## 4 Further Analysis and Discussion
### Results/Implications from Modeling/Comparisons  
The results in Section 3.5 may render the most important outcome of this study, which is the similarity shown and validated based on the application of the comparative research flow that is methodologically proposed. Consequently, AHP-GTMA gives a ROV (\(ROV_{\text{AHP-GTMA}}\)) which is very similar to that obtained using AHP-TOPSIS (\(ROV_{\text{AHP-TOPSIS}}\)). In Phase (1), the similarity is shown when the elements in each ROV are viewed as a list. In these lists, the aggregated absolute distance in rank for the eight alternatives is only 10, while 75% of the alternatives have very similar ranks for both methods (among them, only two have a difference in rank which is strictly greater than one place, and the one with \"maximum deviation\" is only 3). In Phase (2), the small Euclidean distance (i.e., 18) that is measured between these two ROVs (as two vectors in the space) also shows the similarity. Then, Phase (3) finds further supporting evidence that the two ROVs, when viewed as statistical variables, are likely (drawn from) an identical population using the signed-rank test. With the progress of the three phases, in terms of methodology, it is shown or implied that:  
1. _The proposed comparative research flow is applicable. This can be justified from its successful application_. 2. _The ranked-based, multi-measure flow is suitable for the given comparison problem_. For the input of the flow, it requires only the two ROVs that are available from using almost all MADM models. Suchrank-based property of the proposed flow should be valuable, in that it can be easily implemented in decision practice. The multiple measures that are included and used may cross validate the results from each other. Because these measures are designed and established from different theoretical aspects that are intrinsically heterogeneous (i.e., arithmetic, geometrical and statistical), they are particularly useful when any similarity is observed using one measure but further confidence should be sought for from using other measures. Specifically, in this study, the result from using Phase (2)'s geometrical measure provides evidence to the similarity that has been observed in Phase (1)'s arithmetic measure. Moreover, the result from using Phase (3)'s statistical measure further confirms the similarity that has been justified based on the two previous phases. In other words, such a final outcome infers a \"total similarity\" between the two ROVs, which can further support the following Point (3). However, such a multi-measure, cross-validation style should also be a valuable property of the proposed flow, in terms of methodology. 3. _It well supports the target of this study to establish the confidence on applying AHP-GTMA in practice._ As the major experimental result, the AHP-GTMA gives a ROV that is quite similar to that obtained using AHP-TOPSIS, and this has been cross-validated using the multiple tangible or intangible measures. The similarity between the ROVs claims the effectiveness of the AHP-GTMA approach. As discussed previously, since AHP-TOPSIS is a mature and \"widely used\" MADM method, the fact the two ROVs are quite similar supports the claim that AHP-GTMA is (and should also be) another effective and suitable method for solving a selection decision problem. In other words, the result gives confidence that using an AHP-GTMA to solve other types of operational selection decision problems is feasible. 4. _The proposed flow is able to be generalized_. In this study, the methodological flow proposed in Section 3.1 is shown to support the required comparative research process between the ROVs that are individually obtained using AHP-GTMA and AHP-TOPSIS. As most MADM methods give a ROV (even using simple-additive weighting (SAW) or weighted sum model (WSM)) that is behind the final preferential order, the flow can be generalized to identify the similarity between any other pair of ROVs that are produced by any other two MADM approaches, where appropriate.