The Commonplace
Home Three-study pilot Papers Evidence Explore Trends Syntheses Digests References Docs 🎲 Workforce Futures
← Papers
Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review. How this is built →

AI capabilities in construction appear to improve sustainable supply-chain outcomes by enabling green innovation and bolstering resilience, according to a manager-survey of Chinese firms; the evidence is correlational and relies on self-reports.

Do green innovation and resilience mediate the relationship between AI-enabled dynamic capabilities and sustainable supply chain performance?
Kexing Li, Glauco De Vita, Mahdi Bashiri, Yun Luo, Khine S. Kyaw · August 31, 2026 · Cleaner Logistics and Supply Chain
openalex correlational low evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

Structured author observations

Linked only from stored provider relations; the raw author line above is never matched by name.

OpenAlex

Latest observation:

  1. Kexing Li provider ID
  2. Glauco De Vita provider ID
  3. Mahdi Bashiri provider ID
  4. Yun Luo provider ID
  5. Khine S. Kyaw provider ID

Semantic Scholar

Latest observation:

  1. Ke-Xin Li provider ID
  2. Glauco De Vita provider ID
  3. M. Bashiri provider ID
  4. Yun Luo provider ID
  5. K. S. Kyaw provider ID
Survey evidence from Chinese construction firms indicates AI-enabled dynamic capabilities are associated with better sustainable supply chain performance, with green innovation and supply chain resilience jointly mediating the relationship.

Citation observations

Cumulative provider counts captured on specific dates; providers are never combined.

Despite recent advances in the deployment of artificial intelligence technologies for building resilient industries and sustainable development, existing literature largely overlooks the dual mediation role of green innovation and supply chain resilience in how AI-enabled dynamic capabilities relate to the sustainable performance of supply chains. In this study, we extend the dynamic capabilities framework by integrating AI-enabled dynamic capabilities and these two mediator variables in influencing sustainable supply chain performance. Drawing on survey data collected from 634 managers in 423 Chinese construction firms, our empirical validation demonstrates that AI-enabled dynamic capabilities amplify businesses' capacity to sense, seize, and reconfigure resources more rapidly in complex environments. Both green innovation and supply chain resilience exert significant mediation effects. Our theoretical contribution lies in showing that green innovation proactively enhances environmental performance through sustainable practices while supply chain resilience ensures reactive adaptability amid disruptions. These findings offer important implications for how construction industry firms should strategically invest in AI-enabled dynamic capabilities to meet new regulatory requirements, foster sustainable competitive advantages and build resilient supply chains.

Summary

Main Finding

AI-enabled dynamic capabilities significantly improve sustainable supply chain performance in the construction industry, and this relationship is jointly mediated by green innovation (proactive environmental practices) and supply chain resilience (reactive adaptability). Empirical evidence from Chinese construction firms shows AI capabilities speed sensing, seizing, and reconfiguring of resources in complex environments, with both mediators exerting meaningful indirect effects.

Key Points

  • Research gap addressed: prior literature rarely considered the dual mediation role of green innovation and supply chain resilience in the AI–sustainability link; this study integrates both mediators into a dynamic capabilities framework.
  • Core mechanism: AI-enabled dynamic capabilities enhance firms’ ability to sense opportunities/threats, seize them, and reconfigure resources; these capabilities translate into better sustainable supply chain outcomes via:
    • Green innovation — a proactive pathway that elevates environmental performance through sustainable practices and innovations.
    • Supply chain resilience — a reactive pathway that preserves performance by adapting to disruptions.
  • Empirical claim: Both mediators (green innovation and supply chain resilience) have statistically significant mediation effects between AI-enabled capabilities and sustainable supply chain performance.
  • Theoretical contribution: Extends the dynamic capabilities literature by specifying how AI-enabled capabilities operate through dual mediators to produce sustainability gains.
  • Sector focus: Construction industry — a context with high regulatory pressure, infrastructure complexity, and pronounced sustainability/resilience needs.

Data & Methods

  • Data: Survey data from 634 managers across 423 Chinese construction firms.
  • Design: Cross-sectional, manager-reported measures of AI-enabled capabilities, green innovation, supply chain resilience, and sustainable supply chain performance.
  • Analysis: Empirical mediation testing to evaluate indirect effects of AI-enabled capabilities via the two mediators (study reports significant mediation). The approach extends the dynamic capabilities framework to include AI-specific capability constructs.
  • Limitations (implicit from design): Survey-based, cross-sectional data limit causal inference and may be susceptible to common-method bias and endogeneity concerns; further longitudinal or quasi-experimental work would strengthen causal claims.

