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A Network Data Science Approach to People Analytics

A Network Data Science Approach to People Analytics

Nan Wang, Evangelos Katsamakas
Copyright: © 2019 |Volume: 32 |Issue: 2 |Pages: 24
ISSN: 1040-1628|EISSN: 1533-7979|EISBN13: 9781522563839|DOI: 10.4018/IRMJ.2019040102
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MLA

Wang, Nan, and Evangelos Katsamakas. "A Network Data Science Approach to People Analytics." IRMJ vol.32, no.2 2019: pp.28-51. http://doi.org/10.4018/IRMJ.2019040102

APA

Wang, N. & Katsamakas, E. (2019). A Network Data Science Approach to People Analytics. Information Resources Management Journal (IRMJ), 32(2), 28-51. http://doi.org/10.4018/IRMJ.2019040102

Chicago

Wang, Nan, and Evangelos Katsamakas. "A Network Data Science Approach to People Analytics," Information Resources Management Journal (IRMJ) 32, no.2: 28-51. http://doi.org/10.4018/IRMJ.2019040102

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Abstract

The best companies compete with people analytics. They maximize the business value of their people to gain competitive advantage. This article proposes a network data science approach to people analytics. Using data from a software development organization, the article models developer contributions to project repositories as a bipartite weighted graph. This graph is projected into a weighted one-mode developer network to model collaboration. Techniques applied include centrality metrics, power-law estimation, community detection, and complex network dynamics. Among other results, the authors validate the existence of power-law relationships on project sizes (number of developers). As a methodological contribution, the article demonstrates how network data science can be used to derive a broad spectrum of insights about employee effort and collaboration in organizations. The authors discuss implications for managers and future research directions.

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