Journal of Software Engineering and Applications, 2013, 6, 559-563
Published Online November 2013 (
Open Access JSEA
Towards Developing Successful E-Government Websites
Osama Rababah1, Thair Hamtini2, Osama Harfoushi1, Bashar Al-Shboul1, Ruba Obiedat1,
Sahem Nawafleh3
1Department of Business Information Technology, King Abdullah II School for Information Technology, The University of Jordan,
Amman, Jordan; 2Department of Computer Information System, King Abdullah II School for Information Technology, The Univer-
sity of Jordan, Amman, Jordan; 3Department of Management Information System, University of Petra, Amman, Jordan .
Received September 30th, 2013; revised October 24th, 2013; accepted October 31st, 2013
Copyright © 2013 Osama Rababah et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Quality is a key factor to ensuring success of e-government websites. Therefore, a definition for high-quality e-gov-
ernment website is required, as well as, an e-government system’s quality evaluation methodology. This paper iden tifies
quality attributes that are required to assess the quality of an e-government website, which should be consid ered by de-
velopers during the development of e-government applications. The primary goals are identifying, qualifying, catego-
rizing, and ranking these factors, and then defining the interrelations among these quality factors.
Keywords: Web Application Development; E-Government; Quality Evaluation
1. Introduction
An e-government website forms a significant part of the
government framework in advanced countries. It offers
services to people in a context of advanced information
technology, and new public management. Ensuring qual-
ity through website evalu ation arises from the fact that an
e-government website is the most important channel for
public services delivery, and citizen-government interac-
tion. Furthermore, the need to justify government in-
vestment that makes web-based service delivery possible
is yet another reason for website quality assurance.
Past researches show that the website evaluation de-
pends on multiple factors (e.g. download delay, errors in
pages, broken links, server response time) that can be
measured by web diagnostic [1,2]. Since government
sites are becoming increasingly complex, an integral
quantitative evaluation process regarding all relevant
quality characteristics is also a complex issue. This is
caused by the large amount of intervening characteristics,
and by the complex logic relationships among attributes
and characteristics. Besides, some relevant attributes to
evaluate cannot objectively be measured so that they
only can be included after a subjective measurement
made by expert evaluators [3].
Most of the official government websites only offer
basic information for visitors, without always paying
attention to the usability, accessibility, and content man-
agement of the website. For example, making govern-
ment services and infor mation on the e-govern ment web-
sites is not equal to the successful access by users; es-
pecially for persons with disability [4]. It is frequently
the case for people to visit a website which is poorly
structured, difficult to navigate and unfriendly for readers.
Some sites take a long time to download content, which
makes users become impatient and leave. Those sites are
often developed by people who have the perception that a
quality site is the one that demonstrates the latest multi-
media and animation effects [5].
2. Identifying Quality Factor
In this paper, the ISO/IEC 9126 was used as a base mo-
del to identify the basic e-government website quality
factors. The ISO/IEC 9126 standard was developed in
1991 to provide the framework for evaluating software
quality [6].
The model describes an internal and external software
quality. The internal software quality is developer ori-
ented derived from the product itself to satisfy end users’
requirements. On the other hand, the external software
quality provides an appreciation of the quality as seen
from a user’s perspective. Both the internal and external
software qualities are prescribed in six factors (i.e. func-
Towards Developing Successful E-Government Websites
tionality, portability, maintainability, efficiency, usability
and reliability), each of which is further decompos ed into
sub-factors. The model is illustrated in Figure 1.
As the figure shows, modifications on the ISO/IEC
9126 hierarchy were done as our research shows the ne-
cessity of including more characteristics and sub-char-
acteristics after investigation, and receiving experts’
feedbacks through reviews and interviews. In particular,
security, availability, readability, content, navigation and
trustworthiness were added as main factors. Security sub-
factor was removed from “Functionality” and was con-
sidered as a “main” factor that contains many sub-factors
(i.e. Authentication, Privacy, and Access Control) [7].
