A case study of : how the incorporation of Data Analytics in auditing impacts the accounting profession

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A case study of: How the incorporation of Data Analytics in

auditing impacts the accounting profession

Name: Michael Trinh Student number: 11371870 Thesis supervisor:

Date: June 24, 2017

Word count: 6,060 (Thesis is unfinished)

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

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2 Statement of Originality

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

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

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

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1 Introduction

1.1 Background

It’s the management’s responsibility to provide financial statements and it’s the auditor’s responsibility to give the right audit opinion. With an auditor opinion the auditor argues that he is convinced of the fairness and accuracy of the provided financial statements. When an auditor gives an unqualified opinion, it means that the organization does indeed meet these requirements of them depicting a fair and accurate picture of the (financial) state that the organization is in (Hayes & Wallage, 2014).

An auditors opinion always carries the risk of being too subjective a thus more susceptible to bias (Bazerman, Loewenstein, & More, 2012). Previous research conducted by Broeze (2006) suggests that because of the lack of knowledge by auditors in statistical analytics, an auditor’s opinion is less reliable. This is due to the fact that auditors have little understanding of statistical principals and therefor rely too much on subjective risk analyses.

Earley (2015) does mention that in other field’s Data Analytical tools are used to fill up the gabs that a lack of knowledge leaves behind. A research by Dowling & Leech (2014) states that using Data Analytical tools could indeed fill up these gabs, which would mean that audits would improve in terms of accuracy and assurance. This naturally sparked the interest of accounting firms due to the competitive advantage it could deliver.

Research however shows that most accounting firms only use Data Analytics for consultancy purposes and that almost no Data Analytical tools have been implemented in financial audits, which is the core business after all (Earley, 2015).

So even though previous literature show that Data Analytical tools have in been implemented in information systems. There is still a lack of research on the actual use of Data Analytics on the core business of an accounting firm, which is financial audits (Dowling & Leech, 2014).

A research by Chan and Vasarhelyi (2011) states that adopting more Data Analytics into auditing will most likely change the role of an auditor. A side from the reduced labor and time which improves the efficiency and effectiveness of an overall audit (Elliott, 1998). Automatization also requires change in the sense of new procedures such as standardization of data collection and formalization of internal control policies (Chan & Vasarhelyi, 2011).

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1.2 Research question and contribution

For Data Analytics to be fully incorporated in financial audits it is important to study the role of all actors that are involved in the process that allow for technologies like Data Analytical tools to be used in the field. Although Data Analytics already is being used within public accounting firms mostly for consultancy purposes and hardly for audits which is the core business.

I will conduct a research on the process of integrating Data Analytics in financial audits. This provides me with a unique opportunity to actively participate in contributing to the audit field. Since there is a lack of research on the actual use of Data Analytics in financial audits. This case study will attempt to answer the following research question:

‘’How does the institution impact the use of Data Analytics in financial audits?’’

Therefor I will look into the ways Data Analytics are used in the accounting field now and find out what the views are of important actors within the field. In order to do so I will need to get a clearer overview of who these actors are and what their influence might be in integrating innovation into the audit field. I will investigate the roll of the auditor in this process and look further into how they have been influencing this process until this point. This way I will be able to find out the current state of Data Analytics in regards with financial audits and why it is in this state.

By conducting a research on the actual interaction between important actors and agents in this process I could gain a better view on why Data Analytics is not being used more frequently in financial audits. This way I will be able to conclude whether it is due to sociological or technological tendencies that are causing the institution to hesitate on adopting innovative technologies such as; Data Analytics in audit field.

Gaining insight on what drives actors to (possibly) delay this process is a good match with the perspectives of institutional work which looks into the ‘why and how’s’, and less on ‘what and when’s’. This research contributes to the existing theory regarding institutional work, but can also serve as a starting point for (further) adopting Data Analytics into the audit field.

