The relation between the use of data analytics in the audit and the effectiveness and efficiency of the audit process

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The relation between the use of data analytics in the audit and the

effectiveness and efficiency of the audit process.

Name: Yassine Rassir Student number: 6150985

Thesis supervisor: Dhr. Dr. E.E.O. Roos Lindgreen Date: June 25, 2018

Word count: 14285

MSc Accountancy & Control, specialization Accountancy Faculty of Economics and Business, University of Amsterdam

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Statement of Originality

This document is written by student Yassine Rassir who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

Improving the efficiency and effectiveness of the audit is a subject of debate and has been receiving attention recently. Several ways have been used to improve efficiency and effectiveness, such as the implementation of the Sarbanes Oxley Act for improving the effectiveness and the reduction of the sample size for improving the efficiency. Recently, a new way to achieve this improvement has emerged. The use of data analytics in the audit can affect the efficiency and effectiveness of the audit. The effect of data analytics, regarding the efficiency and effectiveness, has not been the subject of extensive research. The research described in this thesis was performed to give insight in the effect of data analytics on the efficiency and effectiveness of the audit. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used to measure the use of data analytics and relevant statements of the audit were used to measure the efficiency and effectiveness of the audit. The UTAUT model and the statements concerning the efficiency and effectiveness were combined in a survey that was distributed among the audit department of PwC Amsterdam. Results of correlation and regression analysis in the current investigation indicate that the use of data analytics affects the effectiveness of the audit. The efficiency of the audit is not affected by the use of data analytics. This is probably due to the fact that the use of data analytics in the audit is still under development. Future research should focus on a larger sample and should be best performed when the use of data analytics has been developed, implemented and in use. Keywords: Data analytics, efficiency, effectiveness.

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1 Introduction

After reading the Green Paper (2010), the conclusion can be drawn that the accounting profession has been under heavy pressure after the financial crisis. The huge losses from 2007 to 2009, that were revealed by numerous of banks, raised the question how auditors could give clean audit reports to their clients (European Commision, 2010). This means that the audit was not effective enough. This pressure led to measures by regulators. In Europe, the Green Paper has been issued and in the United States the Sarbanes-Oxley Act has been implemented.

The pressure on the accounting profession was not only caused by the regulators, but also by the increased competition among firms (Knechel, The business risk audit: Origins, obstacles and opputunities, 2007). This increased competition led to an attention of the fees and costs of the audit. The margin for audit services were under a great deal of strain and firms were beginning to investigate in alternative models for delivering an audit (Knechel, The business risk audit: Origins, obstacles and opputunities, 2007). The attention to the fees and costs led to different changes in the past. One of these changes is the reduction of the hours devoted to audit work (Imhoff, 2003). Another change was the reduction of the sample size (Power, 2003).

The measures by the regulators and the changes in the past took place to improve the effectiveness and the efficiency of the audit. In the public audit profession there is an awareness that the availability of data increased as well as the use of analytics (Appelbaum, Kogan, & Vasarhelyi, 2017). Data Analytics in the audit is the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modelling, and visualization for the purpose of planning or performing the audit (Byrnes, Tom, Stewart, & Vasarhelyi, 2014). The focus in the auditing practice of public accounting firms, regarding data analytics, is to improve the efficiency and effectiveness of the audits (Earley, 2015).

The research of Bierstaker et al. indicates that the use of data analytics allows auditors to complete their tasks within their budget, while testing a larger sample, since analytics allow auditors to diminish their hours spent on testing of internal controls and the substantive testing (Bierstaker, Burnaby, & Thibodeau, 2001). Even though the focus in the auditing practice of public accounting firms is to improve the efficiency and effectiveness of the audits, the effect of data analytics, regarding the efficiency and effectiveness, has not been the subject of extensive research. (Bierstaker, Janvrin, & Lowe, 2014).

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As stated in the previous paragraphs, regarding data analytics, the focus of audit firms is to improve the efficiency and effectiveness of the audit. However, the effect of data analytics, regarding the efficiency and effectiveness, has not been the subject of extensive research. This thesis will explore the effect of the use of data analytics in the audit on the efficiency and effectiveness of the audit by answering the following research question.

RQ1: Does the use of data analytics in the audit influence the effectiveness and efficiency of the audit process?

The answer to the research question is relevant to add knowledge to the existing theory about data analytics. More specific, this paper contributes to current literature because it provides an empirical insight in the effect of data analytics on the audit process. Further, the results of this thesis are relevant for the perspectives of the stakeholders and the auditors on the use of data analytics in the audit and the influence on the audit process. For the auditors, the use of data analytics can influence the effectiveness and efficiency of the audit process. Therefore, the use of data analytics by auditors could have a positive effect on the ability of the auditors to produce a more qualified judgement about the audited object. Because of the enhanced audit quality, as mentioned above, the stakeholders will also benefit of these developments in data analytics as an audit method.

In the current thesis, the Unified Theory of Acceptance and Use of Technology (UTAUT) model and statements regarding efficiency and effectiveness of the audit are combined to form a survey that is then distributed among the audit department of PwC Amsterdam. The collected data is used to test the hypotheses and ultimately answer the research question. The first hypothesis is that the use of data analytics in the audit contributes to the enhancement of the effectiveness of the audit. This hypothesis is supported by the results of this research. The second hypothesis is that the use of data analytics contributes to the enhancement of the efficiency of the audit. This hypothesis is not supported by the results of this research.

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2 Literature review

In this chapter, the literature regarding this thesis will be described. The dependent variables, efficiency and effectiveness, will be discussed in the first paragraph. The independent variable, data analytics, will be explained in the second paragraph. The audit and the audit process will be described in the third paragraph. In the fourth paragraph, the use of data analytics in the audit will be discussed. In the last paragraph, literature and evidence regarding the UTAUT model is outlined.

