期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 2, 页码 3774-3784出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.02.063
关键词
Fuzzy set theory; Back-propagation neural network; IPA; Three-factor theory; Critical service attributes
Importance-performance analysis (IPA) is a simple but effective means of assisting practitioners in prioritizing service attributes when attempting to enhance service quality and customer satisfaction. As numerous studies have demonstrated, attribute performance and overall satisfaction have a non-linear relationship, attribute importance and attribute performance have a causal relationship and the customer's self-stated importance is not the actual importance of service attribute. These findings raise questions regarding the applicability of conventional IPA. Furthermore, Human perceptions and attitudes are subjective and vague. Traditional assessments of service quality or customer satisfaction that used Likert scale to represent customer perceptions based on linguistic assessments are impractical. Moreover. some revised IPA that used statistical methods to acquire the implicitly derived importance of attributes always had some unreality assumptions. Therefore, this study presents a Fuzzy Neural based IPA (FN-IPA) which integrates fuzzy set theory, back-propagation neural network and three-factor theory to effectively and adequately assist practitioners in identifying critical service attributes. (C) 2008 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据