Content uploaded by Hassan M. Selim
Author content
All content in this area was uploaded by Hassan M. Selim
Content may be subject to copyright.
Int. J. Technology Marketing, Vol. X, No. Y, xxxx 1
Copyright © 200x Inderscience Enterprises Ltd.
E-learning critical success factors: an exploratory
investigation of student perceptions
Hassan M. Selim
Department of Business Administration
United Arab Emirates University
P.O. Box 17555, Al Ain, UAE
Fax: (9713) 763–2383
E-mail: hassan.selim@uaeu.ac.ae
Abstract: Information Technology (IT) and intense competition are reshaping
universities worldwide. Universities have begun to utilise and integrate IT in
teaching and learning in order to meet the instructors’ and students’ needs.
E-learning, one of the tools that have emerged from IT, has been integrated
into many university programmes. There are several factors that need to be
considered while developing or implementing university curriculums that
offer e-learning-based courses. Since e-learning is a relatively new learning
technology, this paper is intended to identify and measure its Critical Success
Factors (CSFs) from student perceptions. In line with the literature, four
CSFs were identified and measured, namely, instructor characteristics, student
characteristics, technology infrastructure and university support. Student
attitude towards using e-learning was empirically tested. A sample of 37 class
sections with 538 responses was used to validate the proposed e-learning CSFs.
The results revealed that students perceived instructor characteristics as the
most critical factor in e-learning success, followed by IT infrastructure and
university support. The student characteristics factor was perceived as the least
critical factor to the success of e-learning.
Keywords: e-learning; improving classroom teaching; evaluating CAL
systems; teaching/learning strategies.
Reference to this paper should be made as follows: Selim, H.M. (xxxx)
‘E-learning critical success factors: an exploratory investigation of student
perceptions’, Int. J. Technology Marketing, Vol. X, No. Y, pp.000–000.
Biographical notes: Dr. H.M. Selim is an Associate Professor of MIS at the
Department of Business Administration of the AACSB-accredited College of
Business and Economics at the United Arab Emirates University. He received
his PhD in Management from the MIS Department, Carl Eller Business School
at the University of Arizona, Tucson, USA. His current research interests
include manufacturing information systems, manufacturing cell formation
problem, IT in teaching and learning, and technology acceptance. He has
published in several journals, such as Computers and Education, International
Journal of Distance Education Technologies, Computers and Industrial
Engineering, IIE, Integrated Manufacturing Systems, Industrial Management
and Data Systems, and Omega. He has coauthored two book chapters published
by John Wiley & Sons. Dr. Selim is a member of INFORMS, Decision Science
Institute, IRMA and AIS.
2 H.M. Selim
1 Introduction
The utilisation of Information Technology (IT) in teaching and learning has radically
changed higher education in the past two decades. The new global economy, advances
in IT, and the job market pose complex challenges to university students and
instructors. The job market is competitive and requires computer literacy, critical
thinking, information analysis and synthesising skills. There is a need to build an efficient
learning environment to meet both students’ and employers’ expectations (Ong and Lai,
2006). Advances in information technology are perceived by universities as the solution
to these challenges (Gotthardt et al., 2006). This has created a need to transform
how university students learn by using more modern, efficient and effective alternatives
such as e-learning.
E-learning is internet-enabled learning. Components can include content delivery in
multiple formats, management of the learning experience, and a networked community of
learners/students, and faculty or content developers (Cross, 2004; Gunasekaran et al.,
2002; Wang, 2003). E-learning is defined as the use of computer network technology,
primarily over or through the internet, to deliver information and instructions to
individuals (Ong and Lai, 2006; Welsh et al., 2003). E-learning provides faster learning
at reduced costs, increased access to learning materials, and accountability for all
participants in the learning process. In today’s fast-paced information age, higher
education institutions that implement e-learning provide their students with the ability to
turn change into an advantage to better market the students.
The measure of e-learning success should incorporate different constructs in order
to assess the extent and nature of this success (Wang et al., in press). An instrument
that identifies and measures the Critical Success Factors (CSFs) of e-learning from
stakeholders’ perception will be of great value to researchers, practitioners and higher
education institutions. Identifying and measuring e-learning CSFs can help higher
education institutions to better develop e-learning systems that fit both students’ and
instructors’ expectations. E-learning success can be measured by students’ attitudes
towards e-learning-enabled courses, instructors’ attitudes towards e-learning teaching
tools, and university support to e-learning initiatives.
This study developed a multifactor instrument for identifying and measuring
e-learning CSFs in a higher education institutional context as perceived by university
students. Four CSFs and their indicators were identified and validated. The rest of
the paper is organised as follows. In Section 2, related literature review is presented.
Section 3 presents a description of the research method used in generating the indicators
of each e-learning CSFs. The exploratory factor analysis is introduced and discussed in
Section 4. Conclusions and future work are presented in the last section.
2 Literature review
2.1 E-learning
It was argued by Lockyer et al. (2001) and Ramsden (1992) that higher education
institutions should incorporate proven pedagogical strategies, such as small group work,
cooperative and active learning, peer teaching, and idea sharing and reflection.
Traditional universities were criticised for being devoid of these powerful strategies
E-learning critical success factors
3
because they were difficult to integrate within traditional educational systems (Liaw
et al., 2006). Increasing student enrolment and declining proportions of tenured faculty
worsened the situation. Rosenberg (2001) stated that e-learning provided a reasonable
solution because it drove many of the advantages that helped universities succeed in their
education market. E-learning benefits included the following (Lee et al., 2002; Liaw
et al., 2006; Rosenberg, 2001):
• E-learning is networked and can provide communication anywhere anytime.
• E-learning increases learning effectiveness and information retention.
• E-learning activities centre on students and interactive learning.
• E-learning reduces costs associated with traditional education.
• E-learning is delivered using standard internet technology.
Despite all the interest and the significant literature about e-learning, little research
existed to measure e-learning success or identify its CSFs as perceived by its stakeholders
(universities, instructors and students).
In the past two decades, a substantial number of articles have been published
addressing e-learning. According to Milliken and Barnes (2002), throughout the 1980s
much of the research and development designed to improve learning in higher education
focused on teaching and the ways learning is structured by instructors (Brown et al.,
1982; Dunken, 1983). In the early 1990s, the research included the relationship between
teaching and learning (Ramsden, 1992). In the late 1990s and at the beginning of the 21st
century, effective student learning became the central theme and organising principle of
higher education (Helmi, 2002; Katz, 2002; Lu et al., 2002; Milliken and Barnes, 2002;
Oliver and Omari, 2001; Selim, 2003).
Web-based applications were increasingly utilised as delivery tools in higher
education. Instructors were attracted by the capability of multimedia and websites
to facilitate communication and collaboration among students and instructors (Lockyer
et al., 2001). E-learning-based courses were designed using multimedia computer
environment (Chuang, 1999) and web-based instruction (Human and Kilboume, 1999;
Kaynama and Keesling, 2000; Leon and Par, 2000; Selim, 2003). An analysis of more
than 600 feedback questionnaires at one UK university revealed that some 50% of
students surveyed identified a need for more effective teaching delivery (Pennington,
1994). Students who were exposed to e-learning, compared to those exposed to
conventional learning, generally achieved improved learning effectiveness (Baker et al.,
1997; Beyth-Marom et al., 2003; Lockyer et al., 2001; Tuckman, 2002). Le Grew (1995)
constructed a ‘paradigm shift’ table to show the transformation necessary in higher
education institutions in order to keep up with the changes in communications and
information technology. The shifts are: industrial society to information society,
technology peripheral to multimedia central and institutional focus to learner focus.
The conceptual framework and theories of e-learning have been discussed by a
number of researchers (Alexander, 2001; Alexander and McKenzie, 1998; Cooke
and Veach, 1997; Daniel, 1997; Henry, 2001; Patterson, 1999). Daniel (1997) believed
that the use of ICTs in teaching and learning provided a solution to many issues, such
as cost and quality of educational programmes. Henry (2001) argued that successful
implementation of e-learning requires the same management commitment as other
4 H.M. Selim
mission-critical, university-wide initiatives. Most of all, e-learning needs to be
compelling to students it targets, offering the student a resource that is seen to be
appealing, valuable and productive to their goals and aspirations. Patterson (1999)
discussed the ideas of learning organisations and applied it to universities, suggesting that
they adapt to the changing environment and that they become learning universities.
