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Measuring the Moderating Effect of Gender and
Age on E-Learning Acceptance in England: A
Structural Equation Modeling...
Article in Journal of Educational Computing Research · October 2014
DOI: 10.2190/EC.51.2.b
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MEASURING THE MODERATING EFFECT OF GENDER
AND AGE ON E-LEARNING ACCEPTANCE IN ENGLAND:
A STRUCTURAL EQUATION MODELING APPROACH FOR
AN EXTENDED TECHNOLOGY ACCEPTANCE MODEL
ALI TARHINI
KATE HONE
XIAOHUI LIU
Brunel University
ABSTRACT
The success of an e-learning intervention depends to a considerable extent
on student acceptance and use of the technology. Therefore, it has become
imperative for practitioners and policymakers to understand the factors
affecting the user acceptance of e-learning systems in order to enhance the
students’ learning experience. Based on an extended Technology Acceptance
Model (TAM), the main aims of this study are to investigate the factors
affecting students’ behavioral intention to adopt e-learning technology and to
explore the moderating effect of age and gender on the relationships among
the determinants affecting e-learning acceptance. This study is based on a
total sample of 604 students who used a Web-based learning system at Brunel
University in England. Confirmatory Factor Analysis (CFA) was used to
perform reliability and validity checks, and structural equation modeling
(SEM) was used to test the research model. The results indicate that perceived
ease of use, perceived usefulness, social norm, and self-efficacy were critical
factors for students’ behavioral intention to use e-learning, with the effect
of perceived usefulness found to have the highest magnitude among the
main determinants. We also found that age moderates the effect of PEOU,
PU, and SE on BI, and that gender moderates the effect of PEOU and SN
on BI. However, surprisingly, no significant moderating effect of age on
the relationship between SN and BI was found; results also revealed no
moderating of gender on PU or SE and BI. Overall, the proposed model
achieves acceptable fit and explains 62% of its variance, which is higher than
that explained by the original TAM. Based on these findings, implications
to both theory and practice are discussed.
163
Ó 2014, Baywood Publishing Co., Inc.
doi: http://dx.doi.org/10.2190/EC.51.2.b
http://baywood.com
J. EDUCATIONAL COMPUTING RESEARCH, Vol. 51(2) 163-184, 2014

INTRODUCTION
The use of information and communication technology (ICT) tools to support
e-learning in education is vital at the present time. E-learning has been defined as
learning and teaching facilitated online through network technologies with no
barriers of time and place (NGai, Poon, & Chan, 2007). E-learning environments
reduce the cost of provision and therefore increase revenues for academic insti-
tutions (Ho & Dzeng, 2010). They also afford students more study flexibility and
improve their learning experience and performance (Christie & Ferdos, 2004).
Despite the enormous growth of e-learning in education and its perceived
benefits, the efficiency of such tools will not be fully utilized if the users are not
inclined to accept and use the system. Therefore, the successful implementation
of e-learning tools depends on whether or not the students are willing to adopt
and accept the technology. Thus, it has become imperative for practitioners and
policymakers to understand the factors affecting the user acceptance of Web-
based learning systems in order to enhance the students’ learning experience
(Liaw & Huang, 2011). Within this context, social factors (Schepers & Wetzels,
2007) and individual factors such as computer efficacy (Liaw, 2008) should be
taken into account in the acceptance and adoption of technology.
The Technology Acceptance Model (TAM) (Davis, 1989) is the most fre-
quently cited and influential model for explaining technology acceptance and
adoption. TAM has received extensive empirical support in the IS implementation
area (Venkatesh & Bala, 2008) including, for example, in e-government (Phang
et al., 2006; Walker & Johnson, 2008), e-health (Lanseng & Andreassen, 2007), and
e-learning (Arbaugh et al., 2009; Liu, Chen, Sun, Wible, & Kuo, 2010; Rodriguez
& Lozano, 2011; Sánchez & Hueros, 2010; Zhang, Zhao, & Tan, 2008). The
empirical support leads us to conclude it has acceptable explanatory power.
Although the TAM measures and predicts the acceptance and usage level of
technology, there have been some criticisms concerning the theoretical con-
tributions of the model, specifically its ability to fully explain technology adoption
and usage (Bagozzi, 2007; Benbasat & Barki, 2007; Straub & Burton-Jones,
2007). Furthermore, the existing parameters of the TAM neglected the investi-
gation of other essential predictors and factors that may affect the adoption
and acceptance of technology.
Furthermore, while TAM has generally been found to have acceptable explan-
atory power, the inclusion of moderators could improve this further (Sun &
Zhang, 2006). For example, when including gender and experience in TAM2, the
explanatory power increased from 35 % to 53% (Morris, Davis, & Davis, 2003).
Within this context, a number of researchers have recommended the need to
incorporate a set of moderators that remain largely untested, such as experience
(Venkatesh & Bala, 2008) and cultural background (Qingfei, Yuping, & Shaobo,
2009). In particular, the moderating effects of gender and age have received
relatively little attention in the literature (Wang, Wu, & Wang, 2009).
164 / TARHINI, HONE AND LIU

