CoChat
Sign Up
Cited by 247Robotic Process Automation ApplicationsAuditing, Earnings Management, GovernanceBig Data Technologies and Applications

Big Data and changes in audit technology: Contemplating a research agenda

Open access
Salijeni, George, Samsonova-Taddei, Anna, Turley, Stuart·2019·Accounting and Business Research
DOI 10.1080/00014788.2018.1459458
Open this paper in CoChat — free accountOpen PDF

Abstract

This study explores the most recent episode in the evolution of audit technology, namely the incorporation of Big Data and Data Analytics (BDA) into audit firm approaches. Drawing on 22 interviews with individuals with significant experience in developing, implementing or assessing the impact of BDA in auditing, together with publicly available documents on BDA published within the audit field, the paper provides a holistic overview of BDA-related changes in audit practice. In particular, the paper focuses on three key aspects, namely the impact of BDA on the nature of the relationship between auditors and their clients; the consequences of the technology for the conduct of audit engagements and the common challenges associated with embedding BDA in the audit context. The study’s empirical findings are then used to establish an agenda of areas suitable for further research on the topic. The study is one of the first empirical accounts providing a perspective on the rise of BDA in auditing.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/00014788.2018.1459458

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Salijeni, Samsonova-Taddei, and Turley documented that large firms embedded data analytics in engagement methodology while struggling to articulate what those analytics contributed to sufficiency of evidence.

    “Salijeni, Samsonova-Taddei, and Turley (2019) documented how the large firms embedded data analytics in engagement methodology while struggling to articulate what those analytics contributed to the sufficiency of evidence.”

    From Auditing the Black Box: Rethinking Audit Assurance and Professional Liability in the Age of Artificial Intelligence (2026) · cites this paper as Salijeni, Samsonova-Taddei, and Turley (2019)

    The Introduction and ‘Challenges with embedding BDA tools’ describe firms incorporating analytics into audit approaches and difficulties fitting analytics-derived evidence into standards-based audits. That section also discusses how sufficient evidence is derived, but it does not specifically document large firms struggling to articulate analytics’ contribution to evidence sufficiency. It describes embedding as an ongoing challenge, rather than an accomplished change across large firms. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 61

