Diffusion of Innovation

Diffusion, what is it? Well, you have done the challenging job of finding an idea and developing it into a product/service. All you need to do now is sit back, watch your target market buying/using your innovation, and collect that cash reward.

Well, you probably already know it is not that easy. You need to:

  • spread awareness of your product/service amongst your target market, and
  • get your target market deciding to use/buy your product/service

We call the first diffusion and the later adoption of your innovation.

In this article we’ll explore diffusion. We’ll be looking at Bass’ diffusion and Rogers’ adoption types. As well as Moore’s crossing the chasm, Gladwell’s tipping point and Maloney’s 16% rule.

Perhaps slightly different to other articles you may read on diffusion, I take a network- rather than theory-first view.

If adoption is more your thing, then this is the article for you.

Key take-aways

  • Innovations diffuse (spread) across a social system (your target market) through communication channels over time
  • Network shape and structure can impact diffusion; can be altered to help; and threads the theories together [article]
  • Some in the social system are innovators, some imitators. They can be further divided, such as into Rogers’ classic 5 Adopter Types:
    • Innovators, Early Adopters, Early Majority, Late Majority, and Laggards
    • One adopter type is rarely influenced the same way as the group to their left
    • For high-tech innovations there is a large chasm between Early Adoptors (EA) and Early Majority (EM). The EM do not trust the EA, and the EA want to keep their scarcity.
    • To solve, Moore proposes a strategy of building from a niche beach-head. Maloney suggests we change message and delivery method after 16% have adopted
  • Bass’ mathematical treatment of diffusion backs up Rogers’ adoption curve. It moves the size of each adopter type on from standard deviations to ranges of figures (still close to Rogers). Real life observations are usefully similar to the maths [article]
  • The tipping point is when diffusion (and adoption) takes off rapidly
    • Moore takes a methodological approach. Making incremental improvements from the beach-head to gain additional niche markets until a general product is created
    • Gladwell takes us back to networks, talking of connectors, mavens and salesmen that we can find to accelerate diffusion

So, what’s the diffusion challenge?

At first glance, diffusion is a marketing issue. You need to get people aware of your innovation. You could try and make everyone in your target market aware. In that case it truly is a marketing issue.

However, we are lucky that people also communicate with other people. Our challenge, then, is not so much to make everyone aware ourselves. But it is to make the “right” people aware and encourage them to share information about the innovation to their connections. And for those connections to share with their connections, and so on. That is diffusion.

We think of all these people and connections as a social system, such as that 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

We find information about an innovation difusses through a social system both actively and passively. Active ways are those such as word of mouth, advertising, social influencers. Passive ways are such as others noticing you using an innovation.

The classic definition of diffusion

This brings us to the classic definition of diffusion. It comes from Rogers and 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

We can think that your target market is that social system, or a subset of it. It is also a network.

The role of network shape and structure in diffusion

We find that the shape and structure of a social system can impact diffusion.

Innovation diffusion – getting people in a social system to be aware of your innovation – gets even more interesting and useful if we take a network first perspective. Click To Tweet

For example, a poorly connected network will hamper diffusion. Or, lets say we find an individual that is very well connected, then we might have found an influencer.

Figure 2: Some different network topologies that can impact diffusion (not animated)

I look more into the impact of topology, and what we can do, over in this article.

As we go through this article, I will keep coming back to networks. I believe they help thread the various theories together into a compelling story. But first, let’s clarify diffusion and adoption.

Diffusion: the relation to adoption

Diffusion and adoption are somewhat related.

If diffusion stalls, then adoption is likely to stall as no more people learn about it. Or if too many people don’t adopt then who will diffuse information about the innovation? As we see later, adoption and diffusion curves follow the same pattern.

But they are not the same thing.

You don’t have to adopt an innovation just because you become aware of it (it has diffused to you). Similarly, you don’t have to adopt an innovation to share knowledge of it to others (diffusion). Remember the first version of Google glass? That diffused widely but had very low adoption.

OK, that said, the first observation we can make is that the social system of our target market has two types of actors, each influenced differently.

Innovators and Imitators diffuse innovation

Our first insight is from the work of Bass in the 1960s. We find there are are two types of people/organisations in a diffusion network. Some are eager to actively adopt innovations. Others are more passive.

