Sociological theory of diffusion

Last updated

The sociological theory of diffusion is the study of the diffusion of innovations throughout social groups and organizations. The topic has seen rapid growth since the 1990s, reflecting curiosity about the process of social change and "fueled by interest in institutional arguments and in network and dynamic analysis." [1] The theory uses a case study of the growth of business computing to explain different mechanisms of diffusion.

Contents

Origins of the sociological theory of diffusion

Diffusion emerged as a subfield in early 20th century rural sociology. [2] Bryce Ryan and Neal Gross were influential in laying the initial groundwork on diffusion in sociology. [2] Early studies explained how corn farmers adopted new variants of corn through social diffusion processes rather than economic ones. [2]

The concept of diffusion

In the 1962 book, Diffusion of Innovations, Everett Rogers defines sociological diffusion of innovation as a process in a social system where an innovative idea or concept is spread by members of the social group through certain channels. He identifies four elements that influence how and how quickly a new idea spreads: [3]

In a study by Surry and Farquhar, researchers explain that the theory of diffusion is used in occupations ranging from marketing to agriculture in order to ensure that new products, ideas, and techniques are well adopted by the social group. [4] The concept of diffusion is of particular interest in the marketing field, as this concept affects the success or failure of new ads or products. Understanding this theory helps marketers influence the way the public will perceive each innovation.

The speed at which an innovation spreads through a mass of people depends on how favorably an idea is perceived by the audience. Innovations that are ill matched with existing techniques are not as well accepted and diffused through the group. Social structures are naturally designed in a hierarchy[ citation needed ]; thus, different ideas follow different routes or courses in the hierarchy, depending on the type and source of an innovation. [5]

The study of the diffusion of innovations has led to advancements in awareness of three important aspects of social change: the qualities of an innovation which lead to successful diffusion, the effect of peer networking and conversations when it comes to spreading ideas, and the importance of various "user segments" (Robinson). The theory of diffusion of innovations differs from other theories about the processes of change since most changes are improvements, or "reinventions", of a previously existing product or technique. These changes are generally favorably perceived by the members of the group because they usually are more in line with the values and needs of the group.

There are five important qualities that factor into the success or failure of innovations. First, the relative advantage; that is, whether the new innovation surpasses similar existing ideas in terms of satisfaction and convenience. Second, the compatibility of the new idea with the needs and practices of the group members. Third, the simplicity of the innovation: usually, the simpler the innovation, the more quickly the concept is adopted. Fourth, the "trialability"[ citation needed ] of an innovation; that is, whether it can be tested without commitment for a period of time. An innovation that has an available trial period provides less uncertainty to the group member who will be trying it. Lastly, whether there are observable results with use of the innovation. The more positive and visible results, the higher the likelihood it gets adopted as a permanent idea for the group. [6]

Why diffusion happens

Sociological diffusion occurs when a social group or organization develops an innovation: a new idea or behavior. Diffusion, in the context of corporations and businesses, is a way for an idea to be fleshed out. The diffusion of innovations provides insights into the process of social change: one can observe the qualities that make an innovation successfully spread and the importance of communication and networks. [6] According to Rogers, a new idea is diffused through a decision-making process with five steps: [3]

The key part of the five stages is the decision; this is the main reason why diffusion exists. The decision to either adopt or reject the idea is vitally important. Those responsible for evaluating innovations either determine that the new concept is likely to provide future success, and adopt it, or determine that it is likely to be a failure, and continue to move forward in search of other ideas. It is counterproductive for an organization to invest time, energy, and in most cases money, into a poorly developed or bad idea.

An important aspect of the diffusion and decision process is communication. As an idea further develops and spreads, it flows and moves through an organization by communication. Communication is a necessary condition for an idea to take hold. [7] The innovation depends on a communication network within the organization in order to take root. In Emanuel Rosen's book The Anatomy of Buzz, Rosen points out the importance of communication networks in the spread and development of an idea within an organizational system. (Dobson)

Studies of the diffusion of innovation have shown that new ideas must fit with already established system in order for changes not only to occur, but also to occur easily. (Pinard) An innovation faced with structural or ideological barriers cannot diffuse. On the other hand, if a new idea or innovation has few obstacles and acknowledges places where change is logical, movement to it will occur. (Freeman)

Networks and environment

A firm's interaction with other players, along with its environment and organizational culture, are key in the social theory of diffusion.

