## 4 Results
### Regression Analysis Result
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardized**} & \multicolumn{2}{c}{**Standardized**} & \multirow{3}{*}{**Stg.**} & \multicolumn{2}{c}{**Collinearity**} \\  & \multicolumn{2}{c}{**Coefficients**} & & **Coefficients** & & \multicolumn{2}{c}{**Statistics**} \\ \cline{2-7}  & **B** & **Std. Error** & **Beta** & & & **Tolerance** & **VIF** \\ \hline Constant & \(0.845\) & \(0.309\) & & \(20.731\) & \(0.007\) & & \\ (H1) Convenience & \(0.053\) & \(0.073\) & \(0.066\) & \(0.729\) & \(0.467\) & \(0.413\) & \(2.424\) \\ (H2) Usefulness & \(0.233\) & \(0.110\) & \(0.251\) & \(20.113\) & \(0.036\) & \(0.237\) & \(4.219\) \\ (H3) Enjoyment & \(0.278\) & \(0.080\) & \(0.334\) & \(30.477\) & \(0.001\) & \(0.362\) & \(2.760\) \\ (H4) Anthropomorphism & \(-0.013\) & \(0.061\) & \(-0.017\) & \(-0.217\) & \(0.828\) & \(0.531\) & \(1.882\) \\ (H5) Technical difficulties & \(-0.048\) & \(0.081\) & \(-0.043\) & \(-0.590\) & \(0.556\) & \(0.639\) & \(1.565\) \\ (H6) Burden of expenses & \(-0.047\) & \(0.055\) & \(-0.057\) & \(-0.854\) & \(0.395\) & \(0.753\) & \(1.328\) \\ (H7) Security threats & \(0.265\) & \(0.054\) & \(0.322\) & \(40.909\) & \(0.000\) & \(0.779\) & \(1.284\) \\ (H8) Information reliability & \(0.078\) & \(0.081\) & \(0.067\) & \(0.961\) & \(0.338\) & \(0.689\) & \(1.452\) \\ \hline \hline \multicolumn{7}{c}{**Durbin Watson** = 2.030, **R Square** = 0.507, **ANOVA(Sig.)** = 0.000 } \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of regression analysis of antecedent variables affecting perceived value. Figure 3 shows the results of testing the causal relationship by applying the VAM model. If the results of the antecedent variables that affect the perceived value of the artificial intelligence speakers are summarized as a value-based acceptance model, it can be presented as shown in Figure 3.  
Table 8 is the result of analyzing the effect of the preceding variable on the Perceived value according to the purpose of use. First of all, it was found that users who use peripheral devices for the purpose of operating them are aware of the value of using them for convenience and have an intention to recommend them to others. Users who use it for information retrieval were found to use it because of its usefulness. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardized**} & \multicolumn{2}{c}{**Standardized**} & \multirow{2}{*}{**Sig.**} & \multicolumn{2}{c}{**Collinearity**} \\  & **Coefficients** & & **Coefficients** & & & **Statistics** \\ \cline{2-7}  & **B** & **Std. Error** & **Beta** & & & **Tolerance** & **VIF** \\ \hline Constant & 0.997 & 0.224 & & 40.460 & 0.000 & & \\ (H9) Perceived value & 0.698 & 0.064 & 0.658 & 100.847 & 0.000 & 1.000 & 1.000 \\ \hline \multicolumn{7}{c}{**Durbin Watson** = 1.815, **R Square** = 0.433, **ANOVA(Sig.)** = 0.000} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of analysis of the effect of perceived value on recommendation intention. Figure 3: VAM applied artificial intelligence speaker regression analysis result. positive effect on perceived value for those who use it as a schedule and alarm function. In addition, technical difficulties, cost burden, security threats, and reliability of information did not affect the perceived value. For those who used artificial intelligence speakers for the purpose of listening to music, pleasure had an effect on perceived value. However, perceived technical difficulties appeared to be of no value. It does not provide any perceived value for purposes of dialogue. In other words, the current artificial intelligence speaker is similar to an electronic clock, not an artificial intelligence speaker used for schedule and alarm functions. Table 9 shows the results of analyzing the effect of the antecedent variable on the Perceived value according to age.