Department of Business Administration Master's Program in Accounting
Master's Thesis in Business Administration III, 30 Credits, Spring 2020 Supervisor: Tobias Svanström
Effect of Big Data
Analytics on Audit
An exploratory qualitative study of
data analytics on auditors’ skills and
competence, perception of
professional judgment, audit
efficiency and audit quality
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Abstract
Purpose: The primary goal of this thesis is to provide a deeper understanding of how big
data affect professional judgment, audit efficiency, and perceived audit quality. It also aims to explore the effect of Big Data Analytics (BDA) on the skills and competence required by auditors to perform an audit in a big data environment.
Theoretical perspectives: Theoretical concepts base on previous research and publications
by practitioners and regulators on BDA, professional judgment, audit efficiency and audit quality. Literature was used to derive the research gap and research questions.
Methodology: A qualitative method base exploratory approach. A literature review was
conducted to uncover areas of interest that require more research. The effect of data analytics on the audit was identified as a potential area for research; a focus on audit quality was chosen, including key factors that contribute to overall audit quality. The research is based on semi-structured interviews with auditors from big four audit firms in Sweden.
Empirical foundation: Empirical evidence was generated through an interview with
seven auditors at different levels of the professional hierarchy. Empirical data was analyzed using a thematic data analysis approach.
Conclusions: The findings of this research show that using BDA in the audit methodology
affect the required skills and competence by auditors to carry out audit engagement activities. More IT related skills and knowledge gaining prominent in the audit field. Implementing data analytics will not be efficient in the early stage but will save time as auditors become more familiar with the tools. Data analytics improve audit quality. Auditors use analytics to gain more insight into the client’s business and communicate such insights to clients. It was found that data analytics generate fact-based audit evidence. The visualization ability enables auditors to visualize and analyze audit evidence to guide their professional judgment and decision making.
Key words: Big data, Data analytics, Auditors skills and competence, Audit process, Audit
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Acknowledgment
We would like to express our gratitude to Umea School of Business, Economics, and Statistics for providing us with such a wonderful enabling environment to flourish to our supervisor Tobias Svanström for supporting us throughout the whole process, especially by sharing critical feedback with us.
To our participants, we greatly value the time and effort you devoted to participating in this study in spite of your busy schedules. Your inputs were critically valuable to our study.
We would like to express sincere thanks to our families and classmates for the unconditional support and encouragement during our study.
Finally, a special thanks to the Swedish Institute for the support provided to Hamadou’s education.
UMEÅ 25-05-2020
……… ……… Hamadou Kandeh Mohamad Alsahli
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1. Table of Contents
1. Table of Contents ...VI
1. INTRODUCTION ... 1
1.1. Background ... 1
1.2. Problematization and research gap ... 4
1.3. Research Questions ... 5
1.4. Research purpose ... 5
1.5. Delimitation ... 6
1.6. Structure of the paper ... 6
2. THEORETICAL FRAMEWORK ... 7
2.1. Big Data and Big Data Analytics ... 7
2.1.1 Big Data ... 7
2.1.2 Big Data as Audit Evidence ... 9
2.1.3 Big Data Analytics ... 10
2.1.4 Overview of previous literature ... 12
2.2. Legitimacy Theory ... 13
2.3. Legitimacy in Audit ... 14
2.4. Audit processes ... 15
2.4.1 Pre-audit engagements ... 16
2.4.2 Audit plan ... 16
2.4.3 Assess internal control over financial reporting ... 17
2.4.4 Perform a substantive test on financial transactions ... 17
2.5.5 Report the findings ... 18
2.5. Competence and skills of auditors ... 18
2.5.1 Competence ... 18
2.5.2 Skills ... 19
2.5.3 Effect of Advanced Technology on Skills and Competencies ... 19
2.6. Professional Judgment ... 20
2.7. Audit efficiency ... 22
2.8. Audit Quality ... 23
VII 3.1. Theoretical Methodology ... 27 3.1.2. Research philosophy ... 28 3.1.3. Research Approach ... 30 3.1.4. Research Method ... 31 3.1.5. Research Design ... 32 3.2. Practical Methodology ... 33 3.2.1. Data Collection ... 33 3.2.2. Sampling ... 35 3.2.3 Participant Selection ... 36
3.2.4. Interview Guide and Interview Process ... 37
3.2.5 Data Analysis ... 40
3.2.6 Literature Search ... 42
4. EMPIRICAL RESULTS AND FINDINGS ... 44
4.1. General bachground ... 44
4.2. Skills and competences ... 46
4.3. Professional Judgment ... 49
4.4. Audit Efficiency ... 53
4.5. Audit Quality ... 55
5. ANALYSIS AND DISCUSSION ... 61
5.1. Skills and competence ... 61
5.1.1 Skill sets required to perform an audit in a BDA environment ... 61
5.1.2 The need for a data analytics specialist ... 62
5.1.3 Potential barriers to Future Auditors ... 63
5.1.4 The need to redesign the academic curriculum for Accountants ... 64
5.2. Professional judgment ... 65
5.2.1 Understanding the client Business environment and industry ... 65
5.2.2 Quality of evidence ... 65 5.2.3 Professional experience ... 66 5.3. Audit Efficiency ... 67 5.4. Audit Quality ... 68 5.4.1 Input ... 68 5.4.2 Process ... 68
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5.4.3 Output ... 70
5.4.4 Interaction ... 70
6. CONCLUSION... 72
6.1. General Conclusion ... 72
6.2. Contribution & Implication of research ... 73
6.2.1 Theoretical contribution ... 73
6.2.2 Practical implication... 73
6.2.3 Societal implication ... 73
6.3. Limitations ... 74
6.4. Avenues for future research ... 74
6.5. Quality Criteria ... 75
6.6. Ethical consideration ... 77
7. Reference: ... 79
8. Appendix ... 86
8.1. Appendix 1: Email to prospective participants ... 86
8.2. Appendix 2: Interview guide ... 87
8.3. Appendix 3: Information sheet ... 89
8.4. Appendix 4: List of contacted big four Forms for interview ... 90
Table of figures Figure 1: Connolly’s’ (2012) definition of Big Data ... 8
Figure 2: Paths to expand data analytics in financial statement audits ... 11
Figure 3: Phases of the audit process ... 16
Figure 4: IAASBs’ Framework for Audit Quality ... 24
Figure 5: Data analytics – Impact on audit quality ... 69
Table of tables Table 1: Overview of previous literature ... 12
Table 2: Summary of philosophical assumptions ... 33
Table 3: Participants interview details... 40
Table 4: Phases of the Thematic Analysis (Braun & Clarke, 2006, p. 87) ... 42
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1. INTRODUCTION
In the first chapter of this manuscript, the authors introduce the choice of the research topic and research purpose as well as the overall objective of the thesis. The first chapter provides an overview of the use of technology in audit and the demand for audit quality. Secondly, the authors dive into the theoretical background to motivate the relevance of the topic as a current trend in business administration. Thirdly, the authors identified the research gap and explained how the research would contribute to filling the gap with the research questions. Finally, they outline the research purpose, delimitation of the research, and structure of this paper.
