Diffusion of innovations

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Diffusion of Innovations is a theory of how, why, and at what rate new ideas and technology spread through cultures. The concept has been first studied by the French sociologist Gabriel Tarde (1890) and by German and Austrian anthropologists as Friedrich Ratzel or Leo Frobenius [1]. Its basic ‚epidemiological‘ or ‚internal-influence‘ form was described by H. Earl Pemberton[2], who provided examples of institutional diffusions as postage stamps or compulsary school laws. The publication of a study of Ryan and Gross on the diffusion of hybrid corn in Iowa[3] was the first sustainably visible contribution in a broader interest in innovations which was especially popularized by the textbook of Everett Rogers (1962), Diffusion of Innovations. He defines diffusion as "the process by which an innovation is communicated through certain channels over time among the members of a social system."[1]

Contents

[edit] History

The origins of the diffusion of innovations theory are varied and span across multiple disciplines. Rogers identifies six main traditions that impacted diffusion research: anthropology, early sociology, rural sociology, education, industrial, and medical sociology. The diffusion of Innovation theory has been largely influenced by the work of rural sociologists [4]. In the book Diffusion of Innovations, Rogers synthesizes research from over 508 diffusion studies and produces a theory for the adoption of innovations among individuals and organizations.

[edit] Elements of diffusion of innovations

The key elements in diffusion research are: the innovation, types of communication channels, time or rate of adoption, and the social system which frames the innovation decision process.

[edit] Types of innovation-decisions

There are three types of innovation-decisions within diffusion of innovations. An individual or an organization/social system bases the type of decision on whether an innovation is adopted/rejected. The three types of innovation-decisions are: Optional innovation-decisions, collective innovation-decisions, authority innovation-decisions.

Optional Innovation-Decision
This decision is made by an individual who is in some way distinguished from others in a social system.
Collective Innovation-Decision
This decision is made collectively by all individuals of a social system.
Authority Innovation-Decision
This decision is made for the entire social system by few individuals in positions of influence or power.

[edit] The adoption process

Diffusion of an innovation occurs through a five–step process. This process is a type of decision-making. It occurs through a series of communication channels over a period of time among the members of a similar social system. Ryan & Gross first indicated the identification of adoption as a process in 1943 [5]. Rogers’ categories the five stages (steps) as: awareness, interest, evaluation, trial, and adoption. It should be noted that an individual might reject an innovation at anytime during or after the adoption process. In later editions of the Diffusion of Innovations Rogers’ changes the terminology of the five stages to: knowledge, persuasion, decision, implementation, and confirmation. However the descriptions of the categories have remained similar throughout the editions.

[edit] Five stages of the adoption process

Knowledge
In this stage the individual is first exposed to an innovation but lacks information about the innovation. It should be noted that during this stage of the process the individual has not been inspired to find more information about the innovation.

Persuasion

In this stage the individual is interested in the innovation and actively seeks information/detail about the innovation.

Decision

In this stage the individual takes the concept of the innovation and weighs the advantages/disadvantages of using the innovation and decide whether to adopt or reject the innovation. Due to the individualistic nature of this stage Rogers’ notes that it is the most difficult stage to acquire empirical evidence (Rogers, 1964, p. 83).

Implementation
In this stage the individual employs the innovation on a varying degree depending on the situation. During this stage the individual determines the usefulness of the innovation and may search for further information about it.

Confirmation

Although the name of this stage may be misleading, in this stage the individual finalizes their decision to continue using the innovation and may use the innovation to its fullest potential.

[edit] Rates of adoption

The rate of adoption is defined as: the relative speed with which members of a social system adopt an innovation. It is usually measured by the length of time required for a certain percentage of the members of a social system to adopt an innovation ([6]. The rates of adoption for innovations are determined by an individual’s adopter category. In general individuals who first adopt an innovation require a shorter adoption period (adoption process) than late adapters.
Within the rate of adoption there is a point at which a innovation reaches critical mass. This is a point in time within the adoption curve that enough individuals have adopted an innovation in order that the continued adoption of the innovation is self-sustaining. In describing how an innovation reaches critical mass Rogers’ outlines several strategies in order to help a innovation reach this stage. These strategies are: have an innovation adopted by a highly respected individual within a social network, creating a instinctive desire for a specific innovation. Inject an innovation into a group of individuals who would readily use an innovation, and provide positive reactions and benefits for early adopters of an innovation.

[edit] Characteristics of innovations

Rogers’ defines several intrinsic characteristics of innovations that influence an individual’s decision to adopt or reject an innovation. The relative advantage is how improved an innovation is over the previous generation. Compatibility is the second characteristic, the level of compatibility that an innovation has to be assimilated into an individual’s life. The complexity of an innovation is a significant factor in whether it is adopted by an individual. If the innovation is too difficult to use an individual will not likely adopt it. The fourth characteristic, trialability, determines how easily an innovation may be experimented with as it is being adopted. If a user has a hard time using and trying an innovation this individual will be less likely to adopt it. The final characteristic, observability, is the extent that an innovation is visible to others. An innovation that is more visible will drive communication among the individual’s peers and personal networks and will in turn create more positive or negative reactions.

[edit] Adopter categories

Rogers' defines an adopter category as a classification of individuals within a social system on the basis of innovativeness. In the book Diffusion of Innovations Rogers' suggests a total of five categories of adopters in order to standardize the usage of adopter categories in diffusion research. It should be noted that the adoption of an innovation follows an S curve when plotted over a length of time. The categories of adopters are: innovators, early adopters, early majority, late majority, and laggards [7]

Innovators

Innovators are the first individuals to adopt an innovation. Innovators are willing to take risks, youngest in age, have the highest social class, have great financial lucidity, very social and have closest contact to scientific sources and interaction with other innovators.

