Artificial Intelligence, Economic Decision-Making, and Air Transport Performance: Evidence From G7 Countries

Abstract

This paper examines the role of artificial intelligence (AI) in shaping economic decision-making and improving air transport performance in G7 countries over the period 1996–2021. AI is proxied by scientific publications and interpreted as an indicator of innovation capacity that supports data-driven decision-making and operational efficiency in the aviation sector. To ensure robust empirical inference, the analysis applies second-generation panel data techniques, including cross-sectional dependence and slope homogeneity tests, CIPS unit root tests, and the Westerlund Durbin–Hausman cointegration approach. Long-run relationships are estimated using the fully modified ordinary least squares (FMOLS) method, whereas causality is examined through the Dumitrescu–Hurlin panel causality test. The results reveal that AI-driven innovation has a positive and statistically significant effect on air transport performance, indicating that enhanced information processing and decision-support capabilities contribute to improved logistics efficiency and cargo transport outcomes. In addition, government effectiveness, population, government consumption, GDP growth, and inflation are found to positively influence air transport, highlighting the importance of institutional quality and macroeconomic conditions in shaping sectoral performance. The findings offer important implications for managerial and policy decision-making by emphasizing the role of AI in enhancing operational efficiency, demand forecasting, and strategic planning in the aviation sector. Strengthening AI-oriented innovation systems and institutional capacity can support more effective decision-making processes and improve overall sector performance.