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Purpose – The purpose of this paper is to investigate the factors that determine the acceptance of the WebCT learning system among students of the faculties of Business and Education Sciences at the University of Huelva, and to verify the direct and indirect effects of these factors. Design/methodology/approach – A total of 226 students at the University of Huelva completed a survey questionnaire measuring their responses to six constructs which explain the system usage in the context of e‐learning: technical support (TS); computer self‐efficacy (CSE); perceived ease of use (PEOU); perceived usefulness (PU); attitude (A); and system usage (SU). Structural equation modelling (SEM) was employed for modelling and data analysis. Findings – The most significant results point to the need to rethink the original structural model in terms of the relations of certain variables, although the authors also establish the importance of the direct effect of technical support on perceived ease of use and perceived usefulness among the students. The authors also confirm that WebCT usage and acceptance is directly influenced by perceived usefulness and indirectly by perceived ease of use. Originality/value – The findings in this study have implications for the virtual learning systems managers at the University of Huelva, and for other universities that use online tuition systems. This paper reflects a lack of technical support which students need to use WebCT more efficiently and shows that training courses and technical assistance for students must be extended.
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E-learning and the University of
Huelva: a study of WebCT and the
technological acceptance model
R. Arteaga Sa
´nchez
Department of Financial Economics and Accounting, University of Huelva,
Huelva, Spain
A. Duarte Hueros
Department of Education, University of Huelva, Huelva, Spain, and
M. Garcı
´a Ordaz
Department of Financial Economics and Accounting, University of Huelva,
Huelva, Spain
Abstract
Purpose – The purpose of this paper is to investigate the factors that determine the acceptance of the
WebCT learning system among students of the faculties of Business and Education Sciences at
the University of Huelva, and to verify the direct and indirect effects of these factors.
Design/methodology/approach – A total of 226 students at the University of Huelva completed a
survey questionnaire measuring their responses to six constructs which explain the system usage in
the context of e-learning: technical support (TS); computer self-efficacy (CSE); perceived ease of use
(PEOU); perceived usefulness (PU); attitude (A); and system usage (SU). Structural equation modelling
(SEM) was employed for modelling and data analysis.
Findings – The most significant results point to the need to rethink the original structural model in
terms of the relations of certain variables, although the authors also establish the importance of
the direct effect of technical support on perceived ease of use and perceived usefulness among the
students. The authors also confirm that WebCT usage and acceptance is directly influenced by
perceived usefulness and indirectly by perceived ease of use.
Originality/value – The findings in this study have implications for the virtual learning systems
managers at the University of Huelva, and for other universities that use online tuition systems.
This paper reflects a lack of technical support which students need to use WebCT more efficiently and
shows that training courses and technical assistance for students must be extended.
Keywords Spain, Universities, Students, Virtual learning environment,
Technology acceptance model, WebCT, Virtual learning platforms
Paper type Research paper
Introduction
The new information and communication technologies (ICTs) have been seamlessly
assimilated into university teaching, mainly at the behest of specific groups of
motivated academics.
There are two approaches currently competing in education, the modern and the
postmodern (Uzun, 2012).
The modern approach in education places the teacher and the content of the
material at the centre, while the postmodern approach puts the students and their
needs and interests first.
The understanding of education in our digital age as centred on the student and not
the teacher could have become reality. The argument over learning as formal vs
informal or individual vs social has raged in educational circles. Learning for anybody,
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1065-0741.htm
Campus-Wide Information Systems
Vol. 30 No. 2, 2013
pp. 135-160
rEmerald Group Publishing Limited
1065-0741
DOI 10.1108/10650741311306318
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of Huelva
anywhere, anytime as well as lifelong learning and the concepts of distance learning
form part of this ongoing argument in virtually every educational forum. So we
could say that education is bearing witness to a transition away from the modern
towards the postmodern educational system (Uzun, 2012).
The ICTs have acquired enormous power and gained considerable ground since
their emergence in 1980, opening up a huge divide which is growing daily between the
generation brought up in the past 30 years and the one that came before. Prensky
(2003) states that young people spend an inordinate amount of time in front of the
computer screen in the modern age and children’s brains are evolving to incorporate
the new technologies that take up so much of their time. With this in mind, it is not
simplistic to presume that the new generation of students has already taken the first
step towards becoming autonomous and lifelong learners.
The Web 2.0 platforms and other sources of information on the internet provide
learning environments that can no longer be ignored; they are increasingly consulted
and used by students all over the world. Any computer user can upload and download
documents, images, programs, etc., in order to create, integrate and reuse them in other
applications. In other words, any skill or basic information can be possessed and put
into practice by today’s youth. This leads to a predisposition towards receiving
education via digital platforms, in which case there is a need for teachers who
can guide and help them.
As a result, internet technology in higher education has now become a means to
disseminate course material, communicate and evaluate course work and to improve
the educational processes that support collaborative learning (Maloney, 2007; Nelson
et al., 2009; Augustsson, 2010). Web 2.0 technology, also called the social web, includes
blogs, wikis (Wikipedia), social networks and markers, and these are built to support
collaborative learning (Ajjan and Hartshorne, 2008; Boulos and Wheeler, 2007;
Burden and Atkinson, 2008; O’Reilly, 2005). Web 2.0 technology is highly appropriate
for collaborative learning, the construction and management of collective knowledge,
and social networks and social interaction, which leads to course participants and
teachers becoming more active and personally involved (Ajjan and Hartshorne, 2008;
Greenhow et al., 2009; Hain and Back, 2008; Kok, 2008; Ullrich et al., 2008).
Today there are countless terms that refer to the teaching-learning processes
and online tuition networks, such as e-learning, virtual learning environments (VLEs),
e-training, e-education, blended learning, web-based learning, distance education,
distance learning, etc. These terms are related but nuanced.
We understand e-learning or VLEs to be electronic information systems for the
administrative and didactic support of learning processes in higher education or
vocational training settings which provide students with sufficient resources for
completing tasks systematically (Fry et al., 2009; Strohmeier, 2008; Weller, 2007; S
ˇumak
et al., 2011; Bhuasiri et al., 2012).
The development and application of VLEs flow from their obvious advantages,
such as fomenting greater collaboration and communication, convenience (reduced
costs, better teaching and learning), efficacy, the user’s ability to control his VLE,
personalization, ubiquity, task orientation and the opportunity to promote VLE-guided
teaching-learning (Ozkan and Koseler, 2009; Sitzmann et al., 2006).
