Innovation Diffusion is a theory that seeks to explain how, why, and at what level new ideas and technologies are spreading. Everett Rogers, a professor of communication studies, popularized the theory in his book Diffusion of Innovation; this book was first published in 1962, and is now in its fifth edition (2003). Rogers argues that diffusion is a process in which an innovation is communicated over time among participants in a social system. The origins of innovation diffusion theory vary and extend across multiple disciplines.
Rogers proposes that four major elements influence the dissemination of new ideas: innovation itself, communication channels, time, and social systems. This process is highly dependent on human capital. Innovation must be widely adopted to defend itself. In the adoption rate, there is a point where an innovation reaches critical mass.
The adoption category is the innovator, the initial user, the early majority, the late majority, and the sluggish. Diffusion manifests itself in a variety of ways and is very subject to the kind of adopters and decision-making processes of innovation. The criterion for adoption categorization is innovation, which is defined as the rate at which a person adopts a new idea.
Video Diffusion of innovations
History
The concept of diffusion was first studied by French sociologist Gabriel Tarde in the late 19th century and by German and Austrian anthropologists and geographers such as Friedrich Ratzel and Leo Frobenius. The study of the diffusion of innovation took off in the sub-field of rural sociology in the midwestern United States in the 1920s and 1930s. Agricultural technology is growing rapidly, and researchers are beginning to examine how independent farmers adopt hybrid seeds, tools, and techniques. A study of the adoption of hybrid corn seeds in Iowa by Ryan and Gross (1943) solidifies previous work on diffusion into different paradigms that will be cited consistently in the future. Since its inception in rural sociology, Innovation Diffusion has been applied to a variety of contexts, including medical sociology, communications, marketing, development studies, health promotion, organizational studies, knowledge management, conservation biology and complexity studies, with enormous impact on drug use , medical techniques, and health communication. In organizational studies, the epidemiological forms or basic internal influences are formulated by H. Earl Pemberton, which provides examples of institutional diffusion such as stamps and the standard school ethics code.
In 1962, Everett Rogers, a professor of rural sociology, published his seminal work: Innovation Diffusion. Rogers synthesizes research from more than 508 diffusion studies in various fields that initially influenced theories: anthropology, early sociology, rural sociology, education, industrial sociology, and medical sociology. Using his synthesis, Rogers produced an innovation adoption theory among individuals and organizations. Innovation Diffusion and Rogers' later books are among the most frequently cited in diffusion research. The methodology is closely followed in recent diffusion research, even when the field has evolved into, and has been influenced by, other methodological disciplines such as social network analysis and communication.
Maps Diffusion of innovations
Element
The key elements in diffusion research are:
Characteristics of innovation
The study has explored many characteristics of innovation. Meta-reviews have identified some common characteristics among most studies. This is in line with the characteristics that Rogers originally cited in his review.
Potential adopters evaluate innovation on their relative advantages (the efficiency felt by innovation relative to current tools or procedures), compatibility with pre-existing systems, complexity or learning difficulties, piloting or trial capability, their potential for reinvention for purposes that were initially unintentional), and the effects observed. These qualities interact and are valued as a whole. For example, an innovation may be very complex, reducing the likelihood of being adopted and deployed, but may be highly compatible with substantial gains compared to current tools. Even with this high learning curve, potential adopters may adopt that innovation.
The study also identifies other characteristics of innovation, but this is not uncommon as Rogers compiled above. The uncertainty of the boundaries of innovation can affect its adoption. In particular, innovations with small cores and large edges are easier to adopt. Less risky innovations are easier to implement because of the potential losses of failing integration lower. Innovations that interfere with routine tasks, even when they bring relatively large benefits, may not be adopted because of added instability. Likewise, innovations make tasks easier to adopt. Closely related to relative complexity, the knowledge requirement is the barrier ability to use that is presented by the difficulty of using innovation. Even when there is a high knowledge requirement, support from previous users or other sources may increase the chances of being adopted.
