Australasian Journal of Educational Technology, 2017, 33(2).  

1 

Investigating the effect of learning styles in a blended 
e-learning system: An extension of the technology 
acceptance model (TAM) 
 
Ahmed Al-Azawei 
University of Reading, UK and University of Babylon, Iraq 
 
Patrick Parslow & Karsten Lundqvist 
University of Reading, UK 
 

This study assesses learner perceptions of a blended e-learning system (BELS) and the 
feasibility of accommodating educational hypermedia systems (EHSs) according to learning 
styles using a modified version of the technology acceptance model (TAM). Recently, 
Moodle has been adopted by an Iraqi university alongside face-to-face (F2F) classrooms to 
provide flexible learning and improve understanding. Based on TAM, individual differences 
and perceptions were explored in relationships between learner satisfaction and technology 
adoption. The model was extended to include e-learning self-efficacy, perceived satisfaction, 
and learning styles. Although other variables can be integrated, the proposed framework is 
to investigate the effect of learning styles in predicting satisfaction and BELS acceptance. A 
total of 210 undergraduate students voluntarily took part in the research. Data was gathered 
using a survey instrument and the Index of Learning Styles (ILS) Questionnaire. Partial least 
squares structural equation modelling (PLS-SEM) technique was used to examine the path 
associated between dependent and independent constructs. Unlike prior TAM literature, this 
research highlights the integration of perceived satisfaction and technology acceptance in 
accordance with psychological traits and learner beliefs. Overall, the model achieved an 
acceptable fit and successfully integrated intention to use (ITU) and perceived satisfaction 
(PS). However, psychological differences did not indicate positive impacts on learner 
satisfaction and e-learning adoption. 

 
Introduction 
 
Education has been profoundly affected by the emergence of the Internet and the integration of modern 
technologies. As such, online learning has been used as a form of distance learning. The core advantages 
of online learning are flexibility and accessibility (Wu, Tennyson, & Hsia, 2010). This does not mean that 
e-learning is without its drawbacks. Many issues have to be tackled in this learning mode, for example, the 
absence of a learning environment, lack of e-learning experience, the difficulty of learning and 
understanding without direct guidance, and the weak interaction among learner-learner and learner-
teacher (Bouhnik & Marcus, 2006; Dutton, Dutton, & Perry, 2001; Wu et al., 2010). Accordingly, physical 
classrooms still represent the norm irrespective of size, place, and time constraints (Liu, Chen, Sun, Wible, 
& Kuo, 2010). In order to overcome those identified obstacles and benefit from online learning, blended 
learning has been widely adopted as a compromise between traditional and digital methods. Blended 
learning means “the thoughtful integration of classroom face-to-face learning experiences with online 
learning experiences” (Garrison & Kanuka, 2004; p. 96). According to López-Pérez, Pérez-López and 
Rodríguez-Ariza (2011), blended learning can positively affect learner satisfaction and performance. 
Educational institutions around the world use several learning management systems (LMSs) such as 
Blackboard, WebCT, and Moodle alongside the more traditional classroom settings to provide flexible 
learning and support social constructivist approaches. 
 
Although using LMS is becoming ubiquitous, it is still in its infancy in developing countries. This is due to 
the lack of financial support, low internet bandwidth, and inadequate infrastructure (Tarhini, Hassouna, & 
Abbasi, 2015; Tarus, Gichoya, & Muumbo, 2015). Moreover, lack of e-learning literacy is another reason 
that prevents an effective application of such technologies. In Iraq, for example, universities have recently 
started to apply LMS. Six public universities have registered as using Moodle (Moodle, 2016). However, 
many barriers still affect e-learning successful implementation in Iraq (Al-Din & AlRadhi, 2008). As a 
consequence, further investigation was conducted to reveal common factors that can influence satisfaction 
and BELS adoption. This area of research has not been widely explored in the Middle East in comparison 
to West countries. We adopted TAM as a foundation because it has been extensively used to investigate 



Australasian Journal of Educational Technology, 2017, 33(2).  

2 

technology adoption (Bagozzi, 2007; Shin & Kang, 2015; Sun, Tsai, Finger, Chen, & Yeh, 2008; Wixom 
& Todd, 2005). Furthermore, the core factors of the model can predict learner satisfaction as well (Liaw, 
2008; Sun et al., 2008; Weng & Tsai, 2015). However, there is a concern regarding the appropriateness of 
the model across cultures (Tarhini et al., 2015; Teo, 2008; Srite & Karahanna, 2006). 
 
Consequently, understanding factors that may affect learner satisfaction and technology acceptance can 
help LMS providers in applying special strategies to attract students in adopting this learning form (Park, 
2009). It is a contention of this paper that there is a gap in the research for the Middle East that empirically 
identifies the synergy among psychological and cognitive factors that may influence learner satisfaction 
and/or behavioural intention in a blended learning mode. The current research aimed at addressing this 
deficit. It integrated perceived satisfaction and BELS adoption in order to identify common factors that can 
affect both. The study also attempted to contribute to the existing evidence regarding the soundness of TAM 
across cultural differences. The implications of individual differences in terms of learning styles and gender 
on perceived satisfaction and technology acceptance were also examined. The research mainly focused on 
the role of learning styles in order to understand the feasibility of personalising educational hypermedia 
systems (EHSs) in accordance with this psychological trait. The background to this investigation was a 
literature of psychology that demonstrated the importance of taking into account intrinsic variables in 
explaining human responses to a system (Barrio-García, Arquero, & Romero-Frías, 2015). 
 
Research model and hypotheses 
 
Learners have different characteristics, preferences, and needs (Felder & Silverman, 1988; Graf, 2007). 
Ignoring such individual features and traits may negatively affect learner satisfaction and technology 
adoption. Additionally, learner perceptions towards technology have been examined in two directions: 
perceived satisfaction and technology acceptance (Wixom & Todd, 2005). Prior research, however, showed 
that identical factors can be used to predict both (Capece & Campisi, 2013; Weng & Tsai, 2015). 
 
In this research, the proposed model is based on TAM as a framework to understand the role of individual 
factors. Recently, TAM has attracted significant attention in e-learning research (Ahuja & Thatcher, 2005; 
Liaw, 2008; Shin & Kang, 2015; Sun et al., 2008; Teng, 2015; Teo, 2008). Venkatesh & Davis (1996), on 
the other hand, illustrated that the model can be extended in order to enhance its effectiveness. Legris, 
Ingham and Collerette (2003) critically reviewed TAM, stating that other variables should be included to 
understand those factors that affect technology adoption. This conclusion was also confirmed by Edmunds, 
Thorpe and Conole (2012) to indicate that the two factors of TAM (usefulness and ease of use) may not 
identify all significant components in predicting technology acceptance. Furthermore, one of the debates 
on TAM is that it did not account for user and cultural differences or their influence on technology adoption 
(Bagozzi 2007; Benbasat & Barki 2007; Straub & Burton-Jones 2007). 
 
