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What is available about technology acceptance of e-learning software and systems?
A review and comprehension paper
Mustafa DEĞERLİ * [email protected]
June 11, 2010
* Graduate Student in Department of Information Systems, Informatics Institute, METU
The speedy advances in information and communication technologies (ICT)
have led to their amplified exploitation in teaching and learning contexts (Cappel and
Hayen, 2004). Additionally, International Data Corporation (IDC) estimates that the
value of the e-learning market worth will be between $21 billion and $28 billion by
2008 (Brown, 2006). In this context, Mackay and Stockport (2006) mention that
according to IDC, the revenue from synchronous e-learning exceeded $5 billion by
2006. Stemmed from these facts, applying technology by means of e-learning software
and systems (e-LSS) to facilitate and support learning is an imperative and interested in
application area recently.
Nevertheless, another imperative concern intended for this context is surely the
technology acceptance (TA) of these e-LSS by people, especially by students and
teachers. Even though there are studies conducted in this subject with respect to various
contexts, there is lacking a paper that reviews and summarizes previous studies and by
this way provides a comprehensive guide to let people know about the TA of e-LSS.
This paper aims to compensate this lack for the interested readers wanting to know
about not only the TA concepts, but also about the preceding TA of e-LSS studies.
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In this study, searches were conducted using the online databases ABI/INFORM
Complete, Academic Search Complete, Cambridge Journals Online, Computers &
Applied Sciences Complete, EBSCOhost Databases, Education Research Complete,
Emerald Management Xtra, ERIC, IEL-IEEE/IEE Electronic Library, Library,
Information Science & Technology Abstracts with Full Text, and World Higher
Education Database; with the keywords „„Technology Acceptance Model”, „„TAM”,
„„TAM2”, „„UTAUT,”, „„Universal Theory of Acceptance and Use of Technology”,
“TPB”, “Theory of Planned Behavior”, “IDT”, “Innovation Diffusion Theory”, “e-
learning”, “adoption”, “acceptance”, “educational software”, “e-teaching”, “online
learning”, “online teaching”, and “educational computer systems”.
In this context, apparent non-e-learning and non-technology results were
detached first, and after this, the abstracts of all left behind results were read. Thus, full
versions of all articles that were possibly relevant were retrieved and read. For each
retrieved article, a search of references that might meet inclusion criteria was conducted,
and any of these relevant articles retrieved and the same procedure of analyzing was
applied to these articles. As a consequence of this process, sixteen studies published in
or after 2000 are included in this review.
Before reviewing and summarizing preceding studies and providing a
comprehensive guide on the subject of TA of e-LSS, it is compulsory to have a look at
concept of TA and underlying principles and models related with TA. As indicated by
Dillon and Morris (1996), TA is the user acceptance that is defined as the demonstrable
willingness of the users to employ information technology (IT) for the tasks that it is
intended to support. They argue that demonstrable willingness of the users to use related
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IT must be reached for TA. Moreover, Dillon and Morris also note that every TA
process of IT for intended purposes can be modeled and predicted. In fact, this is a
promising statement, as they argue that thanks to TA theory it is possible to model and
predict any intended ITs' TA. Additionally, in this context, Davis (1993) suggests that
TA is the key factor that determines whether an information system (IS) or IT project is
to be successful or not. Surely, IT or IS projects will be useless and meaningless unless
they are accepted by the intended users for intended purposes.
There are models and theories trying to explain and shape the TA process and its
characteristics. For example, as said by Rogers (1995), innovation diffusion theory
(IDT) says that there are five characteristics of a technology that determine an IT‟s or
IS‟s TA. These are relative advantage, compatibility, complexity, trialability and
observability. According to Rogers, as long as these five concerns are took seriously and
managed well, related IT or IS is to be accepted by intended users for intended purposes.
Furthermore, Technology Acceptance Model (TAM) of Davis, et al. (1989),
Theory of Planned Behavior (TPB) of Ajzen (1991), Technology Acceptance Model 2
(TAM2) of Venkatesh and Davis (2000), and Universal Theory of Acceptance and Use
of Technology (UTAUT) of Venkatesh, et al. (2003) are the models in the literature
mostly used to design, implement and test TA of IT or IS.
