Audit rotation, does it matter?hj.diva-portal.org/smash/get/diva2:1433249/FULLTEXT01.pdf ·...

MASTER THESIS WITHIN: Business Administration

NUMBER OF CREDITS: 30 ECTS

PROGRAMME OF STUDY: Civilekonom

AUTHORS: David Frisk & Karl-Johan Edström

SUPERVISOR: Timur Uman

JÖNKÖPING May 2020

Audit rotation,

does it matter?

A study on audit rotations relationship to audit

quality and its contingencies

i

Acknowledgements

We would like to thank our supervisor Timur Uman for his continuous feedback and

suggestions on how to improve our work. For that we are truly thankful and without you

this thesis would not have been possible. We would also like to express our gratitude to

our opponents who have provided us with valuable feedback throughout this journey.

Lastly, we would like to thank Jönköping University, including teachers and colleagues,

for 4 years of great education and experiences.

THANK YOU!

Jönköping 2020-05-17

David Frisk Karl-Johan Edström

ii

Abstract

Master Thesis, Civilekonomprogrammet

Authors: David Frisk & Karl-Johan Edström

Supervisor: Timur Uman

Examiner: Emilia Florin-Samuelsson

Title: Audit rotation, does it matter? A study on audit rotations relationship to audit

quality and its contingencies

Background & Problematisation: Poor audit quality has historically led to huge

consequences for the society. A low audit quality is often related to a low auditor

independence, which can be caused by the auditor's incentive to maximize personal gain.

In attempts to strengthen the auditor independence and thereby the audit quality, several

audit regulations have been issued, where the mandatory audit rotation has been the

subject to intensive debate. Although the previous research on audit rotation and audit

quality is extensive, few studies investigate the contingency aspects of the relationship

more specifically firm visibility.

Purpose: The purpose of the study is to explain how audit firm rotation and audit partner

rotation relate to audit quality and how this relationship is contingent on firm visibility.

Method: The study is conducted quantitatively using a positivistic deductive approach.

Hypotheses are developed from existing theories and literature in the area. These are later

tested by translating concepts into measurable variables. Audit quality has been measured

through the proxy variable discretionary accruals which was estimated by two variants of

the modified Jones model. The sample consisted out of 58 large-cap firms listed on the

Stockholm OMX stock exchange, constituting a total of 580 firm years.

Conclusion: The results of this study suggest that neither audit partner rotation nor audit

firm rotation has an influence on audit quality. Furthermore, these relationships are not

found to be contingent on firm visibility. The study’s findings contribute to existing

debate on mandatory audit rotation. However, the results need to be interpreted with

certain caution as we cannot be certain that discretionary accruals measured audit quality

as it was intended to do.

iii

Sammanfattning

Examensarbete, Civilekonomprogrammet

Författare: David Frisk & Karl-Johan Edström

Handledare: Timur Uman

Examinator: Emilia Florin-Samuelsson

Titel: Spelar revisorsrotation någon roll? En studie på relationen mellan revisorsrotation,

revisonskvalitet och dess modererande faktorer.

Bakgrund & Problemformulering: Bristfällig revisionskvalitet har historiskt lett till

enorma konsekvenser för samhället. Låg revisionskvalitet är ofta relaterad till en låg

oberoendeställning hos revisorn. Detta kan orsakas av revisorns incitament att maximeras

sin personliga vinning. I försök att förbättra revisorns oberoendeställning, vilket också

skulle kunna öka revisonskvaliteten, har ett flertal regler för revision utfärdats. En av dem

är revisorsrotation, som har varit ämne för debatt. Fastän det finns många tidigare studier

på revisorsrotation, har få studier gjorts på revisonrotation i förhållande till andra

aspekter, i synnerhet företagets synlighet.

Syfte: Syftet med denna studie är att förklara hur rotation av revisionsbyrå samt rotation

av revisonspartner relaterar till revisonskvalitet, och hur detta förhållande påverkas av

företagets synlighet.

Metod: Studien har utförts kvantitativt med en positivistisk deduktiv ansats. Hypoteser

har tagits fram med hjälp av existerande teorier och tidigare litteratur. Dessa har sedan

testat genom att översätta koncept till mätbara variabler. Revisionskvalitet har mätts med

proximal variabeln godtyckliga avskrivningar vilket har estimerats med hjälp av två

varianter av den modifierade Jones modellen. Urvalet bestod av 58 large-cap företag

listade på OMX Stockholms aktiemarknad, vilket utgjorde totalt 580 observerade

företagsår.

Slutsats: Studiens resultat indikerar att varken byte av revisonspartner eller revisionsbyrå

påverkar revisionskvaliteten. Vidare hittar vi inte att dessa sammanband är beroende på

företagets synlighet. Studien kan bidra till den pågående debatten kring behovet av

obligatorisk revisorsrotation. Däremot behöver resultaten tolkas med viss försiktighet

eftersom vi inte kan vara säkra på att godtyckliga periodiseringar mäter revisionskvalitet

som det var tänkt.

iv

Table of Contents

1 Introduction .............................................................................................. 1

1.1 Background ................................................................................................................. 1

1.2 Problematization ......................................................................................................... 4

1.3 Purpose ........................................................................................................................ 7

1.4 Research question ....................................................................................................... 7

1.5 Limitations... ............................................................................................................... 7

2 Literature review ...................................................................................... 9

2.1 Agency theory ............................................................................................................. 9

2.2 The auditor’s role and procedures ............................................................................ 10

2.3 Legitimacy theory ..................................................................................................... 12

2.4 Audit quality ............................................................................................................. 13

2.5 What factors influence audit quality and how .......................................................... 15

2.6 Audit rotation ............................................................................................................ 19

2.6.1 Audit partner rotation ............................................................................................. 19

2.6.2 Audit firm rotation ................................................................................................. 20

2.7 Contingency aspects ................................................................................................. 21

3 Method ..................................................................................................... 23

3.1 Theoretical method ................................................................................................... 23

3.1.1 Research position and scientific strategy ............................................................... 23

3.1.2 Theories of choice .................................................................................................. 24

3.1.3 Source criticism ..................................................................................................... 25

3.2 Empirical method ...................................................................................................... 26

3.2.1 Timespan ................................................................................................................ 26

3.2.2 Sample selection .................................................................................................... 26

3.2.3 Data Collection ...................................................................................................... 27

3.2.4 Limitations ............................................................................................................. 28

3.2.5 Operationalisation .................................................................................................. 28

3.2.5.1 Independent variables ..................................................................................................................... 28

3.2.5.2 Dependent variable ......................................................................................................................... 29

3.2.5.3 Contingency variables ..................................................................................................................... 31

3.2.5.4 Control variables ............................................................................................................................. 32

3.2.5.5 Validity, reliability, and generalizability ........................................................................................ 36

3.2.6 Data analysis .......................................................................................................... 37

3.2.6.1 Descriptive statistic ......................................................................................................................... 37

3.2.6.2 Normal distribution ......................................................................................................................... 37

3.2.6.3 Bivariate correlation analysis .......................................................................................................... 37

3.2.6.4 Multiple regression analysis ........................................................................................................... 38

3.2.6.5 Multicollinearity ............................................................................................................................. 38

4 Empirical analysis .................................................................................. 39

4.1 Descriptive statistics ................................................................................................. 39

4.2 Dependent variable ................................................................................................... 42

4.3 Bivariate Correlation ................................................................................................. 43

4.4 Multiple regressions .................................................................................................. 45

v

4.4.1 Results of multiple regressions .............................................................................. 45

4.4.1.1 Multiple regression with audit partner rotation as the independent variable .................................. 46

4.4.1.2 Multiple regression with audit firm rotation as the independent variable ....................................... 48

4.4.2 Results of multiple regressions for visible and non-visible observations .............. 50

4.4.2.1 Multiple regressions with audit partner rotation as the independent variable for visible

firms………………. ................................................................................................................................... 51

4.4.2.2 Multiple regressions with audit partner rotation as the independent variable for non-

visible firms………….. .............................................................................................................................. 52

4.4.2.3 Multiple regressions with audit firm rotation as the independent variable for visible

firms……………….… ............................................................................................................................... 54

4.4.2.4 Multiple regressions with audit firm rotation as the independent variable for non-visible

visible firms……. ....................................................................................................................................... 56

4.5 Consequences for the hypotheses ............................................................................. 58

5 Discussion ................................................................................................ 60

5.1 Introductory discussion ............................................................................................. 60

5.2 Audit quality and audit partner rotation .................................................................... 60

5.3 Audit quality and audit firm rotation ........................................................................ 62

5.4 Visibility ................................................................................................................... 63

5.5 Control variables ....................................................................................................... 64

5.6 Final discussion ......................................................................................................... 66

6 Conclusion ............................................................................................... 68

6.1 Conclusion ................................................................................................................ 68

6.2 Empirical contributions ............................................................................................. 69

6.3 Theoretical contributions .......................................................................................... 69

6.4 Practical implications ................................................................................................ 71

6.5 Limitations and future research ................................................................................ 72

References .................................................................................................. 74

vi

Tables

Table 1 - Results of descriptive statistics ............................................................ 41

Table 2 - Kolmogorov-Smirnov test for uncoded discretionary accruals ................... 42

Table 3 - Kolmogorov-Smirnov test for coded discretionary accruals ....................... 42

Table 4 - Results of multiple regression ..................................................................... 48






Appendix

Appendix 1 - Excluded companies ............................................................................. 91

Appendix 2 - Histogram DA1 ..................................................................................... 92

Appendix 3 - Histogram DA2 ..................................................................................... 92

Appendix 4 - Histogram coded DA1 .......................................................................... 93

Appendix 5 - Histogram coded DA2 .......................................................................... 93

Appendix 6 - Correlation matrix ................................................................................. 94

1

1 Introduction

In this section, the background is presented, where the reader learns the importance of

audit quality from the perspective of previous financial scandals. In the problematisation,

a discussion is developed on how, and why the audit quality can be impacted by the

auditor, audit rotation and other contingencies. The problematisation culminate in the

research question and the purpose of the study. Lastly, the limitations of the study are

presented.

1.1 Background

In 2001, Enron were caught using off balance sheet subsidiaries to hide losses and debts.

In total losses of close to 600 million dollars, and debts of over 600 million dollars were

hidden from the public for many years (Oppel Jr. & Ross Sorkint, 2001). Because of the

discovery, Enron filed for bankruptcy (Degerfeldt, 2011). Even if Enron used poor

accounting practices, their accounting firm, Arthur Andersen gave its approval. A fatal

mistake for Arthur Andersen, as they also went under consequently (ABC News, 2009).

In all, thousands of jobs, employer benefits, and large amounts of money from investors

were lost (Bragg, 2002). Later, the next year, the telecom company WorldCom filed for

bankruptcy after an internal audit discovered $11 billion dollars in expenses had been

fraudulently accounted for through creative bookkeeping (Colvin, 2005). The bankruptcy

of the once multibillion-dollar company, led to huge monetary losses for investors, as

well as the loss of thousands of jobs. Colvin (2005) further explain that major players in

the industry at the time before the bankruptcy, AT&T, Qwest and Global Crossing ended

up firing employees, committing accounting fraud and, filing for bankruptcy respectively,

all resulting from attempts to be able to compete with the later to be revealed fraudulent

firm.

Lehman Brothers found themselves in a similar position when they filed for bankruptcy

in 2008, which were one of the major players involved in the unfolding of the financial

crisis in that same year (Chu, 2018). To this date Lehman Brothers bankruptcy is the

largest ever to occur. One reason why the bank went under is due to the fact that the bank

had invested, and owned large proportions of mortgage bonds, which were seen as safe

investments, however, this mortgage bonds soon became worthless as the housing bubble

burst (Stow, 2018). Even so, Lehman brothers were able to hide its losses for some time,

2

by for example using an accounting gimmick called Repo 105 (Clark, 2010). Lehman

brothers auditing firm, Ernst & Young has been blamed for detecting errors, but not taking

action (Söderlind, 2010; Friefeld, 2015). The financial crisis in 2008 led to huge

consequences, where people lost their jobs, homes, and tax money because of bailouts for

banks, and impacted the whole world economy (Mathiason, 2008; Uchitelle 2009).

Common for all of the above-mentioned scandals, excluding the huge financial impact on

the individual as well as entities, is that the external auditor either did nothing about the

ongoing financial fraud or did not find any indicators of any wrongdoing in the entities

financials until it was too late. In the light of financial scandals, the reputation for the

audit profession have become a subject of public discussion. After the Enron scandal, the

Swedish CEO of Ernst & Young at the time, declared that the audit profession and the

audit reputation were under pressure (Edling, 2002). A study of a Japanese company that

engaged in accounting fraud in 2006, found that PwC prioritised to increase their audit

quality, to save their international reputation (Skinner & Srinivasan, 2012). Another Big

accounting firm, KPMG is looking to improve their reputation after recent scandals,

through increasing their audit quality (Kinder, 2020).

Audit quality is defined by DeAngelo (1981) as both the probability that an auditor will

discover material misstatements and the probability to report them. Palmrose (1988)

states that audit quality is the level of assurance that an auditor provides to the financial

statements. The International Auditing and Assurance Standards Board (IAASB) breaks

down the concept audit quality, by providing 5 elements of a quality audit. These consist

of; proper values and ethics, sufficient auditors’ knowledge, control procedures in line

with regulations, issuance of timely functional reports and proper relationships with

relevant stakeholders (IAASB, 2014).

Comparing the previously mentioned scandals with the definitions and concepts of audit

quality, one could argue that the audit quality has been poor in all three cases. In Lehman

brothers and in Enron, the auditors most likely did detect flaws in the financial statements,

however they did not take enough actions (i.e. low level of assurance), which per all

mentioned definitions is a lack of audit quality (cf. Francis, 2004). For the WorldCom

scandal, auditors failed to detect the financial fraud (material misstatements), again

displaying a lack of audit quality per all mentioned definitions. This proves the

importance of audit quality, as the consequences of poor audit quality in the mentioned

3

cases, has had a huge impact on society at large. A good audit quality would provide value

to the shareholders. However, even if DeAngelo's (1981) definition is well used, the

consensus on how to define audit quality varies, dependent on what attributes each

definition focus on. Many of the frameworks are also incomplete. This means that it is

not possible for stakeholders to view audit quality in its entirety (Knechel et al., 2013).

Furthermore, it is hard to say whether an auditor should have detected certain

misstatements or if it was right or wrong to issue for instance, a clean audit report for a

company that in a short period of time went bankrupt. Therefore, one could argue that

audit quality becomes more of an abstract concept than reality (Francis, 2004).

In attempts to assure and strengthen the audit quality, changes to the audit profession, as

well as stricter regulations for the providence of non-auditing services has been installed.

This has been done through both company laws and the creation of several audit oversight

boards, with the purpose to protect both public and investors interests (Fuller, 2020).

According to audit firms, the most questionable reforms, introduced because of recent

scandals, is the those regarding audit rotation, (e.g. Ernst & Young, 2015). It is seen as

questionable since switching auditor is thought to results in a loss of client specific

knowledge, start-up costs and disruption of the organisation subject to the audit (Ernst &

Young, 2015). Audit rotation refers to both audit firm rotation and audit partner rotation.

Audit firm rotation is the act of changing the external auditing firm of an entity, while

audit partner rotation refers to the act of changing the auditing partner of an entity without

changing the firm responsible for the auditing (Ernst & Young, 2015).

In 2006, the EU commission issued a new directive regarding audit partner rotation. The

EU Directive 2006/43/EC requires key audit partners in PIE’s to rotate after a period of

7 years. The directive requires a cool down period of 2 years, which implicates that the

partner is not allowed to participate in the audit of that specific firm during the period.

The purpose of this rotation requirement is like the audit firm rotation directive to enhance

the independence of the audit (EUR-Lex, 2006). However, about audit partner rotation

the EU commission later declared that just changing the audit partner that work within

the same firm is not sufficient for increasing audit quality, since an audit firms’ main focal

point will be on client retention. Therefore, pressure will be mounting on the new audit

partner to keep the long-established connection with the client (European Commission,

2014).

4

In 2014 the Council of the European Union introduced such a reform through the

mandatory audit firm rotation directive. The directive imposes periodical breaks for audit

commitments in public interest entities. The objective of the reform is to increase the

audit quality, by strengthening the independence of the auditor. According to the EU

commission, there is an obvious risk with having the same auditor or audit firm for an

extensive period. They state that this would erode the auditor independence, and

consequently damage the professional scepticism. This is due to an extensive relationship

that emerges with the client and the responsible auditor (European Commission, 2014).

Whether regulation results in a higher audit quality remains to be seen. The construct

audit quality is mainly based on individual perception (Gonthier‐Besacier et al., 2016).

This would raise the question if audit quality and audit rotation is correlated at all.

Assuming there is a correlation there might be other underlying factors that needs to be

considered. From previous research we understand that there are a lot of aspects that could

influence audit quality (e.g. Francis, 2004; Hoang Thi Mai Khanh, & Nguyen Vinh

Khuong, 2018), but what they depend on remains unclear.

1.2 Problematization

One way of understanding the importance of auditor’s role and auditor’s independence is

through the agency theory perspective. Within public listed companies, we have two

different parties, where A is in control of the company (Agent), and B are the owner of

the company (Principal) (Fülöp, 2013). However, there might exist misaligned interest

for the two parties. This inclines the principles to install some sort of system that monitors

the agent. (Jensen & Meckling, 1976). Francis (2004) states, referring to the corporate

governance issue, that external auditors have an important role in listed companies where

ownership and control is separated. Watts & Zimmerman (1983) found that agency cost

could be decreased through auditing. This would bridge the gap of interest conflicts

between the two parties, if the auditor is independent, otherwise the monitoring system

would be compromised (DeAngelo, 1981a; Khurana & Raman, 2006). If the

shareholders’ value reduced agency costs, they should be striving for a higher audit

quality. Francis (2004) explains that audit reports of high quality provide useful

information to shareholders and other stakeholders. On the other hand, audit reports with

low quality will provide little to no useful information, consequently, trusting a low-

5

quality audit report could increase information asymmetries and thereby agency costs will

not be reduced.

Arel et al. (2006) explains that there are three threats that could have a negative impact

on the audit quality. The first element is extended relationship between the auditor and

the client, which enhances the closeness between them. This can be connected to the

second element, which is the inclination for the auditor to satisfy their clients. The third

one is the absence of attention to detail, which may origin from a long-term relationship

between an audit firm, or audit partner with their client. Nasution & Östermark (2013)

describes something called belief-perseverance syndrome, which means, in this case, that

an auditor ignores new evidence, and fails to change their opinion. This is an effect of a

long-term relationship between the auditor and client. A closer interpersonal relation

between the audit partner and the client’s CEO has been found to impact the auditor’s

independence negatively in several previous studies (e.g. Kaplan, 2004; Gavious, 2007;

Chi et al., 2005). These problems could be connected to DeAngelo’s (1981a) study, where

she explains that the value of the auditor's opinion increases when the auditor has a greater

incentive to tell the truth. The probability of an auditor to report a discovered breach is

also defined as auditor independence.

Audit rotation is a concept that was introduced as a way of ensuring the auditor

independence, and thereby potentially improving the audit quality. In that case, it would

help to mitigate the three threats that Arel et al. (2006) mentions. (European Commission,

2014). Research on overall audit rotation is mainly focused on investigating audit tenure

and thereby investigating if there is a need for mandatory audit rotation regulations

(Francis, 2004). The main arguments for mandatory audit rotation are that the

independence of the auditor is compromised through the relationship that is built over a

longer tenure (Francis, 2004). It is also argued that rotation resolve conflicts of interest

between the client, audit partner and audit firm (Kalanjati et al., 2019). On the other hand,

it is argued that the economic incentives of the auditor to keep the client, along with the

rotation of other personnel engaged in the auditing will ensure the professional

independence and scepticism of the auditor. Additionally, the acquiring of knowledge

that comes with a new auditor is thought to decrease the quality of the audit initially

(Francis, 2004).

6

When measuring audit rotation through audit tenure, findings of the audit rotations impact

on audit quality have been divergent (Kalanjati et al., 2019). Previous research have both

found a positive relationship between audit rotation and audit quality (e.g. Lu &

Sivaramakrishnan, 2009; Blandón & Bosch, 2013), a negative relationship (e.g. Gul et

al., 2007; Chen et al., 2008; Fargher et al., 2008) and, no relationship at all (e.g Geiger &

Raghunandan, 2002; Knechel & Vanstraelen, 2007). Mentioned studies and most of the

previous research in the area, investigates the effects of audit rotation, but does not

account for the different types of rotation (Mali & Lim, 2018). Kalanjati et al. (2019)

explains that audit rotation can be achieved in one of two ways, either at the audit partner

level, or at the audit firm level. Kalanjati et al. (2019) found that audit partner rotation

increases the audit quality. However, it was also found that audit quality decreases when

the audit firm changed. Similarly, Mali & Lim (2018) found that mandatory audit firm

rotation decreases the audit quality, while Lennox et al. (2014) found that audit partner

rotation at least initially increases audit quality. A longer audit partner or firm tenure is

said to increase the partner or firm’s knowledge of the company subject to auditing

(Kalanjati et al., 2019). Therefore, it is possible that the gap of knowledge is larger with

an audit firm rotation than with an audit partner rotation, subsequently audit quality would

be affected differently. However, the overall divergent findings would suggest that there

are contingent factors that would affect the audit quality and audit rotation

interrelationship. The characteristics of the organisations could possibly be an

explanation (cf. Fiedler, 1964).

Although the research of audit rotation and audit quality is extensive, few researchers

have studied factors that indirectly could impact the interrelationship. One aspect to

consider is the visibility of the firm. Visibility could be explained as certain firm

characteristics that makes the firm more apparent to the society. Brammer & Millington

(2006) explains that the more apparent or visible a firm is, the higher the interest and

attention from society would be, which in turn would increase the political and social

pressure on the given firm. This pressure would also be transferred onto the auditors, who

are responsible for the auditing in more visible firms. Accordingly, Redmayne et al.,

(2010) found that hours spent on auditing increased as the visibility of the firm increased.

Such behavioural pattern suggest that auditors are more likely to increase their effort

when there is more to lose in the eyes of the public. This is consistent with Walo’s (1995)

7

study, which found that auditor tends to excess more caution when dealing with high risk

clients, which could include firms with high visibility.

Characteristics that affect the firm’s visibility commonly include firm size, press

mentions and ownership structure (Bushee & Miller, 2012; Redmayne, 2010; cf. Dienes

et al., 2016). It is understood that the visibility increases the larger the company is in

terms of different attributes (Bushee & Miller, 2012). Press mentions can be understood

to increase the visibility of the firm as the public is exposed to firm specific information

through mentions in the press. Brockman et al. (2017) assumes that the ownership

structure will affect the firm visibility, explaining that a larger institutional ownership

attracts a larger public interest.

In conclusion, the increased caution resulting from the visibility of the firm tends to

change the behaviour of the auditors, which implies that the audit quality could be

affected. For instance, as the behaviour of the auditor changes, the auditor might be more

probable to include material misstatements in the audit report. The auditor might also

perform a larger substantive testing in the investigation, to enhance the assurance for the

auditor. Consequently, this could mean that the relationship between audit rotation and

audit quality is contingent on the audited firm’s visibility.

1.3 Purpose

The purpose of the study is to explain how audit firm rotation and audit partner rotation

relate to audit quality and how this relationship is contingent on firm visibility.

1.4 Research question

How does audit firm rotation and audit partner rotation relate to audit quality and how is

this relationship contingent on firm visibility?

1.5 Limitations

As audit quality is more of an abstract idea, than a fully measurable concept, this study

will need to use a proxy variable to measure the audit quality (cf. Francis, 2004). More

so, this study will be limited to the extent that our proxy variable will estimate audit

quality in a correct way. The same could be said about firm visibility, as it may also be

measured in a variety of different ways, which will also pose as a limitation.

8

The mandatory audit firm rotation regulation has yet to come into full effect, with it being

issued in 2014, with a possible firm tenure of at least 10 years (European Commission,

2014). This may hamper our sampled data, as companies are not forced to change their

audit firm during our sampled years, so a limitation may be the lack of audit firm

rotations.

9

2 Literature review

In this section, the reader is presented with relevant theories and literature related to the

subject of the thesis. The first theory that are presented are the agency theory, to be able

to understand the role and importance of auditing. The second theory is the legitimacy

theory, to be able to understand the importance of audit quality both for auditors and

firms. Later in the section, the reader is provided with a brief overview of the audit quality

influences after which we argue for the importance of audit rotation in terms of audit

quality. Lastly, based on the provided literature, the hypotheses of the thesis are

developed which creates the foundation for the method.

