## 4 Results
The dispersion of ROE, as evidenced by the standard deviation, reflects heterogeneity in banking performance among banks, possibly due to variations in digital adoption, economic conditions, and market structures. Examining the digitalization indicators--Digital Inclusion (LOGDIGINCL), Human Capital (LOGDESI_HC), and Integration of Digital Technologies (LOGDESI_IDT)--reveals their role in shaping banking outcomes. Although these variables exhibit moderate variability, their correlation structure indicates strong interdependence. Notably, LOGDESI_IDT and LOGDESI_HC are highly correlated, suggesting that investments in digital infrastructure and human capital complement each other in fostering financial sector efficiency. The negative relationship between ROE and LOGDESI_IDT, though statistically significant, suggests that a higher intensity of digital integration may not translate directly into immediate profitability gains. This could be attributed to the initial costs of digital transformation, regulatory adjustments, or the time lag required for digital investments to yield efficiency improvements. The macroeconomic environment, as reflected by GDP (LOGGDP) and research and development (RD), also plays an important role in determining banking performance. The negative association between ROE and GDP, despite being significant, suggests that profitability dynamics are more complex than a simple pro-cyclicality assumption. While economic growth generally fosters banking sector expansion, competitive pressures, regulatory compliance costs, or structural banking inefficiencies in highly developed economies may suppress profit margins. Conversely, the positive relationship between RD and ROE suggests that innovation and technological advancement could serve as drivers of profitability, reinforcing the argument that investment in research fosters long-term financial sustainability. The banking sector's structural characteristics, represented by employment in banking (LOGBANKEMPL), electronic banking (LOGEBANK), and ATM density (LOGATM), provide further insights into the operational environment. The strong positive correlation between LOGBANKEMPL and LOGATM underscores the coexistence of traditional and digital banking infrastructures, where workforce intensity remains relevant despite increasing automation. The negative association of banking employment with ROE, though not statistically significant, aligns with the hypothesis that cost structures related to human resources may act as a drag on profitability in an era of increasing digitalization. The inclusion of higher-order moments (MOMENT1 and MOMENT2) within the MMQR framework adds a significant dimension to the interpretation of these relationships. The negative correlation between MOMENT1 and ROE suggests that variations in profitability across quantiles follow a nonlinear trajectory, with lower quantiles experiencing greater instability. The highly significant positive correlation between MOMENT2 and ROE confirms that banking performance exhibits increasing dispersion across quantiles, implying that banks at the upper end of the profitability distribution respond differently to the explanatory variables compared to their lower-performing counterparts. The Jarque-Bera test statistics and the associated probabilities reinforce the presence of non-normal distributions, particularly for ROE, GDP, and digitalization indicators. This further justifies the choice of MMQR as an analytical tool, given its robustness regarding deviations from normality and ability to accommodate heteroskedasticity. The overall interpretation of the findings suggests that while digitalization and macroeconomic variables exert notable influences on banking profitability, their effects are contingent on quantile positioning, operational structures, and the prevailing economic environment in EU countries.