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Relating Cognitive Problem-Solving Style to User Resistance 185 INTRODUCTION There are conflicting views regarding the influence of cognitive style on user resistance. Hirschheim and Newman (1988) 1 , for example, note that a user may resist a system because the mode of presentation of that system does not match the user’s cognitive style. In other words, the system does not follow a problem-solving sequence that is totally acceptable to the user. Huber (1983), however, deduced from his own literary survey that cognitive style theory had not, and probably would never, provide guidelines for system design. No<strong>net</strong>heless, a careful analysis of Huber’s study reveals that it merely rejects attempts to match a system to a given user’s cognitive style. It does not reject attempts to match the systems developer cognitively with a given user. On the contrary, Huber’s study encourages the use of cognitive style theory for personnel selection and placement purposes. Since little direct evidence was found in the IS literature of previous attempts to match systems developers and users cognitively, it was decided to investigate user resistance along this more novel line. The purpose of this study, then, was to test relationships between user resistance to computer-based systems, including those commonly associated with e-Commerce and cognitive style. This term, or the alternative, cognitive problemsolving style is used to denote an individual’s approach to problem solving. The Kirton Adaption-innovation Inventory (KAI) was selected as an instrument to measure cognitive styles. The KAI, based on Adaption-innovation (A-I) theory, measures an individual’s preference for either an “adaptive” or “innovative” approach to problem solving. Adaptive problem-solvers tend to follow prescribed and traditional methods, whilst innovators seek new and often unexpected solutions. These concepts will be expanded on later. SUMMARY OF PRIOR RESEARCH The study covered four areas of prior research. These are: IS user resistance to new systems; resistance to change in other areas not necessarily related to IS (e.g., e-commerce); the influence of cognitive problem-solving styles on resistance to change; and measures of system success and their application. Since ecommerce success is largely a matter of user opinion, the aspect of user satisfaction and its measurement is covered under the discussion of the last area. A link is claimed between user satisfaction (or dissatisfaction) and user resistance, thus the possibility of using user satisfaction as a converse substitute measure of user resistance is considered. Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
186 Mullany and Lay User Resistance - IS Studies and E-Commerce Only five studies of significance were found in the IS literature: those of Keen (1981), Hirschheim and Newman (1988), Bruwer (1984), Markus (1983) and De Brabander and Thiers (1984). The studies of both Keen and Hirschheim and Newman are literature surveys. The former concludes that new systems that represent radical change, as opposed to those that cause incremental or evolutionary change, will be avoided or resisted. Also, since the redistribution of data caused by a new information system is a political resource, the interests of certain groups will be affected. In the latter, the authors define resistance as an adverse reaction to a proposed change, which may be overt or covert. They suggest that the impact of user resistance emerging during implementation may take any of the following forms: low productivity, low effectiveness, high labour turn- over, disputes, absenteeism, psychological withdrawal, aggression, sabotage of machinery, the system being blamed for all difficulties experienced (including incorrect data entries), and lack of management support for the system. Bruwer (1984) studied resistance to computerization in a single organization, where 140 computerized systems, used by about 1,200 clerical staff and 114 managers, were investigated. He claims that older managers with longer experiences are more negative towards computerization than are younger ones. Markus (1983) identified three general theories explaining user resistance from the IS literature, which she then assessed in the light of a single case study. The following were identified as causing resistance: internal individual or group factors, such as a non-analytic cognitive style; factors inherent in the system under implementation, such as technically deficient systems or systems which are not userfriendly; and certain interactions between factors inherent in the user and others intrinsic to the system. In the main, user resistance behaviors took the form of complaints against the system that were considered unfair. This, it will be noted, corroborated the behaviors identified by Hirschheim and Newman. Finally, De Brabander and Thiers (1984) studied certain defective implementation behaviors in the form of not adhering to plans, which resulted in reduced efficiency of task-accomplishment amongst users. They concluded that the reason that their users did not adhere to plans was that they were subject to the sanctioning powers of the corresponding IS specialists. This effect, they noted, was enhanced by the presence of a semantic gap that they define as the employment of differing conceptual definitions for aspects of the same task. As can be seen from the above, there has been little of a definitive nature in the IS literature pertaining to user resistance. None of the work described above attempted to associate user resistance with any aspects of cognitive style. This 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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