Impacts of digitization on auditing: A Delphi study for Germany

Journal Pre-proof Impacts of Digitization on Auditing: A Delphi Study for Germany Victor Tiberius, Stefanie Hirth

PII:

S1061-9518(19)30008-4

DOI:

https://doi.org/10.1016/j.intaccaudtax.2019.100288

Reference:

ACCAUD 100288

To appear in:

Journal of International Accounting, Auditing and Taxation

Accepted Date:

3 November 2019

Please cite this article as: Tiberius V, Hirth S, Impacts of Digitization on Auditing: A Delphi Study for Germany, Journal of International Accounting, Auditing and Taxation (2019), doi: https://doi.org/10.1016/j.intaccaudtax.2019.100288

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Impacts of Digitization on Auditing: A Delphi Study for Germany

Victor Tiberiusa,*, Stefanie Hirthb a

Faculty of Economics and Social Sciences, University of Potsdam, August-Bebel-Str. 89, 14482

Potsdam, Germany b

KPMG AG Wirtschaftsprüfungsgesellschaft, Klingelhöferstr. 18, 10785 Berlin, Germany

Funding

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This research did not receive any specific grant from funding agencies in the public, commercial, or notfor-profit sectors.

Abstract

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The ongoing digitization of the economy presents challenges and opportunities for the auditing profession and requires both auditors and their clients to adapt. Against the background of current

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technological developments in big data analytics, artificial intelligence (AI), and blockchain technology,

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this study examines changes in auditing practices expected by German auditing professionals within the next five to ten years. It addresses the perception of auditing, the auditor–client relationship, regulations, structural and procedural changes for auditing firms, and the profile of the auditing profession. These

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will probably change with new technologies. We surveyed experts as part of a Delphi study in Germany conducted over two rounds. The results show that no far-reaching changes are expected within the given

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time horizon. The annual audit will increasingly evolve toward a continuous audit approach. Despite predominantly uncertain opinions, experts believe that new technologies will not replace the auditor but

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rather will provide relief and support. Even if the job’s necessary requirements make it more difficult to remain in the profession, disruptive effects in auditors’ workplaces are not expected in the near future. Nevertheless, the consequences of using new technologies in the auditing process offer numerous future research opportunities.

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Corresponding Author. E-mail address: [email protected] (V. Tiberius).

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JEL classification M42, O33

Keywords Artificial intelligence; Auditing; Big data analytics; Blockchain technology; Delphi study; Digitization, Germany.

1. Introduction

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Today’s accelerated digitization significantly challenges existing business models and the employment of knowledge workers across all industries (Loebbecke & Picot, 2015). Therefore, also auditing firms and auditors are potentially affected by the further progress of information technology

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(Elliott, 2002), especially in big data analytics (Alles, 2015; Cukier, & Mayer-Schoenberger, 2013; Constantiou & Kallinikos, 2015; Richins, Stapleton, Stratopoulos, & Wong, 2017; Syed, Gillela, &

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Venugopal, 2013), artificial intelligence (AI) (Goertzel, 2007; Nowak, Lukowicz, Horodecki, 2018), and blockchain technology (White, 2017). These rapid advances in digitization involve the potential

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automation of cognitive tasks, in a similar manner that machines replaced physical labor during the industrial revolution (Brynjolfsson & McAfee, 2014). Since these developments could threaten the

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entire auditing industry, they are highly relevant for auditors and their stakeholder groups. Firms need to think ahead and be insightful about technological trends that have the potential to

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change industry rules and create new competition (Hamel & Prahalad, 1994). Therefore, firms embrace foresight “to support decision making, improve long-term planning, enable early warning, improve the

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innovation process, and improve the speed in reacting to environmental change” (Iden, Methlie, & Christensen, 2017: 90), to create a competitive advantage (Anderson, 1997), and to increase profitability (Rohrbeck & Kum, 2018). To achieve these goals and avoid merely speculating about what the future might bring, foresight has adopted several systematic methodologies and techniques, such as the Delphi method, for many years. Despite its importance, technological foresight for the auditing industry is scarce. Because the specific impacts of digitization in auditing are not obvious today, further investigation is required. 2

Against this background, this paper aims to provide foresight to the auditing industry, specifically by using the Delphi method, to examine the probable consequences of digitization-driven changes expected within the next five to ten years. In particular, the study explores digitization’s effects on auditors’ stakeholders (audit addressees, clients, and regulators) and organizations, as well as the auditing profession’s profile. The Delphi study focuses on the German auditing market. For international auditing research, countries’ common features and differences are meaningful. Germany is the fifth-largest economy in the world and one of the leading countries in science and technology. Germany, as part of the European

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Union, shares many auditing practices with other countries, with some differences (Quick, Schenk, Schmidt, & Towara, 2017). German auditing standards largely correspond to the International Standards on Auditing, with only some specifics regarding joint audits, audits of the management reports and early

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warning systems, and audits under the renewable energies law. Further, German and all other EU auditors are not allowed to provide financial accounting, internal audit, management, financial,

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valuation, and actuarial services. While investor protection is considered to be rather weak due to the limited civil liability exposure of statutory auditors, public oversight of the profession is independent.

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Because these differences are not relevant to the paper’s research question, they are not part of the conducted Delphi study. The “Big Four” international audit firms, Deloitte, EY, KPMG, and PwC, have

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an 83% market share among the 160 largest companies in Germany (Fockenbrock, 2011). Therefore, the findings of the study, while not representative of the international auditing industry, are relevant for

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many other developed countries.

