Determinants of key audit matter disclosure and the moderating
effect of auditor litigation risk
Thesis MSc Accountancy and MSc Controlling
University of Groningen
Sam Kranenburg (S3116026)
Herebinnensingel 3A
9711GE Groningen
06 – 40490760
s.j.kranenburg@student.rug.nl
Supervisor: Prof. Dr. D.A. de Waard
5
thof January 2021
Wordcount: 8.957
ABSTRACT: The growing demand for audit information has resulted in the introduction of
key audit matter disclosure to increase transparency of the audit. In this research, a
quantitative research is conducted to verify determinants of the key audit matter disclosure.
The effect of client characteristics, degree of supervision and regulation, and audit firm
rotation on key audit matter disclosure is examined in this paper. Besides, this paper tries to
investigate the moderating effect of auditor litigation risk. The sample of the research consists
of 605 listed companies from nineteen countries for the fiscal year 2018. Results show that
riskier clients; clients with strong supervision and regulation; and audit firm rotation have a
positive effect on the number of key audit matters disclosed. There is no evidence found for
the expected moderating effect of auditor litigation risk.
KEYWORDS: Key Audit Matters, Agency theory, Inspired confidence theory, Hogarth’s
theory, auditor litigation risk, disclosure.
TABLE OF CONTENT
1
INTRODUCTION AND SCIENTIFIC CONTRIBUTION ... 3
1.1 INTRODUCTION ... 3
1.2 SCIENTIFIC CONTRIBUTION ... 5
2
THEORETICAL BACKGROUND ... 6
2.1 AGENCY THEORY... 6
2.2 INSPIRED CONFIDENCE THEORY ... 6
2.3 HOGARTH’S THEORY... 7
3
HYPOTHESIS DEVELOPMENT ... 9
3.1 LITIGATION RISK AND AUDITORS’ LOSS OF REPUTATION ... 9
3.2 CLIENT CHARACTERISTICS: FINANCIAL RISK AND COMPLEXITY ... 9
3.3 SECTOR REGULATION AND SUPERVISION ... 10
3.4 AUDIT FIRM ROTATION ... 11
3.5 CONTROL VARIABLES ... 12
4
METHODOLOGY ... 14
4.1 SAMPLE DESCRIPTION ... 14 4.2 DATA GATHERING ... 14 4.3 STATISTICAL MODEL ... 155
RESULTS ... 16
5.1 DESCRIPTIVE STATISTICS ... 16 5.2 CORRELATION ... 165.3 THE MAIN EFFECT OF THE INDEPENDENT VARIABLES ... 17
5.4 MODERATING EFFECT OF LITIGATION RISK... 18
6
CONCLUSION ... 20
6.1 CONCLUSION AND DISCUSSION ... 20
6.2 IMPLICATIONS, LIMITATIONS AND FUTURE RESEARCH ... 21
7
REFERENCES ... 24
8
APPENDICES ... 32
8.1 APPENDIX 1:CONVERSION RATES ... 32
8.2 APPENDIX 2:PEARSON CORRELATION MATRIX... 33
8.3 APPENDIX 3:VARIATION INFLATION FACTOR (VIF)... 34
8.4 APPENDIX 4:REGRESSION RESULTS REGARDING THE MAIN EFFECT OF THE INDEPENDENT VARIABLES ... 35
8.5 APPENDIX 5:REGRESSION RESULTS REGARDING HYPOTHESIS 2A (INTERACTION BETWEEN LITIGATION RISK AND THE NUMBER OF SEGMENTS) ... 36
8.6 APPENDIX 6:REGRESSION RESULTS REGARDING HYPOTHESIS 2B (INTERACTION BETWEEN LITIGATION RISK AND THE LEVERAGE) ... 37
8.7 APPENDIX 7:REGRESSION RESULTS REGARDING HYPOTHESIS 4(INTERACTION BETWEEN LITIGATION RISK AND THE LEVEL OF SUPERVISION AND REGULATION) ... 38
8.8 APPENDIX 8: REGRESSION RESULTS REGARDING HYPOTHESIS 6(INTERACTION BETWEEN LITIGATION RISK AND AUDIT FIRM ROTATION) ... 39
1
Introduction and scientific contribution
1.1
Introduction
After the financial crisis of 2008-2009, the informativeness of audit reporting was highly criticized (Gold & Heilmann, 2019; Velte & Issa, 2019). The pass/fail model of the audit was mainly criticized for its little communicative value (Church, Davis, & McCracken, 2008). In specific, the auditor’s report often was perceived as little informative and insufficient useful because of its standardized design (Asare & Wright, 2012; Church et al., 2008; IAASB, 2011). Users of the traditional auditor’s reports criticized that the traditional auditor’s reports often are uninformative since almost all publicly listed companies receive the same unqualified auditor’s opinion (Church et al., 2008; Gray, Turner, Coram & Mock, 2011). As a result, users want to have more information on the audit process (Mock et al., 2013). In particular, users are interested in the key matters of the audit (i.e. key risks). Research results show the existing gap between the information users want to have about the audit and on the other hand the available information in the auditor’s report (Gold & Heilmann, 2019).
As a result of the growing demand for audit information and the existing information gap, several institutions and regulators started to design a new auditing reporting model to increase the informativeness of the auditor’s report (Pinto & Morais, 2019). Their focus was to increase the information in the auditor’s report and thereby decreasing the existing information asymmetry between auditors and users of the auditor’s report (Bédard, Gonthier-Besacier, & Schatt, 2014; Cordoş & Fülöpa, 2015). As a response to this growing demand, the International Auditing and Assurance Standards Board (IAASB) issued a new International Standard on Auditing (ISA): 701 – Communication of Key Audit Matters (KAMs), in January of 2015 (IAASB, 2015). The new ISA 701 is part of the new ISA 700 series which introduces significant changes in reporting the audit (Sierra-García, Gambetta, García-Benau & Orta-Pérez, 2019). The new ISA introduces a renewed form of communication between organisations and the users of their reports (Sierra-García et al., 2019). This response from the IAASB shows the belief that a renewed form of audit reporting will increase its communicative value to the users (Sierra-García et al., 2019). This new regulation emphasizes among others the focus on the auditor’s independence and greater transparency of the audit (Pinto & Morais, 2019). One of the most significant newly introduced requirements is the disclosure of KAMs in the auditor’s report (Gold & Heilmann, 2019). KAMs are explained in the ISA 701.8 as “those matters that, in the auditor’s professional judgment, were of most significance in the audit of the financial statements of the current period”. The disclosure of KAMs supports the transparency of the audit and enables the auditor to explain and highlight the matters that need the most awareness (IAASB, 2016). Contrary to the traditional auditor’s report, the revised auditor’s report allows a more customized information disclosure approach on the client- and audit specific observations by the auditor (Gold & Heilmann, 2019).
As a result,auditors now have to decide which KAMs they are going to disclose in their auditor’s reports (Pinto & Morais, 2019). This decision-making process is very complex, since it is based on several elements, sub-processes, and tasks (Einhorn & Hogarth, 1981). The disclosure of the KAMs in the auditor’s report is affected by the outcome of the trade-off between the risk of being sued and the associated reputation damage, and on the other hand, the perceived cost of losing the client (Ferreira & Morais, 2020). Therefore, the characteristics of the client and the relationship with this client are expected to have an important effect on the disclosure of the KAMs (Ferreira & Morais, 2020).
