The Mechanism of Employee Characteristics on Promotion: Building the Predictive Machine Learning Model

Purpose: This study developed a predictive model based on employee characteristics, analyzes multidimensional data to identify predictive factors of promotion outcomes, and provides data-driven insights for organizations to improve talent management. Methods: Decision tree, random forest, and neural network models were built on the existing dataset (N=54,808). Then, the models predictive efficacies were evaluated to select the optimal model using several methods. Results: The accuracy of decision tree was 85.7%. The accuracy of artificial neural network and random forest model was 93%. For promoted class, random forest had higher precision, while neural network performed slightly better in recall and F1 score. For non-promoted class, both models perform almost identically in precision, recall, and F1 score. Conclusion: Random forest and neural network had good predictive efficacy for employee promotion. If avoiding false promotions is more important (i.e., precision matters more), random forest is the better model. If capturing more promotions is critical (i.e., recall matters more), even at the cost of some false positives, then the neural network is slightly better.