# Analysis of EMC Factors on Electronic Devices Using PLS-SEM Method: A Case Study in Vietnam
## 5 Discussion of Findings  
The importance of the path model is assessed from the validity of the measures, which are based on the path coefficients, and also with the significance levels of the path model. The smart-PLS 3.3.0 software is used to analyze the path model and determine the \(p\)-value by starting and calculating in the different path model. The index of the path coefficients and the levels of statistical significance were determined randomly in 10,000 samples of the model's Bootstrapping analysis. Details are shown in Table 6.  
The values of load factor, path coefficient, and \(p\)-value in the structure and path models of the PLS-SEM model are shown in detail in Figure 3. The absolute values of each path are shown in Figure indicated by arrows. The indexes are very important in the structural model of the PLS-SEM model. Table 5 shows the results for each hypothesis in this research model. The formative and reflective models in PLS-SEM analyzed in Figure 3 show that all the \(p\)-values of the observed variables are less than 0.05. This proves that the values of the observed variables positively and closely affect the latent variables (EMI, EMS, and EMC-MMS) of the formative model and the latent variables (EMC) of the reflective model. In the formative model, the observed variables are generated by the same research concept, \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & **Hypothesis** & **Coefficient** & **Standard Deviation** & **T Statistics** & \(p\)**-Value** & **VIF** & **F Square** & \
\begin{tabular}{l} **CI 2.5\%** \\ **Lower** \\ \end{tabular} &
\begin{tabular}{l} **CI 97.5\%** \\ **Upper** \\ \end{tabular} \\ \hline H1 & EM\(\sim\)-EMC & \(-0.135\) & 0.048 & 3.091 & 0.001 & 3.019 & 0.002 & \(-0.209\) & \(-0.102\) \\ H2 & EMS\(\sim\)-EMC & 0.298 & 0.046 & 7.810 & 0.000 & 2.190 & 0.162 & 0.218 & 0.409 \\ H3 & EMC-MMS\(>\)EMC & \(-0.509\) & 0.041 & 12.093 & 0.000 & 2.019 & 0.301 & \(-0.709\) & \(-0.598\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hypothesis results. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **EMI** & **EMS** & **EMC-MMS** \\ \hline EMI & 0.701 & & \\ EMS & 0.519 & 0.709 & \\ EMC-MMS & 0.619 & 0.691 & 0.297 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Discriminant validity-HTMT. and these observed variables need to be closely related to each other. In the reflective model, the observed variables are assumed to produce the parent latent variable through linear association, and no correlation is required between them. They are not interchangeable because each observed variable represents a particular aspect of the latent parent variable. Because of this, the removal of an observed variable has a very high probability of changing the properties of the parent latent variable. Our research found that, by regulations and standards, Electromagnetic Interference (EMI) is the cause of harm to surrounding devices. The electronic components in the circuit are interrupted. The transmission does not meet the standard requirements of the equipment due to the electromagnetic interference components of the power electronic devices, for which they are not designed with anti-interference systems. In specific cases, when the TV screen is placed near the heater, the electromagnetic waves from the heating element cause the signal to be interfered with and the screen does not display or display information blurred, the human eye cannot see clearly content on the TV screen. The continuous on-off state of switches or switches such as transistors, MOSFETs, and circuit breakers are the main cause of electromagnetic interference [16; 21]. Power electronic components located near areas with a lot of electromagnetic interference will be affected by the signal, and sometimes the electromagnetic interference signal that controls the device is affected by electromagnetic interference.