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A Meta Analysis of Factors Affecting Perceived Usefulness and Perceived Ease of Use in The Adoption of E-Learning Systems

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The full potential of e-learning, a trend that is of growing importance lately, will not be reaped unless the users fully utilize the system, triggering extensive research to be conducted in order to provide valuable insight on a myriad of variables influencing user acceptance in e-learning systems. The main purpose of the study is to determine the factors that affect the intention of users to use e-learning and to get results which can guide system developers and researchers. In accordance with this purpose, 203 studies investigating the e-learning acceptance of the users through the Technology Acceptance Model (TAM) were found in the literature. In those studies, variables which are suggested to determine Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) and results of related hypotheses are analyzed. Finally, a model is proposed. In this model, the most widely accepted hypotheses, affecting PU and PEOU according to the literature are included in the original TAM. As a result; it determines Self Efficacy-PEOU, Subjective Norm-PU, Self Efficacy-PU, Interaction-PU, Enjoyment-PEOU, Anxiety-PEOU, Enjoyment-PU, Compatibility-PU, Subjective Norm-PEOU and Interaction-PEOU as variables that have statistical significance in users’ PU and PEOU, respectively. Moreover, the study examines the relationship between the User Satisfaction and original TAM variables, and proposes the Acceptance and Satisfaction Model for E-Learning (ASME) as a model to best explain the dependent variables described above.
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Turkish Online Journal of Distance Education-TOJDE October 2018 ISSN 1302-6488 Volume: 19 Number: 4 Article 1
A META ANALYSIS OF FACTORS AFFECTING PERCEIVED
USEFULNESS AND PERCEIVED EASE OF USE IN THE ADOPTION
OF E-LEARNING SYSTEMS
Rahmi BAKI
Department of Management Information Systems
Aksaray University
Aksaray, Turkey
Dr. Burak BIRGOREN
Department of Industrial Enginering
Kırıkkale University
Kırıkkale, Turkey
Dr. Adnan AKTEPE
Department of Industrial Enginering
Kırıkkale University
Kırıkkale, Turkey
ABSTRACT
The full potential of e-learning, a trend that is of growing importance lately, will not be
reaped unless the users fully utilize the system, triggering extensive research to be
conducted in order to provide valuable insight on a myriad of variables influencing user
acceptance in e-learning systems. The main purpose of the study is to determine the factors
that affect the intention of users to use e-learning and to get results which can guide
system developers and researchers. In accordance with this purpose, 203 studies
investigating the e-learning acceptance of the users through the Technology Acceptance
Model (TAM) were found in the literature. In those studies, variables which are suggested
to determine Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) and results of
related hypotheses are analyzed. Finally, a model is proposed. In this model, the most
widely accepted hypotheses, affecting PU and PEOU according to the literature are included
in the original TAM. As a result; it determines Self Efficacy-PEOU, Subjective Norm-PU, Self
Efficacy-PU, Interaction-PU, Enjoyment-PEOU, Anxiety-PEOU, Enjoyment-PU,
Compatibility-PU, Subjective Norm-PEOU and Interaction-PEOU as variables that have
statistical significance in users’ PU and PEOU, respectively. Moreover, the study examines
the relationship between the User Satisfaction and original TAM variables, and proposes
the Acceptance and Satisfaction Model for E-Learning (ASME) as a model to best explain
the dependent variables described above.
Keywords: E-learning, Technology Acceptance Model, perceived ease of use, perceived
usefulness, user satisfaction.
INTRODUCTION
Recent and exponential developments in information and communication technologies
have caused significant shifts in both corporates’ and users’ working practices, resulting in
individuals being introduced to new paradigms such as e-government, e-commerce, online
banking and e-learning, the last being the most wide-spread and substantial advancement
in the education sector.
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E-learning can be described as the utilization of telecommunications technologies to
transfer information in education and training activities (Sun, Tsai & Finger, 2008). E-
learning connects all education activities conducted by individuals and groups, both online
and offline, through networked or standalone devices, allowing users to access a learning
platform without the restriction of time and space (Naidu, 2006). The system’s competitive
advantage stems through its ability to allow users to direct and customize content via
eliminating a one-size-fits-all approach to education and training (Pantazis, 2002),
facilitating a learning platform that transcends time and space (Trentin, 1997).
Despite having notable advantages, under-utilized systems can pose a problem for
organizations (Venkatesh & Davis, 2000), because information systems are known to
improve organizational performance only when they are used in their full capacity
(Mathieson, 1991). For one to be able to better forecast, assess and enhance user
acceptance, the need to better understand why information systems are accepted or
rejected is vital (Davis, Bagozzi & Warshaw, 1989). As a consequence, researchers have
benefitted from various theories to identify the factors that explain users’ intention to use
e-learning, the most widespread being TAM (Sumak, Hericko & Pusnik, 2011). TAM is a
robust forecast model that is extensively used to assess users’ perceptions of technology
acceptance (Hussein & Saad, 2016).
The model, developed to estimate the adoption and utilization of information technologies,
puts forward that the individuals’ intention to use information technologies has its
foundation in two basic (PU and PEOU) beliefs (Venkatesh & Bala, 2008). In the model,
external variables allow one to understand the factors that most significantly influence PU
and PEOU, while offering guidance in developing action plans that will increase usage
(Legris, Ingham & Collerette, 2003). TAM’s main objective is to lay upon a basis to monitor
the effect of external variables in beliefs, attitudes and actions (Davis, Bagozzi & Warshaw,
1989), leading to many researchers testing and developing the model with different
external variables. A systematic evaluation of all these studies that predicate upon TAM to
assess users’ e-learning acceptance, as well as an analysis of the relationship of PU and
PEOU with all the external variables investigated in the literature will provide valuable
insight to researchers and system developers.
This work examines 203 different studies that explore users’ e-learning acceptance and
identifies 129 external variables to explain PU/PEOU, leading to the testing of 220 different
hypothesis for 714 times. It is being aimed that an extention to TAM is proposed after a
careful examination of external variables with beliefs.
TECHNOLOGY ACCEPTANCE MODEL
TAM is a theoretically validated, robust model that aims to explain computer acceptance
determinants (Davis, Bagozzi & Warshaw, 1989) and comprises of five basic components;
Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Towards Using (A),
Behavioral Intention to Use (I) and Actual Use (A). Being an adaptation of Theory of
Reasoned Action (TRA), TAM identifies two main belief structures, PU and PEOU as attitude
determinants of both the use of intention and actual use of information technologies
(Taylor and Todd, 1995). The model proposes external variables to explain PU and PEOU,
while the latter determines PU and A, the former establishes A and I. Additionally, A affects
I, and I influences U (As shown in Figure 1).
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Figure 1. Technology Acceptance Model (Davis, Bagozzi & Warshaw, 1989)
TAM proposes that individuals’ intention to use information technologies are determined
by two belief structures: PU, the belief that one’s utilization of information technologies
will enhance her work performance and PEOU, the belief that no significant effort will be
spared to use information technologies (Vankatesh & Bala, 2008). PU and PEOU are two
theoretical structures that are the basic determinants of systems usage (Davis, 1989).
In most of the empirical work conducted, it has been shown that PU is a robust determinant
of adoption intention, while PEOU has a relatively less consistent effect (Venkatesh & Davis,
2000). The ‘attitude’ variable is expected to partially mediate the effect of these beliefs on
intention to use. Nonetheless, research show that attitude is not a significant facilitator in
explaining the causal relationship between belief structures and intention to use. (Davis,
Bargozzi & Warshaw, 1989).
According to TAM; PU and PEOU mediates between the effects of various external variables
on the intention to use (Vankatesh & Davis, 2000). Even though TAM and other user
acceptance models have been validated empirically, researchers still add new external
variables to improve the limited specificity and explanatory utility of these models (Tarhini,
Hone & Liu, 2013.b). To improve the explanatory power of the model, incorporating
additional variables or integrating it with other information technologies models is crucial
(Hu, Chau & Sheng, 1999).
Researchers are expected to extend and assess theoretical acceptance models with various
external variables, especially in the field of e-learning. Correlation with TAM is often
supported in e-learning acceptance studies, since the model proves effective in the
investigation of e-learning acceptance technologies (Sumak, Hericko & Pusnik, 2011). This
study evaluates previous research that utilized TAM to assess e-learning acceptance; and
examines the relationship between additional external variables analyzed in these research
with belief structures.
RESEARCH METHOD
A quantitative meta-analysis is conducted to identify the users’ perception of usefulness and
ease of use in e-learning systems. Previous work that benefitted from TAM to examine the
acceptance or usage of e-learning technologies or systems have been carefully evaluated,
resulting in the selection of 203 valid studies to be analyzed. These work comprise of 177
published journal papers, 22 conference papers and 4 PhD thesis. Studies are obtained through
applying key words as Technology Acceptance Model, TAM 2, TAM 3, Perceived Usefulness,
Perceived Ease of Use, Behavioural Intention to Use for TAM; and E-Learning, Learning
Management System, Web-Based Learning, Online Learning, Distant Education, Moodle, Second
Life for e-learning systems.
Following the selection of studies to be analyzed; publications are grouped by their respective
countries, participants, TAM components utilized and variables tested against PU and PEOU (As
shown in Appendix 1). Studies in the scope of this work are conducted in 41 different countries,
respectively in, Taiwan (44), Malaysia (16), Spain (15), China (14), United States of America
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(12), Hong Kong (10), South Korea (8), Iran (8), Turkey (8) and United Kingdom (7). Seven of
these publications are conducted in multiple countries (Abbas (2016), Arenas-Gaitan, Ramírez-
Correa & Rondan-Cataluña (2011), Capece & Campisi (2011), Ramírez-Correa Arenas-Gaitan &
Rondan-Cataluña (2015), Tajudeen, Basha, Michael & Mukthar (2012), Zhao & Tan (2010).
In addition, Armenteros, Liaw, Fernandez, Díaz & Sanchez (2013) carried out their research with
instructors from various countries. When these work are grouped by seven main geographical
regions (As shown in Table 1), it has been seen that the majority of research conducted are
clustered in East Asia and Pacific (104), Europe and Central Asia (41) and Middle East and North
Africa (32), as the distribution of research is remarkably skewed towards East Asian countries
like Taiwan, Malaysia, China, Hong Kong and South Korea.
The work spanned in this study is also classified based on the e-learning user types that the
models developed are tested for. 152 of these research examines the e-learning acceptance
behaviors of students in primary, secondary and tertiary stages. Employees from different
professions (construction professionals, managers, nurses, blue-collar workers, etc.) are
studied in 28 publications and 16 papers span the behaviors of education professionals
(academics, faculty members, instructors, lecturers and teachers). In addition, 5 of these papers
extend their scope to a wider range of citizens. A model developed in one of these studies is
tested both on educators and students while another paper fails to give sufficient information
regarding the user base studied.
Table 1. Distribution of Research by Region (For Studies Conducted in Multiple Countries,
All Countries in Question Are Taken into Consideration)
Region
Number of Studies
East Asia & Pacific
104
Europe & Central Asia
41
Middle East & North Africa
32
North America
16
Latin America & the Caribbean
7
Sub-Saharan Africa
6
South Asia
3
International
1
Total
210
Majority of the information technologies acceptance research that takes the model as a
reference does not include all of TAM’s five main components due to various reasons. For
instance, it is still being debated whether A acts as a robust mediator of the effect of the
belief variables on I, as TRA and TRM proposes (Davis, Bagozzi & Warshaw, 1989). PU,
PEOU and I ensue as the most extensively used variables in the research spanned (As
shown in Table 2).
Table 2. TAM Variable Combinations Used in Literature Reviewed
TAM Variable
Combinations
Used
PU-PEOU-I
PU-PEOU-A-I
PU-PEOU-I-U
PU-PEOU-A-I-U
PU-PEOU-A
PU-PEOU-U
Others
Total
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129 different external variables to be tested as determinants of PU and PEOU are
incorporated into the models studied in all these aforementioned research. Since this
study’s main objective is to identify the factors that affect user beliefs in e-learning
systems, relationships that locate these external variables as the antecedents of A,I, U or
the interdependences between dependent variables are not within the scope of this work.
The effects of 129 different external variables on PU and PEOU are tested in 220 different
hypotheses in 203 publications studied (As shown in Appendix 2). In some of the cases,
researchers have chosen to examine the impact of these independent variables in only one
of the belief structures, where 220 different hypotheses are tested 714 times.
As a result, the hypotheses examined most frequently are ordered as follows: Self Efficacy-
PEOU (71), Self Efficacy-PU (50), Subjective Norm-PU (33), Anxiety-PEOU (19),
Interaction-PU (18), Experience-PEOU (18), Enjoyment-PEOU (16), Experience-PU (14),
Interaction-PEOU (12), Enjoyment-PU (12) and Subjective Norm-PEOU (12). Moreover, it
has been observed that some external variables are tested relatively more frequently
against the belief structures than their counterparts. For instance, Subjective Norm’s
influence on PU and Anxiety’s predictive value on PEOU are examined more frequently than
the variable’s effect on PEOU and PU, respectively.
Among the research studied, the relationship between Self Efficacy and PEOU ranks as the
most validated and acccepted with 58 instances, followed by Subjective Norm-PU (27), Self
Efficacy-PU (24), Interaction-PU (15), Enjoyment-PEOU (13), Anxiety-PEOU (12),
Enjoyment-PU (12), Compatibility-PU (10), Subjective Norm-PEOU (9) and Interaction-
PEOU(8). The most frequently accepted relationships in these research are incorporated
into the ASME proposed in this study.
Factoring in the relatively sporadically validated hypotheses into the model can pose a
threat to its credibility. For example, three of the publications examined find out
Information Quality to significantly influence users’ PU with a positive coefficient.
Nevertheless, these tests do not provide a solid foundation on the validity of this
relationship and the scarcity makes it difficult to find consistent questionnaire items on the
variable studied.
This study reviews and analyses the literature based on the hypotheses between
independent variables and belief structures, rather taking into account the former in an
absolute basis, proposing an extended model as a result. The reason why the study’s
approach is predicated on the most validated hypotheses rather than the external variables
themselves stems from the fact that, if the most frequently used regressors were taken
into account, the Experience variable would have to be incorporated into the model.
Nevertheless, in the literature review conducted, of the 18 publications that examine the
relationship between Experience and PEOU only 8 of them explain a significant pattern.
The statistic is a mere 14 to 5 for the relationship between Experience and PU. Therefore,
independent variables that have no significant effect on belief structures, despite having
been frequently examined, are eliminated from this study.
Another issue that one has to put forward is that, while an external variable is shown to
have a significant effect on one belief structure, a similar relationship may not be pertinent
for the one with the other belief variable. For example, the hypothesis that Anxiety being
a significant determinant of PEOU has been accepted in 12 of the 19 studies conducted. On
the other hand, Anxiety’s influence on PU bears significance in only 3 among 8 models. This
urges the study’s research method to only take into account external variables shown to
have significant effect on PU and PEOU, rather than the frequency in which they are
incorporated in the models spanned.
In conclusion, this study embeds into ASME the external variables that are shown to have
significant effects on e-learning users’ perception of Usefulness and Ease of Use in the
literature review conducted, helping increase the model’s explanatory power.