1.1. Background
The auditing profession has gained much attention in the past decades, particularly after Enron, WorldCom, and other auditing scandals (Alles, 2015, p. 440). An audit involves examining both financial and non-financial records of companies to establish patterns of events. Regulations are being reviewed to adequately guide audit processes and procedures in gathering sufficient evidence. This process is subjective upon the professional judgment of the auditors (Adrian & Viorica, 2015, p. 141). Nowadays, audit engagement involves several stages, such as planning the audit, conducting the client risk assessment, performing internal control tests, collecting evidence, and sharing information to appropriate stakeholders at the various stages of the audit process (Appelbaum et al., 2017, pp. 3- 4). The advancement in technology played and continues to play a crucial role in the evolution of the audit profession. The advent of technology such as digitalization, artificial intelligence, and data analytics drastically changes the methods employed in audit processes geared towards improving engagement output (ICAEW, 2016, p. 6). The continuous development and incorporation of technology into audit methods call for auditors to widen the scope of their knowledge related to using advanced information technology in different stages of the audit process (Salijeni et al., 2019, pp. 98-100; Kokina & Davenport, 2017, p. 115).
For long, auditors have relied on the application of data analytics in the execution of audit engagements. For the past two to three decades, auditors are making the best use of the available technology to gain a better understanding of audit clients and to conduct a risk assessment. More specifically, they gain an overview of industrial risk, and substantive procedures to gather sufficient evidence to issue an informed opinion about management assertion of financial statements (Salijeni et al., 2019, p. 98). Data analytics simply determine the processing of raw data to generate valuable information for decision-making purposes. For long, auditors have used data analytics mainly to improve audit quality and efficiency (Cao, et al., 2015, p. 426).
In recent years, the world is witnessing a growing impact of Big Data (hereafter BD) in business. According to Mckinsey Institute (2011), BD has a significant potential to change the world and not just the business (Alles, 2015, p. 439). Many organizations have invested considerable resources in BD to generate value and increase profit. For instance, by predicting sales numbers and employing new marketing strategies (Alles, 2015, pp.
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440). BD is expected to generate valuable economic benefits for audit firms and audit clients (Salijrni, 2019, p. 11). According to Salijeni (2019, p. 11) it is anticipated that using of BD will generate around £322 billion in UK by 2020 and €425 billion cost reduction in the manufacturing industry in Europe by 2020. Richins et al. (2017) argued that auditing firms should adopt BD proactively in auditing procedures to exploit its potential benefits. In this regard, The Institute of Chartered Accountants of England and Wales (ICAE&W) further states “it is critical for the audit profession to keep pace with these changes and be proactive in understanding how new technology trends can transform the audit approaches” (ICAE&W cited by Joshi & Marthandan, 2018, p. 2).
BD consists of various forms of traditional financial and non- financial data such as email correspondence, records of telephone conversations, text messages from private and business-related, social media platforms, news outlets, and other publications (Appelbaum et al., 2017, p. 4). Furthermore, BD can be described by 4Vs: volume, variety, velocity, and veracity. Volume refers to a large data set. Variety refers to different sources of collecting data. Velocity is how rapidly the data is changing. Veracity is the relevance and integrity of data that changes at a very high level (Vasarhelyi et al., 2015, p. 382; Zhang et al., 2015, p. 470; Yoon et al., 2015, p. 432). Many organizations are increasingly investing in the collection and storage of mass volumes of data in a variety of forms, and the growth is increasing rapidly (Cao et al., 2015, p. 426).
The growing trend in technology development makes it possible to record, store, and measure all kind of data (Cao et al., 2015, p. 423). However, the challenge auditors face is filtering the vast amount of data to obtain relevant and manageable information needed in audit procedures (Brown-liburd & Vasarhelyi, 2015, p. 3). Therefore, big four audit firms have responded to new technological developments by introducing Big Data Analytics (hereafter BDA). BDA can be defined as: ’’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, modeling, and visualization for the purpose of planning or performing the audit’’ (AICPA 2014, cited by Salijeni et al. 2019, p. 98). Audit firms utilize BDA in audit methodology to explore the potential benefits and to serve their clients effectively.
BDA require an ample amount of investment in hardware, software, and skills development to enable the appropriate data mining from the clients and the other third-party organizations (Cao et al., 2015, p. 427). Recently, the big four audit firms have invested a considerable amount in acquiring or developing new BDA tools (Salijeni et al., 2019; p. 98. kokina & Davenport, 2015, p. 116). For instance, Deloitte, in cooperation with Kira System, has started to develop a text mining program to extract significant information from unstructured data (Richins et al., 2017, p. 73). KPMG in cooperation with Formula One Racing and other technology firms, has assigned a $100 million to develop analytics systems that may generate additional value to its clients. Similarly, EY has invested $ 400 million in developing audit methodologies and data analytics capabilities. PWC has developed the Halo platform internally to add data analytics capabilities to its applications (Salijeni et al., 2019, p. 98; Richins et al., 2017, p. 74; Kokina & davenport, 2015, p. 116) and to be ‘a next-generation software application that analyses and assures data using a suite of algorithms’ (PwC, 2014b, p.15; cited by
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Salijeni, 2019, p. 14). Moreover, medium, and small tier auditing firm has used available software like Spotlight, Lavastorm, and Alteryx in order to develop their data analytics abilities (Salijeni et al., 2019, p. 99).
Earley (2015, P. 493) argued that BDA is a game-changer for the audit profession. Other researchers predicted that BDA would make a major change in audit methodology (Gepp et al., 2018; Salijeni, 2019). Cao et al. (2015) argued that BDA has huge potential to improve financial statements audit. There is an increasing interest and recognition of BDA in auditing by academia and audit firms (Appelbaum, 2017, p. 2). However, the auditing profession is different from other businesses. The profession is guided by sets of standards that constrain the audit firms from adopting new technologies. Therefore, auditors should consider the constraints before adopting BDA (Alles, 2015, p. 440) to legitimate or maintain the legitimacy of incorporating BDA in the audit profession (Salijeni et al., 2019, p. 98). Although, this development has resulted to recognition and approval of BDA by international professional auditing bodies such as IAASB and ACCA (Salijeni et al., 2019, p. 96). Regulatory agencies and professional bodies recognize the ability of BDA to minimize the risk of losing physical records. BDA eliminate the use of paper records to digitalized records and cloud storage (Salijeni et al., 2019, p. 96). The regulatory bodies are making efforts to assess the effect of BDA in audit methodology and how it could be regulated (Salijeni, 2019, p. 12).
