Why The Best Technology Isn’t Always The Winner
Anyone who has watched the evolution of technology knows that sometimes, clever new technologies emerge and quickly supplant the incumbents, while others may take years or decades to take off — if they gain widespread traction at all. So for investors, consumers and businesses, the key question is: What is the differentiating factor between the fast winners and the slow losers?
In their paper, “Innovation Ecosystems and the Pace of Substitution: Re-examining Technology S-curves,” published in the Strategic Management Journal, Wharton management professor Rahul Kapoor and Ron Adner, a professor at the Tuck School of Business at Dartmouth College, attempted to answer this question by examining not just competing technologies, but also the ecosystems in which they were embedded. And they have come up with a solid hypothesis. Kapoor recently talked to [email protected] about the implications of their findings for businesses, governments and consumers — both early adopters and the patient mainstream.
Edited excerpts from the conversation appear below.
A Question of Adoption
This research is really looking at a puzzle that we observed, in terms of new technologies being introduced into the market: There are significant differences in terms of how fast they are able to reach mainstream adoption and disrupt existing markets and players. For example, we observed that in the printer space, inkjet printers came about quickly, and rapidly overtook the dot matrix. Then look at HDTV: It took decades for it to achieve mainstream adoption. And then, you can look at things like the Segway, or the Palm types of technologies, which either created some value, or never really reached mainstream adoption.
So the question was really, what explains why some technologies are introduced and immediately supplant the existing technologies, whereas others take decades, or sometimes don’t reach mainstream adoption at all? We tried to find a context where we could observe enough variation in terms of how quick or slow these technology adoption patterns were — but one that helped us to control for a lot of confounding effects, as well. The idea was to find a natural experiment, so to speak. And that helps us to ensure that the source of adoption is not driven by some systematic effects, which we could not observe, and which may make inferences more problematic.
“When you think about the batteries used in electric cars, you must also think about charging stations, and the garages that can fix those electric cars.”
I had some experience in the semiconductor industry, and we came across semiconductor lithography as a technology that would allow us to study this question. What is interesting about the setting is that it’s the engine behind Moore’s Law. Any progress in semiconductors over the last 40 years has been fueled by lithography technology, so it’s important. We know it’s fast-paced, and we thought that it was where we might want to study this question.
We studied 10 different technologies that were introduced in the semiconductor lithography industry over a 40-year period. We interviewed about 30 industry experts to try to get a sense for what drove the different patterns of market adoption. We collected data on technologies, markets, firm, in an effort to really try to understand the factors that might explain these differences. What we found was interesting. You know, a lot of the research and practice focuses on new technologies, and how they interact with the markets. It looks at the question, is the technology better than what’s available now, or not? And that is viewed as explaining whether it’s going to reach mainstream adoption.
We found that is part of the explanation — actually, in our case, a very small part of the explanation. We found that the bigger explanation was not to be found in the new technology, and the market, but within the technology ecosystem: How is the technology created? What are the different elements that make up the technologies?
Another question is, how is the technology used, in terms of other elements or complementary technologies and services? So when you think about the batteries used in electric cars, you must also think about charging stations, and the garages that can fix those electric cars.
And not only that: You need to look at the technology ecosystems for both the new technology and the old technology. What we set out as our goal was, we wanted to explain this variance. What we saw from our field work and heard in our interviews, that by looking at the technology itself, the resolution in terms of whether adoption is going to be fast versus slow is not going to be very clear. So instead, we said, let’s look at the ecosystem. We collected data that systematically described each technology in terms of its ecosystem, and compared it to the pre-existing technology.
When it comes to new technology, there is sometimes what we call an ecosystem emergence challenge: The technology is ready, but the ecosystem still needs some investments, like the charging infrastructure for electric cars, for it to reach mainstream adoption.
But with the old technologies, we also saw that sometimes, you can extend their lifespans by making improvements in components, or in complementary elements. Think about gasoline cars, for example: Nobody thought, 10 or 15 years ago, that they would be getting 30 miles per gallon or even 40 miles per gallon today. But improvements in engines, improvements in fuels, allowed them to get to that point.
Think about the hybrid versus electric car discussion: The hybrid cars introduced were able to grow market share much faster than electric cars. And the main difference was, the hybrid cars could plug and play. There wasn’t an emergence challenge in the ecosystem. Electric cars had these emergence challenges around creating the infrastructure, because they didn’t have the charging stations in place.
After doing this research, we were able to document fairly clearly that if one wanted to understand the likelihood that a new technology was going to come in and immediately disrupt the marketplace, versus taking much longer or that disruption maybe never happening at all, you had to look at the new technology’s ecosystem in terms of emergence challenges, and the old technology’s ecosystem in terms of its extension opportunities. It’s really the joint consideration of these two factors that explains whether you will see a technology make a fast takeoff, or a technology that will never reach mainstream adoption.
I think the research presents some very interesting takeaways for managers, for policymakers, for investors in technology companies, and also for users of technologies, whether they are consumers or businesses. If you are a manager of a firm, whether it’s a new start-up or it’s an established firm, you always have to think about the resources that you’ll have to allocate towards new technologies, and how you transition from an existing to a new technology.
Think about how Kodak shifted from chemical-based photography to the digital photography arena. Think about Netflix moving from a DVD rental business to the online streaming business. Managers can use this framework to set realistic expectations in terms of both whether and when to invest in new technologies. Sometimes, not jumping to the new technology actually makes sense. They will generate more shareholder value, or destroy less shareholder value, by focusing on the existing