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

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Collaborative Innovation Networks

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.

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Notes

  1. 1.

    Some employees had more than one e-mail account in this company. In these cases, we merged the multiple e-mail accounts of an employee into one.

  2. 2.

    http://guardian.galaxyadvisors.com

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Author information

Authors and Affiliations

  1. galaxyadvisors AG, Aarau, Switzerland

    Joao Marcos de Oliveira

  2. Department of Social Networks & Information Systems, University of Bamberg, Bamberg, Germany

    Matthäus P. Zylka

  3. MIT Center for Collective Intelligence, Cambridge, MA, USA

    Peter A. Gloor

  4. Genpact, New York, NY, USA

    Tushar Joshi

Authors
  1. Joao Marcos de Oliveira
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  2. Matthäus P. Zylka
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  3. Peter A. Gloor
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  4. Tushar Joshi
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Corresponding author

Correspondence to Matthäus P. Zylka .

Editor information

Editors and Affiliations

  1. Jilin University, Changchun, Jilin, China

    Yang Song

  2. College of Professional Studies, Northeastern University, Boston, Massachusetts, USA

    Francesca Grippa

  3. MIT Center for Collective Intelligence, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Peter A. Gloor

  4. Department of Management and Economics, University of Beira Interior, Covilhã, Portugal

    João Leitão

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de Oliveira, J.M., Zylka, M.P., Gloor, P.A., Joshi, T. (2019). Mirror, Mirror on the Wall, Who Is Leaving of Them All: Predictions for Employee Turnover with Gated Recurrent Neural Networks. In: Song, Y., Grippa, F., Gloor, P.A., Leitão, J. (eds) Collaborative Innovation Networks. Studies on Entrepreneurship, Structural Change and Industrial Dynamics. Springer, Cham. https://doi.org/10.1007/978-3-030-17238-1_2

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