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A technology acceptance models review
By Mario Arias-Oliva, Universitat Rovira i Virgili, Av. Universitat 1,
43204 Reus, Spain, +34977759800, mario.arias@urv.es and Juan
Carlos Yáñez-Luna, Facultad de Economía, Universidad Autónoma de
San Luis Potosí, Av. Pintores S/N, col. Burócratas del Estado, 78213,
San Luis Potosí, México, jcyl@uaslp.mx
Statement of commitment
At least one of the authors will attend ETHICOMP 2014 conference, at Les Cordelies Paris,
France
Abstract
Due to the current economic, political, technological, social and cognitive conditions the
acceptance and adoption of ICT are critical dimensions for social development. There are in
the literature several researches in the arena of technology acceptance. Most of the models
are based in sociological and psychological constructs that could measure the degree in
which an individual or a company adopt a specific technology (Venkatesh, Morris, Davis, &
Davis, 2003). This work reviews the most used models for measuring the acceptance of
technology and how are they applied in the education context. Also we aim to identify
theoretically the keystone variables of the models reviewed that could imply in the user
adoption of mobile technology.
1. Introduction
Nowadays the new scopes are focusing in mobile devices that allow users to move freely
without losing the interaction with information. The use of mobile devices should be a
motivator to implement the eLearning concepts into educational practice. As discussed in
Ruíz de Querol & Buira (2007) is the same society that changes their lifestyle according to
the comforts of new information and communication technologies. Moreover in the
acceptance terms, Martinez-Torres et al. (2008) points out that any education tool used for
eLearning must be justified according to their effectiveness and relevance to students and
professional groups involved in the education area. The use of these devices is a
breakthrough research on the behaviour of people towards the use of mobile devices in
several studies.
We based this research in the review of the academic literature. We reviewed as well the
different educational environment in which the models of technology acceptance were
tested. Our goal is to identify the main variables involved in user’s adoption of any technology
in an academic institutional context. This paper is structured as follow: In section 2 we will
review the literature to know the state of the art about mobile context and to introduce the
acceptance models. In section 3 we analyse the most actual models that are used to explain
the acceptance of technologies. In section 4 we will discuss the models analysed and give
some future implications.
2. Literature Review.
In the business context, there is a relationship between device and enterprise service.
Cambra, Melero, & Sese (2012) argued that Spanish mobile service companies are not
involved with the operative problems of their customers in creating a lifelong engagement
with the brand. We could suppose that there are high costumer rotations at this business
sector. Authors conclude that it is important to take actions to encourage the engagement in
this sector. Those actions refer in the designs of sceneries in which costumers can interact
with the enterprise or with others clients with experience in mobile devices or services. We
note that those kinds of actions could impact in the decisions of costumers in order to
acquiring any service or products.
In learning context Rosman (2008) observe that with the combination of Social Networks
and mobile technologies enable new forms to learn an teach that influence in society. We
observe mobile devices as an individualized tools that allows people accessing, generating
and sharing information and knowledge in anywhere, anytime and any-device. Gupta (2012)
found that using mobile devices the students gain positive impacts as positive attitude
towards the task, autonomy and usefulness in technical tools and vocabulary. This positive
impacts could be due the new generations are growing within a technological era (Martinez-
Torres et al., 2008). Actually companies are introducing new mobile devices in markets
focusing in several sectors (business, education, entertainment, etc.) due of that, some “habits
of use” are created in population according to the specific technology. According with
Venkatesh, Thong, & Xu (2012) the attitude of using of technology is influenced by the
hedonic motivation, habit and price of the device.
3. Acceptance Models Analysis
3.1. Technology Acceptance Model (TAM).
One of the most used models in this arena is the TAM. Davis (1989) designed the model in
order to measure the degree of acceptance of a technology by individuals. The model is an
adaptation of the Theory on Reasoned Action (TRA) and provides information of how users
accept the ICT; also the model explains theoretically the user’s behaviour towards using ICT.
