This document discusses bridging the gap between researchers and designers. It notes there are often misunderstandings due to differences in skills, culture, roles, languages, and mindsets between the two fields. Researchers favor an information-driven approach while designers prefer inspiration-driven work. The document provides examples of conducting light analysis directly with raw data or using a database for heavier analysis. It also discusses communicating results through presentations or by involving clients throughout the process. The goal is for both data and overall frameworks to inform conceptualization of new design ideas.

1. CONVIVIAL
TOOLBOX
GENERATIVE RESEARCH FOR
THE FRONT END OF DESIGN

3. CHAPTER 10: BRIDGING
A gap between researchers and designers, often
the source of conflict, misunderstandings and
sometimes even a lack of respect:
- skill sets between disciplines or areas of expertise,
- culture and values,
- roles and (perceived) responsibilities,
- (perceived) ownership over parts of the process,
- specialized languages,
- boundaries, edges and domains of the unique
disciplines, and
- mindsets and egos of the people involved.

4. The gaps between researchers and designers are
caused by the differences in the education and
mindset. An information-driven approach is
favoured by those from a research-led perspective.
An inspiration-driven approach is favoured by
those from a design-led perspective.
FOR INFORMING
Conducted by social scientists
Borrows from the scientific method
Values reliability, validity and rigor
Builds upon investigation, analysis and planning
Relies on extrapolation from the past as a way to move
into the future
FOR INSPIRING
Explored and applied by designers
Is discovering its own tenets of good research
Values relevance, generativity, and evocativeness
Built through anticipatory thinking, ambiguity & surprise
Draws primarily from the future, using imagination as
the basis for expression
IDEA

5. The same generative design language is
used both on the research side and on the
design side.
In research, we are investigating what
people say, do, and make in order to
understand their past, present and future
experiences. In design, we are playing in the
design opportunity space with the same tools
and methods in order to seed and to fill the
design space with both ‘stories’ and ‘stuff’.

7. The gaps between researchers and designers are
also reinforced by the tendency of organization to
house researchers and designers in different
groups.
As front-end design and innovation teams become
more interdisciplinary, mini-gaps between people
across additional disciplines become apparent.
All these gaps are, in large part, symptoms of the
expert mindset that each discipline imparts to its
constituents. People feel the need to defend their
expertise since they may be misunderstood or not
even listened in cross-disciplinary settings.
The generative design mindset reveals who the
real experts are: the people we are attempting to
serve through the design process.
The size of the gap is lessened to the extent that
mutual respect exists between the players.

9. Generative design research results in very rich data
sets that invite and benefit greatly from many
levels of analysis.
The process of analysis can influence the formation
of ideas in the conceptual side.
Ways to bridge the gaps:
1. Communication all along: between team
members and stakeholders throughout the
entire process.
2. Immersion-rich events: short, intensive efforts
with many people involved.
3. High-level presentation at the end: the most
likely approach for first-time clients until they
have had the chance to experience the
generative design process once before.

10. To the extent that the client team
members are not immersed in the
everyday experiences of the people
being served through design, there
is the danger of their not
understanding the opportunities
and solutions delivered.

11. Ways to immerse them in the data:
- The sharing of selected video clips can be very
useful as an empathy-generating exercise.
- The development of personas can be a very
good way of providing understanding of and
empathy with ‘users’.
- Extending the persona concept and developing
future scenarios based on the personas.
- Asking the client to predict what the results of
generative tools will reveal and having the client
team members use the generative tools
themselves.
Tools is only the first step in the transformation toward a co-creative culture. Tools need
to be applied via relevant methods often nested within more inclusive methodologies.
The mindset with which the tools are applied in crucial.

12. The generative design research process is dynamic
and very complex in practice. How do you write a
proposal with clear man-hour estimates for a fuzzy
process that you have not executed before? How do
you know what your deliverables will be or how long
the project will take when your task is to explore in the
fuzzy front end? How do you plan for a highly
fluctuating process, and how do you make sure that,
e.g., insights are developed to sufficient depth and the
team doesn’t fixate on a first, superficial idea?
The answer is as simple as it may seem evasive:
practice, experience, and getting the feel for it.

