# Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment
## 5 Data Analysis and Results
### Mediating Effect Analysis  
This section uses the hierarchical regression method as suggested by [44] to verify the mediating effect of attitude between different factors. First, we analyzed the mediating effect when (i) AT is the mediator between the PU and BI and (ii) AT is the mediator between the EU and BI. The results are shown in Table 14. When the partial correlation analysis is performed i.e., without the AT as the mediator, the correlation value between the PU and BI dropped to \(-0.0355\), and the correlation value between the EU and BI also dropped to \(-0.0271\).  
Moreover, to determine the significance of the relationship with the mediator AT, the Sobel test was applied. Because the \(p\)-value is less than \(0.05\) (\(95\%\) confidence level), both paths, i.e., PU\(\rightarrow\)AT\(\rightarrow\)BI and EU\(\rightarrow\)AT\(\rightarrow\)BI, are significant and this confirms that mediating relationships exist between the PU and EU with the BI where the AT is the mediator variable. Additionally, we analyzed the mediating effects considering the EU as the mediator variable between the SI, SC and T with the AT and PU as the mediator variable between the SI, SC and T with the AT as in the research model. Similarly, the semi correlation between the mediator variable and the target variable was weakened by the direct variables. This indicates a mediating effect. We confirm the findings with the Sobel Test as in Table 15. The \(p\)-values are significant at a \(95\%\) confidence level, ensuring that the PU and EU are the mediator variables for the SC, SI and T, leading to the AT. A user with high values of SC, SI and T will perceive the in-vehicle application as useful and easy to use in their driving experience, which will lead to the user having a positive attitude toward the application and eventually having a high intention to use the application.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & **Multiple R** & **Adjusted R2** & **F** & \(p\)**-Value** & **Sig.** \\ \hline T, SC, SI \(\rightarrow\) PU & \(0.82550\) & \(0.678298\) & \(216.7664\) & \(3.52\)E-\(75\) & Yes \\ T, SC, SI \(\rightarrow\) EU & \(0.69949\) & \(0.484246\) & \(97.08173\) & \(4.3\)E-\(44\) & Yes \\ T, SC, SI \(\rightarrow\) AT & \(0.63593\) & \(0.398535\) & \(68.8067\) & \(5.52\)E-\(34\) & Yes \\ T, SC, SI \(\rightarrow\) BI & \(0.494563\) & \(0.237138\) & \(32.81056\) & \(2.12\)E-\(18\) & Yes \\ PU, EU \(\rightarrow\) AT & \(0.601292\) & \(0.357365\) & \(86.36036\) & \(1.91\)E-\(30\) & Yes \\ PU, EU \(\rightarrow\) BI & \(0.509737\) & \(0.254979\) & \(53.53434\) & \(1.18\)E-\(20\) & Yes \\ PU, EU, AT \(\rightarrow\) BI & \(0.897264\) & \(0.803159\) & \(418.5442\) & \(1.4\)E-\(107\) & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 13: Multiple Regression Analysis Results.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Coefficients** & **Std Error** & **t-Stat** & \(p\)**-Value** & **Correlation** & **Semi-Part** \\ \hline PU \(\rightarrow\) AT & \(0.5850\) & \(0.0448\) & \(13.0504\) & \(3.00\)E-\(31\) & \(0.5980\) & \\ AT \(\rightarrow\) BI & \(0.9059\) & \(0.0256\) & \(35.3934\) & \(3.38\)E-\(110\) & \(0.8965\) & \(0.7398\) \\ PU \(\rightarrow\) BI & \(0.5019\) & \(0.0487\) & \(10.3062\) & \(1.41\)E-\(21\) & \(0.5076\) & \(-0.0355\) \\ PU & \(-0.0438\) & \(0.0312\) & \(-1.4037\) & \(0.1614\) & & \\ AT & \(0.9327\) & \(0.0319\) & \(29.2529\) & \(1.20\)E-\(90\) & & \\ \hline EU \(\rightarrow\) AT & \(0.3262\) & \(0.0426\) & \(7.6488\) & \(2.6539E-\(13\) & \(0.4006\) & \\ AT \(\rightarrow\) BI & \(0.9059\) & \(0.0256\) & \(35.3934\) & \(3.3791E-\(110\) & \(0.8965\) & \(0.8323\) \\ EU \(\rightarrow\) BI & \(0.2750\) & \(0.0443\) & \(6.2049\) & \(1.77517E-\(09\) & \(0.3343\) & \(-0.0271\) \\ EU & \(-0.0438\) & \(0.0312\) & \(-1.4037\) & \(0.1614\) & & \\ AT & \(0.9327\) & \(0.0319\) & \(29.2529\) & \(1.20\)E-\(90\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 14: Mediation Analysis (PU affects the outcome of BI indirectly through mediator AT).