Early Adopters

This is second fastest category of individuals who adopt an innovation. These individuals have the highest degree of opinion leadership among the other adopter categories. Early adopters are typically younger in age, have a higher social status, have more financial lucidity, advanced education, and are more socially forward than late adopters (Rogers, 1964, p.185).

Early Majority
Individuals in this category adopt an innovation after a varying degree of time. This time of adoption is significantly longer than the innovators and early adopters. Early Majority tend to be slower in the adoption process, have above average social status, contact with early adopters, and show some opinion leadership

Late Majority
Individuals in this category will adopt an innovation after the average member of the society. These individuals approach an innovation with a high degree of skepticism and the majority of society has to have adopted the innovation. Late Majority are typically skeptical about an innovation, have below average social status, very little financial lucidity, in contact with others in late majority and early majority, very little opinion leadership.

Laggards
Individuals in this category are the last to adopt an innovation. Unlike some of the previous categories, individuals in this category show little to no opinion leadership. These individuals typically have an aversion to change-agents and tend to be advanced in age. Laggards typically tend to be focused on “traditions”, have lowest social status, lowest financial fluidity, oldest of all other adopters, in contact with only family and close friends, very little to no opinion leadership.

[edit] Opinion leaders and communication channels

Through out the diffusion process there is evidence that not all individuals exert an equal amount of influence over all individuals. In this sense there are Opinion Leaders, leaders who are influential in spreading either positive or negative information about an innovation. Rogers relies on the ideas of Katz & Lazarsfeld and the two-step flow theory in developing his ideas on the influence of Opinion Leaders in the diffusion process [8] Opinion Leaders have the most influence during the evaluation stage of the innovation-decision process and late adopters [9]. In addition opinion leaders have a set of characteristics that set them apart from their followers and other individuals. Opinion Leaders typically have greater expose to the mass media, more cosmopolite, greater contact with change agents, more social experience and exposure, higher socioeconomic status, and are more innovative.

[edit] Diffusion in organizations

Innovations are often adopted by organizations through two types of innovation-decisions: collective innovation decisions and authority innovation decisions. The collection-innovation decision occurs when the adoption of an innovation has been made by a consensus among the members of an organization. The authority-innovation decision occurs when the adoption of an innovation has been made by very few individuals with high positions of power within an organization [10]. Unlike the optional innovation decision process, these innovation-decision processes only occur within an organization or hierarchical group. Within the innovation decision process in an organization there are certain individuals termed champions that stands behind an innovation and breaking through any opposition that the innovation may have caused. The champion within the diffusion of innovation theory plays a very similar role as to the champion used within the efficiency business model Six Sigma. The innovation process within an organization contains five stages that are slightly similar to the innovation-decision process that individuals undertake. These stages are: agenda-setting, matching, redefining/restructuring, clarifying, routinizing.

[edit] Consequences of adoption

There are both positive and negative outcomes when an individual or organization chooses to adopt a particular innovation. Rogers states that this is an area that needs further research because the biased positive attitude that is associated with the adoption of a new innovation [11]. In the Diffusion of Innovation, Rogers’ lists three categories for consequences, desirable vs. undesirable, direct vs. indirect, and anticipated vs. unanticipated.

[edit] Diffusion and management

Much of the evidence for the diffusion of innovations gathered by Rogers comes from agricultural methods and medical practice.

Various computer models have been developed in order to simulate the diffusion of innovations. Veneris[12] [13] developed a systems dynamics computer model which takes into account various diffusion patterns modeled via differential equations.

There are a number of criticisms of the model which make it less than useful for managers. First, that technologies are not static, there is continual innovation in order to attract new adopters all along the S-curve, the S-curve does not just 'happen'. Instead, the s-curve can be seen as being made up a series of 'bell curves' of different sections of a population adopting different versions of a generic innovation.

[edit] See also

[edit] References

  1. ^ see the article on Trans-cultural diffusion or Roland Burrage Dixon (1928): The Building of Cultures.
  2. ^ Pemberton, H. E. (1936) 'The Curve of Culture Diffusion Rate', American Sociological Review, 1 (4): 547-556.
  3. ^ Ryan, B. (1943). The diffusion of hybrid seed corn in two Iowa communities. Rural Sociology. 8(1), p. 15-24. The widely recognized mathematical analysis of this study is Griliches, Z. (1957) 'Hybrid Corn: An Exploration in the Economics of Technological Change', Econometrica, 25 (4): 501-522.
  4. ^ Ryan (1943), see above.
  5. ^ Rogers, Everett M. (1964). Diffusion of Innovations, Glencoe: Free Press, p. 79
  6. ^ Rogers, Everett M. (1964). Diffusion of Innovations, Glencoe: Free Press, p. 134
  7. ^ Rogers, Everett M. (1964). Diffusion of Innovations, Glencoe: Free Press, p. 150
  8. ^ Katz, Elihu & Lazarsfeld, Paul (1955). Personal influence: The part played by people in the flow of mass communications, Glencoe: Free Press
  9. ^ Rogers, Everett M. (1964). Diffusion of Innovations, Glencoe: Free Press, p. 219
  10. ^ Rogers, Everett M. (2005). Diffusion of Innovations, Glencoe: Free Press, p. 403
  11. ^ Rogers, Everett M. (2005). Diffusion of Innovations, Glencoe: Free Press, p. 470
  12. ^ Veneris, Yannis (1984). The Informational Revolution, Cybernetics and Urban Modelling, PhD Thesis. University of Newcastle upon Tyne, UK.
  13. ^ Veneris, Yannis (1990). "Modeling the transition from the Industrial to the Informational Revolution". Environment and Planning A 22 (3): 399-416. doi:10.1068/a220399.

[edit] External links

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