Individual user characteristics
Like innovation, the adopters are determined to have properties that affect their possibilities to adopt innovation. An association of individual personality traits has been explored because of their impact on adoption, but with little agreement. Abilities and motivations, which vary in situations unlike personality traits, have a major impact on potential adopters' potential to adopt innovation. Not surprisingly, potential adopters who are motivated to adopt innovations tend to make the adjustments necessary to adopt them. Motivation can be influenced by the meaning that an innovation holds; innovation can have a symbolic value that encourages (or prevents) adoption. First proposed by Ryan and Gross (1943), the overall linkage of potential adopters to the wider community is represented by a city. Potential adopters who frequently visit metropolitan areas are more likely to adopt innovation. Finally, potential adopters who have the power or agents to make changes, especially in organizations, are more likely to adopt innovations than less powerful people over their choices.
Organization Characteristics
Organizations face the possibility of more complex adoption because the organization is an aggregate of the individual and his own system with a series of procedures and norms. Three organizational characteristics match the individual characteristics above: tension for change (motivation and ability), compatibility of the system of innovation (compatibility), and implication assessment (observation). Organizations can feel depressed by the tension for change. If the organizational situation can not be maintained, it will be motivated to adopt innovation to change its fortunes. This tension is often played out among its members. Innovations in accordance with pre-existing organizational systems require less chance change and are easy to assess and more likely to be adopted. A wider organizational environment, often industrial, community, or economic, puts pressure on organizations as well. Where innovation spreads through the organizational environment for any reason, organizations are more likely to adopt it. Deliberately deliberate innovations, including by mandate or political direction, also tend to spread rapidly.
Process
Diffusion occurs through a five-step decision-making process. This occurs through a series of communication channels over a period of time among members of similar social systems. Ryan and Gross first identified adoption as a process in 1943. Rogers' five stages (steps): awareness, interest, evaluation, experimentation, and adoption are an integral part of this theory. One may reject an innovation at any time during or after the adoption process. Abrahamson examines this process critically by asking questions such as: How technically inefficient innovation spreads and what hinders technically efficient innovation from capture? Abrahamson made suggestions as to how organizational scientists can more comprehensively evaluate the spread of innovation. In subsequent editions of Innovation Diffusion, Rogers changed his terminology about five stages to: knowledge, persuasion, decision, implementation, and confirmation. However, descriptions of the categories remain the same throughout the edition.
Decision
Two factors determine the specific type of decision:
- Whether decisions are made freely and voluntarily apply
- Who decides.
Based on these considerations, three types of innovation decisions have been identified.
Adoption rate
The adoption rate is defined as the relative speed at which participants adopt innovation. Levels are usually measured by the length of time it takes for a certain percentage of members of a social system to adopt innovation. The rate of adoption for innovation is determined by the category of individual adopters. In general, individuals who first adopt innovation require a shorter adoption period (adoption process) when compared to a late user.
In the adoption curve at some point, innovation reaches critical mass. This is when the number of individual adopters ensures that innovation is self-sustaining.
adoption strategy
Rogers outlines several strategies to help an innovation reach this stage, including when an innovation is adopted by highly respected individuals in social networks and creates an instinctive desire for specific innovations. Other strategies include injecting innovation into groups of individuals ready to use the technology, as well as providing positive reactions and benefits to early adopters.
Diffusion vs adoption
Adoption is an individual process that details the series of stages experienced by the first hearing of a product until it finally adopts it. Diffusion signifies a group phenomenon, which shows how an innovation spreads.
Category launcher
Rogers defines the adopter category as the classification of individuals in social systems on the basis of innovation. In the book Innovation Diffusion , Rogers shows a total of five categories of adopters to standardize the use of the adopter category in diffusion research. Adoption of innovation follows the S curve when plotted over a period of time. The categories of adopters are: innovators, early users, early majority, late majority, and sluggish. In addition to gatekeepers and opinion leaders in certain communities, change agents can come from outside the community. The change agent brings innovation to a new community - first through the goalkeeper, then through opinion leaders, and so on through the community.