Information technologies have radically altered the way we teach and learn
(Hogo, 2010). Electronic learning offers a new perspective on education and is a
shift in emphasis towards the student with teaching-learning now a permanent process
(Sun et al., 2007).
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Learning becomes a critical strategic resource for organizations that needs to be
handled correctly. ICT provide new tools that endow organizations with the necessary
mechanisms to manage tuition without ever forgetting that the learning is done by the
people who constitute the organization; the human factor determines the success of
learning strategies and knowledge management (Bhatt, 2001). So, our initial reflection
on “e-learning” is to avoid emphasizing technological aspects and focus on the proper
integration of technology in the learning processes.
ICT and the internet alone cannot educate, or replace the teacher. They are powerful
tools whose inclusion necessarily implies a change in the role of the teacher, and an
overhaul of content, study plans and assessment systems.
The integration of new technologies within the educational framework is one of the
main objectives of the new European higher education environment, which means a
re-evaluation of existing educational programmes and managing the changes that are
taking place. When the potential of technology is applied to university education,
traditional teaching is strengthened by the Net, not substituted by it.
The effects of the new communications technologies are most keenly felt in distance
learning, that is, e-learning or internet-assisted learning. Some university degrees can
now be studied in part or entirely on the Net. This new concept of teaching affects
regulated education, and especially lifelong learning and postgraduate studies.
Indeed, e-learning has a decisive role to play in innovation at universities
(Schneckenberg, 2004) since it can contribute to the design and integration of
pioneering educational interventions in the teaching-learning process at the start and
throughout the individual’s professional career ( Franceschi et al., 2009), although
long-term planning is often haphazard.
The online learning platforms are of utmost importance in the virtual teaching
and learning environments, acting as a space within which teachers and
students can interact.
Students can communicate in two different ways in these spaces: asynchronically
(communication that takes place in a different space and time), for example via blogs,
wikis and e-mails; synchronically (communication occurring at the same time in
different spaces) via live chat, webcams and video conferences.
Most of the world’s universities now have computer systems that make it easy to
consult course material and relevant publications, do online tests and receive course
updates, submit tasks and enable teachers and students to communicate, all thanks to
the growth of information technologies. The teacher must be sufficiently skilled in the
design and creation of course activities, making the best use of the widest range of
tools in order to improve learning and increase communication, with all the
possibilities that the Net offers.
There are several course systems on the market for setting up these VLEs; they
vary in sophistication but all share a common methodology.
According to Brusilovsky (2001) and Tsolis et al. (2010) the most widely used
systems are as follows.
Commercial platforms
Web course tools (WebCT) (www.WebCT.com). WebCT was originally developed at
Canada’s University of British Columbia by computer engineer Murray Goldberg.
In 1995, Murray began searching for web-based systems that could be applied to
education. His research showed that the level of student satisfaction and academic
performance could be improved by deploying educational resources based on internet
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web sites. He set about constructing a system to enable the creation of educational
settings based on web pages, and came up with the first version of WebCT.
This virtual teaching platform enabled the creation of flexible learning
environments on the web with versatile, easy to learn and connected solutions.
Blackboard (www.blackboard.com). Blackboard Inc is a software company based in
Washington, DC (USA) that was founded in 1997 as a consultancy with a contract with
the non-profit IMS Global Learning Consortium.
In 2006, Blackboard merged with the rival WebCT company, and the product
previously called WebCT became known as the Blackboard Learning System.
Free research and collaboration platforms
Modular object-oriented dynamic learning environment (Moodle) (www.moodle.org).
Moodle is a free course management system that helps teachers to create their own
online learning communities. It was created by WebCT administrator Martin
Dougiamas who based his design on ideas of collaborative learning. It is a web site
on which all the pedagogical activities related to the transmission and distribution of
course content and material needed for one or various subjects can be done.
Instructors can add tools such as live chat, discussion board, feedback systems and
provide statistics for groups and the educational community in general.
The importance of understanding the motivational factors behind students’
acceptance of e-learning systems is evident if these teaching platforms are to succeed.
Technology acceptance model (TAM) is the most widely used information systems
theory among researchers who model individual usage and acceptance of the new
technologies (Davis et al., 1989). This tool and others such as the upgraded TAM2
(Venkatesh and Davis, 2000) are recognized as robust and reliable instruments for
predicting user acceptance of a broad range of new technologies.
The main aim of this study is to take a model based on the upgraded version of
the TAM proposed by Davis et al. (1989) to investigate the factors that determine the
acceptance of the WebCT learning system among students of the faculties of Business
and Education Sciences at the University of Huelva, and to verify the direct and
indirect effects of these factors.
The results will enable us to adapt our teaching system to the new demands of the
economy and above all to the educational needs of our students.
This paper is structured as follows: we start with a review of the literature as way of
introducing TAM, and look back at research into the uptake of the various educational
systems based on e-learning processes over the years; we then develop the acceptance
model and present our hypotheses, followed by a description of our data gathering
technique; we make a statistical analysis of the data and show the results, and
re-evaluate the hypotheses. Finally, we discuss the results and model limitations, and
offer ideas for future lines of investigation.
Literature review
Although many tertiary education institutions now use the web for learning and
tuition, few studies have researched the factors that influence the student’s decision to
use and accept the WebCT platform.
In 1989, Davis proposed the TAM as an instrument to explain and predict the
adoption and usage of information technology.
Following previous research into information systems (Swanson, 1974; Benbasat
and Dexter, 1986; Franz and Robey, 1986; Markus and Bjorn-Anderson, 1987;
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Swanson, 1987), Davis extended the theory of reasoned action to centre his model on
the behavioural elements at work in the intention to use new technology. This theory
focused specifically on the analysis of the effect of external factors on beliefs, attitudes
and intentions (Davis et al., 1989).
TAM identifies two specific measures that fundamentally affect the decision to
adopt new technology: perceived usefulness (PU) and perceived ease of use (PEOU)
(Davis, 1989; Davis et al., 1989).
TAM echoes the theory of reasoned action by posing that the use of any new
computer technology is determined by behavioural intention. However, TAM differs in
that it incorporates two direct measures of intention: attitude towards the technology
and perceived usefulness. At the same time, perceived usefulness affects attitude. Both
theories concur in that the perceived ease of use of a technology conditions the attitude
towards that technology and the perceived usefulness that comes from using it.
The effect of the external variables is seen in the individual’s beliefs in terms of
perceived usefulness and perceived ease of use.
