USER-CENTRED DESIGN FACTORS IN THE ADOPTION OF A SIMPLIFIED AI AGRICULTURAL ADAPTATION TOOLKIT AMONG SMALLHOLDER FARMERS IN NIGERIA

Abstract

The increasing application of artificial intelligence (AI) in agriculture presents opportunities for improving access to agricultural information, farm decision-making and productivity, but adoption among smallholder farmers depends substantially on whether technologies are understandable, acceptable and compatible with users’ circumstances. This study examined user-design factors associated with behavioural intention to adopt a Simplified AI Agricultural Adaptation Toolkit (SAAAT) among smallholder farmers in Nigeria. Guided principally by the Unified Theory of Acceptance and Use of Technology (UTAUT), the study examined effort expectancy, attitude towards technology, social influence, facilitating conditions, self-efficacy and anxiety as predictors of behavioural intention and performance expectancy. Data were obtained from 300 smallholder farmers selected from Ebonyi, Benue and Taraba States through a multistage sampling procedure. Descriptive statistics, reliability analysis, Pearson correlation and multiple regression were used. The findings indicate that the predictors jointly accounted for substantial variance in behavioural intention (R² = .58) and performance expectancy (R² = .53). Effort expectancy, attitude towards technology, social influence, facilitating conditions and self-efficacy were positively associated with adoption-related outcomes, whereas anxiety showed a negative association. The findings underscore the importance of designing agricultural AI tools around farmers’ practical capabilities, confidence, social environment and access to enabling infrastructure. The study concludes that user-centred design is essential for translating AI opportunities into usable and context-sensitive agricultural innovation for smallholder farmers.