Accordingly, this study attempts to overcome such limitations by considering learning styles and gender. 
The reason behind their choice is that both factors have been regarded as a function of individual and 
cultural differences (Cagiltay & Bichelmeyer, 2000; Chang et al., 2011; Dunn et al. 1990; Gefen & Straub, 
1997; Oxford & Anderson, 1995). 
 
Technology acceptance model (TAM) 
 
Based on the theory of reasoned action (TRA) (Ajzen & Fishbein, 1980) and the theory of planned 
behaviour (TPB) (Ajzen, 1985, 1991), TAM was proposed by Davis (1986). This model was significant for 
TRA which suggests that attitude towards use (ATU) is a mediator between user beliefs (perceived 
usefulness) and behavioural intention. For TAM, however, the expectation of enhancement in job 
performance was described as a direct predictor of intention to use (ITU). The theoretical justification for 
this change is that users may not prefer to use technology, but may still continue to use it because of their 
positive perceptions regarding its impacts on job performance (Davis, Bagozzi, & Warshaw, 1989). This 
modification was regarded as the most important alteration to the TRA (Taylor & Todd, 1995). Although 
the model was extended to propose technology acceptance model 2 (TAM2) (Venkatesh & Davis, 2000), 
unified theory of acceptance and use of technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003), 
and technology acceptance model 3 (TAM3) (Venkatesh & Bala, 2008), TAM has been considered one of 
the most influential theories in information technology (IT) (Benbasat & Barki, 2007; Lee, Kozar, & Larsen, 
2003), and to predict e-learning acceptance (Šumak, Hericˇko, & Pušnik, 2011). 



Australasian Journal of Educational Technology, 2017, 33(2).  

3 

Davis (1986) shows that perceived usefulness (PU) is directly affected by perceived ease of use (PEOU) 
and both, in turn, affect user attitude. Perceived usefulness comes from the user’s beliefs that a particular 
technology can help him/her improve job performance, whereas perceived ease of use comes from the 
beliefs of users that technology is relativity free from mental effort (Davis, 1986). ATU explains users’ 
positive or negative evaluation in accomplishing certain behaviours (Ajzen & Fishbein, 1980). 
Furthermore, TAM assumes a direct effect of PU and ATU on ITU. However, recent research established 
that ATU is a weak mediator between PEOU, PU, and ITU (Szajna, 1996; Venkatesh & Davis, 2000). This 
factor, therefore, was excluded from the analysis of the present research. The main hypotheses of TAM are: 
 

H1: PEOU positively affects PU 
H2: PEOU positively affects ITU 
H3: PU positively affects ITU 

 
E-learning self-efficacy 
 
Another integrated cognitive factor is blended e-learning system self-efficacy (BELSSE). Tarhini, Hone 
and Liu (2014; p. 167-168) defined BELSSE as “a student’s self-confidence in his or her ability to perform 
certain learning tasks using the e-learning system”. Users with low perception regarding their abilities to 
use a technology may not persist in tackling obstacles that face them. It was stated that this factor is a 
determinant of PU and PEOU (Hong, Thong, James, Wong, & Tam, 2001; Ong & Lai, 2006; Venkatesh & 
Davis, 1996). Building on these positive findings, it was incorporated in our proposed model. 
 

H4: BELSSE positively affects PEOU 
H5: BELSSE positively affects PU 
 

Learning styles 
 
According to Barrio-García et al. (2015), literature has shown the significant role of psychological traits 
such as learning styles in explaining and understanding user reactions to systems. Huang (2015) also 
indicates that the exploration of learner intention to use learning technology from a learning styles 
perspective remains scant. Coffield, Moseley, Hall and Ecclestone (2004) found that literature includes up 
to 71 learning style models. Some learning style theories, however, have come to dominate in the empirical 
research such as Dunn and Dunn’s (1974) model, Kolb’s (1981) model, the VARK model (Fleming & 
Mills, 1992), and Felder and Silverman’s (1988) model. For this research, we adopted the Felder and 
Silverman learning styles model (FSLSM) because it has been regarded as significant in educational 
research, and technology enhanced learning (TEL) (Akbulut & Cardak, 2012; Cha, Kim, Park, & Yoon, 
2006; Graf, 2007). The four dichotomies of this model are briefly described as follows: 
 

• Processing (active/reflective): Active learners prefer studying in groups and immediately doing 
learning tasks, while reflective learners use analytical approaches and prefer to study alone. 

• Perception (sensing/intuitive): Sensing learners prefer facts and following the tutors approach in 
problem solving, whereas intuitive learners tend to prefer complex content and apply their own 
innovation approaches. 

• Input (visual/verbal): This dimension refers to the preferred way of receiving information. Visual 
learners prefer video, demonstrations, pictures, and graphs. Verbal learners, in contrast, prefer 
written materials and listen to others explanation. 

• Understanding (sequential/global): Sequential refers to learners who focus on details and study 
step by step, whereas global learners connect all concepts together to understand the bigger picture 
before looking at details. 
 

Some psychologists state that learning styles are one of the most important individual differences that affect 
learner performance and satisfaction (Dunn & Dunn, 1974; Felder & Silverman, 1988; Kolb & Kolb, 2005). 
The key concept underlies the learning styles theory is that learning and teaching styles have to be matched 
in order to improve the learning experience. This was the so-called meshing hypothesis (Pashler, McDaniel, 
Rohrer, & Bjork, 2009). The theory assumes that learning styles can motivate students, enhancing 
achievement, and/or satisfaction. Hence, many studies have adapted EHSs in accordance with those 
learning styles (Akbulut & Cardak, 2012; Brown, 2007; Graf, 2007; Popescu, 2010). 
 



Australasian Journal of Educational Technology, 2017, 33(2).  

4 

Several issues, however, still surround the theory of learning styles. There is the less-than-obvious 
definition of learning styles. Felder (1996; p. 18) defines learning styles as “characteristic strengths and 
preferences in the ways they take in and process information”. This overlaps with other terminologies such 
as cognitive styles and learning approaches. Other critiques exist regarding malleability, lack of reliable 
measurements, which genes are associated with learning styles, and the absence of convincing evidence to 
support their pedagogical implications (Coffield et al., 2004; Mayer, 2011; Peterson, Rayner, & Armstrong, 
2009). 
 
