UNDERSTANDING BEHAVIORAL INTENTION
OF E-LEARNING SYSTEM RE-USE
Yan Li
International College at Beijing, China Agricultural University, Beijing, China
Yanqing Duan
Business and Management Research Institute, University of Bedfordshire, Luton, LU1 3JU, U.K.
Zetian Fu
College of Information and Electrical Engineering, China Agricultural University, Beijing, China
Weizhe Feng
International College at Beijing, China Agricultural University, Beijing, China
Keywords: e-Learning, TAM, ISS, P.R China, Behavioural intention, e-Learning system re-use.
Abstract: With the rapid development of information and communication technologies, e-learning system has
emerged as a new means of education. The learner acceptance of e-learning system has attracted extensive
attention, but how the experience of using the existing e-learning system impacts on their behavioural intent
to the e-learning system re-use has received limited consideration. As the application of e-learning is
gaining its momentum, it is necessary to examine the relationships of e-learners’ experience and their
behavioural intention of re-use. It was argued that the better understanding of the factors affecting the e-
learner’s behavioural intention in the future could help e-learning system researchers and providers to
develop more effective and acceptable e-learning systems. Based on the technology acceptance model,
information system success model and self-efficacy theory, a theoretical framework was developed to
investigate the learner’s behavioural intention to e-learning system re- use. A total of 280 university
students were surveyed to test the proposed structural model. The results demonstrated that perceived
usefulness, perceived ease of use, service quality, course quality and self-efficacy had direct effects on
users’ intention to re-use. Furthermore, self-efficacy affected perceived ease of use which positively
influenced perceived usefulness.
1 INTRODUCTION
The development of Information and
Communication Technologies (ICTs) has provided
significant opportunities for education suppliers to
explore and develop new ways of delivering
educational programmes. As a result, e-learning has
become an emerging phenomenon in revolutionising
processes in which teaching and learning can take
place. Compared with classroom based teaching and
learning, e-learning has received considerable
attention due to its flexibility, low cost and conveni-
ence.
Education is widely considered as key to the
success of a nation like China given increasing
global competition. The demand for higher
education from a growing and widely dispersed
population and an increasingly technology-oriented
economy presents major challenges to the
developers and providers of modern education. A
forecast by Olsen (2003) suggested that “China will
be unable to supply the 20 million university places
needed to meet the demands of its developing
economy”. According to the National Bureau of
218
Li Y., Duan Y., Fu Z. and Feng W..
UNDERSTANDING BEHAVIORAL INTENTION OF E-LEARNING SYSTEM RE-USE .
DOI: 10.5220/0003101302180223
In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2010), pages 218-223
ISBN: 978-989-8425-30-0
Copyright
c
 2010 SCITEPRESS (Science and Technology Publications, Lda.)

