## 5 Discussion  
We validated all the formulated hypotheses by estimating the coefficients of the econometric model at different quantiles of the ROE distribution. The results confirm the existence of an asymmetric effect of emerging technologies on bank performance, indicating that banks with high levels of profitability benefit more from investments in AI and automation, while low profitability institutions fail to transform these investments into a significant competitive advantage. The H1 hypothesis is validated through the observed impact of ATM density across the quantiles of the return on equity (ROE) distribution. The MMQR results demonstrate that ATM density (as a proxy for robotic automation and classified as a mechatronic system) is positively and significantly associated with ROE primarily in the lower quantiles (\(\tau\) = 0.25 and \(\tau\) = 0.50). This indicates that less profitable banks benefit more from ATM implementation in terms of operational efficiency, suggesting that the marginal gains from automation are greater for institutions with initially lower performance. The diminishing or non-significant effect at higher quantiles (\(\tau\) = 0.75 and \(\tau\) = 0.90) reinforces the hypothesis that high-profitability institutions already operate efficiently, thereby exhibiting diminishing marginal returns from basic automation technologies such as ATMs. Hence, the data supports H1 by showing that automation contributes more substantially to profitability enhancement in lower-performing banks. The H2 hypothesis is validated through the observed differential impact of AI-driven variables such as EBANK, DESI_IDT, and DIGINCL across the conditional distribution of ROE. The MMQR results show that these digitalization indicators exert stronger and more statistically significant effects in higher quantiles (\(\tau\) = 0.75 and \(\tau\) = 0.90). For instance, the coefficient for EBANK increases in both magnitude and significance as we move from \(\tau\) = 0.25 to \(\tau\) = 0.90, indicating that banks with higher profitability levels are better equipped to integrate AI tools such as chatbots and robo-advisors and to leverage them for enhanced financial performance. This pattern confirms the heterogeneous nature of AI's contribution: while digital tools are broadly beneficial, their effectiveness is magnified in institutions that already have the financial and technological capacity to adopt and scale these innovations. The observed quantile-dependent behavior empirically supports H2. The validation of H3 is reflected in the interaction between RD (research and development expenditures) and digitalization proxies such as DESI_IDT and DESI_HC. Across quantiles, RD is consistently significant and positively associated with ROE, especially in the upper quantiles. However, its isolated impact is not sufficient. It is when RD is analyzed alongside the DESI indicators that we observe a complementary effect--in countries with higher initial levels of digitalization, the returns to R&D are more pronounced. In contrast, in lower quantiles, where digital readiness is weaker, the effect of RD is either reduced or statistically insignificant. This interaction validates H3 by confirming that R&D effectiveness is contingent upon a foundational level of digital infrastructure and human capital, aligning with the theoretical expectation that innovation outcomes depend on digital maturity. The heterogeneity in the results across quantiles suggests that banking profitability determinants differ not only across banks but also across economic and financial structures in different countries. Several key insights emerge:  
* Economic Growth and ROE: The negative effect of GDP on ROE, particularly at lower quantiles, suggests that in developing economies, macroeconomic expansion does not proportionally benefit all firms, possibly due to capital misallocation, market inefficiencies, or policy-induced distortions.