## 4 Results
### Structural Model Validation  
For the examination of the model fit, the function of Bootstrapping of the Smart PLS Software was used. This function allows verifying the significance of the relationships among the different variables. In this case study, the test used 5000 sub-samples with 0.05 of significance level in one-tailed test, similarly to [PERSON] et al. [91]. We started by analyzing the TAM as proposed by [PERSON] et al. [55], and then we moved to the extended TAM version designed for the context of FARMER 4.0. To determine the predictive power of the model, the coefficient of determination was considered. The coefficient of determination (R\({}^{2}\)) measures the percentage of the variance of the dependent variable that can be attributed to the independent variable. [PERSON] and [PERSON] [92] suggested that for a model to have a minimum explanatory power, R\({}^{2}\) should be significant (t > 1.64) and greater than 0.10. Values of R\({}^{2}\) range from 0 to 1, and the higher the value, the better. Table 7 shows the R\({}^{2}\) values considering only the original TAM. All values were above 0.10, reflecting an acceptable predictive power. The TAM, as proposed by [PERSON] [17] (see Figure 5), is able to explain 70.9% of the variance of farmers behavioral intention (BI) to use the e-learning tool. Furthermore, 56% of farmers' attitude towards use (ATT) is explained jointly by Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). PEOU is apparently responsible for 25.8% of the variance of PU. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **R\({}^{2}\)** & **Mean** & **STDEV** & **T Stat.** & _p Value_ & **Significant** \\ \hline ATT & 0.560 & 0.593 & 0.091 & 6.145 & 0.000 & Yes \\ BI & 0.709 & 0.727 & 0.057 & 12.357 & 0.000 & Yes \\ PU & 0.258 & 0.273 & 0.101 & 2.553 & 0.005 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 7: TAM [PERSON] et al. in the context of FARMER 4.0: Structural Model. Figure 5: TAM validation. Path coefficients are numerically represented on the arrows connecting the different variables in Figure 5. The strength of path coefficients (SPC) and their significance are shown in Table 8. Farmers' attitude towards the e-learning tool influences their intention to use it (\(\beta\) = 0.627), jointly with their perceptions about the usefulness of the tool (\(\beta\) = 0.265). In turn, the attitude towards using the e-learning tool (ATT) is strongly affected by PU (\(\beta\) = 0.675), but the effect of PEOU on the ATT is weak and statistically not significant (\(\beta\) = 0.128. No sig.). Finally, PEOU has a positive and significant effect on PU (\(\beta\) = 0.508). This initial analysis suggests that farmers give the utmost importance to the perceived usefulness of innovations or technologies when considering using them. For the specific context of the study, the TAM version proposed by [PERSON] et al. [55] does not hold entirely. However, it is still a valid aid to describe the adoption behavior of the e-learning tool FARMER 4.0. After validating the TAM as proposed by [PERSON] et al. in 1989, we assessed the proposed TAM extension following the same process. However, the model as proposed in Figure 4 showed a neutral fit considering the observed data (\(\chi^{2}\) = 977.492; NFI = 0.534; the closer the Normed fit index (NFI) to 1, the better the fit [93]). For this reason, similarly to [PERSON] et al. [94], we implemented the post hoc model-fitting strategy suggested by [PERSON] [95] to modify the model and obtain a better fit. Following this model-fitting strategy, every external variable and the corresponding paths were incorporated to the TAM variables and then tested. After several iterations and based on other statistical and theoretical considerations, only the most significant constructs and paths were retained. The final model, presented in Figure 6, showed a significantly better fit (\(\chi^{2}\) = 262.742; NFI = 0.682).