2.1 Agency theory

The agency theory describes situations where one actor (agent) has been given the

authority to represent another actor (principal). In economics, the agent is often seen as

the management control of a firm and the principal as the shareholders (Fülöp, 2013). The

agency theory describes that problems may arise when the interest of the executive

management and shareholder is not aligned, commonly when ownership and management

control is separated (Jensen & Meckling, 1976). The management may work for their

own personal interest instead of the interest of the shareholders, finding reason to do so

through knowledge gaps between the two parties (information asymmetries) (Fülöp,

2013). The difference incentives between the two parties could lead to something called

agency costs (Jurkus et al., 2011). Even so, the shareholders can try to decrease the agency

costs by aligning the interest of the parties, such as performance-based compensation or

moral pressure. The performance-based compensation will reward the CEO when the

CEO has maximised the shareholder value (Donaldson & Davis 1991; Jurkus et al., 2011;

Heath & Norman, 2011).

Agency cost is a broad concept and can arise from a variety of sources. It could consist

of recruitment costs, moral hazard, stealing, corruption, assurance costs or monitoring

costs. (Shapiro, 2005). Monitoring costs is one of those costs that arise from the lack of

trust. Due to the lack of trust, the shareholders (principal) will have an incentive to

supervise and monitor the CEO (Agent) (Eisenhardt, 1989). Shapiro (2005) states that

one way of monitoring the agent is through the auditor, using the financial statements.

However, she also states that the relationship between the auditor and the principal will

10

also pose as an agency relationship, which would also be affected by the agency problems.

So, the natural question that arises is, who monitors the monitors? (Shapiro, 1987). This

paragraph describes that the role of the auditor is to monitor the agent. The principal in

this case be the CEO, or the company. However, the auditor is likely to develop agency

problems with the principal as well.

The agency dilemma between the company and the auditor origins from the contract

between the two. The auditor is fulfilling the contract by providing services and

performing an audit for the given company. In return, the auditor is expected to yield

compensation from the audited company. The problem that arises is that the auditor will

have a personal interest in retaining the client, i.e. the monetary compensation. (Gavious,

2007). Just as the CEO would have different incentives than the shareholders, the

auditor’s incentives would also be different from that of the audited company. Auditors

might be willing to overlook certain material misstatements in the financial statements to

be able to produce a clean audit report, in the belief that this pleases the principals (cf.

Tepalagul & Lin, 2015). However, the possible decrease in reputation as well as the threat

of litigation issues that consequently could arise, is likely to restrict the auditor into

maintaining his professionalism (Tepalagul & Lin, 2015).

Even so, if the auditor would still have monetary interest in retaining a client, it could

harm the independence for the auditor. Hence the auditor’s role as being the median

between the market expectations and the audited company would be disrupted, as the

balance would shift towards the company (Gavious, 2007). One way of mitigating this

agency problem could be mandatory audit rotation, which constrains the auditor from

retaining the client (e.g. Mali & Lim, 2018; Kalanjati et al., 2019). The rotation

mechanism and its impact on auditor independence will be further discussed later in this

section.

2.2 The auditor’s role and procedures

As stated in the agency theory, an auditor's role is to monitor the agent, to bridge the gap

between the principal and the agent. This means that the auditor reviews a given company

(agent), to enhance the trust and assurance that the company's financial reporting are

correct, which will add value to the shareholders (principals) However, the principals is

explained to be far more than just the shareholders, it's important for all stakeholders, and

11

the society as a whole to be able to trust the financial reports and other information that

companies release (Jensen & Meckling, 1976; Eisenhardt 1989; FAR 2018). Pentland

(1993) states that the main purpose of auditing is to enhance trust and give reliability to

the stakeholders, due to the risk that the financial reports will reflect the agents

(CEO/management) own self-interest. This is where the role of the auditor emerges. The

auditor needs to, in a professional and critical approach: plan, investigate and give his or

her assessment in relation to the company’s financial reports and management (FAR,

2018). This procedure is known as the auditing.

The first part, planning, is the procedure of gathering information and knowledge about

the entity that is going to be audited. The information consists of both internal factors,

such as buying and manufacturing processes, and external factors, such as competition

and knowledge about the industry the company is working within. The auditor needs to

understand the company, both internal and external to be able to do an effective auditing.

By having a great knowledge, the auditor is capable of identify where the significant risks

are for the specific company (FAR, 2018). Carrington (2010) states that an inadequate

planning would increase the risk that the audit would be flawed.

The second part is the investigation, which is explained as a substantive testing of the

result and balance accounts from the current recording. This to test if the financial records

are fairly and accurately presented by the company. The auditor should concentrate his

or her work on what they perceive as the most important part (Significant risks), which

can be traced back to the planning phase. The auditor has an ongoing communication with

the company's managers, to be able to ask critical questions, and get answers for any

unclear or misleading statements from the current recording. (FAR, 2018; Carrington,

2010).

The third part is the assessment, where the auditor presents his/her audit statement. The

audit statement should be a written document that is released together with the financial

statements for the audited company and should include the auditor's notations and

observations that were found in the investigation. However, the statement can never give

an absolute certainty for the stakeholders, as the investigation process often only

concentrates on the significant risks, while other accounts are not analysed (FAR, 2018).

12

2.3 Legitimacy theory

Legitimacy theory explains that corporations needs to follow the expectations of society

to remain legitimate. The actions of the corporation can be important for legitimacy, but

in the end, it is the society’s perception of the corporation that decides whether it is

legitimate (Tunks, 1971). Legitimacy theory promotes the idea of an imaginary social

contract between corporations and society. To fulfil the contract, corporations need to

follow certain ethics and values that is of importance to society, most notably nowadays

the environmental impact and awareness of the corporation (Deegan, 2019). If society

perceives that the contract is broken, the corporation will lose legitimacy, and could face

consequences, such as a decrease in sales. Legitimacy can be improved through higher

reliable information disclosure, while a lack of information disclosure or reliability, in

combination with a change in ethics and values can damage the legitimacy (cf. Deegan et

al., 2002).

Legitimacy is a complex matter; it might be hard to understand if your corporation is

legitimate. However, in the context of our study, we suggest that an auditor can either

improve or impair the legitimacy. Through the audit of financial statements and the

control procedures of a corporation, the auditors can ensure or/and explain ensuing of the

social contract in those areas (Chelli et al., 2014). If the auditors explore e.g.

misstatements or creative bookkeeping, they are expected to raise concerns in their audit

report. This would be likely to result in raised concerns from investors and attract the eyes

of the society and probably lead to a decrease in legitimacy. On the other hand, a clean

audit report would improve rather than impair the legitimacy. Consequently, legitimacy

is of importance to corporations (Tilt, 2003). Having that said, legitimacy can still be

impacted or manipulated by the corporation itself (Deegan, 2019), such as recently

discovered manipulations of sustainability reports (Littlefield, 2013).

Comparing legitimacy theory to the previously discussed financial scandals (Enron,

Lehman Brothers and Worldcom), the society would have perceived the legitimacy

contract to have been broken by those firms. Consequently, their reputation and

trustworthiness were harmed. Whether the responsible auditors maintained a high audit

quality is still a subject of discussion. However, the responsible audit firms as well as the

audit profession had seen a decline in legitimacy (Holm & Zaman, 2012). This suggest

that no matter the level of audit quality, the legitimacy can still be impaired by other

13

circumstances (cf. Holm & Zaman, 2012). The decrease in auditor reputation that stems

from the decrease in legitimacy could also harm the perceived audit quality (Skinner &

Zaman, 2012). Consequently, maintaining a high legitimacy is vital for the audit

profession as well (Whittle et al., 2014).

In attempts to restore the legitimacy and thereby the perceived audit quality, regulatory

bodies have, considering financial scandals, such as the previously mentioned, introduced

new audit regulations (Holm & Zaman, 2012; Mali & Lim, 2018). In the context of our

study, regulations on audit rotation is often introduced to meet this objective (e.g.

European Commission, 2014). One of the main cornerstones of the audit rotation

regulation is to enhance the audit independence, and thereby enhance the trust of the audit

profession (European Commission, 2014). As the trust for the audit profession increases,

the legitimacy would increase (Skinner & Srinivasan, 2012), which through previous

reasoning would increase the perceived audit quality. However, if the actual audit quality

is improved or impaired remains to be seen.

This subsection stamps the importance of having an auditor for the retainment of

corporate legitimacy. With the assumption that a higher audit quality provides a lower

probability of financial misstatements, legitimacy is positively correlated with audit

quality. A lower audit quality would imply that the information is less reliable resulting

in a less legitimate corporation. This subsection also provides an important understanding

of the auditors and audit professions need for legitimacy.

2.4 Audit quality

Audit quality is an important factor in relation with legitimacy, to be able to distinguish

whether the legitimacy has been improved or impaired. Just as the term quality, audit

quality is often based on the individual's perception. Consequently, the definitions of this

abstract term are many, and there is no overall superior definition (Francis, 2011). The

most well-cited definition developed by DeAngelo (1981) describes audit quality as a

factor of both the probability to find material misstatement and the probability that the

auditor reports them. Finding material misstatements is referred to as the technical

capabilities of the auditor while the probability that the auditor reports them refers to the

independence of the auditor. Although the definition is simple to understand, it does not

provide any measurements of audit quality. Whether the audit quality is high or low is

14

hard to tell with just DeAngelo’s (1981) definition. However, in the study, DeAngelo

(1981) reasons that higher audit quality is more likely to appear in audit firms where the

customers are few and small. He implies in such cases the audit firms have more to lose

and therefore they would ensure a higher audit quality. If this is true, it would mean that

effort increases audit quality.

In a later study Palmrose (1988) developed a new definition including a measurement for

audit quality. He explains that audit quality is the level of assurance that an auditor

provides to the financial statement. Thus, higher level of assurance would imply a higher

audit quality. This definition is, however, old, and changes to the audit profession would

have changed the definition of audit quality. In a more recent study, Francis (2011)

provides six drivers for audit quality: audit inputs, audit processes, accounting firms, audit

industry and audit markets, institutions, and economic consequences of the audit

outcome. Audit inputs consist of the competence and independence of the personnel as

well as the testing procedures and its reliability. Audit processes refers to decision making

for implementing specific tests and the evaluation of those test. Accounting firm refers to

the impact of the employer of the auditors. Audit industry refers to the impact of operating

in a certain industry. Institutions affect the auditor's work through regulations. Lastly,

similar Palmrose (1988), Francis (2011) describes the probability of economic

consequences to have an impact on audit quality. Francis (2011) further explains that

audit quality should be a continuum ranging from low to high audit quality. He states,

that only focusing on audit failures for audit quality measurements shortens the spectrum

and results in an unfair binary measurement of audit quality. With this broadened

definition Francis (2011) provides a framework for how audit quality measurements

could be extended from the traditional way of solely investigating a spectrum of high and

low audit quality.

In more recent studies on the concept audit quality, Defond & Zhang (2014) states that a

higher audit quality will increase the assurance that the financial report would be of good

quality, hence, the audit quality is a component of the financial reporting quality. They

extend this definition by stating that the financial reporting quality is reliant on the firm’s

inborn characteristics and their reporting system, which would by definition also affect

the audit quality. Laitinen & Laitinen (2015) extends on a previous definition by Knechel

et al. (2013) to create a probability model of audit quality. The audit quality is explained

15

to be a factor of context, inputs, processes, and outcomes of the audit. The context

includes the budget of the auditing firm, the complexity of the audit and the intrinsic risk

of the client. Inputs is described as a factor of the experience and expertise of the audit

which improves their investigational intuition. Processes refers to the efficiency of audit

control systems as well as the performance of the auditor. The outcome of the audit is

seen as the probability that the auditor includes at least one material misstatement in their

audit report, given their context inputs and processes (Laitinen & Laitinen, 2015). Even

if the performance in the factor’s context, inputs and processes is good the audit quality

will not be at a high level if the auditor is not probable to include any material

misstatements in the audit report (cf. Laitinen & Laitinen, 2015). Lastly, Knechel (2016)

understand audit quality to consist out of the auditor independence and auditor

knowledge, where auditor knowledge encompasses both the auditor’s expertise and his

knowledge of the firm subject to auditing. High auditor independence combined with low

auditor knowledge and, low independence combined with high auditor knowledge will

not increase the audit quality. Only when the independence and knowledge are at the same

increasing level, the audit quality is improved (Knechel, 2016).

This subsection provides an understanding of audit quality and its historic development.

To later develop a framework for the audit quality and audit rotation relationship we deem

it important to understand what audit quality persist of. For understanding how audit

quality is impaired or improved, the following section will provide the different aspects

that have been found to impact the audit quality.

2.5 What factors influence audit quality and how

Many studies in this area has emphasized on investigating different factors that could

impact the independence of the auditor, implying that a decrease in the independence

impairs the audit quality (Tepalagul & Lin, 2015). Tepalagul & Lin (2015) investigates

the previous literature findings of 4 potential threats to auditor independence. These

include client importance, non-auditing services, auditor tenure and client affiliation with

the auditing firm.

Client importance can be connected to what was previously mentioned in the agency

theory concept. The client importance becomes a potential threat to the auditor

independence because auditors are being paid by the same company that they perform

16

their audit on. This could lead to the fact that the auditor has a higher incentive to satisfy

their larger clients, and yield to pressure, which would directly harm the auditor

independence, hence, larger clients have a larger economic benefit for the auditor

(Tepalagul & Lin, 2015; Chen et al., 2018). As less companies are required to have a

statutory audit due to a more lenient audit regulation on a national level (e.g.

Justitiedepartementet, 2010), the clientele decreases, which consequently intensifies

client importance (cf. Zhang et al., 2014). However, there are also studies that do not

support this claim (e.g. Chang & Kallapur 2003; Kinney et al., 2004). Chang & Kallapur

(2003) found no significant relationship between client importance measures and

abnormal accruals, used as a proxy for audit quality, which suggest that audit

independence is not affected by client importance. One argument for why client

importance does not seem to affect the auditor independence in these studies might be

due to litigation, which means that the auditor might face legal action, which in turn would

harm his or her reputation (Deangelo, 1981).

Non-auditing services is thought to influence the auditor independence and thereby the

audit quality, as it is argued that an economic bond is created between the client and the

auditor. When the auditor is providing more than auditing services, he/she becomes more

dependent on monetary compensation from the client, which could make him/her willing

to compromise his independence in order to retain the client (Francis, 2004; Tepalagul &

Lin, 2015). However, other researchers take an opposite stance, arguing that providing

non-auditing services increases the audit quality. Through increasing the auditor’s client

knowledge by spending more time on the client, the auditor independence as well as the

auditor expertise is thought to increase, which would mean that the audit quality improves

(Hong-Jo & Cheung, 2017). The overall findings in this area have been divergent, often

depending on the proxy used to observe audit quality (Tepalagul & Lin, 2015). Recent

studies have found both a negative relationship (e.g. Legoria et al., 2017; Hohenfels &

Quick, 2018), positive relationship (e.g. Koh et al., 2013; Kowaleski et al., 2018) and no

relationship (e.g. Bell et al., 2015; Hong-Jo & Cheung, 2017) between non-auditing

services and audit quality. It has also been suggested that just the providence of non-

auditing services can impair the audit quality. The perceived auditor independence is

explained to be decreased when non-auditing services is provided which causes the

perceived audit quality to be impaired (i.e. audit quality) (Kinney et al., 2004).

17

As for non-auditing services, auditor tenure is both argued to increase and decrease audit

quality. The auditors increased client knowledge that comes from a longer audit tenure is

argued to increase the audit quality as the expertise increases. The opposing side argues

that the auditor is probable to develop a close relationship with management over a longer

tenure and act in their favour, compromising his independence and thereby impairing the

audit quality (Francis, 2004). Although the findings on the auditor tenures impact on audit

quality is mixed, the empirical evidence suggests that there are little to no correlation

between the two (Tepalagul & Lin, 2015). Some studies suggest that short audit tenure is

associated with a lower audit quality, when examining the quality of the financial

statements (e.g. Jenkins & Velury, 2008; Bell et al., 2015). At the same time there is no

observed difference in audit quality when comparing medium to long tenure (Jenkins &

Velury, 2008). Other studies have found that the effects of audit tenure are often

dependent on firm characteristics, namely industry, size, and political environment (Gul

et al., 2009; Lim & Tan, 2010).

Client affiliation is explained to be the auditor’s closeness to the client (Firm). Client

affiliation might become a threat due to three issues, firstly, the auditor might see the

client as a future employer. Secondly, the auditor close relationship between the client

(management) might harm the auditor’s relationship with the shareholders, which is the

actual employer, not the management. Lastly, the relationship with former colleagues

might affect the auditor ability to withstand a decrease in independence towards them. To

meet these concerns, some regulations has been put in place, for example the Sarbanes-

Oxley Act from 2002 which requires a cooling off period of 1 year before an auditor are

able to work for a former client (Imhoff, 1978; Tepalagul & Lin, 2015). Some studies

have confirmed these threats and found that an auditor is more likely to issue a clean audit

report when performing an audit on a former employer, (e.g. Lennox 2005; cf. Ye et al.,

2011). Lennox (2005) Also found that the auditor is more likely to issue a clean audit

opinion for companies with affiliated executives (Management). More specific, Guan et

al. (2016) found that auditors, who had a relationship with the executives of their clients

from college, are more likely to issue a clean audit opinion. They also state that

discretionary accruals are reported at a significantly higher level in these types of

companies, suggesting that audit quality are negatively impacted by client affiliation.

18

Having said that, Francis (2004) found that evidence on client affiliation is limited, which

might be since it transpires at a lower level than anticipated.

All the above-mentioned threats may decrease the audit quality as the auditor

independence might decrease. Previous studies have suggested that audit rotation can

alleviate threats connected to auditor’s independence (e.g. Blandon & Bosch, 2013; Mali

& Lim 2018; Kalanjati et al., 2019). Audit rotation is closely connected to audit tenure as

those who argue a longer audit tenure increases audit quality is opposing the idea of

mandatory audit rotation regulations while others who argue the opposite is in favour of

mandatory audit rotation (Tepalagul & Lin, 2015). If the auditor is rotated on a regular

basis, he/she might not be able to develop a relationship with the client and consequently

maintaining his independence (cf. Chen et al., 2018). This would also suggest that the

client affiliations impact on audit quality are reduced, as the relationship is shortened (cf.

Tepalagul & Lin, 2015). Furthermore, retainment of the client is decreasing in importance

as the auditor is obliged to leave the client after a certain time (Chen et al., 2010). Since

the auditor have less incentive in keeping the client, he/she also might not be willing to

compromise his/her independence, consequently the client importance negative impact

on audit quality decreases (cf. Chen et al., 2010). The same goes for non-auditing services,

as the rotation breaks the economic bond between the auditor and the client (cf. Hohenfels

& Quick, 2018). However, some studies argued that the initial lack of client knowledge

that comes with a new auditor harms the audit quality (Lennox et al., 2014; Mali & Lim,

2018).

Regulatory bodies have often attempted to improve the audit quality through regulations

on mandatory audit rotation. In 2006 the European Commission issued a directive on

mandatory audit partner rotation, requiring auditing partners to be switched after serving

7 years as the key audit partner (EUR-Lex, 2006). Some studies on the subject have

suggest that the audit quality increases in the early years following the mandatory audit

partner rotation (Bandyopadhyay et al., 2014; Lennox et al., 2014; Liao & Chi, 2014).

Another study found that the introduction of such regulations has improved the overall

audit quality (Monroe & Hossain, 2013). However, if the mandatory audit rotation leads

to a rotation of the audit firm, the audit quality is decreased (cf. Mali & Lim, 2018; cf.

Kalanjati et al., 2019). Surprisingly in the light of such findings, the European

Commission and other regulatory bodies have introduced similar regulations on audit

19

firm rotation, that requires a rotation of the audit firm after a certain time (European

Commission, 2014). In such cases where the audit quality is impaired by the regulations,

it would become counterproductive, as the purpose is to enhance the auditor’s

independence to improve the audit quality (EU Commission, 2014). In relation, some

countries have introduced audit rotation regulations, but has abolished the regulation after

a short period (e.g. Spain) (Ruiz-Barbadillo et al., 2009).

As explained above the effects of mandatory audit regulations have been divergent in

auditor rotations impacts on auditor independence and consequently the effects on audit

quality. Therefore, we deem audit rotation to be the most important factor to investigate.

Through understanding its impact on audit quality, it will help regulatory bodies to

understand whether there is a need for such regulations or if the regulatory focus should

be put elsewhere for the purposes of improving the audit quality.

2.6 Audit rotation

Audit rotation refers to the act of changing the ultimate responsible external auditor of a

given firm. Accordingly, mandatory audit partner rotation regulations only require the

key audit partner(s) to rotate after certain period in the position (Firth et al., 2012).

Practically this means that audit firms can deploy the same audit team except for the key

audit partner. However, the rotation of the audit partner can sometimes lead to a change

of auditing firm. Such rotations are referred to as audit firm rotation. Audit firm rotation

is thought to decrease the risk of audit firms colluding with a client, while audit partner

rotation is argued to improve the independence of the auditor as there is less time to

develop a close relationship between the individual audit partner and the client (Mali &

Lim, 2018). The main difference between the two types of rotation is thought to be the

information and knowledge gaps that arise after the rotation, which will be extend on

below. As the objectives and argued effects of the different types of audit rotation is

different it is important to make a distinction between the two (cf. Kalanjati et al., 2019).

2.6.1 Audit partner rotation

Audit partner rotation is referred to the act of changing the key audit partner while

maintaining the same audit firm. The empirical evidence on audit partner rotations effect

on audit quality is strongly suggesting at least an initial improvement in audit quality after

audit partner rotation, as a consequence of increased auditor independence (e.g.

20

Bandyopadhyay et al., 2014; Lennox et al., 2014; Liao & Chi, 2014; Kalanjati et al.,

2019). As previously mentioned, the relationship is shortened through the rotation which

enhances auditor independence. This explanation stems from the argument that

independence decreases with a longer the audit tenure, as the risk of collusion increases

(Mali & Lim, 2018). This risk could become apparent, both intentionally, and

unintentionally from the auditor, where the auditor could favour the management instead

of the shareholders. This would deteriorate the role of the auditor as an intermediary

between the management and the shareholders, as the auditing statement become less

reliable, which consequently would decrease the audit quality. Furthermore, as the audit

firm is maintained, the client-specific knowledge is likely to stay intact as auditors in

same audit firm easily can communicate with each other and thereby transfer important

client-specific knowledge. Client-specific knowledge is of major significance on the

auditing process, as the auditor needs to understand the client's industry, accounting

policies and working procedures to be able to make a fair auditing assessment of the

client. The transfer of client specific knowledge would help to alleviate the threat of a

decrease in audit quality due to the transition (e.g. Bobek et al., 2012; Lennox et al., 2014).

As the client-specific knowledge is maintained and the auditor independence is reassured

through audit partner rotation, the audit quality would be improved.

Previous studies have suggested that the fresh perspective that comes with a newly

appointed auditor, after audit partner rotation, increases the likelihood to detect material

misstatements, consequently improving the audit quality (Favere-Marchesi & Emby,

2005; Lennox et al., 2014). This implies that a longer audit partner tenure decreases the

accuracy of the auditor, as the auditor may become inadaptive to changes, due to his or

her comfortability with the client, which would help to motivate a rotation of the audit

partner. Taken together, this study has developed the following first hypothesis:

H1: The number of audit partner rotations is positively correlated with audit quality.

2.6.2 Audit firm rotation

Audit firm rotation is the act of changing the responsible audit firm, that said, the same

audit partner can still be the responsible auditor. The purpose of the audit rotation is to

increase the auditor independence, which would lead to a higher audit quality (European

Commission, 2014). However, the empirical evidence often agree that audit quality is

21

unchanged, or negatively impacted by the firm rotation, which would deteriorate the

outcome in relation to the purpose of the mechanism. (e.g. Jenkins & Vermeer, 2013;

Mali & Lim, 2018; Widyaningsih et al., 2019; Kalanjati et al., 2019). Different

explanations for why the audit quality decreases are provided. Opposite to audit partner

rotation client-specific knowledge would not be shared between the successor and

predecessor audit firm, which as through previous argumentation would impair the audit

quality. The lack of communication could be traced to the fact that these two audit firms

are competing, which would suggest that predecessor firm does not have any interest in

sharing client information, which could increase the quality for successor audit firm

(Kalanjati et al., 2019). The lack of information would lead to the issue that auditors

would have to trust the information given by the audited companies managers, which

could lead to an opportunistic behaviour and aggressive reporting, which have been found

to impair audit quality (Mali & Lim, 2018). A second explanation is that the audit partner

does not change (i.e. the audit partner gets an employment at another audit firm, and his

or her customer are transferred to the new firm) which would hamper purpose of increased

audit independence; hence the same audit partner is still responsible for the auditing.

