Unleashing the value of artificial intelligence in the agri-food sector
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Bibliographic Data
| ID | 21296009 |
|---|---|
| Authors | Meriam Trabelsi (0000-0001-6452-6579, University of Siena, corresponding author), Elena Casprini (0000-0001-5097-8793, University of Siena), Niccolò Fiorini (0000-0002-8734-5858, University of Siena), Lorenzo Zanni (0000-0002-0440-8842, University of Siena) |
| Year | 2023 |
| Volume | 125 |
| Issue | 13 |
| Pages | 482-515 |
| Publication date | 2023-12-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | British Food Journal (JOURNAL) |
| Journal identifiers | ISSN: 0007-070X • E-ISSN: 1758-4108 |
| Publisher | Emerald (PUBLISHER) |
| DOI | 10.1108/bfj-11-2022-1014 |
| OpenAlex | W4386023039 |
| Language | EN |
| Citations received | 7 |
| References cited | 96 |
Purpose This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions. Design/methodology/approach This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions. Findings Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector. Originality/value This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector
Agriculture · Business · Economics · Extant taxon · Field (mathematics) · Food sector · Knowledge management · Originality · Productivity · Qualitative research · Social science · Sociology · Supply chain · Supply chain management · Value (mathematics) · Computer Science · Date Palm Research Studies · Food Supply Chain Traceability · Marketing · Smart Agriculture and AI
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Software survey
| Unique citing works | 7 |
|---|---|
| Citations per year | 3,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 7 |