Auditors have embraced the use of BDA to increase the effectiveness and credibility of the audit reports. Additionally, BDA may have potential effects in reducing the cost of auditing and enhancing the bottom line of audit firms as a result (Alles, 2015, p. 440). In contrast to traditional audit methodology, auditors can perform analysis on an entire population. Through the use of BDA, auditors can improve risk assessment processes, substantive procedures, and the test of internal control (Earely, 2015, p. 495). The development of data analytics tools geared toward improving audit quality and audit efficiency and effectiveness. However, it is imperative to note employing data analytics alone cannot enhance audit quality. It is the combination of data analytics and professional judgment facilitated by data analytics that can improve audit quality (Salijeni et al., 2019, p. 105; ICAEW, 2016, p. 6)
Audit quality has been under continuous scrutiny as a result of failure from major audit engagements such as Enron, WorldCom, Lehman Brothers, Freddie Mac, etc. These incidents lead the audit engagements to be questioned by regulators, governments, and investors. The reliability of audit opinion is dependent upon the validity and quality of audit evidence collected during the auditing process. BDA enables auditors to extract data using simple data structures in place of the traditional record file (ICAEW, 2016, p. 5). Auditors can also make the use of external data sources such as suppliers, customers, banks, and tax agencies. Notably, auditors can perform extraction and analysis of such data independently and this is crucial in gathering sufficient evidence (ICAEW, 2016, p. 5). Most importantly, it is possible to perform the analysis in more detail to gain precise insights into performed activities and business operations. This supports the auditor’s judgment in forming an appropriate audit opinion (ICAEW, 2016, pp. 6- 9)
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1.2. Problematization and research gap
In auditing, BDA have offered several opportunities for auditors’ activities. This includes bankruptcy assessment, management fraud, risk assessment associated with audit engagement, and identification of material misstatements in the financial statements (Cao, et al., 2015, p. 424). Furthermore, instead of the traditional focus on financial transactional data, BDA provides further opportunities for auditors to focus on structured data, semi-structured data, and unsemi-structured data (Alles & Gray, 2015, p. 8). BDA provides a possibility to test the whole population instead of the current traditional sampling approach (Cao et al., 2015, p. 427; Richins, 2017, p. 72). This can lead to enhance audit quality by increasing the sufficiency of audit evidence. Nevertheless, other than the role of accessing massive data, BDA is considered as a complement to internal audit evidence (Yoon et al., 2015, pp. 432- 433). Similarly, BDA provides support to auditors, especially, in the case of traditional audit insufficiency of evidence, and it increases the reliability and relevance of audit evidence (Yoon et al., 2015, pp. 432- 433).
Testing 100% of financial transactions and nonfinancial information is considered a paradigm shift in audit methodology from traditional. Auditors can focus on higher risk transactions to identify patterns and anomalies in which this may lead to higher audit quality. Moreover, BDA in auditing provides opportunities to use non-financial data to make a better professional judgment. In cases like going concerned and valuation, auditors can use BDA to evaluate and establish new models to predict future events (Earley, 2015, pp. 496- 497). Salijeni et al. (2019, p. 106) argued that the rise of BDA in auditing provides possibilities to outsource routine and repetitive tasks. Consequently, this will allow auditors to spend more time on complex issues that need professional judgments. Additionally, this may lead to reducing audit costs and avail audit firms a competitive advantage.
In contrast, previous researchers have found that access to BDA may raise new issues like the ambiguity of information, information overload, and false positives. These may lead to a decreased reliability of information and the benefit of using BDA (Salijeni et al., 2019, p. 99). Furthermore, BDA may cause additional work for auditors, which could result in an increased audit cost (Salijeni et al., 2019, p. 109). Other concerns are related to the knowledge and expertise of auditors. In BDA environment, the auditor should process a large amount of data, and most of them are non-financial data and evaluate anomalies in patterns. The skills and knowledge required to perform such tasks are not yet fully integrated in the academic syllabus. This concern raised an essential question among scholars on what knowledge and expertise auditors should have to work within BDA (Earley, 2015, p. 497). Cao et al. (2015), proposed that audit firms can introduce third-party providers of BDA service which, however, may raise privacy and confidentiality issues.
Previous research identified several pros and cons of using BDA in the audit process. However, as suggested by many researchers there remain several unanswered questions related to the impact of BDA on the audit process (Salijeni et al., 2019, p. 99). Yet, existing literatures in auditing have not identified specific skills required for extracting data from the clients and evaluating anomalies in patterns (Earley, 2015, p. 497). The
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composition of the audit team is another critical factor that would be affected by the advent of BDA, and the issue overlooked in available research (Appelbaum et al., 2017, p. 15). Researchers suggested one of the advantages of using BDA in the methodology will make it possible to outsource some of the processes to others like IT professionals (Salijeni et al., 2019, p. 106). This would raise several concerns in assurance engagements, including confidentiality and privacy, in accessing client’s data (Salijeni et al., 2019, p. 106; Yoon et al., 2016, p. 436; Cao et al., 2015, p. 428). Furthermore, most of the researchers argue that incorporating big data in the audit methodology will contribute to audit quality. However, the concept of audit quality is a subjective term, and achieving high audit quality is dependent upon different factors (Salijeni et al., 2019, p. 111). Achieving higher audit quality will depend on the competence and experience of audit team and methodology used. Finally, there is not enough scientific research on the topic of big data analytics, and available research focuses on the impact of BDA on auditing from a conceptual point of view (Brown-Liburd et al., 2015; Cao et al., 2015; Salijeni et al., 2019). The issue of BDA lacks sufficient empirical evidence from a practical point of view (Salijeni et al., 2019, p. 96). Therefore, more research is needed to exploit this new phenomenon. The above mention factors motivate the authors to conduct this study.
Despite the lack of empirical evidence on the use of BDA on audit methodology, the insufficiently available literature offers a positive view of using BDA in audit methodology (Salijeni et al., 2019; Alles & Gray, 2018). Moreover, prior research calls for more investigation to provide references on the topic from a practical perspective. Many researchers identified key areas worth investigating, and researchers proposed research questions that need to be addressed in future studies (Salijeni et al., 2019; Alles & Gray, 2016; Richins et al., 2017). For instance, what and how the BDA affects auditor’s opinion, audit report and audit evidence? Does BDA increase the audit quality? How BDA affect audit efficiency and professional judgment? What role regulations could play in BDA? (Alles & Gray, 2016, p. 58; Salijeni et al., 2019, p. 112, Appelbaum et al., 2017). This suggest that there is a need to provide empirical support for BDA fit within traditional audit methodology.
1.3. Research Questions
What is the minimum level of skills and competence that are required for auditors to work with BDA tools?
How BDA may impact perception of professional judgment, audit efficiency, and audit quality?
