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Greater adoption of industrial robots raises Chinese manufacturers' new-quality productive forces by strengthening supply-chain efficiency and bargaining power. The gains are concentrated in high-innovation firms, state-owned enterprises and manufacturers in western China.

Artificial Intelligence Empowering New Quality Productive Forces of Enterprises: A Perspective on Supply Chain Resilience
Huan Shu, Chaofeng Li · February 18, 2026 · Sustainability
openalex quasi_experimental medium evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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Higher industrial-robot penetration (used as an AI proxy) significantly increases Chinese manufacturers' 'new quality productive forces' primarily by improving supply-chain efficiency and bargaining power, with larger effects in innovative firms, state-owned enterprises, and western-region firms.

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Developing new quality productive forces represents a core strategy for steering China’s path to modernization and shaping new competitive advantages for the nation. As a leading technology in the new round of technological revolution and industrial transformation, artificial intelligence (AI) serves as a key engine for fostering new quality productive forces. Utilizing panel data from China’s A-share listed manufacturing firms (2012–2024), this study employs the penetration rate of industrial robots to proxy for AI development levels and the entropy method to measure new quality productive forces. From the perspective of supply chain resilience, ordinary least squares (OLS) and instrumental variable (IV) methods are employed to examine the impact of AI on enterprise new quality productive forces and its underlying mechanisms. The findings indicate that AI significantly enhances corporate new quality productive forces, a conclusion that remains robust after addressing potential endogeneity and conducting robustness checks. Mediation analysis reveals that AI reinforces corporate supply chain resilience by improving supply chain efficiency and strengthening supply chain discourse power, which in turn drives the enhancement of corporate new quality productive forces. Heterogeneity analysis indicates that the impact of AI on corporate new quality productive forces is heterogeneous, with particularly pronounced effects observed in firms with higher innovation levels, state-owned enterprises, and firms located in western China. This study contributes new evidence from a supply chain resilience perspective to understand the micro-level pathways through which AI empowers new quality productive forces, and offers targeted policy and managerial recommendations to foster the sustainable development of the manufacturing sector.

Summary

Main Finding

AI adoption—measured by industrial-robot penetration—significantly raises firms’ "new quality productive forces" in China’s manufacturing sector (A‑share listed firms, 2012–2024). This positive effect is robust to endogeneity corrections and various robustness checks. Supply chain resilience (through improved supply‑chain efficiency and greater supply‑chain discourse power) is identified as a key mediation channel. The effect is stronger for high‑innovation firms, state‑owned enterprises, and firms located in western China.

Key Points

  • Outcome measure: "new quality productive forces" constructed as a composite index using the entropy method.
  • AI proxy: firm‑level penetration rate of industrial robots.
  • Main result: higher robot penetration → higher new quality productive forces.
  • Mechanisms: AI enhances supply chain resilience by (1) improving supply‑chain efficiency and (2) increasing supply‑chain discourse power; these mediate the AI → productive‑forces link.
  • Methods: OLS and instrumental‑variable (IV) estimations to address endogeneity; mediation analysis to test mechanisms; robustness checks performed.
  • Heterogeneity: larger effects for (a) firms with higher innovation intensity, (b) state‑owned enterprises, and (c) firms in western China.
  • Contribution: micro‑level evidence that links AI adoption to structural improvement in productive capacity via supply‑chain channels.

Data & Methods

  • Sample: Panel of China’s A‑share listed manufacturing firms, 2012–2024.
  • Key variables:
    • Independent: industrial robot penetration rate (firm/industry level).
    • Dependent: new quality productive forces index (constructed via entropy method from multiple indicators).
    • Mediators: measures of supply‑chain efficiency and supply‑chain discourse power (used to capture supply‑chain resilience).
  • Estimation strategy:
    • Baseline: OLS panel regressions with firm controls and fixed effects (implied).
    • Endogeneity: IV approach used to obtain causal estimates (instrument(s) not specified in the abstract).
    • Mediation: formal mediation analysis to decompose total effect into direct and indirect (via supply‑chain resilience) components.
    • Robustness: multiple sensitivity checks and alternative specifications reported.
  • Limitations (implicit): industrial robots proxy captures manufacturing hardware‑oriented AI/automation rather than software AI; IV details not provided in the summary.