Website content is an important factor which deals
with the characteristics of website information since it is
the major source of value to customers [8]. Table 1 lists
the factors used with a short explanation of each factor.
Based on the academic research exercised, it was
thought that a list of twelve factors will satisfy an as-
sessment of the quality of E-gov ernment websites. These
factors then were extended with sub-factors. The com-
plete list of those forty-nine sub-factors is displayed
within quality factors in Table 1.
3. Qualifying and Rating
A rating system for the factors was built in order to re-
flect the relative importance of the different sub-factors
within a main factor. The results were generated based
on questionnaires from expert specialists in e-govern-
ment website development.
The survey covered eighteen experts from IT compa-
nies and government institutes in Jordan, most of them
acquired development expertise, solid technical back-
ground, and a wide experience in designing and devel-
oping websites. Experts were distributed over five pri-
vate companies and three government institutes. They
were basically asked to respond to the questionnaire by
ordering the sub-factors within each factor according to
the importance of their contribution towards their factor.
One randomly selected expert was identified to vali-
date the content and style of the questionnaire to make
sure that the questions were clear and complete, the ques-
tionnaire’ response was excluded from further analysis.
Given factor S having four sub-factors, namely: SS1 to
SS4, each participant was asked to rate each sub-factor
according to its importance in influencing S, where 1 was
the most important and 4 was the least. Sub-factors with
fewer responses than 50% were removed from the analy-
sis, and the average of received responses was calculated
to fill in the gaps of any missing observations. Once all
results were collected, a weighting scheme was applied
to reflect the rating of the different sub-factors based on
the following fo rmula [9]:
Sub-factor Percent Importance100100(1)MN
where M represents the average rating received on a sub-
factor and N represents the total number of sub-factors
for a given factor. The subtraction from 100 is to reverse
the rating scale of the questionnaire so that the question-
naire rating of “1” has the highest percentage importance.
The final rating achieved has the highest percentage
given to the most important sub-factor, proceeding to the
least important in a descending fashion. One drawback to
this method is that the final rating obtained for each
sub-factor is dependent on the number of sub-factors in
each group; therefore, it affects inter-factor correlations.
To compensate for this problem, a second group of ex-
perts were asked to assess the appropriateness of the cor -
relations as explained in the next section.
Table 1 shows the rating received by each sub factor
within each factor proceeding from the most important
sub-factor to the least important in a descending fashion.
Figure 1. The ISO/IEC 9126.
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Towards Developing Successful E-Government Websites 561
Table 1. Identified quality sub-factors.
Quality Sub-factor Rank Quality Sub-factor Rank Quality Sub-factor Rank
Functionality Content Usability
Accuracy 60 Correctness 67 Understandability 70
Suitability 47 Updated 50 Response Time 63
Interoperability 25 Completeness 46 Learnability 61
Flexibility 22 Relevancy 44 Interactivity 54
Reliability User-Oriented 36 Operability 52
Maturity 43 Concise Content 36 Helpfulness 50
Fault Tolerance 29 Intelligibility 24 Attractiveness 48
Recoverability 27 Navigation Language 48
Security Navigation Structure 55 Customizability 39
Privacy 39 Absence Of Nav iga tion Errors 42 Accessible for users with disabilities 35
Authentication 29 Links Visibility 41 Download Facility 28
Access Control 24 Minimal Path 32 Printing Facility 27
Efficiency External and Internal Links 27 Maintainability
Time Behavior 44 Readability Stability 59
Resource Utilization 15 Clarity 51 Changeability 40
Availability Language Correctness 25 Analyzability 28
24/7 Readiness 41 Style Uniformity 24 Testability 26
Cross Browser Support 9 Trustworthiness Portability
Correctness 32 Adaptability 26
Completeness 12 Conformance 24
4. Factors Relations
In order to build a reliable model, it is important to figure
out the different inferences and causal relations between
the quality factors [10]. Statistical correlation was used in
analyzing the data obtained from the responses to the
questionnaire to establish initial inter-factor relations.
