## 3 Results
### Item Reliability, Variable Interactions, and Multicollinearity  
The Cronbach's alpha reliability coefficient for the five dimensional items of the IGV short scale was 0.90, and correlations among items were all strong to very strong and positive, ranging from 0.57 to 0.80, with \(p<0.001\). Due to the strong correlations between the explanatory variables, a check for statistically significant interactions and a multicollinearity test were executed on the data to exclude item collinearity and multicollinearity. An \(n\)-way ANOVA test served to examine statistically significant interactions between explanatory variables. In Table 2, the authors reported the results of the between-subject-effect test and showed that no significant interactions occurred, with the exception of two pairwise interactions over 26: Usefulness and Beauty, and Usefulness and Clarity.  
Multicollinearity analysis is reported in Table 3.  
In order to compute how much of the model variance was explained by our variables, and whether our variables were mainly explaining our model (and no extra dimensions), we ran a multiple-regression process over them and observed the tolerance statistics, the variable inflation factor (VIF), and the eigenvalue-decomposition matrix.  
The tolerance factor, that is, the difference between total unit variance explained by the model and variance explained by all other explanatory variables, was measured for each explanatory variable. Its reciprocal, the VIF, was interpreted as the proportional increase of variance observed in no multicollinearity because of the multicollinearity between each of the explanatory variables, and one or more of the other explanatory variables. Acceptance thresholds for these two metrics were a VIF under 10 and a tolerance factor above 0.10 or 0.20 (the so called \"rule of 10\", see, for example, [49]).  
Tolerance and VIF are leave-one-out measures of linear dependency among variables and an estimation of their redundancy; these two measures cannot determine the structural form of linear relations [50], nor can they say anything with regard to other forms in which variables can turn out to be related. For this reason, we also examined the decomposition matrix provided by SPSS and reported in Table 4, where the coefficient matrix was decomposed in its principal-component eigenvalues, condition indices, and proportion of variance. Although eigenvalue decomposition and the related indices are weaker indicators of relevant collinearity compared to the VIF and tolerance measures [51], we also inspected them as a further confirmation of the previous tests, considering weak multicollinearity [52].  
\begin{table}
\begin{tabular}{l l l l l l l} \hline  & **Coefficients** & **Std. E** & **Std. Coeff.** & **95\% CI** & \(p\)**-Value** \\ \hline (Constant) & 1.571 & 0.091 & & 1.392 & 1.749 & \(<\)0.001 \\ \hline UsefulM & 0.737 & 0.099 & 0.294 & 0.543 & 0.931 & \(<\)0.001 \\ UsefulnH & 1.096 & 0.104 & 0.451 & 0.893 & 1.300 & \(<\)0.001 \\ \hline IntuitiveM & 0.455 & 0.106 & 0.180 & 0.247 & 0.663 & \(<\)0.001 \\ IntuitiveH & 0.712 & 0.115 & 0.293 & 0.486 & 0.939 & \(<\)0.001 \\ \hline ClarityM & 0.406 & 0.116 & 0.164 & 0.178 & 0.633 & \(<\)0.001 \\ ClarityH & 0.940 & 0.132 & 0.387 & 0.682 & 1.199 & \(<\)0.001 \\ \hline InformativM & 0.139 & 0.113 & 0.057 & \(-\)0.082 & 0.361 & 0.216 \\ InformativH & 0.370 & 0.126 & 0.152 & 0.123 & 0.617 & 0.003 \\ \hline BeautyM & 0.282 & 0.078 & 0.113 & 0.128 & 0.436 & \(<\)0.001 \\ BeautyH & 0.680 & 0.087 & 0.277 & 0.509 & 0.851 & \(<\)0.001 \\ \hline \end{tabular}
\end{table}
Table 1: Linear-regression analysis with a dichotomized version of the items to verify the proportionality of ordinal items with numerical ones.