Four C’s of digital media

Gaurav Mishra wrote a guest blog post at Beth Kanter’s blog, on the 4 C’s of social media, complete with explanations and possible uses of this framework:

1. Content
2. Collaboration
3. Community
4. Collective Intelligence

I like the way that Gaurov puts these on the axes of becoming more visible and at the same time more difficult, as one progresses from content creation to collective intelligence. His rationale for the framework:

If you are a journalist, analyst or academic in the business of understanding social media initiatives, you’ll find the 4Cs Framework really useful. What are the boundary conditions needed to succeed at each layer? What are the boundary conditions needed to move from Content to Collaboration, from Collaboration to Community, and from Community to Collective Intelligence? Can you think of other digital activism or social media initiatives that leverage the Community or Collective Intelligence layers?

Clark Quinn and I have discussed frameworks for social media before and we came up with four C’s from a different perspective in a bit of a back-of-the-napkin exercise. I put them on a scale that made sense to me, with particular regard to network effects, the essence of Web 2.0:

a network effect  is when a good or service has more value the more that other people have it too … Examples include e-mail, IMing, the blogosphere, and even the Web itself.  But what’s not clear from this description is the raw power that is caught up in and represented by network effects.  Most rigorous studies and mathematical formulations reveal that there is tremendous geometric power in network effects.

The figure below is what Clark and I developed as an initial concept on the digital artifacts of social media. As one moves from content creation to contextualization (through grouping, tagging or rating), the potential network effects increase. This gets greater as people connect to the artifacts (through comments, linking or discussion) and then to co-creation, such as mashups or remixes. The basic idea is that as more people manipulate digital objects and give them meaning and context then these objects will gain in value. A YouTube video of an unknown person lip-syncing a popular song has little original value, but when that video (e.g. Numa Numa) gets over 30 million views, links & comments, network effects increase its value to perhaps more than the original song. The creator gets tangible value through the network in the form of guest appearances or fees for another video.

This is still an idea in progress but is another example of why giving up part of your value chain and letting it loose may actually increase value for the creator.