AN ANALYTICAL STUDY ON KNOWLEDGE SHARING WITHIN THE ORGANIZATION
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Abstract
The better management of knowledge within the organization will lead to improved innovation and competitive advantage. The main goal of the firm… better utilization of internal and external knowledge. This core knowledge is found in individuals, communities of interest and their connections. An organization's data is found in its computer systems but a company's intelligence is found, in its biological and social systems. Though it is acclaimed as a good method, there are some setbacks in the process of knowledge sharing[KS] among the employees. This paper explores the possible ways to establish organization using social computing tools to facilitate Knowledge Sharing and create a social data mining among all the members of organization. Social Data Mining Network Analysis (SDMNA) techniques have been used to study KS patterns which take place between employees and departments. This SDMNA graph reveals the structure of social data mining network highlighting connectivity, clustering and strength of relationships between employees.
Figures (4)
![In the WBE, there are 15 employees. Figure 1 is a KS Network that shows the structure of the state of the network that evolved from the WBLE. The nodes represent the learner (the names of learners have been renamed LO - L14 for reasons privacy and anonymity reasons). When there is a line directly connecting two nodes then these nodes are adjacent. When a node is one of a pair of nodes defining the line then the node is incident to the line. The number of lines that are incident with it is called nodal degree [9]. In-degree is the number of lines that are incident to a node while out-degree is the number of lines that are incident from it. Nodes whose degree equals 0 are called isolates. The KS _ network integrates all learners except for one isolate(L14)](https://figures.academia-assets.com/34276846/figure_001.jpg)
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