Farmers’ use of mobile phone applications in Abia state, Nigeria : a thesis submitted in partial fulfilment of the requirements for the Degree of Master of Commerce (Agricultural) at Lincoln University

Abstract

In developing countries such as Nigeria, agriculture is the main source of livelihood where over 70 percent of the population engage in farming. They are mostly smallholders who are often subsistence farmers with minimal use of technology and low productivity. The use of mobile applications in agriculture can help smallholders access agricultural information and financial services, improve access to markets and enhance visibility for supply chain efficiency. Unfortunately, most farmers have not fully exploited these benefits because of lack of uptake in the use of mobile application technology. This study seeks to explore and examine the current level of use of mobile applications for agriculture in Abia State, Nigeria and the factors that affect the uptake of this technology. A conceptual model which builds on the extended Technology Adoption Model (TAM2) was empirically estimated using Structural Equation Modelling (SEM) to examine the factors that influence the adoption of mobile applications. Primary data were collected from a sample of approximately 260 farmers. Data were analysed using descriptive statistics and SEM with the help of IBM SPSS and IBM AMOS software. The study results revealed the current state of mobile application use and the factors that affect the adoption of these applications by farmers. The structural model showed that seven of the direct hypothesised relationships in the research model were supported. Social influence (SI), Perceived usefulness (PU), Information/awareness (IA) and Intention to use (ITU) affected the adoption of mobile applications positively, while perceived risk (PR) and Perceived cost had a negative impact on their adoption. This study contributed extensively to farmers’ technology usage literature through its findings. It proved that extended TAM is a suitable model to explain the factors that influence mobile application adoption behaviour. It helped in bridging the information gap between agricultural application developers and farmers by revealing some important demographic information of farmers such as their age, gender, educational level, the type of farming carried out and most importantly, the factors that affected the adoption and continuing use of mobile applications by farmers.