## 4 Results
### Results of Structural Model  
After verifying the measurement model, we examined the structural model, which showed an acceptable fit to the data (\(\chi^{2}/d.f.=2.54\) (989.71/389, \(p<0.001\)). Goodness-of-fit indices further supported this conclusion: NFI = 0.96, NNFI = 0.98, CFI = 0.99, IFI = 0.99, GFI = 0.84, RMR = 0.033, and RMSEA = 0.066. These results indicate that the model accurately represents the relationships among the variables in the study. The result of standardized path coefficients also supported seven of the ten hypotheses. Consumers' satisfaction with the ibon SST significantly influenced their intention to continue using the technology (\(\beta\) = 0.94, \(p<0.001\)), supporting H2a. However, these factors did not predict intention to adopt the ibon app, rejecting H1 (\(\beta\) = \(-\)0.13, \(p>0.05\)) and H2b (\(\beta\) = 0.18, \(p>0.05\)). A significant relationship between user attitude and intention to adopt the ibon app (\(\beta\) = 0.66, \(p<0.001\)) supported H3. The perceived system quality of the ibon SST significantly influenced user satisfaction (\(\beta\) = 0.24, \(p<0.001\)) and attitude (\(\beta\) = 0.14, \(p<0.05\)) toward the ibon app, supporting both H4a and H4b. However, the relatively modest effect size for H4b suggests that system quality may play a less substantial role in shaping attitudes compared to other factors in the proposed model. While system quality contributes to attitude formation, other variables likely exert stronger influences on users' attitudes toward the mobile app. Future research should investigate whether factors such as perceived usefulness might have a more direct impact on attitude formation. This is because users may prioritize functional benefits over technical system performance. Additionally, perceived ease of use could potentially be a stronger determinant of attitudes toward mobile app, as intuitive interfaces and operational simplicity might be more immediately apparent to users than backend system quality. Perceived convenience significantly affected consumer satisfaction (\(\beta\) = 0.74, \(p<0.001\)), supporting H6a, but did not affect attitude toward the ibon app (\(\beta\) = \(-\)0.12, \(p>0.05\)), rejecting H6b.  
Finally, regarding the virtual attributes of the ibon SST, consumers' perceived system quality and perceived ubiquity significantly affected their attitude toward the ibon app, supporting both H5 (\(\beta\) = 0.44, \(p<0.001\)) and H7 (\(\beta\) = 0.38, \(p<0.001\)). In addition, the research framework determined 80%, 74%, 85%, and 50% of the observed variance regarding users' satisfaction, attitude, continuance intention, and adoption intention, respectively. The results revealed that the applied research model can satisfactorily explain endogenous variables. The results of the structural model are summarized in Table 4 and Figure 3.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Hypothesis** & **Path** & \(\beta\) & **t-Value** & **Result** \\ \hline H1 & CI \(\rightarrow\) AI & \(-\)0.13 & \(-\)0.73 & not significant \\ H2a & SA \(\rightarrow\) CI & 0.94 *** & 22.34 & supported \\ H2b & SA \(\rightarrow\) AI & 0.18 & 1.00 & not significant \\ H3 & AT \(\rightarrow\) AI & 0.66 *** & 12.29 & supported \\ H4a & PSQ \(\rightarrow\) SA & 0.24 *** & 5.13 & supported \\ H4b & PSQ \(\rightarrow\) AT & 0.14 * & 2.33 & supported \\ H5 & VSQ \(\rightarrow\) AT & 0.44 *** & 7.47 & supported \\ H6a & PC \(\rightarrow\) SA & 0.74 *** & 12.93 & supported \\ H6b & PC \(\rightarrow\) AT & \(-\)0.02 & \(-\)1.13 & not significant \\ H7 & PU \(\rightarrow\) AT & 0.38 *** & 6.62 & supported \\ \hline \hline \end{tabular} Note: * \(p<0.05\); ** \(p<0.01\); *** \(p<0.001\).  
\end{table}
Table 4: Hypothesis-testing result.