Of these models, the most usually cited one is the TAM of Davis, et al. Their
work not only provides major contribution to TA literature, but this model is used as a
reference by other studies. TAM of Davis, et al. predicts that TA of any IT is determined
by two factors. These are perceived usefulness (PU) and perceived ease of use (PEOU).
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PU is defined as the extent to which users believe that using the system will enhance his
or her performance regarding the intended purpose. Moreover, PEOU is defined as the
extent to which the users believe that using the system will be free from effort. In
accordance with TAM, both PU and PEOU have major impact on a users‟ attitude
toward using the IT and determining its TA.
The illustrations of the models related with TA, TAM of Davis, et al. (1989),
TPB of Ajzen (1991), TAM2 of Venkatesh and Davis (2000), and UTAUT of
Venkatesh, et al. (2003), are provided below in Figures 1-4. In addition, definitions of
the variables used in these figures are provided in Table 1 below.
As these TA models are crucial to understand the TA studies for TA of e-LSS, it
is a good idea to examine the below figures and the table.
Figure 1: Illustration of TAM
Attitude
Behavioral
Intention to Use
(Acceptance) Actual Use
Perceived
Usefulness
Perceived
Ease of Use
5
Figure 2: Illustration of TPB
Figure 3: Illustration of TAM2
Attitude
Behavioral
Intention Behaviour Subjective
Norm
Perceived
Behavioral
Control
Behavioral
Beliefs
Normative
Beliefs
Control
Beliefs
Subjective
Norm Behavioral
Intention to Use
(Acceptance) Actual Use
Perceived
Usefulness
Perceived
Ease of Use
Image
Job Relevance
Output Quality
Results
Demonstrability
6
Figure 4: Illustration of UTAUT
Variable Definition
Behavior Use (BU) The action, specific or general, whose prediction is of
interest
Behavioral Intention (BI) One specific behavior of interest performed by individuals
with regard to some IT system
Attitude (ATT) An individual‟s evaluative judgment of the target behavior
on some dimension (e.g., good/bad, harmful/beneficial,
pleasant/unpleasant)
Perceived Ease of Use
(PEOU)
An individual‟s perception that using an IT system will be
free of effort
Performance
Expectancy
Behavioral
Intention to Use
(Acceptance) Actual Use
Effort
Expectancy
Social Influence
Facilitating
Conditions
7
Perceived Usefulness
(PU)
An individual‟s perception that using an IT system will
enhance job performance
Subjective Norm (SN) An individual‟s perception of the degree to which
important other people approve or disapprove of the target
Perceived Behavioral
Control (PBC)
An individual‟s perception of how easy or difficult it will
be to perform the target behavior (self-efficacy), of factors
that impede or facilitate the behavior (facilitating
conditions), or of the amount of control that one has over
performing the behavior (controllability)
Effort Expectancy An individual‟s perception that using an IT system will be
free of effort
Performance Expectancy An individual‟s perception that using an IT system will
enhance job performance
Social Influence An individual‟s perception of the degree to which
important other people approve or disapprove of the target
Facilitating Conditions An individual‟s perception of how easy or difficult it will
be to perform the target behavior (self-efficacy), of factors
that impede or facilitate the behavior (facilitating
conditions), or of the amount of control that one has over
performing the behavior (controllability)
8
Image The degree to which one perceives the use of the
technology as a means of enhancing one's status within a
social group
Job Relevance An individual's perception of the degree to which the
technology is applicable to his or her job
Output Quality An individual's perception of how well a system performs
tasks necessary to his or her job.
