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Faculty of economics and business administration a future in accounting without human intervention



UNIVERSITY OF GHENT
FACULTY OF ECONOMICS AND BUSINESS
ADMINISTRATION

A FUTURE IN ACCOUNTING WITHOUT
HUMAN INTERVENTION
Number of words: 17,117

Master’s Dissertation submitted to obtain the degree of:
Master of Science in Business Economics - Accountancy
Academic year: 2017 – 2018

Student: Mélanie Simon
Student number: 01615385
Melanie.Simon@UGent.be
Promotor: Prof. Dr. Patricia Everaert
Patricia.Everaert@UGent.be





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Title page
Title:

A future in accounting without human intervention

Author:
E-mail address:
Student number:

Mélanie Simon
Melanie.Simon@UGent.be
01615385

University:
Faculty:
Study:
Study year:

University of Ghent
Faculty of Economics and Business Administration
Master of Science in Business Economics – Accountancy
2017 – 2018

Promotor:
Email address:

Prof. Dr. Patricia Everaert
Patricia.Everaert@UGent.be

Document status:
Number of words:
Date:
Place:

Final version


17,117
4th of June 2018
Ghent




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Abstract
Objectives:
The aim of this study is to examine the impact of automation on the accounting
profession, in order to answer the question if a future in accounting without human
intervention is possible.
Background:
In order for automation to replace the accountant, technology needs to provide useful
financial information; it needs to be relevant, represented faithfully, comparable,
verifiable, timely and understandable.
Systematic literature study:
32 articles were selected, main subjects identified were: consequences on accounting,
moral decision-making, future role, implications on small accounting firms, implications
on the labour market and solutions.
Methods:
Semi-structured interviews were conducted with accountants from eight different
companies in Belgium and Luxembourg. Professionals have been interviewed regarding
their use of technology and their future perspective on the accounting profession.
Results:
Eight interviews have been conducted, main subjects identified were: the use of
automation, qualitative characteristics, skills and implication on small accounting firms.
Results show that the accountant will be using automation for routine tasks, rather than
being replaced by it. Tasks that require critical-thinking and creativity seem to be more
difficult to be automated. In the coming years, the technology will be able to assist
accountants in non-repetitive tasks. The business model of accounting firms will change
and accountants who are not ready for automation will be at risk of being replaced by
automation. Specific skills will already need to be acquired before starting to work.
Relevance for practice:
Accountants will shift to either advisory or consultancy. IT-, tax- and analytical skills will
have to be developed. Universities will need to change their education programs in order
for future accountants to be ready to work alongside automation.

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Table of content
Introduction
1.1 Context
1.2 Problem Statement
1.3 Research question
1.4 Structure of the thesis

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10
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2. Artificial Intelligence and automation

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3. The Accountant, Auditor and Management Accountant
3.1 Definitions
3.2 A brief history of the accountant
3.3 Financial reporting
3.3.1 Objectives of the financial reporting
3.3.2 Qualitative characteristics of useful financial information

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3.3.2.1 Fundamental qualitative characteristics
3.3.2.2 Enhancing qualitative characteristics

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4. Systematic literature study
4.1 Databases and search strategy
4.2 Study selection
4.3 Quality appraisal
4.4 Data extraction
4.5 Description of the studies
4.6 Consequences on the accounting profession
4.7 Moral decision-making
4.8 Future role
4.9 Implications on small accounting firms
4.10 Implication on the labour market
4.11 Solutions
5. Methodology
5.1 Research design
5.1.1 Interviews
5.1.2 Population
5.1.3 Place
5.1.4 Description of the respondents
5.2 Data collection
5.2.1 Semi-structured interviews
5.3 Reliability and validity
5.3.1 Reliability
5.3.2 Validity
5.3.2.1 Internal validity
5.3.2.2 External validity

5.4 Ethical considerations

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30
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35
35
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37
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6. Results
6.1 Description of the results
6.2 The use of automation in the company
6.3 Qualitative characteristics of the financial information
6.4 Skills
6.5 Small accounting companies

