## 4 Results
### Measurement Assessment
First, the average variance extracted (AVE) for each construct ranged from 0.65 to 0.87 (on-diagonals, Table 3), exceeding the critical value of 0.5 [66]. This indicates that the items within each construct effectively explained a substantial portion of the variance. Second, all factor loadings within each construct were statistically significant at the 0.01 level, with t-values ranging from 14.89 to 23.57. This further supports the convergent validity of our measurement model. Discriminant validity was assessed using a correlation matrix. The AVE for each construct (diagonal values) was more significant than the square of the correlation coefficient between constructs (upper triangle values) [67, 68]. Additionally, the values did not exceed 0.85 [69]. These findings demonstrate that each construct measures a distinct aspect of the construct domain, minimizing overlap and ensuring that other constructs do not unduly influence them in the model. The result shows that our measurement model exhibits sufficient reliability and validity. All measures were self-reported by the same respondents, thereby generating potential common-method bias. Two tests were conducted to examine potential common-method bias following the recommendation of [PERSON] et al. [70]. First, the Harmon single-factor test was employed to verify that the data and constructs were unaffected by common-method bias. We performed factor analysis and determined that the most significant extracted factors contributed to 45% of the total variance extracted, which is less than the threshold value of 50%. This result verified that the research data did not exhibit common-method bias. Moreover, concerns about common-method variance were raised because the same respondents self-reported the items. According to [PERSON] and [PERSON] [71], a full collinearity test was used to assess a potential common-method bias. The variance inflation factors for all constructs (between 1.90 and 3.52) were less than 5 [72], indicating that all variables were distinct and that multicollinearity was absent. In addition, the test employing the common latent factor in CFA was performed [70], with the results revealing the fit of the common latent factor model (\(\chi^{2}\) = 12,212.46 on 405 _d.f._ and RMSEA = 0.285), with the results demonstrating the fit of the common latent factor model (\(\chi^{2}\) = 970.58 on 377 _d.f._ and RMSEA = 0.061). The difference in standardized regression weights between these two models was less than 0.05 for all items. Based on these results, we concluded that common-method bias was not a concern in this study. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **Means** & **S.D.** & **PSQ** & **PC** & **VSQ** & **PU** & **SA** & **AT** & **CI** & **AI** \\ \hline PSQ & 5.23 & 1.29 & **0.79** & 0.53 & 0.38 & 0.32 & 0.50 & 0.35 & 0.55 & 0.25 \\ PC & 5.38 & 1.36 & 0.73 & **0.76** & 0.24 & 0.26 & 0.62 & 0.24 & 0.67 & 0.12 \\ VSQ & 4.83 & 1.26 & 0.62 & 0.49 & **0.75** & 0.53 & 0.24 & 0.58 & 0.26 & 0.34 \\ PU & 4.80 & 1.33 & 0.57 & 0.51 & 0.73 & **0.78** & 0.23 & 0.56 & 0.25 & 0.29 \\ SA & 5.09 & 1.21 & 0.71 & 0.79 & 0.49 & 0.48 & **0.75** & 0.22 & 0.67 & 0.15 \\ AT & 4.98 & 1.30 & 0.59 & 0.49 & 0.76 & 0.75 & 0.47 & **0.85** & 0.22 & 0.45 \\ CI & 5.35 & 1.42 & 0.74 & 0.82 & 0.51 & 0.50 & 0.82 & 0.47 & **0.86** & 0.13 \\ AI & 4.70 & 1.47 & 0.50 & 0.35 & 0.58 & 0.54 & 0.39 & 0.67 & 0.36 & **0.87** \\ \hline \hline \end{tabular} Note: S.D. = standard deviation. On-diagonals are the average variance extracted (AVE) (as indicated in **boldface**). The lower triangle is the correlation coefficient, and the upper triangle is the square of the correlation coefficient. PSQ = physical system service quality; PC = perceived convenience; VSQ = virtual system service quality; PU = perceived ubiquity; SA = satisfaction; AT = attitude; CI = continuance intention; AI = adoption intention.  
\end{table} 
Table 3: Descriptive statistics, variance explained, and correlations.