In this research, the blended e-learning environment was not adapted to individual styles. A one-size-fits-
all teaching approach was used. Hence, learning styles of some learners were certainly mismatched. Even 
if learning styles are ignored, our methodology allows us to reveal the perspectives of every learner and, 
thus, examine implications of learning styles. Although the relationship between learning styles theory and 
e-learning technology has been established (Huang, 2015; Li, 2015), its association with PU, PS, and ITU 
has not been investigated in previous TAM work. However, the learning styles hypothesis assumes such 
link. Furthermore, learning styles as an influential factor on PS is evidently stated (Felder & Brent, 2005). 
Accordingly, we assumed that learning style is a predictor factor of PU, PS, and ITU. 
 

H6: Learning styles positively affect PU 
H7: Learning styles positively affect PS 
H8: Learning styles positively affect ITU 

 
Perceived satisfaction 
 
Wu et al. (2010; p. 157) define learner satisfaction in BELS as “the sum of student’s behavioural beliefs 
and attitudes that result from aggregating all the benefits that a student receives from using BELS”. 
Assessing learner satisfaction in blended learning is very important because this can provide a sophisticated 
view about the effectiveness of blended learning in higher education (Garrison & Kanuka, 2004). According 
to Bolliger and Wasilik (2009), there is a link between learner satisfaction and performance. This means 
that educational institutions should do their best to meet learner needs. The high expectation that a particular 
learning technology that utilises less effort and can enhance learning outcomes may lead to promoting 
learner satisfaction. 
 
Studies pointed out that PU and PEOU influence learner satisfaction (Liaw, 2008; Sun et al., 2008; Weng 
& Tsai, 2015). The correlation between these variables is based on the notion that users will not be satisfied 
if they believe that a particular system will not help to enhance their performance or it is difficult to use. 
Hence, we assumed that both factors positively impact perceived satisfaction (PS). 
 

H9: PEOU positively affects PS 
H10: PU positively affects PS 

 
Figure 1 depicts the proposed model. 
 



Australasian Journal of Educational Technology, 2017, 33(2).  

5 

 
Figure 1. Research framework 
 
Research methodology 
 
This study was carried out at the University of Babylon during the academic year of 2014-2015. It is one 
of the leading universities in the adoption of e-learning in Iraq. The aim was to investigate factors that affect 
technology adoption and perceived satisfaction of BELS as well as highlighting the effectiveness of treating 
learners individually according to their learning styles and gender differences. A mixed-methods survey 
research design was employed. The aim of this approach is to examine the correlation among different 
factors, which can then be generalised (Dash, 2005). Survey research design was appropriate for this study 
because it aims to reveal the causal relationship between the identified constructs. One of the limitations of 
survey design in a positivist paradigm, however, is that the objectivity of research inquiry where human 
behaviours are considered to be passive, identified, and controlled by the external environment (Dash, 
2005). In order to overcome such problems, qualitative data was also gathered to gain further understanding 
of learner perspectives. Qualitative data was grouped using a thematic approach (Runeson & Höst, 2009). 
In this context, however, access to the whole population is very difficult, even impossible. Accordingly, a 
non-probabilistic sampling approach was adopted to select the research sample (Cohen, Manion, & Keith, 
2007). 
 
Context 
 
At the College of Information Technology, Moodle has been used alongside the physical classrooms since 
the end of 2013. Formally, courses were taught via blended learning (F2F and LMS) where all students 
were registered in the system. All learning materials were uploaded to the LMS as pdf or PowerPoint files 
in order to facilitate information retrieval. Moreover, theoretical exams were taken online to avoid the 
paper-based approach. Students were also encouraged to use communication tools such as the forum and 
wiki to support their educational interaction with other colleagues and instructors. 
  
 
 
 

PU 

PEOU 

ITU 

TAM 

H3 

BELSSE 

 

H10 

H4 

H7 

H2 

H5 

H1 

PS 

Perception 

Understanding 

Input 
Le

ar
ni

ng
 S

ty
le

s 

H9 

H8 

H6 

Processing 



Australasian Journal of Educational Technology, 2017, 33(2).  

6 

Participants 
 
This study targeted undergraduate computer science students who attended several courses via BELS. At 
the second semester they were asked, in classes and via the announcement page of Moodle, to fill in the 
online surveys. The research instruments were separately distributed in order to avoid respondent fatigue. 
According to Dillman (2007), the long questionnaire could lead to a low response. From the whole 
requested population, approximately 45.25% (N = 210) students voluntarily completed the research 
instrument (see Appendix A). Out of the total, 169 participants filled out the ILS questionnaire. For the 
purposes of this research however, the total number of cases (210) was used. Table 1 illustrates 
demographic information of the sample. 
 
Table 1 
Demographic Information (N=210) 
Factor N % 
Gender   
Male 88 41.9 
Female 122 58.1 
Age Group   
18-20 124 59.0 
21-23 82 39.0 
24-26 2 1.0 
27-29 1 0.5 
30+ 1 0.5 
Study year   
Freshman 68 32.4 
Sophomore 77 36.7 
Junior 42 20.0 
Senior 23 11.0 
 
Data collection 
 
Prior to data collection, ethics approval was sought, and obtained, using the procedures laid down by the 
ethics committee at the University of Reading. Two questionnaires were administrated online via the 
announcement page of Moodle. It is noteworthy that both were translated into the Arabic language because 
the majority of undergraduate students in Iraq have only a moderate English ability. As such, using the 
English versions could have been unduly time consuming or may have led to lower participation. The 
translation was approved by two Arabic PhD students who are studying in the UK. They were chosen 
because of their high English proficiency. All received questionnaires were valid because all items were 
identified as required to avoid receiving incomplete answers. 

 
The ILS 
This instrument was proposed by Felder and Soloman (n.d.) to infer learning styles according to FSLSM. 
It is freely available and learners can self-assess their learning preferences. They have to choose either (a) 
or (b) of 44 questions where 11 indicators are constructed to diagnose each dimension. This questionnaire 
is widely explained in the literature (Al-Azawei & Lundqvist, 2014; Graf, 2007). Van Zwanenberg, 
Wilkinson and Anderson (2000) suggested assigning 1 to all (a) items and 0 to all (b) items in order to 
facilitate the use of the ILS scores in the standard statistic. For every dimension, this approach derives 
values between 0 and 11 (0, 1: strong tendency for the left side; 2, 3: moderate tendency for the left side; 
4, 5: mild tendency for the left side; 6, 7: mild tendency for the right side; 8, 9: moderate tendency for the 
right side; and 10, 11: strong tendency for the right side). With regard to the reliability and validity of the 
instrument, literature supported both (Cook & Smith, 2006; Felder & Spurlin, 2005; Zywno, 2003). The 44 
questions were followed by an open-ended question highlighting pros and cons of BELS from the learners’ 
perspective. 