To address the aforementioned issues, this study extends the TAM to include
two other determinants, namely, social norm and self-efficacy, as well as gender
and age as moderators, to investigate the extent to which these variables affect
students’ willingness to adopt and use e-learning systems in UK universities.
This article adds to the few studies that have taken into account the critical
role that social and individual factors play in e-learning technology acceptance
(Tarhini, Hone, & Liu, 2013c).
The rest of the article is organized as follows: Section 2 presents and describes
the framework of the research. Section 3 describes the data and methodology.
Section 4 presents and discusses the results, and finally, Section 5 presents the
main conclusions and outlines future work.
Theoretical Framework
This study proposes and tests a conceptual model of e-learning technology
acceptance based on TAM, drawing from previous literature that used TAM
in an educational context. The model extends TAM through the inclusion of
subjective norms (SN) and self-efficacy (SE) as additional predictor variables
and through the inclusion of two individual differences, namely, age and
experience, as moderators. It should be noted that this study has omitted the
Attitude construct from the proposed research model based on Davis’ (1989)
recommendation, which found that the model with the Attitude removed
was a “powerful [model] for predicating and explaining user behavior based
on only three theoretical constructs: intentions, PU, and PEOU” (p. 997). Many
empirical studies also used simplified TAM and found significant casual rela-
tionships between behavior beliefs and behavior intentions. The overall con-
ceptual model is illustrated in Figure 1, and the sections that follow explain
and justify each of the predicted relationships in light of previous findings
from the literature. The boxes represent the constructs that are measured by a
set of items, while the causal relationships (Hypotheses) among these constructs
are represented with arrows.
Perceived Usefulness and Ease of Use
According to Davis (1989), perceived ease of use (PEOU) is defined as ”the
degree to which a person believes that using a particular system would be free of
effort” (Davis, Bagozzi, & Warshaw, 1989 p. 320), while perceived usefulness
(PU) is defined as “the degree to which a person believes that using a particular
system would enhance his/her job performance” (Davis, 1989, p. 453). In the
TAM, both PU and PEOU were theorized as direct determinants of behavioral
intention (BI). The causal relationship between PU and PEOU on BI toward using
the technology is supported in a considerable number of studies (Cheng, Lam, &
Yeung, 2006; Davis et al., 1989; Venkatesh & Davis, 2000) and confirmed in the
context of e-learning studies (Liu et al., 2010; Park, 2009; Rodriguez & Lozano,
INVESTIGATING THE MODERATING EFFECT / 165

2011; Šumak, Heri…ko, & Pušnik, 2011). While the available studies about student
perceptions on using technology support the important role that PEOU and PU
play in predicting BI, the strength of the identified relationships varied con-
siderably between studies. These differences may reflect issues such as the field
of study, sample size, or analysis techniques used.
Based on previous findings, we predict that if students think that a Web-based
learning system is useful and easy to use, then they are more likely to adopt and
use the system. In contrast, students may resist educational technologies if they
are skeptical of its educational value and if they find it hard to use. We therefore
posit the following hypotheses:
H1: Perceived ease of use will have a positive influence on students’
behavioral intention to use a Web-based learning system.
H2: Perceived usefulness will have a positive influence on students’
behavioral intention to use a Web-based learning system.
Subjective Norm
Subjective norm, also known as social norm, is defined as ”the person’s
perception that most people who are important to him or her think he or she should
or should not perform the behavior in question” (Ajzen, 1991; Ajzen & Fishbein,
1980). In other words, SN refers to the social pressure coming from the external
environment that surrounds the individuals and may affect their perceptions
and behaviors of engaging in a certain role (Ajzen, 1991). SN was included in
many theories such as the Theory of Reasoned Action (TRA), the Theory of
Planned Behavior (TPB), and directly and significantly related to behavioral
intention and usage (Ajzen, 1991; Ajzen & Fishbein, 1980).
Figure 1. Theoretical framework.

