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Knowledge grid to facilitate knowledge sharing model in big data community

In many scientific and business areas big data needs to analyze and flow between the users which can help answer questions and solve many problems if it is extracted by experts who are known as data scientists. This group of big data users comes together by merging and supporting knowledge manage...

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Main Author: Hosseinioun, Sara
Format: Thesis
Language:English
English
Published: 2022
Subjects:
Knowledge management
Big data
Online Access:http://psasir.upm.edu.my/id/eprint/112699/1/112699.pdf
http://psasir.upm.edu.my/id/eprint/112699/
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spelling my.upm.eprints.1126992024-10-22T07:45:46Z http://psasir.upm.edu.my/id/eprint/112699/ Knowledge grid to facilitate knowledge sharing model in big data community Hosseinioun, Sara In many scientific and business areas big data needs to analyze and flow between the users which can help answer questions and solve many problems if it is extracted by experts who are known as data scientists. This group of big data users comes together by merging and supporting knowledge management system characteristics as a big data community to help capture and share expertise, experiences, and ideas. Thus, their communication and sharing of knowledge which includes knowledge transferring and knowledge receiving are fundamental for community existence. A knowledge grid as a communication infrastructure can provide a foundation for exchanging huge among of data and information efficiently. However, reliability, accessibility, validity, and security of information are the most concern and affect knowledge sharing among the big data community while the current knowledge sharing model’s approaches to solving and answering these problems had been limited by specific aspects. In this regards a systematic literature review had been conducted to analyze the research gap and influencing factors. The study explored the factors, which affect knowledge sharing and their relationship with the knowledge grid component in the big data community and it listed several factors which influence knowledge sharing that can categorize from the user, organization, and technological aspects. From the previous related literature and theoretical methods, a conceptual model with seven independent variables, motivation, organization relationship, resource sharing rules, top management support, software application quality, data security, and network quality had been designed. The research model defined node density and link strength as a mediator for facilitating knowledge sharing. Based on the model a survey had been designed which was reviewed by three experts for face and content validity before the pilot study on 20 participants. The collected data from the pilot study had been evaluated for internal consistency and the revised questionnaire had been used for empirical analysis. The empirical study had been performed with 106 respondents by using SPSS for descriptive analysis and PLS-SEM for statistical analysis in which nine hypotheses were tested. The results indicated that from nine constructs, six of them are statistically significant in facilitating knowledge sharing. The revised conceptual model had been validated in the developed prototype, reviewed by experts, and the System Usability Score. In the last part of the research, all the research findings and contributions had been represented. Thus, based on the investigated factors that affect knowledge sharing and their relationship with the knowledge grid component in the big data community and hypotheses analysis, the represented knowledge sharing model had been found useful and improved decision making and problem solving among the big data community. 2022-01 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/112699/1/112699.pdf Hosseinioun, Sara (2022) Knowledge grid to facilitate knowledge sharing model in big data community. Doctoral thesis, Universiti Putra Malaysia. Knowledge management Big data English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
topic Knowledge management
Big data
spellingShingle Knowledge management
Big data
Hosseinioun, Sara
Knowledge grid to facilitate knowledge sharing model in big data community
description In many scientific and business areas big data needs to analyze and flow between the users which can help answer questions and solve many problems if it is extracted by experts who are known as data scientists. This group of big data users comes together by merging and supporting knowledge management system characteristics as a big data community to help capture and share expertise, experiences, and ideas. Thus, their communication and sharing of knowledge which includes knowledge transferring and knowledge receiving are fundamental for community existence. A knowledge grid as a communication infrastructure can provide a foundation for exchanging huge among of data and information efficiently. However, reliability, accessibility, validity, and security of information are the most concern and affect knowledge sharing among the big data community while the current knowledge sharing model’s approaches to solving and answering these problems had been limited by specific aspects. In this regards a systematic literature review had been conducted to analyze the research gap and influencing factors. The study explored the factors, which affect knowledge sharing and their relationship with the knowledge grid component in the big data community and it listed several factors which influence knowledge sharing that can categorize from the user, organization, and technological aspects. From the previous related literature and theoretical methods, a conceptual model with seven independent variables, motivation, organization relationship, resource sharing rules, top management support, software application quality, data security, and network quality had been designed. The research model defined node density and link strength as a mediator for facilitating knowledge sharing. Based on the model a survey had been designed which was reviewed by three experts for face and content validity before the pilot study on 20 participants. The collected data from the pilot study had been evaluated for internal consistency and the revised questionnaire had been used for empirical analysis. The empirical study had been performed with 106 respondents by using SPSS for descriptive analysis and PLS-SEM for statistical analysis in which nine hypotheses were tested. The results indicated that from nine constructs, six of them are statistically significant in facilitating knowledge sharing. The revised conceptual model had been validated in the developed prototype, reviewed by experts, and the System Usability Score. In the last part of the research, all the research findings and contributions had been represented. Thus, based on the investigated factors that affect knowledge sharing and their relationship with the knowledge grid component in the big data community and hypotheses analysis, the represented knowledge sharing model had been found useful and improved decision making and problem solving among the big data community.
format Thesis
author Hosseinioun, Sara
author_facet Hosseinioun, Sara
author_sort Hosseinioun, Sara
title Knowledge grid to facilitate knowledge sharing model in big data community
title_short Knowledge grid to facilitate knowledge sharing model in big data community
title_full Knowledge grid to facilitate knowledge sharing model in big data community
title_fullStr Knowledge grid to facilitate knowledge sharing model in big data community
title_full_unstemmed Knowledge grid to facilitate knowledge sharing model in big data community
title_sort knowledge grid to facilitate knowledge sharing model in big data community
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/112699/1/112699.pdf
http://psasir.upm.edu.my/id/eprint/112699/
_version_ 1814054774388031488
score 13.211869

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