# Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia
## 4 Findings
### Structural Measurement Model
This confirms the reliability of the results and supports the validity of the causal relationships between the variables in the model. Next, it is necessary to measure the path coefficients of the structural model proposed earlier. The path coefficients are versions of linear regression weights that can be used in examining the possible causal linkages between statistical variables in the SEM approach. The significance of the proposed hypotheses was tested using \(t\)-statistics generated through bootstrapping in Smart PLS 4.0. Table 12 displays the assessment of the path coefficients, with five hypotheses found to have t-values greater than 1.28, and the \(p\)-value for this study was 0.10. Specifically, the predictors AT (0.006), SN (0.018), PBC (0.002), and AW (0.046) were found to significantly affect the BI readiness of ERS, while one moderator hypothesis was supported, which was TA \(\times\) FC (0.088), regarding the BI readiness of ERS, due to the \(t\)-value > 1.28 and the \(p\)-value < 0.10. Furthermore, as suggested by [105], the \(p\)-value indicates whether there is an effect that exists, but the \(p\)-value will not reveal the size of the effect; therefore, an assessment of the effect size is recommended, as in [98; 99]. The effect size is measured using f\({}^{2}\), with values of 0.02, 0.15, and 0.35 representing small, medium, and large effects, respectively [106]. From Table 12, it can be observed that all constructs have relatively small effect sizes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Construct** & **BI (VIF)** \\ \hline FC & 6.3 \\ \hline AT & 7.054 \\ \hline SN & 7.881 \\ \hline PBC & 9.378 \\ \hline AW & 4.082 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Collinearity assessment. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hypothesis** & **Relationship** & **\(t\)-Value** & **\(p\)-Value** & **Decision** & **f\({}^{2}\)** \\ \hline H1 & FC -¿ BI & 1.138 & 0.127 & Not supported & 0.004 \\ \hline H2 & AT -¿ BI & 2.487 & 0.006 & Supported & 0.017 \\ \hline H3 & SN -¿ BI & 2.096 & 0.018 & Supported & 0.019 \\ \hline H4 & PBC -¿ BI & 2.957 & 0.002 & Supported & 0.031 \\ \hline H5 & AW -¿ BI & 1.686 & 0.046 & Supported & 0.015 \\ \hline H6 & TA \(\times\) FC -¿ BI & 1.353 & 0.088 & Supported & 0.006 \\ \hline H7 & TA \(\times\) AT -¿ BI & 0.159 & 0.437 & Not supported & 0 \\ \hline H8 & TA \(\times\) SN -¿ BI & 1.163 & 0.122 & Not supported & 0.006 \\ \hline H9 & TA \(\times\) PBC -¿ BI & 0.864 & 0.194 & Not supported & 0.004 \\ \hline H10 & TA \(\times\) AW -¿ BI & 0.397 & 0.346 & Not supported & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Hypothesis testing. The \(f^{2}\) contributes to the coefficient of determination or \(R^{2}\). \(R^{2}\) is the proportion of the variation in the dependent variables that is predictable from the independent variables [106]. The \(R^{2}\) values are assessed as follows: 0.25 (weak), 0.50 (moderate), and 0.75 (substantial) [106]. Furthermore, the predictive relevance or \(Q^{2}\) is assessed to measure whether a model has predictive relevance accuracy, specifically when the \(f^{2}\) is relatively small [100]. The minimum value of \(Q^{2}\) is 0, while the closer the \(Q^{2}\) value to the \(R^{2}\), the more predictive the constructs' relevancy [100]. Table 13 shows the value of \(R^{2}\) (0.632), which is a moderate value and indicates that the model explains 63.2% of the variance, and the \(Q^{2}\) is 0.557, which is above the minimum value and closer to the original coefficient \(R^{2}\)[100].