# Binary Decision Support Using AHP: A Model for Alternative Analysis
## 6 Conclusions  
This paper introduces a Binary AHP-based decision support model for binary classification in the context of ICT procurement in the public sector. We adapted the Analytic Hierarchy Process (AHP) to enable a structured and reproducible classification of technological solutions as ICT or non-ICT. The Binary AHP framework organizes the decision process through a hierarchical structure, pairwise comparisons, and consistency analysis, that aim to reduce subjectivity and increase logical consistency.  
We resolved inconsistencies in ICT classification by applying the model to a case from a Brazilian public agency. The global priority vector, which was derived from expert evaluations, provided the basis for classification. The analysis with the inverse priority vector supported the robustness of the model by confirming a balanced weight distribution between alternatives. The results show that solutions dependent on software, data processing, and security received classifications as ICT, while accessories, peripherals, and services unrelated to technology received classifications as non-ICT.  
Public administrators can incorporate the Binary AHP model to support ICT classification decisions. Its use may contribute to greater uniformity and transparency in procurement and regulatory activities. The model also provides a documented decision trail that supports audits and reviews. Its structure enables integration with existing ICT governance and procurement systems.  
This study had some limitations. The case study was restricted to a single public sector context, which may limit the direct transferability of the model to other domains without adaptation. Although structured and validated for consistency, the model relies on expert judgments, which may still introduce some subjectivity. Another limitation is the requirement that all questions be binary since the model explicitly targets dichotomous decision problems. This constraint may restrict its applicability in scenarios where the decision criteria or alternatives do not naturally conform to a binary structure.  
Future research could extend the Binary AHP model to other binary classification contexts, such as policy decisions, project approvals, or risk assessments. Researchers could also explore integrating Binary AHP with other decision-making methodologies or with machine learning techniques to automate parts of the process and enable real-time decision support. Comparative analyses in different organizational and regulatory environments would help evaluate the model's adaptability and identify necessary modifications. Longitudinal studies could examine the stability of the model's classifications as technological and regulatory landscapes evolve.  
Conceptualization, F.R.M., M.A.C.G. and R.R.N.; methodology, R.R.N. and F.R.M.; software, M.A.C.G.; validation, R.R.N., R.R.d.S. and E.G.d.S.; formal analysis, R.R.d.S.; investigation, R.R.N.; resources, R.R.N. and R.R.d.S.; data curation, F.R.M. and M.A.C.G.; writing--original draft preparation, E.G.d.S., F.R.M. and L.A.R.J.; writing--review and editing, E.G.d.S., M.A.C.G., L.A.R.J. and R.R.d.S.; visualization, E.G.d.S. and L.A.R.J.; supervision, R.R.N.; project administration, R.R.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are contained within this article.  
**Acknowledgments:** Project \"TED 33/2023-APPLIED RESEARCH IN PRIVACY AND INFORMATION SECURITY AT THE DIRECTORATE OF PRIVACY AND INFORMATION SECURITY OF THE SECRETARIAT FOR DIGITAL GOVERNMENT\" partially funded this study. Thanks to the Directorate of Privacy and Information Security (DEPSI)/Center of Excellence in Privacy and Security (CEPS)/Secretariat for Digital Government (SGD) of the Ministry of Management and Innovation in Public Services of the Federal Government (MGI).  
**Conflicts of Interest:** The authors declare no conflicts of interest.