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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.
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A technology acceptance models review
By Mario Arias-Oliva, Universitat Rovira i Virgili, Av. Universitat 1,
43204 Reus, Spain, +34977759800, 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,
Statement of commitment
At least one of the authors will attend ETHICOMP 2014 conference, at Les Cordelies Paris,
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 users 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
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
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.
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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... First, the direct sensory experience and bodily involvement that craftsmanship allows can shape social relations and engagement (Borgmann 1987). The idea here is that through skilled practices people give meaning to their world (Coeckelbergh 2012). Through craftsmanship people can come to appreciate the meaning and function that natural environments and processes have for their wellbeing and life. ...
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Indigenous and Local Ecological Knowledge (ILEK) has been recognized for its potential and contribution to sustainable use of natural resources. It has proven difficult, however, to investigate and observe its tacit and embodied character. The objective of this article is to explore ways in which we can theoretically and methodologically understand ILEK. It does so by theorizing ILEK as craftsmanship using literature on practice theory, and analyzing the tacit and embodied nature of craftsmanship of a Sámi craftswoman and an archipelago fisherman through the use of visual methods. Results of this study are used to analyze and discuss how craftsmanship reproduces ILEK and its potential to contribute to environmental sustainability.
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The convergent development of (renewable) distributed electricity sources, storage technologies (e.g., batteries), ‘big data’ devices (e.g., sensors, smart meters), and novel ICT infrastructure matching energy supply and demand (smart grids) enables new local and collective forms of energy consumption and production. This socio-technical evolution has been accompanied by the development of citizen energy communities that have been supported by EU energy governance and directives, adopting a political narrative of placing the citizen central in the ongoing energy transition. But to what extent are the ideals that motivate the energy community movement compatible with those of neoliberalism that have guided EU energy policy for the last four decades? Using a framework inspired by Michel Foucault’s idea of governmentality, we analyze the two political forms from three dimensions: ontological, economic and power politics. For the ontological and the economic dimensions, neoliberal governmentality is flexible enough to accommodate the tensions raised by the communitarians. In the dimension of power politics however, the communitarian logic does raise a fundamental challenge to neoliberal governmentality in the sense that it explicitly aims for a redefinition of the ‘common good’ of society’s energy supply based on democratic premises.
Opracowanie koncentruje się na technice Bulletin Board Discussion (BBD). Jego celem jest charakterystyka wybranych technik badawczych opartych na dyskusji grupowej online oraz wskazanie zalet i dylematów związanych z gromadzeniem danych jakościowych za pomocą BBD. Rezultaty badań literaturowych poparte zostały doświadczeniami wynikającymi z realizacji dwóch badań, które pozwalały na przetestowanie potencjału tej techniki . Prezentując wyniki, skoncentrowano się jedynie na spostrzeżeniach metodologicznych dotyczących procesu zbierania danych. Zastosowano praktykę refleksji, aby ocenić podobieństwa i różnice w zachowaniach uczestników i generowanych treściach w dwóch turach badania.
Współcześnie ilość przechowywanych i generowanych danych rośnie wykładniczo. Dynamika tempa jest wynikiem popularyzacji oraz szerokiego zakresu wdrożeń technologii komputerowej w każdym aspekcie działalności gospodarczej – produkcji, zarządzania łańcuchem dostaw, marketingu, zachowań klientów itp. Generowane dane są przeważnie wynikami prac naukowych, medycznych, badań marketingowych oraz działań finansowych. Jednocześnie dane te mogą być szeroko dostępne dzięki Internetowi, otwartym bazom danych, trendom rynkowym itp. Analiza tych danych tworzy nowe możliwości dla tych jednostek, które mają sposobności oraz chęć pozyskania interesujących i przydatnych informacji w nich zawartych. Rozmiar i zakres nowych zestawów danych przyczynił się do powstania problemu związanego ze skalowalnością tradycyjnych technik statystycznych, które nie są w stanie obsłużyć miliardów rekordów i zmiennych, ponieważ bardzo często konwencjonalna technologia zawodzi przy petabajtach danych. Dlatego współcześnie coraz popularniejsze jest stosowanie sztucznych inteligencji czy koncepcji data mining.
Chapter The chapter addresses the question of humans enchanting robots in the sense that humans tend to perceive robots and think about them as more than just machines as a result of performing magical thinking. It explores the field of Human Robot Interactions and discusses a specific part of robot ethics controversies that seek to rethink our thinking about humans and robots. Next, it examines both classic and more recent anthropological, psychological, and philosophical accounts of magic to identify various analogies between magical thinking, on the one hand, and various phenomena diagnosed by HRI as well as some philosophical ideas regarding robots, on the other. In doing so, it analyzes the reevaluations of the status of magic and magical thinking from the second part of the nineteenth century to the present moment.
