{"title":"基于贝叶斯网络模型的服务要素幸福感识别研究","authors":"Yuho Suzuki, M. Tsubaki, Taro Isobe","doi":"10.1109/ICIM49319.2020.244702","DOIUrl":null,"url":null,"abstract":"Even when consumers receive the same services, they may not have the same experiences of well-being or satisfaction. In this paper, we investigate how consumers experience well-being by classifying consumers into several well-being types using Bayesian network model and identifying what kind of points consumers of each type consider importantly. Furthermore, this paper proposes a method for deriving which service-related variables have higher probabilities of enhancing well-being of each type, as well as the variables that affect experiences of wellbeing according to each type using probabilistic reasoning based on a Bayesian network model.","PeriodicalId":129517,"journal":{"name":"2020 6th International Conference on Information Management (ICIM)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on Identification of Service Elements by Well-being Type Using Bayesian Network Modelling\",\"authors\":\"Yuho Suzuki, M. Tsubaki, Taro Isobe\",\"doi\":\"10.1109/ICIM49319.2020.244702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Even when consumers receive the same services, they may not have the same experiences of well-being or satisfaction. In this paper, we investigate how consumers experience well-being by classifying consumers into several well-being types using Bayesian network model and identifying what kind of points consumers of each type consider importantly. Furthermore, this paper proposes a method for deriving which service-related variables have higher probabilities of enhancing well-being of each type, as well as the variables that affect experiences of wellbeing according to each type using probabilistic reasoning based on a Bayesian network model.\",\"PeriodicalId\":129517,\"journal\":{\"name\":\"2020 6th International Conference on Information Management (ICIM)\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 6th International Conference on Information Management (ICIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIM49319.2020.244702\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 6th International Conference on Information Management (ICIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIM49319.2020.244702","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on Identification of Service Elements by Well-being Type Using Bayesian Network Modelling
Even when consumers receive the same services, they may not have the same experiences of well-being or satisfaction. In this paper, we investigate how consumers experience well-being by classifying consumers into several well-being types using Bayesian network model and identifying what kind of points consumers of each type consider importantly. Furthermore, this paper proposes a method for deriving which service-related variables have higher probabilities of enhancing well-being of each type, as well as the variables that affect experiences of wellbeing according to each type using probabilistic reasoning based on a Bayesian network model.