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Challenges to artificial intelligence‐enabled digital technology adoption in farms: a multi‐method approach
,
Shaofeng Liu,
Xiaoning Chen,
Huilan Chen,
Xinyi Li,
Guoste Pivoraite
International Transactions in Operational Research
Swansea University Author:
Guoqing Zhao
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© 2026 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.
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DOI (Published version): 10.1111/itor.70206
Abstract
Artificial intelligence (AI)-enabled digital technologies have the potential to transform agriculture by supporting decision-making and automating operations. However, their limited adoptions and scholars’ atheoretical explorations constrain our understanding. Drawing on the technology-organization-...
| Published in: | International Transactions in Operational Research |
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| ISSN: | 0969-6016 1475-3995 |
| Published: |
Wiley
2026
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71790 |
| Abstract: |
Artificial intelligence (AI)-enabled digital technologies have the potential to transform agriculture by supporting decision-making and automating operations. However, their limited adoptions and scholars’ atheoretical explorations constrain our understanding. Drawing on the technology-organization-environment (TOE) framework, we investigate the challenges hindering the AI-enabled technology's adoption in farms using a multi-method approach, including semi-structured interviews, thematic analysis, total interpretive structural modeling, and fuzzy cross-impact matrix multiplication applied to classification analysis. Our finding shows novelty in several aspects. First, we identify 13 challenges, some of which are underexplored, such as the absence of effective intermediaries in promoting agricultural technology. Second, we developed a hierarchical and clustering framework to reveal their interrelationships, classifications, and identify the key challenges. Third, we reorganize the TOE framework into a P-TOE model, where “P” represents people's experience, knowledge, and skills. Finally, we offer practical recommendations, including the creation of non-profit technology extension hubs and hands-on training programs to promote inclusive technology adoption. |
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| Keywords: |
artificial intelligence; digital technologies; farming; decision-making; total interpretive structural modeling; MICMAC analysis |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
Swansea University |