## 5 Results
### Structural Equation Modeling and Hypothesis Verification  
Structural models (Figure 3) need to be tested. Structural equation modeling (SEM) is an integrative tool used to test hypothesized relationships between variables [64]. In this study, these are user-perceived factors for the use of AR in the purchase of cultural heritage products, technology acceptance factors for users, psychological experience factors for users, and consumer purchase intention. Figure 3 shows the relationship between the measured variables. The final structural model, which results from applying the refinement criteria mentioned in the modeling section, contains 28 items. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Item** & **Factor Loading** & **CR** & **AVE** & **Cronbach’s Alpha** \\ \hline \multirow{4}{*}{Immersion (IM)} & IM1 & 0.884 & & & \\  & IM2 & 0.840 & & & \\  & IM3 & 0.744 & & & \\  & IM4 & 0.778 & & & \\  & IN1 & 0.786 & & & \\ Interactivity & IN2 & 0.888 & & & \\ (IN) & IN3 & 0.896 & 0.912 & 0.722 & 0.911 \\  & IN4 & 0.823 & & & \\  & AQ1 & 0.757 & & & \\ Aesthetic quality & AQ2 & 0.893 & & & \\  (AQ) & AQ3 & 0.816 & 0.894 & 0.680 & 0.893 \\  & AQ4 & 0.826 & & & \\ Perceived ease of & PEU1 & 0.796 & & & \\   use & PEU2 & 0.794 & 0.881 & 0.649 & 0.879 \\  (PEU) & PEU3 & 0.781 & & & \\  & PU1 & 0.844 & & & \\ Perceived & PU2 & 0.791 & & & \\ usefulness & PU3 & 0.778 & 0.878 & 0.643 & 0.876 \\  (PU) & PU4 & 0.792 & & & \\  & FE1 & 0.818 & & & \\ Flow & FE2 & 0.880 & & & \\ experience & FE3 & 0.909 & 0.925 & 0.754 & 0.917 \\  (FE) & FE4 & 0.864 & & & \\ Consumer & CPI1 & 0.814 & & & \\ purchase & CPI2 & 0.777 & & & \\ intention & CPI3 & 0.829 & 0.877 & 0.640 & 0.875 \\  (CPI) & CPI4 & 0.779 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confirmatory factor analysis. The whole model was then tested. Based on the analysis of the structural model, Table 5 shows values for the structural equation model based on commonly used model fit measures that indicate a good overall model fit [65]. To be specific, the indices of CMIN/DF, IFI, TLI, CFI, and RMSEA demonstrate that the model fits the data well. Finally, Table 6 presents the path values, critical ratios (C.R. ), and significance levels (\(p\)-values). Given that the C.R. exceeds 1.96 and the \(p\)-value is below 0.05, 10 of the 12 paths are statistically significant. Specifically, in the relationship between user perceptive factors and technology acceptance factors (Table 7), immersion is correlated with perceived usefulness (\(\beta\) = 0.171, \(p\) < 0.05), so that H1a holds. Comparatively, interactivity does not constitute a significant effect on perceived usefulness (\(\beta\) = 0.021, \(p\) > 0.05), and aesthetic quality is not a positive influence factor for perceived usefulness (\(\beta\) = 0.023, \(p\) > 0.05), so H2a and H3a are not valid. Meanwhile, immersion (\(\beta\) = 0.149, \(p\) < 0.05), interactivity (\(\beta\) = 0.206, \(p\) < 0.05), and aesthetic quality (\(\beta\) = 0.175, \(p\) < 0.05) were each related to perceived ease of use, with interactivity \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Measurement** & & & & & & & \\
**Indicators** & **CMIN** & **DF** & **CMIN/DF** & **SRMR** & **TLI** & **CFI** & **RMSEA** \\ \hline Measured value & - & - & \textless{}3 & \textless{}0.08 & \textgreater{}0.9 & \textgreater{}0.9 & \textless{}0.08 \\ Reference standard & 534.886 & 333 & 1.606 & 0.057 & 0.979 & 0.981 & 0.032 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Measurement model fit indices. Figure 3: Structural equation modeling. having a more significant effect on perceived ease of use. Thus, H1b, H2b, and H3b were established. Immersion (\(\beta\) = 0.150, \(p\) < 0.05), interactivity (\(\beta\) = 0.155, \(p\) < 0.05), and aesthetic quality (\(\beta\) = 0.135, \(p\) < 0.05) were also correlated with flow experience, respectively, in which interactivity also had a more significant effect on flow experience. Therefore, H1c, H2c, and H3c were established.