  1. Abbott (1988), The system of professions. The essay on the division of expert labour. University of Chicago Press, Chicago, USA.
  2. Alles,G.M.(2015), Drivers of the use and facilitators and obstacles of the evolution of Big Data by the audit profession, Accounting Horizons, Vol.29, No.2, pp.439-449.
  3. Alles, G.M and Gray, G.L (2016), Incorporating Big Data in audits: Identifying inhibitors and a research agenda to address those inhibitors, International Journal of Accounting Information system,Vol.22,pp.44-59.
  4. Alvesson, M., and Karreman, D. (2011), Qualitative research and theory development: Mystery as method. London, Sage Publications .
  5. Andon, P., Free, C. and Sivabalan, P. (2014), The legitimacy of new assurance providers: Making the cap, Accounting, Organizations and Society, 39, pp. 79-96.
  6. Andon, P. Free, C. and O'Dwyer, B. (2015) Annexing new audit spaces: challenges and adaptations, Accounting, Auditing and Accountability Journal, Vol. 28, No. 8, pp.1400 -1430.
  7. Appelbaum, D., Kogan, A., and Vasarhelyi, M.A. (2017), Big Data and analytics in the modern audit engagement: research needs, Auditing: A Journal of Practice and Theory, Vol. 36, No. 4, pp. 11-27.
  8. Arnaboldi, M., Busco, C., and Cuganesan, S. (2017), Accounting, accountability, social media and big data: Revolution or hype?, Accounting, Auditing and Accountability Journal, Vol. 30, No. 4, pp. 762-776.
  9. Bamber, M. and McMeeking,K. (2016), An examination of international accounting standard- setting due process and the implications for legitimacy, British Accounting Review, Vol.48,No.1,pp 59-73.
  10. Bamber, E.M. and Snowball, D. (1988), An experimental study of the effects of audit structure in uncertain task environment, The Accounting Review, Vol.63, No.3, pp. 490-504.
  11. Barnes,PA (2004), The auditor's going concern decision and Types I and II Errors: The Coase theorem,transaction Costs, bargaining power and attempts to mislead, Journal of Accounting and Public Policy,Vol.23, No.6,pp415-440.
  12. Bartram,P. (2013), The value of data, Financial Management, Chartered Institute of Management Accountants, London, March, pp.26-31.
  13. Bhimani, A., and Willcocks, L. (2014), Digitisation, 'Big Data' and the transformation of accounting information, Accounting and Business Research, Vol. 44, No. 4, pp. 469-490.
  14. Brown-Liburd, H., Issa, H. and Lombardi, D. (2015), Behavioral implication of Big Data's impact on audit judgment and decision making and future research directions, Accounting Horizons, Vol.29, No.2, pp. .451-468.
  15. Bi, Z. and Cochran, D. (2014), Big data analytics with applications, Journal of Management Analytics, Vol.1, No.4, pp. 249-265.
  16. Cao.M., Chychyla, R. and Stewart, T. (2015), Big Data Analytics in Financial Statement Audits, Accounting Horizons, Vol.29, No.2, pp.423-429.
  17. Carpenter, B., and Dirsmith, M. (1993), Sampling and the abstraction of knowledge in the auditing profession: An extended institutional theory perspective, Accounting, Organizations and Society, Vol.18, No.1, pp. 41-63.
  18. Curtis,E.,Humphrey, C. and Turley,S.W.(2016),Standards pf Innovation in Auditing, Auditing: A Journal of Practice & Theory,Vol.35,No.3, pp. 75-98
  19. Curtis, E. and Turley, S. (2007), The Business Risk Audit: a longitudinal case study of an audit engagement, Accounting, Organizations and Society, Vol. 32 No.4-5, pp.439-461.
  20. DeFond, M. and Zhang,J. (2014), A review of archival audit research, Journal of Accounting and Economics,Vol.58,Nos.2-3,pp275-326.
  21. Dirsmith, M.W and Haskins, M.E. (1991), Inherent risk assessment and audit firm technology: a contrast in world theories, Accounting, Organizations and Society, Vol.16, No.1, pp 61-90.
  22. Dowling, C., Leech, S. A., and Moroney, R. (2008), Audit Support System Design and the Declarative Knowledge of Long-Term Users. Journal of Emerging Technologies in Accounting, Vol.5, No.1, pp.99-108.
  23. Earley, C.E. (2015), Data analytics in auditing: Opportunities and challenges, Business Horizons (in press)
  24. Elliott, R.K. and Jacobson, P.D. (1998), Audit independence concepts, CPA Journal, Vol. 68 No. 12, pp. 30-33
  25. Fischer, M. J. (1996), Real-izing the benefits of new technolo-gies as a source of audit evidence: an interpretive field study. Accounting, Organizations and Society, Vol.21,pp. 219-242.
  26. FRC ,(2017), Audit Quality Thematic Review: The use of Data Analytics in the Audit of Financial Statements, Financial Reporting Council Limited, London.
  27. Gandomi, A. and Haider, M. (2015), Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, Vol.35, No.2, pp.137-144.
  28. Gendron, Y. (2009), What Went Wrong? The Downfall of Arthur Andersen and the Construction of controllability. Boundaries Surrounding Financial Auditing, Contemporary Accounting Research , Vol. 26, pp. 987-1027.
  29. Guo, K.H, (2016), The Institutionalization of Commercialism in the Accounting Profession: An Identity-Experimentation Perspective. AUDITING: A Journal of Practice & Theory, Vol. 35, No. 3, pp. 99-117.
  30. Holm, C. and Zaman, M. (2012), Regulating audit quality: Restoring trust and legitimacy,Accounting Forum,Vol.36,No.1,pp.51-61.
  31. Humphrey, C. and Moizer, P. (1990), "From techniques to ideologies: An alternative perspective on the audit function", Critical Perspectives on Accounting, Vol.1, No.3, pp. 217-238.
  32. Humphrey, C, O'Dwyer, B & Unerman, J 2017, Re-theorizing the configuration of organizational fields: The IIRC and the pursuit of 'enlightened' corporate reporting, Accounting and Business Research, Vol. 47, No. 1, pp. 30-63.
  33. Jeppessen, K. K. (1998). Reinventing auditing, redefining consulting and independence. European Accounting Review, Vol.7,No.3,pp. 517-539.
  34. Knechel, R. (2007), The Business Risk Audit: Origins, Obstacles and Opportunities, Accounting Organisations and Society , Vol. 32, pp. 101-29, 2007.
  35. Khalifa, R., Sharma, N.,Humphrey, C. and Robson, K. (2007), Discourse and audit change: Transformations in methodology in the professional audit field, Accounting, Auditing and Accountability Journal, Vol. 20,No. 6, pp.825 -854
  36. Krahel, P.J. and Titera, W.R. (2015). Consequences of Big Data and formalization on accounting and auditing standards, Accounting Horizons, Vol.29, No.2, pp. 409-422.
  37. MacLullich, K.K. (2003), The emperor's "new" clothes? New audit regimes: Insights from Foucault's technologies of the self. Critical Perspectives on Accounting, 14(8),791-811
  38. Matthews, D. (2006), A History of Auditing: The changing process in Britain from the nineteenth century to the present day. Routledge, Oxford.
  39. Miles, M. B., and Huberman, A. M. (1994). Qualitative data analysis: an expanded sourcebook. Thousand Oaks, Sage Publications
  40. Newton, J.D. and Ashton ,R.H. (1989), The association between audit technology and audit delay, Auditing: A Journal of Practice and Theory ,Vol.8, No.2(supplement), pp. 22-37.
  41. Omoteso, K., Patel, A., and Scott, P. (2010), Information and Communications Technology and Auditing: Current Implications and Future Directions. International Journal of Auditing, Vol.14,No.2,pp, 147-162.
  42. O'Dwyer, B., Owen, D. and Unerman, J. (2011), Seeking legitimacy for new assurance forms: the case of assurance on sustainability reporting, Accounting, Organizations and Society, Vol. 36 No. 1, pp. 31-52.
  43. O'Leary, D. E. and M. L. Markus. (2006), Microsoft's management reporting: SAP, data warehousing, and reporting tools. Journal of Emerging Technologies in Accounting ,Vol.3,pp, 129-141
  44. Power, M. (1992), From common sense to expertise: The pre-history of auding sampling, Accounting, Organisation and Society, Vol.17, No.1, pp. 37-62.
  45. Power, M. (1995), "Auditing, Expertise and the Sociology of Technique", Critical Perspectives in Accounting, Vol. 6, pp. 317-339.
  46. Power, M. (1997), The Audit Society :Rituals of verification. Oxford University Press ,New York,NY.
  47. Power, M. (2003), Auditing and the production of legitimacy, Accounting, Organizations and Society, Vol.28, No.4, pp. 379-394.
  48. Ramlakun, R. (2015), How Big Data and analytics are transforming the audit. Ernst and Young Global Limited, April , Vol.9, pp. 9-12.
  49. Robson, K., Humphrey, C., Khalifa, R. and Jones, J. (2007), Transforming audit technologies: Business risk audit methodologies and the audit field, Accounting, Organizations and Society, Vol.32, No.4-5, pp. 409-438.
  50. Shafer, W.E. and Gendron, Y., (2005). Analysis of a failed jurisdictional claim: the rhetoric and politics sur-rounding the AICPA global credential project. Accounting, Auditing & Accountability Journal, Vol.18 ,No.4, pp.453 -491.
  51. Sikka, P. (2009), Financial Crisis and the Silence of the auditors, Accounting, Organizations and Society, Vol. 34, No.6-7, pp. 868-873.
  52. Suddaby, R., Cooper, D.J. and Greenwood, R. (2007), Trans-national regulation of professional services: governance dynamics of field level organizational change, Accounting Organizations & Society, Vol. 32 Nos 4/5, pp. 333-362.
  53. Sully,J.M. (1974), Statistical Sampling in Auditing, Journal of the Royal Statistical Society,23(1),71-80
  54. Turley, S., and Cooper, M. (1991), Auditing in the UK. Institute of Chartered Accountants in England and Wales, Prentice Hall, Hertfordshire.
  55. Vasarhelyi, M.A., Kogan,A. and Tuttle, B. (2015), Big Data in Accounting: An Overview, Accounting Horizons,Vol.29 No.2,pp.381-396.
  56. Teeter, R. Alles, M. and Vasarhelyi, M.A (2010), Remote Audit: A Research Framework, Journal of Emerging Technologies in Accounting, Vol.7,No.1,pp.73-88
  57. Titera, W.R.(2013), Updating audit standard-enabling audit data analysis, Journal of Information Systems, Vol.27, No.1, pp. 325-331
  58. Wang, T. and Cuthbertson, R. (2014), Eight issues on Audit data analytics we would like researched, Journal of Information Systems, Vol.29, No.1, pp. 155-162.
  59. Warren, Jr.J.D., Moffitt, K.C. and Byrnes, P. (2015), How Big Data Will Change Accounting, Accounting Horizons, Vol.29, No.2, pp. 397-407.
  60. Williams, J. W. (2013). Regulatory technologies, risky subjects, and financial boundaries: Governing "fraud" in the financial markets. Accounting, Organizations and Society, Vol.38, Nos.6-7,pp. 544-558.
  61. Yoon, K., Hoogduin, L. and Zhang, L. (2015), Big Data as complementary Audit Evidence, Accounting Horizons, Vol.29, No.2, pp. 431-438.

