Artificial Intelligence in agriculture: Capturing stakeholders’ perspectives with a Q methodological approach

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.