Artificial intelligence empowers enterprise innovation: evidence from China’s industrial enterprises

Feng Han and Xin Mao

Applied Economics, 2024, vol. 56, issue 57, 7971-7986

Abstract: Against the background of China’s economic transformation, it is of great practical significance to explore the impact of artificial intelligence on enterprise innovation to promote innovation-driven development strategies. Using patent data from Chinese industrial enterprises and robot data provided by the International Federation of Robotics, this study empirically tests the impact of artificial intelligence on improving the innovation abilities of Chinese enterprises. The study finds the following: (1) Artificial intelligence significantly improves enterprise innovation, and this conclusion remains valid after robustness tests. (2) Artificial intelligence optimizes the skill structure of the enterprise labour force, increases enterprise R&D expenditure, and strengthens the technology spillover effect, thus improving enterprise innovation. (3) The domestic market and the development of the Internet have further strengthened the role of artificial intelligence in promoting enterprise innovation. (4) Artificial intelligence is more helpful in promoting the innovation ability of technology-intensive, general trading, mixed trading, and non-state-owned enterprises. This study provides important policy implications for promoting the deep integration of artificial intelligence and real economy and realizing high-quality economic development.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2023.2289916 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:56:y:2024:i:57:p:7971-7986

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036846.2023.2289916

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().