The allocation of scarce resources to competing information systems project opportunities is a key activity performed by the MIS planning group. Performing this task typically involves consideration of both quantitative as well as qualitative aspects of projects. Research in human information processing and cognitive psychology suggests that decision makers are often subject to biases that tend to assign greater salience to quantitative as opposed to qualitative and intangible factors. To help overcome such biases and to provide flexible decision support to the project selection committee, a knowledge-based system has been developed. Knowledge captured in the system was extracted from industry practitioners responsible for the project selection decision. The system architecture represents an integration of database, modeling, and expert system capabilities. It supports both intelligence and design phases of project selection and can assess the impact of a selected portfolio on an organization's cash flow. The operation of the system is illustrated through an extended example.
Determining user requirements and generating alternative system solutions to meet these requirements are two critical steps in the requirement analysis phase of the system development life cycle. Much of the MIS research in the requirement analysis phase has been devoted to the topic of requirement determination and its verification. Alternative generation and evaluation is left, to a significant degree, to the judgment and expertise of an analyst. This paper proposes a multiple criteria decision making (MCDM) approach for generating and evaluating alternatives when the user requirements are expressed in terms of certain operational criteria such as time, cost, risk, etc. These alternatives form the basis for the user to make the necessary trade-offs.
The knowledge acquisition problem endures as a bottleneck in the construction of expert system knowledge bases. Despite the recent proliferation of techniques and the availability of more sophisticated methods for this task, the interview technique continues to be widely used, especially in business domains. This paper reports the results of an experiment conducted to compare the unstructured knowledge acquisition interview with a specific type of structured knowledge acquisition interview. Structure for the interview was provided by a domain model of the business decision-making activity that attempted to capture the subjective and qualitative aspects of decision making. Senior managers from industry served as the subjects in the experiment. The interview technique was evaluated along efficiency and effectiveness dimensions. Results indicate improved performance with the structured interviewing method.
Extensive coverage of knowledge-based languages has appeared in the recent literature. However, there has been no discussion of the criteria to be used in selecting between a knowledge-based approach and a traditional, that is, non-knowledge-based, approach for a particular application. This paper presents a framework of application-based criteria to assist in this selection. It also applies this decision framework to a number of real and hypothetical applications.