Model	T1_1 Coef	T1_2 Coef	T2_1 Coef	T2_2 Coef	RSE	Adj R2
Original Data	0.080	0.739	0.057	0.013	—	—
KNN Imputation	0.058	0.717	0.186	0.003	0.05	2.39
MICE Imputation	0.076	0.681	0.078	0.013	0.06	2.33
Random Forest	0.069	0.732	0.042	0.007	0.06	2.22
Note: To ensure robustness in handling missing data, we evaluated three imputation strategies: Multiple Imputation using Chained Equations (MICE), Random Forest Imputation (missForest), and k-Nearest Neighbors Imputation (KNN). The coefficients across all four models show consistent directions and significance patterns. The KNN-imputed model shows the lowest Residual Standard Error (RSE = 0.05) and the highest Adjusted R2 (2.39), suggesting that it explains more variance with lower residual noise than other methods. Importantly, the core relationships between trust indicators and SDM remain stable across models, indicating that the imputation did not alter the key findings of the analysis. This supports the robustness of the results and validates the use of KNN for handling missing data.