Theoretical background

Diffusion is defined as "the process by which an innovation is communicated through certain channels over time among members of a social system" (Rogers, 1995, p. 2). Diffusion research has been conducted by researchers from a variety of different disciplines and its origins can be traced back to Tarde (1903). However, in recent years, there has been an integration of concepts and generalisations (Rogers, 1995). Although not without criticism of its application to

information technology (see Larsen and McGuire, 1998), Rogers' (1995) model of innovation diffusion is widely accepted by researchers as useful in an examination of the critical characteristics of technology adoption (Al Qirim, 2003).

According to Rogers' (1995) model, organisations within an established social environment will not all adopt a specific innovation at the same time (Beatty et al., 2001). Rogers (1995) suggests that it is possible to classify organisations into one of five adopter categories determined by their innovativeness relative to other organisations in their social system: innovators, early adopters, early majority, late majority and laggards. Thus, the first research question is: RQ1. Can adopter categories be identified within the intermediary population according to Rogers' (1995) model?

A number of factors have been shown to influence the adoption of innovations. Prior empirical research suggests four determinants of innovation adoption: the characteristics of the innovation, the characteristics of the organisation, the environmental context and the characteristics of the individual decision makers (Kaplan, 1999; Moore and Benbasat, 1996; Premkumar and Roberts, 1999; Thong, 1999; Thong and Yap, 1996). Innovation characteristics are given as: compatibility, complexity, relative advantage, trialability and observability (Rogers, 1995). Of these attributes relative advantage, compatibility and complexity have been found significantly to influence the adoption of systems technologies whilst trialability and observability have not been addressed widely in studies of IT innovation at an organisational level (Beatty et al., 2001).

In terms of organisational characteristics the most frequently measured is size - usually measured through number of employees or revenues and relates positively to adoption (Nguyen et al., 2003). For example, larger firms tend to adopt before smaller firms. Blili and Raymond (1993) recognised that small and medium-sized enterprises (SMEs) - enterprises which are not in the largest 10 to 20 percent of industry firms (OECD, 2000) - encounter unique problems in comparison with larger firms: namely limited financial resources, low skills and minimal strategic management. Additional organisational factors identified as influencing IT adoption include, top management support, quality of IS, user involvement, product champion and resources (Kwon and Zmud 1987).

External environment characteristics that may influence the adoption of IT include competitive pressure, support from technology vendors, pressure from buyers and suppliers (Premkumar and Roberts, 1999). Raymond (2001) argues for enriching Rogers' (1995) model within an interorganisational context, particularly when adoption decisions may be linked to those of business partners. In this context, channels of communication can influence adoption behaviour. Competitors, B2B vendors, suppliers and customers are all potential sources of innovation communication and as such can be viewed as change agents, who are individuals with influence over innovation decisions (Rogers, 1995). Communication channels can be classified into those that are mass media (involving a transmitting medium), those that are interpersonal (face-to-face), those that are homophilous (from a source that shares similar attributes to the individual) and those that are heterophilous (from a source that is

Tina Harrison and Kathryn Waite different or superior in terms of knowledge or skill to that of the individual) (Rogers, 1995). The influence of communication channels has been found to vary according to adopter category. For example, in the case of early adoption amongst farmers Ryan and Goss (1950) found that interpersonal homophilous sources were more important than interpersonal heterophilous sources. Finally the characteristics of the individual decision maker, such as age, experience and psychological traits have also been found to influence adoption (Rogers, 1995):

RQ2. What is the relationship between the rate of adoption as indicated by adopter categories and influencing factors such as organisational size? RQ3. Are there any individual factors that appear to be critical for web site development?

Although each characteristic may exert a separate influence on innovation adoption and diffusion, it has been shown that the combination and interplay between factors within specific contexts is also important (Al-Qirim, 2003). Rogers (1995) outlines the need for context specific research into variables related to innovation adoption, the factors that explain the rate of adoption and the role of communication channels at different stages of adoption. In the context of online banking, Bradley and Stewart (2003) highlight key factors driving banks to adopt online banking are the adoption by other banks, competitive forces, consumer demand and the availability of technology:

RQ4. Do the individual factors combine meaningfully to stimulate web site development?

Early adopters of IT can gain an advantage through early adoption, in the same sense that early entrants into a market can gain advantage (Lambkin, 1988). Early adopters are companies that have perceived the advantage to be gained by action, are likely to be forward thinking, and are less likely to be inhibited by the IT demands placed on companies by moving to the web (O'Keefe et al., 1998). Indeed, the goals and motivations for being an innovator or first adopter can have a significant impact on the decision of whether to adopt and when. Such goals might be to achieve competitive advantage or protect strategic position (Bass, 1980; Johannessen et al., 1999) often promoted by the desire for the organisation to become superior to competitors and to serve customers better or meet the demands of customers better (John and Davies, 2000). However, the extent to which adoption of an innovation can yield a competitive advantage is relatively short-lived. As the innovation becomes more widespread, competitive advantage diminishes and innovation becomes a necessary competitive requirement. It is argued that the need for innovation leads to imitation (Bradley and Stewart, 2003). Hence, the organisational goals or motivations for adopting an innovation and the subsequent use or implementation of the innovation may change over time according to adopter categories:

RQ5. Do the web site development drivers influence the timing of adoption? (i.e. are different adopter categories influenced by different critical factors?) RQ6. Does web site adoption timing influence subsequent use of the technology? (i.e. do the different adopter categories make different use of the technology ?)

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