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The purpose of this study was to understand the knowledge sharing in projects based on knowledge flow patterns. The impact of attrition, thereby leading to a loss of tacit knowledge, inability to capture and reuse knowledge and inability to understand the knowledge flow patterns, which leads to lack of structured workspace collaboration, are frequently faced challenges in organizations. The change in knowledge sourcing behaviors by the current generation workforce has a high reaching impact in driving collaboration among employees.

This paper attempts to study this impact and identify means to improve the effectiveness of collective knowledge sharing via social computing platforms. As part of this study, customized solutions are devised based on knowledge flow patterns prevalent in teams. Knowledge network analysis (KNA), a socio-metric analysis, is performed to understand knowledge flow patterns among employees in a team which helps understand the relationships between team members with respect to knowledge sharing. KNA helps in understanding ties and interactions between human and system resources.

Significant changes were observed in knowledge sourcing and sharing behaviors. Capture of the tacit knowledge of employees further resulted in reducing the impact of knowledge attrition. For instance, targeted communities of practice (CoPs) based on the presence of cliques within teams enabled teams to complete projects effectively and efficiently.

The results are used to identify push and pull networks to enable effective knowledge management (KM). Results of this study reveal that analyzing knowledge flow patterns in a team and deploying a customized social computing platform that is tailored to address the needs of specific knowledge flow patterns within that team, significantly enhances collaborative sharing as opposed to a standardized “one-size-fits-all” platform.

This paper is an original creation after research by the authors for a continuous assessment of KM within the organization.