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Step 2
target-function
TF
j
,
j
= 1, …,
n
, and with
U
h
(
t
)
[
TF
j
],
h
= 1, …,
g
[
TF
j
] the estimated degrees of
importance of requirements
RE
h
[
TF
j
],
h
= 1, …,
g
[
TF
j
] at the moment
t
. There are denoted with
PC
f
[
TF
j
],
f
= 1, …,
d
[
TF
j
] the performance char-
acteristics related to the target-function
TF
j
,
j
=
1, …,
n
. The estimated value weights of these
performance characteristics, at the moment
t
,
are denoted with
V
f
(
t
)[
TF
j
],
f
= 1, …,
d
[
TF
j
] and
calculated with the formula:
In order to meet the business objectives, a set
of target-functions is further formulated
TF
=
{
TF
1
,
TF
2
, …,
TF
n
}, where
n
is the number of
target-functions. To see the consistency of the
target-functions with the business objectives, a
relationship matrix is worked out. It relates the
business objectives (located on the rows of the
matrix) with the target-functions (located on the
columns of the matrix) by means of the so-called
relationship coefficients (located at the intersec-
tions between the rows and the columns of the
matrix). The following categories of relationships
could exist: 0-no relationship; 1-weak/possible
relationship; 3-medium relationship; 9-strong
relationship; 27-very strong/critical relationship
(Brad, 2008). Along each column, at least a strong
relationship must exist such as the corresponding
target-function to be relevant. The estimated value
weight
W
j
(
t
),
j
= 1, …,
n
, at the moment
t
, of each
target-function is calculated with the formula:
g TF
[
]
j
∑
1
V t TF
( )[
]
=
U t TF b TF
( )[
]
⋅
[
],
f
=
1
,
d TF
[
],
j
=
1
,
n
f
j
l
j
lf
j
j
l
=
(2)
where
b
lf
[
TF
j
],
f
= 1, …,
d
[
TF
j
] are the relationship
coefficients of the planning matrix correspond-
ing to the target-function
TF
j
,
j
= 1, …,
n
. The
value weights
V
f
(
t
)[
TF
j
],
f
= 1, …,
d
[
TF
j
] give
a perspective of the most relevant performance
characteristics in relation to the web-based course
with respect to the target-function
TF
j
,
j
= 1, …,
n
, at the moment
t
.
m
∑
W t
( )
=
R t a
( )
⋅
,
j
=
1
,
n
,
(1)
j
k
kj
k
=
1
Step 4
The correlations between performance charac-
teristics to the level of each target-function are
identified. The negative correlations are extracted.
A negative correlation between two performance
characteristics occurs when attempting to im-
prove one of the performance characteristics a
harmful effect is generated upon the other one.
Where negative correlations occur, innovation is
required to define mature solutions of the web-
based course. This kind of innovation is called in
this paper “performance characteristic-oriented
innovation.” TRIZ method is applied at this step
for each negative correlation in order to define
vectors of innovation of the web-based course.
where
a
kj
,
k
= 1, …,
m
;
j
= 1, …,
n
are the relation-
ship coefficients between the business objectives
and the target-functions. It is simple to deduce that
designing for life-cycle of a web-based course
requires consideration of various value weights of
the target-functions at various life-time moments.
Step 3
For each target-function, requirements are for-
mulated and ranked. For ranking, tools like AHP
could be also used. Further, measurable perfor-
mance characteristics are defined and related to
requirements. There are denoted with
RE
h
[
TF
j
],
h
= 1, …,
g
[
TF
j
] the requirements related to the
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