Results Demonstrability The tangibility of the results of using the technology
Behavioral Beliefs An individual‟s belief about consequences of particular
behavior
Normative Beliefs An individual‟s perception about the particular behavior,
which is influenced by the judgment of significant others
Control Beliefs An individual's beliefs about the presence of factors that
may facilitate or impede performance of the behavior
Table 1: Definitions of the Variables Used in TA Models
The first study examined in the context of reviewing papers in TA of e-LSS is
Martinez-Torres, et al.‟s “A technological acceptance of e-learning tools used in
practical and laboratory teaching, according to the European higher education area”
titled study. The objective of their study is to examine the effectiveness of TAM of web-
9
based e-LSS used in practical and laboratory teaching. In the study, Martinez-Torres, et
al. tried to empirically validate the research hypotheses derived from TAM, whose
illustration is provided in Figure 1 above, using the responses to a survey on e-LSS
usage among 220 users. The obtained results of their study strongly support the
extended TAM in predicting users‟ intention to use e-LSS and define a set of external
variables with a major influence in the original TAM variables. However, they found
out that PEOU did not create a significant impact on users‟ attitude or intention towards
e-LSS usage. Martinez-Torres, et al. integrated new factors related to human and social
change processes to the initial TAM to adapt it for the study of e-LSS. These factors
refer to providing students with a new channel to learn, such as providing interactivity
and control, feedback, communicativeness); others refer to factors that can influence
users‟ motivations to use the tool, such as enjoyment, user tools, diffusion,
methodology, user adaptation. To sum up, Martinez-Torres, et al.‟s study concluded that
TAM is there to use to provide TA of e-LSS with some additional extensions.
The second study examined in the context of reviewing papers in TA of e-LSS is
Park‟s “An Analysis of the Technology Acceptance Model in Understanding University
Students’ Behavioral Intention to Use e-Learning” titled study. A sample of 628
university students took part in the related research. In Park‟s study, the general
structural model including e-learning self-efficacy, subjective norm, system
accessibility, perceived usefulness, perceived ease of use, attitude, and behavioral
intention to use e-LSS, is developed based on the TAM. The results of the study are
proved TAM to be a good theoretical tool to understand users‟ acceptance of e-LSS.
10
Additionally, Park noted that e-learning self-efficacy was the most important construct,
followed by subjective norm in explicating the causal process in the model.
The third study examined in the context of reviewing papers in TA of e-LSS is
Hsia and Tseng‟s “An enhanced technology acceptance model for e-learning systems in
high-tech companies in Taiwan: analyzed by structural equation modeling” titled study.
In their study, Hsia and Tseng‟s efforts aimed to integrate two constructs, perceived
flexibility and computer self-efficacy, to examine the applicability of TAM in
explaining employees‟ decisions to accept e-LSS. Their study is based on a sample of
233 employees from 16 high-tech companies at Hsinchu Science Park in Taiwan. The
result of their study significantly supports the extended TAM in predicting employees‟
behavioral intention to use e-LSS. Additionally, results of this study showed that ee-LSS
must be flexible in any time and place. That is perceived flexibility has the most
significant direct and total effect on behavioral intention to use e-LSS. Moreover, Hsia
and Tseng‟s study also showed that computer self-efficacy had a positive effect on
perceived ease of use, perceived usefulness, and perceived flexibility in the context of
TA of e-LSS.
The fourth study examined in the context of reviewing papers in TA of e-LSS is
Liu, et al.‟s “Applying the technology acceptance model and flow theory to online e-
learning users’ acceptance behavior” titled study. In their study, Liu, et al. tested
constructs from IS, TAM, and Human Behavior and Psychology (Flow Theory) in an
integrated theoretical framework of online e-learning users‟ acceptance behavior. Their
study concludes that the most media-rich presentation interface (text-audio-video based
presentations) generated higher levels of PU and concentration than text-audio and
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audio-video based presentations. Additionally, they note that PU and concentration
influence user intentions. Consequently, the study concludes that the TA rate of text-
audio-video based presentations is high thanks to not only its PU but also owing to that
it generates the highest user concentration.