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2 Literature

This chapter provides an overview of the existing literature on Data Analytics which explains the definition and processes with regards to Data Analytics. It also contains literature about the impact Data Analytics might has on the audit role and auditor behavior.

2.1 Data Analytics

The section will be dedicated to give a better understanding of Data Analytics which can be distinguished in two components; Big Data and Data Analytics. To actually get a better overview and to gain a better understanding about both definitions this section will start by explaining how data gets collected to form Big Data. It is worth mentioning that there is a lack of in-depth research on Data Analytics, and since technical innovations have been growing so rapidly it is making this subject interesting but complex at the same time.

Data Analytics derives from Big Data which on his turn derives from the Internet of Things. The Internet of Things can be described as everyday devices that are connected to the internet which are able to identify themselves to other computer systems, devices such as; smartphones and smart refrigerators but also public devices such as check-in portals and vending machines. All the collected identifiable data than forms Big Data, making it possible to be analyzed, turning it into Data Analytics (Syed, Gillela, & Venugopal, 2015).

Most recent example is Facebook which has been in the crossfire of privacy policy issues (Singer, 2018). The whole affair has been about whether and which data they are legally allowed to share, and to which extend this is controllable and transparent for users. This kind of data is a prime example of how Big Data is formed and being used for commercial purposes. According to Vasarhelyi, Kogan and Tuttle (2015) Big Data can be defined in several ways, depending on the industry or field. In the case of the accounting field Data Analytics could be used to test full data sets rather than only testing samples.

However, even when specifically focusing on the accounting field Vasharhelyi, Kogan and Tuttle (2015) do mention that even within the field there are some differences in the approach with regards to the definition. A smaller accounting firm naturally has a smaller accounting information system, making Big Data actually much bigger when compared to the size any big four accounting information system.

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7 Another approach by McAfee & Brynjolfsson (2012) uses volume, velocity and variety as the three pillars to describe Big Data. Volume because of the size of each data set, and it increasing every second. Velocity because of the speed that a data set is created, real time or nearly real time. And variety because of all the different forms it used to gather this information, from social media usage to GPS signals it’s all possible due to technical innovations that took place in the last century.

Runkler (2012) defines Data Analytics as the application of computer systems used to collect and analyze large data sets which helps in the decision-making process. His approach uses a four phased process; preparation, pre-processing, analysis, post-processing. Preparation which is the first phase mostly consists out of planning and data collection and selection. Pre-processing the second phase, involves; filtering, cleaning, correcting, completing and transforming data. The third step, the analysis involves running regression, correlations, forecast and classifying the data. Post-processing, the final phase involves documentation, evaluation and conclusions.

Coa, Chychyla & Steward (2015) describe big as the process of; ‘’inspecting, cleaning

transforming and modeling Big Data with the aim to communicate useful information and patterns, suggest conclusions to support decision making’’. In his same article it is also

mentioned that Data Analytics used in different fields might indeed be also applicable to financial auditing.

The existing literature from different authors tend to describe Data Analytics in comparable manner, however these authors due tend to label these procedures differently and might use more or less phases to do so. Overall the term Big Data is a definition used to describe the gigantic portfolio of data that is growing rapidly (McAfee & Brynjolfsson, 2012). However, Big Data itself as limited value until it actually gets processed and analyzed. This is where Data Analytics comes in, the process of analyzing data with the objective of drawing conclusions used to strategical decision making (Runkler, 2012).

2.2 Impact Data Analytics on audit behavior

In existing research Vasarhelyi, Kogan & Tuttle (2015) state that both accounting academics and practitioners can benefit from gaining more knowledge about the potential of innovative technologies such as; Data Analytics and the challenges which come along when trying to incorporate them (more) into the auditing field.

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8 Although Data Analytics is recognized as an innovative technology which in potential could significantly change accounting as we know it today (Vasarhelyi, Kogan, & Tuttle, 2015). According to Alles (2015) the accounting profession has been showing tendencies of repetitively lagging when it comes to incorporating innovative technology into their field. Alles (2015), in his research specifically doubts the (un)willingness of actors within the accounting institution to adopt technologies such as; Data Analytics.

