Expert system: defection and perfection

Abstract

Unlike other computer‐based information systems, expert systems (ES) are characterized by the satisficing and conservative behavior of their users. Shows that the learning curve may be used to model user dependency on ES technology. Even though user dependency relates to ES quality control parameters (for example, Raggad’s 13 ES quality attributes) only dynamic or late binding features really affect ES dependency: ES learning capability and ES recommendation anticipation. There is hence a learning race between the system and the user. If user learning prevails, then there will be user defection. If system learning prevails, then there will be system perfection. Proposes failure analysis based on user defection due to the absence or underutilization of machine learning. ES owners can adopt this model to design a subsystem capable of transforming user defection analysis into a strategic plan for ES management.

Citation

Raggad, B.G. and Gargano, M.L. (1999), "Expert system: defection and perfection", Logistics Information Management, Vol. 12 No. 5, pp. 395-407. https://doi.org/10.1108/09576059910295878