Cited by

Sorted by citations

Authors
Salijeni, G., Samsonova-Taddei, A., & Turley, S.
Affiliations
  • University of South Wales (GB)
  • Alliance Manchester Business School, University of Manchester, Manchester, UK
  • University of Manchester (GB)
ORCID
Salijeni, George
Venue
Accounting and Business Research
Publisher
Informa UK Limited
Published
2018-04-13
Volume
49
Issue
1
Pages
95-119
ISSN
  • 0001-4788 (ISSN-L)
  • 2159-4260
DOI
10.1080/00014788.2018.1459458
OpenAlex
W2797903672
Cited by
247
References
2
Citations by year
2026: 23 · 2025: 45 · 2024: 59 · 2023: 35 · 2022: 33 · 2021: 26 · 2020: 21 · 2019: 4 · 2018: 1
FWCI
10.77
Type
journal article
Language
en
Open access
Locations
  • Accounting and Business Research (publishedVersion)
  • Aston Publications Explorer (Aston University) (submittedVersion)
  • SSRN Electronic Journal (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Research Explorer (The University of Manchester) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Robotic Process Automation Applications
  • Social Sciences › Business, Management and Accounting › Accounting › Auditing, Earnings Management, Governance
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
Keywords
big data analytics, audit technology, audit practice, auditor-client relationship, technology adoption barriers, public accounting firms, research agenda

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review