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47
51
57

7. Discussion

59

8. Conclusion

61

9. Limitations

63

10. Future research

65

11. Management and policy implementations

67

References

69

Annexes
Annex 1: Printscreen databases
a. ABI/INFORM Collection
b. Accounting, Tax and Banking Collection
c. Web of Science
Annex 2: Overview selected articles
Annex 3: Interview guideline
Annex 4: Confidentiality agreement

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Acknowledgments
The completion of this thesis has been very fascinating and instructive, but also hard
work. These seven months of work have been very challenging for me and I would
like to thank the people who contributed directly or indirectly to the completion of the
work presented in this thesis.
This accomplishment would not have been possible without them.
I would first like to thank my promoter, Prof. Dr. Patricia Everaert. The door to her
office was always open whenever I had questions about my research. I thank her for
advising and guiding me to the right direction during these last couples of months.
I would like to express my gratitude to all the respondents of the interviews for taking
time to participate in this study.
My sincere gratitude also goes out to Inge van der Veen and to Casper van den Berg
for carefully and critically reviewing my thesis.
I would especially like to express my gratitude to my partner for providing me moral
and intellectual support throughout the process of researching and writing this
dissertation. Without him I would not have come this far.

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List of abbreviations
AI

Artificial Intelligence

BDO

Binder Dijker Otte

CEO

Chief Executive Officer

CFO

Chief Financial Officer

CO

Controlling

FI

Financial Accounting

GDPR

General Data Protection Regulation

IASB

International Accounting Standards Board

IBM

International Business Machines

IFRS

International Financial Reporting Standards

IT

Information Technology

KPI

Key Performance Indicator

KPMG

Klynveld Peat Marwick Goerdeler

LPL

Lo Presti Ludovic

PS

Project System



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Introduction
The first chapter describes the context and the problem statement. Then, the research
question will be presented and the structure of the thesis will be described.
1.1 Context
In today’s modern world, a lot of technologic advances have been developed at an
undoubtedly fast rhythm, amplifying the need for companies to invest in Artificial
Intelligence (AI) and automation.
At Google's annual Input/Output developer conference, Google affirmed its desire to
integrate Artificial Intelligence into people’s daily life through a smart personal assistant
(Staff, 2017). Other technology giants as Apple, Facebook, Microsoft and Amazon are
also interested in AI and invest heavily in this technology. The use of Artificial Intelligence
and automation can reduce the need for human labour. This leads to uncertainty
concerning certain professions, such as accounting (Manjoo, 2017).
According to a study conducted by Frey & Osborne (2017), 702 job titles are at risk of
automation. Among these professions, accounting is on top of the list with 94 percent
probability of being computerized in the next two decades (Nagarajah, 2016). Artificial
Intelligence can be integrated into accounting processes and thereby replace humans. In
fact, an artificial agent called Amelia has already started at Shell and Baker Hughes (two
of the biggest gas groups) to take over the duties of accountants and call centre agents.
The system has the ability to understand natural language that allows to interact with
humans. It does not only recognize words, it also understands the meaning of them.
These are tangible signs that the employment of white-collar workers could be
threatened by the rise of Artificial Intelligence (Twentyman, 2017).
Recently, International Business Machines (IBM)'s AI has demonstrated its exceptional
ability to replace humans in performing tasks previously reserved for human intelligence.
The software can answer any question asked by a human in natural language, orally or
in writing, in eight different languages (Tual, 2017).
1.2 Problem Statement
According to an analysis provided by Accenture, 40 percent of transactional accounting
work could be automated by 2020 (Seek, 2017). As an authority on the profession, the
Association of Chartered Certified Accountants is sceptical about the future of the
accountants. The skills that accountants nowadays apply may not be relevant anymore
in the next coming years (Galarza, 2017).