Implications for AI Economics

  • Investment prioritization: Firms should allocate AI investments not only to efficiency gains but explicitly to capability-building (sensing, seizing, reconfiguring) that fosters green innovation and resilience. This reframes AI spending as strategic capital formation with sustainability returns.
  • Returns to AI: Economic value from AI in supply chains includes indirect returns through innovation-driven environmental performance and resilience-driven continuity—these channels can alter cost-benefit calculations for AI adoption.
  • Policy and regulation: Regulators aiming to improve industry sustainability and resilience can incentivize AI adoption (e.g., subsidies, standards) that targets capability development and green innovation, rather than piecemeal technology deployment.
  • Competitive dynamics: Firms that successfully embed AI-enabled dynamic capabilities may gain sustainable competitive advantages — both in complying with tightening environmental regulations and in reducing disruption-related losses — potentially affecting market structure and entry dynamics in capital-intensive sectors.
  • Risk and resilience economics: Incorporating resilience as a mediator highlights that AI investment reduces downside tail risk from disruptions; models of firm behavior and social welfare should account for this insurance-like value of AI-enabled capabilities.
  • Research agenda: Quantify the magnitude of indirect returns via green innovation and resilience (e.g., ROI, risk-adjusted benefits); explore generalizability across sectors and countries; use longitudinal or experimental designs to address causality and to model dynamic investment timing and complementarities between AI, green tech, and supply-chain practices.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on cross-sectional, self-reported survey data from managers, so statistically significant mediation establishes associations but not causal effects; results are vulnerable to common-method bias, reverse causality, and omitted-variable/endogeneity concerns. Methods Rigormedium — The study uses a reasonably large sample (634 respondents across 423 firms) and mediation testing appropriate to the theoretical model, which supports internal consistency and construct testing; however, the cross-sectional single-informant design, likely absence of exogenous variation or quasi-experimental controls, and potential measurement/common-method bias reduce methodological rigor. SampleCross-sectional survey of 634 managers representing 423 Chinese construction firms; manager-reported measures for AI-enabled dynamic capabilities (sensing, seizing, reconfiguring), green innovation, supply chain resilience, and sustainable supply chain performance. Themesinnovation org_design IdentificationCross-sectional manager survey with statistical mediation analysis (likely SEM or regression-based mediation); identification relies on theorized directional relationships and observed controls rather than exogenous variation, time ordering, or instrumentation. GeneralizabilitySingle-country sample (China) may limit applicability to different institutional and regulatory contexts, Industry-specific focus (construction) — findings may not generalize to service sectors or manufacturing with different supply-chain structures, Manager-reported firm-level measures may not align with objective performance indicators, Cross-sectional design prevents inference about dynamics over time or causal direction, Potential sample selection bias if responding firms differ systematically from non-respondents (e.g., larger or more sustainability-focused firms)

Claims (7)

ClaimDirectionOutcomeConfidence & EvidenceDetails
AI-enabled dynamic capabilities significantly improve sustainable supply chain performance among Chinese construction firms. Organizational Efficiency positive Sustainable supply chain performance
Reading fidelity high
Study strength medium
n=634
0.3
AI-enabled dynamic capabilities enhance firms' ability to sense opportunities and threats, seize opportunities, and reconfigure resources in complex construction-sector environments. Organizational Efficiency positive Firms' sensing, seizing, and resource-reconfiguring capabilities
Reading fidelity high
Study strength medium
n=634
0.3
Green innovation significantly mediates the positive relationship between AI-enabled dynamic capabilities and sustainable supply chain performance. Innovation Output positive Sustainable supply chain performance through green innovation
Reading fidelity high
Study strength medium
n=634
0.3
Supply chain resilience significantly mediates the positive relationship between AI-enabled dynamic capabilities and sustainable supply chain performance. Organizational Efficiency positive Sustainable supply chain performance through supply chain resilience
Reading fidelity high
Study strength medium
n=634
0.3
Green innovation and supply chain resilience jointly mediate the relationship between AI-enabled dynamic capabilities and sustainable supply chain performance. Organizational Efficiency positive Sustainable supply chain performance via dual mediation
Reading fidelity high
Study strength medium
n=634
0.3
Green innovation provides a proactive pathway through which AI-enabled dynamic capabilities improve environmental performance and sustainability outcomes. Innovation Output positive Environmental performance and sustainability outcomes
Reading fidelity high
Study strength medium
n=634
0.3
Supply chain resilience provides a reactive pathway through which AI-enabled dynamic capabilities help firms preserve performance by adapting to disruptions. Organizational Efficiency positive Performance preservation and adaptation under supply chain disruptions
Reading fidelity high
Study strength medium
n=634
0.3

Notes