The use of networks

The effects of networks and institutional environment on adoption of innovations can be explained using a social network theory model. In such a model, nodes represent agents (e.g. companies or organizations) and ties represent a connection between two entities (e.g. a company-client relationship or competitive relationship). Diffusion occurs when a novel idea, product, or process is implemented by an agent and permeates through these ties to others. [8]

Internal and external diffusion

Diffusion of information and ideas has been categorized into two modes:

Internal diffusion is the spread of information and innovations within a network, flowing within a single adopting population – a given industry or geographical network. Internal diffusion dynamics require that innovative and early adopter firms introduce new ideas into a network, which are then picked up by the majority of firms and laggard firms. [9] DiMaggio and Powell (1983) [10] argue that firms search for the best ideas and practices and mimic new ideas that prove to work. This phenomenon is known as mimetic isomorphism, [10] and ironically may lead to clustering of firm structure and practices. [8] Additionally, firms are often forced to adopt new ideas as they are constantly competing with other firms; that is, firms want to seem modernized and seek legitimacy in implementing innovative practices.

External diffusion refers to the introduction of ideas to a network from outside actors: firms or other agents on the edge of the network. Outside actors include the mass media and "change agents." Mass media can amplify trends and movements that occur in the marketplace, introducing new innovations to network members, exposing "best-practice" ideas, and conveying new principles. [9] Change agents are usually business professionals (such as lawyers, consultants, bankers, or politicians) who spread new practices or aid in promoting new ideas. [8] These individuals often introduce business models, legal strategies, or investment techniques that are picked up by several entities within a network and continue to diffuse. Often, such external diffusion leads to conformity of a set of corporate strategies or structures, a phenomenon DiMaggio and Powell called "normative isomorphism".

Environmental and cultural factors of diffusion

An agent's environmental and cultural makeup influence the decision to adopt an idea diffusing through a network. Some of the major characteristics of firms that influence their decision to innovate are clustering, weak ties, and firm size.

Clustering', the existence of a group of tightly connected agents, is a frequent concept in network theory. [11] It includes, for example, similar firms locating themselves in close proximity to each other (Silicon Valley for technology firms; New York for banking services). Such clustering and close proximity increases the diffusion rate of ideas for firms within a cluster, as other firms are more likely to adopt an idea if another firm has adopted it within its cluster. [8]

An agent with weak ties has a connection to two or more clusters. [12] These agents are integral in connecting groups, as they provide communication between large clusters. Firms with weak ties can be isolated firms, firms with business in two or more spaces, or those which are external change agents. Firms with weak ties introduce clusters to new, proven methods.

Firm size has been shown to have an influence on the rate of diffusion. Strang and Soule (1998) have shown that large, technical, and specialized organizations with informal cultures tend to innovate much faster than other firms. Smaller and more rigid firms attempt to mimic these "early adopters" in attempt to keep up with competition. [8]

Mathematical treatment

Mathematical models can be used to study the spread of technological innovations among individuals connected to each other by a network of peer-to-peer influences, such as in a physical community or neighborhood. [13]

Complex system (particularly complex network) models can be used to represent a system of individuals as nodes in a network (or Graph (discrete mathematics)). 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 are used to describe the evolution of these states over time. [14]

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. [15] The multiple parameters that influence decisions to adopt, both individual and socially motivated, can be represented by such mathematical models.

Computer models have been developed to investigate the balance between the social aspects of diffusion and perceived intrinsic benefit to the individuals. [16] 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 structure and properties of the network. Two factors in particular 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).

Case study: Diffusion of business computing in organizations

To illustrate how different diffusion mechanisms can have varying effects in individual cases, consider the example of business computing. The 1980s and 1990s saw a rapid paradigm shift in the way many organizations operated; specifically, the rise of computers and related technologies saw organizations adopt these innovations to help run their business (Attewell 1992:1 [17] ). Thus, the diffusion of business computing through organizations during this time period provides an informative case study through which to examine different mechanisms of diffusion and their respective roles.

Networks

The roles of communication networks, as described by traditional theories of diffusion, have been to facilitate information flow about a new innovation and thus remove one of the major barriers to adoption. In this model, those closest to the initial champions of a new innovation are quicker to respond and adopt, while those farther away will take more time to respond (Rogers 1983; [9] Strang and Soule 1998:272 [18] ). This theory about the roles of networks in diffusion, while widely applicable, requires modification in this particular case, among others. Attewell (1992) [17] argues that in this case, knowledge of the existence of computers and their business applications far preceded their eventual adoption. The main barrier to adoption was not awareness, but technical knowledge: knowledge of how to effectively integrate computing into the workplace. Thus, the most relevant networks to the diffusion of business computing were those networks that transmitted the technical knowledge required to utilize the innovation, not those that simply transmitted awareness of the idea behind the innovation.

