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This article, together with a companion video, provides a synthesized summary of a Showcase Symposium held at the 2016 Academy of Management Annual Meeting in which prominent scholars—Denny Gioia, Kathy Eisenhardt, Ann Langley and Kevin Corley—discussed different approaches to theory building with qualitative research. Our goal for the symposium was to increase management scholars’ sensitivity to the importance of theory-method “fit” in qualitative research. We have integrated the panelists’ prepared remarks and interactive discussion into three sections: an introduction by each scholar, who articulates their own approach to qualitative research; their personal reflections on the similarities and differences between approaches to qualitative research, and answers to general questions posed by the audience during the symposium. We conclude by summarizing insights gleaned from the symposium about important distinctions among these three qualitative research approaches and their appropriate usages. The companion video is available on YouTube:
Electronic copy available at:
Joel Gehman*
Alberta School of Business
University of Alberta
Vern L. Glaser*
Alberta School of Business
University of Alberta
Kathleen M. Eisenhardt
Department of Management Science and Engineering
Stanford University
Denny Gioia
Smeal College of Business
Pennsylvania State University
Ann Langley
Department of Management
HEC Montréal
Kevin Corley
W.P. Carey School of Business
Arizona State University
March 8, 2017
* Corresponding Authors.;
We thank the Organization and Management Theory (OMT) Division of the Academy of Management for
sponsoring the original symposium together with co-sponsorship from the Business Policy and Strategy, and
Managerial and Organizational Cognition Divisions; the Alberta School of Business for providing financial support
for videotaping the symposium, and Kara Stephenson Gehman for her editorial assistance in preparing this article.
Electronic copy available at:
This article, together with a companion video, provides a synthesized summary of
a Showcase Symposium held at the 2016 Academy of Management Annual
Meeting in which prominent scholars—Denny Gioia, Kathy Eisenhardt, Ann
Langley and Kevin Corley—discussed different approaches to theory building
with qualitative research. Our goal for the symposium was to increase
management scholars’ sensitivity to the importance of theory-method “fit” in
qualitative research. We have integrated the panelists’ prepared remarks and
interactive discussion into three sections: an introduction by each scholar, who
articulates their own approach to qualitative research; their personal reflections on
the similarities and differences between approaches to qualitative research, and
answers to general questions posed by the audience during the symposium. We
conclude by summarizing insights gleaned from the symposium about important
distinctions among these three qualitative research approaches and their
appropriate usages.
The companion video is available on YouTube:
Management scholars now widely accept qualitative research, with as many qualitative
papers published in the decade between 2000 and 2010 as in the prior two decades (Bluhm,
Harman, Lee, & Mitchell, 2011). Qualitative research has not only grown in quantity, but has
also produced a substantial impact on the field by generating new theories that have shaped
scholars’ understanding of core theoretical constructs (e.g., Bartunek, Rynes, & Ireland, 2006).
However, qualitative research cannot be described as a singular approach: rather, it encompasses
a heterogeneous set of approaches. As a result, although qualitative research methods provide
researchers with diverse philosophies and toolkits for studying and theorizing the actions of
organizations, their members, and their influence on the world, as these tools and methods
proliferate, there is an opportunity for enhanced awareness of, and sensitivity to the unique
assumptions associated with different qualitative methodologies (Sandberg & Alvesson, 2011;
Langley & Abdallah, 2011; Smith, 2015). Notably, different approaches to qualitative research
often presume distinct ontologies and epistemologies, resulting in different assumptions about
the nature of theory and the relationship between theory and method (Morse et al., 2009;
Sandberg & Alvesson, 2011).
As qualitative research has proliferated, we have observed a tendency for qualitative
papers to invoke a mashup of different qualitative citations. For instance, looking at the methods
sections from a sample of qualitative papers we recently reviewed for journals such as Academy
of Management Journal, Administrative Science Quarterly, Journal of Business Venturing,
Journal of Management Studies, and Organization Science, several contained citations to
Eisenhardt (e.g., Eisenhardt, 1989a; Eisenhardt, Graebner, & Sonenshein, 2016), Gioia (e.g.,
Gioia, Corley, & Hamilton, 2013) and Langley (1999) – all in the same paper! Other papers we
reviewed contained citations to some or all of these same three authors, together with others such
as Yin (2009), Strauss and Corbin (1998), Patton (2002), Denzin and Lincoln (2005), Lincoln
and Guba (1985), van Maanen (1979), Golden-Biddle and Locke (2007), Miles and Huberman
(1994), and Garud and Rappa (1994), to name just a few. Although these different
methodological citations may be relevant on their own and in various combinations, more often
it seems that such diverse methods are cited without attending to their different, and potentially
incommensurable assumptions.
Inspired by such experiences, we organized a symposium to help frame our thinking
about how to use qualitative methods (i.e., the tools in our toolbox) in a more disciplined way.
Our basic intuition is that methods are tools; some tools are good for certain purposes, whereas
other tools are good for other purposes. Specifically, at the 2016 Academy of Management
Annual Meeting in Anahiem, California, we brought together three scholars who have been
particularly influential in shaping how we conduct qualitative research in our field: Denny Gioia,
Kathleen Eisenhardt and Ann Langley. Although Denny was unable to attend in person, he
recorded his remarks via video, and Kevin Corley, a longtime collaborator, kindly participated in
the questions and answer session on Denny’s behalf. Table 1 provides an overview of the three
key participants, and an overview of some of their methodological contributions.
-- Insert Table 1 Here --
By organizing this symposium, we aspired to provide a forum for these influential
scholars to present their perspectives on qualitative research and engage in an interactive
discussion with each other and the audience about their methodological similarities and
differences. Although the approaches espoused by these scholars are commonly utilized by
management scholars, by no means do they exhaust the ways that we might engage in theory
building through qualitative research. Rather, these three scholars are notable exemplars and
collectively provide a sense of the range of approaches available to qualitative researchers. We
had three specific goals for the symposium. First, we wanted to provide academy members an
opportunity to hear three leading scholars describe their personal approaches to qualitative
research. Second, we hoped to foreground some important similarities and differences among
these three approaches—thereby fostering greater sensitivity to critical methodological issues
among researchers. Finally, we aimed to generate discussion and debate about appropriate
combinations of qualitative methods, research designs, research questions, and theoretical
We have written this paper to accompany the video of the symposium. In doing so, we
have synthesized the discussion to increase management scholars’ sensitivity to the importance
of theory-method fit in qualitative research. Based on transcripts from the symposium and the
panelists’ presentation materials, we have integrated the panelists’ prepared remarks and
interactive discussion into three sections: an introduction by each scholar to her or his own
approach to qualitative research; their personal reflections on the similarities and differences
between these approaches, and answers to questions posed by the audience during the
symposium. We conclude by summarizing insights gleaned from the symposium about important
distinctions among these three qualitative research approaches and their appropriate applications.
Denny Gioia
Overview. Here’s the opening passage from my recent methods piece with Kevin Corley
and Aimee Hamilton in Organizational Research Methods (ORM):
What does it take to imbue an inductive study with “qualitative rigor,” while still
retaining the creative, revelatory potential for generating new concepts and ideas for
which such studies are best known? How can inductive researchers apply systematic
conceptual and analytical discipline that leads to credible interpretations of data and also
helps to convince readers that the conclusions are plausible and defensible? (Gioia et al.,
2013: 15)
For the past 25 years, I’ve been working to design and develop an approach to
conducting grounded theory-based interpretive research to accomplish just these aims. My main
focus has been on the processes by which organizing and organization unfold, tipping my hat to
my old friend Ann Langley (1999) who articulated the processual view so very well. My
approach revolves around what I consider to be perhaps the single most profound recognition in
social and organizational study: that much of the world with which we deal is socially
constructed (Berger & Luckmann, 1967; Schutz, 1967; Weick, 1979). This recognition means
that studying this world requires an approach that captures the organizational experience in terms
that are adequate at the levels of (a) meaning for the people living that experience, and (b) social
scientific theorizing about that experience.
Quite honestly, I was also motivated to devise a systematic methodology for inductive
research because too many non-qualitative scholars simply don’t believe that inductive
approaches are rigorous enough to demonstrate scientific advancement (see Bryman, 1988;
Campbell, 1975; Popper, 1959). When I started out on this project, I dare say that most
researchers (Kathy Eisenhardt notably excepted) saw qualitative research as a way to report
impressions and cherry-pick quotes that supported those impressions, a variation on the old
theme of “My mind is made up, do not confuse me with the facts.” My assumptions and stances
led me to devise an approach that allows for a systematic presentation of both first-order
analysis, derived from informant-centric terms or codes, and second-order analysis, derived from
researcher-centric concepts, themes and dimensions (see van Maanen, 1979 for the inspiration
for the first-order/second-order terminology).
Some Basic Steps. As the research progresses, I start looking for similarities and
differences among emerging categories. I bend over backwards to give those categories labels
that retain informants’ terms, if at all possible. I then consider the constellation of first-order
codes. Is there some deeper structure or process here that I can understand at a second-order,
theoretical level?
When all the first-order codes and second-order themes and dimensions have been
assembled, I then have the basis for building a data structure. This is perhaps the most pivotal
step in the entire research approach, because it shows the progression from raw data to first-order
codes to second-order theoretical themes and dimensions, which is an important part of
demonstrating rigor in qualitative research. To me, a data structure is indispensable for this style
of work. I kind of have a guiding mantra for the data structure that I express colloquially, which
goes like this: “You got no data structure, you got nothin’.” I know the statement is over-the-top,
but it keeps me focused on obtaining evidence for my conclusions.