Statistics of China (NBS) (2008), the rural
population accounted for 54.3 per cent of the total
population. Due to the backwardness of rural
economic development and inadequate financial
resources, the improvement of rural education has
been seriously restricted. The statistical data shows
that 9.5 per cent of the rural labours are illiterate,
86.2 per cent are educated up to the junior high
school level which is the minimum nine-year
compulsory education in China, only 4.3 per cent
have received the high school education (NBS,
2008). The low education level of rural population
seriously hindered the development of rural
economy. Therefore, continuous education plays an
important role in improving the knowledge and
skills of people living in the rural areas. The rapid
development of information technology and the
Internet provides opportunities for them to
participate in adult continuous study in China.
With the increasing public investment in the
Internet infrastructures in China, the Internet
coverage has been extended to many rural areas,
especially in more developed regions. China Internet
Network Information Center (CNNIC) reported that
the rural Internet users have increased up to 84.6
million at a 60 per cent increasing rate each year
(CNNIC, 2009). E-learning has been considered as
an efficient solution for expanding adult education
and vocational training in rural areas (Ministry of
Agriculture, 2005). Although the applications of e-
learning have been enhanced by the government
promotion programmes and the rapid penetration of
the Internet into the rural regions, only 1.6 per cent
of 700 million rural population has registered in e-
learning programs in 2009 (CNNIC, 2010), despite
the fact that many institutions of higher education
offer e-learning courses and training course in rural
areas.
e-Learning implementation in China are facing
challenges. According to the survey of 150 e-
learning courses by Guo and Yuan (2009), there was
a lack of appropriate e-learning materials. Many
factors can affect e-learning success. For example,
Lang (2006) and Guo (2006) indicated that the
system service and system design were the problems
that could impend the system usage and student
motivation. Therefore, it is imperative to understand
the factors affecting e-learning success. It is argued
that the behavioural intention of re-use can be an
appropriate overall indicator of e-learning system
success.
This study aims to understand how e-learners
experience in the rural areas of China in using
existing e-learning system affects their behavioural
intention of re-use by developing and empirically
validating an integrated theoretical framework based
on Technology Acceptance Model (TAM),
Information System Success (ISS) model and self-
efficacy theory.
2 THE RESEARCH MODEL
The theoretical foundation and constructs were
established based on previous studies. TAM model,
ISS model and self-efficacy theory were simplified
and integrated. TAM model and ISS model
contributed to the “How success of the technology”,
and the self-efficacy theory, as an important
component of social cognition learning theory,
highlighted “How people learn with the technology”.
The research model was adopted to inform the
research hypotheses development.
TAM model which was developed by Davis et
al. (1989), has been widely used to predict user’s
acceptance of technology. In 1996, Davis et al.
modified the original TAM model and suggested
that perceived usefulness and perceived ease of use
has the direct effect on the individual’s intention to
system use. In the meanwhile, the system
characteristics, originated from external variables of
the original model, only provide the mediate effect
on intention by perceived usefulness and perceived
ease of use.
Based on a thorough and systematic analysis of
research publications in MISQ, Journal of
Management Information Systems and Information
Systems Research, DeLone and McLean (2003)
developed a modified ISS model, or D&M model in
2003. Compared with their original ISS model,
service quality is considered as an important new
dimension in the modified ISS model. The
dimension of system use is replaced by intension to
use, and individual benefit by overall benefit. Over
200 research papers have attempted to validate or
apply modified ISS model in various fields of
information system studies.
Self-efficacy was firstly introduced by
psychologist Bandura (1977). The concept has been
commonly used by researchers to investigate the
individual e-learner’s behavior. Computer-efficacy
is defined in this context as “confidence in one’s
ability to perform certain learning tasks using an e-
learning system” (Pituch and Lee, 2006). For
instance, it is found that women usually have more
computer anxiety but less computer self-efficacy
toward the internet (Whitely, 1997). Prior study has
identified that self-efficacy can influence the e-
UNDERSTANDING BEHAVIORAL INTENTION OF E-LEARNING SYSTEM RE-USE
219

learning behavioral intention and performance (Ong
and Lai, 2006; Pituch and Lee, 2006).
Based on TAM, ISS and self-efficacy, this
research developed an integrated model as shown in
Figure 1. In TAM model, the construct on system
characteristics was not clarified and empirically
tested by Davis and Venkatesh (1996). Based on the
related empirical study by Pituch and Lee (2006),
this study defined the system characteristics as:
system functionality, system response and system
interactivity.

Perceived
easeofuse
(
PEOU
)




Behavioural
intentionto
re‐use
(BI)
Perceived
usefulness
(
PU
)

System
response
(SR)
Servicequality
(SQ)
Coursequality
CQ
Self‐Efficacy
(SE)
System
functionality
(SF)
System
interactivity
(
SI
)