Identifying the determinants of the degree of KAM disclosure is relevant for several reasons. First, the more key audit matters disclosed, the less useful the auditor’s report is (Pinto & Morais, 2019). The disclosure of more KAMs makes it more complex for the user to make decisions. Besides the complexity, the disclosure of more KAMs can dilute their importance (Sirois, Bédard & Bera, 2018). Secondly, the disclosure of KAMs attracts the attention of the users and makes the related disclosures in the financial statements more notable (Orquin & Loose, 2013), however increasing the number of KAMs disclosed can reduce the effectiveness of their signalling function (Li, Qi, Tian, & Zhang, 2017). Thirdly, identifying the determinants of KAM disclosure could be helpful for investors. It is important to understand what determines the volume and type of KAM disclosure, since decision-makers, financial markets, and the society rely on the information disclosed in the auditor’s report (Bédard, Gonthier-Besacier, & Schatt, 2014; Christensen, Glover & Wolfe, 2014; Danescu & Spatacean, 2018; Lennox, Schmidt & Thompson, 2017; Trpeska, Atanasovski & Lazarevska, 2017). Investors could include the determinants of KAM disclosure in assessing the appropriateness of the disclosed number of KAMs. Furthermore, since KAM disclosure is considered as more credible than other disclosures (Christensen et al., 2014), users might rely heavily on this disclosure. Given the importance of KAMs in the auditor’s report, it is interesting to examine what elements determine the disclosure by the auditor.
Therefore, this research will focus on the factors which influence the decision of the auditor whether or not to disclose the KAMs of an audit. In specific, this research will investigate the effect of client risk characteristics, client-auditor relationship characteristics, supervision on specific sectors and the litigation risk of a country on the disclosure of KAMs. In line with Pinto and Morais (2019), the independent variables will consist of client risk, sector supervision and regulation and audit firm rotation. Due to the importance of the litigation risk in the decision making whether or not to disclose a specific KAM (Pinto & Morais, 2019; Ferreira & Morais, 2020; In, Kim & Park, 2020; Kipp, 2017), the level of litigation risk will also be included in this research.
According to the review of the Association of Chartered Certified Accountants on the implementation of the extended auditor’s report, it can be concluded that the disclosure of auditors differs between countries. Therefore, this research will also examine whether the differences in the degree of auditor
disclosure between countries can be explained. This research will try to explain the differences between countries by investigating the moderating effect of the auditor litigation risk of a country.
Therefore, the research questions of this research are as follows:
What is the influence of client risk, auditor rotation and sector regulation and supervision characteristics on the disclosure of Key Audit Matters? And to what extent does the level of litigation risk of a country influence these relationships?
In the remainder of this chapter, we will discuss the scientific contribution. Thereafter, the theoretical background, hypothesis development, methodology and the results will be discussed. Finally, the implications, limitations and suggestions for further research will be discussed in the conclusion.
1.2
Scientific contribution
Several studies already have focussed on the consequences of KAM disclosure on several dependent variables (e.g. shareholder decisions, auditor’s liability) (Christensen et al., 2014; Gimbar, Hansen & Ozlanski, 2016; Lennox et al., 2017; Sirois et al., 2018). Gold and Heilmann (2019) state that there already is a substantial body of literature on the consequences of KAM disclosure. However, the literature still lacks on research on the determinants of disclosing the KAMs (Pinto & Morais, 2019). Velte (2018) concludes that the determinants of KAM disclosure are still underinvestigated. Nevertheless, there already are some researches on the determinants of the KAM disclosure. Sierra-Garcia et al. (2019) investigated the KAM reporting in the UK, their research focusses on both the volume of the disclosure (number of KAMs) as well on the content of the disclosure (the subject of the KAMS). Besides, Ferreira and Morais (2020) already researched the relationship between company characteristics and KAM disclosure in Brazil for the fiscal year 2016. Further, Pinto and Morais (2019) also researched the determinants of KAM disclosure, however, for their research, they only used data from the Netherlands, France and the United Kingdom for the fiscal year 2016.
The existing researches on the determinants of KAM disclosure are very limited when it comes to the extent of the used population. For example, Sierra-García et al. (2019) only focus on the UK, while Pinto and Morais (2019) were limited to a small group of European countries and Ferreira and Morais (2020) only looked at Brazilian companies.
This study tries to fill the current gap in the literature by focussing on the determinants of KAM disclosure, instead of the consequences of KAM disclosure (Pinto & Morais, 2019). Further, this study also contributes to the current literature by using a larger population, consisting of companies from countries all over the world. In addition, to the best of my knowledge, this is the first research that includes the auditor litigation risk as a moderating variable.
2
Theoretical background
In this section, the theories used in this research will be discussed. Firstly, the agency theory will be discussed, followed by the inspired confidence theory. Lastly, the theory of Hogarth (1980) will be discussed. After the theory discussion, the hypotheses will be constructed.
2.1
Agency theory
A substantial part of the literature on the disclosure of financial and non-financial information is based on the principal-agent theory (agency theory). The agency theory can be seen as the most traditional theory used for the reporting research (An, Davey & Eggleton, 2011). The agency theory describes the conflict between the incentives of the agent and the principal, due to information asymmetry and self-interest (Agoglia, Hatfield & Lambert, 2015; de Waard, Marra, Kranenburg & van Oorschot, 2020). When the goals of the principal and the agent differ from each other, and the agent get the opportunity to act in his own interest, the agency theory states that the agent will make decisions that serve his own interest, instead of the interest of the principal (Booth & Schulz, 2004; Jensen & Meckling, 1976). In this research, the agency theory can be applied to stakeholders of the audited organisation (principal) and the client of the auditor (agent). The stakeholders demand extra information on the financial statements, since the client might have incentives for biasing the disclosed information. In order to reduce the agency principal conflict, the auditor enters as a third party to review the financial position of the client (Carrington, 2014; Watts & Zimmerman, 1979), which is thereafter presented in the auditor’s report (Carrington, 2014). The focus of the auditor is on communicating private information of the agent to the stakeholders (Moore & Ronen, 1990). In this case, the disclosure of KAMs is a manner to serve the information demand of the stakeholders. By disclosing the KAMs, the information asymmetry between the principal and the agent can be reduced (Shroff, Sun, White & Zhang, 2013). Therefore, the agency theory has an important role in the disclosure of KAMs.
2.2
Inspired confidence theory
Besides the agency theory, this study will also make use of the inspired confidence theory. The inspired confidence theory of Limperg (1985) focusses on the supply and demand of audit services (Dassen, 1989). The demand for audit services is the direct result of having external stakeholders in the organisation. It can be assumed that information disclosed by the management of an organisation might be biased, since there could be conflicts of interest between the management of the organisation and the third parties using this information. This (possible) biased information disclosure creates the demand to have audited reports. The society trusts on the independency and competences of the auditor to form an objective opinion on the disclosed statements of the organisation. Therefore, the audit function is based on the trust and confidence of the users of the statements. The inspired confidence theory is closely
related to the agency theory. Both the agency- and the inspired confidence theory are based on a separation between the management and ownership.
Following the inspired confidence theory, the demand of the auditor is based on the needs of the society. If an auditor cannot meet these needs, his work is not needed anymore. Therefore, an auditor must adopt his practices to the needs of the society. As mentioned in the introduction of this paper, there was a growing demand going on for having more transparency in the audit. Therefore, to serve the society, the auditor must evolve his auditor’s report to improve transparency. Changing
expectations of the society therefore affect the practices of an auditor (Limperg, 1932). As a result, the auditor now has to extend his audit information disclosure, which can be accomplished by disclosing the KAMs of an audit (Oghuvwu & Orakwue, 2018). The inspired confidence theory helps to explain the disclosure of the KAMs by the auditor and therefore the inspired confidence theory is a useful theory in this research.
