## 2 Materials and Methods
One of these is satisfaction with the eID, because if there is low satisfaction with the means of logging in, citizens will not use these services. The second economic factor is the availability of online services, because if there is a low number of online services, citizens are not able to use their desired service. In this analysis using DEA, 6 indices were chosen that may reflect different factors between countries within the EU. Other indices that can be used in this analysis include GDP, HDI, DESI, ICT investment, World Digital Competitiveness Capability Index, number of digital public services for citizens, number of digital public services for businesses, education spending (% of GDP), and others. Based on the selection of values, an input- or output-oriented CCR/BCC model can be created. The EU consists of 27 EU Member States. The aim of our measurement was also determined by the means of measurement or the choice of the model orientation, because we were interested in input-output effects so that we can measure the technological efficiency of e-Government in a given country. The outputs and inputs used in the analysis are unique to the system under consideration. Because we aimed to maximize the outcomes of the technology part of e-Government, we used an output-oriented model [40]. The DEA analysis not only focused on evaluating the efficiency improvement in e-Government for each country, but the countries were also divided into their areas within the EU as much as possible to compare the efficiency scores from the different EU areas. Table 9 shows the inputs and outputs for all DMUs and country breakdown by region. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{3}{*}{**DMU**} & **Region within the EU** & & \multicolumn{3}{c}{**Inputs**} & \multicolumn{3}{c}{**Output**} \\ \cline{3-8}  & **Region within the EU** & & \multicolumn{1}{c}{**Number of Internet Users in [\%]**} & \multicolumn{1}{c}{**DSCI– Movement in [\%]**} & \multicolumn{1}{c}{**Cross R\&D**} & \multicolumn{1}{c}{**Service Online**} & \multicolumn{1}{c}{**Satisfaction**} & \multicolumn{1}{c}{**Interaction with Public**} \\  & **the EU** & & **in [\%]** & **Support** & **\% of GDP** & **in [\%]** & **with eID in [\%]** & **with eID in [\%]** & **Administration in [\%]** \\ \hline \multirow{2}{*}{\(1\)} & western & Luxembourg & 99 & 9 & 20.6 & 92 & 67 & 49 \\ \cline{2-8}  & western & Austria & 88 & 5.7 & 58.8 & 89 & 85 & 67 \\ \hline \multirow{2}{*}{\(3\)} & western & Belgium & 92 & 5.2 & 63.9 & 87 & 72 & 63 \\ \cline{2-8}  & western & France & 92.2 & 5.6 & 43.2 & 90 & 46 & 56 \\ \hline \multirow{2}{*}{\(4\)} & western & Germany & 94 & 6.2 & 57.8 & 87 & 53 & 50 \\ \cline{2-8}  & western & Netherlands & 95.3 & 6.4 & 42.1 & 94 & 86 & 86 \\ \hline \multirow{2}{*}{\(6\)} & eastern & Hungary & 89.3 & 3.1 & 29.4 & 90 & 77 & 81 \\ \cline{2-8}  & eastern & Czech Republic & & 86.8 & 3 & 36.5 & 80 & 44 & 64 \\ \hline \multirow{2}{*}{\(8\)} & eastern & Slovakia & 90 & 3.5 & 16.6 & 63 & 75 & 57 \\ \cline{2-8}  & eastern & Poland & 91.5 & 2.7 & 25.5 & 79 & 77 & 34 \\ \hline \multirow{2}{*}{\(11\)} & eastern & Bulgaria & 70 & 3.9 & 15.5 & 67 & 42 & 24 \\ \cline{2-8}  & eastern & Romania & 78 & 2.8 & 8.5 & 50 & 20 & 12 \\ \hline \end{tabular}
\end{table} 
Table 9: Inputs and outputs data for the DEA method. Correlation analysis is an important criterion for determining appropriate inputs and outputs. We performed correlation analysis for each combination of variables. The results of the correlation analysis are shown in Table 10.  
Individual letters represent selected inputs and outputs:  
* A (number of Internet users in % Penetration),
* B (DSGI--government support),
* C (Gross expenditure on research and development, % GDP), D (service online e-Government),
* E (satisfaction with eID login in [%]),
* F (interaction with public administration). From the results of the correlation analysis, studying the literature on the use of DEA models, and consultation, it can be said that the values between the variables are suitable for DEA analysis.