Technology Acceptance Model (TAM)
TAM is an information technology (IT) theory that dictates how users come to embrace and use technology. The model proposes that when users interact with new technology, a number of factors control their judgment on how and when they will use it as shown below:
- Perceived usefulness (PU). This is described as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis et al, 1989, pp. 995);
- Perceived ease-of-use (PEOU). this is described as “the degree to which a person believes that using a particular system would be free from effort” (Davis et al, 1989, pp. 989).
TAM is an extension of the Theory of Reasoned Action (TRA), a model that predicts that individual attitudes and social norms are the main factors that influence behavioral intent. TAM takes a practical approach towards TRA to the position of an individual considering embracing a specific form of technology, or software.
TAM was originally posited in 1986 by Fred Davis and Richard Bagozzi and advanced in 1989, since then, it has been revised severally. TAM replaces a number of ideas expressed in the TRA model, however, both theories have firm behavioral aspects and presume that when a person creates an intention to act, they will do it without restriction. In the real world, there will be restrictions such as restriction to the freedom to act (Bagozzi et al, 1992, pp. 670). Earlier studies on embrace of innovations also proposed a major role of perceived ease of use. Tomatzky and Klein (1982, pp. 35) examined the relationship between the features of an innovation and its embrace and found that obtaining the matching platform, relative benefits, and user-friendliness had the most noteworthy correlation with embrace across a wide range of innovation forms.
Advances in information technology (IT) systems are altering the way persons interact. Today, we can meet, chat, and work together without requiring the traditional office buildings. For example, with the design of softwares that enable us to plan meetings and aid in decision-making or learning purposes, geographical limitations have faded and this has revolutionized interpersonal communication dynamics. IT is also radically changing teaching and learning methods. As these changes take shape, studies on the scale of acceptance of these technologies has begun to gain prominence among professionals and intellectuals. Developers and software companies are starting to recognize that lack of user acceptance of their products can cause a huge loss of funds and resources (Venkatesh & Davis, 2000, pp. 103).
In investigating user acceptance and application of technologies, the TAM is one of the models frequently referred. The objective of the theory is:
“to provide an explanation of the determinants of computer acceptance that is general, capable of explaining user behavior across a broad range of end-user computing technologies and user populations, while at the same time being both parsimonious and theoretically justified” (Davis et al, 1989, pp. 985).
TAM dictates that if a user realizes the benefits of a specific technology, he/she will believe in a positive user-software relationship. Since effort is a limited resource, a user will most likely embrace software that he perceives as simpler to use than another. As a result, an educational approach with a high focus of PU and PEOU will increase the probability of acceptance of a specific technology. The link between PU and PEOU is that PU arbitrates the influence of PEOU on attitude and anticipated perceptions (Wixom & Todd, 2005, pp. 98).
User acceptance is described as the verifiable readiness within a user group to apply IT for the functions it is designed to support. Despite this definition centering on arranged and intended applications of technology, research shows that personal insight of information technologies are likely to be affected by the major features of the technology. For instance, the scale to which one assesses new technology as beneficial, he/she will most likely embrace it, besides, his insight of the new technology is affected by the way persons around him assess and use the technology.
Application of TAM to Knowledge Management Systems
Several studies have modified Davis’ original theory to give practical facts on the relation between usefulness, user friendliness, and system use. Focus has been on the consistency of this model while other studies have centered on comparing two samples concerning TAM’s reliability. These studies have found a good accuracy and hence validated Davis’ model towards its use in various populations and software.
Money and Turner investigated the applicability of TAM to user acceptance of knowledge management IS, they addressed the following research questions:
- Does TAM explain user acceptance and application of an IS executed to sustain knowledge management purposes?
- Can findings from earlier TAM user acceptance studies be used to investigate user approval of knowledge management IT systems? (Money & Turner, 2004, pp. 6)
The findings from the study were important as they showed that earlier TAM studies may be used in the field of knowledge management. This implies that TAM can be successfully applied in knowledge management systems.
Task Technology Fit (TTF)
TTF is a theory in information technology that states that technology is more likely to have a positive influence on a person’s performance and be used if the abilities of the technology under review corresponds to the functions that the user has to perform (Goodhue & Thompson, 1995, pp. 217). The TTF is made up of 8 aspects: quality; accessibility; authorization; compatibility; user-friendliness; production timelines; systems dependability; and connection with users. Each of these aspects is gauged through the use of between two and ten questions with answers on a scale ranging from one to seven, the degree of agreement increasing from one through to seven. Goodhue and Thompson recognized that the TTF measure, in combination with utilization, is a considerable predictor of user reports of progress in job execution and success that was credited to their application of the system under study.
