Factors Affecting the Adoption of Agricultural Automation Using Davis’s Acceptance Model (Case Study: Ardabil)

Taking into account that traditional agricultural methods reduce the farm performance and make agriculture economically ineffective, development of intelligent machinery is essential for improving the quality of crops and agricultural activities. The most important issue in development of agricultural technologies lies in users’ willingness to adopt it. Therefore, the purpose of this study is to investigate the key factors of Davis’s model in automation acceptance in agriculture. Presented paper describes an applied research with a survey approach through a questionnaire. Questionnaire data were collected from 378 people and respondents include university students, farmers and experts in a randomized sampling from the Ministry of Agriculture. Firstly, the questionnaire data were described in a form of statistical numerical characteristics. Secondly, in order to verify the data normality, the Kolmogorov-Smirnov test was calculated in SPSS and the relationship between the variables was investigated in the conceptual model. Subsequently, hypotheses were tested via appropriate statistical models using LISREL and SPSS software. The results showed that for all hypotheses, the T-test exceeds 1.96 and the significance level is less than 0.05. In such a manner, all hypotheses were confirmed at 95% level, and the path coefficients in the hypotheses H1, H2 and H6 were negative – indicating the negative effect of the independent variable on the dependent variable in the hypothesis. In the other hypotheses, these were positive – indicating the positive effect of the independent variable on dependent variable. By means of modelling, it was found that there was an inverse relationship between social and individual factors with perceived usefulness, as well as an inverse relationship between social factors with perceived ease-of-use, while there was a positive and significant relationship between other factors. According to a set of fitting indices, the research conceptual model was appropriate and Cronbach’s coefficients for each factor were greater than 0.7, suggesting that the questionnaire was valid. On the basis of findings, the better a person understands the usefulness of automation, the more likely this person is to adopt it. Since the risk and issues related to it play an important role in farmers’ decision making, it is recommended that future studies address the issue of risk in adopting precision farming technology.