## 3 Results
### Descriptive Statistical Analysis
Age and area of residence were the primary and most basic variables taken into consideration in our analysis, as the majority of the participants in both cases represent Gen Z and because the area of residence is directly connected to how the Urban Freight Transport system and, specifically, the courier will react locally regarding the installation of new PUPs (and mainly lockers) and the way these areas will be served. Furthermore, income was analyzed due to the fact that using PUPs is a cheaper option than traditional home delivery services, and we wanted to analyze whether it affects the users' perception and level of satisfaction, providing useful insights for courier service providers. The logistic regression model was employed to analyze binary dependent variables, enabling us to assess how demographic factors such as age, gender, education, and income level affect the probability of a specific outcome occurring for a dependent variable. For example, if the dependent variable of interest is the use of courier services, the outcome is \"use of courier services\" (coded as 1), versus \"does not use courier services\" (coded as 0). The logistic regression model then estimates the probability that a given individual will fall into one category (1) versus the other (0) (for example, the likelihood of an individual using a courier service) based on their demographic characteristics. The baseline logistic regression model is formulated as follows:  
\[ \begin{array}{ll}Dependent_{i}=&b_{0}+b_{1}Gender_{i}+b_{2}Age_{i}+b_{3} Age_{i}^{2}+b_{4}University\ student_{i}\\ &+b_{5}Low\ income\ class_{i}+b_{6}High\ income\ class_{i}+b_{7}\varepsilon_{i} \end{array} \]  
where \(Dependent_{i}\) is the dummy variable of interest, taking the value 1 if individual i's response falls into the corresponding category, and 0 otherwise. \(Gender_{i}\) is a dummy variable that takes a value of 1 for male and 0 for female. \(Age_{i}\) represents the age category of individual i, with \(Age_{i}^{2}\) included to account for potential non-linear effects of age on the dependent variable. \(University\ Student_{i}\) is a dummy variable that takes the value 1 for university students and 0 for graduates. The dummy \(Low\ income\ class_{i}\) takes a value of 1 if the individual's monthly income ranges from EUR 0 to 800 and 0 otherwise. Similarly, the dummy \(High\ income\ class_{i}\) takes value 1 if the individual's monthly income exceeds EUR 1600 and 0 otherwise. Finally, \(\varepsilon_{i}\) corresponds to the error term. We estimate the baseline model for each of the different dependent variables, using logistic regressions with clustered standard errors at the municipality level. This approach controls both heteroscedasticity and correlation of the error terms within clusters (Clustered standard errors provide more accurate estimates when observations within the same cluster (i.e., municipality) are more similar to each other than to those from different clusters. This adjustment is necessary because individuals within the same municipality may exhibit correlated behaviors). A more detailed description of all the variables employed in the analysis is presented in Table 3.  
In both Tables 4 and 5, we estimated logistic regression models with robust clustered standard errors at the municipality level.