(Kalanjati et al., 2019). Therefore, the second hypothesis have been developed as follows:

H2: The number of audit firm rotations is negatively correlated with audit quality

2.7 Contingency aspects

Even though a lot of previous research suggests an increase in audit quality after audit

partner rotation and a decrease in audit quality after audit firm rotation, the findings in

the area is not unanimous, some empirical evidence suggest the opposite correlation (Litt

et al,. 2014; cf. Corbella et al., 2015). Jenkins & Vermeer (2013) goes as far as suggesting

that the empirical evidence on audit rotations impact on audit quality is inconclusive if

not worse. This would suggest that there are other factors the affect the audit quality in

relation to audit rotation (cf. Fiedler, 1964).

Previous studies have found that auditors are likely to behave different depending on the

client’s characteristics (i.e. firm characteristics) (Walo, 1995; Redmayne et al., 2010).

Redmayne et al., (2010) found that hours spent on auditing increased as the visibility of

the firm increased. Visibility can include many firm characteristics that attracts the

attention from society and therefore increases both the political and social pressure of on

22

the firm. This pressure is likely to be transferred on to the auditor, as the auditors is subject

to a high level of scrutiny as the visibility increases, consequently their reputation is more

vulnerable (Brammer & Millington, 2006). Hence, the auditors are more likely to act with

more caution when dealing with more visible firms (cf. Walo, 1995). As the behavioural

pattern of the auditor's changes, it is likely to affect the audit quality. Furthermore,

research on audit tenures effect on audit quality have shown that the impairment of audit

quality is often dependant on the firm’s characteristics (Gul et al., 2009; Lim & Tan,

2010). As previously mentioned, audit tenure has a significant impact on the audit quality

in relation to audit rotation (audit partner and audit firm rotation), as both a stronger

relationship is formed between the auditor and the client and the auditors client-specific

knowledge increases the longer the audit tenure is (cf. Chen et al., 2018, Kalanjati et al.,

2019). The auditor might be less willing to compromise his/her independence in visible

firms and therefore the rotation would affect the quality less than in a rotation in a non-

visible firm. Seemingly, the audit quality and audit rotation (audit partner and audit firm

rotation) interrelationship is dependent on the audited firms’ characteristics (i.e.

visibility). Therefore, the third and fourth hypothesis has been developed as follows:

H3: The relation between audit partner rotation and audit quality is contingent on firm

visibility.

H4: The relation between audit firm rotation and audit quality is contingent on firm

visibility.

23

3 Method

This section is divided into two major parts, theoretical method, and empirical method.

In the theoretical method the reader is presented with the research positioning, an

argumentation for the chosen theories and source criticism. In the second part, empirical

method, the data collection is described, along with the operationalisation of the study,

including choice of control variables. Lastly, the section data analysis is presented, where

description of how the empirical tests have been conducted is presented.

3.1 Theoretical method

3.1.1 Research position and scientific strategy

The purpose of the study is to explain how audit firm rotation and audit partner rotation

relate to audit quality and how this relationship is contingent on firm visibility. To fulfil

the purpose, we have opted for a positivistic approach to the science, where the results

for the sample aim to be generalized for the full population, which later can be of help to

the society in the form of e.g. new regulations (Bryman & Bell, 2015).

The positivistic approach is recognized one or more of five characteristics.

1. Sensual knowledge can be recognized and presented as knowledge.

2. Hypotheses is created from theories and tested to allow for explanations of the

phenomena.

3. It is presumed to be conducted in an objective way, allowing for as little as possible

self-interpretation.

4. In order to gain knowledge, facts that create basis for laws has to be obtained.

5. Scientific statements are clearly distinguishable from normative statements (Bryman

& Bell, 2011)

The positivistic approach is mainly conducted inductively (4) or deductively (2). The

inductive approach, where the researchers create theories from existing studies (Bryman

& Bell, 2015), were deselected as previous studies were deemed to be insufficient to

gather knowledge on the contingency aspects in the research problem. Instead, this study

has chosen to implement a deductive positivistic approach, where we theories lay the

24

ground for the hypotheses. The researchers obtain data that is later tested empirically

through translating the sometimes-abstract hypotheses into subjective and measurable

concepts (Bryman & Bell, 2015). For instance, visibility have been translated into three

factors that we understand to be a determiner of the term. Later in the research findings

are analyses and compared with present theories to explain, understand, and prove causal

relationships (Bryman & Bell, 2015).

The deductive approach requires a rigorous sample along with a high amount of

objectivity if the findings are to be generalized for the full intended population (Körner

& Wahlgren, 2015). Seeing as generalization is key for the study to provide guidance for

regulators, we have chosen to investigate the research question quantitatively. A

quantitative approach will allow us to gather large amounts of data (Bryman & Bell,

2015), and as we collect most of our data from annual reports little is left to interpretation

by the researchers, consequently ensuring the objectivity of the study. If the study were

to be conducted qualitatively, the sample size would be smaller and a greater amount of

interpretation would be required by the researchers, consequently threatening the

generalizability of the study (cf. Saunders et al, 2016).

3.1.2 Theories of choice

This study has opted to use four different theories to be able to understand and explain

both the auditor’s role, and how different aspect may affect the audit quality. The given

theories are: Agency theory, legitimacy theory, contingency theory, and behavioural

theory.

Agency theory. As the purpose of this study is to explain if audit rotation affects the audit

quality, we use the agency theory to explain the auditor’s role, and how why the auditor

may need regulations. The agency theory describes the relationship between two actors,

the agent and the principal, and the difficulties that this relationship may face, due to

differences in interest. (Fülöp, 2013). The differences in interest could lead to agency

costs, which could be mitigated using an auditor. This is where the role of the auditor

emergence, to monitor the agent (i.e. management of a company) on the principles (i.e.

shareholders) behalf (Shapiro, 2005). Also, the independence or self-interest dilemma

that may occur for the auditor can also be understood and explained through the agency

theory, as the auditor can be seen as the agent, and the shareholders or the public are seen

25

as the principles. This implies that the auditor also needs certain regulations and control

mechanism not to follow his or her personal interest.

Legitimacy theory. Legitimacy theory are used to describe how corporation uses certain

ethics and values that is of importance to society, to increase the corporation’s legitimacy

towards the public (Deegan, 2019). This is also used to provide another explanation for

why the auditor profession is important, which is to enhance the legitimacy for the

corporations towards the society by controlling the financial statements. It has been found

that if the trust for the audit profession increase, the legitimacy also increases, which

implies that the audit quality is affecting the society. However, if the legitimacy is broken,

regulatory bodies will try to enforce it, by issue new regulations, such as the mandatory

audit partner rotation (EUR- Lex, 2006) and the mandatory audit firm rotation (European

Commission, 2014)

Contingency theory. The contingency theory considers certain characteristics from an

organisation, that will affect the effectiveness of a certain situation (Mcadam et al., 2019).

This helps us to understand that there are other factors that may affect the audit quality in

relation to an audit rotation, stressing the need for contingency aspects in the study.

Behavioural theory. Behavioural theory assumes that people will act according to their

previous experiences and current environment (Kahneman, 2003). The theory is used to

understand how the visibility of a firm can change the audit quality. Through adapting

this theory to the auditor, we understand that he/she is likely to act different dependent

on the visibility of the audited firm, as the environment would be different. If the auditor

changes his/her behaviour the audit quality might be changed.

3.1.3 Source criticism

The academic articles in this study have been searched for and obtained, primarily using

Jönköping University library database PRIMO, and secondary, Google Scholar. To

ensure that the academic articles has high credibility and quality, all academic articles in

this study is peer reviewed. A peer reviewed article is examined by experts for the given

subject before publication, to ensure its quality. To the extent that is possible we have

used articles published in journals that are highly rated on the Academic Journal Guide

(ABS) list. The higher rated the journal is on the ABS list, the higher the reliability of the

study (Bryman & Bell, 2015), which consequently makes this study more reliable.

26

Furthermore, when searching for the articles we also looked for the latest published

articles, to ensure the relevance of their findings in today's environment. We also tried to

find the most relevant articles for our subject, to avoid misinterpretations when relating

their findings to our area of investigation.

3.2 Empirical method

3.2.1 Timespan

To be able to see how audit rotation (both firm and partner) has affected different

companies audit quality, this study will include financial data between the years of 2009-

2018. This timespan can capture the effects of both the mandatory audit partner rotation

and mandatory audit firm rotation EU directive, as they were introduced in 2006 and 2014

respectively (EUR-Lex, 2006; European Commission, 2014). Furthermore, as the 2006

directive requires mandatory partner rotation after a tenure of 7 years, we are guaranteed

at least one audit partner rotation per firm. However, we expect to find fewer audit firm

rotations as the directive has not come into full effect, due to the mandatory audit firm

rotation tenure of 10 years, with the possibility to extend it even further (European

Commission, 2014). This timespan will also provide this study with the latest financial

data available, which improves the relevance of the study.

3.2.2 Sample selection

This study will be performed on Swedish companies, which are listed on OMX

Stockholm large cap. The focus on Swedish companies stems from the fact that Sweden

is a member of the European Union and has chosen to implement both mandatory audit

rotation directives (Regeringen, 2015). Furthermore, to our knowledge very few

published studies in the area have investigated a sample of Swedish firms. The financial

data from listed firms will be easier to obtain than from the non-listed companies as they

are entitled to disclose financial reports for the public (Bolagsverket, 2019). Listed

companies are also obliged to have a statutory auditor, while certain non-listed companies

are exempt from this requirement (Bolagsverket, 2019a). The mandatory firm rotation

directive also excludes most non-listed companies as they are not considered to be public

interest entities (PIEs) (European Commission, 2014).

Large cap entities have per definition a larger market capitalisation, which means that the

auditor becomes increasingly important as there is more to lose for investors in the case

27

of poor audit quality (cf. Redmayne et al., 2010). Large cap entities commonly have more

employees (Holmström, 2019), and might have a larger number of suppliers and total

debts. Therefore, this study argues that it is of greater importance to investigate and

thereby understand the effects of audit rotation on audit quality in large-cap companies,

as an audit failure can have greater consequences on society in such instances.

3.2.3 Data Collection

This study has used the database Business Retriever to gather the large-cap firms’

financial data. Financial data that were unable to obtain through Business Retriever were

manually collected from the firms’ annual reports. Namely, annual audit fees and non-

audit fees, cash flow from operations, changes in revenue for the year 2009 and changes

in accounts receivable for year 2009. Information about the auditor, such as partner and

firm rotation, audit tenure and the responsible auditing firm was gathered manually from

the annual reports. Data on press mentions and years listed on the stock were gathered

from Business Retriever’s media archive and Avanza, respectively. Ownership structure

were at first hand gathered from the annual reports, and secondly from the companies’

website.

The first step in the procedure of collecting data were to obtain the sample of large-cap

companies from Nasdaq OMX Stockholm’s website, where information about each

company's industry classification were obtained. Secondly, all relevant financial and non-

financial data that was available in the database Business Retriever were collected. The

third step was to allocate and collect the missing values from the database, by manually

extracting them from the annual reports of the companies. The last step was to obtain the

non-financial data that were unavailable from the annual reports through the other above-

mentioned sources.

Companies with unavailable financial data for any of the years in the timespan were

excluded. These consisted out of companies founded after 2009 and companies that were

not available in Business Retriever (see excluded companies in Appendix 1).

Furthermore, non-financial data and more specifically ownership structure were

unobtained in a few cases, in which it has been denoted as “missing value”. The total

sample, after the exclusion of above-mentioned firms, amounted to 58 companies which

28

provided 580 company years of data, and a total of 159 audit rotations (firm and partner

rotations combined).

3.2.4 Limitations

As the sample is limited to large cap companies, this study might not provide a

generalizable result for the whole population of listed companies. Through our data

collection we understand that the industry sector for large-cap companies is dominated

by financial and industrial companies, this limits our sample size on the industry sector

as there is a low variation, which also poses a threat to the generalizability of this study.

Another major limitation is that the sample is restricted to Sweden, which might hamper

the findings as country specific factors can have an influence on for instance the audit

quality (Kalanjati et al., 2019). In addition, there is also a possibility that the timespan is

to short and that the overlap of the financial crisis in the sample may affect the findings

(Mali & Lim, 2018). Lastly, the timespan may not capture the effects of the mandatory

audit firm rotation directive from 2014 as the earliest mandatory audit firm rotation occurs

after a 10-year tenure (European Commission, 2014).

3.2.5 Operationalisation

3.2.5.1 Independent variables

The independent variables consist out of Audit partner rotation and Audit firm rotation.

Audit partner rotation - were measured through observing the auditor's report in each of

the company’s annual reports to identify if the responsible audit partner had changed

between the years. A rotation of the audit partner was donated as 1 while a maintained

audit partner was denoted as 0.

Audit firm rotation - were measured in a similar way, with the difference of looking for a

change of the audit firm instead of the audit partner. A rotation of the audit firm was

denoted as 1 while a maintained audit firm were denoted as 0.

The rare cases of pseudo-rotations, when a company is rotating their audit firm while

maintaining the audit partner (Kalanjat et al., 2019), was denoted as 1 in the audit firm

rotation column and 0 in the audit partner column.

Seeing as the number annual reports to investigate were several as well as the importance

of the variable, both researching partners obtained this data independently. In the

29

occurrence of different results from each partner, these were analysed again, to prohibit

any misstatements.

3.2.5.2 Dependent variable

Audit quality - is the dependent variable which was proxied by discretionary accruals.

Discretionary accruals are the difference between normal accruals, such as depreciation

according to plan, and the total accruals (Cornett et al., 2008). Discretionary accruals can

be related to low audit quality, as the use of extreme accounting policies often reflect a

low audit quality, and discretionary accruals can be used to identify such accounting

policies (Defond & Zhang, 2014). Previous studies have found that a high number of

discretionary accruals reflect a low audit quality, while low discretionary accruals often

reflect a high audit quality (Francis & Krishnan 1999; Geiger & Raghunandan, 2002;

DeFond & Zhang, 2014). In studies were audit quality have been proxied by discretionary

accruals to investigate the effects of audit rotation (both firm and partner rotation), both

a negative and positive relationships have been found (e.g. DeFond & Zhang, 2014; Mali

& Lim, 2018; Kalanjati et al., 2019)

As DeFond & Zhang (2014) explain that the proxy variable discretionary accruals have a

high measurement error, we have chosen to include two models for estimating

discretionary accruals. We have opted to use the modified Jones model used by e.g.

Balsam et al. (2003) and Kalanjati et al. (2019), and a modified Jones model used by e.g.

Francis et al. (2013) and Kalanjati et al. (2019). The equations are as follows:

Modified Jones model (e.g. Balsam et al., 2003; Kalanjati et al., 2019):

𝐷𝐴1 =𝑇𝐴𝐶𝐶

𝐴𝑠𝑠𝑒𝑡𝑠− [𝛽0 + 𝛽1 (

1

𝐴𝑠𝑠𝑒𝑡𝑠) +

𝛽2 𝛥𝑅𝐸𝑉

𝐴𝑠𝑠𝑒𝑡𝑠+

𝛽3𝑃𝑃𝐸

𝐴𝑠𝑠𝑒𝑡𝑠]

Modified Jones model controlling for competitor's performance (e.g Francis et al., 2013;

Kalanjati et al., 2019):

𝐷𝐴2 =𝑇𝐴𝐶𝐶

𝐴𝑠𝑠𝑒𝑡𝑠− [𝛽0 + 𝛽1 (

1


𝛽2 (𝛥𝑅𝐸𝑉 − 𝛥𝑅𝐸𝐶)


𝛽3𝑃𝑃𝐸

𝐴𝑠𝑠𝑒𝑡𝑠+ 𝛽4𝑅𝑂𝐴]

TACC denotes total accruals which were estimated and obtained as the difference

between income before extraordinary transactions and cash flow from operations. Assets

were obtained as the company's total assets at the end of the year, ΔREV and ΔREC

denotes the change in net sales and account receivable respectively, obtained as the end

30

of the year value in year t, minus the value in year t-1. PPE is the company's total property,

plant and equipment which also has been obtained as the end of the year value. Lastly,

ROA denotes return on assets which is measured by dividing net income by previous

year’s total assets. How the variables are estimated are consistent with previous studies

(e.g. Kalanjati et al., 2019; Francis et al., 2013; Balsam et al., 2003).

The first modified Jones model estimates the discretionary accruals by subtracting the

non-discretionary accruals from the relative total accruals (TACC/Assets). In the model

non-discretionary accruals is a function of the relative ΔREV and the relative value of

PPE. ΔREV is included in the model as the non-discretionary accruals amounts is found

by a previous study to be dependent on the economic circumstances of the firm (cf. Jones

1981). The assumption is thereby made that ΔREV reflects the economic circumstances

of the firm. The non-discretionary accruals are further estimated by PPE, controlling for

the portion of non-discretionary accruals in the depreciation expenses (Jones, 1981).

Dechow et al. (2012) recognises the function of these two variables to be sufficient for

estimating non-discretionary accruals in cases where revenue has not been manipulated

through misstating the net account receivables. As the amounts in accounts receivables is

recognised as revenue in the model it could lead to an overestimation of the non-

discretionary accruals, consequently underestimating the amount of discretionary

accruals (Dechow et al., 2012).

In second model, the discretionary accruals are estimated through the same principle.

However, some of the flaws of the previous model is mitigated through including ΔREC

in the equation, thereby estimating non-discretionary accruals from cash revenue. This

means through previous reasoning that the risk of overestimating the non-discretionary is

decreased (Dechow et al., 2012). Furthermore, the model controls for competitor’s

performance by including ROA, which is assumed to be an indicator of firm performance

(Francis et al., 2013). With that said, McNichols (2002) explains that these two models

are probable to only capture the basic non-discretionary accruals, which implies that

discretionary accruals are likely to be slightly overestimated, since the non-discretionary

accruals would be underestimated.

To estimate the coefficients for the models the following regressions is used:

For the first model (Modified Jones model):

31

𝑇𝐴𝐶𝐶

𝐴𝑠𝑠𝑒𝑡𝑠= 𝛽0 + 𝛽1 (

1


𝛽2 𝛥𝑅𝐸𝑉


𝛽3𝑃𝑃𝐸

𝐴𝑠𝑠𝑒𝑡𝑠+ ε

For the second model (Modified Jones model controlling for competitor's performance):

𝑇𝐴𝐶𝐶

𝐴𝑠𝑠𝑒𝑡𝑠= 𝛽0 + 𝛽1 (

1


𝛽2 (𝛥𝑅𝐸𝑉 − 𝛥𝑅𝐸𝐶)


𝛽3𝑃𝑃𝐸

𝐴𝑠𝑠𝑒𝑡𝑠+ 𝛽4𝑅𝑂𝐴 + ε

The values of discretionary accruals will be both positive and negative where a positive

value indicates discretionary manipulations increasing earnings and a negative value

indicating discretionary manipulations decreasing earnings (Kalanjati et al., 2019).

However, to be able to run the regression analysis all negative values must be stated as

positive. This then implies that a higher the value of DA1 and DA2 indicates higher

discretionary accruals, i.e. lower audit quality.

Most of the data for the estimation of discretionary accruals were obtained from Business

Retriever. Exceptions were made for cash flow from operations which had to collected

manually from the annual reports for all years, while account receivable, net sales and

total assets were collected manually from the annual reports only for the year 2008.

3.2.5.3 Contingency variables

In this subsection it is explained how the contingency variables were obtained and

measured. For each variable, we elaborate on why specific the measurements are

considered as a determiner of visibility.

Firm size (number of employees). This study has opted to measure firm size in number of

employees. Number of employees affect the visibility as more people are affected by the

company through their economic bond (i.e. salary) (cf. Bushee & Miller, 2012). Number

of employees was obtained as the average total employees for the given year. The variable

was collected from Business Retriever.

Ownership structure. Ownership structure is measured as the percentage of institutional

ownership in the firm. Institutional investors are often recognized as pension funds,

capital investment funds or insurance funds for the public interest. Therefore, a higher

percentage of institutional ownership will increase the visibility, as the public interest in

these companies will be greater (Brockman et al., 2017). The percentage of institutional

ownership was gathered from the annual reports of the companies or when unavailable

from the companies’ website. Commonly the annual report only presented the 10 largest

32

owners and/or Swedish institutional ownership. Therefore, expect the obtained

percentage of institutional ownership to be on the lower border.

Press mentions. This study argues that press mentions increases the visibility of the firm

as the public is exposed with firm specific information through mentions in the press,

either voluntarily through actively searching for information, or unintentionally through

being fed with information from the media. The press, or the media can be seen as a

watchdog, who serves as a intermedia between the public and the company, and in the

case of accounting irregularities, can identify them, and report them to the public (cf.

Hammami & Hendijani Zadeh, 2019; Comiran & Fedyk, 2018). This would not only

affect the specific company in case of negative press exposure, but also for the responsible

auditor, who should be able to detect financial misstatements. Press mentions were

measured through the cumulative number of press mentions of the firm for each year. The

data was obtained from Business Retriever’s media archive where a search was conducted

for the firm name at the time.

Visibility dummy. An index is computed for each of the variables and added together as a

common index of visibility. The average value of the visibility index acts as a determiner

of visibility, where an observation with a visibility index above the average value of the

visibility index is classified as visible and denoted by 1 while observations with an

visibility index below the average value of the visibility index is classified as non-visible

and denoted by 0.

3.2.5.4 Control variables

In this subsection the control variables will be described and motivated along with

information on the measurement and collection of the variables. The above-mentioned

contingency variables are only used as control variables in the testing of hypothesis 1 and

2.

Audit fees. Audit fees can affect the audit quality. Higher audit fees can improve the

auditor’s commitment via the economic incentive, thus improving the audit quality

(DeFond & Zhang, 2014). The audit fees were collected from the company’s annual

reports, more so, the obtained audit fees was normally stated as: statutory audit fee. Often,

companies disclosed audit fees for 1 key audit firm, but also a smaller cost for “other

33

auditing firm” responsible for the statutory audit. This study has chosen to add all

statutory audit fees together as one.

Non-auditing services. Non-auditing services can either improve or impair the audit

quality (e.g. Hohenfels & Quick, 2018; Kowaleski et al., 2018). Audit quality can be

improved as the auditor spends more time with the client and thereby increases his/her

client knowledge. Audit quality can be impaired as an economic bond is created between

the auditor and the client, which might make the auditor willing to compromise his/her

independence to retain the client (Tepalagul & Lin, 2015). As the data for this control

variable were unavailable in Business Retriever, it had to be obtained manually from the

annuals reports of the companies. Normally companies disclosed their non-audit fees in

three ways: tax services, audit relating fees and other services. All three were categorized

and collected as non-auditing fees.

Audit partner tenure and audit firm tenure. Studies have found that an audit partner tenure

and audit firm tenure is closely connected to audit quality (e.g. Bell et al., 2015; Jenkins

& Velury, 2008). A longer audit tenure (both firm and partner tenure) is argued to improve

the audit quality as the client knowledge increases, on the other hand it is also argued that

audit tenure can impair audit quality as a relationship is created between the auditor and

the client which could harm the independence of the auditor (Francis, 2004). This variable

was collected by looking at the auditor’s reports in the annual reports and tracing the

rotation of the key audit partner and audit firm. Audit tenure was measured in terms of

consecutive years at the assignment, with the first year denoted as 1 year of audit tenure.

Leverage. Leverage is controlled for as companies that are highly leveraged often

manipulate their earnings, consequently manipulating their accruals (e.g. Johnson et al.,

2002; Becker et al., 1998; DeFond & Jiambalvo, 1994). Leverage was calculated as total

liabilities divided by total assets. Both components were collected from Business

Retriever as the end of the year value.

Profit or loss. Loss is included as a control variable as previous research has found that

audit quality differs between profitable and less profitable companies (Choi et al., 2010).

This variable was collected by identifying the net income from the previous year in

Business retriever. A profit was denoted as 0, while a loss was denoted as 1.

34

Return on equity. To further control for the financial health of the company, we have

chosen to include the profitability measurement, return on equity. ROE was calculated as

net income divided by the end of the year total shareholders’ equity. All data was

collected from Business Retriever except for total asset in 2008 which had to be collected

manually from the annual reports.

Client firm age. Previous studies have stated that a younger firm tend to face more

financial stress, than an older (Carey & Simnett, 2006). Through this reasoning, we have

chosen to include the firm age as a control variable, as a large financial stress might lead

to a manipulation of earnings (cf. Johnson et al., 2002). Client firm age was measured as

the number of years since the company was first listed on the stock exchange. The data

has primarily been collected from the annual reports of the companies, and secondarily

from the trading platform Avanza.

Firm size (total assets). In a previous study firm size have been found to affect the audit

quality (Purnamasari et al., 2019). According to their study, large firms tends to strive for

a high audit quality, to be able to lower the agency cost that arise from the operational

complexity of large firms. Therefore, large companies often seek to hire the most

experienced auditor from the well-known audit firms, such as those relating to the Big 4.

