Technology Acceptance Model

In business, technology acceptance can be measured by establishing a technology acceptance model. This model measures how employees perceive a system, its perceived usefulness and ease of use. These factors influence intended users’ attitudes toward the technology, which ultimately leads to their behavioral intention to use the system. When a system is perceived as useful and easy to use, it is more likely to be adopted by the intended users.

The technology acceptance model was originally developed by Fred Davis as part of a dissertation at MIT in 1985. At that time, companies wanted to know if investing in new computer technologies would pay off. This was before the internet and windows 3.1, so a technology acceptance model was needed to determine whether the technology was worth the cost. Having a reliable measurement of a technology’s use and acceptance was invaluable to the software vendors and IT managers.

The technology acceptance model has been widely applied in the field of information systems and services (IS). It has provided valuable insights into the process of IS adoption and its impacts. It has also been applied to evaluate the motivations of users in adopting new technologies. This model can help businesses determine the level of adoption of new systems and services, and improve user experience.

The technology acceptance model was derived by identifying core psychological and technological variables that shape acceptance. These variables are then combined to develop a predictive model that helps researchers predict how users will use a new technology. The model is based on theories of psychology and extends the attitude-intention paradigm. A key benefit of using a technology acceptance model is that it allows researchers to understand and predict how a new technology will be received by users.

The technology acceptance model is based on the progress model proposed by Davis, 1989. The model considers factors such as perceived usefulness and perceived ease of use. For example, a home robot may not be perceived as useful by a user until they have a desire to purchase it. This intention will then lead to behavioral integration, which means that the user will eventually use the robot on a regular basis.

In a study of the adoption of eHealth systems in resource-limited settings, an advanced technology acceptance model was used to predict consumer behavior. The model revealed that perceived usefulness and perceived ease of use are the strongest determinants of behavioral intention. Among other factors, technical infrastructure was a predicting factor in resource-limited settings.

The adaptation of the model to a variety of contexts makes it more robust and more predictive. The authors conducted a literature review and identified additional constructs such as price value and habit. Based on these, the researchers modified the original UTAUT model and introduced additional relationships. They also developed five hypotheses for empirical testing.