Vol. 14 No. 3 (2026): Business & Management Studies: An International Journal
Articles

Generative artificial intelligence-supported decision-making systems: A PRISMA 2020-based systematic literature review and thematic synthesis

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  • Fatmanur Yavuz,
  • Zeliha Seçkin
Fatmanur Yavuz
Res. Assist., Aksaray University, Aksaray, Türkiye
Zeliha Seçkin
Prof. Dr., Aksaray University, Aksaray, Türkiye

Published 2026-09-25

Keywords

  • Generative Artificial Intelligence, Large Language Models, Decision-Making, Decision Support Systems, Explainability
  • Üretken Yapay Zekâ, Büyük Dil Modelleri, Karar Verme, Karar Destek Sistemleri, Açıklanabilirlik

How to Cite

Generative artificial intelligence-supported decision-making systems: A PRISMA 2020-based systematic literature review and thematic synthesis . (2026). Business & Management Studies: An International Journal, 14(3), 1241-1264. https://doi.org/10.15295/bmij.v14i3.2819

Copyright (c) 2026 Fatmanur Yavuz- Zeliha Seçkin

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Generative artificial intelligence-supported decision-making systems: A PRISMA 2020-based systematic literature review and thematic synthesis . (2026). Business & Management Studies: An International Journal, 14(3), 1241-1264. https://doi.org/10.15295/bmij.v14i3.2819

Abstract

This study systematically reviews the literature on generative artificial intelligence (GenAI)- and large language model-supported decision-making systems, focusing on research trends, application domains, methodological characteristics, risks, and governance approaches. A Web of Science and Scopus search conducted on May 13, 2026, covering 2020–2026, was reported in accordance with PRISMA 2020. After duplicate removal and eligibility assessment, 31 of 402 records were included in the synthesis. Studies were appraised using MMAT 2018 or design-appropriate JBI checklists; across 249 items, 171 “Yes,” 33 “No,” and 45 “Unclear” judgments were recorded, without calculating an overall score or quality category. The thematic synthesis identified six themes: decision-support capacity; trust and user acceptance; explainability and uncertainty management; human oversight and governance; risks and responsible use; and decision architecture and multi-criteria decision-making. Based on these findings, a six-component Integrative Conceptual Framework for Generative AI-Supported Decision-Making was proposed. The findings indicate that GenAI should function as an explainable, auditable, human-supervised, and institutionally governed decision-support component, while human decision-makers retain authority and responsibility.

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