Generative artificial intelligence and ChatGPT in agriculture supply chain management: a systematic literature review and future research agenda

Purpose This paper presents a comprehensive overview of existing academic research on the application of Generative AI (Gen-AI) and ChatGPT within agricultural supply chains, highlighting current trends, leading publishers, thematic areas and proposing a future research agenda. Design/methodology/approach A bibliometric analysis was conducted using VOSviewer software on a sample of 79 articles retrieved from the Web of Science (WoS) and Scopus databases to identify emerging research topics. Findings The number of publications in this area has increased, and since 2023, academic interest in the application of Gen-AI and ChatGPT to agricultural supply chains has shown a consistent upward trend. Key areas of focus include enhancing prediction accuracy, coordination, automation, employee performance, optimizing supply chain management and improving overall business outcomes. However, concerns also exist, like hallucination, data privacy, security risks, lack of accuracy, copyright issues and ethical implications. Consequently, the risk of ungoverned Gen-AI outputs without human oversight potentially disrupting operations underscores the need for robust governance frameworks to ensure its reliable integration in agricultural contexts. Practical implications Gen-AI and ChatGPT provide several advantages for agricultural supply chains. They enhance forecasting capabilities and coordination among partners, automate stakeholder interactions to increase efficiency, reduce manual tasks to improve employee performance and support data-driven decision-making through advanced analytics, ultimately strengthening supply chain management. Originality/value This study offers a structured and in-depth examination of the Gen-AI and ChatGPT phenomenon in agricultural supply chains. Moreover, it contributes to the literature by identifying existing research gaps and suggesting potential directions for future investigation.

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Publication Details

Journal
British Food Journal
Published
2026-01-06
DOI
https://doi.org/10.1108/bfj-05-2025-0720
Citations
1
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
31.34
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article

Generative artificial intelligence and ChatGPT in agriculture supply chain management: a systematic literature review and future research agenda

Mingxing Li, Asma Javed, Abdul Basit, Yan Ge et al.
1 citations
British Food Journal
Smart Agriculture and AI
31.34
article

Generative artificial intelligence and ChatGPT in agriculture supply chain management: a systematic literature review and future research agenda

Mingxing Li, Asma Javed, Abdul Basit, Yan Ge, Xinping Song
article en
1 citations

Abstract

Purpose This paper presents a comprehensive overview of existing academic research on the application of Generative AI (Gen-AI) and ChatGPT within agricultural supply chains, highlighting current trends, leading publishers, thematic areas and proposing a future research agenda. Design/methodology/approach A bibliometric analysis was conducted using VOSviewer software on a sample of 79 articles retrieved from the Web of Science (WoS) and Scopus databases to identify emerging research topics. Findings The number of publications in this area has increased, and since 2023, academic interest in the application of Gen-AI and ChatGPT to agricultural supply chains has shown a consistent upward trend. Key areas of focus include enhancing prediction accuracy, coordination, automation, employee performance, optimizing supply chain management and improving overall business outcomes. However, concerns also exist, like hallucination, data privacy, security risks, lack of accuracy, copyright issues and ethical implications. Consequently, the risk of ungoverned Gen-AI outputs without human oversight potentially disrupting operations underscores the need for robust governance frameworks to ensure its reliable integration in agricultural contexts. Practical implications Gen-AI and ChatGPT provide several advantages for agricultural supply chains. They enhance forecasting capabilities and coordination among partners, automate stakeholder interactions to increase efficiency, reduce manual tasks to improve employee performance and support data-driven decision-making through advanced analytics, ultimately strengthening supply chain management. Originality/value This study offers a structured and in-depth examination of the Gen-AI and ChatGPT phenomenon in agricultural supply chains. Moreover, it contributes to the literature by identifying existing research gaps and suggesting potential directions for future investigation.

British Food JournalVol. 128(13)
Jiangsu University (CN), Chang'an University (CN), Department of Archaeology (LK), Shanghai Ocean University (CN)
Zero hunger
Openalex Percentile: Top 2%
Smart Agriculture and AI
31.34
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