Assumptions of basing our work on diffusion of innovation

In our last post, we asked you to think about why, as an adult educator, you do the things you do. We suggested that for many of us, especially those working in Cooperative Extension, the “why” was based at least in part in diffusion of innovation theory. Unfortunately, diffusion of innovation theory and the assumptions it leads to are causing us to fall short.

Most of the foundation of diffusion of innovation theory was established more than 50 years ago. In 1941 Bryce Ryan began studying how the innovation of hybrid corn, released in 1928, spread across Iowa. His 1943 study with Neal Gross showed that the adoption of hybrid corn began with a small number of farmers and diffused from there, implying that targeting innovative farmers to adopt innovation would speed up the adoption among all farmers (Stephenson, 2003).

The work of Ryan and Gross led to further studies, most notably by Everett Rogers, who developed the classic adoption curve and the categories of adopters in 1958.

Innovation Adoption Curve
Image by Jurgen Appelo, Flickr, downloaded 12/5/2016,

These categories and the resulting focus on innovators and early adopters have led to serious questions about Cooperative Extension’s reliance of diffusion of innovation theory, including Garry Stephenson’s question, “By Utilizing Innovation Diffusion Theory, have we caused harm in some way to the population we serve?”

Stephenson points out that a focus on innovators can widen gaps in equity. Innovators in agriculture tend to have higher incomes and larger operations than non-adopters. By marketing innovations first to innovators in hopes of influencing others, we may be widening the gap between the haves and the have-nots. In agriculture, non-adopters can be further hurt when bigger operations adopt innovations that increase yields which lowers crop prices.

The focus on innovators is especially concerning in light of the work of Duncan Watts and his colleagues suggesting that under most conditions, social change is driven not by “influentials” (opinion leaders) but by easily influenced individuals influencing other easily influenced individuals (Watts and Dodds, 2007) (Thanks to reader Kevin Gamble, @k1v1n, for pointing out Watts’ work).

Watts says a trend’s success depends less on the person who starts it and more on whether the conditions favor that trend (Thompson, 2008). Creating the right conditions is complex. What makes not just one person, but a majority of people ready for change? It’s complex, and our reliance of diffusion of innovation theory leads us to simplification.

Cooperative Extension tends toward a one-size-fits-all approach that better aligns with our reliance on mass media. If we think of our audience as a homogeneous group, all equally ready for change, we can rely on a single message delivered on a limited number of channels to reach them.

Relying on diffusion of innovation theory also simplifies how we look at problems. It leads us to assume problems can be solved by innovations, especially those devised by “experts,” and especially those “experts” at our land-grant universities. However, not all problems can be solved by innovations. Cooperative Extension is being called upon to help address “wicked problems,” complex social issues that cannot be “solved.”

As we said in our previous post, diffusion of innovation theory is implicit in the logic model which in turn guides our program planning. But, as Thomas Patterson pointed out, our planning model has “failed to result in programs capable of solving ill-defined, complex human problems where there’s disagreement on the desired outcomes” (Patterson, 1993).

We need new theories that support our work in the areas where diffusion of innovation leads us to fall short.

Readers suggest alternate theories

In addition to Kevin’s suggestion that we look at the work of Duncan Watts, readers of our last post also suggested Embracing Chaos and Complexity: A Quantum Change for Public Health (thanks Peg Boyles @ethnobot), the Unified theory of acceptance and use of technology (thanks Jared Decker @pop_gen_JED), Extension 3.0: Knowledge Networks for Sustainable Agriculture from UC Davis  and Adaption – Innovation Theory from Virginia Tech (both thanks to Jeff Piestrak @Jeff_Piestrak). Each of these theories/efforts can serve to complement (and sometimes contradict) the theory of the Diffusion of Innovation.

If you are familiar with any of these theories, or others, which do you think holds the most promise for Cooperative Extension?

Authors: Bob Bertsch (@ndbob), Karen Jeannette (@kjeannette), and Stephen Judd (@sjudd)

This post was published on the Military Families Learning Network blog on January 11,2017.

Creative Commons License This work is licensed under a Creative Commons Attribution 3.0 Unported License.

3 Replies to “Assumptions of basing our work on diffusion of innovation”

    1. Great question Glenn – I think this is one of the main criticisms leveled in the Stephenson paper referenced in the post. When we try to decide in advance who the innovators are, we may find ourselves “returning to the same well” over and over. Historically, in ag, this has meant larger and wealthier farms, who we perceive more able to assume the risk of innovation.

      I think innovators occur within a specific context – as you mention, that includes time, place, and subject. If nothing else, we should be aware of our perceptions of who the innovators are, and how that might impact others.

  1. Glenn & Stephen – This conversation has me wondering to what extent we end up identifying innovators after the fact or in hindsight? In our current historical and cultural context, we seem to commonly conflate innovation, especially technological innovation, with commercial success. If so, how does this affect our ability to perceive and learn from innovations that occur outside the spotlight of commercial success?

    One of the things I appreciated about the Stephenson paper is that it raised some fundamental questions about what we might mean by market success when considering outcomes.

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