## 4 Results
### Model Fit and Path Coefficients  
Using SmartPLS, we evaluated the reliability and validity of the constructs. The Cronbach's alpha coefficients confirm strong internal consistency. The path coefficients and corresponding loadings provide additional validation of the hypotheses (Figure 3):  
**H1:**_Blockchain benefits significantly influence the adoption of blockchain apps. A path coefficient of 0.591 indicates a strong influence, supporting the first hypothesis._  
**H2:**_Blockchain apps positively impact athletes' performance at national and European levels, demonstrated by a moderate path coefficient of 0.243._  
Fornell-Larcker discriminant validity requirements were satisfied, with diagonal matrix values exceeding off-diagonal entries (Table 2). Additionally, the standardized root means square residual (SRMR) of 0.024, below the threshold of 0.05, indicates an excellent model fit [3]  
An exceptional match can be explained by the SRMR (0.024), which has a value of less than 0.05 [35]. The parameters d ULS and d G, which stand for the squared Euclid distance and the geodesic distance, respectively, are utilized to calculate the discrepancy depending on the eigenvalue value [36]. The estimates for SRMR, d ULS, and Chi-Square (saturated = 116.318 and estimated = 116.347) are greater than the saturated model, which stands in for the threshold when the estimated and saturated values of the models are compared (Table 3). The NFI (0.960) score indicates a consistent model because it is quite near the threshold of one.  
The degree to which the exceptionally strong correlations between the variables that predicted the elevated variance of the generated coefficients of regression is determined by the variance inflation factor or VIF. There does not exist collinearity among the variables when the VIF is lower than the conventional threshold of five [36]. In our case all variables  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Block Benefits** & **Block Apps** & **Performance** \\ \hline Blockchain Benefits & & & \\ Blockchain Apps & 0.591 & & \\ Performance & 0.141 & 0.243 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Discriminant validity—Formell–Larcker.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Saturated Model** & **Estimated Model** \\ \hline SRMR & 0.024 & 0.024 \\ d\_ULS & 0.077 & 0.077 \\ d\_G & 0.078 & 0.078 \\ Chi–Square & 116.318 & 116.347 \\ NFI & 0.960 & 0.960 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model fit.  
have lower values than four, meaning that the multicollinearity is not manifest between our variables (Table 4).  
The standard errors for the PLS-SEM results are produced using the predictions from the bootstrapping subsamples. When assessing the significance of PLS-SEM data, SmartPLS software computes T-values, confidence intervals, and standard errors [36]. To evaluate the significance of the PLS-SEM results, T-values, \(p\)-values, and confidence intervals were produced using the previously mentioned data [37]. Model coherence is shown by T-values larger than 1.96 [37] and reduced \(p\)-values (less than 0.01) because the first regression blockchain benefits -> blockchain apps has \(\mu\) = 0.59, DS = 0.043. Tstat = 13.869 and \(p\) = 0.00. The H1 and H2 that were previously mentioned have been met. Because of the extremely low standard deviations and \(p\)-values, we can confirm the high accuracy of our model.