## 6 Evaluation
### Results and Discussion  
In order to assess the data collected through the questionnaires, we made use of partial least squares structural equation modelling (PLS-SEM) [74]. This analysis technique is most appropriate in situations where sample sizes are small and the data to process is not normally distributed [75]. In the proposed experimentation, 40 participants performed the evaluation and Likert scales are intrinsically non-normal. Therefore, these conditions justify the choice of technique employed to test for causality. It should be borne in mind, however, that the recommended minimum sample size ought to be at least 10 times the maximum number of indicators in a construct [76]. In the proposed model, there are at most 3 items pointing at a latent variable, which implies a minimum of 30 samples. Hence, our sample size complies with such criterion. To evaluate the research model using PLS-SEM methods, there are two stages of assessment: (i) the _measurement model_ and (ii) the _structural model_[77]. Data analysis was carried out using the SmartPLS 3.0 tool [78]. Regarding the **measurement model**, it focuses on analysing the relationship between the constructs and their indicators [79]. The aim of exploring this model is to assess its validity. This is carried out by testing that the criteria imposed by certain properties of our latent variables and items are satisfied. Concretely, we evaluate individual item reliability, internal consistency reliability, convergent validity, and discriminant validity [80]. Item reliability is verified through the analysis of indicators' loadings [80]. Research recommends a minimum value of 0.708 [81]. Out of the 15 initial indicators, EOU3 (0.435) and CE1 (0.576) had to be disregarded from the measurement model analysis since they did not attain the required level of reliability. Furthermore, two other loadings that were slightly under the threshold have been maintained, IN1 (0.673) and US3 (0.688). The reason for this is that it can be acceptable to keep factor loadings of a minimum of 0.500 if the other indicators of the same construct present good reliability values [72]. The remaining loadings were between 0.750 and 0.879, thus providing adequate reliability. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Construct** & **Item** & **Question** \\ \hline \multirow{2}{*}{Ease of Use (EOU)} & EOU1 & I found it easy learning how to use and interact with the app and QR codes. \\  & EOU2 & The gamification system is flexible. \\ \multirow{2}{*}{Interactivity (IN)} & IN1 & The use of the gamification platform offered me an interactive museum experience. \\  & IN2 & I felt I had control over my interaction with the platform. \\ \multirow{2}{*}{Cognitive Engagement (CE)} & CE2 & During the visit, I was focused on the information and questions displayed by the platform and I was not easily distracted. \\  & CE3 & Visiting the museum with the gamification platform piqued my interest for telecommunication inventions. \\ \multirow{2}{*}{Learning Outcomes (LO)} & LO1 & The trivia quiz game shows questions that contribute towards consolidating my knowledge about the field of telecommunications. \\  & LO2 & I feel that the use of the platform has allowed me to understand better the information provided about the exhibits. \\ \multirow{2}{*}{User Satisfaction (US)} & US1 & I feel happy with the system after the visit. \\  & US2 & I am satisfied with the system after visiting the museum. \\  & US3 & I found my visit with the gamification platform pleasant. \\ \multirow{2}{*}{Revisit Intention (RI)} & RI1 & The likelihood that I will return to this museum is very high. \\  & RI2 & Given the chance, I would return to the museum and use the gamification platform throughout the visit again. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Constructs and items of the measurement model. Internal consistency reliability is checked by means of the composite reliability indicator, which requires a threshold value of 0.70 to be considered acceptable [79].