GenWorkBalanceNet: A Hybrid Generative AI-Deep Learning Model for Work-Life Balance-Driven Productivity Prediction in IT Workforces
Received: 25 July 2025 | Revised: 15 August 2025, 29 August 2025, and 7 September 2025 | Accepted: 9 September 2025 | Online: 5 November 2025
Corresponding author: Chaya J. Swamy
Abstract
This paper presents GenWorkBalanceNet, a hybrid Generative Artificial Intelligence (AI)-Deep Learning (DL) framework designed for predictive modeling of workforce productivity under diverse Work-Life Balance (WLB) policies. The framework integrates three core components: (i) a transformer-based Generative AI module for synthetic scenario generation, (ii) an attention-enhanced Long Short-Term Memory (LSTM) network for forecasting temporal productivity trends, and (iii) a Shapley Additive Explanations (SHAP)-based interpretability layer for interpretable analysis of key productivity determinants. By augmenting historical Human Resource (HR) records and productivity logs with synthetic policy-driven data, GenWorkBalanceNet addresses critical challenges such as data sparsity, limited policy simulation capacity, and the lack of transparency in conventional analytics approaches. Experimental evaluation on an Information Technology (IT) workforce dataset demonstrates that the proposed model outperforms established baselines, such as Random Forest, Gradient Boosting, and standalone LSTM, achieving a 19-28% reduction in Root Mean Square Error (RMSE), a 27-36% reduction in Mean Absolute Error (MAE), and a 2-7% improvement in R2 score. Scenario-based simulations further reveal that flexible work-hour policies can enhance overall productivity by up to 6-8%, emphasizing the framework's potential as a decision-support tool for HR managers. Overall, GenWorkBalanceNet offers a scalable, interpretable, and data-driven solution for adaptive workforce planning.
Keywords:
generative Artificial Intelligence (AI), Work-Life Balance (WLB), Large Language Models (LLMs), Information Technology (IT) workforce, employee productivity, burnout reduction, retention prediction, Human Resource (HR) policy simulationDownloads
References
H. Sinha, "Predicting Employee Performance in Business Environments Using Effective Machine Learning Models," International Journal of Novel Research and Development, vol. 9, no. 9, pp. a875-881, Sep. 2024.
S. D. Paigude and S. R. Shikalgar, "Deep Learning Model for Work-Life Balance Prediction for Working Women in IT Industry," in Proceedings of the 4th International Conference on Information Management & Machine Intelligence, Jaipur India, Dec. 2022, pp. 1–8. DOI: https://doi.org/10.1145/3590837.3590846
C. Gupta, K. V. S. Rajeswara Rao, and P. Datta, "Support Vector Machine Based Prediction of Work-Life Balance Among Women in Information Technology Organizations," IEEE Engineering Management Review, vol. 50, no. 2, pp. 147–155, Jun. 2022. DOI: https://doi.org/10.1109/EMR.2022.3152520
M. Parekh and D. Shah, "Understanding Work-Life Balance: An Analysis of Quiet Quitting and Age Dynamics using Deep Learning." International Research Journal of Engineering and Technology (IRJET), vol 10, no 6, pp. 1230-1235, Jun. 2023.
S. Das, S. Chakraborty, G. Sajjan, S. Majumder, N. Dey, and J. M. R. S. Tavares, "Explainable AI for Predictive Analytics on Employee Attrition," in Soft Computing and Its Engineering Applications, vol. 1788, K. K. Patel, K. C. Santosh, A. Patel, and A. Ghosh, Eds. Cham: Springer Nature Switzerland, 2023, pp. 147–157. DOI: https://doi.org/10.1007/978-3-031-27609-5_12
A. Ardichvili, K. Dirani, S. Jabarkhail, W. El Mansour, and S. Aboulhosn, "Using generative AI in human resource development: an applied research study," Human Resource Development International, vol. 27, no. 3, pp. 388–409, May 2024. DOI: https://doi.org/10.1080/13678868.2024.2337964
P. Phattharajiranan and V. Muangsin, "Generating Synthetic Population Using Transformer Based Networks," in 2023 27th International Computer Science and Engineering Conference (ICSEC), Samui Island, Thailand, Sep. 2023, pp. 193–200. DOI: https://doi.org/10.1109/ICSEC59635.2023.10329722
W. Narkbunnum and K. Hinthaw, "Interpretable Gradient Boosted Modeling of Employee Attrition: A SHAP-Based Framework for HR Analytics," International Journal of Analysis and Applications, vol. 23, Oct. 2025, Art. no. 236. DOI: https://doi.org/10.28924/2291-8639-23-2025-236
C. Fan, X. Liao, and X. Yang, "Artificial intelligence and enterprise total factor productivity: A human capital requirement perspective," International Review of Economics & Finance, vol. 104, Dec. 2025, Art. no. 104661. DOI: https://doi.org/10.1016/j.iref.2025.104661
J.-L. He, J.-H. Wang, C.-M. Lo, and Z. Jiang, "Human Activity Recognition via Attention-Augmented TCN-BiGRU Fusion," Sensors, vol. 25, no. 18, Sep. 2025, Art. no. 5765. DOI: https://doi.org/10.3390/s25185765
A. Tak, "The Data Mining Techniques for Analyzing Employee Performance and Productivity," International Journal of Science and Research (IJSR), vol. 10, no. 10, pp. 1575–1578, Oct. 2021. DOI: https://doi.org/10.21275/SR231208202957
M. U. Tariq, "AI and Work-Life Balance: Transforming Employee Wellbeing in the Modern Workplace," in Advances in Computational Intelligence and Robotics, E. Ahmed, A. R. Babar, A. Samad, R. I. Ahmed, and G. Beydoun, Eds. IGI Global, 2025, pp. 85–110. DOI: https://doi.org/10.4018/979-8-3693-6507-6.ch004
Employee Performance and Productivity Data. (2024), M. Finsterwald. [Online]. Available: https://www.kaggle.com/datasets/mexwell/employee-performance-and-productivity-data.
Y.-T. Chuang, H.-L. Chiang, and A.-P. Lin, "Insights from the Job Demands–Resources Model: AI’s dual impact on employees’ work and life well-being," International Journal of Information Management, vol. 83, Aug. 2025, Art. no. 102887. DOI: https://doi.org/10.1016/j.ijinfomgt.2025.102887
S. Garg, S. Sinha, A. K. Kar, and M. Mani, "A review of machine learning applications in human resource management," International Journal of Productivity and Performance Management, vol. 71, no. 5, pp. 1590–1610, May 2022. DOI: https://doi.org/10.1108/IJPPM-08-2020-0427
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