As important as the data structure might be, it’s nonetheless only a static photograph of
an inevitably dynamic phenomenon. It allows insight into the content of my informants’ worlds,
the “boxes” in a boxes-and-arrows diagram, if you will. You can’t understand a process unless
you can articulate the “arrows;” thus, that photograph needs to be converted into a movie (Nag,
Corley, & Gioia, 2007) that sets the concepts in motion and constitutes the “holy grail”—the
grounded theory itself. The grounded theory is generated by showing the dynamic relationships
among the emerging concepts. Properly done, the translation from data structure to grounded
theory clearly illustrates the data-to-theory connections that reviewers so badly want to see these
Of course, there’s an opportunity for inspiration in this process, too, of what I like to call
the “Grand Shazzam!” (see Gioia, 2004), some flash of insight about how the revealed processes
explain how or why some phenomenon plays out. I sometimes use a biological metaphor to
describe the transformation from a data structure to a grounded theory model. If you think of the
data structure as the anatomy of the grounded theory, then the grounded model becomes the
physiology of that theory. Writing the grounded theory section then amounts to explaining the
relationship between the anatomy and physiology that yields a systematically derived, dynamic,
inductive theoretical model that describes or explains the processes and phenomena under
investigation. This model chases not only the “deep structure” of the concepts as Chomsky
(1964) so famously put it, but also the “deep processes” (Gioia, Price, Hamilton, & Thomas,
2010) in their interrelationships.
Exemplar Studies. I recently summarized my philosophy of qualitative research in an
Organizational Research Methods article with Kevin Corley and Aimee Hamilton (2013) and an
autobiographical essay in the Routledge Companion to Qualitative Research (Gioia, 2017,
forthcoming). Some of the studies that exemplify this research approach include: Gioia &
Chittipeddi (1991) [a “precursor study” that set the stage], Gioia, Thomas, Clark & Chittipeddi
(1994) [the first study to articulate the methodology in print], Gioia & Thomas (1996), Corley &
Gioia (2004), Nag et al. (2007), Gioia, et al. (2010), Clark, Gioia, Ketchen & Thomas (2010),
Nag & Gioia (2012), and Patvardhan, Gioia & Hamilton (2015).
Kathleen Eisenhardt
Overview. For me, the goal of the “theory building from cases” method is theory – plain
and simple. The method conceptualizes theory-building and theory-testing as closely related.
They’re two sides of the same coin: the former goes from data to theory and the latter from
theory to data. Theory building from cases is centered on theory that is testable, generalizable,
logically coherent and empirically valid. It’s particularly useful for answering “how” questions,
may be either normative or descriptive, and either process (i.e., focused on similarity) or variance
based. Sometimes the goal is to create a fundamentally new theory, while at other times the goal
is to elaborate an existing theory. Regardless of the specifics, the goal is always theory building.
Within this method, theory is a combination of constructs, propositions that link together those
constructs, and the underlying theoretical arguments for why these propositions can explain a
general phenomenon. And again, the goal is strong theory (i.e., theory that is parsimonious,
testable, logically coherent, and empirically accurate).
Theory-building from case studies (Eisenhardt, 1989a; Eisenhardt & Graebner, 2007)
really stems from a combination of two traditions. On the one hand, theory-building from cases
relies on inductive grounded theory building—very much rooted in the tradition of Glaser and
Strauss (1967), where researchers walk in the door and don’t have a preconception of what
relationships they’re going to see. They may have a guess about the constructs, but are
fundamentally going in open-minded, if you will. I think Denny [Gioia] described that very well.
That’s exactly the way I see it as well. On the other hand, theory-building from cases
fundamentally depends on a case study. Here, I’m drawing on Robert Yin (e.g., Yin, 1994,
2009): a case study is a rich empirical instance of some phenomenon, typically using multiple
data sources. A case can be about a group, or an organization. There can also be cases within
cases, so one can imagine a single organization with multiple cases or a single process with
multiple temporal phases. That said, not all qualitative research is theory building from case
studies. Likewise, not all case study research is theory building—sometimes it is deductive.
A case study focuses on the dynamics present in a single setting. A case study can have
multiple levels of analysis (i.e., embedded design). Central to case studies is the notion of
replication logic in which each case is analyzed on its own, rather than pooled with other cases
into summary statistics such as means. That is, each case is analyzed as a standalone entity, and
emergent theory is “tested” in each case on its own. Case studies can include qualitative and
quantitative data. Moreover, data can be collected from the field, surveys, and other sources.
Practitioners of the method often use multiple cases because the generated theory is more likely
to be parsimonious, accurate, and generalizable. In contrast, single cases tend to lead to theory
that is more idiosyncratic to the case, is often overly complex, and may miss key relationships or
the appropriate level of construct abstraction.
Theory building from cases is appropriate in several different research situations. First,
and most typically, case study is appropriate for building theory in situations where there’s either
no theory or a problematic one. For example, Melissa Graebner did work on acquisitions
(Graebner, 2004, 2009; Graebner & Eisenhardt, 2004). If you know the acquisition literature at
all, you know that 95% or more of studies are from the point of view of the buyer, but she took
the point of view of the seller. My work with Pinar Ozcan on networks serves as another
example (Ozcan & Eisenhardt, 2009). If you know network theory, you know that it’s focused on
how the “rich get richer”- i.e., if you have a tie, then you can get another tie, and so forth. We
wanted to look at a situation where the focal actors didn’t have any ties and study how they built
their networks from scratch.
Second, this method is also appropriate for building theory related to complex processes.
For example, situations where there are likely to be configurations of variables, where there are
multiple paths in the data , or equifinality (e.g., see Battilana & Dorado, 2010; Davis &
Eisenhardt, 2011; Hallen & Eisenhardt, 2012). Third, theory building from cases also works well
in situations with “hard to measure” constructs. For example, I think identity is a very hard
construct to measure reliably using surveys (see Powell & Baker, 2014). I think Denny [Gioia]
has also been particularly strong in dealing with “hard to measure” constructs. Another example
is Wendy Smith (2014), who deals with paradox, another construct that’s hard to measure.
Fourth and finally, theory building from cases is also useful when there is a unique exemplar. For
example, Mary Tripsas and Giovanni Gavetti examined Polaroid Corporation, a company that
looked like it had everything going for it, and yet couldn’t change (Tripsas & Gavetti, 2000).
Unique exemplars might be a bit more where Ann [Langley] often plays. In general, I think all of
us are united by process questions—“how do things happen” questions—as opposed to “what”
and “how much” questions.
Some Basic Steps. I believe in knowing the literature, and then looking for a problem or
question where there’s truly no known answer. It’s almost impossible to find those problems
without knowing the literature. I also think that research should at least start with a research
question. It may not be the question of the study in the end or the only question, but I think it’s
“crazy” to start with no question.
The next two steps, research design and theoretical sampling, are particularly important,
regardless of the kind of inductive work, but especially in multi-case research. They might be
less important in single case research, where people are a bit more drawn to an exemplar or
maybe a case that’s particularly convenient. However, in theory building from cases, the
researcher is trying to, on the one hand, control the extraneous variation, and on the other hand,
focus attention on the variation of interest. For example, one research design is what I call the
“racing design”. This is a design where the researcher starts with, let’s say, five firms at a
particular point in time in a particular market and lets them “race” to an outcome . For example,
in my work with Pinar Ozcan in the mobile gaming industry (Ozcan & Eisenhardt, 2009), we
began with five firms with matched characteristics at a particular point in time, and then we
observed what happened over time. Some died, some did well, and some were in the middle. My
work with Doug Hannah on ventures in the U.S. residential solar ecosystem (Hannah &
Eisenhardt, 2017) and with Rory McDonald on ventures in the social investing sector (McDonald
& Eisenhardt, 2017) also rely on this design. Another design is “polar types” (e.g., good and bad)
(see Eisenhardt, 1989b; Martin & Eisenhardt, 2010). Another design is focused on controlling
antecedents. For example, I did some work with Jason Davis on understanding effective R&D
alliances between major incumbents (Davis & Eisenhardt, 2011). Jason read the alliance
literature. He then knew what the antecedent conditions were for effective alliances, (e.g.,
partners before; experience; good resources). Next, he then selected cases with those antecedent
conditions and so, effectively removed alliances that might fail simply because the antecedent
conditions were poor. This control let us focus on uncovering novel process insights. Sam Garg
and I took a similar approach in choosing cases for studying how CEOs engage in strategy-
making with their boards (Garg & Eisenhardt, forthcoming). Research design and the related
theoretical sampling, I think, are critical, particularly in multi-case research. And they are
particularly difficult for the deductive researchers, the ones reviewing our papers, because they
expect random sampling.
The next step is data collection. Here, I think what unites us all is deep immersion in the
setting. Perhaps I and some other researchers use more varied data sources than say Denny
[Gioia] who prefers interviews. For example, ethnography techniques can be very exciting for
questions where informants are not all that helpful – they may not know or even if they do
know, they won’t tell you their thoughts. Other data collection techniques include observation,
interviews (obviously important for most studies), archival surveys, Twitter feeds, etc. Recently,
I did a survey of what people think “qualitative research” means. While no one was able to
articulate a comprehensive definition, the most common definition was: Qualitative research is
based on deep immersion in multiple kinds of data. I think that’s a fundamental characteristic.
Some of us may prefer one data type over others but the inherent feature of “qualitative research”
is multiple types of data that help reveal the focal phenomenon.
The next step is around grounded theory building. When I started, I called what I did
“grounded theory building.” Then there was an interpretivist “beat down” of anybody who used
the grounded theory building term but didn’t exactly follow Strauss and Corbin (1998). What
Walsh and several co-authors including Glaser (see Walsh et al., 2015) are now confirming is
that grounded theory building is a “big tent”—i.e., building a theory from data. It almost
invariably involves collecting data, breaking it up into what Denny [Gioia] calls first order and
second order themes, or what I call “measures” and “constructs” and then abstracting at a higher
level. Regardless of the terms, this process is at the heart of what most theory-building
qualitative researchers are doing.
In theory building from cases, we typically explore multiple cases. The analysis begins
with a longitudinal history of each case or maybe cases within cases. We then do cross-case
pattern recognition. We try to develop measures from the data while we are thinking about
emergent theory. As the theory advances, we incorporate other literature, from both our field and
other fields. For example, because my work with Chris Bingham is on learning (Bingham &
Eisenhardt, 2011), we often considered work from cognitive science, outside our base
disciplines. Then, we iterate among the literature, data and emergent theory to come up with
logical explanations that we term “the whys” for the underlying logic of the emergent
relationships among constructs.
