# How the Pandemic Changes the Factors Influencing Aircraft Utilization: The Case of Korea
## 5 Discussions and Conclusions  
The present research assessed various socio-economic variables related to the number of seats supplied using the SHAP and Random Forest methodologies to avoid confirmation basis and hindsight basis; identify factors influencing aircraft utilization strategies in a situation of uncertainty; uncover hidden relationships among factors; and analyze the hidden relationships based on changes in aircraft utilization rate by air route during the Pandemic. According to the analysis' findings, pre-Pandemic, the coincident index and consumer price index were principal factors. However, the exchange rate (KRW/EUR) became important during the Pandemic. These findings underscore how the Pandemic has shifted airlines' operational sensitivity toward macroeconomic indicators, particularly exchange rates and lagging indexes, suggesting a need for adaptive decision-making frameworks in volatile environments. For example, the European Central Bank (ECB) maintained low interest rates during the early stages of the Pandemic to ensure liquidity. However, the demand for euros has increased due to quantitative easing-related policies and a rise in interest rates in United States (ECB [52]). According to the SHAP and Random Forest analyses, the importance of each factor is dependent on the characteristics of each methodology. The fact that an interest rate (KRW/EUR) was important in both methodologies proves that the fluctuation of interest rates had a substantial effect on the operation of airlines during the Pandemic. On the other hand, the lagging index, which ranked low pre- and during the Pandemic, rose sharply \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{2}{c}{**Pre-Pandemic**} & \multicolumn{2}{c}{**During the Pandemic**} & \multicolumn{2}{c}{**Post-Pandemic**} \\ \cline{2-7}  & \begin{tabular}{c} **Feature** \\ **Importance Vals** \\ **(Percentage)** \\ \end{tabular} & **Rank** & \
\begin{tabular}{c} **Feature** \\ **Importance Vals** \\ **(Percentage)** \\ \end{tabular} & **Rank** &
\begin{tabular}{c} **Feature** \\ **Importance Vals** \\ **(Percentage)** \\ \end{tabular} & **Rank** \\ \hline KOSDAQ\_Index & 16.93\% & 2 & 4.75\% & 3 & 0.14\% & 10 \\ \hline KOSPI\_Index & 17.85\% & 1 & 0.69\% & 5 & 0.16\% & 9 \\ \hline Coincident\_Composite\_Index & 3.99\% & 10 & 0.17\% & 10 & 2.86\% & 3 \\ \hline Leading\_Composite\_Index & 5.72\% & 9 & 0.46\% & 7 & 1.24\% & 5 \\ \hline Price\_Index & 12.87\% & 4 & 0.33\% & 8 & 1.86\% & 4 \\ \hline W/JPY100 & 6.06\% & 7 & 7.02\% & 2 & 0.25\% & 8 \\ \hline W/USD & 6.14\% & 6 & 2.33\% & 4 & 8.90\% & 2 \\ \hline W/CNY & 15.84\% & 3 & 0.68\% & 6 & 0.41\% & 6 \\ \hline W/EUR & 6.06\% & 7 & 83.26\% & 1 & 0.29\% & 7 \\ \hline Lagging\_Composite\_Index & 8.53\% & 5 & 0.31\% & 9 & 83.88\% & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Findings of comparative analysis of grouping factors (Random Forest, LCC). post-Pandemic. This suggests that the lagging index should be interpreted as an indicator of economic recovery specific to certain periods. We conducted the ANOVA and Kruskal-Wallis assay to test whether the three groups differ in terms of averages and distributions. Both methodologies revealed statistically significant differences in certain cases; however, these differences were not consistently observed across all periods, particularly during and post-Pandemic. This suggests that the importance of influencing factors may vary by group and that the characteristics of each group and their respective economic environments could shape these variations. The present research analyzed the effect of the Pandemic on aircraft utilization rate by air route and examined the importance of socio-economic variables in the strategy and operation of airlines. The SHAP and Random Forest analyses hold significance as they identify important factors to be considered for airlines to adapt to changes before, during and after periods of high uncertainty, such as the Pandemic.