Primer on designing and publishing qualitative management research; presented at Baylor University Hankamer School of Business

1. Publishing
Qualita.ve
Research
Joel
West
Keck
Graduate
Ins.tute
The
Claremont
Colleges
October
17,
2018
Research
Methods
Symposium
Series
Hankamer
School
of
Business
Baylor
University

2. Jargon
Check
Related
(but
dis.nct)
terms
• Qualita.ve
data
• Case
study
• Ethnographic
study
• Interview
data
• Induc.ve,
theory-­‐building,
exploratory
Either
Posi+vist
or
Intrepre+vist

3. Qualita.ve
is
Similar
to
Quant
• Importance
of
framing,
contribu.on
• Arduous
review
process
• Wide
variability
in
reviewer
opinions
• Strong
methodological
norms
in
the
field

4. Qualita.ve
is
Different
from
Quant
• Different
forms
of
data
• Different
forms
of
analysis
• Different
standards
of
representa.veness
and
validity
• Different
sorts
of
ques.ons
– Generally
exploratory
rather
than
confirmatory
– Richer
but
less
precise
data
– BeYer
for
"why"
and
"how"
rather
than
"how
o[en"
ques.ons

5. Four
Phases
of
Understanding
1. Learning
methodological
norms
2. Research
design
for
a
specific
study
3. Wri.ng
up
the
study
4. Geang
it
published

6. My
Biases
• 31
ar.cles;
only
a
few
"A*"
journals
– Mostly
innova.on,
some
management,
MIS,
entrepreneurship
– ≈25
chapters
– 4
HICSS
proceedings
– 2
edited
books
• AE
for
Research
Policy,
journal
reviewer
• Posi.vist
industry-­‐
or
firm-­‐level
research

7. 1.
METHODOLOGICAL
NORMS

8. Five
Qualita.ve
Approaches
• Narra.ve
• Phenomenological
• Grounded
theory
• Ethnographic
• Case
study
Creswell
and
Poth,
Qualita+ve
Inquiry
and
Research
Design
4e,
Sage,
2017
Goulding,
"Grounded
theory,
ethnography
and
phenomenology,"
European
Journal
of
Marke+ng,
2005

9. Level
of
Analysis
• Consumer
behavior:
the
individual,
community
• Marke.ng
strategy/MIS:
a
project
• Org
design:
a
group/division
• Strategy:
level
of
the
firm
• Innova.on:
a
technology

10. Management
Norms
• Eisenhardt
and
Graebner,
"Theory
building
from
cases:
Opportuni.es
and
challenges,"
Academy
of
Management
Journal,
2007.
• Eisenhardt
et
al,
"…Rigor
without
rigor
mor.s,"
Academy
of
Management
Journal,
2016.
• Gibbert
et
al,
"What
passes
as
a
rigorous
case
study?"
Strategic
Management
Journal,
2008.

11. Informa.on
Systems
Norms
• Dubé
and
Paré,
"Rigor
in
informa.on
systems
posi.vist
case
research,"
MISQ,
2003.
• Sarker
et
al,
"Qualita.ve
studies
in
informa.on
systems,"
MISQ,
2013.
• Marshall
et
al,
"Does
sample
size
maYer
in
qualita.ve
research?"
Journal
of
Computer
Informa+on
Systems,
2013.

12. Marke.ng
Norms
• Belk,
Handbook
of
Qualita+ve
Research
Methods
in
Marke+ng.
Edward
Elgar
Publishing,
2007.
• Gummesson,
"Qualita.ve
research
in
marke.ng,"
European
Journal
of
Marke+ng,
2005.
• Goulding,
"Grounded
theory,
ethnography
and
phenomenology,"
European
Journal
of
Marke+ng,
2005.

13. 2.
RESEARCH
DESIGNS

14. Research
Design
Key
decisions
in
research
design:
• Research
ques.on(s)
• Literature/gap
• Proof/contribu.on
• Data
collec9on
• Data
analysis
Some
(not
all)
can
be
changed
later

15. Typical
Management
Designs
• Single
case
design
– Firm,
technology,
industry
– Exemplar,
outlier,
unusual
insight
(Tripsas
&
Gavea,
SMJ
2000;
West
&
Wood,
AiSM
2013)
– Used
for
process
studies
(Tripsas,
SMJ
1997)
and
longitudinal
studies
(West,
JMS
2008)
• Compara.ve
case
design
(Eisenhardt)
– "Theore.cal
sampling"
to
show
variance
Typically,
30-­‐50
interviews

