Transformational Leadership in AI-Driven Industry 4.0: Cultivating Adaptive, Ethical, and Resilient Leaders

Abstract

The purpose of this quantitative study is to identify the relationship between the effectiveness of specific transformational leadership traits in Industry 4.0 environments by using regression analysis to examine how these traits (including inspirational motivation, idealized influence, individualized consideration, and intellectual stimulation) impact both employee engagement and job satisfaction among organizational leaders facing challenges from artificial intelligence, machine learning, and predictive analytics. For the utility of the study, the effectiveness of these leadership traits is generally defined as their ability to enhance employee engagement and job satisfaction amidst rapid technological changes, as measured by the Federal Employee Viewpoint Survey (FEVS). The study employs a correlational design using a stratified random sample of 2,000 participants from U.S. federal agencies, ensuring demographic and organizational diversity. Data collection involves responses from the 2023 FEVS dataset, analyzed using regression modeling to assess relationships between leadership traits and employee outcomes. Anticipated findings suggest that transformational leadership traits play critical roles in fostering engagement and satisfaction in disruptive environments. The results aim to provide evidence-based insights for developing leadership programs tailored to address the unique demands of Industry 4.0. Future research recommendations include longitudinal studies to examine how sustained inspirational motivation influences employee engagement and productivity, particularly in the context of AI-driven organizational changes and Industry 4.0 environments, as well as encouraging further exploration into the applicability of findings across industries and organizational contexts beyond federal employees, particularly in sectors such as healthcare, education, finance, and manufacturing facing AI-driven disruptions.