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

人工智能 产业组织 中国 经济 业务 知识管理 计算机科学 政治学 法学
作者
Feng Han,Xin Mao
出处
期刊:Applied Economics [Informa]
卷期号:: 1-16
标识
DOI:10.1080/00036846.2023.2289916
摘要

ABSTRACTAgainst 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.KEYWORDS: Artificial intelligenceenterprise innovation abilityindustrial robotintellectualizationJEL CLASSIFICATION: D22J24O33 AcknowledgementsThe authors take sole responsibility for all the views and opinions expressed in the paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.Notes1 The Hausman test statistic is 1057.53 and the p-value is 0.0000.2 The 2004 industrial robot density by industry in the U.S. is used as an instrumental variable because in the IFR data, the use of industrial robots in the U.S. was zero for each segment of manufacturing industries prior to 2004. Therefore, 2004 is the earliest year for which industrial robot density by industry in the U.S. can be calculated. The model is introduced by estimating this variable’s interaction term with the corresponding year as an instrumental variable.Additional informationFundingThis research was funded by the National Natural Science Foundation of China [Grant No. 72073071], the Qing Lan Project of Jiangsu Province [Grant No. D202062045], and the Postgraduate Research & Practice Innovation Program of Jiangsu Province [Grant No. KYCX22_2109].
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