## 4 Results and Empirical Approach
### Confirmatory Factor Analysis  
Consistent with the method supported by [49], a measurement model was evaluated by conducting CFA. The CFA model was developed with five latent variables, and the results were expected to be correlated with each other. Given the sensitivity of the chi-squared test (\(X^{2}=512.469\); df = 289) and its unreliability in the case of examining model fit in SEM, this study relied on the unlikeness of good-fit indices, including the following: comparative-fit index (CFI), which was 0.94 (>0.9); adjusted-goodness-of-fit index (AGFI), which was 0.85 (>0.8); Tucker-Lewis index (TLI), which was 0.94 (>0.9); the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Latent** & **Standardized** & **Standard** & **Critical** & **R2** & **Mean** & **S.D.** & **Item-Total** & **Cronbach’s** \\
**Variable Items** & **Loading** & **Error a** & **Ratio b** & **R2** & **Mean** & **S.D.** & **Correlation** & **Alpha** \\ \hline ISM & & & & 4.18 & 0.53 & 0.62–0.67 & 0.88 \\ SM1 & 0.68 & 0.10 & 9.63 & 0.46 & & & & \\ SM2 & 0.70 & 0.10 & 10.22 & 0.49 & & & & \\ SM3 & 0.72 & 0.11 & 10.52 & 0.52 & & & & \\ SM4 & 0.69 & 0.11 & 10.16 & 0.48 & & & & \\ SM5 & 0.68 & 0.10 & 9.98 & 0.46 & & & & \\ SM6 & 0.66 & 0.09 & 9.77 & 0.44 & & & & \\ SM7 & 0.72 & 0.10 & 10.44 & 0.51 & & & & \\ SM8 & 0.80 & - c & - c & 0.44 & & & & \\ RSC & & & & & 2.95 & 0.99 & 0.68–0.80 & 0.88 \\ RS1 & 0.71 & 0.05 & 13.57 & 0.51 & & & & \\ RS2 & 0.80 & 0.05 & 16.18 & 0.64 & & & & \\ RS3 & 0.83 & 0.05 & 17.04 & 0.69 & & & & \\ RS4 & 0.88 & - c & - c & 0.77 & & & & \\ ISI & & & & 4.33 & 0.53 & 0.68–0.73 & 0.88 \\ IT1 & 0.74 & 0.08 & 12.99 & 0.55 & & & & \\ IT2 & 0.77 & 0.70 & 13.47 & 0.59 & & & & \\ IT3 & 0.77 & 0.08 & 13.55 & 0.60 & & & & \\ IT4 & 0.77 & 0.07 & 13.57 & 0.59 & & & & \\ IT5 & 0.79 & - c & - c & 0.63 & & & & \\ ISA & & & & & 3.99 & 0.74 & 0.70–0.76 & 0.87 \\ IA1 & 0.76 & 0.07 & 13.43 & 0.58 & & & & \\ IA2 & 0.77 & 0.07 & 13.56 & 0.59 & & & & \\ IA3 & 0.84 & 0.07 & 14.90 & 0.71 & & & & \\ IA4 & 0.81 & - c & - c & 0.65 & & & & \\ ISC & & & & 3.96 & 0.71 & 0.71–0.82 & 0.90 \\ IC1 & 0.82 & 0.08 & 12.99 & 0.68 & & & & \\ IC2 & 0.78 & 0.07 & 13.47 & 0.61 & & & & \\ IC3 & 0.78 & 0.08 & 13.55 & 0.61 & & & & \\ IC4 & 0.75 & 0.07 & 13.57 & 0.56 & & & & \\ IC5 & 0.88 & - c & - c & 0.77 & & & & \\ \hline \hline \end{tabular} Note.: \({}^{a}\) Standard Error is an estimate of the standard error of the covariance; b Critical Ratio is the critical ratio obtained by dividing the estimate of the covariance by its standard error, and a value exceeding 1.96 represents a level of significance of 0.05; c Indicates a parameter fixed at 1.0 in the original solution.  
\end{table}
Table 3: Parameter estimate and convergent and discriminant validity.  
root-mean-square residual (RMR), which was 0.03 (<0.08); and the root-mean-square error of approximation (RMSEA), which was 0.05 (<0.05). All the fit indices were considered to have a good fit with the data.  
Next, the convergent and discriminant validities of five constructs were investigated. Construct reliability (CR) assesses the internal consistency of a measure, and convergent validity exists if the CR value (between 0.87 and 0.90) exceeds the request level of 0.7 [51]. Table 4 shows that the CR values were between 0.87 and 0.90, reflecting a high reasonable standard, giving strong support for the convergent validity of all measurements in the five constructs. The average variance extracted (AVE) was conducted next to assess discriminant validity. The AVE value is the amount of variance obtained by each construct in association with variance due to random measurement error [51]. Discriminant validity exists if the minimum AVE value is 0.5, and the level of the square root of the AVE should be greater than the correlations between any two constructs. The AVE values were between 0.5 (ISM) and 0.65 (RSC, ISC). These results confirmed that the measurement model had relevant psychometric properties.