H3(+)
H12(+)
H9(+)
H10(+)
H7(+)
H11(+)
H4(+)
H5(+)
H6(+)
H2(+)
H1(+)
H8(+)
Figure1: Research model.
In accordance with the research model, 12
hypotheses were developed for the empirical
validation. Table 1 shows 12 hypotheses of the
research.
Table 1: Research hypotheses.
Hypotheses Statements
H1(+) System functionality (SF) will have a positive
effect on perceived usefulness (PU).
H2(+) System functionality (SF) will have a positive
effect on perceived ease of use (PEOU).
H3(+) System response (SR) will have a positive
effect on perceived usefulness (PU).
H4(+) System response (SR) will have a positive
effect on perceived ease of use (PEOU).
H5(+) System interactivity (SI) will have a positive
effect on perceived usefulness (PU).
H6(+) System interactivity (SI) will have a positive
effect on perceived ease of use (PEOU).
H7(+) Perceived ease of use (PEOU) will have a
positive effect on perceived usefulness (PU).
H8(+) System service (SQ) quality will have a
positive effect on behavioural intention of re-
use (BI).
Table 1: Research hypotheses. (cont.)
Hypotheses Statements
H9(+) Course (CQ) quality will have a positive
effect on behavioural intention of re-use (BI).
H10(+) Perceived usefulness (PU) will have a
positive effect on behavioural intention of re-
use (BI).
H11(+) Perceived ease of use (PEOU) will have a
positive effect on behavioural intention of re-
use (BI).
H12(+) Self-efficacy (SE) will have a positive
effect on behavioural intention of re-use (BI).
3 RESEARCH METHODOLOGY
3.1 The Development of Instruments
To measure the latent variables of the model, survey
items were mainly adapted from validated
instruments reported in the relevant studies. For
example, system functionality was measured by 5
items which were mainly adopted from Pituch and
Lee (2006). All items (32 in total) were reviewed by
e-learning experts and researchers in China. Items
related to independent variables were modified to
make them relevant to the e-learning system usage
context of the present study and reviewed by experts.
Five-point Likert scale were used to measure the
respondent’s level of agreement or disagreement of
each survey item statement. In addition to the model
measuring items, the survey also collected
demographic information of the respondent.
3.2 Sampling and Survey Procedure
The sample for this study consisted of 350 part time
e-learning students who were working in rural areas
at undergraduate level of the Higher Education. The
survey questionnaire was revised based on the
feedback of pilot interviews and tests with 45
students from the e-learning programme. The final
questionnaire was distributed to students at the class
room of the local study centers.
With the help of course managers, of the
distributed 350 questionnaires, 280 validated
responses were collected and used for analysis. In
addition to the use of the easy understandable
questionnaire design, the university course
managers’ assistance helped to increase the response
rate in our study.
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4 DATA ANALYSIS
The measurements and research model were tested
using the structural equation modelling (SEM)
technique which has been used in measuring user’s
acceptance of information technology. The computer
software program LISREL (Linear Structural
Relation) 8.80 was used. LISREL consists of
measurement model and structural equation model.
The measurement model should be assessed before
the structural equation model is examined.
According to the recommendation by Anderson and
Gerbing (1988), the minimum sample size for SEM
approach is 200. One rule of thumb found in the
literature is that sample size should be more than 8
times the number of variables in the model (Garson,
2010). Therefore, 280 questionnaires for this study
were considered as acceptable for the SEM analysis
using LISREL.
4.1 Analysis of Measurements
The adequacy of measurements was evaluated by
confirmatory factor analysis, which was proposed to
verify the reliability and validity of the measures.
Eleven assessment of fit measures were used to
evaluate the validity of the research construct.
Multiple criteria measures were used to measure the
model fit, which were absolute fit measures,
incremental fit measures and parsimonious fit
measures. Following the LISREL data analysis
practice, 7 problematic items of the 32 items were
deleted and the measurement model was reassessed.
4.2 Structural Model Testing Results
The significance of the structural model test and
individual direct effects, indirect effects and total
effects were summarized in Table 2 and shown in
Figure 2. Overall, 11 of the 12 hypotheses were
supported with statistical significance.
System functionality, system response and
system interactivity had a significant positive effect
on perceived ease of use (0.475, 0.342, 0.289). The
result also showed that self-efficacy had a direct
effect on perceived ease of use (0.316). These
determinants accounted for 58.9% of the variance in
perceived ease of use. The total effect of perceived
ease of use on behavioural intention was 0.709.
As there was no significant effect of system
interactivity on perceived usefulness (0.101,
p>0.05), hypothesis 5 was not supported. For the
positive relationship between perceived ease of use
and perceived usefulness, the direct effect of
perceived ease of use on perceived usefulness was
0.408. The total effects of system functionality and
system response on perceived usefulness were 0.551
and 0.208 respectively. The variables explained
52.2% of the variance in perceived usefulness. The
total effect of perceived usefulness on behavioural
intention to re-use was 0.735.
Table 2: Effects of dominants on the behavioural intention
to e-learning system re- use.
Latent
variables
I
ndependent
variables
Standardized estimation
Direct
effects
Indirect
effects
Total
effects
PEOU
(
2
R
=0.589)
SF 0.475 0.475*
SR 0.342 0.342*
SI 0.298 0.298*
SE 0.316 0.316*
PU
(
2
R
=0.522)
SF 0.329 0.222 0.551*
SR 0.020 0.188 0.208*
SI 0.096 0.005 0.101
PEOU 0.408 0.408*
BI
(
2
R
=0.609)
SQ 0.611 0.611*
CQ 0.608 0.608*
PEOU 0.277 0.432 0.709*
PU 0.735 0.735*
SE 0.102 0.151 0.253*
* ρ <0.05
System service quality, course quality and self-
efficacy had a significant positive direct effect on
behavioural intention to re-use (0.611, 0.608, 0.102).
The result shows that the total effect of self-efficacy
on behavioural intention to re-use is 0.253. These
determinants accounted for 60.9% of the variance in
behavioural intention of re-use.
0.316*
0.208*
0.253*
0.608* 
0.735* 
0.408*
0.709* 
0.342*
0.101*
0.298*
0.475*
0.551*
0.611*
Perceived
easeofuse



