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H 1
time spent talking
time spent
on problem
Xie et al 2008
Schaufeli 2002
engagement
time spent
thinking
H 2
activation
creates
fun
Schaufeli 2002
H 3
H 4
motivation
committed to
solution
effective
elicitation
identification
H 5
creates
H 6
H 7
reviews
feedback
corrections
validated
results
Schneider 2007
H 8
H 9
clear goal
competencies
insights into
process thinking
Fig. 2. Effective elicitation decomposed into nine hypotheses
we split up the time into more fine granular observations. We hypothesize that
people will spent more time talking ( H 1 ) about the process but also spent more
time to think ( H 2 ) about what they do.
Schaufeli segments engagement into the dimensions activation and identifica-
tion [19]. While activation is already measured with the time spent on the task,
we additionally hypothesize that people have more fun ( H 3 ) as a further aspect
of activation. The dimension of identification inspires us to hypothesize that
people modeling with t.BPM will have more motivation ( H 4 ) to accomplish the
task and will be more committed to the solution( H 5 ) that they shaped.
The second key aspect that we see for effective elicitation is a validated result.
Schneider [20] points out that validation cycles are a time consuming aspect
of requirements elicitation projects. He proposes to create a model during the
elicitation to trigger instant feedback and speedup validation. Validation cycles
are characterized by reviews and adjustments to the model. We hypothesize that
people will do more reviews ( H 6 ) when using t.BPM and apply more corrections
( H 7 ) to their process model.
Finally, validation in model building depends on the competencies of the par-
ticipants. Frederiks [7] proposes that users validate models by deciding on the
significance of information. We propose that this depends on a clear understand-
ing of the modeling goal. We hypothesize that t.BPM provides a clearer goal ( H 8 )
for the elicitation session. Frederiks also proposes that modeling experts guide
the validation by grammatical analysis, in other words their modeling knowledge.
We hypothesize that tangible modeling can create insights into process thinking
( H 9 ) for the user and thereby support the validation process.
We note that the hypotheses are not a forced consequence of the identified
aspects and their building involved interpretation. We come back to this decom-
position when we assess the measurement validity in Section 5.3.
3.2 Experiment Setup and Sampling Strategy
We design the following experimental setup as illustrated in Fig. 3. Subjects get
first conditioned to a certain level of BPM understanding. Afterwards they are
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