Fintech services, financial literacy and financial inclusion among rice farmers in Mwea irrigation scheme, Kirinyaga county

Abstract

Financial inclusion is a key driver of economic development, particularly for rural smallholder farmers who rely on agriculture for their livelihoods. In Mwea, Kirinyaga County, rice farmers face persistent financial exclusion due to limited access to formal credit, savings, and insurance services. Despite the increasing availability of financial technology (FinTech) solutions, such as digital payments, mobile credit, and digital insurance, adoption remains low. Barriers such as high transaction costs, distrust in financial institutions, low financial literacy, and inadequate digital infrastructure hinder farmers' ability to integrate FinTech into their financial activities. This study investigated the role of FinTech services in promoting financial inclusion and examined the moderating effect of financial literacy on FinTech adoption and usage among smallholder rice farmers in Mwea. The study targeted a population of 16,000 rice farmers in Mwea, Kirinyaga County, and employed descriptive research. Using Krejcie and Morgan’s (1970) formula, a sample of 390 farmers were selected through stratified random sampling to ensure a proportional and representative distribution across different farming zones. Data was collected using structured questionnaires, administered through face-to-face interviews to capture both quantitative and qualitative insights. Diagnostic tests assessed residual normality, homoscedasticity, linearity, and multicollinearity, ensuring statistical accuracy. Cronbach’s alpha coefficient was used to measure the reliability of the research instrument, while test-retest reliability was used to verify consistency over time. Hierarchical linear regression analysis was used to analyse the data. Pearson correlation showed all four digital financial services positively and significantly correlated with financial inclusion, with digital insurance strongest (r = 0.768). Multiple regression indicated the services explained 71.9% of financial inclusion variance; digital payments and credit significantly predicted inclusion, while savings and insurance did not. Adding financial literacy as a moderator did not significantly improve the model, though the interaction between digital payments and financial literacy was marginally significant, suggesting a potential moderating effect needing further investigation.