Disaster Management Knowledge Analysis Framework Validated

Dedi I. Inan (), Ghassan Beydoun and Biswajeet Pradhan
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Dedi I. Inan: Universitas Papua
Ghassan Beydoun: University of Technology Sydney
Biswajeet Pradhan: University of Technology Sydney

Information Systems Frontiers, 2022, vol. 24, issue 6, No 17, 2077-2097

Abstract: Abstract In Disaster Management (DM), reusing knowledge of best practices from past experiences is envisaged as the best approach for dealing with future disasters. But analysing and modelling processes involved in those experiences is a well-known challenge. But the efficient storage of those processes to allow reuse by others in future DM endeavours is even more challenging and less discussed. Without an efficient process in place, DM knowledge reuse becomes even more remote as the effort incurred gets construed as a hindrance to more pressing activities during the execution of disaster activities. Efficiency has to also be pursued without compromising the effectiveness of the knowledge analysis and reuse. It is important to ensure that knowledge remains meaningful and relevant after it is transformed. This paper presents and validates a DM knowledge analysis framework (DMKAF 2.0) that caters for efficient transformation of DM knowledge intended for reuse. The paper demonstrates that undertaking knowledge transformation and storage in the context of its use is crucial in DM for both, effectiveness and efficiency of the transformation process. Design Science Research methodology guides the research undertaken, by informing enhancements and how the framework is evaluated. A real case study of flood DM from the State Emergency Service of Victoria State Australia is successfully used to validate these enhancements.

Keywords: Disaster Management; Knowledge Analysis; Knowledge Management; Agent-Oriented Modelling; Metamodeling (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10796-022-10245-w

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