## 3 Results and Discussions
### The Measurement Model  
Table 3 shows the results obtained after testing the convergent and discriminant validity for the construct model. Given that the research focuses on the PLS model, it was decided to test the  
Figure 3: Partial least squares (PLS) algorithm results. convergent validity of the proposed reflective model by using primarily composite reliability, as an alternative to [PERSON]'s alpha (CA) [100; 102]. The arguments that strengthened this decision were mainly related to the [PERSON]'s alpha (CA) indicator which cannot provide a real, perfect estimate of the reliability of the scale-built model. However, the Cronbach's alpha values of each factor were higher than the threshold recommended by [PERSON] [103], namely 0.7, so we can say that in this case, it could also confirm the reliability of the measurement model. Previous studies show that the composite reliability of reflective models must be equal to or greater than 0.6 for exploratory studies [103; 104] equal to or greater than 0.70 for confirmatory constructs [105] and \(\geq\) 0.80 for confirmatory research [106]. The composite reliability of the proposed construct is very high (\(>\) 0.80), a fact confirmed by the size of the indicators obtained by each latent variable. If the composite reliability of the Social Influence factor (0.823) slightly exceeds 0.8, that of the Intrinsic Motivation variable (0.910) approaches 1 (Figure 4). Although it tends to underestimate reliability, [PERSON]'s alpha can confirm the convergent validity of a construct model if the indicators for latent variables are \(\geq\) 0.6 [107]. Six latent factors: BI (0.741), EM (0.717), FC (0.787), PA (0.757), PP (0.771) and SI (0.727) were measured at an acceptable level while the rest exceed the \(\geq\) 0.8 limit set for an extremely good scale (Figure 5) [108]. Therefore, the proposed model fulfills the condition of convergent validity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Latent Variables** & **Cronbach’s Alpha (CA)** & **Composite Reliability** & **Average Variance Extracted (AVE)** \\ \hline AT & 0.800 & 0.872 & 0.639 \\ BI & 0.741 & 0.845 & 0.662 \\ EE & 0.855 & 0.899 & 0.690 \\ EM & 0.717 & 0.875 & 0.778 \\ FC & 0.787 & 0.861 & 0.609 \\ IM & 0.802 & 0.910 & 0.834 \\ PA & 0.757 & 0.841 & 0.570 \\ PC & 0.806 & 0.874 & 0.637 \\ PE & 0.848 & 0.890 & 0.670 \\ PP & 0.771 & 0.856 & 0.602 \\ PR & 0.814 & 0.875 & 0.636 \\ PS & 0.839 & 0.888 & 0.666 \\ SI & 0.727 & 0.823 & 0.542 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Construct reliability and validity. Figure 4: Composite reliability. However, the Average Variance Extracted (AVE) remains the optimal indicator used to test convergent and divergent validity. In the case of reflective models, if the AVE levels of the latency factors exceed the recommended minimum threshold of 0.5 then the convergent validity is confirmed [98; 107; 109], otherwise, the explained variation is exceeded by the error variation [103; 110]. In the present research, the exogenous factors PA (0.570) and SI (0.542) displayed the lowest AVE values but higher than the minimum limit of 0.5. At the opposite pole are the endogenous factors IM and EM whose size AVE was 0.834 and 0.778, respectively. The third endogenous BI factor indicated a moderate AVE of 0.662 (Figure 6). As all AVE values are higher than the minimum acceptable threshold of 0.5, it follows that the construct model meets the condition of convergent validity. The determination of discriminant validity according to the Cornell-Larcker criterion is based on the same AVE indicator [110]. For each factor included in the model, the AVE square roots must be greater than their correlations with any other latent variable [111]. As can be seen in Table 4a,b, the AVE square roots calculated for the 13 factors are distributed in the diagonal cells and the correlations with the other variables are displayed below them. Figure 5: Cronbach’s alpha. Figure 6: Average Variance Extracted (AVE).