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Factors influencing students’
adoption of e-learning: a structural
equation modeling approach
Ali Tarhini
Department of Information Systems,
Sultan Qaboos University College of Economics and Political Science,
Muscat, Oman
Ra’ed Masa’deh
School of Business, University of Jordan, Amman, Jordan
Kamla Ali Al-Busaidi
Department of Information Systems,
Sultan Qaboos University College of Economics and Political Science,
Muscat, Oman, and
Ashraf Bany Mohammed and Mahmoud Maqableh
School of Business, University of Jordan, Amman, Jordan
Abstract
Purpose –This research aims to examine the factors that may hinder or enable the adoption of e-learning
systems by university students.
Design/methodology/approach –A conceptual framework was developed through extending the
unified theory of acceptance and use of technology (performance expectancy, effort expectancy, hedonic
motivation, habit, social influence, price value and facilitating conditions) by incorporating two additional
factors, namely, trust and self-efficacy. Data were collectedfrom students at two universitiesin England using
a cross-sectional questionnaire survey between January and March 2015.
Findings –The results showed that behavioral intention (BI) was significantly influenced by performance
expectancy, social influence, habit, hedonic motivation, self-efficacy, effort expectancy and trust, in their order
of influencing the strength and explained 70.6 per cent of the variance in behavioral intention. Contrary to
expectations, facilitating conditions and price value did not have an influence on behavioral intention.
Originality/value –The aforementioned factors are considered critical in explaining technology adoption
but, to the best of the authors’knowledge, there has been no study in which all these factors were modeled
together. Therefore, this study will contribute to the literature related to social networking adoption by
integrating all these variables and the first to be tested in the UK universities.
Keywords E-learning, Unified theory of acceptance and use of technology,
Structural equation modeling, Technology acceptance, Technology adoption, UTAUT2
Paper type Research paper
1. Introduction
In Western countries and the rest of the world, e-learning, today, is considered as a powerful
and transformative tool to extend the traditional modes of learning and build capacity in
education and training (Mikhaylov and Fierro, 2015;Alfraih and Alanezi, 2016). E-learning “not
only provides academic institutions with efficient means to train and teach individuals, but also
JIEB
10,2
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Received 23 September2016
Revised 16 January2017
Accepted 25 January2017
Journal of International Education
in Business
Vol. 10 No. 2, 2017
pp. 164-182
© Emerald Publishing Limited
2046-469X
DOI 10.1108/JIEB-09-2016-0032
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2046-469X.htm
enablesthemtoefficiently codify and share their academic knowledge”(Al-Busaidi, 2012,
p. 12). E-learning is simply defined as the use of computer technology to deliver education or
training courses to learners; such courses may be studied online, offline or by any mixture of
these modes (Hemming, 2008;Al-Busaidi, 2013). Thus, e-learning gives the students the
flexibility in terms of time and place; it can also be as a mean of education that incorporates
self-motivation, communication, efficiency and technology. E-learning is the acquisition and
use of knowledge distributed and facilitated primarily by electronic means (Janda, 2016;Tetteh,
2016). Driscoll (2002) categorized the benefits of e-learning as strategic and technical benefits.
Strategic benefits are to improve the competitive advantage through e-learning ability to
develop a global workforce, to resend to shorter product development cycles, to manage flatter
organizations, to adjust to employee working hours and to increase skills and knowledge.
Whereas, technical benefits include reducing travel expenses, providing just-in-time learning,
making course updating easier and leveraging existing network infrastructure.
Despite the perceived benefits of e-learning mentioned above, the efficiency of such tools
will not be fully utilized if the students fail to use the system. Hence, the successful
implementation of e-learning tools depends on whether the students are keen to adopt the
system (Clay et al., 2008;Alqirim et al.,2017). Furthermore, Dodge et al. (2009) found that one
of the main challenges for online learning system is the consistently high drop-out rates.
Another study conducted by Patterson and McFadden (2009) revealed that drop-out rates in
online courses have been cited to be 10-20 per cent higher than face-to-face courses. Thus, it
has become imperative for practitioners and policymakers to understand the main factors
that may hinder or influence the adoption of e-learning systems to enhance the students’
learning experience (Liaw and Huang, 2011).
