Dear Rob, Terry, Erik, and Chuck,

Rob's survey of the skills and traits that employers seek in
designers was an excellent example of effective quick and dirty
research. Quick and dirty research is heuristic. As Erik notes, this
form of heuristic research is design process for acquiring
information within time and budget constraints.

This topic raises several issues. The challenge of speed is one.
Standard academic research methods are generally too slow for
industry. Managers are impatient with anything that delays time to
market and return on investment. Time is a factor in the opportunity
costs of any choice against alternative possibilities.

Managers want rapid answers, and skilled industrial research experts
work faster than academic researchers do. In addition, academic
research may not be of high enough quality to meet industrial needs.
Nevertheless, this does NOT mean that designers outside academia are
able to conduct high quality industrial research. This is precisely
why designers today require research training.

Terry points to one solution in the form of relevant current
information. Secondary use and meta-analysis of available data, prior
reports and established findings can be more efficient than
collecting new data. Researchers do well to build on research that
others have already done. This is the reason for doing a literature
review in academic research.

While Rob accepts the value of existing data, he notes that this is
often not enough for industrial innovation. Leading firms require
fresh data when they are attempting something new for which past
results are insufficient. Fresh data helps guide a design process
that would otherwise be uninformed. At the same time, budget
restrictions and time constraints make it imperative to limit data
collection. For this reason, researchers must find ways to generate
useful information with limited, well-chosen samples.

Establishing limits and making robust choices requires experience and
skill. This is why research training is often MORE important for
quick and dirty research than for standard research.

Terry discusses this, writing "quick and dirty research requires
enormous commitment and intellectual critical effort to identify what
can be justifiably inferred from the data. Otherwise, costs of making
faulty decisions can greatly outweigh the savings from cheap data
collection. Getting good outcomes from quick and dirty research can
sometimes be much improved by practical specialists at the
philosophical end of research methodology, critical thinking, [and]
theory of knowledge."

These themes converge in the search for useful information.

As Chuck notes in discussing different forms of simulation, there are
many ways to develop good information from small samples. Several
good examples of such techniques come to mind. One of the most
interesting and least expensive simulation techniques is Pelle Ehn's
famous "cardboard computer." Sonic Rim puts many kinds of modeling to
good use. Imagination Lab uses different forms of play (including
Lego sets) to design organizational processes and structures.
Anthropologist Bryan Byrne - a sometime member of this list - is also
deeply involved in this kind of work.

In my view, industrial research requires an ability to understand and
use a spectrum of research approaches. These range from quick and
dirty approaches on the one hand to careful and systematic inquiry on
the other. The central challenge in each case is choosing appropriate
methods that deal responsibly with constraints.

The Danish form Per Mollerup Designlab offers good examples of
effective research choices. One reason I enjoy working with Per is
the fact that he has a strong research background. He insists on
using part of each project budget for study the problem
appropriately. His understanding of research methods is serious and
robust. While he is a practitioner rather than an academic, he has
been a university-level teacher of statistical methods and he holds a
doctorate. Ass a practitioner, however, his approach to research is
nicely tailored to the competing demands of time, available
resources, and required output.

Our most recent collaboration involved a study for Estonia's national
design policy. Per led a team that included Pekka Korvenmaa from
University of Art and Design Helsinki (UIAH), designer John
Landerholm from Denmark, and me.

In the run-up to the project, we realized that budget and time
constraints would not permit a comprehensive program of field
research and interviews. Instead, we estimated the number of
interviews that would give us MOST of what we needed to know. We
chose the number of interviews that we felt would give us 80% of what
we needed. This number would have to have been multiplied at great
expensive to move from 80% to anything close to 100%.

We informed the clients that budget and time constraints required
this choice. They agreed, and all worked well.

This thread can lead to important reflection on - and distinctions
among - research approaches.

In this, as in many design issues, wise choice - phronesis - makes
the difference between good outcomes and less good.

Making reasonably good decisions under constraints mean less than
perfect outcomes. Nevertheless, less than perfect outcomes are better
than the poor results that result from bad decisions. Learning to
make wise research decisions is another reason that designers benefit
from research methods training.

Best regards,

Ken


--

Ken Friedman, Ph.D.
Associate Professor of Leadership and Strategic Design
Department of Leadership and Organization
Norwegian School of Management

Visiting Professor
Advanced Research Institute
Faculty of Art, Media, and Design
Staffordshire University