Diffusion of innovations
Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread through cultures. Everett Rogers, a professor of communication studies, popularized the theory in his book Diffusion of Innovations; the book was first published in 1962, and is now in its fifth edition (2003). Rogers argues that diffusion is the process by which an innovation is communicated through certain channels over time among the participants in a social system. The origins of the diffusion of innovations theory are varied and span multiple disciplines. Rogers proposes that four main elements influence the spread of a new idea: the innovation itself, communication channels, time, and a social system. This process relies heavily on human capital. The innovation must be widely adopted in order to self-sustain. Within the rate of adoption, there is a point at which an innovation reaches critical mass. The categories of adopters are: innovators, early adopters, early majority, late majority, and laggards. Diffusion manifests itself in different ways in various cultures and fields and is highly subject to the type of adopters and innovation-decision process.
- 1 History
- 2 Elements
- 3 Decisions
- 4 Process
- 5 Rate of adoption
- 6 Adopter categories
- 7 Rogers' five factors
- 8 Failed diffusion
- 9 Heterophily and communication channels
- 10 The role of social systems
- 11 Domains
- 12 Consequences of adoption
- 13 Mathematical treatment
- 14 Criticism
- 15 See also
- 16 References
- 17 External links
The concept of diffusion was first studied by the French sociologist Gabriel Tarde in late 19th century and by German and Austrian anthropologists such as Friedrich Ratzel and Leo Frobenius. Its basic epidemiological or internal-influence form was formulated by H. Earl Pemberton, who provided examples of institutional diffusion such as postage stamps and standardized school ethics codes.
In 1962 Everett Rogers, a professor of rural sociology published his seminal work: Diffusion of Innovations. Rogers synthesized research from over 508 diffusion studies and produced a theory of the adoption of innovations among individuals and organizations.
The origins of the theory cross multiple disciplines. Rogers identifies six main traditions that impacted diffusion research: anthropology, early sociology, rural sociology, education, industrial sociology and medical sociology. The diffusion of innovation theory has been largely influenced by the work of rural sociologists. In 1971, Rogers published a follow-up work: Communication of Innovations; A Cross-Cultural Approach, building on his original theory on the diffusion process by evaluating social systems.
The key elements in diffusion research are:
|Innovation||"an idea, practice, or object that is perceived as new by an individual or other unit of adoption".|
|Communication channels||"the means by which messages get from one individual to another".|
|Time||"The innovation-decision period is the length of time required to pass through the innovation-decision process". "Rate of adoption is the relative speed with which an innovation is adopted by members of a social system".|
|Social system||"a set of interrelated units that are engaged in joint problem solving to accomplish a common goal".|
Two factors determine what type a particular decision is:
- Whether the decision is made freely and implemented voluntarily
- Who makes the decision.
Based on these considerations, three types of innovation-decisions have been identified.
|Optional Innovation-Decision||made by an individual who is in some way distinguished from others.|
|Collective Innovation-Decision||made collectively by all participants.|
|Authority Innovation-Decision||made for the entire social system by individuals in positions of influence or power.|
Diffusion occurs through a five–step decision-making process. It occurs through a series of communication channels over a period of time among the members of a similar social system. Ryan and Gross first identified adoption as a process in 1943. Rogers' five stages (steps): awareness, interest, evaluation, trial, and adoption are integral to this theory. An individual might reject an innovation at any time during or after the adoption process. Abrahamson examined this process critically by posing questions such as: How do technically inefficient innovations diffuse and what impedes technically efficient innovations from catching on? Abrahamson makes suggestions for how organizational scientists can more comprehensively evaluate the spread of innovations. In later editions of Diffusion of Innovation, Rogers changes his terminology of the five stages to: knowledge, persuasion, decision, implementation, and confirmation. However, the descriptions of the categories have remained similar throughout the editions.