E-learning initiatives in teaching and learning that were not successful in achieving
the desired expectations and satisfaction shared common features (Alexander and
McKenzie, 1998). These initiatives did not have sufficient access to technical support
and expertise, and they did not prepare and orient students. Effective and successful
e-learning environments require some form of interaction and collaboration among
students, several researchers recognised the importance of student interaction to improve
performance and satisfaction (Akar et al., 2004; Driver, 2002; Fulford and Zhang, 1993;
Ritchie and Newby, 1989; Vrasidas, 1999). Moore and Kearsley (1996) identified three
types of interactions that allow students to learn effectively in e-learning environments
leading to the success of e-learning initiatives. The first is learner-content interaction that
refers to the student’s interaction with the course materials. This type of interaction is
fostered through an effective and efficient design of electronic or web-based materials
and activities (Freberg, 2000; Selim, 2003; 2005). The second is the learner-instructor
interaction that refers to the student’s interaction with the instructor, which is an essential
component of e-learning (Fulford and Zhang, 1993). The third is the learner-learner
interaction through student collaboration (Hayes, 1990). New information technologies
enable instructors to develop interaction and collaboration among students into the
courses. Most studies indicated that learner-learner interaction is a critical success
factor when measuring student satisfaction with e-learning-based courses (Graham and
Scarbrough, 1999; Phillips and Peters, 1999).
The target of improving university students’ learning efficiency and effectiveness
triggered the question of the extent to which e-learning aids this process and the factors
leading to its success as perceived by students. It is generally acknowledged that very
little has been researched on the CSFs of e-learning from students’ perception (Lee et al.,
2002). This paper reports on an exploratory study aimed to identify and measure the
e-learning CSFs as perceived by a sample of undergraduate students in the College of
Business and Economics at the United Arab Emirates University (UAEU). UAEU is a
large university with 19 000 students enrolled in several undergraduate degrees offered
by nine colleges. UAEU has started offering e-learning-based courses since 1998 and
started implementing a university-wide laptop project at the beginning of 2002 in order to
facilitate e-learning adoption by both students and instructors. The pilot laptop project
included more than 1000 students in three colleges. The AACSB-accredited College of
Business and Economics has 90% of its courses offered using different e-learning
tools and all students enrolled in these courses must have laptop computers as e-learning
facilitating tools. UAEU adopts Blackboard Learning System as an e-learning platform
and as a delivery mechanism to allow students to access several learning tools,
such as discussion boards, chat rooms, course material, and efficient and affective
communication tools. In the next subsection, the literature related to e-learning CSFs
is reviewed and several e-learning CSFs categories that form the core of this study
are identified.
E-learning critical success factors
5
2.2 E-learning critical success factors
The term CSF appeared in the literature in the late 1970s when there was a concern about
why some organisations seemed to be more successful than others, and research was
carried out to investigate the success components (Ingram et al., 2000). Numerous CSF
definitions were introduced in the literature. Rockart (1979) introduced the concept of
CSFs and defined them as “… the limited number of areas in which results, if they are
satisfactory, will ensure successful competitive performance for the organization. They
are the few key areas where ‘things must go right’ for the business to flourish”. Freund
(1988) defined CSFs as “those things that must be done if a company is to be successful”.
CSFs should be few in number, measurable and controllable.
Although e-learning systems are increasingly being used, there is limited
theory-driven research examining the CSFs associated with student attitude towards
e-learning system when that system is used to provide supplementary learning tool for a
traditional class. In December 2002, a workshop in conjunction with the International
Conference on Computers in Education was proposed to discuss and address CSFs
relating to e-learning framework proposed by Al Rawas (2001). The Al Rawas e-learning
framework proposed five categories of factors: organisational context, enabling
technologies, curriculum development, instructional design and e-learning development.
McPherson (2003b) and McPherson and Nunes (2003) used Al Rawas framework to
divide e-learning CSFs into five broad categories. Within each category, a number of
CSFs were identified using cluster analysis (Brook Hall and Concannon, 2003; Coman,
2003; Currier and Campell, 2003; McPherson, 2003a–b; McPherson and Nunes, 2003;
Nunes, 2003; Nunes and McPherson, 2003; Riddy and Fill, 2003). Flood (2004) offered
the 5Ps of e-learning: Presentation, Pedagogy, Promotion, Preparation and Props. The
5Ps were closely interdependent and required an integrated approach to the design of
e-learning architecture.
Several researchers attempted to identify and analyse e-learning CSFs. Papp (2000)
suggested some critical success factors that can assist faculty and universities in
e-learning environment development. Papp’s e-learning CSFs included intellectual
property, suitability of the course for e-learning environment, building the e-learning
course, e-learning course content, e-learning course maintenance, e-learning platform and
measuring the success of an e-learning course. Papp (2000) suggested studying each one
of these CSFs in isolation and also as a composite to determine which factor(s) influence
and impact e-learning success. Benigno and Trentin (2000) proposed a framework for the
evaluation of e-leaning-based courses, focusing on two aspects: the first is evaluating the
learning, and the second is evaluating the students’ performance. They considered factors
such as student characteristics, student-student interaction, effective support, learning
materials, learning environment and information technology.
Volery and Lord (2000) drew upon the results of a survey conducted amongst 47
students enrolled in an e-learning-based management course at an Australian university.
They identified three CSFs in e-learning:
1 technology (ease of access and navigation, interface design and level of interaction)
2 instructor (attitudes towards students, instructor technical competence and
classroom interaction)
3 previous use of technology from a student’s perspective.
6 H.M. Selim
Soong et al. (2001), using a multiple case study, verified that the e-learning CSFs are
human factors, technical competency of both instructor and student, e-learning mindsets
of both instructor and student, level of collaboration, and perceived information
technology infrastructure. They recommended that all these factors should be considered
in a holistic fashion by e-learning adopters. According to studies conducted by Dillon and
Guawardena (1995) and Leidner and Jarvenpaa (1993), three main variables affect the
effectiveness of e-learning environments: technology, instructor characteristics and
student characteristics. Using a survey on the perception of e-learning among
postgraduates enrolled at Curtin Business School, Helmi (2002) concluded that the three
driving forces to e-learning are information technology, market demands and education
brokers, such as universities.
In an attempt to provide a pedagogical foundation as a prerequisite for successful
e-learning implementation, Govindasamy (2002) discussed seven e-learning quality
benchmarks, namely, institutional support, course development, teaching and learning,
course structure, student support, faculty support, and evaluation and assessment. Based
on a comprehensive study by Baylor and Ritchie (2002), the impact of seven independent
factors related to educational technology (planning, leadership, curriculum alignment,
professional development, technology use, instructor openness to change, and instructor
computer use outside school) on five dependent measures (instructor’s technology
competency, instructor’s technology integration, instructor morale, impact on student
content acquisition and higher-order thinking skills acquisition) were studied using
stepwise regression. The study resulted in models explaining each of the five dependent
measures. Within the organisational learning context, Ruiz-Mercader et al. (2006)
assessed the importance of IT to organisational learning capabilities. They empirically
showed that IT investments can boost organisational learning. The reviewed CSFs are
summarised in Table 1 below. The extracted CSFs are presented in the next subsection.
Table 1 E-learning CSFs literature summary
Reference CSFs as perceived by students
Leidner and Jarvenpaa (1993)
Dillon and Guawardena (1995)
Technology
Instructor characteristics
Student characteristics
Papp (2000) Intellectual property
Suitability of the course for e-learning environment
Building the e-learning course
E-learning course content
E-learning course maintenance
E-learning platform
Measuring the success of an e-learning course
Benigno and Trentin (2000) Student characteristics
Student-student interaction
Effective support
Learning materials
Learning environment
Information technology
E-learning critical success factors
7
Table 1 E-learning CSFs literature summary (continued)
Reference CSFs as perceived by students
Volery and Lord (2000) Technology
Instructor
Previous use of technology
Soong et al. (2001) Human factors
Technical competencies of both instructor and student
E-learning mindsets of both instructor and student
Level of collaboration
Perceived information technology infrastructure
Helmi (2002) Information technology
Market demands
Education brokers, such as universities
Govindasamy (2002) Institutional support
Course development and structure
Student and faculty support
Evaluation and assessment
Baylor and Ritchie (2002) Educational technology
Instructor characteristics
Student characteristics
Ruiz-Mercader et al. (2006) Information technology
2.3 Extracted e-learning CSF categories
According to the studies reviewed in Section 2.2 and summarised in Table 1, e-learning
CSFs within a university environment can be grouped into four categories: (1) IT,
(2) instructor, (3) student and (4) university support. The following paragraphs
summarise the characteristics of each CSFs category.