Consequently, this study argues that firm size will affect the audit quality in relation with

audit rotation, as the auditors in larger firms will be more experienced. In line with

Purnamasari er al. (2019) firm size were measured in terms of total assets. The variable

was obtained from Business Retriever looking at the end of the year value of total assets.

Industry. Brammer & Millington (2006) explain that different governance styles occur in

different industries. In the light of that, it is possible that different industries provide

different audit quality. Accordingly, previous studies in the same area of study have also

implemented this variable to control for the fixed effects of the industries (e.g. Kalanjati

et al., 2019). The industry classification for each company were obtained from Nasdaq

OMX Nordic. The obtained industries consist of, industrials, basic materials, health care,

financials, consumer services, consumer goods, technology, and telecommunications.

The company’s industries were denoted as 1 if they were present in the given industry or

0 if they were not. For instance, if a company were present in the financial industry that

column is denoted by 1 and all other industries columns as 0.

35

Year. Year is controlled for, as previous studies have implied that the year of observation

may affect the findings (e.g. Mali & Lim, 2018). Similar to industry the observation year

of the data is denoted as 1 in the column for the specific year and 0 in all other year

columns. For instance, if the data was observed for the year 2009 that column is denoted

by 1 while the other columns (2010 and forward) in denoted by 0.

Discretionary accruals control. As the negative values of discretionary accruals is stated

as positive for the purposes of the regression analysis, we have chosen to include a binary

control variable to see if an upward or downward manipulation of earnings affects the

audit quality. 1 indicates a positive manipulation of earnings while 0 indicates a negative

manipulation of earnings.

Firm size (number of employees). As this study argues that visibility would affect the

audit quality, we include firm size, measured by the total number of employees as a

control variable. As a company could gain more visibility the more employees they have,

the pressure mounts on the auditor (cf. Redmayne et al., 2010, cf. Walo, 1995), which

consequently could affect the audit quality. In accordance, previous study has also opted

to use number of employees to determine the firm size (Fleischer & Goettsche, 2012).

The data on number of employees were extracted from Business Retriver by the average

total employees of for each year.

Ownership structure. As stated, an increasing number of institutional investors, will

increase the visibility of the firm (Brockman et al., 2017). This, as the above mentioned,

will increase the pressure for the auditor, as the stakes are higher due to the public's

interest, hence possibly affect the audit quality (cf. Redmayne et al., 2010). The

percentage of institutional ownership was gathered from the annual reports of the

companies or when unavailable from the companies’ website. Commonly the annual

report only presented the 10 largest owners and/or Swedish institutional ownership.

Therefore, we expect the obtained percentage of institutional ownership to be on the lower

border.

Press mentions. An increasing number of press mentions will, as stated, increase the

firm’s visibility, which in turn could affect the audit quality, as the stakes are higher for

the auditor in the case of a financial scandal due to the public's attention. Previous

research has also found that an increasing amount of press mentions increases the audit

36

effort (Redmayne et al., 2010), which could be argued to be a determiner of audit quality.

As previously mentioned, press mentions were measured through the cumulative number

of press mentions of the firm for each year. The data was obtained from Business

Retriever’s media archive where a search was conducted for the firm name at the time.

3.2.5.5 Validity, reliability, and generalizability

Validity is important to consider, as a higher degree of validity ensures that the variables

represent a fair view of the intended construct, i.e. will the measurement answer the

research question (Menold et al., 2018). To ensure the validity of this study, we have only

included variables and measurements that have been used in previous studies and have

been proven to be a determiner of what it is intended to measure. Furthermore, the

timespan of 10 years decreases the risk of abnormal findings due to extraordinary

economic conditions for a certain year. We have also chosen to include a rigorous number

of control variables to ensure that the proxy variable discretionary accruals actually

measure audit quality.

Reliability seek to explain if the measurement of variables is consistent (Heale et al.,

2015). In our study we expect the reliability to be high as the measurement of the variables

is obtained from annual reports through the database Business Retriever. Variables such

as total assets do not have to be calculated or coded in any way for the purposes of the

test. In cases where data had to be manually estimated, this study used the same template

and coding for all companies. Additionally, certain estimation variables were collected

by both partners to decrease the risk of misstatements.

Generalizability is explained to be the extent to which the findings of an analysis can

present a fair view of the full population that the sample are intended to represent.

Generalizability is largely affected by two factors, the sample size and to which extent

the sample are representative for the measured population (Elliot et al., 2016). This study

has included obtainable companies listed on the OMX Large cap, in a 10-year timespan.

The sample size should present a fair view of the full population of large-cap companies

in Sweden as well as in other countries with similar business environments. More so, all

industries available on the Stockholm OMX large cap is included in the sample to increase

the generalizability of the findings. Lastly, the number of observations is also consistent

with similar studies in the area (e.g. Kalanjati et al., 2019; Hohenfels & Quick, 2018; Mali

& Lim, 2018).

37

3.2.6 Data analysis

The data is compiled in excel and thereafter exported to SPSS to perform several

statistical tests. The tests that have been conducted is the following:

3.2.6.1 Descriptive statistic

The descriptive statistic is computed through a univariate analysis. In this analysis the

variables are individually analysed (Pallant, 2016). The result is a table of descriptive

statistic were the mean, standard deviation, minimum, maximum and total observations

is displayed for each variable. Furthermore, through the statistic “Valid N” the total

number of complete observations is displayed. This gives us an opportunity to find any

misstatement in the data collection as well as providing the reader with an oversight of

the variables used for the analysis.

3.2.6.2 Normal distribution

Secondly an analysis of the dependent variable's normality is performed. A variable that

that follows a normal distribution has most values cantered around the mean value of the

variable while smaller portion of the values gradually differs from the mean. It is

important to control for normality since some test requires the dependent variable to be

normally distributed. The normality of a variable can be measured in many ways such as

Kolmogorov-Smirnov, skewness and the value of kurtosis (Pallant, 2016). Skewness tests

and kurtosis values is appropriate for samples below 200 observations (Tabachnick &

Fidell, 2014). Seeing as our study has well over 200 observations, we have chosen the

Kolmogorov-Smirnov test to control for normality. If the significance level of the

Kolmogorov-Smirnov statistic exceeds 0,05 the variable should be considered to follow

a normal distribution (Pallant, 2016).

3.2.6.3 Bivariate correlation analysis

With bivariate correlation analysis we can understand how two individual variables relate

to each other and how strong this relationship is. The correlation analysis is performed

using the Pearson correlation. The correlation approach is deemed fit as the Pearson

correlation can correlate both continuous variables such as discretionary accruals and

dichotomous variables such as audit partner rotation (Pallant, 2016). The Pearson

correlation computes a correlation coefficient which ranges from -1 to 1. The further away

the value is from 0 the stronger the correlation is. A positive correlation is recognized by

38

a positive value of the coefficient and implicate that an increase in one variable

corresponds to an increase in the other. A negative correlation is recognized by a negative

value of the coefficient and implicate that an increase in one variable corresponds to a

decrease in the other (Pallant, 2016).

3.2.6.4 Multiple regression analysis

Multiple regression analysis is used to understand the relationship between one dependent

variable and two or more independent variables (Bryman & Bell, 2015). The multiple

regression model is suitable to use, when the studies use a limited amount of control

variable and is uncertain of the outcome (Pallant, 2016). Since we have well beyond two

independent variables (including the control variables) specifically for testing the impact

of visibility we deem this test to be appropriate for this study. However, since we have

two measurements of the dependent variable, separate tests are conducted for each. The

multiple regression analysis estimates the degree to which the independent variables can

explain the variation in the dependent variable. This is displayed in the statistic adjusted

R square which ranges from 0-100% (Pallant, 2016). This analysis is suitable for our

study since the test allows us to understand how reliable the model and how accurate our

results is, which is important in order to be able to explain how audit firm rotation and

audit partner rotation relate to audit quality and how this relationship is contingent on

firm visibility and how trustworthy our results is.

3.2.6.5 Multicollinearity

To be able to reliably conduct the multiple regression analysis it is important that the

multicollinearity of the variables is modest. High risk of multicollinearity is recognized

through the VIF-value if it exceeds a certain value and the lower the VIF-value the lower

the risk of multicollinearity in the model (Pallant, 2016). Some researchers recognize the

multicollinearity to be acceptable up to the VIF-value 10 (Pallant, 2016). Other has set

the acceptance level at the VIF-value 4 (O’brien, 2007). For this study we have chosen to

recognize multicollinearity at VIF above 4. This means that any regressions with a VIF-

value above 4 will be carefully interpreted, i.e. less weight is put into the results of those

regressions.

39

4 Empirical analysis

In this section the results and analysis of the empirical tests are presented. Firstly, the

reader is presented with the descriptive statistics, where an overview of the sampled data

can be seen. Thereafter, tests of normality in the dependent variable and results of the

bivariate correlation is presented. The subsequent section of multivariate test and

analysis culminate in the description of the consequences for the hypotheses.

4.1 Descriptive statistics

A total of 58 firms from OMX large cap Stockholm were included in this analysis,

between the years of 2009-2018. In the independent variable audit rotation, we observed

a total of 159 audit rotations from a total sample size of 580 firm years. 118 (20% of the

total observations) were categorized as audit partner rotation, and 41 (7% of the total

observations) as audit firm rotation. The audit partner tenure lasted at average 3,47 years

before a rotation of the auditor occurred, meanwhile the average audit firm tenure lasted

for an average of 9,22 years. Audit fees had an average value of 18,6 MSEK, ranged

between 0,476 to 123 MSEK from 580 samples, while non-audit fees had an average

value of 9,22 MSEK, range, between 0 to 123,6 MSEK.

In the dependent variable discretionary accruals (DA), 49% of the observations had a

positive manipulation of the earnings for DA1 while the corresponding share for DA2

amounted to 52%, which is observed in DA1 ctrl. and DA2 ctrl. respectively. The values

of DA1 and DA2 displays the degree of discretionary manipulation of earning where a

higher value indicates higher discretionary accruals. From the descriptive statistics we

understand that DA1 has a higher observed variation and lower mean than DA2.

The visibility determiner, press mentions had a sample size of 580, and denoted the

number of times a given company has been mentioned in the press during the sampled

years. The variable had a notable range between 68, to 98965, with an average value of

11894. For the variable ownership structure total of 557 samples out of 580 could be

gathered. The mean value of the institutional ownership was 37% and had a large variety

between 0% to 98,4% and the last determiner of visibility, number of employees had a

significant variety, between 14, to 300313, with a mean value of 21444. One missing

value lead to the sample size of 579 out of 580. The common visibility dummy variable

indicates that 45,68% of the observations were considered as non-visible.

40

Of the 58 sampled firms, industrials and financials were the two dominating industries

corresponding to 34% and 24% of the sampled firms, respectively. Telecommunications

and technology were the least obtained industries, each representing only 3% of the total

sample. Total assets ranged between 359 MSEK to 474663 MSEK, with an average value

of 54963 MSEK for a sample size of 580. Client firm age measured in number of years

since the firm was listed on the stock exchange had a maximum value of 121 years, and

a minimum of -5 years. A negative value would mean that the company got listed after

2009, and during the sampled years (2009-2018). The average firm age were 23 years.

Client firm age could be gathered for all the 580 firm years.

Valid N shows the number of samples, which does not include any missing values. In

total, 534 samples out of 580 include all variables, and are seen as complete sets.

41

Table 1 - Results of descriptive statistics

Variable N Minimum Maximum Mean Std. Dev.

DA1 580 ,00003 ,53633 ,03921 ,04363

DA2 580 ,00009 ,42257 ,03820 ,03946

Audit partner rotation 580 0 1 ,20345 ,40291

Audit firm rotation 580 0 1 ,07069 ,25653

Press mentions 580 68 98965 11894,40862 14334,46980

Number of employees 579 14 300313 21444,27461 43364,75155

Ownership structure 557 0 0,984 ,37075 ,19578

Visibility dummy 556 0 1 ,45680 ,49858

DA1 Ctrl. 580 0 1 ,49483 ,50040

DA2 Ctrl. 580 0 1 ,51897 ,50007

Audit partner tenure 580 1 10 3,47414 2,05646

Audit firm tenure 570 1 27 9,22105 5,87601

Audit fees 578 0,476 123 18,64753 23,10706

Non-audit fees 580 0 123,6363636 9,22006 15,02669

Leverage 580 0,09840954 1,306237631 ,53306 ,17507

Total assets 580 359 474663 54963,37414 79956,94077

Profit or Loss 580 0 1 ,07241 ,25940

Return on equity 570 -,37774 1,11112 ,15119 ,11588

Client firm age 580 -5 121 23,03448 21,70096

Year 2009 580 0 1 ,1 ,30026

Year 2010 580 0 1 ,1 ,30026

Year 2011 580 0 1 ,1 ,30026

Year 2012 580 0 1 ,1 ,30026

Year 2013 580 0 1 ,1 ,30026

Year 2014 580 0 1 ,1 ,30026

Year 2015 580 0 1 ,1 ,30026

Year 2016 580 0 1 ,1 ,30026

Year 2017 580 0 1 ,1 ,30026

Year 2018 580 0 1 ,1 ,30026

Industrials 580 0 1 ,344828 ,47572

Basic Materials 580 0 1 ,086207 ,28091

Consumer Goods 580 0 1 ,086207 ,28091

Consumer services 580 0 1 ,086207 ,28091

Financials 580 0 1 ,241379 ,42829

Health Care 580 0 1 ,086207 ,28091

Technology 580 0 1 ,034483 ,18262

Telecommunications 580 0 1 ,034483 ,18262

Valid N (listwise) 534

42

4.2 Dependent variable

The dependent variable audit quality was estimated with the proxy variable discretionary

accruals. These were estimated by using two different models: the modified Jones model,

denoted as DA1, and the modified Jones model controlling for concurrent performance,

denoted as DA2. To know which test to conduct we had to control for normality, i.e. if

the dependent variable follows normal distribution (Pallant, 2016). As the negative values

have been coded as positive to conduct the statistical tests, we control for normality in

both the uncoded data and the coded data. The histograms and thereby the distribution of

values in uncoded discretionary accruals and coded discretionary accruals can be seen in

appendix 1 (DA1) and 2 (DA2) and, 3 (DA1) and 4 (DA2) respectively. The following

tables displays the tests of normality.

Table 2 - Kolmogorov-Smirnov test for uncoded discretionary accruals

Variable Kolmogorov-Smirnov Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

DA1 ,094 580 ,000 ,896 580 ,000

DA2 ,081 580 ,000 ,934 580 ,000

Table 3 - Kolmogorov-Smirnov test for coded discretionary accruals

Kolmogorov-Smirnov Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

DA1 ,185 580 ,000 ,711 580 ,000

DA2 ,167 580 ,000 ,772 580 ,000

The Kolmogorov-Smirnov test is used to control for normality, where a significance level

over 0,05 indicates a normal distribution in the sample (Pallant, 2016). As we can see in

the tables none of the variables has a significance surpassing 0,05, thus we conclude that

the dependent variable is not following a normal distribution. This is, however, a common

observation in larger samples (Pallant, 2016). Variables that are proven to not be normally

distributed can however, be assumed to be normally distributed for conducting

regressions etc. if the sample size surpasses 30 observations (Pallant, 2016). In our case

the number of complete observations amounts to 534, and therefore we assume normality

in the dependent variable going forward in the analysis.

43

4.3 Bivariate Correlation

With correlation analysis we can understand how two individual variables relate to each

other and how strong this relationship is. As we assume normality in the dependent

variables’ correlation can be estimated using the Pearson correlation (Pallant, 2016). The

correlation approach is deemed fit as the Pearson correlation can correlate both

continuous variables such as discretionary accruals and dichotomous variables such as

audit partner rotation (Pallant, 2016). The Pearson correlation coefficient ranges from -1

to 1. The closer the value of the coefficient is to -1 or 1 the stronger the relationship

between the two variables is (Bryman & Bell, 2015). A value from 0,50 to 1,00 or from -

0,50 to -1,00 indicates a strong relationship between the variables, while a semi-strong

relationship ranges from 0,49 to 0,30 or from -0,49 to -0,30 and a weak relationship ranges

from 0,29 to 0,10 or from -0,29 to -0,10 (Pallant, 2016). Furthermore, the coefficient

indicates if the variables is positively correlated or negatively correlated (Pallant, 2016).

A positive correlation is recognized by a positive value of the coefficient and implicate

that an increase in one variable corresponds to an increase in the other. A negative

correlation is recognized by a negative value of the coefficient and implicate that an

increase in one variable corresponds to a decrease in the other (Pallant, 2016). It is

important to note that the conducted correlation test only displays bivariate correlations,

which means that the correlations is only measured between two variables excluding any

potential control variables. The significance level in the correlation indicates the

confidence of the results. The lower the significance the more trustworthy the results are

(Pallant, 2016). For this study we have chosen report significance levels up to 0,05, where

we deem significance levels equal to or below 0,05 as statistically significant. According

to Pallant (2016), significance levels must be below 0,05 to provide at least a semi-strong

confidence in the results. To get a strong confidence in the results the significance level

acceptance must be 0,01 or below.

The dependent variables, audit quality (DA1 & DA2) and the independent variables, audit

rotation (audit firm and audit partner rotation) are the first correlation to be analysed.

From the correlation matrix (Appendix 6) it is understood that the dependent variables

and the independent variables do not correlate as the significance levels for all

correlations is well beyond the acceptance level of 0,05 (Appendix 6). Thus, we conclude

that audit partner and audit firm rotation does not have a direct impact on the audit quality.

The dependent variables DA1 and DA2 displays a strong positive relationship with

44

Pearson correlation coefficient at 0,925. The same is true for the correlation between the

independent variables audit partner rotation and audit firm rotation with a Pearson

correlation coefficient at 0,546 (Pallant, 2016) (Appendix 6).

Secondly the correlations between the dependent variables and contingency variables is

analysed. It is found that none of the contingency variables is significantly correlated with

either estimation of audit quality (DA1 or DA2) (Appendix 6). However, the visibility

dummy variable is correlated with DA1 at the 0,05-significance level. The Pearson

correlation coefficient displays a value of 0,088 implicating a relationship worse than a

weak relationship (Pallant, 2016) (Appendix 6). The positive value indicates that more

exposed firms has higher the discretionary accruals i.e. worse audit quality.

Thereafter, the control variables were analysed against the dependent variables, were a

total of 14 significant correlations were found. Audit fees and non-audit fees is both

correlated with both dependent variables (Appendix 6). All four correlations have a

Pearson correlation coefficient at approximately -0,1 indicating a weak negative

relationship (Appendix 6) (Pallant, 2016). However, despite the weak relationship, this

means that higher audit fees and non-audit fees corresponds to lower discretionary

accruals and thereby higher audit quality. Return on equity and leverage display a weak

positive and a weak negative relationship respectively for both dependent variables. The

last significant correlations were found between the year 2009 and the dependent variable

and the industries consumer services, financials, health care and technology and the

dependent variable (Appendix 6). Those correlations indicate that the specific years or

specific industries resulted in higher or lower discretionary accruals compared to the other

years and industries in the sample.

The next step is to analyse any correlations between the independent variables (audit firm

and audit partner rotation) and the control variables. Unsurprisingly, both independent

variables are negatively correlated with audit firm tenure and audit partner tenure

(Appendix 6). As audit partner rotation or audit firm rotation occurs the audit partner or

audit firm tenure decreases to 0. Of these four correlations, the highest Pearson correlation

coefficient is observed between audit partner tenure and audit partner rotation at -0,609

which indicates a strong negative relationship (Table 3) (Pallant, 2016). Correlations were

also found with specific years, indicating that more or less rotations were observed in a

few years compared to the non-significant years. The years that significantly correlates

45

with either audit firm rotation or audit partner rotation or both were the years 2009, 2010,

2017 and 2018.

The risk of multicollinearity is understood to increase significantly if correlations

between variables with Pearson correlation coefficients that exceed 0,7 are included in

the regression analysis (Pallant, 2016). No such coefficients were found between any

variables except for the correlation between the two measurements of the dependent

variable. Thus, we do not have to exclude any of the independent variables at this step of

the analysis.

4.4 Multiple regressions

In the following section results of the multiple regressions while be presented. A total of

24 multiple regressions have been conducted of which we chosen the 12 regressions that

best can explain the variance in the dependent variable, i.e. the regressions with the

highest adjusted R square. In the first section, results of multiple regressions with audit

partner rotation as the independent variable is presented as we test our first hypothesis,

thereafter, audit partner rotation is substituted by audit firm tenure to test our second

hypothesis. The third and fourth hypothesis is tested by two multiple regressions for each

where one multiple regression is conducted for visible firms and one for non-visible firms.

All regressions have also been tested with both discretionary accruals estimated by a

modified Jones model and discretionary accruals estimated by a modified Jones model

which controls for concurrent performance.

The control variables, the year 2018 and the industry industrials were excluded from all

tests. A significance in any of the other years or industries would then indicate that the

effects of that year or industry had on discretionary accruals is significantly different from

the effect of 2018 or the sector industrials had on discretionary accruals. We also had to

exclude the control variable total assets from all regressions as our sample consisted out

many firms from the financial sector, which we recognized to have disproportionately

high amounts of total assets.

4.4.1 Results of multiple regressions

In this subsection the first and second hypotheses is tested. The visibility dummy variable

is not included in this regression as the multicollinearity would increase as it is computed

46

from three control variables that are included in the test. Only when explicitly stated is

any variable removed or substituted from the regressions.

4.4.1.1 Multiple regression with audit partner rotation as the independent variable

In the first regression analysis H1 is tested with audit partner rotation as the independent

variable and the dependent variable discretionary accruals is estimated with modified

Jones model (DA1).

From table 3 we understand that the regression is strongly significant as p < 0,01. The

value of adjusted R square indicates that the independent variables (including control

variables) explain 12,1% of the variation in the dependent variable (discretionary

accruals). The model does not have a significant multicollinearity as the highest VIF value

amounts to 2,803 and is well below the value 4 which would have indicated high

multicollinearity in the regression (O’brien, 2007). In this regression the independent

variable (audit partner rotation) were found to not have a significant impact on the

dependent variable (discretionary accruals) as the significance level is 0,845 and well

above the acceptance level of p < 0,05. Press mentions (,000) and return on equity (,000)

were found to have a significant positive impact on the dependent variable (discretionary

accruals) as positive beta values is recognized. Leverage (,000) and audit fees (,017) had

the opposite impact on the dependent variable negatively impacting the dependent

variable as negative beta values is recognized. The industry sector financials (,010) and

health care (,033) were found significant, thus we conclude that both sectors had a

significantly different impact on the dependent variable than the excluded sector

industrials.

In the second regression analysis H1 is tested with the dependent variable discretionary

accruals which is estimated with modified Jones model controlling for concurrent

performance (DA2).

The regression is significant at the 0,01 level as the p-value equals 0,000. In this model

the independent variables can explain slightly more of the variation of in the dependent

variable than in the first model as the adjusted R square is 12,4%. The VIF value remains

intact at 2,803, consequently we do not observe high multicollinearity in this model either.

We did not find the independent variable (audit partner rotation) to have a significant

impact on the dependent variable (discretionary accruals) as the p-value is ,663. As with

47

the first regression, press mentions (,002) and return on equity (,000) is found to have a

positive impact on the dependent variable, while leverage (,000) and audit fees (,042)

impact the dependent variable negatively. The industry sector financials (,032) and health

care (,013) were found significant, thus we confirm the previous regressions results and

conclude that both sectors had a significantly different impact on the dependent variable

than the excluded sector industrials.

48

Table 4 - Results of multiple regression

Dependent variable DA1 DA2

Variables Std.B Std.Error Sig. Std.B Std.Error Sig.

