## 4 Data Analysis
### Measurement Model Evaluation (Outer Model)  
The tests for convergent validity, discriminant validity, as well as composite reliability, were all used to assess the reflective constructs of the measurement model (outer model) in the study. The outer loading or loading factor value is utilized to determine the convergent validity. If the outer loading value is greater than 0.7, an indication is judged to fulfill the 'acceptable' category for convergent validity. Other approaches, such as the average variance extracted (AVE) value of every indicator, can be used to assess discriminant validity in addition to monitoring the indicator cross-loading result. For a 'good' model, a value of greater than 0.5 is required. The table demonstrates that each indicator variable has a factor loading value of more than 0.7, indicating that the indicators in this study are reliable. This part utilized to assess the reliability value of indicators on a variable is composite reliability. In the range 0.60-0.70 and 0.70-0.90, the composite reliability value is acceptable and good, respectively; however, in the range 0.90-0.95, it is troublesome. Similarly, if a composite reliability value is greater than 0.7, then the variable can be confirmed to fulfill the composite reliability assessment. Table 4 indicates that each variable's composite reliability (CR) is larger than 0.7 and that is arbitrated as significant. The E-sourcing and E-tendering variables have the highest composite reliability, with values of 0.934 and 0.935. Therefore, all variables may be inferred with excellent composite reliability. In the table, every variable that has an AVE value larger than 0.5 can be considered in the adequate range. As per [135], an AVE value larger than 0.5 is acceptable. The highest AVE value observed is \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & **N** & \
\begin{tabular}{c} **Mean** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Minimum** & **Maximum** \\ \hline E-Tendering & 152 & 4.306 & 0.725 & 3 & 5 \\ \hline E-Reverse auctioning & 152 & 3.307 & 1.014 & 2 & 5 \\ \hline E-Informing & 152 & 4.443 & 0.729 & 3 & 5 \\ \hline E-Sourcing & 152 & 4.096 & 0.877 & 4 & 5 \\ \hline E-Ordering & 152 & 3.874 & 0.864 & 3 & 5 \\ \hline Green Procurement Practices & 152 & 4.324 & 0.580 & 2 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics. from E-reverse auctioning, with an AVE value of 0.801, followed by E-tendering, with a value of 0.783. As a result, each variable may be said to have excellent discriminant validity. There has been some research on assessing discriminant validity, and the standard metric is not an appropriate approach for measuring DV. According to certain disapprovals of the [PERSON] (1981) criteria, they do not adequately detect DV (135,136). Discriminant validity is a statistical measure of the difference between two variables. Based on the empirical assessment, it also analyses the degree to which a variable is different from another (134). Hence, the variance along diagonals (individual variance) must be higher than the variance with the other factors as evident in Table 5. Contrary to popular belief, the square roots of AVE are more discriminant than correlation. The Heterotrait-Monotrait (HTMT) ratio, proposed by (135), is a novel method for computing discriminant validity. In this study, both conventional and innovative approaches were used. Most indicators have factor loadings that range from 0.65 to 0.85. The HTMT ratio is 0.85 for theoretically comparable constructs and 0.90 for conceptually different constructs (135). The HTMT ratio of each build is less than 0.83, as seen in Table 6.