Unveiling Public Service and Prosocial Motivation Through Machine Learning Insights

Public service motivation is defined as an individual’s aspiration to promote community values through contributions to the public sector. In contrast, prosocial motivation reflects the broader community’s inclination to assist individuals. Public service motivation represents a specific type of prosocial motivation directly associated with public service. High levels of public service motivation have been strongly linked to increased prosocial behavior among government employees. Both public service motivation and prosocial motivation have been suggested to influence work outcomes significantly. While substantial research exists on public service and prosocial motivation, a notable gap exists in examining these motivations in non-Western countries' context. This study examines the impact of public service motivation and prosocial motivation on engagement, commitment, and organizational citizenship behavior in the governmental sector in the Kurdistan Region of Iraq via machine learning techniques. Data were gathered from 418 public workers from different cities in the Kurdistan Region. Machine learning methods were used to analyze the data, including support vector machines, k-nearest neighbors, linear regression, and logistic regression. The results reveal that public service motivation predicts employee engagement, organizational commitment, and organizational citizenship behavior. Similarly, prosocial motivation demonstrates a positive relationship with these variables, indicating that public service and prosocial motivation influence public sector employees' overall performance and attitudes.These findings have practical implications for employee motivation strategies in the public sector, particularly for human resource departments aiming to enhance organizational outcomes. Additionally, this study contributes to ongoing discussions on using motivational practices in the public sector and their relationship to administrative efficiency.