Okay, let's jump right in. Today we're doing
a deep dive into something pretty fundamental
if you're interested in how change happens. We're
talking about Everett Rogers' really influential
work from 2003 on the diffusion of innovations.
This is basically the framework that helps explain,
pretty predictably actually, how new ideas or
new tools spread through a group. Exactly. And
it's fascinating because the material we're drawing
on today actually frames this for leaders. It
comes from an excerpt from the book, Efficacious
Technology Management, which is often used with,
say, education leaders or heads of organizations.
So this isn't just theory. It helps explain things
we see all the time, like why some new software
takes off in a company and another just... disappears.
Right. And that's kind of our mission for you
today. We want you to walk away with maybe a
more structured way of looking at how people
adopt new things, stop seeing it as just random
chaos, and start seeing these patterns. You'll
learn about five specific stages, who's in them,
and kind of what makes them tick. And predictability
is the key word there. The source material really
hammers this home. Once an innovation gets into
a community, its spread isn't random. It follows,
well, almost statistical patterns across these
five groups. And there are ways to actually measure
this, which is what makes it so useful, right?
Yeah. Rogers and others use these two main visual
tools, these curves. Let's start with the famous
one, the bell curve. Ah, yes, the bell curve.
So what that shows you is the number of people
at the actual count who fall into of those five
adoption stages at any given time. It's like
a snapshot. If you're rolling out a new system,
this tells you who you're dealing with now. OK,
so it maps the population across the stages.
You see who's an innovator, who's early majority,
and so on. But then there's the other curve,
the S -curve. That's different. Completely different
focus, yeah. The S -curve tracks the cumulative
number of adopters over time. It shows the total
percentage of the population that has adopted
the innovation up to that point. So it starts
slow. Then there's the steep acceleration phase.
And finally, it levels off as you approach saturation.
That's the S -sheep. So bill curve is who's adopting
now. S -curve is how many have adopted in total
so far. And crucially, Not everything makes it
through these curves successfully, right? Absolutely
not. The sources are clear. The innovation itself
has to work. It needs to be efficacious, effective,
efficient. It has to actually be better than
what people were doing before. That's the filter.
Makes sense. OK, let's get into those five stages,
starting at the very beginning of the curve.
The first wave. These are the highest risk takers,
right? The innovators. Only 2 .5 % of the population.
Tiny group, yeah. And their defining trait is
being connected outside their immediate group
or organization. They're the ones reading obscure
journals, going to niche conferences, maybe beta
testing things, months before anyone else. They
bring stuff in from the outside. They're okay
with risk too, I gather. They put time, maybe
even their own money, into trying new things.
Even things that might not work out or ever get
adopted widely. That's exactly it. They're experimenting.
And in today's world, you know, they might be
scattered geographically but connected digitally
through networks. But once they bring that idea
in, the really critical group becomes the next
one. Ah, the early adopters. The next 13 .5%.
Yes. And here's a contrast. Innovators bring
it in, but early adopters are deeply respected
within the organization or community. They're
the opinion leaders, the ones people look to.
So the innovators might find the cool new thing,
but they need the early adopters to make it legitimate
internally. Precisely. Innovators often actively
seek out early adopters. Because if this group
gets on board, the chances of wider adoption
shoot way up. They provide that social proof.
They vet the innovation, show others how it applies,
and basically become the role models. They are
critical change agents. OK, so that's the first
16 % combined, the risk takers and the internal
champions. But getting past 16 %? That's often
the hard part, isn't it? Crossing that gap to
the mainstream. It's famously called crossing
the chasm in some contexts because you're shifting
your focus entirely. You move from people excited
by novelty, the innovators and early adopters,
to people who need practicality and proof. If
the early adopters can't show clear benefits
and success, the innovation often stalls right
there. Which brings us to the biggest chunk of
the population, the majority. Okay. That's a
huge 68 % split in two. Let's start with the
first half, the early majority, 34%. Right. These
are the first ones we'd really call followers.