The fifth study examined in the context of reviewing papers in TA of e-LSS is
Khan and Iyer‟s “ELAM: A Model for Acceptance and Use of E-learning by Teachers
and Students” titled study. In their study, Khan and Iyer propose a conceptual
framework for understanding TA of e-LSS. Their model, namely e-learning acceptance
model (ELAM), is based on the UTAUT of Venkatesh, et al. (2003). ELAM identifies
the key factors in TA of e-LSS as measured by behavioral intention to use the
technology and actual usage. The four determinants of TA of e-LSS are performance
expectancy, effort expectancy, social influence, and facilitating conditions. Specifically,
the following factors are included in facilitating conditions variable in ELAM: reliable
infrastructure, institutional policies, training and support. Additionally, Khan and Iyer
note that since e-learning is associated with individualization of the teaching and
learning process, the learning style of the student and teaching style of the teacher is an
essential factor affecting the TA process for e-LSS.
The sixth study examined in the context of reviewing papers in TA of e-LSS is
Maldonado, at al.‟s “E-learning motivation, Students’ Acceptance/Use of Educational
Portal in Developing Countries” titled study. In their study, Maldonado, at al. tried to
adopt and modify UTAUT model of Venkatesh, et al. by adding a new construct of e-
learning motivation and they applied it to Peruvian context for prediction of the role of
e-learning motivation in TA and use. Furthermore, they found that e-learning motivation
12
plays a decisive role in the adoption and use of e-LSS and they demonstrated that e-
learning motivation is different from conventional learning motivation by means of
adding technology characteristics (like effort expectancy) to traditional motivational
construct. What is more, Maldonado, at al. examined the cyclic effect of the technology
use on e-learning motivation, and they found that e-educational portal use simulates
students‟ e-learning motivation. They also confirmed the importance of influence of
teachers, parent and other peers in TA of e-LSS in schools in Peru context and they used
region and gender as moderating variables in their study.
The seventh study examined in the context of reviewing papers in TA of e-LSS
is Yuen and Ma‟s “Exploring teacher acceptance of e-learning technology” titled study.
In their study, Yuen and Ma attempted to explore a model to understand teachers‟ TA of
e-LSS. In the related study, a self-reported questionnaire was used to examine teacher
acceptance and attitude towards e-LSS. Data were collected from 152 in-service
teachers who were studying in a part-time teacher education program in Hong Kong.
Additionally, TAM was used as the core framework in favor of analysis while additional
constructs were added in order to find a better model to understand teacher acceptance
of e-learning technology. A composite model including five constructs, specifically,
intention to use, perceived usefulness, perceived ease of use, subjective norm and
computer self-efficacy, were formed and tested in the study. It was found that subjective
norm and computer self-efficacy serve as the two significant perception commentators
of the fundamental constructs in TAM. However, contrary to previous literature, PEOU
became the sole determinant to the prediction of intention to use, while perceived
usefulness was non-significant to the prediction of intention to use. In my opinion, the
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reason for this is that the target is not students but teachers for the study. This seems to
indicate that the perceived ease of use amongst teachers is extremely important. As well,
this is just because teachers are different from students with some major respects.
The eighth study examined in the context of reviewing papers in TA of e-LSS is
Moghadam and Bairamzadeh‟s “Extending the Technology Acceptance Model for E-
learning: A Case Study of Iran” titled study. In their study, Moghadam and
Bairamzadeh attempted to extend the TAM to include subjective norm, personal
innovativeness in domain of information technology and self-efficacy to evaluate TA of
e-LSS. Responses from 155 university students were collected to evaluate the proposed
structural model. The results indicated that personal innovativeness in domain of IT has
a direct effect on self-efficacy. Both personal innovativeness in domain of IT and self-
efficacy have unswerving effect on perceived ease of use. Perceived usefulness has a
direct effect on intention of students‟ to accept an e-LSS. Additionally, the study
suggested that e-LSS should include functions that add to efficiency and effectiveness of
teaching and learning, and also to promote the belief of being easy to use. Furthermore,
in their study, Moghadam and Bairamzadeh illustrated the role of personality traits in
TA of e-LSS.
