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Can government digital incentives enhance enterprise artificial intelligence capability? - Evidence from China

Author

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  • Ma, Jingmei
  • Wu, Jie
  • Li, Zhiqing
  • Deng, Kaixin

Abstract

Government digital incentives contribute to improving the enterprise financial environment, enhancing enterprise receptivity to new technologies, and influencing enterprise artificial intelligence (AI) capability. This paper investigates whether and how government digital incentives enhance enterprise AI capability using empirical evidence from Chinese A-share listed enterprises during 2012-2023. By constructing comprehensive indicators to measure enterprise AI capability, we explore the impact of subsidy-based (SDI) and policy-based digital incentive (PDI) on enterprise AI capability and their underlying mechanisms. The findings reveal that: (1) Both SDI and PDI enhance enterprise AI capability, with results remaining robust after robustness tests. (2) SDI positively influence enterprise AI capability by alleviating financing constraints, while PDI exert positive effects through optimizing resource allocation efficiency. (3) Executive IT background positively moderates the relationship between both types of digital incentives and enterprise AI capability. (4) Heterogeneity analysis demonstrates that SDI have significantly stronger effects on enterprise AI capability 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. Compared to non_SOEs, SDI have more significant positive effects on AI capability in SOEs, while PDI demonstrate stronger positive effects on AI capabilities in non_SOEs.

Suggested Citation

  • Ma, Jingmei & Wu, Jie & Li, Zhiqing & Deng, Kaixin, 2026. "Can government digital incentives enhance enterprise artificial intelligence capability? - Evidence from China," Technology in Society, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:teinso:v:86:y:2026:i:c:s0160791x26000473
    DOI: 10.1016/j.techsoc.2026.103258
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