Red Queen Effect 2

Dave Bayless has responded to Critiques of the Red Queen Model, including my comments on this blog (Red Queen Effect, see also Rates of Evolution).

Dave chooses to define innovation as "launching new products". Both John Hagel and I believe that there are other kinds of innovation that are important. But I have a more fundamental concern with Dave's definition - if I don't know exactly what counts as a "new product", then I don't know how to count them. If this year's model has a slightly faster chip than last year's model, or a brushed aluminium case, does that count as a "new product"? Let's say the iPod is a new product, but is the iPhone really a new product, or just a fancy redesign of an old product?

Lots of people in product development have a vested interest in labelling everything as "new improved". Pharma companies spend a small fortune looking for variations on existing drugs, so they can get patent protection for the "new" formula. But if you take these descriptions at face value, you get a fundamentally distorted view of the underlying technology change.

This is why I think we need a rigorous model of technology change, which handles some of the complications I raise in my previous blog entries.

Rates of Evolution

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A fascinating paper by Philip D. Gingerich, shows how the observed rate of evolutionary change (measured in darwins), varies hugely according to the measurement context. (See table at foot of this post). In other words, the observed rate of biological evolution appears to be proportional to the proximity of scientists. Does a similar phenomenon apply to technological evolution?

(Note: I am not assuming that technological evolution is the same as biological evolution - merely that looking at one domain may prompt some interesting and important questions for the other domain.)

I have always been wary of the common belief that technological change is accelerating. I think this belief derives from a combination of proximity, selectivity and distorted perception. I think we can sometimes be disproportionately impressed by the glamour of recent technology, and misled by the commercially-driven measures of intellectual property (such as volumes of patent activity and product releases).

But consider these questions:

Did the lightbulb or bicycle change more

• between the years 1880-1900?
• or between the years 1980-2000?

Did the computer change more

• from 1950 to 1970?
• from 1980 to 2000?

It is certainly true that there have been huge numbers of small modifications to devices such as lightbulbs, bicycles and computers since 1980. There has also been a proliferation of variations and mutations. But will any of this innovation be remembered in fifty years time? From a historical perspective, this kind of detailed technological refinement (or even hyperactivity) may seem rather less significant than the initial burst of technical creativity when the device was taking shape in the first place.

---

Notes

Context	Timescale of Observations	Observed Rate of Change
Laboratory	1.5 - 10 years	60,000 darwins
Colonization studies	70 - 300 years	400 darwins
Post-pleistocene mammels	1,000 - 10,000 years	4 darwins
Fossil record	Millions of years	0.1 darwins

Philip D. Gingerich, Rates of Evolution: Effects of Time and Temporal Scaling. Science 14 October 1983: Vol. 222. no. 4620, pp. 159 - 161

Evolution or Revolution

Mache Creeger asks Evolution or Revolution = Where is the High in High-Tech?

"We work in an industry that prides itself on 'changing the world', one that chants a constant mantra of innovation and where new products could aptly be described as 'this year’s breakthrough of the century'. While there are some genuine revolutions in the technology industry, including cellphones, GPS (global positioning system), quantum computing, encryption, and global access to content, the vast majority of new product introductions are evolutionary, not revolutionary. Real technical breakthroughs are few and far between. Most new products are just a recycling of an earlier idea."

This represents a challenge to the popular belief in the accelerating rate of technical change. I have blogged about this before: Death of Software, Innovation or Refinement.

Bob Wyman complains about Creeger's distinction between evolution and revolution, and insists that Evolution = Revolution. There are undoubtedly some complications in evolutionary theory that Creeger doesn't mention. (Wyman references over a dozen articles in Wikipedia.) But I don't think this alters Creeger's basic argument about the pace of technological change. Wyman suggests that the current situation may be interpreted as part of an evolutionary cycle, and hopes (even predicts) that there is more innovation just around the corner.

"Today's thinkers are no less smart and no less innovative than were the folk working 'back in the day'. The difference is that today we're all still focused on working through the implications of the last revolution. In time we'll exhaust the realm of easily achieved secondary innovations and we'll then be ready to move on to more revolutionary 'Cambrian' times again. It is always like this. It always has been and it always will be."

He appeals vaguely to various intellectual authorities (including Stephen Jay Gould and the entire Santa Fe Institute) in support of this wishful thinking, before falling back on some popular but lightweight business literature (Clayton Christensen: The Innovator's Dilemma, The Innovator's Solution).

Ultimately, the comparison between biological evolution and technological evolution may be useful as an explanatory device, or as a source of interesting hypotheses, but not as a reliable source of predictions about the future.

