This study was conducted to analyze factors affecting acceptance of smart farm technology. Smart farm technology is rapidly being introduced to agriculture in accordance with the progress of the 4th Industrial Revolution, but research on this is still little. Therefore, in this study, based on the unified theory of acceptance and use of technology (UTAUT), a research model reflecting the characteristics of smart farm technology was constructed. To test this, empirical analysis was performed. A survey was conducted for students in smart farm technology education and adult male and female farmers who are currently planning to operate smart farms. Valid 204 sample were used for analysis. The hypothesis test was based on multiple regression analysis using SPSS 24 statistical package. For the mediating effect and moderating effect, Process Macro 3.4 based on the regression equation was used. The results of testing the hypothesis are as follows. First, in the causal hypothesis test, it was shown that performance expectancy, social influence and price value have a significant positive effect on the intention to use smart farm technology. On the other hand, effort expectancy, facilitating conditions were not tested for a significant influence on the use of smart farm technology. As a result of analyzing the mediating effect of trust, it was found that trust plays a mediating role between performance expectancy, effort expectancy, social influence, facilitating conditions, price value and intention to use smart farm technology. In particular, the effort expectancy has not been tested for a direct significant effect on intention to use smart farm technology, but it has been shown to have an impact through trust. Trust was found to be a full mediating between the effort expectancy and the intention to use the smart farm technology. The current IT level of prospective users has been shown to play a moderating role between performance expectancy, facilitating conditions and intention to use smart farm technology. In particular, the IT level was found to strengthen the relationship between performance expectancy and intention to use smart farm technology. Based on the results of these studies, academic and practical implications were suggested.

This study was conducted to analyze the factors influencing participation intention in urban agriculture. The theoretical foundations of the study were Theory of Planned Behavior (TPB) and Pine and Gilmore’s Experiencel Economy. The type of household composition was set as the moderating variable between these independent and dependent variables. In order to test the research model, hierarchical regression analysis was conducted on 439 effective respondents. The empirical analysis results are as follows. Educational experience factors, escapist experience factors, attitudes, subjective norm, and perceived behavioral control, which were set as independent variables to positive (+) influence participation intention in urban agriculture. In addition, the impact of their influence relationship was as follows : perceived behavioral control > attitudes > escapist experience factor > subjective norm > educational experience factor. On the other hand, as a result of analyzing the moderating effect according to the household composition, there was a significant difference in the effect of educational experiences, escapist experiences, and subjective norm on participation intention in urban agriculture. Significant results were not tested on the moderating effects between attitudes, perceived behavioral control, and participation intention in urban agriculture. Based on the results of this empirical analysis, academic and practical implications were presented.

The purpose of the study focuses on the agriculture education services in the changing rural areas conditions such as population decline, aging society, and returning farmers. The study reviews the effects of agricultural education services on returning farmers and local residents for satisfaction, intention for recommendation, and intention to continue participation. Further, the study aims to investigate any difference in the level of satisfaction for two groups. The results suggested that there is a meaningful difference between return-farmers and local residents. Among the demographic variables, age and income showed a notable difference. However, sex, level of education and type of household did not suggest noticeable differences. In addition, the study accessed agricultural education from a service perspective and analyzed its service quality and customer satisfaction, loyalty and relationship using a service profit chain model. Like the result of most other studies, the analysis showed that these had positive relationships. While the study focused on the efficiency of agriculture education training program in agriculture technology centers, the study carries a meaningful value in that it discovered a meaningful difference in the satisfaction level between returning farmers and locals despite the fact that agriculture education was applied as a part of service. In practical terms, the study pointed out the need for consumer-centered education that reflects the characteristics of the groups rather than standardized education.