## 4 Results and Discussion
### Structural Equation Modeling (SEM)  
Model estimation and causal relationship analysis were conducted with the collected data. Subsequently, a statistical model was constructed based on the latent variables' relations, the relations between the latent variables and the observed variables, and the hypothesis model. Figure 4 shows the entire relationship among the latent variables and the observed variables in this study, which consist of risk perception (RP), positive affect (PA), risky behavior (RB), and personal characteristics (PC) such as age (AGE) and riding experience (EX). Hence, a hypothesis model was initially used to investigate the relationship among the latent measures shown in Figure 4 as follows:  
**Hypothesis 1 (H1). **_Risk perception has a negative influence on risky behavior._  
**Hypothesis 2 (H2). **_Positive affect has a positive influence on risky behavior._  
**Hypothesis 3 (H3). **_Positive affect is expected to have a greater influence on risky riding behavior than risk perception._  
To present the model estimation and causal relationship analysis, a statistical model was constructed based on the latent variables' relations, the relations between the latent variables and the observed variables, and the hypothesis model. The statistical software IBM SPSS AMOS (Statistical Package for the Social Sciences--AMOS) was used to test the hypotheses based on the complex variable relationships, and to construct a model to present the causal relationships among the variables. The relationships among the measures included three components, as the risk perception and positive affect of motorcyclists are the measures of factors for their risky behavior. In accordance with the completed Principal Component Analysis (PCA), reliability test, and the hypothesis models, the relationships among the three components--risk perception (RP), positive affect (PA) and risky  
Figure 4: Relationship model of the study. behavior (RB)--and personal characteristics (PC)--which include (AGE) and riding experience (EX)--can be drawn as shown in Figure 5.  
The results obtained from SEM indicate an influential path model from two latent variables--motorcyclists' perception and positive affect--to the other latent variable, risky behavior. The latent variable of risk perception is determined by 11 observed variables. These included Y1 (Frequently changing lane to overtake the vehicle in front. ), Y2 (Speeding up and suddenly braking), Y3 (Exceeding speed limit even feeling unsafe), Y4 (Riding fast on the curve), Y5 (Continue riding although feeling sleep), Y6 (Taking alcohol before riding), Y7 (Run a red light), Y8 (Racing with other vehicles), Y11 (Overtaking/turning without using signal lights), Y12 (Riding without wearing crash helmet), and Y14 (Using mobile phone while riding). Moreover, six observed variables were also determined on latent variable positive affect. The six observed variables consist of Z6 (Taking alcohol before riding), Z7 (Run a red light), Z11 (Overtaking/turning without using signal lights), Z12 (Riding without wearing crash helmet), Z13 (Crossing a stop-junction without fully stopping) and Z14 (Using mobile phone while riding). The validity criteria from studies conducted by [PERSON] and [PERSON] [25] and [PERSON] [26] were used in this study to obtain the goodness of fit of the constructed model. Consequently,  
Figure 5: Relationship of latent variables components in Structural Equation Modeling (SEM). indicators such as Normed Fit Index (NFI) and Comparative Fit Index (CFI) values were lower than the cut-off point. However, values such as \(\chi^{2}\)/df (chi-squared/degree of freedom), Root Mean Square Error of Approximation (RMSEA) and Parsimony-adjusted NFI (PNFI) were statistically acceptable. Table 5 shows the details of the goodness of fit of the constructed model. Table 6 shows the standardized estimate coefficient of the path analysis model, which indicates the relationship of the observed variables with the latent variables.