2.1 Efficiency and effectiveness

This research is focused on the effect of data analytics in the audit regarding the efficiency and effectiveness. There are different definitions with regards to efficiency and effectiveness. Mouzas defines efficiency and effectiveness as the central terms used in assessing and measuring the performance of organizations (Mouzas, 2006). The Institute of Internal Auditors defined efficiency and effectiveness as the degree to which established objectives are achieved (The Institute of Internal Auditors, 2010). These definitions need further explanation. Efficiency can be defined as the optimal use of resources, e.g. time and capital, to achieve the desired output (Knechel & Sharma, 2012). Effectiveness can be defined as the extent to which the set objectives of an organization are achieved (The Institute of Internal Auditors Netherlands, 2016)

For greater clarity, efficiency and effectiveness are each described more specifically. As mentioned earlier, efficiency is the optimal use of resources to achieve the desired output. The desired output of an audit, is to gain a judgement of an auditor that gives users assurance about the reliability of the audited financial statements (International Auditing and Assurance Standards Board, 2012). Providing a judgement, the audit, consists of four phases. The resources that are used during the four phases for the judgement of the auditor, is the time used by the auditor to perform the audit. In conclusion, efficiency of the audit encompasses: the extent of the use of the auditor’s time to perform the four phases of the audit to provide a judgement that gives users assurance about the reliability of the audited financial statements.

Effectiveness is the extent to which the set objectives are achieved. Arens et al. define auditing as the accumulation and evaluation of evidence about information to resolve and report on the degree of consistency between the information and the established criteria (Arens, Elder, & Beasley, 2012). Thus, the overall objective of the audit is accumulating and evaluating

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information to provide a judgement. The auditor has an objective to achieve in every phase of the audit. For example, the objective of the planning phase is to examine whether to accept the client by analyzing the industry of the auditee and evaluate the reasons for the audit (Arens, Elder, & Beasley, 2012). So, effectiveness of the audit can be defined as the extent to which the objectives of the phases of the audit are achieved.

If the audit is efficient, this will lead to a decrease of the working hours of the auditor and subsequently to cost reduction. If the audit is effective, shareholders would have a reasonable assurance regarding the reliability of the financial statements. Important to note, any attempt to improve efficiency without an equal emphasis on effectiveness, is likely to be counterproductive (Institute of Internal Auditors, 2012). An example of the last statement is shortly mentioned in the introduction of this paper, where one of the ways to enhance efficiency is to reduce sample size. This will definitely reduce the hours of labor and thus will improve the efficiency. In this case, however, there is not an equal emphasis on the effectiveness. By reducing the sample size, the reliability of the judgement can decrease and thus the effectiveness of the audit can decrease.

2.2 Data analytics

The independent variable, data analytics, will be explained in this paragraph. This explanation exists out of two sections. In the first section, the DIKW (Data-information-knowledge-wisdom) hierarchy will be described. This will provide an understanding of data. In the second section, the definitions of data analytics will be discussed. This section gives a clear image of data analytics by discussing and comparing the different definitions.

2.2.1 DIKW hierarchy

Data-information-knowledge-wisdom (DIKW) hierarchy is one of the fundamental and widely recognized models in information and knowledge literatures (Rowley, 2007). Rowley (2007) indicates that this model is often quoted, or used implicitly in definitions of data, information, knowledge and wisdom. The DIKW hierarchy is shown in figure 1.

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Figure 1

The DIKW hierarchy (Rowley, 2007)

The article of Ackoff, entitled From data to wisdom, is an important source of the hierarchy (Rowley, 2007). That is why the definitions of data, information, knowledge and wisdom will be based on this article (Ackoff 1989). Ackoff offers the following definitions of data, information, knowledge and wisdom, and their associated transformation processes (Rowley, 2007):

- “Data are defined as symbols that represent properties of objects, events and their environment. They are the products of observation. But are of no use until they are in a usable (i.e. relevant) form. The difference between data and information is functional, not structural”.

- “Information is contained in descriptions, answers to questions that begin with such words as who, what, when and how many. Information systems generate, store, retrieve and process data. Information is inferred from data”.

- “Knowledge is know-how, and is what makes possible the transformation of information into instructions. Knowledge can be obtained either by transmission from another who has it, by instruction, or by extracting it from experience”.

- “Intelligence is the ability to increase efficiency”.

- “Wisdom is the ability to increase effectiveness. Wisdom adds value, which requires the mental function that we call judgement. The ethical and aesthetic values that this implies are inherent to the actor and are unique and personal”.

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Figure 2

Adaptation of the DIKW hierarchy (Chujfi & Meinel, 2017)

Figure 2 shows an adaptation of the DIKW hierarchy, where the transformation from data to information, from information to knowledge and from knowledge to wisdom are shown independently.

2.2.2 Definition

Data analytics can be defined in several ways. In this section, the different definitions will be discussed and compared. This will create a clear definition of data analytics containing all the necessary components.

BusinessDictionary (2018): “the process of evaluating data using analytical and logical reasoning to examine each component of the data provided. This form of analysis is just one of the many steps that must be completed when conducting a research experiment. Data from various sources is gathered, reviewed, and then analyzed to form some sort of finding or conclusion. There are a variety of specific data analysis method, some of which include data mining, text analytics, business intelligence, and data visualizations” (BusinessDictionary, 2018).

Kricheff (2014) defines data analytics as following: “it consists of gathering the proper data and then manipulating and examining it so that you can reach logical conclusions about the data you are looking at.”

Runkler (2012) defines data analytics as: “the application of computer systems to the analysis of large datasets for the support of decisions.”

Cao et al. (2015) defines data analytics as: “the process of inspecting, cleaning, transforming, and modelling Big Data to discover and communicate useful information and patterns, suggest conclusions, and support decision making.”

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The definitions have common and different characteristics. One can infer a short definition of data analytics, based on the definitions above: the use of technology to analyze data with the aim to retrieve useful information out of this data. The definition of Cao et al. (2015) describes data analytics more specific and gives, in comparison to the other definitions, the most compact and clear picture of data analytics.

