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AI uptake in Chinese manufacturing is linked to bigger R&D teams and stronger innovation outcomes; industry structure, firms' innovation strategies and government subsidies amplify these effects.

Research on the Mechanism of Artificial Intelligence-Driven Government Innovation Services to Enhance Manufacturing Innovation Capabilities
Yundong Liu · February 08, 2026 · International Journal of Computer Information Systems and Industrial Management Applications
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Using firm-level panel regressions with two-way fixed effects, the paper finds that AI application is positively associated with larger R&D teams and improved corporate innovation performance, with industry type, innovation strategy, and government subsidies acting as mediators.

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The rapid development of artificial intelligence has injected new momentum into manufacturing enterprises to enhance their independent innovation capabilities and strengthen their competitive advantages. Manufacturing enterprises should actively adapt to this development trend, seize the driving force provided by technological development, and enable Chinese manufacturing to form new advantages in global market competition. Therefore, this paper comprehensively reviews scholars' research on the artificial intelligence transformation of manufacturing and manufacturing innovation capabilities, proposes the theoretical foundation of innovation-driven and endogenous growth, and analyzes the current level of innovation capabilities of manufacturing enterprises under artificial intelligence technology. Using a two-way fixed effects model, we constructed a measurement framework and proposed relevant research hypotheses, designing a basic regression and mechanism testing model to analyze each research hypothesis individually. The findings indicate that the regression coefficient for the impact on the number of R&D personnel is 1.648, which is significantly positive at the 1% level. The application of artificial intelligence can increase the number of R&D personnel in an enterprise and promote employee participation in process innovation, thereby validating Hypothesis 1. Mediation effect tests were conducted simultaneously for industry type, innovation strategy, and government subsidies. When the explained variable was corporate innovation performance, the regression coefficients for industry type, innovation strategy, and government subsidies were 0.186, 0.197, and 0.185, respectively, all of which were significantly positive at the 5% level. This indicates that improvements in industry type, innovation strategy, and government subsidies can positively influence corporate innovation performance, thereby validating Hypotheses 2, 3, and 4.

Summary

Main Finding

The paper finds that artificial intelligence (proxied by industrial robot density) significantly increases manufacturing firms' innovation capacity in China. AI adoption raises R&D staffing (coefficient 1.648, p < 0.01) and improves corporate innovation performance. Industry type, firms' innovation strategy, and government subsidies mediate the AI → innovation relationship (coefficients 0.186, 0.197, 0.185 respectively, all p < 0.05).

Key Points

  • Research question: How do AI-driven government innovation services and AI adoption affect manufacturing innovation capabilities, and through what mechanisms?
  • Theoretical framing: innovation-driven development and endogenous growth (vertical innovation / R&D-based growth).
  • Main empirical result: higher industrial robot density is associated with higher measured innovation (patent applications and related innovation metrics) and a substantial increase in R&D personnel.
  • Mechanisms identified: AI affects innovation partly by
    • increasing R&D human capital (more R&D personnel, greater employee participation in process innovation),
    • interacting with industry type (heterogeneous effects across two‑digit manufacturing industries),
    • reinforcing firms' innovation strategies, and
    • leveraging government subsidies (policy support amplifies AI’s innovation effect).
  • Stylized descriptive facts: China’s total R&D spending rose rapidly 2013–2023 (to ¥3.816 trillion in 2023); R&D personnel FTEs grew strongly but there remains a shortage of high‑level AI talent; regional AI innovation is concentrated in the east though gaps are narrowing; manufacturing innovation service intensity rose modestly (mean increase ~1.65% 2013–2023).

Data & Methods

  • Data scope: China, manufacturing sector at the two-digit industry level, sample period ~2013–2023. Uses national R&D expenditure and R&D personnel series, industry input–output tables, patent/innovation indicators, and industrial robot density by industry.
  • Key variables:
    • Dependent: industry innovation level (log of patent applications / corporate innovation performance measures).
    • Main independent: log(industrial robot density) as proxy for AI adoption.
    • Mediators: industry type, firm innovation strategy, government subsidies; also R&D personnel as an outcome in mechanism tests.
  • Econometric approach:
    • Two‑way fixed effects panel regression (industry and year fixed effects) to estimate the impact of robot density on innovation.
    • Interaction terms to explore heterogeneity across industries.
    • Difference‑in‑differences style identification elements and mediation effect tests to probe mechanisms.
  • Reported coefficients of interest:
    • Impact on number of R&D personnel: 1.648 (significant at 1%).
    • Mediator coefficients (when DV = corporate innovation performance): industry type 0.186, innovation strategy 0.197, government subsidies 0.185 (all significant at 5%).

