## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **Age** & **UM** & **CR** & **CI** & **LTO** & **FI** & **TR** & **SI** & **PEOU** & **PU** & **AT** & **BI** \\ \hline Age & 1 & & & & & & & & & & & \\ UM & \(-\)0.129 \(\ast\) & 1 & & & & & & & & & & \\ CR & 0.164 \(\ast\) & \(-\)0.034 & 1 & & & & & & & & & \\ CI & 0.061 & 0.037 & 0.597 \(\ast\) & 1 & & & & & & & & \\ LTO & 0.028 & \(-\)0.387 \(\ast\) & 0.131 \(\ast\) & 0.146 \(\ast\) & 1 & & & & & & & \\ FL & 0.062 & \(-\)0.143 \(\ast\) & 0.382 \(\ast\) & 0.793 \(\ast\) & 0.503 \(\ast\) & 1 & & & & & \\ TR & \(-\)0.046 & 0.049 & 0.483 \(\ast\) & 0.498 \(\ast\) & 0.163 \(\ast\) & 0.271 \(\ast\) & 1 & & & & \\ SI & 0.052 & 0.024 & 0.501 \(\ast\) & 0.397 \(\ast\) & 0.152 \(\ast\) & 0.337 \(\ast\) & 0.630 \(\ast\) & 1 & 1 & & & \\ PEOU & 0.012 & 0.032 & 0.504 \(\ast\) & 0.396 \(\ast\) & 0.148 \(\ast\) & 0.372 \(\ast\) & 0.764 \(\ast\) & 0.637 \(\ast\) & 1 & & & \\ PU & \(-\)0.019 & 0.037 & 0.513 \(\ast\) & 0.522 \(\ast\) & 0.156 \(\ast\) & 0.401 \(\ast\) & 0.701 \(\ast\) & 0.723 \(\ast\) & 0.761 \(\ast\) & 1 & & \\ AT & 0.107 \(\ast\) & 0.006 & 0.497 \(\ast\) & 0.407 \(\ast\) & 0.147 \(\ast\) & 0.384 \(\ast\) & 0.682 \(\ast\) & 0.712 \(\ast\) & 0.752 \(\ast\) & 0.728 \(\ast\) & 1 & \\ BI & 0.162 \(\ast\) & 0.028 & 0.517 \(\ast\) & 0.381 \(\ast\) & 0.128 \(\ast\) & 0.371 \(\ast\) & 0.598 \(\ast\) & 0.625 \(\ast\) & 0.717 \(\ast\) & 0.701 \(\ast\) & 0.829 \(\ast\) & 1 \\ \hline \hline \end{tabular}  
* \(p<\)0.05, \(\ast\)\(\ast\)\(p<\) 0.01. UM: user mode; CR: cyber-resilience; CI: cognitive instrumentals; LTO: long-term orientation; TR: trust; SI: social influence; PEOU: perceived ease of use; PU: perceived usefulness; AT: attitude; BI: behavioral intention.  
\end{table} 
Table 4: Correlation coefficients between measured variables. Figure 2: Internet of Things (IoT) acceptance model. Table 6 presents standardized effects of direct, indirect, and total effects of exogenous variables on endogenous variables. The direct effect of facilitated appropriation on PU and the indirect effect of user character on attitude are close to being statistically significant. Facilitated appropriation on behavioral intention is at the margin of statistical significance. All other direct, indirect and total effects were statistically significant, at \(p<0.05\). Considering both direct and indirect effects of the variables on behavioral intention, the strongest total effect was from attitude followed by PU and PEOU. The path model revealed that there is a significant indirect effect of the variables on attitude and ultimately on behavioral intention. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Endogenous** & **Exogenous** & **Standardized** & \(p\) & **Standardized** & \(p\) & **Standardized** \\  & & **Direct Effect** & \(p\) & **Indirect Effect** & \(p\) & **Total Effect** & \(p\) \\ \hline FA & UC & 0.398 & 0.01 & - & & 0.398 & 0.01 \\  & CR & 0.128 & 0.007 & - & & 0.128 & 0.007 \\ \hline PEOU & TR & 0.483 & 0.008 & - & & 0.483 & 0.008 \\  & SI & 0.349 & 0.01 & - & & 0.349 & 0.01 \\ \hline PU & TR & 0.136 & 0.008 & 0.247 & 0.03 & 0.383 & 0.008 \\  & SI & 0.302 & 0.008 & 0.141 & 0.008 & 0.443 & 0.008 \\  & CI & 0.161 & 0.01 & - & & 0.161 & 0.01 \\  & FA & 0.058 & 0.05 & - & & 0.058 & 0.05 \\  & PEOU & 0.462 & 0.007 & - & & 0.462 & 0.007 \\  & CR & - & & 0.009 & 0.03 & 0.009 & 0.03 \\  & UC & - & & 0.021 & 0.04 & 0.021 & 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effects of exogenous variables on endogenous variables in IoT acceptance model.