In previous research by Brown-Liburd, Issa & Lombardi (2015) this is confirmed. However, they do add that the incorporation of Data Analytics in the audit field is indeed likely to have some behavioral implications from auditors which could lead to more tempered benefits. These behavioral implications arise from (a lack off) educational background. Throughout their education auditors are thought traditional audit procedures which might not be suitable for effectively and efficiently using Data Analytical tools or even analyzing Big Data. Brown-Liburd, Issa & Lombardi (2015) also mention that although Data Analytics has its benefits, it will also stumble upon its own shortcomings much like the traditional audit procedures.

Brown-Liburd, Issa & Lombardi (2015) discusses four issues that might affect the audit behavior which has direct influence on the audit judgement; ‘’information overload,

information relevance, pattern recognition, and ambiguity’’.

Information overload is described as receiving way too much information to process at once. So much information that it might actually lead to a negative effect on the audit judgement as the judgement might have been done ‘sloppy’ (Brown-Liburd, Issa, & Lombardi , 2015).

Information relevance which also has something to do with the amount of information, is described as an issue in which it is difficult for an auditor to separate the relevant information from the less relevant information. Because too much irrelevant information is likely to negatively affect the auditor judgement quality (Brown-Liburd, Issa, & Lombardi , 2015).

Pattern recognition is described as the capability to search and recognize patterns in an ‘overload’ of information. By recognizing patterns in financial information that may suggests misstatements an auditor can actually assess the risk of a client. The ability to do so using Data Analytical tools can quite easily be educated and trained (Brown-Liburd, Issa, & Lombardi , 2015).

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9 The last issue is ambiguity which tends to come along with Big Data, which is quite unstructured in nature. Big Data often comes in different formats and structures which makes it more complex to use or even create a tool that is compatible to different formats and structures (Brown-Liburd, Issa, & Lombardi , 2015).

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3 Theory

3.1 Theoretical framework: Institutional work

Cohen et al. (1979) is a research article which focusses on the changes in organizational structure due to (innovative) technology. They state that organizational structure tends to adapt to changes in technology. Using Data Analytics in financial audits can be classified as such a new innovative technology that can disrupt the structure of a public audit firm. The research mentions that the adoption of technology may also depend on the organizations management staff, its behavior and actions.

Institutional work is frequently used as framework in the accounting research field to get a better understanding of changes in organizational structure (Currie, Lockett, Finn, Martin, & Waring, 2012).

The way organizational institutionalism is understood varies, while Bjerregaard (2011) reports that in order to get insight on the adoption of an innovative technology within an existing institution: It is important to recognize that changes in organizational structure can arise in several ways and start from several levels within an organization; top-down, bottom-up. It is important to mention the interplay between social and technological factors, specifically when researching the process surrounding the adoption of technology in an existing institution, and having the behavior of actors in play.

Where previous research focuses more on the process, more recent research by Lawrence and Suddaby (2011) emphasized more on the behavior and actions of actors within an organization when incorporating technology into an existing field like auditing. It is stated that institutions arise from purposeful human actions and reactions with one individual key agent of change (Lawrence & Suddaby, 2006).

This research will give insight in the influence that actors within an institution have when it comes to adopting new technology; Data Analytics into the audit field. Like mentioned in the paragraph above this research will indeed focus on the social elements by looking into the interactions between actors and agents within the institution to gain knowledge of who’s and why’s.

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11 Lawrence and Suddaby (2006) describe institutional work as a rough life-cycle. This cycle contains three stages; creating, maintaining, and disrupting. Each of the stages contain several sets of practices which will be discussed and further explained in the next paragraphs.

