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Managing COVID‐19‐related knowledge: A smart cities perspective

Effective management of coronavirus disease 2019 (COVID‐19) and the urgent need to improve epidemic prevention require rapid response and immediate solutions, deploying appropriate knowledge management procedures and facilitating effective decision‐making and managerial efforts. The increased adopti...

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Detalles Bibliográficos
Autores principales: Abdalla, Wala, Renukappa, Suresh, Suresh, Subashini
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9088492/
http://dx.doi.org/10.1002/kpm.1706
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author Abdalla, Wala
Renukappa, Suresh
Suresh, Subashini
author_facet Abdalla, Wala
Renukappa, Suresh
Suresh, Subashini
author_sort Abdalla, Wala
collection PubMed
description Effective management of coronavirus disease 2019 (COVID‐19) and the urgent need to improve epidemic prevention require rapid response and immediate solutions, deploying appropriate knowledge management procedures and facilitating effective decision‐making and managerial efforts. The increased adoption of smart cities (SC) technologies offers various technologies that can support knowledge capturing, acquisition, sharing, and transferring. However, knowledge management practitioners and decision‐makers face various challenges to manage huge data generated from the various SC platforms. Managing COVID‐19‐related knowledge necessitates filtering, cleaning, keeping, and sharing only useful data. Therefore, the aim of this paper is to investigate managing knowledge related to COVID‐19 from a SC perspective. The methodological approach for this study is a systematic literature review. The findings indicate that SC technologies, through the advanced deployment of information communications technology (ICT) applications, have a crucial role in knowledge capturing and sharing. Smart cities strategies enable knowledge extraction through facilitating data collection and analysis over various disparate databases, as well as facilitating quick and accurate handling and analysis of huge and unpredicted amount of data. Managing knowledge related to COVID‐19 pandemic has the potential to improve the planning, treatment and controlling the pandemic, enhance decision‐making, and enable disaster management. However, the managing of a huge amount of complex, unstructured data and information remains a big challenge for COVID‐19 knowledge management (KM) initiatives. The paper proposes a conceptual model and illustrates the various components and links between SC strategies, KM and COVID‐19, and how this can inform, facilitate, and enhance decision‐making to take steps for the path of recovery.
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spelling pubmed-90884922022-05-10 Managing COVID‐19‐related knowledge: A smart cities perspective Abdalla, Wala Renukappa, Suresh Suresh, Subashini Knowledge and Process Management Special Issue Articles Effective management of coronavirus disease 2019 (COVID‐19) and the urgent need to improve epidemic prevention require rapid response and immediate solutions, deploying appropriate knowledge management procedures and facilitating effective decision‐making and managerial efforts. The increased adoption of smart cities (SC) technologies offers various technologies that can support knowledge capturing, acquisition, sharing, and transferring. However, knowledge management practitioners and decision‐makers face various challenges to manage huge data generated from the various SC platforms. Managing COVID‐19‐related knowledge necessitates filtering, cleaning, keeping, and sharing only useful data. Therefore, the aim of this paper is to investigate managing knowledge related to COVID‐19 from a SC perspective. The methodological approach for this study is a systematic literature review. The findings indicate that SC technologies, through the advanced deployment of information communications technology (ICT) applications, have a crucial role in knowledge capturing and sharing. Smart cities strategies enable knowledge extraction through facilitating data collection and analysis over various disparate databases, as well as facilitating quick and accurate handling and analysis of huge and unpredicted amount of data. Managing knowledge related to COVID‐19 pandemic has the potential to improve the planning, treatment and controlling the pandemic, enhance decision‐making, and enable disaster management. However, the managing of a huge amount of complex, unstructured data and information remains a big challenge for COVID‐19 knowledge management (KM) initiatives. The paper proposes a conceptual model and illustrates the various components and links between SC strategies, KM and COVID‐19, and how this can inform, facilitate, and enhance decision‐making to take steps for the path of recovery. John Wiley and Sons Inc. 2022-03-14 /pmc/articles/PMC9088492/ http://dx.doi.org/10.1002/kpm.1706 Text en © 2022 The Authors. Knowledge and Process Management published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Special Issue Articles
Abdalla, Wala
Renukappa, Suresh
Suresh, Subashini
Managing COVID‐19‐related knowledge: A smart cities perspective
title Managing COVID‐19‐related knowledge: A smart cities perspective
title_full Managing COVID‐19‐related knowledge: A smart cities perspective
title_fullStr Managing COVID‐19‐related knowledge: A smart cities perspective
title_full_unstemmed Managing COVID‐19‐related knowledge: A smart cities perspective
title_short Managing COVID‐19‐related knowledge: A smart cities perspective
title_sort managing covid‐19‐related knowledge: a smart cities perspective
topic Special Issue Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9088492/
http://dx.doi.org/10.1002/kpm.1706
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