This chapter explores cognitive problem-solving style and its impact on user resistance, based on the premise that the greater the cognitive difference (cognitive gap) between users and developers, the greater the user resistance is likely to be. Mullany (1989, 2003) conducted an empirical study demonstrating this. This study contradicts the findings of Huber (1983) and supports Carey (1991) in her conclusion that cognitive style theory, as applied to IS, should not be abandoned. Mullany’s findings, in fact, are the opposite. Kirton (1999, 2004) supported Mullany’s results. In particular, Mullany made use of Kirton’s (2004) adaption–innovation theory. The emergent instrument, called the Kirton adaption–innovation inventory (KAI; Kirton, 1999, 2004), was used by Mullany as his measure of cognitive style. Mullany’s study also investigated the relationship between user resistance and user ages and lengths of service in the organisation. It failed to show any relationship between these factors and user resistance. This countermands the findings of Bruwer (1984) and dismisses any intimation that older or longer-serving employees are necessarily more resistant to change as myths.
Ever since the early 1980s, experts have identified user resistance to new systems as an expensive time overhead (see studies by Hirschheim & Newman, 1988, and Markus, 1983). Some authors suggest the greater importance of age and length of service. Bruwer (1984), for instance, claimed to have demonstrated that the older or longer-serving an employee, the more resistant he or she is likely to be to a new computer system. Clarification of issues surrounding user resistance has also highlighted cognitive style theory as potentially important, but to date, its impacts have only been sparsely researched in relation to user resistance, many of the prior studies being open to question. This research, on the other hand, proposes that a system will fail when the developer and user differ significantly in their problem-solving approaches. To reduce user resistance, it thus makes sense to recommend system designs that suit the user’s approach to problem solving.
This issue appears only to have been studied empirically by Mullany (1989, 2003). He formulated the research question, “Is there a relationship between user resistance to a given information system and the difference in cognitive style between the user and the developer?” With the aid of his own instrument for measuring user resistance and the Kirton adaption–innovation instrument (Kirton, 1999) to measure the cognitive styles of users and associated system developers, he found a highly significant relationship between developer–user cognitive style differences and the level of user resistance to systems.
Why no other studies along similar lines have been reported in credible current research is difficult to explain. One possibility is that the literature contains speculative studies, such as that by Huber (1983), that discredit cognitive-style theory as a tool in understanding system success. Other studies, such as that by Carey (1991), while encouraging the continued use of cognitive-style theory in studying system phenomena, do not demonstrate its predictive success in information systems (IS). The remainder of this chapter thus examines the meaning and measure of cognitive style, the measure of user resistance, the specific findings of Mullany (1989, 2003), and outlooks for the future in this area of research.
Key Terms in this Chapter
Adaptor: An adaptor tends to follow traditional methods of problem solving, tending to “do well.” He or she is often seen as “stuck in a groove” ( Kirton, 1999 ).
Innovator: The innovator seeks new, often unexpected, and frequently less acceptable methods. He or she has little regard for traditions, is often seen as creating dissonance, and elicits comments such as, “He wants to do it his own way, not the ‘right’ way” ( Kirton, 1999 ).
R-Score (Resistance Score): A method of measuring user resistance where, at personal interviews with the key user of a given system, the user is asked to list system problems and then to rate the severity of each on a seven-point scale. The sum of severities of all the complaints measures his or her R-score ( Mullany, 1989 ).
KAI (Kirton Adaption-Innovation Inventory): An instrument that measures cognitive problem-solving style. It takes the form of a questionnaire, on which the respondent is asked to rate himself or herself against 32 character traits.
Cognitive Style: An individual exhibits characteristic ways of processing information and, hence, solving problems, known as his or her “cognitive style.”