 
A survey instrument 
A general explanation was provided about the purpose of the study, declaring that all data would be 
manipulated confidentially. This questionnaire included three parts. The first part identified some 
demographic information, while part 2 comprised 17 indicators to infer the five factors (intention to use, 



Australasian Journal of Educational Technology, 2017, 33(2).  

7 

perceived usefulness, perceived ease of use, e-learning self-efficacy, and perceived satisfaction), but two 
items were deleted after conducting factor analysis due to their weak loading. A 7-point Likert scale was 
used which ranging from 1 for (strongly disagree) to 7 for (strongly agree). Part 3 represented an open-
ended question to collect qualitative data. Students were asked to add any comments that related to the use 
of BELS. Although all items were adapted from previously validated questionnaires (Chiu, Hsu, Sun, Lin, 
& Sun, 2005; Venkatesh & Davis, 2000; Wu et al, 2010), we initially tested the first 30 responses to prove 
the constructs validity. All factors achieved an acceptable and good level of reliability, which led to keeping 
all items without any change (Appendix A). 

 
Analysis techniques 
 
Both Statistical Package for Social Science (SPSS) software version 22 and SmartPLS software package 
version 3.0 for Windows 7 were used. Both descriptive and inferential statistics were applied to compute 
mean, standard deviation, frequency, Pearson’s correlation coefficient, Cronbach’s coefficient alpha, 
independent samples t-test, one-way ANOVA, and factor analysis in the former, whereas partial least 
squares (PLS) model, convergent validity, and discriminant validity were computed in the latter. The 
significant alpha of 0.05 was adopted. 
 
Results 
 
The theoretical model was tested using PLS-SEM path modelling (Chin, 1998). SEM has many advantages 
in comparison to traditional statistics such as regression. Reasons for its choice was that firstly, it has the 
ability to examine the association between a series of factors (independent and dependent variables) 
(Tarhini et al., 2015), more specifically for this study, when a dependent variable becomes an independent 
such as PU and PEOU. Secondly, Chin (1998) states that the SEM is an adequate method for predictive 
purposes and development of theory. Moreover, Tobias (1995) indicates the appropriateness and utility of 
this technique in explaining and predicting the behaviour of responses (variables) from the identified factors 
(independent constructs). Finally, this method has been widely used in prior literature to examine the path 
associated between different variables in a research model (Barrio-García et al., 2015; Shin & Kang, 2015; 
Tarhini et al., 2015). 
 
Scores of the ILS 
 
Table 2 draws the preferences of the participants based on the ILS scores. Students were fairly well balanced 
on the understanding dimension (sequential/global). The mean of the sequential preference is 5.871 and the 
mean of the global value is 5.129 which was computed as a complement of 11. For other dimensions, 
students ranged from mild to moderate preferences toward active/reflective, sensing/intuitive, and 
visual/verbal. The most reoccurring styles of participants were active (N = 120, 71%), sensing (N = 143, 
84.6%), visual (N = 140, 82.8%), and sequential (N = 88, 52.1%) which support the assumption of Felder 
and Silverman (1988) regarding the preferences of engineering students. Figure 2 depicts the box plot of 
the four scales. 
 
Table 2 
Descriptive statistics for the learning style dimensions 
Learning Style Scales N Range Min Max Mean SD 
Processing (active/reflective) 169 10.00 1.00 11.00 6.432 1.907 
Perception (sensing/intuitive) 169 10.00 1.00 11.00 7.272 1.963 
Input (visual/verbal) 169 10.00 1.00 11.00 7.574 2.213 
Understanding (sequential/global) 169 11.00 0 11.00 5.781 2.114 

 



Australasian Journal of Educational Technology, 2017, 33(2).  

8 

 
Figure 2. Box plot of the four learning style scales 
 
Descriptive statistics 
 
Table 3 depicts the descriptive statistics. Mean scores of all factors are higher than the midpoint of 3.5 and 
ranged from 4.90 to 5.29, whereas standard deviation ranged from 1.19 to 1.32. This indicates that there 
was a moderate spread of values around the mean. Moreover, skewness and kurtosis indicated that data was 
approximately normally distributed. Further, tolerance and VIF demonstrated that the multi-collinearity 
assumption was not violated. 
 
Table 3 
Descriptive statistics of the model constructs 
Factor Item M SD Skewness Kurtosis Tolerance VIF 
ITU 2 5.29 1.287 -1.429 1.801 0.516 1.938 
PU 3 4.90 1.32 -0.911 0.378 0.523 1.913 
PEOU 4 5.135 1.192 -1.082 1.039 0.533 1.875 
BELSSE 3 5.03 1.294 -0.837 0.237 0.639 1.565 
PS 3 5.076 1.319 -1.204 1.124 0.497 2.010 
 
Pearson correlation was applied to measure the relationship among learning styles, gender differences, and 
the model factors. Table 4 shows that the constructs are significantly correlated and no correlation was 
above 0.8. The highest correlation was between PU and PS (r = 0.662). Neither gender nor learning styles 
significantly correlated with all factors, except for processing dimension where it showed a significant 
relationship with most constructs. However, this dimension was not supported in the PLS equation due to 
the stronger effect of other entered constructs to the equation. In Table 5, ITU and PS were investigated in 
accordance with gender differences and learning styles. None of the groups indicated a significant 
difference, the only exception was processing dimension where active learners were more likely to adopt 
BELS and more satisfied in such learning environments. Table 6 depicts the findings of the one-way 
ANOVA test that measured ITU and PS according to participants’ year of study. It is shown that there is a 
significant difference between groups. Figure 3 illustrates the box plot of the model factors. 
 
 
 



Australasian Journal of Educational Technology, 2017, 33(2).  

9 

Table 6 
Differences between groups according to year of study (one-way ANOVA) 

  Intention to Use (ITU) Perceived Satisfaction (PS)  
Study Year N M SD M SD 
Freshmen 68 5.397 1.128 5.269 1.073 
Sophomore 77 5.383 1.240 5.168 1.351 
Junior 42 4.797 1.565 4.269 1.548 
Senior 23 5.587 1.164 5.666 0.717 
Total 210 5.292 1.287 5.076 1.319 
ANOVA  F(3, 206) = 2.819, P = 0.04 F(3, 206) = 8.127, P < 0.001 

 
 

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Australasian Journal of Educational Technology, 2017, 33(2).  