16. Eisenhardt
method
• Jus.fy
theory
building
• Theore.cal
sampling
of
mul.ple
(4-­‐12)
cases
– Code
variables
between
cases
to
show
variance
• Specific
approach
for
exposi.on:
– "Sketch
emergent
theory
in
the
intro"
– (Usually)
LiYle
or
no
lit
review
– Present
proposi.ons
supported
by
data
– Long
discussion
sec.on
See
Eisenhardt,
Graebner
&
Sonenshein
(AMJ
2016),
Eisenhardt
&
Graebner
(AMJ
2007),
Eisenhardt
(AMR
1989);
Graebner,
Mar.n
&
Roundy
(SO
2012)

17. Other
Methods
1. Eisenhardt
most
cited
but
not
only
method
2. Gioia
method
– Induc.ve,
grounded
theory
– Assumes
socially
constructed
ontology
3. Langley
method:
an
approach
for
process
(rather
than
variance)
research
Gehman
et
al,
"Finding
theory–method
fit:
A
comparison
of
three
qualita.ve
approaches
to
theory
building,"
Journal
of
Management
Inquiry
27,3
(2018):
284-­‐300.

18. Coding
Data
How
do
interviews
get
coded?
• Formal
coding:
grounded
theory
– Typically
with
so[ware
package
(Nvivo,
Atlas..)
– Mul.ple
levels
of
codes
• Informal
coding
– Less
rigorous
examina.on
of
paYerns
• Say
what
you
did
• Don’t
claim
to
do
something
you
didn’t

19. Evolving
Data
Collec.on
• Interview
ques.ons
o[en
evolve
over
.me
– Some
ques.ons
don’t
work
– Others
iden.fy
completely
new
areas
of
inquiry
– Opportunity
to
fix
data
as
it’s
collected
• O[en
possible
to
change
the
ques.ons
– Keep
core
ques.ons,
re-­‐interview
for
new
ones
– Some.mes
you
can’t
change
it
enough

20. 3.
WRITING
UP
RESEARCH

21. Recommended
Ar.cle

22. Pisalls
(1)
Pisalls
are
o[en
similar
to
quan.ta.ve
• Framing
– Confused/unclear
framing
– Framing
doesn’t
match
data
– Framing
doesn’t
match
discussion/contribu.on
• Lit
review
vs.
findings
– Theory
develop:
bias
towards
short
lit
reviews
– What
you
learn
doing
a
study
is
a
finding,
not
part
of
the
lit
review

23. Pisalls
(2)
• Falling
in
love
with
the
data
– Excessive
length
or
detail
– Neglec.ng
generizability
and
the
"so
what"
• Non-­‐standard
research
design
&
ontology
– Common:
you
can’t
test
theory
with
an
N
of
1
– Less
common:
confusing
mixture
of
data
gathering,
collec.on,
analysis

24. Find
Journal-­‐Specific
Exempars
• Each
field
has
its
favorite
authors,
exemplars,
methods
cita.ons
• Each
journal
has
its
own
norms
• Editor(s),
associate
editors,
senior
editors
• Reviewer
pool
• Standards
and
previously
accepted
work
• Find
recent
exemplars
in
that
journal!
• Supplement
with
similar
(and
"beYer")
journals

25. Management
Exemplars
• Academy
of
Management
Journal:
Santos
&
Eisenhardt
(2009),
Hallen
&
Eisenhardt
(2012),
Ben-­‐Menahem
et
al
(2016)
• Strategic
Management
Journal:
Tripsas
(1997),
Bingham
&
Eisenhardt
(2011)
• Strategic
Entrepreneurship
Journal:
Clarysse
et
al
(2011),
Bingham
&
Haleblian
(2012)
• Research
Policy:
O’Mahony
(2003),
Jain
(2012),
Lehoux
et
al
(2014)

26. Informa.on
Systems
Exemplars
• MIS
Quarterly:
Kaplan
&
Ducho
(1988),
Cooper
(2000),
Levina
&
Vaas
(2005),
Markus
et
al
(2006)
• Informa+on
Systems
Research:
Ramesh
et
al
(2012),
Germonprez
et
al
(2017)
• Journal
of
Management
Informa+on
Systems:
Wigand
et
al
(2005)

27. It’s
all
about
the
tables…
• Most
qualita.ve
papers
require
tables
• Breaks
up
text
• Reveals
data
you
used
for
inference
• Forces
you
to
simplify
• Looks
more
“scien.fic”
Diagrams
are
usually
great,
but
not
required

28. 4.
GETTING
RESEARCH
PUBLISHED

29. Typical
Problems
Ordinary
research
problems
• Doesn’t
deliver
on
promises
in
framing
• Poor
execu.on
or
explana.on
• Abstrac.on/generalizability
• Nothing
new
• Doesn’t
(can’t)
address
reviewer
concerns