Although the direct effect of a measure such as perceived usefulness on behavioural
intention runs contrary to the theory of reasoned action, empirical evidence
and various alternative models based on intention (Triandis, 1977; Brinberg, 1979;
Bagozzi, 1982) justify this relation. So, perceived usefulness is a cognitive
element that conditions behavioural intention while attitude is an affective
component (Davis et al., 1989).
Numerous investigators have used and developed TAM, including Armenteros et al.
(2013), Lee and Lehto (2012), Chow et al. (2012), Yeh and Teng (2012), Mathieson (1991),
Szajnak (1996), among others.
Armenteros et al. (2013) explore the behavioural intentions of FIFA instructors
towards education using multimedia material. The conclusions reveal that perceived
usefulness followed by perceived enjoyment, perceived ease of use and quality of the
multimedia instruction marked the instructors’ behavioural intentions to a large
degree when using multimedia teaching material.
Lee and Lehto (2012) use the TAM to identify the determinants that affect
behavioural intention of use with YouTube. This research highlights the motives for
using YouTube in learning tasks.
The results show that behavioural intention was significantly influenced by
perceived usefulness and user satisfaction.
Chow et al. (2012) use TAM to describe the development and evaluation of the
Second Life (SL) online 3D world, a virtual environment for learning.
Yeh and Teng (2012) study the conceptualization of perceived usefulness and state
that the usefulness of the system can be formulated beyond merely improving
its functioning; the authors explore various other usefulness constructs based on
well-established management concepts and the theory of human needs. The study’s
empirical results validate most of the proposed constructs.
Mathieson (1991) compared TAM and the theory of planned behaviour (TPB), both
of which predict the individual’s intention to use an information system. The results
showed that TAM has a slight empirical advantage over TPB.
Research by Szajnak (1996) resulted in an empirical confirmation of the original
TAM. The results confirm that the original TAM is still a valuable tool for predicting
intention to use information systems.
Research on the usage and acceptance of virtual online tuition systems is scarce but
currently on the increase due to the relevance of e-learning in attempts to improve
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our educational system. They provide innumerable tools for a wide range of subjects,
such as document and web page consultation, discussion board, live chat and
videoconferencing, etc.
The most recent studies on the adoption of e-learning systems come from Zhang
et al. (2012) who propose that we extend our knowledge to enable student participation
in online forums by studying the role played by a communication environment.
The results reveal that the psychological safety communication climate influences
student intention to continue to participate directly.
Saade
´et al. (2012) describe the results of critical thought in a VLE. The results
indicate the importance of interactivity which is perceived by students as directed
critical thought vs materials online as resources.
Yoo et al. (2012) study the role of intrinsic and extrinsic motivators in the promotion
of e-learning systems in the workplace in South Korea. The results show that the
intrinsic motivators (the expected effort required, attitude and anxiety) affect
the intentions of the workers in the use of workplace e-learning more strongly than
the extrinsic motivators (performance expectations, social influence and
facilitating conditions).
Escobar-Rodriguez and Monge-Lozano (2011) analyze the intention of students to
use the Moodle platform and thus as a way of improving the teaching-learning process.
They use TAM to specify causal relations, and this theory suggests a significant
positive relation between perceived ease of use and perceived usefulness.
Varol et al. (2010) who studied the acceptance and use of e-learning systems in
Turkey. This field study aimed to understand the beliefs, attitudes and intentions of
students and their interrelations, with the results showing that TAM is valid for
explaining e-learning systems usage. Liu et al. (2010) studied the factors that affected
Taiwanese students in their intention to use an online learning community. They
extended TAM by adding variables such as online course design, user-interface design,
previous online learning experience and perceived interaction.
Park (2009) constructed a model with the following variables: self-efficacy in
e-learning, subjective norm, system accessibility, perceived usefulness, perceived ease
of use, attitude and behavioural intent to use e-learning systems. The author concluded
that the most important construct in the model was self-efficacy in e-learning, followed
by subjective norm, when explaining the model’s causal processes.
Ngai et al. (2007) applied TAM to factors that determine WebCT usage in higher
education institutions in Hong Kong. They extended the model to include a new factor,
technical support, with the results showing that it has a direct, significant effect on the
sensations of perceived ease of use and usefulness.
Other research into e-learning systems has studied the relevance of technical
support (Martins and Kellermanns, 2004; Sa
´nchez and Hueros, 2010), various aspects
of system quality (Pituch and Lee, 2006), encouragement by others (Martins and
Kellermanns, 2004) and computer efficacy and experience (Martins and Kellermanns,
2004; Ong et al., 2004).
Research model and hypotheses
Since TAM was first proposed by Davis, it has been widely accepted by the
scientific community as a reliable instrument for modelling attitudes towards usage
of information systems and predicting intent to use and adopt them. Several
investigations with differing approaches have deployed TAM to measure the degree of
technology adoption (Cheng et al., 2011; Lee et al., 2011; Lee et al., 2009).
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Our study proposes a TAM extension that examines the constructs that affect
adoption of the WebCT system. The study includes variables such as technical support
and computer self-efficacy but excludes intention to use since Ngai et al. (2007), when
testing the validity and reliability of the constructs, demonstrated that teachers’
insistence that students use web-based learning systems has a considerable impact on
the use of learning systems.
The variables in our model are: technical support, computer self-efficacy, perceived
usefulness and ease of use, attitude and system usage (Figure 1).
We expected our model to contain variables and relations that would significantly
affect the adoption of WebCT, and we predicted that technical support and computer
self-efficacy would be both extrinsic and intrinsic factors that would influence the
students’ acceptance of the online tuition system.
Ralph (1991) defined technical support as “people assisting the users of computer
hardware and software products” that can include telephone hotlines, online support
services, automated telephone voice response systems and fax. Igbaria (1990) described
technical support as two dimensional, the first consisting of support for users via the
system’s development tools, user manuals and relevant documents while the second relates
to management support in which the leaders offer maximum encouragement and resources.
Evidently, demonstrable organizational support, including technical support,
foments a more receptive attitude that enhances acceptance and the success of personal
computing systems (Igbaria, 1990; Sa
´nchez and Hueros, 2010).
Chau (1996) states that technical support affects perceived ease of use, and Compeau
and Higgins (1995) report that it also has a positive influence on information
technology usage. In this way, technical support is directly related to the lowering of
computer-related anxiety and helps to develop a more favourable attitude towards new
computing systems. The lack of proper technical support can be a considerable
obstacle to the effective use of new information technology.
With this in mind, we propose the following hypotheses:
H1. Technical support has a positive influence on computer self-efficacy towards
using of WebCT.
