Why does decomposed audit proposal readability differ by audit firm size? A Coh-Metrix approach

Why does decomposed audit proposal readability differ by audit firm size? A Coh-Metrix approach PurposeThis paper aims to introduce the emerging artificial-intelligence-based readability metrics (Coh-Metrix) to examine the effects of firm size on audit proposal readability.Design/methodology/approachCoh-Metrix readability measures use emerging computation linguistics technology to better assess document readability. These metrics measure co-relations of words, sentences and paragraphs on multi-dimensions rather than adopting the unidimensional “bag of words” approach that examines words in isolation. Using eight Coh-Metrix orthogonal principal component factors, the authors analyze the Chang and Stone (2019) data set comprised of 370 hand-collected audit proposals submitted by audit firms for the US state and local governments’ audit service contracts.FindingsAudit firm size has a significant impact on the readability of audit proposals. Specifically, as measured by the traditional readability metric, the proposals from smaller firms are more readable than those submitted by larger firms. Furthermore, decomposed readability metrics indicate that smaller firm proposals evidence stronger (deep) text cohesion, whereas larger firm proposals evidence a stronger narrative structure and higher connectivity (relational indicators) among proposal elements. Unlike the traditional readability metric, however, the emergent readability metrics are uncorrelated with auditor selection.Research limitations/implicationsWork remains to develop and validate Coh-Metrix measures that are specific to the context of accounting and auditing practice. Future research can use emerging readability measures to examine various textual features (e.g. text cohesion) in finance or accounting related documents.Practical implicationsThe results provide practitioners with insight into the proposal writing strategies and practices of larger and smaller firms. In addition, the results highlight the differing audit firm selection outcomes from traditional and Coh-Metrix readability metrics.Originality/valueThis study introduces new data and holistic readability measures to the auditing literature. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Managerial Auditing Journal Emerald Publishing

Why does decomposed audit proposal readability differ by audit firm size? A Coh-Metrix approach

Managerial Auditing Journal, Volume 34 (8): 29 – Sep 2, 2019

Loading next page...
 
/lp/emerald-publishing/why-does-decomposed-audit-proposal-readability-differ-by-audit-firm-hbqrURNoEX
Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0268-6902
DOI
10.1108/MAJ-02-2018-1789
Publisher site
See Article on Publisher Site

Abstract

PurposeThis paper aims to introduce the emerging artificial-intelligence-based readability metrics (Coh-Metrix) to examine the effects of firm size on audit proposal readability.Design/methodology/approachCoh-Metrix readability measures use emerging computation linguistics technology to better assess document readability. These metrics measure co-relations of words, sentences and paragraphs on multi-dimensions rather than adopting the unidimensional “bag of words” approach that examines words in isolation. Using eight Coh-Metrix orthogonal principal component factors, the authors analyze the Chang and Stone (2019) data set comprised of 370 hand-collected audit proposals submitted by audit firms for the US state and local governments’ audit service contracts.FindingsAudit firm size has a significant impact on the readability of audit proposals. Specifically, as measured by the traditional readability metric, the proposals from smaller firms are more readable than those submitted by larger firms. Furthermore, decomposed readability metrics indicate that smaller firm proposals evidence stronger (deep) text cohesion, whereas larger firm proposals evidence a stronger narrative structure and higher connectivity (relational indicators) among proposal elements. Unlike the traditional readability metric, however, the emergent readability metrics are uncorrelated with auditor selection.Research limitations/implicationsWork remains to develop and validate Coh-Metrix measures that are specific to the context of accounting and auditing practice. Future research can use emerging readability measures to examine various textual features (e.g. text cohesion) in finance or accounting related documents.Practical implicationsThe results provide practitioners with insight into the proposal writing strategies and practices of larger and smaller firms. In addition, the results highlight the differing audit firm selection outcomes from traditional and Coh-Metrix readability metrics.Originality/valueThis study introduces new data and holistic readability measures to the auditing literature.

Journal

Managerial Auditing JournalEmerald Publishing

Published: Sep 2, 2019

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off