The ninth study examined in the context of reviewing papers in TA of e-LSS is
Liu, at al.‟s “Impact of media richness and flow on e-learning technology acceptance”
titled study. In their study, Liu, at al. tried to propose an integrated theoretical
framework for the user‟s acceptance behaviour of web-based streaming media for e-
LSS. In their related study, they tested concepts from TAM and human behaviour and
psychology (flow theory) with reference to the TA of e-LSS. In addition to the TAM,
14
flow theory was used to study the influence of user concentration on task activity. The
related study concluded that the most media-rich presentation interface (text–audio–
video presentation) always generates higher levels of PU and concentration than text–
audio-based or audio–video-based presentations. This study further confirms that course
materials that use rich media can promote higher user acceptance through stimulating a
higher PU and concentration.
The tenth study examined in the context of reviewing papers in TA of e-LSS is
Zayim‟s “Instructional technology adoption of medical school faculty in teaching and
learning: faculty characteristics and differentiating factors in adopter categories” titled
study. In her study, Zayim used a mix-method research design, a quantitative
methodology (survey) in conjunction with qualitative methodology (in-depth interviews)
for the purpose of gathering data about characteristics and adoption patterns of medical
school faculty from 155 teaching personnel. The findings provided an evidence for
similarities between adoption patterns of medical school faculty and other higher
education faculty; relatively new tools associated with instruction were not adopted by
majority of the faculty. In this study, additionally it is noted that some differences were
found between early adopters and mainstream faculty in terms of individual
characteristics, adoption patterns, perceived barriers and incentives to adoption and
preferred methods of learning about technology and support.
The eleventh study examined in the context of reviewing papers in TA of e-LSS
is Işık‟s “Perceptions of students and teachers about the use of e - learning / sharing
portal in educational activities” titled study. In his study, Işık conducted a questionnaire
with 200 students of 6th and 7th grade students. In the study, he investigated the
15
perceptions in terms of three aspects: effects of the use of this technology on their
perceived motivation, the perceived usefulness and the perceived ease of use of this
technology. The findings of the study indicated that the students and the teachers
perceived that e-learning / sharing portal technology is a useful and they easy to use
technology for targeted people. In the study, it was found out that the students and the
teachers are satisfied with advantages of the use of this new technology in their learning
environment. In the same way, the teachers and the students stated that using the system
effected students‟ perceived motivation towards the educational activities in a positive
way.
The twelfth study examined in the context of reviewing papers in TA of e-LSS is
Özdemir‟s “The effect of educational ideologies on technology acceptance” titled study.
In his study, Özdemir tried to investigate the effect of both students‟ and academics‟
educational ideologies on TA, and to find out whether there are differences in the PEOU
of technology, PU of technology, attitudes toward technology, and the frequency of use
of technology in education in terms of their educational ideologies. In the study, a
survey design was used. The questionnaire used in the study was developed by making
use of the related literature, and it was administered to 58 academic personnel and 320
students. The results of the study demonstrated that academics‟ educational ideologies
affect their acceptance of technology; specifically they affect the perceived usefulness of
educational technology. Furthermore, there is an effect of students‟ educational
ideologies on the frequency of their use of educational technologies. Educational
ideology is a factor affecting academics‟ perceptions of the usefulness of technology,
and it is a factor affecting the students‟ the frequency of use of educational technology.
16
The thirteenth study examined in the context of reviewing papers in TA of e-LSS
is Tseng and Hsia‟s “The impact of internal locus of control on perceived usefulness and
perceived ease of use in e-learning: an extension of the technology acceptance model”
titled study. In their study, Tseng and Hsia are aimed to broaden the TAM to include
variables related to human factor. Therefore, their mainly effort was to integrate internal
locus of control (ILOC) and computer self-efficacy (CSE), to examine the applicability
of the TAM in explaining employees‟ decisions about TA of e-LSS. Based on a sample
of 204 employees taken from 12 high-tech companies in Taiwan, the results strongly
supported the extended TAM in predicting employees‟ behavioral intention to use e-
learning. It is seen that PU has the most significant direct effect on behavioral intention
to use e-LSS. TAM has been extended in an e-learning context. Specifically, CSE had a
positive effect on PEOU and behavioral intention to use.