Institutions

New institutions, in particular those which acted as educators or consultants, also played an important role in the diffusion of business computing. In order to adapt to evolving trends in business computing, organizations first needed to gain the technical knowledge necessary to operate the technology (Attewell 1992:3-6). [17] The "knowledge barrier" could be reduced or partially circumvented, however, by the formation of new institutions. The new institutions that formed during this time period – such as service bureaus, consultants, and companies creating simplifications of the technology – lowered the knowledge barrier and allow for more rapid diffusion of the ideas and technology behind business computing. This explains the phenomenon in which, at first, many organizations obtained business computing as an out-sourced service. However, after these service institutions effectively lowered the barrier to adoption, many organizations became capable of bringing business computing in-house (Attewell 1992:7-8). [17]

Innovation decisions

Rogers (1983) [9] notes two important ways in which innovations are adopted by organizations: collective innovation decisions, and authority innovation decisions. "Collective innovation decisions" are best defined as a decision that occurs as the result of a broad consensus for change within an organization. "Authority innovation decisions", on the other hand, need only the consensus of a few individuals with large amounts of power within the organization. In the case of organizations adopting business computing, authority decisions were largely impossible. As J.D. Eveland and L. Tornatzky (1990) [19] explain, when dealing with advanced technical systems such as those involved with business computing, “decisions are often many (and reversed), and technologies are often too big and complex to be grasped by a single person's cognitive power – or usually, to be acquired or deployed within the discretionary authority of any single organizational participant." Therefore, a much broader consensus within an organization was required to reach the critical mass of technical knowledge and authority necessary to adapt to business computing. This provided an opportunity for collective innovation decisions within the organization.

See also

Related Research Articles

<span class="mw-page-title-main">Innovation</span> Practical implementation of improvements

Innovation is the practical implementation of ideas that result in the introduction of new goods or services or improvement in offering goods or services. ISO TC 279 in the standard ISO 56000:2020 defines innovation as "a new or changed entity realizing or redistributing value". Others have different definitions; a common element in the definitions is a focus on newness, improvement, and spread of ideas or technologies.

Organizational learning is the process of creating, retaining, and transferring knowledge within an organization. An organization improves over time as it gains experience. From this experience, it is able to create knowledge. This knowledge is broad, covering any topic that could better an organization. Examples may include ways to increase production efficiency or to develop beneficial investor relations. Knowledge is created at four different units: individual, group, organizational, and inter organizational.

In business, diffusion is the process by which a new idea or new product is accepted by the market. The rate of diffusion is the speed with which the new idea spreads from one consumer to the next. Adoption is the reciprocal process as viewed from a consumer perspective rather than distributor; it is similar to diffusion except that it deals with the psychological processes an individual goes through, rather than an aggregate market process.

An Informationcascade or informational cascade is a phenomenon described in behavioral economics and network theory in which a number of people make the same decision in a sequential fashion. It is similar to, but distinct from herd behavior.

Everett M. "Ev" Rogers was an American communication theorist and sociologist, who originated the diffusion of innovations theory and introduced the term early adopter. He was distinguished professor emeritus in the department of communication and journalism at the University of New Mexico.

<span class="mw-page-title-main">Diffusion of innovations</span> Theory on how and why new ideas spread

Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread. The theory was popularized by Everett Rogers in his book Diffusion of Innovations, first published in 1962. Rogers argues that diffusion is the process by which an innovation is communicated over time among the participants in a social system. The origins of the diffusion of innovations theory are varied and span multiple disciplines.

Agricultural extension is the application of scientific research and new knowledge to agricultural practices through farmer education. The field of 'extension' now encompasses a wider range of communication and learning activities organized for rural people by educators from different disciplines, including agriculture, agricultural marketing, health, and business studies.

Technological change (TC) or technological development is the overall process of invention, innovation and diffusion of technology or processes. In essence, technological change covers the invention of technologies and their commercialization or release as open source via research and development, the continual improvement of technologies, and the diffusion of technologies throughout industry or society. In short, technological change is based on both better and more technology.

