## 5 Results
### Measurement Model Evaluation
In the reflective measurement model, in total four indicators' loadings are lower than 0.6. For the internal consistency reliability, [PERSON]'s \(\alpha\) represents the most conservative criterion while CR is a more liberal one. [PERSON]'s \(\alpha\) values are required higher than 0.7, and CR values of 0.60 to 0.70 in the exploratory research are regarded as satisfactory [21; 67]. All the reflective indicators represent good internal consistency reliability. The average variance extracted (AVE) of measured constructs are assessed for convergent validity [68; 69], and the acceptable minimum value of AVE is 0.36 to 0.5 [21; 68; 70]. All the reflective indicators represent a good convergent validity. In conclusion, the formative measurement model has qualified internal consistency reliability and convergent validity, but four indicators failed to show the indicator reliability. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Construct** & **Indicator** & \multicolumn{2}{c}{**Absolute Indicator Contribution**} & \multicolumn{2}{c}{**Significance of Weight**} & \multicolumn{2}{c}{**Multicollinearity**} \\ \hline  & & **Weight** & **Mean** & **Std Dev** & **t-Value** & **Significance** & **VIF** \\ \hline  & bi\_1 & 0.201 & 0.177 & 0.239 & 0.839 & - & 1.195 \\ BI & bi\_2 & 0.605 & 0.580 & 0.192 & 3.156 & \(p<0.01\) & 1.207 \\  & bi\_3 & 0.580 & 0.554 & 0.199 & 2.919 & \(p<0.01\) & 1.042 \\ \hline  & fi\_1 & 0.282 & 0.281 & 0.095 & 2.959 & \(p<0.01\) & 1.847 \\ FI & fi\_2 & 0.258 & 0.248 & 0.107 & 2.406 & \(p<0.05\) & 2.338 \\  & fi\_3 & 0.385 & 0.395 & 0.098 & 3.913 & \(p<0.001\) & 2.350 \\  & fi\_4 & 0.271 & 0.266 & 0.095 & 2.861 & \(p<0.01\) & 1.669 \\ \hline  & rl\_1 & 0.615 & 0.609 & 0.105 & 5.866 & \(p<0.001\) & 1.317 \\ RL & rl\_2 & 0.378 & 0.374 & 0.098 & 3.866 & \(p<0.001\) & 1.382 \\  & rl\_3 & 0.254 & 0.256 & 0.119 & 2.135 & \(p<0.05\) & 1.379 \\ \hline  & bi\_1 & 0.066 & 0.064 & 0.036 & 1.814 & \(p<0.10\) & 1.384 \\  & bi\_2 & 0.120 & 0.116 & 0.032 & 3.771 & \(p<0.001\) & 1.430 \\  & bi\_3 & 0.119 & 0.116 & 0.027 & 4.476 & \(p<0.001\) & 1.217 \\  & fi\_1 & 0.185 & 0.183 & 0.020 & 9.132 & \(p<0.001\) & 2.200 \\ CSCI & fi\_2 & 0.187 & 0.185 & 0.021 & 9.043 & \(p<0.001\) & 2.835 \\  & fi\_3 & 0.199 & 0.197 & 0.020 & 10.046 & \(p<0.001\) & 2.781 \\  & fi\_4 & 0.164 & 0.160 & 0.021 & 7.742 & \(p<0.001\) & 1.921 \\  & rl\_1 & 0.191 & 0.189 & 0.020 & 9.672 & \(p<0.001\) & 1.739 \\  & rl\_2 & 0.164 & 0.162 & 0.020 & 8.176 & \(p<0.001\) & 1.967 \\  & rl\_3 & 0.148 & 0.148 & 0.025 & 5.923 & \(p<0.001\) & 1.739 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of the formative measurement model. In addition, the Cornell-Larcker criterion [68] and cross-loadings are suggested as two main measures to evaluate the discriminant validity of reflective indicators [21]. Tables 8 and 9 shows the results of the discriminant validity evaluation. As shown in Table 9, the square root of each latent construct's AVE should be higher than its highest correlation with other latent constructs [21; 67]. Meanwhile, an indicator loading with its associated latent construct should be higher than its loadings with all the remaining constructs [21; 67] (see Table 8). In conclusion, the formative measurement model has qualified discriminant validity.