after_ethnography.pdf

8/13/2019 After_Ethnography.pdf

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Iota is a new venture that John Cain and I have just started with some other colleagues of ours.

We are doing some new things with user experience research and new kinds of data.

Today I’m going to talk to you about the philosophy we are bringing to the research work we do

through this new business. I’m not going to talk about specic projects, but rather about how we

think, and mostly about where we think the research side of the user experience world is going—

all set in the colliding of new contexts.

A Short History of Market Research

I begin with some of the past decade’s big changes in applied research. I call it market research

for wont of a better term, but I really mean the things that research has brought to design,

and vice versa.

I want to put a certain little spin on this because I think there has been a series of slightly

discontinuous turns, and I want to set up my argument that we have reached a new turn, right now

And as with other kinds of collision, it is as much an opportunity as it is a danger.

After Ethnography

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Rick E. Robinson

Iota Partners

1920s—30s 1950s 1960s 1980s 20102000s

Opinion Research // Advertising Research // Segmentation Models // HCI // User-Centered // Now What?



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So. Market research, when I rst encountered it as I was coming out of an academic context twenty

years ago, was mostly based on opinion research. The big dominant players had all been opinion

pollsters. As that the work of market researchers moved more into the product development world,

maybe in the 1950s or a bit earlier, the big consumer concepts were “attitude & awareness” orintention as in “intent to purchase”—all things largely developed to support the work of

advertising, rather than the work of product development.

Then, when segmentation models rst came out in the mid-1980s, large-scale ones, they really

changed the game again. They conveyed information that had already been available, but there was

a new framework brought to it that made it more useful for a broader range of business

applications. So segmentation was a tool built on top of research data, demographic data, and it

broadened the interplay between product development and research because now you had these

useful characterizations of people.

Just about then also came the rst beginnings of product research in the computer and

communications world and particularly the beginnings of usability studies—very lab-based, but

again, it was for new devices, to solve all the problems of, “How do you try to understand the likely

performance of a product that hasn’t existed before?” This was really the rst time in computer

science that people started trying to deal with future product performance as a market research

question, as in “Will this be adopted?” and “Is this a good feature?” You couldn’t just ask attitude

and awareness questions because nothing like these products had existed before. Now we’ve gone

so far along that path, however, that these questions have really become everyday existence for

researchers. It’s no longer about trying to get responsive information, but trying to understand or

draw some structure and framework around things that don’t exist yet.

About fteen to twenty years ago, user-centered research really picked up the ethnographic

approach from social sciences. That was another shift. Ethnographic research was not new as an

approach in social sciences, but it was new in its application to the product development world and

it really shifted things. It brought the user’s perspective into multiple development processes, not

just in “acceptance” after the fact. So you had a whole different notion of who you needed to pay

attention to. And over the past twenty years, the development of approaches, the development of

language, as well as methodologies to understand the user’s point of view have all been brought

rmly into the development process.

We’ve been talking at this conference about how integrated that approach now seems to be, and yet

there is a feeling among some within the eld that maybe this once-exciting work has plateaued.

Okay, an analogy: When segmentations were rst available, they were enormously differentiating.

Just having a segmentation model was a competitive advantage. Now, everybody uses and works

with segmentation models, so the particulars of a segmentation model are what make it valuable or

not. Just the fact of having one no longer matters as much.

We are in the same situation with ethnographic research and user-centered approaches. Once upon

a time it was very differentiating and extremely valuable to have that information, as opposed to

competitors that were technology-driven, but nobody is purely user-centered or purely

technologically driven anymore.



What Comes After Ethnography?

I started doing ethnographic research and working at the intersection of research and product

design development because I liked the way it could change the world. I liked the way it could

change everyday experience for people. If it’s no longer having that impact, if it’s no longer adisruptive tool, then I want to gure out what’s next.

So here’s what I want to talk about today: What’s after ethnography in the user-centered approach

to product development?

When I say “product development,” I mean it extremely broadly.

Ethnography wasn’t supposed to be, and isn’t necessarily dened as, “eldwork.” The core of the

work has always been the ability to translate the future through some abstracted model back into a

picture of the current reality. So, speaking in terms of traditional anthropological and sociological

methods, using the tools of going out to understand “the other” (other societies, eras, strata) and

then bringing back that information so that it was comprehensible to your peers, has become, in

the applied world, understanding what a future experience might be and making that accessible

in the current context. This next diagram is a simplication of a whole process of ethnographic

work that I have used for a long time, and that Jane Fulton-Suri at IDEO and other people use,

because it is actually derived from Clifford Geertz, an anthropologist. It’s a simplication of

his model.

