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categories. For example, in our prior example, the results might have
been:
Category
Relative Weight
Innovation and learning
0.32
Internal business processes
0.25
Customer
0.21
Financial
0.22
Tot a l
1.00
The results show that the participants believe that the most important
category is innovation and learning. If, within the innovation and learn-
ing category, it is determined that the market-share metric is the most
important, with a local weight of 0.40, then we can calculate the global
outcome by multiplying the local decision weights from level 1 (categories)
by the local decision weight for level 2 (metrics).
Clinton, Webber, and Hassell (2002) provide a good example of the final
calculation, as shown in Table 3.6.
These results indicate that the least important metric is revenue from
the customer category and the most important metric is market share,
from the innovation and learning category.
A study by Utunen (2003) determined the following priorities for
financially based technology measurement: commercialization of tech-
nology, customer focus, technology stock, technology protection, tech-
nology acquisition, competence of personnel, and management focus.
For each indicator, one or more metrics were established, as shown in
Table 3.7.
Phillips (1997) contends that ROI (return on investment) calculation is
not complete until the results are converted to dollars. This includes look-
ing at combinations of hard and soft data. Hard data include such tradi-
tional measures as output, time, quality, and costs. In general, hard data
are readily available and relatively easy to calculate. Soft data are hard to
calculate and include morale, turnover rate, absenteeism, loyalty, conflicts
avoided, new skills learned, new ideas, successful completion of projects,
etc., as shown in Table 3.8.
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