Prioritization of Technological Solutions for the Digital Transformation of SERCOP through AHP–TOPSIS

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Andrés Villarreal Valencia
Jimmy Farinango Alquinga

Abstract

The digital modernization of public procurement is a strategic pillar for strengthening transparency, administrative efficiency, and citizen participation in the public sector. In Ecuador, the National Public Procurement Service (SERCOP) faces the challenge of selecting, from among multiple technological alternatives, the most suitable option to strengthen its digital platform and improve data governance. This study proposes a multi-criteria evaluation model based on the integration of the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods, in order to prioritize strategic criteria—accessibility, data security, efficiency in information management, transparency and accountability, level of technological innovation, and sustainability—and to prioritize digital innovation alternatives. Twelve experts in public management and technological innovation participated, and their assessments established that transparency is the most relevant criterion (40%), followed by technological innovation (22.4%) and efficiency (15.6%). When applying the AHP weights to the TOPSIS model, the “Blockchain Platform for Public Procurement” obtained the highest index of proximity to the ideal solution, positioning itself as the priority alternative for SERCOP. The results demonstrate the relevance of the AHP-TOPSIS approach in complex public decision-making processes and highlight the need to strengthen data governance, digital inclusion, and institutional sustainability.

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Prioritization of Technological Solutions for the Digital Transformation of SERCOP through AHP–TOPSIS. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 41, 244-263. https://fs.unm.edu/NCML2/index.php/112/article/view/911
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How to Cite

Prioritization of Technological Solutions for the Digital Transformation of SERCOP through AHP–TOPSIS. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 41, 244-263. https://fs.unm.edu/NCML2/index.php/112/article/view/911