IT explosion resulted in changes in education. E-learning integration into university
courses is a component of the IT explosion; as a matter of fact, IT is the engine that
drives the e-learning revolution and is conceived as the infrastructure to e-learning
initiatives. The efficient and effective use of IT in delivering e-learning-based
components of a course is of critical importance to the success of, and students’ positive
attitude towards, e-learning. So, ensuring that the university IT infrastructure is rich,
reliable and capable of providing the courses with the necessary tools to make the
delivery process as smooth as possible is critical to the success of e-learning. IT
tools include network bandwidth, network security, network accessibility, audio and
video plug-ins, courseware authoring applications, internet availability, instructional
multimedia services, videoconferencing, course management systems and user interface.
8 H.M. Selim
The instructor plays a central role in the effectiveness and success of
e-learning-enabled courses. Willis (1994) and Collis (1995) believed that it is not the
information technology but the instructional implementation of IT that determines the
effectiveness of e-learning. Webster and Hackley (1997) proposed three instructor
characteristics that affect e-learning success: (1) IT competency, (2) teaching style
and (3) attitude and mindset. Volery and Lord (2000) suggested that instructors provide
various forms of office hours and contact methods with students. Instructors should
adopt interactive teaching style, encourage student-student interaction. It is so important
that instructors have good control over IT and are capable of performing basic
troubleshooting tasks.
University students are becoming more diverse, and demand for e-learning-based
courses is increasing (Gotthardt et al., 2006; Hong, 2002; Lockyer et al., 2001). Students
need to have time management, discipline and computer skills in order to be successful in
the e-learning era. Students’ prior IT experience, such as having a computer at home and
attitude towards e-learning, is critical to e-learning success. As stated before, research
concluded that e-learning-based courses compare favourably with traditional learning,
and e-learning students perform as well or better than traditional learning students
(Beyth-Marom et al., 2003). This shows that students like to use e-learning, if it
facilitates their learning and allows them to learn anytime anywhere in their own way
(Papp, 2000).
E-learning projects that were not successful in achieving their goals did not have
access to technical advice and support (Aldexander et al., 1998; Soong et al., 2001). If
the technical support is lacking, e-learning will not succeed. University administration
support to e-learning is essential for its success. This study limited the e-learning CSFs
categories to those that were supported in the literature while including newly used items
within each CSFs category. The research question that this study attempts to answer is
“What are the critical success factors affecting the success of e-learning initiatives in
order to satisfy students’ needs and expectations?”.
3 Method
The data used in identifying e-learning CSFs were obtained from five courses that
combine both e-learning and traditional learning tools; all of them were laptop-based
courses, and used active and student-centred learning methods. Laptop was a mandatory
tool in laptop-based courses; each student must bring his/her laptop to class in order to
work on in-class assignments and access online material via wireless computer network.
The five courses were:
1 Principles of Financial Accounting
2 Fundamentals of Management
3 Fundamentals of Management Information Systems
4 Principles of Microeconomics
5 Principles of Economics.
E-learning critical success factors
9
Traditional learning tools used in the selected courses are required attendance, regular
textbook, and presence of instructor during the scheduled class time. E-learning tools
used are electronic student-student and student-instructor communication, asynchronous
course material delivered through a Blackboard Learning System web, in-class active and
collaborating learning activities, and student self-pacing pattern. Data were collected
through an anonymous survey instrument administered to 900 undergraduate university
students. Surveys were administered at the end of each course.
3.1 Subjects
Respondents for this study consisted of 538 (334 females and 204 males) undergraduate
students enrolled in five 100-level mandatory laptop-based courses distributed over
37 class sections. Table 2 summarises the demographic profile and descriptive statistics
of the respondents. Student ages ranged from 17 to 28 years, with a mean age of
19.98 years (S.D. = 1.256). Students came from 18 different countries. They have an
average GPA of 2.6 with a standard deviation of 0.54. Participants had eight majors,
namely, accounting, economics, finance and banking, general business, management,
management information systems, marketing and statistics. The exposure to e-learning
technologies of the participating students varied from one to three years; 38.7% had
one-year exposure, 36.6% had two years, and 24.7% had three years of exposure. All
students participated voluntarily in the study.
Table 2 Demographic profile and descriptive statistics of surveyed students
Item Frequency Percentage
Gender Male 204 37.9
Female 334 62.1
Age 17–19 210 39.0
20–22 313 58.2
23–25 12 02.2
26–28 3 00.6
Years at UAEU 1–2 381 70.82
3–4 153 28.44
5–6 4 00.74
Years of e-learning 1 208 38.7
2 197 36.6
3 133 24.7
PC ownership Yes 474 88.1
No 64 11.9
10 H.M. Selim
3.2 Instrument
The literature review suggested that the e-learning CSF categories are student
characteristics, instructor characteristics and IT. This study proposed support as a fourth
e-learning CSF category. University support at different levels is perceived to be critical
for the e-learning success. Each CSF category was represented by a latent construct that
was observed via a group of indicators. Numerous instruments have been developed to
measure e-learning satisfaction. Therefore, various potential indicators exist to measure
each CSF category. A survey instrument was developed that consisted of six sections,
one for each e-learning CSF category and an additional category for the e-learning
acceptance in addition to a demographic characteristics section.
The instructor characteristics construct section included 13 indicators (INS1-INS13),
which assessed the characteristics of instructors (see Appendix for the indicator details).
Indicators INS1 to INS11 were adopted from Volery and Lord (2000) to capture
instructor’s attitude towards the technology, teaching style and control of the technology.
The last two items INS12 and INS13 were adopted from Soong et al. (2001) to complete
the measurement of the instructor’s teaching style.
Twenty-three indicators were used in assessing the students’ characteristics
construct (STUD1-STUD23). The first two indicators measured the students’ motivation
to use e-learning. Indicators STUD3-STUD7 measured the students’ technical
competency. Items STUD8-STUD10 measured students’ mindsets about e-learning.
Items STUD11-STUD16 measured students’ interactive collaboration. The first 16
indicators were adopted from (Soong et al., 2001). Seven additional indicators were
developed to measure the effectiveness of e-learning course content, structure and design
from student perception (see Appendix for details).
Thirteen indicators were developed to measure the technology reliability, richness,
consistency and effectiveness, which represented the information technology construct.
The first eight indicators (TECH1-TECH8) were adopted from Volery and Lord
(2000). The eight indicators measured the on-campus ease of internet access and
browsing, browsing speed, course websites’ ease of use, user interface efficiency,
student-student communication reliability and student-instructor communication. The last
five items (TECH9-TECH13) were developed to capture the effectiveness of the IT
infrastructure and services available at UAEU. They measured consistency of computers
access using the same authentication, computer network reliability and student
information system efficiency.
The university support section consisted of five items (SUP1-SUP5) and all of them
were developed to capture the effectiveness and efficiency of the university’s technical
support, library services and computer labs reliability. The last section was dedicated
to capturing the perceived acceptance of e-learning by students via four indicators
(ELU1-ELU4).
Some of the items were negatively worded. All items used a 5-point Likert-type scale
of potential responses: strongly agree, agree, neutral, disagree and strongly disagree. The
instrument was pre-tested by a random sample of 70 students. Minor changes to the order
and wording of the items resulted from the pre-testers’ opinions. The survey instruments
were distributed during laptop-based lectures and were left to the students to be filled and
returned later. Around 900 instruments were distributed, 538 usable responses were used
E-learning critical success factors 11
giving a 60% response rate. The students were informed that all data were anonymous
and were to be used in assessing the acceptance of e-learning technology at the university
instruction environment. Table 3 shows the mean and variance of each item in the
e-learning assessment instrument.
