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Disaster Management Knowledge Analysis Framework Validated

Author

Listed:
  • Dedi I. Inan

    (Universitas Papua)

  • Ghassan Beydoun

    (University of Technology Sydney)

  • Biswajeet Pradhan

    (University of Technology Sydney
    Sejong University
    King Abdulaziz University
    Universiti Kebangsaan Malaysia)

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.

Suggested Citation

  • Dedi I. Inan & Ghassan Beydoun & Biswajeet Pradhan, 2022. "Disaster Management Knowledge Analysis Framework Validated," Information Systems Frontiers, Springer, vol. 24(6), pages 2077-2097, December.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:6:d:10.1007_s10796-022-10245-w
    DOI: 10.1007/s10796-022-10245-w
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    References listed on IDEAS

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    1. Juhani Iivari, 2015. "Distinguishing and contrasting two strategies for design science research," European Journal of Information Systems, Taylor & Francis Journals, vol. 24(1), pages 107-115, January.
    2. Tongtong Li & Qi Wang & Zheng Xie, 2019. "Disaster response knowledge and its social determinants: A cross-sectional study in Beijing, China," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-10, March.
    3. John Venable & Jan Pries-Heje & Richard Baskerville, 2016. "FEDS: a Framework for Evaluation in Design Science Research," European Journal of Information Systems, Taylor & Francis Journals, vol. 25(1), pages 77-89, January.
    4. Elia, Gianluca & Margherita, Alessandro, 2018. "Can we solve wicked problems? A conceptual framework and a collective intelligence system to support problem analysis and solution design for complex social issues," Technological Forecasting and Social Change, Elsevier, vol. 133(C), pages 279-286.
    5. Dedi Iskandar Inan & Ghassan Beydoun & Simon Opper, 2018. "Agent-Based Knowledge Analysis Framework in Disaster Management," Information Systems Frontiers, Springer, vol. 20(4), pages 783-802, August.
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    Cited by:

    1. Dedi I. Inan & Ghassan Beydoun & Siti Hajar Othman, 2023. "Risk Assessment and Sustainable Disaster Management," Sustainability, MDPI, vol. 15(6), pages 1-5, March.
    2. Dedi I. Inan & Ghassan Beydoun & Siti Hajar Othman & Biswajeet Pradhan & Simon Opper, 2022. "Developing Reusable COVID-19 Disaster Management Plans Using Agent-Based Analysis," Sustainability, MDPI, vol. 14(12), pages 1-22, June.

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