Recent examples of TIB research

The TIB has received little attention in energy research, however support for its use can be found from other research disciplines. The health sciences have used the TIB more widely and studies have shown the model to be effective for a range of social behaviours

(Winzenberg and Higginbotham, 2003). Research by Gagnon, Godin, Gagné, Fortin, Lamothe, Reinharz and Cloutier (2003) concluded that the TIB had advantages over other models due to its more comprehensive approach and Gagnon, Sánchez and Pons (2006) supported the use of the TIB, because it could be applied to a variety of situations in the field of implementation science.

Valois, Desharnais, and Godin (1988) compared predictive strength of the TRA and the TIB, in respect to exercise intention and behaviour, and found the TIB to be a better approach to understanding exercise intentions. Interestingly, the research found that affect had an important influence on intention, and concluded that “the emotional dimension of attitude is the main aspect to consider in the development of health

promotion interventions” (Valois et al., 1988 p470). Boots and Treloar (2000) applied the TIB to investigate the prediction of medical interns‟ attendance of an educational

programme. The research found that the intention to attend was largely predicted by the perceived benefits from the programme, but actual attendance was best predicted by facilitating conditions and habit (Boots and Treloar, 2000). Research by Winzenberg and Higginbotham (2003) investigated factors affecting the intention of educational providers to deliver effective medical education to general practitioners. The types of factors identified through the qualitative research were consistent with the TIB, which provided additional support for the use of the model in future health research (Winzenberg and Higginbotham, 2003).

In the field of environmental psychology, Bamberg and Schmidt (2003) compared the predictive strength of the TPB, NAM and the TIB for travel mode choice and found that the

85

inclusion of habit made the TIB a better predictor of behaviour than either the TPB or NAM. The NAM model explained 14% of the behavioural variance, whereas intention, from the TIB and the TPB, explained around 45% of the variance. After controlling the effect of intention, habit had “a significant, even stronger effect on behaviour” (Bamberg and Schmidt, 2003 p279). Bamberg and Schmidt concluded that car use is a habitual choice process that, rooted in past conscious considerations, usually involves routine shaped automatic associations between situations and habitually chosen options (Bamberg and Schmidt, 2003).

A number of information systems (IS) studies, in fields such as organisational and

managerial economics, provide findings relevant to the TIB and ICE appliance use. Often this branch of economics has used the Technology Acceptance Model (TAM), and its successor TAM2, which are based on the TRA (shown in Figure 3-6 and Figure 3-7) (Legris, Ingham, and Collerette, 2003). However, the TAM models are subject to the same criticisms of rational choice theory, so a number of studies have applied the TIB.

Figure 3-6 Technology Acceptance Model (Legris et al., 2003 p193)

86

Figure 3-7 Technology Acceptance Model 2 (Legris et al., 2003 p200)

Paré and Elam (1995) used the TIB to investigate the discretionary use of computers by knowledge workers. The research found that perceived consequences, affect (anxiety) towards computer use, internal beliefs and habits were dominant factors for the prediction of computer usage. Due to constraints to the study, not all of the factors in the TIB could be investigated and the results could only explain 30% of usage variance. Thus, the authors conclude that future research should include more elements of the TIB (Paré and Elam, 1995). Paré and Elam (1995) also compare their results to similar research by Thompson, Higgins and Howell (1991). Paré and Elam argue that both sets of results

“confirm that Triandis‟ theory of behaviour should be applied for understanding and explaining computer usage behaviour in a voluntary environment” (Paré and Elam, 1995 p226).

Cheung, Chang and Lai (2000) adapted the TIB to help investigate Internet usage at work.

The study excluded the habit construct and used the social factors construct as a direct

87

influence on Internet use and an indirect influence on affect. The construct complexity (the opposite of perceived ease of use in TAM) was also included. In contrast to other

research in this field, Cheung et al. (2000) found that facilitating conditions (i.e. IS support) had the most significant effect on Internet use in the workplace. The research found that social factors had the second most significant role, suggesting that a social environment, which encourages the use of the Internet, makes individuals feel more positive about its use (Cheung et al., 2000). Interestingly, Cheung et al. concluded that the positive impact from the combination of social pressure and the near-term consequences (e.g. usefulness of the Internet) resulted in users‟ affect (i.e. enjoyment) being a less important factor.

Cheung et al. also found that complexity had a significant negative effect on direct Internet use and indirectly through affect and short and near term consequences. Thus, ease of use appears to increase the use of the Internet.

Similar research by Chang and Cheung (2001) investigated graduate students‟ intention to use the Internet. The adapted model found that affect, social factors, facilitating conditions and near-term consequences had positive impacts on intention to use the Internet.

Interestingly, affect was found to be the most important factor in the formation of students‟

intention. Chang and Cheung (2001) also found that complexity had a significant negative indirect effect on students‟ intention to use the Internet. This finding supports previous IS research regarding the „ease of use‟ construct in the TAM and TAM2 (Legris et al., 2003).