3.2 Three stages of Institutional work

3.2.1 Creating institutions

This stage of institutional work can be characterized as the ability for establishing rules and constructing rewards and sanctions that enforce these rules. ‘’Creating institutions highlights

the effortful and skillful practices of interested actors in order for them to create institutions’’

(Lawrence & Suddaby, 2006, p. 228). This ability can be gained through political and economic processes in which an actor establishes a superior position in the field. Within this stage Lawrence and Suddaby (2006) define nine forms of institutional work. In the paragraphs below, these nine forms are separated in three distinguishable categories, and explained in detail. Advocacy, defining and vesting

The first three forms of creating institutional work involve; ‘advocacy’, ‘defining’ and ‘vesting’ which discuss political work that actors have to go through to be able to restructure rules, property rights and boundaries regarding the access to material resources (Lawrence & Suddaby, 2006, p. 221). Combining these three mainly rule based driven forms enables them to strengthen each other which enables the reconstruction of an institution and its structures and practices. Below is a clear description of the three individual forms.

Advocacy is an important form of creating institutional work, ‘advocating’ involves the process of gaining regulatory and political support by lobbying for resources, promoting agendas and proposing new legislation which causes it to be a threat to the existing legislation (Lawrence & Suddaby, 2006, p. 221). When used effectively, advocacy can be a powerful tool that permits actors to influence the way norms are perceived and followed. This form enables actors to change an institutional environment and gain cognitive legitimacy (Lawrence & Suddaby, 2006, p. 222). Lawrence and Suddaby (2006) also mentioned that these different forms of advocacy as mentioned above can allow less powerful actors within the institution to shape the institution environment and acquire cognitive legitimacy.

The second form of creating institutional work is defining, Lawrence and Suddaby (2006) describes defining as; ‘’the construction of rule systems that confer status or identity, define

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boundaries of membership or create status hierarchies within a field’’. Defining involves the

creation of standards and actor certification. From the empirical research examined by Lawrence and Suddaby (2006) it looks like most defining work is more focused on actors and the creation of constructive rules, or rules enabling institutional action. However, compared to the prohibitive nature of regulatory activity, defining emphasizes more on establishing parameters of the future or potential institutional structures and practices (Lawrence & Suddaby, 2006, p. 222).

Third creating institutional work form; Vesting involves the work focused on the creation of rule structures regarding property right. Vesting arises when government authority is used to reallocate property rights (Lawrence & Suddaby, 2006, p. 222). This form of institutional work refers to the micro-processes when creating new actors and new field dynamics by changing the game and its rules of the existing market relations. A common component of vesting which is the negotiation of a regulative bargain between the state or another powerful authority and third party interested actor (Lawrence & Suddaby, 2006, p. 223).

Constructing identities, changing normative associations and constructing normative networks

The second category of creating institutional work refers to the three forms of interaction; ‘constructing identities’, ‘changing normative associations’, and ‘constructing normative networks’. By slowly persuading actors to think differently, slowly influencing them to alter their ways of thinking. Lawrence and Suddaby (2006) refer to this category as the three forms of interaction that provide the basis of forming new institutions. All three forms of interactions focus on the roles, values and norms that are associated with an institution. The institutionalized rules created often complement or are parallel to the established institution. For normative work to succeed it is important to mention that diverging too much due to a conflict within the collective could reduce its force, which gives other actors chance to take over. Below is a clear description of the three individual forms.

Lawrence and Suddaby (2006) refer to ‘constructing identities’ as the form that lays central to the creation of institutions because it describes the interplay between an actor and its field. In institutional theory, the construction of identities have been commonly associated with the development of professions. Which underlines the importance of collective action in accomplishing the construction of identities as a form of normative institutional work (Lawrence & Suddaby, 2006, p. 224).

