# Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment
## 5 Data Analysis and Results
### Linear and Non-Linear Relationship  
A correlation shows the relationship between two variables, while regression allows us to see how one affects the other. Based on the correlation analysis results, we examine the type of regression involving the personal characteristics variables, including gender, age, locality, involvement in accidents, knowledge about in-vehicle applications, locality, self-reported capabilities (vision, hearing, mobility and dexterity) and driving distance per week as shown in Table 16. Non-linear regressions were found for age and intention to use the in-vehicle application, self-reported capabilities and system characteristics, gender and attitude. We compared the standard error values of linear and non-linear regression models. A lower standard error value indicates a better fit model for the variables.  
Table 17 summarizes our findings when we further investigate the personal characteristics variables, which may have a direct influence or moderate the investigated relationships. Knowledge about in-vehicle applications positively influences trust, social influence and system characteristics, which eventually fosters a positive attitude and higher intention to use the application in their driving experience. Because the paper focuses on individual acceptance, locality and knowledge contribute to social influence. Individuals who reside in sub-urban and urban areas may have more exposure to the automotive changing landscape and information, allowing them to react better toward their social cycle views and comments.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Correlation** & **Std Error (Linear)** & **Std. Error (Non-Linear)** \\ \hline Locality \(\rightarrow\) PU & \(-0.12525\) & \(0.05677\) & \(0.00184\) \\ Gender \(\rightarrow\) AT & \(0.15946\) & \(0.08532\) & \(0.00286\) \\ Age \(\rightarrow\) AT & \(-0.1194\) & \(0.03490\) & \(0.00063\) \\ Knowledge \(\rightarrow\) AT & \(0.18236\) & \(0.09378\) & \(0.00372\) \\ Locality \(\rightarrow\) T & \(-0.10759\) & \(0.05602\) & \(0.00198\) \\ Knowledge \(\rightarrow\) T & \(0.14532\) & \(0.09498\) & \(0.00421\) \\ Knowledge \(\rightarrow\) SC & \(0.12307\) & \(0.10096\) & \(0.00424\) \\ Self-reported & \(-0.11057\) & \(0.10396\) & \(0.00425\) \\ Capabilities \(\rightarrow\) SC & \(-0.14126\) & \(0.06822\) & \(0.00234\) \\ Knowledge \(\rightarrow\) SI & \(0.14740\) & \(0.11612\) & \(0.00490\) \\ Gender \(\rightarrow\) BI & \(0.14236\) & \(0.08645\) & \(0.00301\) \\ Age \(\rightarrow\) BI & \(-0.13941\) & \(0.03518\) & \(0.00066\) \\ Knowledge \(\rightarrow\) BI & \(0.18607\) & \(0.09470\) & \(0.00389\) \\ \hline \hline \end{tabular}
\end{table}
Table 16: Personal characteristics variable relationships.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Coefficients** & **Std Error** & **t-Stat** & \(p\)**-Value** \\ \hline PU \(\rightarrow\) AT \(\rightarrow\) BI & \(0.5361\) & \(0.0438\) & \(12.2488\) & \(2.52\)E-28 \\ EU \(\rightarrow\) AT \(\rightarrow\) BI & \(0.3592\) & \(0.0480\) & \(7.4791\) & \(8.03\)E-13 \\ SI \(\rightarrow\) EU \(\rightarrow\) AT & \(0.190105\) & \(0.031894\) & \(5.960593\) & \(6.94\)E-09 \\ SC \(\rightarrow\) EU \(\rightarrow\) AT & \(0.266247\) & \(0.038724\) & \(6.875561\) & \(3.49\)E-11 \\ T \(\rightarrow\) EU \(\rightarrow\) AT & \(0.241852\) & \(0.036435\) & \(6.638004\) & \(1.45\)E-10 \\ SI \(\rightarrow\) PU \(\rightarrow\) AT & \(0.448429\) & \(0.041098\) & \(10.91116\) & \(1.28\)E-23 \\ SC \(\rightarrow\) PU \(\rightarrow\) AT & \(0.430876\) & \(0.040603\) & \(10.612\) & \(1.34\)E-22 \\ T \(\rightarrow\) PU \(\rightarrow\) AT & \(0.241852\) & \(0.036435\) & \(6.638004\) & \(1.45\)E-10 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Results of Sobel Test.