## 3 Results Obtained  
### Application of the ELECTRE I  
The criterion weights were assigned on a scale ranging from 0 (the least important) to 10 (the most important). The points for each criterion provided by each interviewee were calculated column-wise. Then, the sum of the points for each criterion was divided by the total number of points assigned to all criteria, resulting in a normalized weight for each criterion [74], as calculated by Formula (1) (weighting) and presented in Table 3.  
\[w_{h}=\frac{\sum_{j=1}^{n}\ b_{bj}}{\sum_{h=1}^{n}\ \sum_{j=1}^{n}\ b_{bj}} \tag{1}\]  
where \(w_{h}\) is weighting, \(n\) is number of interviewees, \(m\) is number of criteria, \(b_{bj}\) is points, and \(h\) is criterion. Now, a table containing the criteria values must be drawn for the assessment. These figures indicate the differences between the vehicles being compared. It is also necessary to determine whether the criterion value should be minimized, with the ideal criterion value close to 0 (e.g., high noise level, obsolescence, high costs, etc. ), or maximized, with the ideal criterion value close to 10 (e.g., profit, performance, load-carrying capacity, etc.) [75]. Table 4 presents the parameters. The following step involves normalizing the data of the criteria values from 0 to 1, ensuring that no criterion value exceeds another and that all values in the columns are \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Vehicle**} & \multirow{2}{*}{\begin{tabular}{c} **Maximum** \\ **Mileage [km]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Load-Carrying** \\ **capacity [m\({}^{3}\)]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Recharging** \\ **Speed [min]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Purchase** \\ **Price** \\ **[in \(\xi\)]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Load-** \\ **Bearing** \\ **Capacity [t]** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Consumption** \\ **[kWh/100 km]** \\ \end{tabular} } \\ \hline FORD & 284 & 15.1 & 34 & 2026 & 1549 & 29.2 \\ RENAULT & 204 & 13 & 40 & 1700 & 1308 & 34.5 \\ VW & 173 & 10.7 & 45 & 1709 & 1700 & 21.5 \\ OPEL & 330 & 6.6 & 35 & 1566 & 1200 & 26.4 \\ PEUGEOT & 275 & 4.4 & 30 & 1099 & 800 & 16.2 \\ \hline Weighting & 0.19 & 0.2 & 0.15 & 0.09 & 0.21 & 0.16 \\ Criterion nature & max & max & min & min & max & min \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Electric I—parameters of the compared variables. Source: authors according to Ford, 2024; Renault, 2024; VW, 2024; Opel, 2024; Peugeot, 2024. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Maximum** \\ **Mileage [km]** \\ \end{tabular} & \begin{tabular}{c} **Load-Carrying** \\ **Capacity [m\({}^{3}\)]** \\ \end{tabular} & \begin{tabular}{c} **Recharging** \\ **Speed [min]** \\ \end{tabular} & \begin{tabular}{c} **Purchase** \\ **Price [in \(\xi\)]** \\ \end{tabular} & \
\begin{tabular}{c} **Load-** \\ **Bearing** \\ **Capacity [t]** \\ \end{tabular} &
\begin{tabular}{c} **Consumption** \\ **[kWh/100 km]** \\ \end{tabular} \\ \hline Manager & 7 & 7 & 9 & 10 & 8 & 9 \\ Logistics & 10 & 8 & 6 & 2 & 9 & 8 \\ director & & & & & \\ Inbound & 8 & 10 & 5 & 2 & 9 & 6 \\ logistics & & & & & \\ External & 9 & 8 & 6 & 4 & 10 & 5 \\ logistician & 6 & 10 & 5 & 2 & 8 & 7 \\ \hline Total pref. & 40 & 43 & 31 & 20 & 44 & 35 \\ Weight & 0.19 & 0.20 & 0.15 & 0.09 & 0.21 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Weighted preferences. Source: authors. comparable. Without normalization, some criteria might show better values than others simply due to differences in the measurement scale. Normalization ensures the equality of all criteria, and its formula varies according to a specific criterion [67,76]. Formula (2) below is used for minimizing, while Formula (3) is applied for maximizing criteria.