Bossi Francesco, Di Gruttola Francesco, Mastrogiorgio Antonio, D'Arcangelo Sonia, Lattanzi Nicola, Malizia Andrea P, Ricciardi Emiliano
MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy.
Axes Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy.
Front Artif Intell. 2022 Mar 25;5:848015. doi: 10.3389/frai.2022.848015. eCollection 2022.
Internal mobility often depends on predicting future job satisfaction, for such employees subject to internal mobility programs. In this study, we compared the predictive power of different classes of models, i.e., (i) traditional Structural Equation Modeling (SEM), with two families of Machine Learning algorithms: (ii) regressors, specifically least absolute shrinkage and selection operator (Lasso) for feature selection and (iii) classifiers, specifically Bagging meta-model with the -nearest neighbors algorithm (-NN) as a base estimator. Our aim is to investigate which method better predicts job satisfaction for 348 employees (with operational duties) and 35 supervisors in the training set, and 79 employees in the test set, all subject to internal mobility programs in a large Italian banking group. Results showed average predictive power for SEM and Bagging -NN (accuracy between 61 and 66%; F1 scores between 0.51 and 0.73). Both SEM and Lasso algorithms highlighted the predictive power of resistance to change and orientation to relation in all models, together with other personality and motivation variables in different models. Theoretical implications are discussed for using these variables in predicting successful job relocation in internal mobility programs. Moreover, these results showed how crucial it is to compare methods coming from different research traditions in predictive Human Resources analytics.
对于参与内部流动计划的员工而言，内部流动往往取决于对未来工作满意度的预测。在本研究中，我们比较了不同类型模型的预测能力，即：（i）传统的结构方程模型（SEM），以及两类机器学习算法：（ii）回归模型，特别是用于特征选择的最小绝对收缩和选择算子（Lasso），和（iii）分类模型，特别是以k近邻算法（k-NN）作为基估计器的Bagging元模型。我们的目的是研究哪种方法能更好地预测一家大型意大利银行集团中参与内部流动计划的348名员工（从事运营工作）、35名主管（在训练集）以及79名员工（在测试集）的工作满意度。结果显示，SEM和Bagging k-NN的平均预测能力（准确率在61%至66%之间；F1分数在0.51至0.73之间）。SEM和Lasso算法在所有模型中都突出了变革阻力和关系导向的预测能力，以及不同模型中的其他个性和动机变量。本文讨论了在内部流动计划中使用这些变量预测成功工作调动的理论意义。此外，这些结果表明，在预测性人力资源分析中比较来自不同研究传统的方法是多么关键。
