Purpose of this research is determining the success of online shopping stores by an intelligent system. Here a Fuzzy Expert System has been designed with the consideration of website attributes as input variables. The web site success level is determined in this system as output. The rules of systems have been extracted from some ecommerce experts and the systems have been developed with the use of FIS tool of MATLAB software. The final result contains an anticipating model for evaluating level of web site success of shopping centers based on website factors situation. The presented steps have been run in four online bookstores as the empirical study.

1. Full Paper
ACEEE Int. J. on Information Technology , Vol. 3, No. 3, Sept 2013
A Fuzzy Expert System for Assessing the InternetStores
Based on Web Site Attributes
Mahdieh Khalilinezhad1, Ahmad Nadali*2, NiloufarDehghani3, Mohammad Ghazivakili4
1
Department of Computer Engineering, University of Qom,Qom, Iran
Email: A.khalilinezhad@gmail.com
2
Department of Information Technology Management, Science and Research Branch,
Islamic Azad University, Tehran, Iran *Corresponding Author Email: Nadali.ahmad@gmail.com
3
Department of Economics, University of Tehran, Tehran, Iran
Email:Niloofar.dehghani68@gmail.com
4
Department of Telecommunications, Urmia Branch, Islamic Azad University, Urmia, Iran
Email: M.ghazivakili@gmail.com
Index Terms- E-commerce, Internet Stores, Websites Attributes,
Web site Assessment, Fuzzy Expert System.
focuses on designing an Expert System for evaluating success
level of online shopping centers as Output based on criteria
of websites as Input variables. Since the experts’ judgment
isexplained with linguistic variables, using fuzzy functions
and Fuzzy deduction system can be advantageous to build a
knowledge base system for evaluating platforms new ideas.
The remainder of this paper is organized as follows: In
the next Section the concept of web evaluation is defined
and criteria and methods of website assessment are
highlighted. Methodology of Fuzzy Expert System is given
in Section 3. In Section 4 the proposed system & empirical
study are described. In Section 5 the results and discussion
are presented. Finally the article conclusions are drawn in
Section 6.
I. INTRODUCTION
II. THE LITERATURE REVIEW ON WEBSITE EVALUATION
In the last ten years, online shopping has become a
prevalent part of the average consumer ’s shopping
experience. The consumer now has the ability to purchase
virtually anything online; ranging from small ticket items such
as a rubber-band ball to big-ticket items like vacation homes.
With this increase in theonline consumer’s purchasing power
and propensity to purchase online, retailers have become
increasingly willing to develop their e-commerce presence
[1] Moreover, the explosion of the web has determined the
need of measurement criteria to evaluate the aspects related
to the quality in use, such as usability and accessibility of a
web application. The objective is to make a website useful,
profitable, and accessible [2]. Awareness of quality issues
has recently affected every industrial sector. An organization
with a website that is difficult to use and interact with gives
a poor image on the Internet and weakness of an
organization’s position. Therefore, it is important for any
organization to have the ability to make an assessment of the
quality of their e-business websites and services. In the last
decade, numerous studies have focused on the designs of
websites because the design of website is very critical to ebusiness success. Numerous practitioner reports and reviews
have been published seeking to identify the good and bad
features of websites.
The purpose of this paper is to assess ecommerce
websites based on web related attributes. This article mainly
A. The criteria of website evaluation
As the dependency on web services increases, the need
to assess characteristics with website quality and success
increases. Websites characteristics are important; they have
been a constant concern of research in different domains
and they were widely studied in the e-commerce literature
[2]. Website evaluation measures have been proposed in
various contexts in recent years; researchers in this area
struggle to determine important factors for evaluating online
service and marketing.Business and commercial websites were
studied from different perspectives. Some researchers
investigated website features or factors that are critical to ebusiness success, in which they called them critical success
factors [2]. In the context of e-commerce, [3] proposed an
updated DeLone and McLean information system (IS) success
model (henceforth referred to as the Delone and McLean
model) and argued that website success is a multi-dimensional
concept consisting of six interrelated variables – system
quality, information quality, service quality, user satisfaction,
system use, and net benefits. In that article taxonomy, system
quality measures technical success, information quality
measures semantic success, service quality measures
customer service success, and user satisfaction, system use
and net benefits are the measures of website effectiveness.
Within Delone&MCLean model, system quality, information
Abstract—Purpose of this research is determining the success
of online shopping stores by an intelligent system. Here a
Fuzzy Expert System has been designed with the consideration
of website attributes as input variables. The web site success
level is determined in this system as output.
