## 3 Results  
This analysis explores the factors influencing scientific publications in blockchain, privacy, and cybersecurity across different countries. We examine how economic resources, research spending, education, life expectancy, and political systems affect research productivity in these fields. By comparing these areas to overall publication trends, we aim to understand what drives innovation in these important technological domains. The findings offer insights into how countries can support research and development in these rapidly evolving fields. This study provides a foundation for discussing the relationship between societal factors and scientific productivity in the digital age. In what follows, we discuss, comparatively, the relevance of each AHDI component for predicting scientific productivity at the country level. The results are synthesized in Figure 8 and detailed in Table 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Beta Coefficients**} \\ \cline{2-6}
**Dependent Variable** & _Life Expectancy_ & _Average Years_ & _Gross National_ & _Liberal_ & _Research Spending_ \\ (**Publications)** & _at Birth_ & _of Schooling_ & _Income per Capita_ & _Democracy_ & _\% of GDP_ \\ \hline Total per population & **0.125** & 0.027 & 0.535*** & **0.149*** & **0.281*** \\ \hline Blockchain per population & 0.132 & \(\mathbf{-0.165}\)* & 0.736*** & 0.031 & **0.200*** \\ \hline Privacy per population & 0.081 & \(\mathbf{-0.165}\)* & 0.651*** & **0.133** & **0.298*** \\ \hline Cybersecurity per population & 0.001 & 0.063 & 0.651*** & 0.092 & **0.184** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Beta coefficients for the AHDI components in the multiple regression models for the selected research publication topics. Both the dependent and independent variables are transformed with a lg10 function. Source: authors’ analysis. Coefficients marked with * are statistically significant for \(p\) = 0.05; coefficients marked with ** are statistically significant for \(p\) = 0.01; coefficients marked with *** are statistically significant for \(p\) = 0.001. Figure 7: Scatterplots of cybersecurity publications as a function of AHDI in raw numbers and logarithmic form. The logarithmic form indicates a linear relationship. Source: authors’ analysis. (**a**) The total cybersecurity publications per million (\(y\)-axis) as a function of AHDI (\(x\)-axis). (**b**) The lg10 of the total cybersecurity publications per million (\(y\)-axis) as a function of lg10 AHDI (\(x\)-axis). The regression models provide insights into the factors influencing scientific publications in total and on the selected topics, per capita, across countries. Their explanatory power is synthesized in Figure 9.  
Figure 8: Radar visualizations of the beta coefficients in the four regression models. Source: authors’ analysis. Figure 9: Explanatory power (R2 and adjusted R2 values) of multiple regression models. Source: authors’ analysis. The regression models for scientific publications in various fields demonstrate substantial explanatory power, as indicated by their R-squared values. The model for total publications per population shows the highest explanatory power, with an R-squared value of 0.914. This suggests that the selected independent variables account for 91.4% of the variation in the total scientific output across countries, indicating a very strong fit between the model and the data. The models for specific technological fields--blockchain, privacy, and cybersecurity--show somewhat lower but still considerable explanatory power. Their R-squared values range from 0.775 to 0.791. The slightly lower R-squared values for the specific fields compared to the total publications suggest that while the chosen variables are still highly relevant, there may be additional factors influencing research output in these areas that are not captured by the model. This could include field-specific elements such as technological infrastructure, industry partnerships, or national priorities in digital innovation.