This study aims to systematically analyze the impact of drone technology diffusion on future labor markets and occupational structures, and to propose corresponding vocational education and training (VET) strategies. Drawing on prior research in i...
This study aims to systematically analyze the impact of drone technology diffusion on future labor markets and occupational structures, and to propose corresponding vocational education and training (VET) strategies. Drawing on prior research in industry, technology, and policy, the study classifies drone-related job types and identifies key driving factors (KDFs) for scenario planning based on empirical survey data. The derived KDFs—(1) technological and market innovation pressure, (2) polarization of occupational structures, and (3) institutional and regulatory acceptance—form the basis for constructing two future scenarios: high-growth and low-growth.
In the high-growth scenario, our analysis indicates a substantial increase in demand for skilled professionals in areas such as maintenance, traffic management (UTM), and data analysis. These roles require advanced, hands-on training programs and a structured, tiered national certification system. In contrast, the low-growth scenario is characterized by stagnation in simple operational roles, with limited diversity in job functions and minimal training requirements. These findings suggest that the future employment landscape of the drone industry is not inherently uncertain, but rather follows a predictable trajectory shaped by the interplay of technology, institutional support, and market demand.
By linking scenario-based labor forecasting with a competency-based vocational education and training (VET) system, this study provides a strategic policy framework for positioning the drone industry as a catalyst for future job creation. It also offers practical implications for building a talent development system grounded in empirical evidence and aligned with industry transformation.