## 4 Results  
Demographic information was summarized using the IBM Statistical Package for Social Science (SPSS) software, similarly to [PERSON] and [PERSON] [70]. Results are depicted in Table 4. A slight majority of respondents were females (23 out of 42). A high educational level and high frequency of computer use were reported by participants. Twenty-one percent of respondents have experience in agricultural activities of more than 20 years, whereas the 45% have less than 5 years of experience in farming-related activities. To further examine the causal relationships between the proposed model's variables and the corresponding path coefficients, the Structural Equation Modeling (SEM) approach was considered the most appropriate. The SEM is a set of techniques that, by combining factor analysis and multiple regression analysis, allow the study of relationships between one or more independent variables and one or more dependent variables [71]. The discussion by [PERSON] [72] reported by [PERSON] [73], and more recently by [PERSON] [74], exploring the several advantages of the SEM in similar studies, supports the use of the SEM techniques for the present research. Some of the reasons that favor the implementation of SEM over other multiple regression analyses (MRA) include the possibility to make explicit a research theory by defining it in specific constructs, formulate hypotheses about existing relationships between the different constructs, and test the theory and the designed measurements. The software used to adopt the SEM approach in \begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Items Source** \\ \hline Behavioral Intention (BI) & [PERSON] [51], [PERSON] and [PERSON] [52], [PERSON] et al. [53] \\ \hline Attitude Towards Using (ATU) & [PERSON] [51], [PERSON] et al. [54], [PERSON] et al. [30]. \\ \hline Perceived Usefulness (PU) & [PERSON] [51], [PERSON] et al. [30], [PERSON] et al. [55]. \\ \hline Perceived Ease of Use (PEOU) & [PERSON] [51], [PERSON] and [PERSON] [56], [PERSON] et al. [26], [PERSON] [57]. \\ \hline \multirow{2}{*}{Arxiety (ANX)} & [PERSON] et al. [26], [PERSON] and [PERSON] [29], [PERSON] and [PERSON] [58], \\  & [PERSON] et al. [59]. \\ \hline Content Quality (CQ) & [PERSON] and [PERSON] [60], [PERSON] and [PERSON] [61], [PERSON] et al. [34]. \\ \hline Experience (EXP) & [PERSON] and [PERSON] [52], [PERSON] and [PERSON] [62]. \\ \hline Facilitating Conditions (FC) & [PERSON] [51], [PERSON] et al. [54], [PERSON] and [PERSON] [63]. \\ \hline Individual Innovativeness (II) & [PERSON] and [PERSON] [43], [PERSON] et al. [64]. \\ \hline Perceived Enjoyment (PE) & [PERSON] and [PERSON] [56], [PERSON] et al. [34]. \\ \hline \multirow{2}{*}{Self-Efficacy (SE)} & [PERSON] et al. [59], [PERSON] and [PERSON] [60], [PERSON] [36], \\  & [PERSON] et al. [65], [PERSON] and [PERSON] [48]. \\ \hline Social Norm (SN) & [PERSON] et al. [66], [PERSON] and [PERSON] [67]. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Questionnaire items source. the present work was the Smart PLS version 3. This software is based on least-squares estimation to adopt the SEM (PLS-SEM). The PLS-SEM was preferred as the analysis method for this study considering the minimal demands on measurement scales, the sample size, the number of constructs [74; 75], and its increasing popularity to analyze path-coefficients in research models [76]. For a suitable PLS assessment, [PERSON] et al. [77] suggested that the sample size should satisfy at least one of the following conditions: (i) be ten times the largest number of items or questions used to measure one construct, or (ii) be ten times the largest number of paths associated with a particular construct. In our case study, the highest number of questions to measure one construct was three, entailing a minimum sample size of 30 to satisfy one of these conditions. Therefore, our sample, consisting of 42 respondents, is acceptable. Furthermore, a weaker rule also suggested by [PERSON] et al.