In the rapidly evolving world of digital agriculture, understanding what drives farmers to adopt new technologies is crucial for enhancing productivity and sustainability. A recent study published in *Frontiers in Sustainable Food Systems* sheds light on this very topic, offering insights that could reshape how we approach digital transformation in agriculture. Led by Mori W. Gouroubera from the International Center for Tropical Agriculture (CIAT) in Dakar, Senegal, the research employs a sophisticated meta-analytic structural equation modeling approach to synthesize findings from 33 studies involving over 13,000 farmers across 17 countries.
The study focuses on the Technology Acceptance Model (TAM), a widely used framework for understanding how users perceive and adopt new technologies. While TAM has been extensively applied in various contexts, its application in agriculture has yielded inconsistent results. This meta-analysis aims to bring clarity to the debate by examining the relationships between key factors such as perceived ease of use (PEU), perceived usefulness (PU), attitude (AT), behavioral intention (BI), and actual use of digital technologies.
One of the most surprising findings is that perceived ease of use, which TAM posits as a strong predictor of behavioral intention, actually has a weak and sometimes non-significant effect on farmers’ intentions to use digital tools. “This challenges a core assumption of TAM and suggests that farmers are more concerned with the practical benefits of a technology than its ease of use,” explains Gouroubera. In contrast, perceived usefulness consistently emerges as a strong determinant of attitude and behavioral intention, underscoring the importance of demonstrating the tangible benefits of digital technologies to farmers.
The study also highlights the role of external variables, such as facilitating conditions (FC) and social influence (SI), in strengthening key TAM pathways. “Supportive environments and social dynamics play a crucial role in farmers’ acceptance of digital tools,” notes Gouroubera. This finding suggests that policies and interventions aimed at promoting digital agriculture should not only focus on the technology itself but also on creating an enabling environment that includes access to resources, training, and peer support.
Interestingly, the type of digital technology does not moderate TAM relationships, indicating that farmers evaluate digital tools based on their perceived value rather than their technological complexity. This insight could guide developers and policymakers in designing and promoting digital solutions that align with farmers’ needs and preferences.
Despite generally positive behavioral intentions reported in primary studies, the meta-analysis identifies a persistent intention-use gap, with actual digital technology use remaining low. This gap highlights the need for strategies that bridge the divide between intention and action, such as targeted training programs, incentives, and ongoing support.
The implications of this research for the agriculture sector are significant. By understanding the factors that influence farmers’ acceptance of digital technologies, stakeholders can develop more effective strategies to promote adoption and enhance the impact of digital agriculture initiatives. “Tailoring digital agriculture policies to farmers’ perceptions and emphasizing practical benefits and supportive conditions can contribute to the transformation of sustainable food systems,” says Gouroubera.
As the agriculture sector continues to embrace digital transformation, this study provides valuable insights that can shape future developments in the field. By focusing on the factors that matter most to farmers, we can accelerate the adoption of digital technologies and pave the way for a more sustainable and productive future for agriculture.