A number of characteristics of an innovation have been found to affect large areas:
Relative advantage is the degree to which an innovation is perceived as better than the product it supersedes, or competing products. Relative advantage is typically measured in narrow economic terms – for example, cost or financial payback but non-economic factors such as convenience, satisfaction and social prestige may be equally important. The greater the perceived advantage the faster the rate of adoption.
It is useful to distinguish between the primary and secondary attributes of an innovation. Primary attributes, such as size and cost, are invariant and inherent to a specific innovation irrespective of the adopter. Secondary attributes, such as relative advantage and compatibility may vary from adopter to adopter.
Incentives may be used to promote the adoption of an innovation, by increasing the perceived relative advantage of the innovation, subsidizing trials or reducing the cost of incompatibilities.
Compatibility is the degree to which an innovation is perceived to be consistent with the existing values, experience and needs of potential adopters. There are two distinct aspects of compatibility: existing skills and practices; and values and norms. The extent to which the innovation fits the existing skills, equipment procedures and performance criteria of the potential adopter is important, and relatively easy to assess.
So-called network externalities can affect the adoption process. For example, the cost of adoption and use, as distinct from the cost of purchase, may be influenced by: the availability of information about the technology from other users, of trained skilled users, technical assistance and maintenance and of complementary innovations, both technical and organizational.
However, compatibility with existing practices may be less important than the fit with existing values and norms. Significant misalignments between an innovation and an adopting organization will require changes in the innovation or organization, or both. In the most successful cases of implementation, mutual adaptation of the innovation and organization occurs.
Complexity: Complexity is the degree to which an innovation is perceived as being difficult to understand or use. In general, innovations which are simpler for potential users to understand will be adopted more rapidly than those which require the adopter to develop new skills and knowledge.
Trialability is the degree to which an innovation can be experimented with on a limited basis. An innovation that is trialable represents less uncertainty to potential adopters, and allows learning by doing. Innovations which can be trialed will generally be adopted more quickly than those which cannot. The exception is where the undesirable consequences of an innovation appear to outweigh the desirable characteristics. In general, adopters wish to benefit from the functional effects of an innovation, but avoid any dysfunctional effects. However, where it is difficult or impossible to separate the desirable from the undesirable consequences trialability may reduce the rate of adoption.
Observability is the degree to which the results of an innovation are visible to others. The easier it is for others to see the benefits of an innovation, the more likely it will be adopted. The simple epidemic model of diffusion assumes that innovations spread as potential adopters come into contact with existing users of an innovation.
Process of Diffusion:
Research on diffusion attempts to identify what influences the rate of adoption of an innovation. Such taxonomies are fine with the benefits of hindsight but provide little guidance for future patterns of adoption.
Hundreds of marketing studies have attempted to fit the adoption of specific products. In most cases mathematical techniques can provide a relatively good fit with historical data but research has so far failed to identify robust generic models of adoption. In practice the precise pattern of adoption of an innovation will depend on the interaction of demand side and supply side factors.
Demand side model factors:
Demand side models mainly, statistical:
*Epidemic based on direct contact with or imitation of prior adopters;
*Bass, based on adopters consisting of innovators and imitators;
*Probit, based on adopters with different benefit thresholds;
Supply side models mainly sociological:
*Distributing uniformly emphasizes relative advantage of an innovation;
*Dissemination which emphasizes the availability of information;
*Utilization which emphasizes the reduction of barriers to use;
*Communication which emphasizes feedback between developers and users
The epidemic model was the earliest and is still the most commonly used. It assumes a homogeneous population of potential adopters, and that innovations spread by information transmitted by personal contact and the geographical proximity of existing and potential adopters. This model suggests that the emphasis should be on communication and the provision of clear technical and economic information. However, the epidemic model has been criticized because it assumes that all potential adopters are similar and have the same needs.