![]() ![]() We devise a robust ranking method for aggregating feature importance across many folds during cross-validation We carefully study the importance of predictive features which have causal policy-making implications. ![]() We find a new top-performing model (LightGBM) for predicting turnover intention We analyze a real-life, European-wide, and detailed survey dataset to test state-of-the-art ML techniques In summary, the novel contributions of this paper are twofold. This in turn provides an intuitive visual tool for interpreting our results. ![]() Finally, we go beyond correlation-based techniques for feature importance by using a novel causal approach based on structural causal models and their link to partial dependence plots. ![]() We do so by ranking the features using a new procedure that aggregates their model importance across folds. We analyze the main features behind our two best performing models (logistic regression and LightGBM) across multiple folds on the training data for model robustness. We train three interpretable (k-nearest neighbor, decision trees, and logistic regression) and four black-box (random forests, XGBoost, LightGBM, and TabNet) classifiers. This allows the potential employer/policy maker to better understand intended turnover and to identify areas of improvement within the organization to curtail actual employee turnover. Our objective is to train accurate predictive models, and to extract from the best ones the most important features with a focus on such items and themes. The survey includes sets of questions (called items) organized by themes that link an employee’s working environment to her willingness to leave her work. In this paper, we model employee turnover intention using a set of traditional and state-of-the-art machine learning (ML) models and a unique cross-national survey collected by Effectory Footnote 2, which contains individual-level information. Moreover, since one precedes the other, the correct prediction of intended turnover enables employers and policy makers alike to intervene and thus prevent actual turnover. Although the link between the two has been questioned, it is still widely used for studying employee retention as detailed quit data is often unavailable due to, e.g., privacy policies. Turnover intention, which is an employee’s reported willingness to leave the organization within a defined period of time, is considered the best predictor of actual employee turnover. Understanding why employees leave their jobs is crucial for both employers and policy makers, especially when the goal is to prevent this from happening. Footnote 1 For instance, the European Commission includes it in its annual joint employment report to the European Union (EU). It is also important for governments to monitor whether organizations are able to do so as changes in employee turnover can be symptomatic of an ailing economic sector. It is important for organizations to be able to retain their talented workforce as this brings stability and growth. This paper focuses on voluntary dysfunctional employee turnover (henceforth, employee turnover) as the departure of a high-performing employee can have a detrimental impact on the organization itself and the labor market at large. Voluntary turnover is divided further into functional and dysfunctional, which refer to, respectively, the exit of low-performing and high-performing workers. It can be classified as voluntary, when it is the employee who decides to terminate the working relationship, or involuntary, when it is the employer who decides. Employee turnover refers to the situation where an employee leaves an organization. ![]()
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