Likeability versus Competence Dilemma: Text Mining Approach Using LinkedIn Data
Publication: Journal of Management in Engineering
Volume 39, Issue 3
Abstract
Women are significantly underrepresented in construction and engineering industry leadership roles despite having comparable qualifications, experience, and degrees. Improvements require a better understanding of the factors leading to the underrepresentation of females in the construction industry. While prior studies report that a male-dominated culture in construction negatively impacts females’ career advancement, minimal research explores the role congruity theory and the likeability and competency dilemma among construction leaders. When striving to succeed in traditionally male-dominated fields, women face a unique challenge because they need to defy gender stereotypes created by cultural norms, encapsulated by the likeability versus competency dilemma. Females who accomplish traditionally male tasks and demonstrate independence, assertiveness, self-reliance, and power are no longer seen as likeable. Through analyzing publicly available LinkedIn recommendations, this paper proposed a data-driven approach for examining the likeability and competency dilemma for female construction leaders. Results showed that female leaders in construction were seen as competent in the same way as their male counterparts, but far less likeable (a likeability score of 28% compared to 51% for males). Developing a text mining model to predict the gender of the person receiving a recommendation, this paper also highlights unconscious bias in describing and reacting to leaders’ successes. The machine learning model accurately predicted the gender of the person receiving the recommendation with more than 86% accuracy. Despite possessing the sufficient capability to handle traditionally male work successfully, women receive negative judgment from colleagues, creating challenges in their career paths. Finally, the paper contributes to social role theory and role congruity theory by highlighting the differences in gender roles and leadership roles for women in the construction industry.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Journal of Management in Engineering
Volume 39 • Issue 3 • May 2023
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© 2023 American Society of Civil Engineers.
History
Received: Aug 7, 2022
Accepted: Jan 17, 2023
Published online: Mar 9, 2023
Published in print: May 1, 2023
Discussion open until: Aug 9, 2023
ASCE Technical Topics:
- Business management
- Construction engineering
- Construction industry
- Construction management
- Data collection
- Engineering fundamentals
- Engineering profession
- Methodology (by type)
- Model accuracy
- Models (by type)
- Practice and Profession
- Professional development
- Research methods (by type)
- Social network
- Women engineers
- Workplace diversity
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