Examining the students’behavioral intention to use e-learning systems is critical for its
success and continuous adoption by students, instructors and universities. Several models have
been used to examine the determinants of users’behavioral intention to use a certain
technology. Some of these popular models include the technology acceptance model (TAM)
(Davis, 1989), the theory of planned behavior (TPB) (Ajzen, 1991), the theory of reasoned action
(TRA) (Fishbein and Ajzen, 1975), the unified theory of acceptance and use of technology
(UTAUT) (Venkatesh et al., 2003), the unified theory of acceptance and use of technology
(UTAUT2) (Venkatesh et al., 2012). UTAUT and the extended UTAUT2 are among the most
popular models recently used to assess users’behavioral intention to use information
technology in general (Venkatesh et al., 2003;Venkatesh and Bala, 2008;Venkatesh et al., 2012)
and in the e-learning context (Zhang et al., 2008;Teo, 2014;Tarhini et al., 2014a,b;Abu-Shanab,
2014;Masa’deh et al.,2016). Based on UTAUT2, this research aims to examine the most
important factors that affect students’behavioral intention to use e-learning systems in the
universities in the UK. Precisely, this study will investigate the impact of performance
expectancy, effort expectancy, hedonic motivation, habit, social influence, trust, facilitating
conditions, price value and self-efficacy on students’behavioral to use e-learning systems.
British universities’investment in the e-learning system has been increasing, likewise is
the researchers’interest in investigating e-learning acceptance in the UK universities’
context (Tarhini et al., 2014a,b;Tarhini et al., 2015a,b,c). About 95 per cent of participating
institutions in the UK have adopted e-learning systems for students’and instructors’use
(Browne et al.,2006). Mee (2007) assessed e-learning policy and the transformation of
schooling in the UK. “These changes are likely to shape the capacity of students in 2020 to
interact with e-learning systems and will define their expectations of the learning
infrastructure they will encounter”(Callender et al.,2014
, p. 52). This investigation will
enable policymakers at British universities to gain a deeper understanding of the students’
acceptance of e-learning technology and hence develop a better e-learning policy.
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The rest of the paper is organized as follows: the theoretical framework and
hypotheses are presented in Section 2. Section 3 presents the methodology that guided
our research. Data analysis and results of the measurement and structural model are
provided in Section 4. Finally, Section 5 discusses the main findings of the study and
concludes the paper with the implications and limitations of the study.
2. Theoretical framework and hypotheses
This section revises and re-defines a set of factors that seeks to explain the adoption of e-
learning systems by university students. In doing so, this study will extend the UTAUT2
[Performance expectancy (PE), effort expectancy (EE), hedonic motivation (HD), habit (HB),
social influence (SI), facilitating conditions (FCs), price value (PV)] with two additional
factors, namely, trust (TR) and self-efficacy (SE). The proposed theoretical framework is
illustrated in Figure 1, and the subsections which follow explain and justify each of the
predicted relationships in light of previous findings from the literature.
2.1 Performance expectancy
Performance expectancy is defined as the degree to which people believe that using a certain
technology would be usual to them and enhances their performance (Venkatesh et al.,2003).
In this research, performance expectancy refers to the degree to which a person believes that
an e-learning system will enhance his/her educational performance. Many studiesfound that
performance expectancy represents a strong predictor for behavioral intention (BI) of many
Figure 1.
The proposed
research model
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kinds of technologies (e.g. internet banking (Abu-Shanab and Pearson, 2007;Alalwan et al.,
2014), mobile banking (Alalwan et al., 2016), e-government (Rana et al.,2013;Sharma, 2015),
social media and e-learning (Sharma et al.,2016), and e-learning (Teo and Noyes, 2014;Wong
and Huang, 2015;Šumak and Šorgo, 2016;Masa’deh et al.,2016). Hence, the following
hypothesis is proposed:
H1. Performance expectancy will have a positive influence on behavioral intention
toward using e-learning system.
2.2 Effort expectancy
Effort expectancy refers to the degree of ease associated with the use of the system
(Venkatesh et al., 2003) and the extent to which a person believes that the use of the
technology will be free of effort (Yadav et al.,2016). Effort expectancy is similar to perceived
ease of use in the original TAM. Previous research showed that effort expectancy positively
influence behavioral intention (Abu-Shanab et al.,2010;De
cman, 2015;Zuiderwijk et al.,
2015;Sharma et al., 2016). Furthermore, this construct is considered as an essential
determinant of learning behavioral intention to use e-learning systems (Gruzd et al.,2012;
Mtebe and Raisamo, 2014;Teo et al.,2015;Masa’deh et al., 2016;Tarhini et al., 2016a,b,c). In
the context of this study, it is expected that if the students find the system easy to use, then
they are more likely to adopt it. Hence, the following hypothesis is postulated:
H2. Effort expectancy will have a positive influence on behavioral intention toward
using e-learning system.
