{"title":"基于结构方程模型的商业银行操作风险管理影响因素研究","authors":"L. Liang, Fanchen Meng, Li-Jie Cao","doi":"10.1109/ICMLC.2012.6359007","DOIUrl":null,"url":null,"abstract":"Structural equation model (SEM) was employed to analyze the interaction factors that affecting operational risk management (ORM) from questionnaires for commercial banks in the research. Based on lots of international and Chinese documentation and questionnaire, the model was constructed with 20 affecting factors and 4 layers (bank employee, section & sub-branch, head office & branch office and bank conditions). “Bank employee level” is taken as the endogenous latent variable, and other levels are taken as the exogenous latent variables from the research results. According to the modification indices (MI) of the model and the modification corresponding criterion, the model was modified so as to get optimized model. The main innovation includes two parts, one is to design the questionnaire for ORM to get important information, and the other one is to employ SEM to analyze ORM factors.","PeriodicalId":128006,"journal":{"name":"2012 International Conference on Machine Learning and Cybernetics","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Research on affecting factors of operational risk management for commercial bank based on structural equation model\",\"authors\":\"L. Liang, Fanchen Meng, Li-Jie Cao\",\"doi\":\"10.1109/ICMLC.2012.6359007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Structural equation model (SEM) was employed to analyze the interaction factors that affecting operational risk management (ORM) from questionnaires for commercial banks in the research. Based on lots of international and Chinese documentation and questionnaire, the model was constructed with 20 affecting factors and 4 layers (bank employee, section & sub-branch, head office & branch office and bank conditions). “Bank employee level” is taken as the endogenous latent variable, and other levels are taken as the exogenous latent variables from the research results. According to the modification indices (MI) of the model and the modification corresponding criterion, the model was modified so as to get optimized model. The main innovation includes two parts, one is to design the questionnaire for ORM to get important information, and the other one is to employ SEM to analyze ORM factors.\",\"PeriodicalId\":128006,\"journal\":{\"name\":\"2012 International Conference on Machine Learning and Cybernetics\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2012.6359007\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2012.6359007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on affecting factors of operational risk management for commercial bank based on structural equation model
Structural equation model (SEM) was employed to analyze the interaction factors that affecting operational risk management (ORM) from questionnaires for commercial banks in the research. Based on lots of international and Chinese documentation and questionnaire, the model was constructed with 20 affecting factors and 4 layers (bank employee, section & sub-branch, head office & branch office and bank conditions). “Bank employee level” is taken as the endogenous latent variable, and other levels are taken as the exogenous latent variables from the research results. According to the modification indices (MI) of the model and the modification corresponding criterion, the model was modified so as to get optimized model. The main innovation includes two parts, one is to design the questionnaire for ORM to get important information, and the other one is to employ SEM to analyze ORM factors.