The rules of systems have been extracted from some ecommerce experts and the systems have been developed with
the use of FIS tool of MATLAB software. The final result
contains an anticipating model for evaluating level of web
site success of shopping centers based on website factors
situation. The presented steps have been run in four online
bookstores as the empirical study.
© 2013 ACEEE
DOI: 01.IJIT.3.3.1143
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ACEEE Int. J. on Information Technology , Vol. 3, No. 3, Sept 2013
quality, and service quality affect user satisfaction and system
use, which in turn are direct antecedents of net benefits [4].
Other researchers address key issues, ideas and strategies
to be considered in the management of online business from
customer satisfaction perspective, and they assess whether
a website has been built with a customer’s goals in mind.
Another group of researchers investigated the perspective
of web designers in order to elicit factors that they consider
important when designing or developing effective websites
[2]. Other researchers developed generic tools or measurement
frameworks for the assessment of website quality[5], There
are many articles reviewed by authors concentrated on some
important features; they either proposed a framework to
measure the important features of the website or used previous
models to find out to which extent e-business websites
incorporate these important features. [4] Closely linked to
the concept of website quality is the notion of usability. For
the customer to easily consume online, he/she must first find
website useful and easy to use. Website usability has been
defined and measured in many different ways in the table1
we categorized previous research about usability.
the many attempts have been made to address website
evaluation for different organizational sectors and website
categories [4]. In this part we categorized previous research
based on the evaluation methods and different aspect of ebusiness.
Various assessment techniques have been employed to
evaluate websites using subjective approaches based on
individual preferences, such as the Analytic HierarchyProcess
(AHP), the Technique for Order Preference by Similarity to
an Ideal Solution (TOPSIS), the Preference Ranking
Organization Method for Enrichment Evaluation
(PROMETHEE), and the VIKOR [4].
Table 2.show the previous researches based on the
different aspect of e-business.
TABLE II. CATEGORIZATION OF STUDIES BASED ON WEBSITE
EVALUATION METHODS AND E-BUSINESS
Website
Types
Authors
Methods
Evaluation
Criteria
Academic
Website
Bu
yukozkan et
all,
(2007)[11]
Fuzzy
VIKOR
Banking
Website
Miranda,
Cortés,
&Bar riuso,
(2006) [12]
Bu
yukozkan&
Ruan,
(2008)[13]
Buyukozkan
&Cifci(201
2)[14]
Web
Assessment
Index
Right and
understa ndable
content, complete
content,
personalization,
security, naviga tion,
interactivity and user
interface.
Accessibility, spe ed,
navigability and
content.
Travel
Website
Ho and Lee,
(2007)[15]
Factor
Ana lysis
E-commerce
Website
Kuo, Chi,
and Kao,
(2002)[16]
Vander,
M er we&Be
kker,
(2003)[17]
Fuzzy AHP
and ANN
Conceptual
Fr ame work
TABLE I. PREVIOUS RESEARCH ABOUT WEBSITE USABILITY
Authors
Description
Evaluation Criteria
Agrwal&Vente
sh(2002)[6]
Defined usa bility
based on design
elements
Nilsen
(2000)[7]
Extended information
system design
principles for web
Categorized usability
into different aspect
Download delay,
navigability, content,
interactivity,
responsiveness
Navigation, response
time, credibility,
content
Language usability,
layout and graphics,
information
architecture usability,
user interface and
navigation, ge neral
usability
Consistency
,accessibility,
navigation, media use
interactivity, content
Information content,
ease of navigation,
download delay,
website availability
Bai,Law,Wen
(2008)[8]
Hassan and Li
(2005)[9]
Identified web
usability as screen
appearance
Tarafdar and
Zhang
(2005)[10]
Influence factor on
website usability
Gover nment
al Website
Healthcare
Website
The fact that e-commerce itself can be classified as a kind
of information technology dimension and that many business activities are done through the computer and Internet,
including product transactions, advertising, selling services,
etc., reveals the core issue of how Internet businesses can
make themselves the customers’ most trusted and shopped
websites. Shopping websites allow customers to choose
products based on their own needs and then provide businesses transaction platforms through interactive communications to fulfill the transactions. Previous studies have emphasized that the issue of consumer purchase process is
important.
User satisfaction
perspective.
Fuzzy AHP
and Fuzzy
TOPSIS
Tangibles,
responsiveness,
r eliability,
information quality,
assurance and
empathy.
