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Predicting Turnover Tendency of Candidates/Employees Based on Personality Assessment Tests: A Data-Driven Approach

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Digital Economy. Emerging Technologies and Business Innovation (ICDEc 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 531))

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Abstract

Employee turnover constitutes a substantial challenge for organizations, exerting an impact on operational continuity and overarching productivity, thereby incurring concealed costs. Consequently, estimating the likelihood of employee turnover emerges as a valuable endeavor for Human Resource Departments. Such forecasting empowers these departments to proactively undertake measures aimed at averting such occurrences, particularly when staff is deemed pivotal to organizational success. Our research endeavors to explore the predicting capacity inherent in personality assessment test scores for anticipating turnover tendencies among candidates and employees. Utilizing a data-driven methodology, our study aspires to forge a robust correlation between personality traits and turnover. Furthermore, our modeling of turnover probability incorporates the influence of demographic information (e.g., gender, age, and education level), as well as performance and work-period metrics. Utilizing several machine learning methods, we cast the problem of predicting turnover tendency of an individual as a binary classification problem and train separate classifiers for candidates and current employees. Our experimental findings indicate that the differentiation between managers and subordinate employees proves advantageous in predicting turnover tendencies, given the requirements of distinct psychological traits at various hierarchical levels.

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Author information

Authors and Affiliations

  1. Advanced Analytics Department, Yıldız Tech, İstanbul, Türkiye

    Berkay Topçu, Mukaddes Altuntaş, Dilruba Topçuoğlu, Talip Akdemir, Elif Kurt & Zeynep Deniz Cankut

Authors
  1. Berkay Topçu
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  2. Mukaddes Altuntaş
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  3. Dilruba Topçuoğlu
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  4. Talip Akdemir
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  5. Elif Kurt
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  6. Zeynep Deniz Cankut
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Corresponding author

Correspondence to Berkay Topçu .

Editor information

Editors and Affiliations

  1. ESEN, University of Manouba, Manouba, Tunisia

    Mohamed Anis Bach Tobji

  2. ESEN, University of Manouba, Manouba, Tunisia

    Rim Jallouli

  3. Mohammed V University, Rabat, Morocco

    Hicham Sadok

  4. Mohammed V University, Rabat, Morocco

    Kaltoum Lajfari

  5. Mohammed V University, Rabat, Morocco

    Driss Mafamane

  6. Mohammed V University, Rabat, Morocco

    Houda Mahboub

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Topçu, B., Altuntaş, M., Topçuoğlu, D., Akdemir, T., Kurt, E., Cankut, Z.D. (2025). Predicting Turnover Tendency of Candidates/Employees Based on Personality Assessment Tests: A Data-Driven Approach. In: Bach Tobji, M.A., Jallouli, R., Sadok, H., Lajfari, K., Mafamane, D., Mahboub, H. (eds) Digital Economy. Emerging Technologies and Business Innovation. ICDEc 2024. Lecture Notes in Business Information Processing, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-76368-7_5

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  • DOI: https://doi.org/10.1007/978-3-031-76368-7_5

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