Using Machine Learning to Analyze Employee Psychological Contract Fulfillment: A Data-Driven Approach to Predict Job Satisfaction

Workplace commitment, productivity, and job happiness are all significantly impacted by employee psychological contract fulfilment (PCF). Self-reports and surveys are used in traditional PCF assessment procedures, which can be laborious and subjective. Using a data-driven methodology, this study analyses PCF and predicts work satisfaction using machine learning (ML). Different machine learning methods are trained to identify trends in contract fulfilment and satisfaction levels by utilising sentiment analysis, workplace behavioural indicators, and employee feedback. While model evaluation guarantees accuracy and generalisability, feature selection strategies pinpoint important predictors. The findings show that when it comes to forecasting work happiness, ML models-in particular, ensemble learning techniques-perform better than traditional statistical methods. This study demonstrates the potential of AI-driven insights in HRM, which can help firms better engage employees, meet their expectations, and strengthen retention tactics. The study emphasises how ML may revolutionise workplace dynamics by helping to understand and optimise them.