Impact of Employee Engagement on Productivity: A Regression Analysis Approach to Predict Performance Metrics

Employee engagement has become a key driver of organizational success, impacting important performance measures like productivity, turnover, and financial performance. While there is increased literature, quantitative measurement of how levels of engagement, as gauged by employee surveys, translate to tangible productivity measures is not yet fully explored. In this study, the relationship between employee engagement and organizational productivity is explored with the aid of multiple linear regression analysis grounded on actual data from an engagement survey and performance metrics of a multinational organization. The research is underpinned by robust statistical methodology grounded in ordinary least squares (OLS) regression models to quantify how various engagement facets (e.g., autonomy, recognition, communication quality) predict tangible productivity measures (e.g., revenue per employee, task completion rate). Through using established regression analysis, the study demonstrates that employee engagement has a significant and positive effect on productivity measures with statistically significant coefficients (p < 0.01) and explanatory power (adjusted R² > 0.60). The analysis highlights the predictive strength of engagement survey responses and the quantitative underpinning for strategic human resource interventions. Its implications extend to corporate governance, people strategy, and human capital management, encouraging evidence-based approaches to maximize employee experience and organizational performance. DOI: https://doi.org/10.52783/rcp.1171