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The aim of this study is to research the impact of automation in the accounting field. In
order for automation to be able to replace accountants, useful financial information still
has to be provided. Therefor, the attributes that make financial information useful also
have been researched.
1.3 Research question
The following research question is formulated: is a future in accounting without human
intervention possible?
In order to answer this research question, a systematic literature review and interviews
have been conducted. First, all the literature regarding the impact of technology on the
accounting profession has been gathered. Later, several interviews have been
conducted with eight different Belgian and Luxembourgish companies.
1.4 Structure of the thesis
This thesis consists of eleven chapters. The first chapter is the introduction in which the
context, the problem statement and the research question is described. The second
chapter consists of background information regarding Artificial Intelligence and
automation. The third chapter consists of definitions of the accountant, the auditor and
the management accountant. The qualitative characteristics will also be addressed in this
chapter. The systematic literature review can be found in chapter four including the
search strategy and the results. The methodology of the empirical study can be found in
chapter five, where the study design, the data collection and the reliability and validity.
The sixth chapter consists of the results from the semi-structured interviews. The
discussion can be found in chapter seven, in chapter eight the conclusion and in chapter
nine the limitations. Suggestions for future research are described in chapter ten. Finally
in chapter eleven, the management and policy implications of this study are given.

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2. Artificial Intelligence and automation
In order to answer the question if a future in the accounting profession without human
intervention is possible, in this chapter background information about the subject will be
provided. Four things are mainly important: 1) what is Artificial Intelligence and
automation 2) definitions of the accountant, the auditor and the management accountant
3) a brief history of the accounting profession and 4) objectives of the financial reporting
and when automation is useful in light of the accounting profession.
Artificial Intelligence is the theory and development of computer systems that are able to
perform tasks that normally require human intelligence, such as visual perception,
speech recognition, decision-making and translation between languages (Oxford
Dictionary, n.d.a). The system can perform functions that a human brain does, like
learning and problem solving.
The term Artificial Intelligence was first introduced in 1956 by John McCarthy (Smith,
McGuire, Huang et al., 2006), but in 1950 Alan Turing already wrote a paper about the
ability of machines to do intelligent things. The purpose of Turing’s paper was to consider
the question “if machines can think”? He replaced this question by testing if a machine
could replace a human being in the game of imitation. The purpose of the test was to ask
a person to make the distinction between answers given by a machine and those
provided by a human, by communicating via an old teleprinter. Turing predicted that in
the year 2000, the machine would be able to fool 30% of the respondents in a fiveminute test.
Familiar definitions are automation, big data, machine learning and Expert Systems.
Automation is “the technique, method or system of operating or controlling a process by
automating devices, reducing human intervention to a minimum. Automation has a single
purpose: to let machines perform repetitive, monotonous tasks” (Dictionary, n.d.).
“Automation is a technology that actively selects data, transforms information, makes
decisions and controls processes” (Lee & See, 2014).
Big Data is “a set of extremely large data that may be analysed computationally to reveal
patterns, trends and associations, especially relating to human behaviour and
interactions. The more data the machine collects, the more it will be able to learn and the
better it will function” (Oxford Dictionary, n.d.b). “Big Data has the ability to scan large
volumes of data and perform analytics with sophisticated algorithms to facilitate decisionmaking in the accounting function” (Brands & Smith, 2016).

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Machine learning is “the ability of a computer to learn from experience, for instance to
modify its processing on the basis of newly acquired information. It is a type of AI that
focuses on the development of computer programs that can access data and use it to
learn for themselves” (Oxford Dictionary, n.d.c). According to Tynan (2017), “machine
learning systems essentially code themselves, developing their own instructions by
generalizing from examples. The classic example is image recognition, where the
machine learning will identify what is on the image without a human ever telling the
machine what is on the picture.”
Expert system is “a computer system that can provide information and expert advice on a
particular subject. The program asks users a series of questions about their problem and
gives them advice based on its store of knowledge” (Oxford Learner’s Dictionary, n.d.).
Quinn (1990) defined Expert System, as “an interactive computer program that asks the
same questions a human expert would ask, and from the information given to it by the
user, provides the same answer the expert would provide. If a body of knowledge can be
codified into a set of questions and answers, it can be incorporated into an Expert
System software program.”