2.3 Hedonic motivation
Hedonic motivation is defined as the way used to measure user’s perceived enjoyment and
perceived entertainment (Venkatesh et al., 2012). Hedonic motivation was added by
Venkatesh et al. (2012) to their new model to capture the role of intrinsic utilities. Venkatesh
et al. (2012) mentioned that the critical influence of hedonic motivation comes from the
novelty-seeking and innovativeness existing in using new systems. Prior studies examined
hedonic motivation and found that it can play significant part in technology adoption
(Alalwan et al., 2015;Yuan et al.,2015;Arenas-Gaitán et al.,2015) and in e-learning (Raman
and Don, 2013;Teo and Noyes, 2014;Ain et al.,2015;Masa’deh et al., 2016). In the context of
this study, it is expected that if the users feel that the e-learning system will be joyful to use,
and then they will be morelikely to use it.Hence, the following hypothesis is proposed:
H3. Hedonic motivation will have a positive influence on behavioral intention toward
using e-learning system.
2.4 Habit
Habit is defined as the perceptional structure of doing something often and regularly
(Venkatesh et al.,2012). In other words, when an individual repeats an action regularly and
he/she is satisfied with the outcome, the action then becomes habitual (Venkatesh et al.,
2012). Previous studies included habit to understand users’behavior, as prior habitual
behaviors can produce favorable feelings toward the behavior (Ain et al.,2015;Yuan et al.,
2015;Masa’deh et al., 2016;Hsiao et al.,2016). In general, users who frequently use electronic
devices are believed to have more potential to adopt new technologies even prior to use it
(Venkatesh and Zhang, 2010). Nevertheless, some studies found that habit has a negative
Factors
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influence on behavioral intention. For example, Raman and Don (2013) did not find any
correlation between habit and behavioral intention. In the context of this study, it is
expected that if the use of the system becomes a habit for the students, and then they are
more likely to adopt it. Consequently, the following hypothesis is postulated:
H4. Habit will have a positive influence on behavioral intention toward using e-learning
system.
2.5 Social influence
Social influence is defined as the degree to which a person perceives how important it is that
“other people”believe he or she should use a technology (Venkatesh et al., 2003). Conceptually,
social influence captures the role of subjective norms, social factors and image as proposed by
Venkatesh et al. (2003) in the UTAUT. The importance of social influence in shaping
behavioral intention is discussed in many studies (Teo, 2009;Park et al., 2012;Teo and Noyes,
2014;Yuan et al., 2015;Tarhini et al., 2015a,b,c;Sharma et al., 2015;Alzeban, 2016). The direct
impact of social influence on behavioral intention is justified from the fact that people may be
influenced by the opinion of others and thus be involved in a certain behavior even if they do
notwantto.Venkatesh et al. (2003) argue that the effect of social influence occurs only in
mandatory environments and has less influence in a voluntary environment. Consequently,
following the guidelines of UTAUT and because the use of e-learning system is mandatory (i.e.
students must use the e-learning system to complete their course), this research will study the
direct influence of social influence on behavioral influence. This study asserts that students’
behavioral intention toward e-learning systems use will be influenced by their instructors’and
friends’beliefs about the system. Thus, the following relationship is hypothesized:
H5. Social influence will have a positive influence on behavioral intention toward using
e-learning system.
2.6 Trust
Trust was defined by Gefen et al. (2000, p. 161) as “individual willingness to depend based
on the beliefs in ability, benevolence and integrity”. Trust means a subjective expectation
that someone or something is reliable and willing to accept vulnerability (Chen, 2015).
Arguably, trust has been largely discussed over the prior literature as a mechanism adopted
by individuals to mitigate the concerns expected in using a new system (Chen, 2015;Hong
and Cha, 2013;Gao and Bai, 2014;Rauniar et al., 2014;Amaro and Duarte, 2015;Chang et al.,
2015;Petrov et al.,2015;Bokhari et al., 2015;Alalwan et al., 2016). The absence of human
interaction between the user and service provider leads the users to perceive more risks in
using such systems (Rauniar et al.,2014). In the context of this study, it is expected that the
students will be more likely motivated to use the e-learning system if they trust such
systems. Therefore, this study suggests the following hypothesis:
H6. Trust will have a positive influence on behavioral intention toward using e-learning
system.
2.7 Facilitating conditions
Facilitating conditions is defined as the level of perception to use organizational and
technical infrastructure to support the use of new systems (Venkatesh et al.,2003).