1.4. Research purpose
The purpose of this study is to explore and gain a deeper understanding of how the use of BDA in the audit methodology may affect perception of professional judgment, audit efficiency and audit quality from practitioners’ perspectives. This thesis also aims to increase the understanding of skills and knowledge of auditors that are required to perform an audit in the BDA environment. BDA is developing rapidly, and the debate centered around the phenomenon concerning the audit process and methodology continues to
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intensify. Furthermore, this thesis may provide a valuable contribution to the discussion on the subject.
1.5. Delimitation
Some constraints limited the application of the findings of this study. First, the study focuses only on big four Swedish audit firms. Therefore, the findings are relevant to the Swedish context; if this context is altered, the conclusions may change. The study uses qualitative methods, where individual perceptions, knowledge, and experience play a key role. The findings may not be wholly true if perception, knowledge, and experience of the participants differ from the time this research was conducted.
Moreover, this thesis is concerned with a relatively new topic. Another possible constrain is related to access to relevant and sufficient academic and scientific literature. Finally, the authors choose to interview auditors as they are being involved and affected by the new technology. Therefore, it considered that the empirical data would be sufficient to answer the research question in this context.
1.6. Structure of the paper
The structure of this paper is outlined in the following order. The first chapter contains the research background, problem statement, research questions, and research purpose. The second chapter determines the theoretical point of departure. This chapter contains a previously conducted literature review on the topic that is relevant in answering the research questions. Afterwards, the third chapter follows with the research methodology. The chapter is divided into two main sections. The first section is the philosophical assumptions, and the second section contains the practical steps the authors took to collect the necessary data to answer the research questions. The fourth chapter comprises the empirical data obtained from the seven interviews the authors conducted through semi-structured interviews. The Fifth chapter involves the analysis and discussion of the empirical results. Finally, in the sixth chapter, the authors conclude with a summary of the main findings, implications of the findings, limitations of the research work, and identify areas for future research.
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2. THEORETICAL FRAMEWORK
This chapter presents the theoretical point of departure. The chapter provides an overview of the theories related to the topic. First, the chapter discusses the general concept of Big data before moving to the specific use of data analytics in audit methodologies. After deliberating on the use of data analytics in audit, the discussion proceeds with theories of legitimizing the use of big data in an audit. A summary of the audit process in connection to big data is presented. Theories relating to the research questions are then discussed chronologically, starting with auditors’ skills and competence, professional judgment, audit efficiency, and finally, audit quality. Audit quality is presented in accordance with the IAASB framework for audit quality.
2.1. Big Data and Big Data Analytics
2.1.1 Big Data
Big Data (hereafter BD) is a new notion that has been used heavily in different sectors of our life like marketing, health care, and policies. BD becomes the currency of current business, and it is considered as a potential business asset (Appelbaum, 2016, p. 17). BD has brought a wide range of opportunities to businesses. However, firms should effectively exploit these opportunities to generate value from BD. Therefore, companies must have individuals who are capable of associating BD and their analytics to firm strategy and business fundamentals. As stated by the CEO of AICPA, “Big Data has increased the demand for information management specialists, while dramatically increasing the potential for visionary professional growth and positioning. CPAs are perfectly suited to take a leadership role in deciphering and using Big Data to achieve strategic business goals” (Rishins et al., 2017, p. 64). Professional bodies and audit firms started to recognize the significance of BD since 2013 and refer to it in their presentations and publications. The discussion focused on the value that can be extracted in the financial reporting (Salijeni, 2019, p. 41).
External auditors are motivated to use BD in their audit engagements for different reasons. Firstly, the audit’s client has used BD heavily in the decision-making process and accounting judgment, which could have a material impact on financial statements (Appelbaum, 2016, p. 17). Secondly, there exists the aspiration of auditors to use it in risk analysis, assessing client and industry, and confirmation (Appelbaum, 2016, p. 17). Due to reason that the audit profession is highly regulated, BD has been incorporated lately into the audit environment. BD has no unified definition, and the researchers suppose that readers will understand its meaning on an intuitive basis (Vasarhelyi et al., 2015, p. 381). Various definitions of BD can be found on different websites. However, the Wikipedia definition is considered one of the most comprehensive definitions which represent unanimous perspectives. “Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing
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correlations to be found to "spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions’’(Wikipedia; cited by Alles & Gray, 2016, p. 48).
The researchers define BD from different perspectives, on the one hand, is from the content of BD and on the other hand from dimension and characteristics of big data (Alles & Gray, 2015, p. 8). In terms of characteristics, BD is mainly described in 4Vs, namely: volume, variety, velocity, and veracity. Volume refers to a massive amount of data in the database. This volume varies among different companies and different industries, and what may be considered as the BD in one company or industry may not be regarded as such by another. For example, what a small audit firm regards as BD may not be a BD for the big audit firms (Vasarhelyi et al., 2015, p. 382). Variety implies the source of data as internal and external data and different types of data as structured data and unstructured data. Velocity refers to any extent data are being changed and updated continuously. Veracity is the most concerning issue for auditors, and it relates to any extent BD provides truthful information (Yoon et al., 2015, p. 432) and integrity of the data (Alles & Gray, 2015, p. 8). Further, the audit profession defines BD in terms of contents. BD refers to the combination of different types of data as internal and external data, structured and unstructured data, traditional financial data and non-financial data, emails, telephone calls, sensor data, logistics data, blogs, RFID data and social media data (Alles & Gray, 2015, p. 8). Connoly (2012, cited by Alles & Gray, 2015 p. 9) identified BD in terms of mathematical equation “Big Data= Transactions + Interactions + Observations’’ (see figure 1).
Figure 1: Connolly’s’ (2012) definition of Big Data
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Transactions refer to traditional structured financial data that is stored within SQL databases like ERP data systems. Interactions are the interactions between people, things, and firms like social media comments and users’ clickstreams. Observations refer to unstructured data that come from the internet of things (IoT) such as RFID, GPS within mobile devices, and ATMs.
2.1.2 Big Data as Audit Evidence
The main purpose of the auditors is to collect evidence regarding audit engagement to evaluate them to express their opinion regarding financial statements. Audit evidence is a different kind of information that is used by auditors to express their opinions (Appelbaum, 2016, p. 19). According to standards, audit evidence should be sufficient and appropriate. Further, auditors must test physical evidence in the risk assessment process according to audit standards (PCAOB 2010, AS 15; AICPA 2012, SAS 122; IAASB 2009, ISA 500) (Appelbaum, 2016, p. 19).