SN has been characterized in research as an antecedent of BI. The direct
effect of SN on BI is justified from the fact that people may be influenced by
the opinion of others and thus involved in certain behavior even if they don’t
want to be. However, there are inconsistencies in the findings when studying the
direct impact of SN on BI. For example, while some scholars found a significant
influence of SN on BI (Grandon, Alshare, & Kwun, 2005; Park, 2009; Venkatesh
& Morris, 2000), others failed to find any influence. Venkatesh and Davis (2000)
argue that the effect of SN occurs only in mandatory environments and has
less influence in a voluntary environment, which may explain at least some of
the variation between findings.
In studies specifically addressing e-learning acceptance, there is a similar
inconsistency of results. Van Raaij and Schepers (2008) found only an indirect
effect of SN via its influence on PU in a study of VLE acceptance in China. On
the other hand, Park (2009) found a direct effect of SN on BI for e-learning
acceptance in Korea. Our own work in the developing world context of Lebanon
also supported a direct influence of SN on BI (Tarhini, Hone, & Liu, 2013b).
Drawing on past findings and bearing in mind the mandatory nature of
e-learning usage within our research context, it is hypothesized that
H3: SN will have a positive influence on student’s behavioral intention to
use and accept a Web-based learning system.
E-Learning Self-Efficacy
Self-efficacy (SE), as an internal individual factor, has been defined as the
belief “in one’s capabilities to organize and execute the courses of action required
to produce given attainments” (Bandura, 1997, p. 3). In the Social Cognitive
Theory (SCT), SE is a type of self-assessment that helps in understanding human
behavior and performance in a certain tasks (Bandura, 1995, 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 & Higgins, 1995 p. 192). According to
Marakas, Mun, and Johnson (1998), SE is categorized into two types: The first
is related to general use of computers and is known as ”general Computer
self-efficacy,” whereas the second is related to a specific task on the computer and
is known as ”task-specific computer self-efficacy.” Several studies have found SE
to be an important determinant that directly influences the user’s behavioral
intention and actual usage of IT (Downey, 2006; Guo & Barnes, 2007; Hernandez,
Jimenez, & Jose Martin, 2009; Shih & Fang, 2004; Yi & Hwang, 2003),
and e-learning acceptance (Chang & Tung, 2008; Chatzoglou, Sarigiannidis,
Vraimaki, & Diamantidis, 2009; Yuen & Ma, 2008). On the contrary, Venkatesh
et al. (2003) did not find a casual direct relationship between SE and BI.
In the context of this study, e-learning self-efficacy is defined as a student’s
self-confidence in his or her ability to perform certain learning tasks using the

e-learning system. In general, it is expected that e-learning users with higher
levels of self-efficacy are more likely to be willing to accept and use the system
than those with lower self-efficacy. Therefore, consistent with previous research
that integrated self-efficacy as a direct predictor that affects the actual usage of
the system, we propose the following hypothesis:
H4: Computer self-efficacy will have a positive influence on behavioral
intention toward using a Web-based learning system.
Moderating Effects
Age
Research suggests that age is an important demographic variable that has direct
and moderating effects on behavioral intention, adoption, and acceptance of
technology (Chung, Park, Wang, Fulk, & McLaughlin, 2010; Porter & Donthu,
2006; King & He, 2006; Venkatesh et al., 2003; Wang et al., 2009). A number of
authors have speculated that the inclusion of age as a moderator would increase
the explanatory power of a TAM (see Chung et al, 2010). Venkatesh et al. (2003)
reported that age was an important moderator within their UTAUT model. They
found that within an organizational context, the relationship between performance
expectancy (similar to PU) and BI was stronger for younger employees. However,
other studies have failed to replicate this effect. Chung et al. (2010) found no
moderating effect of age on PU’s relationship with intention to engage in online
communities. In the e-learning context, Wang et al. (2009) also failed to find a
moderating effect of age on the relationship between performance expectancy
and intention to use a mobile learning system.
Venkatesh et al. (2003) also found a moderating effect of age on the relation-
ship between effort expectancy (similar to PEOU) and behavioral intention
within their ITAUT model, with the relationship stronger for older users. Wang
et al. (2009) provide support for this finding within their study of m-learning
acceptance. However, Chung et al. (2010) failed to find a moderating effect of age
on the impact of PEOU on BI within the context of online community engagement.
In terms of computer and Internet self-efficacy, it has been found that older
people have low self-efficacy in use of technology (Czaja et al., 2006). The
rationale could be that older adults often think that they are too old to learn a new
technology (Turner, Turner, & Van de Walle, 2007). Previous research also found
that age differences influence the perceived difficulty of learning a new software
application (Morris & Venkatesh, 2000; Morris, Venkatesh, & Ackerman, 2005).
There is clear evidence that younger adults have lower levels of computer anxiety
than their older counterparts (Chaffin & Harlow, 2005; Saunders, 2004) and that
lower levels of computer anxiety are associated with less reluctance to engage
in opportunities to learn new Internet skills (Jung et al., 2010). This relationship
has not previously been considered within an educational context and the current
study therefore explores whether it plays a role within this context.

