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 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.

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