Theories of technological change and innovation attempt to explain the factors that shape technological innovation as well as the impact of technology on society and culture. Some of the most contemporary theories of technological change reject two of the previous views: the linear model of technological innovation and other, the technological determinism. To challenge the linear model, some of today's theories of technological change and innovation point to the history of technology, where they find evidence that technological innovation often gives rise to new scientific fields, and emphasizes the important role that social networks and cultural values play in creating and shaping technological artifacts. To challenge the so-called "technological determinism", today's theories of technological change emphasize the scope of the need of technical choice, which they find to be greater than most laypeople can realize; as scientists in philosophy of science, and further science and technology often like to say about this "It could have been different." For this reason, theorists who take these positions often argue that a greater public involvement in technological decision-making is desired.

Hobart Peyton Young is an American game theorist and economist known for his contributions to evolutionary game theory and its application to the study of institutional and technological change, as well as the theory of learning in games. He is currently centennial professor at the London School of Economics, James Meade Professor of Economics Emeritus at the University of Oxford, professorial fellow at Nuffield College Oxford, and research principal at the Office of Financial Research at the U.S. Department of the Treasury.

<span class="mw-page-title-main">Technology adoption life cycle</span> Sociological model

The technology adoption lifecycle is a sociological model that describes the adoption or acceptance of a new product or innovation, according to the demographic and psychological characteristics of defined adopter groups. The process of adoption over time is typically illustrated as a classical normal distribution or "bell curve". The model indicates that the first group of people to use a new product is called "innovators", followed by "early adopters". Next come the early majority and late majority, and the last group to eventually adopt a product are called "Laggards" or "phobics." For example, a phobic may only use a cloud service when it is the only remaining method of performing a required task, but the phobic may not have an in-depth technical knowledge of how to use the service.

In social dynamics, critical mass is a sufficient number of adopters of a new idea, technology or innovation in a social system so that the rate of adoption becomes self-sustaining and creates further growth. The point at which critical mass is achieved is sometimes referred to as a threshold within the threshold model of statistical modeling.

Innovation management is a combination of the management of innovation processes, and change management. It refers to product, business process, marketing and organizational innovation. Innovation management is the subject of ISO 56000 series standards being developed by ISO TC 279.

The mass-market theory, otherwise known as the trickle across, is a social fashion behavioral marketing strategy established by Dwight E. Robinson in 1958 and Charles W. King in 1963. Mass market is defined as, "a market coverage strategy in which a firm decides to ignore market segment differences and appeal to the whole market with one offer or one strategy." The mechanism focuses on the fashion innovators found within every social economic group and the influences in response to the couture enthusiasts that innovate as part of their stylish aspect.

The technological innovation system is a concept developed within the scientific field of innovation studies which serves to explain the nature and rate of technological change. A Technological Innovation System can be defined as ‘a dynamic network of agents interacting in a specific economic/industrial area under a particular institutional infrastructure and involved in the generation, diffusion, and utilization of technology’.

<span class="mw-page-title-main">Kathleen Carley</span> American social scientist

Kathleen M. Carley is an American computational social scientist specializing in dynamic network analysis. She is a professor in the School of Computer Science in the Carnegie Mellon Institute for Software Research at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the Heinz College, the Department of Engineering and Public Policy, and the Department of Social and Decision Sciences.

Complex contagion is the phenomenon in social networks in which multiple sources of exposure to an innovation are required before an individual adopts the change of behavior. It differs from simple contagion in that unlike a disease, it may not be possible for the innovation to spread after only one incident of contact with an infected neighbor. The spread of complex contagion across a network of people may depend on many social and economic factors; for instance, how many of one's friends adopt the new idea as well as how many of them cannot influence the individual, as well as their own disposition in embracing change.

<span class="mw-page-title-main">Social network</span> Social structure made up of a set of social actors

A social network is a social structure made up of a set of social actors, sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine network dynamics.

Networks are crucial parts of any action taken in a marketplace. Peter Drucker even described the future economy as one of a society of networks. Companies embedded in such networks stand to gain a lot. There are a number of different network models, which have distinct relevance to customers, and marketing initiatives. A network in marketing can be formed either strategically or completely randomly. Marketing channels and business networks have been referred to, by Achrol & Kotler as:

“Interdependent systems of organizations and relations that are involved in carrying out all of the production and marketing activities involved in creating and delivering value in the form of products and services to intermediate and final customers.”

