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

Abstract

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.