A Quantitative Analysis of Transformational Leadership, Diversity, and Inclusion in Federal Logistics Companies

Abstract

This quantitative correlation study examined the relationship between transformational leadership (TL) and diversity and inclusion (D&I) within medium federal contracting agencies in Virginia, North Carolina, South Carolina, Georgia, and Florida. The lack of understanding of how transformational leadership influences the evolution of D&I is responsible for low job satisfaction among acquisition logistics organizations and threatens logistics firms’ sustainability while hindering productivity, performance, and legal compliance. In the federal acquisition sector, meeting clients’ strict demands relies on effective communication and swift coordination between supervisors and teams, and failing to coordinate effectively can lead to costly delays and misinformation. Despite its significant impact on organizational well-being, coordination between leaders and human resource personnel remains unexplored in many parts of the United States. Using stratified sampling, 162 participants, including employees, managers, and policymakers from acquisition logistics firms in the western US, were surveyed with a two-part questionnaire developed using Scarpello and Vandenberg’s (1987) Satisfaction with My Supervisor Scale (SWMSS) and Ashikali and Groeneveld’s (2015) D&I scale. A Cronbach’s test indicated high reliability for the SWMSS (α = 0.950) and D&I (α = 0.993). Findings revealed a strong positive correlation between TL and D&I (ρ = 0.987; p < 0.0001). The adopted linear regression model suggests that increases in TL result in proportionate increases in workplace D&I, indicating that transformational leaders play a critical role in advancing organizational inclusion efforts. Data were collected using an online SurveyMonkey distribution method. Organizations should adopt TL principles to foster inclusive, equitable, and performance-driven workplace cultures. Recommendations include replicating the study in different sectors and regions to enhance generalizability.