Necessary condition An example of data analysis

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 assumption that there is a deterministic relation between team size and success which could be formulated as “Team size seven and up is suffi- cient for success” and “Team size lower than six is sufficient for lack of success”. The trend in this data matrix can be formulated as follows: Proposition 3a: The larger the team size, the more likely the success of an innovation project. Table 9.5 supports the existence of a probabilistic relation between the independent concept management commitment and the dependent concept success. However, such a proposition would not add much to propositions 1a and 1b. Similarly, probabilistic relations between infra- structure and success and between investment and success that could be proposed do not add much to propositions 2a and 2b. These examples demonstrate how statistical tests in surveys, which suggest probabilistic relations, could easily hide factually existing deterministic relations. This is the reason why one always needs to look first for deterministic relations in theory-building research, before looking for probabilistic relations.

9.1.9 Outcome

The likely outcome of the discussed analytic procedures consists of one or more propositions. If the relationship between the concepts A and B in the data matrix is more or less random, the study has failed to gen- erate propositions. If this is the case, another perhaps more intensive Table 9.5 Data matrix regarding management commitment Management Success commitment Case 1 H Y Case 2 H Y Case 3 H Y Case 4 H Y Case 5 M Y Case 6 M N Case 7 M N Case 9 M N Case 8 L N Case 10 L N