# The Evaluation of Industry 5.0 Concepts: Social Network Analysis Approach
## 4 Results and Discussion
### Social Network Analysis
The closeness and betweenness scores of this indicator showed that its influence on other actors of the network was less significant, but the eigenvector score showed that it had a better connection to important sustainability actors. S9 (product revamping or modernization) was not usually found in Serbian manufacturing firms. Similar to S5, S9 had lower closeness and betweenness values, but a higher eigenvector value. S9 was followed by S7 (the use of technologies to recuperate kinetic and process energy), and S6 (the use of technologies for recycling and re-use of water), which showed that the technologies were rarely used for managing energy and water use. This was reflected in the degree, closeness, betweenness, and eigenvector values. S10 (offering take-back services) was also rare in manufacturing firms in the Republic of Serbia, which was seen in low degree, closeness, and betweenness scores; however, this indicator had a better connection to important network actors, which was reflected in the eigenvector score. Lastly, S2 (possession of a certified energy management system) was the least influential actor of the network. Its connection to other actors was not good, and it did not lie on network nodes, but its eigenvector score was the highest. Interestingly, even though sustainability had important factors, they were not interconnected. These data are visually presented in Figure 2.  
Table 5 shows the results of centrality for the resilience approach. Table 5 shows the resilience indicators. Indicators' degrees significantly varied, and their eigenvector values were below 0. The most influential actor of the network was R2 (the use of standardized and detailed work instructions), meaning it was common in Serbian manufacturing firms. This parameter had significant closeness and betweenness values, but its eigenvector score was the lowest, meaning that it did not have a good connection to the other important indicators. The second most important resilience indicator was R5 (implementation of activities raising employees' awareness of data security), which had slightly lower closeness and betweenness scores than R2, and the same eigenvector value. R5 was followed by R6 (the use of software specifically). Even though this indicator had relatively good degree and closeness values, the betweenness value shows that it did not lie on the network nodes; however, its connection with important network actors was better when compared with previously mentioned resilience parameters. The next indicator was R7 (the use of hardware solutions specifically), which showed similar results to R6--despite having relatively good degree and closeness values, its betweenness value was low, but it had a better connection to the important network actors. R1 (the use of internet/network connection in real time for automated data exchange) was also rarely found in Serbian manufacturing firms, resulting in lower closeness and betweenness values. R9 (product revamping or modernization) was not often implemented, but it had a relatively good connection to the other actors in the network, as well as the important ones. On the other hand, its betweenness value was not significant, meaning that it lies on a minority of the  
resilience network nodes. R8 (the use of organizational measures specifically) showed that firms that took part in the research did not specify the use of organizational measures, even though this indicator can impact the implementation and improvement of other resilience indicators. Lastly, R4 (the use of collaborating robots (co-bots)) and R3 (the use of mobile industrial robots) indicated the use of robots in manufacturing. This use did not significantly influence the other resilience indicators, which resulted in low degree, closeness, and betweenness values. Figure 3 presents these data visually.