Research & Skills
Summary Articles Research Lectures
Week 1: Theory Building & Ethics
• Sutton, R. I. & Staw, B. M. (1995). What Theory is Not. Administrative Science Quarterly, 40:
371-384.
Week 2: Quantitative & Qualitative Approaches
• Song, M., Im, S., van der Bij, H. & Song, L.Z. (2011). Does Strategic Planning Enhance or
Impede Innovation and Firm Performance? Journal Product Innovation Management, 28: 503-
520.
• Gephart, R. P. (2004). From the editors: Qualitative research and the academy of management
journal. Academy of Management Journal, 47(4): 454-462.
• Pratt, M. G. (2009). From the editors: For the lack of a boilerplate – tips on writing up (and
reviewing) qualitative research. Academy of Management Journal, 52(5), 856-862.
• Wolfswinkel, J. F., Furtmueller, E. & Wilderom, C. P. M. (2013). Using grounded theory as a
method for rigorously reviewing literature. European Journal of Information Systems, 22: 45-55.
,Week 1: Theory Building & Ethics
Sutton, R. I. & Staw, B. M. (1995). What Theory is Not. Administrative Science Quarterly, 40: 371-
384.
This essay describes differences between papers that contain some theory rather than no theory. There
is little agreement about what constitutes strong versus weak theory in the social sciences, but there is
more consensus that references, data, variables, diagrams, and hypotheses are not theory. Despite this
consensus, however, authors routinely use these five elements in lieu of theory. We explain how each
of these five elements can be confused with theory and how to avoid such confusion.
There is lack of agreement about whether a model and a theory can be distinguished whether a typology
is properly labeled a theory or not, whether the strength of a theory depends on how interesting it is, and
whether falsifiability is a prerequisite for the very existence of a theory.
Parts of an Article that are not Theory
References are not Theory
References to theory developed in prior work help set the stage for new conceptual arguments. Authors
need to acknowledge the stream of logic on which they are drawing and to which they are contributing.
But listing references to existing theories and mentioning the names of such theories is not the same as
explicating the causal logic they contain.
Rather than presenting more detailed and compelling arguments, authors may list the names of prevail-
ing theories or schools of thought, without even providing an explanation of why the theory or approach
leads to a new or unanswered theoretical question.
Authors need to explicate which concepts and causal arguments are adopted from cited sources and
how they are linked to the theory being developed or tested. This suggestion does not mean that a paper
needs to review every nuance of every theory cited. Rather, it means that enough of the pertinent logic
from past theoretical work should be included so that the reader can grasp the author's logical argu-
ments.
Data are not Theory
Much of organizational theory is based on data. Empirical evidence plays an important role in confirming,
revising, or discrediting existing theory and in guiding the development of new theory. But observed
patterns like beta weights, factor loadings, or consistent statements by informants rarely constitute
causal explanations.
Data describe which empirical patterns were observed and theory explains why empirical patterns were
observed or are expected to be observed.
Authors try to develop a theoretical foundation by describing empirical findings from past research and
then quickly move from this basis to a discussion of the current results. Using a series of findings, instead
of a blend of findings and logical reasoning, to justify hypotheses is especially common. Empirical results
can certainly provide useful support for a theory. But they should not be construed as theory themselves.
Prior findings cannot by themselves motivate hypotheses, and the reporting of results cannot substitute
for causal reasoning.
Brute empiricism = we only learn from the paragraph that others had reported certain findings, and so
similar patterns would be expected from the data.
, Quotes from informants or detailed observations may get a bit closer to the underlying causal forces
than, say, mean job satisfaction scores or organizational size, but qualitative evidence, by itself, cannot
convey causal arguments that are abstract and simple enough to be applied to other settings.