Now at the top of this is what ethnographic practice has tried to produce—models of something,

useful representations of something.

I think that perhaps the ur-model of the twentieth century (and hopefully into the twenty-rst) is

the double-helix structure DNA. There is a famous picture of Watson and Crick with the model that

they built—a physical model out of sticks and balls and pipes and lab equipment—in the stairwell

behind their lab. Despite the fact that the double-helix model that they built doesn’t look like what

you can see under a (modern, powerful) microscope, making the physical structure apparent and

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abstract

concrete

present future

messy reality

(less messy) modelof reality

the model you’d like tohave (strategy)



testing out how it might work was very important to them,

both as scientists, but also in seeing if the construct mattered

to other people.

And if you think about the kinds of things that people do with

DNA as a model, now, I think we’re looking at that same sort

of connection: the abstraction of reality into something that

makes it apprehensible by other people and gives you the

ability to change it. That’s what is really important here.

Models

At Iota, we use a very basic denition: a model is a useful

representation of the organization

of _____. Whatever that blank might be—an experience, a

process, a structure. Space. Whatever. People also call them

interpretations, narratives, and frameworks—any of those are

ways of making sense of the world from a user’s point of view,

and thus all of them are forms of explanation. Explanations

are ways that you make sense of phenomena in a new

framework.

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James Watson and Francis Crick with original

model of DNA in 1953

Where hard science research and social science research have gotten into conict with some

applied research, especially “design” research, is at the rational, logical part of building a model out

of the data on user experience. Most of us are really good at that; we can take what we see, the kind

of observations we’ve made, and put them into an order that makes sense, that is rational, that is

logical. But when all anyone asks you is whether those models “predict future behavior,” you are

bound to start to come into conict with the goals and practices of other parts of the organization,

other ways of doing science. And interpretive approaches are usually seen as soft rather

than useful.

Louis Menand is a historian of ideas who has written a lot on the American university, and on the

social organization of universities—how they get funded, how they evolve, why there are different

tiers, what separates different universities both in the United States and in the rest of the world.

Earlier this year, he published a book called The Marketplace of Ideas, which is a summation of a

lot of work that he’s done over the past ve or six years. And one of the pieces that Menand talks

about is “Three Ways of Knowing the World.” He explains that there are people who want to know

the world by describing what things are (science), and at the other end of the spectrum are people

who want to understand what things mean (art), and in between is trying to determine how people

behave. At the core of this middle pursuit, one is trying to connect the way things are to what they

mean. So when Menand talks about this as a coherent system, he says that science and knowledge

of the world has all of these components. There’s science (how things are), there’s art (what things

mean), and there’s what people do with those things. And the interesting part, to me, is that he

talks about entire elds, how entire domains of knowledge, as they evolve over time, shift, and not

just from one end of this to the other. It’s not just a progression from interpretation to hard science

that happens as a eld grapples with different problems that are “differently knowable,” because

the relative balance of what’s valued in each eld will change.



There have been times when the leading edge of natural science was truly an interpretive one, and

then, perhaps at the other extreme, a time when people thought that the future of science was going

to just be completely descriptive, as clearly mapped out and as straightforward as anatomy; all we

had to do was nish off the litany of how all the parts and pieces worked. But recombinant andepigenetic technologies have now made even this seemingly bedrock aspect of biology open up

again, because we have new tools and new ways to know the world. Whether or not we should

recombine these things has become as much a question as whether we can combine these things.

What genes mean is a ercely contested area of knowledge again.

The construction of meaning is at the center of why we study human behavior. I don’t think there

are any people who study behavior just to describe it anymore. You have to get to meaning and to

the question of “Why?”

What things in the world mean, what implications they have for us, necessarily involves social

structure as well as human interpretation.

That is the point we’ve reached in this kind of research.

It would be very nice to know all of these things (what they are, how they behave, what they mean)

about everything that we are engaged in, but it would take lifetimes and millions of dollars. The

point is not to say, “We should be able to know all of these things,” but rather as an organization,

“What should our goal be? As a research practice, or as an applied practice, to what end are we

developing our knowledge of the world and of people?” This question has become more important

than the “hows” of methodology. You have to ask, “Why are we doing this?”

