DocumentCode :
2247475
Title :
Applying a fuzzy measure to evaluate the service quantity of a convenient store
Author :
Lai, Shun-Jen ; Hsieh, Ling-Ling
Author_Institution :
Dept. of Bus. Adm., Asia Univ., Wufeng, Taiwan
Volume :
5
fYear :
2010
fDate :
11-14 July 2010
Firstpage :
2372
Lastpage :
2376
Abstract :
There is a strong link between customer satisfaction and repurchase intention. Thus, the investigation of the overall customer satisfaction has important managerial implications. The weighted arithmetic mean method and regression method are most often ways of evaluating the overall customer satisfaction. The attributes may be redundant or work well together. We need to select important attributes to evaluate the overall customer satisfaction correctly. A fuzzy measure is a good candidate to do that. This study uses Shannon interaction information as fuzzy measure to study how the joint attributes affect the overall customer satisfaction. First, identify the important attributes that would evaluate the overall customer satisfaction by Shannon interaction information. Then, use them to evaluate the overall customer satisfaction. In this study, four methods (i.e., the weighted arithmetic mean method with all attributes, the regression method with all attributes, the weighted arithmetic mean method with important attributes, and the regression method with important attributes) were used to evaluate the overall customer satisfaction. The results show that the regression method with important attributes method which we proposed to evaluate the overall customer satisfaction is the best among the four methods.
Keywords :
customer satisfaction; entropy; fuzzy set theory; regression analysis; retailing; Shannon interaction information; convenient store; customer satisfaction; fuzzy measure; mutual entropy; regression method; repurchase intention; service quantity evaluation; weighted arithmetic mean method; Correlation; Customer satisfaction; Entropy; Joints; Machine learning; Mutual information; Random variables; Entropy; Fuzzy measure; Mutual entropy; Shannon interaction information;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
Type :
conf
DOI :
10.1109/ICMLC.2010.5580668
Filename :
5580668
Link To Document :
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