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3. The Accountant, Auditor and Management Accountant
The field of accounting involves the study of accountancy, auditing, finance, financial
management and tax (Wohlner, n.d.). This thesis will only focus on the accountant, the
auditor and the management accountant. In this chapter, these definitions are given, as
well as a brief history of the accountant. Finally, the financial reporting will be addressed.
3.1 Definitions
The accountant
An accountant is a qualified person who is trained in bookkeeping and in preparation,
auditing and analysis of accounts. Accountants prepare annual reports and financial
statements for planning and decision-making and advise on tax laws and investment
opportunities (Business Dictionary, n.d.). Accounting is the process of measuring and
summarizing business activities, interpreting financial information and communicating the
results to management and other decision makers (Minnesota Libraries, n.d.).
The auditor
When the accounting process ends, auditing begins.
Auditing is the purpose of determining the true and fair representation of the financial
statements. The auditor examines the financial report of an organization.
There are four main steps in the audit process (PriceWaterhouseCoopers, n.d.). The first
step is to determine the auditor’s role and the terms of engagement, which is a letter
signed by the client. The second step is to plan the audit, which includes details of
deadlines and the departments the auditor covers. Once the auditor is aware of the
company's sector and its internal control and has identified the risks, it is necessary to
analyse the accounts more precisely in order to identify the risks of any fraud or errors.
This is the third step. The last and most important element of an audit is reporting the
results. The results are documented in the report of the auditor, including the justification
of the auditor’s opinion. This opinion is the conclusion of all the work carried out during
the audit.

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The management accountant
The management accountant is a professional who assists managers by helping them
making decisions. The management account hence prepares and analyses the financial
statements of a company and analyse its financial performance in order to advice the
managers in the decisions-making process (Institute of Management Accountants,
2008). The management accountant provides the necessary information and advices to
the decision-makers. To forecast the future and to monitor the performance of the
company, managers will need information provided by the management accountants
(Certified Practising Accountant Australia, 2012).
3.2 A brief history of the accountant
The accounting profession has recently been recognized, but the art of accounting is
nearly 6,000 years old (Mason, 1953). The Romans and Egyptians were the first to use
accounting in commercial life. In the Roman Empire, one of the purposes of accounting
was to present the economic situation of the merchants to their customers. The purpose
was to record on the left side of the notebook the use or consumption of resources, while
the right part was used for the origin or production of resources (Alexander, 2002).
Although accounting has indeed been used since the Stone Age, the most significant
development with regards to modern accounting occurred during the Renaissance period
in Italy. Brother Luca Bartolomes Pacioli, an Italian born in 1445, is the inventor of
modern accounting. Pacioli was the first person to publish a work on double-entry
bookkeeping system (Mason, 1953).
The main principles of accounting that are currently used in companies were found at
that time. Modern accounting techniques are based on double entry bookkeeping,
defined as “debit” and “credit”. Three books were necessary to keep adequate records
for every business: a ledger, a journal and a memorandum book. A trial balance has to
be made at the end of each year (Mason, 1953).
Nowadays there are standards set by the International Accounting Standards Boards
(IASB) to guide and harmonize the accounting practices. The IASB develops and
approves International Financial Reporting Standards (IFRS), a set of international
accounting standards to specify how to report the accounts, so that everybody can
understand the business and the reporting from companies situated in different
countries. The Board regularly updates the Conceptual Framework for Financial

