Assessing SME credit rating in supply chain finance with multi-phase QFD-based MULTIMOORA under uncertainty
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| Publicado en: | Technological and Economic Development of Economy vol. 31, no. 6 (Nov 2025), p. 1749-1781 |
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Vilnius Gediminas Technical University
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.3846/tede.2025.23153 |2 doi | |
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| 045 | 2 | |b d20251101 |b d20251130 | |
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| 100 | 1 | |a Gao, Hui | |
| 245 | 1 | |a Assessing SME credit rating in supply chain finance with multi-phase QFD-based MULTIMOORA under uncertainty | |
| 260 | |b Vilnius Gediminas Technical University |c Nov 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Presently, financial institutions have tentatively utilized supply chain finance as a means of assessing small and medium-sized enterprise (SME) credit rating. However, traditional techniques cannot satisfy the requirements of such assessments because financial institutions need to assess SME credit rating from the perspective of the supply chain and core enterprise rather than only from the perspective of SME. In this study, a hybrid technique with quantitative and qualitative criteria called multi-phase quality function deployment (QFD)-based MULTIMOORA under interval type-2 fuzzy set (IT2FS) is proposed to overcome the defects of traditional techniques. First, the quantitative values were converted into IT2FSs using the developed formulas. Second, a multi-phase QFD model is proposed to obtain the SME credit rating matrix by integrating the core enterprise credit rating matrix and the criterion relationship matrices among SME, core enterprises and supply chains. Third, IT2FS-MULTIMOORA is enhanced by considering the improved Borda Rule and extended reference point simultaneously to derive the final rankings; therefore, a weight-determining technique is presented based on the correlation coefficients. Finally, the proposed technique was applied to the SME credit rating assessment problem. Comparisons with other techniques and the sensitivity analysis results provide suggestions for financial institutions to provide loans to SMEs. | |
| 653 | |a Multiphase | ||
| 653 | |a Supply chains | ||
| 653 | |a Small business | ||
| 653 | |a Finance | ||
| 653 | |a Fuzzy sets | ||
| 653 | |a Sensitivity analysis | ||
| 653 | |a Quality function deployment | ||
| 653 | |a Credit ratings | ||
| 653 | |a Correlation coefficients | ||
| 653 | |a Financial institutions | ||
| 653 | |a Small & medium sized enterprises-SME | ||
| 700 | 1 | |a Zhang, Hui | |
| 700 | 1 | |a Liu, Peide | |
| 773 | 0 | |t Technological and Economic Development of Economy |g vol. 31, no. 6 (Nov 2025), p. 1749-1781 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3288188462/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3288188462/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |