## 4 Results
**(c)** Labor and innovation dynamics. **(d)** Digital ecosystem interdependencies. **(e)** Macro-innovation feedback loops. Furthermore, the role of moment-based variables (MOMENT1 and MOMENT2) is underscored by their significant Granger causality toward both ROE and GDP. Their predictive strength validates their use as instruments in quantile regression frameworks addressing endogeneity concerns and enhancing robustness. These findings also reveal the underlying complexity of the feedback loops between modelled performance and the instruments used to estimate them. Digital infrastructure indicators demonstrate predictive relationships with the physical architecture of banking, notably the density of ATMs. Variables such as DESI_HC, DESI_IDT, and digital inclusion are all found to Granger-cause ATM deployment, reflecting how technological readiness and citizen digital behavior shape the evolution of banking interfaces. The influence of GDP on ATM density further supports this conclusion by highlighting the impact of macroeconomic strength on automation investment. The Granger analysis (Table 13a-e and Figure 4) also confirms the predictive power of human capital and technological readiness on banking labor structures. Specifically, DESI_HC and DESI_IDT Granger-cause employment levels in the sector (LOG-BANKEMPL), suggesting that countries with stronger digital skills and corporate digital adoption are more likely to experience restructuring in banking labor demands. This is compounded by the influence of GDP and R&D on employment, which collectively describes an ecosystem where digital readiness, innovation spending, and economic growth co-shape labor market outcomes. Figure 4: Granger causality. Authors own calculation. Note: Red line represents 1% significance; orange line represents 5% significance and dotted gray line represents 10% significance. Interdependencies within the digital ecosystem are also evident. Bidirectional causality between DESI_IDT and DESI_HC confirms a mutual reinforcement mechanism where technological integration and human capital investments drive each other forward. Additionally, E-banking usage (LOGEBANK) significantly predicts both DESI_HC and DESI_IDT, implying that consumer-facing digital behaviors act as stimuli for broader institutional digitalization. Institutional investment (RD) and innovation processes are further shown to participate in predictive loops with GDP and banking performance. R&D both Granger-causes and is caused by GDP, revealing mutual causation and reinforcing the idea that technological investments and economic output evolve in tandem. Moment-based conditions (MOMENT1 and MOMENT2) also predict GDP, reinforcing their macro-level relevance and bolstering their econometric validity. Finally, bidirectional Granger causality between MOMENT1 and MOMENT2 supports the theoretical notion of their internal coherence and interdependence. Their interaction underscores the nuanced behavior of the statistical instruments used, which shape not only model specification but also insight generation. Therefore, the Granger analysis substantiates a dense network of causality across institutional, technological, economic, and instrumental variables. These relationships advance understanding of how innovation and performance are co-determined in the digital banking era. The findings offer a compelling empirical foundation for future policy and strategy formulation in both financial and regulatory spheres. Granger causality tests identify whether one time series variable provides statistically significant information for forecasting another. In this context, the results are interpreted at three levels of statistical significance: strong (1%), moderate (5%), and suggestive (10%) (Table 13a-e and Figure 4). From the policy implications perspective, the Granger results emphasize several aspects:  
1.