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Predicting voluntary turnover through human resources database analysis

机译：通过人力资源数据库分析预测自愿离职

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摘要

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.
机译：目的-本文旨在质疑人力资源（HR）系统中的可用数据是否可以在没有补充调查信息的情况下产生可靠的营业额预测。设计/方法/方法-介绍了用于分析营业额的决策树方法和逻辑回归模型。在一家私营公司的比利时分支机构的真实数据集上说明了该方法。通过ROC曲线（AUC）度量下的面积来评估模型性能。调查结果-结论是人事系统中的数据确实导致了人员流动的有价值的预测。实际意义-提出的方法将自愿离职的决定因素浮出水面。结果为人事部门提供了有用的信息。如果逻辑回归结果导致人员流失的可能性很大，则决策树可以确定面临流失风险的员工组。使用基于数据集的方法，每个公司都可以立即确定自己的营业额风险。原创性/价值-迄今为止，尚未完成对数据驱动的营业额调查方法的研究。

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