## 4 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Path Coeff.** & **T Statistics** & _p Values_ \\ \hline FIT\(\rightarrow\)CUI & 0.643 & 16.169 & 0.000 \\ FIT\(\rightarrow\)PHV & 0.525 & 11.374 & 0.000 \\ FIT\(\rightarrow\)IMM & 0.612 & 16.342 & 0.000 \\ FIT\(\rightarrow\)PUV & 0.514 & 11.272 & 0.000 \\ FIT\(\rightarrow\)SV & 0.469 & 13.420 & 0.000 \\ PHY\(\rightarrow\)CUI & 0.297 & 6.827 & 0.000 \\ PHY\(\rightarrow\)IMM & 0.493 & 9.117 & 0.000 \\ PHY\(\rightarrow\)PUV & 0.353 & 5.895 & 0.000 \\ PHY\(\rightarrow\)SV & 0.378 & 8.669 & 0.000 \\ IMM\(\rightarrow\)CUI & 0.458 & 8.302 & 0.000 \\ IMM\(\rightarrow\)PUV & 0.135 & 2.368 & 0.018 \\ IMM\(\rightarrow\)SV & 0.766 & 35.807 & 0.000 \\ INT\(\rightarrow\)FIT & 0.637 & 19.600 & 0.000 \\ INT\(\rightarrow\)CUI & 0.565 & 18.219 & 0.000 \\ INT\(\rightarrow\)PHV & 0.638 & 17.662 & 0.000 \\ INT\(\rightarrow\)MM & 0.540 & 16.267 & 0.000 \\ INT\(\rightarrow\)PUV & 0.698 & 23.286 & 0.000 \\ INT\(\rightarrow\)SV & 0.414 & 13.619 & 0.000 \\ PUV\(\rightarrow\)CUI & 0.247 & 5.573 & 0.000 \\ SV\(\rightarrow\)CUI & 0.320 & 5.914 & 0.000 \\ \hline \end{tabular}
\end{table} 
Table 8: Path coefficients. Total effects. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **FIT** & **CUI** & **PHV** & **IMM** & **PUV** & **SV** \\ \hline FIT & & 0.091 & 0.380 & 0.159 & 0.098 & \\ CUI & & & & 0.309 & 0.090 & \\ PHY & & 0.035 & & 0.023 & 1.420 \\ IMM & 0.683 & & 0.128 & & 0.121 & \\ PUV & & 0.092 & & & \\ SV & & 0.161 & & & \\ \hline \end{tabular}
\end{table} 
Table 9: _f2_ values. SRMR (Standardized Root Mean Square Residual) evaluates the goodness of fit between the model and the observed data; its value is 0.079. Q\({}^{2}\) is high for PUV (0.473), FIT (0.400), and PHV (0.401); moderate for IMM (0.240) and CUI (0.212); and low for SV (0.101), showing the high predictive relevance of the model. The goodness of fit (GoF) is the geometric mean of the average communality and the average R\({}^{2}\). With the AVE values from Table 2, the average communality is 0.685, and the average R\({}^{2}\) is 0.605. The GoF is 0.643, which exceeds the cutoff value of 0.36 for a large effect size and can be considered satisfactory. With the data and observations from this section, we can confirm the utility of the model in Figure 1. Descriptive statistics of the indicators are in Appendix B. The current research aimed to understand how consumers experience AR apps and what factors influence their intention to reuse them in e-shopping. The findings of the current research indicate the following:  
All the examined variables significantly affect (directly or indirectly) the continued usage intention of AR apps in e-commerce. The current research found that FIT is the most significant variable positively affecting the CUI (the path coefficient is 0.643, see Table 4). FIT influences CUI directly and indirectly, with the indirect impact (0.387) being more substantial than the direct impact (0.256). FIT (see Table 4 and Figure 2) also influences IMM (0.612), PHV (0.525), PUV (0.469), and SV (0.469). Thus, we can conclude that it is essential that the makeup selected by the AR application suits the clients well; the AR application helps to choose shades that suit customers; the customers are satisfied with the photos obtained within the AR application; and customers are satisfied with the products offered by the vendor, which they can try virtually in the AR application. The results show that it takes more than just the fact that the AR app helps users find the right products. Among AR app users, those who feel immersed and delighted when using AR apps, those who find usefulness in using makeup, as well as those who have positive feedback from acquaintances, friends, or family (by using makeup products) will be more tempted to continue using the application and buy makeup products. The result obtained regarding FIT also suggests that if a customer does not find what seems fit, she will not be interested in continuing to use the app or buy products, even if the AR app is technologically excellent.