Technical
support Computer
self-efficacy
Perceived ease
of use
Acttitude System
usage
Perceived
usefulness
H1
H2
H3
H4
H5
H6
H7
H8
H10
H9
H11
H12
Figure 1.
The proposed model for
the acceptance of the
WebCT virtual learning
environment system
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H2. Technical support has a positive influence on the perceived ease of use of
WebCT.
H3. Technical support has a positive influence on the perceived usefulness of
WebCT.
Self-efficacy refers to the levels of confidence individuals have in their ability to carry
out specific courses of action (Bandura, 1982, 1997). The same author hypothesized
(1986) that self-efficacy expectations affect the initiation of an activity, and the effort
and persistence required to successfully carry that activity out. Self-efficacy acts a
self-motivating force (Kankanhalli et al., 2005).
Compeau and Higgins (1995) and Compeau and Huff (1999) define perceived
computer self-efficacy as individuals’ judgement of their capabilities in using a computer
within various information technology contexts. Low confidence in the ability to use new
ICT makes individuals more prone to frustration in the face of obstacles, which in turn
dampens expectations and their capacity to use new technologies. However, those with a
higher estimation of their abilities persevere when faced with difficulties and are not easily
put off by setbacks (Compeau and Higgins, 1995).
In terms of perceived self-efficacy, Albion (2001) showed that this is vital when
explaining teachers’ use of technology in the classroom. The study carried out by Liaw
et al. (2007) showed that the instructors have positive attitudes towards e-learning, and
included variables such as perceived self-efficacy, enjoyment and usefulness and
behavioural intention of use.
This leads us to propose the following hypotheses:
H4. Computer self-efficacy has a positive influence on the perceived ease of use of
WebCT.
H5. Computer self-efficacy has a positive influence on the perceived usefulness of
WebCT.
H6. Computer self-efficacy has a positive influence on attitude towards using of
WebCT.
According to TAM, behavioural intention to use technology is affected by two
mediators: perceived ease of use and perceived usefulness, with the former influencing
the latter. If users feel that the system is easy to use, they will see the usefulness of the
learning platform and will be willing to engage with the technology.
Davis (1989) defined perceived usefulness as “the degree to which a person
believes that using a particular system will enhance his or her job performance”
(Hsu and Lin, 2008).
Perceived usefulness has two dimensions: the perceived usefulness for the
organization and for the individual. The former relates to the financial benefits
(product quality and savings in teaching costs) that the organization can obtain by
adopting a new technology. For the individual, the benefits derive from better work
performance and motivation to use the technology (Robey and Farrow, 1982).
In our study, perceived usefulness of the WebCT learning system is defined
as the degree to which the users believe that use of this system will improve their
academic performance.
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Davis (1989) defines perceived ease of use as “the degree to which a person believes
that using a particular system would be free from effort”. Individuals who perceive that
a system is easy to use are more inclined to believe in its usefulness (Robey and Farrow,
1982) and in the ease with which they can access the system (Amoako-Gyampah, 2007).
Our study defines perceived ease of use of WebCT as the degree to which the user
considers that WebCT usage will not require any great effort (Davis, 1989).
Many researchers have used TAM in their e-learning studies and have found
that perceived ease of use and perceived usefulness have significant effects on the
individual’s behavioural intention to use an e-learning system (Liu et al., 2009; Ong
et al., 2004; Sheng et al., 2008).
TAM proposes that perceived usefulness and ease of use have a direct influence on
attitudes towards new technology usage. Attitude is the degree to which a user is
interested in specific systems, and has a direct effect on intention to use these systems
in the future (Bajaj and Nididumolu, 1998).
Finally, the use of specific computer systems is affected by perceived usefulness
and ease of use (Davis et al., 1989; Igbaria et al., 1997; Selim, 2003).
Based on this, we propose the following hypotheses:
H7. Perceived ease of use has a positive influence on the perceived usefulness
of WebCT.
H8. Perceived ease of use has a positive influence on attitude towards the use
of WebCT.
H9. Perceived ease of use has a positive influence on the use of WebCT.
H10. Perceived usefulness has a positive influence on attitude towards the
use of WebCT.
H11. Perceived usefulness has a positive influence on the use of WebCT.
H12. Attitude to use has a positive influence on the use of WebCT.
Method
Measurements
We used a two-part questionnaire in our study: the first part requires the participant to
fill in personal and academic data; the second part consists of 28 items (Appendix)
to be used to evaluate the six constructs of the model proposed – technical support
(TS), computer self-efficacy (CSE), perceived ease of use (PEOU), perceived usefulness
(PU), attitude (A) and system usage (SU).
The items were adapted from previous studies: technical support was measured on
a scale adapted from Igbaria (1990), computer self-efficacy was calculated with
measurements taken from Compeau and Higgins (1995); the perceived ease of use
and perceived usefulness variables were based on a Davis (1993) register with
modifications, and attitude was measured on a gradation proposed by Ajzen and
Fishbein (1980). These five variables were measured on a seven-point Likert scale
ranging from 1 (“totally disagree”) to 7 (“totally agree”).
To measure the degree of WebCT acceptance, the participants were asked classify
how often they used the system, on a scale of 1 (“never”) to 7 (“very often”).
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Sample and data collection
The object of this study was the WebCT learning system in place at the University of
Huelva. WebCT, as already mentioned, is an internet-accessible course management
system for higher education. The study subjects were students on the Business
Management and Administration degree course at the Faculty of Business Sciences,
and students on the Infant and Primary Education teaching degree course at the
Faculty of Educational Sciences, both at the University of Huelva.
We developed a questionnaire based on our research model to measure the
variables; the questionnaire was completed in class. Following a pre-test, some
questions were rewritten for clarification.
The questionnaire was completed by 266 students from academic year 2008/
2009, in April and May 2009, of which 226 completed questionnaires (85 per cent)
were deemed valid.
The sample profile of participants appears in Table I, which shows that 66.8
per cent of those surveyed were women and 33.2 per cent were men.
Frequency Percentage
Sex
Male 75 33.19
Female 151 66.81
Total 226 100.00
Current academic year
First 1 0.44
Second 25 11.06
Third 123 54.42
Fourth 77 34.07
Total 226 100.00
Occupation
Student 226 100.00
Age
19 6 2.65
20 44 19.47
21 64 28.32
22 44 19.47
23 28 12.39
24 20 8.85
25 9 3.98
26 7 3.10
29 2 0.88
32 1 0.44
37 1 0.44
TOTAL 226 100.00
Degree course
Business management and administration 146 64.40
Infant and primary education 80 35.40
Total 226 100.00
Matriculation type
Official 226 100.00
Erasmus 0 0
Total 226 100.00
Note: n¼226
Tabl e I.
