{"title":"基于解释结构模型的物流信息生态系统风险影响因素研究","authors":"L. Ye, Xiao-shu Hu","doi":"10.1145/3511716.3511756","DOIUrl":null,"url":null,"abstract":"Based on the research results of logistics information ecosystem risk, the author identifies 26 factors that affect logistics information ecosystem risk. The adjacency matrix of 26 influencing factors is constructed according to the relationship between influencing factors. Using Python software to calculate the reachability matrix of influencing factors, and simplify the reachability matrix. Calculate the reachable set, antecedent set and common set of influencing factors. The 26 influencing factors are divided into 6 levels. Rearrange and simplify reachability matrix. The interpretive structural modeling of risk influencing factors of logistics information ecosystem is constructed. The interpretive structural modeling of influencing factors is divided into superficial layer, middle layer and root layer. The factors in the superficial layer include multi-party coordination mechanism, reward and punishment mechanism and system formulation mechanism. The factor in the root layer includes information culture atmosphere. The factors in the middle layer include 22 factors such as logistics information security, logistics information accuracy, logistics information timeliness, logistics information sharing, logistics information adaptability, technical level of logistics employees, logistics personnel equipment configuration, etc. The factor in the root layer not only directly affects the factors in the middle layer and the factors in the superficial layer, but also indirectly affects them. Finally, the paper analyzes the reasons for the formation of the hierarchical relationship of influencing factors, in order to provide theoretical guidance for the stable operation of logistics industry.","PeriodicalId":105018,"journal":{"name":"Proceedings of the 2021 4th International Conference on E-Business, Information Management and Computer Science","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Risk Influencing Factors of Logistics Information Ecosystem Based on Interpretive Structural Modeling\",\"authors\":\"L. Ye, Xiao-shu Hu\",\"doi\":\"10.1145/3511716.3511756\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the research results of logistics information ecosystem risk, the author identifies 26 factors that affect logistics information ecosystem risk. The adjacency matrix of 26 influencing factors is constructed according to the relationship between influencing factors. Using Python software to calculate the reachability matrix of influencing factors, and simplify the reachability matrix. Calculate the reachable set, antecedent set and common set of influencing factors. The 26 influencing factors are divided into 6 levels. Rearrange and simplify reachability matrix. The interpretive structural modeling of risk influencing factors of logistics information ecosystem is constructed. The interpretive structural modeling of influencing factors is divided into superficial layer, middle layer and root layer. The factors in the superficial layer include multi-party coordination mechanism, reward and punishment mechanism and system formulation mechanism. The factor in the root layer includes information culture atmosphere. The factors in the middle layer include 22 factors such as logistics information security, logistics information accuracy, logistics information timeliness, logistics information sharing, logistics information adaptability, technical level of logistics employees, logistics personnel equipment configuration, etc. The factor in the root layer not only directly affects the factors in the middle layer and the factors in the superficial layer, but also indirectly affects them. Finally, the paper analyzes the reasons for the formation of the hierarchical relationship of influencing factors, in order to provide theoretical guidance for the stable operation of logistics industry.\",\"PeriodicalId\":105018,\"journal\":{\"name\":\"Proceedings of the 2021 4th International Conference on E-Business, Information Management and Computer Science\",\"volume\":\"115 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 4th International Conference on E-Business, Information Management and Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3511716.3511756\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 4th International Conference on E-Business, Information Management and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3511716.3511756","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Risk Influencing Factors of Logistics Information Ecosystem Based on Interpretive Structural Modeling
Based on the research results of logistics information ecosystem risk, the author identifies 26 factors that affect logistics information ecosystem risk. The adjacency matrix of 26 influencing factors is constructed according to the relationship between influencing factors. Using Python software to calculate the reachability matrix of influencing factors, and simplify the reachability matrix. Calculate the reachable set, antecedent set and common set of influencing factors. The 26 influencing factors are divided into 6 levels. Rearrange and simplify reachability matrix. The interpretive structural modeling of risk influencing factors of logistics information ecosystem is constructed. The interpretive structural modeling of influencing factors is divided into superficial layer, middle layer and root layer. The factors in the superficial layer include multi-party coordination mechanism, reward and punishment mechanism and system formulation mechanism. The factor in the root layer includes information culture atmosphere. The factors in the middle layer include 22 factors such as logistics information security, logistics information accuracy, logistics information timeliness, logistics information sharing, logistics information adaptability, technical level of logistics employees, logistics personnel equipment configuration, etc. The factor in the root layer not only directly affects the factors in the middle layer and the factors in the superficial layer, but also indirectly affects them. Finally, the paper analyzes the reasons for the formation of the hierarchical relationship of influencing factors, in order to provide theoretical guidance for the stable operation of logistics industry.