## 4 Result
### Model Verification  
To evaluate the fit of the proposed model, in addition to \(\chi^{2}/\)df, this study also selected indicators such as the CFI, GFI, AGFI, RMSEA, and TLI. The CFI (Comparative Fit Index) reflects the degree of improvement of the model fit relative to the baseline model, with values closer to one indicating a better model fit. The GFI (Goodness of Fit Index) measures the consistency between the model data and observed data, with values closer to one indicating a higher model-data fit. The AGFI (Adjusted Goodness of Fit Index) considers the influence of model degrees of freedom on the GFI and is a better indicator of the accuracy of the model fit. The RMSEA (Root Mean-Square Error of Approximation) measures the size of errors between the model and the data, with smaller values indicating more minor model errors, typically considered good if less than 0.08. The TLI (Tucker-Lewis Index), the non-normed fit index, is like the CFI but more strictly penalizes model complexity, with values close to one indicating a good model fit. The fit indices calculation results (Table 4) showed that \(\chi^{2}/\)df = 2.797, GFI = 0.932, AGFI = 0.907, CFI = 0.933, RMSEA = 0.060, and TLI = 0.942, which indicates that the final structural model has a good model fit. After the model was modified, the visual structural diagram of the acceptance intention model was created, as shown in Figure 3. In the figure, observed variables are represented by square symbols, latent variables by elliptical symbols, and error terms by circles. Single arrows represent causal relationships between variables (from cause to effect), and the numbers next to the arrows represent regression coefficients (latent variable \(\rightarrow\) latent variable) and loadings (latent variable \(\rightarrow\) manifest variable). Larger loadings indicate a closer relationship between latent and manifest variables. The path coefficients between all variables are significant at the level of 0.05. According to Figure 3, PP positively affects PU (\(\beta\) = 0.21, \(p\) < 0.001), supporting hypothesis H1, indicating that devices with a higher entertainment value are more likely to be perceived as applicable by users. PEOU has a significant positive effect on PU (\(\beta\) = 0.23, \(p\) < 0.001), supporting hypothesis H2, indicating  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Cronbach’s Alpha** & **Mean** & **Response** & **Mean** & **Factor Loading** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Intention to use \\ (IU) \\ \end{tabular} } & \multirow{3}{*}{0.755} & IU1 & 3.42 & 0.842 \\  & & IU2 & 3.35 & 0.650 \\  & & IU3 & 3.31 & 0.841 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Perceived Risk \\ (PR) \\ \end{tabular} } & \multirow{3}{*}{0.838} & \multirow{3}{*}{1.54} & PR1 & 1.56 & 0.857 \\  & & PR2 & 1.56 & 0.867 \\ \cline{1-1}  & & PR3 & 1.50 & 0.848 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._that when users find the VP-ADD easier to operate, they are more likely to perceive it as applicable. Hypotheses 3 and 4 regarding the positive effects of PEOU and PU on attitudes toward use are accepted, indicating that improving these perceived attributes of the VP-ADD will positively influence user attitudes. The positive effects of PU and attitude toward use on intention to use indicate that H5 and H6 are accepted, emphasizing the importance of usefulness and positive attitudes in promoting the adoption of new technology. In addition, perceived risk (PR) negatively affects the attitude toward use (AU) (\(\beta=-0.11\), \(p<0.05\)), indicating that if the users perceive greater risks, their attitude toward using new devices may be more negative, confirming hypothesis H7. PP positively affects PEOU (\(\beta=0.22\), \(p<0.001\)), implying that more entertaining device features can enhance user perceptions of ease of use, supporting hypothesis H8.