ArticleFrontiers in artificial intelligence2026
Driving factors of agricultural artificial intelligence adoption intention: an empirical study in Shandong province based on innovation characteristics, technology commitment, and individual heterogeneity.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In the "Agriculture 4.0 era," the implementation of agricultural artificial intelligence (AI) has been proven to bring economic and environmental benefits to farmers. Despite its potential advantages, the adoption rate of agricultural AI remains relatively low. To explore the adoption driving mechanism of agricultural AI in major producing areas, this study took 359 agricultural practitioners in Shandong Province as samples, constructed an extended technology acceptance model (TAM)-unified theory of acceptance and use of technology (UTAUT) model integrating technological innovation characteristics, technology commitment, and individual heterogeneity, and used the partial least squares-structural equation modeling (PLS-SEM) method to empirically analyze the influencing factors and moderating effects of adoption intention. The results show that mobility, autonomy, technological interest, and technological control belief significantly and positively affect perceived ease of use; mobility, technological interest, and perceived ease of use have significant positive effects on perceived usefulness; perceived ease of use and perceived usefulness jointly drive the improvement of adoption intention. Educational background and work experience have significant moderating roles: Higher education strengthens the positive impact of technological interest on perceived ease of use, and rich work experience amplifies the promoting effect of technological competence belief (TCM) on perceived ease of use. However, the impacts of autonomy on perceived usefulness and technological competence belief on perceived ease of use and perceived usefulness are not statistically significant, which is closely related to the production characteristics of smallholder farmers and insufficient technological adaptability. This study improves the theoretical framework of agricultural AI adoption, provides an empirical basis for formulating differentiated technology promotion strategies and optimizing technology design, and has important practical significance for accelerating agricultural digital transformation.
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