The fourteenth study examined in the context of reviewing papers in TA of e-
LSS is Henderson and Steward‟s “The Influence of Computer and Internet Access on E-
learning Technology Acceptance” titled study. In their study, Henderson and Steward
tried to investigate whether computer and Internet access influence TA of e-LSS. The
related instrument was administered to 583 business students at two universities in the
Southeast. Regression analysis revealed that computer and Internet access affected the
degree to which students expect Blackboard and the Internet to be easy to use. Computer
and Internet access also affected their attitude towards these technologies. Additional
findings revealed that socioeconomic status and race influenced computer ownership,
convincingly.
17
The fifteenth study examined in the context of reviewing papers in TA of e-LSS
is Roca, at al.‟s “Understanding e-learning continuance intention: An extension of the
Technology Acceptance Model” titled study. In their study, Roca, at al. proposed model
in which the perceived performance component is decomposed into perceived quality
and perceived usability. A sample of 172 respondents took part in this study. The results
suggest that users‟ continuation intention is determined by satisfaction, which in turn is
jointly determined by PU, information quality, confirmation, service quality, system
quality, PEOU and cognitive absorption. More importantly, this study found that the
influence of perceived quality, which is information quality, service quality and system
quality, on confirmation and satisfaction was strong. The empirical results of the related
study showed that information quality had a strong influence on confirmation, and the
effect of information quality on satisfaction was stronger than service quality and system
quality on satisfaction.
The sixteenth study examined in the context of reviewing papers in TA of e-LSS
is Saadé, at al.‟s “Viability of the Technology Acceptance Model in Multimedia Learning
Environments: a Comparative Study” titled study. In their study, Saadé, at al. conducted
a comparative study consisting of 362 students. The related study‟s results suggest that
TAM is a solid theoretical model where its validity can extend to the multimedia and e-
learning context. The study provides a more intensive view of the multimedia learning
system (MMLS) users and is an important step towards a better understanding of the
user behavior on the system and a multimedia acceptance model. The results showed
that PU has a significant impact on student attitude towards using MMLS. Attitude is
confirmed to play an essential role of affecting behavioral intention to use MMLS. The
18
findings validate the TAM as basis for this new model and support the value of attitude
toward MMLS in student acceptance.
Above, all sixteen reviewed studies‟ details provided and explained.
Nonetheless, in below Table 2, studies reviewed and their details are provided, and
purposely the extension variables of these studies on TAM are listed correspondingly.
For a comparative and contrastive, and a general view the below table shall be referred.
#
Title of Study
Sample
Size
Referenced
TA Model
Added / Extension Variables
1 A technological acceptance
of e-learning tools used in
practical and laboratory
teaching, according to the
European higher education
area
220 TAM New channel to learn, such as
providing interactivity and
control, feedback,
communicativeness); factors
that can influence users‟
motivations to use the tool,
such as enjoyment, user tools,
diffusion, methodology, user
adaptation.
2 An Analysis of the
Technology Acceptance
Model in Understanding
University Students‟
628 TAM E-learning self-efficacy,
subjective norm, system
accessibility, perceived
usefulness, perceived ease of
19
Behavioral Intention to
Use e-Learning
use, attitude, and behavioral
intention
3 An enhanced technology
acceptance model for e-
learning systems in high-
tech companies in Taiwan:
analyzed by structural
equation modeling
233 TAM Perceived flexibility and
computer self-efficacy
4 Applying the technology
acceptance model and flow
theory to online e-learning
users‟ acceptance behavior
102 TAM The most media-rich
presentation interface,
perceived usefulness, and
concentration
5 ELAM: A Model for
Acceptance and Use of E-
learning by Teachers and
Students
NA UTAUT Performance expectancy, effort
expectancy, social influence,
and facilitating conditions.
Specifically, the following
factors are included in
facilitating conditions variable:
reliable infrastructure,
institutional policies, training
and support.