Finally, there’s writing. There is a rough formula. I think people who follow what I do or
do similar research have one as does Denny [Gioia]. The typical components of my formula:
overarching diagram, presentation of our findings, themes, propositions, or whatever you want to
call the theoretical framework, and weaving that presentation with case examples to explain the
emergent theory and its underlying theoretical logic. . I’m a “proposition person” if that’s what
my reviewers want. I don’t actually care either way.. If my reviewer says “include propositions”,
I’m good. If not, they’re gone. But presentation of the underlying theoretical arguments (i.e., the
“why’s”) is very important.
Exemplar Studies. I initially articulated my thoughts on the “theory building from cases”
method in the Academy of Management Review (Eisenhardt, 1989a), and extended these thoughts
in the Academy of Management Journal (Eisenhardt & Graebner, 2007) and again more recently
in AMJ (Eisenhardt et al., 2016). Some exemplars have been referenced in my talk, and include ,
Ozcan & Eisenhardt (2009), Battilana & Dorado (2010), Martin & Eisenhardt (2010), Bingham
& Eisenhardt (2011), Davis & Eisenhardt (2011), Hallen & Eisenhardt, (2012), Pache & Santos
(2013), and Powell and Baker (2014) among many others.
Ann Langley
Overview. I do not have a specific method. I also believe that trying to reduce our options
to a single methodology is really not a good idea. However, I do have a position about research,
and it is about the importance of looking at processes. I am interested in any kinds of methods
that can help us understand them. I originally wrote my 1999 paper about process research
methods (Langley, 1999) because I was puzzling over how on earth to analyze complex data
dealing with temporally evolving processes that might be persuasive and theoretically insightful.
The starting point for that paper was that there are two different kinds of thinking that underlie
most of our research: variance thinking and process thinking. Variance thinking is what most of
us actually do as social scientists, which is look at the relationships between variables. However,
I am interested in a different kind of understanding of the world where we think about how
things evolve over time. This form of understanding is very much based on flows of activities
and events. It turns out that variables and events are really quite different entities, so you do very
often need quite different methods to deal with them. For example, you might explain innovation
in two different ways: either by looking at the factors that might be correlated with it (the
variance approach), or by asking what are the activities you actually have to engage in over time
to produce it (the process approach). A fascinating example of how these two forms of thinking
might apply to the same qualitative data on innovation is illustrated by two papers by Alan
Meyer and colleagues from the 1980s (Meyer, 1984 -- a process study; Meyer & Goes, 1988 -- a
variance study).
Why is studying processes over time important? First of all, it is important because time
is the only thing we cannot escape. Time is a very central part of the world we live in and it is
very surprising that a lot of our research still does not take it seriously into account. A second
reason is that process is extremely important from the perspective of practitioners. We may
know, for example, that bigger organizations tend to have economies of scale, and because of
that they may be able to be more profitable, generally speaking. But if you are a small
organization, that does not tell you what to do. You cannot get bigger instantaneously. Using a
variance understanding (i.e., A is better than B) does not capture the movement over time to
move from A to B. The process of becoming bigger can make all the difference and it is this that
an organization will need to understand if it wants to grow. A third reason for studying processes
is that we often forget the huge amount of work and activity that is required to stay in the same
place. The world has to sustain itself, and so the process (i.e., the activities and effort involved) is
very important.
A final reason why process thinking is important is concerned with the multiple and
flowing nature of outcomes. The usual variance study has a single outcome: usually, this is
organizational performance, but that is a static one-time thing. Yet we all know that everything
we do has multiple rippling consequences that spread out over time. There are short-term effects
and there are long-term effects. One of the studies that I did with Jean-Louis Denis and Lise
Lamothe on organizational change (Denis, Lamothe, & Langley, 2001) brought this home to me
rather starkly. We identified cases where CEOs and their management teams were very
successful in achieving change in the shorter term. However, the things that they did in the
process upset so many people that the top management teams broke down and people were
forced to leave and the organizations involved had to start all over again. Process research resists
stopping the clock to focus on unique outcomes. Time and process always go on. In fact, one of
the questions that Joel [Gehman] and Vern [Glaser] asked us to address in this symposium is,
“When do you stop collecting data?” I find that a difficult question because I know that any
stopping point is arbitrary. Classic variance studies seem to overlook this.
Some Basic Steps. There is no one best way to perform process research and I think that
this is an important message that I want to convey here. In my 1999 paper (Langley, 1999), I
described several approaches to data collection and analysis that can be used to study processes.
Moreover, these approaches are not necessarily better or worse than each other; they just produce
different though often equally interesting ways of understanding of the world. I believe that it is
important to know about some of the options that are available.
That said, I do have a few principles and suggestions about how one might try to generate
convincing and theoretically insightful process studies. These are based on my own research and
also on that of others. Notably, if you are interested in process research, I suggest reading the
recent AMJ Special Forum on Process Studies of Change in Organization and Management I
coedited with Clive Smallman, Hari Tsoukas, and Andy Van de Ven, which came out in 2013
(Langley, Smallman, Tsoukas, & Van de Ven, 2013). This is a really nice collection of 13
articles that illustrate different facets of process research (e.g., Bruns, 2013; Gehman, Treviño, &
Garud, 2013; Howard-Grenville, Metzger, & Meyer, 2013; Jay, 2013; Lok & Rond, 2013;
Monin, Noorderhaven, Vaara, & Kroon, 2013; Wright & Zammuto, 2013).
One of the first principles of process research is that you have to actually study things
over time. This is a prerequisite, and it requires rich longitudinal data. Interviews and
observations are typical sources for qualitative data, but other kinds of data can be used as well.
There is, for example, a lovely paper by April Wright and Ray Zammuto (Wright & Zammuto,
2013) in that special issue which is based on temporally embedded archival data; specifically the
minutes of the meetings of the Marylebone Cricket Club which provide in enormous detail a
record of how the rules of cricket actually changed over time and the discussions that led to that.
Many papers in the special issue are based on rich ethnographies (e.g., Bruns, 2013; Jay, 2013;
Lok & Rond, 2013), and others are based on mixed archival and real-time methods (e.g.,
Gehman et al., 2013; Howard-Grenville et al., 2013). The Monin et al. (2013) paper was based
on over 600 interviews describing the integration processes following a mega-merger over
several years.
What is important is that the data fit with the time span of the processes that you are
studying. You can actually do a process study of something that does not last very long (e.g., a
meeting or this symposium), as long as you have longitudinal moment by moment data to
capture it in sufficient detail to derive interesting insights about process. If you are going to be
using interviews, you may wish to interview people about specific factual events that happened
in the past (as Kathy often does in her research). However, if you are interested in people’s
interpretations or cognitions and how those evolved (as Denny likes to do), you probably need to
carry out interviews in real time as processes are evolving because people cannot realistically
remember what their cognitions were three years ago. The data must fit the needs of the project.
In the 1999 paper, I came up with seven ways of analyzing those data once you have
them: narrative, quantification, alternate templates, grounded theory, visual mapping, temporal
bracketing, and comparative cases. I think that all these methods are valuable. However, I also
think that there are probably many other approaches worth considering that I did not include in
that paper. I also think that one point was perhaps not sufficiently emphasized when I wrote it
(although it is there if you read carefully): the fact that these methods can be mixed and matched
in various different ways. They are not completely distinct.
In terms of relating these ideas to the methodologies favored by my colleagues, the
grounded theory method or the way I described it in the 1999 paper is very much what Denny is
proposing. Denny’s work clearly represents one approach to doing process research. I also
included Kathy’s comparative case approach in that original article. For me, this may be another
way of doing process research, although I believe that Kathy’s approach has usually (though not
always) tended to move from original process-based data towards variance theorizing. I have
great admiration for both of these two approaches. I think that both Kathy and Denny have
helped make qualitative research legitimate for all of us, a major advance that we need to thank
them for.
However, there are two other approaches that I like very much, and which I think are
extremely useful for process analysis: visual mapping and temporal bracketing. Both of these are
particularly valuable for examining temporal sequences. A visual mapping strategy is able to
show how events are connected over time, emphasizing for example ordered sequences—events,
activities, choices, entities which we tend to forget about when we are focusing on categories and
variables. Temporal bracketing enables us to simplify temporal flows over time. The problem
with temporality is that new stuff is happening every second. I have found that it is a useful
approximation to try to decompose processes into phases. These phases are not necessarily
theoretically relevant in and of themselves; they are just continuous episodes separated by
discontinuities. They can become units of analysis for comparison over time. This is a different
form of replication, that I have also labeled longitudinal replication. Through this technique it is
possible to explore the recurrence of process phenomena over time (e.g., see Denis, Dompierre,
Langley, & Rouleau, 2011; Howard-Grenville et al., 2013; Wright & Zammuto, 2013).
Exemplar Studies. I articulated some initial thoughts on process theorizing in the 1999
AMR article (Langley, 1999), and extended this thinking in a piece in Strategic Organization
(Langley, 2007). In a paper with Chahrazad Abdallah (Langley & Abdallah, 2011), we contrast
Kathy [Eisenhardt] and Denny’s [Gioia] templates for qualitative research and introduce two
“turns” in qualitative research: the practice turn and the discursive turn. I referred to many
excellent studies in this talk, and would recommend using the AMJ special issue on process
studies as a source of inspiration for qualitative methods and theorizing (Langley et al., 2013).
To highlight the similarities and differences between the three approaches to qualitative
research, we asked each of the senior scholars to reflect on three issues: what constitutes theory,
what do they see as the similarities and differences between the three approaches, and what are
their “pet peeves”?
What Constitutes Theory?
Gioia. My methodology is specifically designed to generate grounded theory, so the
emergent theory rooted in the data constitutes the theory. I have a simple, general view of theory.
As Kevin Corley and I put it “theory is a statement of concepts and their interrelationships that
shows how and/or why a phenomenon occurs” (Corley & Gioia, 2011: 12). Relatedly, theoretical
contributions arise from the generation of new concepts and/or the relationships among the
concepts that help us to understand phenomena. The concepts and relationships developed from
inductive, grounded theorizing should reflect principles that are portable or transferable to other
domains and settings.
