# Effect of IoT Capabilities and Energy Consumption behavior on Green Supply Chain Integration
## 3 Methodology
### Measurement Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Construct** & **Item Loading** & \(t\)**-Statistics** & **Cronbach’s Alpha** & **CR \& AVE** \\ \hline \multicolumn{5}{c}{Internet of Things (IoT)} \\ \hline IoT 2 & 0.707 & 18.63 & & \\ IoT 3 & 0.704 & 16.36 & & \\ IoT 4 & 0.666 & 13.92 & & CR = 0.845 \\ IoT 5 & 0.691 & 14.23 & & AVE = 0.477 \\ IoT 7 & 0.619 & 11.15 & & \\ IoT 9 & 0.751 & 22.63 & & \\ \hline \multicolumn{5}{c}{Energy Consumption Behavior (ECB)} \\ \hline ECB 10 & 0.710 & 15.92 & & \\ ECB 3 & 0.772 & 27.76 & & \\ ECB 4 & 0.780 & 23.66 & & CR = 0.891 \\ ECB 5 & 0.714 & 17.46 & & AVE = 0.577 \\ ECB 8 & 0.768 & 23.03 & & \\ ECB 9 & 0.811 & 31.79 & & \\ \hline \multicolumn{5}{c}{Supplier Integration (SI)} \\ \hline SI 1 & 0.717 & 19.29 & & \\ SI 2 & 0.729 & 21.32 & & \\ SI 4 & 0.734 & 18.46 & & \\ SI 5 & 0.711 & 17.59 & 0.860 & \
\begin{tabular}{c} CR = 0.893 \\ AVE = 0.543 \\ \end{tabular} \\ SI 6 & 0.743 & 22.74 & & \\ SI 7 & 0.765 & 23.53 & & \\ SI 9 & 0.756 & 25.05 & & \\ \hline \multicolumn{5}{c}{Customer Integration (CI)} \\ \hline CI 2 & 0.705 & 15.88 & & \\ CI 3 & 0.731 & 19.74 & & \\ CI 4 & 0.766 & 30.25 & & CR = 0.871 \\ CI 6 & 0.711 & 18.23 & & AVE = 0.529 \\ CI 8 & 0.759 & 21.63 & & \\ CI 9 & 0.689 & 14.50 & & \\ \hline \multicolumn{5}{c}{Green Training (GT)} \\ \hline GT 10 & 0.769 & 26.05 & & \\ GT 2 & 0.741 & 21.44 & & \\ GT 3 & 0.746 & 22.55 & & CR = 0.885 \\ GT 5 & 0.746 & 18.29 & & AVE = 0.563 \\ GT 8 & 0.728 & 20.24 & & \\ GT 9 & 0.769 & 21.96 & & \\ \hline \multicolumn{5}{c}{Green Supply Chain Performance (GSCP)} \\ \hline GSCP 4 & 0.698 & 13.86 & & \\ GSCP 5 & 0.647 & 10.28 & & \\ GSCP 6 & 0.704 & 16.78 & & \\ GSCP 7 & 0.666 & 8.449 & 0.778 &
\begin{tabular}{c} CR = 0.844 \\ AVE = 0.474 \\ \end{tabular} \\ GSCP 8 & 0.710 & 13.18 & & \\ GSCP 9 & 0.702 & 11.41 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Factor loadings, \(t\)-statistics, composite reliability (CR), and average variance extracted (AVE). Content validity is the construct validity of items. The content validity is also called logical validity. The content validity is dependent on the grammar and content of the items. If the respondents can understand the content of all items and their logic, then the instrument has content validity. It can be analyzed through the factor loading of items [92; 93]. If items have a factor loading greater than 0.6, then the instrument has content validity and respondents can understand the instrument. In Table 3, factor loading values show that all items have content validity. Convergent validity refers to the theoretical relation of two constructs. It shows how many measures are related to each other. Convergent validity can be measured through factor loading. If the item has a factor loading greater than 0.6, then items have convergent validity [92; 93]. In this study, the factor loading values are mentioned in Table 3; their values are greater than 0.6 for all items, demonstrating that all factors have convergent validity. Discriminant validity is the opposite of convergent validity. It shows how much constructs are irrelevant to each other. Discriminant validity can distinguish between different constructs. Discriminant validity can be measured in two ways. First, the square root of the average variance extracted (AVE). If the values of the square root of AVE are more than the correlation values of latent variables, then items have discriminant validity [88; 92]. In this study, the Fornell and Larcker method is used to analyze discriminant validity, as mentioned in Table 2. The values of the square root of AVE are also mentioned in Table 2. These values show that all items have discriminant validity. Figure 4 shows the average variance of extracted values of all variables. The second way to measure the discriminant validity is using the factor loadings. If items have high factor loading, then they have discriminant validity [94]. In this study, all items have a factor loading higher than 0.6, as mentioned in Table 3. Therefore, all items have discriminant validity.