Decision Support Systems
Impact of the Union and Difference Operations on the Quality of Information Products
Information Systems Research
Induction over Strategic Agents
Information Systems Research
Journal of Data and Information Quality (JDIQ)
An experimental comparison of real and artificial deception using a deception generation model
Decision Support Systems
Information systems strategy: Past, present, future?
The Journal of Strategic Information Systems
Distrust, fear and emotional learning: an online auction perspective
Journal of Theoretical and Applied Electronic Commerce Research
The bank loan approval decision from multiple perspectives
Expert Systems with Applications: An International Journal
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We consider a new variety of sequential information gathering problems that are applicable for Web-based applications in which data provided as input may be distorted by the system user, such as an applicant for a credit card. We propose two methods to compensate for input distortion. The first method, termedknowledge base modification, considers redesigning the knowledge base of an expert system to best account for distortion in the input provided by the user. The second method, termedinput modification, modifies the input directly to account for distortion and uses the modified input in the existing (unmodified) knowledge base of the system. These methods are compared with an approach where input noise is ignored. Experimental results indicate that both types of modification substantially improve the accuracy of recommendations, with knowledge base modification outperforming input modification in most cases. Knowledge base modification is, however, more computationally intensive than input modification. Therefore, when computational resources are adequate, the knowledge base modification approach is preferred; when such resources are very limited, input modification may be the only viable alternative.