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

Xue Lei et al.

Engineering Economics2026https://doi.org/10.5755/j01.ee.37.1.40527article
ABDC B
Weight
0.50

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.

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

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@article{xue2026,
  title        = {{The Innovation Paradox of AI-Driven Development: Resource Allocation Distortion and Corporate R&D Motivation Loss}},
  author       = {Xue Lei et al.},
  journal      = {Engineering Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.5755/j01.ee.37.1.40527},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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