10 

 
Figure 3. Box plot of the model factors 
 
Instrument properties 
 
Pallant (2013) recommended that Cronbach’s coefficient alpha above 0.7 represents an acceptable level of 
reliability. Generally, the instrument had strong psychometric properties. It achieved excellent internal 
consistency reliability (0.910) and all constructs exhibited acceptable and high reliability where alphas 
ranged from 0.77 to 0.87. For further measurement, a factor analysis was conducted. The data set was 
appropriate to carry out this test because all recommended criteria by Pallant (2013) were met. First, the 
number of observations is more than 150. Second, the correlation matrix revealed higher than 0.3 
correlations among items. Third, the values of Kaiser-Meyer-Olkin (KMO) index and Bartlett’s sphericity 
test as data factorability measurements are 0.890 and p < 0.001 for both tests respectively. Appendix B 
depicts the findings of the principle component analysis (PCA) with Varimax rotation method. The 
presence of five factors model explains 73.98% of the variance. 
 
Convergent and discriminant validity were also examined. Convergent validity means “the extent to which 
indicators of a factor that are theoretically related should correlate highly” (Park, 2009; p. 155). David 
(2012) state that correlation between constructs equal or larger than 0.85 means poor discriminant validity. 
Furthermore, to establish convergent validity, the values of average variance extracted (AVE) and 
composite reliability (CR) should exceed the acceptable level of 0.5 and 0.7 of both measurements 
respectively (Hair, Black, Babin, Anderson, & Tatham, 2006), whereas the discriminant validity can be 
supported when the variance shared between any variable and other variables is smaller than the variance 
that a variable shares with its own constructs (Fornell & Larcker, 1981). Table 7 illustrates that convergent 
and discriminant validities of the instrument were advocated. 
 
Table 7 
Convergent and discriminant validity 
Latent factor AVE 

(>0.5) 
CR 

(>0.7) 
Cronbach’s α Discriminant validity 

BELSSE ITU PEOU PS PU 
BELSSE 0.70 0.875 0.786 0.836     
ITU 0.846 0.916 0.817 0.487 0.920    
PEOU 0.593 0.852 0.771 0.59 0.59 0.770   
PS 0.793 0.920 0.870 0.402 0.607 0.594 0.891  
PU 0.701 0.875 0.787 0.421 0.631 0.525 0.670 0.837 
 



Australasian Journal of Educational Technology, 2017, 33(2).  

11 

Investigating original factors of TAM 
 
In order to understand the actual predictive ability of learning style dimensions on PS and ITU, the model 
was firstly investigated in accordance with the two original constructs of TAM (PU and PEOU). The results 
of PLS are shown in Table 8. Generally, the model achieved an acceptable fit and significance. It explains 
for 49.1% and 53.1% of ITU and PS respectively. Additionally, PEOU was a predictive factor of PU 
(βPEOUPU = 0.424, p < 0.001). In order to predict ITU, PU (βPUITU = 0.443, p < 0.001) and PEOU 
(βPEOUITU = 0.358, p < 0.001) had a significant direct influence on this factor. Similarly, both PU (βPUPS 
= 0.494, p < 0.001) and PEOU (βPEOUPS = 0.335, p < 0.001) were predictors of PS. However, PU was the 
best predictor. The analysis also supports the influence of BELSSE on PEOU (βBELSSEPEOU = 0.590, p < 
0.001) and PU (βBELSSEPU = 0.171, p = 0.02). 
 
Table 8 
The original TAM hypotheses  

Perceived Satisfaction (PS) Intention to Use (ITU) 
Path R2 β p Path R2 β p 
 0.531    0.491   
PU PS  0.494 <0.001 PU ITU  0.443 <0.001 
PEOU PS  0.335 <0.001 PEOU ITU  0.358 <0.001 
PEOU PU  0.424 <0.001 PEOU PU  0.531 <0.001 
BELSSE PEOU  0.590 <0.001 BELSSE 

PEOU 
 0.590 <0.001 

BELSSE PU  0.171 0.02 BELSSE PU  0.171 0.02 
 

Investigating hypotheses of the proposed model 
 
Table 9 illustrates the findings for the path associated with the proposed constructs, whereas Figure 4 
depicts the model after conducting PLS. Seven out of ten hypotheses were confirmed. Based on this 
analysis, none of the learning style dimensions appeared to predict PU, PS and ITU, except for the 
understanding dimension which only had a direct significant effect on PS. In addition, all hypotheses of 
TAM were advocated. The PLS resulted that PEOU (βPEOUPS = 0.331, p < 0.001), PU (βPUPS = 0.486, p 
< 0.001), and understanding dimension (βUnderstandingPS = 0.112, p = 0.050) were predictors of PS 
(R2=0.545). Moreover, BELSSE showed a direct significant influence on PEOU (βBELSSEPEOU= 0.590, p < 
0.001) and PU (βBELSSEPU = 0.181, p = 0.012). In order to identify predictors of ITU, the PLS equation 
revealed that two factors: PU (βPUITU = 0.443, p < 0.001), and PEOU (βPEOUITU = 0.352, p < 0.001) were 
determinant of ITU (R2 = 0.492). 
 
Table 9 
Findings of the research hypotheses 
Dependent 
Factor 

Independent 
Factor 

Direct 
Effect 

t-value P Indirect 
Effect 

t-value Total 
Effect 

Finding 

 
 

ITU 
(R2 = 0.492) 

PU 0.443 6.765 <0.001    Supported 
PEOU 0.352 5.217 <0.001 0.148 4.4550 0.536 Supported 
Learning Styles 
Processing 
Perception 
Input 
Understanding 

 
0.027 
0.021 
-0.006 
-0.036 

 
0.511 
0.361 
0.121 
0.570 

 
0.610 
0.718 
0.904 
0.569 

 
0.005 
0.034 
-0.020 
0.032 

 
0.189 
1.036 
0.569 
0.867 

 
0.032 
0.055 
-0.026 
-0.003 

 
 

Rejected 
 
 

 
 

PS 
(R2 = 0.545) 

PU 0.486 7.713 <0.001   0.486 Supported 
PEOU 0.331 4.821 <0.001 0.202 4.320 0.533 Supported 
Learning Styles 
Processing 
Perception 
Input 
Understanding 

 
0.073 
-0.065 
-0.068 
0.112 

 
1.342 
1.154 
1.395 
1.958 

 
0.180 
0.249 
0.16 

0.050 

 
 

0.037 
-0.022 
0.035 

 
 

1.075 
0.573 
0.843 

 
0.073 
-0.028 
-0.090 
0.147 

 
 

Partially 
Rejected 

 
 
 

BELSSE 0.181 2.503 0.012 0.245 4.740 0.426 Supported 
PEOU 0.415 5.655 <0.001    Supported 



Australasian Journal of Educational Technology, 2017, 33(2).  