Changing the World

The main reason, in the applied world, that we do things is to change the world. I mean that quite

humbly. It’s not simply to respond to what’s already out there, or to address so-called user needs.

(“User needs” is one of my least favorites terms because I think “needs” is a very passive

construction of the point of research.) The idea that we are trying to change behaviors is a central,

important aspect of our approach.

There are two big things going on right now that are changing in the world and having an impact

on research. One of them is pervasiveness. I’m going to talk a lot about the role of sensors and how

sensors have changed what is knowable and what qualies as a good thing to do research on. The

other big change is the continuousness of data. These two things together bring us to a critical

new shift.

I think the word of the decade will be instrument , because it has this nice double meaning (at least

in English). There is “the instrument,” which is a noun, a thing. It can be a scalpel, a musical

instrument; it can be a document of state, something that establishes something. Instruments are

things in the world that affect structures, that affect systems, that affect relationships. Instruments

are things that act and things that enable acts.

But there’s also the verb sense of the word, “to instrument,” which means, in one sense,

implementing a structure, so you can instrument change, you can instrument a process.

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The other, more common usage is instrumenting as “to make apprehensible”—a thermometer

makes temperature differently apprehensible from the physical manifestation of temperature,

which is just being warm, or hot, or chilly.

There is a quote from Max Weber that Clifford Geertz made famous: “Man is an animal, suspended

in webs of signicance that he himself has spun.” The “that he himself has spun” part makes it

social structure. But once you start talking about “webs of signicance,” you’re talking about

meaning at very different levels than individual meaning, right? Individuals have been the target of

much user experience research. But social structures, groups, communities are the target of

research today. And I think that in the near future we are going to be targeting the experience at

the cultural level, not just the cultural as seen through the individual, but the true effort to

understand cultures.

When you think about networks, or webs of signicance, there are of course social structures:

communities, groups, families. There are all kinds of ways of describing how people are connected

to one another. And there’s also what my friend Andrew Sather calls “that interweb thing,” the

connection of a bunch of machines. And what is really the focus today is the internet that has

become many more things, including mobile devices and different platforms, as well as the

internet. We are on the cusp of starting to look at what Danny Miller, among others has called “the

internet of things” or the internet of stuff—the notion that in addition to the platforms that you

might have, your home is also going to be fully connected; that objects, things formerly considered

“dumb” objects, are becoming connected; that sensors are out there, everywhere. Walking through

the airport, you can scan with your smartphone a crazy number of QR codes on billboards, posters,

luggage carts.

Work being done today in health-care research includes instrumenting not only bags of blood but

also all of the different surgical instruments, to start to track how tools move through a hospital. It

started out with the logistics people, this idea of instrumenting simple things. But the idea that you

can tag and track things has made much of the physical, everyday world apprehensible in that

sense of instrumented .

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Going back fteen years or so, to the work of Bruno Latour—a

French sociologist who studied the production of science —what

makes science science—who sort of really started that whole eld

of looking at the interactions among stuff in the material world,

meaning everything, from devices to furniture, clothing, and

people.

Latour coined a way of talking about this: Actor Network Theory.

ANT addresses that “web of signicance.” It says we can talk about

human actors, but importantly we can also talk about nonhuman

actors, and we need to see the network as a thing capable of being

changed by all the actors, and not taken as a given.

So Actor Network Theory really threw into question “what

constitutes an actor” and “what is changeable.”

Actors

Network Context



abstract

concrete

present future

messy reality

(less messy) modelof reality

possible futuremodels

the model you’d like tohave (strategy)

When you start thinking about the kind of modeling that has been done—from the kind of

experience models that were built in US groups ten or fteen years ago, to what is happening now—

the dimension of time becomes extraordinarily important. The very existence of “always on”

changes the nature of the data beast entirely. The work used to be sort of a slice in time, because itpurported to be structural—and I did this as much as anyone, probably—but calling a model

structural, claiming it was structurally a reection of reality, usually ignored the effects of time. And

the new kinds of data that we have available now are making the explicit change of things over time

very important.

I am rethinking the old model of building models vis-à-vis the purpose of doing user research.

