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The mediating role of supply chain resilience in the relationship between AI capabilities and sustainability performance: evidence from manufacturing SMEs

Chamaipon Ratanacharoenchai & Konpapha Jantapoon

Sustainable Futures2026https://doi.org/10.1016/j.sftr.2026.101677article
AJG 1ABDC C
Weight
0.50

What the paper says

This study investigates the complex relationships between artificial intelligence (AI) capabilities, supply chain resilience, and sustainability performance in small and medium enterprises (SMEs) within Thailand's manufacturing sector. Extending Dynamic Capabilities Theory (DCT) to AI-enabled organizational contexts, this research conceptualizes AI as a capability enhancer that transforms traditional sensing, seizing, and reconfiguring processes through algorithmic intelligence, rather than constituting an independent form of algorithmic dynamic capability. Drawing on Dynamic Capabilities Theory and the Resource-Based View, we surveyed 327 manufacturing SMEs implementing AI technologies to examine how AI-enabled sensing, seizing, and reconfiguring capabilities influence organizational outcomes. Using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4.0 software and 5000 bootstrap samples, our findings reveal that AI capabilities are significantly associated with supply chain resilience (R² = 0.542) and positively related to environmental sustainability (β = 0.426, p < .001), while showing no significant direct association with social sustainability (β = 0.073, p = .276). Critically, supply chain resilience fully mediates the relationship between AI and social sustainability, with variance accounted for (VAF) of 69.7%, indicating that SMEs must first achieve operational stability before realizing social benefits. Multi-group analysis reveals significant moderation effects, with firm size, implementation maturity, and industry context shaping how AI capabilities translate into performance outcomes. For SME managers, findings emphasize strengthening AI-driven seizing processes for data-based decision making through investment in real-time analytics platforms. For policymakers, results support designing phased AI maturity programs with staged incentive structures for small manufacturers. For consultants, the study provides a foundation for developing AI-based resilience assessment frameworks and implementation roadmaps.

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https://doi.org/https://doi.org/10.1016/j.sftr.2026.101677

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@article{chamaipon2026,
  title        = {{The mediating role of supply chain resilience in the relationship between AI capabilities and sustainability performance: evidence from manufacturing SMEs}},
  author       = {Chamaipon Ratanacharoenchai & Konpapha Jantapoon},
  journal      = {Sustainable Futures},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.sftr.2026.101677},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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