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200 Mullany and Lay
Table 4: Tests of Hypothesises: Summary of Relevant Statistics
to the innovator’s higher risk operations, and the innovator to motivate potentially
needed changes. To match analysts and users of similar cognitive styles would
deprive the system development effort of this balance. A system developed by two
innovators, for example, would be expected to reach the implementation phase
quickly, but with less groundwork than harder-working adaptors would have done.
Hence debugging after implementation could be extensive. Two adaptors, by
contrast, should take a longer time to implement the system, as they would execute
the analysis and design phases more thoroughly. The debugging effort would thus
be lower. However, a greater enhancing effort would be expected, since certain
novel features which motivated the system’s development are likely to have been
overlooked.
Obviously these conjectures needed testing, and so this study initially attempted
to do so. The mean KAI scores for the analyst and user were used to
measure the extent to which the analyst-user dyads were either two-innovator or
two-adaptor. Associations were then sought between this sample and the length of
implementation time, debugging time and enhancing time. Unfortunately, these tests
had to be abandoned because the data were suspect. Almost none of the analysts
could make a clear distinction between enhancing and debugging times. In fact, 14
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