Unified theory of acceptance and use of technology (UTAUT)
The Unified theory of acceptance and use of technology (UTAUT) is an important theoretical construct that has over the years facilitated the adoption or rejection of technology. When it comes to the focus of intention and usage of UTAUT as a way of addressing the acceptance and rejection of technology is vital. It is closely incorporated in Information Technology and Higher Education. The establishment of (UTAUT) has an important role in selection of e-learning and m-learning technologies. Four main determinants of user behavioral and they include performance expectancy, social influence, effort expectancy, and facilitation of conditions (Thomas et al., 2013).
According to Wu et al. (2011), there are seven dimensions that form UTAUT and they include facilitating conditions (FC), Hedonic Motivation (HM), Performance Expectancy (PE), Effort Expectancy (EE), Habit (HB), and Price Value (PV). The applicability of UTUAT is able to predict 74% of the intention to use technology.
Figure 3. UTAUT (Ahmad, 2014).
The aspect of performance expectancy is outlined by Ahmad (2014), Unified Theory of Acceptance and Use of Technology (UTAUT): a decade of validation and development. In Proceedings of the 4th International Conference on ICT in our Lives (ISSN 2314–8942), as the level of degree in which individuals are able to adopt the belief that the usage of a system has the ability to let them attain gains in job performance. Under the UTAUA, five key variables have been presented by Ahmad (2014), to match performance expectancy. The first variable is perceived usefulness which has been largely coined from both TAM and TAM2 models combined with TPBB to form C-TAM-TPB.
Ahmad (2014), presents the definition of C-TAM-TPB as the user’s subjective probability that create prospects belie that their ability to use a specific system application would lead to increased job performance from an organizational context. Secondly, the aspect of performance expectancy has been outlined by Kumar (2013) as having extrinsic motivation generated from the motivational model. The concept of extrinsic motivation increased perception that a certain activity has the affinity to be performed since people perceive that activity as important in achievement of valued outcomes (Ahmad, 2014).
Another aspect under performance expectancy is Job-fit which inspires the selection of technology by leading to a person using technology believes that its usage can enhance their job (Wang, 2016). Kumar (2013) goes ahead to present a driving factor of technology acceptance or rejection under performance expectancy as a derivative from Innovation Diffusion Theory (IDT). In this case, there is a certain extent in which innovation is seen as being better than its precursor. According to Tarhini et al. (2014), the other theoretical construct entails embracing outcome expectations. In this case, social cognitive theory comes in play to outline the likelihood of perceived consequences of using computers.
As a result, the use of performance expectancy as a significant behavioral predictor remains an essential and reliable predictor of intention behavioral in acceptance and rejection of technology and the theories and models that outline them (Tarhini et al., 2014). The impact of expected performance is therefore moderated by both gender and age factors, where the intention to use is largely exhibited (Ahmad, 2014),
On the other hand, when it comes to effort expectancy, the degree of ease in which the use of a system is given is outlined (Ahmad, 2014). Effort expectancy is therefore matched by three main technology acceptance models. The first one is the perceived ease of use presented under TAM/TAM2 models and is understood as the level at which prospective user expects the system to be free of effort (Tarhini et al., 2014). Secondly, an aspect of complexity is presented in effort expectancy under the Innovation Diffusion Theory combined with Model of PC utilization. The closest definition associated with complexity in relation to effort expectancy and its acceptance is the relativity perception of inability to comprehend and use certain technology (Ahmad, 2014).
The third construct under effort expectancy is consideration of the ease of use (Ahmad (2014). According to Ahmad (2014), innovation diffusion theory is largely responsible for this construct and defines the ease of use as the degree at which users perceive an innovation or technology as difficult to use. As opposed to the Performance Expectancy which largely relies on age and gender in acceptance or rejection of technology, Wang (2016) outlines that Effort Expectancy relies on experience as the moderator to rejection or acceptance of technology. Women who have a higher rate of experience in dealing with technology have a better positive response to technology as compared to those with less.
