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Issue title: Cross-domain Applications of Fuzzy Logic and Machine Learning
Guest editors: Ekaterina Isaeva and Álvaro Rocha
Article type: Research Article
Authors: Liu, Sishia | Ma, Dingb; *
Affiliations: [a] Law and Business School, Wuhan Institute of Technology, Wuhan, China | [b] School of Economics, Wuhan University of Technology, Wuhan, China
Correspondence: [*] Corresponding author. Ding Ma, School of Economics, Wuhan University of Technology, Wuhan, China. E-mail: [email protected].
Abstract: Intellectual property pledge financing is an innovative application of derivative financial instruments, which provides new financing channels for enterprises with intellectual property advantages. Risk evaluation of intellectual property pledge financing is the key to sustainable financing. Given that the factors affecting the risk of intellectual property pledge financing are complex and difficult to quantify, scant studies have used fuzzy analytical network process (FANP) to evaluate the risks of intellectual property pledge financing comprehensively. In order to offer a feasible and versatile tool in risk evaluation of intellectual property pledge financing, this paper summarizes the influencing factors and establishes a risk evaluation model of intellectual property pledge financing through FANP. Based on questionnaire survey and expert evaluation method, this study calculates the super decision matrix, weighted super matrix and the comprehensive weight of each through Super Decision(SD) software. The results indicate that evaluation risk P1, realization risk P2, and moral risk P3 have the greatest impact on the risk evaluation of intellectual property pledge financing, followed by intellectual property management risk. Through shedding light on the risk factors, this study provides a reference for the practice of intellectual property pledge financing management.
Keywords: Intellectual property pledge financing, risk evaluation, fuzzy analytical network process (FANP)
DOI: 10.3233/JIFS-179756
Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 6, pp. 6785-6793, 2020
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