The Diffusion of Innovation, 5th ed by Everett Rogers

The diffusion model is a conceptual paradigm with relevance for many disciplines. The multidisciplinary nature of diffusion research cuts across various scientific fields. There are few disciplinary limits as to who studies innovations. One can understand the social change process more accurately if the spread of a new idea is followed over time as it courses through the structure of a social system.

The diffusion paradigm allows scholars to repackage their empirical findings in the form of higher-level generalizations of a more theoretical nature. It provided a basis for creating a coherent body of generalizations which can be applied to specific cases. Without the diffusion model, the huge body of completed research might just be a mile wide and an inch deep.

This is the implication in diffusion research that an innovation should be diffused and adopted be all members of a social system, that it should be diffused more rapidly and that it should not be re-invented or rejected. The bias leads diffusion researchers to ignore the study of ignorance about innovations, to underemphasize the rejection or discontinuance of innovations, to overlook re-invention, and to fail to study anti-diffusion programs designed to prevent the spread of bad innovations like crack cocaine and cigarettes.
Much diffusion research is funded by change agencies. They have a bias for the adopt of their innovation. In some cases the in innovation is a source of study because of its interest to the investigator. Successful diffusion leaves a trail that can be investigated unlike unsuccessful diffusion. Because of this bias we know much more about (1) the diffusion of rapidly spreading innovations than about slowly diffusing innovations, (2) adoption than about rejection, and (3) continued use rather than about discontinuance. We know more about success than failure.

One way to overcome this bias is to study the diffusion process from the early planning stages. Rearward studies by their nature only look at successful innovations. If you can study successful and unsuccessful innovations in the same social system at the same time your are more likely to eliminate this bias. We need to study the motivation of the adopters and how each re-invents an innovation. We also need to acknowledge that this bias exists.

This bias happens when individuals are blamed for social problems that are the result of the system and not the individual. An unsafe automobile can be the cause of an accident rather than the driver, for example. Late adopters and laggards are often blamed for being late to adopt an innovation. Sponsors of research may bring this bias with them. So is the view that it is easier to change an individual than a system. Individuals are also more accessible to researchers as objects of study than are systems.

Researchers should seek alternatives to using individuals as their sole units of analysis. They should explore network links to find out how information flows in the system. Researchers should keep an open mind about the causes of a social problem and should guard against accepting change agencies’ definitions of diffusion problems, which tend to be in terms of individual-blame. Social and communication structural variables, as well as intra-individual variables, should be incorporated.

One weakness of diffusion research is the dependence upon self-reported recall data from respondents as to their date of adoption. Diffusion researchers have mainly relied on one-shot surveys of respondents. Ideally they should rely on moving pictures of behavior. Alternative designs are (1) field experiments, (2) longitudinal panel studies, (3) use of archival records, and (4) case studies with data from multiple respondents.

The nature of studies in the past have lead to avoiding or ignoring the issue of causality among variables of study. Rogers recommends much greater use of field experiments in diffusion research so as to help avoid the respondent recall problem and to evaluate alternative diffusion strategies. Multiple data points are also recommended which shortens the periods for recall thus making it more accurate. Some studies also allow for point of adoption data like pharmacy records.

In under-developed countries or low socio-economic community sectors, research questions need a different focus. Criteria that guide the choice of innovations should consider public welfare, maintaining low prices for consumers, the influence of society’s social structure on individual decisions, are innovations appropriate, what are the consequence in terms of employment and migration to crowded cities. A focus of the impact on socioeconomic gaps is also appropriate. (See chapt. 11)