## 4 Results and Discussion
### Logistic Regression Analysis of Aspects Influencing Recommendation  
A logistic regression model was employed to assess the impact of various airport services on the likelihood of an airport being recommended by passengers. The model achieved a weighted average precision of 84% on the test dataset, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **F-Statistic** & \(p\)**-Value** \\ \hline Terminal Cleanliness & 2.650 & 0.001 ** \\ \hline Terminal Seating & 16.011 & 0.0001 ** \\ \hline Terminal Signs & 24.453 & 0.0001 ** \\ \hline Food and Beverages & 11.949 & 0.0001 ** \\ \hline Airport Shopping & 2.205 & 0.633 \\ \hline WiFi Connectivity & 13.167 & 0.0001 ** \\ \hline Airport Staff & 13.707 & 0.0001 ** \\ \hline \hline \end{tabular}
\end{table}
Table 1: ANOVA test results across pre-2019 years in August.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **F-Statistic** & \(p\)**-Value** \\ \hline Terminal Cleanliness & 45.57 & 0.0001 ** \\ \hline Terminal Seating & 121.57 & 0.0001 ** \\ \hline Terminal Signs & 225.33 & 0.0001 ** \\ \hline Food and Beverages & 101.23 & 0.0001 ** \\ \hline Airport Shopping & 32.55 & 0.0001 ** \\ \hline WiFi Connectivity & 72.16 & 0.0001 ** \\ \hline Airport Staff & 116.83 & 0.0001 ** \\ \hline \hline \end{tabular}
\end{table}
Table 2: ANOVA test results since 2019.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0 (Not Recommended) & 0.87 & 0.93 & 0.90 & 1085 \\ \hline
1 (Recommended) & 0.73 & 0.58 & 0.64 & 351 \\ \hline Accuracy & & & 0.84 & 1436 \\ \hline Macro Avg & 0.80 & 0.75 & 0.77 & 1436 \\ \hline Weighted Avg & 0.84 & 0.84 & 0.84 & 1436 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification report.  
The model demonstrates high precision (0.87) and recall (0.93) for predicting airports that are not recommended (class 0). However, it exhibits lower precision (0.73) and recall (0.58) for airports that are recommended (class 1), indicating potential areas for model improvement. The coefficients of the logistic regression model, representing the impact of each service on the likelihood of an airport being recommended, are summarized in Table 4.  
Terminal cleanliness (0.968) has the highest positive impact on the likelihood of an airport being recommended, suggesting that cleanliness is a critical determinant in passengers' recommendations. A positive interaction by airport staff (0.606) significantly contributes to recommendations. Airport shopping (0.527) availability and quality positively influence recommendations. Food and beverages (\(-\)0.180), interestingly, has a slightly negative coefficient. Perhaps the quality or pricing of food and beverage options may detract passengers from their overall experience. Terminal seating (0.034), terminal signs (0.051), and WiFi connectivity (\(-\)0.024) are all associated with a very small coefficient, suggesting a lesser impact on recommendations compared to the rest.  
The logistic regression analysis highlights that maintaining high standards in terminal cleanliness, providing excellent airport staff service, and offering quality shopping experiences are crucial factors in enhancing the likelihood of an airport being recommended by passengers. Surprisingly, food and beverage options were found to have a negative impact, warranting further investigation into the specific aspects or services that passengers find dissatisfactory. These insights can guide airport management in prioritizing improvements to maximize passengers' satisfaction and recommendations.