Knowledge that Transforms

This paper reports on the development of an instrument designed to measure the various perceptions that an individual may have of adopting an information technology (IT) innovation. This instrument is intended to be a tool for the study of the initial adoption and eventual diffusion of IT innovations within organizations. While the adoption of information technologies by individuals and organizations has been an area of substantial research interest since the early days of computerization, research efforts to date have led to mixed and inconclusive outcomes. The lack of a theoretical foundation for such research and inadequate definition and measurement of constructs have been identified as major causes for such outcomes. In a recent study examining the diffusion of new end-user IT, we decided to focus on measuring the potential adopters' perceptions of the technology. Measuring such perceptions has been termed a “classic issue” in the innovation diffusion literature, and a key to integrating the various findings of diffusion research. The perceptions of adopting were initially based on the five characteristics of innovations derived by Rogers (1983) from the diffusion of innovations literature, plus two developed specifically within this study. Of the existing scales for measuring these characteristics, very few had the requisite levels of validity and reliability. For this study, both newly created and existing items were placed in a common pool and subjected to four rounds of sorting by judges to establish which items should be in the various scales. The objective was to verify the convergent and discriminant validity of the scales by examining how the items were sorted into various construct categories. Analysis of inter-judge agreement about item placement identified both bad items as well as weaknesses in some of the constructs' original definitions. These were subsequently redefined. Scales for the resulting constructs were subjected to three separate field tests. Following the final test, the scales all demonstrated acceptable levels of reliability. Their validity was further checked using factor analysis, as well as conducting discriminant analysis comparing responses between adopters and nonadopters of the innovation. The result is a parsimonious, 38-item instrument comprising eight scales which provides a useful tool for the study of the initial adoption and diffusion of innovations. A short, 25 item, version of the instrument is also suggested.

We examined 155 information systems research articles published from 1983 to 1988 and found that although this research is not rooted in a single over-arching theoretical perspective, it does exhibit a single set of philosophical assumptions regarding the nature of the phenomena studied by information systems researchers, and what constitutes valid knowledge about those phenomena. We believe that a single research perspective for studying information systems phenomena is unnecessarily restrictive, and argue that there exist other philosophical assumptions that can inform studies of the relationships between information technology, people, and organizations. In this paper, we present two additional research philosophies for consideration-the interpretive and the critical-and for each we provide empirical examples to illustrate how they are used. We conclude by suggesting that much can be gained if a plurality of research perspectives is effectively employed to investigate information systems phenomena.

From a broad perspective, our research can be viewed as investigating the fit of technology to task, the user's view of the fit between technology and task, and the relative importance of each to problem-solving or decision-making performance. The technology investigated in this research is the mode of information presentation. Although there has been a considerable amount of research into problem solving using graphs and tables, until recently the circumstances in which each is more effective have been largely unresolved. Recent research has suggested that performance benefits accrue when cognitive fit occurs, i.e., when factors such as the problem representation and problem solving tools match the characteristics of the task. In this paper, we investigate the effects of the basic paradigm of cognitive fit and extensions to the paradigm in a laboratory experiment that examined the nature of subjects' mental representations as well as problem-solving performance. The experiment, using 128 MBA students in two identical, repeated measures designs, produced the following results: • Performance improved markedly for symbolic tasks when the problem representation matched the task. Performance effects also resulted from matching specific problem-solving skills to the problem representation and the task, and to a lesser extent when the skills matched the task alone. • The incremental effects of matching skills to the problem representation and/or the task were small compared with the primary effects of cognitive fit-that of matching problem representation to task. • A large proportion of problem solvers have insight into the concept of supporting tasks with certain types of problem representation and vice versa. • Participants preferred to use tables rather than graphs; they also preferred to solve symbolic rather than spatial problems. Finally, the problem representation more significantly influenced the mental representation than did task conceptualization. This research suggests that providing decision support systems to satisfy individual managers' desires will not have a large effect on either the efficiency or the effectiveness of problem solving. Designers should, instead, concentrate on determining the characteristics of the tasks that problem solvers must address, and on supporting those tasks with the appropriate problem representations and support tools. Sufficient evidence now exists to suggest that the notion of cognitive fit may be one aspect of a general theory of problem solving. Suggestions are made for extending the notion of fit to more complex problem-solving environments.