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Embedding AI into R&D, decision-making and organizational coordination raises firm competitiveness in China’s embodied-intelligence sector, largely by prompting reconfiguration of competitive structures; infrastructure and scenario-openness measures did not strengthen the effect.

Artificial Intelligence Embedding and Enterprise Competitiveness in the Embodied Intelligence Industry: The Mediating Role of Competitive Structure Reconfiguration
Jiangshan Zhu, Jinguo Xin, Ning Zhang · July 22, 2026 · Administrative Sciences
openalex correlational low evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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Survey evidence from 266 Chinese embodied-intelligence firms shows AI embedding is positively associated with enterprise competitiveness, with competitive-structure reconfiguration partially mediating this relationship, while proposed moderators (data–computing foundation and scenario openness) were not supported.

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Artificial intelligence is increasingly integrated into physical products, industrial scenarios, and innovation ecosystems, yet management research has focused mainly on AI adoption rather than the depth of organizational integration. This study examines how AI embedding affects enterprise competitiveness in China’s embodied intelligence industry and whether competitive structure reconfiguration mediates this relationship. Using survey data from 266 firms and partial least squares structural equation modeling, the analysis shows that AI embedding positively affects both enterprise competitiveness and competitive structure reconfiguration. Competitive structure reconfiguration also improves enterprise competitiveness and partially mediates the focal relationship, with a variance accounted for value of 43.7%. By contrast, the moderating effects of data–computing foundation and scenario openness are not supported. These findings indicate that the competitive value of AI depends not only on adoption but also on its integration into R&D, decision-making, organizational coordination, and scenario development, as well as on the structural changes that follow. The study contributes by distinguishing AI embedding from AI adoption and by identifying competitive structure reconfiguration as a process mechanism linking embedded AI to technological, ecosystem, and rule-based competitiveness.

Summary

Main Finding

AI embedding — the deep integration of AI into R&D, decision-making, organizational coordination, and scenario development — increases firm competitiveness in China’s embodied intelligence industry. Competitive structure reconfiguration (changes in market/competitive structure driven by AI) partially mediates this effect (variance accounted for = 43.7%). Moderating effects of data–computing foundation and scenario openness were not supported.

Key Points

  • Distinction: AI embedding (depth of organizational integration) is conceptually and empirically distinct from mere AI adoption (presence/use).
  • Direct effect: AI embedding positively and directly improves enterprise competitiveness.
  • Mediation: AI-driven competitive structure reconfiguration also increases competitiveness and partially mediates the embedding → competitiveness relationship (VAF = 43.7% → substantial partial mediation).
  • Non-significant moderators: Data–computing foundation and scenario openness did not significantly moderate the relationships tested.
  • Competitive outcomes: The study links embedded AI to technological, ecosystem, and rule-based competitiveness (multiple dimensions of firm advantage).

Data & Methods

  • Context: China’s embodied intelligence industry (products with integrated AI in physical/industrial settings).
  • Sample: Survey of 266 firms.
  • Analytical approach: Partial least squares structural equation modeling (PLS-SEM).
  • Key constructs measured: AI embedding (integration across R&D, decision-making, organizational coordination, scenario development), competitive structure reconfiguration, enterprise competitiveness (multi-dimensional).
  • Key quantitative result: Mediation VAF = 43.7%; moderation tests for data–computing foundation and scenario openness not supported.

Implications for AI Economics

  • Value of integration over adoption: Economic benefits from AI depend on how deeply AI is embedded into firm routines, capabilities, and product scenarios — not just on whether AI is adopted.
  • Mechanism matters: Competitive structure reconfiguration is an important process mechanism — embedded AI changes market structure and competitive positions, which in turn boosts firm competitiveness.
  • Policy and investment: Investments that support organizational integration (training, process redesign, product reengineering) may yield higher returns than investments only in hardware/infrastructure. Infrastructure alone (data–computing foundation) may be necessary but not sufficient.
  • Market structure and dynamics: Widespread embedding can reconfigure competition (entry/exit dynamics, ecosystem roles, regulatory norms), with implications for market concentration and innovation incentives.
  • Measurement and research: AI economic analyses should measure embedding depth and structural-market changes, not just adoption counts or R&D spending. Future work should use longitudinal and objective performance data, other industries and countries, and investigate causal dynamics and welfare implications.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on cross-sectional, self-reported survey data and PLS-SEM associations, which cannot rule out reverse causality, omitted variable bias, or common-method bias; mediation is statistical rather than causal. Methods Rigormedium — Use of PLS-SEM on a moderate sample (n=266) is appropriate for testing latent constructs and mediation, and the paper distinguishes embedding from adoption conceptually; however, reliance on cross-sectional self-report measures, limited discussion of endogeneity controls or robustness checks, and potential sample selection issues reduce methodological rigor. SampleCross-sectional survey of 266 firms operating in China's 'embodied intelligence' industry (firms integrating AI into physical products/industrial scenarios); measures appear to be manager-reported indicators of AI embedding, competitive-structure reconfiguration, enterprise competitiveness, and moderators (data–computing foundation, scenario openness). Themesorg_design innovation IdentificationCross-sectional firm survey analyzed with partial least squares structural equation modeling (PLS-SEM) to test associations and hypothesized mediation; no quasi-experimental or instrumental variable strategy to establish causality. GeneralizabilityChina-specific sample and institutional/regulatory context may not generalize to other countries, Industry-limited to embodied intelligence firms; results may not apply to software-only or services sectors, Moderate sample size and likely non-random sampling limit external validity, Cross-sectional, self-reported data raises concerns about measurement bias and common-method variance, Findings may differ for large multinationals vs small domestic firms (heterogeneity not fully addressed)

Claims (8)

ClaimDirectionOutcomeConfidence & EvidenceDetails
AI embedding positively affects enterprise competitiveness. Firm Productivity positive enterprise competitiveness
Reading fidelity high
Study strength medium
n=266
0.3
AI embedding positively affects competitive structure reconfiguration. Market Structure positive competitive structure reconfiguration
Reading fidelity high
Study strength medium
n=266
0.3
Competitive structure reconfiguration improves enterprise competitiveness. Firm Productivity positive enterprise competitiveness
Reading fidelity high
Study strength medium
n=266
0.3
Competitive structure reconfiguration partially mediates the relationship between AI embedding and enterprise competitiveness, with a variance accounted for (VAF) value of 43.7%. Firm Productivity positive mediating effect of competitive structure reconfiguration on the AI embedding → enterprise competitiveness link
Reading fidelity high
Study strength medium
n=266
VAF = 43.7%
0.3
The moderating effects of data–computing foundation and scenario openness are not supported. Firm Productivity null_result moderating effect of data–computing foundation and scenario openness on the AI embedding → enterprise competitiveness relationship
Reading fidelity high
Study strength medium
n=266
not significant
0.3
The competitive value of AI depends not only on adoption but also on its integration into R&D, decision-making, organizational coordination, and scenario development, as well as on the structural changes that follow. Firm Productivity positive competitive value of AI (enterprise competitiveness)
Reading fidelity high
Study strength medium
n=266
0.3
This study distinguishes AI embedding from AI adoption as a conceptual contribution. Other positive conceptual distinction between AI embedding and AI adoption
Reading fidelity high
Study strength low
not reported
0.15
Competitive structure reconfiguration is identified as a process mechanism linking embedded AI to technological, ecosystem, and rule-based competitiveness. Innovation Output positive process mechanism (competitive structure reconfiguration) linking AI embedding to technological/ecosystem/rule-based competitiveness
Reading fidelity medium
Study strength medium
n=266
0.18

Notes