Table 3 Descriptive statistics of e-learning CSFs indicators
Item Mean S.D. Item Mean S.D.
INS1 3.82 1.01 STUD1 3.87 1.04
INS2 3.68 1.07 STUD2 3.58 1.06
INS3 4.00 1.02 STUD3 4.05 1.06
INS4 3.99 1.00 STUD4 4.00 1.00
INS5 4.00 0.99 STUD5 3.82 1.01
INS6 3.92 0.97 STUD6 3.96 1.04
INS7 3.94 1.00 STUD7 4.01 1.05
INS8 3.86 1.02 STUD8 3.59 1.013
INS9 3.89 0.98 STUD9 3.73 0.99
INS10 3.91 1.02 STUD10 3.54 1.07
INS11 3.86 1.03 STUD11 Dropped
INS12 3.73 1.03 STUD12 3.22 1.07
INS13 3.87 1.01 STUD13 3.30 1.11
STUD14 3.59 1.01
TECH1 4.18 0.99 STUD15 3.10 1.04
TECH2 3.82 1.13 STUD16 3.57 1.03
TECH3 3.88 0.98 STUD17 3.68 1.00
TECH4 4.05 0.90 STUD18 3.61 1.05
TECH5 3.99 0.88 STUD19 3.68 1.04
TECH6 3.75 0.95 STUD20 3.91 0.96
TECH7 3.96 1.01 STUD21 3.73 1.00
TECH8 4.01 0.96 STUD22 3.84 0.98
TECH9 3.99 1.05 STUD23 3.81 0.94
TECH10 3.95 0.97
TECH11 3.91 1.04 SUP1 4.04 0.96
TECH12 4.13 0.91 SUP2 3.86 0.94
TECH13 3.88 0.98 SUP3 3.85 0.93
SUP4 3.69 1.00
ELU1 3.86 1.15 SUP5 3.73 0.97
ELU2 3.67 1.19
ELU3 3.81 1.09
ELU4 3.88 1.14
12 H.M. Selim
Students were asked to rank the four CSFs. The rating for each factor was placed between
1 and 4. Table 4 shows the rank of the four e-learning CSFs as perceived by students. The
instructor characteristics factor was given the first rank, according to 54% of the surveyed
students, as the most critical success factor for e-learning success. This result came in line
with Collis’s (1995) remark that the instructor plays a centric role in the effectiveness of
IT-enabled learning and it is not the technology but the instructional implementation of
the technology that determines the effects on learning. Student characteristics factor came
fourth as the least critical factor for the success of e-learning courses as perceived by
47% of the surveyed students. This rank was dependent on the considered characteristics.
The university support factor was ranked third in the criticality level with 31% of the
surveyed students. The second rank was given to the technology factor.
Table 4 E-learning CSFs ranking
CSF 1 2 3 4 Average
INS 54% (270) 23% (113) 18% (91) 5% (26) 1.74
STD 10% (48) 22% (112) 21% (107) 47% (233) 3.05
TECH 27% (137) 25% (123) 30% (148) 18% (92) 2.39
SUP 9% (45) 30% (152) 31% (154) 30% (149) 2.82
4 Exploratory factor analysis
4.1 Results
Exploratory factor analysis was conducted to identify the underlying critical indicators in
each of the e-learning CSF categories (instructor characteristics, student characteristics,
technology and university support). The same factor analysis was used to validate the
e-learning CSFs categories. LISREL version 8.52 was used to develop the polychoric
correlation and asymptotic covariance matrices used in generating the factor loadings
because all the items were represented by ordinal variables. An oblique factor rotation
method was selected because the ultimate goal of the factor analysis was to obtain several
theoretically meaningful factors. Promax oblique factor rotation method was provided by
Lisrel. Table 5 shows the output results for the Promax-rotated factor loadings. Items
intended to measure the same e-learning CSFs must demonstrate a factor loading of
> 0.50. All factor loadings below 0.5 were ignored.
The 13 items (INS1-INS13) proposed to measure the instructor characteristics
construct as a critical factor of e-learning success were highly correlated with it, as
indicated by the factor loading values of > 0.70 in Table 5. This testifies to the validity of
the indicators used to capture the instructor characteristics factor. The items comprised
in this factor were related to the instructor’s attitude towards students, e-learning skills
literacy and ability to encourage students to interact and ask questions.
E-learning critical success factors 1
3
Table 5 Factor loadings
Item INS ST-COMP ST-COLL ST-CONT TECH SUP ELU
INS1
0.67
0.07 0.07 –0.06 0.10 –0.03 0.04
INS2
0.82
0.00 –0.03 –0.03 –0.01 –0.07 0.10
INS3
0.79
0.06 –0.05 –0.04 0.02 0.08 –0.06
INS4
0.84
–0.04 –0.05 –0.02 0.07 0.06 –0.01
INS5
0.85
0.03 –0.08 –0.02 0.02 0.05 –0.04
INS6
0.84
0.01 0.04 0.01 –0.02 0.01 –0.07
INS7
0.73
0.06 0.01 0.13 –0.04 0.02 –0.06
INS8
0.74
–0.01 0.02 0.14 0.00 –0.01 –0.02
INS9
0.71
0.00 0.04 0.12 0.09 –0.03 –0.06
INS10
0.87
–0.08 0.05 –0.04 0.01 0.00 0.04
INS11
0.87
0.02 0.08 –0.13 –0.02 0.00 0.05
INS12
0.89
–0.02 0.00 0.03 –0.03 –0.04 0.06
INS13
0.85
0.02 –0.02 0.01 0.02 –0.05 0.05
STD1 –0.03
0.87
0.08 –0.01 –0.04 0.01 0.01
STD2 0.08
0.73
0.10 0.01 –0.04 0.03 0.02
STD3 0.02
0.85
–0.04 0.07 0.01 –0.08 0.03
STD4 –0.01
0.74
0.00 0.07 0.11 0.00 0.01
STD5 0.00
0.95
0.01 –0.05 0.04 –0.05 –0.13
STD6 –0.01
0.89
–0.02 0.06 0.06 0.01 –0.14
STD7 0.01
0.77
–0.02 0.03 0.03 –0.05 0.10
STD8 0.03
0.77
–0.06 –0.06 0.02 0.01 0.08
STD9 –0.01
0.64
–0.01 0.08 0.00 –0.02 0.21
STD10 0.00
0.62
0.04 0.03 –0.08 0.10 0.11
STD12 –0.07 0.04
0.83
–0.04 0.04 –0.07 –0.06
STD13 –0.04 0.05
0.85
–0.01 –0.05 –0.01 0.04
STD14 0.15 –0.03
0.75
0.00 –0.03 0.02 –0.03
STD15 –0.06 0.03
0.75
–0.02 0.01 0.01 0.01
STD16 0.16 –0.07
0.69
0.12 –0.04 0.06 –0.02
STD17 0.06 0.01 0.12
0.66
–0.02 0.05 0.05
STD18 0.02 0.07 0.05
0.66
0.06 –0.01 –0.01
STD19 –0.04 0.09 0.09
0.70
0.03 –0.02 –0.07
STD20 0.02 0.01 –0.05
0.76
0.08 –0.01 0.03
STD21 –0.03 0.04 –0.04
0.73
0.08 0.03 –0.01
STD22 0.07 0.00 –0.04
0.74
0.03 0.11 –0.11
STD23 0.00 0.08 –0.04
0.73
0.04 0.03 0.09
Following
Inderscience
style these
should be set
as italics.
