AI 中文总结
本研究以104名AEC从业者为对象，用多种机器学习方法分析发现，BIM参与度比人口统计学特征更能预测工作满意度，其中BIM项目参与度是核心影响因素，为推广BIM应用提供了依据。
AI 中文摘要
建筑信息模型（BIM）已改变了建筑、工程与施工（AEC）行业的工作流程，但其与员工工作满意度的关联仍未得到充分理解。本试点研究旨在探究，在AEC行业从业者中，BIM参与度或人口统计学特征能否更好地预测工作满意度。研究分析了104名参与者的调查反馈，采用斯皮尔曼等级相关、逻辑回归及分类与回归树（CART）建模方法。27项题目的工作满意度指数显示出极佳的内部可靠性。在所有分析方法中，BIM参与度被证实是比人口统计学因素更强的工作满意度预测指标。具体而言，使用BIM完成的项目工作占比是工作满意度的唯一显著预测指标，而年龄、性别、教育水平及专业经验均未显示出显著关联。CART分析进一步确定，BIM项目参与度是与更高工作满意度相关的主要因素。这些发现表明，专业实践中BIM的整合程度，在塑造员工满意度方面可能比个体人口统计学特征发挥更重要的作用。本研究为AEC领域不断发展的人机交互文献作出了贡献，并为支持推动更深层次BIM采用的策略提供了初步证据。
英文摘要
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.