Audit partner rotation -,010 ,006 ,845 -,023 ,005 ,663

Press mentions ,231 ,000 ,000 ,178 ,000 ,002

Number of employees ,007 ,000 ,891 ,017 ,000 ,739

Ownership structure ,049 ,000 ,299 ,031 ,000 ,509

Audit partner tenure -,014 ,001 ,795 -,032 ,001 ,559

Audit firm tenure ,001 ,000 ,980 -,027 ,000 ,561

DA1 Ctrl. ,045 ,006 ,507 ,057 ,005 ,394

DA2 Ctrl. -,114 ,006 ,095 -,078 ,005 ,249

Audit fees -,158 ,000 ,017 -,134 ,000 ,042

Non-audit fees -,068 ,000 ,124 -,085 ,000 ,054

Leverage -,235 ,013 ,000 -,211 ,012 ,000

Profit or loss ,042 ,007 ,330 ,032 ,007 ,468

Return on equity ,213 ,000 ,000 ,235 ,000 ,000

Client firm age ,017 ,000 ,734 -,012 ,000 ,806

Year 2009 ,069 ,009 ,254 ,086 ,008 ,152

Year 2010 -,060 ,009 ,313 -,026 ,008 ,666

Year 2011 ,000 ,008 ,996 ,026 ,008 ,649

Year 2012 ,048 ,008 ,391 ,063 ,007 ,256

Year 2013 ,043 ,008 ,436 ,076 ,007 ,172

Year 2014 ,027 ,008 ,623 ,046 ,007 ,402

Year 2015 ,031 ,008 ,579 ,046 ,007 ,410

Year 2016 ,029 ,008 ,608 ,035 ,007 ,531

Year 2017 -,061 ,008 ,274 -,044 ,007 ,425

Basic Materials -,086 ,008 ,086 -,059 ,007 ,234

Consumer Goods ,013 ,008 ,782 -,014 ,007 ,757

Consumer services -,059 ,008 ,230 -,014 ,007 ,775

Financials ,137 ,005 ,010 ,114 ,005 ,032

Health Care -,104 ,007 ,033 -,121 ,007 ,013

Technology -,068 ,014 ,114 -,066 ,013 ,120

Telecommunications ,044 ,011 ,327 ,066 ,009 ,143

Constant ,055 ,013 ,000 ,051 ,012 ,000

F-value 3,436 ,000 3,513 ,000

Adjusted R square ,121 ,124 Highest VIF-value 2,803 2,803

n=534

4.4.1.2 Multiple regression with audit firm rotation as the independent variable

For this regression, the independent variable audit partner rotation is substituted with

audit firm rotation to test the second hypothesis.

49

The regression with DA1 as the dependent variable is significant (,000) at the higher

degree with a p-value below 0,01. The model has an adjusted R square at 12,1% which

indicates how much of the variation in the dependent variable that can be explained by

the independent variables. The highest observed VIF value is 2,802 (below 4) which

means that the model does not have high multicollinearity (O’brien, 2007, Pallant, 2016).

In this regression no relationship was found between the independent variable (,671)

(audit firm rotation) and the dependent variable (discretionary accruals). Similar to the

previous regressions, press mentions (,000) and return on equity (,000) is found to have

a positive impact on the dependent variable (positive beta values), while leverage (,000)

and audit fees (,015) is found to negatively impact the dependent variable (negative beta

values). Both the financials (,010) and the health care (,034) sector were once again found

to have a significantly different impact on the dependent variable than the excluded sector

industrials.

The second regression with DA2 as the dependent variable does not significantly change

the results. The regression is still significant (,000) at the 0,01-significance level. The

independent variable explains 12,4 % of the variation in the dependent variable (adj. R

square) and the VIF value remains at 2,802. Audit firm rotation (,828) were not found to

have a significant impact on the dependent variable. Press mentions (,002) and return on

equity (,000) are the variables that significantly impacts the dependent variable positively,

while audit fees (,037) and leverage (,000) impacts the dependent variable negatively.

The industries that have a significantly different impact on discretionary accruals

compared to the sector industrials impact on discretionary accruals remains as the health

care (,014) and financial (,033) sector.

50




Audit firm rotation ,020 ,008 ,671 ,010 ,007 ,828

Press mentions ,231 ,000 ,000 ,179 ,000 ,002

Number of employees ,006 ,000 ,907 ,016 ,000 ,745

Ownership structure ,049 ,000 ,298 ,031 ,000 ,514


Audit firm tenure ,010 ,000 ,843 -,021 ,000 ,668

DA1 Ctrl. ,044 ,006 ,517 ,057 ,005 ,396

DA2 Ctrl. -,113 ,006 ,096 -,078 ,005 ,254

Audit fees -,160 ,000 ,015 -,137 ,000 ,037

Non-audit fees -,067 ,000 ,132 -,083 ,000 ,058

Leverage -,235 ,013 ,000 -,210 ,012 ,000

Profit or loss ,041 ,007 ,345 ,031 ,007 ,480

Return on equity ,215 ,000 ,000 ,237 ,000 ,000

Client firm age ,016 ,000 ,738 -,012 ,000 ,806

Year 2009 ,071 ,009 ,241 ,088 ,008 ,147

Year 2010 -,059 ,009 ,326 -,026 ,008 ,668

Year 2011 ,000 ,008 ,994 ,025 ,008 ,663

Year 2012 ,048 ,008 ,393 ,061 ,007 ,271

Year 2013 ,042 ,008 ,451 ,074 ,007 ,185

Year 2014 ,027 ,008 ,630 ,045 ,007 ,419

Year 2015 ,031 ,008 ,580 ,046 ,007 ,415

Year 2016 ,030 ,008 ,595 ,035 ,007 ,524

Year 2017 -,063 ,008 ,259 -,046 ,007 ,404

Basic Materials -,086 ,008 ,084 -,059 ,007 ,234

Consumer Goods ,013 ,008 ,781 -,014 ,007 ,757


Financials ,136 ,005 ,010 ,113 ,005 ,033

Health Care -,103 ,007 ,034 -,120 ,007 ,014

Technology -,068 ,014 ,114 -,066 ,013 ,122


Constant ,053 ,013 ,000 ,049 ,011 ,000

F-value 3,442 ,000 3,507 ,000

Adjusted R square ,121 ,124

Highest VIF-value 2,802 2,802

n=534

4.4.2 Results of multiple regressions for visible and non-visible observations

In this subsection the third and fourth hypothesis will be tested. The visible observations

will be tested first by excluding the non-visible observations from the sample. Thereafter,

51

the non-visible observations are tested by excluding the visible observations. The visible

observations are those who have a visibility index above the mean of the index while the

non-visible observations are those with an index below the mean. As the index is

computed from the visibility determiners and control variables, ownership structure,

number of employees and press mentions these are excluded from the test, to lower the

risk of multicollinearity in the regression. Only when explicitly stated is any variable

removed or substituted from the regressions.

4.4.2.1 Multiple regressions with audit partner rotation as the independent variable for

visible firms

In these two regressions we test if the visible observations change the relationship

between the independent variable (audit partner rotation) and the dependent variable

(discretionary accruals).

The first regression with DA1 as the dependent variable we found significant (,002) at

the 0,01-significance level. The independent variables can explain 11,0% of the variation

in the dependent variable (adj. R square). The model has a low risk of containing

multicollinearity as the highest VIF value is 2,845. Audit partner rotation (,189) were not

found to have a significant impact on the dependent variable. Leverage (,023) and return

on equity (,006) is found to have negative and positive impact on the dependent variable

respectively. Health care (,033) is found to have a significantly different impact on the

dependent variable than the excluded sector industrials had on the dependent variable.

The second regression with DA2 as the dependent variable is also at within the 0,01-

significance level (,003). The independent variables explain 10,3% of the variation in the

dependent variable (adj. R square). The highest VIF value are allocated at 2,845,

consequently having a low risk of high multicollinearity. In this regression, audit partner

rotation (0,317) were found to not have a significant impact on the dependent variable.

ROE (0,09) was found to have a significant positive impact on the dependent variable.

The industry sector health care (,047) is still significant, thus we draw the same

conclusion for this regression as the previous.

52




Audit partner rotation -,107 ,011 ,189 -,082 ,009 ,317


Audit firm tenure -,085 ,001 ,263 -,097 ,001 ,206

DA1 Ctrl. ,075 ,011 ,466 ,109 ,009 ,287

DA2 Ctrl. -,166 ,010 ,095 -,121 ,009 ,224

Audit fees -,029 ,000 ,746 -,024 ,000 ,793

Non-audit fees -,071 ,000 ,376 -,109 ,000 ,178

Leverage -,184 ,027 ,023 -,149 ,023 ,067

Profit or loss ,078 ,012 ,241 ,078 ,010 ,247

Return on equity ,206 ,000 ,006 ,197 ,000 ,009

Client firm age -,033 ,000 ,677 -,033 ,000 ,673

Year 2009 ,141 ,015 ,130 ,168 ,012 ,073

Year 2010 -,016 ,014 ,865 ,033 ,012 ,722

Year 2011 ,032 ,015 ,714 ,070 ,013 ,417

Year 2012 ,122 ,016 ,147 ,115 ,013 ,175

Year 2013 ,081 ,016 ,317 ,115 ,014 ,160

Year 2014 ,002 ,015 ,976 ,035 ,013 ,676

Year 2015 ,009 ,016 ,914 ,026 ,013 ,761

Year 2016 ,049 ,015 ,563 ,079 ,013 ,350

Year 2017 -,056 ,015 ,508 -,011 ,013 ,898

Basic Materials -,038 ,018 ,599 -,030 ,015 ,683

Consumer Goods ,004 ,013 ,949 -,026 ,011 ,710

Consumer services ,112 ,016 ,092 ,099 ,014 ,136

Financials ,100 ,012 ,144 ,071 ,011 ,304

Health Care -,163 ,013 ,033 -,152 ,011 ,047

Technology -,108 ,017 ,095 -,107 ,015 ,097


Constant ,085 ,021 ,000 ,064 ,018 ,001

F-value 2,108 ,002 2,029 ,003



n=244

4.4.2.2 Multiple regressions with audit partner rotation as the independent variable for

non-visible firms

In these two regressions we test if the non-visible observations change the relationship


(discretionary accruals). For both regressions, the technology and the telecommunication

53

sector has been excluded as none of the observations in those sectors had a visibility index

below the median of the index.

The first regression with DA1 as the dependent variable is found to be significant (,007)

at the 0,01 level. The independent variables can explain 7,2% of the variation in the

dependent variable (adj. R square). The highest VIF value are slightly higher than the

above mentioned, even so, it is found to have a low risk of multicollinearity at a level of

3,451. Audit partner rotation (,640) were found not have an impact on the dependent

variable in this this regression as the significance level is above 0,05. Variables that did

have an impact on the dependent variable is found to be, leverage (,002), with a negative

impact and return on equity (,000) with a positive impact. The financial sector was found

to be significant (,017), thus having a different impact than the industrials sector on the

dependent variable.

With DA2 as the dependent variable the regression were still found to be significant

(,002). 8,6% of the variation in the dependent variable can be explained by the

independent variables (adj. R square). The highest VIF value are at the same level as the

previous regression (3,451). The independent variable, audit partner rotation (,837) were

again found not to have an impact on the dependent variable. Once again, the variables

leverage (,003) and return on equity (,000) were found to have an impact on the dependent

variable, were leverage had a negative impact, and return on equity a positive impact.

Again, the financial sector (,038) was found to have a significantly differently impact on

the dependent variable than the industrial sector.

54




Audit partner rotation ,035 ,006 ,640 -,015 ,006 ,837

Audit partner tenure ,012 ,001 ,873 -,021 ,001 ,784

Audit firm tenure ,092 ,000 ,144 ,044 ,000 ,478

DA1 Ctrl. ,010 ,007 ,916 ,007 ,007 ,938

DA2 Ctrl. -,096 ,007 ,365 -,049 ,007 ,639

Audit fees -,018 ,000 ,790 ,004 ,000 ,951

Non-audit fees -,084 ,000 ,174 -,090 ,000 ,142

Leverage -,212 ,014 ,002 -,197 ,014 ,003

Profit or loss ,057 ,010 ,369 ,010 ,009 ,874

Return on equity ,265 ,000 ,000 ,308 ,000 ,000

Client firm age -,002 ,000 ,979 -,043 ,000 ,522

Year 2009 ,065 ,010 ,409 ,071 ,010 ,364

Year 2010 ,021 ,010 ,776 ,014 ,010 ,844

Year 2011 ,043 ,009 ,580 ,043 ,009 ,579

Year 2012 ,044 ,009 ,570 ,076 ,009 ,327

Year 2013 ,071 ,009 ,371 ,090 ,008 ,250

Year 2014 ,094 ,009 ,229 ,091 ,009 ,238

Year 2015 ,082 ,009 ,313 ,082 ,009 ,306

Year 2016 ,059 ,009 ,448 ,029 ,009 ,710

Year 2017 -,049 ,009 ,519 -,057 ,009 ,448

Basic Materials -,036 ,008 ,622 -,010 ,008 ,888

Consumer Goods ,014 ,010 ,834 -,014 ,010 ,836


Financials ,190 ,006 ,017 ,164 ,006 ,038

Health Care -,074 ,008 ,272 -,126 ,008 ,059

Constant ,037 ,014 ,011 ,038 ,014 ,007

F-value 1,901 ,007 2,093 ,002



n=290

4.4.2.3 Multiple regressions with audit firm rotation as the independent variable for visible

firms

In these two regressions we test if the visible observations change the relationship

between the independent variable (audit firm rotation) and the dependent variable

(discretionary accruals).

In the first regression with DA1 as the dependent variable the regression is found

significant (,003) at the 0,01 level. The independent variables can explain 10,3% of the

55

variance in the dependent variable (adj. R square). The risk of high multicollinearity is

low as the highest VIF-value amounts to 2,846. No relationship was found between audit

firm rotation (,947) and the dependent variable. Leverage (,024) and return on equity

(,005) was found to impact the dependent variable negatively and positively, respectively.

The industry sector that have a significantly different impact on the dependent variable

other than the industrials sector is the health care sector with a p-value at 0,044.

Similar results were found in the second regression, with DA2 as the dependent variable.

The regression is significant (,004) at the 0,01 level. The independent variables explain

slightly less of the variation in the dependent variables as the adjusted R square equals

9,9%. The same highest VIF-value is observed at 2,846. However, the only variable that

have significant impact on the dependent variable is return on equity (,007), positively

impacting the dependent variable.

56




Audit firm rotation ,005 ,015 ,947 ,031 ,013 ,688


Audit firm tenure -,073 ,001 ,393 -,074 ,001 ,391

DA1 Ctrl. ,067 ,011 ,513 ,102 ,009 ,322

DA2 Ctrl. -,161 ,011 ,106 -,116 ,009 ,246

Audit fees -,043 ,000 ,633 -,037 ,000 ,683

Non-audit fees -,059 ,000 ,460 -,099 ,000 ,220

Leverage -,184 ,027 ,024 -,151 ,023 ,065

Profit or loss ,076 ,012 ,258 ,074 ,010 ,273

Return on equity ,212 ,000 ,005 ,205 ,000 ,007

Client firm age -,035 ,000 ,656 -,034 ,000 ,671

Year 2009 ,145 ,015 ,126 ,177 ,013 ,062

Year 2010 -,010 ,015 ,920 ,044 ,013 ,641

Year 2011 ,037 ,015 ,672 ,078 ,013 ,370

Year 2012 ,115 ,016 ,177 ,113 ,013 ,184

Year 2013 ,076 ,016 ,354 ,111 ,014 ,173

Year 2014 ,001 ,015 ,989 ,034 ,013 ,682

Year 2015 ,017 ,016 ,841 ,036 ,013 ,673

Year 2016 ,050 ,015 ,559 ,083 ,013 ,333

Year 2017 -,058 ,015 ,499 -,011 ,013 ,896

Basic Materials -,048 ,018 ,514 -,040 ,015 ,584

Consumer Goods ,000 ,013 ,997 -,029 ,011 ,675

Consumer services ,111 ,016 ,096 ,099 ,014 ,137

Financials ,096 ,013 ,166 ,071 ,011 ,310

Health Care -,155 ,013 ,044 -,143 ,011 ,064

Technology -,107 ,017 ,096 -,107 ,015 ,099


Constant ,075 ,022 ,001 ,055 ,019 ,003

F-value 2,028 ,003 1,990 ,004



n=244

4.4.2.4 Multiple regressions with audit firm rotation as the independent variable for non-

visible visible firms

In these two regressions we test if the non-visible observations change the relationship


(discretionary accruals). For both regressions, the technology and the telecommunication

57

sector has been excluded as none of the observations in those sectors had a visibility index

below the median of the index.

The first regression model is found to be significant (,008) at the 0,01 level. 7,2% of the

variation in the dependent variable could be explained by the independent variable (adj.

R square). The highest VIF level are 3,455, which proclaim a low risk of high

multicollinearity. The independent variable, audit firm rotation (,783), were found not to

have a significant impact on the dependent variable. Leverage (,002) was found to have

a negative impact, while ROE (,000) was found to have a positive impact on the

dependent variable. The financial sector (,018) were to be the only sector to have

significantly different impact on the dependent variable compared to the industrials

sector.

The second regression with DA2 as the dependent variable is found significant (,002) at

the 0,01 level. The explanation rate in the independent variables amounts to 8,7% (adj. R

square) The highest VIF-value is as the previous regression found at 3,455 thereby this

model has low risk of holding high multicollinearity. The independent variable, audit firm

rotation (,686) were found to not have an impact on the dependent variable. Leverage

(,003) were found to impact the dependent variable positively while return on equity

(,000) was found to impact the dependent variable in the opposite direction. As for the

previous regression, the financial sector (,036) were to be the only sector to have

significantly different impact on the dependent variable compared to the industrials

sector.

58




Audit firm rotation ,018 ,009 ,783 -,026 ,009 ,686


Audit firm tenure ,097 ,000 ,149 ,035 ,000 ,596

DA1 Ctrl. ,007 ,007 ,941 ,009 ,007 ,922

DA2 Ctrl. -,096 ,007 ,361 -,048 ,007 ,649

Audit fees -,019 ,000 ,784 ,006 ,000 ,936

Non-audit fees -,085 ,000 ,171 -,091 ,000 ,138

Leverage -,213 ,014 ,002 -,197 ,014 ,003

Profit or loss ,057 ,010 ,362 ,010 ,009 ,871

Return on equity ,262 ,000 ,000 ,310 ,000 ,000

Client firm age -,005 ,000 ,942 -,041 ,000 ,546

Year 2009 ,061 ,010 ,437 ,073 ,010 ,347

Year 2010 ,020 ,010 ,779 ,015 ,010 ,832

Year 2011 ,045 ,009 ,560 ,043 ,009 ,574

Year 2012 ,047 ,009 ,549 ,077 ,009 ,321

Year 2013 ,073 ,009 ,358 ,092 ,008 ,243

Year 2014 ,098 ,009 ,208 ,090 ,009 ,243

Year 2015 ,081 ,009 ,320 ,085 ,009 ,291

Year 2016 ,058 ,009 ,456 ,029 ,009 ,704

Year 2017 -,050 ,009 ,519 -,054 ,009 ,479

Basic Materials -,040 ,008 ,591 -,008 ,008 ,917

Consumer Goods ,012 ,010 ,857 -,013 ,010 ,846


Financials ,190 ,006 ,018 ,166 ,006 ,036

Health Care -,075 ,008 ,269 -,126 ,008 ,061

Constant ,038 ,014 ,005 ,038 ,014 ,005

F-value 1,894 ,008 2,099 ,002



n=290

4.5 Consequences for the hypotheses

Previously this study developed and defined four hypothesis that has been tested. This

subsection will provide the results for each hypothesis.

The first hypothesis, H1 “The number of audit partner rotations is positively correlated

with audit quality, could not be confirmed, as the significance levels of the independent

variable, audit partner rotation, were found from the multiple regression analysis to be

way beyond the acceptance level of p < 0,05, at the 0,845 level, and 0,663 respectively.

59

The second hypothesis, H2 “The number of audit firm rotations is negatively correlated

with audit quality” Could not be confirmed either, as the significance levels of the

independent variable, audit firm rotation, were found at 0,671, and 0,828 which is beyond

the acceptance level (P < 0,05).

The third hypothesis, H3 “The relation between audit partner rotation and audit quality

is contingent on firm visibility. This hypothesis could not be confirmed as the independent

variable, audit partner rotation was found insignificant in both the test of visible

observations and the test of non-visible observations, i.e. the results did not change with

higher or lower visibility.

The fourth hypothesis H4: “The relation between audit firm rotation and audit quality is

contingent on firm visibility” could not be confirmed as the independent variable, audit

firm rotation was found to not impact the dependent variable, audit quality in both the test

of visible observations and the test of non-visible observations.

60

5 Discussion

In this section the results of the empirical tests are thoroughly discussed. A discussion is

provided about the audit quality and audit rotation relationship, followed by a discussion

of the contingency aspects influence on the relationship. Later in the section findings in

the control variables is discussed. Finally, a discussion is had about the overall

explanations for the findings.

5.1 Introductory discussion

The purpose of the study was to explain how audit firm rotation and audit partner rotation

relate to audit quality. More so, examine how this relationship were contingent on firm

visibility. This study developed and argued for four hypotheses. The first two hypotheses

are connected to the first research objective, to see how audit firm & audit partner rotation

relate to audit quality. These were as followed: H1: The number of audit partner rotations

is positively correlated with audit quality, H2: The number of audit firm rotations is

negatively correlated with audit quality. The other hypothesis, 3 & 4 are related to the

contingent aspect on firm visibility, namely: H3: The relation between audit partner

rotation and audit quality is contingent on firm visibility. H4: The relation between audit

firm rotation and audit quality is contingent on firm visibility.

To measure audit quality (dependent variable), discretionary accruals has been used as a

proxy variable. Two different models were used to measure audit quality, which are both

similar to each other: the modified Jones model and the modified Jones model controlling

for competitor's performance. The sample consisted out of 58 companies, all listed on the

OMX Stockholm large cap, constituting 580 firm years.

None of the hypothesis could be confirmed from the regression analysis, which raises the

question on why. Therefore, in this section we will discuss the results and the reasons for

the outcome.

5.2 Audit quality and audit partner rotation

H1: The number of audit partner rotations is positively correlated with audit quality.

We argued that audit partner rotation would increase the audit quality from the

independence and client-specific knowledge perspective. The auditor independence

would be improved by shortening the relationship between the firm and the auditor, and

61

as the independence increases the audit quality would increase (Mali & Lim, 2018;

Kalanjati et al., 2019). The client-specific knowledge would remain intact as the

information can be transferred easily between the predecessor audit to the successor

auditor as they work in the same firm. Thereby, the audit quality would not be impaired

by an auditor's lack of information (cf. Mali & Lim, 2018).

The results of both the bivariate correlation test and the multiple regression test showed

that there was no significant relationship between audit quality and audit partner rotation.

Thus, the hypothesis is not confirmed.

Possible explanations for these findings are found in Lennox et al. (2014) who states that

auditors often examine the previous year's auditing for conducting an audit for the present

year. This could mean that the auditor would be extra thorough in the last years of his

tenure to ensure that the auditor independence is sufficient to not risk his/her reputation

(Lennox et al., 2014). In start of the auditor's tenure the independence would be high as

there is not yet a relationship between the auditor and the management (Kalanjati et al.,

2019), at the same time as this relationship is developed over time the auditor would

become keener on ensuring his/her independence as the tenure is closer to ending. This

could mean that the auditor might maintain his/her independence over the full length of

the tenure and therefore, it is less likely that the audit quality is impaired. Another

explanation is that existence of mandatory audit partner rotation regulations has affected

the auditor's independence positively. The auditor know that the client relationship will

only last for a specific time. Therefore, he/she might not be keen to impair the

independence to increase the client satisfaction for the purpose of client retainment (Mali

& Lim, 2018). Furthermore, the level of independence might be determined by the audit

firm's code of conduct. Therefore, the predecessor and the successor auditor might work

under the same ethical principles, and a change between the two will not give a positive

or negative effect for the audit quality.

Previous studies also argue that auditing firms have internal mechanism in place to ensure

high audit quality. This might involve rotating other personnel engaged in the auditing,

i.e. not the key audit partner (Francis, 2004). Another study has found that Big 4 auditors

have stronger incentives and monitoring mechanisms than other firms, which enhances

their audit quality (Che et al., 2020). Seeing as our sample only consists out of Big 4 audit

62

firms these mechanisms might be enough to ensure the auditors independence and thereby

audit quality.

Although abovementioned arguments may help to explain why there might be no

relationship between audit quality and audit partner rotation, the only reason for this

finding could be that the auditors perform their profession in an independent and

professional manner as they have an economic incentive of maintaining their profession

at the audit firm (cf. Francis, 2004).

5.3 Audit quality and audit firm rotation

H2: The number of audit firm rotations is negatively correlated with audit quality.

In opposition to H1, we argued that audit firm rotation would decrease the audit quality,

as the client-specific knowledge would decrease. The predecessor firm would not share

information with the successor firm as they are competitors. The lack of information

would lead to the issue that auditors would have to trust the information given by the

audited companies managers, which could lead to an opportunistic behaviour and

aggressive reporting, which in turn have been found to impair audit quality (Mali & Lim,

2018). Even if the audit independence would increase from the audit firm rotation, the

outcome would still be a negative effect on audit quality (Mali & Lim, 2018).

Both the bivariate correlation test and the multiple regression test found no significant

relation between audit quality and audit firm rotation. Accordingly, the hypothesis is not

confirmed.

