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Relating Cognitive Problem-Solving Style to User Resistance 205
been significant, it is submitted that few of the users responded sufficiently frankly
to give reliable (that is, unbiased) results.
The Roles of System Satisfaction Factors
Hypothesis 3(a) and H3(b) tested significant at p = 0.020. This suggests a
strong negative association between the user’s perception of the accuracy and
reliability of the system, and user resistance. This study thus confirms that accuracy
and reliability are key factors in the issue of user resistance. Insofar as user
resistance and user satisfaction are negatively associated, these results are also in
accordance with the findings of Bailey and Pearson. The higher significance of the
tests for H3(a) and H3(b), together with the rather low value for the correlation coefficients
for H2(a) to H2(d), imply that factors other than cognitive style differences
may play some role in user resistance. However, it must be conceded that the
accuracy and the reliability of the systems were recorded as seen from the point of
view of the user. These assessments, it can be argued, were coloured by the
cognitive styles of the user. For example, an adaptive user might well view an
innovative analyst’s system as a non-conservative, higher-risk tool, in line with the
general adaptor’s views of innovators. Consequently, he would view the system as
less accurate and less reliable. The reverse is as plausible. An innovative user might
view an adaptive analyst’s system as too traditional, failing to encompass all the
novel features that the user believes he needs. Hence, once again, the user may view
the system as less accurate and less reliable. In other words, the significant
associations found for H3(a) and H3(b) actually agree with the predictions of AI
theory.
The Roles of Age and Length of Service
The hypotheses H4(a) and H4(b) support the beliefs that the age and lengths
of service of users are associated with user resistance. These hypotheses were both
rejected at p=0.100. Some doubt may be argued over the result for hypothesis
H4(a), since the ages of users in the sample tested somewhat skew, with
0.050>p>0.020. However, this apparent skewness cannot of itself explain away
a low, distribution-independent correlation, unless it can be shown that the sample
was deliberately biased. It is difficult to see how this was possible in the light of the
research design. Each system investigated was selected without reference to the
user’s age or length of service. This study thus rejects the beliefs that older users,
or users of longer service are more resistant than others to new information systems.
Hypotheses H4(c) and H4(d) examine alternative beliefs; namely, that users
who differ substantially from the analyst in terms of age or length of service are more
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