Seven Factors of Innovation Diffusion

In predicting how your new product idea may take off, you should consider the 1962 work by Everett M. Rogers called Diffusion of Innovations. Rogers also describes the S-shaped growth curve of innovations, and is also known for coining the term “early adopter” – a term familiar to those of you who have read Geoffrey Moore’s famous book, Crossing the Chasm.

Rogers outlines the following seven attributes that determine the rate at which an innovation diffuses in the marketplace. These can serve as a good rule of thumb for forecasting the speed at which a new product or technology will be adopted.

1. Relative Advantage: How much better is your innovation than the incumbent solution?

2. Compatibility: How easily can your innovation fit with the existing infrastructure and ecosystem?

3. Complexity: Is your innovation easy to adopt and use, relative to the current method?

Diffusion of Innovations
Image by Wesley Fryer via Flickr

4. Observability: How easily can customers see the differentiation and benefits of your product?

5. Trialability: How easy can customers pilot or test your product?

6. Social Acceptability: Does your product impact current social norms?

7. Regulatory: Are there legal or bureaucratic issues related to your innovation?

Examples: DVD vs VHS and E-Commerce vs. M-Commerce
OK, so let’s put this to work using a couple of examples that we can all relate to. The diffusion of DVD players was a relatively fast one, where as mobile commerce (or m-commerce) was predicted to be huge back in 2000 ($100 Billion by 2005), but in effect has only recently shown viability. Let’s look at why one was fast, while the other was slow.

Doing a quick back of the envelope analysis, let’s look at each:

DVD M-Commerce
Relative Advantage POSITIVE NEUTRAL
Social Acceptability POSITIVE POSITIVE

When you add up the columns you can understand quickly the net effect of why DVD Players were adopted quickly, whereas M-Commerce was (or is) slow.

A Good Rule of Thumb

So for a 1962 model, it’s pretty darn good. I think if we had to modify it today, I’d add in another item that talks about potential for positive network effects. Overall, this provides us with a useful framework to evaluate new products and technologies, and where / when to make investment decisions when key criteria change for the positive.

Try using it yourself to evaluate a few new products (e.g. Bing vs. Google, Biodiesel fuel, a better mousetrap) and let me know what you come up with.

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