A Network Data Science Approach to People Analytics

A Network Data Science Approach to People Analytics

Nan Wang (Deepmacro LLC, USA) and Evangelos Katsamakas (Gabelli School of Business, Fordham University, USA)
Copyright: © 2019 |Pages: 24
DOI: 10.4018/IRMJ.2019040102

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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2. Background: People Analytics And Dataset

This research proposes a network data science approach to people analytics. In this section, we present a background on people analytics, and briefly describe the research dataset.

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