International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Jan. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 82
Paper Published : 1209
No. of Authors : 3038
  Journal Paper

Paper Title
Inter-Team Boundary Spanning: A Data Science Approach

Abstract
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. Index Terms- Inter-team Boundary Spanning, Data Science, Machine Learning, Knowledge Management


Author - Wietske Van Osch, Charles Steinfield, Yanjie Zhao

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| Published on 2015-10-13
   
   
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