Innovation needs more people working together better

There’s a lot of good discussion going on right now about how social networks and social interactions can support innovation. The challenge is both getting the right people together and having them interact in the right ways.  Crowdsourcing lets individuals generate ideas, but better innovation can come from the interaction of people with a diverse set of skills and interests. How can such a group of strangers, be assembled, and how can it function well together and be productive in a minimal amount of time?

In my view, the quality of the results in collaboration problem-solving or collaborative innovation is a function of the diversity of input and the quality of the interactions among the people.  So if someone could bring together many diverse people and use outstanding group problem-solving methods, they’d get better results than a relatively small group that works well together (e.g., a good team) or a diverse set of people who don’t work together (e.g., Innocentive). Some examples of interesting collaborations that have brought together larger groups than a team and also used methods that enabled people to actually work together (in team-like or quasi-team-like ways) are shown on this chart, with a view of their effectiveness.

 

 Here’s an explanation of each of the methods on the chart.

1. Innocentive is well-known platform for solving problems. Individuals who think they have the answer can submit and be paid if their answer is selected. My understanding from the popular press (correct me if I am wrong) is that many (maybe most) Innocentive problems that are solved are solved by an individual who sees fairly quickly that he or she has some knowledge that can be applied to the problem. There’s not much teamwork.
2. Innocentive (Chinese style) is a reference to something I read (maybe from John Hagel) that in China groups of Innocentive participants collaborate to decide on which problems to attack. So they get the variety of problems from Innocentive and can combine their skills to solve them. In Good to Great, Collins and Porras talk about “getting the right people on the bus” and then figuring out what strategy to pursue. Taking advantage of the stream of Innocentive problems, the Chinese groups have assembled the “right people” and can look for the problems that this group of people is well-positioned to solve. My estimate is that this method will yield greater fruits than what individual Innocentive participants can do.
3. The Netflix Prize challenge. This well-known contest produced every larger groups of collaborators as it neared its end. As the teams grew, they were able to use both traditional improved problem-solving from larger groups as well as collaboration that in some ways was unique to the problem – they were able to mathematically combine algorithms (their solutions to the problem) to get better results.
4. Open Space methods – These methods bring people with a stake in the problem together and then let them work together on what they believe are the important elements. They yield results that cannot be easily predicted but are very powerful.
5. Polymath. This was a high-level mathematical collaboration. It yielded outstanding results through open collaboration of many people. Polymath was conducted on a blog and guided by a set of rules that encouraged people to share ideas that were not complete but that others could build on (or refute early, before too much energy was devoted to them.

The the interesting challenge is figuring out ways of bringing the right people together and structuring their interactions in the best way.