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Ranking Startups Using DEMATEL-ANP-based Fuzzy PROMETHEE II
Version 1
: Received: 24 April 2023 / Approved: 25 April 2023 / Online: 25 April 2023 (03:48:01 CEST)
A peer-reviewed article of this Preprint also exists.
Nguyen, H.T.; Chu, T.-C. Ranking Startups Using DEMATEL-ANP-Based Fuzzy PROMETHEE II. Axioms 2023, 12, 528. Nguyen, H.T.; Chu, T.-C. Ranking Startups Using DEMATEL-ANP-Based Fuzzy PROMETHEE II. Axioms 2023, 12, 528.
Abstract
Accelerators play a critical role in fostering innovative ecosystems and nurturing startups. The evaluation and selection of startups, particularly technology-based startups, for acceleration programs are essential in accelerator economy and management. Assessment of startups requires consideration of numerical and qualitative criteria such as sales, prior startup experience, demand validation, and product maturity. Startups must be ranked based on the varying importance of criteria, which can be identified as a fuzzy multi-criteria decision-making (MCDM) problem. MCDM methods have proven effective in managing complex problems. However, the use of MCDM techniques in startup selection and evaluation of criteria interrelationships from the accelerator perspective is yet to be researched. This study proposes a hybrid DEMATEL-ANP-based fuzzy PROMETHEE II model to rank startups and examine the interrelationships between factors. The final preference values are fuzzy numbers, making a fuzzy ranking method necessary for decision-making. An extension of ranking fuzzy numbers using a spread area-based relative maximizing and minimizing set is suggested to improve the flexibility of existing ranking MCDM methods. Algorithms and formulas are derived and a comparison demonstrates the merits of the proposed method. Finally, a numerical experiment is designed to address the viability of the hybrid DEMATEL-ANP-based fuzzy PROMETHEE II model.
Keywords
DEMATEL; ANP; PROMETHEE II; Ranking Fuzzy numbers; Startups
Subject
Business, Economics and Management, Business and Management
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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