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医疗服务质量评价方法研究综述 被引量:30
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作者 沈蕾 《消费经济》 CSSCI 北大核心 2006年第3期55-59,共5页
本文系统回顾并比较分析了医疗服务质量的概念演变和评价方法的发展过程,指出以患者为中心的医疗服务质量管理越来越受到医疗服务机构的重视。
关键词 医院 服务质量评价方法 研究综述
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Application of Structural Equation Modeling to Evaluate Service Quality of Sportswear Retailing 被引量:1
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作者 李敏 顾彤宇 +1 位作者 杨以雄 洪涛敏 《Journal of Donghua University(English Edition)》 EI CAS 2008年第1期6-10,共5页
Structural Equation Modeling (SEM) used widely in sociology, economics and psychology is adopted. Based on data obtained from marketing research, and using statistical analysis software SPSSll. 0 and LISRELS. 7, The... Structural Equation Modeling (SEM) used widely in sociology, economics and psychology is adopted. Based on data obtained from marketing research, and using statistical analysis software SPSSll. 0 and LISRELS. 7, Theory of Five Dimensions of service quality is proved to be suitable in sportswear retailing in China. It analyzes the relationship among five dimensions and puts them in order of importance as to service quality in sportswear retailing. Advices are given for sportswear retail companies to improve their .Service quality and enhance customer loyalty. 展开更多
关键词 service quality Theory of Five Dimensions SPORTSWEAR Structural Equation Modeling (SEM)
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QoS Evaluation for Web Service Recommendation 被引量:1
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作者 MA You XIN Xin +3 位作者 WANG Shangguang LI Jinglin SUN Qibo YANG Fangchun 《China Communications》 SCIE CSCD 2015年第4期151-160,共10页
Web service recommendation is one of the most important fi elds of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown Qo S property values and ... Web service recommendation is one of the most important fi elds of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown Qo S property values and the evaluation of overall Qo S according to user preferences. Aiming to address these two problems and their current challenges, we propose two efficient approaches to solve these problems. First, unknown Qo S property values were predicted by modeling the high-dimensional Qo S data as tensors, by utilizing an important tensor operation, i.e., tensor composition, to predict these Qo S values. Our method, which considers all Qo S dimensions integrally and uniformly, allows us to predict multi-dimensional Qo S values accurately and easily. Second, the overall Qo S was evaluated by proposing an efficient user preference learning method, which learns user preferences based on users' ratings history data, allowing us to obtain user preferences quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic value for the overall Qo S. The experimental results showed our proposed methods to be more efficient than existing methods. 展开更多
关键词 Web service recommendation QoS prediction user preference overall QoSevaluation
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