Using logistic curves to measure diffusion of innovations is a powerful method of analysis. However there are limits to what can be learned. The methodology helps in understanding how quickly a pervasive technology is adopted. It assumes that the technology “fills all available space” within a market. It therefore also assumes that whatever problems the technology solves are universal problems.
Put another way, if a technology is not universally useful, it tends to peak before a market saturates. This “universality” condition is in evidence when observing that pervasive technologies are adopted not only by all members of one national market but also all nations and through all means of government and regulation. In other words that the jobs that the technologies are hired to do are so important that they bulldoze any and all obstacles placed in the path of adoption.
The only difference is one of timing. Some regions are quicker than others. Institutionalized obstacles essentially defer rather than deter adoption. They impede rather than block.
And I am pretty sure that smartphones solve universal needs and their adoption will be nearly 100%. They also have fairly low impedance given the speed of adoption (50% penetration in most large markets seems to come in less than 5 years.)
That’s the story for the technology, but how value is captured is another story.
Who captures and how it’s captured are questions of commerce not economics. They are informed by competitive advantage and business models. The puzzle seems to be that individual companies don’t capture value in the patterns of Logistic curves. Or at least I don’t think they do.
Consider the graph below.
It shows the transformation of a logistic curve into a linear representation. It’s a log-scale graph of fractional shares of smartphones in general (Blue/green Lines) and individual platforms in the US.
The individual platforms are shown as (Platform x share of all phones/share of all others). This is a “logistic substitution trajectory”.1
The problem is that whereas the overall category (smartphones) reaches saturation, it’s not at all clear that any individual platform does. At least from the data we have so far, there is far too much volatility in platforms to see a clear pattern of monopoly emerging.
I’m not sure if this is “proof” enough to convince anyone that this market supports any number of platforms, but it certainly disproves that there will be only one.2 We’re too far along for churn to convert existing users before saturation sets in.
- Fisher and Pry, 1971, proposed a similar model to study the replacement of old by new technologies. The basic assumptions of the Fisher-Pry model are: (1) The substitution process is competitive. (2) Once substitution by the new technology has progressed as far as a few percent (i.e., “lock-in”), it will proceed until a complete takeover occurs. (3) the rate of fractional substitution F1 is proportional to the remaining substitution 1- F1 possible. Source: Grübler, The Rise and Fall of Infrastructures Physica-Verlag 1990. [↩]
- The odd pattern here seems to be Apple’s consistent (i.e. parallel) adoption. One hypothesis could be that Apple is different in solving the prevailing job to be done while rivals jockey for positional advantage. [↩]