Once upon a time, we talked about the description of reality as if, if you did enough work, and you

used the right approaches, you would have the description of current reality, and that you would

get to a structural, eternal model that you could use. Such a model would then help you predict the

future. Now I think that what we predict is a possible future, and what we can do if we get a messy

model of reality and lots of data is predict multiple possible futures. I think that now we say, if we

produce a product that does x, people’s experience might be this, but we could also produce a

product that does this other thing, and thus change their experience in a very different way. The

important question becomes how do we make that choice? This goes all the way back to that

underlying philosophical notion that we can’t just predict what a product should be, rather we are

making value-informed decisions, driven by what we think is important as to what it ought to be.

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When you start to think about the different ways that the user experience guys talk about the

research, there’s all kinds of things that, once you reect on them, have a great deal to do with

time. The famous early frameworks work, exemplied in articles such as Schank and Abelson’s

Scripts, Plans, Goals, and Understanding—all that stuff has inherent in it, the human default

toward trying to understand what the future is going to be like. But scripts aren’t permanent. If you

think about scripts or plans or goals, all of them are dynamic. But do we have a good way to

represent how they change? Or a way to represent the way they will change in experience

modeling? I don’t think we do, and I think that’s one of the things that we really have to work

toward as a community.



Explanations are almost always sequential. They almost always have cause and effect, or at least

correlation. They can be backward-looking, or they can be forward-looking. Models can be models

of or models for something. You can build a model of the Roman coliseum, or you can build a

model of a house that has yet to be built, and those kinds of things are very different enterprises. Ithink we need to start being much more clear about which one of those modeling projects we are

engaged in.

So when we talk about time, there are a couple of different scales. One is the notion of time in a

second-by-second, minute-by-minute sense. Experience has always been organized on that level,

but there are also all sorts of timescales more on the order of “evolutionary.” The way that things

change over much longer stretches of time.

For example: If I ask you what time it is right now, how many of you would rst think, “I’m going to

run outside real quick and see approximately where the sun is”? Once upon a time, the notion of

the time of day was an extremely concrete experience of the physical world: the sun rising, the sun

directly overhead, the sun setting. Now the notion that it might be 8:42 in the morning is

mediated—and it’s not just that we don’t apprehend where the sun might be—we use watches, we

use clocks, we see the time displayed on our devices, through a system rooted in a cultural

agreement. Noon local, is not necessarily noon in the celestial mechanics sense; the sun might not

be directly overhead because we have systemically agreed on notions like time zones, a twenty-

four-hour division of time, and the way in which we divide minutes into seconds. All of those are

cultural constructs that we have used to help connect us to a phenomenon of human experience

that is thus more abstract than it used to be. We know that the sun is shining outside, and even

though we’re sitting in here, we all know it’s morning. We share this through technology. A British

sociologist named Anthony Giddens wrote about this access really beautifully in his book The

Consequences of Modernity. Giddens talks in very dense but beautiful language about how many

different social experiences are moving from concrete to abstract. And unlike most of his peers at

the time he wrote, who saw this shift as a terrible downfall of modernity, Giddens views it as the

thing that technology enables us to do better, and argues that technology is what gives us access to

(and trust and condence in) those abstract systems. It doesn’t necessarily give us access to where

the sun really is, but it enables us to feel it, to trust that everyone else thinks it’s the same time of

day that I do.

Now the notion that at the level of what seems like basic human experience, we are moving from

very concrete experiences to more abstract notions, is a very different kind of scale than we

normally study as part of “user experience.” Similarly, think of how deeply Cartesian notions of

representing space affects our everyday experience of place. And I’d argue that social media—

Twitter and Facebook—are yielding abstractions of interpersonal communication, of face-to-face

communities and relationships that are being represented and interacted fundamentally

differently—all of this adds up to a new need for technology to provide us with basic trust and

condence in the continued presence of the systems we surround ourselves with. We used to have

interpersonal cues to do all of it for us, but they are yielding to our embrace of technology. That

dynamic is a really interesting lens for looking differently at new stuff in the world.

