How ideas spread

The origins of ideas, and how it is possible for them to spread around, is a topic that I think is probably more interesting that many people may think. At first sight, one may think that ideas just spread by people talking about it and that they grow if they find enough people that find the idea compelling. not differently as how a social network works, the more nodes it has (or people) the more value it is find inside.

The reality seems to be more complex and with different arguments going around. Richard Dawkins published in 1976 a book called ‘The selfish gene’ that started the field of memetics. This field is quite controversial because it compares ideas to genes, that may spread even if it is not good for the person that communicates it.

When I read the previous book, I have to say that the idea that really amazed me was not that ideas, or the core part of an idea, may be able to replicate like genes, but the fact that Dawkins suggest that it may not be that humans use genes to replicate, but genes using humans to keep going on through the time. Or another example that was given by a teacher of mine: the hen does not use eggs; the eggs are the one using hens to replicate themselves.

I found, and still find this example of thinking out of the box very profound. Probably because I have never thought at the time that such a well know fact of the existence and characteristics of genes could lead to a completely different understanding after looking through a completely different lens.

The criticism to this theory is mainly based on the cultural and mainly human factors that are present when ideas spread. The philosopher Mary Midgley was very critic of the idea of memetics because she finds that it tends too much to reductionist, which is probably true. Personally, I tend towards the idea that memetics probably lack some scientific support. Yet this comes from the fact that even Dawkins seemed to have abandoned his own theory later, at least to some extent.

In any case, previous point was to illustrate briefly that this is not an easy topic to have a deep understanding of where ideas come from. And I cannot even imagine how many philosophy books discuss this topic. But there seems to be an easier topic than finding how ideas are generated, which is how ideas spread.

Let’s start with a word of caution, as I wrote in nobody knows anything, it is exceedingly difficult to know if an idea or a project will spread at all. What people can do at best, is to have an educated guess. Therefore, for each new idea (that I will also call innovation) we will never give a clear roadmap to follow to promote new ideas. However, there is a theory that, although very general, is seems that resist the test of time and the application. Let us take a look at the following picture:

Image from Wikipedia

Law of diffusion of innovations

The plot above is known as the law of diffusion of innovations, this theory explains how new ideas, innovations and new technology spread. It was popularized by Everett Rogers in his book Diffusion of Innovations, first published in 1962. As one can imagine, the origin of this theory is multidisciplinary, and being so general, it allows to cover also multiple fields.

This theory works by dividing people in different social systems. These relate people in how eager they are to introduce a new idea or a new technology. In the business world, this will be equivalent on how a company will be eager to adopt a completely novel methodology.

The overall shape of the blue curve is a Gaussian. This is the kind of shape that happens in the nature an incredible amount of times. It is extremely important when making experiments in physics for example, because when you are using real systems, all of them have a tolerance. But that does not mean that, when you measure all results, they will spread equally through the range described by a tolerance (this is called linear distribution). Generally, they will follow the shape of the blue curve: many of the values measured will be around a central value, and as far as we go from this central value, there is first a steep decline, and then is a lower decrease of outliers.

This is already an important lesson: there will be always outliers. For any idea, it does not matter how well defined, proven or introduced, there will be skeptics. In other words, for each person that is on the queue for the next iPhone, you will find a person that keeps trying to buy a flip phone and does not want to hear about smartphones. I will come to this example later.

Spoiler alert: this theory will not tell you how to easily identify people belonging to each social group. In fact, each individual person belongs to different groups at the same time, it depends on the topic. Each group has some well defined traits, but some times they can be difficult to see. A company must then focus on avoiding the people that will never become their clients at the current state of their innovation. It is not unheard, and it ends in a waste of time, money and resources for something that was doomed since the beginning.

Going into the x axis of the curve, which notes the percentage of population, there we have labelled the different social groups for the people through the curve:

Innovators (2.5% of population)

This social group is formed by people that are actively looking for the next new and big thing. They find any new technology exciting and they will ‘suffer the consequences’ of getting the new thing as soon as possible. Think about the people making a queue to get the next iPhone or gaming console. An important trait is that innovators understand that any new technology is not perfect, so they are willing to cope with some errors and failures to use the technology as soon as possible.

Innovators are the first market that any new idea, innovation or technology must convince. In theory, it is fairly easy to get this group to take and test your technology, and if it is impressive enough, they will be the ones that start the flow of the information to the rest of the social groups. Of course, the challenge is to find innovators that can also make the decision and have the power (political or economical) of using resources for this. Due to all the unknowns that anybody faces with a new technology, the innovators are the ones that are willing to take the highest risk. At this early moment, it is impossible to know if the technology will get traction later, no matter how good or nice it looks.

An example of technology that I think that is still trying to flow to other social groups is virtual reality (VR). It is well known that is exist, but the actual implementation is still low. Not sure if it is exactly 2.5%, but I imagine that numbers will not be far. VR has improved vastly technologically but is still looking for the place where the application is useful.

