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Testing the accuracy of employee-reported data: An inexpensive alternative approach to traditional methods

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  • Hales, Douglas N.
  • Sridharan, V.
  • Radhakrishnan, Abirami
  • Chakravorty, Satya S.
  • Siha, Samia M.

Abstract

Although Information Technology (IT) solutions improve the collection and validation of operational data, Operations Managers must often rely on self-reported data from workers to make decisions. The problem with this data is that they are subject to intentional manipulation, thus reducing their suitability for decision-making. A method of identifying manipulated data, digital analysis, addresses this problem at low cost. In this paper, we demonstrate how one uses this method in real-world companies to validate self-reported data from line workers. The results of our study suggest that digital analysis estimates the accuracy of employee reported data in operations management, within limited contexts. These findings lead to improved operating performance by providing a tool for practitioners to exclude inaccurate information.

Suggested Citation

  • Hales, Douglas N. & Sridharan, V. & Radhakrishnan, Abirami & Chakravorty, Satya S. & Siha, Samia M., 2008. "Testing the accuracy of employee-reported data: An inexpensive alternative approach to traditional methods," European Journal of Operational Research, Elsevier, vol. 189(3), pages 583-593, September.
    • Handle: RePEc:eee:ejores:v:189:y:2008:i:3:p:583-593
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    Cited by:

    1. Juan Miguel Campanario & María Angeles Coslado, 2011. "Benford’s law and citations, articles and impact factors of scientific journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 421-432, August.
    2. Theoharry Grammatikos & Nikolaos I. Papanikolaou, 2021. "Applying Benford’s Law to Detect Accounting Data Manipulation in the Banking Industry," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(1), pages 115-142, April.
    3. Hales, Douglas N. & Chakravorty, Satya S. & Sridharan, V., 2009. "Testing Benford's Law for improving supply chain decision-making: A field experiment," International Journal of Production Economics, Elsevier, vol. 122(2), pages 606-618, December.
    4. Lee, Kang-Bok & Han, Sumin & Jeong, Yeasung, 2020. "COVID-19, flattening the curve, and Benford’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    5. Debreceny, Roger S. & Gray, Glen L., 2010. "Data mining journal entries for fraud detection: An exploratory study," International Journal of Accounting Information Systems, Elsevier, vol. 11(3), pages 157-181.
    6. Giuseppe Marzo & Yannick Tazzari & Stefano Bonnini, 2020. "Benford’s Law: genesi, letteratura e applicazioni empiriche," Working Papers 2020019, University of Ferrara, Department of Economics.

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