|Knowledge||The individual is first exposed to an innovation, but lacks information about the innovation. During this stage the individual has not yet been inspired to find out more information about the innovation.|
|Persuasion||The individual is interested in the innovation and actively seeks related information/details.|
|Decision||The individual takes the concept of the change and weighs the advantages/disadvantages of using the innovation and decides whether to adopt or reject the innovation. Due to the individualistic nature of this stage, Rogers notes that it is the most difficult stage on which to acquire empirical evidence.|
|Implementation||The individual employs the innovation to a varying degree depending on the situation. During this stage the individual also determines the usefulness of the innovation and may search for further information about it.|
|Confirmation||The individual finalizes his/her decision to continue using the innovation. This stage is both intrapersonal (may cause cognitive dissonance) and interpersonal, confirmation the group has made the right decision.|
Rate of adoption
The rate of adoption is defined as the relative speed at which participants adopt an innovation. Rate is usually measured by the length of time required for a certain percentage of the members of a social system to adopt an innovation . 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) when compared to late adopters.
Within the adoption curve at some point the innovation reaches critical mass. This is when the number of individual adopters ensures that the innovation is self-sustaining. Illustrating how an innovation reaches critical mass,
Rogers outlines several strategies in order to help an innovation reach this stage, including when an innovation adopted by a highly respected individual within a social network and creating an instinctive desire for a specific innovation. Another strategies includes injecting an innovation into a group of individuals who would readily use said technology, as well as providing positive reactions and benefits for early adopters.
Diffusion vs adoption
Adoption is an individual process detailing the series of stages one undergoes from first hearing about a product to finally adopting it. Diffusion signifies a group phenomena, which suggests how an innovation spreads.
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. 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  In addition to the gatekeepers and opinion leaders who exist within a given community, change agents may come from outside the community. Change agents bring innovations to new communities– ﬁrst through the gatekeepers, then through the opinion leaders, and so on through the community.
|Innovators||Innovators are willing to take risks, have the highest social status, have financial liquidity, are social and have closest contact to scientific sources and interaction with other innovators. Their risk tolerance allows them to adopt technologies that may ultimately fail. Financial resources help absorb these failures. |
|Early adopters||These individuals have the highest degree of opinion leadership among the adopter categories. Early adopters have a higher social status, financial liquidity, advanced education and are more socially forward than late adopters. They are more discreet in adoption choices than innovators. They use judicious choice of adoption to help them maintain a central communication position.|
|Early Majority||They adopt an innovation after a varying degree of time that is significantly longer than the innovators and early adopters. Early Majority have above average social status, contact with early adopters and seldom hold positions of opinion leadership in a system (Rogers 1962, p. 283)|
|Late Majority||They adopt an innovation after the average participant. These individuals approach an innovation with a high degree of skepticism and after the majority of society has adopted the innovation. Late Majority are typically skeptical about an innovation, have below average social status, little financial liquidity, in contact with others in late majority and early majority and little opinion leadership.|
|Laggards||They 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. Laggards typically tend to be focused on "traditions", lowest social status, lowest financial liquidity, oldest among adopters, and in contact with only family and close friends.|
|Leapfroggers||When resistors upgrade they often skip several generations in order to reach the most recent technologies.|
Rogers' five factors
Rogers defines several intrinsic characteristics of innovations that influence an individual’s decision to adopt or reject an innovation.
|Relative advantage||How improved an innovation is over the previous generation.|
|Compatibility||The level of compatibility that an innovation has to be assimilated into an individual’s life.|
|Complexity or simplicity||If the innovation is perceived as complicated or difficult to use, an individual is unlikely to adopt it.|
|Trialability||How easily an innovation may be explored. If a user is able to test an innovation, the individual will be more likely to adopt it.|
|Observability||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.|
Rogers discussed a situation in Peru involving the implementation of boiling drinking water to improve health and wellness levels in the village of Los Molinas. The residents had no knowledge of the link between sanitation and illness. The campaign worked with the villagers to try to teach them to boil water, burn their garbage, install latrines and report cases of illness to local health agencies. In Los Molinas, a stigma was linked to boiled water as something that only the "unwell" consumed, and thus, the idea of healthy residents boiling water prior to consumption was frowned upon. The two-year educational campaign was considered to be largely unsuccessful. This failure exemplified the importance of the roles of the communication channels that are involved in such a campaign for social change. Burt looked at the process of diffusion in El Salvador and asked whether a differential influence exercised by social integration on participation in the diffusion process was more significant than that exerted by other important diffusion relevant variables? 