Correlation is not an indication of cause-and-effect rela-
tionships [11] where changes in one variable impacts,
and is the direct cause of changes in the correlated vari-
able. Correlations merely indicate whether two variables
are in harmony in terms of movement. However, a har-
mony in movement in either the same direction or oppo-
site (inverse) direction provides insight into possible
cause-and-effect relationships. In this paper, correlations
are used with rating scales, but with care. After the com-
pletion of identifying the factors’ significant inter-rela-
tions, the results were reviewed by a panel of experts to
ensure they were comfortable with the results.
The validation of each possible relationship was car-
ried out using a panel of experts to analyze the results of
the correlation analysis to draw conclusions about which
viable inter-dependencies exist amongst sub-factors. The
panel members were invited to a group discussion on
what would be the relevant and important relationships
among the sub-factors. Using Martin’s approach [12], a
stepwise model selection technique combining forward
selection and backwards elimination was used. Every
panel member was asked to select the best causal rela-
tionship, in their view. The selection was iterated one
relationship at a time in a round-robin approach. Not
knowing when a cessation would be reached in this
process, the members continued till their own point of
satisfaction was reached and no further selection was
added to their derived list. At that point a reversal elimi-
nation process started where each member was asked to
return the least desired relation from their possession.
The process continued in a round-robin fashion until the
panel collectively retained 50% of the initially selected
relations. The exercise was concluded at that point. Ta-
ble 2 shows the results of the selection process.
Open Access JSEA
Towards Developing Successful E-Government Websites
Table 2. Final selection f sub-factor interde pendencies iden-
tified quality sub-factors.
Sub-Factor Sub-Factor
influences Correctness
Absence of
Navigation Errors influences Maturity
Authentication influences Privacy
Concise Content influences
Absence of Navigation Errors
Correctness influences
Links Visibility influences Understandability
Navigation Structure influences Understandability
Recoverability influences Fault Tolerance
Stability influences 24/7 Readiness
Testability influences Analyzability
Updated influences Accuracy
User-Oriented influences Language
The same panel of three experts was invited to another
exercise to perform interrelation analysis at the quality
factors level. All the possible factor relationships, as
shown in Table 3, were projected at a display wall. The
panel members were asked to assess the relation “cells”
and provide a score of 0 to 2 where “2” indicates the
presence of strong causal relations and a “0” the lack of
such a relationship. Table 3 lists possible relations
among factors and the rating results received. Table 4
shows the resultant relations inferred.
5. Conclusion
This paper has identified an d ranked the factors and sub-
factors that contribute towards the quality of an e-gov-
ernment website. Furthermore, the relationships among
these factors showing which factors influence others
have been derived. The results provide an important
foundation for the understanding of quality in e-govern-
ment websites that will allow developers to assess the
strengths and weaknesses of their sites in order to know
where to focus further development to achieve the high
quality for e-government success [13,14].
Table 3. Factors polled relations.
Reliability 4
Availability 1 5
Usability 4 1 1
Efficiency 2 2 3 3
Readability 4 1 0 6 3
Content 2 2 0 5 4 6
Navigability 3 1 1 4 4 5 3
Security 3 4 1 3 3 0 3 2
Trustworthiness 3 6 6 4 2 3 5 3 6
Maintainability 2 4 4 0 0 0 0 2 2 1
Portability 1 3 3 2 1 3 0 4 0 1 4
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Towards Developing Successful E-Government Websites 563
Table 4. Finalized selection of factor interdependencies.
Factor Factor
Reliability influences Functionality
Availability influences Reliability
Usability influences Functionality
Readability influences Usability
Efficiency Content influences
Efficiency Navigability influences
Security influences Reliability
Trustworthiness influences
Maintainability influences Availability
Portability influences Maintainability
6. Acknowledgements
This work was supported by The University of Jordan.
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