Disaster Management Knowledge Analysis Framework Validated

Inan, DI; Beydoun, G; Pradhan, B

Disaster Management Knowledge Analysis Framework Validated

Inan, DI Beydoun, G

Pradhan, B

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Information Systems Frontiers, 2022
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Inan, DI
dc.contributor.author	Beydoun, G https://orcid.org/0000-0001-8087-5445
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2022-05-25T06:23:57Z
dc.date.available	2022-05-25T06:23:57Z
dc.date.issued	2022-01-01
dc.identifier.citation	Information Systems Frontiers, 2022
dc.identifier.issn	1387-3326
dc.identifier.issn	1572-9419
dc.identifier.uri	http://hdl.handle.net/10453/157685
dc.description.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.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Information Systems Frontiers
dc.relation.isbasedon	10.1007/s10796-022-10245-w
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [Information Systems Frontiers, 2022. Publication date 01 Jan 2022 The final authenticated version is available online at: [ https://link.springer.com/article/10.1007/s10796-022-10245-w]”
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0806 Information Systems
dc.subject.classification	Information Systems
dc.title	Disaster Management Knowledge Analysis Framework Validated
dc.type	Journal Article
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2022-05-25T06:23:55Z
pubs.publication-status	Published

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.

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http://hdl.handle.net/10453/157685