Analyzing the Contribution of Managerial Skills to the Success of Public Event Projects: A Machine Learning Perspective

Purpose: Identifying critical skills for public event endeavor success from the multiple skill sets is costly, laborious, and time-consuming. Machine learning emerging tools present an opportunity to identify an effective skill set in the project management field. This study aims to reveal the essential skills for public event project success by using machine learning algorithms. Design/Methodology/Approach: Various supervised machine learning algorithms were applied, and the GradientBoosting regressor algorithm performed best with R2-score (0.821005), RMSE (0.220851), and MSE (0.148298) scores to predict the public event project success on 285 sample data. The primary data was collected from the event organizers through a questionnaire. This study concluded that both soft and hard skills are mutually leading to event project success. Findings: The results revealed that leadership and budgeting are the most influential, communication and problem-solving are the medium, and motivation is a low-influential factor to predict the event project's success. Implications/Originality/Value: By employing regression supervised machine learning algorithms, this study not only contributes to existing project management theories but also discloses actionable insights to project managers for developing strategies to build a productive and stable workforce.