An Applied Framework for Classifying the Complexity of Knowledge-Based Systems

Abstract

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 not 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 for development time, budget, staffing, organizational control, and organizational participation.

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