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ACCEPTENCE AND SATISFACTION MODEL FOR E-LEARNING (ASME)
Following the literature review, hypotheses that are most frequently accepted in tests
where external variables are examined against belief structures are incorporated in the
model. These can be listed as follows: Self Efficacy-PEOU, Subjective Norm-PU, Self
Efficacy-PU, Interaction-PU, Enjoyment-PEOU, Anxiety-PEOU, Enjoyment-PU,
Compatibility-PU, Subjective Norm-PEOU and Interaction-PEOU. Apart from 6 regressors
and 10 hypotheses, the model also includes PU, PEOU and I, variables embodied in original
TAM. Satisfaction, a factor that was not included in the original TAM has also been added
to the model.
Research Hypotheses Based on External Variables
Self Efficacy
Self Efficacy is an individuals’ own perception of her talent of accomplishing a duty
(Bandura, 1982). From an e-learning point of view, this description can be paraphrased as
an individual’s self perception of her talent in receiving education via utilizing the e-
learning system. In this meta-analysis, it has been assessed that Self Efficacy is the most
widely used and accepted determinant of users’ Ease of Use perceptions. Moreover, the
hypothesis that Self Efficacy has a significant effect on PU is the second most examined
and the third most accepted in the researched reviewed within the scope of this study. Self-
Efficacy Theory predicts that individuals perform better when they believe they possess the
necessary talents (Barling & Beattie, 1983). Hence, it is expected that users with a higher
degree of Self-Efficacy have stronger intentions to adopt e-learning systems (Hsia, Chang
& Tseng, 2014).
Research show that Self Efficacy directly influences the e-learning users’ perception of Ease
of Use. In the literature review conducted, 58 of the 71 publications that examine Self-
Efficacy’s level of influence on PEOU for e-learning systems confirm the presence of a
significant and positive relationship. This can be explained by the relatively higher level of
perseverance among users with higher levels of Self-Efficacy upon facing problems.
24 of these work accept the hypothesis that there is positive correlation between Self-
Effiacy and PU. It is expected that e-learning systems’ users with high levels of Self-Efficacy
will believe in benefitting from the system without facing a major difficulty. Therefore,
following hypotheses can be put forward:
Hypothesis 1: Self-Efficacy has a positive and significant effect on PU for e-
learning systems.
Hypothesis 2: Self-Efficacy has a positive and significant effect on PEOU for e-
learning systems.
Subjective Norm
Subjective Norm is defined as an individual’s perception of whether the majority of people
important to the individual think she should perform the activity in question (Venkatesh &
Davis, 2000). It can also be referred to as the social pressure perceived on whether to
perform the behaviour or not (Ajzen, 1991). From an e-learning based perspective, one can
also characterize the paradigm as the social pressure one perceives on using e-learning
systems (Agudo-Peregrina, Hernandez-García & Pascual-Miguel, 2014). Even though TRA
theorizes Subjective Norm as a direct determinant of intention, TAM hypothesizes
otherwise (Davis, Bagozzi & Warshaw, 1989). Subjective Norm and social impact are used
interchangeably in various theories (Venkatesh, Morris, Davis & Davis, 2003), this study
follows the same path.
Subjective Norm’s effect on e-learning systems’ users PU has been examined and accepted
in an extensive array of research. In the literature reviewed within the scope of this work,
27 of the 33 publications testing Subjective Norm’s influence on users’ PU accept the
hypothesis of a positive and significant relationship, which is the second most frequently
accepted one among the 220 hypotheses covered. The social pressure on an e-learning
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systems’ user from her esteemed peers, instructors or family to use the aforementioned
system may trigger the user’s perception of the system’s practicality.
The relationship between Subjective Norm and PEOU, despite having been tested less
frequently, implies a positive and significant relationship as well. In 8 of the 12 publications
studied, it has been accepted that Subjective Norm directly influences PEOU. E-learning
systems’ users thought that her esteemed peers should also benefit from the system may
result in the perception of the convenience of the system. In light of all these views, one
can propose the following hypotheses:
Hypothesis 3: Subjective Norm has a positive and significant effect on PU for e-
learning systems.
Hypothesis 4: Subjective Norm has a positive and significant effect on PEOU for
e-learning systems.
Interaction
The key aspects of learning processes can be listed as the interactions between students,
between students and teaching staff as well as the collaboration in learning from these
interactions (Abbad, Morris & Nahlik, 2009). Literature review suggests that increasing
interaction results in higher motivation, boosts the level of satisfaction received from
learning, causes a more optimistic view on learning, triggers effective learning and success
(Donnelly, 2010). Interaction, is as critical as in e-learning as it is in conventional learning
processes. Interaction between students and teaching staff as well as among students is
facilitated via the extensive utilization of e-mails, chat rooms, bulletin boards in e-learning
systems (Pituch & Lee, 2006). Development of e-learning systems is mainly triggered by
technological improvements that facilitate interactions among students (Abbad, Morris &
Nahlik, 2009).
The hypothesis that interaction influences the e-learning systems’ users’ PU has been
examined and accepted in 18 and 15 studies, respectively, the hypothesis ranking fourth
among in the most frequently accepted hypotheses of the literature reviewed. Moreover,
the relationship between Interaction and PEOU has been confirmed to have significance in
8 of the 12 publications spanned. It can be inferred that the advanced interaction level
users build among themselves and with their instructors can have a direct and positive
effect on their PU and PEOU, leading one to propose the following hypotheses:
Hypothesis 5: Interaction has a positive and significant effect on PU for e-
learning systems.
Hypothesis 6: Interaction has a positive and significant effect on PEOU for e-
learning systems.
Enjoyment
Enjoyment is the level an individual perceives her usage of technology as enjoyable without
taking into account the expected performance results (Lubbe & Low, 1999). In e-learning
systems, Enjoyment is closely related to whether the individual deems her usage as
exciting, satisfactory and pleasant (Armenteros, 2013). Enjoyment is an example of
internal motivation and a significant determinant of user acceptance (Shyu & Huang,
2011). In TAM 3, Enjoyment is proposed as an antecedent of PEOU (Venkatesh & Bala,
2008).
Various research have examined whether the enjoyment of an e-learning system’s user
significantly and positively influences her PU. In the 16 publications reviewed within the
scope of this study, 13 accepts this hypothesis. Many software developers include
enjoyable design features in systems, not only aiming to increase the level of Enjoyment
but also bearing the intention to boost the system’s perceived user-friendliness
(Venkatesh, 2000). The lack of enjoyment may cause the user to feel that she has to spare
more effort to use the system. Likewise, in all the 12 research reviewed, Enjoyment is found
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out to significantly and positively affect PEOU. Therefore, the below hypotheses can be
suggested:
Hypothesis 7: Enjoyment has a positive and significant effect on PU for e-learning
systems.
Hypothesis 8: Enjoyment has a positive and significant effect on PEOU for e-
learning systems.
Anxiety
From a computer-science perspective, anxiety is simply the fear and concern upon facing
the probability of using a computer (Venkatesh, 2000), while another definition describes
Computer Anxiety as an individual’s inclination to feel concern about using a computer
(Howard and Smith, 1986). Interaction with a computer can revive strong and negative
feelings in users (Saade and Kira, 2006). Hence, users with a relatively lower level of
anxiety have a higher possibility of interaction with systems (Karaali, Gumussoy & Calisir,
2011).
Research reviewed within the scope of this study found out that the relationship between
Computer Anxiety and PEOU have been tested and accepted more frequently than the one
between Computer Anxiety and PU. (12 of the 19 studies examined found out that
Computer Anxiety is a significant determinant of PEOU whereas only 3 of the 8 publications
do so for the external variable’s relationship with PU). If an individual gets anxious upon
her usage of information technologies, she might perceive the system as complicated and
difficult (Raaij & Schepers, 2008). This lemma can also be replicated for e-learning systems.
Therefore, the following hypothesis can be put forward:
Hypothesis 9: Anxiety has a negative and significant effect on PEOU for e-learning
systems.
Compatibility
Compatibility is the level in which users perceive an innovation to be compatible with their
current values, needs and past experiences (Moore & Benbasat, 1991). A higher level of
Compatibility generally results in a higher level of system acceptance (Tung & Chang,
2008.a), whereas the Diffusion of Innovation Theory classifies innovations’ characteristics
based on their Relative Advantage, Compatibility, Complexity, Trialability and Observability
(Rogers, 1983).
The Relative Advantage and Complexity paradigms in DIT can be used interchangeably with
PU and PEOU in TAM, respectively (Chang & Tung, 2008). Therefore, it has been assessed
that many of the studies examined developed a hybrid model via synthesizing DIT and TAM,
and theorized Compatibility as a pre-determinant of TAM’s belief structures.
Research reviewed within the scope of this study found out that the relationship between
Compatibility and PU have been tested and accepted more frequently than the one between
Compatibility and PU. (10 of the 11 studies examined found out that Compatibility is a
significant determinant of PU whereas only 3 of the 6 publications do so for the external
variable’s relationship with PEOU). A user’s thought that e-learning is harmonious with her
own beliefs, needs and experiences can trigger a positive perception of the system’s value
added. Hence, the following hypothesis can be tested:
Hypothesis 10: Compatibility has a positive and significant effect on PU for e-
learning systems.
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The TAM Variables
Original TAM comprises of PU, PEOU, A, I and U (As shown in Figure 1), where the first two
variables represent the belief structures in TRA. Among the many determinants of system
usage, PU and PEOU are the most important (Davis, 1989). TAM proposes that PU and PEOU
(Venkatesh & Davis, 2000) mediate the impact of many external variables on the intention
to use. External variables help understand the impact scale and scope of PU and PEOU and
provide guidance in designing action plans to facilitate usage (Legris, Ingham & Collerette,
2003).
It is expected from the variable A to mediate the effect of belief variables on I.
Nevertheless, current research show that Attitude does not sufficiently explain the causal
relationship between belief and intention (Davis, Bagozzi & Warshaw, 1989), leading to the
opinion that the connection between A and I is spurious (Venkatesh, Morris, Davis & Davis,
2003). Eliminating A, therefore, could prove valuable in examining PU and PEOU’s influence
on I (Venkatesh, 2000). In line with this view, it has been observed that many studies
frequently use PU, PEOU and I of the TAM components and rule out A (As shown in Table
2). Moreover, TAM proposes that PEOU is a direct determinant of PU, influencing I directly
and through its effect on PU (As shown in Figure 1). In e-learning systems, user’s opinion
on the difficulty of the system can affect her perception on the system’s usefulness.
Therefore, the following hypotheses can be tested:
Hypothesis 11: PEOU has a positive and significant effect on PU for e-learning
systems.
Hypothesis 12: PU has a positive and significant effect on I for e-learning
systems.
Hypothesis 13: PEOU has a positive and significant effect on I for e-learning
systems.
Satisfaction
The main objective of a company is to cater for the needs that increase customer
satisfaction, rather than just rendering goods and services. Therefore; customer
satisfaction is a key factor in gaining competitive advantage (Dominici & Palumbo, 2013).
One of the results of customer satisfaction is the re-purchasing of the good and service
rendered. Similarities can be found between this activity of re-purchasing and the
continuous usage of information technologies (Lee, 2010). User satisfaction is one of the
important criteria that measures the success of information systems, where the variable is
proposed to be one of the six main dimensions of information systems success in the IS
Success Model (DeLone & McLean, 1992).
A considerable amount of research investigating users’ acceptance of e-learning systems
incorporated user satisfaction into TAM and tested its inter-relationships with other TAM
components, even though original TAM does not take into consideration the effect of user
satisfaction on information systems’ acceptance. In all of the 14 publications spanned, PU
has been accepted as a significant determinant of Satisfaction, whereas in 7 of the 10
research examined find out a significant connection between PEOU and Satisfaction.
Relationship between Satisfaction and I and Satisfaction and U was deemed to be
significant in 10 and 2 of the studies investigated, respectively (As shown in Table 3).
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Table 3. Relationship Between Satisfaction Variable & TAM Components in Research
Analyzed
Independent
variable
Dependent
variable
Number of
studies
investigated
Number of
studies that
accept a
significant
relationship
Referances
PU
Satisfaction
14
14
Al-Azawei & Lundqvist (2015), Al-
Azawei, Parslow & Lundqvist (2017),
Al-Hawari & Mouakket (2010), Capece
& Campisi, (2011), Italy & Portugal,
Capace & Campisi (2013), Basic &
Optional, Lee (2010), Lee & Lehto
(2013), Ma, Chao & Cheng (2013), Park,
Son & Kim (2012), Perreira, Ramos &
Chagas (2015), Roca, Chiu & Martinez
(2006), Shih, Chen, Shih & Su (2012)
PEOU
Satisfaction
10
7
Al-Azawei & Lundqvist (2015), Al-
Azawei, Parslow & Lundqvist (2017),
Al-Hawari & Mouakket (2010), Capece
& Campisi, (2011), Italy & Portugal,
Capace & Campisi (2013), Basic &
Optional, Lee (2010), Park, Son & Kim
(2012), Perreira, Ramos & Chagas
(2015), Roca, Chiu & Martinez (2006)
Satisfaction
I
10
10
Cho, Cheng & Lai (2009), Cho, Cheng &
Hung (2009), Lee (2010), Lee & Lehto
(2013), Ma, Chao & Cheng (2013),
Mohammadi (2015.a), Mohammadi
(2015.b), Ramayah & Lee (2012), Roca,
Chiu & Martinez (2006), Shih, Chen,
Shih & Su (2012)
Satisfaction
U
2
2
Mohammadi (2015.a), Mohammadi
(2015.b)
Users’ belief that e-learning systems may influence performance and apprehension of the
system as relatively easy can trigger a positive perception of satisfaction. In addition, user
satisfaction may appear as a vital determinant of the intention to use e-learning systems.
Therefore, the below hypotheses can be suggested:
Hypothesis 14: PEOU has a positive and significant effect on Satisfaction for e-
learning systems.
Hypothesis 15: PU has a positive and significant effect on Satisfaction for e-
learning systems.
Hypothesis 16: Satisfaction has a positive and significant effect on I for e-
learning systems.
As a result of the meta-analysis conducted, Acceptance and Satisfaction Model for E-
Learning (ASME) has been proposed (As shown in Figure 2).
14
Figure 2. Acceptance and Satisfaction Model for E-learning (ASME)
CONCLUSION
The main objective of this study is to identify the factors that influence users’ acceptance
of e-learning systems and hence, guide researchers and systems developers in designing
the necessary corrective measures. In line with this target, research that investigate user
acceptance in e-learning systems via utilising TAM was specified and assessed.
Relationships between TAM’s belief structures, PU and PEOU with the external variables
proposed in these research were analyzed. Hypotheses that were frequently accepted in
the literature were identified and incorporated into the model proposed.
In the meta-analysis conducted, 177 journal papers, 22 conference papers and 4 PhD thesis
that examine user acceptance in e-learning systems via TAM are analyzed where 129
different external variables are proposed as antecedents of belief structures. 220
hypotheses that question the relationship of these external variables with PU and PEOU are
tested 714 times. As a result of this literature review, the most frequently accepted
relationships are ranked as follows: Self Efficacy-PEOU (58), Subjective Norm-PU (27), Self
Efficacy-PU (24), Interaction-PU (15), Enjoyment-POEU (13), Anxiety-PEOU (12),
Enjoyment-PU (12), Compatibility-PU (10), Subjective Norm-PEOU (9) and Interaction-
PEOU (8).