14 H.M. Selim
Table 5 Factor loadings (continued)
INS ST-COMP ST-COLL ST-CONT TECH SUP ELU
TEC1 0.01 0.05 –0.06 –0.07
0.82
0.07 –0.02
TEC2 0.02 0.07 –0.07 –0.03
0.77
0.05 –0.09
TEC3 0.01 –0.05 –0.05 –0.03
0.86
–0.02 –0.08
TEC4 0.05 0.01 –0.05 0.24
0.75
–0.09 –0.02
TEC5 0.07 –0.09 –0.09 0.37
0.67
–0.09 0.06
TEC6 0.11 –0.12 0.13 0.17
0.57
–0.02 0.02
TEC7 –0.07 0.04 0.12 –0.01
0.61
–0.07 0.13
TEC8 –0.02 –0.07 0.09 0.10
0.60
–0.03 0.16
TEC9 –0.01 0.01 0.09 0.00
0.61
0.03 –0.01
TEC10 0.06 0.06 0.00 –0.07
0.67
0.03 –0.01
TEC11 0.00 0.07 –0.04 –0.14
0.78
0.13 –0.06
TEC12 –0.07 0.06 0.03 –0.01
0.65
0.03 0.09
TEC13 0.06 0.04 0.07 0.00
0.55
0.22 –0.01
SUP1 –0.02 0.04 0.00 0.02 0.14
0.71
0.01
SUP2 0.06 –0.06 0.02 0.08 0.09
0.72
–0.02
SUP3 –0.05 0.09 0.10 0.00 0.19
0.65
0.03
SUP4 –0.03 –0.06 –0.06 0.07 0.17
0.75
–0.03
SUP5 0.03 –0.03 –0.04 0.01 0.10
0.71
0.10
ELU1 –0.02 0.26 0.06 0.02 0.05 0.02
0.65
ELU2 0.02 0.27 –0.07 0.04 0.02 0.01
0.64
ELU3 0.04 0.25 –0.04 0.02 –0.03 0.07
0.74
ELU4 0.02 0.26 0.02 –0.04 0.03 0.00
0.78
Three factors emerged from the exploratory factor analysis applied to the 23 indicators
used in measuring student characteristics construct. All items correlation values
(loadings) with the identified factors were > 0.60. The first student factor (ST-COMP)
comprised the first ten indicators of student characteristics (STD1-STD10). All ten items
were related to students’ computer competency and their ability to use and promote
computing technology as it is applied to learning (see Table 5). The second student factor
(ST-COLL) comprised five items (STD12-STD16); all the five indicators were related
to the different types of interactive collaborations, which include student-student and
student-instructor collaborations. Item STUD11 was dropped from any further analysis
because it did not load on any e-learning CSF. All factor loadings of the ST-COLL
factor were > 0.65, which indicated high validity of the factor structure. The third
factor (ST-CONT) comprised the last seven indicators (STD17-STD23). All the seven
indicators were related to e-learning course content and design. All factor loadings were
> 0.65. In general, the student characteristics as a factor contributing to the success of
e-learning initiatives at the university was split into three factors capturing the students’
perceptions about computing literacy, interactive collaboration and e-learning-based
course content.
E-learning critical success factors 1
5
The technology factor of e-learning success was measured by 13 indicators, all of them
loaded with correlations of values > 0.50. The indicators used in the technology factor
were related to the ease of technology access and navigation, visual technology interface,
and the IT infrastructure reliability and effectiveness. Most of the student responses
to the 13 technology items were positive. The students were mostly satisfied with
the on-campus internet access, course websites available via Blackboard, and online
course registration.
The university support factor is the second wing of the technology factor and was
measured using five indicators; all of them had factor loadings of ≥ 0.65. All the items
were related to university support to e-learning initiatives available. Students were,
generally, satisfied with university support. The last factor was related to the students’
usage of e-learning courses as an indicator of e-learning success at the university. The
E-Learning Usage (ELU) factor was measured by four indicators, all of which had high
factor loading values of ≥ 0.64. The ELU factor included the intention of registering in
future e-learning-based courses and the students’ perception about e-learning in general.
Finally, it can be concluded that the indicators used in e-learning CSFs assessment
instrument truly represented the concepts of interest.
E-learning CSFs assessment instrument’s reliability was measured using Cronbach
alpha. Table 6 shows Cronbach alpha values for the seven e-learning CSFs that emerged
from the factor analysis given in Table 5. The suggested accepted value of Cronbach
alpha is ≥ 0.70 (Hair et al., 1998). All factors exhibited a high degree of internal
consistency as the alpha values were ≥ 0.87. It was concluded that the indicators could
be used to measure the factors with acceptable reliability. The average variance extracted,
which reflects the overall amount of variance in the items accounted for by the factor.
The average variance extracted is more conservative than Cronbach alpha as a composite
reliability measure and its accepted value is 0.5 or above (Fornell and Larcker, 1981). As
shown in Table 6, all the average extracted variance values are ≥ 0.69. Average
extracted variance can be used to evaluate the discriminant validity. The square root of
the average extracted variance for each factor should be greater than the correlations
between that factor and all the other factors (Fornell and Larcker, 1981). Table 7 shows
the correlation matrix of the e-learning CSFs and the square root of the average extracted
variance. The discriminant validity does not reveal any problems.
Table 6 E-learning CSFs instrument reliability
CSFs Cronbach alpha Variance extracted
INS 0.95 0.81
ST-COMP 0.95 0.79
ST-COLL 0.87 0.78
ST-CONT 0.90 0.71
TECH 0.92 0.69
SUP 0.90 0.71
ELU 0.93 0.71
16 H.M. Selim
Table 7 Correlation matrix of e-learning CSFs
Factor INS ST-COMP ST-COLL ST-CONT TECH SUP ELU
INS 0.90*
ST-COMP 0.37 0.89*
ST-COLL 0.41 0.39 0.88*
ST-CONT 0.51 0.50 0.47 0.84*
TECH 0.46 0.45 0.37 0.57 0.83*
SUP 0.35 0.35 0.30 0.48 0.57 0.84*
ELU 0.28 0.53 0.35 0.45 0.43 0.38 0.84*
Note: * Square root of the average extracted variance
4.2 Discussion
Through the above results analysis, four e-learning CSFs and an e-learning usage
construct were validated. Empirical results came in support with previous research about
instructor characteristics as one of the e-learning CSFs (Baylor and Ritchie, 2002; Dillon
and Guawardena, 1995; Leidner and Jarvenpaa, 1993; Volery and Lord, 2000). The
following recommendations have emerged from the instructor characteristics exploratory
factor analysis:
• Instructors should be enthusiastic about teaching e-learning-based courses in order to
motivate the students.
• Instructors should be able to handle the technological tools used in e-learning-based
courses, such as e-mail, e-discussion and website maintenance.
• Instructors should show genuine interest level in the students by replying to their
e-mails promptly and allow them to actively contribute to the course content.
• Instructors should rely on e-learning tools, such as online exams, posting
e-announcements, and attract the students to rely on e-learning tools embedded
in the course.
Exploratory factor analysis results and students’ perception about ranking the instructor
characteristics factor as the most critical factor for the success of e-learning-based
courses should encourage higher education institutions to carefully plan short- and
long-term plans for faculty development. This simply indicates that the instructor is the
main key to successful e-learning-based courses in higher education institutions from the
students’ perspective.
The empirical evidence presented in the previous subsection supported previous
research conclusions about student characteristics and skills required to make e-learning
initiatives successful (Baylor and Ritchie, 2002; Benigno and Trentin, 2000; Dillon and
Guawardena, 1995; Leidner and Jarvenpaa, 1993). The recommendations concluded from
student characteristics exploratory factor analysis can be summarised as follows:
• Students should gain a high level of computing competency. They should master
application, such as e-mail, presentation and communication, creative thinking and
all the software applications needed to enhance the e-learning process.
E-learning critical success factors 1
7
• Students should be aware of the differences between learning by construction and
learning by absorption in order to value the e-learning tools.
• Students should rely on e-tools embedded in e-learning course, such as e-mail,
e-discussion, virtual classroom, collaboration and active role in class.
• Students like to attend class. This should motivate the higher education institutions to
promote synchronous e-learning components.
• Most of the student responses to the factor items were positive, indicating a
satisfaction with e-learning-based course content, structure and design.
Most of the surveyed students had been exposed before to computing skills and
e-learning experiences (see demographic data in Table 2). Students’ critical
characteristics were divided into three main categories:
1 student computer competency
2 student attitude towards interactive collaborations with instructors and other students
3 student attitude towards the e-learning course content.
