Kathleen Foley Curley

An applied framework for classifying the complexity of knowledge-based systems

The development and use of knowledge-based (expert) systems has grown dramatically across a broad range of industries. Yet despite its growing importance, the study of expert systems lacks a cohesive framework for differentiating and comparing expert systems initiatives across different applications and in different industrial settings. The problem for IS managers is that a system that works in one situation may ot be appropriate for another. This article presents a classification methodology for the systematic evaluation of a broad range of expert systems. Of primary concern in this study is the measurement of the complexity of such systems. Complexity in the area of expert systems consists of two basic dimensions. The first dimension is the complexity of the underlying knowledge residing with the key experts. The second dimension of the framework focuses on the complexity of the technology incorporated into a given system. This framework is then applied to a sample of 50 successfully developed knowledge-based systems. The results can be used as a foundation for generating research hypotheses and for development time, budget, staffing, organizational control, and organizational participation.

The role of the champion in DSS implementation

Abstract Adoption of a DSS usually involves changes or innovation in an organizations way of doing things. The process of innovation has been the subject of study in other contexts, and research has indicated that the efforts of various champions can be critical to successful innovation. Observations of DSS implementations in several organizations suggest that a system champion can likewise be a key factor in implementation success. Such persons play a variety of roles, including that of reference leader, of missionary or change agent, or of top management surrogate. Implementation strategies can be designed which capitalize on the presence of a champion.

Measuring implementation outcome: beyond success and failure

Abstract This paper proposes a framework for measuring the impact of a computer-based application in an organization. Recognizing the complexity of the implementation process, the framework is multi-dimensional: it seeks to measure the outcome of an implementation effort on both the people and tasks affected as separate from users definition of “success”. The framework can be used to support either the quantitative or case study methodologies used in implementation research. Finally, the authors describe a field test of the framework to demonstrate its usefulness in an organizational setting.

The evolution of intellectual technologies: applying product life-cycle models to MIS implementation

Abstract This article develops the theoretical construct of an “intellectual technology” (as distinct from traditional industrial technologies) as a way to explain the difficulties organizations have in implementing new computer-based applications. Building on the notions of a product and process life-cycle, this concept is used to suggest that non-traditional approaches to the implementation of computer-based applications may be necessary. The “intellectual technology” concept implies that organizations must learn to apply new applications in their particular environment, and that this learning explains the difficulty many firms have in implementing Office Automation, CAD/CAM, Decision Support Systems, and the like.

The strategic use of expert systems for risk management in the insurance industry

The applications focus and development history of two large expert systems for underwriting life insurance cases are used to consider the linkage between business strategy and project focus for major information systems development. It is shown that the project selection and evolution processes of these two companies has resulted in two distinctly different expert system applications, the product of their respective business positioning. The article also presents a detailed description of how expert systems can be used to enhance the productivity and effectiveness of risk management in the life insurance business.