Figure 3: Social network as a diffusion network (animated)

(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 likely to be more complicated, though still following the same concepts).

Bass created a diffusion model – a mathematical formula that shows/predicts how diffusion will work. I look at the formula over here. But in essence, his formula captures this split of innovators and imitators and says:

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

The p and q coefficients in the formula is the split. We can see, p, as representing the innovators (it is called the coefficient of innovation). And q, is the coefficient of imitation. We can visualise Bass’ formula as shown in Figure 5.

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

This says a lot. But for this article lets focus on it saying that early in the diffusion timeline it is innovator types that are important. And that means influences external (to the network) are most appropriate. But quickly, imitators and internal influences, such as word of mouth, take over.

To diffuse an innovation, we need to understand that in a social system, some are innovators/searching for solutions, and the majority are imitators/looking for social proof… Click To Tweet

Interestingly, 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. More on this as well in my deeper look at Bass’ model.

Let’s look at what this means in real life.

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 being used.

You are, according to the early literature, an imitator. Put in a nicer way, by later researchers, you would need to experience the innovation to be comfortable with it. Or 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. This could be advertising, you searching, or connections with other social systems.

Of course, you might be an innovator in some situations and an imitator in others.

It turns out, though, that viewing our target market as just two groups is not sufficient to fully understand challenges in diffusion. This is where we need to look to Rogers’ adopter types.

Rogers’ Five Adopter Types

Rogers’ book Diffusion of Innovations is the classic reference on diffusion theory. It has several useful and important insights. One is the identification of five adopter types: innovators, early adopters, early majority, late majority and laggards.

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

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 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. They are connected to the innovator group. And often we think of these 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 who are a further 34%. 

Those in the early majority often have contact with the early adopters but are making the choice 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, the 16% of customers, defined as the laggards.  These customers are typically resistant to change and hard to get on board.

We can plot the adoption rate through the social system over time. This is the classic curve known as Rogers’ Adoption Curve. You can see this curve in Figure 7. Along with the percentages of target market 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. (non-animated)

Roger’s set the size of each adopter type based on standard deviations from his empirical observations of the literature. Working around the same time, Bass developed a mathematical view.

Bass Diffusion model

Bass’ 1969 mathematical treatment of diffusion is really interesting.
Back in 1962 Bass came up with his imitation model. And that was the basis of our earlier innovator/imitator discussion. His Diffusion model is the natural progression of that earlier model and Rogers work. It is so interesting, I give it an article all to its self.

The model is a formula, a plot of which gives a curve remarkably close to Rogers. Just look at Figure 8.

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

The formula (solid line in first two and dots in bottom three) is pretty close to the actual data. But if you notice, it is also able to map close to real data by adjusting coefficients in the formula (one for innovation and one for imitation). A result of this is we get better sizes for Rogers adopter types than just using standard deviations. Though I have to say, Rogers was pretty close.

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 an innovation is 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.

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 towards. 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 type than between people of different types. 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 biggest instance of this is between the early adopters and the early majority (for high tech innovations). This is the place where the balance between external and internal influences shifts. The place where risk takers are replaced by more pragmatic consumers. This is 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. 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 between each of Rogers’ adopter types that needs to be crossed. Moore noticed that for technological innovations the gap between early adopters and the early majority was quite considerable. He named this gap, the chasm.

Essentially, 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 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 passing across the communication channels in an effective way. And so, diffusion is at considerable risk of stopping.

Figure 9: The chasm as seen in a social network (non-animated)

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 taking the 6 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 craves the principle of social proof. They want things 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. In order 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 often called 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 fulfills the needs of an initial beach head of customers in the early majority. This is when 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.

The strange thing with the tornado is that your strategies need to be opposite to those applied in the bowling alley. Now you have to focus on generic product, mass marketing, commodatisation etc.

Moore’s approach is product/market focused. Gladwell takes a more network focused 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 the 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 right?

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. And a small number in that network are innovator types, searching for solutions to problems, and are influenced from outside the group. A larger 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, the size of these types can be predicted. 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 don’t really trust the former. And the former don’t 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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