Deterministic relation Data analysis

9.1.7.4 Probabilistic relation

Finally, assess whether there is evidence for a probabilistic relation, mean- ing that an increase or decrease in the value of concept A results in a higher chance of an increase or decrease in the value of concept B. The existence of a probabilistic relation can, again, be assessed by rank ordering the cases in the data matrix in accordance with the value of concept A. If, in the resulting rank order, the value of concept B seems also to increase or decrease, though not consistently, then this can be taken as evidence that A and B have a probabilistic relation. The same criteria for assessing whether the probabilistic relation actually exists between A and B in this data set apply as discussed in Chapter 7 for the testing of a probabilistic relation. This relation can then be formulated as followed: Proposition 4: Concept A has a probabilistic relation with concept B. If correctly derived from the data and, thus, proven to be true in the selected cases, the proposition is an appropriate result of the theory- building case study.

9.1.8 An example of data analysis

The following invented example of a data matrix generated in a theory-building study of factors that determine the success of innovation projects, shows ten very diverse cases, five with success and five without success Table 9.1. The table has ten rows, one row for each case, four independent concepts or “success” factors and the dependent concept absence or presence of success.

9.1.8.1 Sufficient condition

A sufficient condition exists if a specific value of concept A always results in a specific value of concept B. In this data matrix, we have four potential success factors and each value of each of these factors could be a sufficient condition for a specific value Yes or No of success. If we look at all four cases with value high on management com- mitment, we see that they all have been successful, whereas the two cases with low levels of management commitment have been unsuccessful. A high level of management commitment, thus, seems to be a sufficient condition for success in this invented example, and a low level of management commitment seems to be a sufficient condition for lack of success. The resulting propositions, thus, are: Proposition 1a: High management commitment is a sufficient condition for success of innovation projects. Proposition 1b: Low management commitment is a sufficient condition for lack of success of innovation projects. If these propositions are true, then it is clear how an innovation project could be made successful. However, these propositions have been built in this invented theory-building case study, and only initially tested. If we continue our inspection with other potential success factors, we see that all three cases with a low value on the concept infrastructure have not been successful. This might lead to the formulation of a third proposition: Proposition 1c: Low infrastructure is a sufficient condition for lack of success of innovation projects. In the same way we could formulate further propositions about team size three being sufficient for lack of success, and team size seven being a sufficient condition for success. But these latter propositions seem to make little sense without additional propositions about the effects of other values of team size. Table 9.1 Data matrix regarding “success” factors of innovation projects Management Infrastructure Investment Team size Success commitment in money Case 1 H H H 10 Y Case 2 H H H 7 Y Case 3 H H H 7 Y Case 4 H H L 6 Y Case 5 M H L 4 Y Case 6 M L L 11 N Case 7 M H L 6 N Case 8 L H L 6 N Case 9 M L L 3 N Case 10 L L L 3 N H⫽high; M⫽medium; L⫽low; Y⫽yes; N⫽no