Prescriptive models

Prescriptive models provide guidelines or frameworks to organise how consumer behaviour is structured, including the order in which the elements should appear. These models prescribe certain cause(s) to get a given effect, that is, they concentrate on what to do to get a given result. Examples of prescriptive models include the Theory of Reasoned Action

(Ajzen and Fishbein, 1980) and the Theory of Planned Behaviour (Ajzen, 1988) which, although have not having been specifically developed for studying consumer behaviour, have been widely used for this purpose. The Technology Acceptance Model (Davis, 1989), developed specifically to study user acceptance of information systems, is also prescriptive as it is based on the Theory of Reasoned Action.

2.8.1. Theories of Reasoned Action (TRA) and Planned Behaviour (TPB)

The Theory of Reasoned Action (Ajzen and Fishbein, 1980) postulates that a person’s behaviour is determined by his intention to perform the behaviour (Figure 2.9). Intention is, thus, seen as the best predictor of behaviour. This intention is, in turn, a function of two basic determinants. The first determinant is an individual’s attitude toward the behaviour.

Attitude is the person’s general feeling of favourableness or unfavourableness for that behaviour and is formed based on the person’s salient beliefs that the behaviour leads to certain outcomes and the evaluation of the outcomes. In other words, whether the outcome of his behaviour will be positive or negative. The second determinant is subjective norm and is related to the influence of the social environment on intentions and behaviour.

More specifically, it refers to the opinions of the person's social environment about him performing the behaviour. The subjective norm is a consequence of the beliefs that specific referents think about whether the individual should, or should not, perform the behaviour, as well as the motivation to comply with these referents. The relative importance of attitudinal and normative components will vary according to the intention under consideration and from one person to another (Ajzen and Fishbein, 1980). However, research suggests that most behaviour is controlled mainly by attitude than by social influence (Cooper and Donald, 2001).

In order to accommodate the influence of variables other than attitude and subjective norm, TRA suggests that additional variables, such as demographics and personality traits, influence intention. However, these variables are regarded as external to the model.

According to Ajzen and Fishbein (1980), “an external variable will have an effect on behaviour only to the extent that it influences the determinants of that behaviour” (p. 9).

Figure 2. 9: The Theory of Reasoned Action Source: Ajzen and Fishbein, 1980

One of the main criticisms to the TRA was the assumption that behaviour is volitional and under control (Ajzen, 1991). In order to overcome this criticism, Ajzen (1988) put forward the Theory of Planned Behaviour. This theory, shown in Figure 2.10, extends TRA by postulating that a third determinant (Perceived Behavioural Control) influences intention.

Perceived Behaviour Control refers to “perceived ease or difficulty of performing the behaviour”

(Ajzen, 1988; p. 132).

Figure 2. 10: The Theory of Planned Behaviour Source: Ajzen, 1980

Attitude toward the

behaviour

Subjective norm

Intention Behaviour

External Variables

Attitude toward the

behaviour

Subjective norm

Perceived behavioural

control

Intention Behaviour

External variables

2.8.2. Technology Acceptance Model

The Technology Acceptance Model (TAM) was first introduced by Davis and colleagues (Davis, 1989; Davis et al., 1989) for predicting user acceptance of information systems.

Theoretically developed upon Fishbein and Ajzen’s TRA,

“the goal of TAM is to provide an explanation of the determinants of computer acceptance that is general, capable of explaining user behaviour across a broad range of end-user computing technologies and user populations, while at the same time being parsimonious and theoretically justified” (Davis et al., 1989, p. 985).

In essence, the model posits that two variables fundamentally determine user acceptance of the technology: perceived usefulness and ease of use (Figure 2.11). Perceived usefulness is the individual’s perception that using the information system will improve his/her performance, whereas ease of use refers to the extent to which the individual expects the information system use to be free of effort (Davis, 1989; Davies et al., 1989; Keen et al., 2004). One important difference between the two variables is that usefulness refers to the outcome of using the system whereas ease of use refers to the process leading to the final outcome (Childers et al., 2001).

Figure 2. 11: Technology Acceptance Model (TAM) Source: Davis et al. (1989)

Similar to TRA, in TAM system usage is determined by behavioural intention. However, behavioural intention is jointly determined by the individual’s attitudes towards using the system and perceived usefulness, with the relative weights estimated by regression instead of self-stated evaluation weights (Davis et al., 1989).

