## 5 Results
### Hypothesis Testing
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Constructs**} & \multirow{2}{*}{**Items**} & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} \\ \cline{3-6}  & & **Loading (0-6)** & **VIF (\(\mathbf{<3}\))** & **Loading (0-6)** & **VIF (\(\mathbf{<3}\))** \\ \hline \multirow{3}{*}{Digital infrastructure capability (DIC)} & DIC1 & 0.902 & 2.514 & 0.75 & 2.856 \\  & DIC2 & 0.761 & 1.337 & 0.641 & 1.554 \\  & DIC3 & 0.832 & 2.192 & 0.634 & 2.26 \\ \hline \multirow{3}{*}{Digital analysis capability (DAC)} & DAC1 & 0.821 & 1.755 & 0.646 & 1.824 \\  & DAC2 & 0.821 & 1.632 & 0.672 & 1.848 \\  & DAC3 & 0.867 & 1.615 & 0.684 & 1.791 \\ \hline \multirow{3}{*}{Strategic support capability (SSC)} & SSC1 & 0.786 & 1.661 & 0.726 & 1.758 \\  & SSC2 & 0.799 & 1.846 & 0.752 & 2.044 \\  & SSC3 & 0.85 & 2.08 & 0.79 & 2.207 \\  & SSC4 & 0.825 & 1.741 & 0.764 & 1.908 \\ \hline \multirow{3}{*}{Supply chain risk management (SCR)} & SCR1 & 0.723 & 1.616 & 0.72 & 1.616 \\  & SCR2 & 0.863 & 2.774 & 0.862 & 2.774 \\ \cline{1-1}  & SCR3 & 0.74 & 1.718 & 0.739 & 1.718 \\ \cline{1-1}  & SCR4 & 0.746 & 1.749 & 0.748 & 1.749 \\ \cline{1-1}  & SCR5 & 0.816 & 2.003 & 0.818 & 2.003 \\ \cline{1-1}  & SCR6 & 0.795 & 2.127 & 0.795 & 2.127 \\ \hline \multirow{3}{*}{Supply chain risk management (SCRM)} & SCR1 & 0.827 & 1.941 & 0.827 & 1.941 \\ \cline{1-1}  & SCRM2 & 0.823 & 1.897 & 0.823 & 1.897 \\ \cline{1-1}  & SCRM3 & 0.805 & 1.664 & 0.805 & 1.664 \\ \cline{1-1}  & SCRm4 & 0.829 & 1.86 & 0.829 & 1.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Assessment of discriminant validity. The evaluation of model fit in the PLS-SEM analysis indicates that both Model 1 and Model 2 exhibit strong explanatory power and acceptable overall fit, the data as shown in Table 6. According to recommended thresholds, an \(R^{2}\) value above 0.26 suggests substantial explanatory power [81], while an SRMR value below 0.08 indicates a good model fit [84]. Additionally, an NFI value above 0.80 is generally considered acceptable [85]. Specifically, Model 1 achieves an \(R^{2}\) of 0.642, demonstrating strong explanatory capability, with an SRMR of 0.06, indicating a good fit, and an NFI of 0.841, reflecting an acceptable fit. Similarly, Model 2 attains an \(R^{2}\) of 0.603, also showing strong explanatory power, with an SRMR of 0.07 and an NFI of 0.825, both meeting acceptable thresholds. These results confirm the robustness of the proposed models in explaining supply chain resilience within the research framework. Model 1 results show a positive correlation between supply chain risk management and SCR (\(\beta\) = 0.561, \(t\) = 9.187) with a substantial impact (\(\beta^{2}\) = 0.436), supporting H1. Digital analytical capability (\(\beta\) = 0.2, \(t\) = 3.829) significantly moderates this relationship, though the effect is small (\(\beta^{2}\) = 0.061), supporting H3b (Figure 4a). However, the moderating effects of digital analysis capability (\(\beta\) = \(-\)0.013, \(t\) = 0.232) and strategic support capability (\(\beta\) = \(-\)0.024, \(t\) = 0.446) are weak and negative, suggesting a slight weakening of the supply chain risk management-SCR relationship with increases in digital analysis capability and strategic support capability, but these effects are not significant due to their small size. Model 2 results also show a positive supply chain risk management-SCR correlation (\(\beta\) = 0.554, \(t\) = 9.125) with a substantial impact (\(\beta^{2}\) = 0.415), supporting H1. Overall digital capability (\(\beta\) = 0.150, \(t\) = 3.479) significantly moderates this relationship with a small effect size (\(\beta^{2}\) = 0.058), supporting H2 (Figure 4b).