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Interactions among biases in costing systems: A simulation approach

  • Stephan Leitner
Chapter
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 662)

Abstract

This simulation study presents results on the effects of distortions in raw accounting data on the quality of the information provided by costing systems. The results give insights into interactions among biases and indicate that multiple biases do not necessarily affect information quality negatively. Surprisingly, in some setups interactions among multiple biases lead to mitigation, or even compensation among biases. Furthermore, findings can constitute the basis for organizational data-quality policies, i.e. the presented results give guidance where (not) to tolerate biases and how to prioritize actions regarding information quality with respect to accuracy and cost of accuracy.

Keywords

Information Quality Cost Information Cost Driver Cost Allocation Cost Category 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    J. A. Brierley. Research into product costing practice: a european perspective. European Accounting Review, 10(2):215-256, 2001.Google Scholar
  2. 2.
    J. Burns and R. W. Scapens. Conceptualizing management accounting change: an institutional framework. Management Accounting Research, 11(1):3-25, 2000.CrossRefGoogle Scholar
  3. 3.
    J. P. Davis, K. M. Eisenhardt, and C. B. Bingham. Developing theory through simulation methods. Academy of Management Review, 32(2):480-499, 2007.CrossRefGoogle Scholar
  4. 4.
    J. S. Demski and G. A. Feltham. Cost determination. A conceptual approach. Iowa State University Press, Ames, Iowa, 1976.Google Scholar
  5. 5.
    C. Drury and M. Tayles. Cost system design for enhancing profitability. Management Accounting, 76(1):40, 1998.Google Scholar
  6. 6.
    R. Ewert and A. Wagenhofer. Interne Unternehmensrechnung. Springer, Berlin, 7. edition, 2008.Google Scholar
  7. 7.
    C. T. Horngren, G. L. Sundem, and W. O. Stratton. Introduction to management accounting. Charles T. Horngren series in accounting. Prentice Hall, Upper Saddle River, NJ, 12. edition, 2002.Google Scholar
  8. 8.
    E. Labro and M. Vanhoucke. A simulation analysis of interactions among errors in costing systems. Accounting Review, 82(4):939-962, 2007.CrossRefGoogle Scholar
  9. 9.
    D. B. Paradice and W. L. Fuerst. An mis data quality methodology based on optimal error detection. Journal of Information Systems, Spring:48-66, 1991.Google Scholar
  10. 10.
    J Reason. Managing the risks of organizational accidents. Ashgate, Aldershot, 1997.Google Scholar
  11. 11.
    T. C. Redman. Data Quality for the Information Age. Artech House, Boston, London, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Controlling and Strategic ManagementAlpen-Adria Universitaet KlagenfurtKlagenfurtAustria

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