# Binary Decision Support Using AHP: A Model for Alternative Analysis
## 4 Case Study
### Issuance of Opinions and Evaluations  
The Binary AHP method required us to conduct pairwise comparisons to evaluate the relative importance of each criterion and subcriterion. In this process, we collected expert opinions from five professionals with expertise in ICT classification, public procurement, and decision analysis. We used the experts' assessments to construct comparison matrices, which served as the basis for prioritizing the decision factors. To ensure the consistency and reliability in the evaluation process, each expert provided pairwise comparisons for the criteria and subcriteria using [PERSON]'s numerical scale. This scale, ranging from 1 to 9, enabled the experts to express their judgments regarding the relative importance of two elements at a time. We structured the comparisons to assess the influence of each criterion on the ICT classification decision by considering aspects such as data-processing requirements, cybersecurity implications, technological integration, and operational dependencies. After collecting the pairwise comparisons, we aggregated and normalized the results to generate priority vectors for each criterion and subcriterion. These priority vectors represented the relative weights assigned to each decision factor, which ensured that the most critical elements exerted a greater influence on the final classification. Table 6 presents an example of a pairwise comparison matrix for one of the main criteria and illustrates the structure and evaluation of the comparisons. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & **1** & **2** & **3** \\ \hline
1 & 1 & 1\(\downarrow\)4 & \(\downarrow\)\(\downarrow\)2 \\
2 & 4 & 1 & 2 \\
3 & 2 & \(\downarrow\)\(\downarrow\)2 & 1 \\ CR: 0.0000 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Example of pairwise comparison matrix (between criteria). A sample matrix illustrating the relative importance assigned to different criteria in the ICT classification process. Figure 2: A representation of the decision hierarchy, showing the relationship between the classification goal, criteria, subcriteria, and decision alternatives. After we completed the pairwise comparisons, we normalized the comparison matrix to ensure all the values appeared on a consistent scale. Table 7 presents the normalized pairwise comparison matrix, where each value resulted from dividing the original matrix elements by the sum of their respective columns. This step ensured that the weight distribution between the criteria remained proportionally adjusted. After constructing the comparison matrices, we calculated the Consistency Ratio (CR) to ensure the logical coherence of the expert evaluations. A CR below 0.10 indicates that the judgments remain consistent and support reliable decision-making. If the CR exceeded this threshold, experts reviewed and refined their assessments to improve the consistency. Following the normalization process, the analysis computed the priority vector for each criterion by calculating the arithmetic mean of each row in the normalized matrix. Table 8 presents the priority vector matrix, which shows the relative importance of each criterion in the classification decision. After completing this process, we obtained normalized priority vectors, which served as the input for the final decision analysis. These values were then applied in the Binary AHP framework to enable a structured comparison of the yes and no classification alternatives. The following sections detail the results of this evaluation and analyze the global priority vectors for both options. With the criteria priority weights established, pairwise comparisons were conducted for the subcriteria under each main criterion. At this level, the process applied the same pairwise comparison, normalization, and priority vector calculation steps. Table 9 presents an example of a pairwise comparison matrix for the subcriteria under Criterion 1.