A Framework for Enhancing Farmer Adoption of Digital Agricultural Technologies

Thesis Abstract

The rapid advancement of digital agricultural technologies (DATs) has the potential to significantly enhance farm productivity, efficiency, and sustainability; however, widespread adoption among smallholder farmers remains a persistent challenge, stemming from factors such as limited awareness, technological illiteracy, socio-economic constraints, and infrastructural deficiencies. This study aims to develop a comprehensive framework to enhance farmer adoption of DATs by identifying key determinants, understanding underlying behavioral influences, and proposing actionable strategies tailored to rural agricultural contexts. The specific objectives include (1) investigating the socio-cultural, economic, and technological factors influencing DAT adoption; (2) applying behavioral and technological acceptance theories—specifically, the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Diffusion of Innovations theory—to model farmer decision-making processes; (3) assessing the role of extension services and peer networks in facilitating adoption; and (4) formulating a practical, context-specific framework to guide stakeholders in promoting DAT uptake. The research adopted a mixed-methods approach, integrating quantitative and qualitative data collection and analysis techniques. The quantitative component employed a cross-sectional survey design targeting smallholder farmers within three administrative districts specializing in crop farming, with a total population of approximately 15,000 farmers. A stratified random sampling technique was used to select 600 participants, ensuring representation across age groups, educational levels, and farm sizes. Data collection instruments included structured questionnaires measuring variables such as technological awareness, perceived ease of use, perceived usefulness, social influence, infrastructural access, and extension service engagement; these instruments were validated through pilot testing and expert review, achieving a Cronbach’s alpha of 0.87. Complementary qualitative data were gathered through focus group discussions and key informant interviews with extension officers, technology providers, and farmer leaders to enrich contextual understanding and validate the survey findings. Data analysis involved descriptive statistics to profile the sample, inferential statistics—specifically, multiple regression analysis—to examine the determinants of DAT adoption, and Structural Equation Modeling (SEM) to test the relationships posited by the integrated theoretical framework. Additionally, thematic analysis was applied to qualitative transcripts to identify contextual factors influencing adoption behaviors. Ethical considerations included obtaining informed consent, maintaining confidentiality, and ensuring voluntary participation, aligned with institutional research ethics standards. Key expected findings include identifying significant predictors of DAT adoption such as perceived ease of use, social influence, infrastructural availability, and perceived relative advantage. The integrated model is anticipated to demonstrate good fit indices, confirming the applicability of UTAUT and Diffusion of Innovations in the rural agricultural context. The findings are expected to reveal that extension services and peer networks significantly mediate the relationship between individual perceptions and actual adoption. The study will also highlight barriers such as limited digital literacy and infrastructural inadequacies, as well as facilitating factors like targeted extension support and community-based peer learning. The contribution to knowledge involves advancing the understanding of behavioral and technological factors influencing DAT adoption through an empirically validated, context-sensitive framework that combines established theories with local contextual variables. This framework offers stakeholders—including policymakers, extension agencies, and technology developers—a practical tool for designing targeted interventions fostering higher adoption rates. The main conclusion underscores the necessity of integrated extension and community engagement strategies informed by the framework to overcome adoption barriers. Recommendations emphasize the expansion of digital literacy programs, infrastructural investments, and participatory extension models tailored to local farmers’ needs and capabilities. The study suggests avenues for future research, such as longitudinal assessments of adoption sustainability and the testing of the framework across different agricultural commodities and geographic regions, to enhance its adaptability and efficacy in diverse rural contexts.