20
6 E-learning motivation,
Students‟ Acceptance/Use
of Educational Portal in
Developing Countries
150 UTAUT E-learning motivation,
influence of teachers, parent
and other peers, region and
gender
7 Exploring teacher
acceptance of e-learning
technology
152 TAM Intention to use, perceived
usefulness, perceived ease of
use, subjective norm and
computer self-efficacy
8 Extending the Technology
Acceptance Model for E-
learning: A Case Study of
Iran
155 TAM subjective norm, personal
innovativeness in domain of
information technology and
self-efficacy
9 Impact of media richness
and flow on e-learning
technology acceptance
NA TAM The most media-rich
presentation interface (text–
audio–video presentation), user
concentration, perceives
usefulness
10 Instructional technology
adoption of medical school
faculty in teaching and
learning: faculty
155 NA Individual characteristics,
adoption patterns, perceived
barriers and incentives to
adoption and preferred
21
characteristics and
differentiating factors in
adopter categories
methods of learning about
technology and support.
11 Perceptions of students and
teachers about the use of e
- learning / sharing portal
in educational activities
200 NA Perceived motivation, the
perceived usefulness and the
perceived ease of use
12 The effect of educational
ideologies on technology
acceptance
378 IDT Educational ideologies
13 The impact of internal
locus of control on
perceived usefulness and
perceived ease of use in e-
learning: an extension of
the technology acceptance
model
204 TAM Integrate internal locus of
control (ILOC) and computer
self-efficacy (CSE)
14 The Influence of Computer
and Internet Access on E-
learning Technology
Acceptance
583 TAM Computer and Internet access
22
15 Understanding e-learning
continuance intention: An
extension of the
Technology Acceptance
Model
172 TAM Perceived usefulness,
information quality,
confirmation, service quality,
system quality, perceived ease
of use and cognitive absorption
16 Viability of the
Technology Acceptance
Model in Multimedia
Learning Environments: a
Comparative Study
362 TAM Attitude
Table 2: Studies Reviewed and Their Details
As sixteen studies reviewed above showed, it is seen that most of the extension
studies referred the TAM to provide a model in order to understand, implement and test
the TA of e-LSS. Moreover, it is seen that the TAM is a venerated theory of TA and it
has a use that has been widely researched in IT practices, and it is an important
theoretical tool for e-LSS research and studies.
Nevertheless, all these studies tried to extend the TAM or any other fundamental
TA models from diverse perspectives. This is just because of the fact that it is necessary
to take into consideration the intended people and intended purpose. As long as intended
people and intended purpose are recognized wholly, by using the fundamental models
23
and principles in relation with TA explained above, it is possible to model and generate
any sort of TA process for e-LSS.
Teachers, students, academicians, designers, purchasers, and all others involved
with e-LSS projects are consistently advised to take into account the fundamental TA
models and TA of e-LSS studies to give support to the design or purchasing process,
training and informational sessions, implementation, and other activities in these
contexts. Surely, to the degree that the factors predicting TA for e-LSS are controllable,
they can be salient levers meant for acceptance and use.
However, there is also a need to continue exploring new theoretically motivated
variables and relationships that can be added to fundamental TA models, or extended
ones. Moreover, it is necessary for researchers to conduct studies for the purpose of
identifying prominent beliefs that actors in e-LSS have on the subject of using e-LSS.
In a word, this paper is written for the interested readers wanting to know about
not only the TA concepts, but also about the preceding TA of e-LSS studies.
24
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Processes, 50, 179 – 211.
Brown, A. (2006). Learning from a distance. Journal of Property Management, 71(4),
42 – 45.
Cappel, J. J., & Hayen, R. L. (2004). Evaluating e-learning: A case study. Journal of
Computer Information Systems, 44(4), 49 – 57.
Davis, F. D. (1993). User acceptance of information technology: Systems
characteristics, user perceptions and behavioral impacts. International Journal
of Man-Machine Studies, 38, 3, 475 – 487.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer
technology: A comparison of two theoretical models. Management Science, 35,
982 – 1003.