Diffusion failed
A failed diffusion does not mean that technology is not adopted by anyone. Conversely, failing diffusion often refers to diffusion that does not reach or approaches 100% adoption due to its own weakness, competition from other innovations, or just a lack of awareness. From a social networking perspective, failed diffusion can be widely adopted within a particular group but fails to impact on people who are farther away. Overly connected networks may suffer rigidity that prevents changes that innovation may bring. Sometimes, some innovations also fail due to lack of local involvement and community participation.
For example, Rogers discusses a situation in Peru involving the application of boiling drinking water to improve health and fitness levels in the village of Los Molinas. The population has no knowledge of the relationship between sanitation and illness. The campaign works with villagers to try to teach them to boil water, burn their garbage, install latrines and report disease cases to local health authorities. In Los Molinas, the stigma is associated with boiling water as something that is just "unwell" consumed, and thus, the idea of ​​healthy residents boiling water before it is consumed is crimped. A two-year education campaign is considered largely unsuccessful. This failure shows the importance of the role of communication channels involved in such campaigns for social change. The diffusion examination in El Salvador determines that there is more than one social network that acts as a communicated innovation. One network carries information and the other brings influence. While one may hear the use of innovation, in the case of Rogers' Los Molinas sanitation, the network of influence and status prevents adoption.
Heterophily and communication channels
Lazarsfeld and Merton first draw attention to the principles of homophilia and its opposite, heterophily. Using their definitions, Rogers defines homophilies as "the extent to which individual pairs interact similarly in certain attributes, such as beliefs, education, social status, and the like". When given a choice, the individual usually chooses to interact with someone similar to himself. Homophilous individuals engage in more effective communication because their similarity leads to greater knowledge enhancement as well as changes in attitudes or behaviors. As a result, homophilous people tend to promote the diffusion of each other. However, diffusion requires a certain degree of heterophilia to introduce new ideas into a relationship; if two individuals are identical, there is no diffusion because there is no new information to be exchanged. Therefore, the ideal situation would involve a homophilous potential adopter in everything, except in the knowledge of innovation.
Healthy behavior promotion provides an example of the necessary balance of homophili and heterophily. People tend to be close to others with similar health status. As a result, people with unhealthy behaviors such as smoking and obesity are less likely to find information and behaviors that promote good health. This presents an important challenge for health communication, as the relationship between heterophilous people is relatively weaker, more difficult to make, and more difficult to maintain. Developing heterophilous relationships with unhealthy communities can improve the effectiveness of diffusion of good health behaviors. After a previously homophilous tie adopted behavior or innovation, other members of the group were more likely to adopt it as well.
Leader of opinion
Not all individuals have the same effect on others. In this sense, opinion leaders are influential in spreading positive or negative information about an innovation. Rogers depends on the ideas of Katz & amp; Lazarsfeld and the two-step flow theory in developing his ideas about the influence of opinion leaders.
The opinion leader has the most influence during the evaluation phase of the innovation-decision process and on the late user. In addition, opinion leaders usually have greater exposure to mass media, more cosmopolitan, greater contact with change agents, more social experience and exposure, higher socioeconomic status, and more innovative than others.
Research carried out in the early 1950s at the University of Chicago tried to assess the effectiveness of broadcast advertising costs on the diffusion of new products and services. The finding is that opinion leadership tends to be organized into a hierarchy in society, with each level in the hierarchy having the greatest influence on the other members of the same level, and at the next level below it. The lowest rates are generally larger in numbers and tend to coincide with the various demographic attributes that may be targeted by mass advertising. However, it was found that direct word-of-mouth and instance words were far more influential than broadcast messages, which were only effective if they strengthened direct influence. This leads to the conclusion that the ad is most appropriately targeted, if possible, to the next to adopt, and not to those who have not been reached by the chain of influence.
