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Introduction: Analytics in Accounting and Auditing
RMIT University, Melbourne, Australia.
University of Gävle, Faculty of Education and Business Studies, Department of Business and Economic Studies, Business administration. Centre for research on Economic Relations (CER), Sweden.ORCID iD: 0000-0002-4436-5920
Mid Sweden University, Sundsvall, Sweden; Centre for research on Economic Relations (CER), Sweden.
RMIT University, Melbourne, Australia.
2023 (English)In: Handbook of Big Data and Analytics in Accounting and Auditing / [ed] Tarek Rana, Jan Svanberg, Peter Öhman, Alan Lowe, Springer Nature , 2023, p. 1-13Chapter in book (Other academic)
Abstract [en]

Big data and analytics offer new opportunities and challenges for academics and practitioners in all business disciplines including accounting and auditing. In the backdrop of increasing growth of emerging technologies, the organizations in public, private and not-for-profit sectors are embracing digital economy and the fourth industrial revolution journey. This requires knowledge of better practice examples, lessons learned and future directions in addressing the new challenges and seizing new opportunities. In this chapter, we discuss the implications of data analytics, artificial intelligence and machine learning on the accounting and auditing practices. We focus on the technological, social, political, economic, institutional, and behavioral aspects of these technologies in the public, private, non-governmental and hybrid contexts. We present state-of-the-art research directions on philosophical, theoretical, methodological, and practical issues, new developments and innovations of big data, analytics, artificial intelligence, machine learning, blockchain, cryptocurrencies and other emerging technologies related to accounting and auditing.

Place, publisher, year, edition, pages
Springer Nature , 2023. p. 1-13
Keywords [en]
Big data Analytics, Artificial intelligence, Machine learning, Digital economy, Accounting, Auditing
National Category
Economics and Business Computer Sciences
Identifiers
URN: urn:nbn:se:hig:diva-42079DOI: 10.1007/978-981-19-4460-4_1ISI: 001145393700002Scopus ID: 2-s2.0-85160700275ISBN: 9789811944604 (print)ISBN: 9789811944598 (electronic)OAI: oai:DiVA.org:hig-42079DiVA, id: diva2:1765731
Available from: 2023-06-12 Created: 2023-06-12 Last updated: 2024-09-11Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf