## 4 Results
The model's goodness-of-fit indices, on the other hand, were well matched to the data: Chi-square (\(\chi^{2}=0.564\)); degrees of freedom (\(df=2\)); the ratio \(\chi^{2}/df\) was 0.282; goodness-of-fit index (GFI = 1); square-root mean square approximation error (RMSEA = 0.000); normalized fit index (NFI = 1); comparative fit index (CFI = 1.000); and, adjusted goodness-of-fit index (AGFI = 0.996). The contrast of hypotheses through the path analysis gathered the support of seven hypotheses out of the 15 initially established, the hypotheses that were not supported were rejected (Table 5). The relationships that supported the hypothesis about having a significant and positive effect on behavioral intention to use IoT were: performance expectancy (H1), facilitating conditions (H10), and attitude toward using technology (H13). Gender had a significant and negative effect on facilitating conditions (H11), and attitude toward using technology (H14). Additionally, age had a significant and positive effect on performance expectancy (H3) and effort expectancy (H6). \begin{table}
\begin{tabular}{c c c c c c} \hline  & **PE** & **EE** & **SI** & **FC** & **ATUT** & **BI** \\ \hline PE & **0.870** & & & & \\ EE & 0.660 & **0.828** & & & \\ SI & 0.903 & 0.711 & **0.860** & & \\ FC & 0.621 & 0.494 & 0.696 & **0.752** & & \\ ATUT & 0.501 & 0.750 & 0.568 & 0.433 & **0.714** & \\ BI & 0.402 & 0.398 & 0.404 & 0.352 & 0.532 & **0.939** \\ \hline \end{tabular} Note: Diagonals represent the average variance extracted, while the other matrix entries represent the squared correlations.  
\end{table} 
Table 4: Discriminant validity measures. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Construct**} & **Item** &
\begin{tabular}{c} **Factor** \\ **Loading** \\ \end{tabular} & **CR** & **AVE** & \(\boldsymbol{\alpha}\) & **Global \(\boldsymbol{\alpha}\)** \\ \hline PE & PE1 & 0.837 & & & & \\ PE & PE2 & 0.863 & 0.926 & 0.758 & 0.939 & \\  & PE3 & 0.886 & & & & \\ PE4 & 0.896 & & & & & \\ \hline EE & EE1 & 0.792 & & & & \\ EE & EE2 & 0.816 & 0.897 & 0.687 & 0.909 & \\ EE & EE3 & 0.850 & & & & \\ EE4 & 0.856 & & & & & \\ \hline SI & SI1 & 0.863 & & & & \\ SI2 & 0.865 & 0.919 & 0.740 & 0.927 & 0.946 \\ SI3 & 0.811 & & & & & \\ SI4 & 0.901 & & & & & \\ \hline FC & FC1 & 0.688 & & & & \\ FC2 & 0.734 & 0.838 & 0.565 & 0.734 & \\ FC3 & 0.820 & & & & \\ FC4 & 0.761 & & & & & \\ \hline ATUT & ATUT1 & 0.728 & & & & \\ ATUT2 & 0.850 & & 0.802 & 0.510 & 0.805 \\ ATUT3 & 0.715 & & & & \\ ATUT4 & 0.528 & & & & \\ \hline BI & BI1 & 0.929 & & & & \\ BI2 & 0.935 & 0.957 & 0.881 & 0.946 & \\ BI3 & 0.953 & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Convergent validity measures and reliability. In the SEM, only the values of significant effects were collected, while the non-significant relations were shown with broken lines to facilitate their interpretation (Figure 2). Thus, the trajectory coefficients of H1, H3, H6, H10, H11, H13, and H14 are shown. Of the total constructs of the UTAUT model, effort expectancy and social influence did not influence the behavioral intention to use IoT. The model determination coefficients (\(R^{2}\)) for each construct were for PE (\(R^{2}\) = 0.015); EE (\(R^{2}\) = 0.018); SI (\(R^{2}\) = 0.004); FC (\(R^{2}\) = 0.018); ATUT (\(R^{2}\) = 0.013); and, BI (\(R^{2}\) = 0.325). Finally, the results indicated differences according to gender and age. At the same time, significant constructs were found in the intention of use IoT by university professors. Figure 2: Structural measurement model. Note: * Significant at \(p<0.05\); ** Significant at \(p<0.01\); *** Significant at \(p<0.001\).