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Relating Cognitive Problem-Solving Style to User Resistance 189 ising advance in the measure of user satisfaction. In their own research, the five of the thirty-nine factors found to be most significant by Bailey and Pearson (1983) were: accuracy (correctness of output), reliability (dependability of output), timeliness (output available at a time suitable for use), relevancy of output, and confidence in the system. There have been various other studies of user involvement or user satisfaction (Eveland, 1977; Olson & Ives, 1981; Robey & Farrow, 1982; Rushinek & Rushinek, 1986), but the conclusions reached do not corroborate or complement each other. DEVELOPMENT AND STATEMENT OF HYPOTHESES The studies by Lusk and Kersnick, Kirton, and Hirschheim and Newrnan suggest the important role of cognitive styles in occupational situations. As was noted, in general, a person will exhibit less resistance to ideas put forward by another person of similar cognitive style. This in turn justifies the submission that user resistance is associated with differences in developer-user cognitive problemsolving styles. This was the basis of the primary hypothesis of this study. The research also attempted to build on the work of other IS researchers by using the instruments of Bailey and Pearson (1983) and Rushinek and Rushinek (1986), and challenging the conclusions of Bruwer (1984). In the light of the foregoing discussion, this study poses the following central questions: i. During system development, implementation and maintenance, is there a relationship between user resistance and cognitive styles (or cognitive style differences) associated with the analyst-user interface? ii. Can cognitive style theory be used to predict certain general aspects and behaviors of a given analyst-user interface during the development and implementation of an information system? iii. Are there factors related to systems or their manner of implementation that are associated with user resistance? iv. Do the ages and lengths of service of analysts and/or users play a role in user resistance? Consequences of the literature-based discussions above lead directly and indirectly to certain hypotheses regarding the causes of user resistance, which fall into the above categories. Before such hypotheses could be developed, however, a reliable instrument to measure user resistance needed to be selected. Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
190 Mullany and Lay The Choice of an Instrument to Measure User Resistance Had the link between user satisfaction and user resistance been assumed, an instrument which measures user satisfaction could have been chosen to yield a converse substitute measure for user resistance. Pearson’s instrument for measuring user satisfaction has been acclaimed as one of the best in the IS literature to date. However, its length (and even the length of the modified version suggested by Ives, et al.) must raise criticism. If the respondent were to complete and return the form on a voluntary basis, then the sample gathered would tend to contain the more conscientious respondents; namely, those who are prepared to fill in and return lengthy questionnaire forms. Since conscientiousness is claimed by Kirton (1984) to be associated with an adaptive cognitive style, the risk of bias is immediately evident with this questionnaire. An instrument other than a self-report questionnaire of the above type was thus indicated. Ives, et al (1983) developed the notion of substitute or “surrogate” measures for entities not capable of direct measurement themselves. In the light of this and the reservations just expressed, an alternative instrument was devised, which constituted a quantified expression of dissatisfaction to a person independent of the user’s organization. The potential weakness inherent in allowing respondents to complete and return their own forms was removed by collecting data at personal, confidential interviews with the users. The user of each system under investigation was asked to enumerate all the problems that he considered or heard had occurred during the implementation or early life of the system. He was effectively invited, in confidence, to make complaints against the system and/ or its manner of development and implementation. Each complaint was recorded, and then the user was asked to weight his complaints in terms of severity on a seven-point scale (see Appendix). The sum of the weights for each complaint was taken as the user’s “resistance score” or “R-score”. The validity of the R-score is argued on the basis that user resistance can only be exhibited in one of four ways: by what the user says or by what the user does (overt resistance), or by what he doesn’t say or doesn’t do (covert resistance). Anyone (or any combination) of these four phenomena, if measured and quantified, would be expected to provide an observable measure of user resistance. Furthermore, as an independent party is not part of the political structure of the organization, comments made to him about the system are likely to be more objective than hearsay filtering back to the management. However, there are two issues that require clarification. Firstly, there is the question of whether or not overt user dissatisfaction, expressed in the form of complaints, is a legitimate measure of user resistance. Both the studies by Hirschheim and Newman (1988) and Markus (1983) suggest a significant associa- 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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