The paper is organized as follows: In the next section, the technological background of

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digitization, exemplarily represented by big data analytics, AI, and blockchain technology, is elucidated briefly, focusing on general linkages with auditing. This serves as a foundation for later Delphi projections. Section 3 explains the Delphi methodology and how the study was conducted. Twenty projections, i.e. statements relevant to auditing changes within the next five to ten years, are formulated, and their plausibility is briefly justified. The fourth section presents the results, both as descriptive statistics and in the form of a coherent scenario. Because several results are rather surprising, the paper then discusses why the auditing experts might have expressed their expectations in this manner. 3

2. Background: Links between digitization and auditing 2.1 Big data analytics Big data has been characterized by the so-called four-V paradigm: volume, velocity, variety, and value (Gantz & Reinsel, 2011). With data storage costs plummeting, the volume of data has grown exponentially during the last few decades (Breuer, 2016). Similarly, the speed of data generation has been rapidly increasing. Data generated from diverse sources, for various purposes, and with no consistent form, can include numbers, text, pictures, audio, videos, and many other types. Therefore, big

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data is stored in an unstructured way. This is especially true for social media data (Syed, Gillela, & Venugopal, 2013; Warren, Moffitt, & Byrnes, 2015). As suggested by Laney (2001), the first three Vs show that conventional software cannot handle big data. However, the newer fourth V stresses that big

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data can have high potential value, especially when understanding customer needs and optimizing products and services (Chen, Mao, & Liu, 2014; Lee, Kao, & Yang, 2014; Ram, Zhang, & Koronios,

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2016). Therefore, big data analytics represents a new processing model to make sense of massive, fast, and unstructured data, turning it into valuable knowledge (Constantiou & Kallinikos, 2015; De Mauro,

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Greco, & Grimaldi, 2016; Laney, 2001; Snijders, Matzat, & Reips, 2012). Big data, which is expected to provide economic growth, also carries societal risks, such as privacy violations and discrimination

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(Cuquet & Fensel, 2018).

For auditing purposes, the use of big data analytics is not obvious, because accounting data even

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with billions of transactions are still “small” in the context of “big data.” In addition, accounting data are usually well structured and include debit and credit accounts. However, even if accounting data are

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not considered “big,” the techniques of big data analytics can be applied to smaller volumes of transactions. Therefore, these processing models can be used to make full audits, rather than partial, random audits, economically meaningful (Bierstaker, Burnaby, & Thibodeau, 2001; Yoon, Hoogduin, & Zhang, 2015). A more indirect application of big data analytics for auditing purposes could be to enrich insights about business transactions with external, non-accounting information, derived from big data (Bierstaker, Burnaby, & Thibodeau, 2001; Yoon, Hoogduin, & Zhang, 2015). This non-accounting data 4

could be matched with accounting data for plausibility (Cao, Chychyla, & Stewart, 2015; Lombardi, Bloch, & Vasarhelyi, 2014). For example, intensive social media discussions about an important deal could be reflected in transactions or asset valuations, or satellite pictures could show construction work when a project is mentioned in the financial report. However, big data analytics also comprise a number of challenges for auditors. For example, auditors have little experience with unfamiliar data sources and therefore may struggle to estimate their appropriateness, reliability, and relevance (Appelbaum, Kogan, & Vasarhelyi, 2017). Furthermore, it is still unclear how to relate unstructured, non-accounting data with structured accounting data in a sensible

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way (Yoon, Hoogduin, & Zhang, 2015).

2.2 AI

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Because no uniform definition of intelligence exists, a clear definition of AI is also problematic (Richins, Stapleton, Stratopoulos, & Wong, 2016; Smith, 2015). In general, AI is supposed to engage in

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activities based on human information processing, such as pattern recognition, learning, and planning (Minsky, 1961). Most AI is applied in language recognition, visual pattern recognition, or logical

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problem solving (Gershman, Horovitz, & Tenenbaum, 2015).

For auditing purposes, AI can find anomalies in accounting data. Currently, machine learning,

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the most important sub-concept of AI (Jordan & Mitchell, 2015), is being used by the “Big Four” auditing companies for data collection and validation (Brennan, Baccala, & Flynn, 2017). For example,

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PwC’s software GL.ai extracts information relevant for accounting from documents (such as contracts) and makes it available to auditors. The more data are fed into the algorithm, the more it can learn (Jordan

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& Mitchell, 2015). Machine learning applications beyond this scope are not identifiable yet (Kokina & Davenport, 2017). However, several other uses of AI are conceivable for the future (Kokina & Davenport, 2017). For example, AI could make inventory processes less prone to human error (Appelbaum & Nehmer, 2017). AI could also be used to improve industry-wide auditing processes and standards. Strong AI could even replace human auditors (Kokina & Davenport, 2017). The World Economic Forum (2015) optimistically expected 30 percent of corporate audits being performed by AI by 2025. 5

2.3 Blockchain technology A blockchain is a decentralized database that chronologically stores information about transactions of any kind (Christidis & Devetsiokiotis, 2016; Dai & Vasarhelyi, 2017; Pilkington, 2016; White, 2017). The database is available to every network member, called a node, which holds an identical copy and validates every new transaction, called a block. As every new block is attached to a prior one, the concatenated blocks build the blockchain. A new block is only attached to the existing blockchain if the majority of nodes agrees. By this need of decentralized validation, later unilateral

Since everybody can read the database, it is also transparent.

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changes in the database are impossible, so the blockchain is considered fraud-proof (Cai & Zhu, 2016).

While cryptocurrencies like Bitcoin are the most well-known application of blockchain

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technology, many other uses are feasible (Dai & Vasarhelyi, 2017). In fact, a blockchain is an alternative to any transaction system currently managed and authenticated by a central intermediary, such as a bank

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(for money or security transfers) or a land registry (for real estate transactions). As authorities certifying the correctness of financial reports, auditors could also be potentially replaced by a blockchain system.

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When a company handles all its transactions via a blockchain system, because all transactions are decentrally validated in real time, they can be trusted by those who accept blockchain technology. An

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additional central audit could be seen as dispensable. However, the potential advantages and risks of

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blockchains for auditing have yet hardly been explored (Dai & Vasarhelyi, 2017).

3. Methodology

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3.1 The Delphi study

To generate a future scenario about the impact of digitization on auditing, a two-stage Delphi

study was conducted online. The Delphi method questions experts over at least two rounds, using a standardized questionnaire and giving structured feedback about the results from the first round to enhance respondents’ consensus (Bell, 1967; Dalkey & Helmer, 1963; Rowe & Wright, 1999; Skulmoski, Hartman, & Krahn, 2007; Woudenberg, 1991).

