Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/10440
Title: | A bibliometric analysis of emergency management using information systems (2000-2016) |
Authors: | Du, Helen S. Dr. KE Xiaobo, Bob Chu, Samuel K. W. Chan, Lok Ting |
Issue Date: | 2017 |
Source: | Online Information Review, 2017, vol. 41(4), pp. 454-470. |
Journal: | Online Information Review |
Abstract: | Purpose The purpose of this paper is to present a statistical analysis of research into emergency management (EM) using information systems (IS) for the period 2000-2016. Design/methodology/approach In this study, research trends in the area of EM using IS are analysed using various parameters, including trends on publications and citations, disciplinary distribution, journals, research institutions and regional cooperation. Through a keyword co-occurrence analysis, this study identifies the evolution of the main keywords in this area, and examines the changes and developments in the main focus of scholars in this period. The study also explores the main research orientations in the field by analysing and integrating the results of two cluster analyses conducted from keyword- and reference-based perspectives, respectively. Findings The area of EM using IS has received increased attention and interest by researchers and practitioners. It is suggested that more cooperation among research institutions is required to help facilitate the further development of the area. Six main research orientations are identified: namely Web 2.0-enabled research, geographic information technology (IT), IT-based research, the contextual use of IT, crisis collaboration research and mass media communication research, since the research area first became popular in 2006. Originality/value This study is the first to comprehensively map the landscape of EM by conducting a bibliometric analysis of the research using IS. The authors’ findings can help academics and emergency managers gain a comprehensive understanding of the research area, and guide scholars towards producing more effective findings. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/10440 |
ISSN: | 1468-4527 |
DOI: | https://doi.org/10.1108/OIR-05-2017-0142 |
Appears in Collections: | Applied Data Science - Publication |
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