Venkatesh et al. (2003) similarly found a moderating effect of age on the
relationship between social influence (similar to social norms) and behavioral
intention, with the relationship stronger for older users. Similarly, Wang et al.
(2009) found that age moderates the relationship between social influence and
BI, and the effect was stronger for older adults who used m-learning technology.
It could be that age increased the positive effect of SN due to a greater need for
affiliation (Burton-Jones & Hubona, 2006; Morris & Vekatesh, 2000).
While a number of studies support the moderating role of age in technology
acceptance, there is still inconsistency in the findings, and the full picture for
e-learning acceptance remains unclear. In the context of this study, it is predicted
that the effect of age on the relationship between PEOU, SE, SN, and BI will be
stronger for older students, while the influence of PU on BI will be stronger for
younger students. Therefore, we propose the following hypothesis:
H5: The influence of determinants (PEOU, PU, SN, SE) toward behavior
intention is moderated by age.
Gender
The consideration of gender in models of behavior was introduced in the
gender schema theory (Bem, 1981) and other technology acceptance models
(e.g., TAM 2 and TPB). Previous studies have shown that men and woman
are different in their decision-making processes, and they usually use different
socially constructed cognitive structures (Venkatesh & Morris, 2000).
Previous research has suggested that gender plays an important role in
predicting usage behavior in the domain of IS research (He & Freeman, 2010;
Venkatesh & Morris, 2000; Venkatesh et al., 2003; Wang et al., 2009). For
example, Venkatesh et al. (2003) found that the explanatory power of TAM
significantly increased to 52% after the inclusion of gender as a moderator. More
specifically, gender was found to have a moderating impact on the influence of
PU, PEOU, SE, and SN on BI and AU (Venkatesh et al., 2003).
Venkatesh et al. (2003)found gender to influence the relationship between
performance expectancy (similar to PU) and BI, with the relationship significantly
stronger for men compared to women. Their findings are consistent with the
literature on social psychology, which emphasizes that men are more pragmatic
compared to women and highly task-oriented (Minton, Schneider, & Wrightsman,
1980). It is also argued that men usually have a greater emphasis on earnings and
are motivated by achievement needs (Hoffmann, 1972; Hofstede, 2005). This
suggests that men place higher importance on the usefulness of the system.
Their argument is also supported by other researchers (Srite & Karahanna,
2006; Terzis & Economides, 2011; Venkatesh & Morris, 2000). However, within
the e-learning context, Wang et al. (2009) failed to find support for the moder-
ating effect of gender on the relationship between performance expectancy and
intention to use mobile learning.

Venkatesh et al. (2003) reported that the intention to adopt and use a system is
more highly affected by effort expectancy (similar to PEOU) for women than for
men. Their results are consistent with gender role studies *Lynott & McCandless,
2000; Schumacher & Morahan-Martin, 2001). However, in the e-learning context,
Wang et al. (2009) failed to find a moderating effect of gender on EE_BI. Where
an effect is found, the reason could be that women compared to men generally
have higher computer anxiety and lower computer self-efficacy (SE). The
difference is based on the correlational relationship, which is closely related to
PEOU, so that higher computer self-efficacy will lead to lowering the importance
of ease-of-use perception (Venkatesh & Morris, 2000). Their argument is
consistent with SCT, which indicates that anxieties and expectancies (self-efficacy
and ease of use) are reciprocal to each other (Bandura, 1986). This would lead to
the prediction that if PEOU is found to be a more salient factor for women, their
rating of their own self-efficacy would be similarly salient, suggesting the
relationship between SE and BI will be stronger for women.
Additionally, several studies have found that gender affects the relationship
between normative beliefs (SN) and BI such that the effect is stronger for women
(Huang, Hood, & Yoo, 2012; Kripanont, 2007; Venkatesh & Morris, 2000;
Venkatesh et al., 2003). Women are found to rely more than men on others’
opinion ( Hofstede & Hofstede, 2005; Venkatesh & Morris, 2000) as they have a
greater awareness of others’ feelings compared to men and therefore are more
easily motivated by social pressure and affiliation needs than men. However, in
contrast, Wang et al. (2009) found that social influence had a significant effect on
BI for males but not for females in their study of m-learning acceptance.
As for age, while there is support for the moderating role of gender in a TAM,
the results are mixed, and a clear consensus of the impact within e-learning
adoption is yet to emerge. Drawing on past research, we predict that the effects of
PEOU and SN will be stronger on BI for women and that the effect of PU on BI
will be stronger for men. We hypothesize the following:
H6: The influence of determinants (PEOU, PU, SN, SE) toward behavioral
intention will be moderated by gender.
Methodology
Participants
The data used to test the research model (see Figure 1) were collected from
British students who use Web-based learning systems in their education at Brunel
University in England. This research applied the nonprobability sampling
technique by convenience sampling to collect the data. The empirical data were
collected from respondents by means of a self-administrated questionnaire con-
taining 29 questions. The respondents were asked to circle their response on
each question that best described their level of agreement with the statements.

