Table_1_Estimating Successful Internal Mobility: A Comparison Between Structural Equation Models and Machine Learning Algorithms.DOCX

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 k-nearest neighbors algorithm (k-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 k-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.

内部流动性往往取决于对未来工作满意度的预测，尤其是在内部流动性计划下的员工。本研究中，我们对比了不同类别模型（i）传统的结构方程模型（SEM），以及两种机器学习算法：（ii）回归器，特别是用于特征选择的绝对最小收缩和选择算子（Lasso），以及（iii）分类器，特别是以k最近邻算法（k-NN）作为基础估计器的Bagging元模型。我们的目标是探讨哪种方法能更好地预测348名（具有操作职责）员工和35名主管（训练集）以及79名员工（测试集）的工作满意度，他们均受一家大型意大利银行集团的内部流动性计划所影响。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算法均在所有模型中突出了对变革的抵抗力和对关系的倾向性，以及在不同模型中其他人格和动机变量。对于使用这些变量预测内部流动性计划中成功的职位迁移的理论影响进行了讨论。此外，这些结果展示了在预测人力资源分析中，比较来自不同研究传统的方法是多么关键。