External

The model also postulates that external variables influence internal beliefs, attitudes and intentions (Davies et al., 1989, p. 988). Yet, a recent literature review (Legris et al., 2003) concluded that there is no clear pattern with respect to the choice of the external variables considered.

The first TAM model posited that the two beliefs about using the innovation (ease of use and usefulness) impacted on intention through attitude. In this sense, this is different from Adoption of Innovations model, who postulates that beliefs about the innovation have a direct impact on the decision to adopt the innovation. However, recently Venkatesh and Davis (2000) proposed a revision of the TAM (usually referred to as TAM2) in which the attitude construct is removed so that the beliefs about ease of use and usefulness are viewed as directly influencing intention (George, 2002). Several researchers have accommodated this change (e.g. Horton et al., 2001; Liaw, 2002; Featherman and Pavlou, 2003; Pavlou, 2003; Luarn and Lin, 2005) whereas others still refer to the original model (e.g. Chen et al., 2002; Ho et al., 2003; Chen and Tan, 2004; Bruner II and Kumar, 2005).

TAM was originally put forward and tested to explain user acceptance of information systems within a work environment. However, a few researchers have attempted to suggest changes that would make it suitable for consumer research. One such initiative was the introduction of a third category of beliefs, tapping the hedonic component of the experience: perceived enjoyment or fun (e.g. Childers et al., 2001; Bruner II and Kumar, 2005). Both studies found perceived enjoyment to be a strong predictor of attitudes toward using the technology.

The TAM model has many strengths that make it potentially suitable for studying the adoption of technological innovations. It is a reliable and robust model, with empirical data extensively supporting and validating the theory (Agarwal and Prasad, 1999; Mathieson et al., 2001; Chen et al., 2002; Henderson and Divett, 2003; Legris et al., 2003; Pavlou, 2003;

Vijayasarathy, 2004; Bruner II and Kumar, 2005). Moreover, it possesses the theoretical property of parsimony (Agarwal and Prasad, 1999; Mathieson et al., 2001) and is focused on technology-based behaviours (Mathieson et al., 2001).

However, there are some limitations associated with using TAM for studying the adoption of technological innovations in a leisure context. First, most of the research has been conducted within a business environment and studies have used either students or workers

to test the model (Legris et al., 2003). Second, there is an assumption that there are no barriers to prevent an individual from using the system if he or she chose to do so (Agarwal and Prasad, 1999; Mathieson et al., 2001; Oh et al., 2003). Thus, it is assumed that the individual has the resources necessary to use, notably access to the technology. Finally, research suggests that the two sets of beliefs may not be sufficient to predict technology adoption in a leisure context, that is, in a context where usage is volitional (Vijayasarathy, 2004; Legris et al., 2003; Agarwal and Prasad, 1999). The general contention is that a richer set of beliefs, such as those found in the work of Moore and Benbasat (1991), might be more appropriate to predict acceptance.

There is some empirical evidence supporting the claims that usefulness and ease of use are not sufficient to adequately explain adoption of a technology for a leisure activity. Several researchers proposed the inclusion of additional sets of beliefs, which have resulted in more predictive models (e.g. Childers et al., 2001; Featherman and Pavlou, 2003; Chen and Tan, 2004; Bruner II and Kumar, 2005). In addition to perceived enjoyment (e.g. Al-Gahtani and King, 1999; Anandarajan et al., 2002; Liaw, 2002; Hsu and Chiu, 2001), other beliefs include social pressure (e.g. Anandarajan et al., 2002), self-efficacy (e.g. Liaw, 2002;

Hsu and Chiu, 2001) and perceived playfulness (Moon and Kim, 2001). Mathieson et al.

(2001), proposed the construct of perceived resources which overlaps with Perceived Behavioural Control of TPB. Several researchers incorporated attributes from DAI in TAM, including image (e.g. Al-Gahtani and King, 1999; Venkatesh and Davis, 2000), result demonstrability (Venkatesh and Davis, 2000; Oh et al., 2003), visibility (Oh et al., 2003), compatibility (Al-Gahtani and King, 1999; Oh et al., 2003) and trialability (Oh et al., 2003).

Therefore, it can be argued that if the researcher aims to understand the outcomes of the adoption process in a leisure context, the DAI set of beliefs is likely to be more appropriate. Not only are the two basic beliefs of TAM included, but also an array of other beliefs that have been shown to influence adoption.