Participation was on a voluntary basis and no financial incentive was offered.
Out of the 1,000 distributed surveys, a 62.4% response rate was achieved (624
participants). After the exclusion of invalid questionnaires due to duplication or
empty fields, we ended up with 602 surveys ready for analysis. Of the 602
participants, the gender split was 315 (52.3%) male and 287 (47.7 %) female.
Their age range varied from 17 to 35 years old, with 370 (61.5%) below 23
years old. In addition, there were 57.6% (347 participants) undergraduates and
42.2.9% (255 participants) postgraduates. In terms of their computer experience,
the majority of the participants (410 participants) were experienced in using
Web-based learning and the Internet.
Measures
To ensure the content validity of the scales, all the items used were drawn from
prior studies related to technology acceptance and e-learning. More specif-
ically, the three constructs, perceived usefulness and perceived ease of use were
five items, whereas behavioral intention was measured using three items. These
constructs were adopted from the work related to TAM (Davis, 1989; Ngai et al.,
2007; Pituch & Lee, 2006), while the constructs, self-efficacy and subjective norm
were measured by six and four items, respectively, and were adopted from the
work of Chang and Tung (2008); Compeau and Higgins (1995); Schepers and
Wetzels (2007); and Venkatesh et al. (2003). The demographic variables were
measured on a nominal scale and all other used items were measured using a
7-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree.
The questionnaire items used are shown in the Appendix.
Data Analysis and Results
The analysis of the research was conducted in two phases. The first phase
examined the descriptive statistics of the measurement items and mainly involved
the analysis of the measurement model to examine reliability and validity of
the model. The second phase involved the analysis of the structural model and
hypothesis testing.
Descriptive Statistics
SPSS was used for the descriptive analysis. As shown in Table 1, the mean
for each construct used in the proposed model was greater than 4.86 for the sample
(N = 602), which indicates that the majority of participants expressed generally
positive responses to the constructs that were measured in this study. The standard
deviation (SD) value in each case is relatively low and ranges between 1.13
and 1.34.

Analysis of Measurement Model
To examine the relationships among the different constructs within the con-
ceptual model, this study employed a confirmatory factor analysis (CFA) based on
AMOS 18.0 (Arbuckle, 2009). We adopted the maximum-likelihood method to
estimate the model’s parameters where all analyses were conducted on variance-
covariance matrices (Hair, Black, Babin, Anderson, & Tatham, 2010). There
are some fit indices that should be considered in order to assess the model’s
goodness-of-fit (Hair et al., 2010; Kline, 2005). First, it was determined using
the minimum fit function c2. However, as the c2 was found to be too sensitive to
sample size (Hu & Bentler, 1999), the ratio of the c2 static to its degree of freedom
(c2/df) was used, with a value of less than 3, indicating acceptable fit (Carmines
& McIver, 1981). These indices are Root Mean Square Residuals (RMSR), the
Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index
(CFI), and the Tucker-Lewis Index (TLI).
As can be shown in Table 2, the first run of the model revealed a good
measurement model fit of the data [c2 = 478.416; df = 179; c2/df = 2.67; TLI =
.963; CFI = .968; RMSR = .104; RMSEA = .053]. Therefore, we can proceed to
assess convergent validity and discriminant validity in addition to reliability
in order to evaluate whether the psychometric properties of the measurement
model are adequate.
Convergent validity checks if the measures of each construct within the
model are reflected by their own indicators (Gefen, Straub, & Boudreau, 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). In order to estimate
convergent validity and the reliability of the factors, we used composite reliability
(CR) and average variance extracted (AVE). According to Hair et al. (2010),
the CR value should be greater than 0.6 and the AVE should be greater than 0.5.
Table 1. Descriptive Statistics of the Constructs
Construct Mean
Std.
deviation
Cronbach’s
alpha
Perceived Ease of Use (PEOU)
Perceived Usefulness (PU)
Social Norms (SN
Self-Efficacy (SE)
Behavioral Intention (BI)
5.37
5.23
4.86
4.95
5.66
1.31
1.26
1.34
1.13
1.27
.92
.92
.83
.84
.89

