## 4 Results
### Application Phase
#### 4.2.1 Static Analysis of the NCMs  
As previously stated in Section 2, a static analysis of NCMs may be utilized to determine (1) the relative relevance of concepts and (2) the indirect and total causal effects between nodes which act as estimators of the degree of influence between pairs of concepts. In our research, we follow the steps depicted in Figure 10 to perform static analysis on NCMs. According to [75,76], centrality is the most important metric of map complexity and is calculated by adding the indegree and outdegree of variables. Actually, centrality indicates how related a variable is to other variables and the cumulative strength of these relationships. Figure 11 shows the pillars from which the metrics for the performance of smart ports are derived, as illustrated in Table 1.  
The above six main factors for the assessment of a smart port's performance problem were considered for our study. These were established using interviews and interaction (see Appendix A for the questionnaire used in this survey) with two experts from academia and industry in the maritime sector. Based on the aforesaid analysis, we can consider the following nodes/concepts for our study:  
C0 Smart port performance  
C1 Operational efficiency  
C2 Technological advancement  
C3 Environmental sustainability  
C4 Customer-centric  
C5 Safety and security  
C6 Financial  
Figure 11: Smart port performance dimensions. Figure 10: Steps for static analysis of NCM. Next, we apply Equation (9) to obtain the aggregated matrix of the combined opinions of the experts which are depicted in Table 3.  
The measures of centrality are calculated using the outdegree measures and indegree, using Equations (2) and (3); the results are shown in Table 4.  
Once the measures of centrality were calculated, the nodes of the NCM were classified. This classification is shown in Table 5 and was utilized in order to understand and analyze the dynamics of the system being modeled. More specifically, transmitter nodes indicate those nodes of the NCM that influence other nodes but are not significantly influenced themselves. On the other hand receiver nodes are those nodes of the graph that are heavily influenced by other nodes but exert little influence on others. Lastly, ordinary nodes represent intermediary processes or factors without playing an extreme role in the system being modelled by the NCM. This classification enables decision-makers to focus on critical aspects of the system for effective intervention and optimization. The total centrality (total degree, _td (vi)_), is calculated using Equation (4) and the results are shown in Table 6.  
Next, the process of de-neutrosophication is applied as it is referred to in [34]. I \(\in\) [0, 1] is replaced by values maximum and minimum. Finally, the interval values are displayed in Table 7. \begin{table}
\begin{tabular}{c c c} \hline
**Node** & **Id** & **Od** \\ \hline OE & 0 & 5+2I \\ \hline TA & 7 & 9 \\ \hline ES & 3+I & 1\(-\)1 \\ \hline CC & 2+I & 2 \\ \hline SS & 3+3I & 2I\(-\)1 \\ \hline F & 2I+1 & 2+2I \\ \hline \end{tabular}
\end{table} 
Table 4: Measures of centrality, outdegree and indegree. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**DIMENSIONS** & **OE** & **TA** & **ES** & **CC** & **SS** & **F** \\ \hline OE & 0 & 0 & 1+I & 2 & 1 & 2 \\ \hline TA & 2 & 0 & 2 & 2 & 2 & 1 \\ \hline ES & 0 & 1 & 0 & 1 & 0 & \(-\)2 \\ \hline CC & \(-\)1 & 2 & 0 & 0 & 1 & 0 \\ \hline SS & \(-\)2 & 2 & 0 & \(-\)1 & 0 & 2I \\ \hline F & 1 & 2 & 0 & \(-\)1 & 2I & 0 \\
Based on Equation (5), the means of the extreme values are obtained to analyze the characteristics to be attended to according to the factors obtained and are shown in Table 8.  
Then, the final priorities of the static analysis of the NCM of our case study can be ordered as follows: TA \(\succ\) OE \(\succ\) F \(\succ\) CC\(\sim\)SS \(\succ\) ES.