The research on network actor theory (ANT) also identifies a significant overlap between the ANT concept and the innovation diffusion that examines the characteristics of innovation and its context among the various stakeholders in the social system to assemble networks or systems that implement innovation.
Other research related concepts to public choice theory found that the hierarchy of influence for innovation is unnecessary, and possibly not, coinciding with official, political, or economic status hierarchies. Elites are often not innovators, and innovations may have to be introduced by outsiders and propagated into hierarchies for top-level decision-makers.
Prior to the introduction of the Internet, he argued that social networking has an important role in the diffusion of innovation, especially tacit knowledge in the book of IRG Solutions - hierarchical incompetence and how to overcome it . The book argues that the widespread adoption of individual computer networks will lead to a much better diffusion of innovation, with a greater understanding of possible deficiencies and the identification of required innovations that will not happen. The social model proposed by Ryan and Gross is extended by Valente who uses social networks as a basis for categorizing adopters rather than relying on system-level analysis used by Ryan and Gross. Valente also looks at individual private networks, which are different applications from an organizational perspective shared by many other scholars.
Recent research by the Wear exhibition, which is particularly regional and rural, has significantly more innovation in communities with stronger interpersonal networks.
Organization
Innovation is often adopted by organizations through two types of innovation decisions: collective innovation decisions and innovation authority decisions. Collective decision occurs when adoption takes place through consensus. The authority's decision comes with the adoption of very few individuals with high positions of power within an organization. Unlike the optional process of innovation decisions, this decision process occurs only within hierarchical organizations or groups. In an organization, certain people are called "champions" who support innovation and break through conflict. The champion played a very similar role as the champion used in the Six Sigma business efficiency model. This process contains five phases that are somewhat similar to the innovative decision-making process of the individual. These stages are: agenda setting, matching, redefining/restructuring, clarification and routine.
Extensions of the
theoryPolicy
Innovation Diffusion has been applied outside its original domain. In the case of political science and administration, policy diffusion focuses on how institutional innovation is adopted by other institutions, at the local, state, or state level. The alternative term is 'policy transfer' where the focus is more on the diffusion agent and the diffusion of policy knowledge, as in the work of Diane Stone. In particular, policy transfers can be defined as "knowledge of how administrative, organizational, and idea-setting policies in one political setting (formerly or present) are used in policy development, administrative arrangements, institutions and ideas in other political settings".
The first concern with regard to policy diffusion focuses on the variation of time or the adoption of state lotteries, but more recent interest has shifted towards mechanisms (competition, learning and coercion) or in diffusion channels where researchers find that regulatory agency creation is transmitted by the state and channel sector. At the local level, checking popular city level policies makes it easy to find diffusion patterns through public awareness measurements. At the international level, economic policy has been thought to move between countries according to local politicians' learning about successes and failures elsewhere and beyond the mandates created by global financial organizations. When a group of countries succeeds with a set of policies, others follow, as exemplified by deregulation and liberalization throughout the developing world after the success of the Asian Tiger. The reintroduction of the regulation in the early 2000s also suggests that this learning process, which will fit under the knowledge and decision stage, can be seen as a lesson learned by following China's successful growth.
Technology
Peres, Muller and Mahajan suggest that diffusion is "a process of market penetration of new products and services driven by social influences, including all interdependencies among consumers affecting various market participants with or without their explicit knowledge".
Eveland evaluates the diffusion of the phenomenological view, which states, "Technology is information, and exists only at a level where people can practice it and use it to achieve value".
The diffusion of existing technology has been measured using the "S curve". These technologies include radio, television, VCRs, cables, flush toilets, clothes washers, refrigerators, home ownership, air conditioners, dishwashers, electric households, telephones, cordless phones, cell phones, airline miles per capita, personal and Internet. This data can act as a predictor for future innovation.
The diffusion curve for infrastructure expresses the contrast in the process of diffusion of personal technology versus infrastructure.