With the continuous advancement in technology, traditional audit evidence becomes no longer sufficient due to the change of the nature and competence of audit evidence (Appelbaum, 2016, p. 19). In their study (2015), Yoon et al. argued that external BD is a complement to traditional client’s data in case of insufficiency of evidence. Therefore, both internal and external auditors should have access to data to ensure that it is secure and trustworthy. External auditor’s access to BD can contribute to the following audit phases. Acquiring additional knowledge about the audit client and industry in the engagement phase. Providing support to the auditor in the risk assessment phase in audit planning. Using non-financial information like social media data in the substantive test phase to conduct a fair value assessment of intellectual property, for example (Appelbaum, 2016, p. 18). Viewing the audit results in deep insight and is comparable with the client's industry in the review phase. Providing auditors more knowledge about their clients in the continuous activities phase (Appelbaum, 2016, p. 18).
Moreover, external data provides support to the auditor to detect fraud because traditional data might hide important information and include motivation and rationalization about individual lifestyle, conduct, and morality. Therefore, evaluating external data like emails can be a helpful tool for auditors to detect fraud (Yoon et al., 2015, p. 433). BD evidence is considered sufficient due to the volume and variety of the data available in real-time. Appropriateness refers to the reliability and relevant evidence (Yoon et al., 2015, p. 432). In the context of using BD as evidence, reliability is a questionable issue. On the one hand, BD evidence is highly reliable as most of the evidence is generated from external sources and retrieved by auditors directly. Further, due to its vast volume it is very difficult to manipulate. On the other hand, the BD in nature is the noise that can lead to false positives and data overload, hence, decrease the reliability of the data (Yoon et al., 2015, p. 433). BD is relevant because of the availability of data in real-time (Yoon et al., 2015, p. 432). Additionally, the relevance of data depends heavily on professional judgment similar to traditional data (Brow-liburd & Vasarhelyi, 2015, p. 6). besides, using BD as audit evidence raises the concern about the verification of the accuracy of data. As most of the data are from external sources and especially if the BD has a significant effect on financial statements (Appelbaum, 2017, pp. 6-7).
10 2.1.3 Big Data Analytics
Alles and Gray (2016, p. 13) stated that “Big Data—whether considered as an evolutionary or revolutionary development in technology—remains a means towards an end and not, as the hype sometimes expresses it, as an end in itself. If auditors are to find value in it, Big Data and related analytics must lead to better audit outcomes”. BD itself has no value; therefore, BD should be analyzed by advanced analytics techniques to extract potential value to help decision-makers to make sound and informed decisions (Gandomi & Haider, 2014, p. 140). The analytics techniques mentioned above are called Big Data Analytics (BDA). BDA has plenty of definitions however in auditing it is defined as “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, modeling, and visualization for the purpose of planning or performing the audit” (AICPA, 2014, cited in Salijeni, 2019, p. 42). The big four audit firms have responded to the later claims to introduce BDA in financial statement audits (Salijeni, 2019, p. 41). They assigned considerable resources to develop new advanced technology internally or in collaboration with specialized firms within this area, such as Microsoft, SAP, and Oracle (Richins et al., 2017, p. 73).
The audit profession is mainly dependent on standards and regulations. Therefore, audit engagement must be conducted in alignment with regulation regardless of the advanced level of the audit's client in terms of IT and accounting complexity (Appelbaum et al., 2017, p. 4). Richins et al. (2017, p. 75) argued that the evolution of auditing standards to consider BDA will determine the role of the auditor within the BDA environment. Further, auditing standards remain unchanged. Therefore, using BD techniques in gathering audit evidence instead of traditional audit methods is still unclear (Richins et al., 2017, p. 75). Some practitioners see the silence of standard setters and regulators as an obstacle to limit their engagement with BDA. These practitioners will extend their use when the standard setters provide answers to their uncertainty about BDA use (Salijeni et al., 2019, p. 110). In this regard, Cao et al. (2015, p. 427) argue that auditing standards should be modified before the auditor adopts BDA. On the other hand, other practitioners see this silence as an opportunity to widely use BDA without considering any possible regulatory restrictions (Salijeni et al., 2019, p. 110). In contrast, standards setting communities argue that the current audit standards allow the auditor to exploit BDA in the audit context due to the flexibility of these standards (Salijeni et al., 2019, p. 110).
Incorporating BDA in audit engagement provides the audit profession an opportunity to use more advanced predictive and prescriptive-oriented analytics (Gyun No et al., 2019, p. 128). Appelbaum et al. (2017, p. 5) argued that close attention should be taken when it comes to discussing analytical procedures and business analytics (BA). Since both terms might not be exchangeable. The analytic procedure is a significant part of the audit process, which includes analyzing data to find out any plausible relationship between financial and non-financial data. At the same time, BA is “the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their operations, and make better, fact-based decisions” (Appelbaum et al., 2017, p. 5).
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BA has three dimensions - domain, orientation, and techniques - that are very useful to provide a deeper understanding of the scope of BA (Appelbaum et al., 2017, p. 6). The domain is the environment where audit teams apply analytics like client’ enterprise and management. The orientation refers to the vision of analytics descriptive, predictive, or prescriptive. The technique is the analytical approach or method (Appelbaum et al., 2018, p. 93). The variety of data, techniques, and client’s enterprise system capabilities are behind the dimensions of BA. Descriptive analytics is backward-looking and provides answers to what happened. It is used extensively in business, and it uses visualization, graphs, Key Performance Indicators (KPIs), dashboards, and descriptive statistics to transform the analysis into valuable information (Appelbaum et al., 2017, p. 6). Predictive analytics consists of different types of techniques that use historical and current data to predict future outcomes (Gandomi & Haider, 2015, p. 143). The majority of businesses use descriptive analytics as the first step of constructing predictive analytics. Prescriptive analytics is future-oriented by taking predictive analytics further. It is considered an optimization approach to analyze and determine the best possible alternatives (Appelbaum et al., 2018, p. 93).
Figure 2: Paths to expand data analytics in financial statement audits
Source: Alles & Gray (2016, p. 45)
Alles & Gray (2016, p. 45) argued that BD and BDA can be interrelated even if both are independent concepts. They determine the association between the two concepts by means of above figure (see figure2). For so long, audit firms have been operating in cell “A,” in which they use traditional tools such as Excel, ACL, and Idea to analyze accounting data. With a small part of non-financial data in cell “B.” Recently, audit firms have started to use visualization tools to analyze 100% of traditional financial data in cell B instead of sampling by following traditional audit procedures (Salijeni, 2019, p. 51). Alles & Gray (2016, p. 45) added that audit firms should move to cell D to fully exploit the opportunities of BD and advanced analytics techniques in an audit. In this regard, Appelbaum et al.
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(2018) established a framework for future research to provide a deeper understanding of the increased use of BDA in audit engagements. Another study by No et al. (2019) proposed a framework to guide auditors through the data selection process and introduce the obstacles in dealing with a high number of outliers in testing the entire data. 2.1.4 Overview of previous literature
To summarize the finding of previous literatures in the areas of BD and BDA in terms of their effects on audit methodology, the table below provides some insights about their role in audit.
