Author List: Yoon, Youngohc; Guimaraes, Tor; O'Neal, Quinton;
MIS Quarterly, 1995, Volume 19, Issue 1, Page 83-106.
As the widespread use and company dependency on expert systems (ES) increase, so does the need to assess their value and to ensure implementation success. This study identifies and empirically tests eight major variables proposed in the literature as determinants of ES success, in this case measured in terms of user satisfaction. IBM'S Corporate Manufacturing Expert Systems Project Center collected information from 69 project managers to support the study. The results clearly support the hypothesized relationships and suggest the need for ES project managers to pay special attention to these determinants of ES implementation success. ES success is directly related to the quality of developers and the ES shells used, end-user characteristics, and degree of user involvement in ES development, as each has been defined in this study. For exploratory purposes, the component items for each of these major variables were correlated with the components of user satisfaction. Based on the results, several recommendations are proposed for ES project managers to enhance the likelihood of project success, including: adding problem difficulty as a criterion for ES application selection; increasing ES developer training to improve people skills, having the ability to model and use a systems approach in solving business problems; shaping end-user attitudes and expectations regarding ES; improving the selection of domain experts; more thoroughly understanding the ES impact on end-user jobs; restricting the acquisition of ES shells based on a proposed set of criteria; and ensuring a proper match of ES development techniques and tools to the business problem at hand.
Keywords: determinants of success; ES development; ES implementation; Expert systems; expert systems success; user satisfaction
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List of Topics

#198 0.205 factors success information critical management implementation study factor successful systems support quality variables related results key model csf importance determinants
#129 0.116 expert systems knowledge knowledge-based human intelligent experts paper problem acquisition base used expertise intelligence domain inductive rules machine artificial task
#253 0.116 user involvement development users satisfaction systems relationship specific results successful process attitude participative implementation effective application authors suggested user's contingency
#3 0.083 problems issues major involved legal future technological impact dealing efforts current lack challenges subsystem related highly present addressing likely recommendations
#97 0.082 set approach algorithm optimal used develop results use simulation experiments algorithms demonstrate proposed optimization present analytical distribution selection number existing
#135 0.061 project projects development management isd results process team developed managers teams software stakeholders successful complex develop contingencies problems greater planning
#248 0.053 computing end-user center support euc centers management provided users user services organizations end satisfaction applications article ibm step field policies