{"title":"基于ELM的教育内容用户付费意愿研究","authors":"Hua Zhang, Yelin Guo, Xin Huo, Yu Li","doi":"10.23977/infse.2023.040504","DOIUrl":null,"url":null,"abstract":": This article uses the elaboration likelihood model (ELM) as the foundational theoretical framework to comprehensively analyze various factors that affect the behavior of users paying for educational content in the central and peripheral pathways. This study fills the gaps in research perspectives and systems from the past and expands the application areas of the elaboration likelihood model. From a practical perspective, the research results of this paper partially address the series of problems in knowledge payment, provide feasible suggestions to enhance user stickiness of educational content payment platforms. Furthermore, the research results can also promote the high-quality development of the content payment industry and provide support for economic recovery after the pandemic.","PeriodicalId":423306,"journal":{"name":"Information Systems and Economics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on User's Willingness to Pay for Educational Content Based on the ELM\",\"authors\":\"Hua Zhang, Yelin Guo, Xin Huo, Yu Li\",\"doi\":\"10.23977/infse.2023.040504\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": This article uses the elaboration likelihood model (ELM) as the foundational theoretical framework to comprehensively analyze various factors that affect the behavior of users paying for educational content in the central and peripheral pathways. This study fills the gaps in research perspectives and systems from the past and expands the application areas of the elaboration likelihood model. From a practical perspective, the research results of this paper partially address the series of problems in knowledge payment, provide feasible suggestions to enhance user stickiness of educational content payment platforms. Furthermore, the research results can also promote the high-quality development of the content payment industry and provide support for economic recovery after the pandemic.\",\"PeriodicalId\":423306,\"journal\":{\"name\":\"Information Systems and Economics\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Systems and Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23977/infse.2023.040504\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems and Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23977/infse.2023.040504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on User's Willingness to Pay for Educational Content Based on the ELM
: This article uses the elaboration likelihood model (ELM) as the foundational theoretical framework to comprehensively analyze various factors that affect the behavior of users paying for educational content in the central and peripheral pathways. This study fills the gaps in research perspectives and systems from the past and expands the application areas of the elaboration likelihood model. From a practical perspective, the research results of this paper partially address the series of problems in knowledge payment, provide feasible suggestions to enhance user stickiness of educational content payment platforms. Furthermore, the research results can also promote the high-quality development of the content payment industry and provide support for economic recovery after the pandemic.