The other important construct in technology acceptance and rejection is social influence (SI). It has been largely defined by Ahmad (2014) as the rate at which important others believe or perceive them to use a new system. In this construct, three key variables present themselves as derived from the technology acceptance models and the way they match social influence (Thomas et al., 2013). This matching is attributed to three main factors, subjective norm, social factors, and image.
When it comes to Subjective norm, the theory of reasoned action (TRA), Theory of planned behavior (TPB), the theory of decomposed planned behavior (DTPB), and TAM/TAM2 combined with C-TAM-TPB derives its concept (Ahmad, 2014). According to Kumar (2013) it is defined as an increased perception as to whether most people who are important to the user think that he or she should not perform the behavior in question.
The other key variable in this case pertains to image. According to Wang (2016) the concept of image is derived from innovation diffusion theory (IDT) and it is largely defined and understood as the perception level at which user’s image or social system in the society is enhanced for using a certain innovation (Wang, 2016). Ahmad (2014) emphasizes that image plays an important role in guiding whether social influence in technology acceptance affects individual behavior through internalization, compliance, and identification mechanisms.
Interestingly, both identification and internalization mechanisms are designed to change and modify individual’s belief structure to align to their potential gains from a social status. In this case, the compliance mechanism leads to changing user’s intentions amidst changing social pressures. For example, when it comes to women they are more inclined towards the opinion of others in their social settings thus the effect of social influence tends to increase when it comes on the opinion of women towards new technology (Ahmad, 2014).
A close look at facilitating conditions entails the beliefs that users have on the role of organizational infrastructure or technical infrastructure towards the support of use of technological innovation. It is guided by three main variables acceptance models (Hornbæk & Hertzum, 2017). The first one is perceived behavioral control which is derived from combined TAM and TPB (C-TAM-TPB), DTPB, TPB, and TRA. A reflection of external and internal constraints on behavioral are considered to include technology facilitating conditions, resource facilitating conditions, and self-efficacy (Tarhini et al., 2014).
The other variable includes facilitating conditions that are largely derived from the Model of PC Utilization (MPCU). This is defined as the aspect of observers making an act easy to accomplish through objective environmental factors that they agree on (Hornbæk & Hertzum, 2017). In close correlation, compatibility forms the third variable which is derived from Innovation Diffusion Theory (IDT). Compatibility is understood as the degree in which past experiences of potential adopters, existing values, and needs align in continued consistency to innovation in question (Kumar, 2013).
According to Ahmad (2014), facilitating conditions tend to have a high predictive power of intention to use as long as the model of effort expectancy is not used as a predictor. However, there are times when facilitating conditions are non-significant in predicting intention of use. This is the time when both effort expectancy and performance expectancy constructs exist at the same time. Hornbæk & Hertzum (2017) affirms that age and experience are essential in regulating the relationship between the intention to use and facilitating conditions. However, in older ages, this effect gets stronger as experience increases.
There are multiple research efforts contributing new moderators to UTAUT. For example, Ahmad (2014), outlines that in UTAUT there is need to take to account the moderating effect of awareness in relation to the use of innovation technologies. Ethnicity and location have also been considered as essential moderating factor in UTAUT when it comes to intention behavioral and acceptance of technology. According to Thomas et al. (2013), behavioral intention to use technology and the aspects of social influence share common moderating factors that range from awareness, age, gender, and experience. For example, when it comes to the aspect of age, younger women have been known to be inclined to social influence, while men lean more on experience as relevant constructs in decision making.
From a theoretical perspective of UTAUT’s role in influencing user’s acceptance and technology adoption, a number of moderating factors that regulate the process were outlined by (Ahmad, 2014). Such factors as experience level, age, gender, and voluntariness to use innovation systems were condensed into individual factors. Under the same individual factors dimension, specialty was also added as another regulating factor (Goh & Karimi, 2014). From the anxiety dimension, such factors as habitual or psychological readiness to adapt change are considered as the main moderate factors to consider.
