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Relating Cognitive Problem-Solving Style to User Resistance 203
one-sided forecasts may be preferable on occasion. For example, suppose that
prior to its embarking upon a joint project, an analyst-user dyad exhibits an absolute
KAI score difference of 10. Then any of the following statements, based on the
values given in Table 6, are acceptable:
After implementation of the system,
1) there is a better than 50% (namely 55%) chance that the user R-score will be
at least 7;
2) there is an approximately 80% chance that the user R-score will be at least 5;
and
3) there is an approximately 80% chance that the user R-score will be no more
than 35.
If user resistance constitutes a high-risk, high-penalty overhead, then onesided
forecasts based on the upper confidence limits give a safe but high “worst
case,” while those based on the lower confidence limits, of course, give the reverse.
A difficulty with which an organization is likely to be faced in the forecasting
of user resistance on this basis is the interpretation of the R-scores. Unlike, for
example, the Centigrade temperature scale, the R-score is not a measure with which
people in general are familiar. Fortunately, this problem can be resolved intuitively
by relating the R-scores to the numbers of complaints made in respect of each of
the systems researched. The Pearson correlation coefficient of the numbers of
complaints versus the corresponding R- scores was found to be 0.9126. This
means that a strong, linear relationship (not merely an association) holds between
these variables. Furthermore, the best-fitting regression line passes through (0, 0),
since zero complaints imply a zero R-score. In other words, the numbers of
complaints and the R-scores are in approximate direct proportion. Based on this
finding, the constant of proportionality was estimated by taking the mean of the
ratios of the R-scores to the numbers of complaints. The mean was found to be
3.913. The R-score is thus approximately four times the number of distinct
complaints that the user will make, in confidence, to an independent consultant, in
respect of the system and/or its manner of implementation.
The strength of the association demonstrated for hypothesis HI not only
facilitates a quantitative forecast of user resistance, but also supports the use of the
R-score as a valid measure of user resistance. Insofar as user satisfaction and user
resistance are related, the R-score is also a potential measure of user satisfaction.
Of course this single study, carried out on one, comparatively small sample of
systems, is insufficient to substantiate the R-score’s general use in a positively
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