2023
DOI: 10.1371/journal.pone.0280239
|View full text |Cite|
|
Sign up to set email alerts
|

MABAC method for multiple attribute group decision making under single-valued neutrosophic sets and applications to performance evaluation of sustainable microfinance groups lending

Abstract: As an important supplement to my country’s financial institutions, micro-loan companies serve "agriculture, rural areas and farmers", small and micro enterprises, and individuals, to a certain extent, alleviating the financing difficulties of such groups and regulating private finance. However, micro-loan companies only lend but do not deposit. In the process of lending, due to inadequate risk management, the risk problem has become increasingly prominent. With the continuous growth of the loan amount of rural… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
4
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(4 citation statements)
references
References 76 publications
0
4
0
Order By: Relevance
“…For example, Zhang et al revised the unbalanced language term sets in Herrera, and developed two optimization models to deal with multi-attribute decision-making problems with multi-granularity unbalanced language information [13,14]; Li et al conducted research on personalized individual semantics in the context of multi-attribute decision-making, which can provide decision makers with personalized digital scales of language terms [15]. At the same time, many new methods have emerged, such as fuzzy EDAS [16,17], ARAS [18], MARCOS [19], MABAC [20], CoCoSo [21], characteristic object method (COMET) [22], and the ideal solution stable preference ranking (SPOTIS) method [23][24][25]. It is undeniable that these methods expand the research of multi-attribute decision-making methods, but there are also some shortcomings.…”
Section: Domestic Patentmentioning
confidence: 99%
“…For example, Zhang et al revised the unbalanced language term sets in Herrera, and developed two optimization models to deal with multi-attribute decision-making problems with multi-granularity unbalanced language information [13,14]; Li et al conducted research on personalized individual semantics in the context of multi-attribute decision-making, which can provide decision makers with personalized digital scales of language terms [15]. At the same time, many new methods have emerged, such as fuzzy EDAS [16,17], ARAS [18], MARCOS [19], MABAC [20], CoCoSo [21], characteristic object method (COMET) [22], and the ideal solution stable preference ranking (SPOTIS) method [23][24][25]. It is undeniable that these methods expand the research of multi-attribute decision-making methods, but there are also some shortcomings.…”
Section: Domestic Patentmentioning
confidence: 99%
“…The modified rough Z -number MABAC approach used Pugh's controlled convergence, rough number, Z -number, consistency theory, and Shannon entropy. Ran ( 2023 ) proposed the maximizing deviation method for determining the attribute weights for the single-valued neutrosophic set (SVNS), and the SVNN-MABAC method was designed for MAGDM under SVNS to evaluate the lending performance of sustainable microfinance organization. With the goal to establish the feasibility of the suggested methodology, Jana et al ( 2023 ) presented a novel method to construct MABAC approach utilizing PyF numbers.…”
Section: Introductionmentioning
confidence: 99%
“…A decision support system is a computer-based system that is able to help decision makers to solve semistructured and unstructured problems using data that has been obtained previously through the process of collecting research data and certain decision support methods [3]. In previous research, decision support systems have been widely used to support decision makers in solving various complex problems [4][5][6][7][8][9]. In a decision support system, there are many methods that can be used, including the Multifactor Evaluation Process [10], VIKOR [11], AHP [12], MABAC [13], MOORA [14], Dll.…”
Section: Introductionmentioning
confidence: 99%