## 5 Results and Discussion
### Assessment of Inner Model  
After completing the outer model or measurement model assessment, the inner model was evaluated. The inner model assessment aims to verify the hypothesized relationships between all the constructs. First of all, the path coefficients of the relationship were tested via a bootstrapping method to obtain the \(t\)-values. In general, path coefficients with \(t\)-values above 1.96 accepted at \(p<0.05\) are considered significant. The results from the analysis are shown in Figure 7.  
The results show that the coefficient of determination of the latent construct of risky behavior was moderate, while positive affect and risk perception were categorized as weak. The findings also imply that 50% of risky behavior is predicted by personal characteristics, positive affect, and risk perception, as shown in Table 9. \begin{table}
\begin{tabular}{c c} \hline \hline
**Indictors** & **VIF** \\ \hline Age & 1.019 \\ Gender & 1.019 \\ Motorcycle Type & 1.046 \\ Trip Distance & 1.043 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Variance inflation factor (VIF) of formative indicators. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Indictors** & **\(t\)-Values** & \(p\)**-Values** \\ \hline Age \(\rightarrow\) PC & 1.999 & 0.046 \\ Gender \(\rightarrow\) PC & 3.825 & 0.000 \\ Motorcycle type \(\rightarrow\) PC & 2.205 & 0.027 \\ Trip distance \(\rightarrow\) PC & 3.316 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Formative indicator outer weights. The findings indicate that four out of six hypotheses can be accepted; the path with the highest significance was PA and RB, with a \(t\)-value of 15.571. The second most significant correlation was the constructs involving PC and RP, with a \(t\)-value of 4.018. Other paths that were accepted as significant were PC with RB and PC with RP, with \(t\)-values of 2.175 and 3.064, respectively. Hypothesis testing also revealed that positive affect had the strongest positive effect on risky behavior, followed by the effect of personal characteristics on positive affect. The path coefficients and their significance values are tabulated in Table 10. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Hypothesis**} & **Hypothesis** & **Path** & \multirow{2}{*}{\(t\)**-Values * **} & \multirow{2}{*}{\(p\)**-Values**} & **Accept/Reject** \\  & **Path** & **Coefficients** & & & **Significance** \\ \hline H1 & RP \(\rightarrow\) RB & \(-\)0.052 & 1.060 & 0.289 & Reject \\ H2 & PA \(\rightarrow\) RB & \(-\)0.670 & 15.571 & 0.000 & Accept \\ H3 & RP \(\rightarrow\) PA & \(-\)0.115 & 1.333 & 0.183 & Reject \\ H4 & PC \(\rightarrow\) RB & \(-\)0.113 & 2.175 & 0.030 & Accept \\ H5 & PC \(\rightarrow\) PA & \(-\)0.320 & 4.018 & 0.000 & Accept \\ H6 & PC \(\rightarrow\) RP & \(-\)0.274 & 3.064 & 0.002 & Accept \\ \hline \end{tabular}
\end{table} 
Table 10: Hypothesis testing. Figure 6: Outcome through assessment of the outer model via PLS algorithm. In this study, two potential mediation models can be identified while assessing personal characteristics as the mediator. The two models' hypothesis paths were PC \(\rightarrow\) PA \(\rightarrow\) RB and PC \(\rightarrow\) RP \(\rightarrow\) RB. In this study, evaluation of the mediation effect revealed that one of the two mediation hypothesis paths could be accepted. It can be observed that personal characteristics (PC) had a significant effect on risky behavior (RB) through positive affect (PA), with a \(t\)-value of 3.885, as shown in Table 11. This finding implies that risky behavior not only has direct effects on positive affect and personal characteristics but that it also has an indirect effect on personal characteristics via positive affect. Risky behaviors (X16) to (X21) were found to be the least common behaviors in both the preliminary and the principal surveys. Therefore, these behaviors were excluded in the subsequent analysis following the SEM analysis.