Faculty Scholarly Dissemination Grants


Asymmetry in Identification of Multiplicity Errors


School of Accounting


Seidman College of Business




A UML class diagram representing an enterprises business processes manifests various company policies in the diagrams multiplicities. Mistakes made in the multiplicities may result in a poorly designed database that does not faithfully represent the enterprises business operations and enforce compliance with its policies. System auditors need to be able to validate an enterprises conceptual data model, including its multiplicities, against the underlying reality. Prior research indicates an asymmetry in the accuracy of system auditors identifying errors in depicted minimum multiplicities. Specifically, auditors seemed more able to identify errors when the underlying reality called for optional participation and the multiplicity was depicted as mandatory than when the reality called for mandatory participation and the multiplicity was depicted as optional, that is the auditors were reluctant to force mandatory participation. Two experiments were conducted in this study to gain additional evidence as to the existence of an asymmetry in minimum multiplicities and to explore whether any asymmetry exists in the identification of errors in maximum multiplicities. Data has been collected but not analyzed for this research-in-progress project. Data will be analyzed and ready to present at the SIG-ASYS Pre-ICIS Workshop on Accounting Information Systems.

Conference Name

Sig-ASYS Pre-ICIS Workshop on Accounting Information Systems

Conference Location

Milan, Italy

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