As shown in Table 3, the average extracted variances were all above 0.567
and above 0.839 for CR. Therefore, all factors have adequate reliability and
convergent validity. Additionally, the total AVE of the average value of variables
used for the research model is larger than their correlation value, therefore
discriminant validity was also established.
Consequently, the internal consistency of the constructs was checked by
Cronbach’s alpha. Cronbach’s alpha measures how well a set of items measures
a single unidirectional latent construct. As shown in Table 1, all the estimated
Cronbach’s alpha values for the proposed model constructs exceeded the cut-off
value of 0.7 (Hair et al., 2010). This suggests that the constructs had adequate
reliability. Therefore, we can now proceed to the analysis of the structural model
phase in order to test the hypotheses.
STRUCTURAL MODEL AND HYPOTHESES
TESTING
We used criteria similar to the measurement model in order to measure the
goodness-of-fit for the model. As can be shown in Table 2, the results of the
structural model showed a good fit with the data. Therefore, the next step is
to test the hypothesized relationships among the independent and dependent
variables and also the moderating effects of age and gender.
The results of the path coefficients in the model are shown in Table 4. As
expected, perceived ease of use, perceived usefulness, social norm, and self-
efficacy were found to have a positive direct effect on behavioral intention to use
Table 2. The Results of the Measurement Model
and Structural Model
Fit index
Recommended
value
Measurement
model
Structural
model
c2
df
c2/df
CFI
RMSR
RMSEA
TLI
NS at p < 0.05
n/a
< 5 preferable < 3
> 0.90
< 0.10
< 0.08
> 0.95
478.41
179
2.67
0.957
0.086
0.053
.958
486.73
184
2.58
.968
.092
.050
.963
Note: Degrees of freedom (df), Comparative fit index (CFI), Root mean square residuals
(RMSR), Root Mean Square Error of Approximation (RMSEA), Tucker-Lewis Index (TLI).

e-learning system (g = 0.2, g = 0.37, g = 0.16, and g = 0.12, respectively), with the
relationship between perceived usefulness and BI having the strongest magnitude.
Thus, the results support H1, H2, H3, and H4.
This study employed multigroup analysis to investigate the moderating effect
of age and gender on the relations among the variables in the proposed model. The
impacts of the moderators were investigated through using multigroup analysis.
In this approach, the data sample is divided into subsamples, and then the same
structural model is run at the same time for both samples. It is then followed by
pairwise comparison in path coefficients across the two groups (high vs. low),
considering the critical ratio for differences among the groups in order to establish
a reliability and validity. However, following Hair et al.’s (2010) recommen-
dation, we first examined the assessment of the measurement model before
proceeding to examine the impacts of moderators on the relationships between
the constructs. Applying the measurement model for each group separately
revealed a good fit with the data.
Table 3. Construct Reliability, Convergent Validity, and
Discriminant Validity
CR AVE SN PU PEOU SE BI
SN
PU
PEOU
SE
BI
0.839
0.926
0.928
0.865
0.900
0.567
0.713
0.722
0.627
0.751
0.753
0.552
0.419
0.419
0.551
0.845
0.663
0.580
0.720
0.849
0.635
0.650
0.792
0.598 0.867
Note: Factor Correlation Matrix with ÖAVE on the diagonal.
Table 4. The Summary of Direct Hypothesized Results
H#
Proposed
relationship
Effects
type
Path
coefficient Results
H1
H2
H3
H4
PEOU (+) ® BI
PU (+) ® BI
SN (+) ® BI
SE (+) ® BI
Direct effect
Direct effect
Direct effect
Direct effect
0.20***
0.37***
0.16***
0.12***
Supported
Supported
Supported
Supported
*p < 0.05; **p < 0.01; ***p < 0.001; NS p > 0.01.

The sample consisted of 315 males and 287 females. In order to examine age
effects, the sample was split into 2 groups (younger and older) using a categorical
variable; this was due to the fact that the highest numbers of participants were
between the ages of 17 and 35. Within the younger age group, there were 370
(61%) students, while within the older age group (age > 23) there were 232
(38.5%) participants. Table 3 represents the moderating effect of age and gender
on the relationship between the exogenous constructs on the relationship with
behavioral intention. As shown in Table 5, a number of hypothesized relationships
of the moderating effect of age and gender were supported. The results are
discussed in the next section. The model accounted for 61% (R2 = 0.61) of the
variance in BI after the inclusion of the moderators.
DISCUSSION
The main aim of this study is to extend the TAM by adding two constructs,
namely, social norm and self-efficacy, as well as two moderators, namely, age
and gender, in order to explore the factors that affect user’s behavioral intention
to use e-learning systems in England. The research sample is based on infor-
mation from 604 students who participated in courses supported by e-learning at
Brunel University.
Determinants of E-Learning Acceptance
Similar to earlier studies (Park, Nam, & Cha, 2012; Teo, Luan, & Sing, 2008),
this study provides empirical support for the theoretical framework for better under-
standing the student’s acceptance of e-learning technology. It is worth noting that
the majority of participants in the study expressed generally positive responses to
the constructs being measured in the model. This means that students were willing
to embrace e-learning as part of their repertoire of learning opportunities.
Table 5. The Summary of the Moderating Effect of
Individual Differences
H# Proposed relationship Effects type z-Score Results
H5
H6
Age × (PEOU, PU, SN, SE) ® BI
Gender × (PEOU, PU, SN, SE) ® BI
Moderating
effect
Moderating
effect
PEOU: 1.96**
PU: –1.7*
SN: –0.588
SE: 1.67*
PEOU: –172*
PU: –1.1
SN: 1.66*
SE: 1.31
Supported
Supported
Not supported
Supported
Supported
Not supported
Supported
Not supported
*p < 0.10; **p < 0.05; ***p < 0.01.

