## 3 Results  
### SNA Method Results  
The study was conducted from 1 November 2023 to 1 February 2024 and involved Polish residents. The population was estimated to be 38,463,689 individuals. Using Formula (6), the required sample size was determined to be at least 384 participants. A total of 1451 individuals participated in the study. Detailed demographic data about the respondents are presented in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Details** \\ \hline \multirow{4}{*}{Gender} & Male: 1016 (70\%) \\  & Female: 363 (25\%) \\  & Other: **44** (3\%) \\  & Prefer not to say: 29 (2\%) \\ \hline \multirow{4}{*}{Age distribution} & \textless{}18: 29 (2\%) \\  & 18-24: 218 (15\%) \\  & 25-34: 508 (35\%) \\ \cline{1-1}  & 35-44: 363 (25\%) \\ \cline{1-1}  & 45-54: 174 (12\%) \\ \cline{1-1}  & 55-64: 116 (8\%) \\ \cline{1-1}  & 65+: 44 (3\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sociodemographic data of respondents. Subsequently, the research results regarding preferences for the technical aspects of vehicles available in peer-to-peer car-sharing systems were analyzed using the Social Network Analysis (SNA) method. To analyze the factors influencing vehicle choice, Anaconda Navigator software (2.5.0 version) was utilized, which distributes Python 3.11.5 version and R programming languages for scientific computations. The primary goal of Anaconda is to simplify package management and deployment. Figure 2 illustrates the structure of the network of technical factors influencing vehicle selection in peer-to-peer car-sharing systems based on the research findings. Factors with the highest values of degree centrality, closeness centrality, and betweenness centrality are marked with specific colors. Green indicates the highest degree centrality, signifying the most direct connections to other factors in the network. Yellow denotes the node with the highest closeness centrality, suggesting its rapid ability to communicate with other factors within the network. The red color highlights the node with the highest betweenness centrality, emphasizing its key role in bridging and connecting various parts of the network. \begin{table}
\begin{tabular}{c c} \hline
**Category** & **Details** \\ \hline \multirow{4}{*}{Education level} & Primary: 73 (5\%) \\  & Secondary: 726 (50\%) \\  & Bachelor: 363 (25\%) \\  & Master/engineering: 261 (18\%) \\  & Postgraduate/doctorate: 29 (2\%) \\ \hline \multirow{2}{*}{Employment status} & Employed full-time: 871 (60\%) \\  & Other: 580 (40\%) \\ \hline \multirow{4}{*}{Average monthly net household income} & Below PLN 2000: 130 (9\%) \\  & PLN 2000–3999: 609 (42\%) \\  & PLN 4000–5999: 421 (29\%) \\  & PLN 6000–7999: 218 (15\%) \\  & PLN 8000–8999: 44 (3\%) \\  & PLN 10,000 and above: 29 (2\%) \\ \hline \multirow{4}{*}{Region of residence} & City above 500,000 inhabitants: 232 (16\%) \\  & City with 100,000–499,999 inhabitants: 509 (35\%) \\  & City below 100,000 inhabitants: 421 (29\%) \\  & Rural area: 289 (20\%) \\ \hline \multirow{4}{*}{Type of housing} & Ownership: 682 (47\%) \\  & Rent: 595 (41\%) \\  & Service apartment: 102 (7\%) \\  & Other: 72 (5\%) \\ \hline \multirow{4}{*}{Number of people in the household} & 1 person: 348 (24\%) \\  & 2 people: 552 (38\%) \\  & 3–4 people: 450 (31\%) \\  & 5 and more people: 101 (7\%) \\ \hline \multirow{4}{*}{Having children} & Yes: 652 (45\%) \\  & No: 799 (55\%) \\ \hline \multirow{4}{*}{Frequency of using vehicles (car, motorcycle)} & Daily: 333 (23\%) \\  & Several times a week: 465 (32\%) \\ \cline{1-1}  & Once a week: 261 (18\%) \\ \cline{1-1}  & Less often than once a week: 276 (19\%) \\ \cline{1-1}  & Never: 116 (8\%) \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._Next, Figure 3 illustrates degree centrality. Degree centrality measures how often a node appears on the shortest paths between pairs of other nodes, which can indicate its role as a \"bridge\" connecting different parts of the network. The graph in Figure 3 reveals that factors F1, F7, F12, and F17 play the most significant roles.