Predicting voluntary turnover through human resources database analysis

Evy Rombaut and Marie-Anne Guerry

Management Research Review, 2018, vol. 41, issue 1, 96-112

Abstract: Purpose - This paper aims to question whether the available data in the human resources (HR) system could result in reliable turnover predictions without supplementary survey information. Design/methodology/approach - A decision tree approach and a logistic regression model for analysing turnover were introduced. The methodology is illustrated on a real-life data set of a Belgian branch of a private company. The model performance is evaluated by the area under the ROC curve (AUC) measure. Findings - It was concluded that data in the personnel system indeed lead to valuable predictions of turnover. Practical implications - The presented approach brings determinants of voluntary turnover to the surface. The results yield useful information for HR departments. Where the logistic regression results in a turnover probability at the individual level, the decision tree makes it possible to ascertain employee groups that are at risk for turnover. With the data set-based approach, each company can, immediately, ascertain their own turnover risk. Originality/value - The study of a data-driven approach for turnover investigation has not been done so far.

Keywords: Human resource management; Turnover; Logistic regression; Database analysis; Human resources analytics; Manpower planning; Wastage (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eme:mrrpps:mrr-04-2017-0098

DOI: 10.1108/MRR-04-2017-0098

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