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dc.contributor.authorHan, Shuihuazh_CN
dc.contributor.authorYe, Yongjiezh_CN
dc.contributor.authorFu, Xinzh_CN
dc.contributor.authorChen, Zhilongzh_CN
dc.contributor.author韩水华zh_CN
dc.contributor.author傅馨zh_CN
dc.contributor.author陈志龙zh_CN
dc.date.accessioned2015-07-22T03:08:10Z
dc.date.available2015-07-22T03:08:10Z
dc.date.issued2014 JANzh_CN
dc.identifier.citationDECISION SUPPORT SYSTEMS, 2014,57:296-308zh_CN
dc.identifier.otherWOS:000330909700026zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/87680
dc.descriptionNational Nature Science Foundation of China [70971112, 71301133, 71371159]; Humanity and Social Science Youth Foundation of Ministry of Education, China [13YJC630033]zh_CN
dc.description.abstractCategory management (CM) plays an increasingly important role in retailing management, as it aids retailers to increase their core competitiveness, maximise profits and ensure a good long-term customer relationship. This technique has been successfully applied to diverse large manufacturers and wholesale retailers. However, it remains a challenging task to directly employ the CM technique in convenience store (CVS) chain(s). This is because CVS chains are often distributed in a variety of areas, each store has impulsive consumers, and the traditional market segmentation attributes (e.g. consumer age, salary, and background) are difficult to collect under such circumstances. This makes it impractical to apply one general CM solution to all CVS chains. Hence, it is crucial to segment a market region and then apply customised CM solutions to the corresponding segments. This paper presents an innovative market segmentation model which is driven by category-role (CR), for the first time, to support CM in CVS chains. A new similarity measure (named HCsim()) and an improved weighted fuzzy K-means clustering algorithm (WFKM) are developed in an effort to cluster the CVSs. The usefulness and applicability of this study is illustrated by means of an empirical study to provide marketing strategy decision support. The derived results are also discussed and compared with existing methods. (C) 2013 Elsevier B.V. All rights reserved.zh_CN
dc.language.isoen_USzh_CN
dc.publisherELSEVIER SCIENCE BVzh_CN
dc.source.urihttp://dx.doi.org/10.1016/j.dss.2013.09.017zh_CN
dc.subjectDECISION-SUPPORTzh_CN
dc.subjectALGORITHMzh_CN
dc.subjectMODELzh_CN
dc.subjectSELECTIONzh_CN
dc.subjectINDUSTRYzh_CN
dc.subjectSYSTEMzh_CN
dc.titleCategory role aided market segmentation approach to convenience store chain category managementzh_CN
dc.typeArticlezh_CN


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