Bridging Decision Tree Modeling and Process Mining for Enhanced Remote Work Productivity Insights During COVID-19

This study examines the key factors influencing employee productivity in remote work settings within Thailand, especially during the COVID-19 era. Utilizing decision tree modeling, weight analysis, and process mining graphs, we explored variables such as remote work experience, gender, age, salary, and job roles to determine their effects on productivity. Decision tree modeling and its weight analysis revealed that remote work experience and gender are the most significant predictors of productivity, with age, salary, and job roles serving as secondary but influential factors. Process mining graphs provided visual insights into work patterns, enabling us to identify workflows and variations across different productivity groups. The findings suggest that organizations can optimize productivity by enhancing remote work experiences and addressing gender disparities. Further, tailored support based on job roles and regional contexts is recommended to create a more effective remote work environment. This research contributes to the understanding of remote work dynamics, offering actionable insights for improving employee performance in a post-pandemic landscape.