## 4 Case Study
### Solution of the Typical Weighted Aggregated Sum Product Assessment Approach  
The WASPAS approach combines WSM and WPM models, which help experts make decisions under the influence of multiple attributes or criteria [35]. The main advantage of the WASPAS approach is that it is highly resistant to ranking reversals of the alternatives considered [42].  
The typical WASPAS approach can only handle complete information provided by the expert. However, expert E4 provided information containing some incomplete information; thus, only the information of experts E1, E2, and E3 was used for calculation. Using the data in Table 5, Equation (2) was used to normalize the evaluation scores of 26 construction industry Internet platforms; then, Equations (4) and (5) were used to calculate the values of \(Q_{i}^{1}\) and \(Q_{i}^{2}\). The results are shown in Table 8. Then, using Equation (13), the \(\lambda\) coefficient value was set to 0.5, and the weights of four evaluations were set to an equal weight to calculate the \(Q_{i}\) value, as expressed in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Platform** & **TM Weighted** & **UN Weighted** & **PF Weighted** & **CS Weighted** & **Aggregated** & **Rank** \\  & **Value** & **Value** & **Value** & **Value** & **Values** & **Values** & **Rank** \\ \hline P1 & 1.340 & 1.487 & 0.278 & 0.160 & 3.265 & 17 \\ P2 & 1.452 & 2.230 & 0.649 & 0.240 & 4.570 & 9 \\ P3 & 1.898 & 2.676 & 0.834 & 0.400 & 5.808 & 1 \\ P4 & 0.782 & 1.635 & 0.556 & 0.133 & 3.106 & 18 \\ P5 & 1.787 & 2.676 & 0.927 & 0.320 & 5.709 & 3 \\ P6 & 1.787 & 2.379 & 0.417 & 0.133 & 4.716 & 8 \\ P7 & 1.228 & 1.784 & 0.556 & 0.240 & 3.808 & 14 \\ P8 & 0.670 & 1.041 & 0.417 & 0.293 & 2.421 & 23 \\ P9 & 0.893 & 1.635 & 0.232 & 0.107 & 2.867 & 19 \\ P10 & 1.452 & 1.784 & 0.278 & 0.267 & 3.780 & 15 \\ P11 & 1.340 & 2.379 & 0.834 & 0.400 & 4.953 & 6 \\ P12 & 1.117 & 1.933 & 0.602 & 0.347 & 3.998 & 13 \\ P13 & 0.893 & 1.933 & 0.371 & 0.133 & 3.330 & 16 \\ P14 & 1.228 & 2.230 & 0.695 & 0.187 & 4.340 & 10 \\ P15 & 0.893 & 1.189 & 0.556 & 0.160 & 2.799 & 20 \\ P16 & 0.558 & 1.041 & 0.741 & 0.293 & 2.634 & 22 \\ P17 & 1.563 & 1.487 & 0.695 & 0.293 & 4.038 & 11 \\ P18 & 0.670 & 0.892 & 0.278 & 0.160 & 2.000 & 25 \\ P19 & 0.558 & 0.892 & 0.695 & 0.187 & 2.332 & 24 \\ P20 & 2.010 & 1.784 & 0.649 & 0.427 & 4.869 & 7 \\ P21 & 1.452 & 1.933 & 0.417 & 0.213 & 4.015 & 12 \\ P22 & 1.563 & 2.676 & 0.695 & 0.133 & 5.068 & 5 \\ P23 & 1.563 & 3.122 & 0.556 & 0.133 & 5.375 & 4 \\ P24 & 2.010 & 2.676 & 0.649 & 0.400 & 5.735 & 2 \\ P25 & 0.447 & 1.041 & 0.278 & 0.133 & 1.899 & 26 \\ P26 & 0.893 & 1.189 & 0.324 & 0.347 & 2.754 & 21 \\ \hline \end{tabular}
\end{table}
Table 7: Calculation results of the FAHP approach.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Platform** & **TM** & **UN** & **PF** & **CS** & \(Q_{i}^{1}\)**Value** & \(Q_{i}^{2}\)**Value** & \(Q_{i}\)**Value** & **Rank** \\ \hline P1 & 4.000 & 3.333 & 2.000 & 2.000 & 0.454 & 0.435 & 0.445 & 20 \\ P2 & 4.333 & 5.000 & 4.667 & 3.000 & 0.675 & 0.671 & 0.673 & 6 \\ P3 & 5.667 & 6.000 & 6.000 & 5.000 & 0.910 & 0.909 & 0.909 & 1 \\ P4 & 2.333 & 3.667 & 4.000 & 1.667 & 0.456 & 0.442 & 0.449 & 18 \\ P5 & 5.333 & 6.000 & 6.667 & 4.000 & 0.874 & 0.869 & 0.872 & 2 \\ P6 & 5.333 & 5.333 & 3.000 & 1.667 & 0.603 & 0.556 & 0.579 & 13 \\ P7 & 3.667 & 4.000 & 4.000 & 3.000 & 0.586 & 0.586 & 0.586 & 12 \\ \hline \end{tabular}
\end{table}
Table 8: The computation results of the typical WASPAS approach.