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Relating Cognitive Problem-Solving Style to User Resistance 187 prompted an examination of studies that had been conducted on resistance in related fields. Resistance to Change in other Areas Rosen and Jerdee (1976) conducted an enquiry into age stereotypes of employees, which suggests that increased resistance to change is a generally believed characteristic of older employees. They found that there was a tendency for younger persons to be considered more receptive to new ideas, and older persons, by implication, more resistant to them. After a further intensive literature survey, however, they found that there was little research evidence to support such beliefs, and concluded that older employees are the potential victims of unjustified discrimination. This study, it should be noted, contradicts the findings of Bruwer . In a study of the impact of manpower-flows on innovation, from which inferences for resistance to change can be drawn, Ettlie (1985) offers support for the premises that innovation is aided by: an influx of new employees (if not too disruptive); the degree to which the organizational structure is decentralized; the complexity of the organization; and the availability of slack resources. Hence, by implication, the same factors must ameliorate resistance to innovation. Cognitive Problem-Solving Styles and Resistance to Change The third area of analysis is that of cognitive problem-solving style and its impact on system success (e.g., e-commerce). If the way in which analysts define and describe tasks can be implicated in the phenomenon of user resistance, then it stands to reason that the cognitive approaches, or problem-solving styles of the analyst and user are also significant. This follows from the fact that definition and description are basic to cognitive information processing. Hence the implication of the semantic gap also suggests the role of cognitive differences between the user and the analyst in user resistance (Zmud, 1983). There have been numerous studies attempting to categorize problem-solving style, such as those of Woodruff (1980) and Lusk and Kersnick (1970), but one of the major research efforts in this area has been performed by Kirton (1976, 1980, 1984, 1985, 1987, 1988) under what he terms Adaption-Innovation theory (Kirton, 1976). Adaption-Innovation theory, Kirton claims, explains in an empirical way many of the anomalies surrounding resistance to change in occupational situations. He proposes that any person can be located on a continuum ranging between two extreme cognitive styles; from an ability to adapt existing technologies (an adaptor) to an ability to use new or different technologies (an innovator). This Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
188 Mullany and Lay proposition, he claims, is relevant to the analysis of organizational change, and offers new insights into the concept of resistance to change. Kirton’s enquiry into the blocking of new initiatives in several large corporate institutions offers a fundamental conclusion regarding resistance to change. In general, a person will exhibit less resistance to ideas put forward by another of similar cognitive style. He further discusses the notion of an “agent for change”, and stresses that it is not necessarily associated with innovation. An agent for change is rather a person “who can successfully accept, and be accepted into, an environment alien to his own”, or ‘’as a competent individual who has enough skill to be successful in a particular environment” (Kirton, 1984). The Kirton Adaption-innovation Inventory (KAI) instrument provides a means for measuring individuals’ cognitive problem-solving styles (Kirton, 1987). These scores are stable, and little variation is reported with time or age. This instrument rates innovators with higher scores than adaptors. These scores have relative as well as absolute significance; hence if A’s KAI score is higher than E’s, it is meaningful to describe A as “more innovative” than E, and B as “more adaptive” than A. As a final point, Huber (1983) in a literary survey concludes that the literature available on cognitive style research in IS does not support a satisfactory basis for recommendations on IS design. Also, he expresses pessimism that this approach will ever provide such guidelines. However, he does recommend cognitive style research in three areas: career counseling, personnel selection and placement, and coaching and training. The second of these has implications for the placing of the most suitable analyst with a given user, or vice versa. This, together with the previous discussion of A-I theory, suggests that the KAI might be used for matching users and analysts successfully. It was thus obvious to select the KAI as the instrument to measure cognitive style in this study. Measures of System Success and User Satisfaction The final research area to be analyzed was that of system success and the related area of user satisfaction. Attempts are found in the literature to identify those attributes of systems that tend to satisfy (and/or dissatisfy) users. In one of the most comprehensive analyses of user satisfaction instruments, Ives, Olson and Baroudi (1983) conducted a psychometric analysis of four “User Information Satisfaction” instruments. These were, Gallagher’s questionnaire, Jenkins and Rickett’s 20-item measure, Larcker and Lessing’s perceived usefulness instrument, and Pearson’s 39-factor instrument. In conclusion, they favoured Pearson’s instrument as the most predictive and exhibiting the greatest construct validity. The shortened version of Pearson’s instrument that they subsequently developed, they considered a prom- 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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