## 4 Results
_First, regulatory regimes_ vary significantly in terms of stringency, innovation support, and openness to fintech integration. For example, countries such as the Netherlands and Estonia have pioneered regulatory sandboxes and open banking frameworks, facilitating rapid adoption of AI and automation tools [126; 127]. Conversely, other jurisdictions may exhibit more conservative regulatory postures, which delay the deployment of novel technologies due to procedural inertia or risk aversion [128]. Second, supervisory intensity--defined by the frequency, scope, and intrusiveness of oversight--shapes how banks allocate resources toward automation. High-intensity supervisory environments, such as those in Germany or France, often impose rigorous compliance protocols that require advanced IT systems and real-time data reporting. This dynamic can incentivize automation but may also elevate short-term costs [129]. Third, digital readiness encompasses infrastructure quality, digital literacy levels, and national investment in broadband and cloud technologies. For instance, Scandinavian countries exhibit strong alignment between public digital policy and private sector innovation, enabling banks to implement high-performance solutions such as predictive AI, algorithmic credit scoring, and fully digitized customer onboarding [130; 131]. In contrast, countries with lower DESI scores face bottlenecks in infrastructure and skills that hinder the effective use of advanced technologies. By incorporating this institutional heterogeneity into our analytical framework, we move beyond a one-size-fits-all interpretation of automation's effects. Instead, we contextualize our findings within the broader political, technological, and supervisory ecosystems that mediate the relationship between digital transformation and banking performance. Furthermore, we have expanded our discussion to highlight the short-term structural costs associated with digital transformation. These include significant upfront investments in IT infrastructure, cybersecurity measures, workforce reskilling, and compliance systems necessary to ensure regulatory alignment and operational continuity during the transition phase [132; 133]. For example, digital migration often incurs temporary productivity slowdowns due to the parallel operation of legacy systems and new platforms. Additionally, initial returns on digital investment may be negative or negligible until systems are optimized, and workforce adaptation is complete. These transition costs are particularly burdensome for small and mid-sized banks that lack economies of scale. Our MMQR results confirm that these structural costs disproportionately impact banks in the lower ROE quantiles, offering an empirical explanation for the initial adverse effects of digital integration. Nevertheless, long-term productivity and efficiency gains offset these costs, especially for institutions with robust digital governance frameworks [133]. Moreover, we have strengthened our analysis on R&D spending by providing case-based illustrations, focusing on banks such as ING and BBVA. Thus, ING's 'Think Forward' digital strategy showcases how a fully integrated data-driven architecture can reduce operational costs and personalize customer experiences, leading to higher profitability [134]. Similarly, BBVA's pioneering use of AI for credit risk modeling and customer segmentation reflects how sustained investment in innovation infrastructure translates into improved asset quality and net interest margins [135; 136]. These cases substantiate our hypothesis that research and development contribute positively to banking performance, particularly for high-performing institutions capable of scaling innovations efficiently. By grounding our econometric findings in real-world strategies, we offer both theoretical and practical insights into how R&D operationalizes digital value creation. Furthermore, the implications of cointegration have also been clarified.