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Although it is often condemned as an imprecise concept, alienation continues to flourish as critique in contemporary philosophy, theology, and psychology, as well as in sociology. Historically originating in Roman law, where it referred to the transfer of land ownership, alienation has since been applied extensively to analyses of labor relations, politics, and culture. In the 19th century, Marx showed that workers’ alienation, their dehumanization and estrangement, was a consequence of the structure of exploitation in capitalist industry. The concern was echoed in Weber’s metaphor of the ‘iron cage’ as an outcome of rationalized structures, as well as in Durkheim’s conceptualization of anomie as a variant of alienation causing socially induced psychological states. Today, while research in the structural tradition does not assume that people necessarily are aware of their condition, researchers who assume that alienation is a conscious experience have invented scales to measure its intensity. Continuing both the structural and the psychosocial traditions, researchers now study alienation in relation to uses of digital technologies and new forms of exploitation in work, as well as in politics and popular culture. Alienation is also studied in families, especially in investigations of parenthood.
This paper explores the ambiguous impact of new information and communications technologies (ICTs) on the cultivation of moral skills in human beings. Just as twentieth century advances in machine automation resulted in the economic devaluation of practical knowledge and skillsets historically cultivated by machinists, artisans, and other highly trained workers (Braverman 1974), while also driving the cultivation of new skills in a variety of engineering and white collar occupations, ICTs are also recognized as potential causes of a complex pattern of economic deskilling, reskilling, and upskilling. In this paper, I adapt the conceptual apparatus of sociological debates over economic deskilling to illuminate a different potential for technological deskilling/upskilling, namely the ability of ICTs to contribute to the moral deskilling of human users, a potential that exists alongside rich but currently underrealized possibilities for moral reskilling and/or upskilling. I flesh out this general hypothesis by means of examples involving automated weapons technology, new media practices, and social robotics. I conclude that since moral skills are essential prerequisites for the effective development of practical wisdom and virtuous character, and since market and cultural forces are not presently aligned to bring about the more salutary of the ambiguous potentials presented here, the future shape of these developments warrants our close attention—and perhaps active intervention.
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The world is flat with Electronic Commerce moving it into new terrains of information exchange and means of conducting business activities. The acceptance of Electronic Commerce as an IT infrastructure depends on the users' conscious assessment of the influencing constructs as could be depicted in Technology Acceptance Model (TAM),Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Innovation Diffusion Theory (IDT), and Technology-Organization-Environment (T-O-E) model. The paper accused TAM and TPB of being traditional and utilitarian-based. Though TPB adds the constructs of perceived behavioral control and subjective norms to the original TAM's characteristic constructs of perceived usefulness (PU) and perceived ease of use (PEOU), both predominantly base analysis on attitudinal variables and view EC as purely productivity tool, communication mediator, or intelligent decision-making partners. Further, TO -E model adds such descriptive constructs as firm's size, consumer readiness, trading partners' readiness, competitive pressure and scope of business operations. In order to make for better explanatory and predictive values, TAM need be integrated with other IT theories that incorporated decision-makers' social and idiosyncratic characteristics. This paper adds to existing body of knowledge on IT acceptance behavior and provides bases for more informed decision by offering such new constructs as company mission, individual difference factors, perceived trust, and perceived service quality. The proposed improved TAM and TO -E Model of Innovation Adoption and Use combines the constructs to form a richer theoretical framework that guides the understanding, explanation and prediction of adoption and use behaviors of IT in an organized system. Abstract: High technology firms sense pressure to constantly innovate and deliver goods and services to the marketplace. Most product introductions do not deliver long-term growing revenue streams or contribute to overall profitability. This study aids in understanding the importance of a new high-technology product, in the B2B sector, attaining a critical mass of customers (industry-wide) by modeling the influences driving technology adoption and development (inflow and outflow models). The paper presents a framework explaining the forces that create a critical mass of customers and the benefits flowing from critical mass in the high-technology arena. The paper's models aid by providing an understanding of delivered product technology marketplace success: short-term vs. long-term. The paper focuses on how firms should scan to determine viability of long-term success and how to invest in products accordingly. Implications to managerial decisions regarding new product launches are provided.