Heterophily and communication channels
Lazarsfeld and Merton first called attention to the principles of homophily and its opposite, heterophily. Using their definition, Rogers defines homophily as "the degree to which pairs of individuals who interact are similar in certain attributes, such as beliefs, education, social status, and the like". When given the choice, individuals usually choose to interact with someone similar to themselves, homophilous individuals engage in more effective communication because their similarities lead to greater knowledge gain as well as attitude or behavior change. However, most participants in the diffusion of innovations are heterophilous, meaning they speak different languages, so to speak. The problem is that diffusion requires a certain degree of heterophily; if two individuals are identical, no diffusion occurs because no new information can be exchanged. Therefore, an ideal situation would involve two individuals who are homophilous in every way, except in knowledge of the innovation.
Not all individuals exert an equal amount of influence over others. In this sense opinion leaders 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.
Opinion leaders have the most influence during the evaluation stage of the innovation-decision process and on late adopters. In addition opinion leaders typically have greater exposure to the mass media, more cosmopolitan, greater contact with change agents, more social experience and exposure, higher socioeconomic status, and are more innovative than others.
Research was done in the early 1950s at the University of Chicago attempting to assess the cost-effectiveness of broadcast advertising on the diffusion of new products and services. The findings were that opinion leadership tended to be organized into a hierarchy within a society, with each level in the hierarchy having most influence over other members in the same level, and on those in the next level below it. The lowest levels were generally larger in numbers and tended to coincide with various demographic attributes that might be targeted by mass advertising. However, it found that direct word of mouth and example were far more influential than broadcast messages, which were only effective if they reinforced the direct influences. This led to the conclusion that advertising was best targeted, if possible, on those next in line to adopt, and not on those not yet reached by the chain of influence.
Other research relating the concept to public choice theory finds that the hierarchy of influence for innovations need not, and likely does not, coincide with hierarchies of official, political, or economic status. Elites are often not innovators, and innovations may have to be introduced by outsiders and propagated up a hierarchy to the top decision makers.
Prior to the introduction of the Internet, it was argued that social networks had a crucial role in the diffusion of innovation particularly tacit knowledge in the book The IRG Solution – hierarchical incompetence and how to overcome it. The book argued that the widespread adoption of computer networks of individuals would lead to much better diffusion of innovations, with greater understanding of their possible shortcomings and the identification of needed innovations that would not have otherwise occurred – the relevance paradox. The social model proposed by Ryan and Gross is expanded by Valente who uses social networks as a basis for adopter categorization instead of solely relying on the system-level analysis used by Ryan and Gross. Valente also looks at an individual's personal network, which is a different application than the organizational perspective espoused by many other scholars.
Innovations are often adopted by organizations through two types of innovation-decisions: collective innovation decisions and authority innovation decisions. The collective decision occurs when adoption is by consensus. The authority decision occurs by adoption among very few individuals with high positions of power within an organization. Unlike the optional innovation decision process, these decision processes only occur within an organization or hierarchical group. Within an organization certain individuals are termed "champions" who stand behind an innovation and break through opposition. The champion plays a very similar role as the champion used within the efficiency business model Six Sigma. The process contains five stages that are slightly similar to the innovation-decision process that individuals undertake. These stages are: agenda-setting, matching, redefining/restructuring, clarifying and routinizing.
The theories of diffusion have spread beyond the original domain. In the case of political science and administration, policy diffusion focuses on how institutional innovations are adopted by other institutions, at the local, state or country level. An alternative term is 'policy transfer' where the focus is more on the agents of diffusion such as in the work of Diane Stone.
The first interests with regards to policy diffusion were focused in time variation or state lottery adoption, but more recently interest has shifted towards mechanisms (emulation, learning and coercion) or in channels of diffusion  where researchers find that regulatory agency creation is transmitted by country and sector channels.
Peres, Muller and Mahajan suggested that diffusion is "the process of the market penetration of new products and services that is driven by social inﬂuences, which include all interdependencies among consumers that affect various market players with or without their explicit knowledge".
