Evaluation of startup companies using multicriteria decision making based on hesitant fuzzy linguistic information envelopment analysis models

• Published in: International Journal of Intelligent Systems Vol. 36; no. 5; pp. 2292 - 2322
• Main Authors: Lin, Mingwei, Chen, Zheyu, Chen, Riqing, Fujita, Hamido
• Format: Journal Article
• Language: English
• Published: New York Hindawi Limited 01.05.2021; John Wiley & Sons, Inc
• Subjects: aggregation operator; Decision analysis; Decision making; evaluation of startup companies; fuzzy; Fuzzy sets; Intelligent systems; Linguistics; multicriteria decision making; Multiple criterion; Numerical analysis; Psychology; Pythagorean fuzzy sets; Robustness (mathematics); Startups
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1002/int.22379

Evaluating startup companies is an important management process for technology business incubators and it is also a typical multicriteria decision‐making (MCDM) problem. There exist various methods that have proposed to solve MCDM problems, but these methods heavily depend on the exact criteria weight values. The decision results of these methods are unstable. Moreover, they cannot provide the improvement suggestions for the nonoptimal startup companies. To overcome these two drawbacks, we propose a novel hesitant fuzzy linguistic decision‐making method to solve the problem of evaluating startup companies. To this end, a novel semantic comparison method based on the experts' psychology and the ratio of score value to deviation degree is proposed to compare the hesitant fuzzy linguistic term sets. Then, a novel definition of hesitant fuzzy linguistic information envelopment efficiency (HFLIEE) is proposed, based on which, a novel hesitant fuzzy linguistic information envelopment analysis (HFLIEA) model and a novel preference model are proposed. By solving these models, all the alternatives can be ranked and nonoptimal alternatives can be improved. Finally, the numerical analysis is given to illustrate the applicability of the proposed models and the robustness analyses of the proposed models are provided. At the same time, they are compared with the previous hesitant fuzzy linguistic decision‐making methods.