# Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment
## 5 Data Analysis and Results
### Causal Relationship  
The causal relationships between the constructs PU, EU, AT, BI, DC, SC and SI are investigated using regression analysis. The results are shown in Table 12. The hypotheses are validated at 95% confidence level. If the null hypothesis is rejected, it is statistically significant that there is a non-zero correlation among the variables, and it can be modeled with the regression equation. Furthermore, the correlation between the predicted value of Y generated in the equation and the actual Y value for each unit refers to the multiple R. The coefficient values provide the impact or weight of a variable toward the entire regression model.  
The assumption is that the combination of independent variables will generate a larger multiple R or correlation than any single variable used as a predictor variable. When single predictors were used (T, SC, SI) to predict the PU, the multiple R values were 0.73987, 0.74992 and 0.72057, respectively. The multiple R value increased to 0.82550 when the predictors were combined. Moreover, the Adjusted R2 indicates the amount of variability being explained by the regression model. Any field that attempts to predict human behavior, such as psychology, typically has R-squared values lower than 0.5. For instance, the value of the Adjusted R2 is 0.6783 when the T, SC and SI were assigned to predict the PU. This shows that the explanatory power of trust, system characteristics and social influence to perceived usefulness is 67.83%. Because the value is more than 0.5, the variables are a good fit to predict the perceived usefulness of in-vehicle applications. This means that the combination of the variables has a significant positive effect on the user-perceived usefulness of in-vehicle applications. When trust, system characteristics and social influence have high values, users are much more likely to perceive that an in-vehicle application is useful. The multiple regression analysis results of different models are summarized in Table 13.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PC Variables** & **VIF** &
\begin{tabular}{c} **Exploratory** \\ **Variables** \\ \end{tabular} & **VIF** \\ \hline Gender & 1.1208 & Trust & 4.2503 \\ Age & 1.1637 & System characteristics & 1.8976 \\ Driving license & 1.0660 & Social influence & 3.4920 \\ Accidents & 1.0405 & Perceived usefulness & 1.5183 \\ Locality & 1.1249 & Perceived ease of use & 1.5183 \\ Knowledge & 1.1070 & & \\ Self-reporting capabilities & 1.1239 & & \\ Driving distance per week (km) & 1.0926 & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Variance Inflating Factor (VIF).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**X\(\rightarrow\)Y** & **Multiple R** & **Coefficient** & **Std. Error** & **t Stat** & _p-Value_ & **Hypothesis** \\ \hline EU \(\rightarrow\) PU & 0.58428 & 0.48622 & 0.03861 & 12.59397 & 1.38E-29 & H1 rejected \\ PU \(\rightarrow\) AT & 0.59797 & 0.32618 & 0.04264 & 7.64880 & 2.65E-13 & H2 rejected \\ EU \(\rightarrow\) AT & 0.40063 & 0.32618 & 0.04264 & 7.64880 & 2.65E-13 & H3 rejected \\ AT \(\rightarrow\) BI & 0.89648 & 0.90591 & 0.02560 & 35.39342 & 3.40E-110 & H4 rejected \\ PU \(\rightarrow\) BI & 0.50761 & 0.50186 & 0.04870 & 10.30616 & 1.41E-21 & H5 rejected \\ EU \(\rightarrow\) BI & 0.33430 & 0.27504 & 0.04433 & 6.20492 & 1.78E-09 & H6 rejected \\ T \(\rightarrow\) PU & 0.73987 & 0.75133 & 0.03906 & 19.23795 & 1.29E-54 & H9 rejected \\ SI \(\rightarrow\) PU & 0.74992 & 0.62271 & 0.03140 & 19.83043 & 7.41E-57 & H7 rejected \\ SC \(\rightarrow\) PU & 0.72057 & 0.6949 & 0.03798 & 18.17872 & 1.37E-50 & H11 rejected \\ T \(\rightarrow\) EU & 0.60368 & 0.73669 & 0.05562 & 13.24611 & 5.74E-32 & H10 rejected \\ SI \(\rightarrow\) EU & 0.47452 & 0.473489 & 0.05021 & 9.42990 & 1.06E-18 & H8 rejected \\ SC \(\rightarrow\) EU & 0.66457 & 0.765272 & 0.04919 & 15.5580 & 1.29E-40 & H12 rejected \\ \hline \hline \end{tabular}
\end{table}
Table 12: ANOVA results.