## 4 Results
### The Measurement Model: Reliability and Validity Analysis  
Reliability and validity tests of the constructs should be conducted to assess the reflective measurement models [73]. Both tests are prerequisites to structural model assessment [74]. These tests aim to assess the internal consistency of the values recorded by the respondents by examining each variable versus its related indicator's values and the validity of the questionnaire's questions. The internal consistency, i.e., reliability, was assessed via [PERSON]'s alpha (CA) and composite reliability (CR) tests, while the validity was assessed by convergent and discriminant validity tests [72].  
[PERSON]'s alpha is used to measure the internal consistency of a construct's items [75]. The reliability of items/factors must be 0.60 or higher. In addition, the composite reliability, which is used to double-check the internal consistency, should have values above 0.7 [76]. The results are reported in Table 2, which indicates that all constructs are reliable since their corresponding Cronbach's alpha values are above 0.6 and their composite reliability values are higher than 0.7. Therefore, the results affirm that the internal consistency of all constructs is reliable and acceptable.  
Convergent and discriminant validity tests were used to assess the correlation between each construct and its related items [77]. Convergent validity is measured by the average variance extracted (AVE) for all constructs' items with a minimum acceptable value of 0.50. This value indicates that the construct explains 50% or more of the variance of its items [76]. The results reported in Table 2 indicate that all AVE values are above 0.5, thus confirming convergent validity.  
Discriminant validity is measured by calculating the square root of all constructs' AVE and comparing the resulting value for each construct with other constructs. Hence, when the correlations within each construct exceed the correlations with all other constructs, the discriminant validity is accepted [78]. The results in Table 3 indicate that the discriminant validity is acceptable.  
In addition, the Heterotrait-Monotrait ratio (HTMT) test was used to double-check the discriminant validity; it measures the degree of differences between overlapping constructs. Accepted values range between zero and one and values less than one indicate good reliability [79]. The HTMT values for this study are below 0.90, as shown in Table 4; therefore, discriminant validity is confirmed.  
\begin{table}
**BDAC** & & & & \\
**DA** & 0.56554 & & & \\
**IC** & 0.62260 & 0.48970 & & \\
**SCA** & 0.53015 & 0.32422 & 0.87068 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: HTMT values.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Construct** & \(\mathbf{R^{2}}\) & **CA** & **CR** & **AVE** \\ \hline DA & & 0.639 & 0.804 & 0.580 \\ BDAC & 0.168 & 0.743 & 0.837 & 0.564 \\ IC & 0.241 & 0.711 & 0.818 & 0.531 \\ SCA & 0.440 & 0.743 & 0.839 & 0.567 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of reliability and validity.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & & **1** & **2** & **3** & **4** \\ \hline
**1** & **BDAC** & **0.75** & & \\
**2** & **DA** & 0.41 & **0.76** & & \\
**3** & **IC** & 0.49 & 0.33 & **0.73** & \\
**4** & **SCA** & 0.4 & 0.23 & 0.66 & **0.75** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Discriminant validity check (square root of the AVE, shown on the diagonal in bold).  
Table 5 depicts the PLS estimation of the factor outer loadings of the reflective indicators. Acceptable values are equal to 0.7 or higher for outer loadings [80] or are lower than 0.7 but above 0.4 if the indicator has been recently established [81]. It is essential to check the outer model by testing the \(t\)-statistics with values above 1.96 to confirm that the outer model loadings are highly significant. Thus, according to the outer loading and \(t\)-test values, the indicators are highly significant.