Part of a series on Innovation Essentials
adopter types crossing the chasm diffusion early group imitators influence majority mavens moore bass people rogers social network tipping point

The Big Picture…

Innovations diffuse – knowledge of them spreads – across a social system through communication channels over time. Your target market is this social system. And a social system is effectively a network. We take a network first look at diffusion in this article.

Some in the social system are innovators and some are imitators. And they can be further divided as Rogers’ classic 5 Adopter Types: Innovators, Early Adopters, Early Majority, Late Majority, and Laggards. Gladwell talks of tipping points and connectors, mavens and salesmen. And Bass gives us a mathematical basis to all this – which nicely aligns with real-life observations.

Can we influence diffusion speed? Well, we know that one adopter type is rarely influenced the same way as the group to their left. And that for high-tech innovations there is a giant chasm between Early Adopters (EA) and Early Majority (EM). Moore proposes building from a niche beachhead with additional niche markets. Maloney suggests we need to change message and delivery method after 16% have adopted. And what of Gladwell’s connectors, mavens and salesmen?

So you’ve identified a hindrance a beneficiary is having. And then you’ve identified a way to help those beneficiaries make progress. All you need now is for those beneficiaries to “hire” your proposal. And to do that, you need to get:

  • awareness of your offering spreading, and
  • your offering used

We call the first diffusion and the later adoption of your innovation. And in this article, we’ll explore diffusion. We’ll be looking at Bass’ diffusion model and Rogers’ adoption types. As well as Moore’s crossing the chasm, Gladwell’s tipping point and Maloney’s 16% rule. If you’re after an article on adoption, then this is the article for you.

Slightly different from other articles you may read on diffusion, we will take a network-first approach.

Your target market is a network (a social system)

Part of your job of commercialising your innovation includes working out your target market, and its size. And you might now be tempted to mass-market/cold-call to make sales and get adoption – to keep hustling.

That is not the best use of your time and money. Awareness of your innovation is the second step to adoption (the first is having something that is useful). And awareness spreads – diffuses as we call it – in a surprisingly predictable manner. It moves through communication channels that connect members of a social system. Your target market is such a social system. As shown in Figure 1.

An animated image showing a set of notes and channels that make up a social system over which an innovation could diffuse.
Figure 1: A network, with nodes and connections (animated

Knowledge of innovations diffuses through a social system both actively and passively. Active ways are those such as word of mouth, advertising, social influencers, etc. And passive methods are, for example, others watching you using an innovation.

The classic definition of diffusion

Rogers gives us the meaning of diffusion in his book “The Diffusion of Innovation“:

Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system.

Rogers – Diffusion of Innovation

Noticing that the social system is a network leads us to consider a couple of things. Firstly, the shape of the social system will impact the success and speed of diffusion. We know that from network theory – and its called the topology of the network. For example, a network with little or poor connections will hamper diffusion. On the other hand, let’s say we find an individual that is very well connected, then we might have found an influencer.

Figure 2: Some Example Network Topologies

I look more into the impact of topology over in this article.

Secondly, are all members of the social system identical? If they are, then mass marketing should be sufficient. However, it is likely that they are not. And that’s what we’ll discuss now. At the very least we find that some members are innovator types and others imitators.

Innovators and Imitators diffuse innovation

Our first insight is from the work of Bass in the 1960s. He found there are two types of people/organisations in a diffusion network. Some, the innovators, are eager and actively seek out and adopt innovations. Others act more passively, and we call them the imitators. Let’s split our network then into two, see Figure 3.

Innovators and Imitators in a social network
Figure 3: Social network as a diffusion network

(A quick confession: in this diagram, and later ones, I simplify things by pretending diffusion and time go from left to right across the network. In practice, the social system is more complicated).

Bass created a diffusion model – a mathematical formula that predicts how diffusion will work. And it shows this split between innovators and imitators very nicely. His model states:

The probability of adoption at time t given that adoption has not yet occurred is equal to: p + (q * cumulative fraction of adopters at time T).

Majahan, Muller, Bass (1995) “Diffusion Of New Products: Empirical Generalizations And Managerial Uses

You can see this model graphically in Figure 4.

