LITERATURE REVIEW

Extensive research works on stock market have been done in field of financial science and in­formation systems. Scholars have identified the factors that influence the individual’s decision to adopt e-commerce platform for transaction.

This study will critically examine extant literatures on eight (8) influential technology ac­ceptance models as follow: Innovation Diffusion Theory (IDT), Social Cognitive Theory (SCT), Theory of Reasoned Action (TRA), Decomposed Theory of Planned Behavior (DTPB) Technology Acceptance Model (TAM), Technology Accep­tance Model 2 (TAM2), the Unified Theory of Acceptance and Use of Technology (UTAUT) and Theory of Planned Behavior (TPB). Table 1 presents a brief discussion on this:

Table 1. Summary of theories of technology acceptance

The technology acceptance and behavioral adoption model has been the main theoretical model underpinning extant works, in predicting factors influencing individual’s intention to adopt new technology. Of significance are the Theory of Reasoned Action (TRA) (Ajzen et al., 1980), Theory of Planned Behavior (TPB) (Ajzen, 1985), and Technology Acceptance Model (TAM), which has emerged to be the most important and perti­nent model. TAM primarily considers the effect of perceived ease of use and perceived usefulness in predicting individual’s intention to adopt new technology (Davis, 1989a; Davis et al.,1989b). A brief description of these intentional models is as follows.

Theory of Reasoned Action (TRA)

Fishbein and Ajzen (1975) developed the theory of reasoned action (TRA) by studying attitudi­nal and normative influences on behaviour.

Also, TRA has found wide application across diverse field. For example, researchers in social psychology have drawn on it in analyzing and comprehending individuals’ social behaviour. Basically, the theory reveals that an individual’s Behavioral Intention (BI) ascertains his specific behavior. Thus, an individual’s Attitude (At) along with the Subjective Norm (SN) ascertains the Behavioral Intention:

BI= At + SN (1)

As Ajzen (1985) asserts, an individual’s inten­tion to behave in a certain way is a linear function of two factors, which are the attitude towards behavior (At) and the subjective/social norm.

Technology Acceptance Model (TAM)

Technology Acceptance Model (TAM) was con­ceptualized to explain individuals’ behaviour when working with computers (Davis, 1989). The model offers clarification on the factors which lead to the acceptance of computer, by providing a clear account on the behaviour of the users towards adopting diverse computing technologies. TAM is actually an extension or modification of the Theory of Reasoned Action (TRA), which has been the fundamental to most studies on the adoption of Information systems (IS). Researchers have employed the model in examining the behaviour of individuals towards adopting Internet platform (Davis, 2000).

The main concept behind the model is that ‘at­titude predicts intention, while intention predicts behaviour’ (Davis, 1989a). Davis also went further to state that the behaviour of a user of a system is influenced by his or her intention to use it, which in turn affect his or her attitude (see Figure 1). Two main factors are identified as below;

1.

2. Perceived ease-of-use of the system (PEU).

Perceived Usefulness (PUS) explains how the use of a technology could enable and enhance a person’s performance on the job. On this, some researchers argue that there exists a direct link between the rate of employing an application and the successful outcome in facilitating a bet­ter performance on the job (Davis et al., 1989b; Pavlou, 2003).

Figure 1. TAM model by Davis (1989a)

Although perceived ease of use (PEU) mea­sures the user’s evaluation on whether an applica­tion can be used in his or her work, yet, Davis et al.(2000) suggested an alternative measure. They explained that PEU is actually the user’s percep­tion on how easy it was to use a given applica­tion when performing his or her work. Equally, the model also recognized the effect of external variables on the perceived ease of use (PEU) and perceived usefulness (PUS). These variables include users support, training, system attributes and documentations (Davis, 1989a).

Most of the studies on Technology Acceptance Model (TAM) have been in the field of information systems, especially in investigating the adoption of Internet, e-finance and e-commerce. Findings have also confirmed both PUS and PEU as strong predicators of attitude, while the latter has been found to be influenced by the former, as well as other extrinsic factors. On the other hand, only the extrinsic factors have been discovered to influence PUS.

TAM’s uniqueness is in the application of fewer numbers of theories to make predictions on user’s intention (Agarwal & Prasad, 1999). Several scholars have deployed the model in an attempt to identify and explain the factors, which influ­ence user’s intention to accept/adopt technology (Siriluck, 2006; Chi- Shing et al., 2007; Jeanette- Taft, 2007). Venkatesh et al. (2003) underscored the need to clarify the difference between user’s intentions to adopt and behaviours towards adopt­ing a technology.

Theory of Planned Behaviour (TPB)

Theory of Planned Behavior (TPB) gained promi­nence in the 1980s, as the most pertinent cogni­tive model to analyze and forecast individual’s distinctive behavior (Ajzen, 1985). Even though it originated from the field of social psychology, however, it has received significant attention across various fields. Fishbein & Ajzen (1975) described it has a comprehensive version of the TRA, which has contributed in resolving the latter’s limitation in explaining the spontaneous behavior of individual. TPB is based on the as­sumption that people behave according to their intentions, which are the result of their personal thoughts, standards and their behavioral control.

Behavioral intention is expressed in terms of attitude, subjective norm and perceived behav­ioural control. Succinctly, a positive attitude and favorable subjective norm along with increased perceived control are expected to exert a positive influence on a person’s intention to behave in a certain way. Thus, a combination of attitude, SN, and PBC will constitute an individual’s behavioral intention, which in turn shapes behaviour. The TPB model is shown in Figure 2:

Figure 2. Constructs of TPB (adapted from Ajzen, 1985)

Based on the relationship among attitudes, SN, PB C, intention and actual behaviour, Aj zen (1985) suggests the following equation to capture TPB;

Accordingly, TPB (Ajzen, 1985, 1991) pro­poses that intention and PBC determine human behaviour, while intention is in turn affected by AT, SN, and PBC. TPB has been employed in studying the behaviour of consumers towards online platform (Bhattacherjee, 2000a; Joo Enget al., 2003; Ramus et al., 2005; Shim et al., 2001).

Application of TAM and TPB Theories in Prior Research

Extant works on Information Systems within the financial domain have drawn from TPB and TAM in order to explain investors’ behaviours and at­titudes, relating to the adoption of online stock trading, word processing application, intentions to use Internet in financial transaction, as well as adoption of IT outsourcing (Ajzen, 2005; Lee-Pi et al., 2003; Michelle, 2005). Studies on the adop­tion of online services have also employed either TAM or TPB, in order to forecast and understand user’s insight on the choice guiding the intention to use a system (Lina Zhou, et al., 2007; Chi-Lee, 2009b). TAM and TPB have been leveraged in attempts to observe and analyze the likelihood of using the Internet for executing trading transac­tions (Lee & Turban, 2001; Tebboune, 2003). A brief summary on prior studies related to these theories is presented in Table 2.

Based on the comprehensive review of litera­ture, the need to clarify the effect of diverse factors on investor’s intention to adopt online trading ser­vice has been less explored. For instance, limited or no attempt has been made within the e-finance domain to develop an integrative model, which can be applied in assessing the effect of diverse factors in predicting user’s intention to adopt on­line stock trading. Specifically, this study aims to bridge the gap in knowledge on the antecedents of individual’s adoption of online stocking trading.