TAM as discussed in Wu & Gao, (2011), suggests the perception towards ease of use (EoU)
and perceived usefulness (PU) of technology and how it influences in the attitudes of use. The
figure 1 shows the TAM model with the main constructs.
Figure 1. The TAM constructs in Davis (1989)
Yu et al. (2005) identify a weakness in the model, it implies that does not include a social
factor influencing in users attitude. Venkatesh and Davis (2000) enforce the social factors
and extended the model to TAM 2. The main goal of the theoretical extension was to include
key determinants on TAM to support Perceived Usefulness and Usage Intention in terms of
social influence. This method could permit design organizational interventions that would
increase user acceptance and usage of new systems, also this model aims to understand how
the effects of these determinants could increase user experience over time. The model is
influenced by two moderators: experience and voluntariness. The figure 2 shows the TAM 2
with the extended constructs. Venkatesh et al. (2003) made a review of several models and
theories in the acceptance context to create the unified theory which would be capable to
predict the acceptance better than TAM.
Figure 2. The TAM 2 constructs in Venkatesh & Davis (2000)
TAM has also been implemented in the enterprise to determine the degree of acceptance of
technology by employees. Venkatesh and Bala (2008), implement a model based on TAM to
help the decision making in organizations, the model was named TAM3. The model combines
TAM2 determinants and the determinants of perceived ease of use. The new determinants
support the variable Perceive Ease of Use to understand how that could enhance employees’
adoption and use of IT. In this context Chen, Chen, & Yen, (2011) focuses in self-efficacy
variable using mobile devices finding that self-efficacy plays a positive role on Perceive ease
of use variable, while it only partially affects Perceive usefulness between employees. The
figure 3 shows the main constructs in TAM 3.
Figure 3. The TAM 3 constructs in Venkatesh & Bala (2008)
Investigations focused on TAM also take into consideration the degree of acceptance and
usefulness by learners and the determinants that directly or indirectly affect to adopting
technologies such as way to facilitating learning tool.
Chow et al (2012) developed and evaluated a virtual environment in healthcare contexts. The
study shows that the system was perceived useful by learners. The determinant used to make
the study was computer self-efficacy; it enables to know the acceptance level of eLearning in
the healthcare curricula. Yoo & Huang, (2011) in similar studies concluded that cultural
environment may influence in how learners accept technologies and how learners use ICT in
learning context. In a mobile context Suki & Suki, (2011) shows some determinants that have
a strong effect in users behaviour and satisfaction to use mobile devices for learning.
Outcomes of studies shows determinants such as perceived mobility, perceived usefulness,
perceived value and intention to reuse had a positively effect on how learners uses mobile
learning.
TAM is also used to study the degree of technology acceptance by lecturers. Al-Busaidi & Al-
Shihi (2010) focused a study on create a framework to evaluate LMS acceptance degree by
lecturers. The study framework focused on Instructor, Organization and Technology
determinants that may influence in the lecturer acceptation. Martinez-Torres et al (2008)
worked with extended TAM to evaluate three eLearning tools. In the outcomes were found
that some determinants did not suggest a significant impact on the attitudes or intention to
usage, they suppose that it can be due that most of the learner have enough skills and
knowledge to use ICT devices.
3.2. Unified Theory of Acceptance and Use of Technology (UTAUT)
In the quest to unify most of the theories and models in the technological acceptance arena
Venkatesh et al. (2003) worked in a review of several constructs from the eight main models
of the last century. The models and theory reviewed were: Theory of Planned Behaviour
(TPB), Technology Acceptance Model (TAM – TAM 2), Combined TAM and TPB (C-TAM-TPB),
Motivational Model (MM), Model of PC Utilization (MPCU), Theory on Reasoned Action
(TRA), Innovation and Diffusion Theory (IDT) and Social Cognitive Theory (SCT).
As its predecessors UTAUT aims to evaluate the degree in which a user has the intention to
use any technology or information systems. The model is based in four main constructs:
performance expectancy, effort expectancy, social influence and facilitating conditions.
