## 4 Results  
The two hypotheses were tested using SEM with SmartPLS v3.0 software, known for its flexibility in analyzing complex data and handling models with multiple causal relationships [72]. Using this software, we assessed digitalization's direct and indirect impact on SDGs in EU countries. SEM also enabled us to identify the mediation effects of economic performance, represented by GDP per capita. Figure 2 shows the \(\alpha\) model, illustrating the direct and indirect influences of selected DESI components on the SDG index (SDGi), along with models 1-5, showing the direct and indirect influences of the DESI components. Figure 1: Research model of digitalization influences on sustainability. Source: author’s design. components on the first five indices associated with the first five goals (SDG1-SDG5). The DESI components are reflexive to the Digital Economy and Society Index latent variable [73]. Other latent variables, such as Economic Performance; Sustainable Development Goals Index; GOAL 1: No Poverty; GOAL 2: Zero Hunger; GOAL 3: Good Health and Well-being; GOAL 4: Quality Education; and GOAL 5: Gender Equality, each have an observable variable (GDP per capita, the aggregate SDG index, and the indices for the first five goals). The outer loadings of exogenous variables (observables) in the six models are above 0.7, increasing the significance of the model [71]. To validate and ensure the reliability of the models, we used the Cornell-Lracker criterion. Table 1 (see Appendix A) presents the discriminant matrices for the six models and the standardized root mean squared residual (SRMR). These values are crucial for confirming the quality of the constructs used in the  
Figure 2: Models for the direct and indirect influence of DESI on SDGi, and SDG1-SDG5. Source: author’s design using SmartPLS v3.0. models. In the discriminant matrix, the values on the main diagonal should be greater than any other value in the row and column (squared correlations) [73]. The SRMR provides a measure of the difference between observed and model-predicted values, highlighting the overall accuracy of the models [74]. Furthermore, [PERSON]'s alpha values (0.909), composite reliability (0.942), and average variance extracted (0.844) for the Digital Economy and Society Index latent variable are high for all six models. Using bootstrapping with a significance level of 0.05 and performing two-tailed tests, we determined the direct and indirect from models \(\alpha\) and 1-5 (Table A4 in Appendix B). Table A4 shows that for models \(\alpha\) and 1-5, hypotheses H1 and H2 are partially validated, although with some exceptions regarding the mediated effects studied by hypothesis H2. For H1, which posits that emerging technologies, measured through the DESI, significantly impact SDG progress in EU countries, the data confirm this hypothesis. All values for the relationship between the DESI, the SDG index, and the SDGs, as well as for most individual goals, are statistically significant and indicate a positive impact. High T-statistic values and \(p\)-values below 0.05 confirm this, suggesting that adopting digital technologies has a robust positive effect on progress toward SDGs. The only exception is perceived for GOAL 2: Zero Hunger, where the relationship between the DESI and GOAL 2: Zero Hunger is not statistically significant (\(p>0.05\)), registering a negative value. For H2, which states that economic performance positively mediates the relationship between emerging digital technologies and SDG progress, the data provide partial support. In model \(\alpha\), the indirect effect of the DESI on SDGs through economic performance is negative and significant (T statistic = 2.839, \(p\) Value = 0.005), indicating a negative mediation. This fact means that although the DESI has a direct positive effect on the SDGs, its indirect effect through GDP per capita is negative.