# Risk Perception and Barriers to Electric Scooter Prevalence
## 3 Results
### SEM Results-Latent Variables
Support for shared ES services significantly predicts the willingness to use them (\(\beta\) = 0.488, \(p\) = 0.013), demonstrating that increased support for these services leads to a higher likelihood of using them. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Estimate** & **S.D.** & \(p\)**-Value** & **S.D.All (\(\beta\))** \\ \hline Positive attitudes & & & & \\ \hline Risk perception & \(-\)0.165 & 0.093 & 0.075 & \(-\)0.131 \\ \hline Willingness to use shared ES service & 0.421 & 0.081 & 0.001\(>\) & 0.372 \\ \hline Safe ES environment & 0.2 & 0.074 & 0.007 & 0.168 \\ \hline Support shared ES service & & & & \\ \hline Positive attitudes & 0.101 & 0.052 & 0.052 & 0.108 \\ \hline Subjective norms & 0.951 & 0.067 & 0.001\(>\) & 0.827 \\ \hline Willingness to use shared ES service & & & & \\ \hline Subjective norms & 0.448 & 0.191 & 0.019 & 0.412 \\ \hline Support shared ES service & 0.462 & 0.186 & 0.013 & 0.488 \\ \hline Willingness to share personal ES & & & & \\ \hline Positive attitudes & 0.236 & 0.061 & 0.001\(>\) & 0.315 \\ \hline Subjective norms & 0.481 & 0.081 & 0.001\(>\) & 0.523 \\ \hline Risk perception & & & & \\ \hline Female attitudes & 0.261 & 0.105 & 0.013 & 0.206 \\ \hline Female risk & \(-\)0.171 & 0.065 & 0.008 & \(-\)0.178 \\ \hline Female attitudes & & & & \\ \hline Subjective norms & 0.24 & 0.052 & 0.001\(>\) & 0.311 \\ \hline Female risk & & & & \\ \hline Subjective norms & 0.292 & 0.067 & 0.001\(>\) & 0.287 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Structural equation results—Regression. Willingness to share personal ES: This is positively influenced by positive attitudes (\(\beta\) = 0.315, \(p\) < 0.001). Individuals with more favorable attitudes towards ES are more likely to share their personal ES. It is also strongly influenced by subjective norms (\(\beta\) = 0.523, \(p\) < 0.001), highlighting the significant role of societal expectations. Risk perception: Female-specific attitudes positively affect risk perception (\(\beta\) = 0.206, \(p\) = 0.013), suggesting that negative perceptions of female use of ES increase overall risk perception. Female-specific risks negatively impact overall risk perception (\(\beta\) = \(-\)0.178, \(p\) = 0.008), indicating that these perceived risks may lower the general perception of risk. Female attitudes and Female risk: Both are significantly influenced by subjective norms, with \(\beta\) values of 0.311 and 0.287, respectively, showing that societal expectations strongly shape gender-specific attitudes and perceived risks. Table 5 provides the following SEM goodness-of-fit indices: Comparative Fit Index (CFI) = 0.908 and Tucker-Lewis Index (TLI) = 0.900: Both values indicate an acceptable fit, as values above 0.9 are considered good. Root Mean Square Error of Approximation (RMSEA) = 0.057: This value is within the acceptable range (0.05-0.08), indicating a good fit. Standardized Root Mean Square Residual (SRMR) = 0.072: This is slightly above the ideal threshold of 0.08, suggesting a reasonable fit. Figure 8: Structural equation model. * \(p\) < 0.05; ** \(p\) < 0.01; *** \(p\) < 0.001. \begin{table}
\begin{tabular}{l c} \hline \hline
**Goodness-of-Fit Indices** & **Value** \\ \hline Comparative Fit Index (CFI) & 0.908 \\ \hline Tucker–Lewis Index (TLI) & 0.900 \\ \hline Root Mean Square Error of Approximation (RMSEA) & 0.057 \\ \hline Standardized Root Mean Square Residual (SRMR) & 0.072 \\ \hline Akaike information criterion (AIC) & 23,364 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SEM goodness-of-fit indices.