# Application of the Theory of Planned Behavior in Autonomous Vehicle-Pedestrian Interaction
## 5 Data Analysis and Results
### Assessment of Measurement Model  
Examining the measurement models is the first step in analyzing the PLS-SEM findings. The authors of [64, 65, 66] recommended that for measuring the measurement model, it is essential to test the individual item reliability/indicator loadings, internal consistency reliability, convergent validity, and discriminant validity. Individual item reliability/indicator loadings: Indicator loadings is the first step in evaluating a measurement model. Loadings greater than 0.708 are suggested since they show that the concept explains more than 50% of the variation in the variable, as well as retain the items if loading between 0.40 and 0.70 [66]. The current study shows adequate indicator loadings; thus, individual item reliability is satisfied, as shown in Table 6.  
Internal consistency reliability: is the second most important measure of reliability. Internal consistency reliability is assessed by composite reliability, Cronbach's Alpha, and rho_A. [PERSON] [66] and [PERSON] [67] proposed a criterion of 0.7 or above for assessing composite reliability, Cronbach's Alpha, and rho_A. As shown in Table 6, the values of composite reliability, Cronbach's Alpha, and rho_A of the study's latent variables are above 0.7, indicating sufficient internal consistency reliability of the measures [PERSON] [66]. Convergent validity: shows the extent to which a construct converges to explain the variance of its items. [PERSON] [68] proposes that convergent validity should be tested with average variance extracted (AVE). Furthermore, as per [PERSON] [69], the AVE should be at least 0.50 or higher to show that a construct has good convergent validity. Referring to Table 6, every construct in this study obtained a minimum of 0.50 AVE values, which indicates that the study shows good convergent validity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Component**} & \multirow{2}{*}{**Dimension**} \\ \hline  & **1** & **2** & **3** & **4** & **5** \\ \hline Q\_12 & & & 0.561 & & & \\ Q\_13 & & & 0.688 & & & Perceived \\ Q\_14 & & & 0.753 & & & Behavioral Control \\ Q\_15 & & & 0.820 & & & \\ Q\_16 & & & 0.715 & & & \\ Q\_17 & & & 0.775 & & & \\ Q\_18 & & & 0.762 & & & \\ Q\_19 & & & 0.597 & & & \\ Q\_20 & & & & 0.689 & & \\ Q\_21 & & & & 0.660 & & \\ Q\_22 & & & & 0.815 & Pedestrian Behavior \\ Q\_23 & & & & 0.767 & & \\ Q\_24 & & & & 0.478 & & \\ \hline \hline \end{tabular} Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 6 iterations.  
\end{table} 
Table 5: _Cont._Discriminant validity: measures the degree to which a variable in the model is empirically different from other constructs. The discriminant validity was determined using the Fornell [68] criterion and the [PERSON] [70] HTMT ratio. As per [PERSON]'s [71] criteria, the square root of the AVE should be greater than the correlations among the latent variables for determining discriminant validity. HTMT values should be <0.85 or must <0.9 as per HTMT ratio [70]. Tables 7 and 8 show the results of the study, which are in the suggested ratio; therefore, it is concluded that discriminant validity is not the issue in the current study.