2.3
Hogarth’s theory
Besides the agency- and the inspired confidence theory, this study will also use Hogarth’s (1980) theory. Using Hogarth’s theory (1980) is in line with other studies investigating the determinants of KAM disclosure (Pinto & Morais, 2019; Morais & Ferreira, 2020; Sierra-Garcia et al., 2019). The theory of Hogarth (1980) focusses on information assimilation for judgment and decision making. By using the theory, the process of the auditor’s decision to disclose whether or not a KAM can be evaluated (Pinto & Morais, 2019). The theory consists of four stages: information acquisition, processing, output and feedback (Pinto & Morais, 2019). In this study, we only focus on the third stage of the theory, the output stage. Hogarth’s theory states that the decision of an auditor to disclose a specific KAM is heavily influenced by the environment as well by the auditor himself (Pinto & Morais, 2019; Sierra-García et al., 2019). The theory used is based on the assumption of Einhorn and Hogarth (1981) who state that the conflict of disclosing a KAM (whether or not disclosing a KAM) differs from the conflict in judgement (whether or not a specific situation, transaction or risk can be seen as a KAM) (Pinto & Morais, 2019).
To solve the disclosure conflict, the auditor can choose between avoidance or confrontation (Einhorn & Hogarth, 1981). In this sense, avoidance does mean that the auditor will not disclose a specific KAM, or he will delay the disclosure (Ferreira & Morais, 2020). The decision of the auditor to avoid KAM disclosure is based on the expected perceived value of the consequences of the disclosure (Ferreira & Morais, 2020). The decision of the auditor to avoid disclosure is expected to happen when the auditor is considered to be less associated with the responsibilities of the consequences of not disclosing a specific KAM, than disclosing it (Ferreira & Morais, 2020). In contradiction, the choice for confrontation means that the auditor makes use of compensatory strategies as expressed in the expected utility model, where the auditor is risk averse (Pinto & Morais, 2019; Ferreira & Morais, 2020). In other words, the disclosure of KAMs by the auditor is affected by the expected consequences of the economic trade-off between
the risk of being sued and the associated loss of reputation, and on the other hand, the risk of losing the client.
3
Hypothesis development
In the context of relevant theory, prior findings and the research questions proposed, this paper argues that client characteristics (number of segments and leverage), regulatory characteristics of the client (level of regulation and supervision), auditor-client relationship characteristics (audit firm rotation) and the level of litigation risk (auditor litigation risk) have an impact on the auditor's identification and disclosure of KAMs. In consequence, the following hypotheses are developed.
3.1
Litigation risk and auditors’ loss of reputation
Auditor litigation risk is a main concern of auditors (Mong & Roebuck, 2005). The literature states that the disclosure of KAMs by the auditor is affected by the auditor’s perceived litigation risk (Pinto & Morias, 2019). Disclosing KAMs has an important advantage to auditors, the disclosure of KAMs makes the auditor less responsible for a misstatement in the financial statements of the client (Kachelmeier, Schmidt & Kristen, 2017). Kachelmeier et al. (2017) further explain that the disclosure of KAMs works as a disclaimer effect. In addition, Brasel, Marcus, Jonathan, Grenier and Reffett (2016) state that users react less negatively when an auditor fails in his task to find a misstatement if related KAMs have been disclosed. So, the addition of mentioning key risks by the auditor in the auditor’s report effectively reduces potential legal actions of users against the auditor. So, this research predicts that auditors disclose more KAMs to reduce their liability by the users. In particular, it can be expected that in countries with a higher auditor litigation risk, the auditor discloses more KAMs (Mong & Roebuck, 2005). The degree of litigation risk will be tested as a moderating effect on the following hypotheses. In other words, this research expects that the level of auditor litigation risk affects the relationships between client characteristics, regulatory characteristics of the client and auditor-client relationship characteristics on the KAM disclosure.
3.2
Client characteristics: financial risk and complexity
Dopuch, Holthausen and Leftwich (1987) found that client characteristics can be used to predict the auditor’s opinion. In particular, the disclosure of losses and solvency issues are found to be significant predictors of the auditor’s opinion (Dopuch et al., 1987). Literature shows that companies with financial problems are indicators for greater risk (Ireland, 2003; Ye, 2011). In general, higher leverage means a higher financial risk (Pinto & Morias, 2019). As the financial risk of a client increases, auditors tend to review the client more thoroughly, and therefore put more effort into the client (Nelson, Ronen & White, 1988). An increase in audit effort to limit the liability of the auditor is expected to result in improved procedures in the audit and therefore a more extended identification of KAMs (Pinto & Morias, 2019). Contrary, when the leverage of a firm increases, managers of the firm have increased incentives to adopt accounting policies that reduce costs but on the other hand increases the risk of certain areas (Pinto &
Morias, 2019). Therefore, this research predicts a positive relationship between the leverage and the number of KAMs. Besides the leverage of the client, this research also expects that auditors tend to disclose more KAMs for more complex clients. In more complex firms, there are more risky areas that could lead to an increase in the number of KAMs disclosed by the auditor (Ferreira & Morais, 2020). Therefore, this research uses the number of segments of a client as a proxy for complexity. The use of this proxy is in line with the current literature (Bédard, Hoitash & Hoitash, 2008; Markanian & Parbonetti, 2007; Pinto & Morias, 2019). Managers in firms that operate in multiple segments do have more incentives to manage earnings due to the agency problems of these diversified firms (Berger & Ofek, 1995; Ozbas & Scharfstein, 2010; Rajan, Servaes & Zingales, 2000). Auditors therefore tend to put more effort in clients that operate in multiple segments, due to their increased risk (Nelson et al., 1988). An increase in audit effort is expected to result in improved procedures in the audit and therefore a more extended identification of KAMs (Pinto & Morias, 2019). This leads to the following hypotheses:
Hypothesis 1: There is a positive association between the firm’s risk and the number of KAMs disclosed.
Hypothesis 1a: There is a positive association between the firm’s leverage and the number of KAMs disclosed.
Hypothesis 1b: There is a positive association between the firm’s number of segments and the number of KAMs disclosed.
Hypothesis 2: The litigation risk of a country moderates the relationship between the firm’s risk and the number of KAMs disclosed in such a way that the relationship is stronger for firms in countries with higher litigation risk.
Hypothesis 2a: The litigation risk of a country moderates the relationship between the firm’s leverage and the number of KAMs disclosed in such a way that the relationship is stronger for firms in countries with higher litigation risk.
Hypothesis 2b: The litigation risk of a country moderates the relationship between the firm’s number of segments and the number of KAMs disclosed in such a way that the relationship is stronger for firms in countries with higher litigation risk.