Variables and hypotheses proposed in the model are identified through a three-phased
approach. First, relationships between variables recurrently accepted in the literature
reviewed and PU/PEOU are analyzed and the most frequently accepted hypotheses are
incorporated into the model. In the second step, a thorough assessment is conducted on
the utilization of TAM’s components and inferences made regarding these variables. In line
with these takeaways, the variables A and U, which make up two of the five components of
the original TAM are excluded from the model proposed. Last, the relationship between
TAM variables and User Satisfaction, a variable not included in the original TAM is
examined. Conforming to the findings of these studies, the position of User Satisfaction in
the model proposed is identified. As a result of this three-phased approach Acceptance and
Satisfaction Model for E-Learning (ASME) is proposed.
In the literature reviewed, only one publication that conducted a meta-analysis of the
studies utilizing TAM within the perspective of users’ e-learning acceptance is attained.
Abdullah and Ward (2016) investigated 107 studies and identified the five most recurrently
used external variables. This study increases the span of the literature review to 203 and
takes into account the most frequently accepted hypotheses, rather than the external
variables. Therefore, the model proposed does not include hypotheses that are not
accepted, despite having been frequently tested or external variables that are found out to
have a significant relationship with only one of the belief structures.
15
Literature reviewed are also classified based on their respective geographical region and
countries, allowing the researchers to investigate the differences of users in different
regions. Most of the literature reviewed was conducted in East Asia and Pacific, while
relatively less publications within the scope of the study originated from Latin America and
the Caribbean, Sub-Saharan Africa and South Asia. It is also observed that the effect of
System Functionality (91.7%), Playfulness (81.8%) and Self-Efficacy (85%) on PU and
PEOU were the most recurrently accepted hypotheses in East Asia and Pacific, Europe and
Central Asia and Middle East and North Africa, respectively. Moreover, Subjective Norm is
expected to have a higher acceptance rate in Eastern cultures where users’ social attributes
are regarded with increased value. The higher acceptance rates of Subjective Norm in
Middle East and North Africa (87,5%) and East Asia and Pacific (85%) compared to Europe
and Central Asia (76,9%) validates this view. It should also be emphasized that Self
Efficacy has a high acceptance rate in Middle East and North Africa (85%) compared to
East Asia and Pacific (67.6%), Europe and Central Asia (52.2%).
Further research should focus upon empirically testing the model on different e-learning
systems, allowing researchers to modify the model based on the structure of the e-learning
system as well as the region the study is conducted.
LIMITATIONS OF STUDY
The study has some limitations that can be addressed in future studies. Firstly, the model
proposed as a result of the literature review, has not been empirically tested. In future
works, the proposed model should be empirically tested and results should be discussed.
Moreover, in the model proposed in this study, according to the literature the most
accepted hypotheses affecting PU and PEOU, proposed by TAM as two main determinants
of intention to use, are suggested. However, some hypotheses that have never been tested
or rarely tested in the literature may also give effective results. In future studies,
researchers should test possible extrinsic variables that they consider possibly effective on
e-learning acceptance, by adding those variables to the suggested model in this study.
Despite the existing limitations, this study may contribute to the e-learning system
developers and researchers working on this field.
BIODATA and CONTACT ADDRESSES of AUTHORS
Rahmi BAKI was born in Ankara, Turkey in 1988. He received her B.A
and M.A. degrees in Industrial Engineering from Gazi University,
Turkey. He is working as a Research Assistant at Management
Information Systems, Aksaray University, Turkey. He is currently
studying his PhD in Industrial Engineering in the Kırıkkale University,
Turkey. His academic interest areas are distance education, facility
layout, supply chain management and logistics management.
Rahmi BAKI
Department of Management Information Systems
Faculty of Economics and Administrative Sciences
Aksaray University, 68100, Aksaray, Turkey
Phone: +90 382 288 24 58
E-mail: rahmibaki@aksaray.edu.tr
16
Dr. Burak BIRGOREN is a Professor of Industrial Engineering at
Engineering Faculty, Kırıkkale University. Dr. Birgoren gained his
Ph.D. in Industrial Engineering at PennState University in 1998. His
academic interest areas are quality control and management,
reliability engineering, production planning and control. He
published 12 journal articles in international indexes, and 4
international book chapters. He has over 400 citations at Google
Scholar.
Burak BIRGOREN
Department of Industrial Engineering
Faculty of Engineering
Kırıkkale University, 71451, Kırıkkale, Turkey
Phone: +90 318 357 4242/1010
E-mail: birgoren@kku.edu.tr
Dr. Adnan AKTEPE is an Assistant Professor Doctor in the
Department of Industrial Engineering at Kirikkale University,
Turkey. He has received the BSc degree in industrial engineering
from Marmara University in 2007; MSc degree in industrial
engineering from Kirikkale University in 2009 and PhD degree from
Gazi University Industrial Engineering Department in 2015. His
research areas include performance management, customer
satisfaction, applications of multi-criteria decision making
methods, fuzzy logic, data mining, service systems and intelligent
techniques in industrial engineering and management science.
Adnan AKTEPE
Department of Industrial Engineering
Faculty of Engineering
Kırıkkale University, 71451, Kırıkkale, Turkey
Phone: +90 318 357 42 42 / 1009
E-mail: aaktepe@gmail.com
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24
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26
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28
APPENDICES
Appendix 1. 203 Publications Aiming to Explain User Acceptance in E-Learning Systems
Study
Territory
Participant
Information
TAM Compenents
External Factors
PU
P
EOU
A
I
U
Abbad, Morris
& Nahlik
(2009)
Jordan
486 Students
+
+
-
+
-
Subjective Norms (-,x), Internet Experience (-,+),
System Interactivity (-,-), Self Efficacy (-,+),
Technical Support (+,-)
Abbas (2016)
Egypt
UK
468 Students
+
+
-
+
-
Egypt: Interpersonel Influence (+,x), External
Influence (+,x), Instructor Influence (+,+)
UK: Interpersonel Influence (-,x), External Influence
(-,x), Instructor Influence (+,+)
Abdel-Wahab
(2008)
Egypt
258 Students
+
+
+
+
-
-
Abdullah, Ward
& Ahmed
(2016)
UK
242 Students
+
+
-
+
-
Experience(-,+), Subjective Norm (-,+), Enjoyment
(+,+), Computer Anxiety (x,-), Self Efficacy (-,+)
Abramson,
Dawson &
Stevens
(2015)
U.S.A
-
+
+
+
+
-
Prior Use of E-learning(-,+), Self Efficacy(-,+),
Subjective Norms(-,+)
Adetiimirin
(2015)
Nigeria
121 Students
-
-
-
-
+
-
Agudo-
Peregrina,
Hernandez-
García, &
Pascual-Miguel
(2014)
Spain
147 Students
+
+
-
+
+
Higher Education: Relevance for Learning (+,x)
Perceived Interaction (+,x), Subjective Norm (-,x),
Self Efficacy(x,-), Computer Anxiety (x,-), Personel
Innovativeness (x,+), Perceived Playfulness (x,-),
Facilitating Conditions (x,+)
Lifelong Learning: Relevance for Learning (+,x)
Perceived Interaction (+,x), Subjective Norm (+,x),
Self Efficacy (x,-), Computer Anxiety (x,+), Personel
Innovativeness (x,-), Perceived Playfulness (x,+),
Facilitating Conditions (x,+)
Al-Adwan, Al-
Adwan &
Smedley
(2013)
Jordan
107 Students
+
+
+
+
-
-
Al-Alak &
Alnawas
(2011)
Jordan
799 Lecturers
+
+
-
+
-
-
Al-Ammary, Al-
Sherooqi, & Al-
Sherooqi
(2014)
Bahrain
109 Students
+
+
-
+
-
Computer Self Efficacy (+,+), System Design &
Features(+,x), Perceived Enjoyment (x,+), Perceived
Mobility Value (+,x), Perceived Interectivity (-,-)
Al-Ammari &
Hamad (2008)
Bahrain
155 Students
+
+
-
+
-
Content Quality (+,+), Computer Self Efficacy (+,+)
Al-Aulamie,
Mansour, Daly
& Adjei (2012)
UK
51 Students
+
+
-
+
-
Enjoyment (+,+), Computer Playfulness (+,-)
Al-Azawei &
Lundqvist
(2015)
Iraq
70 Students
+
+
-
-
-
Learning Styles (-,x), Gender Diversity (-,-), Online
Self Efficacy (+,+)
Al-Azawei,
Parslow &
Lundqvist
(2017)
Iraq
210 Students
+
+
-
+
-
Blended E-learning System Self Efficacy (+,+),
Learning Styles (-,x)
Alenezi (2012)
Saudi Arabia
408 Students
+
+
+
+
+
-
Alenezi, Karim
& Veloo (2010)
Saudi Arabia
408 Students
+
+
+
+
-
-
Alenezi, Karim
& Veloo (2011)
Saudi Arabia
408 Students
+
+
-
+
+
-
Al-Gahtani
(2016)
Saudi Arabia
286 Students
+
+
-
+
+
Subjective Norm (+,x), Image (+,x), Job Relevance
(+,x), Result Demonstrability (-,x), Computer Self
Efficacy (x,+), Perceptions of External Control (x,+),
Computer Anxiety (x,+), Computer Playfulness (x,-),
Perceived Enjoyment (x,+)
Al-Hawari &
Mouakket
(2010)
U.A.E
340 Students
+
+
-
+
-
-
Ali, Ahmed,
Tariq & Safdar
(2013)
Bahrain
425 Students
+
+
-
+
-
Computer Playfulness (x,+), Computer Self Efficacy
(x,+), Computer Anxiety (x,+)
Al-Mushasha
(2013)
Saudi Arabia
224 Students
+
+
+
+
-
University Support (+,+), Computer Self Efficacy
(+,+)
29
Althunibat
(2015)
Jordan
239 Students
+
+
-
+
-
Facilitating Conditions (+,+), Perceived Self Efficacy
(+,+)
Arenas-Gaitan,
Rondan-
Cataluña &
Ramirez-Correa
(2010)
Spain
189 Students
+
+
-
+
+
Result Demonstrability (+,x), Perception of External
Control (x,+), Perceived Enjoyment (x,+)
Arenas-Gaitan,
Ramírez-Correa
& Rondan-
Cataluña
(2011)
Spain
Chile
352 Students
+
+
+
-
+
Spain: Job Relevance(+,x), Result
Demonstrability(+,x), Perception of External
Control(x,+)
Chile: Job Relevance(+,x), Result
Demonstrability(+,x), Perception of External
Control(x,+)
Armenteros,
Liaw,
Fernandez, Díaz
& Sanchez
(2013)
International
88
Instructors
+
+
-
+
-
Previous Experience with Technology (-,-),
Perception Enjoyment (+,+)
Attis (2014)
U.S.A
112
Instructors
+
+
+
+
-
-
Aypay, Celik,
Aypay & Sever
(2012)
Turkey
754 Students
+
+
+
+
-
Facilitating Conditions (+,+), Technological
Complexity (-,+), Computer Self Efficacy (+,-)
Baharin, Lateh,
Nathan &
Nawawia
(2015)
Malaysia
223 Students
+
+
-
+
+
Interactivity (+,+)
Bao, Xiong, Hu