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The Delphi method is suitable for forecasting man-made future states (Woudenberg, 1991). In contrast to causal-deterministic natural development processes, such as the weather, societal futures are based on human intentions, social interactions, and coincidence (Tiberius, 2011). Therefore, societal forecasts can be derived from a group of experts expressing their subjective knowledge and experiencebased opinions. The method has been applied in many fields of business administration and management, such as banking (Bradley & Stewart, 2003), human resources (Poba-Nzaou, Lemieux, Beaupré, & Uwizeyemungu, 2016; Wiggington, 1979), information systems (Hong, Trimi, Kim, & Hyun, 2015; Huang, Wu, & Chen, 2013; Keller & von der Gracht 2014; Koskiala & Huhtanen, 1989;

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Schmidt, Lyytinen, Keil, & Cule, 2001; White, 2017), knowledge management (Scholl, König, Meyer, & Heisig, 2004), manufacturing (Jiang, Kleer, Piller, 2017), marketing (Knutson, Beck, Singh, Kasavana, & Cichy, 2004; Larreche & Montgomerey, 1977; Watson, 2008), new product development

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(Tiberius, Borning, & Seeler, 2019), project management (Bril, Bishop, & Walker, 2006; Kell, Tiwana, & Bush, 2002; Liu, Zhang, Kell, & Chen, 2010), and supply-chain management (Lummus, Vokurka, &

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Duclos, 2005; Melnyk, Lummus, Vokurka, Burns, & Sandor, 2009; Roßmann, Canzaniello, von der Gracht, & Hartmann, 2018). For accounting and auditing research, it has also been used to explore future

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events and trends (Baldwin-Morgan, 1993; Brancheau, Janz, & Wetherbe, 1996; Worrell, Di Gangi, & Bush, 2013).

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Unlike scenario analyses (Gausemeier, Fink, & Schlake, 1998; Kahn & Wiener, 1968), which generate multiple future scenarios, the Delphi method provides the most probable scenario, generated

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by merging multiple expert statements. A pretest is conducted to minimize the risk of misinterpreting statements to the experts. After the first survey round, interim results are revealed to the respondents

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during the second round. Goodman (1987) and Sackman (1975), however, criticize that these disclosures violate the scientific requirement of independent judgment. Delphi study’s goal, though, is the convergence of expressed opinions and the narrowing of the statistical spread, to formulate an unequivocal consensus. The conclusions of a resulting scenario can be drawn from both consensus and dissent.

3.2 Formulation of projections 7

From the perspective of auditing experts, and against the backdrop of current digitization, the Delphi study generates the most probable future scenario over a five- to ten-year time horizon. Since auditing is a broad topic, a full scenario covering every technological change and every aspect of auditing in detail cannot be generated in one step. In particular, the number of projections rated by the experts has to be limited to reach a sufficient response quote and a low dropout rate. Therefore, Delphi study designers have to choose either a broad or a deep scenario. Because prior Delphi studies about auditing do not exist, an exploratory (i.e., broad) rather than a deep scenario is chosen. A broadly scoped Delphi approach covers many specific aspects of auditing but omits details. Later Delphi studies can

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select narrower scopes or partial scenarios and ask more detailed questions.

The scenario is subdivided into six thematic sections: the perception of auditing from the audit addressees’ perspective, the auditor–client relationship, regulation, structural and procedural changes

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for auditing firms, and the profile of the auditing profession.

After selecting the sections and topics of projections, specific future-relevant statements have

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to be formulated. Especially for fields, such as auditing, where future-oriented research is scarce, it is not possible to generate statements solely based on a literature review. Rather, projections have to be

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generated based on plausible conclusions that consider current developments, such as the technological trends discussed earlier. However, even implausible or rather extreme projections can be included in

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Delphi studies, because the same general tendency can be derived from both the rejection of an implausible projection and the inclusion of a plausible one. Against this backdrop, in the following, 20

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projections are formulated for the six selected sections. After each projection, a short explanation is

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given why it can be (but does not have to be) considered plausible.

3.2.1 Changes of audit addressees’ perception The readers of annual accounts, such as shareholders, lenders, and other stakeholders, can be

regarded as auditors’ actual customers, even if they do not pay for auditing services. Therefore, the first section of projections deals with how the perception of auditing, from the audit addressees’ perspective, might change in the future.

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P1: Within the next five to ten years, the amount of intangible assets (e.g., software) in companies’ balance sheets will be much higher than today. With greater valuation flexibility, audits will become less informative for audit addressees. Even for firms that do not offer digital goods, digitization requires high investments in intangible assets, such as software development. Compared with tangible assets, the valuation of intangibles gives accountants more leeway. Accountants who use this leeway liberally can change balance sheet items to such an extent that they might not reflect actual values. For auditors, it is difficult to assess accountants’ valuations, and when their opinions differ, to justify a deviation. Therefore, the

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auditor’s attestation might become less informative for audit addresses.

P2: Within the next five to ten years, audit addressees will trust automated auditing procedures more than manual ones.

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P3: Within the next five to ten years, technology will make auditors’ personal judgments obsolete.

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Auditing firms already use general audit software, such as Audit Command Language or Interactive Data Extraction and Analysis (Bierstaker, Burnaby, & Thibodeau, 2001; Braun & Davis,

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2003; Lin & Wang, 2011; Mahzan & Lymer, 2014), or in-house software, such as Aura by PwC or eAudit by KPMG. All of these support and partly automate auditing procedures (Lombardi, Bloch, &

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Vasarhelyi, 2015). However, human auditors currently still control the process and make decisions. Also, at present, only structured data can be processed automatically, whereas unstructured data needs

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prior preparation (Hunton & Rose, 2010). Completely automated auditing procedures, already common for digital inventories, could also become more important for other elements of financial statements.