3.3
Sector regulation and supervision
Prior research showed that some industries are more difficult to audit than other industries (Hay, Knechel & Wongm, 2006; Simunic, 1980; Ettredge, Xu & Yi, 2014). In particular, financial institutions (i.e. finance, insurance and real estate companies) are difficult to audit due to their complexity,
magnitude and their agency conflicts (Pinto & Morias, 2019). In addition, the opacity of financial institutions does not make the audit easier (Pinto & Morias, 2019). Pinto and Morais (2019) explain that the opacity of financial institutions is mainly due to the complex interrelation of on- and off-balance sheet exposures and due to the wide scope of difficult evaluable risks and values. Besides these difficulties, also the large amount of fair-valued assets makes the audit more complex and difficult (Bratten, Gaynor, McDaniel, Montague & Sierra, 2013). Financial institutions tend to disclose larger and more complex financial reports due to a rise in more complex accounting standards for financial institutions (Pinto & Morias, 2019). This development has resulted in the increasing economic complexity of these institutions. As a response, standard setters have developed and introduced many complex and extensive standards for financial instruments and complex transactions of financial institutions to better capture their real economics (Guay, Samuels & Taylor, 2016). Despite the complexity of an audit of a financial institution, auditors may find the audit of a financial institution less risky (Pinto & Morias, 2019). Since financial institutions have to comply to a high level of regulation and supervision (Pinto & Morias, 2019). As a result, it can be expected that auditors have to perform relative less audit work due to the stronger supervision and regulation of the financial institutions, therefore, it can be expected that auditors disclose fewer KAMs for financial institutions (Dunn & Mayhew, 2004). Ghosh, Jarva and Ryan (2017) support this expectation by showing that extensive regulation and supervision in the financial sector provides the auditor with incentives to limit their effort on the audit. This leads to the following hypotheses:
Hypothesis 3: There is a negative association between the level of regulation and supervision of the sector of the client and the number of KAMs disclosed.
Hypothesis 4: The litigation risk of a country moderates the relationship between the level of regulation and supervision of the sector of the client and the number of KAMs disclosed in such a way that the relationship is weaker for firms in countries with higher litigation risk.
3.4
Audit firm rotation
A long-term relationship between the audit firm and the client could influence the independence of the auditor (Roos, 2012). Long-term relationships between the auditor and the client could result in incentives that threatens the independence and therefore also the professional critical institution of the auditor (Bazerman, Morgan & Loewenstein, 1997). Research on the mandatory rotation of audit firms shows a positive effect of rotation on the independence of the auditor (Ewelt-Knauer, Gold & Pott, 2013). Rotation of audit firms avoids the risk of getting an outdated audit and prevents the audit plan from becoming too static (GAO, 2003). A new auditor is expected to introduce a fresh perspective on the audit (GAO, 2003). Besides the fresh perspective, it can be expected that the independence of the
auditor increases after a rotation, since there is no longer a long-term relationship to maintain. This new fresh perspective and increase in independence could lead to an improvement in KAM identification. This leads to the following hypotheses:
Hypothesis 5: There is a positive association between audit firm rotation and the number of KAMs disclosed.
Hypothesis 6: The litigation risk of a country moderates the relationship between audit firm rotation and the number of KAMs disclosed in such a way that the relationship is stronger for firms in countries with higher litigation risk.
3.5
Control variables
In line with previous studies (Pinto & Morais, 2019; Velte, 2018; Laitinen & Laitinen, 1998; Johl, Jubb & Houghton, 2007; Molyneux & Thornton, 1992; Lennox et al., 2017) this study uses the size of the company, the profitability of the company and the type of legal system in a country as control variables.
Several studies found a positive relationship between the size of the company and the number of KAMs (Pinto & Morais 2019; Velte, 2018). Large companies tend to be more complex and therefore pose a higher risk for auditors (Pinto & Morais, 2019). Since the size of the company can influence the number of KAMs disclosed by the auditor, this research controls for the size of the organisation. The size of the company is measured by the natural logarithm of the total assets at the end of the fiscal year 2018 (Pinto & Morais, 2019). However, not all financial statements are shown in the same currency. Therefore, other currencies than the euro are converted to the euro, to have all the financial data in the euro. The conversion rate of 31 December 2018 is used. This conversion rate can be found in appendix 1.
Besides the size of the company, profitability could also influence the number of KAMs. Following Pinto and Morais (2019), companies with lower profitability tend to be at a higher risk of failure, therefore auditors may need to extend their audit procedures and disclose more KAMs. Contrary, the companies with higher profitability show less probability of default and tend to receive an unqualified audit opinion (Beasley, Carcello, & Hermanson, 1999; Laitinen & Laitinen, 1998; Loebbecke, Eining, & Willingham, 1989), which results in lower litigation between the auditor and the client. Profitability is measured by the return on assets. The return on assets is calculated by hand using the annual reports of the companies.
Finally, this study also controls for the effect of the type of legal system of a country. This study controls for the effect of the type of legal system, since the type of legal system could influence the timing and level of disclosure by companies and auditors (Ball, Kothari & Robin, 2000; Hope, 2003). By making a distinction between common law and civil law, this paper can control for the effect of the different types
of the legal system (Ball et al., 2000; Hope, 2003). In line with previous research (Hope, 2003), a dummy variable is used to classify the different types of the legal system.
4
Methodology
In the previous chapters, the theoretical background and the hypotheses are described. This chapter discusses the testing of the hypotheses. First, the sample and data gathering are discussed. After that, the statistical model is outlined. This chapter also contains a table with all the variables and a short description used in the statistical model.
4.1
Sample description
This research will make use of a quantitative research method to answer the research questions. As mentioned in the introduction, this research has an international focus. The sample of the research consists of companies listed on stock exchanges from Australia, Belgium, Brazil, France, Germany, Hong Kong, Italy, Korea, Malaysia, Mexico, New Zealand, Netherlands, Norway, Singapore, South Africa, Spain, Sweden, Turkey and United Kingdom. All these countries apply the ISAs (IFAC, 2012). The application of the ISAs in auditing is important for this research, since it has become mandatory in the ISAs to disclose key audit matters (Boolaky & Quick, 2016). The specific indices where the companies are retrieved from are: ASX (50), BEL (20), IBOVESPA (73), CAC (40), DAX (30), Hang Seng (50), MIB (40), KOSPI (30), KLSE (30), IPC (30), NZX (50), AEX (25), OBX (25), SGX (30), JSE (50), IBEX (35), OMX (30), SET (50), FTSE (28). This research assumes that the companies and auditors from the main stock exchanges of a country reflect the overall national sentiment in that country, because of the size and significance of companies included in the main stock change of a country. The reference date for the sample is set at the last available fiscal year: 2018, to make the results better generalizable to the present.
Not every company listed on the aforementioned indices is included in the final sample. A small number of auditor’s reports did not contain a KAM section. In addition, for a small number of companies there was no annual report available. Next to that, a small number of auditor’s reports were written in a foreign language, therefore the KAMs of these companies could not be identified. Finally, a small number of companies are listed on multiple stock exchanges. To ensure that some companies are not represented more than once in the sample, duplicates are removed from the sample. For these reasons, the sample of 716 observations is reduced to a final sample of 615 observations.
4.2
Data gathering
The data regarding KAM disclosure, auditor rotation, size and profitability of companies is hand collected from companies’ annual- and auditor reports. This data is collected by the University of Groningen. A team of student employees is responsible for collecting the data regarding KAM disclosure, under supervision of Prof. D. de Waard. The collection of the KAM data is executed very accurately and careful. There are multiple data collection guidelines and second reviews in place to ensure the reliability of the data. In addition, according to Fogarty (2006), at least one variable needs to
be hand collected by the researcher to increase the reliability of the data used for the research. Therefore, the reliability of the data is enhanced by hand collecting a large part of the data by myself. Data regarding the diversification (number of segments), the leverage and the segment of a company is retrieved from the database of Thomas Reuters DataStream. Data regarding the litigation risk is gained from the auditor litigation index of Wingate (1997). This index measures the liability standard for investors to recover damage from auditors when the disclosure of auditors is misleading, insufficient or too negligent. Wingate (1997) uses a 10-point auditor litigation index. A higher score in the index of Wingate (1997), means a higher liability exposure of auditors (Francis & Wang, 2004). In fact, the score means ‘the risk of doing business as an auditor’ in a specific country (Choi & Wong, 2004).
