Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 341

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Robotic Process Automation in Public Accounting

Open access
Cooper, Lauren, Holderness, Darin Kip, Sorensen, Trevor, Wood, David A.·2018·SSRN Electronic Journal

Abstract

SYNOPSIS We investigate the implementation of Robotic Process Automation (RPA) software in public accounting by interviewing RPA leaders at Big 4 firms. RPA software automates the input, processing, and output of data to streamline repetitive, mundane tasks. Many of our findings are unique to accounting. For instance, participants report tax services are furthest along in RPA adoption, followed by advisory and assurance services. Furthermore, RPA has not impacted fees, but there is concern that clients may desire fee reductions due to decreased employee hours. Finally, unlike other technology implementations, RPA adoption is driven primarily by lower-level employees. Similar to other domains, our results indicate massive efficiency and effectiveness gains from RPA implementation. Also, interviewees do not expect reduced head count to result from RPA use. This study is the first to discuss the benefits, opportunities, and challenges to implementing RPA in accounting and serves as a catalyst for future research.

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 · 10.2139/ssrn.3193222

Cites

23 of 23 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 23 references

Cited by

See all 341

Metadata

Authors
Cooper, L., Holderness, D. K., Sorensen, T., & Wood, D. A.
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2018
Volume
33
Issue
4
Pages
15-35
ISSN
  • 0888-7993 (ISSN-L)
  • 1556-5068
OpenAlex
W2901174424
Cited by
341
References
23
FWCI
13.91
Type
journal article
Language
en
Open access
green

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: Robotic Process Automation in Public Accounting. https://app.cochat.ai/papers/10.2139/ssrn.3193222
The paper
Cooper, L., Holderness, D. K., Sorensen, T., & Wood, D. A. (2018). Robotic Process Automation in Public Accounting. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3193222

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.