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Summary Research & Skills For MSc BA (EBM050A05)

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Sutton, R. I., & Staw, B. M. 1995. What theory is not. Administrative Science Quarterly, 40(3): 371-384. Gephart, Jr., R. P. 2004. Qualitative research and the Academy of Management Journal. Academy of Management Journal, 47(4): 454-462. Pratt, M. G. 2009. For the lack of a boilerplate: Tips on writing up (and reviewing) qualitative research. Academy of Management Journal, 52(5): 856-862. Song, M., Im, S., van der Bij, H., & Song, L. Z. 2011. Does strategic planning enhance or impede innovation and firm performance? Journal of Product Innovation Management, 28(4): 503-520. 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(1): 45-55.

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RESEARCH & SKILLS READINGS
1 – SUTTON & STAW (1995)
WHAT IS THEORY NOT?

 identify some common reasons why papers are viewed as having weak theory
We explain why some papers, or parts of papers, are viewed as containing no theory at all rather than
containing some theory

PARTS OF AN ARTICLE THAT ARE NOT THEORY  ALSO IN LECTURE 1

1. REFERENCES ARE NOT THEORY
Listing references to existing theories & mentioning the names of such theories is not the same as
explicating the causal logic they contain
 no theory because no logic is presented to explain why aggression provokes “fight” or why
anger is contagious
References are sometimes used like a smoke screen to hide the absence of theory
Need to do: enough of the pertinent logic from past theoretical work should be included so that the
reader can grasp the author’s logical arguments

2. DATA ARE NOT THEORY
Difference between data and theory
 Data = describes which empirical patterns were observed
 Theory = explains why empirical patterns were observed/ are expected to be observed
Using a series of findings, instead of a blend of findings & logical reasoning, to justify hypotheses is
especially common
 prior findings themselves motivate hypotheses & the reporting of results cannot substitute for
causal reasoning
“That data do not generate theory – only researchers do that.”

3. LISTS OF VARIABLES OR CONSTRUCTS ARE NOT THEORY
Comparative tests of variables shouldn’t be confused with comparative tests of theory, BUT because a
predicted relationship must be explained to provide theory, simply listing a set of antecedents doesn’t
make a theoretical argument
 key issue is why a particular set of variables are expected to be strong predictors

4. DIAGRAMS ARE NOT THEORY
Helpful figures show
 causal relationships in a logical ordering so that readers can see a chain of causation
 or how a 3rd variable intervenes in or moderates a relationship
 or temporal diagrams showing how a particular process unfolds over time

1

, diagrams as stage props rather than the performance itself
 most likely will need to explain the why verbally!
Good theory is normally representational + verbal
Indication of a strong theory: possible to discern conditions in which the major proposition/
hypothesis is most & least likely to hold

5. HYPOTHESES (OR PREDICTIONS) ARE NOT THEORY
Telltale signs that a paper presented hypotheses instead of theory
1. There may be so many hypotheses that none can be adequately explained or motivated
2. When the introduction of a paper ends with a long list of hypotheses, a table of predictions, or
a summarizing figure
Papers with strong theory
 often start with 1-2 conceptual statements & build a logically detailed case
 they have both simplicity & interconnectedness

IDENTIFYING STRONG THEORY  ALSO IN LECTURE 1

Theory Is the answer to queries of WHY?
 About connections among events, story about why acts, events, structure & thoughts occur
 Emphasizes the nature of causal relationships, identifying what comes first as well as the
timing of such events
 Understand the systematic reasons for a particular occurrence/ non-occurrence

THE CASE AGAINST THEORY

more important to isolate a few successful change efforts (those that show consistent positive results)
than it is to understand the causal nuances and underlying any particular outcome
Depends on Journal
1. Journal of Applied Psychology & Personnel Psychology
normally brief reviews of literature along with simple listings of hypotheses
more attention paid to describing the methods, variables, data analysis techniques & findings
 usual reasons for rejecting is that data does not adequately fit the hypotheses or there is a
fatal flaw in the study design
2. Research in Organizational Behaviour
theory development as primary solution
 if data is presented, more used for illustrating rather than testing a theory
Problem: organizational researchers are primarily trained in data collection techniques & the latest
analytical tools – not the nuances of theory building

ARE WE EXPECTING TOO MUCH?

Problem with theory building may be structural: journals could be placing authors in a double blind
1. Editors & reviews plead for creative & interesting ideas, for there to be an important
contribution to organizational theory
1. skewered 2. Authors are for apparent mismatches between their
theory and data
 Author is careful to avoid mentioning variables/ processes that might tip off the reviewers &
editors that something is missing in the article

2

, RECOMMENDATIONS

Issue: few papers are considered strong in both theory & method; journals are forced to make implicit
trade-offs on these dimensions to fill their pages
Recommendation: rebalance selection process between theory & method. People’s natural
inclination is to require greater proof of new/ provocative idea than one they already believe to be true
 Major contributions can be made when data are more illustrative than definitive
Recommendation: if theory building is a valid goal, then journals should be willing to publish papers
that really are stronger in theory than method
In many ways, our journals have already been imposing these proposed standards on qualitative as
opposed to quantitative research. The prevailing wisdom has been that qualitative research is more
useful for theory building than theory testing
 perhaps standards used to judge qualitative papers have the opposite drawbacks of those used for
quantitative papers, with theory emphasized too much and data not emphasized enough


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