<p>This study examines the impact of artificial intelligence (AI) on the logistics performance of G20 countries from 2010 to 2022, with a focus on the moderating effect of accountability, within the framework of Dynamic Capability Theory. Utilizing panel-corrected standard errors (PCSEs) for benchmark regression and two-step System Generalized Method of Moments (GMM) for robustness checks and endogeneity analysis, we assess the relationship between AI investments and logistics efficiency. Our findings reveal that AI positively influences the logistics performance across different economic contexts, with significant effects observed in both developed and developing countries. The robustness analysis, using the number of AI startups as an alternative measure, confirms these results. Moreover, our heterogeneity analysis highlights that high AI funding levels significantly enhance logistics performance, while the impact is negligible in countries with lower AI investments. The analysis of the moderating effect of accountability indicates that while governance improvements contribute positively to logistics efficiency, they may reduce the direct impact of AI investments due to regulatory challenges. These findings imply that policymakers should focus on fostering AI investments and enhancing governance structures to optimize logistics performance, particularly in regions with low AI penetration. Our study underscores the critical role of AI and accountability in advancing global logistics performance.</p>

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Balancing innovation and accountability: AI’s transformative influence on logistics in G20 nations

  • Ya Li,
  • Zheng Guangwen

摘要

This study examines the impact of artificial intelligence (AI) on the logistics performance of G20 countries from 2010 to 2022, with a focus on the moderating effect of accountability, within the framework of Dynamic Capability Theory. Utilizing panel-corrected standard errors (PCSEs) for benchmark regression and two-step System Generalized Method of Moments (GMM) for robustness checks and endogeneity analysis, we assess the relationship between AI investments and logistics efficiency. Our findings reveal that AI positively influences the logistics performance across different economic contexts, with significant effects observed in both developed and developing countries. The robustness analysis, using the number of AI startups as an alternative measure, confirms these results. Moreover, our heterogeneity analysis highlights that high AI funding levels significantly enhance logistics performance, while the impact is negligible in countries with lower AI investments. The analysis of the moderating effect of accountability indicates that while governance improvements contribute positively to logistics efficiency, they may reduce the direct impact of AI investments due to regulatory challenges. These findings imply that policymakers should focus on fostering AI investments and enhancing governance structures to optimize logistics performance, particularly in regions with low AI penetration. Our study underscores the critical role of AI and accountability in advancing global logistics performance.