Research on the Impact of Artificial Intelligence on the High-quality Development of the Logistics Industry
Based on the Perspective of Total Factor Productivity
- https://doi.org/10.2991/assehr.k.211216.033How to use a DOI?
- Artificial intelligence; Logistics industry; Total factor productivity; Technological progress
At present, artificial intelligence technology has become a critical weapon to promote the transformation and upgrading of the logistics industry. This article first describes the mechanism of artificial intelligence affecting the total factor productivity of the logistics industry, and then based on my country’s provincial panel data from 2005 to 2017, using the Malmquist index. The method is used to measure the total factor productivity index of my country’s logistics industry and its decomposed technical efficiency and technological progress index to empirically test the impact of the development level of artificial intelligence on the total factor productivity of the logistics industry. Among them, artificial intelligence mainly affects the total factor productivity of the logistics industry by intelligently upgrading logistics production tools, intelligently configuring logistics resources, and intelligently optimizing logistics links. The empirical results show that artificial intelligence technology has a significant role in promoting the total factor productivity of the logistics industry, and this effect is mainly achieved by promoting the technological progress of the logistics industry.
- © 2021 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Yunping Chen AU - Siying Hu PY - 2021 DA - 2021/12/17 TI - Research on the Impact of Artificial Intelligence on the High-quality Development of the Logistics Industry BT - Proceedings of the 2021 International Conference on Social Sciences and Big Data Application (ICSSBDA 2021) PB - Atlantis Press SP - 165 EP - 168 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.211216.033 DO - https://doi.org/10.2991/assehr.k.211216.033 ID - Chen2021 ER -