Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.4018/IRMJ.2019040102 | INFORMATION RESOURCES MANAGEMENT JOURNAL |
Keywords | Field | DocType |
Collaboration, Data Science, Employee Effort, Employee Engagement, Knowledge Work, Network Analysis, Network Science, People Analytics, Power Laws, Productivity, Talent Analytics | Knowledge management,Network data,Engineering,Analytics | Journal |
Volume | Issue | ISSN |
32 | 2 | 1040-1628 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nan Wang | 1 | 93 | 27.47 |
| Evangelos Katsamakas | 2 | 163 | 13.49 |