## 2 Materials and Methods
The correlation coefficient takes values from the interval \(\langle-1;1\rangle\) and expresses the degree of linear correlation between the variable. A value of \(-1\) indicates absolute indirect dependence, 0 indicates no linear dependence, and 1 indicates absolute direct dependence between two variables. In DEA analysis, it is advisable that the correlation coefficient should not exceed 0.8 or else the efficiency result may be biased. The ideal correlation coefficient is in the range of 0.3-0.8 and, depending on the number of units, is validated at a significance level of 0.05 [42; 43]. To use the DEA method, it is necessary to select the input and output units. Deciding on the appropriate input and output units to achieve the desired result is not an easy task. Thus, the method is used within the EU, which has 27 member states. The total number of inputs and outputs (m + s) is sought to be minimized to achieve a good predictive value of the model. As the number of inputs and outputs increases, all DMUs can become efficient. Hence, the model must satisfy the criterion m + s < n/5. In our study, we use 27 subjects and there is one input or output for every 5 subjects. This means that \(n=27\) and we have to divide this number by 5. This result will give us 5 or 6 inputs or outputs, or a combination of them. The total number of inputs and outputs that can be used is 5 to 6 at most. The input units are chosen from the composite indexes or a separate index. In terms of perspective, the GII: R&D investment index can reflect different levels of economic and social development and serve as an economic input, while the Internet usage index serves as a human input, providing an indication of Internet participation. Regarding the DSGI, it is an economic input to the DEA method. The CCR and BBC models focus on technological efficiency in the framework of e-Government, where 3 outputs have been selected. The models are output-oriented. The 2 units from the e-Government benchmark are chosen as output units. One of the units is the availability of online e-Government services within the country and the other unit is the satisfaction with eID. The last output index chosen is the interaction with online government. For all input and output indices see Table 8. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Used Attributes from the Index** & **Index Attribute** & **Index Direction** \\ \hline Internet usage & Human factor (number of internet users) & Access & - \\ \hline Digital Skills Gap—DSGI & Economic factor (Area of government support) & Access & - \\ \hline Global Innovation—GII & Economic factor (R\&D (\% of GDP) & Access & - \\ \hline e-Government benchmark & Technological factor (Availability of online e-Government services) & Output & + \\ \hline e-Government benchmark & Technology factor (eID satisfaction) & Output & + \\ \hline Interaction with public administration online & Human factor (e-Government—e-Government user communication) & Output & + \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Input and output DMU units. We focused on two areas as inputs. The first area is the human factor, which represents the total number of Internet users in the country. The second area is the economic factor. In the economic factor, we chose to promote digital skills. This was because, if the government does not promote the digital skills of the population, it cannot grow the use of digital or e-Government services. The second economic factor is the share of GDP (Gross Domestic Product) supporting innovation. This factor represents the knowledge society, which is important in the world's leading companies and an important factor for competitiveness and innovation creation. The outputs were chosen analogous to the inputs. That is, one of the output factors is the human factor, which represents the overall communication with the public administration online. Subsequently, two technological factors were chosen that represent the basis for the use of e-Government services.