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Exploring information systems success and organisational big data analytics capabilities in International Financial Reporting Standard 9 adoption

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posted on 28.03.2022, 20:34 by Connor Charles Stead
International Financial Reporting Standard (IFRS) 9 becomes mandatory in January 2018, replacing International Accounting Standard (IAS) 39. The standard introduces a revolutionary forward-looking credit loss model requiring affected entities to calculate and report expected credit losses associated with financial instruments. The new expected credit loss model necessitates entities to use a range of data assets and big data analytical approaches which have not previously been employed for IFRS reporting. Industry analysis has identified the new standard as a practical challenge which impacts data governance frameworks and information systems infrastructures. Extant IFRS, data governance and big data literature does not address the impact of IFRS standard evolution on the data governance frameworks and big data information system infrastructures of affected entities. Moreover, extant literature does not explore the influence of an entity’s capability to conduct big data analytics on their ability to comply with new standard requirements. To address these literature gaps, this study extended DeLone and McLean’s information systems success model with the concepts of organisational big data analytics capabilities and attitudes towards IFRS 9 compliance. The model was then tested by surveying industry professionals involved with international IFRS 9 implementation projects. To evaluate the research model, the results of the survey were analysed using structural equational modelling partial least squares. The primary finding of this study is that the capability of an entity to conduct big data analytics positively affects their employee’s intentions to use IFRS 9 analytics applications and their attitude towards the ability of their entity to comply with IFRS 9. Another key finding is that the quality of the output of information systems used to support IFRS 9 analytics applications positively impacts the intention of affected entity’s employees to use IFRS 9 analytics applications and their attitude towards the ability of the entity to comply with the standard. This study is the first to test an information systems success model in the context of IFRS 9 and is the first to introduce the concept of organisational big data analytics capabilities as an independent variable in information systems success. The study is also relevant to entities which are required to comply with IFRS as it provides empirical research which may help them to develop readiness and capabilities for future IFRS standards.

History

Table of Contents

Chapter 1. Introduction --- Chapter 2. Literature review -- Chapter 3. Research methodology -- Chapter 4. Results -- Chapter 5. Discussion -- Chapter 6. Conclusion -- 8. Reference list -- Appendices.

Notes

Bibliography: pages 127-140 Empirical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

MRes, Macquarie University, Faculty of Business and Economics, Department of Accounting and Corporate Governance

Department, Centre or School

Department of Accounting and Corporate Governance

Year of Award

2017

Principal Supervisor

Savanid Vatanasakdakul

Additional Supervisor 1

Michael Quilter

Additional Supervisor 2

Mauricio Marrone

Rights

Copyright Connor Charles Stead 2017. Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (xii, 195 pages) colour maps

Former Identifiers

mq:70573 http://hdl.handle.net/1959.14/1265606