## 5 Conclusions
By fostering a feedback-driven approach, where both vehicle owners and renters contribute insights to improve platform features, operators can adapt to market needs faster. This model ensures that platform services evolve in response to user preferences, helping to increase trust and long-term engagement. Such strategies have been successful in peer-to-peer sharing models globally and could be critical in expanding the user base in urban settings like Katowice. The research confirms the utility of the social network analysis method in selecting vehicles for car-sharing systems. SNA not only highlights the most important factors for potential users but also reveals the interconnectedness of these factors. The analysis showed that low density and short path lengths in the decision network enable rapid and significant improvements in user satisfaction when optimizing key vehicle features. These applied findings demonstrate the potential of social network analysis in addressing challenges in new modes of transport and mobility, helping operators align their offerings with user preferences and enhance the attractiveness and utilization of peer-to-peer car-sharing systems. The results of the Social Network Analysis (SNA) were further validated through gradient boosting regression, a machine learning technique used to verify the importance of the identified factors. The GBR confirmed that fuel efficiency, battery capacity, and safety features are indeed the most significant predictors of users' vehicle choices, thus reinforcing the accuracy and robustness of the SNA results. This validation supports the conclusion that SNA is a reliable and effective method for analyzing user preferences in car-sharing systems, providing a solid foundation for improving service offerings based on key vehicle attributes. This article, like all research endeavors, is subject to limitations. The primary limitation is the study's exclusive focus on technical factors describing vehicles while neglecting factors that directly describe the systems themselves. Additionally, the scope of factors was intentionally restricted due to the characteristics of the respondents--individuals who had not previously engaged with peer-to-peer car-sharing systems. As such, their insights into the specific shortcomings or advantages of this type of car-sharing service may be limited. Another limitation of this study is the absence of comparative analysis with similar research from other regions or among different user groups. This is primarily due to the lack of existing studies specifically focused on the technical features of vehicles in peer-to-peer car-sharing systems. Additionally, the unique focus on the Katowice region further restricts the possibility of such comparisons. Future research could address these gaps by expanding the geographic and demographic scope of data collection, enabling a broader evaluation of the generalizability and applicability of the findings. Furthermore, the CAWI method used to obtain data from respondents has several limitations that may affect the quality and representativeness of the research results. First, internet access is unequal, which may exclude people from regions with poor internet access, older people, or people with poor digital skills. To mitigate this issue, efforts were made to promote the survey across various platforms, reaching a diverse audience, and the results were carefully analyzed to ensure representation across different age groups and regions. However, in the case of car-sharing services, the internet is a necessary element to use the systems, so this defect did not significantly affect the method's applicability. Second, the risk of lower response quality due to the lack of interviewer supervision is another limitation. To address this, the survey was designed to be simple and intuitive, minimizing the cognitive load on respondents and ensuring that questions were clear and unambiguous. Respondents may also abandon completing long or complex online surveys, which lowers response rates.