More recent research by Bina, Karaiskos and Giaglis (2007) investigated the adoption of mobile data services (MDS) with the TIB and an additional “ease of use” construct taken from TAM. The research found that facilitating conditions (e.g. financial barriers) were of particular significance and concluded that the TIB‟s generic framework provided a useful means to cover the multiplicity of MDS features and specific usage characteristics. The construct of ease of use can also be found in earlier diffusion research under the guise of complexity (Rogers and Shoemaker, 1971). This branch of social research provides a large body of empirical studies that has focused on the adoption of technology. The literature review found that diffusion theory was of particular relevance to this thesis and is described in the following section.

88 3.7.5 Summary of the TIB

Unlike other psychological models the TIB “captures many of the criticisms levelled at rational choice theory” (Jackson, 2005 p95). The examples above highlight that this enables the framework to be adapted to a range of behavioural circumstances, which includes the adoption and use of new technologies. The TIB is one of the few theories to incorporate emotional, habitual, social and contextual factors, alongside the constructs found in rational choice derived models. The TIB‟s habit and contextual constructs also allow the model to link “to people‟s everyday consuming behaviours” (Martiskainen, 2007 p23). Thus, the TIB relates closely to elements of practice theory and traditional

psychological constructs. Jackson provides the following words to succinctly describe the model.

In summary, my behaviour in any particular situation is, according to Triandis, a function partly of what I intend, partly of my habitual responses, and partly of the situational

constraints and conditions under which I operate. My intentions in their turn are influenced by social, normative and affective factors as well as by rational deliberations. I am neither fully deliberative, in Triandis‟ model, nor fully automatic. I am neither fully autonomous nor entirely social. My behaviours are influenced by my moral beliefs, but the impact of these is moderated both by my emotional drives and my cognitive limitations.

(Jackson, 2005 p 95)

89 3.8 Diffusion of Innovations Theory (DIT)

There is a recognised body of research that has focussed specifically on the adoption of new technologies. Much of this research has been undertaken within the established framework of DIT, which is accredited to the work of Rogers and Shoemaker (1971). As a result of further empirical research, there have been a number of revisions to the theory and the most recent, presented by Rogers (2003), reveals a number of issues particularly relevant to this thesis. Rogers describes diffusion as “the process in which an innovation is communicated through certain channels over time among the members of a social system” (Rogers, 2003 p5). Thus, DIT is a social theory concerned with the spread of new ideas, products and social practices, throughout a society or from one society to another (Anable et al., 2006). This is particularly relevant to ICE appliance adoption due to the continuous integration of new technologies into the domestic environment. DIT uses four key constructs to explain the diffusion process: (i) the innovation; (ii) communication channels; (iii) time; (iv) the social system (Rogers, 2003). An overview is provided below in Figure 3-8.

90 Figure 3-8 Diffusion of Innovations Theory (Rogers, 2003)

DIFFUSION OF INNOVATIONS

91 3.8.1 The Innovation

An innovation is defined as “an idea, practice, or object that is perceived as new by an individual or other unit of adoption” (Rogers, 2003 p12). An important aspect of the diffusion of an innovation is the newness of the idea which Rogers argues provides diffusion with a special character. If an individual perceives that an idea is new, then it is an innovation, even though the idea may have been established some time previously.

Consequently, the newness element of an innovation causes a level of uncertainty, and perceived risk, within the process of diffusion (Rogers, 2003). In order for an adopter to overcome this uncertainty there is an important element of information gathering within the decision to adopt. It follows that the quality, availability and the way that information is communicated plays a significant role in diffusion.

DIT applies five key characteristics to explain why there are differences in the rate of diffusion (see points 1-5 below). Innovations that are perceived to possess these characteristics will be adopted more rapidly than other innovations (Rogers, 2003).

1. Relative advantage is the “degree to which an innovation is perceived to be better than the idea it supersedes” (Rogers and Shoemaker, 1971 p22). Relative advantage is often expressed in terms of economic benefit, but it can take a variety of other social forms. All that matters “is whether an individual perceives the innovation

advantageous” (Rogers, 2003 p15).

2. Compatibility is the “degree to which an innovation is perceived as being consistent with the existing values, past experiences, and needs of the receivers” (Rogers and Shoemaker, 1971 p22). Compatibility is fundamentally linked to the social system. If an innovation is incompatible with social values and norms, it will not be adopted as rapidly as an innovation that is considered to be compatible (Rogers, 2003).

3. Complexity is the “degree to which an innovation is perceived as difficult to understand and use” (Rogers and Shoemaker, 1971 p22). Thus, innovations that are more readily

92

comprehended by the members of a social system will be adopted more easily (Rogers, 2003).

4. Trialability is “the degree to which an innovation may be experimented with on a limit basis” (Rogers and Shoemaker, 1971 p23). The process of experimentation (e.g.

adoption through an instalment plan) enables adopters to answer questions of

uncertainty, as it is possible to learn by doing without full commitment (e.g. full financial investment).

5. Observability is “the degree to which the results of an innovation are visible to others”

(Rogers and Shoemaker, 1971 p23). The more easily potential adopters can see the results of an innovation, the more likely they are to adopt. Rogers (2003) highlights that visibility stimulates the discussion of new ideas which facilitates the transfer of information.

Rogers (2003) contends that 49%-87% of the variance in the rate of adoption can be explained by the perceived attributes, which suggests that it is prudent to include this aspect of DIT to investigate the adoption of ICE appliances.