Student computer competency factor included student’s motivation to use e-learning and
the approach that best suits him/her, such as learning by construction or absorption. This
factor supported Soong et al.’s (2001) results about the computer competency of both
students and instructors. Student collaboration factor indicated that the more interactions
the students get exposed to, the more opportunities they have to learn. The e-learning
resources, such as online discussion forums can play a mediating role for collaboration
among students. The students’ attitude towards course content factor captured their
perception about the interactivity, efficiency and effectiveness of Blackboard as a course
management system used by the university as an e-learning resources management tool.
Course management systems made it possible to hold online discussions among student
group members, virtual classrooms and many other e-learning resources. The availability
and timeliness of course materials and e-learning course components were tested by
this factor.
The e-learning information technologies and platforms were addressed by all
researchers as listed in Table 1, which indicated how critical this factor is to
e-learning success. The technology factor empirical analysis revealed the following
recommendations, i.e., higher education institutions should:
• Provide students with easy on-campus access to the web.
• Install enough bandwidth in order to have fast enough web browsing.
• Install a campus-wide single student authentication in order to have access to his/her
data from anywhere in the campus.
• Develop an effective information technology infrastructure that should consist of
highly reliable networking facilities, course management system, student information
system and medium richness.
18 H.M. Selim
Institution support to e-learning was addressed by Benigno and Trentin (2000) and
Govindasamy (2002) as one of the e-learning success CSFs. This study supported
and validated the criticality of the institution support. The validated institution support
components included library services, help desk, computer labs and facilities. The
empirical results extended their validation to testing e-learning success by measuring its
usage. Students indicated positively that they will register in e-learning-based courses in
the future, which indicated a positive attitude towards using the e-learning technology.
5 Conclusion and future work
IT and intense competition are reshaping higher education institutions worldwide.
E-learning has been integrated in several higher education institutions. Consequently,
several adoption-related factors must be carefully evaluated before any adoption attempt
is made by universities and instructors. The adoption of e-learning system is a complex
process of establishing and developing an integrated information technology system. This
study, in line with the literature, identified and measured four critical factors that assist
universities and instructors to adopt e-learning systems. CSFs, which were identified
and measured from student perceptions, included instructor characteristics, student
characteristics (computer competency, interactive collaboration, and course content and
design), technology and support. The four CSFs impact the decision to adopt e-learning
technology in higher education institutions.
A sample of 37 class sections with 900 enrolled students was used to identify and
measure the proposed e-learning CSFs. The students perceived the four factors as
critical success factors in e-learning. The surveyed students indicated that instructor
characteristics factor is the most critical factor followed by support and technology. The
student characteristics factor was perceived as the least critical factor to the success of
e-learning. The students indicated that when a higher education institution attempts to
adopt e-learning-based courses, the following factors should be critically considered:
• Instructors should have sufficient computing skills and enthusiasm in order to
motivate the students.
• Construction of faculty development plans at both the short and long term to
enhance and improve their technology-related skills and interactive learning
different methods.
• Development of student orientation programmes to introduce them to the different
teaching and learning styles using e-learning.
• Enhancement of students’ computing literacy and e-learning applications skills
(e-mail, presentation and creative thinking)
• Construction of an effective information technology infrastructure in order
to facilitate fast web access, e-mail, course management system and other
e-learning services.
• Establishment of e-learning support services.
E-learning critical success factors 1
9
All indicators of the instructor characteristics factor were important and significant
measures. Students perceived the instructor’s enthusiasm, presentation style, friendliness
and interest in students as important and critical factors of e-learning success. Students
perceived instructors’ technical skills as significant to the e-learning success, which came
in line with the results of a study by Demetriadis et al. (2003). Students’ characteristics,
as perceived by the students themselves, were split into three subfactors: student
computing literacy, student interactive collaboration and e-learning course content and
design. Students showed a positive attitude towards e-learning, indicating that “e-learning
encouraged us to search for more facts and participate more actively in the class
than traditional learning methods”. This came in support to the results of another study by
Beyth-Marom et al. (2003).
In the technological dimension, reliability of the IT infrastructure, as perceived by
surveyed students, was very important to the success of e-learning. The critical indicators
were internet accessibility, user interfaces, authentication consistency, student
information system and networking. The university support to students enrolled in
e-learning-based courses was viewed as a critical success factor. Support was not limited
to technical assistance and troubleshooting, but included library and information
availability. Students indicated that they would register in future e-learning-based
courses, assuring their positive attitude and support to e-learning technology and tools.
This study explored the students’ perceptions in identifying and measuring e-learning
critical success factors within a university environment. There is a need to explore
instructors’ perceptions about e-learning CSFs and contrast both instructors’ and
students’ perceptions. Further study can expand on this research to develop a causal
research model that includes all the seven constructs (INS, ST-COMP, ST-COLL,
ST-CONT, TECH, SUP and ELU). The objective of the causal research model would be
to study the effects of the first six factors on e-learning acceptance as indicated by ELU.
The proposed research model can generate causal relationships among the seven CSFs.
Even though this study developed a general instrument for identifying and measuring
e-learning critical success factors as perceived by students, it has some limitations that
could be addressed in future work. First, this study examines a specific context of
e-learning usage. There is a need to test the generalisation of the results of this work to
other forms of e-learning and distance learning contexts. Second, all indicators used in
this study were self-reported rather than observed. There is a need to check the impact of
this on the results obtained. Third, other populations need to be tested in order to validate
the sample size and nature.
In conclusion, this study investigated the critical factors affecting e-learning
technology adoption by universities from students’ perspective. The factors identified and
measured in this study can assist higher education institutions in increasing the efficiency
and effectiveness of the adoption process.
Acknowledgement
My thanks and appreciation to the Research Affairs at UAE University for funding this
research under research grant code of 05-4-11/02.
20 H.M. Selim
References
Akar, E., Ozturk, E., Tuncer, B. and Wiethoff, M. (2004) ‘Evaluation of a collaborative
virtual learning environment’, Education + Training, Emerald Group, Vol. 46, Nos. 6–7,
pp.343–352.
Al Rawas, A. (2001) ‘The challenge of new learning technologies for higher education in Oman.
Towards a national e-learning system: potential and challenges’, Paper Presented at the
University of the 21st Century UNESCO International Conference, Muscat, Oman.
Aldexander, S., McKemzie, J. and Geissinger, H. (1998) An Evaluation of Information
Technology Projects for University Learning, http://www.dest.gov.au/archive/cutsd/
publications/exsummary.html.
Alexander, S. (2001) ‘E-learning development and experiences’, Education and Training, Vol. 43,
Nos. 4–5, pp.240–248.
Alexander, S. and McKenzie, J. (1998) An Evaluation of Information Technology Projects in
University Learning, Canberra, Australia: Department of Employment, Education and
Training and Youth Affairs, Australian Government Publishing Services.
Baker, W., Hale, T. and Gifford, B.R. (1997) ‘From theory to implementation: the mediated
learning approach to computer-mediated instruction, learning and assessment’, Educom
Review, Vol. 32, No. 5.
Baylor, A.L. and Ritchie, D. (2002) ‘What factors facilitate teacher skill, teacher morale and
perceived student learning in technology-using classrooms?’, Computers & Education,
Vol. 39, pp.395–414.
Benigno, V. and Trentin, G. (2000) ‘The evaluation of online courses’, Journal of
Computer-Assisted Learning, Vol. 16, pp.259–270.
Beyth-Marom, R., Chajut, E., Roccas, S. and Sagiv, L. (2003) ‘Internet-assisted versus traditional
distance learning environments: factors affecting students’ preferences’, Computers &
Education, Vol. 41, No. 1, pp.65–76.
Brook Hall, T. and Concannon, F. (2003) ‘Curriculum development CSFs for eLearning
implementation’, Paper Presented at the International Conference on Computers in Education
– Changing Face of HE in the 21st Century: Critical Success Factors (CSFs) for
Implementing eLearning Workshop, Auckland, New Zealand.
Brown, G.A., Bakhter, M. and Youngman, M.B. (1982) Towards a Typology of Lecturing Styles,
Nuttingham, UK: University of Nottingham.