UTAUT constructs are shown in Figure 4. The UTAUT also is moderated in order to sustain
the impact of the four main constructs by four determinants. The determinants that act as
moderators (Gender, Age, Experience and Voluntariness of use) are shown in Figure 4:
Figure 4. The UTAUT Model
In some studies related with UTAUT Carlsson et al. (2006) pointed out that there are a strong
relationship between mobile technology and mobile services, such that users perceive some
functions in the mobile devices. Authors tested the applicability of the UTAUT model to
measure the degree of acceptance and use of the mobile devices and services. In their findings
shown that outcomes were not supported by UTAUT theory due that the model is focused to
test organizations acceptance and the mobile acceptance is considerate more individual.
Wang and Shih (2009) worked in a study to investigate the factors that influencing in citizens
to use information kiosks. In this study the UTAUT was used to explain the variability on
determinants Gender and Age. Their findings show that determinants FC and BI had an
important effect in the use. Other finding was that PE influenced BI more in male than female.
Instead SI had more impact in female than male. In the age evaluation, EE was a stronger
determinant of BI in older than for younger citizens.
In the educational arena there are some researches that measure the degree of acceptance of
technologies in eLearning. El-Gayar and Moran (2006) focused the study in the students’
acceptance for Tablet’s PC. They found that the attitude had the strongest effect. Also PE and
self-efficacy had an important impact on BI. In contrast anxiety and SI does not have an
important contribution in the research.
In mLearning the UTAUT was used to describe the acceptance in academia. Jairak et al.
(2009) found that students have a good perception about mLeaning. The results showed that
PE and EE had a high level of acceptance; that’s means students in the study showed a good
attitude towards using mLearning. Some authors worked it a modified UTAUT in order to
explain new variables that impact in the technology acceptance. Strong et al. (2013) found
that students with a high performance on Self-Efficacy and Self-directedness are more likely
to accept a mobile technology to learn than students with lower performance. Thomas et al.
(2013) added the Attitude in the UTAUT. The aim of the study was to compare some similar
studies and explain the acceptance of mobile technologies in academia. The findings exposed
that cultural and country levels moderate the UTAUT properties. Also they found that
Attitude had an important impact on BI in the study framework, so they suggest to
incorporate the country variable in future works.
However, in order to reach the study of technology acceptance in the consumer’s context
Venkatesh et al. (2012) proposed the UTAUT2. As the first UTAUT was based on extrinsic
motivation, the authors added the variable hedonic motivation as a key predictor in the
consumer behaviour. Also the authors observe a difference between technology acceptance
in an organizational context and in a non-organizational context. The effort expectancy of
employees about the effort and time used in acceptance of a technology is different in a
consumer that they must bear the cost of the technology, in this case the Price Value was
added in UTAUT2 to explain consumers’ actions.
Figure 5. The UTAUT2 model. Venkatesh et al. (2012)
Table 1. The UTAUT 2 main constructs
3.3. Technology Acceptance in organizations
Although the aim of this research is focusing in models oriented on academic institutions, we
think that it is important to show other models that have important impact in firms. There
are two kind of models to analyse the acceptance of technology, first one focuses in analyse
the acceptance in individuals (such as TAM, UTAUT, etc.) and the second one focuses on
analyse the acceptance in firms. In this respect, the Technology-Organization-Environment
Framework (TOE) was used in several researches to measure the degree in which any
organization adopt a new technology or system. According to Zhang et al. (2007) TOE is built
in three contexts: Technological issues, that states to any technology that are very important
to organizations. Organizational issues, that focuses in the firm characteristics (such as scope,
size, etc.) and Environmental issues, focuses in how a firm conducts its business activities.