Information quality,
Secur ity, website
functionality,
customer
r elationships and
responsiveness.
Design and content,
customer education,
security, Interface,
Navigation,
Reliability, Content,
Technical.
III. FUZZY EXPERT SYSTEM METHODOLOGY
Fuzzy expert systems use fuzzy data, fuzzy rules and fuzzy
inference, in addition to the standard ones implemented in
the ordinary expert systems. The fuzzy Inference Systems
(FIS) are very good tools as they hold the nonlinear universal approximation [18]. Fuzzy inference systems can express
human expert knowledge and experience by using fuzzy inference rules represented in “if-then” statements. Following
the fuzzy inference mechanism, the output can be a fuzzy set
or a precise set of certain features. Fuzzy expert systems deal
B. The methods of website evaluation
Drawing a Strategy Canvas for business industry is
© 2013 ACEEE
DOI: 01.IJIT.3.3.1143
Fuzzy AHP
and Fuzzy
TOPSIS
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ACEEE Int. J. on Information Technology , Vol. 3, No. 3, Sept 2013
with phenomena that are uncertain and nonlinear in nature.
The nonlinear characteristics are realized in fuzzy logic by
partitioning the domain-specific rule space, weighting the
rules, and applying the nonlinear membership functions [19].
Fuzzy Inference System (FIS) incorporates fuzzy inference
and rule-based expert systems. There are different types of
fuzzy systems are introduced. Mamdani fuzzy systems and
TSK fuzzy systems are two types of fuzzy systems commonly
used in literature that has different ways of knowledge
representation.TSK (Takagi-Sugeno-Kang) fuzzy system was
proposed in an effort to develop a systematic approach to
generate fuzzy rules from a given input–output data set.
Regarding our problem in which various possible conditions
of parameters are stated in form of fuzzy sets, the Mamdani
fuzzy systems will be utilized due to the fact that the fuzzy
rules representing the expert knowledge in Mamdani fuzzy
systems, take advantage of fuzzy sets in their consequences,
while in TSK fuzzy systems, the consequences are expressed
in form of a crisp function [20].
The general process of constructing such a fuzzy expert
system from initial model design to system evaluation is
shown in Fig.1. This illustrates the typical process flow as
distinct stages for clarity but in reality the process is not
usually composed of such separate discrete steps and many
of the stages, although present, are blurred into each other.
Step3) Determining the membership functions for the
variables
Step4) Specifying the rules for making the relations clear
between Inputs and outputs by experts.
Step5) Developing the Fuzzy Expert System via FIS Tool in
MATLAB Software.
Step6) Implementing the designed system for four online
book store websites.
Step1: The aim is evaluating the success level of online
shopping websites considering to 5 main website factors
status. Since the obtained opinions from the experts about
factors are ambiguous and not precise; evaluation has been
done via linguistic variables. To this purpose, a Mamdani’s
Fuzzy Expert system has been designed.
Step2: According to above mentioned steps, the effective
variables on “website success level” have been extracted
from the previous research of Vander, R., Merwe, Bekker, J
[17] as Input variables (Table3). These input variables include:
Interface, Navigation, Reliability, Content, Technical as main
effective factors that are shown in table 4. Website Success
Level(WSL) has been considered as output of a Mamdani’s
Fuzzy Expert system.
TABLE III. DESCRIPTION OF E-COMMERCE WEB SITE EVALUATION
CRITERIA [17]
Website Attributes
Interface (C1)
Navigation (C2)
Reliability (C3)
Content (C4)
Technical(C5)
Sub Criteria
Graphic design principles, Graphics
and multimedia, Style and text,
Flexibility and compatibility
Logical structure, Ease of use,
Search engine, Navigational
necessities
Product/service-related information,
Company and contact information,
Information quality, Interactivity
Stored customer profile, Order
process, After-order to order receipt,
Customer service
Speed, Security, Software and
database, System design
Step3: In this system, five main factors have been considered
as Inputs and Website Success Level(WSL) as output. The
membership functions of Inputs and Output of designed fuzzy
expert system have been presented in Tables 4&5.
TABLE IV. THE OUTPUT OF FUZZY EXPERT SYSTEM
Output
IV. THE PROPOSED FUZZY EXPERT SYSTEM
In the following section, the circumstance of designing
the fuzzy expert systems for determining Website Success
Level has been described in six steps.
