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Relating Cognitive Problem-Solving Style to User Resistance 207<br />
substantially from the analyst in terms of age or length of service are more resistant<br />
than others to new systems. Since neither the present study nor A-I theory, nor the<br />
literature survey by Rosen and Jerdee support these beliefs, organizations should<br />
be alerted to the possibility of unfair discrimination against older and more<br />
experienced users.<br />
While some discussion of user resistance exists in the literature, no direct,<br />
quantitative measure of this phenomenon had previously been attempted. This led<br />
to the development of the R-score, which is a direct measure of resistance in terms<br />
of observable complaints about the system, and which is significantly associated<br />
with the user’s level of resistance to that system.<br />
Prior to this study, neither had Adaption-innovation theory been applied to IS<br />
development, nor had the KAI instrument been replicated in the IS field. With the<br />
aid of these, it has been shown that matching users to analysts of similar cognitive<br />
style can minimize user resistance. Furthermore, by prior administration of the KAI<br />
instrument to analysts and users, approximate forecasts of user resistance are now<br />
possible. In other words, both A-I theory and the KAI instrument have been shown<br />
to be valuable tools in assessing, understanding and forecasting user resistance (see<br />
Table 6).<br />
This study lends some support to the method used by Bailey and Pearson<br />
(1983) to measure user satisfaction and IS success, in that the two most significant<br />
factors found by them to satisfy users, were also found to be negatively associated<br />
with user resistance (measured as R-scores) in this study. These factors were<br />
system accuracy and reliability. This further suggests that low user resistance is<br />
indeed associated with high user satisfaction, confirming that resistance and<br />
satisfaction can be used as inverse, surrogate measures for one another. The user’s<br />
R-score in the post-implementation phase is thus indicated as a possible measure<br />
of system success. However, more research would be required to ensure that it is<br />
not also significantly dependent upon the cognitive style of the investigator: a factor<br />
that would preclude its use as a standard measure. The speed with which the Rscore<br />
can be assessed, though an interviewing technique, would make it an<br />
attractive option to the more protracted Pearson-type questionnaire for measuring<br />
system success.<br />
AREAS FOR FURTHER RESEARCH<br />
As previously mentioned, there may be a dependence of the R-score on the<br />
cognitive style of the investigator since the R-score is determined as a result of his<br />
interaction with the respondent at an interview. In a further study, the effect of the<br />
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