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Reporting to facilitate the use of IFRS Standards. The Conceptual Framework includes
qualitative characteristics of the financial information to help accountants decide what
information to provide and how to present it.
In the next subchapter, the objectives of the financial reporting and the qualitative
characteristics will be described. In accounting, automation is useful when it is able to
perform the same tasks as accountants do. In other words: AI needs to provide useful
financial information. Useful financial information, as described by the Conceptual
Framework (Ernst & Young, 2010), will be explained.
3.3 Financial reporting
The conceptual framework for financial reporting sets out the concepts for the
preparation of the financial statements for external users. The conceptual framework is
structured according to the following hierarchy:
-

The objectives of the financial reporting are stated;

-

The qualitative characteristics of the information contained in the financial
statements;

-

The definition, recognition and measurement of the elements from which financial
statements are constructed (assets, liabilities, equity, income and expenses).

The following chapter explains the objectives of the financial reporting and the qualitative
characteristics of useful financial information (Deloitte, n.d.).

3.3.1 Objectives of the financial reporting
The purpose of financial reporting is to provide useful financial information about an
entity to potential investors, lenders and other creditors who use that information to make
decisions about buying, selling or holding equity or debt instruments and providing or
settling loans or other forms of credit (IFRS, n.d.).
To achieve this objective, the financial reports must provide information on the economic
resources of the entity, their counterparty and the transactions and other events and
circumstances that affect them. The degree of usefulness of financial information
depends on the qualitative characteristics.

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3.3.2 Qualitative characteristics of useful financial information
The qualitative characteristics of financial information, as set out in the Conceptual
Framework of the IASB are fundamental to identify the types of information that are most
likely to be useful for the purpose of making decisions about the reporting entity based
on the information presented in its financial report (Ernst & Young, 2010).
The revised Framework distinguishes two types of qualitative characteristics that are
necessary to provide useful financial information: fundamental qualitative characteristics
and enhanced qualitative characteristics.
3.3.2.1 Fundamental qualitative characteristics
By fundamental qualitative characteristics is meant: the relevance and the faithful
representation of financial information.
Relevant information is capable of influencing the decision made by users. It is capable
of making different decisions if it has predictive value, confirmatory value or both.
Predictive value helps users in predicting or anticipating future outcomes. Confirmatory

value enables users to check and confirm earlier predictions or evaluations.
The Financial reports represent economic phenomena in words and numbers. To give a
perfectly faithful picture, the financial information must have three characteristics: it must
be complete, neutral and free of errors. The revised Framework acknowledges limitations
in achieving a faithful representation; financial information might not be totally free from
errors. However, a faithful representation is achieved if no errors or omissions affect the
description of economic phenomena and the process applied to produce reported
information has been selected and applied without errors.

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3.3.2.2 Enhancing qualitative characteristics
The usefulness of financial information is enhanced when it is comparable, verifiable,
timely and understandable. The purpose is to enhance the relevant and faithfully
financial information.
Comparable:

User decision-making implies making choices between various options, such as selling
or holding an investment or investing in one entity over another. Therefore, information is
more useful if it can be compared with other items, in different periods within a set of
financial statements and across different reporting entities.
Verifiable:

Verifiability assumes that different well-informed and independent observers could come
to a consensus -but not necessarily to a complete agreement- on whether a particular
depiction of an event or transaction is a faithful representation.
Timely:

Timeliness of financial information involves the need to make the information available in
time to decision-makers, in order to influence their decisions. The information should not
be significantly delayed; otherwise it will be of little or no value. However, some
information may also continue to be useful after the end of a reporting date because, for
example, some users may need to identify and evaluate trends.
Understandable:

The information is understandable when it is classified, characterized and presented in a
clear and concise manner. A company's financial information should be presented in
such a way that a person with a reasonable knowledge of business and finance, and the
willingness to study the information, should be able to comprehend it.

