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Procurement 4.0: A Three-Pillar Framework for Ethical AI Adoption in Small Businesses

Senali Amarasuriya & Kristie A. Abston

Journal of Small Business Strategy2026https://doi.org/10.53703/001c.157792article
ABDC C
Weight
0.50

What the paper says

Small and Medium Enterprises (SME) in the U.S. face significant procurement challenges, like inefficiencies in supplier selection, cost inflation, and low bargaining power. While artificial intelligence (AI) offers transformative solutions, its adoption remains inconsistent due to ethical concerns, implementation barriers, and fears of workforce displacement. This paper introduces a three-pillar framework for AI-driven procurement in SMEs to address and mitigate these concerns. The framework consists of three pillars: Intelligent Automation & Decision Augmentation, which addresses how AI enhances, rather than replaces, human decision-making; Ethical Procurement Ecosystems, which incorporates practices ensuring fairness, transparency, and sustainability in AI-driven supplier networks; and lastly, Dynamic Regulatory Adaptation, which aligns AI use with evolving legal and industry standards. An implementation roadmap is provided along with suggested strategies for addressing anticipated challenges. The paper concludes with policy recommendations to guide SMEs in adopting AI responsibly in the context of best procurement practices, ensuring efficient supply chain management.

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https://doi.org/https://doi.org/10.53703/001c.157792

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@article{senali2026,
  title        = {{Procurement 4.0: A Three-Pillar Framework for Ethical AI Adoption in Small Businesses}},
  author       = {Senali Amarasuriya & Kristie A. Abston},
  journal      = {Journal of Small Business Strategy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.53703/001c.157792},
}

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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

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.