Implications for AI Economics

  • For policy:
    • AI adoption is a channel to build endogenous innovation capacity; public support (subsidies, innovation services) complements firm AI investments and amplifies innovation returns.
    • Policies should target industry heterogeneity: tailored support is needed because AI’s innovation effects differ by industry.
    • Human capital remains crucial: governments and firms should invest in high‑level AI talent and reskilling to realize AI’s innovation potential.
    • Strengthening basic research and IP protections is important given China’s relatively low share of basic R&D and the need to improve original innovation conversion.
  • For firms:
    • Investing in AI (including robotics) can increase R&D headcount and process innovation participation, supporting longer‑term capability building.
    • Aligning AI adoption with explicit innovation strategies yields larger innovation gains.
  • For research:
    • The study highlights industry‑level channels through which AI affects innovation and supports models of capital‑biased technological change feeding endogenous growth.
    • Future empirical work should address remaining endogeneity (e.g., instrument AI adoption), disaggregate AI beyond robot density (software/algorithms, data infrastructure), and analyze firm‑level heterogeneity and productivity/quality outcomes rather than patents alone.

Limitations noted by the authors (implicit): reliance on robot density as an AI proxy, industry‑level aggregation, and potential endogeneity between AI adoption and innovation investment—areas for deeper follow‑up.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on observational panel correlations with two-way fixed effects but no clear exogenous source of variation or strategies to address likely endogeneity (e.g., reverse causality, omitted time-varying confounders), so causal claims are weak. Methods Rigormedium — The authors use standard econometric techniques (two-way fixed effects, mediation tests) appropriate for panel data and test multiple hypotheses, but they do not appear to implement stronger identification checks (instrumental variables, difference-in-differences with exogenous shocks, event-study / pre-trends, robustness to alternative AI measures), limiting rigor for causal inference. SampleFirm-level panel data on Chinese manufacturing enterprises (years and sample size not specified in the summary); key variables include AI application/adoption, number of R&D personnel, corporate innovation performance, industry type, firm innovation strategy, and government subsidies. Themesinnovation adoption labor_markets IdentificationFirm-level panel regression with two-way fixed effects (firm and time), plus mediation analysis for industry type, innovation strategy, and government subsidies; no exogenous variation or instrumental variables reported. GeneralizabilityChina-only sample — institutional and policy environment (including subsidies) may not generalize to other countries, Limited to manufacturing sector — findings may not apply to services or other industries, Potential sample selection (firms that report AI use) and unclear measurement of 'AI application' limits external validity, Time period unspecified — results may reflect a particular stage of AI diffusion, Firm heterogeneity (size, tech intensity) not fully detailed, constraining applicability across firm types

Claims (6)

ClaimDirectionOutcomeConfidence & EvidenceDetails
The application of artificial intelligence can increase the number of R&D personnel in an enterprise. Employment positive number of R&D personnel
Reading fidelity high
Study strength medium
1.648
0.3
The application of artificial intelligence promotes employee participation in process innovation. Innovation Output positive employee participation in process innovation
Reading fidelity medium
Study strength medium
not reported
0.18
Improvements in industry type can positively influence corporate innovation performance. Innovation Output positive corporate innovation performance
Reading fidelity high
Study strength medium
0.186
0.3
An innovation strategy can positively influence corporate innovation performance. Innovation Output positive corporate innovation performance
Reading fidelity high
Study strength medium
0.197
0.3
Government subsidies can positively influence corporate innovation performance. Innovation Output positive corporate innovation performance
Reading fidelity high
Study strength medium
0.185
0.3
This paper uses a two-way fixed effects model and a measurement framework to test hypotheses about AI's impact on manufacturing enterprise innovation. Other null_result methodological approach (model specification)
Reading fidelity high
Study strength high
not reported
0.5

Notes