Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 242

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Consequences of Big Data and Formalization on Accounting and Auditing Standards

Krahel, John Peter, Titera, William R.·2015·Accounting Horizons

Abstract

SYNOPSIS: Accounting and auditing standards help manage a compromise between users' desire for more information and the costs to prepare and transmit that information. Previously forced to manage a paucity of information, businesses now look to capitalize upon the massive volumes of data and metadata that fill petabytes of space in their servers. Likewise, data from many sources and in many forms, many of which are irrelevant, inundate investors. Accounting and auditing standards have not kept pace, maintaining an emphasis on presentation, aggregation, and sampling. This essay argues that a change in standards to focus on data, the processes that generate them, and their analysis, rather than their presentation, will add value and relevance to the accounting profession, empower end users, and improve the efficiency of the capital markets.

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.

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

Integrity signals

Registry

Crossref · 10.2308/acch-51065

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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.

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

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

Show all 2 references

Research works printed but not linked: 31

  1. Amir, 2014, The role of accounting disaggregation in detecting and mitigating earnings management, Review of Accounting Studies, 19, 43, 10.1007/s11142-012-9220-9 · doi:10.1007/s11142-012-9220-9
  2. Badertscher, 2012, A convenient scapegoat: Fair value accounting by commercial banks during the financial crisis, The Accounting Review, 87, 59, 10.2308/accr-10166 · doi:10.2308/accr-10166
  3. Boritz, 2013, A content analysis of auditors' reports on IT internal control weaknesses: The comparative advantages of an automated approach to control weakness identification, International Journal of Accounting Information Systems, 14, 138, 10.1016/j.accinf.2011.11.002 · DOI
  4. Brousseau, 2014, Does fair value accounting contribute to market price volatility? An experimental approach, Accounting & Finance, 54, 1033, 10.1111/acfi.12030 · doi:10.1111/acfi.12030
  5. Bryan, 2013, How fair values and accounting structures allow triple-counting income: Implications for standard setters, market participants, and academics, Journal of Accounting, Auditing & Finance, 28, 79, 10.1177/0148558X12468130 · doi:10.1177/0148558x12468130
  6. Cao, 2011, The effect of litigation risk on management earnings forecasts, Contemporary Accounting Research, 28, 125, 10.1111/j.1911-3846.2010.01045.x · DOI
  7. Chen, 2013, Fair value accounting and managers' hedging decisions, Journal of Accounting Research, 51, 67, 10.1111/j.1475-679X.2012.00468.x · doi:10.1111/j.1475-679x.2012.00468.x
  8. Dongarra, 2012, Anatomy of a globally recursive embedded LINPACK benchmark, 2012 IEEE Conference on High Performance Extreme Computing, Waltham, MA, September 10–12, 10.1109/HPEC.2012.6408679 · DOI
  9. Elder, 2013, Audit sampling research: A synthesis and implications for future research, Auditing: A Journal of Practice & Theory, 32, 99, 10.2308/ajpt-50394 · DOI
  10. Fairfield, 1996, Accounting classification and the predictive content of earnings, The Accounting Review, 71, 337
  11. Hardgrave, 2013, RFID-enabled visibility and retail inventory record inaccuracy: Experiments in the field, Production and Operations Management, 22, 843, 10.1111/poms.12010 · DOI
  12. Holmes, 1996, An exploration of the espoused organizational cultures of public accounting firms, Accounting Horizons, 10, 26
  13. Huang, 2014, Tone management, The Accounting Review, 89, 1083, 10.2308/accr-50684 · DOI · Paper page
  14. Humpherys, 2011, Identification of fraudulent financial statements using linguistic credibility analysis, Decision Support Systems, 50, 585, 10.1016/j.dss.2010.08.009 · DOI · Paper page
  15. Issa, 2014, A predictive ordered logistic regression model as a tool for quality review of control risk assessments, Journal of Information Systems, 28, 209, 10.2308/isys-50808 · DOI
  16. Jackson, 2010, The allowance for uncollectible accounts, conservatism, and earnings management, Journal of Accounting Research, 48, 565, 10.1111/j.1475-679X.2009.00364.x · DOI · Paper page
  17. Johnson, 2001, The impact of securities litigation reform on the disclosure of forward-looking information by high technology firms, Journal of Accounting Research, 39, 10.1111/1475-679X.00014 · DOI · Paper page
  18. Laux, 2009, The crisis of fair-value accounting: Making sense of the recent debate, Accounting, Organizations and Society, 34, 826, 10.1016/j.aos.2009.04.003 · DOI · Paper page
  19. Laux, 2010, Did fair-value accounting contribute to the financial crisis?, Journal of Economic Perspectives, 24, 93, 10.1257/jep.24.1.93 · DOI · Paper page
  20. Lee, 2012, The effect of quarterly report readability on information efficiency of stock prices: The effect of quarterly report readability on information efficiency of stock prices, Contemporary Accounting Research, 29, 1137, 10.1111/j.1911-3846.2011.01152.x · DOI · Paper page
  21. Lenard, 2000, An analysis of fuzzy clustering and a hybrid model for the auditor's going concern assessment, Decision Sciences, 31, 861, 10.1111/j.1540-5915.2000.tb00946.x · DOI
  22. Loughran, 2011, When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks, The Journal of Finance, 66, 35, 10.1111/j.1540-6261.2010.01625.x · DOI · Paper page
  23. Marschak, 1954, Toward an economic theory of organization and information, Decision Processes, 3, 187
  24. McCarthy, 1982, The REA accounting model: A generalized framework for accounting systems in a shared data environment, The Accounting Review, 57, 554
  25. Rahman, 2010, Frequency of corporate announcements via stock exchange web sites and market efficiency, Journal of Accounting, Auditing & Finance, 25, 457, 10.1177/0148558X1002500308 · doi:10.1177/0148558x1002500308
  26. Reiner, 2005, RFID in healthcare, Healthcare Purchasing News, 29, 74
  27. Sheridan, 2012, The future is now: XBRL emerges as career niche, Journal of Accountancy, 213, 122
  28. Sorter, 1969, An “events” approach to basic accounting theory, The Accounting Review, 44, 12
  29. Vasarhelyi, 2012, AIS in a more rapidly evolving era, Journal of Information Systems, 26, 1, 10.2308/isys-10280 · doi:10.2308/isys-10280
  30. Warren, 2015, How accounting will be changed by Big Data, Accounting Horizons, 29, 10.2308/acch-51069 · DOI · Paper page
  31. Yeh, 2014, Going-concern prediction using hybrid random forests and rough set approach, Information Sciences, 254, 98, 10.1016/j.ins.2013.07.011 · doi:10.1016/j.ins.2013.07.011

Cited by

See all 242

Metadata

Authors
Krahel, J. P., & Titera, W. R.
Venue
Accounting Horizons
Published
2015-02-01
Volume
29
Issue
2
Pages
409-422
ISSN
  • 0888-7993 (ISSN-L)
  • 1558-7975
OpenAlex
W2055684928
Cited by
242
References
2
FWCI
75.15
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: Consequences of Big Data and Formalization on Accounting and Auditing Standards. https://app.cochat.ai/papers/10.2308/acch-51065
The paper
Krahel, J. P., & Titera, W. R. (2015). Consequences of Big Data and Formalization on Accounting and Auditing Standards. Accounting Horizons. https://doi.org/10.2308/acch-51065

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.