Measuring annual report narratives disclosure

Empirical evidence from forward-looking information in the UK prior the financial crisis

Abed Suzan, Basil Al-Najjar, Clare Roberts

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract


Purpose:
This paper aims to investigate empirically the common alternative methods of measuring annual report narratives. Five alternative methods are employed, a weighted and un-weighted disclosure index and three textual coding systems, measuring the amount of space devoted to relevant disclosures.

Design/methodology/approach:
The authors investigate the forward-looking voluntary disclosures of 30 UK non-financial companies. They employ descriptive analysis, correlation matrix, mean comparison t-test, rankings and multiple regression analysis of disclosure measures against determinants of corporate voluntary reporting.

Findings:
The results reveal that while the alternative methods of forward-looking voluntary disclosure are highly correlated, important significant differences do nevertheless emerge. In particular, it appears important to measure volume rather than simply the existence or non-existence of each type of disclosure. Overall, we detect that the optimal method is content analysis by text-unit rather than by sentence.

Originality/value:
This paper contributes to the extant literature in forward-looking disclosure by reporting important differences among alternative content analyses. However, the decision regarding whether this should be a computerised or a manual content analysis appears not to be driven by differences in the resulting measures. Rather, the choice is the outcome of a trade-off between the time involved in setting up coding rules for computerised analysis versus the time saved undertaking the analysis itself.
Original languageEnglish
Pages (from-to)338-361
Number of pages24
JournalManagerial Auditing Journal
Volume31
Issue number4/5
DOIs
Publication statusPublished - 2016
Externally publishedYes

Fingerprint

Annual reports
Empirical evidence
Disclosure
Financial crisis
Narrative disclosure
Voluntary disclosure
Content analysis
Design methodology
Disclosure index
Measuring system
Multiple regression analysis
Trade-offs
Correlation matrix
T-test
Ranking

Cite this

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abstract = "Purpose:This paper aims to investigate empirically the common alternative methods of measuring annual report narratives. Five alternative methods are employed, a weighted and un-weighted disclosure index and three textual coding systems, measuring the amount of space devoted to relevant disclosures.Design/methodology/approach:The authors investigate the forward-looking voluntary disclosures of 30 UK non-financial companies. They employ descriptive analysis, correlation matrix, mean comparison t-test, rankings and multiple regression analysis of disclosure measures against determinants of corporate voluntary reporting.Findings:The results reveal that while the alternative methods of forward-looking voluntary disclosure are highly correlated, important significant differences do nevertheless emerge. In particular, it appears important to measure volume rather than simply the existence or non-existence of each type of disclosure. Overall, we detect that the optimal method is content analysis by text-unit rather than by sentence.Originality/value:This paper contributes to the extant literature in forward-looking disclosure by reporting important differences among alternative content analyses. However, the decision regarding whether this should be a computerised or a manual content analysis appears not to be driven by differences in the resulting measures. Rather, the choice is the outcome of a trade-off between the time involved in setting up coding rules for computerised analysis versus the time saved undertaking the analysis itself.",
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Measuring annual report narratives disclosure : Empirical evidence from forward-looking information in the UK prior the financial crisis. / Suzan, Abed; Al-Najjar, Basil; Roberts, Clare .

In: Managerial Auditing Journal, Vol. 31, No. 4/5, 2016, p. 338-361.

Research output: Contribution to journalArticle

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