List of Variables or Constructs are not Theory
A theory must also explain why variables or constructs come about or why they are connected. Com-
parative tests of variables should not be confused with comparative tests of theory, however, because
a predicted relationship must be explained to provide theory; simply listing a set of antecedents (or even
a causal ordering of variables as in LISREL models) does not make a theoretical argument. The key
issue is why a particular set of variables are expected to be strong predictors.
Diagrams are not Theory
Diagrams or figures can be a valuable part of a research paper but also, by themselves, rarely constitute
theory. More helpful are figures that show causal relationships in a logical ordering, so that readers can
see a chain of causation or how a third variable intervenes in or moderates a relationship. Also useful
are temporal diagrams showing how a particular process unfolds over time. On occasion, diagrams can
be a useful aid in building theory.
The logic underlying the portrayed relationships needs to be spelled out. Text about the reasons why a
phenomenon occurs, or why it unfolds in a particular manner, is difficult to replace by references to a
diagram.
Good theory is often representational and verbal. The arguments are clear enough that they can be
represented in graphical form. But the arguments are also rich enough that processes have to be de-
scribed with sentences and paragraphs so as to convey the logical nuances behind the causal arrow.
One indication that a strong theory has been proposed is that it is possible to discern conditions in which
the major proposition or hypothesis is most and least likely to hold.
Hypotheses (or Predictions) are not Theory
Hypotheses can be an important part of a well-crafted conceptual argument. They serve as crucial
bridges between theory and data, making explicit how the variables and relationships that follow from a
logical argument will be operationalized. Hypotheses do not (and should not) contain logical arguments
about why empirical relationships are expected to occur. Hypotheses are concise statements about what
is expected to occur, not why it is expected to occur.
Identifying Strong Theory
Theory is about the connections among phenomena, a story about why acts, events, structure, and
thoughts occur. Theory emphasizes the nature of causal relationships, identifying what comes first as
well as the timing of such events. Strong theory, in our view, delves into underlying processes so as to
understand the systematic reasons for a particular occurrence or nonoccurrence. As Weick (1995) put
it succinctly, a good theory explains, predicts, and delights.
Summary Articles Research Lectures
Week 1: Theory Building & Ethics
• Sutton, R. I. & Staw, B. M. (1995). What Theory is Not. Administrative Science Quarterly, 40:
371-384.
Week 2: Quantitative & Qualitative Approaches
• Song, M., Im, S., van der Bij, H. & Song, L.Z. (2011). Does Strategic Planning Enhance or
Impede Innovation and Firm Performance? Journal Product Innovation Management, 28: 503-
520.
• Gephart, R. P. (2004). From the editors: Qualitative research and the academy of management
journal. Academy of Management Journal, 47(4): 454-462.
• Pratt, M. G. (2009). From the editors: For the lack of a boilerplate – tips on writing up (and
reviewing) qualitative research. Academy of Management Journal, 52(5), 856-862.
• Wolfswinkel, J. F., Furtmueller, E. & Wilderom, C. P. M. (2013). Using grounded theory as a
method for rigorously reviewing literature. European Journal of Information Systems, 22: 45-55.
,Week 1: Theory Building & Ethics
Sutton, R. I. & Staw, B. M. (1995). What Theory is Not. Administrative Science Quarterly, 40: 371-
384.
This essay describes differences between papers that contain some theory rather than no theory. There
is little agreement about what constitutes strong versus weak theory in the social sciences, but there is
more consensus that references, data, variables, diagrams, and hypotheses are not theory. Despite this
consensus, however, authors routinely use these five elements in lieu of theory. We explain how each
of these five elements can be confused with theory and how to avoid such confusion.
There is lack of agreement about whether a model and a theory can be distinguished whether a typology
is properly labeled a theory or not, whether the strength of a theory depends on how interesting it is, and
whether falsifiability is a prerequisite for the very existence of a theory.
Parts of an Article that are not Theory
References are not Theory
References to theory developed in prior work help set the stage for new conceptual arguments. Authors
need to acknowledge the stream of logic on which they are drawing and to which they are contributing.