Eveland evaluated diffusion from a phenomenological view, stating, “Technology is information, and exists only to the degree that people can put it into practice and use it to achieve values”
Diffusion of existing technologies has been measured using "S curves". These technologies include radio, television, VCR, cable, flush toilet, clothes washer, refrigerator, home ownership, air conditioning, dishwasher, electrified households, telephone, cordless phone, cellular phone, per capita airline miles, personal computer and the Internet. This data can act as a predictor for future innovations.
Consequences of adoption
Both positive and negative outcomes are possible when an individual or organization chooses to adopt a particular innovation. Rogers states that this area needs further research because of the biased positive attitude that is associated with innovation. Rogers lists three categories for consequences: desirable vs. undesirable, direct vs. indirect, and anticipated vs. unanticipated.
In contrast Wejnert details two categories: public vs. private and benefits vs. costs.
Public versus private
Public consequences comprise the impact of an innovation on those other than the actor, while private consequences refer to the impact on the actor. Public consequences usually involve collective actors, such as countries, states, organizations or social movements. The results are usually concerned with issues of societal well-being. Private consequences usually involve individuals or small collective entities, such as a community. The innovations are usually concerned with the improvement of quality of life or the reform of organizational or social structures.
Benefits versus costs
The benefits of an innovation obviously are the positive consequences, while the costs are the negative. Costs may be monetary or nonmonetary, direct or indirect. Direct costs are usually related to financial uncertainty and the economic state of the actor. Indirect costs are more difficult to identify. An example would be the need to buy a new kind of pesticide to use innovative seeds. Indirect costs may also be social, such as social conflict caused by innovation. Marketers are particularly interested in the diffusion process as it determines the success or failure of a new product. It is quite important for a marketer to understand the diffusion process so as to ensure proper management of the spread of a new product or service.
The diffusion of an innovation typically follows an S shaped curve which often resembles a logistic function. Mathematical programming models such as the S-D model apply the diffusion of innovations theory to real data problems.
Complex Systems models
Complex network models can also be used to investigate the spread of innovations among individuals connected to each other by a network of peer-to-peer influences, such as in a physical community or neighborhood.
Such models represent a system of individuals as nodes in a network (or graph). The interactions that link these individuals are represented by the edges of the network and can be based on the probability or strength of social connections. In the dynamics of such models, each node is assigned a current state, indicating whether or not the individual has adopted the innovation, and model equations describe the evolution of these states over time.
In threshold models the uptake of technologies is determined by the balance of two factors: the (perceived) usefulness (sometimes called utility) of the innovation to the individual as well as barriers to adoption, such as cost. The multiple parameters that influence decisions to adopt, both individual and socially motivated, can be represented by such models.
Computer models have been developed to investigate the balance between the social aspects of diffusion and perceived intrinsic benefit to the individuals. When the effect of each individual node was analyzed along with its influence over the entire network, the expected level of adoption was seen to depend on the number of initial adopters and the network's structure and properties. Two factors emerged as important to successful spread of the innovation: the number of connections of nodes with their neighbors and the presence of a high degree of common connections in the network (quantified by the clustering coefficient).
Using the logistic function, researchers were able to provide new insight into market penetration, saturation and forecasting the diffusion of various innovations, infrastructures and energy source substitutions, Kondretiev waves,
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 diffusion. Veneris developed a systems dynamics computer model that considers various diffusion patterns modeled via differential equations.
A number of criticisms imply limits to its usefulness for managers. First, technologies are not static. Continual innovation attracts new adopters all along the S-curve. The S-curve does not just 'happen'. Instead, the s-curve may be made up of a series of 'bell curves' at different sections of a population adopting different versions of a generic innovation.
Rogers placed the contributions and criticisms of diffusion research into four categories: pro-innovation bias, individual-blame bias, recall problem, and issues of equality.
One of the cons of the approach is that the communication process involved is a one-way information flow. The message sender has a goal to persuade the receiver, and there is little to no reverse flow. The person implementing the change controls the direction and outcome of the campaign. In some cases, this is the best approach, but other cases require a more participatory approach.