Dillon, A., & Morris M. (1996). User acceptance of information technology: theories
and models. In: M. Williams (ed.), Annual Review of Information Science and
Technology, Vol. 31, (Medford, NJ: Information Today).
Henderson, R. G., & Stewart, D. L. (2007). The Influence of Computer and Internet
Access on E-learning Technology Acceptance. Business Education Digest 2007
Issue XVI, 3 – 16.
25
Hsia, J. W., & Tseng, A. H. (2008). An Enhanced Technology Acceptance Model for E-
Learning Systems in High-Tech Companies in Taiwan: Analyzed by Structural
Equation Modeling. 2008 International Conference on Cyberworlds, 39 – 44.
Işık, A. (2009). Perceptions of students and teachers about the use of e - learning /
sharing portal in educational activities. METU, 2009.
Khan, F. U., & Iyer, S. (2009). ELAM: A Model for Acceptance and Use of E-learning
by Teachers and Students. International Conference on e-learning (ICEL),
Toronto, Canada, July 2009.
Liu, S. H., Liao, H. L., & Peng, C. J. (2005). Applying the technology acceptance model
and flow theory to online e-learning users’ acceptance behavior. Issues in
Information Systems, 4(2), 175 – 181.
Liu, S., Liao, H., & Pratt, J. A. (2009). Impact of media richness and flow on E-learning
technology acceptance. Computers & Education, 52(3), 599 – 607.
Mackay, S., & Stockport, G. J. (2006). Blended learning, classroom and e-learning. The
Business Review, 5(1), 82 – 88.
Maldonado, U. P. T., Khan, G. F., Moon, J., & Rho, J. J. (2009). E-learning motivation,
Students' Acceptance/Use of Educational Portal in Developing Countries: A
Case Study of Peru. 2009 Fourth International Conference on Computer
Sciences and Convergence Information Technology, 2009, 1431 – 1441.
26
Martínez-Torres, M. R., Toral Marín, S.L., García, F. Barrero, Vázquez, S. Gallardo,
Oliva, M. Arias, & Torres, T. (2008). A technological acceptance of e-learning
tools used in practical and laboratory teaching, according to the European
higher education area. Behaviour & Information Technology, 27: 6, 495 – 505.
Moghadam, A. H., & Bairamzadeh, S. (2009). Extending the Technology Acceptance
Model for E-learning: A Case Study of Iran. 2009 Sixth International Conference
on Information Technology: New Generations, 2009, 1659 – 1660.
Özdemir, D. (2004). The effect of educational ideologies on technology acceptance.
METU, 2004.
Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding
University Students' Behavioral Intention to Use e-Learning. Educational
Technology & Society, 12 (3), 150 – 162.
Roca, J. C., & Chiu, C. M. (2006). Understanding e-learning continuance intention: An
extension of the technology acceptance model. Human-Computer Studies. v64
i6. 683 – 696.
Rogers, E. M. (1995). Diffusion of Innovations (4th ed.). New York: Free Press.
Saadé, G. R., Nebebe, F., & Tan W. (2007). Viability of the Technology Acceptance
Model in Multimedia Learning Environments: A Comparative Study.
Interdisciplinary Journal of Knowledge and Learning Objects, Vol. 3, 175 – 184.
27
Tseng, A. H., & Hsia, J. W. (2008). The Impact of Internal Locus of Control on
Perceived Usefulness and Perceived Ease of Use in E-Learning: An Extension of
the Technology Acceptance Model. 2008 International Conference on
Cyberworlds, 2008 – 815 – 819.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology
acceptance model: Four longitudinal field studies. Management Science, (46:2),
186 – 204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of
information technology: Toward a unified view. MIS Quarterly, (27:3), 425 –
478.
Yuen, H. K., & Ma, W. K. (2008), Exploring teacher acceptance of e-learning
technology. Asia-Pacific Journal of Teacher Education, 36(3), 229 – 243.
Zayim, N. (2004). Instructional technology Adoption of Medical School Faculty in
Teaching and Learning: Faculty Characteristics and Differentiating Factors in
Adopter Categories. METU, 2004.