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Issue title: Ethical Computational Intelligence for Cyber Market
Guest editors: Oscar Sanjuán Martínez, Giuseppe Fenza and Ruben Gonzalez Crespo
Article type: Research Article
Authors: Zhu, Chengkea | Wang, Junshanb; *
Affiliations: [a] College of Economics and Management, Hefei University, Hefei, China | [b] School of International Trade and Economics, University of International Business and Economics, Beijing, China
Correspondence: [*] Corresponding author. Junshan Wang, School of International Trade and Economics, University of International Business and Economics, Beijing, 100029, China. E-mail: [email protected].
Abstract: With the development of technology and artificial intelligence(AI), financial leasing is used frequently in China’s equipment manufacturing industries, especially after 2008, which attracts our attention. This paper focuses on the problem. A theoretical model is provided in the paper to explain the motivation that why financial leasing is used in equipment manufacturing industries. In this paper, we employ multi-level fuzzy linear regression model to analyze data of manufacturing equipment industry of China in order to discover impact between sales revenue and financial leasing. We also discover credit sale forecasting using this model. We discover that economic leasing has a constructive important effect on sales revenue in total samples, which is consistent with the theoretical framework. However, the results are different in sub-industries, which shows that economic leasing has a constructive and important effect on sales profits in some sub-industries, but some of them are not. Furthermore, we also find that asset characteristics has a substantial effect on financial leasing decision. The outcome demonstrates that the more precise the asset, the easier it can be leased.
Keywords: Financial leasing, sales revenue, equipment manufacturing industry, promotion, artificial equipment intelligence, fuzzy linear regression model
DOI: 10.3233/JIFS-189667
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8477-8484, 2021
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