Circular supply chain practices and sustainability performance: does AI capability and green entrepreneurial orientation matter?

Francis Kamewor Tetteh et al.

The Bottom Line2026https://doi.org/10.1108/bl-05-2025-0094article
AJG 1ABDC C
Weight
0.50

What the paper says

Purpose This study aims to investigate how artificial intelligence (AI) capabilities and green entrepreneurial orientation (GEO) amplify the relationship between circular supply chain practices (CSCPs) and sustainability performance in resource-constrained emerging economies. Design/methodology/approach Using survey data from 384 manufacturing firms in Ghana, the study uses structural equation modeling and Hayes PROCESS macro to test the direct effects of four CSCP dimensions (circular procurement, circular design, circular production and end-of-life product management) on sustainability performance, as well as the moderating roles of AI capabilities and GEO. Findings All four CSCP dimensions significantly enhance sustainability performance. However, AI capabilities and GEO exhibit dimension-specific moderating effects, strengthening only the circular procurement-sustainability performance and circular production-sustainability performance relationships but not affecting circular design or end-of-life product management relationships. Originality/value To the best of the authors’ knowledge, this study provides the first empirical evidence of dimension-specific boundary conditions in the CSCP-sustainability performance relationship, extends the natural resource-based view by identifying technological and entrepreneurial capabilities as strategic mechanisms and offers practical guidance for targeted AI and GEO investments in circular transformation initiatives within emerging economy contexts.

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https://doi.org/https://doi.org/10.1108/bl-05-2025-0094

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@article{francis2026,
  title        = {{Circular supply chain practices and sustainability performance: does AI capability and green entrepreneurial orientation matter?}},
  author       = {Francis Kamewor Tetteh et al.},
  journal      = {The Bottom Line},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/bl-05-2025-0094},
}

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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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