# Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia
## 4 Findings
### Reflective Measurement Model  
Before proceeding to the PLS-SEM's structural model assessment (stage 2), certain quality criteria must be met in Stage 1. This involves evaluating the reliability, convergent \begin{table}
\begin{tabular}{c c c c c} \hline
**Construct** & **Mean** & **Standard Deviation** & **Excess Kurtosis** & **Skewness** \\ \hline FC & 4.828 & 1.362 & 0.315 & \(-\)0.435 \\ \hline AT & 5.319 & 1.215 & 0.45 & \(-\)0.553 \\ \hline SN & 4.888 & 1.357 & 0.299 & \(-\)0.496 \\ \hline PBC & 5.106 & 1.222 & 0.632 & \(-\)0.459 \\ \hline AW & 4.569 & 1.426 & 0.136 & \(-\)0.44 \\ \hline BI & 4.814 & 1.329 & 0.165 & \(-\)0.373 \\ \hline \end{tabular}  
* Scale: “1 = Strongly Disagree”, “2 = Slightly Disagree”, “3 = Disagree”, “4 = Neutral”, “5 = Agree”, “6 = Slightly Agree”, “7 = Strongly Agree”.  
\end{table} 
Table 6: Descriptive analysis. Figure 4: Initial framework on Smart PLS 4.0.  
validity, and discriminant validity. Reliability pertains to the extent to which a measurement is stable and free from random error [97]. In the context of PLS-SEM, composite reliability (CR) is the preferred metric [98]. To establish reliability, the CR should be at least 0.70 [97]. Convergent validity measures the degree to which one attribute is associated with others within the same dependent variables [99]. To ascertain convergent validity, we calculated the average variance extracted (AVE) to assess item factor loadings and their significance [97; 98; 99]. The AVE of the measurement model should exceed 0.50, indicating that the variables explain at least 50% of the variance in the outer models [98]. Factor loadings determine an item's contribution to its constructs, and they should be at least 0.708 [99; 100]. Table 7 indicates that all item loadings exceed the minimum threshold of 0.708 for their respective constructs. Additionally, they contribute to an AVE value that surpasses the minimum threshold of 0.50 for FC, AT, SN, PBC, AW, and BI. Moreover, the minimum CR value of 0.70 is met, signifying that the measurement models satisfy the reliability and convergent validity requirements. \begin{table}
\begin{tabular}{c c c c c} \hline
**Construct** & **Item** & **Loading** & **AVE** & **CR** \\ \hline \multirow{6}{*}{FC} & FC5 & 0.879 & & \\ \cline{2-5}  & FC6 & 0.919 & & \\ \cline{2-5}  & FC7 & 0.867 & 0.802 & 0.953 \\ \cline{2-5}  & FC8 & 0.917 & & \\ \cline{2-5}  & FC9 & 0.894 & & \\ \hline \multirow{6}{*}{AT} & AT1 & 0.814 & & \\ \cline{2-5}  & AT2 & 0.851 & & \\ \cline{2-5}  & AT3 & 0.844 & & \\ \cline{2-5}  & AT4 & 0.831 & & \\ \cline{2-5}  & AT5 & 0.862 & 0.709 & 0.956 \\ \cline{2-5}  & AT6 & 0.849 & & \\ \cline{2-5}  & AT7 & 0.846 & & \\ \cline{2-5}  & AT8 & 0.862 & & \\ \cline{2-5}  & AT9 & 0.815 & & \\ \hline \multirow{6}{*}{SN} & SN1 & 0.794 & & \\ \cline{2-5}  & SN2 & 0.8 & & \\ \cline{2-5}  & SN4 & 0.856 & & \\ \cline{2-5}  & SN5 & 0.874 & & \\ \cline{2-5}  & SN6 & 0.854 & & \\ \cline{2-5}  & SN7 & 0.849 & & \\ \cline{2-5}  & SN9 & 0.741 & & \\ \cline{2-5}  & SN10 & 0.765 & & \\ \hline \end{tabular}
\end{table} 
Table 7: Reflective measurement model. The next step involves assessing the discriminant validity of the model, which can be done using one of three methods [98]. The first method is the Fornell and Larcker Criterion, which examines the extent to which indicators load more strongly on their respective constructs than on other constructs [101]. Table 8 illustrates that all constructs in the model exhibit satisfactory discriminant validity. This implies that the constructs are distinct from one another with every value in its own construct is higher than other construct, demonstrating that the model displays satisfactory discriminant validity. It indicates that the study's constructs are not highly correlated with each other.