Demographic information
of the participants
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The table also shows that 54.4 per cent of participants were third-year students,
and 28.3 per cent were aged 21.
Table II shows the frequency response to each of the items and the mean and
standard deviation for the WebCT system.
We observe that the item with highest mean is PEOU2, “It is easy to get course
material from the platform”, with 5.38, it being the item that the students are most
satisfied with. The lowest mean was recorded for TS2, “the support technicians are
available for consultation at all times”, with 3.26, the item that the students are least
happy with. They find that technical support is insufficient to enable them to improve
their usage of WebCT.
The highest standard deviation is 1.78, for item PU4, “using the platform makes it
easier to learn at university”, which is the item that produced the widest or narrowest
dispersal of opinion among the students; and the lowest is 1.3, for item PU1, “using the
platform improves my academic performance”, this being the item that produced
the narrowest and widest dispersal of opinion among the students.
Analysis and results
First, the psychometric properties were analysed with the SPSS statistical program,
and the testing of the model and the checking of the psychometric properties were done
with Amos 6.0 and Lisreal 8.80, the most widely used structural equation software
programs on the market. The analysis process followed recommendations by Hair et al.
(1998) and was carried out in two parts:
(1) Exploratory analyses to check the validity of the variables proposed and
compare the initial reliability of the scales.
(2) A causal and confirmatory factor analysis to verify the dimensionality in the
exploratory study and to allow the established scales to be purged. This
analysis makes it easier to check the psychometric properties of the factors
that constitute the model and to contrast the structural relations proposed.
Our study randomly divided the sample of 226 students into two sub-samples, S1 with
30 per cent of the data, and S2 containing 70 per cent (Ngai et al., 2007). The first
analysis examined S1, using the SPSS program. Then Amos 6.0 and Lisreal 8.80
software programs were used on S2 for the second causal and confirmatory analysis.
Analysis of the measurement model
This study examined the validity and reliability of the variables by means of an
exploratory analysis.
The SPSS program was used to test the variables’ validity by analysing the main
components with Kaiser’s Varimax rotation, as recommended in the literature (Kaiser,
1970, 1974; McDonald, 1981; Hair et al., 1999).
The initial analysis of the main components showed that we had to extract five
components: technical support (TS), perceived ease of use (PEOU), perceived
usefulness (PU), attitude (A) and system use (SU). This was due to the fact that
the computer self-efficacy (CSE) variable loaded on perceived ease of use (PEOU), so we
eliminated CSE from our model. The items TS3, TS4, TS5 and TS6 were also
eliminated as they loaded on a new component that did not coincide with the rest of the
items in the technical support (TS) construct. This could be because those surveyed did
145
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WebCT 1% 2% 3% 4% 5% 6% 7% Mean SD
Technical support 3.44 1.18
TS1 The platfor m provides assistance when you have technical problems 8.0 11.9 19.0 39.4 14.6 6.2 0.9 3.63 1.31
TS2 Technical support staff is available for consultation at any time 12.8 19.9 21.7 26.1 14.2 4.4 0.9 3.26 1.42
Perceived usefulness 4.45 1.40
PU1 I learn more efficiently when I use the platform 1.3 4.9 9.3 20.8 32.3 25.2 6.2 4.78 1.30
PU2 Using the platform improves my academic performance 8.0 10.6 10.6 22.1 24.8 14.6 9.3 4.26 1.69
PU3 Using the platform makes me a more efficient learner 6.2 8.0 10.7 23.1 23.1 20.9 8.0 4.44 1.61
PU4 Using the platform makes it easier to learn at university 8.8 11.1 13.7 18.6 20.8 15.9 11.1 4.23 1.78
PU5 Using the platform gives me more control over my learning 7.1 8.4 13.8 23.1 22.7 13.3 11.6 4.32 1.68
PU6 In general, I find that the platform is advantageous for my lear ning 3.5 7.5 14.2 17.3 22.6 19.5 15.5 4.68 1.65
Perceived ease of use 5.33 1.31
PEOU1 I find it easy to use the platform 1.8 2.7 5.3 16.4 20.4 27.4 26.1 5.38 1.44
PEOU2 It is easy to get course material from the platfor m 2.2 3.1 4.0 15.0 21.7 27.4 26.5 5.39 1.46
PEOU3 The platfor m is simple and easy to understand 1.8 3.1 10.2 15.1 20.4 25.3 24.0 5.21 1.52
PEOU4 In general, I believe the platform is easy to use 1.8 3.1 3.1 19.1 20.0 27.1 25.8 5.37 1.43
Attitude 4.44 1.42
A1 Learning on the platform is fun 10.2 8.8 16.4 29.2 18.6 11.5 5.3 3.93 1.60
A2 Using the platform is a good idea 5.3 4.4 7.5 15.0 23.0 23.9 20.8 5.01 1.67
A3 The platfor m provides a pleasant way to learn 4.9 5.3 15.9 23.5 23.5 18.1 8.8 4.45 1.54
A4 In general, I like using the platform 8.0 8.0 13.8 16.5 25.0 17.0 11.6 4.40 1.74
System use 4.35 1.52
SU1 I use the platform (1 never to 7 very often) 5.3 4.0 10.2 19.5 22.1 20.8 18.1 4.84 1.65
SU2 The number of hours I spend on the platform per week is:
(1 none to 7 many hours) 8.0 15.1 17.8 22.2 20.9 9.3 6.7 3.88 1.64
Table II.
A statistical description
of the item in the
questionnaire
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not realise that the WebCT platform provided tools (fax, mail systems, etc.) which
could be used to request assistance on technical issues.
We then carried out a second analysis of the main components without the computer
self-efficacy variable and items TS3, TS4, TS5 and TS6 of the technical support
construct, resulting in a new hypothesis, H13, which proposed that technical
support had a positive influence on attitude to use WebCT.
In the second WebCT factor analysis, the Kaiser-Meyer-Olkin (KMO) index stood
at 0.843, suggesting that the factor analysis was feasible. Five components were
extracted, which explains the 79.36 per cent in the survey’s response variance, using
the Kaiser-Guttman rule for eigenvalues above 0.82.
The results of the new analysis of the main components in Table III show that the
load values of each variable item are above 0.4 (Nunnally, 1978), except the 0.35 for item
2 of the attitude construct (A2) but which is still close enough to the recommended
value. As a result we verify that the mean points are sufficiently valid.
