## 4 The Results
### Classification of the Infrastructure Assessment of Bike-Sharing System
#### 4.3.3 Model Fit and Reliability  
Model fit statistics confirm moderate to strong relationships between infrastructure and perceived benefits. Correlation coefficients (R) range from 0.380 to 0.588, with R\({}^{2}\) values between 0.144 and 0.346--indicating that up to 34.6% of the variance in perceived benefits can be explained by infrastructure-related variables. Adjusted R\({}^{2}\) values remain high for economic benefits (B3 = 0.261) and integration with public transport (B11 = 0.209),affirming the robustness of the model. Standard errors (0.866-1.356) reflect moderate variability, leaving room for improvement while supporting the model's reliability in guiding infrastructure enhancements. The findings underscore infrastructure's pivotal role in shaping perceptions of bike-sharing. Technological aspects--such as mobile app usability and real-time information--strongly influence user views on affordability, efficiency, and integration. Conversely, weak points in bike maintenance and appearance highlight areas for improvement. Diverse bike types and seamless integration with public transport emerge as critical levers for increasing system flexibility and adoption. From an innovation perspective, the results emphasise the transformative impact of system design, digital integration, and infrastructure improvements on user satisfaction. Reliable, intuitive applications--free of errors--are key to presenting bike-sharing as a modern, user-friendly transport solution. For the first time, full integration with public transport positions bike-sharing as part of a comprehensive, multimodal transit network, offering seamless first- and last-mile connectivity. Opportunities for further innovation include predictive maintenance through IoT-enabled sensors, improved frame design (e.g., ergonomic, lightweight, or customizable bikes), and flexible docking strategies such as geofenced zones or virtual stations. These enhancements would improve convenience, reduce clutter, and adapt better to urban conditions. Cost-related innovations could involve dynamic pricing, subscriptions, or incentive-based models that promote regular use while increasing system equity--particularly among low-income users. Environmentally focused developments, such as renewable-powered charging stations and recycled bike materials, could strengthen the sustainability narrative. Finally, integrating health-oriented features into mobile apps--such as fitness tracking or gamification--could attract wellness-conscious users and reinforce cycling as a long-term lifestyle habit. Together, these innovations would not only address current limitations but also position bike-sharing systems as adaptive, inclusive, and future-ready urban mobility solutions. Figure 4 presents the Hierarchical Model of the Bike-Sharing System in Poland, illustrating the interdependencies between various infrastructural and operational components affecting the effectiveness and efficiency of bike-sharing networks. The model highlights significant factors like physical infrastructure, technological integration, and policy support, all of which play critical roles in defining the usability and user experience of bike-sharing services. Specifically, the diagram represents the interface between docking stations, bike lanes, and multimodal transport integration, emphasising the need for total connectivity among them. Based on the analysis of the hierarchical structure, the model helps to determine the loopholes in the existing system and provides a template for improvements to be implemented in the future, which could potentially enhance accessibility, sustainability, and general users' experience. One of the significant focuses of the hierarchical model is on the relevance of infrastructure quality in promoting the success of bike-sharing schemes, particularly in post-industrial zones, such as Silesia in Poland.