4.3
Statistical model
This study is conducted in a quantitative way by performing statistical tests. In line with previous studies (Ferreira & Morais, 2020; Pinto & Morais 2019; Sierra-Garcia et al., 2019), a multiple regression model is used to test the hypotheses of this research. The Ordinary least squares model is employed, defined as follows:
KAM = 𝛽
0+ 𝛽
1SEG + 𝛽
2LEV + 𝛽
3LIT + 𝛽
4SUP + 𝛽
5ROT + 𝛽
6SIZE + 𝛽
7PROF + 𝛽
8LAW + 𝜀
iIn this formula, 𝛽0 is constant, 𝛽i represent the independent and control variables used in this study and the 𝜀i is the error. Table 1 gives an overview of the variables used.
Table 1: Description of variables
Variable Measure Type of
variable
Dependent
KAM Number of key audit matters per organisation. Interval
Independent
SEG Number of segments in which the organisation is active. Interval
LEV Leverage of the company. Interval
LIT Litigation risk of a country, using Wingate’s (1997) index. Interval SUP Sectors (i.e. financial institutions) with stronger supervision and
regulating. Financial institutions = 1 and other = 0.
Dummy
ROT Auditor rotation. New auditor = 1 and same auditor as last year =0. Dummy
Control
SIZE The natural logarithm of the total assets of the organisation Interval PROF Return on assets calculated by hand with information from the annual
report
Interval
LAW The country in which the auditor’s report is issued type of legal
5
Results
In this chapter, the results of the conducted research are presented. This chapter is organised as follows. The first paragraph provides an overview of the descriptive statistics. The second paragraph discusses the correlation between the variables of this research. Further, the third paragraph provides the results of hypotheses 1a, 1b, 3 and 5. Here, only the main effect of the independent variables is analysed. Lastly, the fourth section provides the results of hypotheses 2, 4 and 6. These hypotheses test the moderating effect of the auditor litigation risk.
5.1
Descriptive statistics
Table 2 gives a representation of the descriptive statistics of the variables used in this research. The table shows the sample size, mean, standard deviation, minimum and maximum of the variables. The mean of the number of key audit matters is 2,87 (St.Dev. = 1,46). The average number of segments an organisation operates in is 5,15 (St.Dev.= 2,32). The mean of the leverage is 43,10 (St.Dev.= 24,08), which means that the assets of the organisations from the sample are on average 43,1% financed by debt. The average Wingate score of the sample is 6,22 (St.Dev.= 2,39). The mean of financial institution dummy is 0,35, which means that 35% of the sample is a financial-, insurance- or real estate institution. The mean of the auditor firm rotation dummy is 0,06, which implicates that 6% of the organisations in the sample had a new audit firm in 2019 compared to 2018. The mean of the law-dummy is 0,59 (St.Dev.=0,49), which means that 59% of the sample is based in a country with a code law legal system. The mean of the return on assets is 5,34 (St.Dev.=6,13).
Table 2: Descriptive statistics
Variables N Mean Std. Deviation Minimum Maximum
KAM 615 2,87 1,46 0 8 SEG 615 5,15 2,32 1 10 LEV 615 43,10 24,08 0,00 43,10 LIT 615 6,22 2,39 2,42 6,22 SUP 615 0,35 0,47 0 1 ROT 615 0,06 0,22 0 1 SIZE_LOG 615 23,28 1,80 18,48 28,89 LAW 615 0,59 0,49 0 1 PROF 615 5,34 6,13 -20,89 46,72
5.2
Correlation
Appendix 2 and 3 respectively represent the Pearson correlation matrix and the variance inflation factor (VIF) table between the dependent and independent variables. Field (2013) states that correlations higher than 0,7 at a 5% significance level, indicates a multicollinearity problem. The highest measured correlation value in this research is 0,242 between the number of KAMs and the leverage. Although
some variables correlate significantly, multicollinearity is not a concern in this research (Gujarati & Porter, 2003). The VIF values should not exceed 10 (Hair, Black, Babin, & Anderson, 2010). It was verified that a multicollinearity problem is unlikely, since all the VIF values are lower than 10.
5.3
The main effect of the independent variables
The linear regression method is used to test our hypotheses. The analysis is based on six steps, shown in appendix 4. The first step consists of analysing the control variables of our study, thereafter we separately tested our hypotheses and finally the complete model is analysed.
First, in model 1 (appendix 4) only the control variables are analysed. This analysis shows an adjusted R-squared of 0,041, which implicates that 4,1% of the change in the number of KAMs disclosed is explained by the effect of all control variables together. Size (β = 0,066, p<0,05) and profitability (β = -0,042, p<0,01) both show a significant effect on the number of KAMs disclosed. For the type of legal system, there is found a positive effect (β = 0,180) on the number of KAMs, however, this relationship is not significant.
The second model is used to test hypothesis 1a, which assesses the relationship between the number of segments in which the organisation operates and the number of KAMs. This hypothesis assumes a positive relationship between the number of KAMs and the number of segments. To eliminate the effect of the size, profitability and type of legal system on the key audit matter disclosure, these variables are included as control variables in the model. Model 2 (appendix 4) presents the results of the performed regression to test hypotheses 1a. When including the effect of the number of segments in the model, the explanatory power raises from 4,1% in model 1 (control variables) to 6,6% in model 2 (control variables and the number of segments). Model 2 (appendix 4) shows a significant positive relationship (β= 0,101, p<0,01) between the number of segments an organisation is active in and the number of KAMs disclosed for that organisation. This implicates that organisations that operate in more segments, do have significant more KAMs in their auditor’s reports. Therefore, hypothesis 1a can be accepted.
The third model is used to test hypothesis 1b. In line with hypothesis 1a, hypothesis 1b also focusses on client characteristics. However, hypothesis 2b assumes a positive relationship between the leverage of the organisation and the number of KAMs. In line with the previous model, there will be controlled for the effect of the size, profitability and type of legal system. Model 3 (appendix 4) presents the results of the performed regression to test hypotheses 1b. When including the effect of the leverage in the model, the explanatory power raises from 4,1% in model 1 (appendix 4)(control variables) to 7,5% in model 3 (appendix 4) (control variables and the leverage). Model 3 (appendix 4) shows a significant positive relationship (β= 0,012, p<0,01) between the leverage and the number of KAMs disclosed. This means that organisations with higher leverage significantly have more KAMs in their auditor report. Therefore, hypothesis 1b can be accepted.
Model 4 (appendix 4) is used to test hypothesis 3. Hypothesis 3 assumes a negative relationship between strong regulation and supervision and the number of KAMs. Model 4 (appendix 4) also controls for the effect of the control variables. Model 4 (appendix 4) presents the results of the performed regression to test hypotheses 3. When including the effect of the leverage in the model, the explanatory power raises from 4,1% in model 1 (appendix 4)(control variables) to 4,5% in model 4 (appendix 4) (control variables and supervision and regulation). Model 4 (appendix 4) shows a significant positive relationship (β= 0,230, p<0,10) between the regulation and supervision and the number of KAMs disclosed. This implicates that organisations with stronger supervision and regulation do have significantly more KAMs in their auditor report. Since hypothesis 3 assumes a negative relationship, while there is found a positive relationship, hypothesis 3 can be rejected.
