Artificial Intelligence in agriculture
Capturing stakeholders’ perspectives with a Q methodological approach
Bibliographic Data
| ID | 21295129 |
|---|---|
| Authors | Serena Mandolesi (0000-0001-5565-6902, Universita Politecnica delle Marche Department of Agricultural, Food and Environmental Sciences (D3A)), R Zanoli (0000-0002-7108-397X, Universita Politecnica delle Marche Department of Agricultural, Food and Environmental Sciences (D3A)), Gabriella Esposito (0009-0006-2327-0611, University of Turin Department of Management “Valter Cantino”) |
| Year | 2026 |
| Pages | 1-16 |
| Publication date | 2026-05-08 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | British Food Journal (JOURNAL) |
| Journal identifiers | ISSN: 0007-070X • E-ISSN: 1758-4108 |
| Publisher | Emerald (PUBLISHER) |
| DOI | 10.1108/bfj-07-2025-0933 |
| OpenAlex | W7160395766 |
| Language | EN |
| References cited | 46 |
Purpose This study explores how stakeholders perceive and evaluate Artificial Intelligence (AI) in sustainable agriculture. While AI is often promoted as a solution to food security and climate challenges, its adoption raises ethical, social, and institutional concerns that remain underexplored. Design/methodology/approach Using Q methodology, the study captures subjective viewpoints from 20 stakeholders, including farmers, nutritionists, journalists, and health professionals. A Q sample of 30 statements was ranked and analysed through inverted factor analysis, revealing four distinct perspectives: “The Concerned Skeptic”, “The Critical Adopter”, “The Responsible Environmentalist”, and “The Technological Optimist”. Despite differences, all groups expressed concern over the digital divide and access inequalities. Findings The findings challenge linear models of technology adoption and highlight the value of context-sensitive, inclusive governance. Originality/value By integrating the Social Construction of Technology framework and extended Technology Acceptance Models, the study contributes a structured and interpretive understanding of how artificial intelligence is socially constructed in agriculture, offering practical insights for more equitable and responsible innovation
Sample (material) · Sustainability · Value (mathematics) · Viewpoints · Agriculture Sustainability and Environmental Impact · Q Methodology Applications · Smart Agriculture and AI
Kade
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Smart Farming
User Acceptance of Information Technology
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A longitudinal investigation into multilevel agile & ambidextrous strategic dualities in an information technology high performing Emne
One hundred and twenty-five years of the British Food Journal
Unleashing the value of artificial intelligence in the agri-food sector
Digital strategic collaborations in agriculture
Scoring NutriScore
From Value Sensitive Design to values absorption – building an instrument to analyze organizational capabilities for value-sensitive innovation
The Environmental Behaviour of Farmers – Capturing the Diversity of Perspectives with a Q Methodological Approach
A systematic review on the impact of Artificial Intelligence in the agri-food supply chain
Mixed Method Lessons Learned From 80 Years of Q Methodology
Doing Q ethodology
Forest owners’ perceptions of machine learning
A Set-Theoretical Analysis of the Pathway(s) to Digital Innovation in Developing Democracies
Beyond polarization
Growing algorithmic governmentality
Automated pastures and the digital divide
| Citation velocity | historical |
|---|---|
| Highly cited | No |