## 4 Empirical Analysis
### The NRM Analysis Based on the DEMATEL Technique  
The DEMATEL technique was adopted to construct the structure of the network relationships map (NRM) for the digital transformation of the Med-Tech industry. When users are making decisions about the digital transformation of the Med-Tech industry, there are many criteria they may consider. The most common problem they face is how these aspects impact each other. Therefore, before making development decisions, it is necessary to know the essential aspects and then make useful improvements to enhance the overall capability of digital transformation. When a decision-maker needs to develop many aspects, the best way to handle this is to determine the aspects that most impact others and improve them. Recent studies have used the DEMATEL techniques for solving complex problems, such as the analysis of user interface [47], failure sorting evaluation system [48], the innovation policy portfolios of Taiwan's SIP Mall industry [49], the evaluation system of e-learning programs [50], the evaluation system of knowledge management strategy [51], the value-created systems of science (technology) parks [20], and the key success factors of block-chain for sustainable supply chain [52], the selection of R&D projects for energy sector based on the fuzzy based approach [53]. This study divides DEMATEL into five steps: (1) Calculate the original average matrix, (2) calculate the direct influence matrix, (3) calculate the indirect influence matrix, (4) calculate the full influence matrix, and (5) analyze the NRM (network relation map). 1. Calculate the original average matrix Respondents were asked to indicate the influence that they believed each aspect exerts on each of the others according to a scoring scale ranging from 0 to 4. \"0\" means no influence, whereas \"4\" means \"extreme influence\" between aspect/criterion. \"1\", \"2\", and \"3\" mean \"low influence\", \"medium influence\" and \"high influence\" respectively [54]. As shown in Table 4, the influence that \"PC\" has on \"CR\" is 2.688, which means \"medium influence.\" The influence that \"RS\" has on \"PC\" is 2.713, which also means \"medium influence\" as shown in Table 4.
2. Calculate direct influence matrix  
From Table 4, we processed the \"Original average influence matrix\" (\(A\)) by Equations (1) and (2) and got the \"Direct influence matrix\" (\(D\)). As shown in Table 4, the diagonal items of \(D\) are all 0, and the sum of a row is at most 1. Then we got Table 5 by adding up rows and columns. In Table 6, the sum of row and column for \"Operation management (OM)\" is 1.980, which is the most important influence aspect. On the other hand, the sum of row and column for \"Regulatory system (RS)\" is 1.884, which is the least essential influence aspect. \[\mathbf{D}=\mathbf{s}\mathbf{A},\ \mathbf{s}>\mathbf{0} \tag{1}\]  
where  
\[s=\underset{i,j}{min}[1/\underset{1\leq i\leq n}{max}\sum\
olimits_{j=1}^{n} a_{ij},1/\underset{1\leq j\leq n}{max}\sum\
olimits_{i=1}^{n}a_{ij}],s= \underset{i,j}{min}[1/\underset{1\leq i\leq n}{max}\sum\
olimits_{j=1}^{n} a_{ij},1/\underset{1\leq j\leq n}{max}\sum\
olimits_{i=1}^{n}a_{ij}],i,j=1,2, \ldots,n \tag{2}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Aspects** & **PC** & **OM** & **CR** & **RS** & **ME** & \\ \hline Professional competence (PC) & 0.000 & 2.700 & 2.688 & 2.663 & 2.850 & 10.900 \\ Operation management (OM) & 2.813 & 0.000 & 2.938 & 2.575 & 2.788 & 11.113 \\ Critical resources (CR) & 2.963 & 2.975 & 0.000 & 2.588 & 2.713 & 11.238 \\ Regulatory system (RS) & 2.713 & 2.638 & 2.575 & 0.000 & 2.863 & 10.788 \\ Market expansion (ME) & 2.575 & 2.825 & 2.638 & 2.563 & 0.000 & 10.600 \\ \hline Total & 11.063 & 11.138 & 10.838 & 10.388 & 11.213 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The original average influence matrix (A). (4)  
Calculate the full influence matrix  
The full influence matrix \(T\) can be derived from Equation (4) or (5); Table 8 is the calculated full influence matrix.