Although the original TTF model functions on the personal level of analysis, Zigur and Buckland (1998, pp. 316) give a parallel model functioning at the group level. Since its inception, TTF has found use in the framework of various forms of information systems. This theory, similar to TAM, has undergone several modifications to correspond to the objectives of the particular research. A schematic diagram of the model according to Goodhue and Thompson is shown below:
The connection between IT and individual or group performance has been a continuing concern in IS studies. Many research studies into the question of task have been undertaken since the mid 20th century, these studies have been undertaken in organizational literature and from the group process angle. Many intellectuals of group behavior have stated that the form of task plays a vital role in the group’s communication process and performance. This led to the creation of the Task/Technology fit model. This model maintains that a connection between business tasks and IT is vital to explain and forecast success of information systems (IS) (Goodhue and Thompson, 1995; Zigurs and Buckland, 1998). For a number of proactive application of IS, it has been proven that there is a positive relationship between TTF and IS success measures, and even on individual performance. This theory has found use in various areas such as in mobile technology (Gebauer & Tang, 2008, pp. 2).
The TTF dictates that the success of an IS should be linked to the fit between task and technology. In this scenario, success has been linked to individual and group performance. For group support systems, a particular theory of TTF was formed (Zigurs
& Buckland, 1998, pp. 327) and later tried, this theory gave a complete list of the conditions of group support systems to fit group tasks. For mobile ISs, TTF has been proven to be generally applicable, as earlier mentioned. However, more specific questions relating to the use of TTF to mobile IS have not had any response.
The TTF defines and builds on two elementary connections between task and technology, i.e.
- The link between the task characteristics and the necessity for various functions and aspects of a technology; and
- The link between fit technology needs, technology performance, and its assessment from the user’s point of view.
Goodhue and Thompson (1995, pp.225) forwarded a complete technology-to-performance version of the TTF that incorporated the features of IT, tasks, and the sole user as descriptive variables for technology use and individual performance. Zigurs and Buckland later modified the TTF to fit that of group decision making (GSS) processes. Studies have shown that excellent fit between tasks and technology will lead to better group performance, the creators defined fit as “ideal profiles composed of an internally consistent set of task contingencies and GSS elements that affect group performance” (Zigurs & Buckland, 1998, pp. 323) and performance as the realization of group objectives to be implemented for individual task scenarios.
Application of the TTF to Mobile Information Systems
The concept of TTF can help us to classify aspects that are vital to sustain a business task, hence lead to the success of technologies. An example of such an idea is in the application of mobile technology to sustain an increasingly mobile labor force (Gebauer & Tang, 2008, pp. 2). However, applying the TTF brings new challenges as earlier studies mainly centered on the functionality presented by the technology, and have done little on the context of the technology being used. However, studies have shown that the use-context may have a less vital influence on the conditions of task technology fit based on the following:
- It can be seen that non-functional aspects, for example, weight and size, play a more important role in mobile than in non-mobile use environment;
- Functional requirements may change as business tasks are frequently executed differently in mobile and non-mobile use environments.
Due to the visible shifts in business tasks and associated technology needs, it becomes essential to evaluate the applicability of the TTF to mobile technologies and mobile use contexts, and to determine the requirements of theory modifications and extensions.
In a study of the applicability of the TTF to mobile technology, the following research questions were forwarded:
- What is the influence of user mobility on the manner in which a specific task is performed?
- How does user-mobility influence the user’s insight of the significance of non-functional elements of the technology? And
- How does user mobility influence a user’s approval of the fit between task and technology?
A study of 216 business operators who use mobile technology through equipment that include regular mobile phones and portable computers had the following findings.
- The association between task complexity and functional needs of the mobile equipment is greater for highly mobile users than for those that are less mobile.
- User mobility is associated with increased insight from the users of the significance of a number of non-functional characteristics.
- For a number of uses and elements, highly mobile users showed a stronger connection between task-technology fit, and an general assessment of the technology.
From these findings, we deduce that user mobility requirements need to be taken into consideration when applying the TTF to mobile IS. Besides, the study gives information on the system needs of an increasingly mobile labor force (Gebauer & Tang, 2008, pp. 14).
Bagozzi, R. P., Davis, F. D., and Warshaw, P. R. (1992). Development and test of a theory of technological learning and usage. Human Relations, 45(7), 660-686.
Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, (35:8), 1989, pp. 982-1003.
Gebauer, J., and Tang, Y. (2008). Applying the Theory of Task−Technology Fit to Mobile Technology: The Role of User Mobility. International Journal of Mobile Communications, Vol. 6 (3), 2008.
Goodhue, D. & Thompson R. L. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly, (19:2), 1995, pp. 213-236.
Klein, T., and Tomatzsky, L. G. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29, 28-45.
Money, W., and Turner, A. (2004). Application of the Technology Acceptance Model to a Knowledge Management System. Hawaii International Conference on System Sciences, vol. 8, pp.80237b, Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS’04) – Track 8, 2004.
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, (46:2), 2000. 186-204.
Wixom, B. & Todd P. (2005). A Theoretical Integration of User Satisfaction and Technology Acceptance. Information Systems Research, Vol. 16, Issue 1, 2005, pp.85-102.
Zigurs, I., and Buckland, B. K. (1998). A Theory of Task/Technology Fit and Group Support Systems Effectiveness. MIS Quarterly 22(3), 1998. 313-334.