2.3 Audit and audit process

Information risk reflects the possibility that the information used for decisions was inaccurate (Arens, Elder, & Beasley, 2012). This means that reliable information is important for decision makers, such as stakeholders and managers. Arens et al. further indicated that the current society becomes more complex, which leads to the possibility of receiving unreliable information (2012). The auditor performs an audit to reduce the likelihood of receiving unreliable information. Auditing is the accumulation and evaluation, performed by a competent and independent person, of evidence about information to determine and report on the degree of correspondence between the information and established criteria (Arens, Elder, & Beasley, 2012). A more professional definition of auditing is described in the International Standards on Auditing 200: “Obtaining reasonable assurance about whether the financial statements a whole are free from material misstatement, whether due to fraud or error, thereby enabling the auditor to express an opinion on whether the financial statements are prepared, in all material respects, in accordance with an applicable financial reporting framework and to report on the financial statements, and communicate as required by the ISAs, in accordance with the auditor’s findings.” (International Federation of Accountants, 2009).

There are four stages in the audit (Arens, Elder, & Beasley, 2012): - Planning;

- Test of controls and substantive tests on transactions; - Analytical procedures and tests of details of balances; - Conclusion and reporting.

The first stage is about the client acceptance, performing business risk assessment and determining the materiality level (Arens, Elder, & Beasley, 2012). Auditors examine whether to accept the client by analyzing the industry of the auditee and evaluate the reasons for the audit (Arens, Elder, & Beasley, 2012). Hereafter, the auditor attains a sufficient understanding of the business and the industry of the client in order to make a proper business risk assessment,

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(Arens, Elder, & Beasley, 2012). Materiality is defined as follow: misstatements, including omissions, are considered to be material if they, individually or in the aggregate, could reasonably be expected to influence the economic decisions of users taken on the basis of the financial statements (International Auditing and Assurance Standards Board, 2009). Further in the ISA 320 is stated that the determination of materiality is a matter of professional judgement and that the auditor must consider: the circumstances surrounding the entity; both the size and nature of misstatements; and the information needs of the users as a group. PwC gives a similar description of the planning phase. The planning activities consists of accepting the client, meet the independence requirements, forming the audit team and performing other procedures to determine the nature, timing and extent of procedures to be performed in order to conduct the audit in an effective manner (PricewaterhouseCoopers, 2017). Further PwC describes the planning phase includes the performing of risk assessment to identify and assess the risks that could lead to a material misstatement in the financial statements. Finally, PwC describes that once the risks have been assessed, an overall audit strategy and a detailed audit plan to address the risks of material misstatement in the financial statements is developed.

During the second stage, the auditors carry out test of controls and substantive tests on transactions (Arens, Elder, & Beasley, 2012). Hereafter the auditors can determine the control risk on which they can base the amount of evidence that is necessary. PwC indicates, when gathering and evaluating evidence, auditors should apply professional skepticism and judgement (PricewaterhouseCoopers, 2017). If there is a case of weak internal control, more evidence has to be gathered to verify the monetary amounts of transactions and balance sheet items in the subsequent phase (Arens, Elder, & Beasley, 2012).

Hereafter the third stage of the audit will follow. In this stage the auditors carry out analytical procedures and test of details of balances (Arens, Elder, & Beasley, 2012). Analytical procedures consist of evaluations of financial information made by a study of plausible relationships among both financial and nonfinancial data (Public Company Accounting Oversight Board, 1989). Test of details of balances include tracing figures to supporting documentation to determine if transactions are valid, properly classified, accurate and complete and the tests include recalculating and confirming recorded information (The Institute of Internal Auditors, 2012). This stage is the execution stage and consists of substantive procedures.

During the fourth stage, the auditors draw an overall conclusion in the final phase of the audit, whether or not the financial statements are free from material a misstatement, which is

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referred to as the auditor’s opinion (Arens, Elder, & Beasley, 2012). PwC describes that during the fourth stage the auditors practice professional judgement and form their overall conclusion, which forms the basis of the audit opinion, based on the tests that have been carried out, the evidence they have gathered and the other work they have performed (PricewaterhouseCoopers, 2017). This stage is about concluding and reporting.

2.4 Data analytics in the audit

There are different definitions of data analytics in the audit. Two definitions will be discussed in this paragraph. Byrnes’ et al. (2014) defines data analytics in the audit as following: “Data Analytics in the audit is the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modelling, and visualization for the purpose of planning or performing the audit”. Titera (2013) defines data analytics as: “the computer-assisted examination of information underlying financial statements or other subject matter being audited”. These definitions support the short definition of data analytics mentioned above in chapter 2.2. The differences between the definition of data analytics in general and the definition of data analytics in the audit, is that the latter is more specific concerning an audit.

Data analytics can be employed in any stage of the audit and this is shown in figure 3 (Titera, 2013). Chapter 2.3 discussed that the planning stage is about client acceptance, performing business risk assessment and determining the materiality level. To perform these tasks, the auditor needs to analyze the client, the industry of the client and the business of the client. Data analytics could be useful in helping the auditor to gain an understanding of the business, including significant accounts and processes, to assess risks of material misstatement and to plan the audit accordingly (Titera, 2013).

The execution stage in figure 3 consists of the second stage and the third stage of the audit. During the second stage, the auditor performs tests of controls and substantive tests on transactions. During the third stage, the auditor performs analytical procedures and test of details of balances. In the execution stage, data analytics can provide high-quality evidence regarding possible material misstatements because with the use of data analytics, the analysis could be based on the population as a whole (Titera, 2013).

The last stage in figure 3, reporting, corresponds with the last stage of the audit described in chapter 2.3. This stage is about concluding and reporting. Data analytics could be

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employed in this stage and provide a reasonable check, as well as an overview of subsequent activity (Titera, 2013).

Figure 3

The Role of Data Analytics on an Audit (Titera, 2013)

Verver (2015) discussed in his paper an example of the use and the effects of data analytics in the audit: “a large car rental company transformed audit processes and reportedly reduced traditional audit work by 10,000 hours annually by using automated analysis to test all revenue transactions on an ongoing basis”. Verver further states that additional tests identified nearly US$1 million a year in incorrect commission payments and multiple instances of payroll fraud that may not have been discovered through manual methods. The effect of the use of data analytics in the audit is that all revenue transactions could be tested, the traditional audit work hours will decrease and the possibility to discover mistakes will increase.