Table 1: Overview of previous literature
Author Research Approach Findings
S ali je n i et al., 2019
Qualitative :22 interviews with
individuals who have experience in
implementing, evolving, or
evaluating the effect of BDA in audit. Looking also on published paper on BDA within the audit.
the study looks at BDA effect on relationship between auditor and their clients as a result changing in analyzing, storage and using financial and non-financial data. Looking also on BDA effect on changing audit methodology. Overview about the challenges of implementing BDA on audit process. the findings are used to establish agenda for future research.
Ric
h
in
s e
t al., 2017
Conceptual: looking on pervious literature and publication of big four firms.
Accounting professions have knowledge and experience to work with structured data which help them to make a shift to deal with unstructured data. Therefore, BDA complement the skills and knowledge of accountant instead of replacing them. However, the curriculum of professional bodies and education must be adjusted to cope with potential challenges of BDA.
B rown -L ib u rd et al., 2015
Conceptual: looking on pervious
literature from auditing and
psychology to find out the behavioral implications of BD on professional judgment.
implementing BD in audit process does not come without pitfall. As a massive amount of data from different sources to be processed could affect the cognitive ability of auditor to form professional judgement and decision-making process.
E
ar
ley 2015
Conceptual: looking on pervious paper and popular press that issued in recent years.
Data analytics is a game changer in the way auditor conduct audit. Addressing the problem of data analytics like skills and knowledge of auditors, integrity and relevance of available data and stakeholder expectation of data analytics for further research.
Cao e
t
al., 2015
Conceptual: looking on pervious literature, publication of big four firms and ISA, etc.
BDA does not use heavily in audit. However, its use could increase the efficiency and effectiveness of financial statement audit.
Alles &
Gray, 2016
Conceptual: looking on pervious literature, publication of big four firms, white paper, and blogs of practitioner
provide discussion about advantage and disadvantage of using BD in audit. The authors also suggested areas for further research to fill the gap in literature.
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2.2. Legitimacy Theory
According to Salijeni (2019, p. 56) adopting new technology in the audit field like BDA in this case, audit firms should prove to their stakeholder that BDA is adequate and socially desirable to meet audit requirements. In other words, audit firms should gain legitimacy from their stakeholders like clients, regulators, and standard setters. Legitimacy defined by Suchman (1995, p. 574) “Legitimacy is a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions.” Legitimacy within the legitimacy theory is about the idea of the social contract between organizations and societies they operate within. The legitimacy will be affected negatively in case the organization does not comply with the terms of the social contract, which can lead to a legitimacy gap (Deegan, 2018, p. 2315).
The literature in legitimacy theory is divided into two schools: strategic and institutional. The strategic approach discusses legitimacy from a managerial perspective by focusing on the organization's effort to gain societal support. In contrast, the institutional approach deals with legitimacy from an industry perspective as a broader point of view (Suchman, 1995, p. 572). In his study of 1995, Suchman stated that legitimacy is visible in form three types, namely: pragmatic legitimacy, moral legitimacy, and cognitive legitimacy. Every kind of legitimacy requires different behavior. Pragmatic legitimacy, which involves actions that support the best interest of an organization's immediate audience (O’Dwyer et al., 2011, p. 36). It also comprises the organization's activities and effects on broader political, economic, and social audience’s well-being (Suchman, 1995, p. 572). Pragmatic legitimacy is also divided into three types: exchange, influence, and dispositional legitimacy (O’Dwyer et al., 2011, p. 36). In relation to influence legitimacy, auditors seek to gain legitimacy from other groups like regulators, in addition to legitimacy from immediate audiences such as clients (Salijeni, 2019, p. 63).
Moral legitimacy is the second type of legitimacy. Suchman (1995, p. 579) identified moral legitimacy as “reflects a positive normative evaluation of the organization and its activities’’. The main point in moral legitimacy is the assessment of constituents to determine if the practice is the right thing to do (O’Dwyer et al., 2011, p. 36). Moral legitimacy has four types, namely, consequential, procedural, structural, and personal legitimacy (Suchman, 1995, p. 579). These types are determined based on the evaluation of outputs and consequences, techniques and procedures, categories and structures, and leadership and representative (O’Dwyer et al., 2011, P. 36).
Cognitive legitimacy includes practices that pursue objectives and activities which are accepted by constituents as taken for granted, ultimate, and desirable (O’Dwyer et al., 2011, p. 36). According to Suchman (1995, p. 582), taken for granted “is distinct from the evaluation: one may subject a pattern to positive, negative, or no evaluation, and in each case (differently) take it for granted.” Therefore, this type is very difficult to attain because, it depends on cognition instead of interest or evaluation. In relation to this point, sustainability assurance is taken for granted by constituents due to the necessity and relevance of this kind of practice in sustainability reporting (O’Dwyer et al., 2011, p. 36).
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The types mentioned above of legitimacy co-exist in most real-world settings, and only in some cases, there is a distinction among each other (O’Dwyer et al., 2011, p. 36). For instance, audit firms’ efforts to legitimizing non-audit services (pragmatic legitimacy) may compromised auditors' independence. Which, in turn, can affect the whole legitimacy of the audit profession (Salijeni, 2019, p. 65). According to Suchman (1995), the distinction appears in two salient points. Firstly, self-interest is the main purpose of audiences in the pragmatic legitimacy assessment. Whereas, the cultural rules are the base in assessing moral and cognitive legitimacy. Secondly, pragmatic, and moral legitimacy depends on cost-benefit assessments and ethical judgments of audiences (Suchman, 1995, p. 585) whereas, cognitive legitimacy is based on cumulative experiences of audiences (O’Dwyer et al., 2011, p. 36). Suchman (1995, p. 585) argued based on these observations that are moving from the pragmatic to the moral and to cognitive legitimacy; legitimacy becomes more difficult to attain and sometimes needs to reinforce one type to another. In relation to implementing BDA in audit, audit firms seek to gain pragmatic and moral legitimacy. As incorporating BDA in the audit process is at the early stages; additionally, legitimacy process starts from pragmatic and moral legitimacy (Salijeni, 2019, p. 65)
2.3. Legitimacy in Audit
The legitimacy in audit has been discussed from two perspectives. The first point of view considers auditing as a tool or mechanism to generate legitimacy, while the other point of view, auditing seeks to be legitimized (Salijeni, 2019, p. 65). Power (2003, p. 380) suggests that the legitimacy of both perspectives (producing or seeking) is co-produced. The audit firms and professional bodies cultivate a lot of efforts to standardize the audit process. However, there are differences in the style and application of audit routines. These differences are between two approaches - the structured formal style approach, and the other approach that gives a room for professional judgment. According to Power (2003, p. 381) the difference relies in the nature of auditing as a social entity dependent on “deeply embedded perspectives’’ instead of a series of technical steps.
