ArticleScientific reports2023
Study on the influence mechanism of adoption of smart agriculture technology behavior.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Advancing occupational and environmental safety in greenhouse agriculture: an extended elaboration likelihood model of safety technology adoption.Scientific reports · 2026Article
- Factors affecting Iranian farmers' intention to adopt digital agriculture: an integrated TPB-TAM approach.Scientific reports · 2026Article
- Smart farming approaches in medicinal plant cultivation: a review of techniques, benefits, and sustainability.Planta · 2026Review
- Determinants of maize irrigators' intention to adopt water-saving technology in South Africa.Scientific reports · 2025Article
- Research on farmers' willingness to grow high-quality rice based on the TAM-TPB model: evidence from China.Frontiers in nutrition · 2025Article
- The synergistic effects of microcredit access and agricultural technology adoption on maize farmer's income in Kenya.PloS one · 2025Article
- The impact of ethical commitments on the intention to adopt digital agricultural technologies.Scientific reports · 2024Article
- Factors influencing the intention to use the ICD-11 among medical record officers (MROs) and assistant medical record officers (AMROs) in Ministry of Health, Malaysia.Scientific reports · 2024Article
- Driving rural industry revitalization in the digital economy era: Exploring strategies and pathways in China.PloS one · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smart agricultural (SA) technology has become a technological support for modern agriculture. By exploring the decision-making process and psychological motivation of farmers in adopting SA technology, it is conducive to achieving the popularisation of SA technology and promoting the modernisation of agriculture. Based on microscopic research data, a Structural Equation Model (SEM) is used to analyse the influencing factors and extent of cotton farmers' adoption of SA technologies, using Deconstructive Theory of Planned Behavior (DTPB) as the analytical framework. This was combined with in-depth interviews to further reveal the motivations and influencing mechanisms of cotton farmers' adoption of SA technologies. The results show that under the behavioural belief dimension, cotton farmers value the positive effect of perceived usefulness even though the risk of the technology itself has a dampening effect on adoption intentions. Under the normative belief dimension, superior influence influenced the willingness to adopt SA technologies to a greater extent than peer influence. Under the control belief dimension, factors such as self-efficacy and information channels influence willingness to adopt technology and behaviour. In addition, behavioural attitudes, subjective norms, and perceived behavioural control all contribute to cotton farmers' willingness to adopt SA technologies, and can also influence behaviour directly or indirectly through willingness to adopt. Policy and technology satisfaction positively moderate the transition from willingness to behaviour. Therefore, preferential policies are proposed to reduce the cost of adopting SA technologies; to continuously improve the level of SA technologies; to establish SA technology test plots to provide a reference base; and to increase knowledge training on SA and expand access to information.
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