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Prepare for takeoff: improving asset measurement and audit quality with drone-enabled inventory audit procedures

Author

Listed:
  • Margaret H. Christ

    (University of Georgia)

  • Scott A. Emett

    (Arizona State University)

  • Scott L. Summers

    (Brigham Young University)

  • David A. Wood

    (Brigham Young University)

Abstract

Auditors increasingly employ technologies to improve audit quality. Using a design science approach, we examine whether using drones and automated counting software can improve audit quality and thus financial reporting. We assess three dimensions of audit quality—efficiency, effectiveness, and quality of documentation. We show that auditors can perform inventory counts with these technologies much more efficiently than they can with manual techniques, decreasing count time in our study from 681 h to 19 h. Similarly, auditors can maintain or improve audit effectiveness, decreasing error rates in our study from 0.15% to 0.03% while providing higher-quality audit documentation. Interviews with national-level partners and audit standard setters highlight impediments to adopting these technologies, including firm concerns about being first movers combined with inability of standard setters to provide guidance at a pace that matches the pace of technological development. Collectively, our results suggest that technology-enabled inventory audits can improve audit quality and further regulatory guidance on using such technologies would enhance adoption.

Suggested Citation

  • Margaret H. Christ & Scott A. Emett & Scott L. Summers & David A. Wood, 2021. "Prepare for takeoff: improving asset measurement and audit quality with drone-enabled inventory audit procedures," Review of Accounting Studies, Springer, vol. 26(4), pages 1323-1343, December.
    • Handle: RePEc:spr:reaccs:v:26:y:2021:i:4:d:10.1007_s11142-020-09574-5
    DOI: 10.1007/s11142-020-09574-5
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    References listed on IDEAS

    as
    1. Earley, Christine E., 2015. "Data analytics in auditing: Opportunities and challenges," Business Horizons, Elsevier, vol. 58(5), pages 493-500.
    2. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    3. Anton Korinek & Joseph E. Stiglitz, 2018. "Artificial Intelligence and Its Implications for Income Distribution and Unemployment," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 349-390, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Goodson, Brian M. & Grenier, Jonathan H. & Maksymov, Eldar, 2023. "When law students think like audit litigation attorneys: Implications for experimental research," Accounting, Organizations and Society, Elsevier, vol. 104(C).
    2. Peters, Christian P. H., 2023. "The microfoundations of audit quality," Other publications TiSEM 6a2b12a5-6060-4544-883b-e, Tilburg University, School of Economics and Management.
    3. Anastassia Fedyk & James Hodson & Natalya Khimich & Tatiana Fedyk, 2022. "Is artificial intelligence improving the audit process?," Review of Accounting Studies, Springer, vol. 27(3), pages 938-985, September.
    4. Xiaomeng Lucock & Victoria Westbrooke, 2021. "Trusting in the “Eye in the Sky”? Farmers’ and Auditors’ Perceptions of Drone Use in Environmental Auditing," Sustainability, MDPI, vol. 13(23), pages 1-20, November.
    5. Benjamin P. Commerford & Sean A. Dennis & Jennifer R. Joe & Jenny W. Ulla, 2022. "Man Versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence," Journal of Accounting Research, Wiley Blackwell, vol. 60(1), pages 171-201, March.
    6. Gu, Yu & Dai, Jun & Vasarhelyi, Miklos A., 2023. "Audit 4.0-based ESG assurance: An example of using satellite images on GHG emissions," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
    7. Ruhnke, Klaus, 2023. "Empirical research frameworks in a changing world: The case of audit data analytics," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 51(C).
    8. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).

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    More about this item

    Keywords

    Drones; Automated software; Inventory counting; Inventory; Design science;
    All these keywords.

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing
    • M48 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Government Policy and Regulation

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