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USING FUZZY TOPSIS AND REGRESSION BASED WEIGHTS TO RANK E-COMMERCE WEBSITES
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Date
2019-07-01
Author
ÖZKAN, NECMETTİN FIRAT
Gökalp Yavuz, Fulya
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Website usability is a widespread study area which incorporates researches from various disciplines. Several methods are available to evaluate usability of a website. Checklists, heuristic evaluations, expert evaluations, surveys and user tests are presented as the most popular methods for the evaluation of websites. Especially e - commerce websites’ usability has a critical importance due to the rising in competence and the rising in the number of e-commerce websites. The diversity of e-commerce websites evokes typical example of multi criteria decision making (MCDM) issue in daily life of humanity. Since different user groups have different expectations from the websites, the evaluation method should be structured to include appropriate measurement criteria. The main aim and the target users of the website should be considered attentively while defining the evaluation method. In this study, the most visited (Based on Alexa.com list in January, 2018) e -commerce websites in Turkey are compared in terms of usability. Criteria are decided based on the study which was published by Lee and Kozar (2015). Two different weighting approaches are integrated to TOPSIS method. One of these approaches is fuzzy expert evaluation and the other one is weighted regression estimate. We aim to discover different impressions between e-commerce website designers and users on the same criteria to provide a comprehensive viewpoint.
Subject Keywords
E-Commerce
,
Website
,
Fuzzy TOPSIS
,
Regression
,
Multicriteria Usability
URI
https://hdl.handle.net/11511/38814
Journal
Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi
DOI
https://doi.org/10.31796/ogummf.563348
Collections
Department of Statistics, Article
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N. F. ÖZKAN and F. Gökalp Yavuz, “USING FUZZY TOPSIS AND REGRESSION BASED WEIGHTS TO RANK E-COMMERCE WEBSITES,”
Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi
, vol. 2, pp. 116–124, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38814.