A Machine Learning-Based Forecast Model for Career Planning in Human Resource Management: A Case Study of the Turkish Post Corporation

Abstract

In sustainable and competitive business management, it is crucial for organizations to consider organizational change and transformational leadership in human resource (HR) management to adapt to the changes in their environment. This capability enables large-scale enterprises to maintain their presence in an increasingly competitive environment through enhanced management capacity. Enterprises that adopt transformational leadership in HR management must equip leadership candidates with competencies such as creating a shared vision, providing appropriate role models, encouraging the adoption of group goals, meeting high performance expectations, and providing individual support and the development of intellectual stimulation. By identifying potential leadership candidates using a decision support model, the necessary competencies can be developed through in-service training and experiential learning during their careers. Innovative and effective approaches to identifying leadership candidates can be developed by analyzing complex big data using advanced artificial intelligence (AI) techniques. In this article, a forecast model using machine learning (ML) algorithms for a human resource management career planning approach was developed for the Turkish Post Corporation (PTT) and it was tested to predict potential leadership candidates by analyzing the big data of 5000 employees. The Turkish Post Corporation ML algorithms were applied to 100 randomly selected data points using the k-Nearest Neighbor (kNN), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) algorithms to predict the types of titles held by the staff employed at PTT. The kNN, GB, RF, and SVM algorithms achieved accuracy rates of 96%, 91%, 73%, and 41%, respectively. The case study results indicate that promotion decisions in large-scale and rooted enterprises can be effectively modeled using big data and ML algorithms, highlighting significant potential for HR management and leadership development practices in the public sector.