Perceived Usefulness and Adoption Intention of IoT-Enabled Smart Farming Technologies Among Agriculture Graduates in Maharashtra: A Quantitative Study

Abstract

The swift adoption of the Internet of Things (IoT) technologies in the agricultural sector has changed the conventional farming process as it allows making decisions based on the data, optimizing resources, and producing sustainably. Although the use of smart farming solutions has become more relevant, the successful implementation of the solutions is mainly determined by the perception and intentions of future agricultural professionals. The proposed quantitative research paper focuses on the perceived usefulness of technologies and adoption intention of IoT-based smart farming technologies among the agrarian economy graduates in Maharashtra, a state with a heterogeneous agrarian economy and an increasing trend in technological involvement in agriculture.

It is based on the Technology Acceptance Model (TAM) and addresses the issue of getting the meaning of perceived usefulness on how it can impact the intention of graduates to adopt the IoT applications, i.e., smart sensors, automated irrigation systems, soil monitoring tools, and data analytics platforms. The primary data were gathered using a designed questionnaire that was given to the graduates in the field of agriculture at the selected universities and colleges in Maharashtra. The statistical methods used to evaluate relationships between perceived usefulness and adoption intention were descriptive analysis, correlation analysis, and regression analysis.

The results show that graduates with the major of agriculture tend to have a positive attitude towards the use of IoT-enabled smart farming technologies, as they see the opportunities to increase productivity and lower the input costs and sustainability of agricultural activities. The perceived usefulness was also found to be a strong predictor of the intention to adopt, which serves as an argument in favor of showing a practical advantage and applicability of smart farming tools in the real world. Nonetheless, the issues of cost, technical complexity, and lack of practical exposure were found to be the factors that can moderate the adoption decisions.

The research adds to the current body of literature by offering empirical data of the technology acceptance of the future agricultural practitioners in a developing agricultural setting. The findings have useful policy implications on policymakers, learning institutions, and technologists to develop specific training modules, curriculum implementation policies, and sensitization efforts that can empower IoT use in agriculture. Finally, increasing the perceived usefulness among the graduates of the agriculture profession may be the key factor in quickening the change in the smart farming technologies in Maharashtra.