Diffusion of Innovations Theory: Case Studies and Discussion

The Diffusion of Innovations theory was the leading theory in agricultural extension post World War II until the 1970s and there is still considerable interest in it today in agricultural extension, as these case studies demonstrate. It is particularly relevant when extension is concerned with adoption of a particular technology (i.e. a technology transfer approach to extension).  However, there are also experiences and situations that have not suited the application of the theory so well.

The theory is explained in greater detail in our earlier post: Diffusion of Innovations Theory – Adoption and Diffusion. That post discusses the theory, which is attributed to Everett M. Rogers and also some critique. Rogers was convinced that the spread of innovations amongst a population of people is a process of social change.  It explains how, over time, an idea or product gains momentum and spreads (or diffuses, hence the name) through a specific population or social system. The speed at which diffusion occurs is dependent upon the nature of the social system (society) in question and what means of communication is available within the social system.

Rogers developed adopter categories to ‘measure’ innovativeness of farmers to produce a statistical model (normal distribution curve), which shows distribution of the five adopter categories over the average time it takes for an innovation to be adopted:

  1. Innovators (2.5% of social system population)
  2. Early Adopters (13.5% of social system population)
  3. Late Majority (34% of social system population)
  4. Laggards (16% of social system population)

     (Source: Adopter categorization on the basis of innovativeness – Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press)

The Cooperative Extension Service in the USA experience

Garry Stephenson observed that since the 1950’s, the Innovation Diffusion Theory has been used to train Extension staff and Tertiary level courses on extension have included it in their courses. His review of extension articles into the early 2000’s, shows it being frequently cited, (Stephenson, 2003). His article, “The Somewhat Flawed Theoretical Foundation of the Extension Service”, examines the history, influence, and impacts of the innovation diffusion theory on the Extension Service in the U.S.A.

Today, the theory that underlies much of our Extension programming is based largely on research from this era – the 1940s, ’50s and ’60s, (Stephenson, 2003).

Stephenson concluded that segments of the innovation diffusion literature have stood the test of time, those that relate to the characteristics of innovation, the stages of the adoption process, and the effect of the interaction of farmers on adoption. He also states the most controversial area has been the theory’s focus on the most innovative farmers and the undesirable consequences of using this approach.

The particular challenges he documents are:

  1. A Pro-Innovation Bias – the implication that an innovation should be diffused and adopted by all farmers.  The act of innovating is regarded as being positive and to reject adopting an innovation (regardless of circumstances), is considered negative.
  2. Individual Blame Bias – the extension agencies themselves, are not blamed for not responding to the actual needs of farmers, but rather the farmers who do not adopt the innovation are blamed for not responding and doing so.
  3. Bias in Favour of Larger and Wealthier Farmers – “Development agencies tend to provide assistance especially to their innovative, wealthy, educated and information-seeking clients. Following this progressive, or ‘easy to convince’ diffusion strategy, leads to a lower degree of equality. For example, more progressive farmers are eager for new ideas and have the economic means to adopt; they can also more easily obtain credit if they need it. Because they have larger farms, the direct effect of their adoption on total agricultural production is also greater” (Rogers, 1995: 128-129). Consequently, the rich get richer and poor get poorer.  The questions are posed: Does the focus on the theory favour practices and technology that only larger and wealthier farmers are able to adopt readily? Should there be equal focus to ensure the viability of medium and smaller farms, particularly in respect to their contribution to rural communities?
  4. Issue of Equality – The potential negative impacts of focussing on diffusing innovations that have a production focus are not well considered.  For example:  Unemployment, migration of rural people, inequitable distribution of income and how these issues impact on the overall needs of famers and rural communities.

Diffusion of Innovation Theory and Integrated Pest Management (Diffusion does not occur as simply as for one off technologies)

The book, ‘Integrated Pest Management: Dissemination and Impact’ (2009) has a chapter on the Diffusion of Innovations Theory and how it relates to the dissemination of Integrated Pest Management (IPM) technology. The book discusses the IPM systems in developed countries: North America, Europe and Australia; as well as in the developing countries of Africa, Asia and Latin America.

CSIRO in Australia explains that Integrated Pest Management (IPM) is an effective combination of a wide range of management methods including plant resistance, refuge crops and effective sampling to name just a few. It is an ecosystem approach to crop production and protection, that combines different management strategies and practices to grow healthy crops and minimise the use of pesticides.

“IPM is a combination of different technologies (a cluster of technologies) that has not diffused the way simple one off technologies do. IMP technology has sophisticated demands and is a complex system. It is a knowledge and skill intensive innovation. This complexity does not fit well with the Diffusion of Innovation Theory. It is further complicated by the fact that there is no agreement on what constitutes adoption of IPM.  Almost all diffusion research projects and studies on rate of adoption, have concentrated on single innovation/practice and not a mix of practices.”

