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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 Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
206 Mullany and Lay resistant than others to the analyst’s new systems. All sets of readings for these hypotheses tested normal. The results showed no support for these beliefs (at p = 0.100) either, and they are also rejected by this study. Unfair discrimination of older employees, as suggested by Rosen and Jerdee, might occur in the IS area to older users. Such discrimination could well be motivated by studies such as Bruwer’s, which suggest, inter alia, that older and longer-serving personnel are most resistant to new computer systems. Attention is drawn to the fact that neither the present study nor A-I theory, nor the literature survey by Rosen and Jerdee support these beliefs. Unfair discrimination could certainly cost both individuals and organizations dearly in terms of unnecessary retrenchment and/ or transfer of experienced staff. CONCLUSION Subject to the postulate that the user R-score is a legitimate measure of user resistance, certain causes and impacts of user resistance have been demonstrated by this study. It has been shown that matching analysts and users of similar cognitive styles can minimize user resistance. However, care is suggested in applying such a policy indiscriminately, since there may be disadvantages in so doing despite this study’s failure to show any. The high degree of association between user R-scores and absolute analyst-user KAI score differences permits approximate forecasts of the former given the latter. Two phenomena associated with analyst-user dyads where the analyst is the more innovative, were implied by the results. The first of these is a tendency for the user to view the analyst as brief and confident, seldom wasting time on side issues. The second is (less convincingly) that the user may comment on the unexpected speed with which the analyst comprehends system requirements or specifications. It is suggested that these features be noted as diagnostic signs of the more innovative nature of the analyst, especially in situations where formal testing has not or cannot be conducted. This study finds in favor of the hypotheses that perceived accuracy and reliability of the system are associated with lower user resistance. Consequently, further insights may be gained into a given user-analyst dyad by questioning the user on the accuracy and reliability of a system. For example, the less accurate and less reliable a user considers a system, the more user resistance can be expected, and consequently (from hypothesis HI), the greater the analyst-user cognitive style difference is likely to be. There is no support for the beliefs that the ages and lengths of service of users are associated with user resistance: or for the alternative beliefs that users who differ Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
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Acquisition Editor: Mehdi Khosrow-P
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The Economic and Social Impacts of
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About the Authors 259 Eric Cloete B
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