Implementation of Internet of Things depends on intention: young farmers’ willingness to accept innovative technology

Abstract

Innovative applications of smart technology constitute a current trend in agricultural development. This study employed a technology acceptance model to explore the intention of young farmers to apply Internet of Things systems in field-level management of Taiwanese farms. An online questionnaire was used to collect data regarding farmers aged 45 years or younger who were currently engaged in agricultural production. Statistical analysis of 241 valid questionnaires revealed that young farmers’ intention to use innovative technologies was affected mainly by perceived organizational support, followed by average annual turnover, perceived usefulness, perceived ease of use, and sense of trust in the system supplier. This study suggests that agricultural administration agencies should consider farmers’ farming needs and intention to use; agencies should employ problem-solving and design thinking when developing smart agriculture policies. Insightful design of incentives and guidance measures enables young farmers to maximize achievement and to minimize effort.

Farmers’ Knowledge, Attitude, and Adoption of Smart Agriculture Technology in Taiwan

“…These findings indicate that larger farm size or volume of business may enhance farmers' investments in SA technologies. This finding accords with those of previous studies [13,25,26] that reported a positive relationship between farm size or revenue and innovation adoption. Most sociodemographic characteristics were not significantly associated with SA adoption, except age, which was positively correlated with SA adoption.…”

“…These emerging concepts have slight differences in terms of the emphasis on specific technological applications. Most of these concepts share common traits and values in incorporating new and intelligent technologies into farming practices and introducing resource use efficiency approaches that minimize production costs, reduce farming risks, and increase productivity [ 13 ]. The inventory of the European smart agricultural knowledge and innovation systems (Smart-AKIS) program indicates that SA is mainly related to three interconnectable new technology categories: farm management information systems, precision farming, and agricultural automation.…”

“…Studies have focused on the effects of psychological factors on individual behaviors, such as social learning theory, the theory of reasoned action, and the theory of planned behavior [ 18 , 19 ]. Few studies on agriculture have identified associations between farming practices, attitudes, and other psychological determinants [ 13 , 20 ]. Furthermore, the theory of planned behavior, an extension of the theory of reasoned action, has been extensively applied and tested in various fields [ 21 ].…”

“…The theory of planned behavior identifies hierarchical relations between various beliefs and attitudes affecting behavior. The educational goals of agricultural training programs are multifaceted; such programs are expected to improve the target group’s knowledge level and change their attitudes and adoption behaviors [ 13 , 22 ]. This study employed a comprehensive knowledge–attitude–practice (KAP) model based on the previous literature, to further investigate the relationships in the KAP model of participants in the SA training course.…”

Estimated the Willingness to Pay Levels for the Adoption of the Internet of Things-IoTs Technology: An Empirical Study in Swiftlet Farming in Binh Thuan Province, Vietnam

“…The WTP is also the market demand curve for that commodity. This approach has been effectively applied and adopted in the previous studies related to different fields of study with especially in rural and farming activities (Asrat, Belay and Hamito, 2004;Othman et al, 2009;Energyplus, 2011;Divilly, 2018;Etsay, Negash and Aregay, 2019;Haile, 2019, 2020;Chuang, Wang and Liang, 2020;Kansanga et al, 2020;ÖZSAYIN, 2020).…”