Diagnosing decision quality

Authors:
Michael J. Davern;Ravi Mantena;Edward A. Stohr
Affiliations:
Department of Accounting & Business Information Systems, The University of Melbourne, Australia;William E. Simon Graduate School of Business Administration, University of Rochester, Rochester, NY, United States;Howe School of Technology Management, Stevens Institute of Technology, Hoboken, NJ, United States
Venue:
Decision Support Systems
Year:
2008

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Abstract

Human decision making is error-prone and often subject to biases. Important information cues are misweighted and feedback delays hamper learning. Experimentally, task information has been shown to be valuable in improving decision making. However, such information is rarely available. Generalizing from lab-based approaches, we present a new field methodology for the ''diagnosis of decision quality'' (DDQ) that helps decision makers discover such information. We illustrate our approach in the context of hotel revenue management and demonstrate how it can identify context-specific systematic errors in decision making in a manner that facilitates adaptive changes and improved performance.