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