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Because automated auditing procedures reduce human errors, auditing in the future could possibly use more automated auditing procedures than manual ones. For example, drones are expected to reduce human errors in the inventory process (Appelbaum & Nehmer, 2017). P4: Within the next five to ten years, the expectation gap, especially regarding future-oriented risk statements in the management report, will have increased substantially. As stated in Projection 1, qualitative statements in management reports can become more important for auditors because of valuation insecurities. This might also affect executives’ statements 9

about future risks and forecasts. Because risk orientation has become a major focus for auditors after global auditing scandals (Curtis & Turley, 2007), executives’ statements about future risks have become increasingly important auditing data. Such statements can be assessed in relation to plausibility but not in relation to probabilities. Therefore, the expectation gap could increase.

3.2.2 Changes in the auditor–client relationship Apart from audit addressees, paying clients (the companies whose annual reports are being audited) represent another important stakeholder group. However, since their perceptions and

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expectations can change, the second section of projections deals with possible changes in the relationship between auditors and their clients.

P5: Within the next five to ten years, clients will not regard current price models as appropriate,

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because increased automation will erode fees.

As automated procedures substitute for manual ones (Projection 2), less manpower will be

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needed, massively decreasing firms’ costs. Therefore, auditors will initially face increasing IT investments. But in the long term, as noted by several studies, IT will make auditing significantly more

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efficient (Bierstaker, Burnaby, & Thibodeau, 2001; Lombardi, Bloch, & Vasarhelyi, 2015; Omoteso, Patel, & Scott, 2010). If auditors can capture a large proportion of the value generated by automation,

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their profits could increase. However, if clients become aware of the extent of these cost savings, their willingness to pay high auditing fees might decrease. If auditors are forced to wholly or largely pass cost

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reductions to clients in the form of lower fees, amortizing IT investments would become more difficult, and profits would remain stable or even sink.

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P6: Within the next five to ten years, the auditor–client relationship will become more tense, because digital transparency leads to higher liability risks for clients. Digitization is usually accompanied by a higher degree of transparency. This makes it possible

for auditors to more easily uncover violations of orderly accounting practices or other problematic procedures. In this situation, auditors who wish to gain access to clients’ digital data or who need to use auditing software in clients’ firms might encounter client resistance.

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P7: Within the next 5 to 10 years, clients who use blockchain technology for transactions will regard formal auditing as obsolete. Clients who use blockchain technology will question whether or not formal auditing is still necessary. Because all blockchain transactions are already decentrally approved by multiple participants, they are regarded as transparent, secure, and trustworthy. Third-party certification authorities may no longer be required (Dai, & Vasarhelyi, 2017), and additional audits might become obsolete.

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3.2.3 Regulatory changes

Regulators comprise a third important stakeholder group for auditors. As technological progress proceeds, regulatory changes might be required. Therefore, the third section of projections addresses

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possible regulatory changes.

P8: Within the next five to ten years, a substantial regulatory gap between the new digital

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business reality and auditing standards will exist.

In many cases, technological progress is faster than legislation and private regulation. Because

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regulation can only react to (but usually not anticipate) external changes, a temporal gap between new situations and their regulation is common. This could also take place for auditing regulations. Since

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technological changes potentially affect almost all aspects of accounting and auditing, current regulatory standards may require major adjustments (Appelbaum, Kogan, & Vasarhelyi, 2017). And because

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international negotiations about new standards are time-consuming, regulatory authorities may need longer than five to ten years to fully process their adoption.

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P9: Within the next five to ten years, accounting and auditing standards will be established by AI.

If this regulatory gap could be defined today, disruptors might look for a way to prevent it from

happening. A rather extreme solution for this problem is that auditing standards could be established by AI rather than by human beings. P10: Within the next five to ten years, auditing standards will contain few margins of discretion.

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The final interpretation and application of specific auditing standards rely on the discretion of the individual auditor. Auditing algorithms using AI could potentially make such margins of discretion obsolete, because AI would immediately identify the relevant standard and apply it to the accounting issue correctly (Kokina & Davenport, 2017).

3.2.4 Structural changes for auditors After addressing the auditors’ stakeholders, the fourth and fifth sets of projections deal with organizational aspects of the auditors themselves, namely, feasible structural (Section 4) and procedural

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changes (Section 5).

P11: Within the next five to ten years, automation will relieve auditors from routine tasks in favor of more complex tasks, such as judgment.

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P12: Within the next five to ten years, the profile of the auditor’s profession will have completely shifted from classic auditing to consulting.

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While automated auditing procedures could save time for auditors, at least for simple auditing tasks, they could increase auditor unemployment. Frey and Osborne (2017) project that 94% of

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accountants’ and auditors’ work could be automated in the near future. However, it is also possible that auditors’ additional time will be used for new and more complex tasks that cannot be automated, such

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as decision making or consulting. Furthermore, auditors, such as accountants, could support data scientists performing exploratory, big data analyses, by analyzing unstructured data (Richins, Stapleton,

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Stratopoulos, & Wong, 2017).

P13: Within the next five to ten years, continuous auditing, rather than annual auditing, will be

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the new standard.

An annual audit of a client’s financial year, which is the current standard, provides non-timely

information compared with financial data or press releases that are often immediately available (Appelbaum, Kogan, & Vasarhelyi, 2017; Eilifsen, Knechel, & Waliage, 2001). Business news is available instantly, and client stakeholders will probably demand more frequent, more recent, or even continuous real-time audits (Elliott, 2002; Lombardi, Bloch, & Vasarhelyi, 2015; Zhang, Yang, & Appelbaum, 2015). This demand could be satisfied with real-time data exchanges between clients and 12

auditors using, for example, eXtensible Business Reporting Language (XBRL) (Valentinetti & Rea, 2013), as well as automated accounting and auditing procedures based on big data analyses (Cao, Chychyla, & Stewart, 2015; Lombardi, Bloch, & Vasarhelyi, 2014; Vasarhelyi, Kogan, & Tuttle, 2015; Warren, Moffitt, & Byrnes, 2015). To date, these processes are currently more popular in accounting than in auditing (Gepp, et al., 2018). P14: Within the next five to ten years, technological changes will displace most small and midsized auditing firms. In the near term, auditing firms will have to make sizable investments in technological

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infrastructure and build new competencies (Projection 5). Small and mid-sized auditing firms predominantly serving small and mid-sized clients (Chaney, Jeter, & Shivakumar, 2004) could struggle to afford these investments while also remaining competitive. Along with anticipated pricing pressures,

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this could lead to consolidations or a substantial market shakeout.