30. Qualita.ve
Problems
Problems
specific
to
qualita.ve
studies:
• Confusing
mess
of
story
or
data
• Missing
insights
from
data
• Ontological
impossibility
(suggest,
not
prove)

31. Theory
Building
on
the
Fron.er
• Research
opportuni.es
on
the
fron.ers
of
science
are
like
opportuni.es
on
the
19th
century
Western
fron.er
• Qualita.ve
researchers
are
trappers
– They
live
off
the
land
at
at
the
edge
of
the
fron.er
– They
work
in
a
world
without
fences
• Quan.a.ve
researchers
are
the
farmers/ranchers
– They
put
up
fences,
bring
order,
civiliza.on
– Goal:
consistent,
efficient,
reliable
produc.on
• When
the
seYlers
show
up,
a
trapper
needs
to
find
a
new
fron.er

32. LEARN
FROM
MY
MISTAKES

33. #1:
MISQ
• In
2003,
MIS
student
Jason
Dedrick
&
I
conduct
11
interviews
on
Linux
adop.on
by
firms
• Almost
no
research
on
how
firms
adopt
standards
• Combine
org
innova.on
adop.on
literature
with
standards
literature
• June
2003:
submit
"An
Exploratory
Study
into
Open
Source
Plasorm
Adop.on"
to
HICSS
• Sept
2003:
submit
to
special
issue
workshop
• March
2004:
submit
to
MISQ
special
issue
on
"Standard
Making:
A
Cri.cal
Research
Fron.er
for
Informa.on
Systems"

34. Take-­‐away
From
Reviews
• Fixable
problems:
• Rushed
to
special
issue
• Put
off
contribu.on
to
the
last
minute
• Needed
more
Eisenhardt-­‐style
data
coding
• Not
fixable
problem:
• Interview
data
about
open
source,
not
standards
• One-­‐.me
opportunity:
first
(and
only)
MISQ
special
issue
on
standards
• Conclusion:
• Important
research
ques.on
• “A”
journal
pub
doomed
by
poor
design
that
didn’t
fit
(or
couldn’t
be
expanded
to
address)
special
issue

35. #2.
SEJ
2018
• Went
5
rounds
for
SEJ
special
issue
on
"open
innova.on"
• Data:
interviews,
secondary
data
on
28
3D
prin.ng
entrepreneurs
– Ques.on:
what
explains
variance
on
openness?
• Nonstandard
qualita.ve
research
design

36. Difficulty
Finding
Exemplar
• Iden.fied/studied
20
published
studies
– 8
AMJ;
3
SEJ;
2
JBV,
RP,
SMJ;
1
ASQ,
ISR,
JPIM
– Typically
4-­‐10
cases,
rich
data
on
each
case
• Bingham
&
Haleblian
(SEJ
2012):
7
cases,
45
interviews
• Our
study
– 1
interview
for
each
of
28
cases
– 71%
<
3
years
old,
most
1-­‐3
employees
– Who
else
do
you
interview
in
a
new
firm?
• Secondary
data
essen.al
to
sa.sfy
reviewers

37. MIXED
METHODS

38. Mixed
Methods
• Each
form
of
data
has
its
weaknesses
• Mul.ple
data
sources
allow
for
triangula.on
Common
mixed
method
designs
1. Quan.ta.ve
&
qualita.ve
– Quan.ta.ve
provides
generalizability
– Quali.a.ve
(pre-­‐
or
post-­‐)
explains
what’s
measured
2. Qualita.ve:
interview
and
archival
– Qualita.ve
provides
insight
– Archival
is
objec.ve
and
o[en
longitudinal
Some.mes
includes
quan.fying
qualita.ve
data

39. Example:
Jarvenpaa
&
Leidner
(1999)
• Experiment:
350
students
from
six
con.nents
par.cipate
in
online
simula.on
• Data:
individual
surveys,
email
archive
– 29/75
teams
have
complete
data
– Code
4
categories;
pick
3
exemplars
per
category
• Content
analysis
of
email
from
12
teams
• Surveys
quan.fy
variance,
qualita.ve
data
explains
the
“how”
and
“why”
Jarvenpaa
and
Leidner,
"Communica.on
and
trust
in
global
virtual
teams,"
Organiza+on
Science,
1999.