Eisenhardt. Theory is a combination of constructs, relationships between constructs, and
the underlying logic linking those constructs that is focused on explaining some phenomenon in
a general way. Assume we have Construct A and Construct B (or second order code). The
underlying logic for why A might lead to B is extremely important, that’s “the whys.” What are
the one, two, three logical reasons why A and B might be related? The reason could be a logical
argument. It could draw on prior research in our field or elsewhere, or on what the informants
say. Or it might draw on all of these sources. Let’s say you studied a bunch of companies and
observed that CEOs with blue eyes did better. If you can’t come up with an underlying reason
why blue-eyed CEOs perform better, then you don’t have a theory. You just have a correlation.
This is a really important point.
Langley. Depending on which analytic strategies you use, the kind of theory that you will
produce will be different. If you’re using a narrative strategy and using the grounded theory
strategy of the type that Denny [Gioia] and Kevin [Corley] are talking about, you are going to be
developing an interpretive theory. You are going to be focusing on the sense given by
participants to a phenomenon. If you are using a comparative strategy or a quantitative strategy,
you are going to be talking about a different kind of theory more focused on prediction. I think
that this is what Kathy [Eisenhardt] is talking about. She is interested in identifying causes and
relationships between variables which are demonstrated empirically in the data and which also
have a theoretical explanation attached to them that can be generalized and tested.
Another kind of theoretical product is a pattern. When you identify similarity in
sequences of events for a phenomenon across different organizations, you have a surface pattern.
Visual mapping may be very good for deriving such patterns, but this has other problems
because it may not provide you with an understanding of why those patterns are there. Another
kind of theorizing focuses on mechanisms i.e., the set of driving forces that are underlie and
produce the patterns that we see empirically. I particularly like Andy Van de Ven’s (1992)
analysis of different kinds of theoretical mechanisms underlying processes of change and
development, although I do not think that the mechanisms he proposes necessarily exhaust all
Methodological Similarities and Differences
Gioia. Ann Langley is the purest among us. She does pure process research and it is
beautiful. I consider myself a pure interpretivist, but sometimes I think Ann thinks I’ve gone
astray with my focus on systematic techniques for studying process. My work is much different
from Kathy Eisenhardt’s, as her work is usually based on multi-case study comparisons and
focused in some way on, what I might term, hypothesis assessment.
Beyond a basic assumption that the organizational world is essentially socially
constructed, my methodological approach is predicated on another critical assumption that my
informants are “knowledgeable agents.” I know that term is a classic grandiose example of
academese, but all it means is that people at work know what they are trying to do and that they
can explain to us quite knowledgeably what their thoughts, emotions, intentions, and actions are.
They get it. They’re not even close to Garfinkel’s (1967) rich notion of cultural dopes, so I
always, always, always foreground the informants’ interpretations.
Above all, I’m not so presumptuous that I impose prior concepts, constructs, or theories
on the informants to understand or explain their understandings of their experiences. I go out of
my way to give voice to the informants. Anyway, my opening stance is one of well-intended
ignorance. I really don’t pretend to know what my informants are experiencing and I don’t
presume to have some silver-bullet theory that might explain their experience. I adopt an
approach of willful suspension of belief concerning previous theorizing.
Here’s a quick example of why it’s important to suspend prior theory. Twenty-five years
ago I was researching strategic change in academia. At the time, the received wisdom was that
strategic managers thought about issues as either threats or opportunities. I just wasn’t sure that
was true in academia, so in my interviews of university upper-echelons executives, I pointedly
did not use those terms. Perhaps surprisingly, in three months of interviews, not once did any of
them refer to issues in threat-opportunity terms. They saw issues as either “strategic” or
“political.” When the study was over, I asked about it. One of the informants said to me, “Oh, I
can use those terms if you like, but that’s just not the way we think about the issues around
Of course, I’m never completely uninformed about prior work. I’m not a dope or a
dummy either, but I try not to let my existing knowledge get in the way. I assume that I’m a
fairly knowledgeable agent, too. I’ve worked in responsible positions in organizations. I
understand the organizational context from an on-the-ground, gotta-make-a-decision-now point
of view, not merely from an abstract theoretical perspective.
The implications of these assumptions are, however, pretty profound. Perhaps most
importantly, it puts me, the researcher, in the role of glorified reporter of the informants’
experiences and their interpretations of those experiences. I’m not at all insulted by this
subordinate role. I guess I get a little jealous of other forms of qualitative research that give
people what I call a license to be brilliant, whereas I am bound by my oath to be faithful to my
informants’ constructions of reality. I’ve discovered over the years that my self-imposed restraint
gives me a different kind of creative license, actually.
Eisenhardt. Initially, I’d like to observe that there are more similarities than differences
among the approaches to qualitative research represented here. That being said, when qualitative
researchers are theory building, whether it’s myself or Denny [Gioia] or Ann [Langley], there are
other people who are theory building too, and they’re using formal models, or they might be
armchair theorizing. As a group, we contrast with those other methods. I like to use the analogy
that just as math keeps formal theory honest, it’s data and being true to the data that keeps our
theory building honest -which is why we’re not just reporting what we feel like saying.
To further elaborate, I am a big believer that a lot of us who are doing theory building
research are basically all doing the same thing and on the same team. We’re all using diverse
data sources with deep immersion in the phenomenon. We’re all doing theoretical sampling, not
random sampling. And, we’re all doing grounded theory building, whether we’re following the
bible of grounded theory building or the spirit of grounded theory building by going from data to
theory. I think that’s what unites the panel, and what unites much of qualitative research.
Although there are qualitative researchers who have other aims, the people who see themselves
as theory builders are all doing these. When I read over the article that Kevin [Corley], Denny
[Gioia] and Aimee [Hamilton] wrote (Gioia et al., 2013), I’m mostly agreeing : “I know this. I
believe this. This is where I’m coming from too.”
I think we’re probably all in agreement that rigor is about a strong theory that’s logical,
that’s parsimonious, that’s accurate. We have concepts or second order themes. We know what
they are—they’re defined, distinct, well-measured andwell-grounded. And we’re coming up with
theory that is insightful. I think regardless of who you are in this room—whether you’re an
ethnographer, an interpretivist, a multi-case person or a process person, whoever you might be—
at the end of the day, if you’re a theory builder, then you must ask yourself: Is my theory a strong
theory in the traditional sense?
Now, to discuss some of the differences between my approach to theory-building and
Denny or Ann’s approach. For me, theory building from cases is an inductive approach that is
closely related to deductive theory testing. They are two sides of the same coin. In comparison
with interpretivist and ethnographic approaches, the goal is generalizable and testable theory. As
such, it is not solely focused on descriptions of particular situations or privileging the subjective
perspective of participants. I used to call myself a positivist. I don’t do that much anymore – it’s
a loaded term. But I also don’t cringe at positivism. Finally, my approach and theory building
from cases broadly is not locked into an epistemological or an ontological point of view, but it is
often locked into a 40-page limit. A multiple case study author has a much different writing
challenge than a single case author.
Regarding page limits, a criticism of my work and the work of other multi-case authors
from some reviewers is “We don’t see enough description.” My response is, “How are we going
to fix that in 40 pages?” We can’t, and so we can’t take the same approach to writing as single-
case authors. There’s really quite a difference, I think, in the writing challenge that we have. So
while some readers are looking for stories, multiple case papers are necessarily written in terms
of theory with case examples, and not as a single narrative story.
Beyond writing differences, the analytic techniques and presentation of data are distinct.
In theory building from cases, researchers use a variety of techniques for cross-case analysis
techniques as they iterate across cases and at later stages, with the extant literature. There is also
openness with regard to how data are coded and displayed. This stems from the belief that
different data, research questions, and even researchers may call for distinctive approaches to the
specifics of coding and display.
One final specific difference to observe: Denny [Gioia] said, “I couldn’t live without a
data structure.” While theory building from cases has measures and constructs that constitute a
data structure, I don’t want to present a “data structure” in my papers. A data structure has no
data in it, and so takes up precious journal space that is already tight. Instead I show the reader
the data structure in a series of construct tables that tie particular measures of the construct to
specific cases. So, don’t make me do a data structure! Likewise, I don’t want a “data and
themes” table. There are two problems in multiple cases. First of all, you have to fit all the cases
into the table. Then secondly, you have to show that the data for Case 1 are fitting (or not) with
Case 2, Case 3, Case 4 etc. If you use a data and themes table, you can’t show the systematic
grounding of each construct in each case because you are showing only a piece here, and a piece
there. So the replication logic across cases is obscured. Replication logic requires systematically
observing constructs and relationships in each case - Case 1, Case 2, Case 3. If multi-case
research is forced into a data structure table and especially a data and themes table, it’s deeply
problematic—certainly for the kind of work I do and, I think, for other people conducting
multiple case studies.
Langley. I think my key point here is that I am not proposing a single method or template
for doing qualitative research. However, I am arguing for the need to consider phenomena
processually and for finding suitable ways of doing this. Process researchers seek to understand
and explain the world in terms of interlinked events, activity, temporality and flow (Langley et
al., 2013) rather than in terms of variance and relationships among independent and dependent
variables. There are a variety of qualitative designs and analytic strategies that one can adopt to
capture and theorize processes, each having advantages and disadvantages in terms of what can
be revealed and understood. It might be reassuring for some to have a clear-cut template for
doing successful work of this nature (and I personally see Denny’s and Kathy’s approaches as
fairly template-like although they might deny it). In contrast, I am not proposing a single
approach, and indeed, I believe that any specific template is bound to have blind spots—and that
it is better to welcome diversity.
There are, however, a few common elements that I think are important for qualitative
process research. First, since process research is about evolution, activity and flow over time, this
needs to be reflected in the data. Process studies are longitudinal and data need to be collected
over a long enough period to capture the rhythm of the process studied. In addition, while
process researchers often use retrospective interviews as part of their databases, real time
observation or time-stamped archival data and repeated interviews are generally important to
capture processes as they occur, rather than merely their retrospective reconstruction. Second,
the analysis process itself needs to focus on temporal relations among events in sequence in
order to develop process theory.