14. CHAPTER 07: ANALYSIS
One option is not to analyze the data at
all but to immerse yourself and the team
in the raw data. We call this ‘immersion
for inspiration only’

15. The analysis of qualitative data is always an
adventure of exploration. Data sources include:
- secondary research
- ethnographic field research
- interviews
- informal conversations
- questionnaire results
- photographs taken by participants
- workbook data, diary entries, toolkit exercises
(may be annotated)
- artifacts made by participants
- discussions between participants
- notes and reflections by the research team
- mindmaps and idea dumps by team members

17. The form of the data is ‘messy’. This
messiness means that the activity of figuring
out what the data mean will not be simple or
fast, nor will be easy to prescribe how it
should be done in advance of its collection.
Dealing with this type of data is a skill that
comes with training, experience, and
reflection. It is not a fixed and rigid
procedure.
Analysis involves interpreting the data,
making comparisons to theories and to
other data, searching for patterns,
generalizing findings, and finding evidence
to support your conclusions.

18. https://stangarfield.medium.com/yet-another-myth-the-dikw-pyramid-scheme-a059ba595b30
… knowledge is not a result merely of filtering or
algorithms. It results from a far more complex
process that is social, goal-driven, contextual,
and culturally-bound. We get to knowledge —
especially ‘actionable’ knowledge — by having
desires and curiosity, through plotting and play,
by being wrong more often than right, by talking
with others and forming social bonds, by
applying methods and then backing away from
them, by calculation and serendipity, by
rationality and intuition, by institutional
processes and social roles. Most important in
this regard, where the decisions are tough and
knowledge is hard to come by, knowledge is not
determined by information, for it is the knowing
process that first decides which information is
relevant, and how it is to be used…

19. The distinction between data and information is
that the latter is interpreted by the researcher. The
same piece of data may lead to different
interpretations.
The data have no meaning. Meaning is actively
chosen by the researcher. Whereas data are
typically physical, interpretations are symbolic.
Knowledge is generalized, abstracted from the
individual data and information bits about which it is
made, and if we succeed in making a successful
theory, it can predict further events, and further
data that can be extracted from the evidence.
Much of the formal training of researchers is to give them understanding and skill in
managing this process getting usable data into the research system, and working it up
to knowledge.

20. The levels in an
abstraction hierarchy
are related by
‘laddering’, by asking
the questions ‘why’ to
move up and ‘how’ to
move down. They form
a ‘means-ends
hierarchy’.

21. The journey from gathering to
conceptualization differs from project to
project. It can be very brief or involve
intensive methodical searches for
patterns and interpretations.
Many people are more comfortable on
one side (research) or the other
(design) of the gap.
There are various levels at which
bridging can take place. It can be done
straight from the raw data, leading to
little ideas. For example this can be
straightforward suggestions by users.

23. When the bridge is crossed at the
information level, ideas that emerge
through interpretation and abstraction
usually are bigger, less superficial ideas.
Finally, at the knowledge level, a theory is
developed, and this can lead to a ‘big
picture’, which can form the basis for new
ideas which are more radical, fundamental,
and/or substantial.
The most intensive forms of analysis should
also carry the more holistic ‘touch and feel’
of the lower levels along with the big
insights.

25. Path A: Immersion for inspiration only
You expose the design team to the raw data
without substantially selecting, interpreting, or
organizing it. An ‘inspiration event’: a prepared
and facilitated session (from half a day to events
over several weeks) in which the team reviews
the data, samples it, and draws its own
conclusions.
This path makes sense when the primary goal is
to use the artifacts and data to quickly inspire
design development teams. It is a good way to
learn about generative design research. It is a
good way to convince a first-time and possibly
sceptical client. It might be appropriate when
team members have many years of experience
and a deep understanding.

26. Immersive events for inspiration rely on four
types of activities: immersion, role playing,
storytelling, and making:
1. Immerse the team in the topic. Ask the
team to complete the same exercises that
were filled out by the participants.
2. Use the data to tell stories from the field.
3. Encourage empathy with roleplaying.
4. Give them generative toolkits to use.
Create toolkits to encourage creativity. Not
to generate concepts but to come to know
the research participants in many ways.

27. What comes out of an inspiration event? In
this path, many insights may remain tacit.
Some can be made explicit, but typically the
outcomes are not explicit or generalized
conclusions, but rather a sense of ‘reality
check’. This gives the team a chance to
connect to the richness of the participants’
lives. Inspiration, in the form of many small
ideas, is also quite likely.
The generative sessions and inspiration
events may even be merged.

28. Path B: Analysis on the wall
We call it light analysis, can usually take place in
one or two intensive sessions lasting about a day
each. Best when your sample size is small, such
as 6 or 7 participants.
The real value is that it can provide information
and inspiration simultaneously.
It can be handled by a lone researcher but to get
the most out of data is much higher when a team
is analyzing the data together.
Before the workshop all members will have read
the transcripts and marked interesting quotes
together with an interpretation in their own words.
Analysis on the wall requires diligence and
rigor. In several ways, the workshop to analyze
data on the wall is similar to a generative session.

29. Path C: Analysis with a database
‘Heavy’ analysis necessary when your sample
size is larger than 10 or 12 participants and when
you are interested in making comparisons
between groups. Also recommended when there
is an abundance of data.
A disadvantage is that it hides the richness of the
data inside a computer.
The more visual and visible you can make the
data, the better you can analyze it, especially if
working collaboratively.
Different forms of data require different types of
analysis. You could actually spend months on a
heavy analysis.
Meet daily to discuss preliminary insights and
hypotheses about the data.

30. Analysis can be a creative process.
Premature conclusions can bias data analysis,
leading to missed insights and opportunities.
This can be the case with creative people such
as designers who are eager to get into the
conceptualization phase.
So be aware of when a statement is ‘raw data’,
or an interpretation by a team members, or a
loose idea, or a general conclusion.
Encourage intuitive leaps. Document them as
they occur. It is impossible to prevent creative
people from making such leaps. Encourage the
leaps but tag them accordingly. Then put them
aside as the analysis progresses. Verify the
intuitive leaps later on.

31. Design your analysis tools. Analyzing data can be
inspiring and enjoyable. Just as you devote
attention to the making of workbooks or collage
toolkits, you should devote attention to the
research materials.
Work from both ends simultaneously. Some
people thrive on getting the details perfect while
others are driven to see the big picture.
Encourage the use of both modes.
Encourage the use of personality and working
style inventories by members of the team. This
way of knowing oneself can be very useful.
Encourage people to play on their strengths.
Be ready to be surprised. Don’t try to predict the
outcome of analysis. And don’t use prescriptive
categorization schemes. Be open to the patterns
that will reveal themselves.

32. The real value of a thorough analysis is in
crossing the research/design gap at a higher
level of knowledge, enabling the identification
and communication of a ‘big picture’. The big
picture or framework connects backward to
the data, and serves to organize it in a way
that reveals previously unseen patterns and
structures. The big picture points to the future
and serves to suggest or provoke new ways
of thinking about the design problem.

33. CHAPTER 08: COMMUNICATION
Communication takes place after the
analysis and before the design
conceptualization. However, these
may run in parallel.
There are tradeoffs on whether to
use presentation or participation in
the communication of project results.

34. Three approaches to communication: high-
level final presentation, immersion-rich event
at the end, and communication all along.
It can be effective to bring a rough prototype
of all the research materials and to engage
the client in first trying out these materials.
You might invite the client team to predict
what they think the results of the end-user
input will be. This can be quite revealing as
it tends to show a large gap in the
understanding and empathy that the client
team members have toward the people who
were involved.

35. Communication all along: involve a
dedicated member of the client team
when the analysis is coming together. The
second most opportune time is in the data
collection stage. The development of
personas during the analysis is another
way to bring the client into the process
and to generate empathy.
Document the process in such a way that
would allow you to create a full
documentary of the process at the end, if
needed.

39. Communication of generative design
research results does not follow a
prescribed plan or formula. The plan
will depend upon the people involved,
the content to be delivered, the timing,
the budget, etc. It is best to approach
this with an open mindset each time.

40. CHAPTER 09: CONCEPTUALIZATION
Even raw data can lead to small ideas.
Low levels of the analysis hierarchy are
more prone to providing inspiration for
ideas than are the higher levels which are
more likely to provide information,
knowledge and, hopefully, insight.
You need to provide both raw data and
the big picture in communicating results
of the generative design research process.

41. We make use of both smaller and larger ideas. Ideas
can be as small and fragmentary as ‘solar power could
suffice’. A concept is a much larger idea that is a proposal
for ‘the whole thing’. But a concept need not be
complete as it will be further developed by other
stakeholders in the process. Inputs to conceptualization:
- ideas from the data
- photos from the field
- insights from the analysis
- constraints regarding possible solutions
During conceptualization, it helps to make things
visible. Having ideas on the wall helps the team stay
aware of the progress and point to ideas and
combinations of ideas. As ideas, concepts and solutions
are developed, post the content on the wall and work
on them as well.

