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Handbook of Big Data and Analytics in Accounting and Auditing
(CER)
Mid Sweden University, Faculty of Human Sciences, Department of Economics, Geography, Law and Tourism. University of Gävle. (CER)
Mid Sweden University, Faculty of Human Sciences, Department of Economics, Geography, Law and Tourism. (CER)ORCID iD: 0000-0001-5731-0489
2023 (English)Collection (editor) (Other academic)
Abstract [en]

This handbook collects the most up-to-date scholarship, knowledge, and new developments of big data and data analytics by bringing together many strands of contextual and disciplinary research. In recent times, while there has been considerable research in exploring the role of big data, data analytics, and textual analytics in accounting, and auditing, we still lack evidence on what kinds of best practices academics, practitioners, and organizations can implement and use. To achieve this aim, the handbook focuses on both conventional and contemporary issues facing by academics, practitioners, and organizations particularly when technology and business environments are changing faster than ever. All the chapters in this handbook provide both retrospective and contemporary views and commentaries by leading and knowledgeable scholars in the field, who offer unique insights on the changing role of accounting and auditing in today’s data and analytics driven environment. Aimed at academics, practitioners, students, and consultants in the areas of accounting, auditing, and other business disciplines, the handbook provides high-level insight into the design, implementation, and working of big data and data analytics practices for all types of organizations worldwide. The leading scholars in the field provide critical evaluations and guidance on big data and data analytics by illustrating issues related to various sectors such as public, private, not-for-profit, and social enterprises. The handbook’s content will be highly desirable and accessible to accounting and non-accounting audiences across the globe. 

Place, publisher, year, edition, pages
Springer Nature , 2023. , p. 564p. 1-564
Keywords [en]
Accounting, Big Data, Business Intelligence, Data Analytics, Textual Analytics
National Category
Business Administration
Identifiers
URN: urn:nbn:se:miun:diva-48485DOI: 10.1007/978-981-19-4460-4Scopus ID: 2-s2.0-85160716726ISBN: 978-981-19-4459-8 (print)OAI: oai:DiVA.org:miun-48485DiVA, id: diva2:1767029
Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2024-04-01Bibliographically approved

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Svanberg, JanÖhman, Peter

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