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Employee Turnover Prediction From Email Communication Analysis

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Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13589))

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Abstract

One of the biggest problems faced by companies is the sudden departure of employees from the company. Such events may even result in a serious paralysis of the functioning of enterprises in the event of resignation from work by people holding significant positions. Therefore, an extremely important issue is to develop techniques that will allow detecting the planned resignation of a given employee well in advance. Gaining knowledge about the factors influencing this type of events may allow for taking actions aimed at counteracting them. This work proposes a proprietary method based on the use of artificial neural networks to predict employees leaving work and to indicate which of the possible analyzed reasons are the most significant. Ultimately, the proposed system achieved an efficiency of 74 %.

The work was supported by The National Centre for Research and Development (NCBR), the project no POIR.01.01.01-00-1288/19; 2020-22.

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Author information

Authors and Affiliations

  1. Czestochowa University of Technology, al. Armii Krajowej 36, Czestochowa, Poland

    Marcin Korytkowski, Jakub Nowak & Rafał Scherer

  2. Network Perspective Ltd., Wroclaw, Poland

    Anita Zbieg, Błażej Żak, Gabriela Relikowska & Paweł Mader

Authors
  1. Marcin Korytkowski
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  2. Jakub Nowak
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  3. Rafał Scherer
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  4. Anita Zbieg
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  5. Błażej Żak
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  6. Gabriela Relikowska
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  7. Paweł Mader
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Corresponding author

Correspondence to Rafał Scherer .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Korytkowski, M. et al. (2023). Employee Turnover Prediction From Email Communication Analysis. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13589. Springer, Cham. https://doi.org/10.1007/978-3-031-23480-4_21

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  • DOI: https://doi.org/10.1007/978-3-031-23480-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23479-8

  • Online ISBN: 978-3-031-23480-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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