Predicting Project Success Using HR Analytics: A Study of Human Capital Metrics and Outcomes

With the current data-based economy, the capability to calculate project success with the help of human resource (HR) analytics has become all the more useful. The research examines these dependencies between major measures of human capital and project outcomes in a sample of 300 projects in technology, finance and consulting industries. Employing a blend of organizational HR information systems and a structured sense of employee survey, reports were obtained on engagement, leadership performance, team unity, skill performance, and absenteeism and turnover rates. The analysis was conducted using both the standard multiple regression and complex machine learning regressor techniques, also in the form of random forest and support vector machines (SVM), testing to which level they are useful predictors. Findings established that the employee engagement, leader effectiveness, team integration along with proficiency of skills is substantial favourable indicators of the project success whereas turnover along with absenteeism are damaging. Of all the tested models, random forest method proved to have the greatest predictive accuracy surpassing regression and SVM models. These results provide evidence that HR analytics can become a strategic instrument used in improving the performance of projects and provide useful recommendations to managers. Recent research adds to the emerging body of scientific literature about predictive HR analytics and the necessity of companies to develop workforce data capabilities and people orientation. Suggestions are provided to enhance human capital practices, analytical capacity and ethical use of data in project-driven contexts.