The discriminant validity technique was used to test validity and enable the correct
interpretation of relations between the constructs. This concept refers to the accuracy
by which the measure used does not correlate too closely to the measures of other
constructs from which it is supposed to differ theoretically.
The discriminant validity evaluation in this study found that there is no value
of 1 in the correlation between each pair of constructs, with a confidence interval of
Components
Factor 1:
perceived
usefulness
Factor 2:
perceived
ease of use
Factor 3:
system
usage
Factor 4:
technical
support
Factor 5:
attitude
PU1 0.69
PU2 0.75
PU3 0.88
PU4 0.86
PU5 0.82
PU6 0.77
PEOU1 0.81
PEOU2 0.84
PEOU3 0.87
PEOU4 0.84
SU1 0.83
SU2 0.96
TS1 0.81
TS2 0.79
A1 0.68
A2 0.35
A3 0.60
A4 0.54
Cronbach’s a0.93 0.92 0.80 0.70 0.83
Eigenvalues 8.32 2.14 1.73 1.27 0.82
Accumlated explained variance (%) 46.24 58.12 67.74 74.80 79.36
Note: Extraction method: analysis of main components. Rotation method: Kaiser’s Normalization
Varimax
Table III.
Results of the analysis
of the main
WebCT components
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between 95 and 99 per cent. This allows us to deduce that each variable represents
a different concept since they are not perfectly correlated.
In the correlations matrix of the data set in Table IV, the correlations above 0.3 were
statistically significant at 0.01. The majority of the correlations between the items were
significant at values equal to or higher than 0.3, significance registering at 0.1.
The correlations are stronger between items that measure the same variable than
between items that measure different variables, demonstrating the convergent and
discriminant validity of our model.
The reliability of each construct and dimension was measured, based on the notion
that reliability is the required, though not absolute, condition for the validation of a
construct.
Reliability, according to George and Mallery (1995), is related to the fact that the
instrument used for measurement produces the same results whenever it is applied
to the same person and in the same circumstances. Thus, the instruments normally
used in social sciences are considered reliable if they obtain similar results, regardless
of who administers them and how. We tested the reliability of the variables with
Cronbach’s a, the most widely used tool for this type of analysis.
In our study, the Cronbach acoefficients for each of the five variables are greater or
equal to the 0.7 threshold acceptable for determining reliability. Table III shows the
coefficients ranging from 0.70 to 0.93, which indicates that the instrument is reliable
and internally coherent.
Test of structural model
After eliminating the computer self-efficacy variable and items TS3, TS4, TS5 and
TS6, we tested the proposed structural model with structural equation models (SEM),
which were chosen for their capacity to examine relations between constructs with
multiple measurement scales ( Jo
¨reskog and So
¨rbom, 1996).
Amos 6 and Lisreal 8.80 software programs were used in a confirmatory factor
analysis of the second sample (S2). The maximum likelihood method of estimation was
used (Hu and Bentler, 1995).
We tested the model’s goodness-of-fit by using goodness-of-fit indexes (GFIs)
recommended by Hu and Bentler (1995), Hair et al. (1995) and Hu et al. (1999), such as
the w
2
-test, the GFI, the adjusted goodness-of-fit index (AGFI), the normed fit index
(NFI), the comparative fit index (CFI) and the root mean square error of approximation
(RMSEA).
Table V shows that the goodness-of-fit of all the statistics is within an acceptable
range, except the GFI which is sufficiently close to the recommended score to be
acceptable.
The results of the structural model are shown in Tables VI and VII, and in Figure 2.
Table VI shows the results of the coefficient of the determination R
2
for each
endogenous variable. It is important to underline the explanatory power of the
perceived usefulness, attitude and system usage variables, with R
2
values of 43, 75 and
27 per cent, respectively.
Figure 2 and Tables VI and VII show the important structural relation between
the variables studied. All the hypotheses were considered significant except H13,
H9 and H12. The results for H13 reveal that the relation between technical
support and attitude is not significant (b¼0.06, p40.05). However, it is noticeable
that perceived usefulness and ease of use have a strong indirect effect on attitude
(b¼0.33, po0.01).
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TS1 TS2 PU1 PU2 PU3 PU4 PU5 PU6 PEOU1 PEOU2 PEOU3 PEOU4 A1 A2 A3 A4 SU1 SU2
TS1 1
TS2 0.385** 1
PU1 0.077 0.036 1
PU2 0.012 0.076 0.567** 1
PU3 0.086 0.013 0.673** 0.744** 1
PU4 0.047 0.122 0.606** 0.670** 0.796** 1
PU5 0.039 0.018 0.649** 0.728** 0.657** 0.733** 1
PU6 0.115 0.087 0.718** 0.580** 0.708** 0.651** 0.701** 1
PEOU1 0.038 0.005 0.588** 0.480** 0.432** 0.417** 0.531** 0.489** 1
PEOU2 0.07 0.104 0.661** 0.482** 0.388** 0.440** 0.557** 0.534** 0.738** 1
PEOU3 0.132 0.114 0.460** 0.382** 0.257* 0.277* 0.418** 0.294* 0.719** 0.741** 1
PEOU4 0.14 0.191 0.534** 0.463** 0.304* 0.327** 0.423** 0.405** 0.687** 0.789** 0.760** 1
A1 0.294* 0.342** 0.406** 0.393** 0.423** 0.268* 0.303* 0.318* 0.410** 0.447** 0.506** 0.557** 1
A2 0.018 0.05 0.640** 0.571** 0.576** 0.488** 0.495** 0.614** 0.721** 0.643** 0.622** 0.600** 0.512** 1
A3 0.039 0.039 0.592** 0.612** 0.679** 0.571** 0.555** 0.657** 0.493** 0.493** 0.316* 0.428** 0.548** 0.611** 1
A4 0.093 0.077 0.607** 0.549** 0.572** 0.544** 0.565** 0.742** 0.456** 0.495** 0.385** 0.481** 0.469** 0.582** 0.690** 1
SU1 0.215 0.084 0.287* 0.346** 0.336** 0.405** 0.308* 0.274* 0.343** 0.316* 0.310* 0.231 0.081 0.302* 0.257* 0.377** 1
SU2 0.169 0.085 0.019 0.246 0.186 0.308* 0.163 0.003 0.196 0.104 0.336** 0.076 0.145 0.133 0.054 0.191 0.749** 1
Note: *,**Correlations significant at 0.05 and 0.01 levels, respectively
Tabl e IV.
WebCT correlations
matrix
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H9 posed a relation between perceived ease of use and WebCTusage, was also rejected.
