A Network Data Science Approach to People Analytics

Nan Wang and Evangelos Katsamakas
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Nan Wang: Deepmacro LLC, USA
Evangelos Katsamakas: Gabelli School of Business, Fordham University, USA

Information Resources Management Journal (IRMJ), 2019, vol. 32, issue 2, 28-51

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:rmj000:v:32:y:2019:i:2:p:28-51

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