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An analysis of expert systems for business decision making at different levels and in different roles

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  • J S Edwards
  • Y Duan
  • P C Robins

Abstract

This paper begins by analysing decision making activities and information requirements at three organizational levels and the characteristics of expert systems (ESs) intended for the two different roles of supporting and replacing a decision maker. It goes on to review the evidence from many years of commercial use of ESs at different levels and in different roles, and to analyse the evidence obtained from a pilot experiment involving developing ESs to fulfil two different roles in the same domain. The research finds that ESs in a replacement role prove to be effective for operational and tactical decisions, but have limitations at the strategic level. ESs in a support role, as advisory systems, can help to make better decisions, but their effectiveness can only be fulfilled through their users. In the experiments, an expert advisory system did not save a user's time, contrary to the expectations of many of its users, but an ES in a replacement role did improve the efficiency of decision making. In addition, the knowledge bases of the ESs in the different roles need to be different. Finally, the practical implications of the experience gained from developing and testing two types of ESs are discussed.

Suggested Citation

  • J S Edwards & Y Duan & P C Robins, 2000. "An analysis of expert systems for business decision making at different levels and in different roles," European Journal of Information Systems, Taylor & Francis Journals, vol. 9(1), pages 36-46, March.
  • Handle: RePEc:taf:tjisxx:v:9:y:2000:i:1:p:36-46
    DOI: 10.1057/palgrave.ejis.3000344
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    Cited by:

    1. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

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