It is also important to recognize that the analytic approaches to sensemaking that we
adopt quite clearly influence the theoretical forms and types of contributions that we are able to
make. For example, interpretations based on a narrative strategy or grounded theory provide a
sense of participants’ lived experiences (as in Denny’s approach); predictions based on a
comparative or quantitative strategy provide a sense of causal laws (more like Kathy’s
approach); patterns based on visual mapping provide a sense of surface structure; and
mechanisms based on a narrative strategy, alternate templates or temporal composition provide a
sense of driving forces. Above all, it is important to remember that there is still room for
creativity! I would hate that a symposium like this might imply that there are only three
approaches to seeing the world qualitatively. There are many approaches, some perhaps
remaining to be invented. There are however some substantive differences between the different
approaches to qualitative research, and I have outlined some detailed thoughts on this in a recent
article (Langley & Abdallah, 2011).
Pet Peeves
Gioia. There are a number of issues that I would like to address about the way the
methodology I’ve been developing has been implemented over the years by others (see also
Gioia, 2017, forthcoming). The first is that the first-order or second-order terminology seems to
have become increasingly prevalent in recent years. As my friend, Royston Greenwood, put it in
a good-natured ribbing not long ago, “Is that it, then? Are we all going to talk only in terms of
first- and second-order findings in our research reporting now? Is that a good thing?” My answer
is: “Oh, good grief! I hope not.” No, it’s not a good thing. I’m a big tent kind of guy. I have no
desire to see the particular systematic approach that I’ve developed become the template for
qualitative research.
Another colleague said that the approach is creating a kind of arms race where each
study has to outdo the other on demonstrating its qualitative rigor. Lord, I hope that’s not true
either, especially when it gets the point of feeling that we need to include coding reliability
statistics in our reporting. That sort of outcome will play directly into the hands of critics who
see the methodology as an example of creeping positivism, a statement that gives me the heebie-
I developed this approach mainly because I’m also an evidence-based guy. I just believe
that the presentation of evidence matters. I’ve become my own victim, too. One of my recent
reviewers said, “This Gioia-methodology approach is just becoming too common,” and asked if I
couldn’t please figure out some other approach. Oh, the benefits of blind review! – Gioia being
asked not to use the Gioia methodology! I love it! If I were a bigger, more understanding guy, I
should probably be receptive to the request. Yet, I’m not sure reviewers would ask people not to
use multiple regression, for instance, if it were appropriate to answer the research question
Finally, I’m concerned that so many scholars seem to be treating the methodology
mainly as a presentational tactic, which offends my sensibilities. I designed this thing as a
systematic way of thinking about designing, executing, and writing up qualitative research—the
“full Monty.” The approach is meant to systematize your thinking while providing the
wherewithal to discover revelatory stuff. It galls me to think that people are using it as just a
formulaic presentational technique. Remember: it’s a methodology, not just a method or set of
cookbook techniques.
Eisenhardt. In a new AMJ paper (Eisenhardt et al., 2016), we write about rigor and rigor
mortis. What’s rigor mortis? It’s requiring specific formats like a data structure. I understand
why it works for Denny [Gioia] butI don’t think it works for everybody. Data and themes tables
don’t work well for everybody or in multi-case research either. And, they don’t work well
outside of interview data, orwith time varying data. Second, rigor mortis involves following rigid
analysis steps as if there’s a bible – e.g., turning grounded theory building into a religion, not a
technique. My third pet peeve, related to rigor mortis is excessive transparency. What matters is
the sampling and the data. I don’t need to know every step of the journey. I don’t even want to
know every step of the journey. Instead, I want to get to the findings. In collecting data for our
article (Eisenhardt et al. 2016), we surveyed about 30 qualitative researchers—not just
researchers like me, but all kinds. Most everybody writes their Methods section as linearized; “I
did Step 1, Step 2, Step 3, Step 4.But, this is the equivalent of “kabuki theater” for most people.
We all use a much more creative process that can’t accurately be turned into a linear, mindless,
step-by-step description. That just isn’t what we do.
I also have a couple of idiosyncratic preferences. I like multiple cases better than single,
although I recognize there are unique exemplars, andsometimes data challenges. I also think that
some single case studies are actually multi-case because the authors actually do break up the case
and compare. I will say, however, that I’ve never seen (in my own studies) a single case that told
me nearly as much as two, three, four cases told me. A single case is just too idiosyncratic and
leads to an over-determined theory in the mathematical sense.
The second thing I prefer is theory i.e., explicit and generalizable theory. So I’m
interested in why A and B go together, not just that A and B do go together. I’m also actually
happy to engage with deductive research and with its concepts like controls and measures
because (at the end of the day) we theory build and deductive researchers theory test. I say “we
rule”; they do our work. Seriously, I think that we should connect to deductive researchers.
Langley. I am not sure that I would call these pet peeves, but when we edited the special
issue of Academy of Management Journal (Langley et al., 2013), we did come across some
examples of process research that somehow failed in their mission to capture processes
insightfully, even though they involved studying processes empirically over time. Most of these
papers were rejected on the grounds that they made “no theoretical contribution.” So what does
this mean exactly? Let me elaborate on some of the patterns we noticed.
A first problem is simply generating a narrative without any obvious theorization. For
example, one reviewer noted: “The case is interesting and well written. It could be useful in a
strategic management course.” That will not get you published. A second problem I have noticed
is what I call anti-theorizing: this involves pitting your case against a “received view,” which is
usually a very rational kind of theorizing, and saying, “Well, actually it’s not like that.” This
approach to attempting to make a contribution may have worked in the past, but that is no longer
the case. Saying that “things are messy” is simply not enough. A third problem is what I call
“illustrative theorizing.” This is what happens when you start with a theory and apply it to your
qualitative process data. This is tempting, but is not particularly convincing. The author is simply
labeling things that happened according to a preconceived theory. As one reviewer of a paper
submitted to the special issue noted, “The analysis is a form of labeling: here’s something that
happened and here is what it would be called in our theoretical framework. This is not a test of
the framework, but a mapping exercise.” The fourth approach that does not seem to work all that
well is finding regularities but not really explaining them – I call this “pattern theorizing” and
mentioned it above. An interesting example I always give for this is based on a very nice piece
of process research by Connie Gersick (1988), which is about how groups with deadlines make
decisions. She found with eight different groups that, bang in the middle, they shift the way they
are thinking and working. Is that really a theory? As such, I do not think it is. It is just an
empirical pattern. One of the things that Connie has mentioned when writing about this study in
a later publication (Gersick, 1992) is that the lack of an obvious theoretical explanation was what
gave her trouble in publishing the paper, despite the clear empirical pattern. She did in fact
eventually find a theoretical explanation and wrote another paper supporting this, developing an
interesting analogy between her findings and other phenomena that have a punctuated
equilibrium structure (Gersick, 1991). Finally, another form of problematic process theorizing I
call patchwork theorizing (or bricolage), in which authors just take a few ideas from here, a few
ideas from there, a little bit from elsewhere, and stick the whole thing together in a kind of
mashup. Unfortunately, readers will not usually see this as a contribution, as it lacks coherence
and integration.
As a counterpoint to these problematic issues, I would also like to point to examples of
the kinds of theorizing that can make a theoretical contribution and that were successful in the
special issue of AMJ. For instance, Philippe Monin and colleagues examined how dialectics and
contradiction constitute a process motor (Monin et al., 2013) explaining sensemaking and
sensegiving patterns over time during a complex merger. Joel [Gehman] and colleagues have a
very nice paper on multi-level interaction between micro processes and macro processes and
how one grew out of the other (Gehman et al., 2013). A third kind of contribution is focused on
the dynamics of stability, i.e., the work you need to do to stay in the same place (Lok & Rond,
2013). In fact, a final point I would like to make is that what makes a theoretical contribution in
process research is itself a moving target (or a processual phenomenon). The kinds of theoretical
framings that appeared insightful in earlier decades no longer have the same attraction today.
Part of the common challenge of doing qualitative research (and I think Denny and Kathy would
agree with me here) is in fact the continual push for novelty.
On Controlling Variance
Corley (substituting for Gioia). Something that is very important in Kathy’s method is
controlling variance, and then really focusing on the specific variance you’re interested in
studying. In contrast, one of the things that comes out of an interpretivist perspective is this
notion that variability in peoples’ experiences – and their understanding of that experience – is
really interesting. As a grounded theorist trying to understand the phenomenon from the
experience of those living that phenomenon, I want to gather as many varied perspectives on the
phenomenon as possible. I think that this leads partly to the need or desire at some point to begin
to try to structure the data, because as an interpretive theorist I’m out collecting a lot of data and
I’m trying to make sense of it and figure out how this helps me understand the phenomenon
better. Then I have to pivot a little bit and say, “How can I help my reader understand this
phenomenon, because they don’t have the benefit of being absorbed in all these varied data.”
So interestingly, interpretivists have a rather different way of thinking about variance;
we’re much less interested in controlling variance and more interested in capturing variability
and trying to understand why that variability exists. This leads to the need to find a way to
structure the data so that our readers can understand it better.
Eisenhardt. One of the reasons why controlling variance comes up in my world is
multiple cases. I think that this actually is the huge difference. If Denny or Ann were doing an
identity study at a major university and they wanted to do a multi-case study, would they control
the variance by looking at another major university or would they try to create variance by
looking at a corporation or government? I think the big difference is that, in a multi-case study,
once we specify the focal phenomenon and research question, we then think carefully about
where to control v. create variance in the research design.
Langley. Obviously, process approaches do not emphasize the explanation of variance. I
can see that when you want to explain variance and you only have a small sample, you really
need to control for everything except the central elements that you are interested in. What I see
as one of the differences between Denny [Gioia]’s and Kathy [Eisenhardt]’s approach is in what
the final theoretical product looks like and what kind of generalization might be conceivable
from that? Those who follow Kathy’s approach develop constructs from a series of cases that
enable them to explain differences. In doing so, they abstract out all of the richness of the
particular stories to focus on those specific things that make the difference. That is a very
important thing to do. To do it well, you need to control for extraneous variance on things you
are not focusing on. Whereas in interpretive research such as that favored by Denny and Kevin,
you might want all that messiness to be present and visible, because interpretivists have a
different conception of what generality is. Rather than talking about generalizability, they would
talk about transferability. To achieve this, you need to include as much richness as possible in
your account so that the readers themselves can see to what degree the story you are telling finds
resonance. For me, that is an entirely different approach to theorizing. One is not better than the
other; they both contribute to our understanding in different ways. However, you do need to
know which of these you want to do when you’re developing a study.