Behavioural
intentionto
re‐use
Perceived
usefulness
System
response
Servicequality
Coursequality
Self‐efficacy
System
functionality
System
interactivity
*ρ<0.05
Figure 2: Model testing results.
UNDERSTANDING BEHAVIORAL INTENTION OF E-LEARNING SYSTEM RE-USE
221

5 DISCUSSION
AND CONCLUSIONS
The results of data analysis demonstrate that most of
the causal relationships between constructs are well
supported. Comparing with system response and
system interactivity, system functionality has the
strongest effect on perceived ease of use and
perceived usefulness. It seems that a complete and
consistent system interface and function design
make the system easier to use, thus can motivate the
users to re-use the system. It is validated that system
functionality and system response indirectly
influence the learners’ intention of system re-use. It
is found that system interactivity has no significant
effect on perceived usefulness. Because of the
multiple communication tools provided by the
Internet, such as email, MSN, QQ and telephone,
which are easy to use and popular in China, learners
and teachers may choose one of those tools instead
of making interactions using e-learning system tools.
However, system interactivity positively affects the
behavioural intention of re-use, mediated by the
perceived ease of use.
Perceived usefulness has the strongest direct
effect on the learner’s intention of e-learning system
re-use. It seems that people living in rural areas
concern more about the outcomes of e-learning
because it is important for them to improve their
personal qualifications and enhance their career
prospective. Coping with the rapid economic
development in rural areas in China, people pay
more attention to continuous education. The
advantages of e-learning can provide the
opportunities for satisfying the demand. Perceived
ease of use is found to have the significant influence
on behavioural intention, but less direct effect than
perceived usefulness. This suggests that it is also
important for learners to use easy-to-use and user
friendly e-learning systems. Our findings reveal that
the perceived ease of use positively affects the
perceived usefulness although there are some
criticism on the relationship between perceived ease
of use and perceived usefulness (e.g. Mathieson,
1991; Venkatesh, 2000; Roca and Gagne, 2008).
Service quality and course quality appear to be
the significant determinant of learner’s behavioural
intention of e-learning system re-use. The results
suggest that the staff support and appropriate
interaction between students and teachers strongly
affect the e-learners motivation to re-use e-learning
systems in the future. Due to students’ limited access
to the learning facilities and the education centres in
rural areas, teachers’ support of learning through
regular communications, interactions and tutoring is
essential. Also, the course quality, such as the course
content, assessment methods and supplementary
information, affect the learner’s intention to re-use
the e-learning system.
Learners’ computer self-efficacy appears to have
a significant effect on behavioural intention of re-
use. The study identifies that computer self-efficacy
is also related to the perceived ease of use. This
finding is consistent with prior e-learning study. For
example, computer-efficacy was examined with a
significant positive effect on perceived ease of use
of e-learning (Venkatesh, 1996), and with significant
positive effects on perceived usefulness and negative
effects on perceived credibility of e-learning (Ong et
al., 2004).
Through the developing and empirically testing
the extended research model, a number of interesting
implications are generated, which would help e-
learning managers and practitioners in rural areas of
China to improve the effectiveness of e-learning.
These implications include:
1. To improve the learning productivity and
effectiveness, it was essential for managers and
designers to use appropriate system technologies
which could effectively facilitate the teaching and
learning process.
2. As the service quality and course quality are
significantly affect the learner’s intention of e-
learning system re-use, pedagogical principles,
including principles of developing and structuring
the course content should be employed in the
development and evaluation of relevant content.
3. Considering the low education level of the
people living in rural areas, the system use
instructions and manuals are necessary to provide
useful guidelines and improve the ease of use.
4. e-Learning programmes developed for learners
in rural areas of china must be practical and relevant
to learners’ personal development goal and work
requirements, so the learners can benefit from the
l
earning outcomes and improve their work
performance eventually.
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