& Kibelloh
(2013)
China
137 Students
+
+
-
+
-
General Computer Self Efficacy (+,+), Specific
Computer Self Efficacy (+,+)
Basoglu &
Ozdogan
(2011)
Turkey
81 Students
+
+
+
-
-
Mobility (-,x), Peer Influence (-,x), Computer Self
Efficacy (x,+), Personal Innovativeness (x,-), User
Interface (x,-)
Bhatiasevi
(2011)
Thail&
207 Students
+
+
-
+
-
Computer Self Efficacy (-,+), System Functionality
(-,+), Teaching Materials (+,+)
Brown, Ingram
& Thorp (2006)
South Africa
171 Students
+
+
-
+
+
Compatibility (+,-), Perceived Enjoyment (x,-), Self
Efficacy (x,+)
Calisir,
Gumussoy,
Bayraktaroglu
& Karaali
(2014)
Turkey
546 Workers
+
+
+
+
-
Image(-,x), Perceived Content Quality(+,x),
Perceived System Quality(x,+), Anxiety (x,+)
Cabada,
Estrada,
Hernandez,
Bustillos &
Reyes-García
(2017)
Mexico
43 Students
+
+
+
+
-
-
Capece &
Campisi (2011)
Italy
Portugal
253 Students
+
+
+
+
-
-
Capace &
Campisi (2013)
Italian
5083
Employees
+
+
-
-
-
-
Chang, Yan &
Tseng (2012)
Taiwan
158 Students
+
+
+
+
-
Perceived Convenience (x,+)
Chang, Tseng,
Liang & Yan
(2013)
Taiwan
125 Students
+
+
-
+
-
Perceived Convenience (+,+)
Chang, Chao &
Cheng (2015)
Taiwan
682 Nurses
+
+
+
+
-
Perceived Risk (+,-)
Chang, Hajiyev
& Su (2017)
Azerbaijan
714 Students
+
+
-
+
-
Subjective Norm (+,-), Experience (+,+), Enjoyment
(+,+), Computer Anxiety (+,+), Self Efficacy (-,+)
Chang & Liu
(2013)
Taiwan
60 Students
+
+
-
+
-
Augmented Reality (+,+), Content Quality (+,+),
Environment Interaction (+,+)
Chang & Tung
(2008)
Taiwan
212 Students
+
+
-
+
-
Compatibility (+,x)
Chen, Lin, Yeh
& Lou (2013)
Taiwan
218 Students
+
+
-
+
-
Perceived Enjoyment (+,+), System Characteristics
(+,+)
Chen & Tseng
(2012)
Taiwan
402 Teachers
+
+
-
+
-
Motivation to Use (+,+), Computer Anxiety (-,+),
Internet Self Efficacy (+,+)
Cheng (2011)
Taiwan
328
Employees
+
+
+
+
+
Network Externality (-,+), Interpersonal Influence
(+,x), External Influence (+,x), Content Quality
(+,x), System Response, System Interactivity (+,+),
System Functionality (+,+), Computer Self Efficacy
(-,+), Internet Self Efficacy (-,+), Cognitive
Absorption (+,+)
Cheng (2012)
Taiwan
483
Employees
+
+
-
+
-
Course Content Quality (+,+), Course Design Quality
(-,+), Support Service Quality (+,+), System
Functionality (+,+), System Interactivity (+,+),
System Response (+,-), User Interface Design
(+,+),Instructor Attitude Towards E-learners (+,x)
30
Cheng (2013)
Taiwan
218 Nurses
+
+
-
+
-
Learner-System Interaction (+,+), Instructor-
Learner Interaction (+,+), Learner-Learner
Interaction (+,+)
Cheng (2014)
Taiwan
225 Students
+
+
-
+
-
Controllability (+,+), Responsiveness (+,+), Two
Way Communication (+,+), Personalization (+,+)
Cheng (2015)
Taiwan
486 Users
+
+
-
+
-
Navigation (+,+), Convenience (+,+), Compatibility
(+,+)
Cheung & Vogel
(2013)
Hong Kong
136 Students
+
+
+
+
+
Perceived Resource (x,+), Compatibility (x,+),
Sharing (+,x)
Cho, Cheng &
Lai (2009)
Hong Kong
445 Students
+
+
-
+
-
Perceived Functionality (+,x), Perceived User-
Interface Design (-,+), Perceived System Support
(x,+)
Cho, Cheng &
Hung (2009)
Hong Kong
445 Students
+
-
-
-
+
-
Chow, Herold,
Choo & Chan
(2012)
Hong Kong
206 Students
+
+
-
+
-
Computer Self Efficacy (+,+)
Chow, Chan, Lo,
Chu, Chan & Lai
(2013)
Hong Kong
128 Students
+
+
+
+
-
Computer Self Efficacy (-,+)
Cigdem &
Topcu (2015)
Turkey
115
Instructors
+
+
-
+
-
Subjective Norm (+,+), Technological Complexity
(x,+), Application Self Efficacy (-,+)
Coskuncay &
Ozkan (2013)
Turkey
224
Academicians
+
+
-
+
-
Application Self Efficacy (+,+), Subjective Norm
(+,+), Technological Complexity (x,+)
Davis & Wong
(2007)
New Zeal&
964 Students
+
+
-
+
+
Subjective Norm (+,x), Output Quality (+,x),
Flow/Playfullness (x,+)
De Smet,
Bourgonjon,
Wever,
Schellens &
Valcke (2012)
Belgium
505 Teachers
+
+
-
-
+
Personal Innovativeness toward IT (+,+),
Experience (x,+), Subjective Norm (+,x)
Deshp&e,
Bhattacharya &
Yammiyavar
(2012)
India
40 Students
+
+
+
+
+
Computer Friendliness Experience+Knowledge (x,-)
Escobar-
Rodriguez &
Monge-Lozano
(2012)
Spain
162 Students
+
+
-
+
-
Perceived Usefulness for Professors (+,x), Perceived
Compatibility with Student Tasks (-,+), Training
(+,-)
Fadare,
Babatunde,
Akomolafe &
Lawal (2011)
Nigeria
458 Students
+
+
+
+
-
-
Fagan, Kilmon
annd P&ey
(2012)
U.S.A
158 Students
+
+
-
+
-
Personal Innovativeness in the Domain of IT (+,+)
Farahat (2012)
Egypt
153 Students
+
+
+
+
-
Social Inflence (+,+)
Floental (2016)
U.S.A
156 Students
-
+
+
-
-
-
Freitas,
Ferreira, Garcia
& Kurtz (2017)
Brazil
260 Students
+
+
+
+
-
Interactivity (+,x), Technical Support Availability
(x,+)
Harmon (2015)
U.S.A
195 Students
+
+
-
+
-
Personal Innovativeness (+,-)
Hashim (2008)
Maleysia
261
Employees
+
+
+
-
-
-
Hei & Hu
(2011)
China
253 Students
+
+
+
+
-
Social Inflences (-,x)
Hidayanto,
Febriawan,
Sucahyo &
Purw&ari
(2014)
Indonesia
74 Students
+
+
+
+
+
Task Technology Fit (-,+)
Ho, Ke, Liu
(2015)
Hong Kong
131 Students
+
+
+
+
-
-
Hsia, Chang &
Tseng (2014)
China
223
Employees
+
+
-
+
-
Locus of Control (+,+), Computer Self Efficacy (x,+)
Hsia & Tseng
(2008)
Taiwan
233
Employees
+
+
-
+
-
Computer Self Efficacy (+,+), Perceived Flexibility
(+,x)
Hsiao & Chen
(2015)
Taiwan
60 Students
+
+
-
+
-
Mobile Learning Self Efficacy (+,+), Task Technology
Fit (+,+)
Hsu & Chang
(2013)
Taiwan
82 Students
+
+
+
+
-
Perceived Convenience (+,x)
Huang, Lin &
Chuang (2007)
Taiwan
313 Students
+
+
+
+
-
Perceived Mobility Value (+,x), Peceived Enjoyment
(x,+)
Hussein,
Aditiawarman
& Mohamed
(2007)
Indonesia
147 ogrenci
+
+
-
+
-
Computer Self Efficacy (+,-), Convenience (x,-),
Instructional Design (+,+), Technological Factor (-
,+), Instructor’s Characteristic (x,-)
Hussein (2017)
Malaysia
151 Students
+
+
+
+
-
-
Ibrahim, Leng,
Yusoff, Samy,
Malaysia
95 Students
+
+
-
+
-
Instructor Characteristics (-,x), Computer Self
Efficacy (-,+)
31
Masrom &
Rizman (2017)
Ifinedo (2006)
Estonia
72 Students
+
+
-
+
+
Technology Characteristics (+,+), User
Characteristics (+;+)
Indahyanti &
Sukarjadi
(2014)
Indonesia
60 Students
+
+
+
+
+
-
Islam (2013)
Finl&
249 Students
+
+
-
-
+
-
Ismail, Razak,
Zakariah, Alias
& Aziz. (2012)
Malaysia
215 Students
+
+
-
+
-
-
Jan & Contreras
(2011)
Peru
89 ogrenci
+
+
+
+
+
-
Jung (2015)
South Korea
189 Students
+
+
-
+
+
Instant Connectivity (+,x), Compatibility (+,x),
Interaction (+,x), Content Enrichness (+,x),
Computer Self Efficacy (+,x)
Kang & Shin
(2015)
Guney Kore
251 Students
+
+
-
+
-
Self Efficacy (+,-), Systematic Lecture Content (-,-),
Subjective Norm (+,+), System Accessibility (-,+)
Karaali,
Gumussoy &
Calisir (2011)
Turkey
546 Workers
+
+
+
+
-
Social Influence (+,x), Facilitating Conditions (x,+),
Anxiety (x,+)
Khor (2014)
Malaysia
125 Students
+
+
+
+
-
-
Kilic, Guler &
Celik (2015)
Turkey
416 Students
+
+
+
-
-
Interactive Whiteboard Self Efficacy (+,+),
Perceived Learning (+,+)
Kim, Kim & Han
(2013)
South Korea
60 Teachers
+
+
+
+
-
-
Lai & Ulhas
(2012)
Taiwan
96 Students
+
-
-
+
-
Compatibility (+,x), Convenience (+,x), Perceived
Enjoyment (+,x)
Lau & Woods
(2008)
Malaysia
342 Students
+
+
-
+
+
Technical Quality (-,+), Content Quality (-,+),
Pedagogical Quality (+,+), Self-Efficacy (-,-),
Internet Experience (-,-)
Lau & Woods
(2009)
Malaysia
312 Students
+
+
-
+
+
Technical Quality (-,+), Content Quality (-,+),
Pedagogical Quality (+,+), Self-Efficacy (-,-),
Internet Experience (-,-)
Lee, Cheung &
Chen (2005)
Hong Kong
544 Students
+
+
+
+
-
-
Lee (2006)
Taiwan
1085
Students
+
+
-
+
+
Content Quality (+,x), Perceived Network Externality
(+,+), Computer Self Efficacy (+,+), Course
Attributes (-,-), Subjective Norms (+,x)
Lee (2008)
Taiwan
1107 ogrenci
+
+
-
+
-
Internal Computing Support (+,+), Internal
Computing Training (+,+), Internal Equipment
Accessability (-,-), External Computing Support
(+,+), External Computing Training (-,+), External
Equipment Accessability (-,+)
Lee, Yoon & Lee
(2009)
South Korea
214 Students
+
+
-
+
-
Instructor Characteristics (+,x), Teaching Materials
(+,x), Design of Learning Contents (x,+)
Lee (2010)
Taiwan
363 Students
+
+
+
+
-
Confirmation (+,x)
Lee, Hsieh & Ma
(2011)
Taiwan
357
Employees
+
+
-
+
-
Organizational Support (+,-), Management Support
(-,+), Computer Self Efficacy (-,+), Individuals’
Experience with Computers (-,+), Task Equivocality
(-,-), Task Interdependence (-,+), Subjective Norm
(+,+)
Lee, Hsieh &
Hsu (2011)
Taiwan
552
Employees
+
+
-
+
-
Compatibility (+,-), Complexity (+,+), Relative
Advantages (+,+), Observability (-,-), Trialability
(+,+)
Lee, Hsieh &
Chen (2013)
Taiwan
332
Employees
+
+
+
+
-
Organisational Support (+,+), Computer Self Efficacy
(-,+), Prior Experiences (+,+), Task Equivocality (+,-
)
Lee, Hsiao,
Purnomo
(2014)
Indonesia
326 Students
+
+
-
+
-
Computer Self Efficacy (-,+), Internet Self Efficacy
(+,+), Instructor Attitude Toward Students (-,x),
Learning Content (+,+), Technology Accessibility
(x,+)
Lee & Lehto
(2013)
Guney Kore
432
Respondents
+
+
-
+
-
Task Technology Fit (+,x), Content Richness (+,x),
Vividness (+,x), YouTube Self Efficacy (+,x)
Lefievre (2012)
France
404 Students
+
+
-
+
+
Computer Playfulness (x,+), Perceived Enjoyment
(x,-), Computer Anxiety (x,+), Result
Demonstrability (+,x), Relevance (+,x)
Letchumanan &
Tarmizi (2011)
Malaysia
169 Students
+
+
+
+
+
Gender (-,-)
Li, Duan &
Alfrod (2012)
China
280 Students
+
+
-
+
-
System Funcationality (+,+), System Response
(+,+), System Interactivity (-,+), Self Efficacy (x,+)
Lin, Chen & Yeh
(2010)
Taiwan
214 Students
+
+
-
+
-
Perceived Enjoyment (+,x), System Characteristics
(+,x), Course Features (x,+), Self Efficacy (x,+)
Lin (2013)
Taiwan
212 Students
+
+
-
+
-
Underst&ing U-learning (+,+), Assimilating U-
learning (+,+), Applying U-learning (+,+)
Lin, Persada &
Nadlifatin
(2014)
Taiwan
302 Students
+
+
+
+
-
Perceived Interactivity (+,+)
Little (2016)
U.S.A
318 Nurses
-
-
+
+
-
-
Liu, Liao & Peng
(2005)
Taiwan
88 Students
+
+
+
+
-
E-learning Materials Presentation Types (+,x)
32
Liu, Liao & Pratt
(2009)
Taiwan
88 Students
+
+
+
+
-
E-learning Materials Presentation Types (+,x)
Liu (2010)
U.S.A
126 Students
+
+
-
+
+
Wiki Self Efficacy (-,+), Online Posting Anxiety (-,-)
Liu, Li &
Carlsson
(2010)
China
209 ogrenci
+
+
-
+
-
Personal Innovativeness (+,+)
Lo, Hong, Lin &
Hsu (2012)
China
45 Students
+
+
+
-
-
-
Lo, Liu & Wang
(2014)
Taiwan
35 Students
+
-
+
+
-
-
Loukis, Pazalos
& Salagara
(2012)
Greece
98
Professionals
+
+
-
-
+
-
Lowe,
D’aless&ro,
Winzar, Laffey
& Collier (2013)
UK
144 Students
+
+
+
+
