Key factors in women’s managerial advancement in the construction industry: insights from machine learning

Abstract

Despite ongoing efforts to promote gender diversity in the Australian construction industry, women remain significantly underrepresented in managerial positions. Differing from previous studies using traditional survey or interview approaches, this study applied career capital theory and analyzed 1,595 LinkedIn profiles with 11 features, related to work experience, network size, educational background, and industry recognition. Predictive modeling was conducted using MATLAB’s Classification Learner, applying multiple machine learning algorithms to assess the significance of those features in predicting managerial level. The results identified current employer size as the strongest predictor of female managerial levels. Women in small enterprises were more likely to reach top management, while those in large companies more likely remained in lower managerial levels. Experience duration also had a significant impact, but progression plateaued beyond seven years, indicating tenure alone does not drive advancement. Follower and connection count demonstrated a notable contribution, emphasizing the importance of professional visibility. Contrary to traditional assumptions, recommendation count and highest education level had lower relevance, while construction-related degrees, certifications, awards, and courses showed minimal impact. This study sheds light on the barriers and contributors of women’s managerial advancement and provides practical recommendations for policymakers and industry stakeholders to foster inclusive and equitable workplaces.