Eisenhardt. First, I think that my cases are probably as rich as Denny’s - although maybe
not quite. But as I was trying to say before, it is not possible to write about five cases with the
same richness as one case when there is a 40 page or so limit. It’s not possible.
Second, my coauthors and I have also lately been told by some reviewers that we can’t
have a process study and a variance study in the same study. I think that this is also not true. The
confusion arises from the multiple meanings of “process.” Process can refer to events over time
as Ann notes. Most of us doing qualitative research take this kind of longitudinal perspective.
But process can also mean similarity which contrasts with variance. In theory building from
cases, a researcher can be looking at two or three companies and see a given process like
socialization occurring in different ways (variance). In fact, Anne-Claire Pache and Filipe Santos
(Pache & Santos, 2013) have a very nice paper on social aid organizations where the
administrative processes are different – i.e., a variance study of process phenomena. Finally, an
update on Ann’s diagram may be that the diagram has a particular view of variance studies that
implies static antecedents (not time varying processes) and outcomes.
Langley. I think you can mix process and variance, but it is hard to put all of that in one
paper. I have tried that, but reviewers tend to push you to either drop cases to provide more
richness, or to develop comparisons with clearly distinct outcomes. I also think that in a process
study, multiple case studies can serve a different kind of role from the one that Kathy is
suggesting by showing how similar processes occur in different contexts, rather than
emphasizing variance (see for example, Abdallah, Denis, & Langley, 2011; Bucher & Langley,
2016; Denis et al., 2001). This is a very powerful way to show that the process that you were
describing actually has some generality. It is not just something that you found in one particular
context, but rather similar sorts of dynamics are occurring in very different places.
Eisenhardt. That’s also something we theory building from cases researchers think
about, too. We’re trying to figure out where we want the variation, how we want to handle
generalizability, where we want to control for the variation that we don’t care about. In designing
our research, we’re balancing all of them – i.e., variation, control, and generalizability. In the
ideal multi-case world, Denny might replicate his university-based study of identity in a
corporation, and then see what parts of the process in the university are the same in the
corporation, what parts are different, and why.
On the Creative Process
Eisenhardt. I read Ann Langley’s work and get great ideas about the creative process. I
don’t think Denny and Kevin have quite articulated theirs (and I haven’t articulated mine), but I
suspect we’re all doing pretty similar things because we’re trying to see what the data are saying.
We’re trying to figure out different ways to look at our data to see fresh insights. For example, I
might mix and match: let’s compare Cases A and B or let’s compare Periods 1 and 2.
Corley. I think another thing that pops out to me is that part of this process is really
getting lost in your data. From an interpretivist’s perspective, that means I need to go out and
collect a lot of data and struggle my way through it and really try to understand what’s going on.
I know Joel [Gehman] and Vern [Glaser] are interested in this notion of theoretical sampling, and
at these key points looking at your data going, “Okay. What do I not understand? And where
could I go in my context to get data that would help me understand that?” That process of
gathering a lot of data and getting lost in it and then finding your way through it so that when
you come out you have, for me, a plausible explanation of what’s going on, is a really key part of
the creative process. Not that it’s necessarily different, but it’s something that I think you don’t
pick up in a lot of methodology texts and how-to type of articles. It’s that messiness that is the
creative process.
Langley. On this topic, I recently published paper with Malvina Klag in International
Journal of Management Reviews titled “Approaching the Conceptual Leap” (Klag & Langley,
2013). It confirms what Kathy and Kevin have been saying, but includes another idea which is
that there is a kind of dialectic process occurring here. For example, being very, very familiar
with your data—being inside your data, your data being inside you—is extremely important. Yet
on the other hand, it is also so important to detach yourself from it at some point, because
otherwise you just get completely crushed by it.
For example, there is nothing like coming to the Academy of Management meeting and
being forced to do a PowerPoint presentation that you are not ready to do for making a creative
leap, provided the data are inside you. If not, you could probably still make a creative leap, but it
might not have anything to do with the data, which would not be good. That dialectic between
being immersed in the data and separating yourself from it is important. Other kinds of dialectics
are important as well, such as being able to talk to a lot of other people without being too
influenced by them and being able to draw insights from the literature not only in your field, but
in other disciplines as well.
On the other hand, accepting the role of chance is also very important in the creative
process. Our paper (Klag & Langley, 2013) really talks about these different dialectics and the
importance of combining the systematic disciplined side of research with the free imaginative
side. Karl Weick (1989, 1999), if you remember, talked about theorizing as “disciplined
imagination,so essentially what we are saying is a reflection of that tension between the
systematic discipline part and the freeing up part. You must have both. I think if you stay too
close to the data, you end up with something that’s very mechanical, but if you’re just
freewheeling, you finish up with something that has no relation to anything that’s actually
grounded. Both are needed to develop strong and valuable theoretical insight.
On the Replicability of Findings
Corley. I think if you read what interpretivists believe and understand the philosophical
underpinnings of interpretivism, you wouldn’t expect two different people walking in with the
same research question to find exactly the same explanation for the same phenomenon. I think
perhaps this explains why it’s difficult for a lot of our colleagues who, having been trained in
much more positivistic quantitative methods, struggle with what we do, because we’re not
making truth claims about what we find. What we are doing is providing some deep insights into
phenomena that we couldn’t obtain without engaging the people who experienced it.
Determining whether or not these insights are “true” (according to some consensual criterion) is
the next step in the process. We must test these theoretical insights in lots of different contexts.
Our job as interpretivists is to go out there and gain new insights into a phenomenon from the
people who are living it. So, I would not expect someone who had been at my research site
asking the same questions I did to come up with the same grounded model that I did, because
they’re not me. They didn’t interact with my informants in the same way.
Eisenhardt. I have an alternative view. I think if you asked my research questions in my
cases, you would get pretty much the same answer that I got. What I do think would be different
is the questions that would be asked. Ann might choose a different question or Kevin might
choose another different question that was interpretivist. But I think that if you used my question,
you would see what I saw. So I differ on this point.
On Induction vs. Deduction
Eisenhardt. In connecting with our deductive friends, I do think that theoretical sampling
is mind-blowing, and so one does have to explain that concept. But I also think that there are
many similarities between the two approaches. So if we’re actually doing the same thing as
deductive researchers like measuring constructs, then we should use the same terms. That’s why
I use “measures” and “constructs”, not the terms “first” and “second order codes”. I don’t think
that inventing more terms adds value. If we’re actually doing something genuinely different, then
we should call it something else like theoretical sampling and replication logic. Finally, my
deductive editors often like propositions, and if so, I usually provide them.
Corley. I tend to push back when they ask for propositions because propositions are not
always the best output of inductive research. I agree that propositions can be a useful way of
transitioning from inductive insights to deductive testing, but some inductive efforts produce
deeply meaningful insights that can’t be easily reduced to proposition-type language.
Langley. I personally think that we over-emphasize the idea of induction, that we are
completely theory free. I actually think that what we are doing is abduction rather than induction.
Induction for me implies that you are generalizing from empirical observation and that there is
not really any a priori theory there, which is illusory. I think that to develop a richer
understanding of the world, we do need to connect to prior theory.
In most of my studies, we go into a site with some vague idea about the kinds of
concepts and ideas that we are interested in. We collect some data that make us think about some
other angles that might be interesting and then we go to the literature and search for theories that
would be relevant. Usually, when we do that, we can see how theories that are relevant can take
us part, but not all, of the way to an enhanced understanding and it is the remaining piece that we
contribute. Thus, both deduction and induction are present in a kind of cycle. The word for that,
is abduction, which means connecting what you see in the empirical world with theoretical ideas,
which are also out there and can be further developed.
Of course, you do have to have something over and above what is already expressed in
theories. That’s why I said that the labeling approach to theorizing does not work. A typical
example I give is actor-network theory. Actor-network theory, unfortunately, is so wonderful in
that you can explain everything with it if you just label things the correct way. However, you
will not make a contribution to actor-network theory by doing that because it will stay the same.
It has not moved; you have not added to it. You do need to be able to extend theory. Quite often,
my studies have a section called theoretical framework where I say, “Well, this is what the
theory says but this is what we don’t know.” That gives me enough to move forward.
This symposium led to several major insights. Overall, the panelists agreed that there is
some commonality between the different qualitative approaches. For instance, Kathy Eisenhardt
concluded: “Let’s get past those minor points. Let’s focus on doing great research and let’s
remember that 90 percent of the academy is composed of deductive researchers, so let’s play on
the same team.” Although this is certainly something to be celebrated, this does not necessarily
mean that anything goes. Within the “big tent” of qualitative research, there are different pockets
or niches of scholars with their own toolkits and methodologies that should be engaged or
leveraged thoughtfully. In our concluding thoughts, we highlight three takeaways for scholars
using qualitative research: (1) in determining what qualitative approach to use, it is important to
have a clear theoretical goal and objective for your research—this theoretical purpose animates
the decisions made about research design; (2) every qualitative theory-method package, while
potentially providing some degree of template or exemplar, nonetheless needs to be customized
for a particular research context; (3) it is important to create a theory-method package “fit,” in
which the methodological tools and their particular configuration are suited to the research
question and theoretical aims of the project.
First, the purpose of a research study is very important. The scholars in this presentation
explicitly or subtly described several different potential purposes that research seeks to theorize
or explain. For example, do you want to understand what characteristics of a firm are associated
with superior performance, perhaps using extant constructs? Are you attempting to understand
how organizational actors in a social setting understand their circumstances or surroundings? Are
you attempting to understand processual relationships among events? Different purposes of
research result in the need to use and to discover different types of concepts and relationships
among concepts. One takeaway from this session: If you want to generate a theory that can be
tested deductively, the Eisenhardt method may be the place to start; if you want to understand the
lived experiences of informants, the Gioia method may be the place to start; and if you want to
understand temporal or practice dynamics in organizational life, Langley’s approach may be a
source of inspiration. By the same token, there seem to be rather limited circumstances when a
single paper would appropriately draw on many of the specifics of all three approaches.
