Then the proposition could be formulated, for example, as a deter- ministic condition e.g. a necessary condition, and the combination
of factors is then the independent concept, which is a necessary condi- tion for the effect. The complex model is reduced to again the simple
model and testing the propositions is straightforward. Chapter 4
Box 10 More complex conceptual models
Our book focuses on relatively simple causal relations in which one concept causes another concept, as shown in Figure A below.
However, more complex models are possible as well. For example, it is possible that
concept A has an effect on B via another “intervening” or “mediating” concept. A medi- ating
concept is a concept that links the independent and the dependent concept in a proposition and which is necessary for the causal relation between the independent
and the dependent concept to exist. This is shown in Figure B. First A affects C and then C affects B. Separate propositions can be formulated and tested about the relation
between A and C, C and B, and A and B.
It is also possible that a concept C has a moderating effect on the relation between
A and B. A moderating concept is a concept that qualifies the relation between the independent and the dependent concept in a proposition. For example, the relation
between A and B only exists or is stronger if C has a certain value. This is shown in
Concept A cause
Concept B effect
Concept A cause
Concept C mediator
Concept B effect
Figure A
Simple causal relation between concept A
and concept B
Figure B
A causal relation with the mediating also
called intervening effect of concept C
Figure C. The propositions can be formulated and tested in terms of the effect of A on B for different values of C.
Other possible conceptual models have more than one causal factor or more than one effect. This is shown in Figures D and E, respectively. If there are more causal fac-
tors, the proposition can be formulated in terms of combinations of factors that must be present in order to have an effect. If there are more effects, the proposition can be
formulated such that the causal factors can have more than one effect.
Concept A cause
Concept B effect
Concept C moderator
Concept A cause
Concept B effect
Concept C cause
Concept A cause
Concept B effect
Concept C effect
Figure C
A causal relation with the moderating
effect of concept C on the relation
between A and B
Figure D
A causal relation with more than
one causal factor
Figure E
A causal relation with more than
one effect
4.5 Outcome and implications
Testing consists of comparing the “facts as observed” in the instances studied with the expectations formulated in the hypothesis, which is
derived from the proposition. This “observation of facts” is called measurement, which itself consists of the collection of data and the
coding of these data. The result of these two procedures is a score that represents the value of a concept in the observed instance of the object
of study see Appendix 1 “Measurement” for a further discussion.
In this book we do not discuss how a hypothesis is tested in a statis- tical analysis. We will discuss qualitative analysis as applied in theory-
testing case study research in Chapters 5–7. The result of a test is either a confirmation of the hypothesis or a rejection. Both a confirmation
and a rejection require an interpretation of what is the most likely explanation of the outcome.
■
Is it possible that the outcome is not correct because of methodological or practical limitations and errors?
■
Does the outcome require rethinking and possibly reformu- lation of the proposition?
■
Does the outcome require a reformulation of the boundaries of the domain of the theory?
A common-sense idea of a scientific test is that the desired outcome is always a confirmation of the expectation, meaning that the theory is cor-
rect. This is true in the sense that the aim of theory development is to build correct statements about the object of study and that, therefore,
it is hoped that the theory is able to produce correct expectations, par- ticularly when it is fully established and specified after a long process of
development. However, from the viewpoint of theory development, a confirmation of a hypothesis is not stimulating for further improve-
ment and specifying of the current theory, particularly in “most likely” instances in which it was expected to find a confirmation anyway. If the
theory is not yet fully developed, it is hoped that new instances of the object of study will be found, in which the theory does not seem to
hold, because such rejections of the theory stimulate revisions.
Theory-testing, thus, is not only a strategy for confirming a hypothesis but is also a way by which one aims to learn more about the object of
study by identifying instances in which the hypothesis as presently formulated is rejected. This means that one purposively tries to find
confirmations in “least likely” instances in which an outcome either a confirmation or a rejection is expected to be productive in terms of
theory development. Chapter 4
After a hypothesis is confirmed or rejected in one study “one- shot”, replications are needed in order to enhance the robustness and
the generalizability of the proposition. A replication strategy must be formulated in accordance with the researcher’s answers to such ques-
tions as listed above regarding the outcome of the previous test. Testing of propositions by replication follows the same procedures as
initial testing of propositions discussed above.
4.6 Summary
Theory-testing research is testing a proposition of a theory by confirm- ing or rejecting a hypothesis that is derived from that proposition in an
instance of the object of study or in a group of instances or a popula- tion. After a hypothesis is confirmed or rejected in one study “one-
shot”, replications are needed in order to enhance the robustness and the generalizability of the proposition.
Four types of proposition can be distinguished: a sufficient condi- tion If there is A, then there will be B, a necessary condition B exists only
if A is present, a deterministic relation If A is higher, then B is higher, and a probabilistic relation If A is higher, then it is likely that B is higher.
Many business and management problems are formulated as necessary conditions, but most business research tests probabilistic relations.
We argue that the experiment is the preferred research strategy for testing all types of proposition. In an experiment the independent
concept A is manipulated and its effect on the dependent concept B is investigated. Confirmation in a well-conducted experiment is strong
evidence for the existence of a causal relation. However, in the actual practice of business research, it is often not possible to create experi-
mental conditions. If experimental research is not feasible, survey research is a good alternative for testing a probabilistic relation and
case study research is a good alternative for testing deterministic con- ditions or relations.
The survey is the second-best research strategy for testing a prob- abilistic relation. In the survey, a sample of a population is selected for
the test, and a statistical analysis is conducted in order to test for proba- bilistic relations between the independent and dependent concepts.
The survey is the third-best strategy for testing deterministic relations.
Despite the widespread belief that case study research is not an appro- priate research strategy for theory-testing, we show that the case study
is the second-best research strategy for testing deterministic relation. The single case study is the second-best strategy for testing a sufficient
