network assessment -- Superstorm sandy; 025.04
• DOI: 10.1108/OIR-09-2016-0280
• Languages: English
• ISSNs: 1468-4527

Abstract

Abstract : Purpose: The purpose of this paper is to elaborate pros and cons of two coding methods: the rapid network assessment (RNA) and the manual content analysis (MCA). In particular, it focuses on the applicability of a new rapid data extraction and utilization method, which can contribute to the timely coordination of disaster and emergency response operations. Design/methodology/approach: Utilizing the data set of textual information on the Superstorm Sandy response in 2012, retrieved from the LexisNexis Academic news archive, the two coding methods, MCA and RNA, are subjected to social network analysis. Findings: The analysis results indicate a significant level of similarity between the data collected using these two methods. The findings indicate that the RNA method could be effectively used to extract megabytes of electronic data, characterize the emerging disaster response network and suggest timely policy implications for managers and practitioners during actual emergency response operations and coordination processes. Originality/value: Considering the growing needs for the timely assessment of real-time disaster response systems and the emerging doubts regarding the effectiveness of the RNA method, this study contributes to uncovering the potential of the RNA method to extract relevant data from the megabytes of digitally available information. Also this research illustrates the applicability of MCA for assessing real-time disaster response networks by comparing network analysis results from data sets built by both the RNA and the MCA.