Social Computing Platforms Leveraging Knowledge Flow Patterns

Abstract

The loss of tacit knowledge, inability to capture and re use knowledge and lack of structured workspace collaboration are frequently faced challenges in organizations. The change in knowledge sourcing behaviors by the current generation workforce has impacted the knowledge flow patterns in organizations. The paper attempts to analyze the impact of existing knowledge flow patterns in an organization and identify means to improve the effectiveness of knowledge sharing initiatives in the organization via social computing platforms. Use of social computing platforms enables efficient workspace collaboration. As part of this study, customized solutions are calibrated based on knowledge flow patterns prevalent in teams. Knowledge Network Analysis (KNA) is a sociometric analysis performed to identify knowledge flow patterns and interactions among people in a team. The outcomes of the KNA describe the relationships between team members and knowledge flows. KNA showcases the communication ties prevalent in teams and interactions between human and system resources. The results of these analyses are used to identify push and pull networks which in turn enable effective knowledge management. Results of this study reveal that analyzing the knowledge flow patterns in a team and deploying a customized social computing platform that is tailored to address the specific knowledge flow patterns within that team significantly enhances the impact as opposed to a standardized “one-size-fits-all” platform. Impacts seen include a significant change in knowledge sourcing behaviors, resulting in capture of the tacit knowledge of employees, which further results in reducing the impact of employee movements & attrition. For instance, targeted Communities of Practice based on the presence of cliques in knowledge flow patterns within teams enabled the teams to be able to complete projects efficiently and sustain the quality of deliverables.