12 

PU 
(R2 = 0.309) 

Learning Styles 
Processing 
Perception 
Input 
Understanding 

 
0.012 
0.076 
-0.045 
0.073 

 
0.192 
1.351 
0.587 
0.891 

 
0.848 
0.16 

0.557 
0.373 

   
0.012 
0.076 
-0.045 
0.073 

 
 

Rejected 

PEOU 
(R2 = 0.348) 

BELSSE 0.590 10.228 <0.001   0.590 Supported 

 

 
Figure 4. Results of the research framework 
 
Qualitative data 
 
To gain a wider understanding of learner perspectives, a qualitative approach was used. In a mixed method 
approach qualitative and quantitative data have to be integrated rather than keeping them separated. 
Researchers can initially report quantitative findings and follow this by qualitative themes that could either 
support or reject them (Creswell, Klassen, Clark, & Smith, 2011). It is worth mentioning that all of the 
collected comments were in the Arabic, but the first author has translated them into the English language 
keeping the same meaning as mentioned by participants. A total of 48 students responded to the open-ended 
questions. Their perspectives were analysed by using the thematic approach. All comments were coded 
based on the identified themes. Generally, six advantages and one issue were highlighted by participants. 
Table 10 demonstrates the number and percentage of students who identified similar themes. It may be 
worth considering that some participants have reported more than one benefit or drawback of e-learning 
application. 
 
  

β=0.112* 
 

PU 
R2=0.309 

PEOU 
R2=0.348 

ITU 
2=0.492R 

TAM 

β=0.443*** 
 

BELSSE 

 

β=0.486*** 
 

β=0.590*** 
β=0.352*** 

 

β=0.181* 
 

β=0.415*** 
 

PS 
R2=0.545 

Perception 

Understanding 

Input 

L
ea

rn
in

g 
St

yl
es

 

β=0.331*** 
 

Processing 

Significant effect 
Insignificant effect 



Australasian Journal of Educational Technology, 2017, 33(2).  

13 

Table 10 
The generated themes from the qualitative analysis 

BELS Advantages 
Theme Number % 
A useful tool in teaching and learning 15 31.25 
Improving ‘learner-teacher’, ‘learner-learner’, and ‘learner-content’ interaction 10 20.83 
Providing alternative opportunities to understand a subject 9 18.75 
Expanding students’ knowledge 2 4.16 
Saving learning time and effort 2 4.16 
Promoting intellectual abilities and individual skills of students 2 4.16 

The Issue Associated with BELS Application 
Theme Number % 
The use of Moodle in online tests 19 39.58 
 
Discussion 
 
In this study, we extended TAM by incorporating learning styles as well as integrating technology 
acceptance and learner satisfaction in one model. The overall results did not support the influence of 
learning styles to contribute to the critique of this psychological education theory. However, the research 
advocated the use of TAM across cultural differences, revealed the influence of self-efficacy on PU and 
PEOU, and integrated satisfaction and behavioural intention in one model. 
 
Our research model confirmed all of the hypotheses from the original TAM (Table 8). According to 
Venkatesh and Davis (2000), TAM typically accounts for 40% of the variance. The results from the present 
study exceeded the typical results of TAM to explain 49.1% of variance by assuming a direct effect between 
PEOU and ITU and without the mediation of ATU. Such findings support the appropriateness of TAM 
across-cultural differences. In accordance with other research, PU had the most direct and strongest effect 
on ITU in comparison to other factors (Davis et al., 1989; Park, 2009; Tarhini et al., 2015; Teo, 2008; Teo, 
2009). This research is in agreement with such studies. Gefen and Straub (2000) explain the reason that 
underlies the weak effect of PEOU. They mentioned that this factor relates to task nature which assesses 
the intrinsic features of a technology, for example, flexibility, clarity, and ease of learning. PU, on the other 
hand, represents a response of users to assess a technology’s extrinsic features such as outcome and 
technology assistance to achieve tasks. Accordingly, extrinsic features have a stronger influence on 
technology adoption. This can also be attributed to student experience or the nature of the learning 
technology. Tarhini et al. (2015) stated that the significance of PEOU is shown in the early stage of 
adoption, whereas it has a little to no effect on a population that is highly experienced in a particular 
technology. 
 
Some of the prior literature has shown a correlation between gender differences and technology adoption. 
Gefen and Straub (1997) pointed out that PEOU and PU of men and women were affected by gender, but 
it did not influence ITU. In the study performed by Gefen and Straub (2000), women showed less interest, 
use, and adoption of educational computer tools than men. To some extent, Ong and Lai (2006) supported 
these outcomes. However, it was found that gender is a predictor of ITU as well. Tarhini et al. (2014), on 
the other hand, stated that gender did not moderate PU, PEOU, and ITU. Similarly, Vanderheyden and De 
Baets (2015) concluded that gender differences were neither correlated with learner performance nor 
satisfaction. Our analysis suggests that both men and women had a similar behavioural intention to use 
BELS and PS irrespective of their gender differences as pinpointed in Table 5. In Table 6, it is exhibited 
that there was a significant difference in both ITU and PS among the students in accordance with their study 
level. Post-hoc Tukey's HSD tests indicated that junior students had significantly lower ITU and PS than 
the other three groups at the 0.05 level of significance. This may be associated with other external variables 
such as its use in a non-interactive method for this group of students, whereas learners need to be highly 
motivated in order to accept such technologies in learning whether in a voluntarily or a non-voluntarily 
learning environment. 
 
The learning style hypothesis was not confirmed in this research, which is consistent with other studies 
(Hong, 2002; Shaw, 2012). Learning style dimensions were not predictors for the dependent constructs 
(PU, PS, and ITU). Understanding dimension, on the other hand, was the only one that showed weak, but 



Australasian Journal of Educational Technology, 2017, 33(2).  

14 

significant influence on PS. Furthermore, the outcomes suggest that learning style dimensions had a mild 
predictive ability on PU, PS, and ITU. Such findings are in agreement with the research performed by 
Brown et al. (2009). In their study, learning styles were also shown to have a limited influence on nine 
subscales of online learning environments. In this research, even though the examined learning environment 
adopted a one-size-fits-all approach, learners rated PS and ITU similarly irrespective of their individual 
styles as demonstrated in Table 5. The only exception was for processing dimension. The t-test analysis 
exhibited that active learners were more likely to accept BELS and be satisfied. Based on FSLSM, active 
learners do not like to be passive participants in educational activities. They tend to discuss, brainstorm, 
question, and be involved practically. Giving these features, the LMS with its communication tools such as 
forum and wiki represents a suitable learning platform to serve their preferences. This outcome is 
compatible with the study conducted by Li (2015) where it was found that active learners possessed 
significantly higher intention towards an interactive learning technology (wiki) than the reflective group. 
Generally, the claim that there is no adequate evidence to justify incorporating learning styles could not be 
refuted. 
 