Figure 4: Bass saw that adoption of innovation started with innovator types in a network who are overtaken by imitator types as time progresses

Figure 4 says a lot. First, early in the diffusion timeline, it is the innovator types that are important. But relatively quickly, imitators influence takes over. It also tells us that the number of innovator types is lower than imitators.

In fact, the figure exaggerates the size of innovators to make them visible. How big are these two groups? Well, the coefficients, p and q, in the formula represent the innovators (p) and imitators (q). Studies have shown that the average value for the coefficient of innovation (p) is 0.03. And, the average coefficient of imitation (q) is 0.38.

If you’re into the maths behind Bass’ model, then I look at the formula in some more detail over in this article. That also includes some links to an Excel version of the model to play with.

But what does this mean in real life? Let’s see.

Some people in your network are imitators…

Remember the last time you discovered something new and innovative. Most likely, a friend or colleague introduced it to you. We would say you had an internal influence (to the network) acting on you. And, this was probably through word of mouth, or perhaps you saw the innovation in use.

You are, according to the early literature, an imitator. Or as later researchers put it in a nicer way: you need to experience the innovation to be comfortable with it. And psychologists would say you need to have social proof.

Some people are innovators…

However, I’m sure you can think of examples where you were the first to find and use an innovation. Often you’re connected to the initial innovator, or you are actively searching for a solution to a problem you have. Being in the innovator group, we are said to have had an external influence (to the network) acting on you. Which could be advertising, you searching, or connections with other social systems.

It turns out, though, that viewing our target market as just two groups is not sufficient to fully understand the challenges of diffusion. Instead, Rogers’ introduced five adopter types.

Rogers’ Five Adopter Types

Rogers’ book Diffusion of Innovations is the classic reference on diffusion theory. It has several useful and valuable insights. And the one we pick up on here is his identification of five adopter types: innovators, early adopters, early majority, late majority and laggards. This allows us to update our view of the social system/network as shown in Figure 5.

Figure 5: How Bass’ innovator/imitator insight relates to Rogers’ five adopter types

And, whilst we call these adopter types, it also affects diffusion. Remember that as time progresses, diffusion is more reliant upon internal influence and social proof. Let’s explore these adopter types, starting with the innovators.

Innovators

Rogers found that a group of people he called innovators are the ones most likely to have your new innovation diffused to them (and to adopt). These people are willing to take risks and are typically closely related to the industry producing the innovation.  About 2.5% of your target market are these innovators.

Early Adopters

Around 13,5% of your target market are also risk-takers, like the innovator group. However, they will wait for your innovation to get some market hold before adopting.  These are your early adopters. And they are usually closely connected to the innovators. Often we can see early adopters as leaders/thought influencers.

The Early and Late Majority

After the early adopters, there are the two largest categories of adopters. First, the early majority who are 34% of your target market. And second, the late majority, a further 34%. 

Those in the early majority often have contact with the early adopters but are choosing to adopt much later. This group includes your average customer. The late majority need to overcome a degree of scepticism before adopting. As such, they are doing so after the average customer has.

Laggards

Finally, we find the hardest group to persuade. Making up 16% of customers, these are the laggards. These customers are typically resistant to change and hard to get on board.

One observation Moore makes is the following (which we look at lower down).

…any of the adopter groups will have difficulty accepting an innovation if it is presented to them in the same way as the group to the immediate left.

Moore, Crossing the Chasm

We often look at Rogers’ adopter types through his adoption curve.

Rogers’ Adoption Curve

We can plot the adoption rate through the social system over time. And we see that in Figure 6. This curve is known as Rogers’ Adoption Curve. And it shows the above percentages of the target market predicted for each adopter type.

Figure 6: Rogers’ five adopter types, when they start adopting in time, and what percentage of your target market are in each adopter type.

Rogers’ set the size of each adopter type based on standard deviations from his empirical observations of the literature. And as a rule of thumb, they are good to use. Even more interesting is the work that Bass continued upon. That shows results very close to Roger’s curve.

