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Mirror, Mirror on the Wall, Who Is Leaving of Them All: Predictions for Employee Turnover with Gated Recurrent Neural Networks

In: Collaborative Innovation Networks

Author

Listed:
  • Joao Marcos Oliveira

    (galaxyadvisors AG)

  • Matthäus P. Zylka

    (University of Bamberg)

  • Peter A. Gloor

    (MIT Center for Collective Intelligence)

  • Tushar Joshi

    (Genpact)

Abstract

Employee turnover is a serious issue for organizations and disrupts the organizational behavior in several ways. Hence, predicting employee turnover might help organizations to react to these mostly negative events with, e.g., improved employee retention strategies. Current studies use a “standard analysis approach” (Steel, Academy of Management Review 27:346–360, 2002) to predict employee turnover; accuracy in predicting turnover by this approach is only low to moderate. To address this shortcoming, we conduct a deep learning experiment to predict employee turnover. Based on a unique dataset containing 12 months of time series of e-mail communication from 3952 managers, our model reached an accuracy of 80.0%, a precision of 74.5%, a recall of 84.4%, and a Matthews correlation coefficient value of 61.5%. This paper contributes to turnover literature by providing a novel analytical perspective on key elements of turnover models.

Suggested Citation

  • Joao Marcos Oliveira & Matthäus P. Zylka & Peter A. Gloor & Tushar Joshi, 2019. "Mirror, Mirror on the Wall, Who Is Leaving of Them All: Predictions for Employee Turnover with Gated Recurrent Neural Networks," Studies on Entrepreneurship, Structural Change and Industrial Dynamics, in: Yang Song & Francesca Grippa & Peter A. Gloor & João Leitão (ed.), Collaborative Innovation Networks, pages 43-59, Springer.
    • Handle: RePEc:spr:seschp:978-3-030-17238-1_2
    DOI: 10.1007/978-3-030-17238-1_2
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