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2.5 The Unified Theory of Acceptance and Use of Technology

Figure 4

Unified Theory of Acceptance and Use of Technology (Venkatesh, Morris, Davis, & Davis, 2003)

The Unified Theory of Acceptance and Use of Technology (UTAUT) model is shown in the figure above. This model consists of four constructs that play a significant role as direct determinants of user acceptance and usage (Venkatesh, Morris, Davis, & Davis, 2003). The determinants are performance expectancy, effort expectancy, social influence and facilitating conditions. Gender, age, experience and voluntariness are the key moderators of this model. These key moderators affect the results of the UTAUT model. Behavioral intention and use behavior are the results of the UTAUT model.

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2.5.1 Determinants

Performance expectancy (PE) is defined as the degree to which an individual believes that using the system will help him or her to attain gains in job performance (Venkatesh, Morris, Davis, & Davis, 2003). Venkatesh et al. (2003) discussed that performance expectancy is the strongest predictor of intention. Worth noting, this is the case both in a voluntary as mandatory environment. As can be seen in figure 4, the influence of performance expectancy on behavioral intention, is moderated by gender and age. Men tend to be highly task-oriented and therefore performance expectancies, which focus on task accomplishment, are likely to be especially salient to men (Venkatesh, Morris, Davis, & Davis, 2003). Younger workers may place more importance on extrinsic rewards (Venkatesh, Morris, Davis, & Davis, 2003). This means that men and younger employees have a stronger effect on the determinant performance expectancy.

Effort expectancy (EE) is defined as the degree of ease associated with the use of the system (Venkatesh, Morris, Davis, & Davis, 2003). Effort expectancy has a significant influence on behavioral intention, both in a voluntary and mandatory environment. Figure 4 shows that effort expectancy is moderated by gender, age and experience. Effort-oriented constructs are expected to be more salient in the early stages of a new behavior, when process issues represent hurdles to be overcome, and later become overshadowed by instrumentality concerns (Venkatesh, Morris, Davis, & Davis, 2003). Further, effort expectancy is more salient for women than for men. More specific, since increased age has been associated with difficulty in processing complex stimuli, effort expectancy is more salient for younger women (Venkatesh, Morris, Davis, & Davis, 2003).

Social influence (SI) is defined as the degree to which an individual perceives that important others believe he or she should use the new system (Venkatesh, Morris, Davis, & Davis, 2003). Individual’s behavior is influenced by the way in which they believe others will view them as a result of having used the technology. Social influence has an impact on individual behavior through three mechanisms: compliance, internalization and identification. Compliance: individuals are more likely to comply with others’ expectations when those referent others have the ability to reward the desired behavior or punish nonbehavior (Venkatesh, Morris, Davis, & Davis, 2003). Internalization: altering an individual’s belief structure (Venkatesh, Morris, Davis, & Davis, 2003). Identification: causing an individual to respond to potential social status gains (Venkatesh, Morris, Davis, & Davis, 2003). Women tend to be more sensitive to others’ opinion and older people need more unity and thus they

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find social influence to be more salient when they form an intention to use the system (Venkatesh, Morris, Davis, & Davis, 2003).

Facilitating conditions (FC) is defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system (Venkatesh, Morris, Davis, & Davis, 2003). This determinant is operationalized to include aspects of the technological and/or organizational environment that are designed to remove barriers to use the system (Venkatesh, Morris, Davis, & Davis, 2003). Figure 4 shows that facilitating conditions is moderated by age and experience and has a direct effect on use behavior. `

2.5.2 Moderators

As shown in figure 4, key moderators affect the results of the UTAUT model. The key moderators are gender, age, experience and voluntariness. The gender of a user of the system influences performance expectancy, effort expectancy and social influence. The age of a user of the system affects all the determinants. The extent to which the users have experience in similar systems affect effort expectancy, social influence and facilitating conditions. Voluntariness of use, mandatory or voluntary environment regarding the use of the system influences only social influence.

2.5.3 Results of UTAUT model

Behavioral intention is the result of the UTAUT model. Performance expectancy, effort expectancy and social influence are the determinants that influence behavioral intention. Behavioral intention has not been defined by Venkatesh et al. (2003). Alwahdi and Morris (2008) define behavioral intention as the person’s subjective probability that he or she will perform the behavior in question. Pavlou (2003) discussed that there is a high correlation between intentions and actual use and that this positive relationship between behavioral intention and action is described by the theory of reasoned action and the theory of planned behavior. Venkatesh et al. (2003) concluded that behavioral intention is an important predictor of the actual usage.

Use behavior is the result of all the determinants. Behavioral intention has a direct effect on use behavior and is influenced by performance expectancy, effort expectancy and social influence. Facilitating conditions is the last determinant which influences the use behavior. Venkatesh et al. (2003) argue that the empirical results of their research indicates that facilitating conditions have a direct influence on usage beyond that explained by behavioral intention alone.

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3 Hypothesis development

Efficiency can be defined as the optimal use of resources, e.g. time and capital, to achieve the desired output (Knechel & Sharma, 2012). So, efficiency of the audit is the extent of the use of the auditor’s time to perform the four phases of the audit to provide a judgement that gives users assurance about the reliability of the audited financial statements. Effectiveness can be defined as the extent to which the set objectives of an organization are achieved (The Institute of Internal Auditors Netherlands, 2016). Therefore, effectiveness of the audit can be defined as the extent to which the objectives of the phases of the audit are achieved.

Audit clients continue to shift toward electronic data storage to enhance efficiency and auditors who make use of new technology will be rewarded with improvements in audit efficiency and effectiveness (Bierstaker, Burnaby, & Thibodeau, 2001). Data analytics is new technology that could be useful in the audit. Data analytics can be defined as the use of technology to analyze data with the aim to retrieve useful information out of this data. To be more specific, data analytics in the audit can be defined as the computer-assisted examination of information underlying financial statements or other subject matter being audited (Titera, 2013).