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4. Systematic literature study
The fourth chapter discusses the methodology of the literature study and the obtained
results from the systematic literature study.
4.1 Databases and search strategy
The databases that were searched for relevant studies are: ABI/INFORM Collection,
Accounting, Tax and Banking Collection and Web of Science. The searches were
conducted in January 2018 (Week 2). The following search strategy was used:
(accountancy OR audit OR accounting OR auditing) AND (automation OR technology
OR artificial intelligence OR robots) AND (Future)
In the database ABI/INFORM, this search strategy resulted in 764,477 articles, in
Accounting, Tax and Banking Collection it resulted in 81,490 articles and finally in Web of
Science it resulted in 5,695 articles. Due to the number of articles found in ABI/INFORM
Collection and Accounting and Tax and Banking Collection, the following filters were
used: full-text, peer reviewed, scholarly journals and articles. These filters led to 75,236
articles in ABI/INFORM and 7,159 articles in Accounting, Tax and Banking Collection. No
filters were used in the database Web of Science. The total number of articles found by
this searching strategy was 88,090. In annex 1, a screenshot of all databases can be
found.
4.2 Study selection
One reviewer searched for relevant studies using the search strategy described above.
The selection of studies was determined by two steps: the studies were first filtered on
relevance of the title (n=164) and after that, the studies were filtered on relevance of the
abstract (n=92). Duplicates and studies written in another language than English were
excluded (n=44). The remaining studies (n=48) were read full-text and articles not related
to the different technologies as described in Chapter 2 and not related to the future of
accounting were excluded. The 32 selected articles underwent quality appraisal.
The flow chart of the selection process can be found on the next page.

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Figure 1: Flow chart of the selection process

4.3 Quality appraisal
The quality assessment of the selected articles was conducted by using Hawker, Payne,
Kerr (et al., 2002)’s framework. This framework includes nine questions, regarding the
following domains: abstract and title, introduction and aims, method and data, sampling,
data analysis, ethics and bias, results, generalizability and implications and usefulness.
Each of the domains can be scored from 1 (very poor) to 4 (good), with a maximum
score of 36. High quality is defined as 30-36 points and medium quality as 24-29 points.
Of the 32 articles, 13 articles had a high-quality score and 13 articles had a medium
quality score. 6 articles had a lower quality score, but the findings were yet seen as
interesting for this research.
The framework for quality appraisal can be found in table 1, in table 2 to 5 the quality
assessment of the selected articles can be found.

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1. Abstract and title

Did they provide a clear description of the study?

2. Introduction and aims

Was there a good background and clear statement of the aims?

3. Method and data

Is the method appropriate and clearly explained?

4. Sampling

Was the sampling strategy appropriate to address the aims?

5. Data analysis

Was the description of the data analysis sufficiently rigorous?

6. Ethics and bias

Have ethical issues been addressed, and what has necessary
ethical approval gained? Has the relationship between
researchers and respondents been adequately considered?

7. Results

Is there a clear statement of the findings?

8. Generalizability

Are the findings of this study transferable to a wider population?

9. Implications and usefulness

How important are these findings to policy and practice?