They watch the early adopters. They need to see
it working, see the bugs ironed out, get confirmation
from people they trust inside the system. They're
more deliberate. Maybe a bit skeptical. I remember
that old quote Rogers used. Oh, the Alexander
Pope one. Be not the first by whom the new is
tried, nor the last to lay the old aside. That
perfectly captures them. They wait for credible
evidence from those early adopters. Then they
tend to make the decision to adopt. They don't
want to be first, but they also don't want to
be left behind. So if you're trying to spread
an idea, your messaging has to change here. Less
cutting edge, more proven results. Exactly. You
need case studies, testimonials from peers, solid
data showing it works, and the risk is low. Okay,
that makes sense. Now for the second half of
that main group, the other 34%, the late majority.
This group adopts... Only when it feels like
almost everyone else already has. When the innovation
is becoming the standard, their motivation shifts
again. It's less about advantage and more about,
well, pressure. Pressure. Like social pressure
or practical pressure? Both, really. There are
growing expectations to use the new thing. Maybe
the old tools are becoming harder to use or aren't
supported anymore. Think about economic reasons,
too. Or just the sheer inconvenience of sticking
with the old way when everyone else has moved
on. They adopt when pretty much all the uncertainty
is gone. It feels safer to switch than to stay
put. Hmm. So they're responding more to necessity
and the established norm. Yeah. The tipping point
has well and truly passed by the time they come
on board. Which leads us to the final group.
The last 16%. The later adopters, Rogers and
others sometimes use the term laggards historically,
right? They did, though Rogers himself became
critical of the blame often associated with that
term. But yes, this group, the later adopters,
they tend to hold onto the traditional ways the
longest. They often adopt only when the old way
is simply no longer an option. And they tend
to stick together socially. communicating mostly
with others who are also late to adopt. That's
often the case, yes. They can form a sort of
closed social circle, reinforcing their existing
views and practices. But here's where Rogers
adds that really important layer of insight.
OK. He saw how organizations often blame these
individuals, call them resistant, stubborn, you
know, laggards. But he argued against that. He
said, wait a minute. Don't just blame the person.
Right. He suggested that if you actually study
why this group is delaying adoption, you often
learn something critical about the organization
itself. Their reasons for delaying might point
to real problems, maybe lack of resources, poor
training, maybe the innovation doesn't actually
work well for their specific role or context.
Things that the earlier adopters who might have
had more resources or flexibility could overcome,
but that represent genuine barriers for this
last group. Wow, okay. That flips the perspective
completely. Their resistance isn't just stubbornness,
it could be a signal about systemic issues. It
could be valuable feedback, exactly, a diagnostic
tool for the organization hidden in the reasons
for delay. That's powerful. Okay, let's quickly
recap the five groups then. We have innovators,
2 .5%, the external connectors, risk -takers.
Then early adopters, 13 .5%, the respected internal
opinion leaders. They bridge the gap. Then the
bulk. the early majority, 34%, the deliberate
followers who need proof from early adopters,
followed by the late majority, another 34%, who
adopt due to pressure and necessity once it's
the norm. And finally, the later adopters, the
last 16%, who hold out the longest, and whose
reasons for delay Roger says we should really
pay attention to. Exactly. And knowing these
predictable stages, these percentages, It gives
you a map. You can look at any new tool or practice
spreading in your workplace or even in society
and get a quick sense of where it is in that
life cycle. Are you still in the early adopter
phase or are you trying to convince the late
majority? It changes everything about how you
approach it. Which brings us to our final thought
for you the listener to kind of chew on. It comes
directly from that insight about the later adopters.
If Rogers is right, And this framework generally
holds true, meaning about 16 % of any group will
likely be the last to adopt. And if he specifically
warns against blaming them individually, then
the challenge becomes listen to them, understand
their rationale. So here's the question for you.
In your own organization, when you think about
the people who seem slowest to adopt a new change,
what might you be missing? What organizational
barrier, what resource issue, what perspective
that's only visible from their position might
you be overlooking?