Consistent with previous research findings (Chang & Tung, 2008; Park et al.,
2012; Tarhini, Hone, & Liu, 2013a; Zhang et al., 2008), our results indicate that
perceived ease of use, perceived usefulness, social norm, and self-efficacy were all
significant determinants of behavioral intention to use e-learning, with PU having
the strongest relationship with BI. It is therefore believed that students who found
the system useful in their learning process and also found the system easy to use
were more likely to adopt the system. Therefore, in order to attract more users of
e-learning, policymakers should improve the content quality of their e-learning
systems by providing sufficient, up-to-date content that can fit the students’ needs.
It is advised that software developers should design more usable interfaces to
encourage students with poorer computer skills to use the system.
We also found that social norm was a significant determinant of behavioral
intention to use e-learning. The results support the findings of prior research
(Park, 2009; Park et al., 2012; Tarhini, Hone, & Liu, 2014a, 2014b). Our results
support the role of instructors and other peers on the behavior and perceptions of
other students to adopt the system. In this context, the instructor should announce
to the students that using the 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, then the number of later e-learning adopters is likely to
grow rapidly (Rogers, 2003).
Self-efficacy was found to play an important role in predicting student’s
behavioral intention to use the e-learning. 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.
Moderating Effect of Age and Gender
This study further investigated the moderating effect of age and gender on the
relationship between the exogenous constructs (PEOU, PU, SN, SE) and the
endogenous construct (BI). The results indicate that age moderates the relation-
ship between PEOU, PU, SE and BI, however no differences were detected in
terms of SE on BI. Also, our results indicate that gender moderates the relation-
ship between PEOU, SN, and BI, while no moderating effects were found in
terms of PU and SE on BI. The results of these two variables will be discussed in
the following sections sequentially.
Age
As expected, age was found to moderate the relationships among most of
the predictors and behavioral intention (see Table 5). This result was expected, as
previous research shows that age plays an important role in the acceptance of

technology (Morris et al., 2005; Taylor & Todd, 1995; Venkatesh et al., 2003;
Wang et al., 2009).
A stronger relationship between PU and BI was demonstrated for younger
users compared to older users within our sample. This supports the earlier work
of Venkatesh et al. (2003) and unlike Wang et al. (2009) supports the applicability
of this finding within an e-learning context.
For PEOU, the relationship with BI was found to be stronger for older users,
supporting the previous work of both Venkatesh et al. (2003) and Wang et al.
(2009). Ease of use may be a more salient factor for older users who may be less
confident in their ability to use technology. On the other hand, no significant
moderating effect of age on the relationship between social norm and behavioral
intention was found. This is in contrast to previous work by Venkatesh et al.
(2003) and Wang et al. (2009), who both found a stronger relationship for older
users. The failure to find a significant effect here may have several possible
explanations. It is possible, for instance, that if the difference in age between the
two parts of the sample had been greater, a moderating effect might have emerged.
This research also demonstrated the additional moderating effect of age on
the relationship between self-efficacy and behavioral intention. This relationship
was stronger for the older students. This suggests that a lack of confidence in
using technology may present more of a barrier to use for older students com-
pared to younger students. To our knowledge, this result has not been shown
before in e-learning acceptance and may warrant further exploration.
Gender
Gender was found to moderate some of the relationships within the extended
TAM (see Table 5). However, in contrast to the work of Venkatesh et al. (2003),
no significant moderating effect of gender was found on the relationship between
PU and BI. Wang et al. (2009) similarly failed to find a moderating effect of
gender on this relationship in an e-learning context. It may therefore be the case
that, within an educational context, males and females do not differ in terms of the
emphasis they place on task completion, at least not to the extent found in other,
more general computing domains. This hypothesis may be worthy of further study.
On the other hand, gender was found to moderate the relationship between
PEOU and BI such that the relationship was stronger for females. Previous work
by Wang et al. (2009) had failed to find support for this effect within an educa-
tional context, but our findings are consistent with the work of Venkatesh et al.
(2003). This suggests that female students tend to place more emphasis on ease
of use of the system when deciding whether or not to adopt the system.
The results also showed that the relationship between SN and BI was stronger
for females. This result was expected from the literature where several studies
report that men are less likely to accept behavior even if it is confirmed by a
majority of people (He & Freeman, 2010; Hu, Al-Gahtani, & Hu, 2010; Venkatesh

