Pathways to Leadership in the Australian Construction Industry: A Comparative Social Network Analysis Using LinkedIn Profiles
Diya Yan, Yi Ding, Cynthia Changxin Wang, Riza Yosia Sunindijo, Zhengyi Yang
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Abstract
Despite ongoing efforts to promote gender equity in the construction industry, women remain underrepresented at senior leadership levels. Prior studies have attempted to investigate women's professional networks in construction through traditional survey- or interview-based approaches, which are often limited in scale and unable to capture the complexity of career mobility patterns. This study adopts a previously underexplored data source, LinkedIn, to examine and compare the career networks of female and male top managers in the Australian construction industry. Using 914 publicly available LinkedIn profiles, social network analysis was conducted in Python using the NetworkX library to construct affiliation-based career networks derived from shared organizational experience. Multiple network metrics were applied to compare network connectivity, cohesion, community structure, core and periphery positioning, and robustness. The results show that female top managers are more densely connected, more locally cohesive, and more globally integrated than their male counterparts. Female career networks also exhibit fewer but larger communities, deeper and more cohesive core structures, and greater robustness to the removal of structurally important individuals. These findings extend career capital theory by highlighting the gendered differences in professional networks and provide evidence-based guidance for women, organizations, and policymakers supporting equitable leadership pathways.
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BibTeX
@article{buildings16040727,
title = {Pathways to Leadership in the Australian Construction Industry: A Comparative Social Network Analysis Using LinkedIn Profiles},
author = {Yan, Diya and Ding, Yi and Wang, Cynthia Changxin and Sunindijo, Riza Yosia and Yang, Zhengyi},
volume = {16},
issn = {2075-5309},
url = {http://dx.doi.org/10.3390/buildings16040727},
doi = {10.3390/buildings16040727},
number = {4},
journal = {Buildings},
publisher = {MDPI AG},
year = {2026},
month = Feb,
pages = {727}
}
