Issues come and go, and some of them even come back. Here’s a cut and paste of a recent blog post called “Dangerous(ly seductive) curves”
or “smacking your (rule of) thumb with the hammer marked ‘brain’”.
Narratives are great. They help you arrange (or even create) facts that fit in a nice orderly view of the world. If there is a graph to go along with the narrative, they’re even more comforting. I mean, it’s science, right?
So, two of my favourite dangerous curves are the issue life-cycle model of Anthony Downs (1972) and the Hype-Disappointment Cycle that the Gartner Consultancy came up with. I’ll discuss each in turn.
The lifecycle model talks about how issues come onto the public radar, everyone gets alarmed and exuberant, then realised the actual cost of doing anything [which is often forcefully highlighted by the industry under attack] and then the ‘issue’ gets kicked into the long grass and forgotten (or perhaps, not to be too cynical, actually got resolved. It can happen.)
Downs’ article was fab, but – as a lot of work shows since – there were plenty of unanswered questions. Why do some issues catch hold and others don’t? Which ones sputter out? How, exactly, do issues move up the cycle? What are the different actors (social movements, governments, industry) doing in each stage? (Thus theDialectic Issue LifeCycle Model)
Hype cycles talk about that initial exuberance behind a Shiny New Technology, with it getting boostered by folks who want (you) to believe it is the Answer To Everything. And then reality intervenes, everyone loses interest/moves to the next SNT. Some hardcore fans stick around, dust off the battered tech, and it slowly climbs in stature and usage.
Except…. As Borup et al. (2006: 291-2 )put it
However seductive, there are a number of serious problems with this form of representation. First and foremost, the model is too general in not providing enough room for the kinds of variation and unpredictability that characterize the place of expectations in technological, let alone, social change. Many cases, for example, do not show a neat slope of enlightenment, and simply stop at disillusionment or continue with a new inflation of expectations. Critically, this way of thinking about change re-introduces a highly linear understanding of a technology’s path dependency and fails to account for the way artefacts or technologies actually change over time in a continual and practical process of reconfiguring and being reconfigured in use.
So, the lesson is, don’t let an eye-pleasing curves distract you. Same probably goes for diffusion of innovation curves, but I am hardly acquainted with that, so wouldn’t pretend adequacy.
Borup, M. Borwn, N. Knorad, K. and Van Lente, H. 2006. The Sociology of Expectations in Science and Technology. Technology Analysis & Strategic Management. Vol. 18, (¾), pp.285-298.
Downs, A. 1972. Up and down with ecology – the issue attention cycle The Public Interest Vol. 28 (2)