Relation of Rogers’ adoption curve to Bass’ Diffusion model

Rogers’ adoption curve allows us to understand adopter types in a broad manner. What I find quite fascinating is Bass’ 1969 mathematical treatment of diffusion. That builds on his 1962 imitation model, and, I give it an article all to its self.

Bass’ model produces curves remarkably close to Rogers’ adoption curve. And the impressive part is that mapped to real data you can identify the curve. Just look at Figure 7 which shows real sales volumes of various technologies together with an overlaid adoption curve.

Figure 7: Bass’ diffusion model compared to real-world data (non-animated)

One outcome of Bass’ work is we obtain better size values for Rogers’ adopter types than standard deviations. Though I have to say, Rogers approach of using standard deviations was pretty close. For example, we find innovators make up 0.8%-2.8% where Rogers said 2.4%.

But, before we get too happy, there is a bias problem when it comes to innovation.

Pro-innovation Bias

A challenge with diffusion is that we believe innovations are always good. There is a pro-innovation bias.

…an innovation should be diffused and adopted by all members of a social system, that it should be diffused more rapidly, and that the innovation should be neither re-invented nor rejected.

The Diffusion of Innovation, Rogers.

What we tend to believe is that once a node in the network hear’s about an innovation they will adopt and they will diffuse the innovation further. This is what we see with viral infections, such as the novel coronavirus spread in early 2020.

The bias means research focuses on adoption rather than rejection. I believe this bias can be particularly harmful in services innovation. There, rejection is a real aspect. Think of banks moving from the high street to online. Or supermarkets moving to self-service checkouts.

But, diffusion can fail. And it risks doing so more at the boundaries between adopter types than elsewhere. One reason is that internal influences work better between people of the same kind. Moore, with his marketing background, highlights that:

…any of the adopter groups will have difficulty accepting an innovation if it is presented to them in the same way as the group to the immediate left.

Moore, Crossing the Chasm

And, he identifies that the most significant instance of this is between the early adopters and the early majority (for high tech innovations). It is here where the balance between external and internal influences shifts. And where more pragmatic consumers replace risk-takers. Welcome to Moore’s chasm.

Crossing the Chasm – a diffusion problem

It turns out for high-tech innovations the gap between the early adopters and the early majority is considerable. So considerable, that Moore called it a chasm. See Figure 8. And his book “Crossing the Chasm” describes why this chasm occurs and ways to cross it. He also updates Rogers’ adopter type names with his own. For example, the early adopters become the visionaries.

Figure 8: There is a gap we must cross between each of Rogers’ adopter types. Moore noticed that for technological innovations, the gap between early adopters and the early majority was quite considerable. He named this gap, the chasm.

Moore sees the pragmatic early majority are not keen on listening to the visionary early adopters. Pragmatists see visionaries as:

  1. lacking respect for colleagues’ experiences.
  2. taking a greater interest in technology than in their industry.
  3. failing to recognize the importance of existing product infrastructure.

Additionally, he sees pragmatists as being concerned with the overall disruptiveness of high-tech innovations.

From a network perspective, the information about the innovation is not efficiently passing across the communication channels (Figure 9). And so, diffusion is at considerable risk of stopping.

Figure 9: The chasm as seen in a social network

Moore suggests several approaches to help cross the chasm using analogies of bowling alleys and tornadoes. We will come to this shortly. But first, let’s look at the insight from another marketer.

Maloney’s 16% Rule

Whereas Moore attributes the chasm to the early majority not trusting the early adopters. Maloney thinks the chasm is due to the early adopters not wanting to share what they have discovered.

Malony takes the six principles of persuasion introduced in the book “Influence: The Psychology of Persuasion“. From which, he identifies that innovators and the early adopters crave the principle of scarcity. They want things others don’t have. Whereas, the early majority desires the principle of social proof. They want what they see others having. If this social proof idea sounds familiar, we talked about this earlier when looking at imitators.

Figure 10: Maloney’s 16% Rule – messaging and media needs to change between the first 16% adopters and the remaining (non-animated)

Given the different cravings, and standing on Moore’s no group is persuaded the way the group to the left is insight, Maloney came up with his 16% rule. This states:

Once you’ve reached 16% adoption of any innovation, you must change your messaging and media strategy from one based on scarcity to one based on social proof, in order to accelerate through the chasm and the tipping point.

