Technology Adoption Curves
Technology Adoption Curves
Introducing the third and final pillar for our research, the one that gave inspiration for our name.
“Any sufficiently advanced technology is equivalent to magic.” - Arthur C. Clarke
Introduction
Firstly, this briefer article was much slower for me to get out. Working full-time and publishing content like this can be challenging, so I will make sure I put more time aside to do this moving forward. It is also a point to raise to my current reader base that I one day hope to make this my full-time job, so at some point in the future I will take this publication to a paid one. I don’t see this happening for another 12 months, but I thought I should be upfront about my intentions so it doesn’t come as a shock if and when I make that decision.
When I came up with the name of this publication, I already had the three key pillars of my research laid out: Bitcoin's principles, Public Interest Technologies, and technology adoption curves. While the Curve in the Bitcoin Curve comes from this, I also recognise that the adoption curve is a very simplistic and incomplete way to explain this pillar.
Technology adoption curves are just the outcome of how technology is adopted by the population; they don’t explain why it occurs this way. For this research, we need to understand why so that we can take these learnings and practically apply them in other industries we are looking to scale Bitcoin into.
This article will discuss this at a high level, and the next one will provide more academic detail. Even if you aren't a Bitcoiner, given my current network, you will likely be a technologist to some degree and so this should be something you will get value from as it can be applied in your field.
With that, let’s dive in
Technology Adoption
The Adoption S Curve
When I read blog posts, Twitter conversations and LinkedIn posts about technology adoption and/or Bitcoin adoption, they often include something about technology adoption curves, S curves or something along those lines. It often shows Bitcoins adoption alongside other famous technologies like the car, microwave, internet etc. which gives the communities reading these a lot of hope and confidence that we are on the winning path. So what do these look like?
Figure 1: Technology adoption s curve1
The above, sourced from the Whatfix blog, is a good overlap of the two adoption curves that I’ve seen when anyone talks about technology adoption. The yellow line is the S curve, and the blue is the bell curve. The only difference is that the S curve is a cumulative curve, and the bell curve is an adoption level at a point in time.
These are great tools when trying to measure Bitcoin adoption against previous technology adoption rates as an indicator of whether we are on the right track and also whether the pace at which we are seeing adoption is faster or slower when compared to other technologies. It gives us the information to make some decisions on where to focus our time and attention, but what it is being mainly used for at the moment is to understand the risk of the asset. A fast adoption curve would indicate less risk, and one that’s slowing down or flat-lining is high risk.
The challenge of using these alone is that they lack the detailed information to take any meaningful action in response to what we see. I get stuck asking, “Why is this happening?” without any meaningful direction on what to consider.
As I dive into the research on scaling Bitcoin beyond money, I need a more robust framework that provides a historical view of what drives adoption so that I can draw out the success criteria from the past and embed them into Bitcoin scaling to de-risk adoption.
This is where academia comes in.
Academia
How does the academic literature approach this? Technology Acceptance Models (TAM’s)
Figure 1: Extensions of the technology acceptance model2
Before diving into the above I’ll set the scene by saying words matter. When I read the above, I can see that there is an intention behind the words used to describe each element that needs to be properly understood in order to understand the model completely. the ones that pop out to me are
Perceived
Attitude
Intention
The reason for the above three is that each of these has a subjective nature that adds a level of complication to drawing conclusions as there isn’t a purely objective measurement basis to refer to. That isn’t a bad thing because that’s the world we live in, but it does mean that we need to take that into account when drawing conclusions.
I’ll be diving into this in far more detail in the next article, so I will leave you with this.
There is a heavily subjective lens to the technology adoption model
This links to the subjective nature of value
Technology adoption is person centric.
In my next article, I will break this down in more detail and start to build out my version of this framework for the work I will be doing with The Bitcoin curve.
Impacts on Bitcoin
In a recent podcast I did with Robin Seyr, I spoke briefly on technology adoption but highlighted that what it will take to orange pill someone into adoption will change over time. In my mind, it's easier to get those innovators and early adopters because they often have a higher risk tolerance and a change/innovation mindset, so the barriers to adoption for them are far lower.
As we enter the later stage of the early adopters and the start of the early majority, we are now talking about everyday people who will have lower risk tolerances, have a higher aversion to change and will have a more diverse set of values and needs.
For Bitcoin to continue its successful trajectory of adoption, we as a Bitcoin community need to start thinking differently about how we articulate value, as seen in the technology acceptance model above, so that our messages land and shift non-users into users. If we continue with the same narrative, we are likely to encounter resistance from the early majority because it doesn’t address their needs.
In short and to be blunt, it isn’t about us, it’s about them. And if we want them to adopt, we must put them at the heart of everything we do.
With the ETFs launched, we have opened the door for the institutional investors to start stacking sats. That, in its way, is a good thing as it’s forcing a shift in the legacy system to start moving across onto Bitcoin. The problem that this creates is that the ability for retail to front-run the institutions is drawing to a close. If we all want to see more retail investors adopt before the institutions, it is incumbent on us to create the value propositions to get more of the early majority onboard as soon as possible. The slower we are in our message to them, the more value the institutions can capture and maintain the wealth inequality they currently enjoy.
I want to be on the side of the retail investor, not the institution and so I will do what I can to make the Bitcoin message accessible to this next cohort of retail investors. And my ask if you are reading this is that you do the same.
Conclusion
In one of my first articles, I quoted Einstein as saying.
“If you can’t explain it simply, you don’t understand it well enough.” - Albert Einstein.
But the flip side to this is that simplicity born in the absence of the detail is even worse
What's become clearer to me is that the way in which we talk about technology adoption in the mainstream is very simple. However, it lacks the detail and scope of what this topic actually is. We fixate on adoption curves but don't think about why that curve exists and what the inputs are that drive it forward. This creates a sense of complacency because the perceived risks of adoption vs. the actual risks are misaligned.
When you look at the academic literature you see a more robust overview of technology adoption models that paints a more complex picture but is simple in its depiction. This is what I’ll be using to ensure that any research that is being done explores all avenues so that the recommendations I may provide are backed with the details needed to make them a success.
In the next article, I will explore the academic view of technology adoption models in more detail and unpack all the key elements. I’ll also incorporate my previous article on value so that you can see the interplay between these two complex themes.
Also if you were interested in watching my recent podcast with Robin Seyr here it is
Thanks for reading and see you in the next one.
https://whatfix.com/blog/technology-adoption-curve/
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14, 81-95.