On the other hand, from adaptation timeline dimension, the aspect of allowing time to absorb change is considered. Therefore, three main moderate factor category dimensions namely individual factors, anxiety, and adaptation timeline come out strongly as the main aspects to consider in UTAUT (Kumar, 2013). On the other hand, based on Ahmad (2014), UTAUT based open adoption model considers new sets of moderators as position and awareness. Goh & Karimi (2014) outlines that gender, age, experience, and position have been outlined as the most important moderators. In addition to age was outlines as despite being a moderating effect, it also has an effect in behavioral intention as well as usage of open access. The aspect of awareness as a moderator is outlined to only have effect on usage of open access.
Based on research presented by Tarhini et al. (2014) age is presented as an important demographic variable that has adoption, acceptance of technology, and behavioral intention. The explanatory power of TAM is increased by age as a moderator. The UTAUT model embraces age as an important factor in influencing the level of technology acceptance and behavioral intention. When it comes to younger employees, the relationship between BI and performance expectancy was stronger.
When considering internet self-efficacy and computer, Ahmad (2014) outlines that older people have lower efficacy in computer usage as compared to younger people. The rationale behind this finding is that older people tend to think that they are too old to understand new technologies. As a result, younger adults have a lower level of anxiety as compared to older people when learning or using new technology. Thomas et al. (2013) goes ahead to claim that there is a close relationship between behavioral intention and social influence. It also has a direct effect in that it regulates the relationship between BI and social influence.
A number of studies have shown that there is close correlation between age and e-learning acceptance as an innovation technology. It is predicted that in the near future, the effect of age on the interrelationship between SE, PEOU, BI, and SN will be better and stronger for older students. As a result, the correlation between age and technology acceptance in UTAUT has been outlined to be inversely proportional to age. As age increases, the lower the level of interest and positive attitude towards technology (Ahmad, 2014). The ease of use is a major determinant in the attitude that users, based on age, develop when it comes to appreciation of technology.
When it comes to experience, the field of human-computer interaction HCI has in the recent past launched a new concept of study that embraces the concept of “User Experience” (UX) when it comes to technology selection and acceptance. In the exploration of technology acceptance, most models focus on usability of the technology in terms of time for task completion, and learning time. However, user experience is designed to consider such factors as cognitive orientation constructs in TAM, aesthetics, and emotions and their roles in influencing user selection and their intention to adopt and accept innovative technology (Goh & Karimi, 2014).
The level of user experience that technology users have has a lot of influence in determining whether they will adopt the technology. Ahmad (2014) asserts that understanding the shape, knowledge and adoption of Information Technology is important in human technology interaction. Both UTAUT and TAM have a close influence in determining the level of user experience. According to Hornbæk & Hertzum (2017) as users continue to relate with technology, they theory attitude and perception on the technology changes based on the ease of use and other factors.
According to Irani (2000) prior experience among users during their interactions with technology has continued to be at consideration. It is through increased interactions and usage of technology that has cultivated a positive aspect among users. However, the true sense is that the experience can lead to dominant majority attitude within both UTAUT and basin decisions.
Hornbæk & Hertzum (2017) affirms that user experience tend to make them choose the type of technology they are going to use. Per say, if the user tends to have more time using any given technology, they get attached to it as long as it has profits. The outlined experience ensures that only the most conversant and efficient type of technology is chosen by the user. This will help users determine whether to embrace technology or reject it (Irani, 2000).
A close correlation exists between user experience and the attitude that is developed by users as they interact with technology. When users find that technology has ease of use, has flexibility, and can be trusted and relied on in terms of security, they are motivated to use it more often. A higher degree of conversancy on technology as a result of increased experience is also developed (Ahmad, 2014).
Gender as a Moderating Variable
When it comes to consideration of “Gender” as an essential aspect in determination of models of behavior that relate to technology acceptance or rejection, the gender schema theory approves it (Tarhini et al., 2014). According to Suki & Suki (2017), studies done on technology acceptance models, that is TPB and TAM 2, have shown that the decision processes taken by men and women tend to be different in terms of socially constructed cognitive structures. Per say, when it comes to the prediction of usage behavior in the domain of IS research, gender plays a crucial role in facilitation of that process.
