## 4 Discussion
Upon detailed analysis of the results obtained from the social network analysis, it can be determined that the leading technical factors influencing the choice of a vehicle for a peer-to-peer car-sharing system include the following:  
Fuel consumption in urban, extra-urban, and mixed cycles: For car-sharing users, the economics of usage are crucial, including low fuel consumption, which directly impacts the costs associated with using the vehicle. Battery capacity in kWh (for EVs) and real range under various conditions: For electric vehicles, battery range and efficiency are essential to ensure mobility without the need for frequent charging. Availability of advanced assistance packages: This includes autonomous driving packages and the presence of advanced safety packages. The least important factor was found to be the warranty period. Fuel consumption and battery efficiency in electric vehicles (EVs) were identified as the most crucial factors. This underscores the growing cost awareness and interest in sustainable transport among car-sharing users. Unlike organized B2B car sharing, where the cost of fuel is typically included in the rental price, these costs are not covered in peer-to-peer car sharing. This issue is significant from both an economic and a sustainable development perspective, particularly concerning the interest in electric vehicles. To validate the results obtained from the SNA, a Gradient Boosting Regression (GBR) analysis was conducted to assess whether the key factors identified by the SNA--such as fuel efficiency, battery capacity, and safety systems--were indeed the most significant predictors of user preferences for vehicle selection. The GBR results confirmed the findings from the SNA, with the same factors emerging as the most influential in the decision-making process. This strong agreement between the two analytical methods underscoresthe reliability of the SNA outcomes, demonstrating that the network structure accurately reflects the underlying patterns in user preferences. The validation using GBR highlights that SNA not only captures the central relationships between vehicle attributes but also mirrors the real-world decision-making process of users. This dual-method confirmation reinforces the robustness and applicability of SNA in the context of peer-to-peer car-sharing platform management, proving it to be an effective and efficient method for understanding decision-making dynamics. Referring to research by [PERSON] and [PERSON] [75], it is noteworthy that they emphasized that lower operating costs and reduced environmental impact are primary motivations for car-sharing users. Another critical factor identified was vehicle safety, understood as the presence of safety systems and advanced driver assistance packages. Interestingly, respondents expressed significant interest in vehicles equipped with advanced driver assistance systems, such as autonomous driving packages. This behavior may indicate a readiness to use vehicles equipped with new technologies. It is important to note that while vehicles with autonomous driving packages are not yet available in Polish peer-to-peer car-sharing systems, they can be rented in other countries, such as the United States. This finding aligns with research on the objectives of using car-sharing systems, which indicates that one of the primary purposes is to test new technologies and modern vehicles [76]. In peer-to-peer car sharing, operational management should focus on supporting vehicle owners with tools and services that help them manage their vehicles more effectively. Providing data analytics on car usage, fuel consumption, and renter preferences can empower owners to make decisions about maintaining or upgrading their vehicles. Platforms can also use AI and machine learning to predict user demand and suggest pricing strategies for owners, as seen in platforms like Getaround, where dynamic pricing is based on demand and availability.