The example of Verver (2015) in chapter 2.4 indicates that through the use of data analytics, all the revenue transactions could be tested, the traditional audit work hours will decrease and the possibility to discover mistakes will increase. This means that the use of data analytics enhances the efficiency of the audit because of the decrease of the traditional audit work hours. Further, the use of data analytics enhances the effectiveness of the audit as a result of an increase of the possibility to discover mistakes and as a result of the possibility to test the whole population.

Earley (2015) point out that the use of data analytics has four primary benefits: 1) auditors can test a greater number of transactions than they do now, 2) audit quality can be increased by providing greater insights into clients’ processes, 3) fraud will be easier to detect because auditors can leverage tools and technology that they already use, 4) auditors can provide services and solve problems for their clients that are beyond current capabilities by utilizing external data to inform audits.

All the benefits and implications about the use data analytics in the audit indicate that the use of data analytics enhances the effectiveness and efficiency of the audit process. Combined, these theoretical arguments lead to the following hypotheses:

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H1: the use of data analytics contributes to the enhancement of the effectiveness of the audit. H2: the use of data analytics contributes to the enhancement of the efficiency of the audit.

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4 Research method and design

The dependent variable in this research is efficiency and effectiveness of the audit. Based on the literature, this variable might be positively influenced by the independent variable, the use of data analytics in the audit. As mentioned by Verver (2015), the use of data analytics can reduce the traditional audit work and discover fraud that may not have been discovered through manual methods. So, based on the literature, the use of data analytics should enhance the efficiency and effectiveness of the audit. This research investigates whether this is also the case in practice.

The design of this investigation is quantitative, where a questionnaire survey has been sent out. The survey consists of two sections and was drawn upon the basis of previously described theories. The first section of the survey contains questions to collect the demographic data of the participants. The second section of the survey encompasses statements to measure the efficiency, effectiveness and the use of data analytics in the audit.

The use of data analytics is measured with the use of the UTAUT model. This model consists of a survey developed by Venkatesh et al. (2003). With the help of the survey, the four determinants are measured (see section 2.5.1). The first three determinants form the behavioral intention. Pavlou (2003) discussed that there is a high correlation between intentions and actual use. To complete the measurement of the use of data analytics, the UTAUT model combines the behavioral intention with the last determinant to form the use behavior. Venkatesh et al. (2003) argue that the last determinant has a direct influence on usage beyond that explained by behavioral intention alone.

Efficiency and effectiveness are measured by upgrading the UTAUT survey with statements concerning efficiency and effectiveness based on previously described theories. As mentioned in the literature, efficiency can be defined as the extent of the use of the auditor's time to perform the four phases of the audit to provide a judgment that gives users assurance about the reliability of the audited financial statements. The statements of the efficiency of the audit are based on the time the auditor spends to perform the four phases of the audit. As discussed in the literature, effectiveness can be defined as the extent to which the objectives of the phases of the audit are achieved. The objectives of the phases are described in chapter 2.3. The statements of the effectiveness are based on the achievement of the objectives of the four phases. The complete survey can be found in the appendix.

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The participants of the survey had the option to rate the statements regarding the use of data analytics with: strongly disagree, disagree, neutral, agree and strongly agree. The statements regarding efficiency are about the time the auditor spends for the audit. These statements could be rated with: very low, low, average, high and very high. The ratings regarding the statements about the effectiveness of the audit depended on the statements. The ratings for every statement can be found in the appendix. The responses of the participants were merged into numbers. For example, strongly disagree was merged to 1, disagree to 2, neutral to 3, agree to 4 and strongly agree to 5. The variables were measured by taking the average of the corresponding statements. The averages of the statements regarding the efficiency were reversed. For example, if the statement was rated with 4, it was reversed to 2. This reversal took place because the statements were negative regarding efficiency. If the statements were rated high, it meant that the audit was not efficient. To make the statements positive, the ratings were reversed. The variables which were extracted from the survey were efficiency, effectiveness, performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention and use behavior. The average of performance expectancy, effort expectancy and social influence formed the behavioral intention. Use behavior consists of behavioral intention combined with the average of facilitating conditions.

The participants in this research were firstly recruited by placing an article in the newsletter of the office. A personal approach was used to recruit more participants. The participant could participate through a link in the newsletter or through a link that was sent to them personally. The article that was placed in the newsletter and the email that was sent to auditors after a personal approach can be found in the appendix. The first step was to receive as many reactions as possible. Hereafter, a selection was made. Participants with no experience as an external auditor were excluded. If participants did not have experience as an external auditor, it means that they don’t have any experience using data analytics in the audit at PwC and their opinion regarding the UTAUT model wouldn’t be reliable. Furthermore, respondents that filled in incomplete questionnaires were also excluded.

The survey was drawn up online utilizing the website http://www.thesistoolspro.com. The purpose of the survey was introduced in the article in the newsletter and also in the email that was sent personally. Furthermore, the participants were instructed at the beginning of the survey. This instruction noted that this survey was not a comparison between persons, not a comparison between BU's or a comparison between accounting firms to remove biased opinions. All the surveys, partly or completely filled in, could be downloaded from this

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website. All the data was extracted from the website in excel format. This data sheet was then converted to an SPSS dataset which tested the formulated hypotheses.

There were two datasets created in SPSS. The first dataset consisted of all the responses regarding the statements of the UTAUT model for the measurement of the use of data analytics. For the purpose of reliability, Cronbach’s Alpha was calculated to estimate the reliability of the use of data analytics in PwC. Nunnally (1978) discussed in his book that the threshold of Cronbach’s Alpha is 0.70. This is also the threshold used in this research.

The second dataset consisted of the following four variables: 1) an average of the rates of the statements concerning efficiency 2) an average of the rates of the statements concerning effectiveness, 3) the averages of the rates of the statements regarding PE, EE and SI that formed the variable BI, 4) the average of the rates of the statements regarding FC in a combination with BI that formed the variable UB.

