The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Big Data Analytics: Opportunity or Threat for the Accounting Profession?
Abstract
Contrary to Frey and Osborne's (2013) prediction that the accounting profession faces extinction, we argue that accountants can still create value in a world of Big Data analytics. To advance this position, we provide a conceptual framework based on structured/unstructured data and problem-driven/exploratory analysis. We argue that accountants already excel at problem-driven analysis of structured data, are well positioned to play a leading role in the problem-driven analysis of unstructured data, and can support data scientists performing exploratory analysis on Big Data. Our argument rests on two pillars: accountants are familiar with structured datasets, easing the transition to working with unstructured data, and possess knowledge of business fundamentals. Thus, rather than replacing accountants, we argue that Big Data analytics complements accountants' skills and knowledge. However, educators, standard setters, and professional bodies must adjust their curricula, standards, and frameworks to accommodate the challenges of Big Data analytics.
Overview
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.
Integrity signals
Registry
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
Beyond the hype: Big data concepts, methods, and analytics
Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226
Why Are There Still So Many Jobs? The History and Future of Workplace Automation
Autor, D. H. (2015) · Journal of Economic Perspectives · doi:10.1257/jep.29.3.3 · Cited by 3,917
Management control systems as a package—opportunities, challenges and research directions
Malmi, T., & Brown, D. A. (2008) · Management Accounting Research · doi:10.1016/j.mar.2008.09.003 · Cited by 1,757
Polanyi's Paradox and the Shape of Employment Growth
Autor, D. (2014) · doi:10.3386/w20485 · Cited by 323
Cited by
The digital transformation of external audit and its impact on corporate governance
Manita, R., Elommal, N., Baudier, P., & Hikkerova, L. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2019.119751 · Cited by 533
The Data Analytics Journey: Interactions Among Auditors, Managers, Regulation, and Technology*
Austin, A. A., Carpenter, T. D., Christ, M. H., & Nielson, C. S. (2021) · Contemporary Accounting Research · doi:10.1111/1911-3846.12680 · Cited by 172
Behavioral challenges to professional skepticism in auditors’ data analytics journey
Li, X. (2022) · Maandblad voor Accountancy en Bedrijfseconomie · doi:10.5117/mab.96.78525 · Cited by 11
Metadata
- Authors
- Richins, G., Stapleton, A., Stratopoulos, T. C., & Wong, C.
- Venue
- Journal of Information Systems
- Publisher
- American Accounting Association
- Published
- 2017-06-01
- Volume
- 31
- Issue
- 3
- Pages
- 63-79
- ISSN
- 0888-7985 (ISSN-L)
- 1558-7959
- OpenAlex
- W3122214705
- Cited by
- 361
- References
- 6
- FWCI
- 54.08
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Big Data Analytics: Opportunity or Threat for the Accounting Profession? https://app.cochat.ai/papers/10.2308/isys-51805
- The paper
- Richins, G., Stapleton, A., Stratopoulos, T. C., & Wong, C. (2017). Big Data Analytics: Opportunity or Threat for the Accounting Profession? Journal of Information Systems. https://doi.org/10.2308/isys-51805
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