Chuang, Y.R. (1999) ‘Teaching in a multimedia computer environment: a study of the effects of
learning style, gender, and math achievement’, Interactive Multimedia Electronic Journal of
Computer-Enhanced Learning, Vol. 1, No. 1.
Collis, B. (1995) ‘Anticipating the impact of multimedia in education: lessons from the literature’,
Computers in Adult Education and Training, Vol. 2, No. 2, pp.136–149.
Coman, P. (2003) ‘Critical success factors for eLearning delivery’, Paper Presented at the
International Conference on Computers in Education – Changing Face of HE in the 21st
Century: Critical Success Factors (CSFs) for Implementing eLearning Workshop, Auckland,
New Zealand.
Cooke, T. and Veach, I. (1997) ‘Enhancing the learning outcome of university distance education:
an Australian perspective’, International Journal of Educational Management, Vol. 11, No. 5,
pp.203–208.
Cross, J. (2004) ‘An informal history of eLearning’, On the Horizon, Emerald Group Publishing
Limited, Vol. 12, No. 3, pp.103–110.
Currier, S. and Campell, L. (2003) ‘Learning technologies critical success factors for eLearning
implementation: educational technology interoperability standards’, Paper Presented at
the International Conference on Computers in Education – Changing Face of HE in the
21st Century: Critical Success Factors (CSFs) for Implementing eLearning Workshop,
Auckland, New Zealand.
E-learning critical success factors 21
Daniel, J.S. (1997) ‘Why universities need technology strategies’, Change, Vol. 29, No. 4,
pp.11–17.
Demetriadis, S., Babas, A., Tsoukalas, I. and Palaigeorgiou, G. (2003) ‘“Cultures in negotiation”:
teachers’ acceptance/resistance attitudes considering the infusion of technology into schools’,
Computers & Education, Vol. 41, No. 1, pp.19–37.
Dillon, C.L. and Guawardena, C.N. (1995) ‘A framework for the evaluation of
telecommunications-based distance education’, Paper Presented at the 17th Congress of the
International Council for Distance Education, Open University, Milton Keynes.
Driver, M. (2002) ‘Exploring student perceptions of group interaction and class satisfaction in the
web-enhanced classroom’, The Internet and Higher Education, Vol. 5, No. 1, pp.35–45.
Dunken, M.J. (1983) ‘A review of research on lecturing’, Higher Education Research and
Development, Vol. 2, No. 1, pp.63–78.
Flood, J. (2004) ‘Successful online learning – the five Ps’, Turkish Online Journal of Distance
Education, Vol. 5, No. 2, pp.1–3.
Fornell, C. and Larcker, D. (1981) ‘Evaluating structural equation models with unobservable
variable and measurement error’, Journal of Marketing Research, Vol. 18, pp.39–50.
Freberg, L. (2000) ‘Integrating internet resources into the higher education classroom’, Syllabus,
Vol. 13, No. 7, pp.48–50.
Freund, Y.P. (1988) ‘Critical success factors’, Planning Review, Vol. 16, No. 4, pp.20–25.
Fulford, C.P. and Zhang, S. (1993) ‘Perceptions of interaction: the critical predictor in distance
education’, American Journal of Distance Education, Vol. 7, No. 3, pp.8–21.
Gotthardt, M., Siegert, M.J., Schlieck, A., Schneider, S., Kohnert, A., Gro[beta], M.W., et al.
(2006) ‘How to successfully implement e-learning for both students and teachers’, Academic
Radiology, Vol. 13, No. 3, pp.379–390.
Govindasamy, T. (2002) ‘Successful implementation of eLearning: pedagogical considerations’,
The Internet and Higher Education, Vol. 4, Nos. 3–4, pp.287–299.
Graham, M. and Scarbrough, H. (1999) ‘Computer-mediated communication and collaborative
learning in an undergraduate distance education environment’, Australian Journal of
Educational Technologies, Vol. 15, No. 1, online.
Gunasekaran, A.M., Donald D. and Shaul, D. (2002) ‘E-learning: research and applications’,
Industrial and Commercial Training, Vol. 34, No. 2, pp.44–53.
Hair, J., Anderson, R., Tatham, R. and Black, W. (1998) ‘Multivariate data analysis’, 5th ed.,
Prentice Hall.
Hayes, E. (1990) ‘Adult education: context and challenge for distance educators’, American
Journal of Distance Education, Vol. 4, No. 1, pp.25–38.
Helmi, A. (2002) ‘An analysis on impetus of online education Curtin University of Technology,
Western Australia’, The Internet and Higher Education, Vol. 4, No. 3, pp.243–253.
Henry, P. (2001) ‘E-learning technology, content and services’, Education and Training, Vol. 43,
No. 4, pp.249–255.
Hong, K-S. (2002) ‘Relationships between students’ and instructional variables with satisfaction
and learning from a web-based course’, The Internet and Higher Education, Vol. 5,
pp.267–281.
Human, S.E. and Kilboume, L.M. (1999) ‘Using web-based instruction in an interpersonal skills
course’, Journal of Management Education, Vol. 23, No. 5.
Ingram, H., Biermann, K., Cannon, J., Neil, J. and Waddle, C. (2000) ‘Internalizing action learning:
a company perspective. Establishing critical success factors for action learning courses’,
International Journal of Contemporary Hospitality Management, Vol. 12, No. 2, pp.107–113.
Katz, Y. (2002) ‘Attitudes affecting college students’ preferences for distance learning’, Journal of
Computer Assisted Learning, Vol. 18, No. 1, pp.2–9.
22 H.M. Selim
Kaynama, S.A. and Keesling, G. (2000) ‘Development of a web-based internet marketing course’,
Journal of Marketing Education, Vol. 22, No. 2.
Le Grew, D. (1995) Global Knowledge: Superhighway or Super Gridlock: Applications
of Media and Technology in Higher Education, National Institute of Multimedia Education,
Chiba, Japan.
Lee, J., Hong, N.L. and Ling, N.L. (2002) ‘An analysis of student’s preparation for the virtual
learning environment’, The Internet and Higher Education, Vol. 4, No. 3, pp.231–242.
Leidner, D.E. and Jarvenpaa, S.L. (1993) ‘The information age confronts education: case studies on
electronic classrooms’, Information Systems Research, Vol. 4, No. 1, pp.24–54.
Leon, R.V. and Par, W.C. (2000) ‘Use of home pages in teaching statistics’, American Statisticians,
Vol. 54, No. 1.
Liaw, S-S., Huang, H-M. and Chen, G-D. (2006) ‘Surveying instructor and learner attitudes
towards eLearning’, Computers & Education, in press.
Lockyer, L., Patterson, J. and Harper, B. (2001) ‘ICT in higher education: evaluating outcomes for
health education’, Journal of Computer Assisted Learning, Vol. 17, pp.275–283.
Lu, J., Yu, C-S. and Liu, C. (2002) ‘Learning style, learning patterns, and learning performance in a
WebCT-based MIS course, Information & Management, in press.
McPherson, M. (2003a) ‘Organizational critical success factors for managing the implementation
of eLearning in higher education’, Paper Presented at the International Conference on
Computers in Education – Changing Face of HE in the 21st Century: Critical Success Factors
(CSFs) for Implementing eLearning Workshop, Auckland, New Zealand.
McPherson, M. (2003b) ‘The ICCE 2002 CSF workshop: the changing face of HE in the 21st
century: critical success factors for implementing eLearning’, Paper Presented at the
International Conference on Computers in Education – Changing Face of HE in the
21st Century: Critical Success Factors (CSFs) for Implementing eLearning Workshop,
Auckland, New Zealand.
McPherson, M. and Nunes, M. (2003) ‘A framework to support elearning management’, Paper
Presented at the International Conference on Computers in Education – Changing Face of HE
in the 21st Century: Critical Success Factors (CSFs) for Implementing eLearning Workshop,
Auckland, New Zealand.
Milliken, J. and Barnes, L.P. (2002) ‘Teaching and technology in higher education: student
perceptions and personal reflections’, Computers & Education, Vol. 39, No. 3, pp.223–235.
Moore, M.G. and Kearsley, G. (1996) Distance Education – A Systems View, Belmont, CA:
Wadsworth Publishing.
