Description
With the ubiquity of data, new opportunities have emerged for the application of data science and machine learning approaches to help enhance the efficiency and effectiveness of knowledge management. With the growing use of social media technologies in enterprise settings, one specific area of knowledge management warranting the use of big data analytics involves cross-boundary knowledge creation and management. The objective of this paper is to develop and test a machine learning approach that can assist knowledge managers in detecting three types of intra-organizational boundary spanning activities with the goal of predicting and improving such important outcomes as team effectiveness, collaboration, knowledge sharing, and innovation.
Recommended Citation
Steinfield, Charles; Van Osch, Wietske; and Zhao, Yanjie, "Intra-Organizational Boundary Spanning: A Machine Learning Approach" (2015). AMCIS 2015 Proceedings. 13.
https://aisel.aisnet.org/amcis2015/BizAnalytics/GeneralPresentations/13
Intra-Organizational Boundary Spanning: A Machine Learning Approach
With the ubiquity of data, new opportunities have emerged for the application of data science and machine learning approaches to help enhance the efficiency and effectiveness of knowledge management. With the growing use of social media technologies in enterprise settings, one specific area of knowledge management warranting the use of big data analytics involves cross-boundary knowledge creation and management. The objective of this paper is to develop and test a machine learning approach that can assist knowledge managers in detecting three types of intra-organizational boundary spanning activities with the goal of predicting and improving such important outcomes as team effectiveness, collaboration, knowledge sharing, and innovation.