MULTICRITERIA ASSESSMENT IN TECHNOLOGICAL DECISION MAKING: A COMPARATIVE SYSTEMATIC REVIEW BETWEEN THE SAPEVO-M AND SAPEVO-H2
Abstract
This study aimed to conduct a systematic literature review to compare the applicability, methodological characteristics, and limitations of the SAPEVO-M and SAPEVO-H² models in supporting technological decision-making, seeking to understand how these methods can contribute to more consistent and effective processes in complex scenarios. It is a Systematic Literature Review (SLR), conducted according to the PRISMA 2020 protocol, with searches carried out in the Scopus, ScienceDirect, and IEEE Xplore databases. The analysis, of a qualitative and comparative nature, selected 15 studies. The results reveal a predominance of SAPEVO-M, applied in business and sectoral contexts such as supplier evaluation, employee performance, reverse logistics, health, and agriculture, highlighting its methodological flexibility and practical applicability. Several studies also explored hybrid versions of the model, combining it with methods such as FAHP, TOPSIS, and PROMETHEE, which reinforces its versatility. In contrast, SAPEVO-H² appeared in only three publications, all linked to the same research group, suggesting an early stage of dissemination. Despite its low representativeness, it showed potential for high-complexity decision-making, especially in public policy and national defense, by structuring multiple hierarchical levels and integrating different decision-making scales. It is concluded that the SAPEVO family models represent promising tools in the field of multicriteria decision-making, but they are at different stages of maturity. While SAPEVO-M is already consolidated in practical applications, SAPEVO-H² requires further empirical exploration.
Author Biographies
Department of Materials and Metallurgical Engineering of the Polythecnical School of the University of São Paulo. São Paulo – SP, Brazil.
Department of Industrial Engineering of Fluminense Federal University. Rio de Janeiro – RJ, Brazil.
Department of Industrial Engineering of Fluminense Federal University. Rio de Janeiro – RJ, Brazil.
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