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C. Hwang, K. Yoon (1981)
Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey, 186
Chen-Tung Chen (2000)
Extensions of the TOPSIS for group decision-making under fuzzy environmentFuzzy Sets Syst., 114
G. Liang (1999)
Fuzzy MCDM based on ideal and anti-ideal conceptsEur. J. Oper. Res., 112
O. Kulak, C. Kahraman (2005)
Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approachInternational Journal of Production Economics, 95
C. Kahraman, U. Cebeci, D. Ruan (2004)
Multi-attribute comparison of catering service companies using fuzzy AHP: The case of TurkeyInternational Journal of Production Economics, 87
P. Dabholkar, Dayle Thorpe, Joseph Rentz (1996)
A measure of service quality for retail stores: Scale development and validationJournal of the Academy of Marketing Science, 24
Kenneth Boyer, R. Hallowell, A. Roth (2002)
E-services: operating strategy—a case study and a method for analyzing operational benefitsJournal of Operations Management, 20
L. Zadeh (1971)
Similarity relations and fuzzy orderingsInf. Sci., 3
T. Chu, Y.-C. Lin (2003)
A Fuzzy TOPSIS Method for Robot SelectionThe International Journal of Advanced Manufacturing Technology, 21
M. Wolfinbarger, M. Gilly (2003)
eTailQ: dimensionalizing, measuring and predicting etail qualityJournal of Retailing, 79
Kahraman Kahraman, Ruan Ruan, Dogan Dogan (2004)
Fuzzy group decision‐making for facility location selectionInt J Prod Econ, 87
M. Tang, G. Tzeng (1998)
A hierarchy fuzzy MCDM method for studying electronic marketing strategies in the information service industry, 1998
Renhong Zhao, Rakesh Govind (1991)
Algebraic characteristics of extended fuzzy numbersInf. Sci., 54
(1002)
International Journal of Intelligent Systems DOI
Shu-Jen Chen, C. Hwang (1992)
Fuzzy Multiple Attribute Decision Making - Methods and Applications, 375
Guangquan Zhang, Jie Lu (2003)
An Integrated Group Decision-Making Method Dealing with Fuzzy Preferences for Alternatives and Individual Judgments for Selection CriteriaGroup Decision and Negotiation, 12
Sheng-Hshiung Tsaur, Te-Yi Chang, Chang-Hua Yen (2002)
The evaluation of airline service quality by fuzzy MCDM.Tourism Management, 23
C. Kahraman, D. Ruan, I. Doğan (2003)
Fuzzy group decision-making for facility location selectionInf. Sci., 157
Zadeh Zadeh (1965)
Fuzzy setsInform Contr, 8
E. Lee, R.-J. Li (1988)
Comparison of fuzzy numbers based on the probability measure of fuzzy eventsComputers & Mathematics With Applications, 15
Shan-Huo Chen (1985)
Ranking fuzzy numbers with maximizing set and minimizing setFuzzy Sets and Systems, 17
Chu Chu (2003)
Lin YC. A fuzzy TOPSIS method for robot selectionInt J Adv Manuf Technol, 21
Chang Liu, K. Arnett (2000)
Exploring the factors associated with Web site success in the context of electronic commerceInf. Manag., 38
T. Chu (2002)
Facility Location Selection Using Fuzzy TOPSIS Under Group DecisionsInt. J. Uncertain. Fuzziness Knowl. Based Syst., 10
O. Vaidya, Sushil Kumar (2006)
Analytic hierarchy process: An overview of applicationsEur. J. Oper. Res., 169
J. Fodor, M. Roubens (1994)
Fuzzy Preference Modelling and Multicriteria Decision Support, 14
A. Kaufmann, M. Gupta (1988)
Fuzzy mathematical models in engineering and management science
(1965)
Fuzzy sets. Inform Contr 1965;8:338–353
Tian-Shy Liou, Mao-Jiun Wang (1992)
Ranking fuzzy numbers with integral valueFuzzy Sets and Systems, 50
E‐service evaluation is a complex problem in which many qualitative attributes must be considered. These kinds of attributes make the evaluation process hard and vague. Cost–benefit analyses applied to various areas are usually based on the data under certainty or risk. In case of uncertain, vague, and/or linguistic data, the fuzzy set theory can be used to handle the analysis. In this article, after the evaluation attributes of e‐services and the fuzzy multi‐attribute decision‐making methods are introduced, a fuzzy hierarchical TOPSIS model is developed and applied to an e‐service provider selection problem with some sensitivity analyses. The developed model is a useful tool for the companies that prefer outsourcing for e‐activities. It is shown that service systems can be effectively evaluated by the proposed method. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 547–565, 2007.
International Journal of Intelligent Systems – Wiley
Published: May 1, 2007
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