- Central media
- Collaborative innovation network
- Critical mass (sociodynamics)
- Delphi technique
- Hierarchical incompetence
- Hierarchical organization
- Information Revolution
- Information Routing Group
- Interlock diagram
- Interlock research
- Lateral communication
- Lateral diffusion
- Lateral media
- Lazy User Model
- Opinion leadership
- Pro-innovation bias
- Public Choice Theory
- Relevance paradox
- Sociological theory of diffusion
- Tacit knowledge
- Technological revolution
- The Wisdom of Crowds
- Diane Stone, ‘Transfer Agents and Global Networks in the ‘Transnationalisation’ of Policy’, Journal of European Public Policy, 11(3) 2004: 545–66.
- Diane Stone, ‘Non-Governmental Policy Transfer: The Strategies of Independent Policy Institutes’, Governance: An International Journal of Policy and Administration, 13 (1) 2000: 45–70.
- Diane Stone, ‘Learning Lessons and Transferring Policy Across Time, Space and Disciplines’, Politics, 19 (1) 1999: 51–59.
- David L. Loudon & Albert J. Della Bitta, Consumer Behaviour (fourth edition), 1993.
- Wejnert, Barbara (2002). "Integrating Models of Diffusion of Innovations: A Conceptual Framework". Annual Review of Sociology (Annual Reviews) 28: 297–306. doi:10.1146/annurev.soc.28.110601.141051. JSTOR 3069244.
- Diffusion of Innovations, 5th Edition. Simon and Schuster. 16 August 2003. ISBN 978-0-7432-5823-4.
|last1=in Authors list (help)
- Rogers 1962, p. 150.
- Kinnunen, J. (1996). "Gabriel Tarde as a Founding Father of Innovation Diffusion Research". Acta Sociologica 39 (4): 431. doi:10.1177/000169939603900404.
- Spier, L. (1929). "The Building of Cultures.RolandB. Dixon". American Anthropologist 31: 140. doi:10.1525/aa.1929.31.1.02a00130.
- Pemberton, H. E. (1936) 'The Curve of Culture Diffusion Rate', American Sociological Review, 1 (4): 547–556.
- "institutional diffusion | World Bank Blogs". Blogs.worldbank.org. 2009-11-16. Retrieved 2014-07-17.
- Ryan, B.; Gross, N. (1943). "The diffusion of hybrid seed corn in two Iowa communities". Rural Sociology 8, (1): 15–24.
- Communication of innovations: a cross-cultural approach. Free Press. 1971.
|last1=in Authors list (help);
|last2=in Authors list (help)
- Rogers 1983, p. 11.
- Rogers 1983, p. 17.
- Rogers 1983, p. 21.
- Rogers 1983, p. 21, 23.
- Rogers 1983, p. 24.
- Rogers 1962, p. 79.
- Newell, S. (2001). "Management Fads and Fashions". Organization 8 (1): 5–15. doi:10.1177/135050840181001.
- Rogers 1962, p. 83.
- Rogers 1962, p. 134.
- Fisher, J.C. (1971). "A simple substitution model of technological change". Technological Forecasting and Social Change 3: 75–88. doi:10.1016/S0040-1625(71)80005-7.
- Rogers 1962, p. 282.
- Rogers 1962, p. 283.
- Burt, R. S. (1973). "The differential impact of social integration on participation in the diffusion of innovations". Social Science Research 2 (2): 125. doi:10.1016/0049-089X(73)90015-X.
- Rogers 1983, p. 18.
- Rogers 1983, p. 19.
- Personal Influence, the Part Played by People in the Flow of Mass Communications. Transaction Publishers. 1970. ISBN 978-1-4128-3070-6.
|last1=in Authors list (help);
|last2=in Authors list (help)
- Rogers 1962, p. 219.
- Radford, Scott K. (2011). "Linking Innovation to Design: Consumer Responses to Visual Product Newness". Journal of Product Innovation Management 28 (s1): 208–220. doi:10.1111/j.1540-5885.2011.00871.x.