Further, the conflict between structure and judgment in the audit process is related to one mechanism and the other organism. The mechanism seeks an integrative formal audit approach, which depends on algorithmic knowledge, while organisms focus on whole knowledge instead of specificity as the whole is greater than individual parts. In their study, Dirsmith and Haskins (1991 cited by Power, 2003, p. 381) examine the conceptual conflict within the assessment of inherent risk, and they found that audit firms use broader factors in assessing the inherent risk when they follow an organic approach. They also found that the structure-judgment could not be realized as a perfect approach, even in the predominance of the structure audit approach. This predominance increases the need for legitimacy and transparency to standardize processes for management control purposes. Therefore, legitimacy and control are very significant in the structure audit approach, even if it is not associated with enhancing audit efficiency (Power, 2003, p. 381).
Some researchers argue that incorporating technologies into the audit is used to legitimize program and audit ideas (Robson et al., 2007; Andon & Free, 2014; cited by Saljini, 2019, p. 68). The use of technologies either aims to help stakeholders to achieve their objectives or as a tool to finalize work. Further, these technologies appear to main stakeholders like
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clients, professional bodies, educators, regulators, and standard setters as appropriate and desirable because they are associated with shared norms and cultural value of auditing (Salijeni, 2019, p. 68). In their study Robson et al. (2007, p. 433) found that audit firms introduce a new audit methodology known as Business Risk Audit (hereafter BRA). To respond to their failure in detecting management fraud and enhancing their understanding of business risk and add value to their clients. Audit firms collaborate with educators and professional bodies to seek pragmatic legitimacy by introducing the BRA as a solution for auditing problems. The collaboration with researchers and educators helps the audit firms to develop BRA and gain exchange legitimacy from their clients through their publication hence pragmatic legitimacy. Audit firms apparently gain moral legitimacy by engaging standard setters and regulators in BRA (Salijeni, 2019, p. 69).
In relation to BRA, another study conducted by Curtis and Turley (2007 cited by Salijeni, 2019, p. 69) showed that audit firms in their effort to gain pragmatic legitimacy must convince their auditors of the positive effect of using BRA in an audit. In contrast, the concerns of the auditor were on the impact of BRA on their professional role in the audit process, which can raise the demand to generate moral legitimacy. In this regard, O’Dwyer et al. (2011, p. 41) showed in his study that audit firms should appeal to a wide range of their main stakeholder for legitimacy. He also adds that the production of legitimacy is needed in some cases, to manipulate stakeholders to achieve this purpose. Further, if firms fail to maintain pragmatic legitimacy, moral legitimacy can be gained through manipulation. Based on the aforementioned, adopting new technology in audit required producing both moral and pragmatic legitimacy as a cultural value. For instance, audit technology can enhance the effectiveness and efficiency of the audit (Salijeni, 2019, p. 69). Salijeni (2019, p. 70) argues that adopting new audit ideas or using new technology from other fields in an audit may cause challenges and, therefore, will require careful consideration of context and key audiences.
2.4. Audit processes
The audit process is a series of interrelated activities an auditor has to perform during an audit engagement. This process starts from the pre-engagement to the point where the audit is completed and review, and opinion is reviewed and eventually signing the audit report (Leung et al., 2015, p. 238). During the audit process the auditor gathers information to understand the client’s internal environment, assess the client-specific industry, and gather enough audit evidence that will subsequently enable the auditor to form an informed audit opinion (Eilifsen et al., 2014, p. 20). Eilifsen et al. (2014, pp. 17- 20) and Messier Jr, et al., (2017, pp. 18- 20), divide the audit process into seven stages and are highly interdependent and some iterated. Furthermore, PwC (2013, pp. 8- 9), summed up these seven stages into five key interrelated stages. Below, these five key stages are visualized and further discussed.
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Figure 3: Phases of the audit process
Source: PWC (2013, p. 9) 2.4.1 Pre-audit engagements
The initial phase of the audit process; it involves assessing the viability of the audit clients. Audit standards require an auditor to establish appropriate policies and procedures for accepting or rejecting potential audit clients or retaining current clients (PwC 2013, p. 8). The reason for establishing such rules is to minimize the risk of an auditor being involved with a client that lacks integrity, as this compromises the independence of auditors (Eilifsen et al., 2014, p. 68; Messier et al., 2017, pp. 71-72). Before accepting an engagement with a client, the audit firm should consider key issues regarding the engagement. The auditor should perform a background check about the integrity of the client’s shareholders, senior management charged with governance (Eilifsen et al., 2014, p. 17; Messier Jr et al., 2017, p. 18).
Further, the scope and objective of the engagement, duration of the audit, the responsibility of management and the auditors and the audit fee all should be stipulated in the engagement letter (Eilifsen et al., 2014, pp. 71-72; Messier Jr et al., 2017, pp. 74- 75). As part of the pre-audit engagement activities, the auditor should also consider value-added services such as tax planning, transaction support, IT consulting, and internal reporting process (Leung et al., 2015, pp. 318- 320). This will give the auditor opportunity to provide additional service and hence, benefit from a holistic view of the client’s internal environment. This will be a chance to understand the entity's business risk and provide a recommendation to mitigate those risks (Duh et al., 2007, n d).
2.4.2 Audit plan
Auditing standards ISA 300 require the auditor to develop a proper plan to carry out the audit activities (Leung et al., 2015, p. 327). Proper planning is essential in fulfilling the audit objective and meeting the terms and conditions of the engagement agreement (PWC, 2013, p. 8). The planning process involves all the aspects the auditor should consider in devising a strategy and plan for implementing the audit. This phase of the audit involves resource planning, risk assessment, and setting materiality (Messier et al., 2017, p. 78).
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Upon reaching an agreement to carry out the audit on a client’s financial statements, the auditor is going set out all the resources that will be required to perform the audit (Fukukawa et al., 2011, p. 85). This involves assembling the right audit team with the right blend of skills and expertise and setting the time frame to conduct the audit (Eilifsen et al., 2014, p. 75).
ISA 315 instructs auditors to perform a risk assessment of clients to identify any risk material misstatement that may be contained in the financial statements (PwC, 2013, p. 8). Auditors apply their gathered knowledge of the client during the initial phase of the audit, understanding of the industry and the environment in which the client operates to identify and assess the risk that could lead to a material misstatement (PwC, 2013, p. 8). A key process of the planning phase is setting materiality for the financial statement. Materiality is making decisions about what needs to be considered significant on performance, and disclosure in significant accounts (Messeir et al., 2017, pp. 78 -79). The auditor uses the set materiality level to determine if misstatement in an individual account or collectively misstate the financial statements (Eilifsen et al., 2014, pp. 83-85). The auditor applies professional judgment and experience in calculating the material level. Hence, auditors use both qualitative and quantitative mechanisms to assess materiality. Materiality is very important in the audit process; it also helps auditors form their audit opinion.
