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Public auditing, analytics, and big data in the modern economy

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TitleInfo
Title
Public auditing, analytics, and big data in the modern economy
Name (type = personal)
NamePart (type = family)
Appelbaum
NamePart (type = given)
Deniz
NamePart (type = date)
1963-
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Deniz Appelbaum
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Vasarhelyi
NamePart (type = given)
Miklos A
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Miklos A Vasarhelyi
Affiliation
Advisory Committee
Role
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chair
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NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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Graduate School - Newark
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Text
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theses
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2017
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2017-05
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2017
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xx
Language
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eng
Abstract (type = abstract)
There is an increasing recognition in the public audit profession that the emergence of big data as well as the growing use of analytics by audit clients has brought new concerns and opportunities. The first chapter introduces and identifies a number of these issues as that are facing the auditor in the modern economy. The second chapter primarily addresses one of these concern: what is the extant research on analytical procedures in the audit engagement? This dissertation proposes that the answers to these issues should start with an examination of the extant external audit research. However, an updated review of this research does not exist. Accordingly, 301 papers are identified regarding analytical procedures in the audit engagement. These papers are organized by technique, audit phase, and other attributes for understanding. This analysis is then presented as an External Audit Analytics (EAA) framework, which is subsequently expanded with the concepts of business analytics. Specifically, this synthesis organizes this literature, thereby offering guidelines regarding possible approaches for more complex and data driven analytics in the engagement. The third chapter elaborates and expands upon the next six issues and discusses additional aspects for contemplation by researchers and the profession. The fourth chapter discusses the issues of Big Data when it is being considered as Audit Evidence, particularly in the context of external big data. In this age of big data many sources of evidence are untraceable and their provenance unverifiable. This chapter provides guidance regarding provenance of big data, allowing it to be regarded as reliable evidence for external auditors. Finally, the fifth chapter concludes. These chapters discuss and illuminate broadly many issues facing the profession since clients are more automated and are capturing more data. These chapters also contribute to audit literature regarding external audit analytics and reliability of big data audit evidence. Big data and analytics are dramatically changing the business environment and their processes. Business methods are changing, capabilities are being added, anachronistic functions are being eliminated, and processes are being substantially accelerated. The same paradigm change should occur with the audit profession, and this dissertation provides some of the needed ideas to motivate such a shift.
Subject (authority = RUETD)
Topic
Management
Subject (authority = ETD-LCSH)
Topic
Auditing
Subject (authority = ETD-LCSH)
Topic
Big data
Subject (authority = ETD-LCSH)
Topic
Quantitative research
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Rutgers University Electronic Theses and Dissertations
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ETD_8140
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electronic resource
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Extent
1 online resource (xiv, 340 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Deniz Appelbaum
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Title
Graduate School - Newark Electronic Theses and Dissertations
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rucore10002600001
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Identifier (type = doi)
doi:10.7282/T3R49TQ2
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Appelbaum
GivenName
Deniz
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-04-27 22:27:39
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Name
DENIZ APPELBAUM
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Affiliation
Rutgers University. Graduate School - Newark
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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2017-05-31
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2018-05-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 31st, 2018.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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