For example, a research conducted by Mouakket (2018) showed that there was a significant 52% increase in the explanatory power of TAM when the inclusion of gender moderator was made. This meant that when it came to the influence of SE, BI, PU, PEOU, and SN, gender inclusion had a moderating effect. Based on the research, it was found that there was a stronger relationship for men compared to women when it came to performance expectancy. This means that men are highly task oriented and highly pragmatic as compared to women from a social psychology point of view.
In natural settings, men are more oriented to success and achievement needs thus creating a great emphasis on earnings as compared to women. This translates that when it comes to a system, men place higher interest in its usefulness. Therefore, gender has been found to be an essential moderating aspect when it comes to the intention to adopt and use a system, putting to consideration the effort expectancy when it comes to women than men (Suki & Suki, 2017). As outlined by xxx, anxieties and expectancies are reciprocal to each other, and in this case, women have a higher computer anxiety and lower self-efficacy as compared to men. This means that the ease-of-use perception is largely lowered when it comes to women (Tarhini et al., 2014).
Moreover, when it comes to consideration of normative beliefs, women are largely affected than men in consideration of its influence in technology acceptance and rejection (Marangunić & Granić, 2015). According to Mouakket (2018) the effect of normative beliefs is stronger in women than men. Women tend to rely heavily on the opinion of others before making decisions as compared to men due to their greater ‘awareness to others’ aspect, and are therefore easily motivated by social pressure. This means that men have little affiliation need pressures as compared to women when it comes to the acceptance or rejection of technology.
The use of UTAUT and TAM models in making decisions on usage of technology on learning and teaching environments is important. Just like students in a learning environment, teachers have different influencing factors that determine their attitude and level of acceptance or rejection of innovation technology. In UTAUT, gender, age, experience, and voluntariness have important roles that influence choices made (Hornbæk & Hertzum, 2017).
For example, when using learning management systems, teaching styles also play important roles in determining teaching experience and technology usage. The first influencing factor is performance expectancy. The perception that certain technology is going to impact positively on the expected results is considered. Increased in perceived usefulness impacts technology acceptance based on the teaching style applied. If one teaching style is effective for teachers in terms of ease of use, then it is considered. The risk perception in using the technology also considered (Ahmad, 2014).
According to Goh and Karimi (2014) another main factor influencing teaching style under UTAUT is effort expectancy. The ease of use has been presented as a major determinant of intent to use technology in e-learning environment. A greater impact of accepting e-learning is dependent on the teaching technology being used and how teachers and students respond to it. Other factors have been depicted as facilitating conditions also play an important role in enhancement of teaching technology.
Based on research conducted by Gawande et al. (2016), when it comes to teaching style inventory, scores are fair separately on each scale. Different predominant teaching styles presents themselves when utilizing technology. The first one is Expert style; this entails the aspect of the instructor possessing knowledge and skills that the students require. The teaching approach encourages students to enhance their capacity by displaying detailed knowledge.
Another teaching style as presented by Gawande et al. (2016), is the formal authority approach. The teacher in this case is respected because he or she tends to possess status among students. The core focus of the style is to provide both negative and positive feedback, ensure learning goals are established, create course expectations in regard to the technology being utilized, and ensuring that the strategy needed to learn is provided.
In Personal Model, the teacher tends to teach using personal example. In the process of implementing the model, students use a prototype to learn how to think and behave. It encourages increased emulation of the instructor’s views based on the structure they need to learn. When it comes to the facilitator teaching style, emphasis is made on teacher-student interactions (Chiu & Ku, 2015).
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81-95.
Mouakket, S. (2018). The role of personality traits in motivating users’ continuance intention towards Facebook: Gender differences. The Journal of High Technology Management Research, 29(1), 124-140.
Suki, N. M., & Suki, N. M. (2017). Determining students’ behavioural intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education, 15(3), 528-538.
Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163-184.
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