Development of a Methodology Based on Fuzzy Logic for Solving the Problem of Evaluating a Startup Team Under Uncertainty

Abstract Aim/Purpose The purpose of the article is to develop a methodology for evaluating startup teams and to create a corresponding computer model based on multicriteria analysis and fuzzy-logic decision-making. Particular attention is paid to determining both qualitative and quantitative characteristics of the team, and obtaining a generalized integral assessment of the startup team under uncertainty. Design/methodology/approach An integrated evaluation method is proposed that combines the principles of the fuzzy set approach and expert evaluation and is implemented as a fuzzy inference system in MATLAB. The developed model used different initial characteristics of the startup team as input parameters. For this, formulas were identified, described, and utilized to calculate the values of these evaluation parameters. The set of linguistic variables and a system of rules for processing fuzzy data were defined. Literature data, expert and investor assessments, and case studies of real startup projects served as the empirical basis for the study. Findings The results demonstrate that the proposed approach enables a fairly objective and comprehensive assessment of a startup team’s quality, considering multiple assessment criteria, their interrelationships, and the combination of qualitative and quantitative input data, all within the context of significant uncertainty. The methodology ensures the objectivity and repeatability of the assessment, making it a valuable decision-support tool for various situations and participants within the startup community. Research implications/limitations The study is limited by the amount of data on real startup teams for model verification, which leaves much to be desired, as well as the need for further empirical substantiation and adjustment of the fuzzy model as a whole, including formulas for input parameters, linguistic variables, and decision rules, based on expert opinions. Possible areas for further research include adapting the method to different stages of startup development, taking into account their field of activity, size, and other specific features, and enabling more accurate model adjustment across various practical cases. Originality/value/contribution The article’s originality lies in integrating fuzzy logic with multicriteria analysis to assess the human factor in startups. A useful contribution involves creating a practice-oriented tool that enhances the accuracy and reliability of team analysis, which is essential for startups themselves, business angels, venture funds, accelerators, and other participants in the startup community.