Promotion and resignation in employee networks

Jia Yuan, Qian Ming Zhang*, Jian Gao, Linyan Zhang, Xue Song Wan, Xiao Jun Yu, Tao Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Enterprises have put more and more emphasis on data analysis so as to obtain effective management advices. Managers and researchers are trying to dig out the major factors that lead to employees' promotion and resignation. Most previous analyses are based on questionnaire survey, which usually consists of a small fraction of samples and contains biases caused by psychological defense. In this paper, we successfully collect a data set consisting of all the employees' work-related interactions (action network, AN for short) and online social connections (social network, SN for short) of a company, which inspires us to reveal the correlations between structural features and employees' career development, namely promotion and resignation. Through statistical analysis, we show that the structural features of both AN and SN are correlated and predictive to employees' promotion and resignation, and the AN has higher correlation and predictability. More specifically, the in-degree in AN is the most relevant indicator for promotion, while the k-shell index in AN and in-degree in SN are both very predictive to resignation. Our results provide a novel and actionable understanding of enterprise management and suggest that to enhance the interplays among employees, no matter work-related or social interplays, can be helpful to reduce staffs' turnover risk.

Original languageEnglish (US)
Pages (from-to)442-447
Number of pages6
JournalPhysica A: Statistical Mechanics and its Applications
Volume444
DOIs
StatePublished - Feb 15 2016

Keywords

  • Complex networks
  • Employee networks
  • Human resource
  • Promotion
  • Resignation

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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