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Challenges to artificial intelligence‐enabled digital technology adoption in farms: a multi‐method approach

Guoqing Zhao Orcid Logo, Shaofeng Liu, Xiaoning Chen, Huilan Chen, Xinyi Li, Guoste Pivoraite

International Transactions in Operational Research

Swansea University Author: Guoqing Zhao Orcid Logo

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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-...

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Published in: International Transactions in Operational Research
ISSN: 0969-6016 1475-3995
Published: Wiley 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71790
first_indexed 2026-04-24T07:30:25Z
last_indexed 2026-05-22T20:27:03Z
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spelling 2026-05-20T10:51:41.9594320 v2 71790 2026-04-24 Challenges to artificial intelligence‐enabled digital technology adoption in farms: a multi‐method approach 2ff29aa347835abe2af6d98fa89064b4 0009-0003-9537-9016 Guoqing Zhao Guoqing Zhao true false 2026-04-24 CBAE 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. Journal Article International Transactions in Operational Research 0 Wiley 0969-6016 1475-3995 artificial intelligence; digital technologies; farming; decision-making; total interpretive structural modeling; MICMAC analysis 4 5 2026 2026-05-04 10.1111/itor.70206 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2026-05-20T10:51:41.9594320 2026-04-24T08:25:26.7926747 Faculty of Humanities and Social Sciences School of Management - Business Management Guoqing Zhao 0009-0003-9537-9016 1 Shaofeng Liu 2 Xiaoning Chen 3 Huilan Chen 4 Xinyi Li 5 Guoste Pivoraite 6 71790__36754__699c457889004fda9ee6ebde57b24511.pdf 71790.VoR.pdf 2026-05-15T15:08:00.0911762 Output 1326241 application/pdf Version of Record true © 2026 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Challenges to artificial intelligence‐enabled digital technology adoption in farms: a multi‐method approach
spellingShingle Challenges to artificial intelligence‐enabled digital technology adoption in farms: a multi‐method approach
Guoqing Zhao
title_short Challenges to artificial intelligence‐enabled digital technology adoption in farms: a multi‐method approach
title_full Challenges to artificial intelligence‐enabled digital technology adoption in farms: a multi‐method approach
title_fullStr Challenges to artificial intelligence‐enabled digital technology adoption in farms: a multi‐method approach
title_full_unstemmed Challenges to artificial intelligence‐enabled digital technology adoption in farms: a multi‐method approach
title_sort Challenges to artificial intelligence‐enabled digital technology adoption in farms: a multi‐method approach
author_id_str_mv 2ff29aa347835abe2af6d98fa89064b4
author_id_fullname_str_mv 2ff29aa347835abe2af6d98fa89064b4_***_Guoqing Zhao
author Guoqing Zhao
author2 Guoqing Zhao
Shaofeng Liu
Xiaoning Chen
Huilan Chen
Xinyi Li
Guoste Pivoraite
format Journal article
container_title International Transactions in Operational Research
container_volume 0
publishDate 2026
institution Swansea University
issn 0969-6016
1475-3995
doi_str_mv 10.1111/itor.70206
publisher Wiley
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description 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.
published_date 2026-05-04T12:57:51Z
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score 11.106347

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