But listing references to existing theories and mentioning the names of such theories is not the same as
explicating the causal logic they contain.
Rather than presenting more detailed and compelling arguments, authors may list the names of prevail-
ing theories or schools of thought, without even providing an explanation of why the theory or approach
leads to a new or unanswered theoretical question.
Authors need to explicate which concepts and causal arguments are adopted from cited sources and
how they are linked to the theory being developed or tested. This suggestion does not mean that a paper
needs to review every nuance of every theory cited. Rather, it means that enough of the pertinent logic
from past theoretical work should be included so that the reader can grasp the author's logical argu-
ments.
Data are not Theory
Much of organizational theory is based on data. Empirical evidence plays an important role in confirming,
revising, or discrediting existing theory and in guiding the development of new theory. But observed
patterns like beta weights, factor loadings, or consistent statements by informants rarely constitute
causal explanations.
Data describe which empirical patterns were observed and theory explains why empirical patterns were
observed or are expected to be observed.
Authors try to develop a theoretical foundation by describing empirical findings from past research and
then quickly move from this basis to a discussion of the current results. Using a series of findings, instead
of a blend of findings and logical reasoning, to justify hypotheses is especially common. Empirical results
can certainly provide useful support for a theory. But they should not be construed as theory themselves.
Prior findings cannot by themselves motivate hypotheses, and the reporting of results cannot substitute
for causal reasoning.
Brute empiricism = we only learn from the paragraph that others had reported certain findings, and so
similar patterns would be expected from the data.
, Quotes from informants or detailed observations may get a bit closer to the underlying causal forces
than, say, mean job satisfaction scores or organizational size, but qualitative evidence, by itself, cannot
convey causal arguments that are abstract and simple enough to be applied to other settings.
List of Variables or Constructs are not Theory
A theory must also explain why variables or constructs come about or why they are connected. Com-
parative tests of variables should not be confused with comparative tests of theory, however, because
a predicted relationship must be explained to provide theory; simply listing a set of antecedents (or even
a causal ordering of variables as in LISREL models) does not make a theoretical argument. The key
issue is why a particular set of variables are expected to be strong predictors.
Diagrams are not Theory
Diagrams or figures can be a valuable part of a research paper but also, by themselves, rarely constitute
theory. More helpful are figures that show causal relationships in a logical ordering, so that readers can
see a chain of causation or how a third variable intervenes in or moderates a relationship. Also useful
are temporal diagrams showing how a particular process unfolds over time. On occasion, diagrams can
be a useful aid in building theory.
The logic underlying the portrayed relationships needs to be spelled out. Text about the reasons why a
phenomenon occurs, or why it unfolds in a particular manner, is difficult to replace by references to a
diagram.
Good theory is often representational and verbal. The arguments are clear enough that they can be
represented in graphical form. But the arguments are also rich enough that processes have to be de-
scribed with sentences and paragraphs so as to convey the logical nuances behind the causal arrow.
One indication that a strong theory has been proposed is that it is possible to discern conditions in which
the major proposition or hypothesis is most and least likely to hold.
Hypotheses (or Predictions) are not Theory
Hypotheses can be an important part of a well-crafted conceptual argument. They serve as crucial
bridges between theory and data, making explicit how the variables and relationships that follow from a
logical argument will be operationalized. Hypotheses do not (and should not) contain logical arguments
about why empirical relationships are expected to occur. Hypotheses are concise statements about what
is expected to occur, not why it is expected to occur.
Identifying Strong Theory
Theory is about the connections among phenomena, a story about why acts, events, structure, and
thoughts occur. Theory emphasizes the nature of causal relationships, identifying what comes first as
well as the timing of such events. Strong theory, in our view, delves into underlying processes so as to
understand the systematic reasons for a particular occurrence or nonoccurrence. As Weick (1995) put
it succinctly, a good theory explains, predicts, and delights.
