Sufficient condition An example of data analysis

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

9.1.8.2 Necessary condition

A necessary condition exists if a specific value of concept B only exists if there is a specific value of concept A. In this data matrix, we have two values Yes or No of success and, therefore, we can see whether one or more of the potential success factors have the same value in each of the successful cases Table 9.2 and, next, whether one or more of the potential success factors have the same value in each of the unsuccess- ful cases Table 9.3. We can see in Table 9.2 that only infrastructure has the same high value in all five successful projects. We can formulate this finding as follows: Proposition 2a: A high value of infrastructure is a necessary condition for success of innovation projects. In the same way, we see in Table 9.3 that all five unsuccessful projects have a low level of investment. We can formulate this finding as follows: Proposition 2b: A low level of investment is a necessary condition for lack of success of innovation projects. Table 9.2 Data matrix regarding successful 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 Table 9.3 Data matrix regarding unsuccessful innovation projects Management Infrastructure Investment Team size Success commitment in money 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

9.1.8.3 Deterministic relation

A deterministic relation entails that an increase or decrease in the value of concept A consistently results in a change in a consistent direction in the value of concept B. This type of relation, thus, assumes that both the independent and the dependent concept have more than two values and these values have a rank order. There is one inde- pendent concept that has more than two values in a rank order man- agement commitment, but the only dependent concept success has only two values. Therefore, we cannot identify a candidate determinis- tic relation in this data matrix.

9.1.8.4 Probabilistic relation

A probabilistic relation entails that an increase or decrease in the value of concept A results in a higher or lower chance of an increase or decrease in the value of concept B. The existence of a probabilistic relation can 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. In this data matrix, we can perform this procedure for all four independent concepts. Table 9.4 supports the existence of a probabilistic relation between team size and success. Only two cases case 5 and case 6 violate the Table 9.4 Data matrix regarding team size Team size Success Case 6 11 N Case 1 10 Y Case 2 7 Y Case 3 7 Y Case 4 6 Y Case 7 6 N Case 8 6 N Case 5 4 Y Case 9 3 N Case 10 3 N