Early Work of Technology Acceptance

        Innovation diffusion theory focuses on the behaviour of uncertainty reduction among potential adopters when technological innovations are introduced (Rogers 1983). IDT measures the level of technology acceptance by examining the overall innovation decision process in the adoption of a technology across various categories of adopters on the basis of the speed at which they take up innovations (Rogers 1995). Innovation diffusion theory (IDT) has four main elements, namely innovation, communication channels, time and social system (Rogers 2003). “An innovation is an idea, practice, or project that is perceived as new by an individual or other unit of adoption” (Rogers, 2003, p. 12). On the other hand, a communication channel is “a process in which participants create and share information with one another in order to reach a mutual understanding” (Rogers 2003, p. 5), while a social system is “a set of interrelated units engaged in joint problem solving to accomplish a common goal” (Rogers, 2003, p. 23).

        Rogers (2003) also introduced five attributes of innovations namely relative advantage, compatibility, complexity, trialability and observability. Relative advantage is “the degree to which an innovation is perceived as better than the idea it supersedes” (Rogers 1995, p. 250). The extent of relative advantage is often indicated in terms of economic profitability but it may be measured in other ways, such as a social perspective (Rogers 1995, p. 212). Compatibility is “the degree to which an innovation is perceived as consistent with existing values, past experiences, and needs of potential adopters” (Rogers 1995, p. 250). Rogers (1995, p. 250) posited a relationship between perceived compatibility and rate of adoption. Complexity is “the degree to which an innovation is perceived as relatively difficult to understand and use” (Rogers 1995, p. 250). He also suggested that there is a negative relationship between complexity of an innovation and its rate of adoption (Rogers 1995). Trialability is “the degree to which an innovation may be experimented with on a limited basis” (Rogers 1995, p. 251). Rogers (1995, p. 251) suggested that the perceived trialability of an innovation can increase its rate of adoption. Finally, Observability is the “degree to which the results of an innovation are visible to others” (Rogers 1995, p. 251). This also means that the perceived observability of an innovation is positively related to its rate of adoption (Rogers 1995). This model of innovation-decision (see Figure below) is counted among the best-known theories on the adoption of new technology.

        Bandura (1986) is credited with introducing social cognitive theory in his important book, The social foundations of thought and action: a social cognitive theory. SCT explains psychosocial functioning using a logic of triadic reciprocal causation involving personal determinants, behaviour and environmental influences. The way in which the results of behaviours are interpreted informs and alters people’s environments and the personal traits they possess, which in turn informs and alters their subsequent behaviours. This three-way interaction across these elements is shown in the figure below.

        Fishbein and Ajzen (1975) formulated the Theory of Reasoned Action (TRA) as a way to obtain more in-depth understanding about how attitudes and beliefs are interrelated with performance of individual intentions. TRA is an intention-based model originating from the field of social psychology. Social psychology researchers are not concerned with classifying the characteristics of a technology but are more interested in factors that determine the behaviour of a person. A general survey of current research shows that most modern research on technology adoption is premised on behavioural intentions. The TRA model has a good record in predicting and explaining a diverse array of human behaviour (Ajzen & Fishbein 1980, p. 4). The primary assumption of this model is that an individual can generally be considered  as a rational being who makes systematic use of information and considers the implications of his/her actual behaviour before engaging in a given behaviour (Ajzen & Fishbein 1980, p. 5). Subsequently, an individual’s behavioural intention is defined by two factors namely attitude towards behaviour and subjective norm. Attitude towards behaviour is “---an individual’s positive or negative feelings (evaluative affect) about performing the target behaviour” (Ajzen & Fishbein 1980, p. 216). Subjective norm define as “---a person’s perception that most people who are important to him think he should or should not perform the behaviour in question” (Fishbein & Ajzen 1975, p. 302).

        IS researchers have often utilised this theory to study the determinants of usage behaviour in IT innovations (Han 2003). A comparative study by Teo and Schaik (2012) used TRA, TPB, TAM and an integrated model to determine the most parsimonious model and assess the effect of each construct in these models on intention to use technology among pre-service teachers in Singapore. The study found that these four models succeeded in accounting for more than 50 per cent of observed variance on intention to use, even though an increase in the number of constructs did not increased their explanatory power. Between the models, little difference was found between the integrated model and the other models. The construct of attitude emerged as the most significant determinant of the intention to use technology. The same result was echoed by Liang and Yeh (2011) who found that a user’s attitude contributed to the intention to continue playing mobile games.

        The theory of planned behaviour (TPB) builds on TRA and refines its focus to provide a theoretical framework that “---dealing with behaviours over which people have incomplete volitional control” (Ajzen 1991, p. 181). It includes a third determinant called ‘perceived behavioural control’ which recognises that not all behaviours are under an individual’s volitional control (Ajzen 1991, p. 181). According to the TPB model, people’s attitudes toward behaviour, subjective norms, and perceived behavioural control can predict their intention to perform a certain behaviour (Ajzen 1991, p. 179). Attitude toward behaviour includes highly subjective behavioural elements arising from personal experiences and dispositions that influence an individual’s favourable or unfavourable evaluation using a certain technology (Ajzen 1991, p. 188). Subjective norm is “---the perceived social pressure to perform or not to perform the behaviour” (Ajzen 1991, p. 188).

        TPB has provided the theoretical foundation for 222 studies available in the Medline database, and 610 studies available in the PsycINFO database, from 1985 to January 2004 (Francis et al. 2004, p. 2). The TPB model still cannot account for a large proportion of variance in both intentions and behaviours (Baltic 2005, p. 245).

        Yi et al. (2006) integrated TAM, IDT and TPB to analyse the adoption of PDAs in medical treatment among physicians in the United States. They found that perceived usefulness, subjective norm (SN), and perceived behavioural control exert influence on usage intention, but perceived ease of use does not. Personal innovation characteristics also have an effect on perceived behavioural control, perceived ease of use and subjective norm (Yi et al. 2006). Nasri and Charfeddine (2012) found that social norm has a significant effect on adoption of internet banking in Tunisia, particularly in the early stages when users have only a limited direct experience. This study also found that the construct of perceived behavioural control influences the intention to adopt internet banking (Nasri & Charfeddine 2012).