Following ITU hypotheses, the study demonstrated that PU and PEOU were predictors of behavioural 
intention. This finding is in agreement with the assumption of TAM and supporting literature that states PU 
represents the best dominant determinant of ITU (Park, 2009; Shin & Kang, 2015; Tarhini et al., 2015; Teo, 
2009). It is also consistent with other studies that PEOU has a direct significant influence on ITU (Liu et 
al., 2010; Tarhini et al., 2014; Weng & Tsai, 2015). 
 
Pertaining to PS hypotheses, studies consistently indicated that PEOU and PU are important factors in 
predicting PS. Although both constructs predicted PS, PU indicated a larger effect than PEOU as in the 
prior research (Drennan, Kennedy, & Pisarski, 2005; Sun et al., 2008; Weng & Tsai, 2015). This highlights 
a rational link between these constructs, suggesting that the beliefs of learners in BELS usefulness have a 
crucial effect on their satisfaction. With regard to PEOU, learners may search for an alternative learning 
method if a particular learning technology requires a lot of effort to achieve a task and this, in turn, may 
affect their satisfaction. 
 
BELSSE demonstrated a strong effect on PEOU and a mild significant impact on PU which means that this 
factor may affect PS and ITU indirectly. Furthermore, the effect of BELSSE on PEOU was larger than its 
influence on PU. One possible interpretation is that learners believe that blended e-learning is a useful 
learning technology regardless of their personal skills, but less experience in the use of a particular 
technology means the need for more effort to achieve a particular task. According to Ong and Lai (2006), 
self-efficacy had a greater impact on PEOU than PU. This finding was confirmed in this research. 
Additionally, Shin and Kang (2015) found that self-efficacy was a significant predictor of PEOU, whereas 
it did not influence PU. This is because less experienced students feel that they need greater effort to 
accomplish their tasks properly. Hence, educational institutions, particularly in developing countries, 
should provide a structured guideline and special training courses in order to improve student self-
confidence. As a consequence, our expectation is that Iraqi students need further training courses to 
encourage them using BELS as well as promoting their individual skills. 
  
Qualitative data presented that in order to successfully implement BELS, some challenges need to be 
addressed. Most comments, however, revealed that the advantages outweighed the disadvantages to be 
compatible with the quantitative analysis. The main responses were grouped into two categories: benefits 
and issues as shown in Table 10. 
 
On the pros side, 31.25% of them believed that e-learning is useful software that has the opportunity to 
improve learning and teaching in higher education. In addition, 20.83% of students found that the use of e-
learning alongside the physical classrooms has promoted the interaction with other peers, lecturers, and 
learning content. This outcome is in agreement with the conclusion of Emelyanova and Voronina (2014) 
that e-learning systems can promote the implementation of the social constructivist learning theory. E-
learning can also provide an alternative approach to absorbing a topic. This advantage was identified by 
18.75% of participants. Fewer students (4.16%) stated that providing additional resources on the e-learning 
system can assist them to expand their knowledge. Others (4.16%) found that its effective use can save time 
and effort. Similarly, 4.16% of respondents indicated that students can enhance their intellectual abilities 
by experiencing a new learning method such as via educational technology. These advantages were 
reflected by some of the students’ comments below: 



Australasian Journal of Educational Technology, 2017, 33(2).  

15 

 
Blended learning is very important where a topic is explained firstly in details in a classroom 
and all questions are discussed and answered. Then, in Moodle, the topic can be discussed 
again by using external information or ideas that may have a strong relationship to the topic. 
 
… [BELS] keeps the interaction between students and subjects and it facilitates obtaining 
particular information when it is needed … 
 
The advantages are: students do not need to use social networks to get lectures and this, in 
turn, saves time, a topic can be discussed with a lecturer after reading it, the questions of 
students can assist the lecturer to identify weaknesses of students and, then, addressing them 
in a shortest time, and other students can benefit from these questions. In my opinion, there 
are no any disadvantages of this learning mode, on the other hand, it responds to the digital 
era … 

 
On the cons side, the inappropriate use of BELS in online tests can negatively affect student willingness to 
accept it. This issue was highlighted by 39.58% of participants. A possible reason is that some instructors 
might have a lack of experience in the use of BELS in online tests. Thus, conducting effective and diverse 
training programs can assist to address this issue. Moreover, other highlighted reasons by respondents were 
the limited type of questions in the web-based exams and the inconsistency between monthly and final 
exams since the latter is based on the paper-based approach. Therefore, students highly recommended the 
use of either online-based or paper-based tests. Examples of students’ responses that reported this issue are: 
 

It is a limited system and without a high benefit because [online-based] questions and tests 
are either true-false or multiple choices. In general, it is Okay, but it should be developed 
further to obtain more advantages… for me as a student, I visit the system once a week to 
download the lectures… 
 
One test method should be followed either online-based or paper-based tests because of the 
difference in the style of questions between e-learning and traditional tests. 
 

To sum up, although students faced some issues in the e-learning system, they believe that the benefits of 
blended learning far outweigh the drawbacks. 

 
Implications, recommendations, and limitations 
 
This study added a number of values in comparison to previous research. Firstly, TAM was rarely examined 
in Arabic populations and the soundness of the model across-cultures is significant to the research. Even 
though it is stated that Eastern and Western students differ in many aspects such as the social relationships, 
physical environment, autonomy, and learning styles (Chang et al., 2011), the present study exhibited that 
the original factors of TAM (PU and PEOU) can be used to determine technology acceptance regardless of 
such differences. Thus, it clearly contributes to the existing evidence about the robust construction of TAM. 
Secondly, identical factors were successfully used to predict adoption behaviour and perceived satisfaction. 
This means that both constructs can be integrated into one model instead of proposing separate models to 
explain each one. Finally, the psychological trait (learning styles) was extended to examine their 
implications on PU, PS, and ITU. Contrary to the theoretical assumption of educational psychologists 
(Felder & Silverman, 1988), the results revealed that this trait had a limited predictive power on the research 
variables, and there was no significant difference between students’ willingness to adopt e-learning and 
their satisfaction based on learning styles. Although the integration of learning styles theory did not 
positively influence the model, it empirically contributes to the existing critique regarding its impacts on 
education. 
Based on the findings, the study has drawn four recommendations: 
 

1. Accommodating hypermedia systems in accordance with learning styles are not recommended due 
to the weak correlation between learning styles, perceived satisfaction, and technology acceptance. 
This supports literature that pointed out learning styles did not influence academic performance as 
well (Al-Azawei, Al-Bermani, & Lundqvist, 2016; Gomes & Mendes, 2010; Prajapati, Dunne, 
Bartlett, & Cubbidge, 2011); 



Australasian Journal of Educational Technology, 2017, 33(2).  

