Exploring Employee Retention among Generation Z Engineers in the Philippines Using Machine Learning Techniques

Abstract

Generation Z represents a significant portion of the current workforce and is poised to become dominant in the engineering field. As the new generation arises, employee retention becomes a crucial topic in the Philippines. Hence, this study explored the factors influencing employee retention among Generation Z engineers in the Philippines using machine learning feature selection (filter method’s permutation, wrapper method’s backward elimination, and embedded method’s Least Absolute Shrinkage and Selection Operator) and classifiers (support vector and random forest). A total of 412 participants were gathered through a purposive sampling technique. The results showed that six out of seven investigated features were found to be significant factors impacting Generation Z engineers’ intention to remain in a company. These six features were supervisor support, company attachment, job satisfaction, contribution, emotional support, and shared value, organized in descending order of feature importance. These were further explained by fifteen significant subfeatures representing each feature. Only one feature, servant leadership, was deemed insignificant. These findings were extracted from the optimal combination of machine learning algorithms. Particularly, feature selection’s backward elimination brought 85.66% accuracy, and the random forest classifier further enhanced the accuracy value to 90.10%. In addition, the model’s precision, recall, and F1-score values were 89.50%, 90.10%, and 88.90%, respectively. This research also provided practical insights for the company executives, organizational leaders, and human resources department seeking to enhance employee retention strategies. These implications were based on the significant features influencing Generation Z engineers’ retention, ultimately contributing to the long-term success and competitiveness of organizations.