# Predictors of Engagement in Virtual Reality Storytelling Environments about Migration
## 5 Hypotheses Testing and Results
### Evaluation of the Formative Measurement Model  
The formative measurement model consists of the following exogenous variables with their corresponding indicators (see Figure 3): immersion, presence, agency, usability, and UX (User eXperience). To evaluate this measurement model, redundancy analysis was applied to assess the convergent validity, as well as analysis of collinearity (VIF) and analysis of the outer loadings [72]. The results are shown in Table 1. In Table 1, the outer loadings are estimated by the PLS algorithm, using partial linear regressions where the indicators for each construct are independent variables and the formative construct acts as the dependent variable. The equations that are used by the PLS algorithm to estimate the outer loadings are presented in [80]. Moreover, convergent validity is \"the extent to which a measure correlates positively with other measures of the same construct using different indicators\" ([72], p. 140). According to [PERSON] et al. [72], to estimate convergent validity, the idea is to test the correlation between a formatively measured construct and a reflective measure of the same construct. To do that, we applied the redundancy analysis method in which every variable with its constructs acts as an exogenous variable that is connected to an endogenous variable with one reflective indicator that corresponds to the average value of all indicators. Finally, after running the PLS algorithm, the strength of the path linking the two constructs indicates the validity of that construct. Values above 0.7 are indicative of good validity. Regarding collinearity, in PLS-SEM, the variance inflation factor (VIF) is analyzed to determine the correlation of each indicator with respect to the other indicators. VIF values between 5 and 10 or higher indicate multicollinearity. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{3}{*}{**Formative** **Constructs**} & **Indictors for the Construct** & **Outer Loadings** & **Convergent Validity— Redundancy** & **Collinearity (VIF \(<\) 5)** & **Composite Reliability (\(>\)0.7)** & **AVE (\(>\)0.5)** \\ \hline \hline \multirow{3}{*}{Immersion} & IMM\_1 & 0.954 & 1.000 & 2.507 & & 0.940 & 0.888 \\  & IMM\_2 & 0.929 & & 2.507 & & & \\  & PRE\_1 & 0.675 & 1.000 & 1.345 & & & \\  & PRE\_2 & 0.622 & & 1.335 & & & \\  & PRE\_3 & 0.850 & & 1.654 & & & \\  & PRE\_4 & 0.740 & & 1.355 & & & \\  & & & & & 0.787 & 0.650 \\ Agency & AGE\_1 & 0.877 & 1.000 & 1.101 & & & \\  & AGE\_2 & 0.723 & & 1.101 & & & \\  & USA\_1 & 0.815 & 1.000 & 2.065 & & & \\  & USA\_2 & 0.782 & & 2.411 & & & \\  & USA\_3 & 0.840 & & 2.611 & & & \\  & USA\_4 & 0.686 & & 2.141 & & & \\  & USA\_5 & 0.664 & & 2.048 & & & \\  & USA\_6 & 0.777 & & 1.870 & & & \\  & USA\_7 & 0.624 & & 1.633 & & & \\  & UK\_1 & 0.835 & 1.000 & 1.273 & & & \\  & UK\_2 & 0.639 & & 1.292 & & & \\  & UK\_3 & 0.723 & & 1.661 & & & \\  & UK\_4 & 0.584 & & 1.605 & & & \\  & UK\_5 & 0.620 & & 1.581 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Outer loadings, convergent validity, composite reliability, and AVE of the formative measurement model. In terms of reliability and to tackle the limitations of the Cronbach alpha coefficient, composite reliability is used for internal consistency. According to [PERSON] et al. [72], composite reliability is calculated based on the outer loadings of the indicators. Values of composite reliability range from 0 to 1, with values above 0.7 indicating satisfactory reliability. According to [PERSON] et al. [72], the average variance extracted (AVE) is a measure to establish convergent validity. This measure is calculated as the mean value of the squared loadings of the indicators associated with each one of the constructs. Values higher than 0.5 in a construct indicate that (on average) the construct explains more than half of the variance in the respective construct.