期刊名称:International Journal of Software Engineering and Its Applications
印刷版ISSN:1738-9984
出版年度:2016
卷号:10
期号:4
页码:83-92
DOI:10.14257/ijseia.2016.10.4.09
出版社:SERSC
摘要:The data used in this study includes guarantee-related data from January 2007 to September 2014, as well as data of search volume provided by Naver Trend. As a result of the hierarchical regression analysis, it turned out that in step 1, the volume of credit guarantee searching affected guarantee supply while the default amount did not. In addition, it is expected that as the volume of credit guarantee searching increases, guarantee supply will also be expended. In step 2, it turned out that the amount of default normalization affected guarantee supply. It is expected that as the amount of default normalization increases, guarantee supply will also be expanded. Finally, in step 3, it turned out that both the volume of credit guarantee searching and the amount of default normalization affected guarantee supply while the default amount did not. As for the explanation power of the three models, that of step 1 was 8.3%, that of step 2 was 13.7%, and that of step 3 was 19.3%. As the default amount affected guarantee supply, Sobel Test was conducted to measure the mediation effect of the volume of credit guarantee searching and the amount of default normalization. As a result, it turned out that the volume of credit guarantee searching had mediation effect while the amount of default normalization did not. The objective of this study is to examine the effect of guarantee accidents of the Regional Credit Guarantee Foundation, which provides public finance service on guarantee supply. This indicates the necessity of examining the causality through another regression analysis after a Granger Causality test. In terms of demands for guarantee supply, further research may be necessary on referring to the volume of credit guarantee searching.
关键词:Volume of Credit Guarantee Searching; Guarantee Supply; Default ; Amount; Amount of Default Normalization; Mediation Effect; Hierarchical Regression