## 4 Results
Similarly, behavioral intention (BI) has a considerable impact on actual use (UB), with a coefficient of 0.530 and a \(t\)-value of 5.746, reaffirming its role as a key predictor of adoption behavior. Effort expectancy (EE) also shows a significant relationship with behavioral intention (BI) (\(\beta\) = 0.214, \(t\) = 2.079, \(p\) = 0.001), although its impact is lower than performance expectancy. On the other hand, facilitating conditions (FC) have a significant effect on actual use (UB) (\(\beta\) = 0.161, \(t\) = 2.041, \(p\) = 0.031), suggesting that access to resources and support influences the effective use of AI. However, its impact on behavioral intention (BI) is very low and not significant (\(\beta\) = 0.034, \(t\) = 0.384, \(p\) = 0.001). Social influence (SI), with a coefficient of 0.044 and a \(t\)-value of 0.469, shows a weak relationship with behavioral intention, although it remains statistically significant (\(p\) = 0.023). This suggests that peer perception has a minor impact on students' adoption of AI. Finally, to evaluate the overall model fit, the standardized root mean square residual (SRMR) was analyzed, yielding a value of 0.062. This result indicates a good model fit, as it falls below the recommended threshold of 0.08 in the PLS-SEM literature. A low SRMR value suggests minimal discrepancy between the observed and estimated covariance matrices, supporting the adequacy of the proposed model [33]. These findings reinforce the validity of the UTAUT2 model in predicting students' acceptance of ChatGPT. After conducting these analyses, we proceeded to examine the influence of other variables, such as gender, age, and field of study, on the degree of ChatGPT acceptance. For all cases, the hypotheses were formulated as follows:  
* Null Hypothesis (H\({}_{0}\)): There are no significant differences based on gender, with an alpha risk of 0.05 for the different variables tested. * Alternative Hypothesis (H\({}_{1}\)): There are significant differences based on gender, with an alpha risk of 0.05 for the different variables tested. Starting with the gender variable, and applying the Mann-Whitney U test, we obtained the results presented in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Original** & \multirow{2}{*}{**Sample Mean (M)**} & \multicolumn{2}{c}{**Standard**} & \multirow{2}{*}{**T Statistics**} \\  & **Sample (O)** & & & **Deviation** \\  & & & **(STDEV)** & & **(1O/STDEV1)** \\ \hline BI \(>\) UB & 0.530 & 0.527 & 0.092 & 5.746 & 0.000 \\ EE \(>\) BI & 0.214 & 0.219 & 0.103 & 2.079 & 0.001 \\ FC \(>\) BI & 0.034 & 0.038 & 0.088 & 0.384 & 0.001 \\ FC \(>\) UB & 0.161 & 0.165 & 0.079 & 2.041 & 0.031 \\ PE \(>\) BI & 0.596 & 0.591 & 0.099 & 5.999 & 0.000 \\ SI \(>\) BI & 0.044 & 0.046 & 0.093 & 0.469 & 0.023 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Path coefficients. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **PE** & **EE** & **SI** & **FC** & **BI** & **UB** \\ \hline Mann–[PERSON] U & 3982.5 & 4078.0 & 3653.0 & 3977.5 & 3612.0 & 3490.0 \\ Wilcoxon W & 11,732.5 & 11,828.0 & 11,403.0 & 11,727.5 & 11,362.0 & 11,240.0 \\ Z & \(-\)0.972 & \(-\)0.721 & \(-\)1.838 & \(-\)0.997 & \(-\)1.976 & \(-\)2326 \\ Sig. & 0.331 & 0.471 & 0.066 & 0.319 & 0.048 (P) & 0.020 (P) \\ \hline \hlineThe results obtained only allow for the rejection of H0 at a significance level of \(p\leq 0.05\) concerning \"behavioral intention\" and \"usage behavior\". To determine which gender these differences favored, a rank test was applied, yielding the values presented in Table 8 for the two accepted alternative hypotheses. Regarding age, the Kruskal-Wallis test was applied, and the results obtained are presented in Table 9.  
In this case, the H0 hypotheses rejected at \(p\leq 0.05\) were those related to the non-existence of significant differences concerning age and the following UTAUT2 dimensions: \"Usage Behavior\", \"Behavioral Intention\", and \"Effort Expectancy\".