TY - JOUR
T1 - The Impact of Intelligent Logistics on Logistics Performance Improvement
AU - Ye, Aishan
AU - Cai, Jiayi
AU - Yang, Zhenjie
AU - Deng, Yangyang
AU - Li, Xiaohua
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/1
Y1 - 2025/1
N2 - The logistics industry is essential to global economic development but continues to grapple with challenges related to quality improvement, cost reduction, and efficiency enhancement. Addressing these issues is crucial for promoting high-quality growth within the sector. The emergence of intelligent logistics—leveraging automation, data analytics, and Internet of Things (IoT) technologies—offers a promising approach to transforming traditional logistics operations. This study develops a theoretical framework that integrates these intelligent logistics components to investigate their mechanisms and limitations in influencing logistics performance. Using an empirical analysis of Chinese provincial panel data, we identify significant disparities in logistics industry performance across the provinces, with most regions exhibiting an initial improvement followed by a subsequent decline. Our findings reveal a notable spatial interaction effect between intelligent logistics and logistics performance, indicating that intelligent logistics substantially enhance performance. However, the impact varies by region: it significantly promotes performance in the eastern and western regions but has a limited effect in the central and northeastern regions, potentially due to distortions in production factors and other regional specificities. Additionally, the degree of openness to the outside world positively influences logistics performance in the western region. The proposed mechanisms are validated in all regions except the eastern region. This study provides valuable insights for policymakers on leveraging intelligent logistics to improve logistics industry performance, highlighting the need for region-specific strategies to maximize the benefits of intelligent logistics technologies.
AB - The logistics industry is essential to global economic development but continues to grapple with challenges related to quality improvement, cost reduction, and efficiency enhancement. Addressing these issues is crucial for promoting high-quality growth within the sector. The emergence of intelligent logistics—leveraging automation, data analytics, and Internet of Things (IoT) technologies—offers a promising approach to transforming traditional logistics operations. This study develops a theoretical framework that integrates these intelligent logistics components to investigate their mechanisms and limitations in influencing logistics performance. Using an empirical analysis of Chinese provincial panel data, we identify significant disparities in logistics industry performance across the provinces, with most regions exhibiting an initial improvement followed by a subsequent decline. Our findings reveal a notable spatial interaction effect between intelligent logistics and logistics performance, indicating that intelligent logistics substantially enhance performance. However, the impact varies by region: it significantly promotes performance in the eastern and western regions but has a limited effect in the central and northeastern regions, potentially due to distortions in production factors and other regional specificities. Additionally, the degree of openness to the outside world positively influences logistics performance in the western region. The proposed mechanisms are validated in all regions except the eastern region. This study provides valuable insights for policymakers on leveraging intelligent logistics to improve logistics industry performance, highlighting the need for region-specific strategies to maximize the benefits of intelligent logistics technologies.
KW - dynamic spatial panel model
KW - intelligent logistics
KW - logistics performance
KW - spatial heterogeneity
UR - http://www.scopus.com/inward/record.url?scp=85215789769&partnerID=8YFLogxK
U2 - 10.3390/su17020659
DO - 10.3390/su17020659
M3 - Article
AN - SCOPUS:85215789769
SN - 2071-1050
VL - 17
JO - Sustainability
JF - Sustainability
IS - 2
M1 - 659
ER -