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Can Government Digital Incentives Enhance Enterprise Artificial Intelligence Capability? Empirical Evidence from Chinese A-share Listed Enterprises

Jie Wu et al.

Science, Technology and Society2026https://doi.org/10.1177/09717218261427081article
AJG 2ABDC B
Weight
0.50

What the paper says

This article investigates whether and how government digital incentives enhance enterprise AI capability (AIC) using empirical evidence from Chinese A-share listed enterprises during 2011–2024. By constructing comprehensive indicators to measure enterprise AIC, we explore the impact of subsidy-based (SDI) and policy-based digital incentive (PDI) on enterprise AIC and their underlying mechanisms. The findings reveal that: (a) Both SDI and PDI enhance enterprise AIC, with results remaining robust after robustness tests. (b) SDI positively influence enterprise AIC by alleviating financing constraints (FC), while PDI exert positive effects through optimising resource allocation efficiency. (c) Executive IT background positively moderates the relationship between both types of digital incentives and enterprise AIC. (d) Heterogeneity analysis demonstrates that SDI have significantly stronger effects on enterprise AIC in eastern regions compared to western and central regions, whereas PDI show more pronounced positive effects in central regions compared to eastern and western regions. SDI exert markedly stronger effects in high-tech industries than in non-high-tech industries, while PDI demonstrate greater positive impacts in non-high-tech industries compared to high-tech industries. Compared to non-state-owned enterprises (SOEs), SDI have more significant positive effects on AIC in SOEs, whereas PDI exhibit stronger positive effects on AI capabilities in non-SOEs.

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https://doi.org/https://doi.org/10.1177/09717218261427081

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@article{jie2026,
  title        = {{Can Government Digital Incentives Enhance Enterprise Artificial Intelligence Capability? Empirical Evidence from Chinese A-share Listed Enterprises}},
  author       = {Jie Wu et al.},
  journal      = {Science, Technology and Society},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/09717218261427081},
}

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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

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.