Corn farmers intention towards IOT in agriculture production in Kelantan

• Main Author: Nur Aqilah Nawayee
• Format: Undergraduate Final Project Report
• Language: English
• Published: 2022
• Online Access: http://discol.umk.edu.my/id/eprint/13490/1/NUR%20AQILAH%20NAWAYEE.pdf; http://discol.umk.edu.my/id/eprint/13490/

This study was conducted to identify influential factors of corn farmers' intention towards IoT in agriculture production among corn farmers in Kelantan since farmers lack of acceptance, awareness, and knowledge about the Internet of Things (IoT). The main issue that arises in the agriculture sector is climate change, pest and disease, and low agriculture production. Besides that, some farmers do not want to accept the uses of IoT or smart farming in their farming. The sample size is 50 respondents of corn farmers and conducted in the Kelantan area. Then, this study carried out three objectives to determine the level intention of corn farmers towards IoT in agriculture production in Kelantan, to investigate the relationship of attitude, subjective norms, and perceived behavior control on intention of corn farmers towards IoT in agriculture production in Kelantan and to identify the most influential factor of corn farmer's intention towards IoT in agriculture production in Kelantan. Next, this study uses the methods of purposive sampling, which is a distribution of survey questionnaires by Google Forms among corn farmers in Kelantan by applying the Theory of Planned Behavior (TPB). The finding for this research study resulted in a high level of the level intention of corn farmers towards IoT in agriculture production, and the result also show there is a positive relationship between attitude, subjective norms, and perceived behavior control of corn farmers intention towards IoT in agriculture production in Kelantan. Next, the most influential factor is subjective norms corn farmers intention towards IoT in agriculture production in Kelantan. Then, the Confirmatory Factory Analysis also showed that it has an acceptable model fit where the SRMR value is less than 0.10 or 0.08. The value will be considered a good fit for this research study.