One explanation for why the audit quality is not impaired after an audit firm rotation

might be due to the information sharing aspect. As this study anticipated that the client

information sharing between two different audit firm would be low, it is possible that this

is not the case. It is feasible that the audit firms actually share client information

extensively between each other. If that is the case, it would mean that the differences in

the rotation mechanism between audit partner and audit firm would fade. Thereby, audit

partner rotation and audit firm rotation could impact audit quality in the same way. With

that reasoning in mind, the independence might not be affected by audit firm rotation

either. Therefore, similar arguments to those provided for hypothesis 1 could be adapted

to this argumentation.

63

The audit quality could be maintained as the auditing firm ensure independence in the last

years before rotation. The presence of regulations decreases the audit firms' efforts to

maintain the client through decreasing independence (Mali & Lim, 2018). As for the

differences between the code of conduct in different audit firms, it might be absent. In

this study, all sampled companies used a Big 4 audit firm, and as previous studies has

found that this type of firms often provide a high audit quality (DeFond & Zhang, 2014),

the code of conduct relating to the auditor independence might be similar throughout the

Big 4 audit firms (Che et al., 2020). A rotation between Big 4 auditing firm could then

mean that the auditor independence is less impacted. The internal mechanisms discussed

in the previous subsection might also act as an explanation to why the audit quality

remains unchanged with audit firm rotation.

Lastly, it should also here be stated that the audit firms’ working professionalism that

stems from their economic incentive might alleviate the effects that audit firm rotation

may have on audit quality (Francis, 2004).

5.4 Visibility

H3: The relation between audit partner rotation and audit quality is contingent on firm

visibility.

H4: The relation between audit firm rotation and audit quality is contingent on firm

visibility.

In the development of both H3 & H4 we argued that the effects on audit quality from the

audit rotation, both partner and firm are contingent on firm visibility. Hence, the

relationship between audit partner rotation or audit firm rotation and audit quality is

different depending on the client's firm visibility (Walo, 1995; Redmayne et al., 2010).

Our main argument was that the auditors behave different in visible firms. Auditors’ were

found by previous studies to increase their effort to ensure high audit quality.

Furthermore, as the visibility increases the pressure increases on the auditor. Auditors in

visible firms may feel that their reputation is at stake and therefore act more cautiously in

those situations (Brammer & Millington, 2006). An auditor would be less willing to

compromise his/her independence in more visible firms and therefore the effects on audit

quality after audit rotation would be different.

64

Although the bivariate correlation shows that more exposed firms has lower audit quality,

we did not find audit firm rotation or audit partner rotation significant in either the

multiple regression test for visible observations or the multiple regression test for non-

visible observations. This means that the relationship between audit quality and audit

partner rotation or audit firm rotation is not different for visible or non-visible

observations. Thus, we fail to confirm both hypotheses.

This might mean that the auditors maintain their professionalism and act similar no matter

the visibility of the firm. The argued increase in effort in more visible firm may just be

dependent on the size of the company (cf. Brammer & Millington, 2006). An auditor is

probably expected to put more effort in to a larger client to maintain the same audit quality

for all clients. The argumentation about auditor independence may also be applied here.

If the independence is maintained, we are not likely to find that the audit quality and audit

rotation relationship is contingent on firm visibility.

The sample might also be an explanation for why we could not confirm the hypothesis.

The fact that the sample consisted only out of large-cap companies could have harmed

the variation. Hence, all companies listed on the OMX could be asserted to be highly

visible, and the auditor would have a high pressure to perform well in all the sampled

companies. If we instead would have included smaller firms in our sample, the outcome

might have been different. Furthermore, the non-finding could relate to the chosen

variables for visibility. This study included 3 variables that determent each sampled firm's

visibility. However, there might be other variables that could explain a company's

visibility level more adequate, which also could have had a different outcome for the

hypothesis if those were included.

5.5 Control variables

Visibility dummy. The visibility dummy variable was found in the bivariate correlation

test to have a positive relationship with dependent variable, discretionary accruals. This

means that more visible firms correspond to higher discretionary accruals and therefore

lower audit quality. More visible firms tend to manipulate their earnings to improve their

public image and investor relations (cf. Shu & Chiang, 2014). This would mean that the

bigger a firm is, the more it has to lose, and as the pressure is mounting from the public

and from investors to perform well, it will enhance the incentive to maximize profits, by,

65

for example manipulate earnings. Thus, the relationship does not necessarily have to do

with audit quality. However, this finding may also indicate that auditors do not increase

their effort in more visible firms, as an increased effort is unlikely to result in a decrease

in quality (cf. Redmayne et al., 2010). The auditors may maintain their professionalism

and approach each task appropriately regardless of the client’s visibility. More visible

firms can be more complex to audit and therefore it is possible that the auditors need to

differentiate effort or expertise for such clients to maintain a high audit quality (cf.

Redmayne et al., 2010).

Audit fees. The variable audit fees were found in the bivariate correlation to have a

negative relationship with discretionary accruals, i.e. high audit fees correspond to low

discretionary accruals. As the bivariate correlation does not tell us which variable that

affect which, we cannot here determine which variable that affects the other variable.

Audit fees might be higher since the audit firm provide higher audit quality (lower

discretionary accruals) or the other way around. However, in several of the multiple

regression tests audit quality is found to negatively impact the discretionary accruals. This

can be explained by the auditor's economic incentive. As the economic incentive is

growing with a higher audit fee so is the audit quality (DeFond & Zhang, 2014).

Non-audit fees. Non-audit fees are only found negatively significant in the bivariate

correlation. Thus, we cannot be sure if higher non-audit fees accruals lead to lower

discretionary accruals (lower audit quality) fees or the other way around. However, it

could be argued that higher non-audit fees increase the audit quality as the client specific

knowledge is increased as the auditors spend more time on the client (Tepalagul & Lin,

2015).

Press mentions. Press mentions is found in a few multiple regressions to have a positive

influence on discretionary accruals, i.e. a negative influence on audit quality. As press

mentions is a determiner of visibility in this study the same logic can be applied here as

with the visibility dummy variable. Firms would then manipulate their earnings to

preserve their image in the press (cf. Shu & Chiang, 2014). If we assume the relationship

has to do with audit quality, more press mentions would indicate lower audit quality. A

possible explanation could be that the press often reports “bad press” (cf. Baumgartner &

Chaqués Bonafont, 2015). This could mean that companies that misbehaves, financially

or socially are more likely to be mentioned in the press. Previous studies have found

66

companies with higher financial distress are more likely to have a lower audit quality (Du

& Lai, 2018). With this reasoning, companies that are frequently mentioned in the press,

might be more likely to have a lower audit quality.

Return on equity. Return on equity is found in most multiple regressions to have a positive

influence on discretionary accruals. This would mean that earnings manipulation is more

common in firms with higher earnings. However, the discretionary accruals can be used

to manipulate earnings upward or the equity downward and thereby increasing the return

on equity (Houmes & Skantz, 2010). Thus, this result needs to be interpreted carefully.

Leverage. The variable leverage is found in several multiple regressions to negatively

influence discretionary accruals. This indicates that highly leveraged firms do not

excessively manipulate their earnings through discretionary accruals. As the equity and

debt might have been manipulated through discretionary accruals this interpretation might

not hold true (cf. Houmes & Skantz, 2010).

Industry dummies. From the multiple regression tests, we recognized that the healthcare

and financials sector had a significantly different impact on the dependent variable,

discretionary accruals, than the industry sector industrials had on the dependent variable.

Either these sectors have different audit quality, or the manipulation of earnings differs

between the two. Previous studies have found that companies that work within industry's

that have inferior product market pricing, more often engage in discretionary earnings

accruals (Datta, 2013). This could be a possible explanation for why specifically

healthcare & financial sector were found to have a significantly different impact on our

dependent variable.

5.6 Final discussion

Our model assumed that there would be differences in the outcome between audit partner

rotation, and audit firm rotation in relation to our dependent variable, audit quality. We

also argued that the visibility level of the client firm would influence the results for our

dependent variable in relation with audit rotation. However, our findings did not support

any of our assumptions or arguments.

There are a variety of previous literature in this area, which we have based our model on.

Kalanjati et al. (2019) found that audit partner rotation improves the audit quality while

67

audit firm rotation impairs the audit quality. Mali & Lim (2018) found that audit firm

rotation is not likely to improve audit quality, while audit partner rotation more likely to

improve audit quality rather than impar it. Most of the previous literature included in this

study have found that audit rotation affects audit quality either positively or negatively.

However, to our knowledge no studies have been conducted on the Swedish market.

Although, extensive research has been conducted in countries with similar business

environments, we cannot rule out that the behaviour of the Swedish population,

specifically auditors and firms, may influence the results of this study. A study of

compliance with an IAS standard reviled that Swedish firms are more likely to comply

with the standard than Dutch firms (Hartwig, 2013). Even if, it is very difficult to

generalize the behaviour of certain populations, Hartwig’s (2013) study indicates that

there are differences of corporation behaviour between countries. Therefore, Swedish

firms and auditors might be more compliant with audit regulations. And if that holds true,

it is less likely that the audit quality is impaired or improved by an audit rotation,

consequently this helps to explaining our findings.

The choice of proxy variable for audit quality may have affected the findings of all

hypotheses. Although, financial reporting quality is considered by previous research to

be the superior proxy for audit quality, there is low consensus on how to best measure a

firms financial reporting quality (Defond & Zhang, 2014). Financial reporting quality can

be measured through, discretionary accruals, accruals quality, accounting conservatism

and more (Defond & Zhang, 2014). In this study we used discretionary accruals to proxy

audit quality. Higher discretionary accruals would indicate lower audit quality. Another

choice of proxy variable for audit quality, such as accruals quality (Defond & Zhang,

2014) might had resulted in a different outcome for our hypotheses. Furthermore,

previous studies have had a low consensus on how to estimate discretionary accruals

(Defond & Zhang, 2014). Even though, we have opted to use two different modified Jones

models to estimate our proxy variable there is still a possibility for estimation error. Taken

together, the findings of this study need to be interpreted with caution.

68

6 Conclusion

In this section the study’s conclusions are provided by answering the research question

and relating back to the purpose of the study. The empirical contributions, theoretical

contributions and practical implications of the study is discussed and elaborated on.

Lastly, the limitations of the study are presented along with suggestions for future

research.

6.1 Conclusion

The purpose of the study was to explain how audit firm rotation and audit partner rotation

relate to audit quality and how this relationship was contingent on firm visibility. The

research question was formulated as follows:

How does audit firm rotation and audit partner rotation relate to audit quality and how is

this relationship contingent on firm visibility?

The study was conducted with a positivistic deductive approach (Bryman & Bell, 2015),

where four hypotheses were formulated from the theoretical framework consisting of the

following existing theories: Agency theory which we used to explain the auditor's role as

well as the problem that may arise in the relationship between the auditor and the client

(Fülöp, 2013), legitimacy theory which we used to display the importance of both the

auditing profession, and high audit quality (Deegan, 2019), contingency theory which

was used to introduce visibility as a contingency aspect (Mcadam et al., 2019). And lastly

behavioural theory which was used to explain how a firm's visibility can alter audit firm

rotation and audit partner rotations relationship with audit quality (Kahneman, 2003).

The hypotheses were tested quantitatively on 58 large-cap firms between the years 2009-

2018 listed on the OMX Stockholm stock exchange. Audit quality was assigned the proxy

variable discretionary accruals, which were estimated by two different modified Jones

models. The empirical tests consisted out of bivariate correlation tests and multiple

regression tests.

Our study found that audit partner rotation does not significantly impact audit quality.

This may be explained by the increased effort by the auditor in the last year of his/her

tenure and/or that the auditor values his/her integrity and maintains high audit quality. No

significant impact was found between audit firm rotation and audit quality either. This

69

may be explained similarly as above but also that client specific knowledge has a lower

impact on audit quality than anticipated. When testing if the relationship was contingent

on firm visibility, no significant difference was found between on how audit firm rotation

and audit partner rotation related to audit quality. This finding can once again be

explained by the professionalism of the auditor. Furthermore, as the sample only

consisted out of large cap firms it is possible that all firms should be considered as visible.

Altogether, this study does not find any evidence that audit quality is affected, either by

audit partner rotation or audit firm rotation. More so, we could not confirm that this

relationship is contingent on firm visibility.

6.2 Empirical contributions

The study’s empirical contributions mainly consist out of the findings in the descriptive

statistics. We observe that large-cap companies listed on OMX Stockholm stock

exchange are more likely to change their audit partner, than their audit firm. Accordingly,

the average audit firm tenure is significantly longer than the average audit partner tenure.

This might be a consequence of the mandatory audit partner rotations shorter allowed

tenure as well as the mandatory audit firm rotation was later introduced and has not come

into full effect yet (EUR-Lex, 2006; European Commission, 2014). This could also be an

indication of firms not being willing to rotate their audit partner or audit firm voluntarily.

Thus, this study contributes to the behaviour of auditors and audit clients.

We further contribute by finding that large cap companies are almost equally likely to

manipulate earnings negatively as positively. Possibly because companies want to

equalize the result over time. There are also large differences amongst the firms on the

monetary compensation to auditors. This might have to do with the complexity or the size

of the firm or that certain firms have internal expertise and are therefore less reliable on

auditors. Lastly, losses are recorded less than once every ten years.

6.3 Theoretical contributions

This study provides several theoretical contributions. We contribute to the mandatory

audit rotation debate by providing evidence of no relationship between either audit firm

rotation or audit partner rotation and audit quality. The same applies audit quality in

relation to audit rotation and firm visibility (contingency aspects). Previous studies have

commonly found a relationship (either negative or positive) between audit quality and

70

audit rotation (e.g. Francis, 2004; Tepalagul & Lin, 2015). This might indicate one or

more of three things. Firstly, the proxy for audit quality, discretionary accruals, might not

provide a fair measurement for audit quality (DeFond & Zhang, 2014). It is possible that

discretionary accruals do not measure earnings management or financial reporting quality

and therefore is not a good proxy for audit quality (cf. DeFond & Zhang, 2014).

Furthermore, a previous study explains that discretionary accruals might be a too

simplistic measurement of earnings management and therefore possibly an inappropriate

way of estimating earnings management (Jackson, 2018). Secondly, as previously

mentioned there is low consensus in previous studies on how to best estimate

discretionary accruals (DeFond & Zhang, 2014) and although we use two measurements

for discretionary accruals our results might indicate that both models only poorly estimate

discretionary accruals. Lastly, our findings might be in line with previous studies,

however, as the phenomena publication bias are well known, articles that do not reject

the null hypothesis are less favoured to be published (Upton & Cook, 2014). In our field

of study this would mean that articles that find no relationship between audit rotation and

audit quality are less likely to be published. The findings in the area would then appear

to be leaning towards a relationship between the two even if that is not the case.

We further contribute to the research on audit quality, by providing an indication of press

mentions negative effect on audit quality. We explored that firms that are more exposed

in the press commonly has lower audit quality. The finding is opposed to what previous

studies argued the effect of higher media visibility would be (Brammer & Millington,

2006). This might be a consequence of the bad press phenomena discussed earlier.

Financially distressed companies are more likely to be mentioned in the press but also to

have lower audit quality (Du & Lai, 2018). Thus, we provide a fresh perspective of press

mentions effect on audit quality. Furthermore, visibility was found to have a negative

correlation with audit quality, it could be explained by the enlarged complexity of a more

visibly firm that an auditor needs to adapt to, by either increasing their effort, or their

expertise. If the auditors maintain the same effort between companies, we will see a lower

audit quality for more visible firms, which might be the explanation for our findings.

Some contributions can be made from the inconsequent findings of audit fees positive

effect on audit quality. Although, the relationship can be found in only a few of the

71

multiple regression test, this finding further strengthens the argument that higher auditor

economic incentive leads to higher audit quality (DeFond & Zhang, 2014).

Lastly, contributions are found in the industry sectors different impact on audit quality.

We contribute to further research on audit quality, by exploring a recurring difference in

the financial and healthcare sectors impact on audit quality compared to the industry

sector industrials impact on audit quality.

6.4 Practical implications

As this study found no correlation between a higher audit quality and audit rotation, it

could indicate that the concept of audit rotation is flawed and unnecessary. This could

have implications for the positive accounting theory, which suggest that an individual

will always act in their own interest, often to benefit economically (Watts & Zimmerman,

1986). Through this theory, an auditor would harm the audit quality by for instance

compromising his/her independence to maintain a client (cf. Watts & Zimmerman, 1986).

Seeing as this study found no relationship between audit quality and audit rotation, it

suggests that the auditors maintains their audit quality throughout their tenure. Seemingly

the positive accounting theory is not applicable to the auditing profession. However, we

cannot know if the audit quality is maintained at a high level or a low level after audit

rotation. If the audit quality before and after audit rotation is low, it is still possible that

the individual auditors act in their own interest, only that the both the predecessor and

successor auditor compromise his/her independence equally. Thus, the positive

accounting theory may hold true for the auditing profession. Therefore, the audit rotation

mechanism in this case, will not help to alleviate the problem. Yet, the length of the

mandatory audit rotation tenure can be sufficient to maintain the audit quality. If the

length of the mandatory audit partner rotation would have been 10 years instead of 7

years, the outcome could have been different, i.e. a strong relationship between the auditor

and the client, which could deteriorate the audit quality, takes a longer time to develop

(cf. Mali & Lim, 2018). This could implicate that mandatory audit rotation regulations

fulfils its purpose of ensuring auditor independence (European Commission, 2014).

Our findings also have practical implications for auditing clients. The clients may object

to changing auditor as there is a threat of lower initial audit quality due to a lower client-

specific knowledge that comes with a new auditor (Kalanjati et al., 2019). However, our

72

findings suggest that the drop-in client-specific knowledge do not contribute to lower

audit quality. This implicates that auditing clients can be assured that the audit quality is

maintained with after audit rotation and therefore they do not have to become captive to

the auditor. Furthermore, auditor clients can assure high audit quality by increasing the

auditing payments, as we found that higher audit fees lead to higher audit quality.

There are also several practical implications for the auditors and the audit profession.

Firstly, the auditor independence seems to be maintained at a high level, which give

reassuring trust for the auditors and the audit profession. Secondly, the visibility level did

not affect the audit quality in relation to audit rotation, which would implicate that the

auditors hold a consistent professionalism to all clients. However, higher audit fees were

found to improve the audit quality, which would suggest that the economic incentive of

the auditors hampers the auditing outcome (DeFond & Zhang, 2014). Thirdly, our

findings indicate that auditors need to improve expertise or effort on more visible clients

to maintain the same audit quality across the field. Lastly, as we found that the healthcare

and the financial sector had a different impact on audit quality than the industrial sector,

this implies that auditors should act more cautiously in specific industries as the audit

quality is seemingly dependent on what industry the client operates in.

6.5 Limitations and future research

The sample of the study which consisted out of 58 firms listed on the Stockholm OMX

stock exchange, presents several limitations to this study. The generalizability of the study

is compromised by the rather small sample as well as the focus on large firms. Therefore,

the findings of the study may not be generalized for all firms in Sweden let alone

populations of firms in other countries. Furthermore, the focus on large firms may altered

the findings of hypothesis three and four as it is possible that all firms listed on large cap

should be considered visible. However, there is an opportunity for future researchers to

replicate our study with a larger sample consisting out of firms from different size

segment and/or firms outside of Sweden, thereby improving the generalizability and

ensuring a variation in firm visibility.

Another limitation is found in the timespan of the sample. As the 2014 European directive

is yet to come into full effect at the time of the study (European Commission, 2014), we

may have observed less audit firm rotations than if the study were to be conducted in 10

73

years. The mandatory audit partner rotation directive has already come into full effect as

it was introduced in 2006 (EUR-Lex, 2006), consequently giving this sample significantly

more audit partner rotations than audit firm rotations. Therefore, the generalizability of

the audit firm rotation findings might be less robust. A future study could replicate our

approach when the maturity of the mandatory audit firm rotation has grown. Additionally,

it would be interesting to include firm years before 2006 to see how audit quality has

changed after the mandatory partner rotation directive was introduced.

As previously discussed, the audit quality proxy variable, discretionary accruals, might

present limitations to this study. We cannot with certainty know that the proxy variable

measures what it is intended to measure. Possibly another proxy variable would have

provided a better measurement for audit quality (DeFond & Zhang, 2014). Furthermore,

the low consensus on how to measure discretionary accruals might have led to estimation

error (DeFond & Zhang, 2014), which consequently could have altered the findings of

the study. A future study could use a different proxy variable for audit quality and if

similar results are found the robustness of the findings could be improved. As audit

quality is a rather abstract concept, there is also an opportunity to investigate audit quality

qualitatively through interviews with auditors etc.

Lastly, this study has not separated voluntary audit rotations from mandatory audit

rotations. Possibly there is a difference between how audit quality is affected when the

audit rotation is voluntary or mandatory. It would be interesting for future research to

distinguish the two to provide more assuring evidence to the debate on mandatory audit

rotation.

74

References

ABC News. (2009). Arthur Andersen Goes Out of Business. ABC News. Retrieved

2020-01-20 from: https://abcnews.go.com/Business/Decade/arthur-andersen-

business/story?id=9279255

American Institute of Certified Public Accountants (AICPA). (1992). Statement of

Position Regarding Mandatory Rotation of Audit Firms of Publicly Held Companies.

SEC Practice Section. New York, NY: AICPA.

Arel, B., Brody, R., & Pany, K. (2006). Findings on the Effects of Audit Firm Rotation

on the Audit Process under Varying Strengths of Corporate Governance. Advances in

Accounting, Incorporating Advances in International Accounting, 22, 1–27.

https://doi.org/10.1016/S0882-6110(06)22001-7

Balsam, S., Krishnan, J., & Yang, J. (2003). Auditor industry specialization and

earnings quality. Auditing-A Journal Of Practice & Theory, 22(2), 71–97.

https://doi.org/10.2308/aud.2003.22.2.71

Bandyopadhyay, S., Chen, C., & Yu, Y. (2014). Mandatory audit partner rotation, audit

market concentration, and audit quality: Evidence from China. Advances in Accounting,

30(1), 18–31. https://doi.org/10.1016/j.adiac.2013.12.001

Baumgartner, F., & Chaqués Bonafont, L. (2015). All News is Bad News: Newspaper

Coverage of Political Parties in Spain. Political Communication, 32(2), 268–291.

https://doi.org/10.1080/10584609.2014.919974

Becker, C., Defond, M., Jiambalvo, J., & Subramanyam, K. (1998). The Effect of Audit

Quality on Earnings Management*. Contemporary Accounting Research, 15(1), 1–24.

https://doi.org/10.1111/j.1911-3846.1998.tb00547.x

Bell, T., Causholli, M., & Knechel, W. (2015). Audit Firm Tenure, Non‐Audit Services,

and Internal Assessments of Audit Quality. Journal of Accounting Research, 53(3),

461–509. https://doi.org/10.1111/1475-679X.12078

Blandon, J., & Bosch, J. (2013). Audit tenure and audit Qualifications in a low litigation

risk setting: An analysis of the Spanish market. Estudios De Economia, 40(2), 133–156.

http://dx.doi.org/10.4067/S0718-52862013000200002

https://abcnews.go.com/Business/Decade/arthur-andersen-business/story?id=9279255

https://abcnews.go.com/Business/Decade/arthur-andersen-business/story?id=9279255

https://doi.org/10.1016/S0882-6110(06)22001-7

https://doi.org/10.1016/S0882-6110(06)22001-7

https://doi.org/10.2308/aud.2003.22.2.71

https://doi.org/10.1016/j.adiac.2013.12.001

https://doi.org/10.1080/10584609.2014.919974

https://doi.org/10.1111/j.1911-3846.1998.tb00547.x

https://doi.org/10.1111/1475-679X.12078

http://dx.doi.org/10.4067/S0718-52862013000200002

http://dx.doi.org/10.4067/S0718-52862013000200002

75

Bragg, R. (2002). Enron’s Collapse: Workers; Workers Feel Pain of Layoffs And

Added Sting of Betrayal. The New York times. Retrieved 2020-01-20 from:

https://www.nytimes.com/2002/01/20/us/enron-s-collapse-workers-workers-feel-pain-

layoffs-added-sting-betrayal.html

Brammer, S., & Millington, A. (2006). Firm size, organizational visibility and corporate

philanthropy: an empirical analysis. Business Ethics: A European Review, 15(1), 6–18.

https://doi.org/10.1111/j.1467-8608.2006.00424.x

Brockman, P., Subasi, M., & Uzmanoglu, C. (2017). Investor Conferences, Firm

Visibility, and Stock Liquidity. Financial Review, 52(4), 661–699.

https://doi.org/10.1111/fire.12136

Bobek, D., Daugherty, B., & Radtke, R. (2012). Resolving audit engagement challenges

through communication.(Report). Auditing: A Journal of Practice & Theory, 31(4), 21–

45. https://doi.org/10.2308/ajpt-50210

Bolagsverket (2019a) Revisor i aktiebolag. Bolagsverket. Retrieved 2020-03-25 from

https://bolagsverket.se/ff/foretagsformer/aktiebolag/arsredovisning/delar

Bolagsverket (2019), Delar och bilagor i årsredovisningen. Bolagsverket. Retrieved

2020-03-25 from:


Bryman, A., & Bell, E. (2015). Business research methods (4. ed.). Oxford: Oxford

Univ. Press.

