Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.32244

Evaluation and Selection of Startup Ideas Using MCDM: A Comparative Study of TOPSIS, AHP, VIKOR, PROMETHEE II and ELECTRE II

Sara Rekkal ORCID...1, Kahina Rekkal ORCID...2, Mohamed Amine Yakoubi ORCID...3
1 Department of Mathematics and Computer Science, Faulty of Exact Sciences, Tahri Mohammed University of Bechar, Bechar, Algeria
2 Department of Electrical Engineering, University of Naama, Naama, Algeria
3 Computer Science Research Laboratory, Computer Science Department, Faculty of Technology, Badji Mokhtar Annaba University, Annaba, Algeria

Background: The selection of startup ideas to fund and develop is a complex decision-making problem regarding a multitude of conflicting criteria such as profitability, growth potential, risk, initial costs and social impact. Traditional single-criteria approaches are therefore insufficient to grasp this multidimensional complexity. This is the reason why multi-criteria decision-making (MCDM) methods are well adapted to this context.

Objective: The aim of this study is to compare several widely recognized MCDM methods to find the most promising startup ideas. Besides, we compare the consistency, robustness and convergence of the rankings obtained under controlled weighting scenarios. These scenarios are intended to simulate possible decision priorities and to test the robustness of the methods.

Methods: Five MCDM methods (AHP, TOPSIS, VIKOR, PROMETHEE II and ELECTRE II) are applied to the same dataset of startup ideas evaluated according to their profitability, growth potential, risk, initial costs and social impact. Different criterion weighting scenarios are considered to analyse the influence of decision-makers\\\\\\\' preferences and to test the robustness of the resulting rankings.

Results: The results show strong convergence between the applied MCDM methods, despite their different underlying principles. The variations in the weighting of criteria affect intermediate rankings, but the top-ranked startup ideas remain stable across methods, demonstrating the robustness of the evaluation process.

Conclusion: The proposed multi-criteria evaluation framework is characterized by its reliability, transparency and robustness. By combining and comparing the different MCDM methods, it provides decision-makers with a valuable, evidence-based tool for selecting startup ideas and making investment decisions.

Keywords: Startup idea selection; Multi-criteria decision-making; Analytic hierarchy process; Technique for Order of Preference by Similarity to Ideal Solution; ELimination Et Choix Traduisant la REalité; VI¹eKriterijumska Optimizacija I Kompromisno Re¹enje.

Received: February 8, 2026; Revised: June 4, 2026; Accepted: July 1, 2026; Prepublished online: August 22, 2026 

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