The data was first approached by calculating Pearson correlation coefficients between the variables. This analysis was used to assess possible associations between the variables. The test of significance was two-tailed. P-values <0.05 were considered statistically significant. The variables used to test the hypotheses were the independent variables efficiency and effectiveness and the dependent variables behavioral intention and use behavior. Significant associations were then selected for linear regression analysis with 95% confidence intervals. This test allowed estimation of size effects between the different variables with a regression formula. Furthermore, the previously described variables from the UTAUT model were reported.

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5 Empirical results

The example of Verver (2015) in chapter 2.4 indicates that through the use of data analytics, all the revenue transactions could be tested, the traditional audit work hours will decrease and the possibility to discover mistakes will increase. Earley (2015) point out that the use of data analytics has four primary benefits: 1) auditors can test a greater number of transactions than they do now, 2) audit quality can be increased by providing greater insights into clients’ processes, 3) fraud will be easier to detect because auditors can leverage tools and technology that they already use, 4) auditors can provide services and solve problems for their clients that are beyond current capabilities by utilizing external data to inform audits. These theoretical arguments led to the following hypotheses.

H1: the use of data analytics contributes to the enhancement of the effectiveness of the audit.

H2: the use of data analytics contributes to the enhancement of the efficiency of the audit. The UTAUT model variables, of which the use of data analytics consists, were analyzed to explain the use of data analytics in PwC. Cronbach’s Alpha was calculated to predict the reliability of the measurement of the use of data analytics (threshold 0.70). Associations between the variables were assessed by calculating the Pearson correlation coefficient. Size effects were estimated with linear regression analysis. Results of the Pearson correlation coefficients and the linear regressions were considered statistically significant when P<0.05.

The sample of this research consisted of 45 auditors working at PwC in the office of Amsterdam during the period of February 2018 till June 2018. The average age of these auditors was 28.6 years. The portion of male participants was 66.7% and female participants 33.3%. A major part of the participants had a Master of Science Degree or a Post-Master Degree. Furthermore, none of the participants was an expert in IT. The average experience as an auditor was not high, the minimum was 1 year and the maximum was 16 years. Table 1 summarizes the descriptive statistics regarding all participants.

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Table 1

Descriptive statistics

Frequency Percent Min. Max. Mean Std. Deviation

Age 21,00 40,00 28,60 3,92

Gender Male 30 66,7

Female 15 33,3

Educational attainment Bachelor's Degree 5 11,1 Master of Science 21 46,7 Post-Master 19 42,2

Experience as Auditor 1,00 16,00 5,64 3,69

IT experience Novice 18 40,0

Intermediate 27 60,0

An outline of the variables derived from the UTAUT model can be found in Table 2. Performance expectancy, effort expectancy, social influence and facilitating conditions form the variables behavioral intention and use behavior. The averages of all variables that form behavioral intention and use behavior were positive and above the average (Likert scale from 1 to 5). That means that the following degrees were above average: 1) the degree to which an individual believes that using the system will help him or her to attain gains in job performance was above the average 2) the degree of ease associated with the use of the system and 3) the degree to which an individual perceives that important others believe he or she should use the new system 4) the degree to which an individual believes that an organizational end technical infrastructure exists to support use of the system. Worth noting, participant rate that the use of data analytics makes it possible to perform tasks more quickly with a mean of 3.80 and rate that the use of data analytics increases their productivity with a mean of 3.76.

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Table 2

Variables UTAUT model

Minimum Maximum Mean

Std. Deviation

I find data analytics useful in my job 3,00 5,00 4,13 0,63

The use of data analytics makes it possible to perform tasks more quickly 2,00 5,00 3,80 0,79

The use of data analytics increases my productivity 3,00 5,00 3,76 0,65

The use of data analytics increases my chances of getting promoted 1,00 4,00 2,53 0,81

Average performance expectancy 3,56

The use of data analytics is clear and understandable 2,00 5,00 3,38 0,86

It is easy for me to become skillful at using data analytics 1,00 5,00 3,40 0,84

In my opinion, data analytics is easy to use 1,00 5,00 3,38 0,89 Learning to use data analytics is easy for me 2,00 5,00 3,60 0,69

Average effort expectancy 3,44

People who are able to influence my behavior have the opinion that I should use

data analytics 2,00 5,00 3,51 0,79

My superiors have been helpful in the use of data analytics 2,00 5,00 3,18 0,78

In general, our firm has supported the use of data analytics 3,00 5,00 3,89 0,61

Average social influence 3,53

PwC provides the resources necessary to use data analytics 2,00 5,00 3,73 0,78 If necessary, a person or a group of persons is available for assistance with data

analytics difficulties 2,00 5,00 3,76 0,77

PwC offers training course(s) for the use of data analytics 2,00 5,00 3,73 0,72

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Calculated Cronbach's Alpha values can be found in Table 3 and 4. In Table 3, the part that forms the variable BI is tested. In Table 4, the part that forms the UB is tested. The threshold for the Cronbach’s Alpha was 0.70 and was based on the statement of Nunnaly.

Table 3

Reliability variable use of data analytics (BI)

Cronbach's Alpha 0,693

Cronbach's Alpha Based on Standardized Items 0,684

N of Items 11

Table 4

Reliability variable use of data analytics (UB)

Cronbach's Alpha 0,720

Cronbach's Alpha Based on Standardized Items 0,717

N of Items 14

The statements and ratings of the dependent variables, efficiency and effectiveness, can be found in table 5. The averages of both efficiency and effectiveness are above the average of the Likert scale.

