## 3 Empirical Results  
### Efficiency Evaluation over the Period 2013-2017  
In this study, the Super-SBM model was used to evaluate the efficiency of 24 ICT companies in Vietnam during 2013-2022 with three selected inputs (Cost of goods sold, Operation cost, and Total assets) and two outputs (Net sale and Profit afte-tax). This study uses DEA-Solver Pro V13.2 to run DEA Super-SBM. Before obtaining the efficiency scores, the authors would like to test the correlation between inputs and outputs as when employing DEA approach, the researcher concerns ensuring not merely that the relationship between input and output indicators is isotonic, but also that the linear relation determines an efficiency measure of position relative to the frontier toward each DMU. In this paper, a Pearson correlation is conducted to define the level of alignment between two variables, whereas a higher correlation coefficient implies a closer relation and vice versa. The results of correlation testing presented at Table 1 indicate that there exists the linear correlation between inputs and output with the positive associations. Thus, the correlation correlates well with the prerequisite condition of the DEA model. After making sure of the correlation between inputs and outputs, DEA Super-SBM is applied to obtain the efficiency scores of 24 ICT companies over the period 2013-2017 and Table 2 reports the obtained results. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Year 2017** & **Cost of Goods Sold** & **Operation Cost** & **Total Asset** & **Net Sales** & **Profit after Tax** \\ \hline Cost of Goods sold & 1 & 0.9606 & 0.9659 & 0.9973 & 0.9840 \\ Operation Cost & 0.9606 & 1 & 0.9885 & 0.9450 & 0.9918 \\ Total Asset & 0.9659 & 0.9885 & 1 & 0.9524 & 0.9839 \\ Net sales & 0.9973 & 0.9450 & 0.9524 & 1 & 0.9735 \\ Profit after Tax & 0.9840 & 0.9918 & 0.9839 & 0.9735 & \(1\) \\ \hline \end{tabular}
\end{table} 
Table 1: Correlation between variables (2017). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**DMU** & **2013** & **2014** & **2015** & **2016** & **2017** & **Average** \\ \hline DMU1 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ DMU2 & 2.244 & 1.875 & 1.787 & 1.714 & 1.720 & 1.868 \\ DMU3 & 3.288 & 2.028 & 0.729 & 0.532 & 0.554 & 1.426 \\ DMU4 & 0.743 & 0.901 & 0.935 & 0.887 & 1.004 & 0.894 \\ DMU5 & 1.072 & 1.197 & 1.099 & 1.233 & 1.091 & 1.138 \\ DMU6 & 0.641 & 1.360 & 1.046 & 0.891 & 0.752 & 0.938 \\ DMU7 & 0.674 & 0.691 & 0.736 & 0.715 & 0.758 & 0.715 \\ DMU8 & 0.492 & 0.477 & 0.526 & 1.282 & 1.402 & 0.836 \\ DMU9 & 1.202 & 1.150 & 1.325 & 1.303 & 1.164 & 1.229 \\ DMU10 & 4.613 & 2.766 & 3.326 & 8.926 & 4.220 & 4.770 \\ DMU11 & 0.772 & 1.082 & 1.044 & 1.327 & 1.287 & 1.102 \\ DMU12 & 2.323 & 1.105 & 0.997 & 0.839 & 0.935 & 1.240 \\ DMU13 & 0.947 & 0.951 & 0.954 & 0.903 & 0.898 & 0.931 \\ DMU14 & 0.839 & 0.921 & 1.061 & 0.851 & 0.862 & 0.907 \\ DMU15 & 0.674 & 0.700 & 2.082 & 1.316 & 2.296 & 1.414 \\ DMU16 & 0.691 & 0.725 & 0.722 & 0.716 & 0.723 & 0.715 \\ DMU17 & 0.599 & 0.689 & 0.719 & 0.793 & 0.770 & 0.714 \\ DMU18 & 0.766 & 0.759 & 0.751 & 0.777 & 0.835 & 0.777 \\ DMU19 & 0.708 & 1.514 & 1.180 & 1.048 & 1.571 & 1.204 \\ DMU20 & 0.787 & 0.835 & 0.824 & 0.736 & 0.893 & 0.815 \\ DMU21 & 1.188 & 1.312 & 1.305 & 1.425 & 1.474 & 1.341 \\ DMU22 & 0.855 & 0.906 & 0.854 & 0.923 & 1.003 & 0.908 \\ DMU23 & 0.912 & 1.845 & 1.319 & 1.155 & 1.136 & 1.273 \\ DMU24 & 0.872 & 0.835 & 0.783 & 0.760 & 0.818 & 0.814 \\ Average & 1.204 & 1.151 & 1.129 & 1.335 & 1.215 & 1.207 \\ \hline \end{tabular}
\end{table} 
Table 2: The efficiency of ICT companies (2013–2017). On average, of 24 ICT companies, 12 companies were found to be inefficient with the corresponding efficiency score lower than 1. For these inefficient, the average score ranges from 0.714 to 0.938, with the inefficiency in performance indicating the unbalance between inputs and outputs. On the other hand, the half of selected companies showed better performance with the corresponding score being equal and higher than 1.