Mental model for assessing innovation

From HorizonMuseum

David’s comments about mission driving adoption made me think harder and more reflectively about the algorithm I’m using to assess short and mid-range likelihood of adoption and impact for these innovations. I thought it might be useful to be a bit more transparent—both to illuminate my own thought process and to seek feedback that might improve my (and our) thinking. So here’s a rough guide to how I assess the probability that a particular innovation is going to have an impact in a particular time-frame. I’m really just noodling this up, so it’s possible I’ve omitted something important to my own thinking; it’s also possible that I’ve omitted something that should be important to my own thinking but currently isn’t. Your thoughts very welcome.

• Value proposition: what does the innovation promise to accomplish at its best? At its most likely? Is that value proposition one that institutions in the target market will see and accept analytically, or is it one that they will have to be shown empirically by some group of thought-leaders? Immediate acceptance of the value proposition usually implies an incremental improvement on an existing behavior, and drastically shortens the adoption curve: 1-2 year widespread adoptions may become possible. Even if thought leaders get behind a wholly novel innovation and push, adoption curves lengthen to a minimum of 3-5 years and usually longer for widespread penetration.

• Interest alignment: who wins and who loses from the innovation? Lots of great ideas falter because they help those with no power to adopt them, while harming those with the power to block them. Win-win innovations have the shortest adoption curves; zero-sum innovations can be almost as fast or even faster if the winners are those currently in power, or very slow or stalled if the winners are those without power and they would gain power vis a vis the current winners. Truly disruptive innovations are often negative-sum (everyone currently involved loses something, so that a new crop of entrants can benefit). OER is a potential example of this.

• Sustainability model: some people might prefer the phrases “business case” or “business model,” but I work in open source, so this phrasing is more congenial. This assessment draws heavily from the assessment of value propositions and interest alignments: if enough participants cannot make a compelling case on a steady-state basis for why they should continue to support an initiative, it’s not going to survive. But one needs to look further, too; for instance, it’s common for a sustainability plan to base itself implicitly on an assumption that it will be the only or the dominant player in a market (the seeds-of-its-own-destruction fallacy), but success breeds imitation, so that assumption is usually a red-flag. It is commonplace for project leaders and pundits to entirely ignore the sustainability model in their focus on the coolness or prospective benefits of the project, but intelligent institutional adoptions tend to be driven strongly by sustainability assessments—and it’s a bad idea to base your short or long-term planning on the assumptions that institutions will irrationally adopt something that they don’t know how to keep going. If the hype quota is high but the sustainability model is poor or contains the seeds of its own destruction, the innovation may flourish in the very short term but won’t last. Virtual worlds are a great example of an innovation with a huge sustainability-model problem.

• Cost: in a perfect world, cost would only matter in terms of benefit or return on investment; in the real world, excessive cost can prevent an institution from investing even if the benefits would be substantial. Other things equal, projects that reduce costs or work within existing budget categories are more sustainable, and more likely to be adopted quickly and easily.

One can easily add a lot of richness to this basic framework; for instance, one can look for synergies between innovations and other innovations or established practices. But most of that richness can also be handled within one of these four categories, as far as I can see.