Second, it is important to customize the method for your research context. Research
situations are different and require the use of tools and techniques in different ways. On the one
hand, some tools and techniques might be used in multiple approaches to qualitative research.
For example, a general technique such as the constant comparative method for coding (i.e.,
Strauss & Corbin, 1998) might be used across multiple approaches to qualitative research. On the
other hand, techniques such as visual mapping might be generally applied, but will need to be
customized for particular studies. That said, given the different onto-epistemological
assumptions embedded in these methods packages, seemingly common concepts are likely to
have different meanings and implications as you move from one method to another. For
example, a concept such as replication differs quite a bit among the approaches. In Eisenhardt’s
approach, replication is central: without replication across cases the researcher is left with just a
particular story. In Langley’s approach, the logic of replication is temporal (e.g., see Denis et al.,
2011). In Gioia’s approach, replication functions at the level of codes. So qualitative researchers
can look to techniques that are shared across approaches, but the needs and idiosyncrasies of
every research project will require customization. To sum up: Denny, Kathy and Ann each agree
that their method should be used flexibly. A methodology is not a cookbook; rather, it provides
scholars with orienting principles and tools that always need to be modified and customized.
Third, it is important to create theory-method-package “fit.” This goes beyond ensuring
that a study’s methods are internally consistent to encompass the relationships among methods
and the research question one is asking and theoretical contribution(s) one intends to make. The
ontologies, epistemologies, and even types of theories differ among approaches. It is important
for people to customize approaches for their research designs; it is fundamental that scholars
doing qualitative research are sensitive to the linkages between methods and theory. From a
method point of view, although a given method may be suitable to many tasks, this does not
mean it is suitable to every task. Similarly, from a theory point of view, although there may be
more than one way of making a theoretical contribution, the kind of theoretical contribution one
aspires to make has implications for the kinds of methodological choices that are appropriate. In
sum, in designing a study, qualitative researchers need to find theory-method fit.
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Kathy Eisenhardt is the Stanford W.
Ascherman M.D. Professor in the
Stanford School of Engineering
and Co-Director of the Stanford
Technology Ventures Program.
Denny Gioia is the Robert & Judith
Auritt Klein Professor of
Management in Penn State’s
Smeal College of Business.
Ann Langley is the holder of the
Chair in Strategic Management in
Pluralistic Settings in the
Department of Management at
HEC Montréal.
Key works
Her path-breaking article, “Building
Theories from Case Study
Research,” (Eisenhardt, 1989a)
has been cited more than 32,000
times according to Google
Her ideas in the article have been
elaborated in others (see
Eisenhardt & Graebner, 2007;
Eisenhardt et al., 2016).
Her comparative case method has
been widely cited and used by
scholars in BPS, OMT, TIM
divisions of the Academy of
Management, and beyond.
He pioneered a grounded-theory
inspired method in his paper,
“Sensemaking and Sensegiving in
Strategic Change Initiation”
(Gioia & Chittipeddi, 1991), with
more than 2,300 citations
according to Google Scholar.
This method was recently codified in
a paper “Seeking Qualitative
Rigor in Inductive Research:
Notes on the Gioia Methodology
(Gioia, Corley, & Hamilton,
The “Gioia Method” has been
especially influential with
scholars in OMT, MOC and other
divisions of the Academy of
She has been a strong proponent for
theorizing from process data,
writing an influential article,
“Strategies for Theorizing from
Process Data” (Langley, 1999),
cited more than 3,000 times
according to Google Scholar.
She guest edited a 2013 special issue
in the Academy of Management
Journal that featured process
research (Langley et al., 2013).
Her ideas have been used by scholars
from OMT, ODC, SAP and other
divisions of the Academy of
... To answer the second research question: what are the managerial and organizational capabilities that enable sustained high growth and how are they created and enacted over time?, we also adopted a single case study approach (Gehman et al., 2018). The selected case in this study was Takeaway, one of the biggest food order and delivery platforms worldwide and the fastest growing organization in the Netherlands. ...
... Given the limited theoretical insights and empirical evidence about the team-level dynamic managerial capabilities we carried out an inductive theory building approach (Gehman et al., 2018;Gioia et al., 2012) and conducted an in-depth case study of the business model innovation process of the biggest independent fashion retailer in the Netherlands. This case is particularly suited for this study because for a traditional physical retailer to introduce an online business model indicates a designed, novel and nontrivial change to the business model (Kim & Min, 2015). ...
... Given the limited theory and evidence about the team-level interaction of dynamic managerial capabilities we draw on an inductive theory-building case study design as it gave us the opportunity to gain deep insight into this rather under researched phenomenon (Gehman et al., 2018;Gioia et al., 2012). We adopt an in-depth single case study design as it allows us to capture the longitudinal, processual nature of the BMI process (Langley, 1999). ...
In today’s business environment which is characterized by its high environmental dynamism driven by technological disruptions and the disruptive growth of innovative business models, firms face the important challenge of continuous adaptation to internal and external change. On the one hand, firms with innovative business models that achieve high growth face the challenge of adapting their organizational practices to the internal and external environments that are in constant flux. On the other hand, incumbent firms face the challenge of adjusting to the changing market conditions, caused by fast growing new entrants, in order to protect their competitive position. While this phenomenon has attracted significant research attention in recent years, there still remain important gaps in our understanding of how firms and top managers deal with the critical questions of high growth and innovation in the pursuit of successful adaptation. To address this omission, we conduct two single in-depth case studies and one multiple-case study. Throughout these empirical studies, we examine: (1) how the top management team of an incumbent firms combines the dynamic managerial capabilities of its top managers in order to adapt its firm’s business models to protect its competitive position; (2) how a scale-up company creates and enacts dynamic capabilities that enables it to sustain high growth over prolonged periods of time; and (3) how scale-ups design scalable business models that enable them to create and capture value over prolonged periods of high growth. We build our theorizing on three related research topics that have gained momentum in recent years: Business Models, Dynamic Capabilities, and High-Growth Firms, often referred to as Scale-ups. Our insights have important implications for our understanding of how firms and top managers deal with the important, yet complex challenges of sustaining high growth and the challenge of protecting their firm’s competitive position against its fast-growing competitors.
... This case study is positioned in the tradition of interpretive research. Because our goal was to gain an understanding of actors' experiences through their own frames of interpretation, we followed the criteria for the interpretivist research process and analytical rigour (Gehman et al., 2018;Gioia et al., 2013;Symon et al., 2018). ...
... The analysis adopted an abductive process (Dubois and Gadde, 2002) typical of a qualitative case study, with the first inductive analytical step followed by a deductive one. We started with a thematic inductive analysis (see Gehman et al., 2018;Gioia et al., 2013) to progressively discern emerging patterns in our material. Using NVivo software, we coded and analysed the field material stepwise. ...
Full-text available
Why do highly skilled migrants encounter difficulties getting a skilled job? In this study, instead of searching for an answer in migrants’ characteristics, we turn to organizations and ask: why do organizations underemploy migrants? With an in-depth qualitative study of a program for highly-skilled migrants’ labour integration in Sweden, we show that highly skilled migrants are perceived as a potential threat to organizational norms and habits. Using the relational theory of risk – approaching risk as socially constructed – the study provides a novel explanation for highly-skilled migrants’ underemployment. It shows an organization logic protecting corporate practices seen as ‘normal’ from a perceived disruption that employing highly-skilled migrants could possibly cause. Theoretical contributions to the understanding of highly-skilled migrants’ employability are threefold: (1) the field assumption that organizations are favorable to hiring migrants is challenged, (2) highly-skilled migrants’ underemployment is explained through a protective organizational logic, and (3) we stress the necessity to problematize an implicit reference to organizational normality when recruiting.
... Given that this article aims at theory development rather than theory elaboration, this study adopts the Gioia data analysis technique (Gehman et al., 2018;Gioia, Corley, & Hamilton, 2013). This method can be effectively used to analyse small samples because, instead of focusing on comparing a certain number of cases, it centres on eliciting a data structure composed of first-order, secondorder, and aggregate dimensions based on theoretical sampling to stimulate theoretical insights (Gioia et al., 2013). ...
This study provides new insights into the role of subsidiary managers in the practice of global business models of multinational enterprises in transforming economies. Drawing on the global business model literature and through semi-structured interviews with a leading Norwegian maritime multinational enterprise in China, we have developed and critically explored a theoretical framework for uncovering how subsidiary managers understand and manage the tensions between the headquarters based in a western country and the subsidiaries based in a transforming economy. More specifically, when implementing the global business model in the transforming economy, subsidiary managers need to undertake effective management of structural, behavioural, and cultural tensions along with the global integration-local responsiveness dilemma. Subsidiary managers can contribute to solving structural tensions between the headquarters and subsidiary by undertaking effective market sensing and knowledge transfer activities to integrate the transforming economies into the MNE's global production networks. Meanwhile, they need to make effective relationship management to solve behavioural and cultural tensions.
... As there is little firm knowledge about how entrepreneurs mobilize different types of resources from their local communities, we employed an open and iterative analysis approach guided by our emerging insights (Gehman et al., 2018;Glaser and Strauss, 1967;Eisenhardt, 1989). We used the qualitative data analysis software Maxqda and multiple data displays (Miles et al., 2017) to code and analyze our data. ...
In this study, we move beyond the predominant focus entrepreneurship researchers have put on the acquisition of financial capital from professional investors by exploring how, and with what effects, entrepreneurs can mobilize all required resources—financial, human, physical, and social—from local communities. Our temporal analysis of the resource mobilization processes of seven cases of community-based enterprises (CBEs) reveals four sets of activities with distinct goals and effects, which explain how entrepreneurs can meet or even exceed their resource mobilization goals by mobilizing a greater variety of resources from a broader base of resource providers. Importantly, the findings show how entrepreneurs can achieve a multiplier effect meaning that they can perpetuate the inflow of significant amounts of unsolicited resources by continuously engaging in activities targeted at creating a sense of identification and ownership, which require comparatively little extra effort and resource inputs. We synthesize our findings in a framework of community resourcefulness in new venture creation. This framework adds a new perspective of resourcefulness as “getting more from many,” and demonstrates that resourceful behavior is not necessarily about individuals' ability to respond to situational constraints but also about their ability to recognize and seize situational resource potentials. Our findings have important implications for our understanding of resourcefulness in entrepreneurship and the nascent body of literature on community-based enterprises.
