MULTI-CRITERIA DECISION MAKING METHODS FOR DETERMINING COMPUTER PREFERENCE INDEX

In helping consumers to make a wise decision in purchasing a personal computer (PC), the use of multi-criteria decision analysis methods is a way to provide ranking of the attributes of PCs and to construct the computer preference index (CPI). This paper employs the Rank Ordered Centroid (ROC) metho...

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Main Authors: Maznah Mat Kasim, haslinda ibrahim, Mahmoud S. Bataineh
Format: Article
Language:English
Published: UUM Press 2011-03-01
Series:Journal of ICT
Online Access:https://www.scienceopen.com/document?vid=f878d2fd-dc0f-492e-b580-7c0081ab850a
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spelling doaj-372e123b0c104228b7ed669c5b4851da2021-08-02T22:27:37ZengUUM PressJournal of ICT1675-414X2011-03-0110.32890/jict.10.2011.8115MULTI-CRITERIA DECISION MAKING METHODS FOR DETERMINING COMPUTER PREFERENCE INDEXMaznah Mat Kasimhaslinda ibrahimMahmoud S. BatainehIn helping consumers to make a wise decision in purchasing a personal computer (PC), the use of multi-criteria decision analysis methods is a way to provide ranking of the attributes of PCs and to construct the computer preference index (CPI). This paper employs the Rank Ordered Centroid (ROC) method to do the ranking. The findings reveal that the most important attribute is the CPU, followed by the hard drive, the price, the memory card, the warranty, the size, the screen resolution, the Ethernet, the weight and the DVD. Whilst, the CPI is constructed by using a Multi-Criteria Decision Making (MCDM), called the Simple Additive Weighting (SAW) method. Analysis of data from 25 PCs from four brands, Toshiba, Dell, HP Compaq and Acer, presents the CPI. The resulted CPI shows that two HP Compaq models, Presario V3632 and V3653 are preferred most, while, Acer Aspire 4920-5AA0516MI is the least preferred.  https://www.scienceopen.com/document?vid=f878d2fd-dc0f-492e-b580-7c0081ab850a
collection DOAJ
language English
format Article
sources DOAJ
author Maznah Mat Kasim
haslinda ibrahim
Mahmoud S. Bataineh
spellingShingle Maznah Mat Kasim
haslinda ibrahim
Mahmoud S. Bataineh
MULTI-CRITERIA DECISION MAKING METHODS FOR DETERMINING COMPUTER PREFERENCE INDEX
Journal of ICT
author_facet Maznah Mat Kasim
haslinda ibrahim
Mahmoud S. Bataineh
author_sort Maznah Mat Kasim
title MULTI-CRITERIA DECISION MAKING METHODS FOR DETERMINING COMPUTER PREFERENCE INDEX
title_short MULTI-CRITERIA DECISION MAKING METHODS FOR DETERMINING COMPUTER PREFERENCE INDEX
title_full MULTI-CRITERIA DECISION MAKING METHODS FOR DETERMINING COMPUTER PREFERENCE INDEX
title_fullStr MULTI-CRITERIA DECISION MAKING METHODS FOR DETERMINING COMPUTER PREFERENCE INDEX
title_full_unstemmed MULTI-CRITERIA DECISION MAKING METHODS FOR DETERMINING COMPUTER PREFERENCE INDEX
title_sort multi-criteria decision making methods for determining computer preference index
publisher UUM Press
series Journal of ICT
issn 1675-414X
publishDate 2011-03-01
description In helping consumers to make a wise decision in purchasing a personal computer (PC), the use of multi-criteria decision analysis methods is a way to provide ranking of the attributes of PCs and to construct the computer preference index (CPI). This paper employs the Rank Ordered Centroid (ROC) method to do the ranking. The findings reveal that the most important attribute is the CPU, followed by the hard drive, the price, the memory card, the warranty, the size, the screen resolution, the Ethernet, the weight and the DVD. Whilst, the CPI is constructed by using a Multi-Criteria Decision Making (MCDM), called the Simple Additive Weighting (SAW) method. Analysis of data from 25 PCs from four brands, Toshiba, Dell, HP Compaq and Acer, presents the CPI. The resulted CPI shows that two HP Compaq models, Presario V3632 and V3653 are preferred most, while, Acer Aspire 4920-5AA0516MI is the least preferred.  
url https://www.scienceopen.com/document?vid=f878d2fd-dc0f-492e-b580-7c0081ab850a
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AT haslindaibrahim multicriteriadecisionmakingmethodsfordeterminingcomputerpreferenceindex
AT mahmoudsbataineh multicriteriadecisionmakingmethodsfordeterminingcomputerpreferenceindex
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