Beyond Demographics: BIM Engagement and Job Satisfaction Among AEC Professionals, A Machine Learning Pilot Study

Building Information Modeling (BIM) has transformed workflows across the Architecture, Engineering, and Construction (AEC) industry, yet its relationship with employee job satisfaction remains insufficiently understood. This pilot study investigates whether BIM engagement or demographic characteristics better predict job satisfaction among AEC professionals. Survey responses from 104 participants were analyzed using Spearman rank correlations, logistic regression, and Classification and Regression Tree (CART) modeling. 27 items Job Satisfaction Index demonstrated excellent internal reliability. Across all analytical approaches, BIM engagement emerged as a stronger predictor of job satisfaction than demographic factors. Specifically, the proportion of project work completed using BIM was the only significant predictor of job satisfaction, whereas age, gender, education level, and professional experience showed no significant relationships. The CART analysis further identified BIM project involvement as the primary factor associated with higher job satisfaction. These findings suggest that the extent of BIM integration in professional practice may play a more important role in shaping employee satisfaction than individual demographic characteristics. The study contributes to the growing literature on human technology interactions in the AEC sector and provides preliminary evidence to support strategies that promote deeper BIM adoption.