## 2 Materials and Methods
The log-log scale helps in depicting a consistent, multiplicative relationship between the AHDI and total publications, thereby improving the robustness and accuracy of our analysis. The visualizations for the total publications per capita are presented in Figures 4-7 below. While we tried to introduce measures of country-level digitalization in the regression analysis, specifically the Network Readiness Index (NRI), which has values across a wide range of countries, this variable was highly correlated with GNI/capita and created collinearity issues. Statistical analyses were performed using the IBM SPSS version 29.0.2.0 software, employing linear regression with the Enter method for the independent variables. The visualizations were generated using Microsoft Excel (Office 365). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Subject** & **Query (Keywords)** & **World Count** & **EU Count** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Total} & \multicolumn{2}{c}{“the” (capturing all the publications with a generic keyword)} & \multirow{3}{*}{27,105,149} & \multirow{3}{*}{8,330,370} \\  & & & & \\ \hline
2 & Privacy & “Privacy” & 104,802 & 30,896 \\ \hline
3 & Blockchain & “Blockchain” & 36,460 & 10,888 \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{Cyber Security} & “Cybersecurity” OR & \multirow{3}{*}{22,376} & \multirow{3}{*}{7545} \\  & & “Cyber Security” OR & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Research topics and publications included in analysis. Source: authors’ analysis. The study encompasses 16 regression models, with 4 primary models reported in the main text and the remaining 12 presented only in the Supplementary Materials. The preliminary analysis revealed multicollinearity issues with an additional digitalization indicator, which was subsequently excluded. The Supplementary Materials contain models using both raw numbers and log transformations, as well as models solely utilizing AHDI  
Figure 4: Scatterplots of the total publications as a function of AHDI in raw numbers and logarithmic form. The logarithmic transformation permits linear regression modeling. Source: authors’ analysis. (**a**) The total publications per million (\(y\)-axis) as a function of AHDI (\(x\)-axis). (**b**) The lg10 of the total publications per million (\(y\)-axis) as a function of lg10 AHDI (\(x\)-axis). Figure 5: Scatterplots of blockchain publications as a function of AHDI in raw numbers and logarithmic form. The logarithmic transformation permits linear regression modeling. Source: authors’ analysis. (**a**) The total blockchain publications per million (\(y\)-axis) as a function of AHDI (\(x\)-axis). (**b**) The lg10 of the total blockchain publications per million (\(y\)-axis) as a function of lg10 AHDI (\(x\)-axis). Figure 6: Scatterplots of privacy publications as a function of AHDI in raw numbers and logarithmic form. The logarithmic transformation permits linear regression modeling. Source: authors’ analysis. (**a**) The total privacy publications per million (\(y\)-axis) as a function of AHDI (\(x\)-axis). (**b**) The lg10 of the total privacy publications per million (\(y\)-axis) as a function of lg10 AHDI (\(x\)-axis). indicators and those incorporating research spending as a percentage of GDP. The inclusion of research spending enhanced the predictive value without introducing collinearity concerns. Consequently, the article reports only log-transformed models incorporating all five predictors. This approach provides a comprehensive statistical foundation for the research findings.