Expected behavior test is known as a structure-oriented behavior test, which evaluates the validity of the structure of the model by comparing expected outcomes of the real world as
understood by the experts and models outcome patterns in certain cases (Senge 1980;
Barlas 1989). The entire model or a sub-model could be used for expected behavior test
simulation (Barlas 1996). The expected behavior is captured qualitatively as patterns rather
than quantities. Under certain conditions, experts may expect the outcome variable to have
a fall, a rise, a fall followed by a rise, a delayed fall, a delayed rise or oscillation (Carson
and Flood 1990).
Since the final FCM that emerged in this study, consists of 366 concepts, it was neither
practical nor reasonable to run the
expected behavior test on the entire
model. Therefore, the model was broken
into sub-models with a comprehensible
number of concepts with some overlaps,
before being examined.
Inspired by modularity classes suggested
by Gephi as shown in Figure 27, the final
FCM network was broken into 7 sub-
FCM networks as discussed below. (See
Figure 28, Figure 30, Figure 32, Figure
34, Figure 36, Figure 38 and Figure 40).
Figure 27- Modular classes determined by Gephi Modularity logic
115 This approach ensured that the entire model was tested, although part by part, thus validity
of the entire model was practically concluded from the validity of smaller sub-sets that are
intelligible.
Sub-FCM1; From the list of 30 variables of creativity hub as shown in Figure 28, two
variables, “autonomy” and “creative thinking skills” were activated–initial value was set
as +1 and clamped. Expectation was to see the value of individual creativity to increase,
which is what was observed as shown in Figure 29.
Sub-FCM2; From the list of 40 variables of new product development hub as shown in
Figure 30 , two variables, “stage gate approach” and “a visible roadmap” were activated–
initial value was set as +1 and clamped. Expectation was to see the value of new product
development to increase, which is what was observed as shown in Figure 31.
Sub-FCM3; From the list of 94 variables of exploratory-exploitative hub as shown in
Figure 32, two variables, “formalization” and “centralization of decision making” were
activated–initial value was set as +1 and clamped. Expectation was to see the value of
“exploitative innovation” to raise while the value of “exploratory innovation” drops, which
is what was observed as shown in Figure 33.
Sub-FCM4; From the list of 62 variables of contextual ambidexterity hub as shown in
Figure 34, three variables, “trust”, “stretch” and “discipline” were activated–initial value
was set as +1 and clamped. Expectation was to see the value of “contextual ambidexterity”
to increase, which is what was observed as shown in Figure 35.
116
Sub-FCM5; From the list of 45 variables of innovation performance hub as shown in
Figure 36, two variables, “Bottom-up communication” and “high dependency on top
management [for decision making]” were activated–initial value was set as +1 and -1
respectively and clamped. Expectation was to see the value of “knowledge and innovation”
to increase, which is what was observed as shown in Figure 37.
Sub-FCM6; From the list of 40 variables of knowledge hub as shown in Figure 38, two
variables, “contractors risk of failure” and “[Collaboration with] suppliers” were activated–
initial value was set as -1 and +1 respectively and clamped. Expectation was to see the
value of “knowledge and innovation” to increase, which is what was observed as shown in
Figure 39.
Sub-FCM7; From the list of 59 variables of innovation hub as shown in Figure 40, two
variables, “redundancy and slack” and “evaluating methods emphasizing linearity,
efficiency and control” were activated–initial value was set as -1 and +1 respectively and
clamped. Expectation was to see the value of “knowledge and innovation” to increase,
which is what was observed as shown in Figure 41.
In conclusion, all the seven scenarios that were run using the sub-FCMs gave the expected
results, indicating that these are compatible with an a priori understanding of how system
works in the real life. The assumption is that since the behavior of the sub models have
been verified, the model as a whole is also likely yield results consistent with a priori
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