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3.2.5 Procedural changes for auditors

Apart from structural changes for auditors (Section 4), future technological shifts might cause

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procedural changes.

P15: Within the next five to ten years, AI will be able to make auditing decisions with a high

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degree of discretion.

Even if lower margins of discretion in auditing standards do not occur (Projection 10), a high

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degree of automation would still be possible if AI could learn from current auditing literature how to apply auditing standards correctly. The more often machine learning techniques are used to analyze

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documents and to set up reports, the higher the probability of finding irregularities (Kokina & Davenport, 2017). Therefore, in the future, AI could make auditing decisions involving a high degree of discretion.

P16: Within the next five to ten years, full rather than random audits will be the new standard. In current manual auditing procedures, partial business transactions are selected on a random basis to ensure, with a reasonable probability, that all accounting procedures, including the non-assessed ones, are compliant with the standards of orderly accounting (Projection 1). With the ongoing 13

automation of routine auditing procedures, especially those using big data analyses, it can become possible to conduct full audits that cover all transactions of the client’s firm. P17: Within the next five to ten years, auditing risks will be eliminated completely. Based on Projection 16, the question arises whether full audits will reduce or even eliminate auditing risks completely. This could be possible with AI-based automation.

3.2.6 Changes in the auditor’s professional profile In the final section of projections, auditing as a profession is addressed to explore changes for

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future generations of auditors.

P18: Within the next five to ten years, high IT and data expertise will be required, at the expense of traditional business skills.

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As digitization increases and clients’ (digital) business models constantly change, complex auditing and auditor requirements could also occur. In auditing firms, more IT specialists and data

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scientists might be employed. This increasing specialization raises the question of whether business administration, accounting, or auditing graduates will continue to staff auditing firms, rather than IT-

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oriented employees (Appelbaum, Kogan, & Vasarhelyi, 2017).

P19: Within the next five to ten years, the auditing profession will become less attractive for

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young professionals because of increasing examination requirements. With an increasing number of requirements, the already challenging auditing certification exam

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could become even more complex, making the profession less attractive and leading to a lower number of potential auditors in the future.

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P20: Within the next five to ten years, massive job losses will occur. Corresponding to Projection 11, supply and demand in the labor market for auditors could

decrease with the growth of automated auditing procedures. As noted in Projection 11, massive job losses could result (Frey & Osborne, 2017).

3.3 Panelists

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Selecting appropriate respondents for a Delphi study involves questioning how an expert can be characterized or how his expertise can be measured (Welty, 1972). In this study, though, these questions are not an issue. The panel consisted of auditors registered with the public auditors’ register of the German Auditor Chamber, university auditing professors, regulators, and IT experts in auditing software. All can be undoubtedly seen as experts in the auditing profession. We recruited 250 respondents from all regions in Germany. Eight could not be reached, and three stated they did not wish to participate. Of the remaining 239, 110 participated in the first round, with 101 completed responses, and 83 participated in the second round, with 76 completed responses.

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Because the study took place during auditors’ “busy season,” the 46.0 percent response rate and 75.5 percent repetition rate can be considered high. Detailed demographics of the panel can be found in Table

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1.

Table 1

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Panel demographics. Panel Demographics

1st Round (N = 101)

2nd Round (N = 76)

Gender

n 83 18 n 7 28 30 30 5 0 n 70 15 7 8 2 3

n 63 13 n 4 21 23 25 2 1 n 53 8 2 8 2 3

Age

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Younger than 30 yrs 30–40 yrs 41–50 yrs 51–60 yrs Older than 60 yrs No Answer

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Male Female

Affiliation

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Audit Big Four firm Not in the Big Four Self-employed auditor University auditing professor Regulator IT expert in auditing software

% 82 18 % 7 28 30 30 5 0 % 69 15 7 8 2 3

% 83 17 % 5 28 30 33 3 1 % 70 11 2,5 11 2,5 3

Change

N −20 −5 N −3 −7 −7 −5 −3 +1 n −17 −7 −5 -

% +1 −1 % −2 +3 −2 +1 % +1 −4 −4,5 +3 +0,5 -

3.4 Data collection The questionnaire contained the 20 projections presented above. The respondents were asked to either agree or disagree with these statements, using a four-point Likert scale (“do not agree,” “rather 15

do not agree,” “rather agree,” and “agree”). Because an even-numbered Likert scale has the advantage of no neutral responses, the tendency toward the center could be avoided. The two survey rounds were conducted between September 10–24, 2018, and October 1–15, 2018. The study was conducted anonymously since anonymity is a central feature of Delphi studies (Woudenberg, 1991). Critics argue that anonymity may cause participants to respond irresponsibly (Goodman, 1987; Hill & Fowles, 1975), especially when self-fulfilling prophecies are expected. However, in this study, technological progress has a higher impact on future development than the

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expression of the experts’ views.

4. Results

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4.1 Descriptive statistics

The results of a Delphi study are not based on a commonly formulated group consensus. Rather,

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they are derived from individuals’ aggregated responses. To calculate a statistical distribution, the

“rather agree”: 3, and “agree”: 4.

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response options received the following numerical values: “do not agree”: 1, “rather do not agree”: 2,

To avoid statistical biases, and because it is more robust toward outliers (Rowe & Wright, 1999),

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the median has been established as the preferred statistical average for Delphi studies. To interpret aggregated responses, the scattering around the median is measured by the inter-quartile range (IQR),

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the difference between the upper quartile (x0,75) and the lower quartile (x0,25). A small IQR represents a high level of consensus, whereas a high IQR stands for a high level of dissent. After the first survey

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round, the scattering range of responses is supposed to decrease as they approximate the median (x0,5). No projection received full consent (Median 4). The experts somewhat agreed (Median 3) to

projections 1, 5, 8, 11, 13, 16, and 19. They somewhat disagreed (Median 2) with projections 2, 4, 6, 7, 12, 14, 15, 18, and 20. Projections 3, 9, 10, and 17, the most extreme, were completely rejected (Median 1). During both rounds, the respondents agreed or disagreed with the same projections. For Projection 2, the median slightly changed from full to partial agreement. The disagreement with 16

Projection 10 increased during the second round. While no changes occurred for the other projections, a scattering of opinions about seven projections decreased, a target of the Delphi method’s iterative design. For projections 6, 9, 12, 17, and 18, the IQR decreased by 1.00, and for Projection 20, it was 2.00. Therefore, the experts reached a higher overall consensus. Critics argue that a decrease of IQR represents a majority impact and a tendency toward the group median rather than an approximation to the most probable future development (Bardecki, 1991; Hill & Fowles; 1975; Rowe & Wright, 1999). Detailed results from the Delphi study for both rounds can be found in Table 2. Table 3 summarizes the

Table 2 Descriptive statistics.