IPM is location specific and it requires extended periods of time for experiments, trials, repetitions and validations, in a given location. It requires the farmer to have a clear understanding about the IPM tactics and those tactics may vary from crop to crop and area to area. It needs a planned strategy of imparting knowledge and skill and active learning and active adoption by the farmers.

It is suggested that diffusion researchers should employ an “action research” design to study the IPM implementation and feed the results to help develop farmer-acceptable IPM systems. The coordination of all the stakeholders in an agricultural innovation system approach, needs to emphasise the outcomes of technology and knowledge generation and adoption of IPM practices, rather than merely the strengthening of research and extension systems.

The reality is that the spread of IPM does not readily follow the Diffusion of Innovation Theory approach, with it’s focus on Innovators and the subsequent diffusion to Early Adopters etc, as it is complex and a lot is needed to happen within a complex system.

Innovation Adoption in Agriculture: Innovators, Early Adopters and Laggards

Diederen et al (2003) collected survey and interview data from 1075 farms participating in the Dutch Farm Accountancy Data Network (FADN), maintained at the Agricultural Economics Research Institute (Landbouw-Economisch Institut, (LEI).  Farmers in the FADN received a short questionnaire in which they were asked to answer two key questions : i) whether they had adopted and implemented an important innovation in this period, and ii) whether they could indicate for this innovation their position on the diffusion curve.

“We found that innovators and early adopters differ from laggards with regard to structural characteristics like size, market position, age and solvency. However, we also discovered that these structural characteristics (except for age), do not distinguish innovators from early adopters.  Instead we found that innovators differ from early adopters with regard to behavioural characteristics, such as the valuation of external information, the source of innovative ideas and the way they co-operate.” (Diederen et al, 2003)

“If a farmer invests, he either chooses to innovate (to be the first user of an innovation among his competitors), or to adopt an innovation which is already used by others, but which is still relatively new, or he chooses to adopt a mature technology and a farmer can also choose not to adopt anything new at all. The literature on innovation adoption suggests a number of factors that might contribute to the explanation as to whether a farmer prefers to be an innovator, an early adopter, a late adopter or a non-adopter.” (Diederen et al, 2003)

Contributing Factors (Source: Diederen, et al 2003)

Farm size: Farmers with larger businesses are more likely to adopt relatively new innovations.  Many innovations are characterised by fixed costs. This leads to scale economies: the rate of return on adoption is higher for larger farms. Furthermore, larger farms tend to be characterised by some degree of division of labour, more professional management and a larger capacity to bear risk. Most studies find a positive relationship between size and adoption. Some question this result, because smaller farmers may cooperate or are more willing to take the risk and costs associated with early adoption, because they are looking for new niches and opportunities.

Market position: Farmers that produce for heterogeneous markets are likely to adopt innovations earlier.  Farmers are more likely to be able to capture the benefits of innovations when delivering to markets that allow for some degree of price differentiation.  For example, the markets for flowers or for branded vegetables are more heterogeneous than the markets for grain, fresh dairy and meat.

Solvency: Farmers that have larger financial resources of their own, are likely to adopt innovations earlier. Investment in innovations often require fixed expenditures and are more risky than investments in mature technologies. Therefore, credit constraints may hamper adoption behaviour. The importance of this argument, may be limited to capital-intensive innovations. Diederen et al, 2003, found that there is mixed evidence on the credit constraint hypothesis.

Age of the farmer: The younger the farmer, the more likely he is to adopt innovations early in his life cycle. Older farmers, on average, have a lower level of education, which may be correlated with the ability to judge opportunities to innovate. Older farmers may have a shorter time horizon and be less inclined to invest in novelties.

Sectoral dummies: The more technological opportunities a sector faces, the more farmers are inclined to adopt early. The more regulation and protection a sector faces, the less farmers are inclined to adopt early. The number of technologies used in the production process differs from sector to sector. Greenhouse horticulture for example, uses many different technologies (e.g. for climate control, light control, transport and logistics, sorting, feed composition). On the other hand the number of technologies used in the production process in dairy farming is more limited (feeding and milking systems mainly). New technologies that are relevant for greenhouse horticulture appear much more frequently on the market, than new technologies for dairy farming.

Also, in horticulture, innovations are more likely to be superseded by new innovations before they reach an advanced stage of diffusion, than in dairy farming. Hence, more farmers in horticulture will adopt innovations that are in an early stage of diffusion, than those in dairy farming.

