Living up to Metcalf's law on social innovation communities

In my presentation last week at spigit's customer summit, I presented some statistics on spigit's social innovation communities including a comparison between actual connections established over a social network and the maximum possible connections - the basis for Metcalf's law (which is really not a law but we will continue the tradition for the time being). Given the warm reception from the audience at my explanation, I thought I will dig into this a little bit more. This blog post is the result of my investigation.

First let me set some context on Metcalfe's Law. Metcalfe, inventor of the Ethernet protocol, was originally talking about how the "value" of a network of compatibly communicating machines grows quadratically (not linearly). I found this post that shows a reproduction of his original slide. The basis for this assertion is the fact that in a network of "n" nodes, each node can communicate with n-1 nodes hence there are n*(n-1)/2 possible pair-wise connections on such a network. The value of a network is really in its power of establishing communication among its nodes. The number of possible connections is of the order O(n²) as n becomes large which leads to Metcalfe's law.

The mathematics works the same for any network so it is possible to apply the basic thought to other networks including human ones. If each person in an on-line community communicate with all other members, the total number of communication links will be of the order O(n²). Although this may be the ideal scenario, what's the reality like? I performed some analysis on Spigit communities to answer that question. The chart below shows the number of connections in the network plotted against the size of the social network. Three types of connections are plotted:

1. Conversational - Post-response connections between original poster of an idea, blog post, forum thread etc. and a commenter. Note that multiple exchange between two people count as one connection.
2. Conversations and Evaluations - In addition to conversations, this includes voting/rating interactions between two users
3. All connections - These include viewer-poster and linked-in style connections in addition to 1&2

This sanitized version of the graph does not show the numerical data but does depict the trends I observed in my analysis. The chart also shows a plot of linear and nlog(n) functions to give readers an idea about the order of change. Looking at the chart and the numbers behind it, it is clear that the number of conversational connections increases more or less linearly with the community size. The number of connections representing all interactions certainly did not grow at a quadratic rate but the fluctuations were too high to draw any meaningful conclusion. The linear variation in conversational connections is consistent with the long tail pattern of conversations found in all communities. There are a relatively fewer number of individuals who are extremely well connected, but the majority of users are part of the long tail and have a limited number of connections that is independent of the community size.

It is evident from the chart that there is a significant variation in the connectivity utilization between different communities. When I examined the dynamics within more successful communities (in terms of user connections), I found three key factors:

1. Well-defined innovation process -  All successful communities have a very well defined process consisting of idea sourcing, evaluation, and selection. The process is well publicized and executed as promised.
2. Incentives - Most communities have fine-grained incentives for encouraging broader participation in addition to allocating funding for implementing selected ideas. The incentives range from tangible items like gift cards to recognition by leadership to purely monetary rewards corresponding to the play money earned in Spigit's virtual economy.
3. Emphasis on "social" - Some of the well connected communities emphasize social interactions by promoting blogging, open exchange of thoughts on discussion forums, and (now) traditional collaboration over wikis. Innovation is embedded within this larger context.

It is interesting to note that not every successful community incorporated all three factors. Also in general increased connectivity did not necessarily lead to a better result in terms of number of quality ideas that get selected for implementation. That is strongly correlated only to the first two factors. Conversely in some communities,  organizational culture inhibited people from freely collaborating on proposed ideas, but the outcome has been significantly better compared to traditional processes due to support from the executive team and strong innovation process management. This is an important point to consider when setting up enterprise communities. Whereas communication within consumer-oriented social networks is very often an end in itself, for enterprise communities communication is a means to an end. For spigit communities, the goal is the selection and implementation of ideas that provide business value and achieving that requires channeling community interactions to that end.

It's also worth mentioning a few patterns that I thought I would see but didn't. I didn't find any correlation between different business sectors and network connectivity nor did I find more interactions within communities that had higher percentage of knowledge workers. There was C-level involvement in a couple of cases but in many communities the innovation effort was lead by an internal champion who was passionate about making the innovation effort successful. Finally the size of the community didn't seem to have much effect on the networking pattern even though it did have an effect on the number of participants that posted on the innovation site.