Before I reply to comments from KatB & Simon Edhouse on The Tagging Growth Curve, a quick reminder that this series of postings has as it’s primary point of departure the idea that the smoothly drawn analyst’s curve for describing technology growth misrepresents the noisy and unpredictable fluctuations of reality. The outcome is that this conversation is as much about the models currently in use in the technology industry for framing how technologies grow, change, and spread, as about the specifics of tagging.
At some point, it may be necessary to branch these topics in pursuit of further clarity; on either, or both in relation.
@Simon: Incremental growth along these two paths is what we’re seeing at the moment. The apparently slow increase in the size of the total population of people and organizations who’ve adopted tagging is consistent with the idea that innovation happens during those periods when the rate of diffusion is slowed, due to the increased requirements of crossing a community boundary.
A partial list of those requirements would be:
* lower cost
* rising user experience quality
* social facilitators: advocates, ambassadors, evangelists
* conceptual bridges – like ‘horseless carriage’ – to overcome the increased friction of reconciling the new and different with an old frame of reference lagging in terms of awareness and understanding of tagging
The graph charting the curve for the rate of innovation – if it’s possible to chart something as slippery as innovation – would likely appear as the inverse of the growth curve. A curve for innovation would show spikes (for quality of innovation, quantity of innovation, or both…?) during the intervals when the spread of tagging is slower.
Anyone who wants to understand the mechanisms, rates, trajectories, etc. of technology growth should really look at diffusion and innovation as they inter-relate. Doing otherwise seems like trying to understand population sizes based only on the single factor of births or deaths, but not both working together.
@KatB: Technology innovation is definitely a social process – good of you to surface that angle explicitly. Social tagging is – well – inherently social. Taking that for granted (which says a lot about my initial frame of reference), I brought in the biological model without mentioning this given.
My knowledge of the Technology Acceptance Model is limited. What makes it a good model for the new explicitly social technologies? As opposed to the previous (non-social) technologies prevalent when TAM was formulated?
My goal in suggesting punctuated equilibrium as a better model than the Gartner Hype Cycle was to explore the fit of a different way of looking at tagging than is customary for the broad IT frame of reference. That does not discounting any other kind of model directly. There may be fundamental conflicts between one or more of the various models on the table that I’m unaware of at the moment, but then I’m unaware of them
Another question to ask in testing how well punctuated equilibrium fits as a model for diffusion is “Does punctuated equilibrium apply to open source technologies?”
Ravi of Luinuxhelp posted a mind map of the lineage of linux distributions in April of 2006 that surfaced in the open source and linux communities. The updated version of that map looks like this:
And these two images (from Patterns and Rates of Species Evolution by Michael J. Benton) illustrate speciation as it appears under punctuated equilibrium; first in general, and then specifically for bryozoans in the Caribbean.
We should keep two things in mind while considering these evolutionary charts: the illustrations come from completely different frames of reference; and apparent similarity – visual or otherwise – is no guarantee of genuine similarity on any level. But there is still compelling likeness in these renderings of the lineage of an open source OS, and “tiny colonial animals that generally build stony skeletons of calcium carbonate, superficially similar to coral”. Clearly, in the real world, linux distros and bryozoans are wildly unlike. Yet their evolutionary trajectories may show some of the same patterns at this level of abstraction.
Which means that punctuated equilibrium as a model might have something to say about open source software in general. [Here I have to say that informed contributions from the linux community are welcome, as I'm not qualified to discuss it's workings in any detail.] And if such is the case, then punctuated equilibrium seems to correspond to the known patterns of diffusion and evolution in the two major spaces in which software technologies evolve at the moment (until another production model arises) – commercial, and open source.
And just in case there’s any question about my professional qualifications for discussing evolutionary mechanisms, it should be clear I am in no way a sociobiologist, ethologist, geneticist, evolutionary biologist, morphologist, etc. So I’m borrowing freely from other fields to seek a new model, which means I run the risk of borrowing concepts from either (or both) in ways that don’t make sense in their original contexts.