Pathways to Leadership: Analyzing Career Trajectories of Asian American Leaders in the Construction Industry
Year: 2025
Type: conference-paper
Abstract: With the growing focus on fostering a diverse and inclusive workforce, understanding the barriers and opportunities for underrepresented groups in leadership roles has become increasingly important.This study examines the career trajectories of Asian American professionals in the Architecture, Engineering, and Construction (AEC) industry, highlighting the factors influencing their advancement into leadership positions.Utilizing a scraped dataset of over 21,000 LinkedIn profiles from Engineering News Record's (ENR's) Top 400 construction firms, the research investigates how educational attainment, professional experience, geographic distribution, and professional skills affect career progression.To explore these patterns, the study uses two analytical approaches: comparative analysis to identify differences in career trajectories, and regression analysis to quantify the impact of key variables on leadership attainment.The results indicate that, despite high levels of education and a broad range of professional skills, Asian Americans encounter barriers that limit their progression into leadership roles.The logistic regression analysis further reveals that while advanced degrees and extensive work experience provide some advantages, they also are not enough to counteract the stereotypes and biases ingrained in workplace norms.Additionally, the regression analysis suggests that career progression is also strongly influenced by frequent job changes, underscoring the critical role of career mobility in professional advancement.Based on the results, this research highlights the need for targeted interventions to ensure equitable leadership development within the AEC industry.Organizations must go beyond surface-level diversity initiatives, committing to impactful strategies that cultivate inclusive organizational cultures and embrace diverse leadership styles.
Authors: Jonathan Ma, Dongyang Zhen, Qingbin Cui
Language: English
Subfield: Gender Studies
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W7128094697