The impact of artificial intelligence applications on enterprise innovation performance: A case study of the manufacturing industry

Rongfei Zheng; Chunhui Yuan

doi:10.54254/3029-0880/2025.21116

Research Article

Open access

Published on 20 February 2025

Download pdf

Zheng,R.;Yuan,C. (2025). The impact of artificial intelligence applications on enterprise innovation performance: A case study of the manufacturing industry. Advances in Operation Research and Production Management,4(1),7-17.

Export citation

The impact of artificial intelligence applications on enterprise innovation performance: A case study of the manufacturing industry

Rongfei Zheng *^,1, Chunhui Yuan ²

¹ Beijing University of Posts and Telecommunications
² Beijing University of Posts and Telecommunications

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/3029-0880/2025.21116

Abstract

The application of artificial intelligence (AI) is of significant importance in driving the innovation and development of enterprises. This paper explores how AI applications affect enterprise innovation performance from a micro-level perspective. Based on the Resource-Based View (RBV) and Dynamic Resource-Based View (DRBV), the study empirically tests the impact of AI technology application on the innovation performance of manufacturing enterprises using data from A-share listed manufacturing companies between 2015 and 2023. The research results show that: (1) The application of AI significantly enhances the innovation performance of manufacturing enterprises, and this effect remains significant across various robustness tests. This suggests that the application of AI is a key driver for the efficient utilization of production factors, improving corporate competitiveness and economic growth. Manufacturing enterprises should actively adopt AI technologies to enhance their innovation capabilities and facilitate the conversion of innovation outcomes into economic benefits. (2) Innovation and R&D resources play a significant mediating role in the process by which AI applications enhance innovation performance, with the mediating effect of R&D personnel allocation being the strongest, while the mediating effect of R&D funding allocation is relatively weaker. This finding provides new insights into optimizing the allocation of innovation resources, particularly emphasizing the irreplaceable role of human capital in technological innovation. By optimizing human resource allocation, enterprises can further enhance the marginal benefits of AI applications and promote the continuous development of innovation capabilities. This study provides a theoretical foundation and practical insights for empowering manufacturing industry innovation through AI technology.

Keywords

artificial intelligence applications, Innovation performance, innovation resource allocation

View pdf

References

[1]. Zhang, X. (2023). The social changes that may be triggered by next-generation artificial intelligence technology (ChatGPT). Industrial Economic Review, 3, 22–30.

[2]. Liu, J., Chang, H., Forrest, J. Y. L., & Yang, B. (2020). Influence of artificial intelligence on technological innovation: evidence from the panel data of China’s manufacturing sectors. Technological Forecasting and Social Change, 158, 120142.

[3]. Fadziso, T. (2018). The impact of artificial intelligence on innovation. Global Disclosure of Economics and Business, 7(2), 81–88.

[4]. Sun, L., & Li, S. (2021). Research on the influence path of artificial intelligence empowerment on innovation performance: Based on fuzzy qualitative comparative analysis. Science and Technology Management Research, 41(23), 11–19.

[5]. Li, G., & Bai, Y. (n.d.). How does the application of artificial intelligence influence innovation performance in manufacturing enterprises? Economic and Finance Review, 1–13.

[6]. Aghion, P., Jones, B. F., & Jones, C. I. (2018). Artificial intelligence and economic growth. In The Economics of Artificial Intelligence: An Agenda (pp. 237–282). University of Chicago Press.

[7]. Sun, W., & Liu, Y. (2023). A study on the impact mechanism of artificial intelligence on the labor market. East China Economic Management, 37(3), 1–9.

[8]. Mi, J., Jiang, L., & Li, Z. (2020). A study on the application of artificial intelligence technology in advancing the upgrading of China’s manufacturing industry. Humanities Journal, 9, 46–55.

[9]. Meng, F., Xu, Y., & Zhao, G. (2022). Research on the impact mechanism of “intelligent +” on innovation performance in manufacturing enterprises. Research Management, 43(9), 109–118.

[10]. Li, C., He, S., Tian, Y., Sun, S., & Ning, L. (2022). Does the bank’s FinTech innovation reduce its risk-taking? Evidence from China’s banking industry. Innovation and Knowledge, 7(3), 100219.

[11]. Zhao, L., Tao, H., & Cao, H. (2023). Empirical research on the impact of artificial intelligence technology on employment: Micro evidence from service enterprises. Information Technology and Management Applications, 2(3), 124–137.

[12]. Liu, T., Zhou, B., & Liang, B. (2024). Artificial intelligence, green innovation, and corporate carbon performance. Economic System Reform, 4, 193–200.

[13]. Liu, Q., Zhang, P., Yang, P., & Lei, Y. (2023). The impact of digital empowerment on technological innovation efficiency in manufacturing. Statistics and Decision, 23, 80–84.

[14]. Fang, H. (2024). The impact of artificial intelligence applications on corporate innovation and its mechanism: Evidence from China’s A-share listed companies. Science and Technology Management Research, 44(15), 157–167.

[15]. Li, M., & Huang, H. (2022). Industrial robot applications and innovation performance in manufacturing enterprises: The mediating effects of R&D investment and employee knowledge capabilities. Chinese Personnel Science, 3, 73–84.

[16]. Hu, S., Wang, L., & Zhao, H. (2021). The application of artificial intelligence, human-machine collaboration, and labor productivity. Chinese Population Science, 5, 48–62, 127.

[17]. Liu, B., & Liu, A. (2023). Digital transformation, factor allocation, and corporate innovation efficiency. Economic System Reform, 5, 121–128.

[18]. Xu, P., & Xu, X. (2020). The logic and analytical framework of enterprise management transformation in the era of artificial intelligence. Management World, 36(1), 122–129, 238.

[19]. Yao, J., Zhang, K., Guo, L., & Feng, X. (2024). How does artificial intelligence improve corporate production efficiency? Based on the perspective of labor force skill structure adjustment. Management World, 40(2), 101–116, 133, 117–122.

[20]. Zhu, G., & Wang, K. (2024). Artificial intelligence applications and green innovation in manufacturing enterprises. Industrial Technology Economics, 43(9), 73–81.

[21]. Guan, J. C., & Wang, J. X. (2004). Evaluation and interpretation of knowledge production efficiency. Scientometrics, 59(1), 131–155.

[22]. Roberts, D. L., & Candi, M. (2024). Artificial intelligence and innovation management: Charting the evolving landscape. Technovation, 136, 103081.

Cite this article

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Journal：Advances in Operation Research and Production Management

Volume number: Vol.4

ISSN：3029-0880(Print) / 3029-0899(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).