Facilitating conditions are considered as one of the environmental factors that affect users’
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perception of how easy or difficult it is to perform a task. In other words, facilitating
conditions provide the external resources that are needed to facilitate the performance of a
particular behavior (Ajzen, 1991). In the context of workplace technology use, facilitating
conditions are believed to include the availability of training and provision of support. In the
context of this study, facilitating conditions will be measured by the perception of students
of whether they are able to access the required resources and the necessary support to use
the e-learning services. Prior studies have examined the critical impact of facilitating
conditions on technology adoption and acceptance (Teo, 2010) and within the e-learning
context (Teo, 2010;Abu-Shanab et al., 2010;Deng et al., 2011;Tarhini et al., 2014a,b,2016a,b,c;
Ain et al., 2015;Sharma et al., 2016;Wong and Huang, 2015;Wang, 2016). Hence, it is critical to
examine whether facilitating conditions have a direct influence on the adoption of the e-learning
system, as the absences of facilitating resources may represent barriers to usage (Wang, 2016).
Therefore, this proposes the following hypothesis:
H7. Facilitating conditions will have a positive influence on behavioral intention toward
e-learning system.
2.8 Price value
Price value is defined as a “consumer’s cognitive trade-off between the perceived benefits of
the application and the monetary cost for using it”(Venkatesh et al.,2012, p. 161). In
comparison with organizational setting, where the required facilities to use technology are
freely available, students are responsible for carrying the associated monetary cost of using
such systems. The students are more likely to adopt the e-learning systems when they find
that the financial cost of using these systems is not much compared to the benefit of using
such system. There are many studies that have supported the considerable impact of price
value or perceived value on behavioral intention (Kim and Niehm, 2009;Chen, 2015;Lim and
Cham, 2015;Yuan et al., 2015;Arenas-Gaitán et al., 2015;De
cman, 2015). Therefore, this
study proposes the following hypothesis:
H8. Price value will have a positive influence on behavioral intention toward e-learning
system.
2.9 Self-efficacy
Self-efficacy –as an internal individual factor –is defined as individuals’judgments about
their capabilities to organize and execute the courses of action required to produce given
attainments (Bandura, 1997, p. 3). In the social cognitive theory (SCT), self-efficacy is a type
of self-assessment that helps the understanding of human behavior and performance in a
certain task (Bandura, 1997). In the context of IT, self-efficacy has been defined as “an
individual’s perceptions of his or her ability to use computers in the accomplishment of a
task rather than reflecting simple component skills”(Compeau and Higgins, 1995,p.192).
Prior studies have found self-efficacy to be a critical predictor that directly affects the user’s
behavioral intention (Downey, 2006;Guo and Barnes, 2007;Hernandez et al., 2009;Abbasi
et al., 2015) and e-learning acceptance (Chang and Tung, 2008;Yuen and Ma, 2008;Park,
2009;Chatzoglou et al.,2009;Tarhini et al., 2015a,b,c;Wong and Huang, 2015). On the
contrary, Venkatesh et al. (2003) did not find a casual direct relationship between self-
efficacy and behavioral intention. In the context of this study, self-efficacy is defined as a
student’s self-confidence in his/her ability to perform certain learning tasks using the
e-learning system. It is expected that e-learning users with higher level of self-efficacy are
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more likely to adopt the system than those with lower self-efficacy. Hence, this study
postulates the following hypothesis:
H9. Self-efficacy will have a positive influence on behavioral intention toward using
e-learning system.
3. Methodology
3.1 Participants and procedure
This research is based on a non-probabilistic and self-selection sampling method, a
convenience sample. More specifically, data were collected by means of self-administrated
questionnaire containing 44 questions from two British universities who use e-learning tools
as part of their teaching between February and April 2015. All participants were volunteers
and no financial incentive was offered. Participants took about 12 min to complete the
questionnaire. After screening for missing data, we retained 366 questionnaires for data
analysis. These included 55.4 per cent male and the mean age range varies from 17 to 34
years, with 73.5 per cent experienced in using computers and e-learning tools.
3.2 Instrument
The items used in the proposed research model were adopted from previous studies related
to UTAUT2 proposed by Venkatesh et al.(2012). Specifically, performance expectancy and
behavioral intention were measured using five items, whereas effort expectancy, social
influence, hedonic motivation, price value, habit and facilitating conditions were measured
using four items. These items were adopted from Venkatesh et al. (2012) and related work
Tarhini et al. (2016a,b,c), Teo et al. (2016),Sharma et al. (2016). Four items were used to
measure self-efficacy and trust and were adopted from Zhang et al. (2008),Garrido-Moreno
et al. (2008) and Kim and Niehm (2009). These items were anchored on a seven-point Likert
scale, ranging from 1 –strongly disagree to 7 –strongly agree.