Our study differs from others in that the relation between both variables is
insignificant (b¼0.09, p40.05), although perceived ease of use has a moderate indirect
effect on WebCT usage via perceived usefulness and attitude (b¼0.28, po0.05).
H12 proposed that attitude influences WebCT usage. Our study, like others
(Ngai et al., 2007), found that attitude has no significant influence on platform usage
(b¼0.14, p40.05).
Our study also rejected H1,H4,H5 and H6 due to the elimination of the computer
self-efficacy variable.
Discussion
E-learning systems represent an alternative to traditional face-to-face educational
methods. Many institutions have now installed these systems to satisfy the needs of
students who can now study without the hindrances imposed by space and time.
The aim of this investigation was to examine the factors that motivated students at
the University of Huelva to accept the WebCT platform. Our study proposed a research
model to analyse the variables affecting WebCT acceptance via an extension of TAM.
The variables in our model were technical support, perceived self-efficacy, perceived
usefulness, perceived ease of use, attitude and end usage.
We expected our model to produce variables and relations that were significant.
We also believed that technical support and perceived self-efficacy would be
intrinsic and extrinsic factors that would affect WebCT acceptance. The data on the
students were gathered via questionnaire and analysed by SEM. The results suggest
that most of the hypotheses were proven. The discussion now turns to the results
relating to each hypothesis.
Goodness-of-fit index Recommended value Results in the study
w
2
/degrees of freedom p3 1.9
Goodness-of-fit index (GFI) X0.90 0.85
Adjusted goodness-of-fit index (AGFI) X0.80 0.80
Comparative fit index (CFI) X0.90 0.98
Normed fit index (NFI) X0.90 0.96
Root mean square error of approximation (RMSEA) p0.08 0.08
Tabl e V.
Statistical summary
of the model’s
goodness-of-fit
Perceived ease
of use
Perceived
usefulness Attitude System usage
Direct
effects
Indirect
effects
Direct
effects
Indirect
effects
Direct
effects
Indirect
effects
Direct
effects
Indirect
effects
Technical support 0.34** 0.18* 0.19** 0.06 0.33** 0.20**
Perceived ease of use 0.57** 0.38** 0.31** 0.09 0.28*
Perceived usefulness 0.55** 0.33* 0.07
Attitude 0.14
R
2
0.11 0.43 0.75 0.27
Notes: *po0.05; **po0.01
Table VI.
The effects of the variables
on the acceptance of the
WebCT learning system
150
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Our research highlights the importance of our extrinsic variable (technical support)
which is closely related to perceived ease of use and, as in other studies (Ngai
et al., 2007; Sa
´nchez and Hueros, 2010), to perceived usefulness. It also has a
considerable indirect effect on attitude, showing that when students have good
technical backup, understood as personal assistance via the web, fax or e-mail, etc.,
they are much more motivated and are keener to learn, and much more receptive to
using the WebCT system.
In other words, the better the technical service or assistance provided to students
to help solve their problems, the more useful and easier WebCT platform usage is
considered to be.
We believe that our research results make a contribution to the teaching aspects of
ICT. The University of Huelva is constantly evolving, and despite its small size, its
vitality and potential is underlined by the priority it gives to the constant improvement
of online tuition systems.
This improvement is due to the support provided by the University of Huelva for its
teachers in WebCT usage. But our study also reveals the lack of tuition and support for
students in e-learning. The University of Huelva needs to dedicate more time, resources
WebCT hypotheses Hypotheses bStatistic Valuation
Technical support has a positive influence on
computer self-efficacy in the use of WebCT.
H1 Eliminated
Technical support has a positive influence on
the perceived ease of use of WebCT
H2 0.34** 3.54 Accepted
Technical support has a positive influence on
the perceived usefulness of WebCT
H3 0.18* 2.23 Accepted
Computer self-efficacy has a positive
influence on the perceived ease of use
of WebCT
H4 Eliminated
Computer self-efficacy has a positive
influence on the perceived usefulness
of WebCT
H5 Eliminated
Computer self-efficacy has a positive
influence on attitude to use WebCT
H6 Eliminated
Perceived ease of use has a positive influence
on the perceived usefulness of WebCT
H7 0.57** 6.36 Accepted
Perceived ease of use has a positive influence
on attitude to use WebCT
H8 0.38** 4.85 Accepted
Perceived ease of use has a positive influence
on usage of WebCT
H9 0.09 0.69 Rejected
Perceived usefulness has a positive influence
on attitude to use WebCT
H10 0.55** 6.17 Accepted
Perceived usefulness has a positive influence
on usage of WebCT
H11 0.33* 2.10 Accepted
Attitude to using the system has a positive
influence on usage of WebCT
H12 0.14 0.76 Rejected
Technical support has a positive influence on
attitude to use WebCT
H13 0.06 1.04 Rejected
Notes: *po0.05; **po0.01
Table VII.
Results of the hypotheses
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E-learning and
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and personnel to strong technical support for students in order to motivate them to use
e-learning platforms.
The results of our study also show that platform usage is directly affected by
perceived usefulness and indirectly by perceived ease of use. Usage is also influenced
by attitude, albeit slightly, which could be due to pressure from teachers which
conditions students’ attitude in that it obliges them to use these virtual teaching
platforms to study and pass certain subjects.
The importance of the perceived usefulness variable through its direct or indirect
effects (via attitude) on WebCT usage suggests that teachers must make the most of the
platform, since they do not normally use all the tools available to them, so that the
students see that the online learning system can be useful.
The perceived ease of use variable evaluated in our study, in accordance
with Selim (2003), has no direct relation to WebCT usage, although an indirect
and significant relation does exist via perceived usefulness and attitude. The
insignificant relation of the direct effects is consistent with other recent studies (Chau
and Hu, 2002; Szajnak, 1996; Wu and Wang, 2005). Further investigating the issue,
Venkatesh et al. (2003) did not find any direct post-implementation effects of
perceived ease of use, only pre-implementation effects. The significant relation by
means of indirect effects suggests that when we have an ill-informed, preconceived
idea that using a system is difficult, we might really believe that the system is
complicated to use and that the potential benefits are not worth the effort required to
achieve them, without even having tried to use the system. Therefore, teachers need
to design learning strategies that enable students to gain confidence and competence
in carrying out particular activities, and system designers must focus on creating
intuitive, user-friendly environments to make it easier for students to use the
Perceived
ease of use
R2=0.11
Perceived
usefulness
R2=0.43
Technical
support Attitude
R2=0.75 System usage
R2=0.27
TS1
TS2
PEOU1 PEOU2 PEOU3 PEOU4
PU6PU5PU4
PU3
PU2PU1
A1
A4 A3
A2
SU2
SU1
0.67 0.88 0.89 0.88 0.86 0.87
0.86
0.62
0.80 0.82 0.87 0.89
0.82 0.76
0.91
0.89
0.84
0.81
0.34
0.06
0.18
0.57 0.38
0.55
0.09
0.33
0.14
Figure 2.