The evolutionary timescale gives us another intersecting set of terminology to work with. Joseph

Schumpeter, in the 1930s and 1940s, wrote about bringing an evolutionary perspective to the

industrial world, to how to think about social and technological consequences in business. What

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Schumpeter did was talk about “Creative Disruption and Destruction” (emphasis mine). So if you

think about what Darwin did, he really moved the perception of the order of the world from “it’s a

natural order” to “it’s a dynamic: evolution.” But that was still something that just unfolded. We

were included in it but we didn’t drive it. What Schumpeter did, through his work on CreativeDisruption/Destruction was say that we (humanity, societies) have a role in that change. That we

are now actively changing the evolutionary pattern, asserting that all of these things—competition,

selection, disruption—all of this evolutionary-scale terminology can now apply to business, to

systems of technology, and to social structures. But once we do that, we are getting back to

consciously choosing which path we’re going to take, and that’s really difcult to do.

Much Bigger Samples

So how might we actively engage in selecting which future we’re heading to, given that technology

can now provide data at a different level of granularity, all different kinds of things are now

apprehensible, and the data is continuously available? We can start to think, instead of studying

ve people, or twenty people, at a population level. We can start to think about the billions of

readers/users. We can start talking about doing research on signicant subsamples of those, and

not at a purely speculative level. We can start to think about closing the gap between projecting

from samples and descriptions of actual behavior at the population level—almost impossible before

“big data,” but something that we can actually contemplate through an instrumented world.

One of the things that I think is most interestingly headed that way is the emergence of proprietary

online communities. Companies like Communispace and Passenger are creating very particular

groups, much more interesting than demographic slices, and following them with a really

intriguing combination of online and off-line sources over an extended period of time. So you have

a thousand cardiac physicians being followed to nd out how they are reading science journals,

what kinds of drugs they’re prescribing, what kind of procedures they’re discussing with their

patients, and how they see their particular populations. You have new levels of granularity available

now, not just the scale that ethnography made possible in seeing one person day after day after day

but by seeing much larger groups of people continuously available in multiple parts of the world.

Sensors

A sensored environment that tells you everything from the movement of toilet paper on the roll to

the consumption of food in the fridge, to whether the windows are open, gives you an extremely

different picture of the experience of people in the home than you can get from asking them

questions or even from coming in and observing them for a day or so. The key, I think, to making

sensors really productive for product development, is to move from simply building a sensored

environment, because what good does one sensor reading, one moment give you? You have to use

the data as it unfolds over time. And we need better models of ways of doing that. Longitudinal

research was extremely important in the 1940s and 1950s for psychology, for epidemiology, but it

got to be extremely expensive because it was so labor-intensive. Now, thanks to technology, it’s

coming back into affordability again. There are ways to study large groups of people over a long

period of time with signicant granularity that don’t cost you hundreds of thousands of hours of

the time of even graduate students, let alone professionals.

That all sounds very serious, and it sort of comes back to “well, if we could have a perfect mirror-

world model,” meaning that if we had some sort of data on every aspect of daily life then we would

know exactly what’s going to happen.

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I don’t think that will ever be the case. It’s the wrong thing to

expect of research.

So we come back to the meaning of instrumentation, and to myother candidate for word of the decade: play.

It Might Get Loud, a lm by Davis Guggenheim, the maker of An

Inconvenient Truth, is a documentary that looks at three different

generations of electric guitarists. It’s an incredible lm about the

balance of expertise, the nature of the instrument itself, and what

it means to explore and play. These guys are playing music, but

they’re also playing with musical structure, they’re playing with

pop culture, and they are taking themes and twisting them as they

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play. In the lm, they talk at length about how the actual physical guitars affect their world and

affect the work that they do. What’s really remarkable is how structurally similar that relationshipis to what new techniques and science have enabled people to do with genes and genetics. You

think about the kind of play that’s been made available for inventing new species, and you get back

to something that’s really important in thinking about research work: it’s not as simple as

description that leads to prediction. There is always inherent, in the best research work, a sense of

play, of something driving the choices made in thinking through multiple possibilities and taking

some responsibility for what it ought to be, not just what it will be. That’s very difcult and very

different from Mission, Vision, and Value statements. You can’t just say, “We want to develop a

good user experience.” Those kinds of nostrums are not as valuable now that we know how much

products affect daily life.

Ethnographic work, I think was the last big choice, the last big shift, in user experience research

because it said, “Go see people.” It urged developers and designers. “Don’t just imagine from

yourself what it must be like to use this technology; go see it in action.” And I think what’s

happening now is the advent of an ability to create instrumented worlds, and then treat them truly

experimentally. But what I want to urge is that when we do that, we think very carefully about what

we’re choosing to do, and how we are choosing to do it, and why.

That’s it. Thank you.

after_ethnography.pdf

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