From environmental orientation to supply chain sustainability: the mediating role of AI-enabled circular economy practices

Shuili Yang et al.

Chinese Management Studies2026https://doi.org/10.1108/cms-09-2025-0921article
AJG 1ABDC C
Weight
0.50

What the paper says

Purpose Due to pressures such as climate change, regulatory demands and stakeholder expectations, several firms have embraced an environmental orientation (EO). Despite this shift, many firms struggle to translate their EO into measurable, sustainable outcomes. To address this gap, this study draws on the practice-based view to examine the association of EO with sustainable supply chain performance (SSCP). Design/methodology/approach This study surveyed 240 Chinese firms. Data were analyzed using hierarchical regression and bootstrapping methods to test the proposed mediation and moderation effects. Findings The results reveal that, among the three dimensions of artificial intelligence (AI)-enabled circular economy practices, only AI-enabled management systems (AI-MS) and AI-enabled eco-design (AI-ED) positively mediate the EO–SSCP association. AI-enabled investment recovery (AI-IR) does not play a significant mediating role. Furthermore, AI maturity and firm size serve as critical boundary conditions. Low-to-medium AI maturity firms rely more on AI-ED, whereas high AI maturity firms shift toward AI-MS. Firm size moderates the effectiveness of AI-IR, showing promise for small and medium-sized enterprises but constraints in larger enterprises. Originality/value The findings delineate the pathways and boundary conditions through which EO translates into SSCP, thereby informing tailored AI-enabled circular strategies for firms at different digitalization stages and scales.

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https://doi.org/https://doi.org/10.1108/cms-09-2025-0921

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@article{shuili2026,
  title        = {{From environmental orientation to supply chain sustainability: the mediating role of AI-enabled circular economy practices}},
  author       = {Shuili Yang et al.},
  journal      = {Chinese Management Studies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/cms-09-2025-0921},
}

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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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