The Technology Acceptance Model and Other User Acceptance Theories

The Technology Acceptance Model and Other User Acceptance Theories

Joseph Bradley
DOI: 10.4018/978-1-60566-659-4.ch015
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As global business markets become increasingly competitive, firms look to information technology to manage and improve their performance. Timely and accurate information is a key to gaining performance efficiency. Yet, firms may invest in technology only to find that their users are not willing to accept and use the new technology. This chapter explores the technology acceptance model and other theories of user acceptance.
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“There is nothing more difficult to plan, more doubtful of success, nor more dangerous to manage than the creation of a new order of things…Whenever his enemies have the ability to attack the innovator, they do so with the passion of partisans, while others defend him sluggishly, so that the innovator and his party alike are vulnerable. (Machiavelli, 1513, from Rogers, E. M., Diffusion of Innovations, 2003)”

The above quote from the 16th Century demonstrates that resistance to innovation is not unique to information systems, but has been with us for a long time with any type of innovation. Industry has turned to information systems technology to become more competitive in controlling resource use and costs to face increased global competition. The successful implementation of information systems ranging from simple applications, such as word processing and spreadsheets, to more complicated applications, such as enterprise resource planning systems, requires user acceptance. Yet, users are not always willing to accept the new technology. Academics and practitioners will benefit from a better understanding user acceptance. With this knowledge, user response can be predicted and systems modified to improve acceptance. Davis et al. (1989) propose a model of how users deal with the adoption of new technologies.

Davis et al (1989) developed the Technology Acceptance Model (TAM) based on the Theory of Reasoned Action (Ajzen and Fishbein, 1980). The TAM uses two variables, perceived usefulness (PU) and perceived ease of use (PEOU), as determinants of user acceptance. A key element of the TAM is behavioral intent which leads to the desired action, use of the system.

This article will first look at the theoretical development of the TAM beginning with the Expectancy-Value Theory and the Theory of Reasoned Actions. The TAM is introduced and described. A discussion of the impact of TAM on information systems research follows together with the limitations of the model. Extensions of TAM and alternative theories of user acceptance are then discussed. Lastly, a current discussion of the future of TAM is presented.



The theoretical roots of TAM can be found in the expectancy-value model and the theory of reasoned action.

Expectancy-Value Theory

The expectancy value theory was developed to understand motivations underlying the behavior of individuals. Behavioral intent is posited as the immediate precursor of a particular behavior. If we understand the elements that influence intention, we can better predict the likelihood of an individual engaging in a behavior. “Individuals choose behaviors based on the outcomes they expect and the values they ascribe to those expected outcomes” (Borders, Earleywine & Huey, 2004, p. 539). Expectancy is “the measurement of the likelihood that positive or negative outcomes will be associated with or follow from a particular act” (Mazis, Ahtola & Kippel, 1975, p. 38). The strength of the expectancy and the value attributed to the outcome will determine the strength of the tendency to act (Mazis et al., 1975, p.38). A simple example demonstrated by Geiger and Cooper (1996) is that college students who valued increasing their grades were more willing to increase their effort in the course.

Key Terms in this Chapter

Coping Model: Adaptation strategies to significant information systems events consisting of benefits maximizing, benefits satisficing, disturbance handling, and self-preservation. Expected outcomes of these strategies are restoring emotional stability, minimizing the perceived threats of the technology and improving user effectiveness and efficiency (Beaudry and Pinsonneault, 2005).

User Acceptance: This term describes the willingness of a user of information systems technology to adopt and accept new IT initiatives.

Theory of Reasoned Action (TRA): A theory found in social psychology literature which explains the determinants of consciously intended behaviors (Fishbein and Ajzen, 1975; Ajzen and Fishbein, 1980).

Innovation Diffusion Theory: A theory of adoption of new technology based on individual user characteristics, information sources and communications channels, and innovation characteristics (Rogers, 1983).

Perceived Usefulness (PU): One of the two key variables in the technology acceptance model. PU directly influences both attitude toward systems use and behavioral intention to use the system. PU is influenced by perceived ease of use.

Theory of Planned Behavior (TPB): A theory which extends the theory of reasoned action to include users who do not have complete control over the use of an innovation. A variable of perceived behavioral control is added to the TRA.

Technology Acceptance Model (TAM): TAM is a model of user acceptance of information systems technology based on the theory of reasoned action. Two variables perceived usefulness and perceived ease of use lead to attitude toward use, behavioral intention to use and use of the system.

Decomposed Theory of Planned Behavior: A variation of the theory of planned behavior which breaks down attitudinal, normative and control beliefs into a set of more measureable variables.

Task-Technology Fit Model (TTF): A model of user acceptance that relates actual use to tool functionality and task characteristics (Dishaw et al., 2002).

Perceived Ease of Use (PEOU): One of the two key variables in the technology acceptance model. Perceived ease of use will lead to attitude toward use, behavioral intention to use and actual use. PEOU also influences the second key variable, perceived usefulness.

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