4. Data analysis
In this study, SPSS was used to compute the descriptive statistics, whereas the analysis of
the data was conducted in two phases. The first phase used confirmatory factor analysis
(CFA) and involved the analysis of the measurement model in examining reliability and
validity of the proposed research model, whereas the second phase involved the analysis of
the structural model and hypotheses testing.
4.1 Descriptive analysis
As can be shown in Table I, the mean values of all items were above the mid-point of 3.5
which suggests the respondents had given generally positive responses to the items being
measured. The standard deviations ranged from 0.98 to 1.55 which shows a narrow spread
around the mean.
4.2 Analysis of measurement model
A CFA based on AMOS 21.0 was used to examine the relationships among the constructs
within the proposed research model (Arbuckle, 2012). This was conducted using the
maximum-likelihood estimation (MLE) procedure to estimate the model’s parameters, where
all analyses were conducted on variance-covariance matrices (Hair et al., 2010). To assess the
model goodness-of-fit, Kline (2010) and Hair et al. (2010) recommended some fit indices that
should be considered. These indices are root mean square residuals (RMSR), the root mean
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square error of approximation (RMSEA), comparative fit index (CFI), adjusted goodness-of-
fit index (AGFI), goodness-of-fit index (GFI), normed fit index (NFI) and parsimony normed
fit index (PNFI).
Table II shows the level of acceptance fit and the fit indices for our sample after the
improvement in model fit. To obtain a better model fit, three indicators (SE3, SI4 and PV2)
were deleted from the initial measurement model to ensure good model fit. The results of the
model revealed a good fit with the data (Table II). It is clear from the table that all fit indices
were in the recommended range. Hence, we can proceed to assess convergent validity,
discriminant validity in addition to reliability to evaluate if the psychometric properties of
the measurement model are adequate.
4.3 Construct reliability, convergent validity and discriminant validity
Convergent validity checks if the measures of each construct within the model are reflected
by their own indicators (Gefen et al.,2000). This will ensure unidimensionality of the
multiple-item constructs and will help in eliminating any unreliable indicators (Bollen, 1989).
Whereas, discriminant validity checks whether the measures of different concepts that are
supposed to be unrelated are statistically different (Gefen et al., 2000). Composite reliability
(CR) and average variance extracted (AVE) were used to assess the reliability, convergent
validity and discriminant validity as recommended by Hair et al. (2010).Hair et al. (2010)
suggest that CR should be above 0.7 to establish good reliability; the AVE should be above
0.5 and the CR should be greater than the AVE to establish convergent validity. Whereas,
the total AVE of the average value of variables should be larger than their correlation value
to support discriminant validity (Hair et al., 2010).
Table II.
Model fit summary
for the final
measurement and
structural model
Fit index Recommended value Measurement model Structural model
x
2
/df <5 preferable <3 2.93 2.90
Goodness-of-fit index (GFI) >0.90 0.932 0.935
Adjusted goodness-of-fit index (AGFI) >0.80 0.844 0.845
Comparative fit index (CFI) >0.90 0.925 0.925
Root mean square residuals (RMSR) <0.10 0.083 0.085
Root mean square error of approximation
(RMSEA) <0.08 0.069 0.070
Normed fit index (NFI) >0.90 0.917 0.919
Parsimony normed fit index (PNFI) >0.60 0.746 0.747
Table I.
Descriptive statistics
of the constructs
Construct Mean Standard deviation
Performance expectancy (PE) 5.36 1.41
Effort expectancy (EE) 5.25 1.26
Social influence (SI) 4.20 1.53
Hedonic motivation (HD) 5.08 1.18
Price value (PV) 5.32 0.98
HB (HB) 5.40 1.37
Trust (TR) 4.82 1.32
Self-efficacy (SE) 5.11 1.55
Behavioral intention (BI) 5.45 1.25
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The results in Table III show that the AVE and CR of all the constructs were above 0.536
and above 0.787, respectively, suggesting that the constructs had adequate reliability and
convergent validity. In addition, the square root of AVE is higher than their correlation
values which suggest that all the constructs illustrated sufficient discriminant validity.
4.4 Structural model and hypotheses testing
The next step after establishing good convergent and discriminant validity was to assess
the structural model to test the proposed relationships.
As can be shown in Table IV, the results showed that students’behavioral intention to
use e-learning systems was significantly influenced by performance expectancy, social
influence, habit, hedonic motivation, self-efficacy, effort expectancy and trust, in their order
of influencing strength and explained 70.6 per cent of the variance in behavioral intention.
These results provide support for H1,H2,H3,H4,H5,H6 and H9. Contrary to our
expectations, facilitating conditions and price value did not have an effect on behavioral
intention. Hence, H7 and H8 were not supported in this study.