Table 1: Framework for Quality Appraisal

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AlHtaybat
et al.,
2017

Anony
mous,
1987

Arntz
et al.,
2017

Baldwin
et al.,
2006

Beaman
et al.,
2007

Blum,
1966

Chase
et al.,
1991

Chelliah,
2017

Cole
et al.,
1992

4

2

4

4

4

2

4

3

2

4

2

4

3

3

2

3

3

2

4

2

4

3

4

2

2

2

2

4. Sampling

4

2

4

3

4

2

2

2

2

5. Data analysis

4

2

4

3

4

3

2

2

3

6. Ethics and bias

4

2

3

3

4

2

2

2

2

7. Results

4

3

3

2

3

3

3

3

3

8. Generalizability

4

3

4

3

4

3

3

3

3

9. Implications and
usefulness

4

3

4

2

3

3

3

3

3

Total

36

21

34

26

33

22

24

23

22

1. Abstract and
title
2. Introduction and
aims
3. Method and
data

Table 2: Framework for Quality Appraisal

Coyne
et al.,
2017

David,
2015

Frey
et al.,
2013

Gamage,
2016

Gonzalez et
al., 2012

Henry
et al.,
2015

Herbert
et al.,
2016

Kim et al.,
2017

2

3

4

3

4

3

2

3

2

2

4

2

4

3

3

3

2

3

3

3

4

2

3

4

4. Sampling

2

3

4

3

4

2

3

4

5. Data analysis

3

3

4

3

3

2

3

4

6. Ethics and bias

2

3

3

3

3

3

3

4

7. Results

3

3

4

3

4

3

3

4

8. Generalizability

3

3

4

3

3

3

3

4

3

3

3

3

3

3

3

4

22

26

33

26

32

24

26

34

1. Abstract and
title
2. Introduction and
aims
3. Method and
data

9. Implications
and usefulness
Total

Table 3: Framework for Quality Appraisal

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Kokina
et al.,
2017

Liu et
al.,
2014

Marcello
et al.,
2017

Moudud-UlHuq, 2014

Omar,
1993

Oschinski
et al.,
2017

Özdoğan,
2017

Parham
et al.,
2012

1. Abstract and title

3

2

2

3

4

4

3

3

2. Introduction and
aims

3

3

2

4

4

4

3

2

3. Method and data

4

3

4

4

2

3

3

3

4. Sampling

3

3

4

4

3

3

3

3

5. Data analysis

3

3

3

3

3

4

3

3

6. Ethics and bias

4

3

2

3

3

4

3

3

7. Results

3

3

2

3

4

4

3

3

8. Generalizability

2

3

3

3

3

3

2

3

9. Implications and
usefulness

2

3

3

3

4

4

3

3

Total

27

26

25

30

30

30

26

26

Table 4: Framework for Quality Appraisal

Rattunde
et al.,
2016

Sangster,
1994

Silverman,
1966

Sorgner,
2017

Tuzhilin,
2004

Wilson et
al., 1992

Zarowin,
1994

3

3

3

4

3

3

3

3

4

3

4

4

3

3

3

4

3

4

2

4

2

4. Sampling

3

4

3

4

2

4

2

5. Data analysis

4

4

3

4

2

4

3

6. Ethics and bias

4

4

3

3

2

3

3

7. Results

4

4

3

4

3

4

3

8. Generalizability

4

3

3

4

2

3

3

9. Implications
and usefulness

4

3

3

4

3

3

3

Total

32

33

27

35

23

31

25

1. Abstract and
title
2. Introduction
and aims
3. Method and
data

Table 5: Framework for Quality Appraisal

23


4.4 Data extraction
From the included articles, the data was gathered using a standard data extraction form:
author, title, year, country, study objective, method and findings. The data of the articles
used can be found in annex 2.
4.5 Description of the studies
The included studies vary in method of study, location of study and time of study.
Regarding the study methods, the 32 studies that are included can be categorized in
sixteen review studies (Kokina & Davenport, 2017, Oschinski & Wyonch, 2017, Chelliah,
2017, David, 2017, Coyne, Coyne & Walker, 2017, Rattunde, 2016, Gamage, 2016,
Henry & Hicks, 2015, Liu & Vasarhelyi, 2014, Moudud-Ul-Huq, 2014, Baldwin, Brown &
Trinkle, 2006, Silverman, 1996, Omar, 1993, Chase & Shim, 1991, Anonymous, 1987 &
Blum, 1986), eight survey studies (Özdoğan, 2017, Sorgner, 2017, Gonzalez, Sharma &
Galetta, 2012, Parham, Noland & Kelly, 2012, Beaman & Richardson, 2007, Sangster,
1994, Tuzhilin, 2004 & Wilson & Sangster, 1992), four interview studies (Al-Htaybat &
Von Alberti-Alhtaybat, 2017, Marcello, Ray, Carmichael et al., 2017, Herbert, Dhayalan &
Scott, 2016 & Zarowin, 1994), three quantitative studies (Kim, Kim & Lee, 2017, Arntz,
Gregory & Zierahn, 2017 & Frey & Osborne, 2013) and one case study (Cole & Hales,
1992).