Bushee, B., & Miller, G. (2012). Investor Relations, Firm Visibility, and Investor

Following. The Accounting Review, 87(3), 867–897. https://doi.org/10.2308/accr-10211

Carey, P., & Simnett, R. (2006). Audit partner tenure and audit quality. Accounting

Review, 81(3), 653–676. https://doi.org/10.2308/accr.2006.81.3.653

Carrington. T. (2010). Revision 1. uppl.; Malmö : Liber

Chen, C., Lin, C., & Lin, Y. (2008). Audit Partner Tenure, Audit Firm Tenure, and

Discretionary Accruals: Does Long Auditor Tenure Impair Earnings Quality?*.

Contemporary Accounting Research, 25(2), 415–445. https://doi.org/10.1506/car.25.2.5

https://www.nytimes.com/2002/01/20/us/enron-s-collapse-workers-workers-feel-pain-layoffs-added-sting-betrayal.html

https://www.nytimes.com/2002/01/20/us/enron-s-collapse-workers-workers-feel-pain-layoffs-added-sting-betrayal.html

https://doi.org/10.1111/j.1467-8608.2006.00424.x

https://doi.org/10.1111/fire.12136

https://doi.org/10.2308/ajpt-50210



https://doi.org/10.2308/accr-10211

https://doi.org/10.2308/accr.2006.81.3.653

https://doi.org/10.1506/car.25.2.5

76

Chen, S., Sun, S., & Wu, D. (2010). Client Importance, Institutional Improvements, and

Audit Quality in China: An Office and Individual Auditor Level Analysis. The

Accounting Review, 85(1), 127–158. https://doi.org/10.2308/accr.2010.85.1.127

Chen, S., Li, Z., & Chi, W. (2018). Client importance and audit quality: evidence from

China. Asia-Pacific Journal of Accounting & Economics, 25(5), 624–638.

https://doi.org/10.1080/16081625.2016.1268061

Che, L., Hope, O., & Langli, J. (2020) How Big-4 Firms Improve Audit Quality. (n.d.).

Management Science. https://doi.org/10.1287/mnsc.2019.3370

Chelli, M., Richard, J., & Durocher, S. (2014). France’s new economic regulations:

insights from institutional legitimacy theory. Accounting Auditing & Accountability

Journal, 27(2), 283–316. https://doi.org/10.1108/AAAJ-07-2013-1415

Chi, W., Huang, H., Liao, Y., & Xie, H. (2009). Mandatory Audit Partner Rotation,

Audit Quality, and Market Perception: Evidence from Taiwan*. Contemporary

Accounting Research, 26(2), 359–391. https://doi.org/10.1506/car.26.2.2

Choi, J.-H., Kim, C., Kim, J.-B. and Zang, Y. (2010), Audit office size, audit quality,

and audit pricing. AUDITING: A Journal of Practice & Theory, 29 (1), 73-97.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1009&context=soa_research

Chu, B. (2018). Financial crisis 2008: How Lehman Brothers helped cause 'the worst

financial crisis in history'. Independent. Retrieved 2020-01-20 from:

https://www.independent.co.uk/news/business/analysis-and-features/financial-crisis-

2008-why-lehman-brothers-what-happened-10-years-anniversary-a8531581.html

Clark, A. (2010). Lehman Brothers: Repo 105 and other accounting tricks. The

Guardian. Retrieved 2020-01-25 from:

https://www.theguardian.com/business/2010/mar/12/lehman-brothers-repo-105-enron

Colvin, G. (2005). The other victims of Bernie Ebbers's fraud. Money CNN. Retrieved

2020-01-20 from:

https://money.cnn.com/magazines/fortune/fortune_archive/2005/08/08/8267650/index.h

tm

https://doi.org/10.2308/accr.2010.85.1.127

https://doi.org/10.1080/16081625.2016.1268061

https://doi.org/10.1108/AAAJ-07-2013-1415

https://doi.org/10.1506/car.26.2.2

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1009&context=soa_research

https://www.independent.co.uk/news/business/analysis-and-features/financial-crisis-2008-why-lehman-brothers-what-happened-10-years-anniversary-a8531581.html

https://www.independent.co.uk/news/business/analysis-and-features/financial-crisis-2008-why-lehman-brothers-what-happened-10-years-anniversary-a8531581.html

https://www.theguardian.com/business/2010/mar/12/lehman-brothers-repo-105-enron

https://money.cnn.com/magazines/fortune/fortune_archive/2005/08/08/8267650/index.htm

https://money.cnn.com/magazines/fortune/fortune_archive/2005/08/08/8267650/index.htm

77

Comiran, F., Fedyk, T., & Ha, J. (2018). Accounting quality and media attention around

seasoned equity offerings. International Journal of Accounting & Information

Management, 26(3), 443–462. https://doi.org/10.1108/IJAIM-02-2017-0029

Corbella, S., Florio, C., Gotti, G., & Mastrolia, S. (2015). Audit firm rotation, audit fees

and audit quality: The experience of Italian public companies. Journal of International

Accounting, Auditing and Taxation, 25(C), 46–66.

https://doi.org/10.1016/j.intaccaudtax.2015.10.003

Cornett, M., Marcus, A., & Tehranian, H. (2008). Corporate governance and pay-for-

performance: The impact of earnings management. Journal of Financial Economics,

87(2), 357–373. https://doi.org/10.1016/j.jfineco.2007.03.003

Cronqvist, H., Makhija, A., & Yonker, S. (2012). Behavioral consistency in corporate

finance: CEO personal and corporate leverage. Journal of Financial Economics, 103(1),

20–40. https://doi.org/10.1016/j.jfineco.2011.08.005

Custódio, C., & Metzger, D. (2014). Financial expert CEOs: CEO׳s work experience

and firm׳s financial policies. Journal of Financial Economics, 114(1), 125–154.

https://doi.org/10.1016/j.jfineco.2014.06.002

Dang, C., Li, Z., & Yang, C. (2018). Measuring firm size in empirical corporate

finance. Journal of Banking and Finance, 86, 159–176.

https://doi.org/10.1016/j.jbankfin.2017.09.006

Datta, S., Iskandar-Datta, M., & Singh, V. (2013). Product market power, industry

structure, and corporate earnings management. Journal of Banking and Finance, 37(8),

3273–3285. https://doi.org/10.1016/j.jbankfin.2013.03.012

Deangelo, L. (1981a). Auditor independence, low balling, and disclosure regulation.

Journal of Accounting & Economics, 3.

http://search.proquest.com/docview/213014012/

Deangelo, L. (1981). Auditor size and audit quality. Journal of Accounting and

Economics, 3(3), 183–199. https://doi.org/10.1016/0165-4101(81)90002-1

https://doi.org/10.1108/IJAIM-02-2017-0029










https://doi.org/10.1016/0165-4101(81)90002-1

78

Dechow, P., Hutton, A., Kim, J., & Sloan, R. (2012). Detecting Earnings Management:

A New Approach. Journal of Accounting Research, 50(2), 275–334.

https://doi.org/10.1111/j.1475-679X.2012.00449.x

Deegan, C. (2019). Legitimacy theory. Accounting, Auditing & Accountability Journal,

32(8), 2307–2329. https://doi.org/10.1108/AAAJ-08-2018-3638

Deegan, C., Rankin, M., & Tobin, J. (2002). An examination of the corporate social and

environmental disclosures of BHP from 1983-1997. Accounting, Auditing &

Accountability Journal, 15(3), 312–343. https://doi.org/10.1108/09513570210435861

DeFond, M., & Zhang, J. (2014). A review of archival auditing research. The Journal of

Accounting and Economics, 58(2-3), 275–326.

https://doi.org/10.1016/j.jacceco.2014.09.002

Defond, M., & Jiambalvo, J. (1994). Debt covenant violation and manipulation of

accruals. Journal of Accounting and Economics, 17(1), 145–176.

https://doi.org/10.1016/0165-4101(94)90008-6

Degerfeldt, B. (2011). Tioårsjubileum för Enronskandalen. Svenska Dagbladet.

Retrieved 2020-01-20 from: https://www.svd.se/tioarsjubileum-for-enronskandalen)

Dienes, D., Sassen, R., & Fischer, J. (2016). What are the drivers of sustainability

reporting? A systematic review. Sustainability Accounting, Management and Policy

Journal, 7(2), 154–189. https://doi.org/10.1108/SAMPJ-08-2014-0050

Donaldson, L., & Davis, J. (1991). Stewardship Theory or Agency Theory: CEO

Governance and Shareholder Returns. Australian Journal of Management, 16(1), 49–

64. https://doi.org/10.1177/031289629101600103

Du, X., & Lai, S. (2018). Financial Distress, Investment Opportunity, and the Contagion

Effect of Low Audit Quality: Evidence from China. Journal of Business Ethics, 147(3),

565–593. https://doi.org/10.1007/s10551-015-2986-5

Edling, L. (2002). Enron-skandalen pressar svenska revisorer. Affärsvärlden. Retrieved

2020-01-21 from: https://www.affarsvarlden.se/bors-ekonominyheter/enron-skandalen-

pressar-svenska-revisorer-6732761

https://doi.org/10.1111/j.1475-679X.2012.00449.x

https://doi.org/10.1108/AAAJ-08-2018-3638

https://doi.org/10.1108/09513570210435861



https://doi.org/10.1016/0165-4101(94)90008-6

https://www.svd.se/tioarsjubileum-for-enronskandalen

https://doi.org/10.1108/SAMPJ-08-2014-0050

https://doi.org/10.1177/031289629101600103

https://doi.org/10.1007/s10551-015-2986-5

https://www.affarsvarlden.se/bors-ekonominyheter/enron-skandalen-pressar-svenska-revisorer-6732761

https://www.affarsvarlden.se/bors-ekonominyheter/enron-skandalen-pressar-svenska-revisorer-6732761

79

Eisenhardt, K. (1989). Agency Theory: An Assessment and Review. The Academy of

Management Review, 14(1), 57–74. https://doi.org/10.2307/258191

Elliot, M., Fairweather, I., Olsen, W., & Pampaka, M. (2016). Generalizability. In A

Dictionary of Social Research Methods. Retrieved from:

http://www.oxfordreference.com/view/10.1093/acref/9780191816826.001.0001/acref-

9780191816826-e-0158

Ernst & Young. (2015). The audit mandatory rotation rule: the state of the art. Retrieved

2020-01-22 from: https://www.ey.com/Publication/vwLUAssets/ey-the-audit-

mandatory-rotation-rule-the-state-of-the-art/$FILE/ey-the-audit-mandatory-rotation-

rule-the-state-of-the-art.pdf

EUR-Lex (2006). Directive 2006/43/EC of the European Parliament and of the Council.

of 17 May 2006 on statutory audits of annual accounts and consolidated accounts,

amending Council Directives 78/660/EEC and 83/349/EEC and repealing Council

Directive 84/253/EEC. EUR-Lex. Retrieved 2020-01-22 from: https://eur-

lex.europa.eu/legal-content/EN/TXT/?qid=1476260328229&uri=CELEX:32006L0043

European Commission. (2014). Reform of the EU Statutory Audit Market - Frequently

Asked Questions. European Commission. Retrieved 2020-01-22 from:

https://ec.europa.eu/commission/presscorner/detail/en/MEMO_14_256

FAR (2018). FARs Samlingsvolym Revision. Stockholm: FAR förlag.

Fargher, N., Lee, H., & Mande, V. (2008). The effect of audit partner tenure on client

managers’ accounting discretion. Managerial Auditing Journal, 23(2), 161–186.

https://doi.org/10.1108/02686900810839857

Favere-Marchesi, M., & Emby, C. (2005). The impact of continuity on concurring

partner reviews: an exploratory study.(Generally Accepted Accounting Principles).

Accounting Horizons, 19(1), 1–10. https://doi.org/10.2308/acch.2005.19.1.1

Fiedler, F. E (1964). A contingency model of leadership effectiveness. Advances in

Experimental Social Psychology, 177(1), 149-190. https://doi.org/10.1016/S0065-

2601(08)60051-9

https://doi.org/10.2307/258191

http://www.oxfordreference.com/view/10.1093/acref/9780191816826.001.0001/acref-9780191816826-e-0158


https://www.ey.com/Publication/vwLUAssets/ey-the-audit-mandatory-rotation-rule-the-state-of-the-art/$FILE/ey-the-audit-mandatory-rotation-rule-the-state-of-the-art.pdf



https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1476260328229&uri=CELEX:32006L0043

https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1476260328229&uri=CELEX:32006L0043

https://ec.europa.eu/commission/presscorner/detail/en/MEMO_14_256

https://doi.org/10.1108/02686900810839857

https://doi.org/10.1108/02686900810839857

https://doi.org/10.2308/acch.2005.19.1.1

https://doi.org/10.1016/S0065-2601(08)60051-9

https://doi.org/10.1016/S0065-2601(08)60051-9

80

Firth, M., Rui, O., & Wu, X. (2012). How Do Various Forms of Auditor Rotation

Affect Audit Quality? Evidence from China. International Journal of Accounting,

47(1), 109–138. https://doi.org/10.1016/j.intacc.2011.12.006

Fleischer, R., & Goettsche, M. (2012). Size effects and audit pricing: Evidence from

Germany. Journal of International Accounting, Auditing and Taxation, 21(2), 156–168.


Francis, J. (2011). A Framework for Understanding and Researching Audit Quality.

Auditing, 30(2), 125–152. https://doi.org/10.2308/ajpt-50006

Francis, J. (2004). What do we know about audit quality? The British Accounting

Review, 36(4), 345–368. https://doi.org/10.1016/j.bar.2004.09.003

Francis, J., & Krishnan, J. (1999). Accounting accruals and auditor reporting

conservatism. Contemporary Accounting Research, 16(1), 135–165. Retrieved from


Francis, J., & Michas, P. (2013). The contagion effect of low-quality audits. Accounting

Review, 88(2), 521–552. https://doi.org/10.2308/accr-50322

Friefeld, K. (2015). Ernst & Young settles with N.Y. for $10 million over Lehman

auditing. Reuters. Retrieved 2020-01-20 from: https://www.reuters.com/article/us-ernst-

lehman-bros/ernst-young-settles-with-n-y-for-10-million-over-lehman-auditing-

idUSKBN0N61SM20150415

Fuller, J. (2020). Time for the Big Four to rethink auditing’s purpose. Financial Times.

Retrieved 2020-01-21 from: https://www.ft.com/content/fa0040fe-3095-11ea-a329-

0bcf87a328f2

Fülöp, M. T. (2013). Audit function role in achieving an efficient corporate governance

- Conceptual model proposal. Studia Universitatis “Vasile Goldis” Arad. Seria Stiinte

Economice, 23(4), 87–97. http://search.proquest.com/docview/1664920590/

Gavious, I. (2007). Alternative perspectives to deal with auditors’ agency problem.

Critical Perspectives on Accounting, 18(4), 451–467.

https://doi.org/10.1016/j.cpa.2006.01.011

https://doi.org/10.1016/j.intacc.2011.12.006



https://doi.org/10.1016/j.bar.2004.09.003



https://www.reuters.com/article/us-ernst-lehman-bros/ernst-young-settles-with-n-y-for-10-million-over-lehman-auditing-idUSKBN0N61SM20150415



https://www.ft.com/content/fa0040fe-3095-11ea-a329-0bcf87a328f2

https://www.ft.com/content/fa0040fe-3095-11ea-a329-0bcf87a328f2




81

Geiger, M., & Raghunandan, K. (2002). Auditor tenure and audit reporting failures.

Auditing, 21(1), 67–78. http://search.proquest.com/docview/216738658/

Gonthier‐Besacier, N., Hottegindre, G., & Fine‐Falcy, S. (2016). Audit Quality

Perception: Beyond the “Role‐Perception Gap.” International Journal of Auditing,

20(2), 186–201. https://doi.org/10.1111/ijau.12066

Guan, Y., Su, L., Wu, D., & Yang, Z. (2016). Do school ties between auditors and client

executives influence audit outcomes? Journal of Accounting and Economics, 61(2-3),

506–525. https://doi.org/10.1016/j.jacceco.2015.09.003

Gul, F., Fung, S., & Jaggi, B. (2009). Earnings quality: Some evidence on the role of

auditor tenure and auditors’ industry expertise. Journal of Accounting and Economics,

47(3), 265–287. https://doi.org/10.1016/j.jacceco.2009.03.001

Gul, F., Jaggi, B., & Krishnan, G. (2007). Auditor independence: evidence on the joint

effects of auditor tenure and nonaudit fees.(Report). Auditing: A Journal of Practice &

Theory, 26(2), 117–142. https://doi.org/10.2308/aud.2007.26.2.117

Gul, F., Sun, S., & Tsui, J. (2003). TRACKS: Audit Quality, Earnings, and the

Shanghai Stock Market Reaction. Journal of Accounting, Auditing & Finance, 18(3),

411–427. https://doi.org/10.1177/0148558X0301800306

Hammami, A., & Hendijani Zadeh, M. (2019). Audit quality, media coverage,

environmental, social, and governance disclosure and firm investment efficiency.

International Journal of Accounting & Information Management, 28(1), 45–72.


Hartwig. (2013). Swedish and Dutch listed companies’ compliance with IAS 36

paragraph 134. International Journal of Disclosure and Governance, 12(1), 78–105.

https://doi.org/10.1057/jdg.2013.33

Heale, R., Twycross, A., & Heale, R. (2015). Validity and reliability in quantitative

studies. Evidence-Based Nursing, 18(3), 66–67. https://doi.org/10.1136/eb-2015-10212

Heath, J., & Norman, W. (2004). Stakeholder Theory, Corporate Governance and

Public Management: What can the History of State-Run Enterprises Teach us in the


https://doi.org/10.1111/ijau.12066



https://doi.org/10.2308/aud.2007.26.2.117

https://doi.org/10.1177/0148558X0301800306


https://doi.org/10.1057/jdg.2013.33

https://doi.org/10.1136/eb-2015-10212

82

Post-Enron era? Journal of Business Ethics, 53(3), 247–265.

https://doi.org/10.1023/B:BUSI.0000039418.75103.ed

Hoang Thi Mai Khanh, & Nguyen Vinh Khuong. (2018). Audit Quality, Firm

Characteristics and Real Earnings Management: The Case of Listed Vietnamese Firms.

International Journal of Economics and Financial Issues, 8(4), 243–249.

https://doaj.org/article/4d50a9fcdb224d74b5bb0a36d8ed8c4e

Hohenfels, D., & Quick, R. (2018). Non-audit services and audit quality: evidence from

Germany. Review of Managerial Science, 1–49. https://doi.org/10.1007/s11846-018-

0306-z

Holmström, C (2019). Sveriges största företag. Ekonomifakta. Retrived 2020-03-26

from https://www.ekonomifakta.se/Fakta/Foretagande/Naringslivet/Sveriges-storsta-

foretag/

Hong-Jo, P., & Cheung, J. (2017). Does Non-Audit Service Compromise Audit

Quality? Journal of Applied Business Research, 33(2), 309–320.

https://doi.org/10.19030/jabr.v33i2.9903

Holm, C., & Zaman, M. (2012). Regulating audit quality: Restoring trust and

legitimacy. Accounting Forum, 36(1), 51–61.

https://doi.org/10.1016/j.accfor.2011.11.004

Houmes, R., & Skantz, T. (2010). Highly Valued Equity and Discretionary Accruals.

Journal of Business Finance & Accounting, 37(1-2), 60–92.

https://doi.org/10.1111/j.1468-5957.2009.02179.x

Imhoff, E. (1978). Employment Effects on Auditor Independence. The Accounting

Review, 53(4), 869–881. Retrieved from


IAASB (2014). A framework for audit quality key elements that create an environment

for audit quality. International Federation of Accountants. Retrieved 2020-01-22 from:

https://www.ifac.org/system/files/publications/files/A-Framework-for-Audit-Quality-

Key-Elements-that-Create-an-Environment-for-Audit-Quality-2.pdf

https://doi.org/10.1023/B:BUSI.0000039418.75103.ed

https://doaj.org/article/4d50a9fcdb224d74b5bb0a36d8ed8c4e

https://doi.org/10.1007/s11846-018-0306-z

https://doi.org/10.1007/s11846-018-0306-z

https://www.ekonomifakta.se/Fakta/Foretagande/Naringslivet/Sveriges-storsta-foretag/

https://www.ekonomifakta.se/Fakta/Foretagande/Naringslivet/Sveriges-storsta-foretag/

https://doi.org/10.19030/jabr.v33i2.9903

https://doi.org/10.1016/j.accfor.2011.11.004

https://doi.org/10.1111/j.1468-5957.2009.02179.x


https://www.ifac.org/system/files/publications/files/A-Framework-for-Audit-Quality-Key-Elements-that-Create-an-Environment-for-Audit-Quality-2.pdf

https://www.ifac.org/system/files/publications/files/A-Framework-for-Audit-Quality-Key-Elements-that-Create-an-Environment-for-Audit-Quality-2.pdf

83

Jackson, A. (2018). Discretionary Accruals: Earnings Management ... or Not? Abacus,

54(2), 136–153. https://doi.org/10.1111/abac.12117

Jenkins, D., & Velury, U. (2008). Does auditor tenure influence the reporting of

conservative earnings? Journal of Accounting and Public Policy, 27(2), 115–132.

https://doi.org/10.1016/j.jaccpubpol.2008.01.005

Jenkins, D., & Vermeer, T. (2013). Audit firm rotation and audit quality: evidence from

academic research. Accounting Research Journal, 26(1), 75–84.

https://doi.org/10.1108/ARJ-11-2012-0087

Jensen, M., & Meckling, W. (1976). Theory of the firm: Managerial behavior, agency

costs and ownership structure. Journal of Financial Economics, 3(4), 305–360.

https://doi.org/10.1016/0304-405X(76)90026-X

Jones, J. (1991). Earnings management during import relief investigations. (includes

appendices). Journal of Accounting Research, 29(2), 193–228.

https://doi.org/10.2307/2491047

Johnson, E., Kuhn, J., Apostolou, B., & Hassell, J. (2013). Auditor perceptions of client

narcissism as a fraud attitude risk factor.(Report). Auditing: A Journal of Practice &

Theory, 32(1), 203–219. https://doi.org/10.2308/ajpt-50329

Johnson, M., Nelson, K., & Frankel, R. (2002). The Relation Between Auditor’s Fees

for Non-audit Services and Earnings Quality. IDEAS Working Paper Series from

RePEc. Retrieved from http://search.proquest.com/docview/1698318047/

Jurkus, A., Park, J., & Woodard, L. (2011). Women in top management and agency

costs. Journal of Business Research, 64(2), 180–186.

https://doi.org/10.1016/j.jbusres.2009.12.010

Kahneman, D. (2003). A perspective on judgment and choice - Mapping bounded

rationality. American Psychologist, 58(9), 697–720. https://doi.org/10.1037/0003-

066X.58.9.697

Kalanjati, D., Nasution, D., Jonnergård, K., & Sutedjo, S. (2019). Auditor rotations and

audit quality. Asian Review of Accounting, 27(4), 639–660.

https://doi.org/10.1108/ARA-10-2018-0182

https://doi.org/10.1111/abac.12117


https://doi.org/10.1108/ARJ-11-2012-0087

https://doi.org/10.1016/0304-405X(76)90026-X

https://doi.org/10.1016/0304-405X(76)90026-X

https://doi.org/10.2307/2491047





https://doi.org/10.1037/0003-066X.58.9.697

https://doi.org/10.1037/0003-066X.58.9.697

https://doi.org/10.1108/ARA-10-2018-0182

https://doi.org/10.1108/ARA-10-2018-0182

84

Kalelkar, R., & Khan, S. (2016). CEO Financial Background and Audit Pricing.

Accounting Horizons, 30(3), 325–339. https://doi.org/10.2308/acch-51442

Kaplan, R. (2004). The mother of all conflicts: auditors and their clients.(Symposium:

Evaluation and Response to Risk by Lawyers and Accountants in the U.S. and E.U.).