Projection x0.25 No.

x0,.

x0.75

2nd round (N = 76) IQR

x0.25

x0,5

x0.75

IQR

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na

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17

x0.25

x0.5

x0.75

0 0 0 0

IQR

1.00 1.00 0 1.00

0 0 0 −1.00 0 0 0 0

1.00 0 1.00

0 0 0

1.00 0 1.00

0 0 0 0 0 0 −1.00 −1.00 0 −1.00 0 0

1.00 0 1.00 1.00

0 0 0 0

0 0 0 0 −1.00 −1.00 0 0 0 0 0 0

0.25 1.00 0

0.75 0 0

0 0 −0.75 0 0 0 0 −1.00 −1.00

0 1.00

0 0

0 −1.00 −1.00 0 0 0

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Section 1: Changes of Audit Addressees’ Perception 2.00 3.00 3.00 1.00 2.00 3.00 3.00 1 2.00 3.00 3.00 1.00 2.00 2.00 3.00 2 1.00 1.00 1.00 0 1.00 1.00 1.00 3 2.00 2.00 3.00 1.00 2.00 2.00 3.00 4 Section 2: Changes in the Auditor–Client Relationship 2.00 3.00 3.00 1.00 2.00 3.00 3.00 5 2.00 2.00 3.00 1.00 2.00 2.00 2.00 6 1.00 2.00 2.00 1.00 1.00 2.00 2.00 7 Section 3: Regulatory Changes 2.00 3.00 3.00 1.00 2.00 3.00 3.00 8 1.00 1.00 2.00 1.00 1.00 1.00 1.00 9 1.00 2.00 2.00 1.00 1.00 1.00 2.00 10 Section 4: Structural Changes for Auditors 3.00 3.00 4.00 1.00 3.00 3.00 4.00 11 2.00 2.00 3.00 1.00 2.00 2.00 2.00 12 2.00 3.00 3.00 1.00 2.00 3.00 3.00 13 2.00 2.00 3.00 1.00 2.00 2.00 3.00 14 Section 5: Procedural Changes for Auditors 1.00 2.00 2.00 1.00 1.75 2.00 2.00 15 2.00 3.00 3.00 1.00 2.00 3.00 3.00 16 1.00 1.00 2.00 1.00 1.00 1.00 1.00 17 Section 6: Changes in the Auditor’s Professional Profile 2.00 2.00 3.00 1.00 2.00 2.00 2.00 18 2.00 3.00 3.00 1.00 2.00 3.00 3.00 19

Difference

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1st Round (N = 101)

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acceptances and rejections of the 20 projections.

0 0 0 0

0 0 0 0 −1.00 −1.00 0 0 0

0

1.00

Theses

Rating

1, 5, 8, 11, 13, 16, 19

Rather agree

2, 4, 6, 7, 12, 14, 15, 18. 20

Rather disagree

3, 9, 10, 17

Disagree

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4.2 Scenario

0 −1.00 −2.00

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1.00 2.00 3.00 2.00 2.00 2.00 2.00 20 x0.25 = Lower quartile x0.5 = Median x0.75 = Upper quartil IQR: Inter-quartile range (x0.75 – x0.25) = Approximation of the quartile toward the median = Deferral of the median = Reduction of IQR Table 3 Agreement and disagreement with projections.

According to the interviewed experts, over the next five to ten years, greater digitalization in

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auditing will not change audit addressees’ perceptions noticeably. Valuations of intangible assets, such as software, will impede consistent information for auditors’ valuations, but the personal judgment of

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auditors, based on manual procedures, will still be perceived as important and reliable. Despite technological progress, massive job losses among auditors will not occur. Auditors will

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remain important partners, even at companies that embrace automation. However, clients are expected to demand lower fees, which will place stress on the auditor–client relationship. The increasing regulatory gap between auditing standards, digital business realities, and margins

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of discretion will impede a comprehensive use of new technologies. However, routine procedures will

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be automated, and auditors will focus on aspects that require judgment. Discretionary decisions will remain with auditors, and their tasks will remain the same. Technological progress will establish real-time corporate reporting. Therefore, continuous,

rather than annual, auditing will be the new standard. Full audits will replace random sampling. However, audit risks will not disappear. Regarding the auditors’ job profile, fundamental knowledge about business administration and accounting will remain important but will be complemented by IT knowledge. More difficult auditor 18

exams will decrease the attractiveness of the auditing profession. Small and mid-sized auditing firms will remain in the auditing industry.

5. Discussion 5.1 Experts’ expectations 5.1.1 Changes of audit addressees’ perceptions In the first section, four out of five projections were rejected. But since these projections were formulated in a negative way, this represents a positive future outlook.

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Even though only 12 percent of the respondents completely agreed with Projection 1, the majority of the expert group expected that software investments will be more immaterial than material assets. The group expected that it will become increasingly difficult to show adequate and comparable

standards exist for software valuation and its depreciation.

