Ranking Startups Using DEMATEL-ANP-Based Fuzzy PROMETHEE II

Abstract

In entrepreneurship management, the evaluation and selection of startups for acceleration programs, especially technology-based startups, are crucial. This process involves considering numerical and qualitative criteria such as sales, prior startup experience, demand validation, and product maturity. To effectively rank startups based on the varying importance of these criteria, a fuzzy multi-criteria decision-making (MCDM) approach is needed. Although MCDM methods have been successful in handling complex problems, their application in startup selection and evaluating criteria interrelationships from the accelerator perspective is underexplored. To address this gap, a hybrid DEMATEL-ANP-based fuzzy PROMETHEE II model is proposed in this study, facilitating startup ranking and examining interrelationships among factors. The resulting preference values are fuzzy numbers, necessitating a fuzzy ranking method for decision-making. An extension of ranking fuzzy numbers using a spread area-based relative maximizing and minimizing set is suggested to enhance the flexibility of existing ranking MCDM methods. Algorithms, formulas, and a comparative analysis validate the proposed method, while a numerical experiment verifies the viability of the hybrid model. The final ranking of four startup projects is A4<A1<A3<A2 which indicates that startup project A2 has the highest comprehensive potential, followed by startup project A3.

Comparative analysis of fuzzy multi-criteria decision-making methods for quality of service-based web service selection

“…The FP method have been used recently for transport mode analysis [22], ranking startups [23], and for ensemble feature selection [24]. See the steps in ( 40)-( 49) for the FP application in the present comparative study.…”

“…-Step 4: regulate the FPIS, A* and 𝐹𝑁𝐼𝑆, 𝐴 − . The FPIS and FNIS of the alternatives can be defined as ( 22) and (23).…”

Novel Method for Ranking Generalized Fuzzy Numbers Based on Normalized Height Coefficient and Benefit and Cost Areas

“…Revathi and Valliathal [16] used the centroid method for ordering non-normal fuzzy numbers with more parameters, which is investigated using level analysis, which gives flexibility to the expert's opinion. Nguyen and Chu [17] proposed a DEMATEL-ANP-based fuzzy PROMETHEE II to rank startups, in which areas based on a subject's confidence level were suggested and height was not considered. He et al [18] introduced a new fuzzy distance based on a novel interval distance that considers all points within the intervals by using the concept of integration to calculate the average distance between all points belonging to two intervals.…”

Novel Method for Ranking Generalized Fuzzy Numbers Based on Normalized Height Coefficient and Benefit and Cost Areas

“…However, this method cannot be used to rank fuzzy numbers with different centroids and heights (see Example 3 in Section 2.3). Revathi & Valliathal (2021) used centroid method for ordering non-normal fuzzy numbers with more parameters is investigated using level analysis, which gives flexibility to the expert's opinion Nguyen & Chu (2023). proposed a DE-MATEL-ANP-Based fuzzy PROMETHEE II to rank startups in which areas based on a subject confidence level was suggested and height was not considered He et al (2023).…”