Enhancing Employee Productivity and Satisfaction in Malaysian SMEs Using Explainable AI-Based Predictive Modeling

This study investigates the application of Explainable Artificial Intelligence (XAI) in predicting employee productivity and job satisfaction in Malaysian small and medium enterprises (SMEs). A predictive modeling framework using Random Forest and SHAP (SHapley Additive exPlanations) is designed to forecast employee outcomes and identify the key drivers influencing workplace productivity and satisfaction. Data from 150 employees across 10 SMEs was collected through surveys, focusing on variables such as autonomy, workload, managerial feedback, and digital tool usage. Results indicate strong predictive performance, with XAI explanations highlighting autonomy and workload as the most influential factors. By integrating XAI into HR analytics, managers can make transparent, data-driven decisions that enhance employee trust, adoption, and engagement. This study contributes to HR management and AI literature by demonstrating a novel framework for explainable workforce analytics tailored to SMEs.

Explainable AI, Predictive Modeling, Employee Productivity

1. SME Corporation Malaysia, “Annual SME Report,” 2022. [Google Scholar] [Crossref]

2. S. Basnet, “The Impact of AI-Driven Predictive Analytics on Employee Retention Strategies,” International Journal of Research and Review, vol. 11, no. 9, pp. 50–65, Sep. 2024, doi: 10.52403/ijrr.20240906. [Google Scholar] [Crossref]

3. M. Sun, F. Zhu, and X. Sun, “Influencing factors of construction professionals’ burnout in China: a sequential mixed-method approach,” Engineering, Construction and Architectural Management, vol. 27, no. 10, pp. 3215–3233, Oct. 2020, doi: 10.1108/ECAM-02-2020-0106. [Google Scholar] [Crossref]

4. S. Y. Bansal, B. Kaur, and J. R. Saini, “A Novel Optimized Approach for Machine Learning Techniques for Predicting Employee Attrition,” in 2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/SMARTGENCON56628.2022.10084237. [Google Scholar] [Crossref]

5. A. Y. Aránega, C. G. Montesinos, R. C. Urueña, and R. C. Sánchez, “Digitalization intention in entrepreneurs and SMEs: a structural equation model of digital behavior and work-life balance,” International Entrepreneurship and Management Journal, vol. 21, no. 1, Dec. 2025, doi: 10.1007/s11365-024-01049-2. [Google Scholar] [Crossref]

6. G. Marín Díaz, J. J. Galán Hernández, and J. L. Galdón Salvador, “Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making,” Mathematics, vol. 11, no. 22, Nov. 2023, doi: 10.3390/math11224677. [Google Scholar] [Crossref]

7. A. Tursunbayeva, “Human resource technology disruptions and their implications for human resources management in healthcare organizations,” Apr. 29, 2019, BioMed Central Ltd. doi: 10.1186/s12913-019-4068-3. [Google Scholar] [Crossref]

8. X. Gao, J. Wen, and C. Zhang, “An Improved Random Forest Algorithm for Predicting Employee Turnover,” Math Probl Eng, vol. 2019, 2019, doi: 10.1155/2019/4140707. [Google Scholar] [Crossref]

9. C. Molnar, Interpretable Machine Learning A Guide for Making Black Box Models Explainable. [Online]. Available: https://christophm.github.io/ [Google Scholar] [Crossref]

10. A. M. Antoniadi et al., “Current challenges and future opportunities for xai in machine learning-based clinical decision support systems: A systematic review,” Applied Sciences (Switzerland), vol. 11, no. 11, Jun. 2021, doi: 10.3390/app11115088. [Google Scholar] [Crossref]

11. A. Bahalul Haque, EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI): MAKING AI UNDERSTANDABLE FOR END USERS. [Google Scholar] [Crossref]

12. S. Seppänen, DIGITAL TRANSFORMATION IN SMES: DRIVERS, CHALLENGES, AND THE IMPACT OF EMERGING TECHNOLOGIES ON DECISION-MAKING AND MANAGEMENT CONTROL. [Google Scholar] [Crossref]

13. A. R. Tarar, “Employee Retention Strategies in Small and Medium Sized Companies,” 2021. [Google Scholar] [Crossref]

14. Daria Kalishina, “Algorithmic customer churn prediction and targeted intervention: Optimizing customer lifetime value in data-sparse SME environments,” World Journal of Advanced Research and Reviews, vol. 26, no. 1, pp. 593–603, Apr. 2025, doi: 10.30574/wjarr.2025.26.1.1045. [Google Scholar] [Crossref]

15. B. Abedin, “Managing the tension between opposing effects of explainability of artificial intelligence: a contingency theory perspective,” Internet Research, vol. 32, no. 2, pp. 425–453, Mar. 2022, doi: 10.1108/INTR-05-2020-0300. [Google Scholar] [Crossref]

16. I. Omemma Evans-Uzosike, C. Gbemisola Okatta, B. O. Otokiti, O. Gift Ejike, and O. T. Kufile, “Optimizing Talent Acquisition Pipelines Using Explainable AI : A Review of Autonomous Screening Algorithms and Predictive Hiring Metrics in HRTech Systems,” International Scientific Refereed Research Journal Available online, 2024, doi: 10.32628/SHISRRJ. [Google Scholar] [Crossref]

17. C. Molnar, Interpretable Machine Learning. Leanpub, 2020.. [Google Scholar] [Crossref]

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