-
Affinity (+,+), Risk Tolerance (x,+)
Ma, Chao &
Cheng (2013)
Taiwan
650 Nurses
+
+
-
+
+
Task Technology Fit (+,x), Computer Sef Efficacy (-
,x)
Mafunda, Swart
& Bere (2016)
South Africa
49 Students
+
+
+
-
+
-
Macharia &
Nyakwende
(2009)
Kenya
200 Students
+
+
-
+
+
Competition Pressure (+,-), Government Support
(+,+), ICT Vendors Support (+,-), Perceived Socio
Economic (+,+)
Martin (2012)
Oman
210 Students
& Educators
+
+
-
+
+
Subjective Norm (+,x), Extrinsic Motivation (-,x),
Intrinsic Motivation (x,+), Technology Experience
(+,-), System Interactivity (+,-), Information Privacy
(x,-)
Martinez-
Torres, Marin,
Garcia,
Vazquez, Oliva
& Torres (2008)
Spain
220 Students
+
+
-
+
+
Methodology, Accessibility (x,+), Reliability (x,+),
Enjoyment (x,+), Interactivity & Control (+,x)
Moghadam &
Bairamzadeh
(2009)
Iran
155 Students
+
+
-
+
-
Subjective Norm (+,x), Personal Innovativeness in
Domain of Information Technology (-,+), Computer
Self Efficacy (x,+)
Mohammed &
Karim (2012)
Malaysia
160 Students
+
+
-
+
-
Computer Application Anxiety (-,-), Self Efficacy (-,-)
Mohammadi
(2015.a)
Iran
390 Students
+
+
-
+
+
-
Mohammadi
(2015.b)
Iran
390 Students
+
+
-
+
+
-
Moreno,
Cavazotte &
Alves (2016)
Brazil
251 Students
+
+
+
+
-
System Interactivity (+,x), Social Influence (-,x),
Output Quality (-,x), Cognitive Absorbtion (+,+), Self
Efficacy (x,+), Facilitating Conditions (x,+), Prior
Experience (x,-)
Motaghian,
Hassanzadeh &
Moghadam
(2013)
Iran
115
Instructors
+
+
-
+
+
Information Quality (+,+), System Quality (-,-),
Service Quality (-,+), Subjective Norm (+,+), Self
Efficacy (-,+)
Nan, Xun-hua &
Guo-qing
(2007)
China
121 Students
+
+
+
+
-
Training Impression (x,+), Technology Facilitating
Condition (x,-), Perceived Enjoyment (+,x), Personal
Innovativeness of IT (-,x), Job Relevance (+,x),
Substitutability (-,x)
Ngai, Poon &
Chan (2007)
Hong Kong
836 Students
+
+
+
-
+
Technical Support (+,+)
Okazaki &
Santos (2012)
Brazil
446
Faculty
Members
+
+
+
+
+
-
Ong, Lai &
Wang (2004)
Taiwan
140
Engineers
+
+
-
+
-
Computer Self-Efficacy (+,+)
Ong & Lai
(2006)
Taiwan
156
Employees
+
+
-
+
-
Computer Self-Efficacy (+,+)
Ouyang, Tang,
Rong, Zhang,
Yin & Xiong
(2017)
China
234 Students
+
-
-
+
-
Confirmation (+,x)
Padilla-
Melendez,
Garrido-
Moreno &
Aguila-Obra
(2008)
Spain
225 Students
+
+
+
+
-
Computer Self Efficacy (x,+)
Padilla-
Melendez,
Aguila-Obra &
Garrido-
Moreno (2013)
Spain
484 Students
+
+
+
+
-
Males: Perceived Playfulness (+,+)
Females: Perceived Playfulness (+,+)
Park (2009)
South Korea
628 Students
+
+
+
+
-
E-learning Self Efficacy (+,+), Subjective Norm (+,-),
System Accessibility (-,+)
Park, Lee &
Cheong (2008)
U.S.A
191
Instructors
+
+
-
+
+
Motivation (+,+), Instructional Technology Cluster
(-,-)
33
Park, Nam &
Cha (2012)
South Korea
288 Students
+
+
+
+
-
Mobil Learning Self Efficacy (-,+), Major Relevance
(+,-), System Accessibility (-,+), Subjective Norm
(+,-)
Park, Son & Kim
(2012)
South Korea
408
Professionals
+
+
-
-
-
Enjoyment (+,-), Computer Anxiety (+,+), Social
Influence (+,x), Organizational Support (-,+),
Information Quality (+,x), System Quality (-,+)
Perreira,
Ramos &
Chagas (2015)
Brazil
192 Students
+
+
-
-
+
-
Pituch & Lee
(2006)
Taiwan
259 Students
+
+
-
-
+
System Functionality (+,+), System Interactivity
(+,-), System Response (+,+), Self-Efficacy (-,+),
Internet Experience (-,-)
Poelmans,
Wessa, Milis,
Bloemen &
Doom (2008)
Belgium
200 Students
+
+
-
+
-
Information Quality (+,x), System Quality (x,+)
Post (2010)
U.S.A
134 Students
+
+
+
+
-
Subjective Social Norm (+,x), Perceived Compatbility
(+,x)
Premchaiswadi,
Porouhan &
Premchaiswadi
(2012)
Thail&
86 Students
+
+
-
+
-
-
Punnose
(2012)
Thail&
249 Students
+
+
-
+
-
Computer Self-Efficacy (x,+), Conscientiousness
(+,x), Subjective Norms (+,x)
Purnomo & Lee
(2012)
Indonesia
306
Employees
+
+
-
+
-
Management Support (+,+), Computer Self Efficacy
(-,-), Prior Experience (+,+), Computer Anxiety
(+,-), Compatibility (+,+)
Raaij &
Schepers
(2008)
China
40 Managers
+
+
-
-
+
Personal Innovativeness in the Domain of
Information Technology (-,+), Computer Anxiety
(x,+), Social Norms (+,x)
Ramayah & Lee
(2012)
Malaysia
250 Students
-
-
-
+
-
-
Ramírez-Correa
Arenas-Gaitan
& Rondan-
Cataluña
(2015)
Chile
Spain
389 Students
+
+
-
+
+
Result Demonstrability (+,x), Perceived Enjoyment
(x,+), Perception of External Control (x,+)
Rejón-Guardia,
Sanchez-
Fernandez &
Muñoz-Leiva
(2013)
Spain
135 Students
+
+
-
+
-
Subjective Norms (+,x), Image (+,x)
Rezaei,
Mohammadi,
Asadi &
Kalantary
(2008)
Iran
120 Students
+
+
-
+
-
Internet Experience (+,-), Computer Anxiety (x,-),
Age (-,x), Computer Self Efficacy (x,-), Affect (x,-)
Roca, Chiu &
Martinez
(2006)
Spain
172 Workers
+
+
-
+
-
Confirmation (+,+), Computer Self Efficacy (x,+),
Internet Self Efficacy (x,+)
Roca & Gagne
(2008)
Spain
166 Workers
+
+
-
+
-
Perceived Autonomy Support (+,x), Perceived
Competence (+,+), Perceived Relatedness (-,x),
Perceived Playfulness (+,+)
Ros,
Hernandez,
Caminero,
Robles,
Barbero, Macia
& Holgado
(2014)
Spain
80 Students
+
+
-
+
-
Gadget Design (+,-), Container Design (x,+),
Previous Experience (-,-)
Saade, Nebebe
& Tan (2007)
Canada
362 Students
+
+
+
+
-
-
Saade & Kira
(2006)
Canada
114 Students
+
+
+
-
-
Affect (-,+), Anxiety (-,+)
Sadeghi,
Saribagloo,
Aghdam &
Mahmoudi
(2014)
Iran
275 Teachers
+
+
+
+
-
Masculinity (+,+), Uncertainty Avoidance (+,+),
Individualism (+,-), Power Distance (+,+)
Sanchez-
Franco (2010)
Spain
431 Students
+
+
-
+
-
Flow (+,+)
Sanchez &
Hueros (2010)
Spain
226 Students
+
+
+
-
+
Technical support (+,+), Computer self-efficacy (-,-)
Seet & Goh
(2012)
New Zelal&
54 Students
-
-
-
+
-
-
Seif, Rastegar,
Ardakani &
Saeedikiya
(2013)
Iran
120 Students
+
+
-
+
-
Pleasure seeking (+,+), Applicability (+,+)
34
Shah, Bhatti,
Iftikhar,
Qureshi &
Zaman (2013)
Pakistan
400 Students
+
+
-
+
-
Information Quality (+,x), Service Quality (+,+),
System Quality (x,+)
Shah, Iqbal,
Janjua & Amjad
(2013)
Pakistan
172
Employees
+
+
-
+
-
Learning Objectives (+,+), Demographic Factors (-,-)
Shen & Chuang
(2010)
Taiwan
350 Students
+
+
+
+
-
Interactivity (+,+)
Shen & Eder
(2009)
U.S.A
77 Students
+
+
-
+
-
Computer Playfulness (x,+), Computer Self Efficacy
(x,+), Computer Anxiety (x,-)
Shih, Chen,
Shih & Su
(2012)
China
304 Students
+
+
+
+
-
-
Shroff, Deneen
& Ng (2011)
Hong Kong
72 Students
+
+
+
+
-
-
Shyu & Huang
(2011)
Taiwan
307 Students
+
+
+
+
+
Perceived E-government Learning Value (+,x),
Perceived Enjoyment (x,+)
Smith & Sivo
(2012)
U.S.A
517 Teacher
+
+
-
+
-
Social Presence (+,+)
Sanchez,
Hueros & Ordaz
(2013)
Spain
226 Students
+
+
+
-
+
Technical Support (+,+), Computer Self Efficacy (-,-)
Song & Kong
(2017)
Hong Kong
102 Students
+
+
+
+
-
Subjective Norm (+,x), Facilitating Conditions (-,+),
Self Efficacy (+,+), Anxiety (-,+)
Suki & Suki
(2012)
Malaysia
100 Students
+
+
+
+
-
-
Tajudeen,
Basha, Michael
& Mukthar
(2012)
Malaysia &
Nigeria
247 Students
+
+
+
+
+
-
Tan (2015)
Taiwan
370 Citizens
+
+
+
+
+
-
Tarhini, Hone &
Liu (2013.a)
UK
604 Students
+
+
-
+
+
-
Tarhini, Hone &
Liu (2013.b)
Lebanon
569 Students
+
+
-
+
+
-
Tarhini, Hone &
Liu (2014)
Lebanon
569 Students
+
+
-
+
+
-
Tarhini, Hone &
Liu (2015)
Lebanon
UK
1173
Students
+
+
-
+
+
-
Tarhini,
Hassouna,
Abbasi &
Orozco (2015)
Lebanon
235 Students
+
+
+
+
-
-
Tarhini, Hone,
Liu & Tarhini
(2017)
Lebanon
569 Students
+
+
-
+
+
-
Teo (2011)
Singapore
189 Students
+
-
-
-
-
Learning Environment (-,x), Course Delivery (+,x),
Tutor Attribute (+,x), Facilitating Conditions (+,x)
Tobing,
Hamzah, Sura &
Amin (2008)
Malaysia
314 Students
+
+
-
+
-
System Adaptability (+,+)
Tran (2016)
Vietnam
396 Students
+
+
+
-
-
System Functionality (x,+), Language Capability
(x,+), Computer Self Efficacy (x,+), Extraversion
(x,+), Openness (x,-)
Trayek &
Hassan (2013)
Malaysia
120 Students
+
+
+
-
-
-
Tselios,
Daskalakis &
Papadopoulou
(2011)
Greece
102 Students
+
+
+
+
-
-
Tseng & Hsia
(2008)
Taiwan
204
Employees
+
+
-
+
-
Internal Locus of Control (+,+), Computer Self
Efficacy (x,+)
Tung & Chang
(2008.a)
Taiwan
228 Students
+
+
-
+
-
Compatibility (+,x)
Tung & Chang
(2008.b)
Taiwan
267 Students
+
+
-
+
-
Compatibility (+,x)
Ursavas (2015)
Turkey
311 Teachers
+
+
-
+
-
-
Veloo & Masood
(2014)
Malaysia
100
Employees
+
+
-
+
-
Relative Advantage (+,+), Compatibility (-,-),
Complexity (-,+), Trialability (-,-), Observability (+,-)
Wang & Wang
(2009)
Taiwan
268
Instructors
+
+
-
+
+
Information Quality (+,x), System Quality (-,+),
Service Quality (x,+), Subjective Norm (+,x), Self
Efficacy (x,+)
Williams &
Williams
(2009)
UK
237 Students
+
+
+
+
-
Incentive to Use (+,x), Faculty Encouragement (-,x),
Peer Encouragement (+,x), Awareness of System
Capabilities (-,x), Access to System (-,x), Technical
Support (+,x), Prior Experience (-,x), Self Efficacy
(-,x)
Wu & Chen
(2017)
China
252
Respondents
+
+
+
+
-
Individual Technology Fit (-,+), Task Technical Fit
(+,+), Openness (-,+), Reputation (+,x), Social
Recognition (+,x), Social Influence (+,x)
35
Wu & Gao
(2011)
U.S.A
101 Students
+
+
+
+
-
Perceived Enjoyment (+,x)
Wu & Zhang
(2014)
China
214
Employees
+
+
+
+
-
Reliability (+,+), Accessibility (-,+), Accuracy (+,x),
Completeness (+,x), Sociality (+,x)
Wu, Kuo & Wu
(2013)
Taiwan
392 Students
+
+
-
+
-
Ipad Self Efficacy (x,+)
Yang & Lin
(2011)
Taiwan
377
Employees
+
+
-
-
+
Social Influence (+,x), Computer Self Efficacy (x,+)
Yi-Cheng,
Chun-Yu, Yi-
Chen & Ron-
Chen (2007)
Taiwan
214 Students
+
+
-
+
+
Perceived Enjoyment (+,x), System Features (+,x),
Characteristics of Teaching Materials (x,+), Self
Efficacy (x,+)
Yuen & Ma
(2008)
Taiwan
152 Teachers
+
+
-
+
-
Subjective Norm (+,+), Efficacy (-,+)
Zare &
Yazdanparast
(2013)
Iran
379 Students
+
+
-
+
-
Computer Playfulness (x,+), Perceived Enjoyment
(+,+), Facilitative Condition (+,+), Cognitive
Absorption (+,+)
Zhang, Zhao &
Tan (2008)
China
121 Students
+
+
-
+
+
-
Zhao & Tan
(2010)
Canada
China
282 Students
+
+
-
+
-
-
Note: Expressions in parentheses indicate the tested relationship between the external
variable & the belief variable. Value (+) in parantheses indicates that the relationship is
found to be significant, value (-) in parantheses indicates that the relationship is found to
be insignificant, value (x) in parantheses indicates that the relation is not tested.
Appendix 2. 129 Variables Proposed as Determinants of PU & PEOU in E-Learning Systems
& 220 Hypotheses Tested in the Literature Reviewed
No.
Independent
Varible
Dependent
Variable
Inv.
Acc.
