Generative AI use and advisory performance among agricultural extension agents in Benin

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dc.creatorGouroubera, W. Mori
dc.creatorSegnon, Alcade C.
dc.creatorGouthon, Morrisson
dc.creatorMoumouni-Moussa, Ismail
dc.creatorZougmore, Robert B.
dc.date2026-05-26
dc.date2026-06-02T08:02:39Z
dc.date.accessioned2026-06-27T13:32:41Z
dc.descriptionHarnessing Generative Artificial Intelligence (GenAI) offers promising avenues to enhance agricultural advisory services. Yet, understanding extension agents' engagement with such technologies remains limited. Using the Technology Acceptance Model (TAM) as an analytical lens, this study investigates how agricultural extension agents in Benin interact with GenAI and its impact on their advisory performance. We surveyed 240 extension agents across six districts and applied Partial Least Squares Structural Equation Modeling to examine relationships among perceived usefulness, workload, time pressure, attitudes, behavioral intentions, GenAI use, and performance. Results reveal that GenAI use is positively associated with improved advisory effectiveness. Workload and pressure emerge as key motivators for GenAI use, while perceived usefulness strongly predicts both positive attitudes toward GenAI and perceived ease of use. However, contrary to TAM assumptions, attitude has a negative influence on behavioral intention, a paradoxical engagement pattern implying that while extension agents value GenAI, they hesitate to rely fully on it, reflecting concerns about professional judgment, accountability, and trust in AI outputs. Finally, attitude, pressure, and behavioral intention indirectly affect the performance of extension agents using GenAI. This study contributes to agricultural extension research and AI governance debates by revealing how professional intermediaries navigate tensions between technological promise and institutional responsibility, offering insights for capacity-building and policy frameworks that promote responsible AI integration.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/183153
dc.identifier.urihttp://hdl.handle.net/123456789/62734
dc.languageen
dc.publisherFRONTIERS MEDIA
dc.rightsOpen Access
dc.sourceGouroubera, W.M.; Segnon, A.C.; Gouthon, M.; Moumouni-Moussa, I.; Zougmore, R.B. (2026) Generative AI use and advisory performance among agricultural extension agents in Benin. Frontiers in artificial intelligence 9: 1763406. ISSN: 2624-8212
dc.subjectagricultural extension
dc.subjectbenin
dc.subjectartificial intelligence
dc.subjecttechnology adoption
dc.subjectagricultural extension systems
dc.subjectbehavioural sciences - behavioral science
dc.subjectbehavioral intention
dc.titleGenerative AI use and advisory performance among agricultural extension agents in Benin
dc.typeJournal Article

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