Author
Listed:
- Haiying Lin
- Mengtian Wang
- Meng Guo
- Zhijuan Wang
- Ziyuan Cheng
- Wenlong Li
- Muhammad Umer Arshad
Abstract
Technological innovation drives high-quality economic development, and artificial intelligence (AI) represents a new impetus for developing productive forces with new qualities. AI is becoming a focal point in economic development plans and national strategies worldwide due to its contribution to economic growth and the transformation of traditional production methods. This paper examines the impact and mechanism of AI on the export technological complexity of Chinese manufacturing enterprises from a corporate perspective. It utilizes data from listed manufacturing companies on the Shanghai and Shenzhen A-shares from 2008 to 2021 and employs a fixed-effects model. The results indicate that: (1) AI positively promotes the export technological complexity of Chinese manufacturing enterprises, with more pronounced effects in regions with higher export technological complexity. (2) Heterogeneity analysis indicates that AI significantly enhances the export technological complexity across various categories of enterprises. Particularly notable impacts are observed among state-owned enterprises, light textile enterprises, and enterprises located in the eastern and central regions. (3) Mechanism analysis reveals that AI indirectly promotes the export technological complexity of manufacturing enterprises by improving labor structure and enhancing corporate innovation capabilities. This study proposes relevant policy recommendations from four aspects: strengthening AI technology research and application, optimizing labor structure, enhancing corporate innovation development, and promoting balanced AI development.
Suggested Citation
Haiying Lin & Mengtian Wang & Meng Guo & Zhijuan Wang & Ziyuan Cheng & Wenlong Li & Muhammad Umer Arshad, 2026.
"Research on the impact of artificial intelligence on the export technological complexity of chinese manufacturing enterprises: An analysis based on mediating effects,"
PLOS ONE, Public Library of Science, vol. 21(7), pages 1-23, July.
Handle:
RePEc:plo:pone00:0351061
DOI: 10.1371/journal.pone.0351061
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