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A Network Data Science Approach to People Analytics

Author

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  • Nan Wang

    (Deepmacro LLC, USA)

  • Evangelos Katsamakas

    (Gabelli School of Business, Fordham University, USA)

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.

Suggested Citation

  • Nan Wang & Evangelos Katsamakas, 2019. "A Network Data Science Approach to People Analytics," Information Resources Management Journal (IRMJ), IGI Global, vol. 32(2), pages 28-51, April.
    • Handle: RePEc:igg:rmj000:v:32:y:2019:i:2:p:28-51
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IRMJ.2019040102
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    Cited by:

    1. Clotilde Coron, 2021. "Quantifying Human Resource Management: A Literature Review," Post-Print halshs-03212718, HAL.

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