... The data analysis method used in this paper also contributes to project management and open innovation research. First, it builds on the strengths of a processual approach to organizational phenomena ( Gehman et al., 2018 ;Langley, 1999Langley, , 2007Van de Ven & Poole, 2005 ). As such, it enables one to tackle difficult "how " questions in both strands by tracking the dynamic emergence of an outcome of interest over time, instead of inadvertently oversimplifying complex processes in typically reified variable relationships ( Thompson, 2011 ). ...
How do organizations use the experience from projects to build a systematic capability to manage open innovation projects? Drawing upon Project Management and Open Innovation capability-building frameworks, we studied the crusade of an industrial company to create an open innovation capability. In that sense, we applied an Event Structure Analysis (ESA) to evaluate the event network, which evidenced a four-stage process: closed mode, open driver, vanguard project, project-to-organization. Results demonstrate, from causal connections, that the referred capability can be leveraged from the execution of key projects, especially from a vanguard project. Our study contributes to Project Management theory by reveling that previous experiences in both project and organizational levels offer a fertile ground for the emergence of a vanguard project. For the open innovation field, this paper provides a project-oriented approach to the discussion of open innovation's adoption in mature firms.
... We considered how different institutional settings impact on EFs' local embeddedness. Given that we did not intend to show causality or correlation between the institutional setting and the local embeddedness of EFs, but instead aimed to obtain rich, in-depth data, we considered individuals with their own perception of reality and, thus, adopted the interpretivist paradigm (Gehman et al., 2018). Additionally, a qualitative methodology served best our research objective of identifying the formal and informal mechanisms that impact on EFs' local embeddedness in different institutional settings given that literature advises the performance of qualitative investigations when the studied phenomenon is under-investigated and under-theorised. ...
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The interaction between institutional settings and Entrepreneurial Families (EFs) is two-fold. Extant literature has attempted to understand how institutional settings can affect Family Businesses’ embeddedness. Both perspectives are complementary and necessary to recognise that EFs are not only locally embedded in their territories, but they are also entrenched in institutions. Despite this, how different institutional settings impact on EFs’ local embeddedness remains unexplored. To fill this gap, we combine institutional theory and family business research to perform a qualitative investigation. Drawing on the Varieties of Capitalism institutional categorisation, an exploratory study is carried out by including four European regions from countries that are positioned as a Coordinated Market Economy (CME) (Germany), a Liberal Market Economy (LME) (United Kingdom) and two cases of Mediterranean capitalist system (France and Spain). 43 semi-structured interviews were conducted across the regions and analysed through an open-coding process. Findings unveil that EFs’ local embeddedness is conditioned by different institutional settings in different ways, namely through codified mechanisms (CME and Spain) and through nonformalised mechanisms (LME and France). These are unfolded in 20 mechanisms, which contribute to territorial policies adjustments depending on the category of institutional setting where EFs are locally embedded.
... To explore this question, we conducted a qualitative case study of modern slavery in contemporary United Kingdom. While a crucial step in qualitative research is finding a "theory-method fit" (Gehman et al., 2018), "theory building from case study research is particularly appropriate" when "little is known about a phenomenon, current perspectives seem inadequate because they have little empirical substantiation, or they conflict with each other or common sense" (Eisenhardt 1989, p. 548). For modern slavery, "not only does the field lack a deep theoretical understanding on modern slavery, but it also suffers from deficiencies in terms of its empirical understanding at the organizational level and of the overall business side" (Phung & Crane, 2018, p. 180). ...
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Despite growing attention from companies and regulators looking to eradicate modern slavery, we know little about how slavery works from a business perspective. We address this gap by empirically examining innovations in the business models of modern slavery, focusing on how the business models of slavery in advanced economies have evolved since slavery was legally abolished. While continuities exist, novel business models have emerged based on new actors, activities, and linkages. We categorize these as four innovative models per actors involved (producer/intermediary) and how value is created and captured (revenue generation/cost reduction), and discuss implications for research, policy, and practice.
... We follow the advice in the literature for structured qualitative analysis (Eisenhardt, 1989;Voss et al., 2002;Barratt et al., 2011). Specifically, we follow the eight-step procedure proposed by Eisenhardt (1989) because it is compatible with our research objective, which seeks to explain an a priori identified relationship in a complex process (Gehman et al., 2018). Eisenhardt's eight steps are: ...
Purpose: Why some assembly factories implement a lean program faster than others is an enduring puzzle. We examine the effect of a fundamental characteristic of every assembly factory-its rhythm of production. Design/methodology/approach: We designed a multi-method study and collected data from a leading global equipment manufacturer that launched a lean program across its factory network. We use quantitative data gathered from internal company documents to test our hypothesis that production rhythm affects the pace of lean implementation. We then analyze qualitative data from interviews and factory visits to derive theoretical explanations for how production rhythm affects lean implementation. Findings: Consistent with our hypothesis, we present evidence that factories with faster production rhythms implement lean faster than those with slower rhythms. This evidence is consistent with learning theories as well as the literature on organizational routines and forms of knowledge. We propose a theory of the relation between rhythm and learning in lean implementation. Research limitations/implications: The hitherto unexplored relation between production rhythm and lean implementation raises intriguing questions for scholars and ushers new insights into how organizations learn to implement lean. Practical implications-Organizations need to calibrate their expectations for lean implementation pace when their factories have widely different production rhythms and find ways to mitigate any adverse effects slower rhythms may have. Organizations can alleviate the unfavorable context of slower rhythms by inculcating practices in the factory that emulate the learning environment present in faster-paced factories. Originality/value: We contribute novel quantitative and qualitative evidence that production rhythm affects lean implementation through learning-based mechanisms.
... The data material collected for this study, was generated in accordance with established practice in multiple case studies (Eisenhardt, 1989;Eisenhardt & Graebner, 2007;Gehman et al., 2018), where qualitative data is collected eclectically using a combination of interviews, informal observations and documents. The primary data source for this study was interviews with the ventures' principal founders. ...
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The mobilization of resources is an essential challenge for entrepreneurs. Existing research suggests that access to standard and high-quality resources is an important condition for entrepreneurial success, yet such resources are often out of reach for entrepreneurs. In this study, we explore entrepreneurial resource mobilization in resource-constrained peripheral locations. We identify three activities together constituting an underlying logic of spatial bricolage, defined as making do with the resources at hand in the immediate spatial context. Further, we suggest that the likelihood and prevalence of this logic of action is both situational and dispositional, as individual and contextual factors combine to generate important differences in the resource mobilization activities of the entrepreneurs. Our study contributes to a contextualized understanding of entrepreneurship by showing how spatial bricolage as a distinct logic can help entrepreneurs overcome resource constraints, and how the spatial context incorporates an important dimension of what constitutes ‘at hand’ in entrepreneurial bricolage.
... 2021, 11, 2 6 of 18 with Syrian women having refugee experience. The role of the qualitative analysis is to nuance and put in context the quantitative findings (Gehman et al. 2018). Although the interviewees are not highly skilled, we argue that the material provides valuable insights about the life of Syrian women with refugee experience in Sweden. ...
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One of the main challenges faced by refugee hosting states is the labour market integration of newcomers, which can be achieved to some extent through the creation of small businesses. This paper analyses the individual level determinants of the entrepreneurial intentions of highly-skilled women with refugee experience. The study adds a new perspective to the conversation about highly-skilled migrant women analysed so far, mostly as family reunion migrants joining economic migrants. It also contributes to the relatively new research on refugee entrepreneurship by adopting an unusual perspective for looking at highly skilled women. The empirical analysis embedded in the context of Sweden is two-fold. First, it is done in SPSS on the sample (N = 98) drawn from the 2017 Swedish Invandrarindex data set with the use of binary logistic regression. Second, the findings from the quantitative analysis are nuanced with the analysis of two case studies based on SSI with Syrian women having refugee experience. The results show that the gender variable does not predict the effect on entrepreneurial intentions. The findings confirm the importance of previous self-employment and leadership experience and indicate the potential importance of entrepreneurial role models, the cultural aspect of entrepreneurial intentions and the role of an encouraging environment in the host country.
Prior research has advanced several explanations for entrepreneurial success in nascent markets but leaves a key imperative unexplored: the business model. By studying five ventures in a nascent financial-technology market, we develop a novel theoretical framework for understanding how entrepreneurs effectively design business models: parallel play. Similar to parallel play by preschoolers, entrepreneurs engaged in parallel play interweave action, cognition, and timing to accelerate learning about a novel world. Specifically, they (1) borrow from peers and focus on established substitutes for their services or products, (2) test assumptions, then commit to a broad business-model template, and (3) pause before elaborating the activity system. The insights from our framework contribute to research on optimal distinctiveness and to the learning and evolutionary-adjustment literatures. More broadly, we blend organization theory with a fresh theoretical lens—business-model processes—to highlight how organizations actually work and create value.
This comprehensive bookcollects contributions from leading international scholars to highlight the diverse qualitative approaches available to organizational researchers, each grounded in its own philosophy. The editors provide a cutting edge, globally oriented resource on the state of qualitative research methodologies, helping readers to grasp the theories, practices, and future of the field.
This article discusses how acquired leaders create value in the integration of technology firms. Many acquisitions fail to achieve their desired ends because of ineffective post-deal implementation. Implementation may be especially difficult in technology acquisitions, which are often motivated by the desire to obtain and transfer tacit and socially complex knowledge-based resources. Since these forms of knowledge are difficult to transfer, a high degree of post-deal integration may be required in technology acquisitions. Yet paradoxically, integration may also lead to the destruction of the acquired firm's tacit knowledge through employee turnover and the disruption of organizational routines. Studies have generally viewed post-merger integration as a process that happens to the acquired firm, rather than as an activity in which the acquired leaders are active and essential participants. Value creation from acquisitions can be conceptualized in terms of two distinct dimensions: realization of expected value, and realization of serendipitous value. Expected value refers to those benefits that motivated the buyer to undertake the acquisition. Serendipitous value, in contrast, refers to value that was not anticipated by the buyer prior to the deal.