## 4 Results and Discussion
### Baseline Results
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Code** & **Barrier** & \(D\) & \(R^{T}\) & **(\(D\)+\(R^{T}\))** & **Rank (\(D\)+\(R^{T}\))** & **(\(D\)\(-\)\(R^{T}\))** & **Rank (\(D\)\(-\)\(R^{T}\))** & **Category** \\ \hline B1 & Cybersecurity threat & 6.35 & 6.00 & 12.35 & 10 & 0.35 & 5 & Net cause \\ B2 & Costly & 7.06 & 6.38 & 13.43 & 5 & 0.68 & 1 & Net cause \\ B3 & Skills gap of the human capital & 6.89 & 7.03 & 13.92 & 2 & \(-\)0.14 & 8 & Net effect \\ B4 & Aprehensive stakeholders & 6.78 & 6.36 & 13.14 & 7 & 0.42 & 4 & Net cause \\ B5 & Lack of training resources & 7.50 & 6.85 & 14.35 & 1 & 0.65 & 2 & Net cause \\ B6 & Lack of collaboration & 6.77 & 6.19 & 12.95 & 9 & 0.58 & 3 & Net cause \\ B7 & Knowledge gap for the & & & & & & & \\  & customization of & 6.43 & 6.90 & 13.33 & 6 & \(-\)0.47 & 9 & Net effect \\  & curriculum design & & & & & & & \\ B8 & Insufficient available & 6.93 & 6.97 & 13.90 & 3 & \(-\)0.03 & 7 & Net effect \\  & technologies & & & & & & & \\ B9 & Health issues & 5.19 & 5.80 & 10.98 & 12 & \(-\)0.61 & 11 & Net effect \\ B10 & Time constraint for & 6.26 & 6.80 & 13.06 & 8 & \(-\)0.54 & 10 & Net effect \\  & material preparation & & & & & & & \\ B11 & Complexity of & 6.30 & 7.43 & 13.73 & 4 & \(-\)1.14 & 12 & Net effect \\  & learning platforms & & & & & & & \\ B12 & Insufficient foundation in & 6.29 & 6.03 & 12.32 & 11 & 0.26 & 6 & Net cause \\  & basic education & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The prominence and relation vectors. \begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Data** \\ \hline The ordered triplet set, \(T^{*}\) & \(\left\{ \begin{array}{l}(0.71,5,11),(0.68,5,8),(0.67,5,3),(0.67,2,11),(0.66,8,11), \\ (0.66,3,11),(0.66,5,7),(0.66,5,10),(0.65,6,11),\ldots,(0.34,9,9)\end{array} \right\}\) \\ Dispatch \(-\) node set, \(T^{Di}\) & \(\left\{5,5,5,2,8,3,5,5,6,\ldots,9\right\}\) \\ \(T_{i}^{Di}\) sets and \(MDE_{i}^{Di}\) values & \(T_{1}^{Di}=\{5\}\), \(MDE_{1}^{Di}=\{5,5\}\), \(MDE_{2}^{Di}=\{5,5\}\), \(MDE_{2}^{Di}=0\); \(T_{3}^{Di}=\{5,5,5\}\), \\ \(MDE_{2}^{Di}=0\); \(T_{4}^{Di}=\{5,5,5,2\}\), \(MDE_{4}^{Di}=0.028\); \(\ldots\) \\ Set of \(MDE_{i}^{Di}\) values & \(\{0,0,0,0.028,0.021,0.016,0.025,0.034,0.027,\ldots,0\}\) \\ Maximum \(MDE_{i}^{Di}\) & \(0.034  
human capital (B3), apprehensive stakeholders (B4), lack of training resources (B5), lack of collaboration (B6), and insufficient available technologies (B8). The indirect factors category is composed of knowledge gap for the customization of curriculum design (B7), time constraints for material preparation (B10), and complexity of learning platforms (B11). The independent factor consists of health issues (B9). We focus our attention on the key factors category and identify the crucial barriers. The lack of training resources (B5) yields the most significant barrier in this category. It influences the skills gap of human capital (B3), insufficient available technologies (B8), time constraints for material preparation (B10), and complexity of learning platforms (B11). It implies that the lack of training materials affected the nexus of human and technological requirements essential to the implementation of EDUC4. Thus, government and HEIs must concentrate their resources and efforts to ensure that the lack of training resources must be addressed for the benefit of the stakeholders. This finding is consistent with the observations of [PERSON] and [PERSON] [65]. The rank order of the key barriers is as follows: (B5) \(\succ\) (B11) \(\succ\) (B2) \(\succ\) (B8) \(\succ\) (B3) \(\succ\) (B7). The proposed Fermatean fuzzy DEMATEL classifies five key barriers from among those identified by [PERSON] et al. [10]: lack of training resources (B5), skills gap of the human capital (B3), insufficient available technologies (B8), the complexity of learning platforms (B11), and costly (B2).