A comprehensive list of ten major expert systems (ES) related factors likely to affect users' jobs has been defined, including problem importance, problem difficulty, developer skill, domain expert quality, user characteristics, user satisfaction, shell quality, user involvement, management support. and system usage. Impact on the job has been defined in terms of eleven items dealing with changes in job importance, amount of work, accuracy requirements, skills needed, job appeal, feed-back about performance, freedom in how to do the job, opportunity for advancement, job security, relation with peers, and job satisfaction. Data were collected on sixty-nine expert systems developed through IBM's Corporate Manufacturing Expert Systems Project Center in San Jose, California. The results show that the major variables having the most impact on users' jobs are problem importance, problem difficulty, domain expert quality, user satisfaction with the ES. shell quality, and user involvement in ES development. Based on the results, recommendations are made for corporate and ES development managers to increase the likelihood that ES will have a desirable impact on users' jobs.
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.