There are many models have been developed to understand the factors affecting the acceptance of computer technology such as Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975, Ajzen & Fishbein, 1980), Theory of Planned Behavior (TPB) (Ajzen, 1985, 1991), Technology Acceptance Model (TAM) (Davis, 1989), Decomposed Theory of Planned Behavior (DTPB) (Taylor & Todd, 1995), and Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). TRA proposes that individual beliefs influence attitudes which will create intentions that will generate behavior. There are two major factors that determine behavioral intentions which are the person’s attitude toward the behavior and subjective norms. Attitude toward the behavior refers to the person’s judgment that performing the behavior is good or bad according to his or her belief. While the subjective norms are a function of normative beliefs that reflect the person’s perception of social pressures put on him or her to perform or not to perform the behavior in question.
TPB is an expansion of the TRA. TPB includes the construct, perceived behavioral control to measure and account for the extent to which users have complete controls over their behavior. Perceived behavioral control relates to the extent to which the person believes that she or he has control over personal or external factors that may facilitate or constrain the behavioral performance.
TAM pioneered by Davis advances the TRA by postulating that perceived usefulness (PU) and perceived ease of use (PEU) are key determinants that lead to the actual usage of a particular technology or system. Perceived usefulness is the degree to which an individual believes that using a particular system would enhance his or her productivity while perceived ease of use is the degree an individual believes that using a particular system would be free of effort.
DTPB was formulated through combination of both TAM and TPB, which was intended for providing better understanding of behavioral intention by
concentrating on the factors that are likely to impact systems use. This model explores dimensions of subjective norms and perceived behavior control through decomposing them into particular belief perception whilst constructs from the innovation characteristics has also been regarded as the basis of DTPM formulation. DTPB also offer a clearer understanding of behavior and behavioral intention by giving detailed information about impacts of normative and control beliefs over system usage.
UTAUT had synthesized the eight prominent user acceptance models including the TRA, TAM, the motivational model (MM), TPB, a model combining the technology acceptance model and the theory of planned behavior (C-TAM-TPB), the model of PC utilization (MPCU) (Ronald et al., 1994; Thompson & Higgins, 1991), the innovation diffusion theory, and the social cognitive theory (SCT) (Compeau et al., 1999 and Compeau & Higgins, 1995). This model was formulated with four core determinants of intention and usage including performance expectancy, effort expectancy, and social influence and facilitating conditions and also with up to four moderators of key counting gender, experience, age and voluntariness of use. However, the authors argue that in order to strengthen this model, the culture also need to take consideration since different culture can affect the acceptance behavior among user towards the computer technology.
There are more theories that had been developed and many variables had used to evaluate the IS/IT acceptance rate. For example, the interactive model of technology acceptance and satisfaction (IMTAS) which integrate the user satisfaction with and user acceptance of IT. This model expands user satisfaction and user acceptance into SME sector simultaneously as two basic constructs of system usage behavior (as the key measure of IT success) while addresses specific characteristics of SMEs such as resource constraints, management method and direct interaction of SME users with external environments. Base on this model, user satisfaction can be influenced by user involvement, system quality, and information quality. High quality of information and system enable the user to produce good decision making, hence, increase the user satisfaction.
However, user involvement is the key determinant of user satisfaction since high user involvement allow the user involve in major area of the system which this give the user opportunity to enjoy most of the benefits of the system. User satisfaction will increase the usage of the system and the usage will be even higher if the system has high user friendliness. Another key determinant that influence the system usage in SMEs is user computer competence. User computer competence can be enhance through providing training by assuming that assistance of SMEs’ external environment have a crucial impact on the success of newly implemented IS by increasing SME user acceptance and satisfaction since SMEs are typically suffering from lack of resources such as internal expertise, knowledge and user skills. The training can influence the perceived ease of use and perceived of usefulness among user, hence, influence the user attitude and intention which than influence the actual usage of a system in SMEs.
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