Figure 6. The TOE framework (Tornatzky & Fleischer, 1990) cited in (Baker, 2012)
Another framework to measure the acceptance of technology in organization is the Diffusion
of Innovations. The DOI is described in Rogers (1983, p. 5) as “the process by which an
innovation is communicated through certain channels over time among the members of a
social system”. In this way, communication is important to exchange information in two ways
in order to take the best decision. Beck (2006) pointed out that there are some differences in
the concept of “diffusion” between the innovation theory and diffusion theory. According to
Beck in the innovation theory an innovation is referred as a process and in the diffusion
theory an innovation is referred as an object or a product of technological progress which are
recognized by potential adopters as something new. In this respects, Rogers (1983, p. 15)
and cited in (Ataizi, 2009) described five characteristics of innovation that are perceived and
influenced by adopters in order to adopt a new technology. Relative advantage. The degree
to which an innovation is perceived as better than the idea it supersedes. Compatibility. The
degree to which an innovation is perceived as being consistent with the existing values, past
experiences, and needs of potential adopters. Complexity. The degree to which an
innovation is perceived as difficult to understand and use. Trialability. The degree to which
an innovation may be experimented with on a limited basis. Observability. The degree to
which the results of an innovation are visible to others.
The diffusion theory deals with the adoption, the speed and degree of penetration, and the
distribution of an innovation (Beck, 2006). Arpaci et al. (2012) discussed that many
researchers used to combine both TOE and DOI in order to explain better the adoption of
technology in organizations. Awa et al. (2010) critiqued the TAM arguing that it is a good
descriptor in the acceptance of technology, but in other hand there are some missing
constructs that could be important to explain the adoption behaviour. In order to reduce the
gaps in the constructs the authors developed a model combining the TAM and TOE. Their
research focuses focus on some factors such as individual difference, facilitating conditions,
social influence, organization norms, perceived trust and perceived service quality
transforming the model to a whole model in which researchers could explain and predict the
adoption of technology. Wang et al. (2010) used TOE in a research with the adoption of radio
frequency in firms. They proposed that the adoption should not include technology as the
one, also it have to focus on internal factors and the external environment. In their findings
observe that some variables such as information intensity, complexity, compatibility, firm
size, competitive pressure, and trading partner pressure, are significant in the adoption of
radio frequency technologies. However, variables such as relative advantage, top
management support, and technology competence were not significant in the adoption of the
technology.
4. Discussion and future implications
Several researches about acceptance models have been released. We focused our framework
in recent models that impact in the acceptance of technology in academia. Across time the
models have been modified or as we review the models have been combined with each other.
Researchers try to explain the adoptions process focusing on motivators that could be
extrinsic or intrinsic. Those motivators impact on the acceptance or adoption of a technology.
We consider that the technology is changing quickly and the Life-Cycle of technology is
shorter than others times. In this case, we suggest that it is important to evaluate timing
variables (e.g. perceived lifetime) to moderate the acceptance behaviour. Also we observe
that the newest models (TAM 2, 3 and UTAUT 1, 2) consider the impact of social influence,
facilitating conditions, Perceived Usefulness and Perceived ease of use variables in the
acceptance of technologies. However, in the organization adopting models we observe that
there are variables focused in the characterization of the firm, but also we identify some
similar variables that in the individual models such as Technical Support, Observability,
Complexity and Relative advantages.
In this paper we analyse the most used models in technology acceptance in order to increase
the literature review in the arena. The sociologist and psychologist theories are the base for
the models we described. The individual and organizational contexts were evaluated
theoretically in order to differentiate the aims of the acceptance in technology. The models
reviewed focuses in the adoption of a technology, service or system, but we think that it is
important to take in consideration in future researches a post-adoption comparative factors
in the acceptance as suggest by (Zhou, 2011). A possible future implication is to contrast the
results of the models reviewed in this paper in a similar context in order to compare the
results and its explaining in the acceptance of technology.
Acknowledgment
The contribution of Yáñez-Luna in this study was supported by PROMEP in coordination with
the Autonomous University of San Luis Potosí, México. Under the project reference number:
PROMEP/103.5/11/5517 and Folio: UASLP-245
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