we can ask questions about who are initial adopters?
Are they people with high degree, low degree, what accounts for different
speeds in a diffusion process, why might there be an eventual slowdown?
There's a whole series of questions that come up.
And let's start by looking at, at one of the early studies of this.
it was a study by Coleman, Katz and Menzel in the 1960s and they were looking
at the adoption of a new drug by doctors, and in particular, there was a drug that
had just been developed. it was an antibiotic and it needed to be
prescribed by doctors. And there was a question of so their idea
was a, a doctor adopted this new drug if they prescribed it at some point in time,
okay? And so there was a question of how many
how, how much time it took for different doctors to start prescribing the drug.
And so, what they did is they, they kept track of the doctors over time and kept
track of which doctors had prescribed the drug by some time period.
So, six months out from the development of this drug, how many, or from the legal
adoption by, of this drug. Had some doctors prescribed it?
Eight months out, how many had prescribed it?
Ten months out and so forth. And what they did was they, before they
started this they surveyed the doctors. And they asked which other doctors would
you go to for advice. Okay.
And so then they kept track of, that, that gives a network of, of doctors, and
in particular, we can break subjects into three different categories, they broke
them into three different categories. They had 36 doctors that were not named
by anybody else. So nobody said that they would go to that
person for advice. 56 doctors were named by one or two
others. And 33 were named by at least three
others, three or more others. Okay, so these were different doctors, in
terms of how many other people would say they would come to this person for
advice. And then they kept track of the adoption
rates over time. And the ones that hadn't been named by
anyone else, by six months out, 31% of them had started prescribing this new
drug. By eight months out, 42%.
By ten months, 47%. by 17 months, it was 83%.
And when you look at the, the doctors that have been named by one or two
others, it started at 52% six months out, it was at two thirds, roughly, by eight
months, 70, and so forth. So, so here we're seeing higher adoption
rates at an earlier time. And then when you're named by three or
more others, it was even higher. they were picking even higher reaching
higher rates at an earlier time. And so forth.
So what this shows is that the diffusion process actually differed based on the
position of the doctors in the network. there's been follow-up studies that
there's some difficulties in doing these kinds of studies and making sure that
you've got a clean test. Because it could be that whether you're
named by three or more others also correlates with other things which could
correlate with whether or not you heard about this drug from advertising or other
sources. You might be pressured directly by the
drug company. So, there's a, a series of other things
that might be accounting for these kinds of results.
There's been a series of follow up studies that have tried to make sure
that. The, the kind of finding here is actually
robust and not something that's just the spurious correlation.
But in any case what we do see is differences here based on the the
connectant and, and that seems to hold up if you, if you look at the, the, the data
with more scrutiny. so here, I just plotted based on how many
months out, what those rates were. And what you see is the ones that were
named by nobody else, had lower rates at each point in time.
Eventually they reached the ones by one or two others.
and the one or two others are below the ones named by three or more others, but
you're, so you're seeing adoption rates different adoption rates based on, on the
degree of the individuals involved. And that's one thing we can pay attention
to in trying to model diffusion and understand diffusion, why is it that it
might vary by the degree of the individual?
And there'll be fairly intuitive explanations for that, as you might
expect. let's take a look at another example
which is another fairly famous one. this is data analyzed by V Griliches in a
paper in 1957 and It goes back to data that had been
collected earlier in the 30s and 40s on the adoption of hybrid corn among farmers
in different areas of the United States. So basically hybrid corn where you had
mixed the genetic material of different corn species was being developed
actually, you know, this kind of husbandry of corn has gone on for a
millenia. But it was being marketed and developed
in a new way in the 1930s. And the yields of the corn that was being
produced in this way were somewhere between 15 to 25% higher.
Than the existing corn strains. So you could, you could get much higher
yields from the corn seed that they were being developed at this point and time.
And this type of corn eventually just completely replaced the old single.