References
1
Accessibility
PU
4
4
Kang & Shin (2015), Park (2009), Park, Nam & Cha (2012),
, Wu & Zhang (2014)
2
Accessibility
PEOU
7
6
Kang & Shin (2015), Lee, Hsiao, Purnomo (2014), Martinez-
Torres, Marin, Garcia, Vazquez, Oliva & Torres (2008), Park
(2009), Park, Nam & Cha (2012), Williams & Williams
(2009), Wu & Zhang (2014)
3
Accuracy
PU
1
1
Wu & Zhang (2014)
4
Affect
PU
1
0
Saade & Kira (2006)
5
Affect
PEOU
2
1
Rezaei, Mohammadi, Asadi & Kalantary (2008), Saade &
Kira (2006)
6
Affinity
PU
1
1
Lowe, D’aless&ro, Winzar, Laffey & Collier (2013)
7
Affinity
PEOU
1
1
Lowe, D’aless&ro, Winzar, Laffey & Collier (2013)
8
Age
PU
1
0
Rezaei, Mohammadi, Asadi & Kalantary (2008)
9
Anxiety
PU
8
3
Chang, Hajiyev & Su (2017), Chen & Tseng (2012), Liu
(2010), Mohammed & Karim (2012), Park, Son & Kim
(2012), Purnomo & Lee (2012), Saade & Kira (2006), Song
& Kong (2017)
10
Anxiety
PEOU
19
12
Abdullah, Ward & Ahmed(2016), Agudo-Peregrina,
Hernandez-García, & Pascual-Miguel, Higher Education &
Lifelong Learning (2014), Al-Gahtani (2016), Ali, Ahmed,
Tariq & Safdar (2013), Calisir, Gumussoy, Bayraktaroglu &
Karaali (2014), Chang, Hajiyev & Su (2017), Chen & Tseng
(2012), Karaali, Gumussoy & Calisir (2011), Lefievre
(2012), Liu (2010), Mohammed & Karim (2012), Park, Son
& Kim (2012), Purnomo & Lee (2012), Raaij & Schepers
(2008), Rezaei, Mohammadi, Asadi & Kalantary (2008),
Saade & Kira (2006), Shen & Eder (2009), Song & Kong
(2017)
11
Applicability
PU
1
1
Seif, Rastegar, Ardakani & Saeedikiya (2013)
12
Applicability
PEOU
1
1
Seif, Rastegar, Ardakani & Saeedikiya (2013)
13
Applying
PU
1
1
Lin (2013)
14
Applying
PEOU
1
1
Lin (2013)
15
Assimilating
PU
1
1
Lin (2013)
36
16
Assimilating
PEOU
1
1
Lin (2013)
17
Augmented Reality
PU
1
1
Chang & Liu (2013)
18
Augmented Reality
PEOU
1
1
Chang & Liu (2013)
19
Autonomy Support
PU
1
1
Roca & Gagne (2008)
20
Awareness of
System Capabilities
PU
1
0
Williams & Williams (2009)
21
Cognitive Absorption
PU
3
3
Cheng (2011), Moreno, Cavazotte & Alves (2017), Zare &
Yazdanparast (2013)
22
Cognitive Absorption
PEOU
3
3
Cheng (2011), Moreno, Cavazotte & Alves (2017), Zare &
Yazdanparast (2013)
23
Compatibility
PU
11
10
Brown, Ingram & Thorp (2006), Chang & Tung (2008),
Cheng (2015), Jung (2015), Lai & Ulhas (2012), Lee, Hsieh
& Hsu (2011), Post (2010), Purnomo & Lee (2012), Tung &
Chang (2008.a), Tung & Chang (2008.b), Veloo & Masood
(2014)
24
Compatibility
PEOU
6
3
Brown, Ingram & Thorp (2006), Cheng (2015), Cheung &
Vogel (2013), Lee, Hsieh & Hsu (2011), Purnomo & Lee
(2012), Veloo & Masood (2014)
25
Compatibility with
Student Tasks
PU
1
0
Escobar-Rodriguez & Monge-Lozano (2012)
26
Compatibility with
Student Tasks
PEOU
1
1
Escobar-Rodriguez & Monge-Lozano (2012)
27
Competence
PU
1
1
Roca & Gagne (2008)
28
Competence
PEOU
1
1
Roca & Gagne (2008)
29
Competition
Pressure
PU
1
1
Macharia & Nyakwende (2009)
30
Competition
Pressure
PEOU
1
0
Macharia & Nyakwende (2009)
31
Completeness
PU
1
1
Wu & Zhang (2014)
32
Complexity
PU
3
1
Aypay, Celik, Aypay & Sever (2012), Lee, Hsieh & Hsu
(2011), Veloo & Masood (2014)
33
Complexity
PEOU
5
5
Aypay, Celik, Aypay & Sever (2012), Cigdem & Topcu
(2015), Coskuncay & Ozkan (2013), Lee, Hsieh & Hsu
(2011), Veloo & Masood (2014)
34
Confirmation
PU
3
3
Lee (2010), Roca, Chiu & Martinez (2006), Ouyang, Tang,
Rong, Zhang, Yin & Xiong (2017)
35
Confirmation
PEOU
1
1
Roca, Chiu & Martinez (2006)
36
Conscientiousness
PU
1
1
Punnose (2012)
37
Container Design
PEOU
1
1
Ros, Hernandez, Caminero, Robles, Barbero, Macia &
Holgado (2014)
38
Content Quality
PEOU
6
5
Al-Ammari & Hamad (2008), Chang & Liu (2013), Cheng
(2012), Kang & Shin (2015), Lau & Woods (2008), Lau &
Woods (2009)
39
Content Quality,
Content Richness
PU
11
8
Al-Ammari & Hamad (2008), Calisir, Gumussoy,
Bayraktaroglu & Karaali (2014), Chang & Liu (2013), Cheng
(2011), Cheng (2012), Jung (2015), Kang & Shin (2015),
Lau & Woods (2008), Lau & Woods (2009), Lee (2006), Lee
& Lehto (2013)
40
Controllability
PU
1
1
Cheng (2014)
41
Controllability
PEOU
1
1
Cheng (2014)
42
Convenience
PU
5
5
Chang, Yan & Tseng (2012), Chang, Tseng, Liang & Yan
(2013), Cheng (2015), Hsu & Chang (2013), Lai & Ulhas
(2012)
43
Convenience
PEOU
3
2
Chang, Tseng, Liang & Yan (2013), Cheng (2015), Hussein,
Aditiawarman & Mohamed (2007)
44
Course Attributes
PEOU
2
1
Lee (2006), Lin, Chen & Yeh (2010)
45
Course Attributes
PU
1
0
Lee (2006)
46
Course Delivery
PU
1
1
Teo (2011)
47
Demographic
Factors
PU
1
0
Shah, Iqbal, Janjua & Amjad (2013)
48
Demographic
Factors
PEOU
1
0
Shah, Iqbal, Janjua & Amjad (2013)
49
Design
PU
3
2
Al-Ammary, Al-Sherooqi, & Al-Sherooqi (2014), Cheng
(2012), Hussein, Aditiawarman & Mohamed (2007)
50
Design
PEOU
3
3
Cheng (2012), Hussein, Aditiawarman & Mohamed (2007),
Lee, Yoon & Lee (2009)
51
Enjoyment
PU
12
12
Abdullah, Ward & Ahmed (2016), Al-Aulamie, Mansour, Daly
& Adjei (2012), Armenteros, Liaw, Fernandez, Díaz &
Sanchez (2013), Brown, Ingram & Thorp (2006), Chang,
Hajiyev & Su (2017), Chen, Lin, Yeh & Lou (2013), Lai &
Ulhas (2012), Lin, Chen & Yeh (2010), Nan, Xun-hua & Guo-
37
qing (2007), Park, Son & Kim (2012), Wu & Gao (2011), Yi-
Cheng, Chun-Yu, Yi-Chen & Ron-Chen (2007), Zare &
Yazdanparast (2013)
52
Enjoyment
PEOU
16
13
Abdullah, Ward & Ahmed (2016), Al-Ammary, Al-Sherooqi,
& Al-Sherooqi (2014), Al-Aulamie, Mansour, Daly & Adjei
(2012), Al-Gahtani (2016), Arenas-Gaitan, Rondan-
Cataluña & Ramirez-Correa (2010), Armenteros, Liaw,
Fernandez, Díaz & Sanchez (2013), Brown, Ingram & Thorp
(2006), Chang, Hajiyev & Su (2017), Chen, Lin, Yeh & Lou
(2013), Huang, Lin & Chuang (2007), Lefievre (2012),
Martinez-Torres, Marin, Garcia, Vazquez, Oliva & Torres
(2008), Park, Son & Kim (2012), Ramírez-Correa Arenas-
Gaitan & Rondan-Cataluña (2015), Shyu & Huang (2011),
Zare & Yazdanparast (2013)
53
Experience
PU
14
5
Abbad, Morris & Nahlik (2009), Abdullah, Ward &
Ahmed(2016), Abramson, Dawson & Stevens (2015),
Armenteros, Liaw, Fernandez, Díaz & Sanchez (2013),
Chang, Hajiyev & Su (2017), Lau & Woods (2008), Lau &
Woods (2009), Lee, Hsieh & Ma (2011), Lee, Hsieh & Chen
(2013), Martin (2012), Pituch & Lee (2006), Purnomo & Lee
(2012), Rezaei, Mohammadi, Asadi & Kalantary (2008), Ros,
Hernandez, Caminero, Robles, Barbero, Macia & Holgado
(2014)
54
Experience
PEOU
18
8
Abbad, Morris & Nahlik (2009), Abdullah, Ward &
Ahmed(2016), Abramson, Dawson & Stevens (2015),
Armenteros, Liaw, Fernandez, Díaz & Sanchez (2013),
Chang, Hajiyev & Su (2017), De Smet, Bourgonjon, Wever,
Schellens & Valcke (2012), Deshp&e, Bhattacharya &
Yammiyavar (2012), Lau & Woods (2008), Lau & Woods
(2009), Lee, Hsieh & Ma (2011), Lee, Hsieh & Chen (2013),
Martin (2012), Moreno, Cavazotte & Alves (2017), Pituch &
Lee (2006), Purnomo & Lee (2012), Rezaei, Mohammadi,
Asadi & Kalantary (2008), Ros, Hernandez, Caminero,
Robles, Barbero, Macia & Holgado (2014), Williams &
Williams (2009)
55
External Computing
Support
PU
1
1
Lee (2008)
56
External Computing
Support
PEOU
1
1
Lee (2008)
57
External Control
PEOU
5
5
Al-Gahtani (2016), Arenas-Gaitan, Rondan-Cataluña &
Ramirez-Correa (2010), Arenas-Gaitan, Ramírez-Correa &
Rondan-Cataluña, Chile & Spain(2011), Ramírez-Correa
Arenas-Gaitan & Rondan-Cataluña (2015)
58
External Equipment
Accessability
PU
1
0
Lee (2008)
59
External Equipment
Accessability
PEOU
1
1
Lee (2008)
60
External Influence
PU
3
2
Abbas, Egypt & UK (2016), Cheng (2011)
61
Extraversion
PEOU
1
1
Tran (2016)
62
Facilitating
Conditions
PU
5
4
Althunibat (2015), Aypay, Celik, Aypay & Sever (2012),
Song & Kong (2017), Teo (2011), Zare & Yazdanparast
(2013)
63
Facilitating
Conditions
PEOU
9
8
Agudo-Peregrina, Hernandez-García, & Pascual-Miguel,
Higer Education & Lifelong Learning (2014), Althunibat
(2015), Aypay, Celik, Aypay & Sever (2012), Karaali,
Gumussoy & Calisir (2011), Moreno, Cavazotte & Alves
(2017), Nan, Xun-hua & Guo-qing (2007), Song & Kong
(2017), Zare & Yazdanparast (2013)
64
Flexibility
PU
1
1
Hsia & Tseng (2008)
65
Flow
PU
1
1
Sanchez-Franco (2010)
66
Flow
PEOU
2
2
Davis & Wong (2007), Sanchez-Franco (2010)
67
Gadget Design
PU
1
1
Ros, Hernandez, Caminero, Robles, Barbero, Macia &
Holgado (2014)
68
Gadget Design
PEOU
1
0
Ros, Hernandez, Caminero, Robles, Barbero, Macia &
Holgado (2014)
69
Gender
PU
1
0
Letchumanan & Tarmizi (2011)
70
Gender
PEOU
1
0
Letchumanan & Tarmizi (2011)
71
Gender Diversity
PU
1
0
Al-Azawei & Lundqvist (2015)
72
Gender Diversity
PEOU
1
0
Al-Azawei & Lundqvist (2015)
73
Government Support
PU
1
1
Macharia & Nyakwende (2009)
74
Government Support
PEOU
1
1
Macharia & Nyakwende (2009)
75
Image
PU
3
2
Al-Gahtani (2016), Calisir, Gumussoy, Bayraktaroglu &
Karaali (2014), Rejón-Guardia, Sanchez-Fernandez &
Muñoz-Leiva (2013)
38
76
Incentive to Use
PU
1
1
Williams & Williams (2009)
77
Individual
Technology Fit
PU
1
0
Wu & Chen (2017)
78
Individual
Technology Fit
PEOU
1
1
Wu & Chen (2017)
79
Individualism
PU
1
1
Sadeghi, Saribagloo, Aghdam & Mahmoudi (2014)
80
Individualism
PEOU
1
0
Sadeghi, Saribagloo, Aghdam & Mahmoudi (2014)
81
Information Privacy
PEOU
1
0
Martin (2012)
82
Information Quality
PU
5
5
Motaghian, Hassanzadeh & Moghadam (2013), Park, Son &
Kim (2012),Poelmans, Wessa, Milis, Bloemen & Doom
(2008), Shah, Bhatti, Iftikhar, Qureshi & Zaman (2013),
Wang & Wang (2009)
83
Information Quality
PEOU
1
1
Motaghian, Hassanzadeh & Moghadam (2013)
84
Innovativeness
PU
7
4
De Smet, Bourgonjon, Wever, Schellens & Valcke (2012),
Fagan, Kilmon annd P&ey (2012), Harmon (2015), Liu, Li &
Carlsson (2010), Moghadam & Bairamzadeh (2009), Nan,
Xun-hua & Guo-qing (2007), Raaij & Schepers (2008)
85
Innovativeness
PEOU
9
6
Agudo-Peregrina, Hernandez-García, & Pascual-Miguel,
Higher Education & Lifelong Learning (2014), Basoglu &
Ozdogan (2011), De Smet, Bourgonjon, Wever, Schellens &
Valcke (2012), Fagan, Kilmon annd P&ey (2012), Harmon
(2015), Liu, Li & Carlsson (2010), Moghadam &
Bairamzadeh (2009), Raaij & Schepers (2008)
86
Instant Connectivity
PU
1
1
Jung (2015)
87
Instructional
Technology Cluster
PU
1
0
Park, Lee & Cheong (2008)
88
Instructional
Technology Cluster
PEOU
1
0
Park, Lee & Cheong (2008)
89
Instructor Influence
/ Charecteristic
PU
8
6
Abbas, Egypt & UK (2016), Cheng (2012), Cheng (2013),
Lee, Yoon & Lee (2009), Lee, Hsiao, Purnomo (2014), Teo
(2011), Ibrahim, Leng, Yusoff, Samy, Masrom & Rizman
(2017)
90
Instructor Influence
/ Charecteristic
PEOU
4
3
Abbas, Egypt & UK (2016), Cheng (2013), Hussein,
Aditiawarman & Mohamed (2007)
91
Interface
PU
2
1
Cheng (2012), Cho, Cheng & Lai (2009)
92
Interface
PEOU
3
2
Basoglu & Ozdogan (2011), Cheng (2012), Cho, Cheng & Lai
(2009)
93
Internal Computing
Support
PU
1
1
Lee (2008)
94
Internal Computing
Support
PEOU
1
1
Lee (2008)
95
Internal Equipment
Accessability
PU
1
0
Lee (2008)
96
Internal Equipment
Accessability
PEOU
1
0
Lee (2008)
97
Interpersonal
Influence
PU
3
2
Abbas, Egypy & UK (2016), Cheng (2011)
98
Job Relevance
PU
6
6
Al-Gahtani (2016), Arenas-Gaitan, Ramírez-Correa &
Rondan-Cataluña, Chile & Spain (2011), Lefievre (2012),
Nan, Xun-hua & Guo-qing (2007), Park, Nam & Cha (2012)
99
Job Relevance
PEOU
1
0
Park, Nam & Cha (2012)
100
Language Capability
PEOU
1
1
Tran (2016)
101
Learning
PU
1
1
Kilic, Guler & Celik (2015)
102
Learning
PEOU
1
1
Kilic, Guler & Celik (2015)
103
Learning Content
PU
1
1
Lee, Hsiao, Purnomo (2014)
104
Learning Content
PEOU
1
1
Lee, Hsiao, Purnomo (2014)
105
Learning
Environment
PU
1
0
Teo (2011)
106
Learning Objectives
PU
1
1
Shah, Iqbal, Janjua & Amjad (2013)
107
Learning Objectives
PEOU
1
1
Shah, Iqbal, Janjua & Amjad (2013)
108
Learning Styles
PU
2
0
Al-Azawei & Lundqvist (2015), Al-Azawei, Parslow &
Lundqvist (2017)
109
Learning Value
PU
1
1
Shyu & Huang (2011)
110
Locus of Control
PU
2
2
Hsia, Chang & Tseng (2014), Tseng & Hsia (2008)
111
Locus of Control
PEOU
2
2
Hsia, Chang & Tseng (2014), Tseng & Hsia (2008)
112
Management
Support
PU
2
1
Lee, Hsieh & Ma (2011), Purnomo & Lee (2012)
39
113
Management
Support
PEOU
2
2
Lee, Hsieh & Ma (2011), Purnomo & Lee (2012)
114
Masculinity
PU
1
1
Sadeghi, Saribagloo, Aghdam & Mahmoudi (2014)
115
Masculinity
PEOU
1
1
Sadeghi, Saribagloo, Aghdam & Mahmoudi (2014)
116
Materials
Presentation Types
PU
2
2
Liu, Liao & Peng (2005), Liu, Liao & Pratt (2009)
117
Mobility
PU
3
2
Al-Ammary, Al-Sherooqi, & Al-Sherooqi (2014), Basoglu &
Ozdogan (2011), Huang, Lin & Chuang (2007)
118
Motivation
PU
3
2
Chen & Tseng (2012), Martin (2012),Park, Lee & Cheong
(2008)
119
Motivation
PEOU
3
3
Chen & Tseng (2012), Martin (2012),Park, Lee & Cheong
(2008)
120
Navigation
PU
1
1
Cheng (2015)
121
Navigation
PEOU
1
1
Cheng (2015)
122
Network Externality
PU
2
1
Cheng (2011), Lee (2006)
123
Network Externality
PEOU
2
2
Cheng (2011), Lee (2006)
124
Observability
PU
2
1
Lee, Hsieh & Hsu (2011), Veloo & Masood (2014)
125
Observability
PEOU
2
0
Lee, Hsieh & Hsu (2011), Veloo & Masood (2014)
126
Openness
PU
1
0
Wu & Chen (2017)
127
Openness
PEOU
2
1
Tran (2016), Wu & Chen (2017)