Attitude regarding innovation, existence of follow-up activities and innovation expenditure ratio: Farmers adopt earlier if they regard the search for innovation as a permanent business activity and if innovations lead to follow-up activities; farmers adopt innovations earlier if they invest more in innovation related activities (courses, extension services, professional advice) on a regular basis.

Valuation of internal sources of information and source of innovative ideas: Farmers who consider external sources of information to be important, are more likely to adopt innovations early; farmers who consider internal sources of information to be more important are less likely to adopt early. Farmers who get their ideas from external sources are more likely to adopt early in the life cycle.

Degree of co-operation: Farmers who develop innovations by themselves, or in co-operation with others, or who adapt innovations developed elsewhere are earlier adopters than those who buy new technologies off the shelf.

Eagerness to protect intellectual property: Farmers that seek intellectual property protection are true innovators. Individual behavioural characteristics of the farmers may also explain this phenomenon.

 

Diffusion of Innovation Theory, now and into the future

From an agricultural extension perspective, the following conclusions are current:

  1. The theory is still applicable and can be used to great effect: The theory has stood the test of time and had very considerable influence on agricultural extension planning and programming and the adoption of innovations in rural communities, world wide and the results that have followed from it’s use. Both in terms of intended results and unforeseen ones. The adopter categories and their characteristics, do help explain likely diffusion in many cases, but it has been shown that as complexity of either the innovation, the industry or the environmental circumstances increase; then other factors start having significant influence.
  2. It is best suited to technological type innovations that are less complex:  Those that are more one off, less complex and technologically based. For more complex innovations and more complex and less heterogeneous extension environments; other theories and approaches should also be considered and integrated into a planning and implementation framework.
  3. The potential for bias must be assessed and planned for: Extension professionals need to be aware of the potential for bias in the extension of innovations when using the theory. That is, the uptake of any one innovation may be influenced by anyone of a number of factors. These factors may give farmers and farm businesses, with a particular set of characteristics, a particular advantage. To ensure the uptake or otherwise success of other famers and farm businesses, who do not have those particular advantageous characteristics, may take some alternative effort, which must be planned and allowed for; if the innovation is to diffuse into those businesses. Extension efforts need to assess the potential for bias and plan and implement accordingly.
  4. Farmer’s personal characteristics play a part: i.e. age, personality, degree of connectedness with others, history of using service providers, degree of comfort with using external information; will all have an influence on how likely they are to adopt innovations. Extension professionals need to cater for diversity amongst a farming population, in order to ensure broad uptake.
  5. The type of farm industry involved will have an effect: Farming industries who are more complex and capital intensive of themselves, have a greater likelihood to be accustomed to innovations and implementing them, than other industries.  For example, the intensive horticultural industries versus the extensive, rain fed pastoral industries. Extension efforts will need to be different between industries as a result.
  6. The potential impact on all stakeholders should first be considered: Extension professionals, when using the theory to plan and implement the extension of an innovation. Need to first consider the needs of all stakeholders that may be potentially influenced by the uptake of the innovation and what the likely outcomes for them may be. It also needs to analyse the environmental factors at play (physical, societal, market forces, etc).  Not just the potential economic benefit of the innovation for those that may be innovators or early adopters. The potential impact on all famers and farm businesses individually and also collectively, as part of a broader rural community should be considered. For example, Jim Hightower (1972) observed the impact when there is a particular focus on extending innovations:

“Corporate agriculture’s preoccupation with scientific and business efficiency, has produced a radical restructuring of rural America, that has been carried into urban America.”

 

Taking such factors into consideration, is likely to lead to more effective extension planning and programming and more effective and appropriate outcomes, when using the Diffusion of Innovations Theory.

 

Sources and Further Information

E. Rogers (1995). The Diffusion of Innovations. New York Free Press. ISBN 0029266718 9780029266717 0028740742 9780028740744

J. Hightower (1972). Hard Tomatoes, Hard Times (The Failure of the Land Grant College Complex). Washington, D.C. Agriculture Accountability Project.

G. Stephenson (2003). The Somewhat Flawed Theoretical Foundation of the Extension Service. Journal of Extension. August 2003, Vol 41, Number 4.

R. Peshin (2009). Integrated Pest Management Volume 2: Dissemination and Impact. Springer. ISBN 978-1-4020-8990-9

P. Diederen., H. Meijl., A. Wolters., K. Bijak. Innovation Adoption in Agriculture: Innovators, Early Adopters and Laggards. Cahiers d’économie et sociologie rurales, n° 67.

extensionAUS, Extension Practice. (2016). Diffusion of Innovations Theory – Adoption and Diffusion.

E. Rogers (2003). Adopter categorization on the basis of innovativeness of innovations, (5th ed.). New York: Free Press

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