Modelo de avaliação de startups utilizando metodologia multicritério de apoio a decisão

Abstract

Regardless of a firm’s size or industry, identifying and evaluating indicators associated with key activities is essential for effective decision-making. In the context of incubated startups, this process becomes more challenging due to high levels of uncertainty, the dynamic nature of business models, and limited managerial maturity, which hinder structured and consistent evaluations. The main objective of this research was to develop a multicriteria decision support model for the evaluation of incubated startups, considering criteria identified in the literature and refined through expert input, including aspects related to the five dimensions of the Reference Center for Supporting New Ventures (CERNE), sustainable development, and the external environment. Methodologically, the study was structured into four stages: (i) identification of criteria through a systematic literature review guided by the Methodi Ordinatio; (ii) selection of an optimal set of criteria with expert support using the Elimination et Choix Traduisant la Réalité (ELECTRE I) method; (iii) weighting of criteria and evaluation dimensions based on the relative frequency of citations in the literature; and (iv) application of the model to incubated startups using the Multi-Attributive Border Approximation Area Comparison (MABAC) method for ranking alternatives, complemented by sensitivity analysis. The model was structured into seven evaluation dimensions: entrepreneur/team, technology, market, capital, management, external environment, and sustainable development, and applied to seven startups within the same innovation incubator located in the Southwest region of Paraná, Brazil. The results indicate that the model can clearly differentiate the evaluated startups and produce stable rankings across the analyzed variations, thereby demonstrating its robustness. Sensitivity analysis revealed low dependence on changes in criteria weights, generally preserving the extreme positions in the ranking. It is concluded that the proposed model provides a structured, transparent, and practical approach for evaluating incubated startups, enabling more informed strategic decision-making by incubators and managers and supporting the prioritization of actions aimed at startup development and sustainability. Furthermore, the research offers a theoretical contribution by integrating and systematizing dispersed criteria in the literature into a robust multicriteria model, advancing the structuring of knowledge on startup evaluation and addressing existing gaps in this field.