128
Organizational
Support
PU
3
2
Lee, Hsieh & Ma (2011), Lee, Hsieh & Chen (2013), Park,
Son & Kim (2012)
129
Organizational
Support
PEOU
3
2
Lee, Hsieh & Ma (2011), Lee, Hsieh & Chen (2013), Park,
Son & Kim (2012)
130
Output Quality
PU
2
1
Davis & Wong (2007), Moreno, Cavazotte & Alves (2017)
131
Pedagogical Quality
PU
2
2
Lau & Woods (2008), Lau & Woods (2009)
132
Pedagogical Quality
PEOU
2
2
Lau & Woods (2008), Lau & Woods (2009)
133
Peer Influence
PU
3
2
Basoglu & Ozdogan (2011), Cheng (2013), Williams &
Williams (2009)
134
Peer Influence
PEOU
1
1
Cheng (2013)
135
Personalization
PU
1
1
Cheng (2014)
136
Personalization
PEOU
1
1
Cheng (2014)
137
Playfulness
PU
4
4
Al-Aulamie, Mansour, Daly & Adjei (2012), Padilla-
Melendez, Aguila-Obra & Garrido-Moreno, Male & Female
(2013), Roca & Gagne (2008)
138
Playfulness
PEOU
11
8
Agudo-Peregrina, Hernandez-García, & Pascual-Miguel,
Higher Education & Lifelong Learning (2014), Al-Aulamie,
Mansour, Daly & Adjei (2012), Al-Gahtani (2016), Ali,
Ahmed, Tariq & Safdar (2013), Lefievre (2012), Padilla-
Melendez, Aguila-Obra & Garrido-Moreno, Males & Females
(2013), Roca & Gagne (2008), Shen & Eder (2009), Zare &
Yazdanparast (2013)
139
Pleasure seeking
PU
1
1
Seif, Rastegar, Ardakani & Saeedikiya (2013)
140
Pleasure seeking
PEOU
1
1
Seif, Rastegar, Ardakani & Saeedikiya (2013)
141
Power Distance
PU
1
1
Sadeghi, Saribagloo, Aghdam & Mahmoudi (2014)
142
Power Distance
PEOU
1
1
Sadeghi, Saribagloo, Aghdam & Mahmoudi (2014)
143
Relatedness
PU
1
0
Roca & Gagne (2008)
144
Relative Advantage
PU
2
2
Lee, Hsieh & Hsu (2011), Veloo & Masood (2014)
145
Relative Advantage
PEOU
2
2
Lee, Hsieh & Hsu (2011), Veloo & Masood (2014)
146
Relevance for
Learning
PU
2
2
Agudo-Peregrina, Hernandez-García, & Pascual-Miguel,
Higher Education & Lifelong Learning (2014)
147
Reliability
PU
1
1
Wu & Zhang (2014)
148
Reliability
PEOU
2
2
Martinez-Torres, Marin, Garcia, Vazquez, Oliva & Torres
(2008), Wu & Zhang (2014)
149
Reputation
PU
1
1
Wu & Chen (2017)
150
Resource
PEOU
1
1
Cheung & Vogel (2013)
151
Responsiveness
PU
1
1
Cheng (2014)
152
Responsiveness
PEOU
1
1
Cheng (2014)
153
Result
Demonstrability
PU
6
5
Al-Gahtani (2016), Arenas-Gaitan, Ramírez-Correa &
Rondan-Cataluña, Spain & Chile (2011), Arenas-Gaitan,
Rondan-Cataluña & Ramirez-Correa (2010), Lefievre
40
(2012), Ramírez-Correa Arenas-Gaitan & Rondan-Cataluña
(2015)
154
Risk
PU
1
1
Chang, Chao & Cheng (2015)
155
Risk
PEOU
2
1
Chang, Chao & Cheng (2015), Lowe, D’aless&ro, Winzar,
Laffey & Collier (2013)
156
Self Efficacy
PU
50
24
Abbad, Morris & Nahlik (2009), Abdullah, Ward & Ahmed
(2016), Abramson, Dawson & Stevens (2015), Al-Ammary,
Al-Sherooqi, & Al-Sherooqi. (2014), Al-Ammari & Hamad
(2008), Al-Azawei & Lundqvist (2015), Al-Azawei, Parslow
& Lundqvist (2017), Al-Mushasha (2013), Althunibat
(2015), Aypay, Celik, Aypay & Sever (2012), Bao, Xiong, Hu
& Kibelloh, General & Spesific (2013), Bhatiasevi (2011),
Chang, Hajiyev & Su (2017), Chen & Tseng (2012), Cheng,
Computer & Internet (2011), Chow, Herold, Choo & Chan
(2012), Chow, Chan, Lo, Chu, Chan & Lai (2013), Cigdem &
Topcu (2015), Coskuncay & Ozkan (2013), Hsia & Tseng
(2008), Hsiao & Chen (2015), Hussein, Aditiawarman &
Mohamed (2007), Ibrahim, Leng, Yusoff, Samy, Masrom &
Rizman (2017), Jung (2015), Kang & Shin (2015), Kilic,
Guler & Celik (2015), Lau & Woods (2008), Lau & Woods
(2009), Lee (2006), Lee, Hsieh & Ma (2011), Lee, Hsieh &
Chen (2013), Lee, Hsiao, Purnomo, Computer & Internet
(2014), Lee & Lehto (2013), Liu (2010), Ma, Chao & Cheng
(2013), Mohammed & Karim (2012), Motaghian,
Hassanzadeh & Moghadam (2013), Ong, Lai & Wang (2004),
Ong & Lai (2006), Park (2009), Park, Nam & Cha (2012),
Pituch & Lee (2006), Purnomo & Lee (2012), Sanchez &
Hueros (2010), Sanchez, Hueros & Ordaz (2013), Song &
Kong (2017), Yuen & Ma (2008)
157
Self Efficacy
PEOU
71
58
Abbad, Morris & Nahlik (2009), Abdullah, Ward & Ahmed
(2016), Abramson, Dawson & Stevens (2015), Agudo-
Peregrina, Hernandez-García, & Pascual-Miguel, Higher
Education & Lifelong Learning (2014), Al-Ammary, Al-
Sherooqi, & Al-Sherooqi. (2014), Al-Ammari & Hamad
(2008), Al-Azawei & Lundqvist (2015), Al-Azawei, Parslow
& Lundqvist (2017), Al-Gahtani (2016), Ali, Ahmed, Tariq &
Safdar (2013), Al-Mushasha (2013), Althunibat (2015),
Aypay, Celik, Aypay & Sever (2012), Bao, Xiong, Hu &
Kibelloh, General & Spesific (2013), Basoglu & Ozdogan
(2011), Bhatiasevi (2011), Brown, Ingram & Thorp (2006),
Chang, Hajiyev & Su (2017), Chen & Tseng (2012), Cheng,
Computer & Internet (2011), Chow, Herold, Choo & Chan
(2012), Chow, Chan, Lo, Chu, Chan & Lai (2013), Cigdem &
Topcu (2015), Coskuncay & Ozkan (2013), Hsia, Chang &
Tseng (2014), Hsia & Tseng (2008), Hsiao & Chen (2015),
Hussein, Aditiawarman & Mohamed (2007), Ibrahim, Leng,
Yusoff, Samy, Masrom & Rizman (2017), Kang & Shin
(2015), Kilic, Guler & Celik (2015), Lau & Woods (2008), Lau
& Woods (2009), Lee (2006), Lee, Hsieh & Ma (2011), Lee,
Hsieh & Chen (2013), Lee, Hsiao, Purnomo, Computer &
Internet (2014), Li, Duan & Alfrod (2012), Lin, Chen & Yeh
(2010), Liu (2010), Moghadam & Bairamzadeh (2009),
Mohammed & Karim (2012), Moreno, Cavazotte & Alves
(2017), Motaghian, Hassanzadeh & Moghadam (2013),,
Ong, Lai & Wang (2004), Ong & Lai (2006), Padilla-
Melendez, Garrido-Moreno & Aguila-Obra (2008), Park
(2009), Park, Nam & Cha (2012), Pituch & Lee (2006),
Punnose (2012), Purnomo & Lee (2012), Rezaei,
Mohammadi, Asadi & Kalantary (2008), Roca, Chiu &
Martinez, Computer & Internet (2006), Sanchez & Hueros
(2010), Sanchez, Hueros & Ordaz (2013), Shen & Eder
(2009), Song & Kong (2017), Tran (2016), Tseng & Hsia
(2008), Wang & Wang (2009), Williams & Williams (2009),
Wu, Kuo & Wu (2013), Yang & Lin (2011), Yi-Cheng, Chun-
Yu, Yi-Chen & Ron-Chen (2007), Yuen & Ma (2008)
158
Service Quality
PU
2
1
Motaghian, Hassanzadeh & Moghadam (2013), Shah,
Bhatti, Iftikhar, Qureshi & Zaman (2013)
159
Service Quality
PEOU
3
3
Motaghian, Hassanzadeh & Moghadam (2013), Shah,
Bhatti, Iftikhar, Qureshi & Zaman (2013), Wang & Wang
(2009)
160
Sharing
PU
1
1
Cheung & Vogel (2013)
161
Social Presence
PU
1
1
Smith & Sivo (2012)
162
Social Presence
PEOU
1
1
Smith & Sivo (2012)
163
Social Recognition
PU
1
1
Wu & Chen (2017)
164
Sociality
PU
1
1
Wu & Zhang (2014)
165
Socio Economic
Factors
PU
1
1
Macharia & Nyakwende (2009)
41
166
Socio Economic
Factors
PEOU
1
1
Macharia & Nyakwende (2009)
167
Subjective Norm
PU
33
27
Abbad, Morris & Nahlik (2009), Abdullah, Ward &
Ahmed(2016), Abramson, Dawson & Stevens (2015),
Agudo-Peregrina, Hernandez-García, & Pascual-Miguel,
Higher Education & Lifelong Learning (2014), Al-Gahtani
(2016), Chang, Hajiyev & Su (2017), Cigdem & Topcu
(2015), Coskuncay & Ozkan (2013), Davis & Wong (2007),
De Smet, Bourgonjon, Wever, Schellens & Valcke (2012),
Farahat (2012), Hei & Hu (2011), Kang & Shin (2015),
Karaali, Gumussoy & Calisir (2011), Lee (2006), Lee, Hsieh
& Ma (2011), Martin (2012), Moghadam & Bairamzadeh
(2009), Moreno, Cavazotte & Alves (2017), Motaghian,
Hassanzadeh & Moghadam (2013), Park (2009), Park, Nam
& Cha (2012), Park, Son & Kim (2012), Post (2010),
Punnose (2012), Raaij & Schepers (2008), Rejón-Guardia,
Sanchez-Fernandez & Muñoz-Leiva (2013), Song & Kong
(2017), Wang & Wang (2009), Wu & Chen (2017), Yang &
Lin (2011), Yuen & Ma (2008)
168
Subjective Norm
PEOU
12
9
Abdullah, Ward & Ahmed(2016), Abramson, Dawson &
Stevens (2015), Chang, Hajiyev & Su (2017), Cigdem &
Topcu (2015), Coskuncay & Ozkan (2013), Farahat (2012),
Kang & Shin (2015), Lee, Hsieh & Ma (2011), Motaghian,
Hassanzadeh & Moghadam (2013), Park (2009), Park, Nam
& Cha (2012), Yuen & Ma (2008)
169
Substitutability
PU
1
0
Nan, Xun-hua & Guo-qing (2007)
170
Support Service
Quality
PU
1
1
Cheng (2012)
171
Support Service
Quality
PEOU
1
1
Cheng (2012)
172
System Adaptability
PU
1
1
Tobing, Hamzah, Sura & Amin (2008)
173
System Adaptability
PEOU
1
1
Tobing, Hamzah, Sura & Amin (2008)
174
System
Characteristics
PU
2
2
Chen, Lin, Yeh & Lou (2013), Lin, Chen & Yeh (2010)
175
System
Characteristics
PEOU
1
1
Chen, Lin, Yeh & Lou (2013)
176
System Features
PU
1
1
Yi-Cheng, Chun-Yu, Yi-Chen & Ron-Chen (2007)
177
System Functionality
PU
6
5
Bhatiasevi (2011), Cheng (2011), Cheng (2012), Cho,
Cheng & Lai (2009), Li, Duan & Alfrod (2012), Pituch & Lee
(2006)
178
System Functionality
PEOU
6
6
Bhatiasevi (2011), Cheng (2011), Cheng (2012), Li, Duan &
Alfrod (2012), Pituch & Lee (2006), Tran (2016)
179
System Interactivity
/ Interaction
PU
18
15
Abbad, Morris & Nahlik (2009), Agudo-Peregrina,
Hernandez-García, & Pascual-Miguel, Higher Education &
Lifelong Learning(2014), Al-Ammary, Al-Sherooqi, & Al-
Sherooqi (2014), Baharin, Lateh, Nathan & Nawawia
(2015), Chang & Liu (2013), Freitas, Ferreira, Garcia & Kurtz
(2017), Cheng (2011), Cheng (2012), Cheng (2013), Jung
(2015), Li, Duan & Alfrod (2012), Lin, Persada & Nadlifatin
(2014), Martin (2012), Martinez-Torres, Marin, Garcia,
Vazquez, Oliva & Torres (2008), Moreno, Cavazotte & Alves
(2017), Pituch & Lee (2006), Shen & Chuang (2010)
180
System Interactivity
/ Interaction
PEOU
12
8
Abbad, Morris & Nahlik (2009), Al-Ammary, Al-Sherooqi, &
Al-Sherooqi (2014), Baharin, Lateh, Nathan & Nawawia
(2015), Chang & Liu (2013), Cheng (2011), Cheng (2012),
Cheng (2013), Li, Duan & Alfrod (2012), Lin, Persada &
Nadlifatin (2014), Martin (2012), Pituch & Lee (2006), Shen
& Chuang (2010)
181
System Quality
PU
3
0
Motaghian, Hassanzadeh & Moghadam (2013), Park, Son &
Kim (2012), Wang & Wang (2009)
182
System Quality
PEOU
6
5
Calisir, Gumussoy, Bayraktaroglu & Karaali (2014),
Motaghian, Hassanzadeh & Moghadam (2013), Park, Son &
Kim (2012), Poelmans, Wessa, Milis, Bloemen ve Doom
(2008), Shah, Bhatti, Iftikhar, Qureshi & Zaman (2013),
Wang & Wang (2009)
183
System Response
PU
3
3
Cheng (2012), Li, Duan & Alfrod (2012), Pituch & Lee (2006)
184
System Response
PEOU
3
2
Cheng (2012), Li, Duan & Alfrod (2012), Pituch & Lee (2006)
185
System Support
PEOU
1
1
Cho, Cheng & Lai (2009)
186
Task Equivocality
PU
2
1
Lee, Hsieh & Ma (2011), Lee, Hsieh & Chen (2013)
187
Task Equivocality
PEOU
2
0
Lee, Hsieh & Ma (2011), Lee, Hsieh & Chen (2013)
188
Task
Interdependence
PU
1
0
Lee, Hsieh & Ma (2011)
189
Task
Interdependence
PEOU
1
1
Lee, Hsieh & Ma (2011)
42
190
Task Technology Fit
PU
5
4
Hidayanto, Febriawan, Sucahyo & Purw&ari (2014), Hsiao
& Chen (2015), Lee & Lehto (2013), Ma, Chao & Cheng
(2013), Wu & Chen (2017)
191
Task Technology Fit
PEOU
3
3
Hidayanto, Febriawan, Sucahyo & Purw&ari (2014), Hsiao
& Chen (2015), Wu & Chen (2017)
192
Teaching Materials
PU
2
2
Bhatiasevi (2011), Lee, Yoon & Lee (2009)
193
Teaching Materials
PEOU
2
2
Bhatiasevi (2011), Yi-Cheng, Chun-Yu, Yi-Chen & Ron-Chen
(2007)
194
Technical Quality
PU
2
0
Lau & Woods (2008), Lau & Woods (2009)
195
Technical Quality
PEOU
2
2
Lau & Woods (2008), Lau & Woods (2009)
196
Technical Support
PU
4
4
Abbad, Morris & Nahlik (2009), Ngai, Poon & Chan. (2007),
Sanchez & Hueros (2010), Sanchez, Hueros & Ordaz (2013)
197
Technical Support
PEOU
6
5
Abbad, Morris & Nahlik (2009), Freitas, Ferreira, Garcia &
Kurtz (2017), Ngai, Poon & Chan. (2007), Sanchez & Hueros
(2010), Sanchez, Hueros & Ordaz (2013), Williams &
Williams (2009)
198
Technological Factor
PU
1
0
Hussein, Aditiawarman & Mohamed (2007)
199
Technological Factor
PEOU
1
1
Hussein, Aditiawarman & Mohamed (2007)
200
Technology
Characteristics
PU
1
1
Ifinedo (2006)
201
Technology
Characteristics
PEOU
1
1
Ifinedo (2006)
202
Training
PU
3
2
Escobar-Rodriguez & Monge-Lozano (2012), Lee, Internal &
External (2008)
203
Training
PEOU
3
2
Escobar-Rodriguez & Monge-Lozano (2012), Lee, Internal &
External (2008)
204
Training Impression
PEOU
1
1
Nan, Xun-hua & Guo-qing (2007)
205
Trialability
PU
2
1
Lee, Hsieh & Hsu (2011), Veloo & Masood (2014)
206
Trialability
PEOU
2
1
Lee, Hsieh & Hsu (2011), Veloo & Masood (2014)
207
Two Way
Communication
PU
1
1
Cheng (2014)
208
Two Way
Communication
PEOU
1
1
Cheng (2014)
209
Uncertainty
Avoidance
PU
1
1
Sadeghi, Saribagloo, Aghdam & Mahmoudi (2014)
210
Uncertainty
Avoidance
PEOU
1
1
Sadeghi, Saribagloo, Aghdam & Mahmoudi (2014)
211
Underst&ing
PEOU
1
1
Lin (2013)
212
Underst&ing
PU
1
1
Lin (2013)
213
University Support
PU
2
1
Al-Mushasha (2013), Williams & Williams (2009)
214
University Support
PEOU
1
1
Al-Mushasha (2013)
215
Usefulness for
Professors
PU
1
1
Escobar-Rodriguez & Monge-Lozano (2012)
216
User Characteristic
PU
1
1
Ifinedo (2006)
217
User Characteristic
PEOU
1
1
Ifinedo (2006)
218
Vendors Support
PU
1
1
Macharia & Nyakwende (2009)
219
Vendors Support
PEOU
1
0
Macharia & Nyakwende (2009)
220
Vividness
PU
1
1
Lee & Lehto (2013)
... according to the meta-analysis research of Baki et al. (2018), self-efficacy, satisfaction, anxiety, and subjective norms influence perceived ease of use. In the context of technology, self-efficacy is an individual's perception of their ability to use technology. ...
