## 4 The Results
### Classification of the Infrastructure Assessment of Bike-Sharing System  
Table 3: A summary of the challenges encountered by users of bike-sharing systems, analysed through fundamental statistical measures. These challenges are interpreted using indicators, such as the mean, median, minimum, maximum, standard deviation, skewness, and kurtosis, which collectively illustrate users' perceptions and experiences. The analysis of infrastructural variables reveals generally neutral perceptions among users, with most mean scores clustering around 3.0. Features such as Application--Ease of Use (M = 3.31), No Errors in Rental/Payment Process (M = 3.05 and 3.18), and Ability to Check Available Bikes (M = 3.35), are functional but not perceived as particularly innovative. Skewness and kurtosis values are generally low and slightly negative, suggesting a moderate spread in opinions and a lack of strongly polarised views. Standard deviations for these variables (approx. 1.0-1.08) indicate moderate variability, with some users reporting issues--particularly in rental and payment processes--while others experienced smooth functionality. These findings suggest the system is reliable but lacks features that would distinguish it as technologically advanced. Figure 2: Identified factors of benefits of bike sharing. Physical infrastructure, including Bike Path Networks (M = 3.00), Docking Station Density (M = 2.99), and Bike Path Surface Quality (M = 3.11), was similarly rated as adequate but unnispiring. Skewness near zero reflects balanced perceptions, while slight leftward skew in variables, such as Bike Condition and Surface Quality, indicates that some users face notable but non-critical issues. The system performs its core functions but does not exceed expectations in design, quality, or integration. The Visual Condition of Bikes (M = 3.02) and Integration with Public Transport (M = 3.07) were also rated neutrally, suggesting that users find these aspects acceptable but lacking in novelty or design sophistication. Cost of Using City Bikes (M = 3.23) was considered fair, though not competitively distinctive. Variability in responses points to differing price sensitivity, but overall, the pricing model is seen as conventional. Additional features, such as Bike Racks (M = 3.15), Baskets (M = 3.14), and Bike Type Variety (M = 3.01; SD = 1.25), are valued as functional, yet not innovative. High standard deviations here reflect diverging user needs and expectations, particularly regarding variety and practicality. Overall, the findings suggest that while the system is perceived as functional and reasonably well-performing, it does not stand out in terms of innovation. The lack of standout features in areas, such as app design, bike availability, multimodal integration, or infrastructure quality, points to significant room for improvement. Enhancements in technological integration, creative infrastructure solutions, and differentiated bike fleets could elevate the system beyond basic utility and position it as a leading example of innovative urban mobility. Factor analysis was conducted to consolidate factors into variables, subsequently applying normalised Varimax rotation. The Kaiser-Meyer-Olkin (KMO) measure for sampling adequacy reached 0.81, indicating the correlation matrix is suitable and supports the use of factor analysis in this particular study. According to [PERSON]'s criterion, two factors with eigenvalues exceeding 1 should be retained. Similarly, [PERSON]'s scree plot criterion also recommended retaining two factors. The factor analysis examining the assessment of bike-sharing systems resulted in two distinct factors, collectively accounting