Hickey, P J (2023) A study of gender diversity in u.S. Architecture, engineering, and construction (AEC) industry leadership. PhD thesis, University of Maryland, College Park, USA.
Abstract
Anecdotally, men dominate the Architecture, Engineering, and Construction (AEC) field. This study contributes to the body of knowledge by quantifying the gender composition of the c-suite, identifying differences in career paths between women and men, and gathering in-depth information on women engineering executives’ professional stories. Using the industry recognized Engineering News Record (ENR) Top 400 largest companies, initial phase of this research found women filled 3.9% of engineering executive positions in 2019, reducing to 3.5% in 2021. However, certain sub-segments, highlighted by firms with a public commitment to diversity, ENR Top 100 Green companies, and larger organizations, offer more opportunities to women. Exploring further into individual and collective career paths, researchers applied web scraping algorithms to extract LinkedIn data for 2,857 industry leaders. Data found that women work for more companies (+56%), hold more positions (+19%), earn more advanced degrees (53.9% to 31.2%), assemble larger professional networks (+14%), yet remain significantly underrepresented (-83%). Confirming a difference between the career paths of women and men, Machine Learning (ML) modeling predicted profile genders with 98.95% training sample and 89.53% testing sample accuracy. Final stage of research incorporates interviews with women engineering executives, seeking to learn about pathways and barriers in their respective and collective professional journeys and test the findings from the initial two phases of this study. An overriding theme throughout the progressive study, recommendations for increasing women’s representation include directed Science, Technology, Engineering, and Math (STEM) scholarships for young girls, targeted recruiting of women, establishing mentoring relationships, and creating nurturing cultures to retain early and mid-career women.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Cui, Q |
| Uncontrolled Keywords: | accuracy; women; leadership; learning; training; professional; machine learning; culture; gender; interview |
| Index terms: | women, executive, girl, modelling, testing, career, mentoring, machine learning, body of knowledge, interview, accuracy, commitment, science |
| Subjects: | analytical methods, knowledge management, professional practice, training, psychology, practitioner, specialized education, demography, artificial intelligence, data collection methods, sociology, professional development |
| Topics: | Education, Engineering Principles, Organizational Design, Urban Studies, Digital Applications, Human Resources, Roles and Professions, Research Practice, Information Management |
| Descriptive scope: | 3 PCE |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here