## 4 Results  
A structural model was created to confirm the accuracy of the model. Various fit and reliability indices were calculated to assess the overall fit and suitability of the proposed model. Table 3 presents an overview of the obtained and widely accepted values. Figure 1 provides a visual representation of the proposed model together with the estimated parameter values and associated \(p\)-values. The results show that, with a confidence level greater than 95%, four of the ten proposed hypotheses were accepted (\(p\) < 0.05). Table 4 presents the findings for each of the hypotheses. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Variables** & **Label** & **Effect** \\ \hline
1 & Network readiness index & NRI & Positive \\ \hline
2 & Sustainable development goal rank & SDG & Positive \\ \hline
3 & Corruption control & CC & Positive \\ \hline
4 & Democracy level & DeM & Positive \\ \hline
5 & Economic freedom & EF & Negative \\ \hline
6 & Income inequality & GiC & Negative \\ \hline
7 & Foreign reserve & RiS & Positive \\ \hline
8 & Human development index & HDI & Positive \\ \hline
9 & Regulatory quality & RQ & Negative \\ \hline
10 & Corruption perception index & CPI & Positive \\ \hline
11 & Country CBDC adoption status & STA & Dependent \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The variables’ labels and expected effects. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Value** & **Interpretation** \\ \hline Average path coefficient (APC) & APC = 0.158, \(p\) = 0.044 & \\ \hline Average R-squared (ARS) & ARS = 0.052, \(p\) = 0.167 & \\ \hline Average adjusted R-squared (AARS) & AARS = \(-\)0.118, \(p\) = 0.080 & \\ \hline Average block VIF (AVIF) & AVIF = 3.316 & Acceptable if \(\leq\) 5, ideally \(\leq\) 3.3 \\ \hline Average full collinearity VIF (AFVIF) & AFVIF = 5.444 & Acceptable if \(\leq\) 5, ideally \(\leq\) 3.3 \\ \hline Tenenhaus GoF (GoF) & GoF = 0.227 & Small \(\geq\) 0.1, medium \(\geq\) 0.25, large \(\geq\) 0.36 \\ \hline Simpson’s paradox ratio (SPR) & SPR = 0.700 & Acceptable if \(\geq\) 0.7, ideally = 1 \\ \hline R-squared contribution ratio (RSCR) & RSCR = 0.585 & Acceptable if \(\geq\) 0.9, ideally = 1 \\ \hline Statistical suppression ratio (SSR) & SSR = 0.800 & Acceptable if \(\geq\) 0.7 \\ \hline Nonlinear bivariate causality direction & NLBCDR = 0.400 & Acceptable if \(\geq\) 0.7 \\ ratio (NLBCDR) & \hline \hline \end{tabular}
\end{table} 
Table 3: Indices of the model’s fit and reliability. The graphs (Figure 2) below illustrate the most significant findings. The above graph in Figure 2 indicates that the relationship between GiC, which represents income inequality (**H8**), and CBDC adoption is negative (\(\beta=-0.2\), \(p<0.04\)). Central bank digital currencies are more widespread in nations with moderate to small income inequalities compared to those with wide wealth gaps. The relationship between the corruption perception index (CPI) (**H3**) and the adoption of CBDCs is strong and positive (\(\beta=0.2\), \(p=0.04\)). The graph in Figure 3 illustrates how the variable \"CPI\" is positively correlated with the variable \"STA\", indicating that as the public view of governance and trust in the government improves, CBDC adoption correspondingly increases. The curve in the graph in Figure 4 indicates that countries with very poor regulatory quality (RQ) have a significantly positive link with CBDC adoption, whereas countries with medium levels of RQ have a relatively weak association. However, when the level of regulatory quality reaches its highest point, adoption rates further increase.