Model 5 (appendix 4) is used to test hypothesis 5. Hypothesis 5 assumes a positive relationship between audit firm rotation and the number of KAMs. The model also includes the control variables to eliminate their effect. Model 5 (appendix 4) presents the results of the performed regression analysis to test hypothesis 5. When including the effect of auditor rotation in the model, the explanatory power declines from 4,1% in model 1 (appendix 4) (control variables) to 3,9% in model 5 (appendix 4) (control variables and auditor rotation). Model 5 (appendix 4) shows a positive relationship (β= 0,113) between auditor rotation and the number of KAMs disclosed. This implicates that organisations with a new audit firm compared to last year do have more KAMs in their auditor report. However, since this relationship is not significant, therefore hypothesis 5 can be rejected.
Lastly, the sixth model is used to test the complete model (except for the moderating effect of litigation risk, see section 5.4). The sixth model (appendix 4) includes all independent and control variables. The purpose of this sixth model is to investigate the explanatory power of all variables together. Appendix 4 presents the results of the performed regression to test the complete model. The explanatory power increases from 4,1% in model 1 (appendix 4)(control variables) to 9,5% in model 6 (appendix 4) (complete model).
5.4
Moderating effect of litigation risk
The linear regression method is used to test the hypotheses containing the moderating effect of the litigation risk of the auditor. The analysis is based on three steps per hypothesis. The first step consists of analysing the control variables of our study, thereafter we analyse the effect of adding the standardized independent variables and in the final step, we analyse the contribution of the interaction between the independent and moderating variable.
Hypothesis 2 assumes that the relationship between the firm’s risk and the number of KAMs is stronger for firms in countries with high litigation risk. This research uses two different measures for measuring the risk of the firm: the number of segments (complexity) and the leverage of the firm (financial risk).
Appendix 5 shows the results of the analysis to test hypothesis 2a. The explanatory power of the model including the interaction effect between the number of segments and the litigation risk is 7,1%. Model 3 (appendix 5) shows a positive relationship (β= 0,074) between the interaction of the number of segments and the litigation risk on the number of KAMs. However, this relationship is not significant. Therefore, hypothesis 2a can be rejected.
Besides, appendix 6 presents the results of the analysis to test hypothesis 2b. The explanatory power of the model is 8,1%. Model 3 (appendix 6) of the analysis shows a positive relationship (β= 0,084) between the interaction of the leverage of the company and litigation risk on the number of KAMs. However, this relationship is not significant. Therefore, Hypothesis 2b can be rejected.
Hypothesis 4 assumes that the relationship between the level of supervision and regulation and the number of KAMs is weaker for countries with higher litigation risk. Appendix 7 presents the results of the analysis to test hypothesis 4. The explanatory power of the model is 4,9%. Model 3 (appendix 7) shows a negative relationship (β= - 0,014) between the interaction of the supervision and the litigation risk on the number of KAMs. However, this relationship is not significant. Therefore, hypothesis 4 can be rejected.
Hypothesis 6 assumes that the relationship between auditor firm rotation and the number of KAMs is stronger for firms in countries with high litigation risk. Appendix 8 shows the results of the analysis to test hypothesis 6. The explanatory power of the model is 4,4%. Model 3 (appendix 8) shows a negative relationship (β= - 0,022) between the interaction of the audit firm rotation and the litigation risk on the number of KAMs. Since hypothesis 6 assumes a positive relationship, hypothesis 6 can be rejected.
6
Conclusion
In this chapter, the research questions will be answered. Firstly, the results of the research will be discussed. Secondly, the theoretical and practical implications of this research are discussed. Thereafter, the limitations of this research are discussed. Finally, suggestions and recommendations for future research will be presented.
6.1
Conclusion and discussion
This study aims to identify and verify several determinants of the number of KAMs disclosed. In addition, this paper tries to explain cross-country differences by the effect of auditor litigation risk on the expected relationships. To the best of my knowledge, this is the first research that tries to explain differences in the number of KAMs disclosed by taking into consideration the moderating effect of the auditor litigation risk of a country. In more particular, the research came about as a result of the paper of Velte (2018). He suggested for more research on the determinants of KAM disclosure. In addition, this research also fills the recommendations for further research of Pinto and Morais (2019). Pinto and Morais (2019) stated that there was a need for research on the determinants of KAM disclosure for a larger sample than just a few countries. This paper fulfils this need by having a sample consisting of 615 organisations, represented on the main indices of nineteen countries. Based on prior literature and the used theories, six hypotheses were developed.
The first hypothesis supposes that firm characteristics affect the number of KAMs disclosed. Hypothesis 1 is divided into two separate hypotheses; H1a supposes that the financial risk of the client has a positive effect on the number of KAMs disclosed and H1b supposes that the complexity of the client has a positive effect on the number of KAMs disclosed. The regression shows a positive significant effect for both hypotheses. Hypothesis 2 supposes a moderating effect of the auditor litigation risk on the relationships between the client characteristics and the number of KAMs disclosed. In specific, hypothesis 2a supposes that the positive relationship between the financial risk of the client and the number of KAMs disclosed is stronger for firms in countries with a relatively high litigation risk and hypothesis 2b supposes that the positive relationship between the complexity of the client and the number of KAMs disclosed is stronger for firms in countries with relative high litigation risk. No significant results have been found for hypothesis 2a and 2b. The third hypothesis supposes that firms with strong supervision and regulation have less key audit matters in their auditor reports. However, for this hypothesis there is found an unexpected significant positive relationship between firms with stronger supervision and regulation and the number of KAMs disclosed.
This finding is remarkable, since Pinto and Morais (2018) did provide evidence for a negative relationship. The reason for this difference could be explained by the difference in the samples used for our researches. The research of Pinto and Morais (2018) only contained companies from the Netherlands, France and the United Kingdom, while the sample of this research consists of nineteen countries (including non-European countries). The level of regulation and supervision regarding financial institutions do significantly vary between countries (Kara, 2016), this might explain the difference between our findings.
The fourth hypothesis supposes a moderating effect of the auditor litigation risk on the relationship between the level of supervision and regulation of the client and the number of KAMs disclosed. However, no significant relationship has been found. The fifth hypothesis supposes a positive relationship between audit firm rotation and the number of KAMs disclosed. The regression analysis showed a significant positive relationship. Lastly, the sixth hypothesis supposes a positive moderating effect of the auditor litigation risk on the relationship between the audit firm rotation and the number of KAMs disclosed. However, no significant relationship has been found for hypothesis 6.
Finally, the research questions “What is the influence of client risk, auditor rotation and sector
regulation and supervision characteristics on the disclosure of Key Audit Matters? And to what extent does the level of litigation risk of a country influence these relationships?” can be answered. As
mentioned, the results show that client characteristics (complexity and financial risk) and auditor rotation do have a positive significant effect on the number of KAMs disclosed. For sector regulation and supervision, we also found a positive significant effect on the number of KAMs disclosed, however, this relationship was expected to be negative, in line with the paper of (Pinto & Morais, 2018). Nevertheless, no moderating effect regarding the auditor litigation risk is found.
6.2
Implications, limitations and future research
The findings of this research contribute to the existing literature by providing several theoretical implications for the used theories. To the best of my knowledge, this is the first research that uses the agency, the inspired confidence as well Hogarth’s (1980) theory to explain and verify possible determinants of KAM disclosure. Firstly, the agency theory and inspired confidence theory are extended by the results of this study. We found that auditors are more likely to disclose KAMs when the client is riskier. Auditors of relatively risky clients fulfilled their role, as described by the agency and inspired confidence theory, by mitigating the information asymmetry between the client and the user of the auditor ’s report by being more transparent about the key audit matters of the audit. Besides, thi s research also extends Hogarth’s theory because of the findings regarding the audit firm rotation. Hogarth’s (1980) theory is based on the trade-off between the probability of being suited and the reputation loss (disclosing too few KAMs), and on the other hand the expected loss of losing a client (disclosing too many KAMs).