16 

2. Educational institutions should consider other factors to enhance student satisfaction and 
behavioural intention towards LMS rather than exerting time and effort into integrating learning 
styles; 

3. Training courses should be delivered to all learners in order to enhance their self-confidence and 
this, in turn, can positively influence learner beliefs and technology acceptance; and 

4. Training sessions should also be provided for instructors in order to extend their knowledge about 
how e-learning can be effectively implemented. 

 
In spite of the significant findings of this study, it suffers from many limitations which may be subject to 
further research. The first weakness is that the sample was from one college. Although it was sufficient to 
represent the population in higher education, recruiting samples from several universities and using random 
sampling approach can improve the generalisability of the findings. The other limitation is that the overall 
results of the proposed model may indicate a need for further research in order to integrate more variables. 
This may enhance the model’s ability to predict perceived satisfaction and technology adoption. 
 
Conclusion 
 
Blended learning combines the advantages of traditional and online learning. It is widely applied to support 
F2F learning as well as responding to user needs. Investigating learner satisfaction and intention to use e-
learning alongside physical classrooms can predict whether learners will continue using this technology or 
not. Based on TAM, this study integrated perceived satisfaction and technology acceptance into one model. 
TAM was extended by incorporating other determinants, namely, learning styles and self-efficacy. The 
proposed model successfully identified the key factors that can explain learner satisfaction and BELS 
acceptance in the Middle East where few studies have investigated predictors of both factors in comparison 
to the West nations. 
 
Gender differences did not show a significant association with any of the model’s factors. Additionally, 
learning styles failed to predict PU, PS, and ITU. This finding added more to the existing debate regarding 
the absence of convincing evidence to advocate learning styles theory. As a result, it is recommended that 
learner needs should be considered from the start rather than accommodating EHSs according to learning 
styles. Moreover, our research supports the existing literature regarding the soundness of TAM constructs 
irrespective of cross-cultural differences. 
 
Acknowledgment 
 
The authors are grateful to the Iraqi Ministry of Higher Education and Scientific Research for supporting 
this study. They would also like to thank the anonymous reviewers for their valuable feedback that led to 
further improvement of the research. 
 
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Corresponding author: Ahmed Al-Azawei, a.al-azawei@pgr.reading.ac.uk  
 
Australasian Journal of Educational Technology © 2017. 
 
Please cite as: Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the effect of learning 
styles in a blended e-learning system: An extension of the technology acceptance model (TAM). 
Australasian Journal of Educational Technology, 33(2), 1-23. https://doi.org/10.14742/ajet.2758  
  

http://dx.doi.org/10.1016/j.compedu.2009.12.012
mailto:a.al-azawei@pgr.reading.ac.uk
https://doi.org/10.14742/ajet.2758


Australasian Journal of Educational Technology, 2017, 33(2).  

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Appendix A 
The research instrument blended learning (BL) 

 
Item Question  Reference 
Intention to Use (ITU)   
ITU1 Assuming I have access to e-learning (Moodle) in BL, I intend to use it.  Adapted from 

Venkatesh & 
Davis (2000) 

ITU2 Given that I have access to e-learning (Moodle), in BL, I predict that I 
would use it. 

 

Perceived Usefulness (PU)   
PU1 Using e-learning (Moodle) improves my performance in BL.  Adapted from 

Venkatesh & 
Davis (2000) 

PU2 Using e-learning (Moodle) in BL increases my scientific performance.  
PU3 Using e-learning (Moodle) in BL enhances my learning effectiveness.  
Perceived Ease of Use (PEOU)   
PEOU1 The interaction with e-learning (Moodle) is clear and understandable.  Adapted from 

Venkatesh & 
Davis (2000) 

PEOU2 Interacting with e-learning (Moodle) in BL does not require a lot of 
mental effort. 

 

PEOU3 I find e-learning (Moodle) to be easy to use in BL.  
PEOU4 I find the environment of e-learning (Moodle) to be easy to use.  
Blended E-Learning System Self-Efficacy (BELSSE)   
BELSSE1 I can use e-learning (Moodle) in BL, if there is no one around to tell me 

what to do as I go. 
 Adapted from 

Wu et al. 
(2010) BELSSE2 I can use e-learning (Moodle) in BL, if I had never used a system like it 

before. 
 

BELSSE3 I can use e-learning (Moodle) in BL, even if there is no assistant 
illustration tool with the system. 

 

Perceived Satisfaction (PS)   
PS1 I am satisfied with the BELS efficiency.  Adapted from 

Chiu et al. 
(2005) 

PS2 I am satisfied with the BELS effectiveness.  
PS3 Overall, I am satisfied with the BELS.  
  



Australasian Journal of Educational Technology, 2017, 33(2).  

23 

Appendix B 
Factor analysis 

 
  Factors 
Latent 
Factor 

Item Factor loading (> 
0.7) 

1 2 3 4 5 

ITU ITU1 0.917 .360  .733   
ITU2 0.922   .816   

PU PU1 0.848   .565 .610  
PU2 0.798    .719  
PU3 0.864 .424   .698  

PEOU PEOU1 0.755     .644 
PEOU2 0.654     .854 
PEOU3 0.853 .510  .326  .471 
PEOU4 0.803 .341 .404 .438  .457 

BELSSE BELSSE1 0.847  .725   .310 
BELSSE2 0.823  .781    
BELSSE3 0.839  .827    

PS PS1 0.906 .852     
PS2 0.907 .797     
PS3 0.858 .710   .318  

 Variance %  18.979 15.834 14.505 12.471 12.191 
Notes. Rotation converged in 8 iterations. Loading less than 0.3 was excluded. 
 


	Ahmed Al-Azawei
	University of Reading, UK and University of Babylon, Iraq
	Introduction
	Research model and hypotheses
	Technology acceptance model (TAM)
	E-learning self-efficacy
	H4: BELSSE positively affects PEOU
	Learning styles
	Perceived satisfaction
	Figure 1 depicts the proposed model.
	Figure 1. Research framework
	Research methodology
	Context
	Participants
	Table 1
	Data collection
	A survey instrument
	Analysis techniques
	Results
	Scores of the ILS
	Table 2
	Figure 2. Box plot of the four learning style scales
	Descriptive statistics
	Table 3
	Table 6
	Table 4
	Figure 3. Box plot of the model factors
	Instrument properties
	Table 7
	Investigating original factors of TAM
	Table 8
	Investigating hypotheses of the proposed model
	Table 9
	Figure 4. Results of the research framework
	Qualitative data
	Table 10
	Discussion
	Implications, recommendations, and limitations
	Conclusion
	Acknowledgment
	References
	The research instrument blended learning (BL)
	Appendix B
	Factor analysis