# Effect of IoT Capabilities and Energy Consumption behavior on Green Supply Chain Integration
## 3 Methodology
### Structural Model  
The structural model can be assessed through the explanatory factors (R\({}^{2}\)) of the model with their significant values. The validation of the model with the explanatory factors is mentioned in Figure 5. PLS-SEM does not require any distribution assumptions or parametric technique to check the validity of data. It works on the bootstrapping method to test the significant value of path coefficients [95; 96]. The default bootstrapping value in software is 500, which was recommended in the previous study to gain precise results [97; 98]. However, in this study, 5000 subsamples underwent the bootstrapping method to get more precise results of path coefficients. The values of path coefficients, \(t\) values, and significant values are mentioned in Table 4.  
The ultimate goal of the structural model is to explain the variance of endogenous variables. The variance can be explained through R\({}^{2}\)[90; 98]. There are no hard and fast rules for differentiating between low and high values of variance. In the literature, it was suggested that the range of R\({}^{2}\) values can be 0.02, 0.13, and 0.26 [99], showing a low-, medium-, and high-variance explanation, respectively. In this manner, the research model path coefficients of GSCP have high values of R\({}^{2}\). Figure 5 shows the path coefficient values of all variables.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Path Coefficients** & **\(t\)-Statistics** & **\(p\) Values** & **Results** \\ \hline CI \(\rightarrow\) GSCP & 0.326 & 4.603 & 0.000 & Supported \\ ECB \(\rightarrow\) CI & 0.586 & 11.90 & 0.000 & Supported \\ ECB \(\rightarrow\) SI & 0.210 & 3.122 & 0.002 & Supported \\ GT \(\rightarrow\) GSCP & \(-\)0.362 & 1.691 & 0.091 & Supported \\ IoT \(\rightarrow\) CI & 0.141 & 3.148 & 0.002 & Supported \\ IoT \(\rightarrow\) SI & 0.306 & 5.371 & 0.000 & Supported \\ SI \(\rightarrow\) GSCP & 0.838 & 3.437 & 0.001 & Supported \\ CI*GT (GSCP) \(\rightarrow\) GSCP & \(-\)0.007 & 0.063 & 0.950 & Not Supported \\ SI*GT (GSCP) \(\rightarrow\) GSCP & \(-\)0.025 & 0.163 & 0.870 & Not Supported \\ IoT*ECB (CI) \(\rightarrow\) CI & \(-\)0.138 & 6.543 & 0.000 & Supported \\ IoT*ECB (SI) \(\rightarrow\) SI & \(-\)0.237 & 8.008 & 0.000 & Supported \\ ECB \(\rightarrow\) CI \(\rightarrow\) GSCP & 0.191 & 4.684 & 0.000 & Supported \\ IoT \(\rightarrow\) CI \(\rightarrow\) GSCP & 0.047 & 2.127 & 0.033 & Supported \\ IoT*ECB (CI) \(\rightarrow\) CI \(\rightarrow\) GSCP & \(-\)0.045 & 3.875 & 0.000 & Supported \\ ECB \(\rightarrow\) SI \(\rightarrow\) GSCP & 0.178 & 2.164 & 0.030 & Supported \\ IoT \(\rightarrow\) SI \(\rightarrow\) GSCP & 0.256 & 2.928 & 0.003 & Supported \\ IoT*ECB (SI) \(\rightarrow\) SI \(\rightarrow\) GSCP & \(-\)0.199 & 3.186 & 0.001 & Supported \\ \hline \hline \end{tabular}
\end{table}
Table 4: Research model validation.  
Figure 5: Path coefficients of research variables.  
In this study, PLS-SEM structural equation modeling shows all the path coefficient values regarding the variables and factor loadings of each item that it has calculated. Figure 6 shows the values of all items factors loadings and their path coefficients of GSCP.