40. #3.
JMS
• Context:
Applica.on
of
1
technology
(Shannon
theory)
to
deep
space
communic.ons
• Broad
mix
of
data
– Scien.fic
publica.ons
&
awards
– Archival
data
on
27
NASA
probes,
1960-­‐1978
– 11
primary
interviews
with
engineers
– Previous
oral
histories
with
key
inventors
– Secondary
data
on
two
key
MIT
spinoff
companies
Commercializing Open Science: Deep Space
Communications as the Lead Market for Shannon
Theory, 1960–73
Joel West
San José State University
ABSTRACT Recent research on the commercialization of scientific discoveries has emphasized
the use of formal intellectual property rights (notably patents) as a mechanism for aligning
the academic and entrepreneurial incentives for commercialization. Without such explicit
intellectual property rights and licensing, how is such open science commercialized? This
paper examines the commercialization of Claude Shannon’s theory of communications,
developed at and freely disseminated by Bell Telephone Laboratories. It analyses the first 25
years of Shannon theory, the role of MIT in developing and extending that theory, and the
importance of deep space communications as the initial market for commercialization. It
contrasts the early paths of two MIT-related spinoffs that pursued this opportunity, including
key technical and business trajectories driven by information theory. Based on this evidence,
the paper provides observations about commercializing open science, particularly for
engineering-related fields.
INTRODUCTION
Industries typically enjoy long periods of relatively predictable incremental innovation,
punctuated by irregular bursts of discontinuous technological innovation. Such discon-
tinuities enable new, previously unexplored trajectories for technological innovation
(Dosi, 1982; Nelson and Winter, 2002). From these new technological trajectories many
opportunities arise for new products, new firms and new industries (Anderson and
Tushman, 1990; Nelson, 1995).
In many cases, such technological breakthroughs can be traced back to basic research
disseminated through the peer-reviewed process of open science, often from public
research institutions such as universities. In some cases, the discontinuous improvement
can be traced to a single discovery, whereas in other cases, it builds upon a stream of
research in open science.
Address for reprints: Joel West, San José State University, BT555, 1 Washington Square, San José, CA
95192-0070, USA (Joel.West@sjsu.edu).
© Blackwell Publishing Ltd 2008. Published by Blackwell Publishing, 9600 Garsington Road, Oxford, OX4 2DQ, UK
and 350 Main Street, Malden, MA 02148, USA.
Journal of Management Studies 45:8 December 2008
0022-2380

41. Review
(round
1)
• "I
would
like
to
applaud
the
authors
for
their
detailed
data
collec.on
and
their
descrip.on
of
the
journey
between
science
à
commercializa.on
as
it
pertains
to
Shannon’s
informa.on
theory.
This
is
a
fascina.ng
case
descrip.on.
The
key
task
for
the
authors
is
to
make
the
paper
more
theore.cally
precise
so
that
its
insights
can
then
be
compared
and
contrasted
with
prior
work/alternate
models
of
technological
development.
"

42. Typology
of
Case
Studies
Welch
et
al
(2011),
JIBS,
740-­‐762

43. “Contextualized”
Explana.on
• "Overall,
case
studies
that
emphasised
causal
explana.on
…
were
in
the
minority.
…
[W]e
paid
aYen.on
to
how
authors
in
this
quadrant
were
able
to
combine
the
inherent
strength
of
the
case
study
to
contextualise
with
its
explanatory
poten.al.
…
• "In
this
quadrant,
authors
were
more
open
about
the
explanatory
aims
of
their
paper
…
what
typifies
the
authors’
language
is
a
very
par.cular
view
of
causality
as
a
complex
and
dynamic
set
of
interac.ons
that
are
treated
holis.cally.
"
Welch
et
al
(2011),
JIBS,
p.
753-­‐754

44. West
(2008):
Final
Framing
• "Technological
breakthroughs
can
[o[en]
be
traced
back
to
basic
research
disseminated
through
the
peer-­‐reviewed
process
of
open
science,
o[en
from
public
research
ins.tu.ons
such
as
universi.es.
• "But
how
does
such
open
science†
get
commercialized?
In
par.cular,
absent
an
explicit
policy
to
align
the
interests
of
scien.sts
and
firms,
how
does
the
knowledge
disseminated
in
open
science
become
incorporated
into
the
offerings
of
for-­‐profit
companies?"
†
Paul
A.
David
(1998),
‘Common
agency
contrac.ng
and
the
emergence
of
open
science
ins.tu.ons,’
American
Economic
Review,
88

45. CONCLUSIONS

46. Publishing
Qualita.ve
Reseach
• Interes.ng
data
and
phenomenon
• Important
unanswered
ques.on
• Engage
powerful
theories
• Legi.mate
methods
and
research
design
• Clarity
of
exposi.on
• Clear
alignment
of
ques.on,
framing,
data,
contribu.on