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Comparing the Strength of Auditing and Reporting Standards and Investigating their Predictors in Europe and Asia

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  • Pran Krishansing Boolaky
  • Barry J. Cooper

Abstract

type="main"> The World Economic Forum conducted an opinion survey to determine the strength of auditing and reporting standards (SARS) in 133 countries. It then assigned a score for SARS to each country as one of its global competitive indices. This is a unique dataset on SARS at country level. Using this dataset, this paper compares SARS scores for 72 countries (41 European and 31 Asian). A multi-phase regression analysis is employed to empirically investigate the predictors of SARS using five sub-models. Findings from the study support existing theory and add new findings to the auditing and reporting literature at a regional level. It suggests that there are nine predictors of SARS which are similar for both Europe and Asia but with different magnitude. In Asia the efficiency of the legal framework and the size of the foreign export market are also significant predictors of SARS compared to Europe.

Suggested Citation

  • Pran Krishansing Boolaky & Barry J. Cooper, 2015. "Comparing the Strength of Auditing and Reporting Standards and Investigating their Predictors in Europe and Asia," Australian Accounting Review, CPA Australia, vol. 25(3), pages 292-308, September.
    • Handle: RePEc:bla:ausact:v:25:y:2015:i:3:p:292-308
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    File URL: http://hdl.handle.net/10.1111/auar.12058
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    References listed on IDEAS

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    Cited by:

    1. Jamaani, Fouad & Ahmed, Abdullahi D., 2021. "Modifier effects of country-level transparency on global underpricing difference: New hierarchical evidence," International Review of Financial Analysis, Elsevier, vol. 74(C).
    2. Oleh Pasko, 2017. "Perception of Accounting and Auditing Standards by Users in Context of Formation of Institutional Openness in Regulation," Oblik i finansi, Institute of Accounting and Finance, issue 1, pages 68-75, March.
    3. Andra Maria ACHIM (NAȘCA), 2018. "The Strength Of Auditing And Reporting Standards In Relation To Financial Reporting Quality," SEA - Practical Application of Science, Romanian Foundation for Business Intelligence, Editorial Department, issue 17, pages 193-197, September.
    4. Stewart Jones & Nurul Alam, 2019. "A machine learning analysis of citation impact among selected Pacific Basin journals," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(4), pages 2509-2552, December.

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