How can we measure the true pace of technological change? Some people use patent activity as a metric, but this metric is made almost meaningless by the vast number of trivial patents, as both Creeger and Wyman agree. How can we decide which are the major innovations? Creeger mentions a few, but Wyman has his doubts even about these.

The success of evolutionary theory is based in part on painstaking observation and classification by generations of biologists. We simply don't have an equivalent body of knowledge about technological innovation. What we have is large amounts of hype and hot air, and a relatively small number of detailed scientific studies of particular innovations.

Even in the absence of detailed empirical data, however, it is always useful to step back from the current obsession with technical wizardry, and try to get a bigger picture of technological change. In this spirit, I welcome both Creeger's polemic and Wyman's reinterpretation, while remaining cautious about the inevitable subjectivity of both.

Technorati Tags: evolution innovation technology

Differential Adoption

Where there are two competing technologies in a given space, we may be able to learn something interesting from the differential patterns of adoption. (In studying a technology in relation to its adoption space, we get a better understanding of both.)

RSS and Atom represent two competing standards for internet syndication. James Snell recently posted a quick note of some of the technical differences: So what's the deal with Atom? He has now discovered some evidence of differential adoption between RSS and Atom. It turns out RSS and Atom really are different.

However, we need to interpret this evidence carefully. Technology often goes in clusters - one technology drags other technologies on its coattails - and it is not always obvious which technology is the determining factor in the user selection.

(In biological evolution, there is a phenomon known as genetic coupling, which links together apparently distinct features and prevents them from developing independently of one another. Thus natural selection doesn't prove the advantages of a single feature in isolation, merely the aggregate advantages of some group of features. Similar coupling often happens with interdependent technologies, and this complicates the study of technology adoption.)

In this particular example, the user preference for either Atom or RSS is correlated to a user preference for different news readers (e.g. Bloglines versus RSS readers).

• Bloglines readers are more likely to subscribe to Atom.
• Atom readers are more likely to use Bloglines.

There are many possible ways of explaining this correlation. Maybe this has something to do with the characteristics of the Bloglines user (of which I'm one). Or perhaps it has something to do with the design of the Bloglines subscription mechanism. Where a blog offers both an RSS feed and an Atom feed, Bloglines readers are given the choice. (For my part, I always choose the Atom feed.)

In his recent book Democratizing Innovation (free download here), Eric von Hippel discusses lead users and identifies three characteristics as follows:

• ahead of the majority of users in their populations with respect to an important market trend,
• expecting to gain relatively high benefits from a solution to the needs they have encountered there,
• and a significant source of innovation - many of the novel products they develop for their own use will appeal to other users too and so might provide the basis for products manufacturers would wish to commercialize
  

James Snell refers to this book, and suggests that Atom users may be lead users in this sense. But the differences between Atom and RSS (as described by James) don't seem to warrant this suggestion. Is there any evidence that these Atom users are actually exploiting the technical differences between Atom and RSS, and/or generating significant quantities of user-centred innovation?

Technorati Tags: Atom-enabled innovation RSS technology adoption

Enacted Technology

In her work on the Virtual State, Jane Fountain has made an important distinction between what she calls Objective Technology and Enacted Technology. She points out that technological outcomes are largely driven by the Enacted Technology, and this is only indirectly dependent on the Objective Technology.



For many years we have been making a similar distinction: between Technology-as-Designed and Technology-in-Use. (Our concept of Technology-in-Use is broadly the same as Fountain’s concept of Enacted Technology, but we are uneasy with her characterization of Objective Technology.)

As technology change management practitioners, we can make the following observations.

Firstly, there is a many-to-many relationship between Designed Technology and Enacted Technology. Not only can the same Designed Technology be enacted in many different ways (even within the same organization), but the enacting process can sometimes nullify design differences. (In other words, technological features which designers think highly significant can often prove irrelevant in the context of use.)

The procurement process often focuses on the Designed Technology, or the so-called Objective Technology, although there may be some attention on how the technology has been enacted elsewhere. This means that the procurement process often fails to make good connections to the desired/expected outcomes, while often spending lots of time on technical features with minor impact on outcomes.

Secondly, there may be loose co-evolution between the Designed Technology and the Enacted Technology, but without tight synchronization. Sometimes new features are added to the Design that only slowly filter into practice; sometimes new practices emerge in the Enacted Technology and only get reflected in the Design after some delay.

Thirdly, the gap between Designed Technology and Enacted Technology is itself a subject of interest, and can reveal something about the maturity of both the technology and the organization.