The Innovation Paradox of AI-Driven Development: Resource Allocation Distortion and Corporate R&D Motivation Loss

Xue Lei; Ziyan Zhang; You Chen; Chang Liu; Shouchao He

doi:10.5755/j01.ee.37.1.40527

Authors

Xue Lei School of Management, Shanghai University, China
Ziyan Zhang School of Management, Shanghai University, China
You Chen School of Management, Shanghai University, China
Chang Liu School of Management, Shanghai University, China
Shouchao He School of Economics and Management and Wenzhou Research Center for Digital Economy and Digital Governance, Wenzhou University of Technology, China

DOI:

https://doi.org/10.5755/j01.ee.37.1.40527

Keywords:

Artificial Intelligence, Enterprise Innovation, Resource Allocation, Crowding-out Effect, Innovation Talent, Professional Technical Equipment

Abstract

As artificial intelligence technology rapidly develops, enterprises pursuing intelligent transformation may face innovation resource allocation dilemmas. On one hand, the construction and maintenance of AI systems require substantial financial investment, and this high-cost pressure may crowd out resources traditionally allocated to innovation activities. On the other hand, excessive dependence on AI may lead enterprises to neglect talent cultivation and equipment updates, resulting in technological path lock-in. Based on resource allocation theory, principal-agent theory, and path dependence theory, this paper uses Chinese A-share listed companies from 2013-2023 as research samples and employs text analysis methods to construct an enterprise AI application intensity index to explore the impact of AI on enterprise innovation behavior and its mechanisms. The research finds that: (1) AI application has a significant crowding-out effect on enterprise innovation; (2) this crowding-out effect is primarily realized through two channels: reducing innovation talent investment intensity and cutting professional technical equipment configuration; (3) heterogeneity analysis shows that characteristics such as state ownership, high market competition, low financing constraints, high technology intensity, and high management shareholding can effectively mitigate AI's inhibitory effect on innovation. This research not only deepens theoretical understanding of the relationship between AI and enterprise innovation but also provides practical guidance for optimizing innovation resource allocation during enterprise digital transformation.

Author Biographies

Xue Lei, School of Management, Shanghai University, China

Xue Lei is a scholar in Management Science at Shanghai University. He received his Ph.D. from Shanghai University. His research interests span corporate sustainable development, environmental policy impacts, technological innovation, and regional economics. Dr. Lei has published extensively in journals such as Economics Letters, Energy Economics, Journal of Environmental Management, and Finance Research Letters. He serves as Associate Editor for Politická ekonomie and Editorial Board Member for Oeconomia Copernicana.
Ziyan Zhang, School of Management, Shanghai University, China

Ziyan Zhang is a researcher at the School of Management, Shanghai University. Her research focuses on technological innovation management, corporate strategy, and the intersection of artificial intelligence and business development. She has contributed to research on environmental technology success factors and green innovation. Her work explores how emerging technologies reshape corporate behavior and innovation ecosystems.
You Chen, School of Management, Shanghai University, China

You Chen is a researcher at the School of Management, Shanghai University. His research interests include supply chain resilience, digital transformation, and green innovation. He has published in journals such as Scientific Reports, Sustainability, and PLOS ONE. His recent work examines how AI-enabled manufacturing enterprises enhance supply chain resilience through organizational change and how digital technology drives green innovation efficiency.
Chang Liu, School of Management, Shanghai University, China

Chang Liu is a researcher at the School of Management, Shanghai University. His research focuses on operations research, supply chain optimization, and sustainable logistics. He has published in Engineering Optimization and Sustainability, with expertise in mixed integer programming, closed-loop supply chain network design, and emergency logistics. His work addresses optimization challenges under uncertain environments.
Shouchao He, School of Economics and Management and Wenzhou Research Center for Digital Economy and Digital Governance, Wenzhou University of Technology, China

Shouchao He is a researcher at the School of Economics and Management, Wenzhou University of Technology, and affiliated with the Wenzhou Research Center for Digital Economy and Digital Governance. His research interests include digital economy, regional economic governance, and innovation policy. He focuses on understanding how digital transformation affects economic development and corporate innovation strategies in China.