## 5 Results
### Measurement Model  
The [PERSON]'s test (\(p<0.01\)) showed that there is no normal distribution in all items of the model. As a result, the partial least squares (PLS) method was used as the most adequate method [86, 87, 88]. Also, to test the research hypotheses, we applied the bootstrapping method and used SmartPLS version 3.0 for the analysis [89].  
Model estimation was the initial stage in data analysis. An evaluation of the internal consistency as well as discriminant validity of the instrument items are included in the measurement model test. To ensure validity, a test's internal consistency should be validated before it can be used for study or examination. Internal consistency refers to the extent to which all of the items in a test measure the same notion or construct and is thus linked to the test's interrelatedness [90].  
In this study we use three measures to validate internal consistency:  
* Composite reliability (CR) estimations generate an internal consistency reliability coefficient based on the proportion of variation explained by the test items compared to the overall variance of the composite test score [91].
* AVE is the average amount of variance in observed variables that a latent construct is able to explain. It is required to have an AVE of 0.5 [92, 93].
* Cronbach's alpha was introduced by [PERSON] in 1951 [94] to provide a measure of a test's internal consistency; it is expressed as a number between 0 and 1 [94].  
Thus, we examined internal consistency via composite reliability (CR) in Figure 2, the average variance (AVE) in Figure 3, and Cronbach's alpha in Figure 4. Values were all above the threshold of 0.60 (ranged from 0.700 to 0.909). The AVE values vary within the limits of 0.625 to 0.822, CR values ranged from 0.833 to 0.936, everything well above the minimum recommended of 0.50 and 0.70, respectively. Table 1 shows the factor loadings for each construct (27 in total), which all exceed the recommended value of 0.70.  
Figure 3: Average Variance Extracted (AVE).  
Figure 2: Composite Reliability.  
Discriminant validity was examined to determine whether the AVE square root of each construct was greater than its highest correlation with any other construct, and the results demonstrated that there is no discriminant validity issue in the data (see Figure 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Construct** & **Item** & **Mean** & **Std. Dev.** & **Factor Loadings** \\ \hline \multirow{3}{*}{**Innovation**} & I1 & 5.49 & 1.31 & 0.875 \\  & I2 & 5.53 & 1.28 & 0.825 \\  & I3 & 5.66 & 1.19 & 0.803 \\ \hline \multirow{3}{*}{**Privacy Concern**} & PC1 & 5.40 & 1.39 & 0.907 \\  & PC2 & 5.67 & 1.29 & 0.874 \\ \hline \multirow{3}{*}{**Trust in Technology**} & TT1 & 5.52 & 1.28 & 0.866 \\  & TT2 & 5.66 & 1.18 & 0.805 \\ \cline{1-1}  & TT3 & 5.52 & 1.29 & 0.829 \\ \cline{1-1}  & TT4 & 5.61 & 1.17 & 0.841 \\ \hline \multirow{3}{*}{**Perceived Vulnerability**} & PV1. & 5.65 & 1.24 & 0.843 \\  & PV2 & 5.51 & 1.35 & 0.746 \\ \cline{1-1}  & PV3 & 5.59 & 1.32 & 0.781 \\ \hline \multirow{3}{*}{**Perceived Severity**} & PS1 & 5.59 & 1.27 & 0.817 \\  & PS2 & 5.57 & 1.34 & 0.789 \\ \cline{1-1}  & PS3 & 5.18 & 1.52 & 0.786 \\ \hline \multirow{3}{*}{**Self-Efficacy**} & SE1 & 5.31 & 1.34 & 0.861 \\  & SE2 & 5.42 & 1.32 & 0.766 \\ \cline{1-1}  & SE3 & 5.35 & 1.32 & 0.839 \\ \hline \multirow{3}{*}{**Perceived Response Efficacy**} & PRE1 & 5.42 & 1.28 & 0.855 \\ \cline{1-1}  & PRE2 & 5.60 & 1.19 & 0.781 \\ \cline{1-1}  & PRE3 & 5.29 & 1.40 & 0.835 \\ \hline \multirow{3}{*}{**Perceived Response Cost**} & PRC1 & 4.57 & 1.79 & 0.920 \\ \cline{1-1}  & PRC2 & 4.70 & 1.76 & 0.877 \\ \cline{1-1}  & PRC3 & 4.61 & 1.79 & 0.901 \\ \cline{1-1}  & PRC4 & 4.87 & 1.62 & 0.846 \\ \hline \multirow{3}{*}{**Behavioral Intention**} & BI1 & 5.40 & 1.36 & 0.917 \\ \cline{1-1}  & BI2 & 5.49 & 1.33 & 0.897 \\ \hline \end{tabular}
\end{table}
Table 1: Measurement.  
Figure 4: Cronbach’s Alpha.