42. The education of designers
is now moving from a
preoccupation with the
making of stuff to a
preoccupation with making
stuff for people in the
context of their lives.

43. By putting making together with telling and
enacting, you can empower people who are
not skilled in making.
An effective big picture:
- is simple. The top layer expresses the whole
idea and fits on a single page.
- is memorable. Others can take away the gist
of it without having to take notes.
- is expansive. There are other layers with
increasing levels of detail.
- contains parts from all the data. Any data item
is connected to the top layer.
- evokes new directions in thinking. By
simplifying, it reveals new ways to see.

44. CHAPTER 06: GATHERING DATA
Workplan:
1. Review what is already known
2. Team formation and induction
3. Understand current context of use/experience
4. Screen and recruit participants
5. Plan what participants will go through
6. Create the materials
7. Pilot test the plan and materials
8. Revise and produce research materials
9. Sensitize the participants
10. Conduct interviews or workshops
11. Document the data

45. Most research firms are not yet familiar with
generative design thinking and the use of
toolkits in participatory sessions.
Moderators are usually skilled at conducting
traditional focus groups but these skills do not
usually prepare them for the open-ended
quality and the application of generative tools.
It is best to do your own facilitating of the
sessions.
We have found that a small sensitizing packet
(that takes no more than 20 minutes) and a
one-hour session is a good place to start.

46. Find the balance between
‘sketchy’ and ‘designed’ toolkits
and materials to motivate and
empower participants

47. HOW TO MAKE A TOOLKIT
There is an art to making a good toolkit. The
more toolkits you make and see the results of
(what people make with them), the better you
will become at making them. The feedback
about the use of toolkits is absolutely crucial for
this expertise to grow.
Toolkits are a new form of visual literacy that is
legible by all. Toolkit creation is one instance
where having training in design might hinder
the process. Toolkits need to make sense and
appeal to non-designers so overdesigning the
toolkit is something to be avoided.

48. HOW TO MAKE A TOOLKIT
- is it for stimulating the expression of
memories and feelings?
- is it a means for the participants to share their
understanding of an experience, a product or
an environment that they are familiar with?
- is it for facilitating the expression of dreams
and aspirations for experience?
- it is primarily to gain empathy with
participants?

49. HOW TO MAKE A TOOLKIT
Step 1: Write the instructions, do this
collaboratively
Step 2: Brainstorm words that relate to the topic
(i.e., purpose of the toolkit)
Step 3: Look for visual material; do not hunt
for images too literally
Step 4: Review the trigger items. Have a
balance of positive, neutral, and negative
stimuli; include both ambiguous and concrete
stimuli; include a variety of visual styles; include
different ages, races, and genders; decide
whether an idea is better expressed visually or
verbally

50. HOW TO MAKE A TOOLKIT
Develop the look and feel of your toolkit and
instructions to fit the language (jargon,
aesthetics, expectations) of the participants.
Step 5: Fill in the holes in the trigger set; it
should be broad and open-ended enough so
that people can express a very wide range of
memories, feelings, understandings, or ideas.
One way to discover the holes in the trigger
set is for the team members to use the
trigger set to express the widest range of
experiences (related to the topic) that they
can jointly imagine. Once the holes are
revealed, one person can fill them in.

51. HOW TO MAKE A TOOLKIT
Step 6: Review the final set. Keep the total
number of items between 150 and 200 words
and pictures. A good toolkit will give them the
open-endedness they need to express
whatever they have in mind.
Intuition wins. You can ‘load’ the toolkit with
triggers that you have assumptions about. You
may have a preconceived idea for a new
product or service. You can make it into a
trigger and see if any of the participants
expresses interest in it.
Step 7: Design the toolkit. Randomization will
provoke connections, but require longer
viewing time. Clustering words apart from
images is good to promote quick viewing.

52. The tools are there to support
the expression and creativity of
the participants, not a machine
to put people through.

53. Some design researchers enjoy and
excel at the planning and organizing
process. Others are just happy that
someone else enjoys doing that. But
most people who find themselves doing
this type of fieldwork LOVE the part
where they get to meet new people and
learn about their lives and their dreams
for the future. Memories of these
sessions with people will stay with you
for many years to come.

54. https://www.bispublishers.com/convivial-toolbox.html