The Technology Acceptance Model (TAM) is the most influential and widely used model for technology acceptance (Özbek et al., 2014; George and Kumar, 2013). Although the model has become generalised as a tool for studying technology or Information System (IS) usage, even at organisational level (Lucas et al., 2007), the model was primarily developed with the
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aim of understanding why people accept or reject computers at a personal level (Sheikhshoaei and Oloumi, 2011; Lucas et al., 2007; Davis et al., 1989).
TAM was introduced by Davis in the late 1980s (Akman and Mishra, 2015; Özbek et al.
2014; Saricam, 2014; Wentzel et al., 2013; Bradley, 2012; Cua, 2012; Davis et al., 1989).
TAM was tested and validated in the stages of its development. Davis first introduced the concept of technology acceptance in his doctoral thesis at the Sloan School of Management, Massachusetts Institute of Technology (MIT) in 1986. However, the TAM model was subjected to further tests and validation in two main studies in 1989 (Davis, 1989; Davis et al., 1898). These tests confirmed the validity of the two main constructs of the model which are perceived usefulness (PU) and perceived ease of use (PEOU).
TAM is grounded in the Theory of Reason Action (TRA) (Teo and Jarupunphol, 2015). TRA is a theory based in social psychology, and was jointly formulated by Fishbein and Ajzen in 1975. TRA argues that an individual’s behaviour is a function of both the individual’s attitude toward a specific behaviour and the social influences and norms surrounding it (Zhou, 2008). The model has four main components that attempt to explain people’s use of technology. These are beliefs, attitudes (A) and subjective norms (SN), behavioural intention (BI) and actual behaviour or use (Bradley, 2012). TRA states that there is a causal connection between the four components in that beliefs shape attitudes which in turn affect behavioural intention and ultimately actual behaviour or use. A person’s attitudes are, furthermore, influenced by subjective norms which Kwon and Chidambaram (2000) have described as social pressure exerted by the person to perform (or not perform) the behaviour.
TAM differs from TRA in that it omits the SN element of TRA, and most importantly, replaces the component of beliefs with two new constructs: Perceived usefulness (PU) and perceived ease of use (PEOU). PU is defined as "the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989, p.320).
PEOU, in contrast, refers to "the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989, p.320). PU and PEOU both directly influence the user’s attitude toward using new information technology which in turn leads to the user’s behavioural intention (BI) to use (Bradley, 2012). Besides directly influencing attitude, PU also influences BI which leads to computer usage. According to Davis et al. (1989, p.986), the PU-BI relationship is based on the idea that “within organisational settings, people form
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intentions towards behaviours they believe will increase their job performance, over and above whatever positive or negative feelings may be evoked towards the behaviour per se.”
This is so because enhanced performance is instrumental to achieving various rewards that are extrinsic to the content of the work, such as pay increments and promotions.
A number of studies (Davis, 1989; Davis et al., 1989) have found that PU is more influential than PEOU in driving usage behaviour. Davis (1989) argues that usefulness is strongly linked to usage than ease of use; stating that users are driven to adopt an application primarily because of the functions it performs for them, and how easy or hard it is to get the system to perform those functions. He goes on to say that although difficulty of use can discourage adoption of an otherwise useful system, no amount of ease of use can compensate for a system that does not perform a useful function. TAM further posits that PEOU has an effect on PU but not the other way round. This finding has been confirmed in a recent study by Park et al. (2009). Conspicuously missing in TAM from the original TRA is the construct of SN.
Davis et al. (1989) justifies the decision to exclude the SN to the uncertainty of the theoretical and psychometric status of the construct.
Figure 2. 1: The Technology Acceptance Model (TAM) (Source: Davis et al., 1989).
