Propositions that express a deterministic relation

Concept A in a deterministic relation can be forced or “recoded” into a condition by specifying a cut-off point that dichotomizes this concept. For values below the cut-off point, condition A is considered to be absent; for values above the cut-off point condition A is con- sidered to be present. In a similar way, the effect concept B can be forced into a dichotomous concept.

4.2.4 Propositions that express a probabilistic relation

Propositions that express a probabilistic relation between concept A and concept B can be formulated as: If A is higher, then it is likely that B is higher. A probabilistic relation is a relation in which both A and B on average increase or decrease at the same time. It is assumed that A causes B. A probabilistic relation can be visualized as a scatter plot of instances of the object of study of interest, as shown in Figure 4.4, which, on the average, illustrates an increase in concept B due to an increase in concept A. Note that there can be pairs of instances in which A increases and B decreases, which would not be possible in a deterministic relation. In our example this could be formulated as: “If there is more top man- agement commitment, then it is likely that the innovation project is more successful”. Probabilistic relations between A and B can be on 70 Concept A Concept B Figure 4.3 Scatter plot of instances indicating a continuous increasing deterministic relation between concept A and concept B average increasing, or decreasing, continuously, or not continuously, depending on the proposition. Also note that we do not mean that vari- ation as shown in Figure 4.4 is due to “measurement error”. Figure 4.4 depicts the variation of the actual values of the concepts of the object of study. These actual values are interpreted as a representation of an underlying, “realistic”, probabilistic relation.

4.3 Business relevance of propositions

We have presented two different types of propositions: deterministic propositions and probabilistic propositions. We consider deter- ministic propositions as “stronger” than probabilistic ones because they explain more and sometimes all variation in a dependent con- cept and, therefore, can often predict effects in individual instances. Deterministic propositions make the theory more powerful. Further- more, deterministic propositions if supported in many replications are very useful for practitioners. An insight that tells you how to act or not to act in order to create a “critical” condition for success or for the absence of failure is often more useful in managerial practice than an insight that tells you how to increase the likelihood of success. This is not to say that absolute certainty about an effect can be achieved, but an “almost certainty” see Box 8 is a powerful ground for decision making. The distinction between deterministic conditions and probabilistic relations reflects two different types of knowledge that managers might need for their decision making. Typically, managerial problems Chapter 4 Concept A Concept B Figure 4.4 Scatter plot of instances indicating a probabilistic relation between concept A and concept B