How to give your idea the best chance of success
I enjoyed the responses to this tweet from Nick Parker.
A whole range of technologies was shared, here are a few interesting examples:
“The idea of IoT (Internet of Things) in general. Not a single tipping point but a convergence of: (fairly cheap) sensors everywhere, better network bandwidth, better batteries, and the cloud.” ⟶ Tweet from @seanmfdineen
“Accurate mechanical timekeeping — Took 500 years to get from the first mechanical clock to Harrison’s first sea clock. A lot of technical innovations, as well as a lot of public and private investment, were needed to get to that point.” ⟶ Tweet from @AGMonro
“Maybe mundane, but LEDs suddenly passed some sort of cost/performance barrier a few years ago and went from being the little diodes on stereos to changing the way cities look.” ⟶ Tweet from @snillockcirtap
“Shipping containers … first used in the 1760s … became mainstream in 1960s/70s” ⟶ Tweet from @davegentle
It got me thinking about the tipping point or critical mass of innovation and its usefulness for understanding the broader theories of change.
For a long time, I think education has focused on the wrong part of innovation theory.
Diffusion of Innovation
Before we get into talking about the pattern or curve of adoption that a new idea takes, it is worth reminding you of the work of Everett Rogers.
Back in 1962, he wrote The Diffusion of Innovations, where he looked at the rate at which an idea spreads through a community.
Although telecommunications and digital technology later co-opted this model to explain why your Dad doesn’t use a smartphone, the original innovation Rogers studied was in agriculture.
He looked at the shift from farmers using particular variants of crops and livestock to more widespread adoption of a new way of doing things.
What encourages the adoption of a new idea?
Rogers proposed five main factors that contribute to the rate of adoption.
He hypothesised that there is a direct relationship between the characteristics of the innovation (relative advantage, compatibility, observability, complexity and trialability — how easy it is to try out) and the percentage of people who adopt it over time.
It is these characteristics that I find the most fascinating and which are often overlooked:
#1 Compatibility
Why would your father want to have a smartphone? Because his friends, family and colleagues already do! Technology has to be a good fit for people’s lives and interests. Innovation must be compatible enough with existing beliefs, values and practices.
#2 Trialability
The ratio of effort it takes to try something out versus the benefits gained from doing so. In an interview exploring these ideas, Rogers stated that “the more convenient a test is for you, the less involved or complicated it is to get into a trial, the easier it is going to be for you to make up your mind about trying a new idea”. This suggests a threshold effect — you’ll try something if there’s little risk and the benefits outweigh whatever barrier might exist between thinking about trying and doing it.
#3 Complexity
The more complicated the idea, the more time and effort to try it out. If there is a choice of which new thing to try out, this factor suggests that people choose the more straightforward option. Rogers says that an innovation will be adopted when it is “simple enough to understand and use, but complex enough to offer challenges”.
#4 Relative Advantage
This one also gets considered in behavioural economics through concepts such as loss aversion (people are far more risk-averse when it comes to losing something than gaining the same thing). This is probably the one we most want to understand of all the factors. It is, after all, the idea that anyone adopting a new technology must have more to gain than they have to give up.
#5 Observability
I find this factor fascinating because it suggests that whether people take up the idea depends less on its benefits than how easy it is for them to see other people using it. This one is fascinating in the context of education. We all know that using the latest technology in class isn’t enough; people also have to see it used well and learn how to get started with it themselves.
Adoption Curves
Rogers proposed that a new idea or technology would typically follow a bell curve to diffuse through a community. This is referred to as the S-curve, adoption or diffusion curve. The emphasis was on members of a social system and our labels for them.
There was a time when every education keynote — yes, even mine — provoked an audience to think about the different groups of people that adopt an innovative idea.
The segment names directly illustrate the group’s propensity to adopt an innovation.
- Innovators
- Early adopters
- Early majority
- Late majority
- Laggards
Over time, these groups adopt an innovation at different rates and represent different community proportions.
While this pattern of adoption is not universal, it happens often enough that we can use it as a general way of understanding how people will accept new things.
Focusing on labelling people and their response to the idea is a dead end. It is much more helpful to think about the characteristics of your idea that might be changed to encourage adoption and acceptance.
Your Talking Points
I have two key provocations for you to ponder and reflect upon.
- Within the theory of diffusion, we are not attempting to change a person’s label from one to another. It is not people that change but the innovation itself as, over time, it improves, changes and diffuses throughout a system.
- Identify a new programme you are starting this year — your innovation — and score it according to the five essential characteristics: relative advantage, compatibility, observability, complexity and trialability. How could it improve?