5. Discussion, implications and suggestions for future work
5.1 Discussion and conclusion
The main objective of this study is to examine the main factors that may influence or hinder
the adoption of e-learning systems in the UK. A conceptual framework was developed
through extending the unified theory of acceptance and use of technology (UTAUT2) by
Table III.
Construct reliability,
convergent validity
and discriminant
validity (factor
correlation matrix
with HAVE on the
diagonal)
Item CR AVE SE PE EE SI HB PV HD TR FC BI
SE 0.911 0.742 0.864
PE 0.924 0.703 0.556 0.845
EE 0.906 0.711 0.642 0.634 0.850
SI 0.854 0.536 0.496 0.532 0.475 0.798
HB 0.892 0.612 0.403 0.688 0.562 0.440 0.779
PV 0.864 0.689 0.530 0.579 0.579 0.451 0.562 0.865
HD 0.787 0.702 0.744 0.641 0.530 0.459 0.522 0.720 0.759
TR 0.880 0.625 0.572 0.713 0.638 0.512 0.758 0.618 0.601 0.864
FC 0.875 0.710 0.422 0.555 0.456 0.419 0.690 0.525 0.572 0.663 0.866
BI 0.920 0.732 0.585 640 0.553 0.439 0.586 0.611 0.522 0.601 0.654 0.778
Table IV.
The research model
results
H# Proposed relationship Estimate Result
H1 PE –>BI 0.255*** Supported
H2 EE —>BI 0.116* Supported
H3 HD —>BI 0.128* Supported
H4 HB —>BI 0.198*** Supported
H5 SI —>BI 0.229*** Supported
H6 TR —>BI 0.104* Supported
H7 FC —>BI 0.095 Not supported
H8 PV —>BI 0.071 Not supported
H9 SE —>BI 0.121* Supported
Notes: *p<0.05; ***p<0.001
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incorporating two additional factors, namely, trust and self-efficacy. This study has
successfully supported, both theoretically and empirically, the ability of UTAUT2 to be a
useful theoretical framework for better understanding the student’s behavioral intention to
use e-learning technology within the British context. More specifically, the results showed
that students’behavioral intention to use e-learning tools was significantly influenced by
performance expectancy, social influence, habit, hedonic motivation, self-efficacy, effort
expectancy and trust, in their order of influencing the strength and explained 70.6 per cent of
the variance in behavioral intention. Contrary to our expectations, facilitating conditions
and price value did not have an influence on behavioral intention.
More specifically, our results indicate that performance expectancy was a significant
determinant of behavioral intention to use e-learning. It is therefore believed that students who
found the system useful in their learning process will be more willing to adopt the e-learning
system. Hence, to attract more users of e-learning, instructors should improve the content quality
of their e-learning systems by providing sufficient, up-to-date content that can fitthestudents’
needs. The results also showed that effort expectancy influenced the behavioral intention of
students to accept and use e-learning system. This construct is considered as an essential
determinant of learning behavioral intention to use e-learning systems (Mtebe and Raisamo,
2014). In the literature, effort expectancy has significant influence on willingness to use online
systems (Tarhini et al., 2015a,b,c, Alalwan et al.,2015). Our results suggest that training is not
necessary for individuals who are skilled in using technology; however, it is crucial for the less
skilled ones, as those users will form their perceptions about using the e-learning system on the
ease of use of the system no matter how useful the system is. Hence, to promote the ease of use of
e-learning, system designers should provide a system which promotes ease of use.
The two factors (hedonic motivation and habits) are tested in this research to explore the
impact and significant level on users’behavioral intention of e-learning systems. Hedonic
motivation refers to user’s perceived enjoyment and entertainment, whereas habit refers to
perceptional structure of doing something often and regularly. The results showed that both
factors are critical determinants of behavioral intention, which is consistent with the
findings of (Adedoja et al.,2013;Bakar and Razak, 2014;Elkaseh et al., 2015;Masa’deh et al.,
2016) and also consistent with the UTAUT2 proposition. In other words, if the use of the
system becomes a habit for theusers and also feel that the e-learning system will be joyful to
use, then they are more likely to use it. Hence, hedonic motivation and habit are critical in
expanding the scope and generalizability of UTAUT2 to the e-learning environment.
