Technical Papers
Aug 3, 2022

Use of LinkedIn Data and Machine Learning to Analyze Gender Differences in Construction Career Paths

Publication: Journal of Management in Engineering
Volume 38, Issue 6

Abstract

Will women and men follow distinctively different paths to achieve executive engineering leadership positions in the US architecture, engineering, and construction (AEC) industry? Using Engineering News Record’s (ENR’s) 2019 Top 400 list, this research analyzed LinkedIn profiles for over 2,800 executives to assess career differences between genders. Statistical comparisons of important features, highlighted by number of companies, titles, education, and network size, revealed a significant impact of gender on individual career paths. A key finding was that men ascend to leadership with a single firm throughout their career, outpacing women almost fourfold (37% to 10%). Applying random forest (RF) as an ensemble classifier, researchers successfully predicted profile gender with accuracy of 98.95% for training and 89.53% for testing samples. Collating and categorizing the activities and milestones of individual and collective executives offer insight regarding successful experiences, skills, and choices to reach leadership roles. This creates a roadmap for current and future early and midlevel professionals to model their own vocational journey and accelerate progression up the corporate ladder. From an industry perspective, firms deprive themselves and customers of the proven wide-ranging benefits of diversity.

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Data Availability Statement

All data were collected through publicly available information sources. Raw data and data profiles will be available from the corresponding author by reasonable request subject to the LinkedIn data sharing and privacy policies.

Acknowledgments

Ms. Veronica Hercules and Mr. Adam Johnson from the University of Maryland helped with data collection and cleaning. Their efforts proved invaluable to the construction of this paper. Comments from Journal of Management in Engineering reviewers improved the theoretical underpinnings, hypothesis refinement, and overall structure of the manuscript.

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Journal of Management in Engineering
Volume 38Issue 6November 2022

History

Received: Oct 24, 2021
Accepted: May 19, 2022
Published online: Aug 3, 2022
Published in print: Nov 1, 2022
Discussion open until: Jan 3, 2023

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Paul J. Hickey [email protected]
Ph.D. Student, Dept. of Civil and Environmental Engineering, Univ. of Maryland, College Park, MD 20742. Email: [email protected]
Ph.D. Student, Dept. of Civil and Environmental Engineering, Univ. of Maryland, College Park, MD 20742. ORCID: https://orcid.org/0000-0002-4703-1248. Email: [email protected]
Qingbin Cui, A.M.ASCE [email protected]
Professor, Dept. of Civil and Environmental Engineering, Univ. of Maryland, College Park, MD 20742 (corresponding author). Email: [email protected]

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