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13 The second form of this category; ‘changing normative associations’, is a different form of interaction but also with the aim on creating new institutions. The work involves reformulating normative associations, which means; re-making the connections between sets of practices and its moral and cultural foundations (Lawrence & Suddaby, 2006, p. 224). Time has learned that these practices have frequently led to new institutions, complementary or similar to existing institutions. This form of interaction however, did not involve a direct threat or challenge to pre-existing institutions, but rather supported them. At the same time this lead to actors to questioning them. Changing normative associations refers to manipulating the relationship between norms and the institution that created them (Lawrence & Suddaby, 2006). The third form of creating institutional work in this category aims to create new institutions and is referred to as; ‘constructing normative networks’ by Lawrence and Suddaby (2006). Normative networks are interorganizational connections through which practices become an institution and institutional work. These normative networks possess the power to provide normative compliance, monitor and evaluate of these practices. This form brings together actors from different fields, to form a coalition that is able to alter the relationship between actors in a field by changing the normative assumptions.

Mimicry, theorizing and educating

The final category focusing on the creation of institutions involves; ‘mimicry’, ‘theorizing’ and ‘educating’. Lawrence and Suddaby (2006) describe the actions within this category as; ‘’actions designed to alter abstract categorizations in which the boundaries of meaning systems are altered’’ (Lawrence & Suddaby, 2006, p. 221). These three forms of institutional work emphasize on the cognitive elements of an institution. Cognitive elements contain; ‘’the beliefs, assumptions and frames that inform action by providing meaningful and understandable interaction patterns to be followed’’ (Lawrence & Suddaby, 2006, p. 228).

Mimicry is described by Lawrence and Suddaby (2006) as work that draws on existing institutional work, in order to articulate and legitimate new practices and structures. This means that existing institutional patterns can be used in the process of creating a new institution by leveraging existing sets of practices within these institution. Associating new elements with existing elements in some way might ease the adoption because it decreases the contrast between new and old. Part of the success of mimicry in creating new institutional structures is that it associates new elements with older once to make these new structure more clear and accessible, while highlighting the insufficiencies and shortcomings of past structures.

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14 Theorizing is ‘’the development and specification of abstract categories, and the

elaboration of chains of cause and effect’’ (Lawrence & Suddaby, 2006, p. 226). In other

words; the development of concepts and beliefs to support new institutions (Lawrence & Suddaby, 2006, p. 228). A key aspect in theorizing is highlighting new concepts and practices, giving them the chance to become part of the part of the cognitive map of the field. Lawrence and Suddaby (2006, p. 226) refer to this as ‘naming’, which represents the first step that provides the foundation a chance for further theorizing. This however does not mean that naming could be referred to as agreeing to these concepts and practices.

Educating is the last form of institutional work in this category, this form aims at educating actors with the skills and knowledge which enables them to participate in new practices or interact with new structures in support of the new institution (Lawrence & Suddaby, 2006, p. 227). This is important because new institutions are likely to introduce information which was unknown before, therefor it is needed to educate actors with additional skills and knowledge they need (Lawrence & Suddaby, 2006, p. 228).

3.2.2 Maintaining institutions

This stage of institutional work can be characterized as the actions needed to maintain institutions. As most institutions naturally don’t have the ability to automatically reproduce itself over time, the emphasis of this stage will be focusing on the work that is required to continue an institution, therefor it is likely that a lot of maintaining work is needed. Lawrence and Suddaby (2006, p. 230) mention that the work of maintaining institutional work in generally involves; supporting, repairing or recreating social mechanisms that ensure compliance. Within this stage Lawrence and Suddaby (2006) identify six forms of institutional work which focus on maintaining an institution. In the paragraphs below, these six forms are separated in two distinguishable categories, and explained in detail.

Enabling work, policing and deterring

The first three forms of maintaining institutional work involve; ‘enabling work’, ‘defining’ and ‘deterring’, these three forms mainly focus on the maintenance of institutions by ensuring adherence to rule systems. These three forms together form a force that is an important foundation and key pillars for an institution. Without these three forms the foundation of an institution would be likely to crumble, leaving only empty threats and promises rather than

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15 self-activating means of institutional control. Below is a clear description of the three individual forms.