The result of the
structural model
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platform. If students encounter a lot of difficulties in using the platform, they
will abandon it.
Following previous studies, we initially used the computer self-efficacy intrinsic
variable in our investigation. The results of the exploratory analysis show that this
variable had to be eliminated because it loaded on to the same component as perceived
ease of use, that is, they were indistinguishable as variables.
Conclusions
The traditional role of the student in education is to be the passive reader and listener,
and to cram for exams. However, theories on how we learn show that real knowledge,
which the student never forgets, is acquired when the learner plays an active role,
constructs things and puts them into practice, resolves problems, analyses situations
and searches for answers, etc.
This teaching-learning process has received a significant boost from internet
usage in education. The student actively participates in the construction of his own
knowledge. Tools and resources are now on hand, such as course tutors, mutual
support from fellow students, discussion boards, live chat, course content, etc., and all
of it whenever and wherever the student logs on to his course.
The worldwide development of web resources for higher education has increased
rapidly in recent years, providing students with an ever wider range of educational
resources in various formats: video, photography, audio, text, etc. (Zhang and Zhou, 2003).
At the same time, with the growth in the quantity and quality of internet
connections, more and more people, students especially, are demanding access to
online course management systems. As a result, we need to understand the factors that
prompt students to adopt and use WebCT in order to include them in the development
of this technological tool.
The findings in this study have implications for the virtual learning systems
managers at the University of Huelva, and for other universities that use online tuition
systems. We must extend training courses and technical assistance for students
whether by e-mail, live chat, discussion boards, direct personal assistance, etc.
The proposed model is revealing for the importance of the technical support variable,
which the statistics show to have the lowest mean as it registered the most
student dissatisfaction.
We also suggest that teachers show greater commitment to using these e-learning
systems, since it goes without saying that the acquisition of the relevant skills by the
teacher is the key to the success of the teaching-learning process.
The significance of the perceived usefulness variable in WebCT acceptance implies
that the teacher becomes a determining factor, because he has direct contact with the
students and is aware of the obstacles that hinder usage and the support needed for
the integration of these tuition systems in the classroom. The teacher must motivate
the students to use this tool more efficiently and effectively.
Like all studies our research has its limitations, such as the age of the data used,
but even so we believe this study is still relevant since it gives a very clear picture of
student motivation in the use and acceptance of WebCT e-learning; and since the age
range of the students involved in these educational processes remains the same over
the academic years, it is admissible that their perception of technology oriented to
e-learning systems is very similar, which could help future research to clarify other
possible student motivations towards these systems and the new ones that will surely
appear in the future to facilitate e-learning.
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E-learning and
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of Huelva
In addition, the differences in student experience with the system and the time spent
using it. The coefficient of determination R
2
of system usage (R
2
¼0.27) is moderately
good but always susceptible to improvement, meaning that there are other variables,
apart from those proposed, that could influence system usage and thus improve this
determination coefficient. This study included only two non-TAM variables (technical
support and perceived usefulness), and other variables that might have significantly
influenced the acceptance of WebCT were not considered for the sake of brevity. The
small sample size together with the rigorous requirements of the SEM techniques could
have also influenced the results.
Future investigations should study other variables that might affect WebCT
usage, such as teacher support, mutual support among students, previous computer
knowledge, teachers’ demands, etc. Other complimentary or comparative studies with
other universities that use e-learning systems could also be carried out.
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Appendix. Questionnaire statements used in the study
About the authors
R. Arteaga Sa
´nchez is an FPDI predoctoral fellow (2009-present) at the Faculty of Business
Administration, specifically in the Department of Financial Economics and Accounting at the
University of Huelva. Her research interests are centered on the impact of the internet on
education, banking, e-commerce and the acceptance and usage of new information technologies
such as Moodle, WebCT, enterprise resource planning (ERP) and social networks in education
Technical support
TS_1 The platform provides assistance when you have technical problems
TS_2 Technical support staff is available at any time
TS_3 I can contact support staff via fax when there is a technical problem
TS_4 I can contact support staff via e-mail when there is a technical problem
TS_5 Technical assistance can be called up via Web request forms
TS_6 In general, the platform provides good technical support
Perceived usefulness
PU_1 My learning is more effective when using the platform
PU_2 Using the platform improves my academic performance
PU_3 Using the platform enhances the efficacy of my learning
PU_4 Using the platform makes it easier to learn at university
PU_5 Using the platform I have more control over my learning
PU_6 In general, I find the platform to be an advantage for my learning
Computer self-efficacy
CSE_1 I can access course content on the platform
CSE_2 I can navigate freely for course content on the platform
CSE_3 I can use the platform without detailed instruction on its use
CSE_4 I can overcome obstacles that occur when I use the platform
CSE_5 I can use the platform if system manuals are available
CSE_6 In general, I am competent in using the platform
Perceived ease of use
PEOU_1 Learning how to use the platform is easy for me
PEOU_2 It is easy to obtain course material from the platform
PEOU_3 It is clear and easy to understand how to use the platform
PEOU_4 In general, I believe the platform is easy to use
Attitude
A_1 Learning on the platform is fun
A_2 Using the platform is a good idea
A_3 The platform is an attractive system on which to learn
A_4 In general, I like using the platform
System usage
SU_1 I connect to the platform. (1 – never to 7 – very often)
SU_2 The hours I spend using the platform per week range from:
(1 – none to 7 – many hours)
159
E-learning and
the University
of Huelva
and business. R. Arteaga Sa
´nchez is the corresponding author and can be contacted at:
rocio.arteaga@decd.uhu.es
A. Duarte Hueros is a Lecturer in the Department of Education at the University of Huelva.
She focuses on educational technology and design, media production and education materials.
M. Garcı
´a Ordaz is a Lecturer at the Faculty of Business Administration, specifically in the
Department of Financial Economics and Accounting at the University of Huelva. She focuses on
digital accounting, online consumer behavior, enterprise resource planning (ERP) and social
networks in education and business.
To purchase reprints of this article please e-mail: reprints@emeraldinsight.com
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