Table 5

Descriptive statistics Efficiency and Effectiveness

Minimum Maximum Mean

Std. Deviation Give your opinion about the size of the time spent on the audit 1,00 5,00 3,40 0,84 Give your opinion about the size of the time spent at the planning stage 1,00 5,00 3,36 0,83 Give your opinion on the amount of time spent collecting the audit evidence 1,00 5,00 3,20 0,84 Give your opinion on the size of the time spent for the interim audit 2,00 4,00 2,93 0,69 Give your opinion on the amount of time spent for the end-of-year audit 2,00 5,00 3,47 0,97

Average efficiency 3,27

Give your opinion about the strength of the customer analysis for performing the risk assessment

2,00 5,00 3,44 0,81

Give your opinion about the size of the sample being tested 2,00 5,00 3,38 0,98 Give your opinion about the size of the audit evidence that is collected 2,00 5,00 3,71 0,82 Give your opinion about the reliability of the audit evidence 3,00 5,00 3,93 0,58 Give your opinion about the possibility of discovering errors in the interim audit 2,00 5,00 3,42 0,75 Give your opinion about the possibility of discovering errors in the year-end audit 2,00 5,00 3,76 0,74

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H1: the use of data analytics contributes to the enhancement of the effectiveness of the audit. To test these hypotheses, the Pearson correlation coefficient test was performed to investigate a possible connection between the use of data analytics and the effectiveness of the audit. The correlations between the variables are described in Table 6. Effectiveness is correlated with behavioral intention and use behavior with a significance level of 0.012 for behavior intention and 0.022 for use behavior.

H2: the use of data analytics contributes to the enhancement of the efficiency of the audit. The Pearson correlation coefficient is also performed to find out whether there is a connection between the use of data analytics and efficiency of the audit. Efficiency is not correlated with behavioral intention and not correlated with use behavior. The significance levels are in both cases higher than the threshold of 0.05.

Table 6

Correlations

BI UB

Effectiveness Pearson Correlation ,371* ,341*

Sig. (2-tailed) 0,012 0,022

N 45 45

Efficiency Pearson Correlation 0,116 0,154

Sig. (2-tailed) 0,447 0,313

N 45 45

*. Correlation is significant at the 0.05 level (2-tailed).

Since there is a correlation between the use of data analytics and the effectiveness, linear regression was performed to measure how large the effect is from the use of data analytics in the audit and the effectiveness of the audit. Results of the linear regression analysis with 95 % confidence intervals can be found in Table 7. P-values remain the same in the linear regression analysis as the values found in the Person correlation coefficient. This is explained by the fact that the linear regression test is a simple regression and not a multiple regression.

Table 7

Linear regression analysis with effectiveness as the dependent variable.

Unstandardized β-values t P-value 95,0% CI for β

BI 0,459 2,623 0,012 0,106; 0,812

UB 0,455 2,381 0,022 0,070; 0,841

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6 Discussion and conclusion

Audit clients continue to shift toward electronic data storage to enhance efficiency and auditors who make use of new technology will be rewarded with improvements in audit efficiency and effectiveness (Bierstaker, Burnaby, & Thibodeau, 2001). Bierstaker et al. made this statement in their paper. This statement can be substantiated by the previously mentioned example of Verver (2015) which indicated that through the use of data analytics, all the revenue transactions could be tested, the traditional audit work hours will decrease and the possibility to discover mistakes will increase. The statement and the substantiation of this by the example of Verver implies that the use of data analytics in the audit affects the efficiency and the effectiveness of the audit.

The results of this research support the literature regarding the effectiveness of the audit due to the use of data analytics. The rating (3.27) of the effectiveness of the audit is above average (Table 5). The possibility of discovering errors, with ratings of 3.42 and 3.76, is above the average. This corresponds with the literature of Verver indicating that the possibility to discover mistakes will increase. The sizes of the samples being tested, with ratings of 3.38 and 3.71, is also above the average. This corresponds with the literature of Verver indicating that a larger or even the whole population could be tested. The significance level of the Pearson correlation coefficient is below the threshold of 0.05 in the current thesis. This means the effectiveness of the audit correlates with the use of data analytics in the audit.

The rating (3.27) of the efficiency of the audit is above the average. The use of data analytics makes it possible to perform tasks more quickly is rated in table 2 with a mean of 3.80. The use of data analytics increases the productivity is rated with a mean of 3.76. These ratings indicate that the efficiency of the audit is caused by the use of data analytics and corresponds to the literature of Verver indicating traditional audit work hours will decrease. The use of data analytics is not the cause of this. The significance level of the Pearson correlation coefficient is far above the threshold. This means there is no connection between the use of data analytics and the efficiency of the audit. The results of this research do not support the literature regarding the efficiency of the audit due to the use of data analytics.

An interview about data analytics with an employee/respondent can be found in the appendix. This interview makes clear why the results of this research, regarding the efficiency of the audit due to the use of data analytics, do not correspond with the literature. The employee clearly indicates that over time he is of the opinion that data analysis would influence the

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efficiency of the audit. The process to test the reliability of the received data dump that would be imported in the tool makes the audit less efficient. The moment that the use of data analytics in the audit is fully developed, the use of data analytics will most likely enhance the efficiency of the audit.

Worth noting, the use of data analytics can be measured with the variable BI and the variable UB. Pavlou (2003) discussed that there is a high correlation between intentions and actual use. The UTAUT model combines the behavioral intention with the last determinant to form the use behavior. Venkatesh et al. (2003) argue that the last determinant has a direct influence on usage beyond that explained by behavioral intention alone. Cronbach’s Alpha was calculated to test the reliability of BI and UB. The results correspond with the theory described above.

There are several limitations that influence the results of this research. The size of the sample is relatively small. This decreases the overall reliability of the results. Secondly, this research is based on PwC and this makes it difficult to generalize these results. Moreover, the implementation of data analytics in the audit is still under development. This can be the reason that the use of data analytics in the audit does not affect the efficiency of the audit. Another limitation of the current study is its design. All results are derived from subjective reported data, which is influenced by the individual perceptions of all employees. However, the in-depth interview performed supports the findings, which suggests a decent amount of accuracy of the found data.

Follow-up research is certainly recommended. A longitudinal design following the implementation of data analysis with larger sample sizes is recommended. This would make the research more reliable. Further, the data could be best collected from more accounting firms. The recommendation would be the Big Four accounting firms, since the use of data analytics in the audit is more applied by them. Hereby, the research would have a larger sample and it would be more generalizable. At last, it would be better to perform a follow-up research when the use of data analytics in the audit has been fully implemented and in use. Based on the literature, the results of the follow-up research would support the hypotheses that the use of data analytics affects the efficiency of the audit.