Social influence refers to the degree to which a person perceives how important it is that
“other people”believe he or she should use a technology. In the context of e-learning, peers’
and instructors’opinions can affect other opinions and beliefs. The results showed that
social influence has a direct influence on student’s behavioral intention to use e-learning
system. In fact, the impact of social influence on an individual can vary based on different
factors such as culture, age and education. The results of this research are consistent with
the findings of Masa’deh et al. (2016) and Elkaseh et al. (2015). It is therefore advisable for
instructors to announce to the students that using the e-learning system is mandatory and it
is also advised that practitioners should persuade users who are familiar with the system to
help in promoting it to other users. Thus, when the number of e-learning users reaches a
critical mass point, the number of later e-learning adopters is likely to grow rapidly (Tarhini
et al., 2015a,b,c). This emphasizes the need to consider implementation strategies that
develop buy-in from those within the wider social environment.
Trust refers to the degree of reliability and willingness of someone or something to
accept vulnerability. The results showed that trust play a very significant role in behavioral
intention of students to accept and use e-learning system. The analysis result of trust in the
Factors
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173
literature showed that it very critical in motivating students in accepting e-learningsystems.
In 2015, a research group investigated the relationship between intention to use academic e-
learning and trust. They found that trust significantly influences the user’s behavioral
intention (Petrov et al., 2015). In general, trust plays a very critical role in acceptance and
accelerate adoption most of the digital systems. Thus, this research is aligned with many
other researches in the literature.
However, the results showed that the degree of facilitating conditions to use an e-learning
system has no significant influence on students’behavioral intention to use e-learning
system. This hypothesis is not supported while accounting for an estimate of 0.095 in
explaining overall students behavioral intention to use e-learning system which is
inconsistent with some pervious literature (Bakar and Razak, 2014;Masa’deh et al.,2016).
Bakar and Razak (2014) research confirmed that there is a positive impact between self-
efficacy and continuance intention to use e-learning. It is clear that individuals with higher
self-efficacy induce a more active learning process (Chung et al., 2010). Therefore, IT teams
should provide both on- and off-line support in addition to training and this is necessary to
increase e-learning self-efficacy. Training is very useful in boosting self-confidence in the
use of technology and eventually individuals who demonstrate higher self-confidence in
using technology are more likely to use the system. Furthermore, the results showed that the
degree of price value to use an e-learning system has no significant positive influence on
students’behavioral intention to use e-learning system. This hypothesis is not supported
while accounting for an estimate of 0.071 in explaining overall students’behavioral intention
to use e-learning system.
Finally, self-efficacy was found to play a critical role in predicting student’s behavioral
intention to adopt the e-learning system. Self-efficacy has also been implicated in inducing a
more active learning process (Chung et al.,2010). Therefore, policymakers should provide
both on- and off-line support in addition to training which is necessary to increase e-learning
self-efficacy.
5.2 Contributions to theory and practice
This study draws several implications for theory, methodology and practice. From the
theoretical point of view, the core outcomes of this research are to develop a conceptual
research model that allows a better understanding of the factors that affect the users’
behavioral intention to use e-learning technology in the UK. This study concludes that
performance expectancy, effort expectancy, hedonic motivation, habit, social influence, trust
and self-efficacy play important roles on student’s behavioral intention to use e-learning in
the UK. The applicability of UTAUT2 is limited in the educational settings as much of the
research has been carried out in non-educational contexts. Consequently, Venkatesh et al.
(2012) emphasizes on the importance of testing UTAUT2 in different cultures to enhance its
generalizability and validity. Hence, this study extended UTAUT2 with two new factors,
namely, trust and self-efficacy, which add a further step to the studies that take into account
the individual, social and organizational factors in technology acceptance and adoption.
Another significant contribution of this work is to demonstrate the importance of trust as an
antecedent to behavioral intention within the context of e-learning adoption. This variable
has previously been suggested as potentially important but had not been investigated
thoroughly in empirical work on UTAUT2, nor had it been investigated in relation to
e-learning acceptance. The results of our study validate and confirm that trust is an
important consideration in the study of e-learning adoption.
This study helps to better understand the characteristics of the students in the UK, which
can help policymakers, educators and experts to understand what the students expect from
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the learning management systems. This can help the management achieve the most
effective deployment of such system and also helps them improve their strategic decision-
making about technology in the future, they can decide on the best approach that fits their
students before implementing any new technology. Additionally, for the system developer
of e-learning systems, this research provides the opinions of the British university students
on the important factors that affect the adoption and acceptance of such system which will
help them understand how they could improve their learning management systems.
Similarly, the users (students) can understand what motivations and factors drive them into
accepting the use of technology.