In this research, these steps briefly have been followed:
Step1) Clarifying the objective
Step2) Selecting the Input and output variables with the use
of previous studies
© 2013 ACEEE
DOI: 01.IJIT.3.3.1143
Interval
W
ebsite
Success Level
(W
SL)
Fig. 1. Process flow in constructing a fuzzy expert system [20]
[0 1]
Type of
membershi
p function
P-shape
Linguistic terms
Very Low(VL), Low(L),
Medium(M), High(H),
Very High(VH)
After specifying Input and Output variables, membership
functions by the experts have been defined for the variables
which are shown in Fig 2 to Fig 7.
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ACEEE Int. J. on Information Technology , Vol. 3, No. 3, Sept 2013
TABLE V. THE INPUTS OF FUZZY EXPERT SYSTEM
Inputs
Inter fa ce
Navigation
Reliability
Content
Technical
Interval
Type of
me mbershi
p function
[0 1]
Ga ussian2
[0 1]
Gbell
[0 1]
Gbell
[0 1]
Gaussian
[0 1]
Gaussian
Linguistic terms
Very Low(VL), Low(L),
M edium(M), High(H),
Very High(VH)
Low(L) , Medium(M)
High(H)
Very Low(VL), Low(L),
M edium(M), High(H),
Very High(VH
Low(L) , Medium(M)
High(H)
Very Low(VL), Low(L),
M edium(M), High(H),
Very High(VH
Fig.5. Three Gaussian2 Membership function for Content
Fig.6. Five Gaussian Membership function for Technical
Fig.2. Five Gaussian2 Membership function for Interface
Fig.7. Five P-shape Membership function for Website Success Level
(WSL)
Fig.3. Three Gbell Membership function for Navigation
TABLE VI. T HE
RULES OF
FUZZY EXPERT SYSTEM
C1
1
2
3
4
5
6
7
8
9
10
Fig.4. Five Gbell Membership function for Reliability
C3
C4
C5
WSL
VH
M
L
H
M
VL
H
VH
VL
M
M
H
M
M
H
M
L
L
H
H
H
M
L
VH
VL
H
H
H
M
H
H
L
M
H
M
L
M
H
L
H
H
M
VL
M
H
M
VL
M
H
VH
VH
M
VL
H
M
L
L
H
VL
VH
Step5: The system according to the obtained rules from
experts about the relationbetween Input variables and Output
has been designed via MATLAB software. Here, Fuzzy
Inference System (FIS) in MATLAB fuzzy logic toolbox as
Step4: To design the systems, we needed the rules which
determine the relation between the input and output
variables.The 15 obtained rules can be viewed in Table 6.
© 2013 ACEEE
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ACEEE Int. J. on Information Technology , Vol. 3, No. 3, Sept 2013
user friendly interface has been used.
Step6: The system is able to determine the website success
level based on the five effective website attributes. Regarding
the proposed fuzzy expert system, four E-commerce websites
have been evaluated as empirical study, as shown in next
section.
V. RESULTS AND DISCUSSION
The final objective of this study was to present a Fuzzy
Expert system to evaluate the website success level for any
E-commerce website. According to the experts’ opinions as
the inputs, we applied it for 4 online shopping stores and the
following results have been presented (Fig 8 to Fig11).
Fig 11: The assessed success Level of Website 4 by designed system
As a result, website success level (WSL) of Website 1
would be 0.667 out of 1 , for Website 2 would be 0.806 out of
1, for Website 3 would be 0.524 out of 1 and for Website 4
would be 0.709 out of 1. These results show that website 2 is
best online shopping and website 4 is better than website 1
and website 3 is worst. Finally, website 2 is successful for
attracting audiences only based on its website good
attributes.
VI.CONCLUSION
In this article we have tried to review previous studies
related to features of good websites specially e-shopping
websites and then it has been investigated different methods
that are used for evaluating web site quality in different aspect
of e-business website such as: e-learning, e-banking, egovernment, e-travel, e-commerce, e-shopping and healthcare
websites. In this study website success level for online
shopping stores has been evaluated according to five website
factors. Here, the effective variables on websites have been
considered as the system inputs and the website success
level as the system output. The rules have been obtained by
the use of E-commerce experts opinions. According to these
rules, a Fuzzy expert system has been designed. This model
helps to rank the shopping websites and it can be used for
assessing websites in other areas.
Fig 8: The assessed success Level of Website 1 by designed system
ACKNOWLEDGEMENT
Here, we appreciate from the WBB Team experts who
have given their knowledge to the researchers and supported
this research.
Fig 9: The assessed success Level of Website 2 by designed system
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