Understanding early adoption

I am currently in a discussion about following early adopters sparked from Kathryn Greenhill’s article. Unfortunately, after getting in on the discussion (not before), I decided to get a better understanding of what Everett Rogers was doing with his Diffusion of Innovations. I then realized that I was bandying about the “early adoption” phrase without having a clear understanding about how diffusion studies are done and what they represent.

A diffusion study looks at the adoption of a technology over time. Let’s call a sample technology “Tickle me Elmo” or TME to give it a high-tech-sounding acronym. The first thing you do is perform a “macro diffusion” study (Fichman, 1992). A macro diffusion study simply counts the number of people who adopt a technology over a particular time period. Everett and others discussed how this diffusion occurs in an S-curve. That is, adoption is slow at first (represented by a more flat curve at the beginning of the time period), then it hits a point where it takes off (represented by a steep curve mid-way), and then it tapers (represented by the curve going flat again).

In diffusion studies, the top of the curve represents 100% adoption of the technology. What percentage-based studies do not tell us is how many people that 100% represents. 6 billion? A few million? The point here is that:

The S-Curve measures adopters only, not non-adopters.

That means that some people do not even make it to “laggard.” Also, it means that the maximum possible adoption will vary from technology to technology. I have TME (but was a very late adopter), but I never had Hush Puppies or a hula hoop. I had Firefox early on, but I don’t have a hi-fi stereo with subwoofers or a large-screen television. Given this information, libraries ought to consider the possible capacity of a market as well as the modes of adoption when they look at new innovations and technologies.

The next thing diffusion experts do is take these “macro diffusion” studies and chop them up into statistical categories. To quants-oriented people, this means 2 and 3 standard deviations from the mean. So, early adopters are actually the name for those who, statistically, adopt a technology early, within 2 standard deviations of the mean adoption rate. Innovators are 3 standard deviations from the mean. What Rogers did was talk to these folks and he discovered that there were certain traits like financial standing and education level that could describe (in general) what an “early adopter” was like. So that brings a second point:

Early adoption is a generalization based on statistical data, not specific personality traits.

Personality traits do not make the early adopter. They merely coincide with early adoption. Just because someone is dynamic, leader-like, interesting or whatnot does not mean they will be an early adopter in all scenarios. This means libraries cannot rely on particular individuals as “early adopters” who will in turn predict the success or failure of a new technology. An early adopter is just that — someone who, for whatever reason, adopts a technology early.

So, the phrase “I am an early-adopter” is a bit of a misnomer. People are early adopters of some things and late adopters of others. Using early adoption as a predictor for a broader adoption is extrapolation, which has its validity problems. You can predict (with some accuracy) into the short term, but not the longer term or very long term.
This is because these studies are all “after the fact” studies. They happened in history, but that doesn’t mean they will happen in the future.

Another important point is that these studies only apply to the markets that were studied themselves. We cannot infer from the early adopters of one innovation that all early adopters of all technologies have these same traits.

And what can you say about innovations or ideas that came from the past? Yoga got adopted as a health benefit millenniums ago, yet the S-curve happened again in the 70s and again in the 2000s. Pilates was developed out of the tradition of yoga as well — so is it an innovation or and example “slow growth” conservative style?

And what does this mean to librarians? Well, in developing technology, it means you want to get the advice of people who have tried it. The person who tried a technology may be anyone from the organization. There are techies out there that do not have an iPod. You need to ask the early iPod user whether the tech will catch on (and why), not the person in the organization who generally likes to try new stuff. I was an early MUD gamer and chatter, but it took me a long time to get to Flickr. That’s not because I’m afraid of technology, but because I’m not that interested in photography (a prerequisite for people getting interested in a site that organizes photographs).

Also, in this paradigm it makes sense that we should talk not only to early adopters but also the innovators. The only caveat is that the early adopters are going to be more compelling in their arguments for the technology. Then, (surprise!) libraries have to make decision for themselves about the “stickiness” of the technology.

In the end, I guess my message is that the way to understanding the applicability of new technologies is to learn learn learn as an organization. That means you want to hire learners and help them learn as much as possible.  It means you also need to give everyone an opportunity to play with new innovations, if they have something they want to try.

The other thing I’d like to add is that innovativeness will have its cycles with individuals. There are some bugs that I caught as a youngster (like MUDs), that I now do not have (I have too many “first life” responsibilities to even consider a “second life).” But on the whole, I find I am adopting things earlier now than I did when I was younger. That’s because I have the financial resources to try out new toys. I’m also willing to bet that second lifers will be forced to change their behaviors as things like careers, marriages, children, family illnesses, declining physical fitness and other things come around (as they almost always do).

As I write this myself, I am more than happy to say that I will be taking a break from playing with technology to play some floor hockey with teens with Salvation Army volunteers. Long live adoption of old fun as well as new tech!  And long live personal growth for everyone!

3 thoughts on “Understanding early adoption

  1. Hi Ryan. Thanks for your thorough reading. I agree that we should be talking about early adoptors not as a demographic class, but as a statistical class.

    What I was trying to say in my original piece was that we shouldn’t try to get immediate uptake from all our users for a service – which was how I used to define its success. We can aim to only have 2.5% uptake or 16% uptake and know that things are actually on track.

    I did then go on to talk about “early adopters” almost as a demographic when I was talking about user education – but I guess what I was getting at was that the traditional “how to” instuction that we use for the late adopters may not be appropriate if we are aiming at people who tend to adopt most technological change early. We could add in a discussion about how this fits in with other new technologies out there and could be enhanced – I know that this is often what I want to know at the same time as the “how to”s.

    I think how quickly a technology reached the 16% uptake rate would be worth noting in its evaluation. Yes, it’s ony an extrapolation, but that’s how the bell curve works – giving a statististical pattern to predict events. Of course the adoption could go pear shaped, and very few real life events match statistical preditions, but it is still a useful tool.

    We shouldn’t use statistical models as our only guide for new services – I agree that we need to learn, learn learn and use our own professional judgement. And keep having old and new fun along the way.

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  2. Hey Kathryn — for some reason I had to remove your comments from my spam filter. I hope no other comments got lost! Honestly, I’m not the sort to censor those with whom I disagree (or have disagreed).

    Tell them also to Google “the other librarian” for kicks. My nom de plume is used for everything from corny jokes to erotic stories. Libelous!

    “You couldn’t understand all of the words the other librarian said because the speaker was upside down.” — yes, I do have a habit of hanging presenters by the ankles when I introduce them!

    “Bonus points if you can locate the other librarian in the picture.” — (A game better than “Where’s Waldo”!)

    “Another librarian and I started petting it and talking to it, and the other librarian named her Beanie.” — I gave her a monographed burrito to go with it too!

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