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Recent Research Reviews Journal

Employee Productivity Analytics and Prediction: A Dynamic Productivity and Burnout Modelling Engine (DPBME) for Privacy-Aware Workforce Analytics

Open Access
Published: 15 September, 2026
Pages: 355-378
  • Lakmali Karunarathne , Lakmali Karunarathne
    Lecturer, Department of Computer Science and Data Science, York St John University, London Campus, London, United Kingdom
    Lecturer, Department of Computer Science and Data Science, York St John University, London Campus, London, United Kingdom
  • Swathi Ganessan , Swathi Ganessan
    Deputy Associate Dean, Department of Computer Science and Data Science, York St John University, London Campus, London, United Kingdom
    Deputy Associate Dean, Department of Computer Science and Data Science, York St John University, London Campus, London, United Kingdom
  • Nalinda Somasiri , Nalinda Somasiri
    Associate Dean, Department of Computer Science and Data Science, York St John University, London Campus, London, United Kingdom
    Associate Dean, Department of Computer Science and Data Science, York St John University, London Campus, London, United Kingdom
  • Kavindu Karunarathne Kavindu Karunarathne
    Research Assistant, Department of Computer Science and Data Science, York St John University, London Campus, London, United Kingdom
    Research Assistant, Department of Computer Science and Data Science, York St John University, London Campus, London, United Kingdom
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How to Cite

Karunarathne, Lakmali, Swathi Ganessan, Nalinda Somasiri, and Kavindu Karunarathne. 2026. “Employee Productivity Analytics and Prediction: A Dynamic Productivity and Burnout Modelling Engine (DPBME) for Privacy-Aware Workforce Analytics”. Recent Research Reviews Journal 5 (2): 355-78. https://doi.org/10.36548/rrrj.2026.2.007.
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Keywords

Workforce Analytics
Burnout Prediction
Productivity Modelling
Explainable AI
SHAP
GDPR Compliance
Predictive HR Analytics

Abstract

Digital collaboration tools and project management systems provide continuous data about workforce behavior and activities. Conventional workforce analytics are mainly concerned with historical performance and turnover of employees, with no predictive abilities with respect to future productivity or signs of burnout. In this paper, we propose the Dynamic Productivity and Burnout Modelling Engine (DPBME) that combines the productivity model, designation cohort decline index, and burnout risk score while maintaining explainability and privacy principles. Tested on the dataset of 35,000 employee records, Ridge Regression, Random Forest, and Gradient Boosting Machine (GBM) algorithms are evaluated, with GBM being the most effective one (R² = 0.9072). The obtained burnout risk score corresponds well to the real Burn Rate (Pearson r = 0.6562). The sensitivity analysis demonstrates stable parameters' values for the risk of burnout, while the SHAP analysis revealed the importance of mental fatigue and workload as predictors of productivity. The results are presented associatively and cover limitations, governance, and privacy aspects in accordance with the GDPR Article 22 standards.

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