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Promotion and resignation in employee networks

Author

Listed:
  • Yuan, Jia
  • Zhang, Qian-Ming
  • Gao, Jian
  • Zhang, Linyan
  • Wan, Xue-Song
  • Yu, Xiao-Jun
  • Zhou, Tao

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.

Suggested Citation

  • Yuan, Jia & Zhang, Qian-Ming & Gao, Jian & Zhang, Linyan & Wan, Xue-Song & Yu, Xiao-Jun & Zhou, Tao, 2016. "Promotion and resignation in employee networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 442-447.
  • Handle: RePEc:eee:phsmap:v:444:y:2016:i:c:p:442-447
    DOI: 10.1016/j.physa.2015.10.039
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    References listed on IDEAS

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    1. Liu, Jian-Guo & Ren, Zhuo-Ming & Guo, Qiang, 2013. "Ranking the spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4154-4159.
    2. Nailin Bu & Jean-Paul Roy, 2005. "Career Success Networks in China: Sex Differences in Network Composition and Social Exchange Practices," Asia Pacific Journal of Management, Springer, vol. 22(4), pages 381-403, December.
    3. Hu, Hai-Bo & Wang, Xiao-Fan, 2008. "Unified index to quantifying heterogeneity of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3769-3780.
    4. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    5. Manju K. Ahuja & Dennis F. Galletta & Kathleen M. Carley, 2003. "Individual Centrality and Performance in Virtual R& D Groups: An Empirical Study," Management Science, INFORMS, vol. 49(1), pages 21-38, January.
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    Cited by:

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    2. Cai, Meng & Wang, Wei & Cui, Ying & Stanley, H. Eugene, 2018. "Multiplex network analysis of employee performance and employee social relationships," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1-12.
    3. Jian Gao & Tao Zhou, 2017. "Quantifying China's Regional Economic Complexity," Papers 1703.01292, arXiv.org, revised Nov 2017.
    4. Cem Çağrı Dönmez & Abdulkadir Atalan, 2019. "Developing Statistical Optimization Models for Urban Competitiveness Index: Under the Boundaries of Econophysics Approach," Complexity, Hindawi, vol. 2019, pages 1-11, November.
    5. Chen, Ling-Jiao & Gao, Jian, 2018. "A trust-based recommendation method using network diffusion processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 679-691.
    6. Yang, Xiao & Gao, Jian & Liu, Jin-Hu & Zhou, Tao, 2018. "Height conditions salary expectations: Evidence from large-scale data in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 86-97.

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