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A finer-grained approach to assessing the “quality” (“quantity” and “richness”) of risk management disclosures


Jia, J and Munro, L and Buckby, S, A finer-grained approach to assessing the 'quality' ('quantity' and 'richness') of risk management disclosures, Managerial Auditing Journal, 31, (8/9) ISSN 0268-6902 (2019) [Refereed Article]


Copyright Statement

Copyright 2016 Emerald Group Publishing Limited

DOI: doi:10.1108/MAJ-12-2014-1135


Purpose This paper aims to examine the "quality" of narrative risk management disclosures (RMD) from a "quantity" and "richness" (width and depth) perspective, utilising a finer-grained approach. Evidence is then provided on the relationships between RMD quality and the corporate determinants driving that quality.

Design/methodology/approach Within a multidimensional quality disclosure framework, annual report narrative RMD from the top 100 Australian Securities Exchange (ASX) listed companies precisely "matched" for the 2010 and 2012 years were examined using semantic content analysis. The relationship between the dimensions and sub-dimensions of RMD "quantity" and "richness", and various corporate characteristics were explored using ordinary least squares (OLS) regression analysis.

Findings The results indicate that RMD are considerably lacking in quality, from the "quantity", "width" and particularly the "depth" dimension and sub-dimensions for both years. Many companies provide "boiler plate" RMD over consecutive years and many do not comply with the intent of the ASX Corporate Governance Principles and Recommendations under the "if not, why not" regime (ASX CGC, 2010). Company size and cross-listing were found to be the primary determinants of higher quality RMD and, to a lesser extent, firm risk. Some evidence was found that "quality" RMD were less likely where companies are more highly leveraged and when their shareholders are more concentrated.

Research limitations/implications Although two coders independently coded the RMD and specific decision rules were followed, the subjectivity inherent in conducting semantic content analysis into the dimensions and sub-dimensions of the framework cannot be completely eliminated. However, by adopting a finer-grained approach, this study contributes to the global literature on the quality of RMD. Previous studies are extended by analysing and testing the individual dimensions and sub-dimensions of "quantity" and "richness" which provides new empirical evidence and a more comprehensive portrayal of RMD quality and a greater understanding why some companies are more likely to disclose higher quality RMD than others.

Practical implications These results provide useful and predominantly new empirical evidence on the quality of RMD for practitioners, regulators and researchers. As many companies are not complying with the "intent" of the "if not, why not" approach, these results support the argument for mandated narrative RMD regulations at an international level.

Originality/value The multidimensional framework of RMD "quantity" and "richness" provides a basis for examining not only how much is disclosed, but what is disclosed and how. In adopting a finer-grained approach, this study analyses and tests the individual dimensions and sub-dimensions of the framework. This provides a deeper understanding of the overall quality of RMD and the determinants driving RMD quality for the sample companies.

Item Details

Item Type:Refereed Article
Keywords:risk management, determinants, quality, disclosures, quantity, richness
Research Division:Commerce, Management, Tourism and Services
Research Group:Strategy, management and organisational behaviour
Research Field:Corporate governance
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in commerce, management, tourism and services
UTAS Author:Jia, J (Dr Jing Jia)
ID Code:131780
Year Published:2019 (online first 2016)
Web of Science® Times Cited:9
Deposited By:TSBE
Deposited On:2019-04-04
Last Modified:2020-04-30
Downloads:7 View Download Statistics

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