{"title":"餐厅员工技术使用意愿:考虑外部因素的技术接受模型验证","authors":"S. Ham, W. Kim, H. Forsythe","doi":"10.1080/10507050801978422","DOIUrl":null,"url":null,"abstract":"ABSTRACT The study aims to examine if the Technology Acceptance Model (TAM) works for restaurant operations using computing systems. In addition, we pursued other external variables, which were not included in the original TAM, to see how they affect perceived ease of use, perceived usefulness, and intention to use. The external factors were user characteristics, system quality and organizational support. The survey collected data from restaurants in Kentucky, and the response rate was 25% based on the total contacts eligible. SPSS 15.0 and AMOS 7.0 were used for the data analysis. Structural Equation Modeling (SEM) was the primary analysis used to examine the proposed hypotheses developed in fulfilling the study objectives. The SEM statistics supported all the proposed hypotheses but one. The SEM results were interpreted relative to industry implications.","PeriodicalId":341174,"journal":{"name":"Journal of Hospitality & Leisure Marketing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":"{\"title\":\"Restaurant Employees' Technology Use Intention: Validating Technology Acceptance Model with External Factors\",\"authors\":\"S. Ham, W. Kim, H. Forsythe\",\"doi\":\"10.1080/10507050801978422\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT The study aims to examine if the Technology Acceptance Model (TAM) works for restaurant operations using computing systems. In addition, we pursued other external variables, which were not included in the original TAM, to see how they affect perceived ease of use, perceived usefulness, and intention to use. The external factors were user characteristics, system quality and organizational support. The survey collected data from restaurants in Kentucky, and the response rate was 25% based on the total contacts eligible. SPSS 15.0 and AMOS 7.0 were used for the data analysis. Structural Equation Modeling (SEM) was the primary analysis used to examine the proposed hypotheses developed in fulfilling the study objectives. The SEM statistics supported all the proposed hypotheses but one. The SEM results were interpreted relative to industry implications.\",\"PeriodicalId\":341174,\"journal\":{\"name\":\"Journal of Hospitality & Leisure Marketing\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"34\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hospitality & Leisure Marketing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/10507050801978422\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hospitality & Leisure Marketing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10507050801978422","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Restaurant Employees' Technology Use Intention: Validating Technology Acceptance Model with External Factors
ABSTRACT The study aims to examine if the Technology Acceptance Model (TAM) works for restaurant operations using computing systems. In addition, we pursued other external variables, which were not included in the original TAM, to see how they affect perceived ease of use, perceived usefulness, and intention to use. The external factors were user characteristics, system quality and organizational support. The survey collected data from restaurants in Kentucky, and the response rate was 25% based on the total contacts eligible. SPSS 15.0 and AMOS 7.0 were used for the data analysis. Structural Equation Modeling (SEM) was the primary analysis used to examine the proposed hypotheses developed in fulfilling the study objectives. The SEM statistics supported all the proposed hypotheses but one. The SEM results were interpreted relative to industry implications.