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Cited by 272

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Genre Analysis of Corporate Annual Report Narratives: A Corpus Linguistics-Based Approach

Rutherford, B. A.·2005·Journal of Business Communication

Abstract

The study reported on in this article analyzes the U.K. Operating and Financial Review(OFR) as a genre of accounting narrative, employing word frequencies to identify genre rules. Evidence is found of rhetorical ploys within the genre and of differences in word frequencies, suggesting the existence of subgenres, related to the exigencies of the rhetorical situation. The genre employs language biased toward the positive (the “Pollyanna effect”), despite authoritative guidance that the OFR should be expressed in neutral terms. Evidence of subgenres includes differential propensity to employ positive language and differences in the rhetorical ploys adopted in connection with marketing strategy, corporate recovery, selfreference, comparative analysis, and gearing (leverage). The study also demonstrates the value of a corpus linguistics approach in analyzing accounting narratives.

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Metadata

Authors
Rutherford, B. A.
Venue
Journal of Business Communication
Published
2005-10-01
Volume
42
Issue
4
Pages
349-378
ISSN
0021-9436 (ISSN-L)
OpenAlex
W2045828589
Cited by
272
References
30
FWCI
56.52
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Genre Analysis of Corporate Annual Report Narratives: A Corpus Linguistics-Based Approach. https://app.cochat.ai/papers/10.1177/0021943605279244
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Rutherford, B. A. (2005). Genre Analysis of Corporate Annual Report Narratives: A Corpus Linguistics-Based Approach. Journal of Business Communication. https://doi.org/10.1177/0021943605279244

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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.