Of the 32 studies, 19 were conducted in the United States (Coyne et al., 2017, Kokina et
al., 2017, David, 2017, Marcello et al., 2017, Rattunde, 2016, Henry et al., 2015, Liu et
al., 2014, Frey et al., 2013, Parham et al., 2012, Gonzalez et al., 2012, Beaman et al.,
2007, Baldwin et al., 2006, Tuzhilin, 2004, Silverman, 1996, Zarowin, 1994, Cole et al.,
1992, Chase et al., 1991, Anonymous, 1987 & Blum, 1986), seven were conducted in the
United Kingdom (Al-Htaybat et al., 2017, Kim et al., 2017, Özdoğan, 2017, Chelliah,
2017, Herbert et al., 2016, Sangster, 1994 & Wilson et al., 1992), two in the Netherlands
(Arntz et al., 2017 & Omar, 1993), one in Canada (Oschinski et al., 2017), one in India
(Moudud-Ul-Huq, 2014), one in Australia (Gamage, 2016) and one in Russia (Sorgner,
2017).
The year the studies took place varies from 1986 to 2017. Ten studies took place in 2017
(Coyne et al., Sorgner, Al-Htaybat et al., Marcello et al., Kokina et al., Kim et al., Arntz et
al., Özdoğan, Oschinski et al. & Chelliah), three studies in 2016 (Gamage, et al.,
Dhayalan & Scott), two studies in 2015 (David & Henry et al.), one study in 2014
(Moudud-Ul-Huq), one study in 2013 (Frey et al.), one study in 2014 (Liu et al.), two
studies in 2012 (Parham et al., & Gonzalez et al.), one in study 2006 (Baldwin et al.), one

24


study in 2007 (Beaman et al.), one study in 2004 (Tuzhilin), one study in 1996
(Silverman), two studies in 1994 (Sangster, Zarowin), one study in 1993 (Omar), two
studies in 1992 (Cole et al. & Wilson et al.) one study in 1991 (Chase et al.), one study in
1987 (Anonymous) and one study in 1986 (Blum).
Main subjects identified were: 1) consequences on the accounting profession, 2) moral
decision-making, 3) future role, 4) implication on small accounting firms, 5) implications
on the labour market and 6) solutions. Each of these topics will be discussed in the next
paragraph.

4.6 Consequences on the accounting profession
The first step is to distinguish routine tasks (which can easily be automated) and nonroutine tasks (which are more difficult to be performed by machines or software). Jobs
that require critical thinking and human contact will not be automated soon (Oschinski et
al., 2017). These occupations need high-level creativity and training. Jobs that generally
consist of routine tasks do not require a level of high education and only little human
interaction is needed compared to non-routine tasks. Non-routine tasks can be divided
into manual occupations and intellectual occupations. Manual occupations generally
require lower qualifications than cognitive jobs that generally require a high level of
education (Oschinski et al., 2017).
Herbert et al. (2016) explored the possibilities for transforming the way professional work
in the future, by using automation. The study describes that since automation is used to
eliminate routine and repetitive tasks, it will allow employees to focus on more creative,
non-structured tasks that require more thinking. While focusing more on creative, nonstructured tasks, the value of the accountant`s contributions will increase. Kim et al.
(2017) examined the relative quantities of jobs that are susceptible to become
computerized in the future and concluded that jobs that require little creativity or complex
training (routine occupations), are most likely to be replaced. Jobs that require critical
thinking and human contact will not be easily automated. These occupations require
high-level creativity and training. Tuzhilin (2004) found the same results by examining
current trends in the technology-driven automation and the effect that it will have on
different jobs. The author describes that repetitiveness, stability and structure are the
characteristics of jobs that can be automated. In other words: routine production jobs can
be performed by automation. Arntz et al., (2017) demonstrated that empirical

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