Implications for AI Economics

  • Microeconomic mechanism: AI adoption can raise firm‑level productive quality not only by automating tasks but also by strengthening supply‑chain resilience—improving throughput, reducing disruptions, and increasing bargaining/coordination power within supply networks.
  • Policy implications:
    • Promote targeted AI/robot adoption in manufacturing—subsidies, tax incentives, and public investment in robotics and related infrastructure.
    • Support supply‑chain upgrades (digitalization, standards, data sharing) so AI gains translate into systemic resilience and productivity improvements.
    • Tailor support to heterogeneity: prioritize high‑innovation firms, state‑owned firms as anchors, and western regions to narrow regional disparities.
    • Invest in workforce retraining, complementary digital skills, and organizational capabilities to realize AI benefits.
  • Research implications:
    • Measurement: broaden AI proxies beyond industrial robots to include software/algorithmic AI and firm‑level digitalization indicators.
    • Identification: document and report IV strategies and alternative causal designs (e.g., instrumental shocks, policy experiments).
    • Scope: extend analysis to non‑manufacturing sectors, longer horizons, and cross‑country comparisons to assess generalizability.
  • Broader economic insight: findings support the view that AI’s economic value is amplified when it operates through network and coordination channels (supply chains), suggesting that policies fostering both AI adoption and supply‑chain modernisation will maximize gains in national productive capabilities.

Assessment

Paper Typequasi_experimental Evidence Strengthmedium — Strengths include use of longitudinal firm-level data, IV methods to address endogeneity, robustness checks, mediation analysis, and heterogeneity tests; key weaknesses are the reliance on industrial-robot penetration as a proxy for AI (potential measurement error and conflation with general automation), lack of instrument detail and disclosed diagnostics in the abstract, and possible omitted variable or reverse-causality concerns for the mediation pathways. Methods Rigormedium — The study applies standard econometric tools (panel OLS, IV, mediation, heterogeneity analysis) appropriate for causal inference in observational data, but rigor is limited by an unclear instrument description and strength, potential measurement issues with the constructed 'new quality productive forces' index and the AI proxy, and inherent limitations of mediation analysis for establishing causal mechanisms without experimental variation. SamplePanel of China A-share listed manufacturing firms, 2012–2024; firm-year observations of listed manufacturers only; AI proxied by firm/region-level industrial robot penetration; outcome is a constructed index of 'new quality productive forces' measured via the entropy method; includes firm-level controls and heterogeneity splits by innovation level, ownership (SOE vs non-SOE), and region (eastern/central/western China). (Exact number of firms/observations not reported in the abstract.) Themesproductivity org_design IdentificationFirm-level panel regression (OLS) combined with an instrumental-variables (IV) approach that uses variation in industrial-robot penetration as a proxy for AI adoption to isolate causal effects; mediation analysis is used to test channels through supply-chain efficiency and supply-chain bargaining power. (Abstract does not report the specific instrument or first-stage diagnostics.) GeneralizabilityOnly publicly listed manufacturing firms in China — excludes private, small, unlisted firms and services sectors, Uses industrial-robot penetration as proxy for AI, which may not capture software-based AI or broader AI adoption, Findings may be China-specific due to institutional, policy and supply-chain structures, Study period (2012–2024) covers particular technological and policy shocks that may limit extrapolation to other periods, Constructed outcome index ('new quality productive forces') is context-dependent and may not map cleanly to standard productivity/welfare measures

Claims (6)

ClaimDirectionOutcomeConfidence & EvidenceDetails
AI (proxied by the penetration rate of industrial robots) significantly enhances corporate new quality productive forces. Firm Productivity positive new quality productive forces
Reading fidelity high
Study strength medium
not reported
0.48
The positive effect of AI on corporate new quality productive forces remains robust after addressing potential endogeneity and after conducting robustness checks. Firm Productivity positive new quality productive forces
Reading fidelity high
Study strength medium
not reported
0.48
AI reinforces corporate supply chain resilience by improving supply chain efficiency and strengthening supply chain discourse power. Organizational Efficiency positive supply chain efficiency; supply chain discourse power (components of supply chain resilience)
Reading fidelity high
Study strength medium
not reported
0.48
Improvements in supply chain efficiency and supply chain discourse power (induced by AI) mediate the effect of AI on enhancing corporate new quality productive forces. Firm Productivity positive new quality productive forces (mediated by supply chain efficiency and supply chain discourse power)
Reading fidelity high
Study strength medium
not reported
0.48
The impact of AI on corporate new quality productive forces is heterogeneous: effects are particularly pronounced in firms with higher innovation levels, in state-owned enterprises, and in firms located in western China. Firm Productivity positive new quality productive forces
Reading fidelity high
Study strength medium
not reported
0.48
The study proxies AI development level by the penetration rate of industrial robots and measures new quality productive forces using the entropy method. Other mixed measurement approach for AI (robot penetration) and for new quality productive forces (entropy-based index)
Reading fidelity high
Study strength low
not reported
0.24

Notes