## 3 Results
### SNA Method Results
Conversely, factor F23 was identified as the least important factor. Figure 3: Degree centrality of the analyzed factors. Figure 2: Technical factors influencing the choice of vehicles for peer-to-peer car-sharing systems. Subsequently, Figure 4 shows the centrality of proximity, which refers to the average distance of a node from all other nodes in the network, suggesting its ability to quickly reach other nodes. The data obtained show that factors F7, F12 and F17 are characterized by the highest closeness centrality value. Next, Figure 5 presents the results of the centrality of inequality. The betweenness centrality shows how often a node appears on the shortest paths between pairs of other nodes, which may indicate its role as a \"bridge\" connecting different parts of the network. The results indicate that F12 has the highest value, while F23 has the lowest value. Table 4 presents in detail the values obtained based on the analysis of the degree centrality, closeness centrality, and betweenness centrality measures characterizing the analyzed factors. Figure 4: Closeness centrality of the analyzed factors. Figure 5: Betweenness centrality of the analyzed factors. Based on the data presented in Table 4, several key insights can be drawn regarding the factors influencing vehicle selection on peer-to-peer car-sharing platforms. First, the high degree centrality values for nodes F1, F7, F12, and F17 (all at 0.3913) indicate that these vehicle attributes are the most interconnected with other important features, highlighting their pivotal role in the decision-making process of potential users. Specifically, these attributes, such as fuel consumption, vehicle range, and advanced driver assistance systems, emerge as critical factors for users. This finding aligns with the broader trend of increasing user emphasis on cost efficiency and safety, which suggests that platform operators should prioritize promoting vehicles that excel in these areas to attract and retain customers. Second, the closeness centrality values for nodes F7, F12, and F17 (0.6053) suggest that these attributes are not only central but also highly accessible, meaning they influence the decision-making process more quickly than other features. This implies that vehicles with these attributes should be prominently featured on the platform to ensure they are easily discoverable by users. The speed with which these factors are considered by users underscores the importance of clear and efficient vehicle filtering or highlighting these key attributes during the browsing process, which could lead to higher conversion rates in vehicle rentals. Third, the high betweenness centrality values for nodes F17 (0.1001) and F10 (0.1243) indicate that these attributes serve as critical intermediaries, connecting other important factors in the decision network. Features related to advanced safety systems and comfort, such as autonomous driving or driver assistance technologies, appear to play a central role in facilitating users' decisions. The inclusion of vehicles equipped with these features on a platform, along with the clear communication of their availability, could significantly enhance the platform's attractiveness and foster greater trust among users, especially those seeking higher standards of technology and safety. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node** & **Degree Centrality** & **Closeness Centrality** & **Betweenness Centrality** \\ \hline F1 & 0.3913 & 0.5897 & 0.0929 \\ F2 & 0.3478 & 0.5476 & 0.0672 \\ F3 & 0.2609 & 0.5476 & 0.0375 \\ F4 & 0.2174 & 0.5227 & 0.0302 \\ F5 & 0.1739 & 0.52  
These findings collectively suggest that platforms should prioritize vehicles with advanced fuel efficiency, safety, and technological features to align with user preferences and optimize decision-making processes on the platform. Next, the density was calculated according to Formula (6):  
\[D=\frac{2L}{N(N-1)}=0.2536 \tag{7}\]  
The density in the graph is approximately 0.2536.