## 6 Evaluation
### Results and Discussion
Scores ranged from 0.741 to 0.854, therefore lying above the minimum and, hence, deeming construct reliability values satisfactory. The convergent validity measure is the _\"extent to which the construct converges to explain the validity of its items\"_[80]. The indicator used to assess convergent validity is the average variance extracted (AVE) for every indicator in each latent variable. Literature suggests that the AVE values must be greater than 0.50 [82] to confirm convergent validity of the results. Our values were between 0.593 and 0.745, higher than the threshold. The aforementioned reliability results together with convergent validity scores are summarised in Table 4.  
With respect to discriminant validity, it is verified when each indicator shows a strong correlation with the latent variable it is linked to in the model, while having a weak correlation with the rest of the constructs. [PERSON] and [PERSON] [83] define a criterion that suggests that discriminant validity between two latent variables has been fulfilled if the AVE for each individual construct is greater than the shared variance (the square of the correlation coefficient) between pairs of constructs. Table 5 shows that discriminant validity is demonstrated, because the square root of each construct's AVE (bold elements of the diagonal) is greater than any value in that corresponding row and column. Once the measurement model is adequate, the **structural model** can be assessed. It explores the significance of the paths between latent variables [84], which means that this model indicates whether or not the raised hypotheses are supported by the results. We configured the SmartPLS tool to calculate the structural model taking into account the recommended number of 5000 bootstrap samples [74]. The results obtained are reflected in Table 6. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Construct** & **Item** & **Loading** & **CR** & **AVE** \\ \hline \multirow{2}{*}{\begin{tabular}{l} Ease of Use \\ \end{tabular} } & EOU1 & 0.750 & 0.750 & 0.600 \\  & EOU2 & 0.799 & & \\ \multirow{2}{*}{\begin{tabular}{l} Interactivity \\ \end{tabular} } & IN1 & 0.673 & 0.741 & 0.593 \\  & IN2 & 0.855 & & \\ \multirow{2}{*}{\begin{tabular}{l} Cognitive Engagement \\ \end{tabular} } & CE2 & 0.776 & 0.794 & 0.659 \\  & CE3 & 0.846 & & \\ \multirow{2}{*}{\begin{tabular}{l} Learning Outcomes \\ \end{tabular} } & LO1 & 0.865 & 0.847 & 0.734 \\  & LO2 & 0.849 & & \\ \multirow{2}{*}{\
\begin{tabular}{l} User Satisfaction \\ \end{tabular} } & US1 & 0.854 & 0.851 & 0.658 \\  & US2 & 0.879 & & \\ \multirow{2}{*}{
\begin{tabular}{l} Revisit Intention \\ \end{tabular} } & RI1 & 0.868 & 0.854 & 0.745 \\  & RI2 & 0.858 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the measurement model. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & CE & EOU & IN & LO & RI & US \\ \hline \multirow{2}{*}{\begin{tabular}{l} CE \\ \end{tabular} } & **0.812** & & & & & \\  & 0.438 & **0.775** & & & & \\ \multirow{2}{*}{\
\begin{tabular}{l} IN \\ \end{tabular} } & 0.463 & 0.199 & **0.770** & & & \\  & 0.485 & 0.079 & 0.342 & **0.857** & & \\ \multirow{2}{*}{
\begin{tabular}{l} RI \\ \end{tabular} } & 0.319 & 0.182 & 0.189 & 0.557 & **0.863** & \\  & US & 0.629 & 0.364 & 0.393 & 0.685 & 0.495 & **0.811** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Discriminant validity based on [PERSON]’s criterion. As is shown in Table 6, two out of seven hypotheses have been rejected: H1 and H4. Thus, based on these results we can infer that, although the platform appears to be easy to use by visitors, this quality does not contribute towards the interactivity of the gamification system. Also, the interactive qualities of the platform do not necessarily affect visitors' satisfaction with it and with the museum experience. The platform's ease of use positively influences visitors' cognitive engagement during the museum visit (\(\beta\) = 0.361; \(p<0.05\)), therefore H2 is validated.