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values on a balance sheet, leading to less information for audit addressees. In particular, no special

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Interestingly, 36% of the participants disagreed with Projection 1, possibly because it actually consists of two statements, rising immaterial assets and rising valuation problems. Some experts might

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have agreed with the first statement but not with the second and therefore rejected the projection completely. The second statement may have been rejected because new standards for software valuation

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and depreciation are expected to be set in the nextfive to ten years. The results were similar for Projection 2. The majority of the respondents did not expect a higher

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degree of trust in automated versus manual auditing procedures. However, since 41% held the opposite position, no clear group opinion resulted. Even if not statistically significant, Projection 2 was rejected

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more by respondents who are 41 years or older than by younger professionals, possibly because of a distinction between positivistic and normative projections: Even though all projections were limited to developments over the next fove to ten years, some respondents might have answered not what they expect but what they hope will happen. This effect can be observed primarily for threatening projections. In such cases, the respondents repressed unpleasant expectations by replacing them with their ideal version of the future (often, a current or a past situation).

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This possible effect was even more apparent for Projection 3, a more extreme version of Projection 2, which stated that automated audits will completely replace manual ones. While this outcome is possible in general, 97 percent of the respondents rejected Projection 3. Other than wishful thinking, another reason for this high rejection rate could be that the first four projections explicitly took the audit addressees’ perspective. However, audit addressees’ perceptions can be decoupled from or even opposed to actual technological progress: Even if automated processes objectively were superior to manual ones, humans might still trust and prefer human judgments over automated, anonymous ones. A narrow majority of 51 percent of the experts did not anticipate an increasing expectation gap

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regarding future-oriented risk statements in management reports. Perhaps, the experts did not expect regulators to set narrower standards for margins of discretion, which would prevent the expectation gap to rise. Further, experts who did see an increasing expectation gap possibly did not see that auditors are

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responsible for this development. For full and partial audits, since no secure judgment is possible when audit risks remain (Projection 17), this can also lead to an expectation gap among audit addressees.

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However, almost half of the respondents somewhat agreed with Projection 4. Therefore, it remains rather

oriented risk statements.

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unclear if auditors expect audit addressees to face an increasing expectation gap regarding future-

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5.1.2 Changes in the auditor–client relationship

In this section, three of the four negatively formulated projections were (somewhat) rejected by

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the expert group. The majority agreed with one projection. Sixty-one percent of experts expected that automation will put pressure on current price models

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for auditing services. Thirty-nine percent opposed this view, with one participant completely rejecting the projection. Interestingly, most of the rejecting respondents work in a “Big Four” firm, which has greater pricing power than smaller firms. Similar to Projection 1, and less obviously in Projection 5, the projection consists of two separate statements: fee erosion and the automation that caused it. Some auditors might recognize automation’s cost-reduction effects but not see reduced fees as a consequence.

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Eighty-two percent of the experts agreed about Projection 6. They do not expect that tension in the auditor–client relationship will increase due to digital transparency, leading to higher liability risks for clients. Projection 7 proposed that blockchain technology, which allows a decentralized verification of all transactions, could make audits completely obsolete within the next five to ten years. This projection was rejected by a clear majority of 82 percent of the participants. Several possible explanations may be behind this high consensus. First, wishful thinking could be a factor. Because the rather extreme projection represents a threat to the whole audit profession, respondents may have substituted their real

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expectation with a more idealistic one. Second, blockchain technology can only depict single transactions, primarily regarding the profit and loss statement versus other parts of the balance sheet. For example, items such as goodwill, trade names, or provisions somewhat depend on individual

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choices, with a margin of discretion. Since these are the main subjects of auditing, blockchain

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technology can produce neither benefits nor risks for the auditing industry.

5.1.3 Regulatory changes

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This section showed respondents’ ambivalent expectations. One projection was approved, whereas the other two were rejected.

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Fifty-four percent of the respondents expected that the substantial regulatory gap between a new digital business reality and auditing standards will not be closed over the next five to ten years

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(Projection 8). Although considerable differences existed within the group, no clear differences between sub-groups could be found. Projection 8 implicitly consists of two statements: one, that digitization will

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require new standards, and two, that regulators will not manage to set new standards within the given timeframe of five to ten years. Possibly some of the experts see no need for changed standards due to digitization, since the main concepts of accounting and auditing are not affected, whereas others see a need for new standards but are more confident with regulators’ reaction speed. In contrast, the rather extreme Projection 9 was rejected by 97% of the respondents. The projection stated that new auditing standards could be set by AI rather than by a human regulatory authority. The experts clearly did not see AI technology progressing fast enough to make this projection 21

realistic. However, the fact that one participant agreed and another somewhat agreed with Projection 9 shows that the idea is not completely far-fetched. For Projection 10, almost 90 percent of the expert group expected that auditing standards will still contain margins of discretion within the next five to ten years. Only a minority expects discretionary powers to disappear due to complete disclosure and transparency in all transactions. However, similar to Projection 7, a distinction should be made between simple and automatable transactions in the profit and loss account and more complex valuations in the balance sheet that cannot be automated because

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they require (human) judgments.

5.1.4 Structural changes for auditors

In this section, two projections received a significant consensus, whereas the other two

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projections show rather ambivalent expectations among the expert group members.

In particular, Projection 11 obtained high approval ratings. Ninety-three percent expected that

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digitization will lead to a reduced workload for simple auditing routines, giving auditors more time to concentrate on more complex and demanding tasks. The experts, therefore, expect digitization to

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positively affect auditors’ everyday work. A narrow minority of participants did not believe this hypothesis.

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According to the expert group’s ratings of Projection 12, the reduced workload proposed in Projection 11 will not mainly be used for non-auditing tasks, such as IT compliance or data protection

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consulting. Rather, auditors will remain focused on auditing procedures. A reason for this assessment could be that, especially for Big Four auditors who represented the majority of the panel, a large

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proportion of their consulting business is derived from prior auditing services. Therefore, reduced auditing services would also result in a loss of consulting services. By contrast, smaller auditing firms or sole auditors could be forced to substitute auditing with consulting services. A clear majority of the respondents agreed to Projection 13, which stated that annual auditing will probably be replaced by continuous or even real-time auditing. This is plausible, considering both the advancing technology and the capital market’s demand for prompter and more reliable financial reports. Major price corrections at stock markets after quarterly reports that deviate from prior 22

expectations could be reduced or even completely avoided if investors and speculators had transparent, current, and reliable financial information. Stock-listed companies which engage in continuous or even real-time auditing would be preferred by investors. Pioneering companies would probably force others to follow, establishing a new standard for certain exchanges. The expert group (somewhat) disagreed with Projection 14, which stated that technological progress would displace most small and mid-sized auditing firms within the next five to ten years. This projection, like the others, concerns the German audit market which, like many other markets, is dominated by the “Big Four.” This projection is plausible because more than one quarter of the

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participants agreed to it, as only large auditors are expected to be able to make the necessary investments in technology. However, most respondents regarded the given timeframe as too short.

