Diffusion Of Innovation Revisited – A Great Idea or Solution Looking For A Problem?
I have been blogging on innovation and new product development now for a better part of five years sharing with you the things I have learned both as a practitioner as well as a student, researcher and contributor to the body of knowledge related to innovation and new product development.
I have covered a lot of topics in my articles and most recently have focused my articles on the Jobs-To-Be-Done Innovation framework as a reliable and predictable method to improve the odds that your innovation and new product development efforts will be successful.
As I close this year, I wanted to revisit a couple of my earlier articles I published on the theory of diffusion of innovations. It’s the seminal theory that explains why it is so hard to introduce truly new products and ideas. I’ll also share in these articles, proven strategies on how to manage early market diffusion dynamics.
I have added graphics to the original articles to help visualize the concepts better. And I have edited and broken down the original articles into smaller and digestible chunks to make them easier to read.
I’ll also add comments to my post on how the Jobs-To-Be-Done innovation framework fits into the diffusion of innovation theory – and how to use it to manage the uncertainty and risk associated with truly new and bold ideas.
So let’s start here:
The Diffusion of Innovation: What it is and where it began
To be historically accurate, Joe M. Bohlen and George M. Beal in 1957 at Iowa State are credited as the first to describe the theory of the technology adoption lifecycle (TALC) as a sociological model, with its purpose to track the purchase patterns of hybrid seed corn by farmers.
Rogers’ points out in his 4th edition (1995) at the time of his first edition there were already 405 publications about the topic. Rogers significantly broaden the model and is recognized as the seminal thought leader in diffusion theory. His book is worth the read albeit I warn you in advance, it is a text book written with academia in mind. (I have only read the 4th edition. The 5th addition addresses how the internet impacts the diffusion theory – it’s on my reading list.)
Rogers’ Salient Points on Diffusion of Innovation Theory
Rogers wasn’t necessarily talking about technology diffusion per se, though as we will see later, technology diffusion has become a prime focus of diffusion theory. But rather diffusion is the broader study of how communities respond to discontinuous innovations.
Discontinuous innovations can be ‘ideas” or new products or services requiring the end user and the marketplace to dramatically change their past behavior with the promise of gaining equally dramatic new benefits.
The Familiar S-Shaped Diffusion Curve
Diffusion theory can be graphically explained using the now familiar S shaped diffusion curve which is nothing more than the accumulated number of adopters plotted over time.
Figure 1: The Diffusion S Curve:
As described by Rogers “The S-shaped adopter distribution rises slowly at first when there are few adopters in each time period. It then accelerates to a maximum until half of the individuals in the system have adopted. Then the S-curve increases at a gradually slower rate as fewer and fewer remaining individuals adopt the innovation.” Rogers goes on to explain “This S-shaped curve is normal. Why? The reasoning rest on the role of information and uncertainty reduction in the diffusion process.”
The S-shaped curve takes off once interpersonal networks become activated in spreading subjective evaluations of an innovation from peer to peer in a social system. The part of the diffusion curve from about 10% to 20% adoption is the heart of the diffusion process. After that point, it is often impossible to stop the further diffusion of a new idea, even if one wished to do so.
The S-curve begins to level off after half the individuals in a social system have adopted, because each new adopter finds it increasingly difficult to tell the new ideas to a peer who has not yet adopted, for such non-knowers become increasingly scarce.
The Diffusion Model Bell Curve
When the S curve is plotted over time on a frequency basis, the also familiar bell-shaped curve results. Thus the same adoption data can be represented by either curve, but most often the bell curve is used in describing the adoption life cycle.
Figure 2: The Diffusion Model Bell Curve
As explained by Geoffrey Moore in his book “Inside the Tornado,” the bell curve can be used to categorize and explain the process and players in the adoption distribution.
When a market is confronted with an opportunity to switch to a new paradigm, say typewriters to word processors, customers self-segregate along an axis of risk aversion:
Risk immune innovators moving to the forefront – these are the innovators
Risk allergic laggards retreat to the rear of the line (quills still firmly in hand) – these are the laggards.
In between these two ends are:
Looking at the diffusion bell curve we can see that each of the segments in the bell curve represent a standard deviation from the norm. Where the early majority and late majority are defined within 1 standard deviation, the early adopters and laggards are 2 deviations and the innovators are 3 deviations from the norm.
In my next article in the theory of innovation diffusion series, I’ll discuss the characteristics of each segment described in the diffusion model bell curve in greater detail.
Be innovative by trying something new! See you soon.