‘Enabling work’ is described by Lawrence and Suddaby (2006) as the creation of rules to enable the survival and continuation of institutions and their respected activities. This means providing them with facilities, supplements and support, even if this also involves the creation of authorizing agents, new roles or diverting resources (Lawrence & Suddaby, 2006, p. 230).

Lawrence and Suddaby (2006) describe maintaining an institution through enforcement, auditing and monitoring as policing. Policing of is used to ensure compliance, the activities of policing can involve the use of sanctions and inducements, frequently used together at the same time by the same agent (Lawrence & Suddaby, 2006). However, Lawrence and Suddaby (2006, p. 231) do mention that the use of enforcement to ensure compliance is not always a necessary strategy in professionalized fields, because in these cases usually auditing and monitoring is enough to ensure compliance.

The third and final form of maintaining institutional work with the aim to ensure compliance is called deterrence. Deterrence focusses on ‘’establishing coercive barriers to

institutional change which involve the threat of coercion to inculcate the conscious obedience of institutional actors’’ (Lawrence & Suddaby, 2006, p. 232). Lawrence and Suddaby (2006,

p. 232) also mention that effective deterrence is very dependent on legitimate authority of a coercive agent and that it may originate from the threat of economic coercion.

Valourizing and demonizing, mythologizing and embedding and routinizing

The final three; ‘valourizing/demonizing’, ‘mythologizing’ and ‘embedding & routinizing’ are more focused on maintaining an institution by reproducing existing norms and belief systems. It’s worth mentioning that coercive work, which is rule-based used to maintain institutions are more visible than their cognitive or normative counterparts. Actors involved in such work, as well as the complying actors are aware of the effects and purpose of these actions (Lawrence & Suddaby, 2006, p. 232).

Valourizing and demonizing involve work wherein actors identify and review the morality of participants within the field to enact the institutionalized beliefs, but also as a way of maintaining the power of those beliefs (Lawrence & Suddaby, 2006, p. 232).The work of maintenance provided by the form of valourizing and demonizing contains giving positive and negative examples to demonstrate the normative foundations of an institution. As results of

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16 these positive and/or negative examples the state of the institution can get reinforced or deteriorated (Lawrence & Suddaby, 2006, p. 232).

Mythologizing represents a form of maintaining an institution that focusses on the past, rather than the present. An important way in which actors aim to preserve the normative pillars of institutions is by mythologizing their history. This means providing work ‘’to create and

sustain a myth using a story and occasion to tell it’’ (Lawrence & Suddaby, 2006, p. 233).

Embedding and routinizing the last form within this category of maintaining institutional, Lawrence and Suddaby (2006, p. 233) characterize this form as reproducing through the stabilizing influence of embedded actions and routine practices. Containing actions and practices such as; training, education, hiring and certificated routines and ceremonies of celebration. This form of embedding and routinizing involves integrating these embedded and routine practices into the participant’s daily routines and organizations practices (Lawrence & Suddaby, 2006, p. 233).

3.2.3 Disrupting institutions

The last stage of institutional work are the practices aimed at disrupting an institution, this stage involves an offensive or undermining attitude towards the mechanisms in place at existing institutions. Until today not much is known about disruptive work, and most of what is known does not come directly from consciously undermining an existing institution. Also the use of disruptive work usually takes an accomplished, experienced actor with a lot of knowledge about the profession, for it to have the right effect. This stage of institutional work focusses mainly on the relationship between an institution and the social controls that give them the ability to continue their practices. This usually involves; ‘’disconnecting rewards and

sanctions, disassociating moral foundations and undermining assumptions and beliefs all disrupt institutions by lowering in some way the impact of those social controls on non-compliance’’ (Lawrence & Suddaby, 2006, p. 238). Lawrence and Suddaby (2006) identify

three forms of institutional work which focus on disrupting an institution; ‘Disconnecting sanctions’, ‘disassociating moral foundations’ and ‘undermining assumptions & beliefs’. In the paragraphs below, these three forms are explained in detail.