Consequence of adoption
Positive and negative results are possible when an individual or organization chooses to adopt a particular innovation. Rogers stated that this area needs further research because of the biased positive attitudes associated with innovation. Rogers lists three categories for the consequences: desirable vs. not desirable, direct vs. indirect, and anticipated vs not anticipated.
Instead, Wejnert outlines two categories: public vs. personal and vs. benefits cost.
Public versus private
The public consequences consist of the impact of innovation on those other than the actor, while the personal consequences refer to the impact on the actor. Public consequences usually involve collective actors, such as a country, state, organization or social movement. The result is usually related to community welfare issues. Personal consequences usually involve individuals or small collective entities, such as communities. Innovation is usually associated with improving the quality of life or reform of organizational or social structures.
Benefits versus cost
The benefits of an innovation are obviously positive consequences, while the cost is negative. Costs can be either money or non-monetary, direct or indirect. Direct costs are usually associated with financial uncertainty and the economic state of the actor. Indirect costs are more difficult to identify. An example is the need to buy new types of pesticides to use innovative seeds. Indirect costs may also be social, such as social conflicts caused by innovation. Marketers are very interested in the process of diffusion because it determines the success or failure of a new product. It is important for marketers to understand the diffusion process to ensure proper management of the deployment of new products or services.
Mathematical care
The diffusion of an innovation usually follows an S-shaped curve that often resembles a logistical function. Mathematical programming models such as the S-D model apply the diffusion of innovation theory to real data problems. In addition, agent-based models follow a more intuitive process by designing individual level rules to model the diffusion of ideas and innovations.
Complex system model
Complex network models can also be used to investigate the spread of innovation among individuals who are connected to each other by networks of peer-to-peer influences, such as in physical or environmental communities.
The models represent individual systems as nodes in the network (or graph). The interactions that connect these individuals are represented by the edge of the network and can be based on the probability or strength of social connections. In the dynamics of the model, each node is given its current status, indicating whether the individual has adopted the innovation, and the model equations describe the evolution of these countries over time.
In the threshold model, the uptake of technology is determined by the balance of two factors: the (perceived) benefits (sometimes called utilities) of innovation for the individual as well as barriers to adoption, such as cost. The various parameters that influence the decision to adopt, whether individual or social motivation, can be represented by models such as a series of nodes and connections representing a real relationship. Borrowing from social networking analysis, each node is an innovator, adopter, or potential user. Potential adopters have a threshold, which is a fraction of their neighbors who adopt innovations that must be achieved before they will adopt. Over time, each prospective adopter looks at his neighbor and decides whether he should adopt it based on the technology they use. When the effects of each individual node are analyzed along with their influence over the entire network, the expected adoption rate is seen to depend on the number of early adopters and the structure of networks and property. Two factors emerged as important for successful deployment of innovation: the number of node connections with their neighbors and the presence of high-level general connections in the network (measured by the co-efficient grouping). These models are excellent in showing the impact of opinion leaders relative to others. Computer models are often used to investigate the balance between the social aspects of diffusion and the perceived intrinsic benefits to the individual.
Criticism
Because there are more than four thousand articles in many disciplines published in Innovation Diffusion, with most being written after Rogers created a systematic theory, there have been some widely adopted changes to the theory. Although each study applies theory in a slightly different way, this lack of cohesion has made the theory stagnant and difficult to implement with the consistency of new problems.
Diffusion is difficult to measure because human and human tissues are very complex. It is very difficult, if not impossible, to gauge exactly what causes the adoption of an innovation. This is important, especially in health care. Those who encourage the adoption of new health or medical technology behaviors need to be aware of the many forces that act on the individual and his decision to adopt new behaviors or technologies. The diffusion theory can never explain all the variables, and therefore may lose critical predictors of adoption. These variable variables also lead to inconsistent research results, reducing heuristic values.
Source of the article : Wikipedia
0 Comments