The Journal of Corporation Law, 29(2), 363–383. https://doi.org/10.2139/ssrn.556623

Khurana, I., & Raman, K. (2006). Do Investors Care about the Auditor’s Economic

Dependence on the Client?*. Contemporary Accounting Research, 23(4), 977–1016.

https://doi.org/10.1506/D171-8534-4458-K037

Kinder, T. (2020). Bill Michael fights to restore KPMG’s image and profits. Financial

Times. Retrieved 2020-02-21 from: https://www.ft.com/content/9002e2ac-20b3-11ea-

b8a1-584213ee7b2b

Kinney, W., Palmrose, Z., & Scholz, S. (2004). Auditor Independence, Non‐Audit

Services, and Restatements: Was the U.S. Government Right?. Journal of Accounting

Research, 42(3), 561–588. https://doi.org/10.1111/j.1475-679X.2004.t01-1-00141.x

Knechel, W. (2016). Audit Quality and Regulation. International Journal of Auditing,

20(3), 215–223. https://doi.org/10.1111/ijau.12077

Knechel, W., Krishnan, G., Pevzner, M., Shefchik, L., & Velury, U. (2013). Audit

Quality: Insights from the Academic Literature. AUDITING: A Journal of Practice &

Theory, 32(Supplement 1), 385–421. https://doi.org/10.2308/ajpt-50350

Knechel, W., & Vanstraelen, A. (2007). The relationship between auditor tenure and

audit quality implied by going concern opinions. Auditing: A Journal of Practice &

Theory, 26(1), 113–131. https://doi.org/10.2308/aud.2007.26.1.113

Knechel, W., Sharma, D., & Sharma, V. (2012). Non-Audit Services and Knowledge

Spillovers: Evidence from New Zealand. Journal of Business Finance & Accounting,

39(1-2), 60–81. https://doi.org/10.1111/j.1468-5957.2011.02268.x

Koh, K., Rajgopal, S., & Srinivasan, S. (2013). Non-audit services and financial

reporting quality: evidence from 1978 to 1980. Review of Accounting Studies, 18(1), 1–

33. https://doi.org/10.1007/s11142-012-9187-6

https://doi.org/10.2308/acch-51442

https://doi.org/10.2139/ssrn.556623

https://doi.org/10.1506/D171-8534-4458-K037

https://www.ft.com/content/9002e2ac-20b3-11ea-b8a1-584213ee7b2b

https://www.ft.com/content/9002e2ac-20b3-11ea-b8a1-584213ee7b2b

https://doi.org/10.1111/j.1475-679X.2004.t01-1-00141.x

https://doi.org/10.1111/ijau.12077


https://doi.org/10.2308/aud.2007.26.1.113

https://doi.org/10.1111/j.1468-5957.2011.02268.x

https://doi.org/10.1007/s11142-012-9187-6

85

Kowaleski, Z., Mayhew, B., & Tegeler, A. (2018). The Impact of Consulting Services

on Audit Quality: An Experimental Approach. Journal of Accounting Research, 56(2),

673–711. https://doi.org/10.1111/1475-679X.12197

Körner, S., & Wahlgren, L. (2015). Statistiska metoder (3. ed.). Lund: Studentlitteratur.

Laitinen, E., & Laitinen, T. (2015). A probability tree model of audit quality. European

Journal of Operational Research, 243(2), 665–677.

https://doi.org/10.1016/j.ejor.2014.12.021

Legoria, J., Rosa, G., & Soileau, J. (2017). Audit quality across non-audit service fee

benchmarks: Evidence from material weakness opinions. Research in Accounting

Regulation, 29(2), 97–108. https://doi.org/10.1016/j.racreg.2017.09.001

Lennox, C. (2005). Audit quality and executive officers’ affiliations with CPA firms.

Journal of Accounting and Economics, 39(2), 201–231.


Lennox, C., Wu, X., & Zhang, T. (2014). Does Mandatory Rotation of Audit Partners

Improve Audit Quality? The Accounting Review, 89(5), 1775–1803.


Liao, H., & Chi, W. (2014). Audit quality during the first year of mandatory audit-

partner rotation. NTU Management Review, 25(1), 233–258.

https://doi.org/10.6226/NTURM2014.APR.GE30

Lim, C., & Tan, H. (2010). Does Auditor Tenure Improve Audit Quality? Moderating

Effects of Industry Specialization and Fee Dependence*. Contemporary Accounting

Research, 27(3), 923–957. https://doi.org/10.1111/j.1911-3846.2010.01031.x

Litt, B., Sharma, D., Simpson, T., & Tanyi, P. (2014). Audit Partner Rotation and

Financial Reporting Quality. Auditing, 33(3), 59–86. https://doi.org/10.2308/ajpt-50753

Littlefield, S. (2013). Security, independence, and sustainability: Imprecise language

and the manipulation of energy policy in the United States. Energy Policy, 52, 779–788.

https://doi.org/10.1016/j.enpol.2012.10.040

https://doi.org/10.1111/1475-679X.12197

https://doi.org/10.1016/j.ejor.2014.12.021

https://doi.org/10.1016/j.racreg.2017.09.001




https://doi.org/10.6226/NTURM2014.APR.GE30

https://doi.org/10.1111/j.1911-3846.2010.01031.x


https://doi.org/10.1016/j.enpol.2012.10.040

86

Lu, T., & Sivaramakrishnan, K. (2009). Mandatory audit firm rotation: Fresh look

versus poor knowledge. Journal of Accounting and Public Policy, 28(2), 71–91.


Mali, D., & Lim, H. (2018). Conservative Reporting and the Incremental Effect of

Mandatory Audit Firm Rotation Policy: A Comparative Analysis of Audit Partner

Rotation vs Audit Firm Rotation in South Korea. Australian Accounting Review, 28(3),

446–463. https://doi.org/10.1111/auar.12206

Mathiason, N. (2008). Three weeks that changed the world. The Guardian. Retrieved

2020-01-20 from: https://www.theguardian.com/business/2008/dec/28/markets-credit-

crunch-banking-2008

Mcadam, R., Miller, K., & Mcsorley, C. (2019). Towards a contingency theory

perspective of quality management in enabling strategic alignment. International

Journal of Production Economics, 207, 195–209.

https://doi.org/10.1016/j.ijpe.2016.07.003

McNichols, M. (2002). Discussion of the Quality of Accruals and Earnings: The Role of

Accrual Estimation Errors. The Accounting Review, 77(s-1), 61–69.

https://doi.org/10.2308/accr.2002.77.s-1.61

Menold, N., Bluemke, M., & Hubley, A. (2018). Validity. 14(4), 143–145.

https://doi.org/10.1027/1614-2241/a000159

Monroe, G., & Hossain, S. (2013). Does audit quality improve after the implementation

of mandatory audit partner rotation? Accounting and Management Information Systems,

12(2), 263–279. Retrieved from http://search.proquest.com/docview/1491755645/

Nasution, D. and Östermark, R. (2013), “Auditor fee dependence, auditor tenure, and

auditor independence: the case of Finland”, International Journal of Accounting,

Auditing and Performance Evaluation, Volume 9(3), 224-246.

https://doi.org/10.1504/IJAAPE.2013.055895

O’brien, R. (2007). A Caution Regarding Rules of Thumb for Variance Inflation

Factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-

6



https://doi.org/10.1111/auar.12206

https://www.theguardian.com/business/2008/dec/28/markets-credit-crunch-banking-2008

https://www.theguardian.com/business/2008/dec/28/markets-credit-crunch-banking-2008

https://doi.org/10.1016/j.ijpe.2016.07.003

https://doi.org/10.2308/accr.2002.77.s-1.61

https://doi.org/10.1027/1614-2241/a000159


https://doi.org/10.1504/IJAAPE.2013.055895

https://doi.org/10.1007/s11135-006-9018-6

https://doi.org/10.1007/s11135-006-9018-6

87

Oppel Jr, R., & Ross Sorkint, A. (2001). Enron’s Collapse: The Overview. The New

York times. Retrieved 2020-01-20 from:

https://www.nytimes.com/2001/12/03/business/enron-s-collapse-the-overview-enron-

corp-files-largest-us-claim-for-bankruptcy.html

Pallant, J. (2016). SPSS survival manual: a step by step guide to data analysis using

IBM SPSS (6. ed.). Maidenhead: Open University Press.

Palmrose, Z. (1988). An Analysis of Auditor Litigation and Audit Service Quality. The

Accounting Review, 63(1), 55. http://search.proquest.com/docview/1301316123/

Pentland, B. (1993). Getting comfortable with the numbers: auditing and the micro-

production of macro-order. Accounting, Organizations and Society, 18(7).


Purnamasari, D., & Negara, H. (2019). The Effect of Auditor Reputation, Audit Tenure,

and Firm Size on Audit Quality (A Study of Manufacturing Companies Listed on the

Indonesia Stock Exchange for the 2013-2017 Period). International Journal of

Computer Networks and Communications Security, 7(6), 104–108.


Redmayne, N., Bradbury, M., & Cahan, S. (2010). The effect of political visibility on

audit effort and audit pricing. Accounting & Finance, 50(4), 921–939.

https://doi.org/10.1111/j.1467-629X.2010.00350.x

Regeringen. (2015). Nya regler för revisorer och revision, Delbetänkande av

Utredningen om EU:s revisionspaket. Retrieved 2020-03-26 from

https://www.regeringen.se/49c838/contentassets/f3c9d163c2184cc4b825d93c37910ed3/

sou-2015-49-nya-regler-for-revisorer-och-revision.pdf

Justitiedepartementet. (2010). En frivillig revision Retrived 2020-03-04 from:

https://www.regeringen.se/49bb7e/contentassets/819445a4a4114fe9978d543f7884f2e6/

en-frivillig-revision-ju-10.11

Ruiz-Barbadillo, E., Gomez-Aguilar, N., & Carrera, N. (2009). Does mandatory audit

firm rotation enhance auditor independence? Evidence from Spain.(Report). Auditing: A

https://www.nytimes.com/2001/12/03/business/enron-s-collapse-the-overview-enron-corp-files-largest-us-claim-for-bankruptcy.html

https://www.nytimes.com/2001/12/03/business/enron-s-collapse-the-overview-enron-corp-files-largest-us-claim-for-bankruptcy.html




https://doi.org/10.1111/j.1467-629X.2010.00350.x

https://www.regeringen.se/49c838/contentassets/f3c9d163c2184cc4b825d93c37910ed3/sou-2015-49-nya-regler-for-revisorer-och-revision.pdf

https://www.regeringen.se/49c838/contentassets/f3c9d163c2184cc4b825d93c37910ed3/sou-2015-49-nya-regler-for-revisorer-och-revision.pdf

https://www.regeringen.se/49bb7e/contentassets/819445a4a4114fe9978d543f7884f2e6/en-frivillig-revision-ju-10.11

https://www.regeringen.se/49bb7e/contentassets/819445a4a4114fe9978d543f7884f2e6/en-frivillig-revision-ju-10.11

88

Journal of Practice & Theory, 28(1), 113–135.

https://doi.org/10.2308/aud.2009.28.1.113

Saunders, M., Lewis, P., & Thornhill, A. (2016). Research methods for business

students (7. ed.). New York: Pearson Education.

Shapiro, S. (1987). The Social Control of Impersonal Trust. American Journal of

Sociology, 93(3), 623–658. https://doi.org/10.1086/228791

Shapiro, S. (2005). Agency theory. Annual Review of Sociology, 31.

https://doi.org/10.1146/annurev.soc.31.041304.12215

Shu, P., & Chiang, S. (2014). Firm size, timing, and earnings management of seasoned

equity offerings. International Review of Economics and Finance, 29, 177–194.

https://doi.org/10.1016/j.iref.2013.05.011

Skinner, D., & Srinivasan, S. (2012). Audit quality and auditor reputation: evidence

from Japan. (Case study). Accounting Review, 87(5), 1737–1765.


Stow, N. (2018). Why did Lehman Brothers collapse and what caused the global

financial crisis in 2008? The Sun. Retrieved 2020-01-20 from:

https://www.thesun.co.uk/news/7265360/lehman-brothers-bank-collapse-crisis-

bankruptcy/

Söderlind, O. (2010). Misstänkt jättebedrägeri i Lehman-härva. Affärsvärlden.

Retrieved 2020-01-20 from: https://www.affarsvarlden.se/juridik_affarer/misstankt-

jattebedrageri-i-lehman-harva-6685498

Tabachnick, B., & Fidell, L. (2014). Using multivariate statistics (6. ed., Pearson new

international edition). Harlow: Pearson.

Tepalagul, N., & Lin, L. (2015). Auditor Independence and Audit Quality: A Literature

Review. Journal of Accounting, Auditing & Finance, 30(1), 101–121.

https://doi.org/10.1177/0148558X14544505

https://doi.org/10.2308/aud.2009.28.1.113

https://doi.org/10.1086/228791

https://doi.org/10.1146/annurev.soc.31.041304.12215

https://doi.org/10.1016/j.iref.2013.05.011



https://www.thesun.co.uk/news/7265360/lehman-brothers-bank-collapse-crisis-bankruptcy/

https://www.thesun.co.uk/news/7265360/lehman-brothers-bank-collapse-crisis-bankruptcy/

https://www.affarsvarlden.se/juridik_affarer/misstankt-jattebedrageri-i-lehman-harva-6685498

https://www.affarsvarlden.se/juridik_affarer/misstankt-jattebedrageri-i-lehman-harva-6685498

https://doi.org/10.1177/0148558X14544505

89

Tilt, C. (2003). Review article: Accounting, auditing and accountability journal: Special

issue on legitimacy theory. Social and Environmental Accountability Journal, 23(2),

18–20. https://doi.org/10.1080/0969160X.2003.9651701

Tunks, L. (1971). Hurst: The Legitimacy of the Business Corporation ni the law of the

United States. Washington Law Review, 47.


Uchitellet, L. (2009). U.S. lost 2.6 million jobs in 2008. The New York Times. Retrieved

2020-01-20 from: https://www.nytimes.com/2009/01/09/business/worldbusiness/09iht-

jobs.4.19232394.html

Upton, G., & Cook, I. (2014). Publication bias. In A Dictionary of Statistics. Retrieved

from

http://www.oxfordreference.com/view/10.1093/acref/9780199679188.001.0001/acref-

9780199679188-e-2409

Walo, J. (1995). The effects of client characteristics on audit scope. Auditing, 14(1),

115. http://search.proquest.com/docview/216737896/

Watts, R., & Zimmerman, J. (1983). Agency Problems, Auditing, and the Theory of the

Firm: Some Evidence. The Journal of Law and Economics, 26(3), 613–633.

https://doi.org/10.1086/467051

Watts, R., & Zimmerman, J. (1986). Positive accounting theory. Englewood Cliffs:

Prentice-Hall.

Whittle, A., Carter, C., & Mueller, F. (2014). “Above the fray”: Interests, discourse and

legitimacy in the audit field. Critical Perspectives on Accounting, 25(8), 783–802.


Widyaningsih, I., Harymawan, I., Mardijuwono, A., Ayuningtyas, E., & Larasati, D.

(2019). Audit firm rotation and audit quality: Comparison before vs after the

elimination of audit firm rotation regulations in Indonesia. Cogent Business &

Management, 6(1). https://doi.org/10.1080/23311975.2019.1695403

https://doi.org/10.1080/0969160X.2003.9651701


https://www.nytimes.com/2009/01/09/business/worldbusiness/09iht-jobs.4.19232394.html

https://www.nytimes.com/2009/01/09/business/worldbusiness/09iht-jobs.4.19232394.html




https://doi.org/10.1086/467051

https://doi.org/10.1086/467051


https://doi.org/10.1080/23311975.2019.1695403

90

Ye, P., Carson, E., & Simnett, R. (2011). Threats to auditor independence: the impact of

relationship and economic bonds.(Report). Auditing: A Journal of Practice & Theory,

30(1), 121–148. https://doi.org/10.2308/aud.2011.30.1.121

Zhang, Z., Zhang, Z., & Law, R. (2014). Relative importance and combined effects of

attributes on customer satisfaction. The Service Industries Journal, 34(6), 550–566.

https://doi.org/10.1080/02642069.2014.871537

https://doi.org/10.2308/aud.2011.30.1.121

https://doi.org/10.1080/02642069.2014.871537

91

Appendix 1 - Excluded companies

AAK AB Kindred Group AB

Addtech AB Kinnevik AB

Alfa Laval L E Lundbersföretagen AB

Ahlström-Munksjö AB Lundin Energy AB

Arion Banki AB Lundin Mining AB

Assa Abloy AB Medicover AB

Atrium Ljungberg AB Nordea Bank Abp.

Attendo AB Nordic Entertainmentgroup AB

Autoliv AB Nyfosa AB

Bonava AB Pandox AB

Bravida AB Resurs Holding AB

Demetic Group AB Samhällsbyggnadsbolaget i Norden AB

Epiroc AB Svenska Cellulosa AB SCA

Essity AB SEB AB

EQT AB Svenska Handelsbanken AB

Evolution Gaming Group AB Stora Enso AB

Fastighets AB Balder Swedbank AB

Fastpartner AB Tieto Sweden AB

Fenix Outdoor International AB Veoneer AB

Industrivärlden AB

92

Appendix 2 - Histogram DA1

Appendix 3 - Histogram DA2

93

Appendix 4 - Histogram coded DA1

Appendix 5 - Histogram coded DA2

94

Appendix 6 - Correlation matrix

Variables Correlation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 DA1 Pearson Correlation 1,000

2 DA2 Pearson Correlation ,925** 1,000

3 Audit partner rotation Pearson Correlation ,012 ,020 1,000

4 Audit firm rotation Pearson Correlation ,017 ,021 ,546** 1,000

5 Press mentions Pearson Correlation ,073 ,069 ,016 -,021 1,000

6 Number of employees Pearson Correlation -,062 -,030 -,018 -,029 ,376** 1,000

7 Ownership structure Pearson Correlation ,022 -,014 -,003 -,005 ,129** -,050 1,000

8 Visibility Pearson Correlation ,088* ,054 -,019 ,010 ,420** ,385** ,518** 1,000

9 Audit partner tenure Pearson Correlation -,029 -,061 -,609** -,332** -,039 -,001 ,037 ,064 1,000

10 Audit firm tenure Pearson Correlation -,047 -,049 -,151** -,390** ,054 ,237** -,135** ,003 ,171** 1,000

11 DA1 Ctrl. Pearson Correlation ,009 ,020 -,020 ,023 -,091* ,014 -,035 -,047 ,008 ,062 1,000

12 DA2 Ctrl. Pearson Correlation -,042 -,037 -,053 -,031 -,045 ,045 -,015 -,013 ,064 ,128** ,692** 1,000

13 Total assets Pearson Correlation -,026 -,050 ,021 ,020 ,577** ,310** ,173** ,312** -,012 ,000 -,012 ,031 1,000

14 Audit fees Pearson Correlation -,082* -,104* ,024 -,008 ,620** ,504** ,194** ,467** ,013 ,127** -,078 -,040 ,695** 1,000

15 Non-audit fees Pearson Correlation -,128** -,145** -,014 -,024 ,351** ,359** ,088* ,202** ,020 ,012 -,032 -,033 ,661** ,542** 1,000

16 Leverage Pearson Correlation -,126** -,148** ,008 ,026 ,005 ,130** -,152** -,154** ,030 ,047 ,037 ,083* -,084* ,096* -,096*

17 Profit or loss Pearson Correlation ,070 ,038 -,026 ,001 ,035 -,056 ,103* ,087* ,039 ,030 -,064 ,003 -,034 -,043 -,049

18 Return on equity Pearson Correlation ,125** ,220** ,031 ,041 -,071 ,085* -,131** -,042 -,097* -,089* ,173** ,015 -,102* -,062 -,091*

19 Client firm age Pearson Correlation -,034 -,055 ,026 -,018 ,402** ,325** ,107* ,201** -,039 ,145** -,052 -,040 ,370** ,572** ,302**

20 Year 2009 Pearson Correlation ,098* ,081 -,040 -,025 ,162** -,025 -,020 ,089* ,013 -,108** -,157** -,139** -,046 -,004 -,036

21 Year 2010 Pearson Correlation ,002 ,010 -,083* -,047 ,206** -,022 -,001 ,107* ,085* -,061 ,038 ,033 -,040 -,028 -,012

22 Year 2011 Pearson Correlation -,013 -,003 -,054 -,047 ,037 -,006 -,006 ,011 ,119** -,023 ,026 ,022 -,031 -,022 -,009

23 Year 2012 Pearson Correlation ,021 ,017 ,017 -,047 -,030 ,002 -,028 -,026 ,069 ,012 ,003 ,010 -,026 -,023 -,005

24 Year 2013 Pearson Correlation ,007 ,025 ,089* ,043 -,021 ,005 -,028 -,062 -,035 ,018 ,003 ,045 -,017 -,016 -,009

25 Year 2014 Pearson Correlation -,020 -,005 ,074 ,020 -,063 ,009 ,011 -,026 -,066 ,021 ,038 ,010 ,006 -,010 ,016

26 Year 2015 Pearson Correlation ,005 ,008 ,031 ,020 -,056 ,009 ,001 -,031 -,094* ,038 -,054 -,047 ,011 ,005 ,026

27 Year 2016 Pearson Correlation ,023 ,003 -,026 -,047 -,045 ,008 ,003 -,019 -,052 ,062 -,020 -,013 ,032 ,010 ,016

28 Year 2017 Pearson Correlation -,072 -,077 ,031 ,087* -,082* ,011 ,030 -,007 -,041 ,041 ,015 -,024 ,045 ,022 -,008

29 Year 2018 Pearson Correlation -,050 -,058 -,040 ,043 -,107** ,008 ,037 -,036 ,001 -,002 ,107** ,102* ,067 ,067 ,022

30 Industrials Pearson Correlation -,016 -,015 -,006 ,026 ,073 ,311** -,007 ,230** ,009 ,032 ,080 ,089* -,035 ,252** -,023

31 Basic Materials Pearson Correlation -,035 -,032 ,013 ,011 -,039 -,113** -,084* -,137** -,053 ,101* -,181** -,184** -,070 -,143** -,067

32 Consumer Goods Pearson Correlation -,012 -,060 ,028 ,059 -,075 -,031 ,048 ,002 ,022 -,124** -,083* -,110** -,101* -,015 -,047

33 Consumer services Pearson Correlation ,014 ,127** ,013 -,037 ,149** ,010 -,254** -,088* -,050 ,068 -,071 -,147** -,091* -,081 -,050

34 Financials Pearson Correlation ,093* ,072 -,015 -,030 -,215** -,242** -,122** -,317** ,056 ,042 ,183** ,285** -,030 -,270** -,229**

35 Health Care Pearson Correlation -,080 -,105* -,033 ,011 -,125** -,120** ,154** ,027 ,004 -,172** ,015 -,049 ,046 -,095* ,236**

36 Technology Pearson Correlation -,070 -,094* -,002 -,052 ,352** ,170** ,300** ,211** -,011 ,075 ,021 ,031 ,269** ,365** ,264**

37 Telecommunications Pearson Correlation ,069 ,071 ,022 -,015 ,102* -,022 ,168** ,211** -,025 -,040 -,168** -,177** ,226** ,123** ,222**

* Correlation is significant at the 0,05 level

** Correlation is significant at the 0,01 level

95

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

1,000

-,127** 1,000

,069 -,176** 1,000

-,004 -,074 -,013 1,000

-,028 ,151** -,140** -,069 1,000

-,043 ,084* ,073 -,054 -,111** 1,000

-,010 ,018 ,001 -,038 -,111** -,111** 1,000

,037 -,049 ,037 -,023 -,111** -,111** -,111** 1,000

,027 -,027 -,021 -,008 -,111** -,111** -,111** -,111** 1,000

,007 -,027 -,040 ,008 -,111** -,111** -,111** -,111** -,111** 1,000

,001 ,040 ,034 ,023 -,111** -,111** -,111** -,111** -,111** -,111** 1,000

,013 -,071 ,043 ,038 -,111** -,111** -,111** -,111** -,111** -,111** -,111** 1,000

,009 -,049 ,028 ,054 -,111** -,111** -,111** -,111** -,111** -,111** -,111** -,111** 1,000

-,011 -,071 -,015 ,069 -,111** -,111** -,111** -,111** -,111** -,111** -,111** -,111** -,111** 1,000

,129** -,077 ,177** ,017 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 1,000

-,203** ,009 -,116** ,043 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 -,223** 1,000

,236** ,056 -,067 ,043 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 -,223** -,094* 1,000

-,131** -,015 ,215** -,047 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 -,223** -,094* -,094* 1,000

,122** ,044 -,089* -,111** ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 -,409** -,173** -,173** -,173** 1,000

-,241** ,033 -,081 -,115** ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 -,223** -,094* -,094* -,094* -,173** 1,000

-,049 -,016 -,145** ,396** ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 -,137** -,058 -,058 -,058 -,107* -,058 1,000

-,052 -,016 -,041 -,066 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 -,137** -,058 -,058 -,058 -,107* -,058 -,036 1,000

Audit rotation, does it matter?hj.diva-portal.org/smash/get/diva2:1433249/FULLTEXT01.pdf ·...

Documents

Transcript of Audit rotation, does it matter?hj.diva-portal.org/smash/get/diva2:1433249/FULLTEXT01.pdf ·...