2.2.1 Strengths of the TAM
According to Davis et al. (1989, p. 985), the goal of TAM is “to provide an explanation of the determinates of computer acceptance that is general, capable of explaining user behaviour
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across a broad range of end-user computing technologies and user populations, while at the same time being both parsimonious and theoretically justified.” To a greater extent, TAM has managed to live up to this expectation. To begin with, TAM has proven to be a powerful model in explaining usage behaviour. Legris et al. (2003) reviewed research done with this model and underscored its value for understanding the use of IT. Their findings showed that this model can predict about 40% of the factors that influence the use of IT. Another study conducted by Meister and Compeau (2002) revealed that TAM has routinely explained up to 40% of usage intentions and 30% of system usage. Similarly, Davis et al. (1989) conducted a study involving 107 MBA students at the University of Michigan to measure students’
attitudes towards a word processing software, Write One. A questionnaire was administered to the students soon after introduction to the program and at the end of the semester. The aim was to measure their attitudes towards the software using TAM and TRA. The results of the study indicated that TRA accounted for 32% of variance at the beginning of first semester and 26% of the variance at the end of second semester. TAM, on the other hand, explained 47% and 51% of BI’s variance respectively. It was again observed that TRA explained 7% of A’s variance during the first semester and 30% during the second semester whilst TAM explained 37% and 36% during these periods. Findings of these studies show that TAM is more powerful than some of its competitors, TRA in particular. Secondly, the main constructs of the model, PU and PEOU, have been tested and found to have a direct and indirect effect on both attitudes and BI hence having positive effect on actual usage (Bradley, 2012). In this, the model helps to explain user behaviour in system usage.
TAM has been described by Igbaria et al. (1995) as simple, having high validity (Chau, 1996), being parsimonious and powerful model that can be used for the explanation and prediction of usage intentions and acceptance behaviour (Yi & Hwang, 2003). Moreover, TAM has proven to have reliable and valid constructs (Chin and Todd, 1995), and is an adequate model in predicting students’ IT usage and learning satisfaction (Cheng et al., 2015). According to Saricam (2014), the advantage of TAM is the capability of the model in identifying why a particular system is not accepted and, in showing how appropriate corrective steps can be taken. This could in turn lead to wider adoption and use of information systems (IS), a development that cannot only benefit organisations, but also individuals using them. Probably the best summary of the model is the one that was provided by King and He (2006) who described TAM as powerful, highly reliable, valid and robust predictive model that may be used in a variety of contexts. The positive reviews that TAM
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has received, to a great extent, indicates that the model has largely lived up to the expectations of Davis et al. (1989) who created it.
2.2.2 Weaknesses of TAM
Although TAM and its extensions have proven over time to be the most influential technology adoption model (Wentzel et al., 2013), it has its own weaknesses. Venkatesh (2000), for instance, argued that the application of the TAM beyond the workplace raises problems because its core constructs do not fully reflect the variety of task environments and how well the technology meets the requirements of that task. Park (2010) argues further that because the original model was intended to be general and parsimonious, it lacked an ability to identify antecedent variables that could influence PU and PEOU. Moreover, TAM is limited in scope in that its two constructs mainly focus on the technology itself thereby leaving out other important aspects in the adoption process. Scholars such as Bagozzi (2007) have indicated that TAM has neglected group, social and cultural aspects of technology adoption whilst Van de Wijngaert & Bouman (2009) and Wentzel et al. (2013) found that personal characteristics, fun and context characteristics play an important role in technology adoption. The limited scope of the model is further reflected in its performance. Bradley (2012), for instance, observed that most studies reviewed based on TAM explained about 40% of the variance in usage intentions and behavior.
As previously discussed, the other weakness levelled against TAM was that it lacked an ability to identify antecedent variables that could influence PU and PEOU (Park, 2010).
Venkatesh and Davis (2000) and Venkatesh and Bala (2008) have attempted to address this weakness through the development of TAM2 and TAM3 respectively. In TAM2, Venkatesh and Davis (2000) posited that subjective norm, image, job relevance, output quality, result demonstrability, and perceived ease of use are determinants of PU. In TAM3, Venkatesh and Bala (2008) added computer self-efficacy, perceptions of external control, computer anxiety, computer playfulness, perceived enjoyment and objective usability as determinants of PEOU (Venkatesh and Bala, 2008). The expanded TAM models have proven more powerful than the original model. TAM2, for instance, accounts for 60% of the variance in the drivers of user intentions (Baker, 2010). TAM3, on the contrary, had an explanatory power of between 43% and 67% of use intention (Venkatesh and Bala, 2008). Nevertheless, UTAUT explained up to 69% of the variance in use intention. It is for these reasons that TAM and its extensions
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have been overlooked in this study as the researcher searches for a model that has greater coverage and more explanatory power.