- Economic policy making in evolutionary perspective, by Ulrich Witt, Max-Planck-Institute for Research into Economic Systems.
- The IRG Solution: Hierarchical Incompetence and how to Overcome it. Souvenir Press. 1 January 1984. ISBN 978-0-285-62662-1.
|last1=in Authors list (help)
- Valente, T. W. (1996). "Social network thresholds in the diffusion of innovations". Social Networks 18: 69. doi:10.1016/0378-8733(95)00256-1.
- Rogers 2003, p. 403.
- Berry, Frances Stokes (1990). "State Lottery Adoptions as Policy Innovations: An Event History Analysis". The American Political Science Review 84 (2): 395. doi:10.2307/1963526.
- Simmons, B. A.; Elkins, Z. (2004). "The Globalization of Liberalization: Policy Diffusion in the International Political Economy". American Political Science Review 98. doi:10.1017/S0003055404001078.
- Fabrizio Gilardi (July 2010). "Who Learns from What in Policy Diffusion Processes?". American Journal of Political Science 54 (3): 650–666. doi:10.1111/j.1540-5907.2010.00452.x.
- Jordana, J. (2011). "The Global Diffusion of Regulatory Agencies: Channels of Transfer and Stages of Diffusion". Comparative Political Studies 44 (10): 1343–1369. doi:10.1177/0010414011407466.
- Peres, Renana (2010). "Innovation diffusion and new product growth models: A critical review and research directions". International Journal of Research in Marketing 27 (2): 91–106. doi:10.1016/j.ijresmar.2009.12.012.
- Eveland, J.D. (1986). "Diffusion, Technology Transfer and Implementation". Knowledge Creation, Diffusion, Utilization 8 (2): 303–22.
- Moore, Stephen; Simon, Julian (Dec 15, 1999). "The Greatest Century That Ever Was: 25 Miraculous Trends of the last 100 Years, The Cato Institute: Policy Analysis, No. 364".
- Grübler, Arnulf (1990). "The Rise and Fall of Infrastructures: Dynamics of Evolution and Technological Change in Transport". Heidelberg and New York: Physica-Verlag.
- Rogers 2005, p. 470.
- Wejnert 2002, p. 299.
- Wejnert 2002, p. 301.
- Hochbaum, Dorit S. (2011). "Rating Customers According to Their Promptness to Adopt New Products". Operations Research 59 (5): 1171–1183. doi:10.1287/opre.1110.0963.
- What Math Can Tell Us About Technology's Spread Through Cities.
- How does innovation take hold in a community? Math modeling can provide clues
- Watts, D. J. (2002). "A simple model of global cascades on random networks". Proceedings of the National Academy of Sciences 99 (9): 5766–5720. doi:10.1073/pnas.082090499.
- McCullen, N. J. (2013). "Multiparameter Models of Innovation Diffusion on Complex Networks". SIAM Journal on Applied Dynamical Systems 12 (1): 515–532. doi:10.1137/120885371.
- Ayres, Robert (1989). "Technological Transformations and Long Waves".
- Grübler, Arnulf (1990). The Rise and Fall of Infrastructures: Dynamics of Evolution and Technological Change in Transport. Heidelberg and New York: Physica-Verlag. pp. 12–25.
- Marchetti, Cesare (1996). "Pervasive Long Waves: Is Society Cyclotymic".
- Veneris, Y. (1990). "Modelling the transition from the industrial to the informational revolution". Environment and Planning A 22 (3): 399. doi:10.1068/a220399.
- Giesler, Markus (2012). "How Doppelgänger Brand Images Influence the Market Creation Process: Longitudinal Insights from the Rise of Botox Cosmetic". Journal of Markeing 76 (6): 55–68. doi:10.1509/jm.10.0406.
- The Diffusion Simulation Game, about adopting an innovation in education.
- The Pencil Metaphor on diffusion of innovation particularly ICT in education.
- Diffusion of Innovations, by Jon Roland, summarizing ideas in the field.
- Diffusion of Innovations,Strategy and Innovations. The D.S.I Framework by Francisco Rodrigues Gomes, Academia.edu share research.