Even though, based on the literature, the results of the follow-up research would support the hypotheses regarding the effect of data analytics on the efficiency of the audit, the practice could prove the opposite. Since data analytics affects the effectiveness of the audit, it is possible

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that the use of data analytics could create more work. For example, it might be that auditors should document multiple deviations cause of testing the entire population. The creation of more work for the auditor means the extent of the use of the auditor's time to provide a judgement would increase. This could be the reason data analytics would not enhance the efficiency of the audit or even weaken the efficiency of the audit.

The use of data analytics affects the effectiveness of the audit and this corresponds with the literature. The literature describes that the use of data analytics also affects the efficiency of the audit. The results of this research do not support this description. Based on the interview in the appendix, this could possibly be caused by the fact that the use of data analytics is still in development. This research is important to add knowledge to the consisting theory of data analytics. It is a relatively new academic subject, and the effect of data analytics on the efficiency and effectiveness of the audit is unexplored. This paper contributes to current literature because it provides an empirical insight into the effect of data analytics on the efficiency and effectiveness of the audit. Accounting firms can use this paper to gain knowledge about the effect of data analytics on the audit and to know where to focus their investments on.

RQ1: Does the use of data analytics in the audit influence the effectiveness and efficiency of the audit process?

Based on the results of this research, the answer to the research question is yes and no. The use of data analytics influences the effectiveness and does not influence the efficiency of the audit. Worth noting, it is recommended to perform a follow-up research when the use of data analytics has been developed, implemented and in use. The expectation is that the use of data analytics in the audit will influence the efficiency of the audit.

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9 Appendix: Email

Geachte heer/mevrouw,

In het kader van mijn masterscriptie ben ik momenteel een onderzoek aan het verrichten naar de efficiency en effectiviteit van data-analyse in de audit. Om vast te kunnen stellen of data-analyse een impact heeft op de efficiency en effectiviteit van de audit, heb ik een survey opgesteld die u zou kunnen invullen. Gezien het nu busy season is, heb ik de survey beperkt gehouden zodat deze binnen 10 minuten ingevuld kan worden. Via de onderstaande link kunt u deelnemen aan mijn onderzoek en een bijdrage leveren aan de wetenschap.

Alvast dank voor u hulp.

Link (Nederlands): https://www.thesistoolspro.com/survey/7coa45ab0ef1e1a1d1

Dear Sir / Madam,

For my master thesis I am currently conducting a research on the efficiency and effectiveness of data analysis in the audit. In order to assess whenever data analysis leads to higher efficiency and

effectiveness during an audit I have prepared survey, which you can fill-in. Since it is busy season I have kept the amount of questions small so that survey can be filled in within 10 minutes. You can participate via the link below. Thanking in advance for your participation

Link (English): https://www.thesistoolspro.com/survey/h5ig95ab253b69df02

Met vriendelijke groet/ yours sincerely,

-- Yassine Rassir Scriptant Tel: +31 (0)6 48 72 22 29 E-mail: yassine.rassir@pwc.com PricewaterhouseCoopers Accountants N.V. (KvK 34180285)

Thomas R. Malthusstraat 5 | 1066 JR | Postbus 90357 | 1006 BJ | Amsterdam

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10 Appendix: Survey

Dear visitor, welcome to this questionnaire. You are invited to participate in a study about efficiency and effectiveness of the audit. Your cooperation is very important to me, your participation contributes to the collection of data for my research. The collected data are analyzed anonymously. Moreover, your participation is completely voluntary. If you have any questions, comments or difficulties, please contact Yassine Rassir at yassine.rassir@pwc.com. Filling in the questionnaire takes 5 - 10 minutes.

This survey is part of a research. The aim of the research is to improve the efficiency and effectiveness of the audit. Furthermore, it is important to note that this survey is not a comparison between persons, not a comparison between BU's or a comparison between accounting firms.

The first questions in this survey serve to collect the demographic data of the participants. What is your gender?

What is your age?

What is your highest level of education?

What is your experience as an external auditor (in years)? What is your IT experience (novice- intermediate - expert)? In which office do you work (country - city)?

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In order to be able to assess the following statements as well as possible, you could make a comparison with a situation in which the audit is fully automated and your time use is very low. Please bear in mind that these statements serve to measure efficiency and not to assess the extent to which the audit is automated. Give your opinion about:

Likert Scale: Very low – Low – Average – High - Very high Give your opinion about the size of the time spent on the audit

Give your opinion about the size of the time spent at the planning stage Give your opinion on the amount of time spent collecting the audit evidence Give your opinion on the size of the time spent for the interim audit

Give your opinion on the amount of time spent for the end-of-year audit Likert scale: Very Weak – Weak – Average – Strong – Very strong

Give your opinion about the strength of the customer analysis for performing the risk assessment

Likert Scale: Very small- Small- Average – Big – Very Big Give your opinion about the size of the sample being tested

Give your opinion about the size of the audit evidence that is collected

Likert Scale: Very unreliable – Unreliable – Average – Reliable – Very unreliable Give your opinion about the reliability of the audit evidence

Likert Scale: Very small chance – Small chance – Average – Big chance – Very big chance Give your opinion about the possibility of discovering errors in the interim audit

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Likert Scale: Strongly disagree – Disagree – Undecided – Agree – Strongly agree I find data analytics useful in my job

The use of data analytics makes it possible to perform tasks more quickly The use of data analytics increases my productivity

The use of data analytics increases my chances of getting promoted The use of data analytics is clear and understandable

It is easy for me to become skillfull at using data analytics In my opinion, data analytics is easy to use

Learning to use data analytics is easy for me

People who are able to influence my behaviour have the opinion that I should use data analytics My superiors have been helpful in the use of data analytics

In general, our firm has supported the use of data analytics PwC provides the recources necessary to use data analytics

If necessary, a person or a group of persons is available for assistance with data analytics difficulties

The relation between the use of data analytics in the audit and the effectiveness and efficiency of the audit process