## 4 Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Variables** & **KHS** & **TMBE** & **HEMS** & **KER** & **PZM** & **EMJ** & **RNC** & **RAM** & **PNM** & **RA** & **SEC** & **BTM** & **STC** & **MB** \\ \hline
**KHS** & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline
**TMBE** & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline
**HEMS** & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline
**KER** & 1 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline
**PZM** & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
**EMJ** & 1 & 0 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 1 \\ \hline
**RNC** & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
**RAM** & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 1 & 1 & 1 & 1 \\ \hline
**PNM** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline
**RA** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 0 \\ \hline
**SEC** & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline
**BTM** & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 1 & 1 \\ \hline
**STC** & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 1 & 1 \\ \hline
**MB** & 1 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Final received matrix (Ref: Authors). that cause the mentioned variable (Table 9). Finally, the structural model is presented in 9 levels according to Figure 3.  
Low-level variables are the most influential (TMBE and HEMS) and higher-level variables are the most influenced (PZM and RNC). Research model variables can now be identified in terms of penetration and dependency using MICMAC analysis. In MICMAC analysis, the type of variable is determined  
Figure 3: Leveling of indicators (Ref: Authors). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Degree of Dependence** & **Influence Rate** \\ \hline KHS & 8 & 9 \\ \hline TMBE & 1 & 14 \\ \hline HEMS & 2 & 13 \\ \hline KER & 7 & 12 \\ \hline PZM & 5 & 1 \\ \hline EMJ & 5 & 10 \\ \hline RNC & 11 & 1 \\ \hline RAM & 10 & 7 \\ \hline PNM & 9 & 7 \\ \hline RA & 9 & 5 \\ \hline SEC & 10 & 10 \\ \hline BTM & 11 & 6 \\ \hline STC & 12 & 4 \\ \hline MB & 11 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Influence power and dependence of structural model variables (Ref: Authors). according to its impact on other variables, and after determining the influence or influence and the dependence of factors, all word factors on Industry 5.0 can be classified in the banking industry (Figure 4). MICMAC analysis is a method for graphical representation of variables based on their influence and dependence in structural-interpretive modelling. Based on the power of dependence and the influence of variables, a coordinate system can be defined and divided into four equal parts. MICMAC analysis is based on the power of influence (effective) and the degree of dependence (impressive) of each variable and allows further investigation of the range of each variable. As it is known, it is located in the first region of PZM. This indicates that it operates almost separately from the entire system. This component has little effect on other components and in fact, the relationship of these components with other components is very limited and insignificant. In the second area, there are RNC, RA, STC, RAM, PNM, and BTM indices, which show the low penetration of this index and have a high dependence on other indices in the model. In the third area are the MB, SEC, and KHS indices, which have a strong influence as well as a dependency force. These variables are in fact components that are unstable, meaning that taking any action on these components, in addition, directly affects other components. It can also affect the component itself in the form of feedback from other components.