## 4 Results
### Evaluation of SEM Models (Structural Equations Modeling)
#### 4.3.2 Structural Model Assessment  
Analysing the values of the coefficient of determination R\({}^{2}\) of the most significant endogenous latent variables of the model should be the first step in evaluating the structural model ([PERSON], 1996) (p. 57). Accordingly, the procedures for assessing the structural model include assessing the model's explanatory capacity by analysing the coefficient of determination (R2) and assessing the prediction capacity by utilising the Cohen indicator (f\({}^{2}\)) and the estimates for coefficients (Path coefficients) through bootstrapping. For Millennials, the UI and PI have an R\({}^{2}\) of 0.586 and 0.616, respectively, while for Centennials, the UI and PI have an R\({}^{2}\) of 0.593 and 0.477, respectively. These are the two endogenous latent variables. R\({}^{2}\) values of 0.20 are regarded as high in the area of consumer behaviour research, per [PERSON] ([PERSON], 2017). The R\({}^{2}\) of both endogenous latent variables (UI and PI) for the two study groups (Millennials and Centennials) are regarded as high as the current research falls within this theme. We immediately proceeded to the second component of assessing the model's predictive capacity: the effect size; the Cohen indicator ([PERSON], 1996) evaluates whether a latent variable's omission significantly affects the endogenous latent variable ([PERSON], 2017). According to [PERSON] (2017), f\({}^{2}\) values of 0.02 are considered small, 0.15 considered medium, and 0.35 considered large. Table 14 refers to the millennial group, and Table 14 refers to the Centennials, f\({}^{2}\) values whose effects were considered moderate and high are presented. \begin{table}
\begin{tabular}{c c c c c c} \hline Centennials & PC & SI & NI & PI & PR & UI \\ \hline PC & & & & & \\ \hline SI & 0.650 & & & & \\ \hline NI & 0.162 & 0.190 & & & \\ \hline PI & 0.606 & 0.791 & 0.162 & & \\ \hline PR & 0.149 & 0.092 & 0.123 & 0.066 & \\ \hline UI & 0.763 & 0.833 & 0.313 & 0.683 & 0.048 \\ \hline \end{tabular}
\end{table} 
Table 13: Ratio Heterotrait-Monotrait (Centennials). Source: Own elaboration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline Millennials & PC & SI & NI & PI & PR & UI \\ \hline PC & & & & & \\ \hline SI & 0.695 & & & & \\ \hline NI & 0.138 & 0.119 & & & \\ \hline PI & 0.726 & 0.84 & 0.164 & & \\ \hline PR & 0.158 & 0.145 & 0.188 & 0.098 & \\ \hline UI & 0.817 & 0.697 & 0.143 & 0.717 & 0.066 \\ \hline \end{tabular}
\end{table} 
Table 12: Ratio Heterotrait-Monotrait (Millennials). Source: Own elaboration. Consequently, the PI has a large effect on UI (\(\ell^{2}=0.445>0.35\)), and SI has a large effect on PI (\(\ell^{2}=0.516>0.35\)). Considering the results of the Centennials, PC has a moderate effect on UI (\(\ell^{2}=0.235<0.35\)); SI has a moderate effect on PI (\(\ell^{2}=0.245<0.35\)); and SI has a large effect on UI (\(\ell^{2}=0.366>0.35\)). Lastly, individual analysis of the coefficients (Path coefficients) is carried out; according to [PERSON] [77], it provides a partial empirical validation of the relationships considered in the hypotheses between latent variables. The significance of the coefficient (\(p\)-value) will depend on its standard error, which can be obtained through bootstrapping. In this context, a significance level of 5% was considered so that the corresponding critical value would be 1.96; for the test to be considered valid, it must be greater than 1.96 or \(p<0.05\)[78]. As can be seen in Table 14, referring to the bootstrapping data of the Millennials sample, we have the hypotheses H\({}_{3}\) (SI has a positive influence on PI), H\({}_{3}\) (SI has a positive influence on UI), H\({}_{6}\) (PI has a positive influence on UI) and H\({}_{1}\) (UI has a positive influence on HF) were considered valid, given that t \(>1.96\) and \(p<0.05\). The other hypotheses, H\({}_{3}\) (NI positively influences UI) and H\({}_{5}\) (PR influences PI), were not supported.