The findings of this research are in line with Hogarth’s theory, by finding a positive significant effect of auditor rotation on the number of KAMs disclosed. Since it is the first-year audit of the new audit firm, the auditor has less to lose, as there is no long-term relationship to maintain. Therefore, auditors are more likely to disclose relative many KAMs.
Besides the theoretical implications, this research also provides some practical implications. The findings could offer insights for investors, who are seen as the main users of the auditor’s report (Kausar, Taffler & Tan, 2017). Investors can use the find ings of this research in assessing the extended auditor’s report. In more specific, investors can use the determinants found in this paper to assess the appropriateness of the number of KAMs disclosed in the auditor’s report. Further, this research also provides practical implications for standard setters, like the IAASB. Since the introduction of the extended auditor’s report, little is known about the determinants that do affect the KAM disclosure of the auditor. Therefore, this paper is relevant to the standard setters by underlining the components that influence the identification and the subsequent disclosure of a KAM by the auditor (Pinto & Morais, 2019). This could, for example, be relevant for standard setters for gaining an understanding of the KAM disclosure process and in developing guidelines for KAM disclosure Finally, this research gives useful insights to financial managers. The results give a better understanding of the determinants that influence the process of the auditor whether or not to disclose a certain KAM (Pinto & Morais, 2019).
Nevertheless, this research has some limitations. Firstly, the data of the research is collected by a group of students. Although, there are several controls in place to ensure the reliability of the data (e.g. data collection guidelines, random checks, second reviews), nonetheless it is challenging to ensure that all students are completely consistent in their data extraction. Therefore, there could be subjectivity in the data. Secondly, the sample only consists of the largest, listed, companies of each country. Only the main indice s of the countries in the sample are used. In addition, 89% of the companies in the sample are audited by at least one big 4 auditor. Therefore, the generalizability to smaller firms and non-big 4 auditors could be limited. Lastly, this research only uses cross-sectional data, as the sample only contains data on the fiscal year 2018. Therefore, the results may be biased by the timing of this research.
Further research could investigate whether or not the results of this paper hold for a longer period of time. Researching a longer period of time could test the presence of an expected learning effect (Choe, 2002). Secondly, little is known about the KAM disclosure by non -big 4 auditors. Therefore, further research could investigate the differences between b ig 4 and non-big 4 auditors. Thirdly, further research could take into consideration the characteristics and the background of the auditor, since he or she is responsible for the process of whether or not to disclose a KAM. These characteristics could include, among others, the age, political
preference, gender or education of the auditor. Fourthly, further research could focus on the content of the disclosed KAMs. For example, the number of words used to describe the KAM, the writing style or the classification of the KAM.
This research tried to explain cross-country differences by the moderating effect of the level auditor litigation risk of a country. However, we did not find any significant relationshi p regarding this moderating effect. Therefore, further research might focus on other determinants that could explain cross-country differences. For example, culture could explain differences in disclosure between countries ( Van der Laan Smith, Adikhari & Tondkar, 2005).
7
References
An, Y., Davey, H., & Eggleton, I. R. (2011). Towards a comprehensive theoretical framework for voluntary IC disclosure. Journal of Intellectual Capital.
Agoglia, C. P., Hatfield, R. C., & Lambert, T. A. (2015). Audit team time reporting: An agency theory perspective. Accounting, Organizations and Society, 44, 1-14.
Asare, S. K., & Wright, A. M. (2012). Investors’, auditors’, and lenders’ understanding of the message conveyed by the standard audit report on the financial statements. Accounting Horizons, 26(2), 193-217.
Ball, R., Kothari, S.P., & Robin, A. (2000). The effect of international institutional factors on properties of accounting earnings. Journal of Accounting and Economics, 29, 1- 51.
Barnes, C.M., Dang, C.T., Leavitt, K., Guarana, C.L. & Uhlmann, E.L. (2018). Archival data in micro-organizational research: A toolkit for moving to a broader set of topics. Journal of
Management, 44(4), 1453-1478.
Bazerman, M. H., Morgan, K. P., & Loewenstein, G. F. (1997). The impossibility of auditor independence. Sloan Management Review, 38, 89-94.
Beasley, M. S., Carcello, J. V., & Hermanson, D. R. (1999). Fraudulent Financial Reporting: 1987-1997, An Analysis of U.S. Public Companies. New York: COSO.
Bédard, J., Gonthier-Besacier, N., & Schatt, A. (2014). Analysis of the Consequences of the Disclosure of Key Audit Matters in the Audit Report.
Bédard, J., Hoitash, U., & Hoitash, R. (2008). Audit pricing and internal control disclosures among non-accelerated filers. Research in Accounting Regulation, 20, 103–126.
Boolaky, P.K. and Quick, R. (2016). Bank director’s perceptions of expanded auditor’s reports.
International Journal of Auditing, 20(2), 158-174.
evaluation judgments under agency problem conditions. Accounting, Organizations and Society, 29(5-6), 473-488.
Brasel, K., Marcus, M. D., Jonathan, H., Grenier, J. H., & Reffett, A. (2016). Risk disclosure preceding negative outcomes: The effects of reporting critical audit matters on judgments of auditor liability. Accounting Review, 91(5), 1345–1362.
Bratten, B., Gaynor, L. M., McDaniel, L., Montague, N. R., & Sierra, G. E. (2013). The audit of fair values and other estimates: The effects of underlying environmental, task, and auditor-specific factors. AUDITING: A Journal of Practice, 32 (1), 7-44.
Carrington, T. (2014). Revision. 2 ed. Stockholm: Liber
Choe, J.M. (2002). The organizational learning effects of management accounting information under advanced manufacturing technology. European Journal of Information Systems, 11, 142–158.
Church, B. K., Davis, S. M., & McCracken, S. A. (2008). The Auditor's Reporting Model: A Literature Overview and Research Synthesis. Accounting Horizons, 22(1), 69-90.
Choi, J., & Wong, T.J. (2004). Audit markets and legal environments: an international investigation.
Working paper, The Hong Kong University of Science and Technology.
Christensen, B. E., Glover, S. M., & Wolfe, C. J. (2014). Do critical audit matter paragraphs in the audit report change nonprofessional investors' decision to invest? Auditing: A Journal of Practice &
Theory, 33(4), 71-93.
Cohen, J., Krishnamoorthy, G., Peytcheva, M., & Wright, A. (2013). How does the strength of the financial regulatory regime influence auditors’ judgments to constrain aggressive reporting in a principles-based versus rules-based accounting environment? Accounting Horizons, 27(3), 579–601.
Cordoş, G. S., & Fülöpa, M. T. (2015). Understanding audit reporting changes: Introduction of key audit matters. Accounting and Management Information Systems, 14(1), 128–152.
Danescu, T., & Spatacean, O. (2018). Audit opinion impact in the investors’ perception–empirical evidence on Bucharest Stock Exchange. The Audit Financial journal, 16(149), 111-111.
Dassen, R. (1989). De Leer van het Gewekte Vertrouwen: Agency avant la lettre?. Maandblad voor
accountancy en bedrijfseconomie, 63, 341.
de Waard, D., Marra, T., Kranenburg, S., & van Oorschot, M. (2020). Transparent Carbon Disclosures: depth in Carbon-reporting of Dutch listed and non-listed companies. Maandblad Voor Accountancy en
Bedrijfseconomie, 94 (7-8), 275-284.
Dunn, K., & Mayhew, K. (2004). Audit firm industry specialization and client disclosure quality.
Review of Accounting Studies, 9, 35-58.
