DisruptiveInnovation.Org - Latest Discussions

Collaboration And Conformability

Horace Dediu, 9/14/2016

Ideas take time to gain acceptance. Even the best ideas are not instantly recognized as such. The process by which a population changes its mind was observed by sociologist Rogers in the 1960s. The observations were conducted on a particular subset of ideas: technological innovations. Rogers observed that good ideas such as better seeds or boiling drinking water take time to “diffuse” into a population of adopters.

Once the process was identified, he was able to categorize adopters by their willingness to accept change. Thus we came to the classic Innovators, Early Adopters, Early Majority, Late Majority and Laggards categories of adopter personalities. Underlying this categorization is a mathematical model of the rate of adoption. This is called the Diffusion Curve which shows the percent of a population that adopts the technology over time. It is an S-shaped curve that spans from 0 to a maximum penetration, up to 100% of the population.

This S-curve came to be the most commonly used measurement of the rate that ideas come to be accepted. From a user’s point of view, technologies are fundamentally ideas: a vendor proposes that it’s a good idea to use a smartphone and although most people are skeptical, after a certain time and after many product iterations most agree that it’s a good idea to do so and they purchase the product and bring it into their lives. Note that the first version of the idea is not for everybody. For a unanimous agreement of utility, the product must be improved. But as a few people agree it’s a good idea, their support gives fuel to the producer to make it better, attracting more adopters.

The adoption curve is a dual feedback process. Adopters influence each other on the benefits and support improvements in the product which leads to more acceptance. The question we have been asking ourselves is what determines the rate of this feedback. What are the forces that accelerate adoption?

In order to develop a sense of what accelerates adoption, we have to understand how ideas become innovations. Before an S-curve begins its rise, there has to be an invention followed by a period of gestation as the idea is built into a product and, if the product solves a customer need, the formation of a market. If the technology is widely useful then the products using it are “adopted” by a majority and the technology “diffuses” into the general population. Ultimately the technology is universally accepted and it “saturates” the addressable market.

Technological diffusions have been measured for at least two centuries. The earliest were for transportation canals built in 18th century England. In the 19th century, as industrialization took hold, diffusions became more common with transportation and communication alternatives. In the 20th century new enablers in the form of motors, fuels and production methods led to increasing consumerization and miniaturization. Today new digital technologies seem to appear and spread very quickly.

But are new technologies being adopted more quickly? Are all technologies quick today? Will they be quicker in the future? Were they slower in the past?

The historic data is not all consistent. There were many old technologies which grew very quickly. In 1930s America, the radio was adopted very quickly. So was the TV in the 50s. The oldest of all, the printing press, diffused throughout medieval European cities in less than 50 years. Conversely, there are many technologies today which are slow to rise. Hybrid cars are diffusing more slowly than diesel cars. Electric cars are diffusing slower than the Model T, new forms of renewable energy production are growing more slowly than the original electric grid. Healthcare and education reform is far slower to be adopted than the rise of hospitals and primary/secondary schools in the 19th century.

To look for patterns, we looked at 104 technology diffusions. The data shows significant variations in adoption speed for technologies that got started around the same time. For example the Kodak Brownie camera and the automobile both reached 10% of their markets around 1915. However the democratization of photography took 20 years while the automobile took over 70 years. Refrigerators and TVs were quick while the contemporary washing machines and dishwashers were slow.

Even toward the late 20th century and the rise of transistor electronics, the VCR was quick but the video game console was slow. The early 21st century is awash with internet/computing- based technologies which rise very quickly but there are innovations which seem slow and stubborn. We are facing a crisis of transformation in healthcare, education, energy, banking, transportation and government. Manufacturing and agriculture might also be reaching low- growth crises.

It's tempting to believe that Moore’s Law, which correlates well with computing innovation rates, will solve these crises. But there seem to be some technologies which are disobeying the law. It we look back far enough we can see that well before Moore’s Law, there were other technological “laws” which led to rapid improvements in performance. In the 19th century steam engine efficiency grew exponentially. In the early 20th century innovations related to internal combustion and turbines meant that vehicle speed grew exponentially. In the mid 20th century, the miniaturization of electric motors meant their specific output increased exponentially. Steam, internal combustion, electric motors were all epoch-making enablers but, during those same epochs there were anomalous laggards. Not all diffusions conform to technological law governance.

Diagram: Absorbtion Rates

To find what else might be at work we compared products with similar enablers but differing diffusion rates. What we saw was that rapid growth was correlated with the absorbability of the innovation in the adopter’s life. We call this “conformability” to user circumstances or behavior. For example, the refrigerator rose quickly in popularity because it was easy to fit into any kitchen while a contemporary washing machine was slow because there was no room to install it it in an apartment or into a farmhouse with no plumbing. We developed a way to quantify conformability by asking whether the adopter faced a minimum of five independences: purchase, assistance, time, space and learning.

We also observed a pattern of increasing granularity of value networks among the rapid diffusions. We call this “collaborative” producer behavior. Smartphones rose quickly because they leveraged modular software, components, distribution via network operators and pre-existing internet content while electric cars rose slowly because they require integration, defeat current distributors’ business models and need to be driven on roads built without charging infrastructure. We tested collaboration by asking whether the producer could recruit ecosystems, obtain network effects, distribution, supplier networks and leverage existing infrastructures.

In combination, conformability and value network creation proved to be powerful accelerants. This has led us to stating that creating independence of purchase simultaneously with a dependence on partnership leads to rapid adoption. We thus define a “modular business architecture” as the combination of conformable demand creation and collaborative demand fulfillment. The duality of independence of purchase and dependence of supply explains the push/pull or market creation. We think of this as “modularity” since the product or service acts as a module which slots easily into an adopter’s world while simultaneously allowing partners to couple it to new business models that create additional points of value capture. The product has few dependencies as far as the buyer is concerned but has many dependencies as far as the seller is concerned.

Network effects and ecosystems tend to be underestimated which is why we expect wearables and cryptocurrencies to be very quickly adopted. On the other hand, physical infrastructure inertias are also underestimated. For example large-scale industrial 3D printing depends on new materials and replacement of production methods currently in use, requiring re- learning and adaption will delay adoption. Electric cars suffer from fueling infrastructure, behavioral changes, ecosystem issues (grid capacity), a lack of network effects and distribution questions. Photovoltaic power lacks in all aspects of conformability to the current power production systems and does not benefit from ecosystems or network effects to offset this. For this reason these technologies will be slower.

The observation that speed of adoption is governed by collaboration and conformability, or making solutions fit for purpose and doing so with the help of others is intuitively obvious. These forces of adaptability and collaboration are natural ways to make improvements. We can’t argue against working together and making what customers’ circumstances demand. And yet when we look at societal grand problems we see many attempts to explain the lack of improvements on other causes. Complexity, intransigence, inertia are easy targets but history shows that these are symptoms and not causes. The causes are actually far simpler. There is a lack of agreement on purpose of action and a lack of understanding of how to get there.

Businesses are organisms which respond to these challenges with profit models that align objectives and plans of action. It’s a great lesson on how to make progress.