A Few Innovation Ideas

How do we get ideas to spread? It’s a critical question, and one of the ways that we distinguish between invention and innovation. For me, an invention is a clever new idea, an innovation is a clever new idea that is packaged up in way that enables it to spread. There’s a big difference between the two. I ran across several interesting articles today that shed some light on how to get our ideas to spread.

The first is a profile of Duncan Watts from Fast Company. The article looks at the question of whether or not you need to spread ideas primarily through targeting superconnectors, an idea put forward by Malcolm Gladwell in The Tipping Point. Watts suggests that peoples’ tendency to be receptive to new ideas has a greater impact on whether the idea spreads than who starts spreading the idea does. This result is very similar to the findings of our colleague Andrew Stephen.

This has some interesting implications. One is that ‘Influentials’ actually aren’t any more effective at sparking trends than normal people. This leads directly to the second point, which is that ideas spread most effectively when the time is right. Taken together, this makes it very hard to predict which ideas will spread, and it also makes it difficult to develop a strategy to make your ideas go viral.

I think that the best response to this is actually to approach innovation alogorithmically. What this basically means is that the way to innovate is to generate a lot of ideas, figure out ways to try them out cheaply and quickly, and then scale-up the ones that seem most promising. The FC article describes an advertising strategy devised by Watts that functions very similarly to this, and I think that it is the way to go.

The second article that caught my eye was Atul Gawande’s New Yorker piece on health care reform in the US. In assessing the bill that the Senate passed last night, Gawande applauds the way that this experimentation mechanism is built into the bill. It does not specify precisely how the new health care system will function, rather, it provides a platform for encouraging experiments and a path for getting the best new ideas to spread. Gawande describes how this approach worked during reform of the US agricultural system at the start of the 20th century, including the efforts of Seaman Knapp in Terrell, Texas:

Knapp knew that the local farmers were not going to trust some outsider who told them to adopt a “better” way of doing their jobs. So he asked Terrell’s leaders to find just one farmer who would be willing to try some “scientific” methods and see what happened. The group chose Walter C. Porter, and he volunteered seventy acres of land where he had grown only cotton or corn for twenty-eight years, applied no fertilizer, and almost completely depleted the humus layer. Knapp gave him a list of simple innovations to follow—things like deeper plowing and better soil preparation, the use of only the best seed, the liberal application of fertilizer, and more thorough cultivation to remove weeds and aerate the soil around the plants. The local leaders stopped by periodically to confirm that he was able to do what he had been asked to.

In a very poor year for cotton, Porter’s profits jumped substantially. This led him to use the new ideas over his entire farm. Many other local farmers followed suit, and the federal government gave Knapp more funds to expand the program. This was happening all over the country, and it transformed the agricultural industry – first in the US, then around the world.

So, again, try things, figure out what works, then scale up. That’s an innovation algorithm that works.

Note: If you’re reading this and you feel bad about thinking about work in the middle of the holiday season, remember that we’re just doing what Chris Brogan is telling us to do:

Student and teacher of innovation - University of Queensland Business School - links to academic papers, twitter, and so on can be found here.

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12 thoughts on “A Few Innovation Ideas

  1. I agree, with “…the best response to this is actually to approach innovation alogorithmically.” Generating a lot of different ideas will bring more diversity. I believe that the more diversity we have as innovators the more ideas there are to chose from, and when there is more to chose from as a result there will be better ideas. Something i’ve learned from this article is the idea that Chris Brogan has about competitors slacking from Thanksgiving to New Years. Now that I think about what he has said, I believe he is completely right.

  2. Thanks for the feedback Nate. I’m glad that you agree about the benefits of algorithmic innovation – I think it is a sound strategy.

  3. Let a thousand flowers bloom! And it’s even faster to do nowadays with some many tools at our disposal, you can get anything setup within a week and give yourself a month to experiment.

    Algorithmic innovation, I like that term!

  4. Thanks Jorge! I’m still not entirely sure how to set up an innovation algorithm, but it does seem like a good idea!

  5. I don’t think I would agree:
    – that creating many options first is efficient
    – that getting folks aligned (to one) after that is likely

    I do think I agree that
    – finding who wants “what”
    – helping them seek “what” might work
    – trying (only) ideas that seem most viable
    – using, measuring, and scaling (only) those
    might work sooner, and better, and algorithmically(?)
    I’d call that “Collaboration On Purpose”

    Divergence versus convergence..
    – isn’t that the question(?)

  6. Thanks for the comment George. I guess I’ll be happy with 4 agreements versus 2 disagreements!

    I’m not a big fan of creating a lot of options beforehand either, actually – what I’m really trying to get at is the idea that we should try a lot of things, which is a bit different.

  7. What I heard was the question of how to gain momentum via the early adopter thought leaders, ‘influencials’, and super-connectors, etc. My observation is that these efforts are akin to “pushing water uphill” – and out of sequence.

    Conversely, a reference to the agricultural “transformation” example given above suggests that going with gravity worked so much better:
    – all stakeholders already wanted one result (profit)
    – one stakeholder was willing to risk one crop cycle
    – one ‘expert’ offered insight and specific instruction
    – one test/iteration was risked (with great results)
    – many other stakeholders willingly followed/engaged
    because of less risk to adopt what was a specific and proven approach (not many and varied and unproven).

    The wise County Agent already knew the outcome (or could reasonably expect success because of many and varied testing back at the Ag Experiment Station). He wasn’t testing many things with the farmer, just demonstrating one that would work.

    My caution was of course not to go test a bunch of unproven concepts, or worse yet implement those that have already proven elsewhere to be ‘unfit’.

    Applying your algorithmic approach is great, if we take what has already been shown to work (like in health-care) and scale that up as you suggest. I don’t think you will have to seek early adopters to gain momentum but may be over-run with willing co-conspirators to implement that innovation, and to enjoy that mutually-beneficial ‘change’.

    I applaud all risk takers like farmers who must seek innovation, and do adjust (ever so cautiously), just to survive. We should all be so prudent. I shudder at turning bureaucrats loose with discretionary money to “try a bunch of things” ( since they are so prone to scattering more weeds, or turning the hogs loose to pillage the crops).

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