<span class="mw-page-title-main">Christianization of the Roman Empire as diffusion of innovation</span>

Christianization of the Roman Empire as diffusion of innovation looks at religious change in the Roman Empire's first three centuries through the lens of diffusion of innovations, a sociological theory popularized by Everett Rogers in 1962. Diffusion of innovation is a process of communication that takes place over time, among those within a social system, that explains how, why, and when new ideas spread. In this theory, an innovation's success or failure is dependent upon the characteristics of the innovation itself, the adopters, what communication channels are used, time, and the social system in which it all happens.

References

  1. ↑ Strang, David; Sarah Soule (1998). "Diffusion in Organizations and Social Movements: From Hybrid Corn to Poison Pills". Annual Review of Sociology. 24: 265–290. doi:10.1146/annurev.soc.24.1.265.
  2. 1 2 3 Everton, Sean F.; Pfaff, Steven (2022). "Historical and Comparative Research on Social Diffusion: Mechanisms, Methods, and Data". Social Science History. 46 (2): 431–472. doi:10.1017/ssh.2021.46. ISSN   0145-5532. S2CID   246049542.
  3. 1 2 Rogers, Everett (2003). Diffusion of Innovations. New York: Free Press.
  4. ↑ Surry, D; J Farquhar (1997). "Diffusion Theory and Instructional Technology". Journal of Instructional Science and Technology. 2 (1): 269–278.
  5. ↑ Jeanty, Jacquelyn. "Social Theory of Diffusion".{{cite web}}: Missing or empty |url= (help)
  6. 1 2 Robinson, Les. "A Summary of Diffusion of Innovations" (PDF). Retrieved November 19, 2012.
  7. ↑ Strang and Soule
  8. 1 2 3 4 5 Strang, David. "Diffusion in Organizations and Social Movements; From Hybrid Corn to Poison Pills" (PDF). Retrieved 18 Nov 2012.
  9. 1 2 3 4 Rogers, Everett (1983). The Diffusion of Innovation (Third Ed.). New York, NY: Free Press.
  10. 1 2 DiMaggio and Powell, Paul and Walter (1983). The Iron Cage Revisited (PDF).
  11. ↑ Mishra, Schreiber, Stanton, Tarjan, Nina, Robert, Isabelle, Robert (2007). "Clustering Social Networks". Algorithms and Models for the Web-Graph. Lecture Notes in Computer Science. Vol. 4863. pp. 56–67. doi:10.1007/978-3-540-77004-6_5. ISBN   978-3-540-77003-9.{{cite book}}: CS1 maint: multiple names: authors list (link)
  12. ↑ Granovetter, Mark. "The Strength of Weak Ties" (PDF). Archived from the original (PDF) on 2013-06-01. Retrieved 18 Nov 2012.
  13. ↑ "What Math Can Tell Us About Technology's Spread Through Cities". Bloomberg CityLab. April 10, 2013.
  14. ↑ "How does innovation take hold in a community? Math modeling can provide clues". Archived from the original on 2016-12-04. Retrieved 2014-05-30.
  15. ↑ Watts, D. J. (2002). "A Simple Model of Global Cascades on Random Networks" (PDF). Proceedings of the National Academy of Sciences of the United States of America. 99 (9): 5766–5771. Bibcode:2002PNAS...99.5766W. doi: 10.1073/pnas.082090499 . JSTOR   3058573. PMC   122850 . PMID   16578874.
  16. ↑ McCullen, N. J.; Rucklidge, A. M.; Bale, C. S. E.; Foxon, T. J.; Gale, W. F. (2013). "Multiparameter Models of Innovation Diffusion on Complex Networks". SIAM J. Appl. Dyn. Syst. 12 (1): 515–532. arXiv: 1207.4933 . doi:10.1137/120885371.
  17. 1 2 3 4 Attewell, Paul (1992). "Technology Diffusion and Organizational Learning: The Case of Business Computing". Organization Science. 3 (1): 1–19. doi:10.1287/orsc.3.1.1. JSTOR   2635296.
  18. ↑ Strang, David; Sarah Soule (1998). "Diffusion in Organizations and Social Movements: From Hybrid Corn to Poison Pills". Annual Review of Sociology. 24: 265–290. doi:10.1146/annurev.soc.24.1.265. JSTOR   223482.
  19. ↑ Eveland, J.D.; L. Tornatsky (1990). The Processes of Technological Innovation. Lexington, MA: Lexington Books.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.