Early adopters (13.5% of population)

Once we start to go higher in the diffusion curve, the risk that people are willing to take starts to go lower, so the technology should be robust at this time. Early adopters are a social group that is eager to change, but only if it is clear for them that there is a benefit. Following the example with the iPhone or gaming console, these people will not be there to buy on day one, but they will be the first people to buy the new technology after the long queues clear.

There is an important caveat with early adopters, unlike innovators, early adopters will only adopt to a new technology if there is a benefit for them. This benefit can be diverse, more money, efficiency, productivity… but there must be clear that the hassle of adopting new technology will come with a new benefit. That said, early adopters do understand that this is still early days for the technologies, so although robustness starts to be a problem, they are willing to still cope with the lack of features. In fact, this group will easily engage to add features that help them more, so they can be an invaluable source for feedback.

This group is usually formed by respected members with a central position in a social system, so they usually have a large communication network. This is one of the reasons of how important they are, on one hand, it is possible to get a lot of early feedback early, on the other hand, the will provide advice and information to the following social groups, giving the idea or technology validation on the eyes of more risk averse populations.

We get here to a particularly important topic. Any famous and innovative technology that you may remember that did not make it to the next social group got stuck here, in what Geoffrey A. Moore called the chasm. Which is the leap between early adopters to the early majority. It should be noted that Moore’s book Crossing the chasm: Marketing and selling high-tech products to mainstream customers is focused on disruptive or discontinuous innovations, or in other words, technology that requires a significant change of behavior by the customer.

I think that VR has not yet reached a large introduction in this group because the amount of successful cases of using the technology is still low. Some very innovative companies are working hard is illustrating the advantages of adopting VR, but we are still in a point where there is not a clear case where adopting VR is a no-brainer. I would even say that the amount of new information is decreasing, which is not good to reach the next social group.

Early majority (34% of population)

The difficulty in making the leap between the previous group and this one is mainly pragmatism. This is the common trait of this social group: until a technology has proven itself to be useful, the early majority will not adopt it. The early majority will deliberately look for evidence, which will come mainly from the early adopters. If there is enough validation of the benefits of a new technology, the early majority make the change to adopt it.

This first group of the majority does tend to adopt innovations before the average people but will not be as quick as the early adopters and they do expect a finished product. On the other hand, once a technology reaches this degree of diffusion, it will find itself with a massive amount of market share readily available. They do also provide a snowball effect that leads to exponential growth, as the yellow line in the previous plot indicate.

Late majority (34% of population)

Once we cross the 50% of the population, we reach a point were the average user is very reticent to make any change. The main way for the late majority to adopt a new technology is by peer pressure, economic necessity or any other reason that may make the change mandatory. There will not be any motivation to make the change by themselves.

At this time, any technology or product must be well supported, extensively reviewed, extensively validated and feature complete (or as much as this can be possible). A common trait of this social group is that many times they do not want to commit resources to adopt the new technology, maybe because they have fewer resources, or just pure skepticism. Even social norms should indicate that the new technology is desirable: one can think that once more of the 50% population has adopted a new product or technology, it must be highly desirable. This may be the case, but depending on the specifics of the technology, we may find cultural problems where a technology will not be accepted because it goes against some group’s tradition or internal values. We must realize that at this moment we are starting to focus on smaller parts of society that are not eager to make any change.

Laggards (16% of population)

The last social group is the most risk averse, traditional and conservative. Probably in many cases also the most vulnerable economically with limited access to resources. To follow the previous example, these are the people that sill use a flip-phone and cannot buy a console because it does not work with their CRT TV anymore, even if they have the resources to buy it. We find here also highly isolated groups or individuals, so the lack of connection to the other social groups makes social pressure nonexistent.

The only way to reach this part of the population, is if there is simply no other way to function without a new technology. Think about what happens if the old CRT TV breaks. A person of this group will by a new flat TV, but only because there is no way to use the old technology.

Criticism and final thoughts

Unfortunately, the steps required to use this theory to be able to introduce your innovation or idea are extremely difficult to execute. If this was not the case, anybody will be able to do it. The problem with social systems as described in the theory, is that you cannot really identify an early adopter before meeting with the person, and even so, it may not be possible for that person to have the influence in the company to be able to introduce your technology. In simple words, if the person that loves to try to implement your novel technology cannot write checks or make decisions, the project is unlikely to progress.

There is some other criticism to the theory, which Rogers already tackle, but I find quite interesting: in general, it is problematic to apply any theory that cannot be measured properly. The amount of market introduction is not a clear parameter and depends on how it is calculated. Also, it is unlikely for a company to use only one or a few methods of communication, so it will never be clear the interaction between different ways of communication. One method by itself may be useful, but the use of multiple ones may have a compounding effect.

Studies seem to suggest that the diffusion of innovation is capable of explain reasonably well the spread of ideas. However, we should always be careful of not simplify too much the problem that we are facing. Guidelines help, but we still need to work on the execution of our strategy.

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