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Governing AI in sustainable public procurement: towards an ESG-oriented conceptual architecture

Alessia Sciarrone & Mario Calabrese

VINE Journal of Information and Knowledge Management Systems2026https://doi.org/10.1108/vjikms-07-2025-0309article
ABDC B
Weight
0.50

What the paper says

Purpose This study aims to explore how artificial intelligence (AI) can contribute to the sustainable transformation of public procurement systems. It addresses the gap of theoretical models capable of aligning environmental, social and governance (ESG) goals into digital governance models by providing a framework for responsible and inclusive AI adoption. Design/methodology/approach The study adopts a conceptual, theory-building approach based on iterative synthesis and the integration of multiple theoretical perspectives. It prioritizes framework development over hypothesis testing. Findings The study proposes the Governance Architecture for Sustainable Public Procurement (GASPP) framework for AI-driven public procurement, linking technological readiness, governance, organizational capacity and sustainability into four interconnected pillars and operationalizing it through a multi-level strategic map. Originality/value GASPP extends the technology-organization-environment framework by integrating insights from resource-based view and Institutional Theory and adding a sustainability-oriented dimension. It offers a unified, multi-level model that links AI adoption in public procurement to ESG performance and public value creation.

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https://doi.org/https://doi.org/10.1108/vjikms-07-2025-0309

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@article{alessia2026,
  title        = {{Governing AI in sustainable public procurement: towards an ESG-oriented conceptual architecture}},
  author       = {Alessia Sciarrone & Mario Calabrese},
  journal      = {VINE Journal of Information and Knowledge Management Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/vjikms-07-2025-0309},
}

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Governing AI in sustainable public procurement: towards an ESG-oriented conceptual architecture

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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.