Nunes, M. (2003) ‘Instructional design CSFs for eLearning’, Paper Presented at the International
Conference on Computers in Education – Changing Face of HE in the 21st Century: Critical
Success Factors (CSFs) for Implementing eLearning Workshop, Auckland, New Zealand.
Nunes, M. and McPherson, M. (2003) ‘Learning support in constructivist eLearning environment’,
Paper Presented at the International Conference on Computers in Education – Changing
Face of HE in the 21st Century: Critical Success Factors (CSFs) for Implementing eLearning
Workshop, Auckland, New Zealand.
Oliver, R. and Omari, A. (2001) ‘Student responses to collaborating and learning in a web-based
environment’, Journal of Computer Assisted Learning, Vol. 17, No. 1, pp.34–47.
Ong, C-S. and Lai, J-Y. (2006) ‘Gender differences in perceptions and relationships among
dominants of e-learning acceptance’, Computers in Human Behavior, Vol. 22, pp.816–829.
Papp, R. (2000) ‘Critical success factors for distance learning’, Paper Presented at the Americas
Conference on Information Systems, Long Beach, California, USA.
Patterson, G. (1999) ‘The learning university’, The Learning Organization, Vol. 6, No. 1, pp.9–17.
Pennington, G. (1994) Improving Learning in Higher Education: A Case Study in Designing
an Initial Professional Development Program for Academic Staff, Committee of
Vice-Chancellors and Principals of the Universities of the United Kingdom, Universities Staff
Development Unit, UK.
E-learning critical success factors 2
3
Phillips, M.R. and Peters, M.J. (1999) ‘Targeting rural students with distance learning courses:
a comparative study of determinant attributes and satisfaction levels’, Journal of Education for
Business, Vol. 74, No. 6, pp.351–356.
Ramsden, P. (1992) Learning to Teach in Higher Education, London, UK: Routledge.
Riddy, P. and Fill, K. (2003) ‘Technological CSFs for eLearning implementation’, Paper Presented
at the International Conference on Computers in Education – Changing Face of HE in the
21st Century: Critical Success Factors (CSFs) for Implementing eLearning Workshop.
Ritchie, H. and Newby, T.J. (1989) ‘Classroom lecture/discussion vs. live television instruction:
a comparison of effects on student performance, attitude and interaction’, American Journal of
Distance Education, Vol. 3, No. 3, pp.36–45.
Rockart, J.F. (1979) ‘Chief executives define their own needs’, Harvard Business Review, Vol. 57,
No. 2, pp.81–93.
Rosenberg, M.J. (2001) E-learning, Strategies for Delivering Knowledge in the Digital Age,
New York: McGraw Hill.
Ruiz-Mercader, J., Merono-Cerdan, A.L. and Sabater-Sanchez, R. (2006) ‘Information technology
and learning: their relationship and impact on organizational performance in small businesses’,
International Journal of Information Management, Vol. 26, No. 1, pp.16–29.
Selim, H.M. (2003) ‘An empirical investigation of student acceptance of course websites’,
Computers & Education, Vol. 40, No. 4, pp.343–360.
Selim, H.M. (2005) ‘Videoconferencing-mediated instruction: success model’, Journal of Distance
Education Technologies, Vol. 3, No. 1, pp.62–80.
Soong, B.M.H., Chan, H.C., Chua, B.C. and Loh, K.F. (2001) ‘Critical success factors for on-line
course resources’, Computers & Education, Vol. 36, No. 2, pp.101–120.
Tuckman, B.W. (2002) ‘Evaluating ADAPT: a hybrid instructional model combining web-based
and classroom components’, Computers & Education, Vol. 39, pp.261–269.
Volery, T. and Lord, D. (2000) ‘Critical success factors in online education’, The International
Journal of Educational Management, Vol. 14, No. 5, pp.216–223.
Vrasidas, R. (1999) ‘Factors influencing interaction in an on line course’, American Journal of
Distance Education, Vol. 13, No. 3, pp.22–36.
Wang, Y. (2003) ‘Assessment of learner satisfaction with asynchronous electronic learning
systems’, Information & Management, Vol. 41, No. 1, pp.75–86.
Wang, Y-S., Wang, H-Y. and Shee, D.Y. (in press) ‘Measuring e-learning systems success in an
organizational context: scale development and validation’, Computers in Human Behavior,
Corrected proof.
Webster, J. and Hackley, P. (1997) ‘Teaching effectiveness in technology-mediated distance
learning’, Academy of Management Journal, Vol. 40, No. 6, pp.1282–1309.
Welsh, E., Wanberg, C., Brown, K. and Simmering, M. (2003) ‘E-learning: emerging uses,
empirical results and future directions’, International Journal of Training and Development,
Vol. 7, No. 4, pp.245–258.
Willis, B. (Ed.) (1994) ‘Enhancing faculty effectiveness in distance education’, Distance
Education: Strategies and Tools, Englewood Cliffs, NJ: Educational Technology
Publications, Inc.
24 H.M. Selim
Appendix E-learning CSFs instrument
Instructor characteristics (INS)
INS1 The instructor is enthusiastic about teaching the class
INS2 The instructor’s style of presentation holds my interest
INS3 The instructor is friendly towards individual students
INS4 The instructor has a genuine interest in students
INS5 Students felt welcome in seeking advice/help
INS6 The instructor encourages student interaction
INS7 The instructor handles the e-learning units effectively
INS8 The instructor explains how to use the e-learning components
INS9 I feel the instructor is keen that we use the e-learning-based units
INS10 We were invited to ask questions/receive answers
INS11 We were encouraged to participate in class
INS12 The instructor encourages and motivates me to use e-learning
INS13 The instructor is active in teaching me the course subjects via e-learning
Student characteristics (STUD)
STUD1 E-learning encourages me to search for more facts than the traditional
methods.
STUD2 E-learning encourages me to participate more actively in the discussion than
the traditional methods
STUD3 I enjoy using personal computers
STUD4 I use the personal computers for work and play
STUD5 I was comfortable with using the PC and software applications before I took
up the e-learning-based courses
STUD6 My previous experience in using the PC and software applications helped me
in the e-learning-based courses
STUD7 I am not intimidated by using the e-learning-based courses
STUD8 I learn best by absorption (sit still and absorb)
STUD9 I learn best by construction (by participation and contribution)
STUD10 I learn better by construction than absorption
E-learning critical success factors 2
5
STUD11 I do not read/participate in the discussion group
STUD12 I only read messages in the discussion group
STUD13 I do read, as well as participate in the discussion group
STUD14 The instructor initiated most of the discussion
STUD15 The students initiated most of the discussion
STUD16 The instructor participated actively in the discussion
STUD17 I found the instructions on using the e-learning components to be
sufficiently clear
STUD18 I found the course content to be sufficient and related to the subject
STUD19 It was easy to understand the structure of the e-learning components
STUD20 It was easy to navigate through the Blackboard/course web
STUD21 The e-learning components was available all the time
STUD22 The course materials were placed online in a timely manner
STUD23 I perceive the design of the e-learning components to be good
Technology (TECH)
TECH1 Easy on-campus access to the internet
TECH2 Did not experience problems while browsing
TECH3 Browsing speed was satisfactory
TECH4 Overall, the website was easy to use
TECH5 Information was well structured/presented
TECH6 I found the screen design pleasant
TECH7 I could interact with classmates through the web
TECH8 I could easily contact the instructor
TECH9 I can use any PC at the university using the same account and password
TECH10 I can use the computer labs for practicing
TECH11 I can rely on the computer network
TECH12 I can register courses online using Banner
TECH13 Overall, the information technology infrastructure is efficient
26 H.M. Selim
Support (SUP)
SUP1 I can access the central library website and search for materials
SUP2 I can get technical support from technicians
SUP3 I think that the UAEU e-learning support is good
SUP4 There are enough computers to use and practice
SUP5 I can print my assignments and materials easily
E-learning usage/acceptance (ELU)
ELU1 I intend to register in courses that use e-learning methods
ELU2 E-learning is a failure and a bad idea
ELU3 E-learning is an effective method of learning
ELU4 I like the idea of using e-learning
