Technology Acceptance Model (TAM)

TRA and TPB are not specifically developed as models of technology acceptance or adoption, but can explain technology acceptance to some extent, as described above with reference to some studies. Fred D. Davis sought to build a specific model for technology acceptance in (1989) as part of his doctoral dissertation in the Massachusetts Institute of Technology (MIT). Davis conducted an explorative study to find the determinant factors of users’ acceptance of information technology (electronic mail and file editor in study 1 and PC-based graphic systems in study 2). He identified two factors, namely perceived usefulness (PU) and perceived ease of use (PEOU). Perceived usefulness is defined as the degree to which a person believes that using particular systems would enhance his/her job or task at hand, while perceived ease of use is defined as the degree to which a person believes that using a system would be free of effort (Davis (1989), p. 320). His study confirmed that both factors explained users’ acceptance of information technology in the two studies.

The Technology Acceptance Model was based on Fishbein and Ajzen’s Theory of Reasoned Action (TRA). However, Davis did not consider subjective norm to be a relevant factor in a workplace context in which technology use is often mandatory. He was interested in examining the varied behaviour of users in the adoption of technology per se.

His research results informed him that users’ behaviour is different because they possess different attitudes toward the technology. This attitude, according to Davis, is formed by users’ perceptions about the usefulness of the technology and the degree of the technology’s ease of use. The confirmed model, widely known as the Technology Acceptance Model (TAM), is represented in Figure 2.8.

FIGURE 2.8 TECHNOLOGY ACCEPTANCE MODEL

Source: Davis (1989)

Davis’s article about TAM study has received more than 1,000 citations in articles about technology acceptance. Bagozzi (2007) even mentioned that TAM has outperformed other models (such as TRA and TPB) in terms of explained variances across studies. The model is also famous for its parsimonious yet powerful explanatory ability (Davis, Bagozzi

& Warshaw 1989a); Goodhue (2007); Premkumar & Bhattacherjee (2008)). Venkatesh et al. (2007) and Venkatesh (2006) noted that this model has undergone many replications and generalisation tests in a range of countries and contexts even more diverse than the original study. The authors recorded that TAM has been applied in the field of marketing, advertising and information adoption.

Nevertheless, studies focusing on TAM have generated inconclusive results about this model. Ma and Liu (2004) and Yousafzai, Foxall and Pallister (2007) conducted a meta-analysis of TAM-related studies to consolidate their findings and generate a common understanding of the influences of each factor in different technology adoption contexts.

Their research indicated that both perceived usefulness and perceived ease of use are good predictors of intention to adopt technology, but they express a caution with regard to contextual influences. Yousafzai et al. (2007) differentiated these contexts into three categories: field (real work situation), lab (both work and non-work situations) and

Perceived usefulness

Intention to

adopt Technology

adoption Attitude

toward adoption Perceived

ease of use

voluntary (non-work situation). They stated that the significance of each factor tends to vary as the context varies.

TAM has demonstrated its power to predict an individual’s intention to adopt new technology. The parsimonious structure of TAM allows it to be applied in any context of technology adoption, as proven by numerous studies. It is also easy to administer and test. However, as demonstrated by Davis et al. (1989a), the model can only explain variance of up to 50%, which means that half of the variance still cannot be explained by TAM. Bagozzi (2007) stated that the major weakness of TAM comes from its strength. Its parsimonious quality renders it unable to explain variations of technology adoption in the real world, a fact that has been noted by Davis et al. (1989). They suggested expanding TAM by including other factors.

In line with Davis et al. (1989a) and Bagozzi (2007) above, Goodhue (2007) has observed two blind spots of TAM. First, TAM ignores the situation after adoption, and secondly, it overlooks other factors that might influence perceived usefulness and ease of use. The first omission creates ambiguity about the relationship between adoption and performance; yet this is a crucial factor since people adopt technology in order to improve their performance. Furthermore, understanding use behaviour after the adoption stage is important for understanding how technology is diffused and how the use of technology affects future intention to adopt similar or new versions of the technology.

Different approaches have been taken to improving TAM. These include: modifying the basic structure of TAM by adding other factors to the main model (e.g. adding the

‘perceived enjoyment’ variable); extending the model to include ‘use behaviour’ (e.g. the Technology Adoption and Use Model developed by Bagozzi 2007); extending TAM backward by linking perceived usefulness and perceived ease of use with individual or situational factors (e.g. the Technology Readiness Acceptance Model introduced by Lin et al. 2007); or introducing new predictors and moderating variables into the model (e.g. the Unified Theory of Acceptance and Use of Technology developed by Venkatesh et al.

2003). Modification, forward extension and the addition of moderators to TAM are discussed first. These approaches differ from the backward extension in that the latter substantially extends the core model of TAM. This approach incorporates the complex world of human psychology, which adds a level of complexity to TAM. The forward extension is not so substantial since it only introduces one variable, i.e. use behaviour.

The above mentioned approaches used to improve TAM have also been implemented in a number of more recent studies. One that demonstrated a comprehensive modification and well-designed approach was conducted by Crabbe, Standing and Standing (2009), who investigated mobile banking adoption in Ghana. Through this research, they intended to

develop a more comprehensive model based on TAM. This study reveals how rigorous and dynamic the studies of TAM are, which are performed to enhance the power, implementability and expandibility of TAM.

2.4.4 The search for an integrative model: the Unified Theory of Acceptance and