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5.1.5 Procedural changes for auditors

Whereas two of the projections delivered clear opinions of the expert group, one was

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characterized by great uncertainty.

In particular, Projection 15 was clearly rejected. Therefore, the majority of the auditing experts

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did not expect that AI will be able to make decisions on auditing issues involving a high degree of discretion. Tasks such as asset valuations are seen as too complex to be conducted by a computer.

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Considering what AI is already able to perform today, this view is rather optimistic. AI now can perform many tasks long considered to be accessible only to humans. Skepticism was shown by the approval of

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Projection 15 by only 22 percent of the respondents. Projection 16 showed a high degree of uncertainty among the panel members. While a narrow

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majority of the respondents expected full audits to be the new standard, 43 percent (somewhat) disagreed. The proportion of Big Four auditors among the agreeing respondents was very high. Possibly, they hope to be technologically able to conduct full audits in the near future, whereas smaller auditors are hesitant. Another reason for this disparity could be that the possibility for full audits is not only dependent on the auditors’ technological infrastructure but also on the clients’. Many respondents do not expect clients to digitize their accounting as fast as their auditors do. Also, the timeframe may be too narrow for many study participants. Yet another reason could be that many auditors do not see the 23

clear advantage of full audits compared with partial, random audits. The question is whether the additional work and expense of full audits are proportionate to their additional values. A slight majority seems to expect that full audits will be completely automated and, therefore, cause no additional work or expense and that full audits will garner higher trustworthiness among audit addressees. Projection 17, eliminating auditing risks, was rejected by 78 percent of the respondents. This result has to be seen in connection with prior Projection 16 regarding full audits. Since study participants were uncertain about the arrival of full audits, it is no surprise that Projection 17 was rejected. Additionally, many experts expect human auditors to play a crucial role in the auditing process in the

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future (e.g., Projection 15), making elimination of human errors impossible. Some respondents also probably believed that a completely automated auditing process cannot be faultless.

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5.1.6 Changes in the auditor’s professional profile

In the last section, two projections show quite clear judgments, whereas one projection is

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characterized by high uncertainty.

A clear majority of the panel expects specific auditing knowledge and skills rather than IT

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knowledge to still dominate auditors’ job requirements in the future (Projection 18). The respondents expect that even if IT knowledge becomes more relevant in auditing firms, this will be provided by IT

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experts, not by auditors. This judgment is plausible considering the industries’ very different job domains and job requirements.

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A clear majority of the respondents (somewhat) agreed to Projection 19, which states that increasing examination requirements will lessen graduates’ interest in the field. Because this projection

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does not explicitly refer to technological progress, all situational factors leading to this judgment have to be included. The last two to three decades have seen increasing examination requirements due to a massively growing body of knowledge. Combined with high examination failure rates, the attractiveness of the profession has to be very high. Two important factors determining this attractiveness are reputation and income. In the last two decades, international reporting scandals have partly undermined the general public’s trust in auditors. Future scandals could advance this development. Regarding

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income, as seen in Projection 5, auditing fees are expected to get under pressure. This could also lead to reduced remuneration for auditors. Finally, a clear majority of the expert group does not see massive job losses for auditors in the near future. This relates especially to the rejections of projections 3, 7, and 9, which anticipate that technological progress will not occur very quickly and will not threaten the role of humans in the auditing process.

5.2 Limitations

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As with all research, this study has several limitations. First, the scope of examined technologies was limited to keep the length of the survey short and to avoid a higher termination rate. Therefore, not all possible relevant technological aspects could be addressed in this study. Rather, we focused on

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current, advancing key technologies and the general automation of digital processes (big data analytics, AI, and blockchain technology). The impact of other advancing technologies, such as cloud computing

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or as yet unknown technologies, was not assessed. An expansion of this approach could yield promising results.

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Second, a professional bias in the expert panel must be acknowledged. As stated in Projection 18, IT skills could become more important than fundamental business administration, accounting, and

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auditing skills. This possibility is not adequately represented in the panel, which predominantly consisted of auditors. Experts who work in a field tend to be more optimistic about the future

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developments of their field than outsiders (Sackman, 1975). A larger number of experts could have led to a less conservative scenario. A study involving more IT-oriented panel could have been more

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negative.

6. Conclusion

In this study, 20 projections about the future of the auditing, caused by automating digital processes, were developed and evaluated by a panel of German auditors, university professors of auditing, regulators, and IT experts of auditing software.

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The resulting scenario showed that the respondents did not expect major changes for the auditing practice within the next five to ten years. The findings demonstrate that professionals have a rather optimistic view about the impact of technological progress on their field. These findings appear surprising considering the massive changes digitization has caused in most established industries worldwide, including Germany. The findings contradict Frey and Osborne’s (2017) prediction of an extinction of the accounting profession, similarly affecting auditors. In contrast, the respondents in the German Delphi study saw no major threats. Technological progress is expected to support rather than to threaten the auditing profession.

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Nevertheless, the expectation that everything stays, more or less, the way it currently is has to be considered potentially dangerous. Big data analytics, AI, blockchain technology, and other advancing technologies have the potential to extinguish any human interference in the auditing process. In

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particular, big data could complement original accounting data and enhance reporting quality. AI could conduct the same audit procedures and judgments as a human colleague in the future. And when every

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single transaction is decentrally verified by numerous blockchain members, an overall quality seal could

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appear redundant.

Disclosures

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This research did not receive any specific grant from funding agencies in the public,

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commercial, or not-for-profit sectors.

Acknowledgements

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We would like to thank Robert Larson and the two anonymous reviewers for their valuable and constructive comments on a prior version of this paper.

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