Einhorn, H., & Hogarth, R. (1981). Behavioral decision theory: Processes of judgment and choice.
Annual Review of Psychology, 32, 53–88.
Ettredge, M. L., Xu, Y., & Yi, H. S. (2014). Fair value measurements and audit fees: Evidence from the banking industry. Auditing: A Journal of Practice & Theory, 33(3), 33-58.
Ewelt-Knauer, C., Gold, A., & Pott, C. (2013). Mandatory audit firm rotation: A review of stakeholder perspectives and prior research. Accounting in Europe, 10(1), 27-41.
Field, A. (2013). Discovering statistics using IMB SPSS Statistics. Sage.
Fogarty, T.J. (2006). Publishing in academic accounting: Practical advice and healthy iconoclasm.
Methodological Issues in Accounting Research: Theories and Methods, 515-534.
Ferreira, C., & Morais, A. I. (2020). Analysis of the relationship between company characteristics and key audit matters disclosed. Revista Contabilidade & Finanças, 31(83), 262-274.
Francis, J.,& Wang, D. (2004). Investor protection, auditor conservatism and earnings quality: are Big 4 audit firms conservative only in the United States? Working Paper: University of Missouri and
University of Nebraska.
General Accounting Office (GAO) (2003). Public Accounting Firms: Required Study on the Potential Effects of Mandatory Audit Firm Rotation. Washington, DC: United States General Accounting
Gimbar, C., Hansen, B., & Ozlanski, M. E. (2016). The effects of critical audit matter paragraphs and accounting standard precision on auditor liability. The Accounting Review, 91(6), 1629-1646.
Gold, A., & Heilmann, M. (2019). The consequences of disclosing key audit matters (KAMs): A review of the academic literature. Maandblad Voor Accountancy en Bedrijfseconomie, 93(1/2), 5-14.
Gray, G. L., Turner, J. L., Coram, P. J., & Mock, T. J. (2011). Perceptions and misperceptions regarding the unqualified auditor’s report by financial statement preparers, users, and auditors.
Accounting Horizons, 25(4), 659–684.
Gujarati, D. N., & Porter, D. C. (2003). Basic econometrics (4th ed.). New York: McGraw-Hill.
Hackenbrack, K., & Nelson, M. W. (1996). Auditors’ incentives and their application of financial accounting standards. Accounting Review, 71, 43–59.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data
analysis, 5(3), 207-219.
Hay, D. C., Knechel, R. W., & Wong, N. (2006). Audit fees: A meta-analysis of the effect of supply and demand attributes. Contemporary Accounting Research, 23(1), 141–191.
Hogarth, R. M. (1980). Judgement and Choice: The Psychology of Decision. New York: John Wiley &
Sons.
Hope, O.K. (2003). Firm-level Disclosures and the Relative Roles of Culture and Legal Origin.
Journal of International Financial Management and Accounting, 14(3), 218 - 248.
In, C. Y., Kim, T., & Park, S. (2020). Key Audit Matters for Production-to-Order Industry and Conservatism. International Journal of Financial Studies, 8(1), 5.
International Federation of Accountants (IFAC). (2012). IFAC member body compliance program. Retrieved from: https://www.ifac.org/system/files/uploads/MBD/Basis-of-ISA-Adoption-by-Jurisdiction-August-2012.pdf
International Auditing and Assurance Standards Board (IAASB). (2011) Enhancing the value of auditor reporting: Exploring options for change. Retrieved from:
International Auditing and Assurance Standards Board (IAASB). (2015). International Standard on Auditing (ISA) 701 (NEW), Communicating Key Audit Matters in the Independent Auditor’s Report. IAASB. Retrieved from: https://www.ifac.org/ publications-resources/international-standard-auditing-isa701-new-communicating-key-audit-matters-i
International Auditing and Assurance Standards Board (IAASB). (2016). The New auditor’s report: a comparison between the ISAs and the US PCAOB preproposal. Retrieved from:
https://www.ifac.org/system/files/publications/files/IAASB‐Auditor‐Reporting‐Comparison‐between‐ IAASB‐Standards‐and‐PCAOB‐Reproposal_0.pdf
Jamal, K., & Tan, H. (2010). Joint effects of principles-based versus rules-based standards and auditor type in constraining financial managers’ aggressive reporting. Accounting Review, 85(4), 1325–1346.
Jensen, M. C., & Meckling, W. H. (1979). Theory of the firm: Managerial behavior, agency costs, and ownership structure. Economics social institutions. 163-231. Springer, Dordrecht.
Johl, S., Jubb, C.A., & Houghton, K.A. (2007). Earnings management and the audit opinion: evidence from Malaysia. Managerial Auditing Journal, 22(7), 688-715.
Kachelmeier, S. J., Schmidt, J. J., & Kristen, V. (2017). The Disclaimer Effect of Disclosing Critical Audit Matters in the Auditor’s Report.
Kara, G. (2016). Bank capital regulations around the world: what explains the differences?.
Kausar, A., Taffler, R.J., & Tan, C.E.L. (2017). Legal regimes and investor response to the auditor’s going-concern opinion. Journal of Accounting, Auditing and Finance, 32(1), 40–72.
Kipp, P. (2017). The effect of expanded audit report disclosures on users’ confidence in the audit and the financial statements.
Laitinien, E.K., & Laitinen, T. (1998). Qualified audit reports in Finland: evidence from large companies. European Accounting Review, 7(4), 639-653.
Lennox, C. S., Schmidt, J. J., & Thompson, A. (2017). Is the expanded model of audit reporting informative to investors? Evidence from the UK.
Li, L., Qi, B., Tian, G., & Zhang, G. (2017). The contagion effect of low-quality audits at the level of individual auditors. Accounting Review, 92(1), 137–163.
Limperg, T. (1932). De functie van den accountant en de leer van het gewekte vertrouwen. Maandblad
voor accountancy en bedrijfseconomie, 10 (2), 193-196.
Lin, K., & Fraser, I. (2008). Auditors’ ability to resist client pressure and culture: Perceptions in China and the United Kingdom. Journal of International Financial Management and Accounting, 19(2), 161– 183.
Loebbecke, J. K., Eining, M. M., & Willingham, J. J. (1989). Auditor’s experience with material irregularities: Frequency, nature, and detectability. Auditing: A Journal of Practice & Theory, 9(1), 1– 28.
Markanian, G., & Parbonetti, A. (2007). Firm complexity and board of director composition.
Corporate Governance: an international review, 15(6), 1224–1243.
Mayhew, B. W., Schatzberg, J. W., & Sevcik, G. R. (2001). The effect of accounting uncertainty and auditor reputation on auditor objectivity. Auditing: A Journal of Practice & Theory, 20, 49–70.
Mock, T. J., Bédard, J., Coram, P. J., Davis, S. M., Espahbodi, R., & Warne, R. C. (2013). The audit reporting model: Current research synthesis and implications. A Journal of Practice & Theory, 32(1), 323–351.
Molyneux, P., & Thornton, J. (1992). Determinants of European bank profitability: a note. Journal of
Banking and Finance, 16(6), 1173-1178.
Mong, S., & Roebuck, P. (2005). Effect of audit report disclosure on auditor litigation risk.
Accounting & Finance, 45(1), 145-169.
Moore, G., & Ronen, J. (1990). Asymmetric information and external auditing. Auditing: A Journal of
Practice and Theory, 234-242.
Nelson, M., Elliot, J., & Tarpley, R. (2002). Evidence from Auditors about managers’ and auditors’ earnings management decisions. Accounting Review, 77, 175–202.
