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characteristics also play an important role in adoption decisions. Larger farms are
generally more likely to adopt modern technologies due to greater financial resources
and economies of scale, whereas smallholder farmers often face higher barriers to
adoption. Institutional support mechanisms such as credit access and extension services
are therefore critical for enabling farmers to invest in new technologies. However, the
literature also highlights considerable variation in adoption outcomes across regions,
suggesting that local socioeconomic, cultural, and policy contexts significantly shape
adoption behaviour (Ruzzante et al., 2021).
Recent meta-analytic research further identifies key drivers and barriers affecting
the adoption of precision agriculture and digital farming technologies. Kroupová et al.
(2024) found that farmer characteristics, particularly age and education, significantly
influence adoption decisions, with younger and better-educated farmers showing a
greater propensity to adopt digital technologies. Farm characteristics are also important,
as larger and more capital-intensive farms are more likely to implement technological
innovations due to economies of scale and greater financial capacity. In addition,
perceived economic benefits, including improved profitability and return on investment,
serve as strong motivations for adoption. Institutional support, such as advisory services
and technical assistance, can facilitate adoption by addressing knowledge gaps among
farmers. Nevertheless, adoption patterns vary significantly across regions due to
differences in agricultural policies, technological infrastructure, and socioeconomic
conditions, highlighting the importance of considering contextual factors when promoting
digital agriculture technologies (Kroupová et al., 2024).
To explain farmers’ technology adoption behaviour, researchers have increasingly
applied theoretical frameworks from economics, sociology, and information systems.
Several models are commonly used in the literature, including the Technology Acceptance
Model (TAM), Technology Readiness and Acceptance Model (TRAM), Technology
Readiness Index (TRI), and Agricultural Knowledge and Innovation Systems (AKIS)
framework (Arangurí et al., 2025). These frameworks highlight the importance of
perceived usefulness, perceived ease of use, and innovation readiness in shaping
technology adoption decisions. Similarly, the Theory of Planned Behaviour (TPB)
emphasises the role of attitudes, subjective norms, and perceived behavioural control in
influencing behavioural intentions (Ajzen, 1985).
A systematic literature review by Rosário et al. (2022) shows that sociopsychological
models have become increasingly prominent in research on agricultural innovation
adoption. Constructs such as attitude, perceived usefulness, perceived ease of use, and
subjective norms are frequently used to explain farmers’ decision-making processes. The
growing use of statistical techniques such as structural equation modelling has further
facilitated the integration of sociopsychological constructs into adoption studies.
However, the review also highlights certain limitations. Many studies repeatedly apply
similar constructs without adequately accounting for contextual differences across
agricultural systems, which may reduce the explanatory power of adoption models.
Therefore, recent research suggests adopting integrative and multidisciplinary
approaches that combine economic, technological, and sociopsychological perspectives to
better capture the complexity of farmers’ decision-making processes (Rosário et al., 2022).
Overall, the existing literature highlights that the adoption of digital technologies in
agriculture is a complex process shaped by the interaction of technological,
socioeconomic, institutional, and behavioural factors. These determinants vary across

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