2.4.3 Assess internal control over financial reporting
A company’s management and board of directors build a system of internal control purposely to enable the company to keep track of required financial reporting reliability, achieve operational efficiency, and fulfill compliance with legal requirements (Eilifsen et al., 2014, p. 176). In this phase of the audit, the auditor utilized the COSO framework for internal control to assess the internal control environment of the client (Eilifsen et al., 2014, p. 179; Messier et al., 2017, p. 180). This assessment is geared to determine whether a company's internal control system is reliable, produces timely reports, and transparent in terms of external financial reporting (Messier et al., 2017, p. 180). The auditor performs a test of control for mainly two reasons, to be able to assert that the system is reliable or to establish level substantive procedures required to generate enough sufficient audit evidence (PwC, 2013, p. 8).
2.4.4 Perform a substantive test on financial transactions
Regardless of the strength of the internal control over financial reporting, the auditors could not give a hundred percent reliance on the system (PwC, 2013, p. 9). International auditing standards require the auditor to perform substantive tests on some, if not all the financial transactions. This is particularly relevant in the process of obtaining relevant and appropriate audit evidence to guide the audit opinion (Messier et al., 2017, pp. 191- 192). When carrying out substantive procedures, auditors can either utilize manual procedures or make use of Computer-Assisted Auditing Techniques (CAATs) (Eilifsen et al., 2014, p. 139). During this process, auditors carry out an analytical review of documents, make inquiries to company personnel, customers, suppliers, also observation, and inspection to company warehouses (PwC, 2013, p. 9). The professional standard requires auditors to use professional judgment and skepticism to gather evidence through the review documents, and reports (PwC, 2013, p. 8). Sufficient and appropriate evidence must be obtained for
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auditors to justify their conclusion on management assertion on financial statements (Leung et al., 2015, p. 440).
2.5.5 Report the findings
The last stage of the audit process is to evaluate the finding and choose the appropriate audit opinion to produce the report to the users of financial statements (Leung et al., 2015, p. 784). The final product of the audit process is the auditors’ report on the findings. The audit report communicates to management and other users of financial statements how true and fair the financial statements are (Leung et al., 2015, p. 254). The audit report usually contains information such as the scope of the audit, the right and responsibility of auditors and management, key accounting policies, and the audit opinion (Messier et al., 2017, p 599). According to Eilifsen, et al. (2014, pp. 590- 595) there are four main audit opinions available for auditors to choose from. These are qualified, unqualified, disclaimer or adverse audit opinion.
2.5. Competence and skills of auditors
2.5.1 Competence
Competence has a variety of definitions, and the word competence is used in everyday language, and it has different meanings referring to ability, capability, and effectiveness (Weinert, 1999, p. 3). Weinert (1999, p. 4) defined the competence concept as a combination of individual prerequisites such as abilities, proficiency, and skills that are significant to achieve specific objectives. In their study, Hager & Gonczi (1996, p. 2) identified competence as a comprehensive and integrated approach that goes beyond the tasks, skills, and knowledge. This approach contains a variety of attributes required to perform key tasks. These attributes have cognitive attributes such as (knowledge, problem-solving strategies, communication, pattern recognition, and critical thinking), interpersonal skills, effective attributes, and technical skills. Training and assessment in the generic approach are considered as a strategy to train and assess candidates' attributes. These attributes also are assessed in isolation from any achieved work. Consequently, it may raise concerns for future performance evaluation (Hager & Gonczi, 1996, p. 2).
The aforementioned is competence in general. International Organization for Standardization (IOS) issued a guideline (ISO 19011: 2011) to manage auditing systems, and it includes the required competencies for auditors. Achieving the objectives of audit processes depends heavily on the competences of individual auditors and the audit team who are involved in all stages of the audit process. The evaluation process of auditors should consider personal behavior, knowledge, and ability to implement what they gained through education, work experience, and training. This process should include an audit program and its goal. Further, the audit team should have the diversity of competences that is sufficient to achieve audit goals; on the other word, each auditor in the team should not have the same competences. Based on the outcome of the evaluation audit process, the audit team should be selected, additional training is required to enhance competence, and evaluate the auditor’s performance continuously (BSI, 2011, pp. 24-26).
19 2.5.2 Skills
The term skills are used by an educational psychologist and human capital theorists. Skills refer to individual worker properties instead of the propriety of a job (Vallas, 1990, p. 379). This perspective considers skills as an independent variable in the prediction of the variation in salary levels. In contrast, a sociological point of view focuses on the property of the job. It considers skills as a dependent variable in explaining the variation of skill levels over time (Vallas, 1990, p. 380). Currently, technology has a major impact on auditor work, and it could make their job easier and more efficient. However, technology cannot substitute specific skills like human intellect, judgment, and leadership. In relation to auditor skills, KPMG, in collaboration with Forbes Insight, determine in their study five essential skills. These skills are required from auditors to enhance audit quality and should be used in accompanying innovative technology (Forbes Insights, 2018; KPMG, 2017a, pp. 5-12).
The first skill is strong communication skills which is considered the most important one. It helps auditors to spread their ideas, suggestions, and thoughts during the meeting and interview and negotiation with their clients. Emotional intelligence is the second skill. Even though financial client records might be disorganized and contain the possibility of fraud, auditors must always maintain patience to ensure everything is accurate. The third skill is critical thinking and business acumen. Auditors can analyze objectives and evaluate information and fact. Further, the auditor must gain insights into the client’s business and industry to pose the right questions. The fourth skill is professional skepticism. It has a significant role in designing and executing an audit engagement as well it helps auditors to recognize and overcome bias and make an appropriate judgment. The last skill is interpersonal skills. Since the auditor deals with different clients in different situations, therefore, the success of the auditor requires exceptional people skills, for instance, empathy which helps auditors to gain a better understanding of the client’s point of view (Forbes Insights, 2018; KPMG, 2017a, pp. 5-12).
2.5.3 Effect of Advanced Technology on Skills and Competencies
Advanced technology played and continues to play an influential impact on the skills and competencies required for future work. Recently, advanced technology like artificial intelligence and robotics calls individuals to think in different ways to adjust their skills and competences to adopt these technologies (David, 2015, p. 5). David (2015, p. 5) argues that advanced technology like artificial intelligence and robots can replace workers in performing routine and repetitive work, whereas, in some work that requires judgment and flexibility, advanced technology is still so far to replace workers. Therefore, individuals should consider creativity, flexibility, and judgment as the most significant competence to avoid any risk of technology substitution in the future (The Institute for the Future, 2011, pp. 8-10).
The Institute for the Future (2011) identified the ten main skills and competencies that may be required in the future. These skills are sense making, social intelligence, novel and adaptive thinking, cross cultural competency, computational thinking, new media literacy, transdisciplinary, design mindset, cognitive load management and virtual collaboration. Social intelligence refers to making a connection with people directly and deeply to