5.3 Limitations and future work
This study has some potential limitations that need to be identified and discussed. First,
data were collected from the students using a convenience sampling technique and
consequently should not necessarily be considered representative of the population. Second,
although our results find support for UTAUT2 in England, generalizability of the findings
to other countries should be treated with caution. Third, this research uses a cross-sectional
method and quantitative survey to collect the data. Although the questionnaire has strong
theoretical literature and was carefully distributed to students, using a purely quantitative
analysis limits the ability to have an in-depth view of the phenomena being investigated
which is mainly found in qualitative research. Hence, using this method only is justified
because of time and resources taking into consideration that the research aim and objectives
have been achieved. Therefore, future research could use a variety of methodologies
(interviews, qualitative methods, longitudinal study, etc.) to understand the adoption and
acceptance of technology. In addition, as user’s behavior may differ depending on cultural,
social, situational, beliefs and technology acceptance level and because the findings of our
study are context-specific (the UK), it would be more typical to investigate if our developed
model may hold for different nationalities and different geographical countries, like other
developed or less developed countries. This will be valuable in assessing the robustness and
the validity of the research model across different cultural settings. Therefore, future
research could replicate our study among mono- and multi-cultural samples. Furthermore,
future research may extend our study to integrate other potential constructs of interest to
increase the explained variance of UTAUT2. In addition, further research could consider a
set of individual differences such as culture and demographic characteristics and then a
more complete picture of the dynamic nature of individual technology may begin to emerge.
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Further reading
Khasawneh, R.T. and Abu-Shanab, E.A. (2013), “E-government and social media sites: the role and
impact”,World Journal of Computer Application and Technology, Vol. 1 No. 1, pp. 10-17.
Vijayasarathy, L.R. (2004), “Predicting consumer intentions to use on-line shopping: the case for an
augmented technology acceptance model”,Information & Management, Vol. 41 No. 1, pp. 747-762.
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Appendix 1. Survey constructs and their items
Performance expectancy
PE1. I find e-learning system allow me to accomplish learning tasks more quickly.
PE2. Using e-learning system improves my learning performance.
PE3. Using e-learning system makes it easier to learn course content.
PE4. Using e-learning system increases my productivity.
PE5. Using e-learning system enhances my effectiveness in learning.
Effort expectancy
EE1. Learning how to use the e-learning system is easy for me.
EE2. My interaction with the e-learning system is clear and understandable.
EE3. I find e-learning system easy to use.
EE4. It is easy for me to become skillful at using e-learning system.
Hedonic motivation
HD1. Computers and e-learning services make learning more interesting.
HD2. Learning using computers and e-learning services is fun.
HD3. I like using computers.
HD4. I look forward to those aspects of my learning activities that require me to use computers.
Habit
HB1. The use of internet and e-learning system has become a habit for me.
HB.2 I am addicted to using internet and e-learning system for educational purposes.
HB3. I must use internet and e-learning in my learning activities.
HB4. Using internet and e-learning system has become natural to me.
Social influence
SI1. My instructors thinks that I should participate in the e-learning activities.
SI2. My colleagues think that I should participate in the e-learning activities.
SI3. Management of my university thinks that I should use the e-learning activities.
SI4. The opinion of non-academic groups (e.g. friends and family) is important to me.
Trust
TR1.The website presents enough online security.
TR2. I trust that my activities while using the e-learning system are secure and private.
TR3. I believe my personal information on the e-learning system will be kept confidential.
TR4. Overall, I am not worried to use the e-learning system because other people will not be able to
access my account.
Facilitating conditions
FC1. I have the knowledge necessary to use the e-learning system.
FC2. The technology necessary (computers, cables, modems, etc.) for the internet and e-learning use
in my university is modern and updated.
FC3. I have the resources necessary to use the e-learning system.
FC4. When I needed help to use the e-learning system, guidance was available to me.
Factors
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Price value
PV1. Using the free resources such as e-libraries helped me to save money and effort.
PV2. Using emails to communicate with other student groups help me to save my expense and effort.
PV3. Internet is reasonably priced.
PV4. Internet is a good value for the money.
Self-efficacy
SE1. I am confident of using the e-learning system even if there is no one around to show me how to
do it.
SE2. I am confident of using the e-learning system even if I have only the online instructions for
reference.
SE3. I am confident of using the e-learning system even if I have never used such a system before.
SE4. I am confident of using the e-learning system as long as someone shows me how to do it.
Behavioral intention
B1. I intend to use the e-learning system for preparing for the exam and course work.
BI2. Given the chance, I intend to use the e-learning system to do different things, from downloading
lecture notes and participating in chat rooms to learning on the Web.
BI3. I predict I would use e-learning system in the next semester.
BI4. In general, I plan to use e-learning system frequently for my coursework and other activities in
the next semester.
BI5. I intend to engage in e-learning routinely.
Corresponding author
Ali Tarhini can be contacted at: ali.tarhini@hotmail.co.uk
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