This article investigates the economic consequences of data errors in the information flows associated with business processes. We develop a process modeling-based methodology for managing the risks associated with such data errors. Our method focuses on the topological structure of a process and takes into account its effect on error propagation and risk mitigation using both expected loss and conditional value-at-risk risk measures. Using this method, optimal strategies can be designed for control resource allocation to manage risk in a business process. Our work contributes to the literature on both ex ante risk management-based business process design and ex post risk assessments of existing business processes and control models. This research applies not only to the literature on and practice of process design and risk management but also to business decision support systems in general. An order-fulfillment process of an online pharmacy is used to illustrate the methodology.
The need to ensure reliability of data in information systems has long been recognized. However, recent accounting scandals and the subsequent requirements enacted in the Sarbanes-Oxley Act have made data reliability assessment of critical importance to organizations, particularly for accounting data. Using the accounting functions of management information systems as a context, this paper develops an interdisciplinary approach to data reliability assessment. Our work builds on the literature in accounting and auditing, where reliability assessment has been a topic of study for a number of years. While formal probabilistic approaches have been developed in this literature, they are rarely used in practice. The research reported in this paper attempts to strike a balance between the informal, heuristic-based approaches used by auditors and formal, probabilistic reliability assessment methods. We develop a formal, process-oriented ontology of an accounting information system that defines its components and semantic constraints. We use the ontology to specify data reliability assessment requirements and develop mathematical-model-based decision support methods to implement these requirements. We provide preliminary empirical evidence that the use of our approach improves the efficiency and effectiveness of reliability assessments. Finally, given the recent trend toward specifying information systems using executable business process models (e.g., business process execution language), we discuss opportunities for integrating our process-oriented data reliability assessment approach--developed in the accounting context--in other IS application contexts.