& Morris, 2000; Wang et al., 2009). Conversely, women were found to rely
more than men on others’ opinion (Hofstede & Hofstede, 2005; Venkatesh &
Morris, 2000) as they have a greater awareness of others’ feelings compared to
men and are therefore more easily motivated by social pressure and affiliation
needs than men (Venkatesh & Morris, 2000).
No moderating effect of gender was found on the relationship between SE
and BI. Contrary to our prediction, SE did not appear to be a more salient factor
in the adoption decision for woman compared to men. This is somewhat sur-
prising in light of the finding that PEOU was more important to women as part
of their adoption decision, since we might expect similar patterns to emerge in
relation to both anxieties and expectancies (Bandura, 1986), and this was indeed
the pattern we observed in relation to the moderating impact of age.
CONCLUSION
This study extended the TAM to include two other determinants, namely, social
norm and self-efficacy, as well as the gender and age as moderators, to investigate
the extent to which these variables affect students’ willingness to adopt and use
e-learning systems in UK universities. This article adds to the few studies that take
into account the critical role that social and individual factors play in technology
acceptance. The results show that PU, PEOU, SN, and SE had a direct influence on
behavioral intention to use the e-learning. Our results support existing findings
within an educational context showing that age moderates the relationship of
PEOU and BI (Wang et al., 2009) and that gender moderates the relationship
between PU and BI (Wang et al., 2009). Further, we extend support for a moder-
ating effect of age on PU - > BI and a moderating effect of gender on PEOU, SN,
and BI (Venkatesh et al., 2003) to an educational context. We also demonstrate a
new finding of a moderating effect of age on the relationship between SE and BI.
However, surprisingly, no significant moderating effect of age on the relationship
between SN and BI was found; also, no moderating of gender on PU, SE, and BI
was found. Overall, the proposed model achieves acceptable fit and explains
for 62% of its variance, which is higher than that of the original TAM. This reveals
the adequate ability of the proposed model to predict and explain student’s
behavioral intention to use e-learning.
As with any research, this study has some limitations. Firstly, we did not
consider the moderating effect of other demographic characteristics such as
educational level, experience, or culture. Secondly, data were collected from
students using a convenience sampling technique and thus should not neces-
sarily be considered representative of the population. Additionally, most of the
respondents were in the age range of 17 to 28, therefore there was not a big
generational gap between the two groups. However, this is representative of the
typical user profile within higher education institutions. These limitations should
be taken into account when generalizing from the work presented here.

APPENDIX: The Questionnaire Items
Name of
construct
Origin from
literature Items of construct
Perceived
Usefulness
Perceived
Ease of
Use
Behavioral
Intention
Self-
efficacy
Subjective
Norm
Davis (1989);
Ngai et al. (2007);
Pituch & Lee (2006)
Davis (1989);
Ngai et al. (2007);
Pituch & Lee (2006)
Davis (1989);
Ngai et al. (2007);
Pituch & Lee (2006)
Compeau &
Higgins (1995);
Chang & Tung
(2008); Park (2009)
Venkatesh &
Davis (2000);
Venkatesh et al.
(2003); Schepers
& Wetzels (2007)
Using the Web-based learning system will
•allow me to accomplish learning tasks more quickly
•improve my learning performance
•make it easier to learn course content
•increase my learning productivity
•enhance my effectiveness in learning
•Learning to operate the Web-based learning system is
easy for me.
•I find it easy to get the Web-based learning system to
do what I want it to do.
•My interaction with Web-based learning system is clear
and understandable.
•It is easy for me to become skillful at using the
Web-based learning system.
•I find the Web-based learning system easy to use.
•Given the chance, I intend to use the Web-based
learning system to do different things, from down-
loading lecture notes and participating in chat rooms
to learning on the Web.
•I predict I would use the Web-based learning system in
the next semester.
•In general, I plan to use the Web-based learning
system frequently for my coursework and other
activities in the next semester.
I am confident of using the Web-based learning system:
•Even if there is no one around to show me how to
do it;
•Even if I have only the online instructions for
reference;
•As long as I have just seen someone using it before
trying it myself;
•As long as I have a lot of time to complete the job for
which the software is provided;
•As long as someone shows me how to do it.
•My instructors think that I should participate in the
Web-based learning activities.
•Other students think that I should participate in
•Management at my university thinks that I should use
•Generally speaking, I would do what my instructor
thinks I should do.

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121 St. Johns Building
Department of Information Systems & Computing
Brunel University
Kingston Lane
Uxbridge UB8 3PH
e-mail: Ali.Tarhini@brunel.ac.uk
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