Maloney’s 16% rule

Maloney effectively suggests marketing to the first 16% (the innovators and the early adopters) should emphasise the scarcity and be through PR or other more exclusive means. To cross the chasm, and reach after the 16%, the marketing needs to change to emphasise social proof and to be through mass media to drive that “I need it too” feeling.

If we can get over the chasm, and avoid the 16% issue, then the next aspect we are looking at is hitting the tipping point.

The Tipping Point and diffusion

At a point in time when you are in the early majority, diffusion (and adoption) of your innovation accelerates. This point is the tipping point.

Figure 11: The tipping point is where your innovation really starts to take off

Both Moore and Gladwell consider this tipping point. Moore’s “Inside the Tornado“, talks about high-tech innovations being in the bowling alley, the tornado and eventually on Main Street. Gladwell’s “The Tipping Point” considers this from an epidemic perspective and concludes the need to find specific types of people to accelerate through the tipping point: connectors, mavens, and salespersons.

Bowling alleys, Tornados and Main Street

Moore suggests creating a complete niche product that fulfils the needs of an initial beachhead of customers in the early majority. At this point, you have the first taste of the mass market. You are now into the bowling alley. Here you need to keep knocking down more and more niche markets by extending your product. Now you are establishing yourself as having a product suitable for the mainstream. You are building a reputation and forging alliances.

At the next point in time, you hope to be pulled into a tornado of a host of pragmatists buying your product. Now you are in Moore’s tornado.

But, there is a strange thing with the tornado. Your strategies need to be opposite to those applied in the bowling alley. Now you have to focus on generic product, mass marketing, commoditization etc.

Moore’s approach is product/market focused. Gladwell takes a more network-first view.

Connectors, Mavens and Salesmen

Gladwell’s “The Tipping Point” circles us back to network structures and metrics. He identifies, amongst other things, a law of the few. That in any social network there are three key actor types:

  • Connectors – are connected to many people (i.e. have high degrees of centrality) and excel at linking people together, including across different social networks (they cross structural holes and often have weak ties)
  • Mavens – we rely on these people to connect us to new information. Gladwell sees then as “starting word of mouth epidemics”. A modern interpretation could see them as social media influencers.
  • Salesmen – these are the networks persuaders.

Ideally, you should identify the connectors, mavens and salesmen in the network of your target market. And then determine the best way to persuade then to diffuse. Doing so should accelerate diffusion.

Adding weight to this, Keller and Berry’s book, “The Influentials“, suggests that “One American in ten tells the other nine where to shop and what to buy”.

Is Gladwell correct?

However, one researcher pushed back against this in 2008. He revisited the early experiments that informed Gladwell’s book and found different results. Finding that in most cases a normal person, rather than influencer, was responsible for the tipping point.

A trend’s success depends not on the person who starts it, but on how susceptible the society is overall to the trend – not how persuasive the early adopter is, but whether everyone else is easily persuaded.

Is the tipping point toast?

Despite this, spending on social media influencers keeps growing.

I’ll leave it to you to decide if this means influencer marketing is actually getting better results. Or if we as a population are becoming more easily persuaded.

Wrapping Up

We’ve seen that diffusion is spreading the message of an innovation over channels between entities in a social system over time. That social system is a network of your target market. A small part of the network contains innovator types. They are searching for solutions to problems and are influenced from outside the group. A more significant number observe the innovation in use and start using it themselves. These are imitators or known as those requiring social proof. They are influenced internal to the group through, for example, word of mouth.

The whole network can be better divided into adopter types: innovators, early adopters, early and late majority, and laggards. Remarkably, we can predict the size of these types. But there is a gap to cross between each type as they have different values. And we saw that we need to alter our communication approach to each group since any particular type will not be persuaded by the techniques used by the group to their left.

In high tech, there is a big gap between the early adopters and early majority. The latter do not trust the former. And the former does not want to give up the scarcity that they value to the masses. We have to change our message and channel between the first 16% of adopters and the rest.

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