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Mobile technology is pervasive at institutions across the U.S. The study was framed with self-efficacy theory, self-directed learning theory, and the unified theory for acceptance and use of technology. The purpose of this study was to assess undergraduate students’ behavioral intention towards mobile technology acceptance in agricultural education courses. The population was undergraduate agricultural leadership students (N = 687) in a department of agricultural education at a land-grant university. Random sampling was employed to assist the researchers in answering the study’s objectives and to generalize findings to the target population. Survey research was employed as the data collection method and descriptive statistics, correlations, and multiple regression were implemented to analyze the data. Three hundred forty-four students were surveyed and 88.10% (n = 303) of the sample responded to the survey. Self-efficacy, level of self-directedness, and GPA explained 32% of the variance of students’ behavioral intention to use mobile technology. The data suggested students are accepting the use of mobile technology in academic settings to enhance learning. By developing a better comprehension of factors that influence student’s behavioral intentions with mobile technology, institutions may improve student learning and better assist institutions achieve strategic objectives through disseminating institutional information with mobile technology.
Conference Paper
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The objectives of this research are to assess the likelihood of acceptance in mobile learning (m-Learning) and study main factors that effect to use m-Learning that focus on higher education students in Thailand. The researchers use a quantitative and qualitative approach to survey on 390 students. The samples are selected on the probability basis that using the stratified random sampling under the different area by divided into 2 groups: (1) the private universities and (2) the public universities in Thailand. We use questionnaires for collecting the data. In addition, the modified acceptance framework that based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted to determine the factors that influence the students' intention to use m-Learning. The results from statistical analysis show that the acceptance level of students on m-Learning is in the high level.
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In this paper, we compare the utility of modified versions of the unified theory of acceptance and use of technology (UTAUT) model in explaining mobile learning adoption in higher education in a developing country and evaluate the size and direction of the impacts of the UTAUT factors on behavioural intention to adopt mobile learning in higher education. The data were obtained through a web survey of university students and the models are estimated in a structural equations modelling framework. Many of the UTAUT relationships are confirmed, but some are contradicted. The results suggest that culture and country level differences moderate the UTAUT effects, hence, a straightforward application of the model regardless of the context can lead to non-detection of important relationships and to suboptimal mobile learning promotion strategies. Including attitude in the model is also a prudent modification since it increases its explanatory power.
Mobile technologies are a future in e-learning technologies. The paper presents the details of using mobile devices and wireless technologies that could be used for m-learning in education and training. Mobile devices can have more processing power, slicker displays, and more interesting applications than were commonly available on desktop machines ten years ago, and educators are quickly realizing their potential to be used as powerful learning tools. However, the application of mobile technologies to learning contexts must take into account a number of factors. Above all other things, we must consider how mobile learning can be used to provide learners with better opportunities and enhanced learning outcomes. This paper is concerned about the problems of using mobile devices and wireless technologies, a differentiation between learning and technology as the driver for mobile learning approaches and than the classification of mobile learning activities. M-learning is the exciting art of using mobile technologies to enhance the learning experience. Mobile phones, PDAs, Pocket PCs and the Internet can be blended to engage and motivate learners, any time and anywhere. Handheld devices are emerging as one of the most promising technologies for supporting learning and particularly collaborative learning scenarios; mainly because they offer new opportunities for individuals who require mobile computer solutions that other devices cannot provide. The highly personalized nature of digital mobile devices provides an excellent platform for the development of personalized, learner-centric educational experiences. In paper is emphasized the importance of considering learning over technology, and suggest a pedagogically based framework for developing learner-centric m-learning. The evolution in education and training at a distance can be characterized as a move from distance learning to e-learning and m-learning (mobile learning).
Adoption of Information Technologies (ITs) is a crucial decision for the growth, productivity, competitiveness, and even survival in a competitive market. Organizations adopt IT innovations to sustain their competitive position as well as to create competitive advantage. Tornatzky and Fleischer (1990) developed a framework named TOE that comprises three key determinants that affect organizational adoption: technology, organization, and environment. This framework has been used successfully in the study of adoption within organizations. Based on this framework, the purpose of this paper is to conduct a systematic review of the literature to understand important adoption factors of Information Technologies in organizations.
This paper applies the extended technology acceptance model (exTAM) in information systems research to the use of clickers in student learning. The technology acceptance model (TAM) posits that perceived ease of use and perceived usefulness of technology influence users' attitudes toward using and intention to use technology. Research subsequent to TAM has added perceived enjoyment as a factor in predicting attitude and behavioural intentions. This study tests the validity of this extended TAM model while applied to clickers via data collected from three macroeconomics classes. Path analytic results show that most of the hypotheses are supported in the expected directions, providing evidence that exTAM is applicable to examining factors influencing learner attitude and behaviour in relation to the use of interactive learning technologies, such as clickers in the classroom.