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Audit Risk Evaluation Using Data Envelopment Analysis with Ordinal Data

Author

Listed:
  • Gholam R. Amin
  • Osama El‐Temtamy
  • Samy Garas

Abstract

This study examines the data envelopment analysis (DEA) model for audit risk evaluation which was initially developed by Bradbury and Rouse (2002) and reinterpreted by Davutyan and Kavut (2005). Bradbury and Rouse (2002) apply the standard DEA model for audit risk factors, including judgemental (ordinal) measures. In the presence of ordinal data, efficiency analysis in DEA requires appropriate models to be applied instead of the standard DEA model. Accordingly, audit risk evaluation based on the standard DEA model is not assessed appropriately because the risk factors are qualitative and ordinal measures. Hence, we employ an appropriate DEA model to accurately evaluate audit risk in the presence of ordinal data. In light of the prior two studies, our results demonstrate the appropriateness of the ordinal DEA model.

Suggested Citation

  • Gholam R. Amin & Osama El‐Temtamy & Samy Garas, 2022. "Audit Risk Evaluation Using Data Envelopment Analysis with Ordinal Data," Abacus, Accounting Foundation, University of Sydney, vol. 58(3), pages 589-602, September.
    • Handle: RePEc:bla:abacus:v:58:y:2022:i:3:p:589-602
    DOI: 10.1111/abac.12254
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    References listed on IDEAS

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    1. Cook, Wade D. & Zhu, Joe, 2006. "Rank order data in DEA: A general framework," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1021-1038, October.
    2. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
    3. Michael E. Bradbury & Paul Rouse, 2002. "An Application of Data Envelopment Analysis to the Evaluation of Audit Risk," Abacus, Accounting Foundation, University of Sydney, vol. 38(2), pages 263-279, June.
    4. Nurhan Davutyan & Lerzan Kavut, 2005. "An application of data envelopment analysis to the evaluation of audit risk: a reinterpretation," Abacus, Accounting Foundation, University of Sydney, vol. 41(3), pages 290-306, October.
    5. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    6. Michael De Martinis & Keith Houghton, 2019. "The Business Risk Audit Approach and Audit Production Efficiency," Abacus, Accounting Foundation, University of Sydney, vol. 55(4), pages 734-782, December.
    7. Boritz, Je, 1986. "The Effect Of Research Method On Audit Planning And Review Judgments," Journal of Accounting Research, Wiley Blackwell, vol. 24(2), pages 335-348.
    8. Ali Emrouznejad & Guo-liang Yang & Gholam R. Amin, 2019. "A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(7), pages 1079-1090, July.
    9. Sueyoshi, Toshiyuki & Shang, Jennifer & Chiang, Wen-Chyuan, 2009. "A decision support framework for internal audit prioritization in a rental car company: A combined use between DEA and AHP," European Journal of Operational Research, Elsevier, vol. 199(1), pages 219-231, November.
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