## 5 Conclusions  
The relationships among the three latent variables, namely risk perception, positive affect, and risky behavior, were studied. This study found that the latent variables risk perceptions and positive affect had an impact on risky riding behavior. Comparing the three latent variables, risk perception had a weak positive influence on risky riding behavior. It was expected that risk perception would have a negative relation with risky behavior, but based on the results obtained from SEM, the estimated value obtained was positive. This is probably due to the limited sample size, which could have led to inaccuracy in the results. On the other hand, this study showed there is a significant relationship between risky behavior and positive affect resulting from risky behavior in the region of Malaysia, in particular. Therefore, this study reveals that engagement of Malaysian riders in risky behaviors is mostly affected by the positive emotions resulting from the positive outcomes of the behavior. An example from the results depicting this scenario is that motorcyclists consistently cross stop junctions without fully stopping, although this is risky. This finding provides a possible direction for policymakers to pursue in order to reduce the number of accidents involving motorcyclists by addressing the high degree of positive affect experienced by Malaysian riders. However, from the findings obtained, risk perception shows a low positive relationship with the risky riding behavior (estimate coefficient = 0.036), which does not really fit the initial expectation. This may be due to the unsatisfactory number of samples collected, which could lead to inaccuracy in the findings. In fact, not all of the observed variables were considered significant after undergoing PCA. Observed variables that obtained factor loadings lower than 0.5 and also explained less than 20% of the variance of the items were considered to be insignificant. Nevertheless, a model of estimations and a causal relationship analysis were conducted by constructing a statistical model. SEM was used for the analysis. Based on the model, the relationships among the latent variables risk perception, positive affect, and risky riding behavior were clearly shown. The age and riding experience (i.e., the number of years for which motorcyclists had possessed a riding license) of the motorcyclists were addressed in the model. Based on the results obtained, the age of the motorcyclists significantly affected their risky behavior, with an estimate coefficient of \(-0.037\). This shows that elderly motorcyclists seldom perform risky riding behavior while riding on the road. Additionally, riding experience had a positive relationship with motorcyclists' risk perception (estimate coefficient = 0.012) and a negative relationship with positive affect (estimate coefficient = \(-\)0.032). This shows that motorcyclists that have only recently obtained their motorcycle license tend to think that the behaviors in question have lower risk and are more likely to engage in a the particular risky riding behavior. The following suggestions and recommendations are put forward to improve the further study of related research on risky riding behavior with respect to motorcyclists' personal preferences and risk perception:  
1. Larger sample size is required to ensure the accuracy of the results obtained. The greater the amount of data obtained, the more accurate the analysis process will be. 2. The gender of the respondents should be controlled so that both genders of respondents have a balanced number. 3. As the results indicate that risk perception has a low positive relationship with risky riding behavior (estimate coefficient = 0.036), which was not expected, this study could be further improved through the collection of an increased number of samples, which should provide a more consistent finding. 4.