Disconnectingsanctions,disassociatingmoralfoundationsandunderminingassumptions&beliefs

Disconnecting sanctions/rewards is described by Lawrence and Suddaby (2006, p. 235) as a form of work within the institutional work aimed at disrupting an institution by disconnecting

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17 sanctions and/or rewards. This form involves undermining attitude towards the mechanisms in the existing (leading) institution. A form of undermining is working state and non-state actors through state-systems to disconnect sanctions and rewards from set certain set of practices, technologies or rules (Lawrence & Suddaby, 2006, p. 235). This is mainly done using jurisdiction that has enough impact to directly invalidate the former powerful institutions (Lawrence & Suddaby, 2006, p. 238).

Disassociating moral foundations is described as the potential to ‘’disassociating the

practice, rule or technology from its moral foundation as appropriate within a specific cultural context’’ (Lawrence & Suddaby, 2006, p. 236). So rather than directly attacking the

foundations, the institution are getting disrupted by indirect sets of practices and actions which undermine this same foundation. Lawrence and Suddaby (2006, p. 237) also found that actors who use these dissociative practices and actions to disrupt an institution are likely to be the largest firms and professional associations, the so called ‘elites’ within the profession have quite a high status within the field due to their level of prestige. While these elites first adopted new practices on the basis of technical reason, studies have showed that actors who were used to using their status to develop and spread technical changes that justified being different. Which put late adopter under pressure to follow and reform (Lawrence & Suddaby, 2006, p. 237).

The last form of disruptive institutional work is undermining assumptions and beliefs. Like the name implies this form of disrupting involves undermining the supposed effects with regards to the changes that established institutions have brought. But it can also involve an innovation that overrides rules or assumptions made by established institutions, or just by slowly proving a concept by an established institution wrong. So although there is not much documentation about these forms of disruptive work, Lawrence and Suddaby (2006, p. 237) still were able to distinguish two different types within this disruptive work. The first one is being breaking the existing institutional assumptions by innovation. And the second one being gradual undermining of existing institutional assumptions through contrary practice.

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4 Methodology

4.1 Research setting

As mentioned in the introduction the goal of this thesis is to provide more insight on why Data Analytics is not being used more frequently in financial audits. As theoretical frame I chose institutional work because this theory applies for the subject ‘why and how’, which is a match with my overall research which investigates the process in which an innovation gets incorporated in the audit field.

This is interesting because all though previous literature shows that Data Analytical tools have indeed been implemented in information systems. There is still a lack of in depth research on why there perhaps is, still a delay on the actual use of Data Analytical tools in the core business of an accountancy firm; financial auditing (Dowling & Leech, 2014).

Through means of a thesis internship at a big four firm a possibility was opened to indeed gain more insights on the process of these developments. The reason for conducting a case study at a Big Four firm is that they are on top of the accounting market, and thus are likely to be the best representative of the changes in the accounting field. More specifically the Big Four firm involved in this case study; has one of the biggest if not the biggest Data Analytics and innovations department throughout all the Big Four firms.

4.2 Data Collection

Data will be collected through semi-structured interviews, with notable actors that are involved in the process of these developments, but also with financial auditors who will be working alongside the Data Analytical tools. Objective is to find out the why’s and how’s why there is a delay in the use of (more) Data Analytical use, but also to find out how Data Analytical tools are operationalized and experienced by actors in the (financial) audit field.

For this research to be successful it is needed that the interviewees work on different positions and levels within the organization. In order to paint a complete picture and get a variety of points of views with regards to the process of incorporating (more) Data Analytics into financial audits, interviewees should have different specializations as well, from Data Analytics and IT to financial audits.

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19 Interviews will be held in English as much as possible to maintain consistency making it directly transcribable, however if interviewee wishes for the interview to be held in Dutch, than this interview will be transcribed and translated to English.

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