... In the context of technology, self-efficacy is an individual's perception of their ability to use technology. the influence of self-efficacy on perceived ease of use can be explained by the persistence of users with higher self-efficacy to learn a system (Baki et al., 2018). thus, this research proposed the following hypothesis: ...
... this result is in line with a study conducted by Purnamasari et al. (2021) that examined consumers' purchase decisions in relation to paylater and found a negative influence of perceived ease of use on user decisions. self-efficacy influences perceived ease of use: users who are most confident in their ability to learn a system tend to use it more consistently (Baki et al., 2018). this self-efficacy then influences consumers' perceptions of ease of use. ...
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These days, paylater, the most well-known alternative credit option in e-commerce, is widely accessible. This research examines the evolving landscape of paylater preferences among Generation Z in Indonesia and Malaysia. The study employs quantitative methodology using a nonexperimental design that measures the effect of perceived ease of use, perceived usefulness, and perceived trust on the intention to use paylater. The research presents two studies. Study 1 had 500 participants from Indonesia who were paylater and non-paylater users. The results of Study 1 revealed that perceived usefulness and perceived trust positively affect the intention to use paylater; however, perceived ease of use has a negative effect on the intention to use paylater. Study 2 had 165 participants from Malaysia who were also paylater and non-paylater users. The results of Study 2 were similar to those of Study 1. That is, perceived usefulness and perceived trust have a positive effect on the intention to use Paylater, but perceived ease of use has a negative effect on the intention to use paylater. This research contributes to the existing literature on consumer behaviour and financial technology adoption by providing insight into the trust dynamics related to paylater preferences among Generation Z in Indonesia and Malaysia.
... Previous research in the field of educational technology has consistently shown that perceived ease of use is a critical determinant of technology acceptance and adoption among students [55,56]. Studies [57][58][59] have found that students are more likely to use and engage with technology tools that they perceive as easy to use [35,41]. Therefore, this study's construct of perceived ease of use is aligned with existing literature underscores its importance in understanding students' acceptance and utilization of AI-powered tools like ChatGPT in educational research contexts. ...
... This finding contradicts some previous research indicating that perceived usefulness significantly impacts technology usage [15]. However, it aligns with studies suggesting that perceived usefulness may not always directly translate into actual usage behavior [57]. One possible explanation could be that while students may perceive ChatGPT as useful, they may encounter barriers or lack sufficient motivation to incorporate it into their research activities. ...
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This study investigates the determinants of ChatGPT adoption among university students and its impact on learning satisfaction. Utilizing the Technology Acceptance Model (TAM) and incorporating insights from interaction learning, collaborative learning, and information quality, a structural equation modeling approach was employed. This research collected valuable responses from 262 students at King Faisal University in Saudi Arabia through the use of self-report questionnaires. The data's reliability and validity were assessed using confirmation factor analysis, followed by path analysis to explore the hypotheses in the proposed model. The results indicate the pivotal roles of interaction learning and collaborative learning in fostering ChatGPT adoption. Social interaction played a significant role, as researchers engaging in conversations and knowledge-sharing expressed increased comfort with ChatGPT. Information quality was found to substantially influence researchers' decisions to continue using ChatGPT, emphasizing the need for ongoing improvement in the accuracy and relevance of content provided. Perceived ease of use and perceived usefulness played intermediary roles in linking ChatGPT engagement to learning satisfaction. User-friendly interfaces and perceived utility were identified as crucial factors affecting overall satisfaction levels. Notably, ChatGPT positively impacted learning motivation, indicating its potential to enhance student engagement and interest in learning. The study's findings have implications for educational practitioners seeking to improve the implementation of AI technologies in university students, emphasizing user-friendly design, collaborative learning, and factors influencing satisfaction. The study concludes with insights into the complex interplay between AI-powered tools, learning objectives, and motivation, highlighting the need for continued research to comprehensively understand these dynamics.
... These findings are in accordance with the work of Mtebe and Raphael (2018) and Tawafak et al. (2018), where they confirm the dimensions of how the usefulness of the elearning system affects the students' satisfaction. Perceived ease of use and students' satisfaction toward the adaptive elearning system has been reported to increase student satisfaction and engagement in learning, as well as students' need for online learning services that accord to their preferences (Baki et al., 2018;Daneji et al., 2019;Safsouf et al., 2020;Sunkara & Kurra, 2017;Tawafak et al., 2018). ...
... Electronic learning, known interchangeably as online learning or e-learning, represents a structured method for both formal and non-formal education, facilitated through technological means. This pedagogical method empowers students and lecturers to actively engage in learning process using electronic media [3]. The adoption of e-learning aims to enhance student grasp of subject matter, cultivate active participation, nurture self-directed learning competencies, and elevate the caliber of instructional content [4]. ...
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... Regarding to the second research question whether multimedia instruction, content quality, and LMS self-efficacy affect perceived ease of use, only LMS self-efficacy exerted the perceived ease of use variable for the experienced users (Davis, 1989;Park et al., 2019) as shown in Table 6. The third research question analyses whether perceived usefulness and perceived ease of use play a positive effect on students' behavior intention (Baki et al., 2018;Lee & Hung, 2015;Park, 2009). For both novice and experienced users, perceived usefulness and perceived ease of use seemed to have a significant effect on behavior intention to use the online platform as results shown in Table 7. ...
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Learning about entrepreneurship is very widespread in the world of education, one of which is at the level of higher education, especially in Indonesia. This can be seen from the existence of one of the Independent Learning Campus (MBKM) programs initiated by the Ministry of Education and Culture (Mendikbud) regarding entrepreneurship. This program encourages campuses to integrate entrepreneurship courses into their curricula. During the process of forming this curriculum, campuses need to know the factors that encourage the desire to be entrepreneurial. This study aims to find out what factors encourage entrepreneurial intentions in undergraduate students. The number of respondents in the study amounted to 30 undergraduate students from province of South Sulawesi and East Java, Indonesia. This research method is a quantitative approach which is analyzed using PLS. Results of this study show that perceived desirability, locus of control, self-efficacy, and entrepreneurial education (universities courses support) are factors affecting entrepreneurial intention among undergraduates from province of South Sulawesi and East Java, Indonesia.
... This model was originally proposed by Davis and has become the most widely used model to explain user acceptance of new technologies. TAM was developed from Theory of Reasoned Action and provides a basis for tracking how external variables influence beliefs, attitudes, and intentions to use new technologies (Baki et al., 2018;Lewis, 2019). This model has been used to predict acceptance of new IT and has proven reliable in explaining acceptance behaviour in several areas of information systems (Sayekti & Putarta, 2016). ...
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Educational games are becoming a learning trend for elementary school students today because of their fun nature and attractive appearance which makes students interested in trying them. Pancasila educational games are also designed to make learning more interesting. Testing of educational games and their compatibility with human-computer interaction needs to be done so that games are more optimal when implemented and distributed more widely. The purpose of this study was to determine the results of testing the acceptance of Pancasila educational games using the characteristics of the technology acceptance model. This study involved 300 respondents of early childhood, aged 5-6 years in 3 schools in East Java, Indonesia. Modelling and data analysis using SEM AMOS. The results showed that as many as 97.4% of students liked and were helped by this educational game. Another important factor is in terms of perceived ease of use game 99.8% of students like this aspect and from the aspect of perceived usefulness, 95% of students choose this aspect. Where both give good value to the Pancasila educational game. As for the development of educational games, they will be readjusted to the conditions of learning in each school.
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Technology integration in higher education has been widely recognised for its multifarious benefits. Nevertheless, arising from various factors, the prevalence of technostress poses a substantial impediment to learning effectiveness. In response, this study employs visualisation analysis and systematic review techniques to formulate a comprehensive model that encompasses variables related to technostress. Based on a systematic selection from 1,861 publications, 83 publications were included to model predictors and outcomes of higher-education students’ technostress. Our findings reveal that the COVID-19 pandemic has spurred growing academic interest in technostress, owing to concerns about the stressful and anxious nature of remote learning. Existing research on this topic predominantly relies on technology acceptance models and theories, with ongoing expansions incorporating variables from multiple research domains. In particular, external factors assume pivotal roles as predictors of technostress, along with subdimensions related to technostress. The impact of technostress can be observed in various aspects, such as learning experiences and performance outcomes. The findings of this study provide valuable insights for future research endeavours, facilitating further exploration and informing technology-enhanced teaching practice.
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Self-efficacy theory predicts that people will perform better when they believe they have the skills necessary for success. It also suggests, however, that believing in long-term rewards for success ("response-outcome expectations") does not correlate with adequate performance. This paper supports the generality of self-efficacy theory and provides evidence that self-efficacy beliefs predict insurance sales performance, whereas response-outcome expectations did not. A questionnaire was developed to measure self-efficacy beliefs and response-outcome expectations using 200 insurance sales representatives. Regression analyses were computed on a different sample of 97 insurance sales representatives using four separate dependent variables (calls-per-week; number of policies sold; sales revenue and a composite performance index on which actual sales commission was based). (1) These analyses established a correlation (but no causal relationship) between self-efficacy beliefs and sales performance. (2) The generality of self-efficacy theory in a business setting is suggested by the relationship between self-efficacy and objective measures of sales performance. (3) The relevance of these results, and the importance of integrating them into the practice of organizational behavior modification is discussed.
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The aim of this study is to determine the factors affecting blue-collar workers’ intention to use a web-based learning system in the preimplementation phase in the automotive industry. For that purpose an extended technology acceptance model (TAM) is proposed, which included factors such as image, perceived content quality, and perceived system quality as additions to the basic model. Data collected from 546 blue-collar workers were used to test the proposed research model by using Linear Structural Relations software LISREL, Version 8.54. The findings of the study indicate that perceived usefulness is the strongest predictor of behavioral intention to use a web-based learning system. In addition, a high proportion of perceived usefulness is explained by perceived content quality, and perceived ease of use is explained by perceived system quality and anxiety.
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The purpose of the research reported in this paper was to study the acceptability of e‐learning instruments either as substitutes for or a complement to traditional face‐to‐face education for people with a humanistic background. We conducted an international comparison between students (attending humanistic studies) in two different European countries: Italy and Portugal. The comparison allowed us to focus on similarities and differences in on‐line educational programs and in two different settings and allowed for appropriate statistical analysis. Starting from data and observations, we propose an acceptance model of Information and Communication Technologies (ICT) and a measurement framework. In particular, the study includes both reliability and construct validity measures. To date there has been little research effort investigating the implications of switching from the traditional educational frameworks towards innovative on‐line ones in an international comparative context. Our results show that students with a humanistic background may not be prepared to fully use technical tools as instruments of education. Therefore ICT instruments must be introduced in humanistic faculties via a stepwise approach which allows a gradual but definitive assimilation of the innovative technological learning instruments. The outcomes of our study have been directly applied on a real case: the Master Program in Human Resource Management (HRM) in the University of Rome ‘Tor Vergata’ where an e‐learning platform has been implemented as a complementary to the traditional approaches to education. Furthermore our model is currently under investigation in large industrial companies with the aim of measuring and improving the e‐learning education in the field of production and distribution of energy. Copyright © 2011 John Wiley & Sons, Ltd.