Effects of AI on the Accounting Profession and Providing Possible Solutions to Its Negative Effects

Introduction

Background

Many companies around the world are redesigning their business processes to maximise their operational efficiencies (Kirschner & Stoyanov 2018). The accounting industry is one of the key tenets of corporate operations that are experiencing such changes (Cortese & Walton 2018). Their effects are heightened by the introduction of artificial intelligence (AI), which has been linked to high levels of productivity, accuracy and low operating costs (Fox 2018). Fox (2018) defines AI as the ability to help machines think, almost as a human being does. This type of technology equips machines or computers to make predictions about operational issues and adjust them to boost performance (Kirschner & Stoyanov 2018). Similarly, they can adapt to their environments the same way as human biology equips people to do the same (Carriço, 2018).

Professions that require the meticulous implementation of tasks are deemed the biggest beneficiaries of AI because it adds another layer of efficiency to their tasks (Birtchnell 2018). This outcome often occurs by extending a computer’s normal input and output processes to increase productivity (Felzmann, Villaronga, Lutz & Tamò-Larrieux 2019). Based on a general review of industry reports, AI is increasingly being used to conduct administrative duties and present accounting results in business through various structural changes (Fox 2018). Some common accounting tasks linked with AI include quarterly close procedures, procurement planning, developing accounts payables, answering audit queries and expense management (Ferri, Lusiani & Pareschi 2018).

Kirschner and Stoyanov (2018) support the above assertion by saying that most bookkeeping tasks, which are traditionally associated with accounting, are already being partially done through AI. For example, the technology is already being used to compute accounts payables and receivables (Geiger 2017). Relative to this discussion, observers have reported that some accounting firms have already integrated AI tasks to initiate payments and match purchase orders (Kirschner & Stoyanov 2018).

Some data entry procedures are also being undertaken using AI (Geiger 2017). The same outcome is true for data categorisation processes. For example, some global accounting firms, such as Deloitte, have adopted an advanced usage of AI to analyse the language used in contracts when reviewing client agreements (Birtchnell 2018). They have also relied on the same processes to analyse financial trends (Geiger 2017).

The healthcare field has also started using AI to improve industry outcomes and enhance efficiency (Pakdemirli 2019a; Liu, Keane & Denniston 2018). For example, some facilities use it to analyse healthcare claims for thousands of patients (Pakdemirli 2019b).

In this regard, potential complexities have been observed during the initial stages of claim processing (Pakdemirli 2019b). The accounting sector has also used AI in the healthcare field by subjecting transaction approval processes, which are ordinarily done by human beings, to linked processes (Cortese & Walton 2018). Consequently, payroll, auditing and tax remittance procedures are increasingly being undertaken by AI-aided systems (Birtchnell 2018). A broad review of these processes and their importance to accounting tasks shows that AI has the potential to oversee the implementation of complicated tasks, which have been traditionally a preserve of human intelligence (Bechmann & Bowker 2019; Mann 2017).

The trend towards the adoption of AI in accounting partly stems from increased work pressures that have forced most professionals to look for alternative resources for completing their project tasks (Rana 2018). Consequently, artificial intelligence (AI) has emerged as a technique that works much as human resources do in completing organisational tasks (Ferri et al. 2018). The only difference it has with other methods is that it is much more efficient and effective in completing accounting processes (Carriço 2018). It has also gained popularity among most organisations because it could help managers to complete tasks quicker than when they use conventional ways and without asking for a salary at the end of the process (Carriço 2018). This is why some managers prefer it to human labour (Rana 2018).

Stano, Kuruma and Damiano (2018) say that artificial intelligence significantly escalates the level of computing in the accounting field. Notably, it helps professionals to make appropriate changes in their decision-making processes that would be beneficial to the realisation of their professional or industry goals. In this regard, computers can adjust systemic processes through machine-based learning, as would be the case if human beings did the same job (Rana 2018). The accounting profession could significantly benefit from this capability because it is characterised with the fulfilment of role tasks and the improvement of human capabilities (Rana 2018; Carriço 2018). Firms or companies that have developed a strong competitive advantage in the last decade based on this competence could attribute their success to the AI development platform.

The MIT-Boston Consulting Group is one organisation that supports the above-mentioned assertion because it recently reported that about 80% of professionals believe that AI contributes to the development of competitive advantages in the accounting field (Rana 2018). The same report suggested that about 70% of people associate improved productivity in organisational processes to technological development (Rana 2018). This idea has encouraged many accountants to increasingly relying on AI to assess large volumes of data – a process that was previously time-consuming when done by human beings (Stano et al. 2018).

Small and medium-sized organisations do not have the same resources that large organisations do to employ AI or develop associated products for their internal processes (Carriço 2018). However, Dahbi, Ezzine and Moussami (2017) say that AI will become more readily available and affordable to different firms in the future. Already, industry observers have demonstrated the positive impact of this technology on the marketing sector and predict that the accounting field would not be spared either (Carriço 2018). Indeed, AI technology has created significant cost reductions and improved productivity in multiple business sectors (Kirschner & Stoyanov 2018). The same benefits are likely to be realised in the accounting field as well. This advantage is in addition to the gains made through improved accuracy and precision in the development of new products and the provision of quality services (Kirschner & Stoyanov 2018).

Although AI is set to revolutionise how industry processes are undertaken, generating all this information without the involvement of an accountant would be unwise because there is a place for human intervention in accounting processes, especially when making meaningful conclusions from the data generated (Dahlin 2019). In other words, even though AI could lead to job losses, there is a place for accountants in the analysis of data. However, their professional roles need to change from the traditional model, which is akin to the use of a calculator to make sense of data to a consultancy and advisory role that gives direction on what to do about the pieces of information obtained (Dahbi et al. 2017). This way, they would be advancing the profession and at the same time helping their clients to grow.

Artificial Intelligence poses several advantages and disadvantages to the accounting field. However, most professionals in this sector are ill-equipped to manage them (Cortese & Walton 2018). The need for training is consequently emphasised to equip them to not only respond well to this trend but also understand its potential impact on their careers (Rana 2018). For example, Levine (2019) posits that artificial intelligence could change a company’s structure, while Oleinik (2019) adds that it may have a significant impact on organisational culture as well. Therefore, managers have to make changes to embrace some of these developments, as they need not oppose the trend but exploit it to realise their organisations’ objectives. This dissertation focuses on investigating the effects of AI on the accounting profession and providing possible solutions to its negative effects from the prisms of education and training. The purpose of the study is explained below.

Problem Statement

Although artificial intelligence is largely regarded as the frontier technology tool for the next generation of business progress, there is little understanding of its impact on different fields of business and accounting (Clarke, Chambers & Barry 2017). In this regard, many uncertainties are associated with artificial intelligence because, through a poor understanding of the concept, myths and misconceptions about its effects are widespread (Cortese & Walton 2018). For example, questions linger regarding the probability of artificial intelligence to solve all problems humans can address (Clarke et al. 2017). Here, issues relating to the limits of artificial intelligence should be reviewed because the debate revolves around whether human and artificial intelligence are equal.

Another issue that affects the implementation of AI in the accounting field is whether it is dangerous or potentially harmful to the livelihoods of professionals in the sector considering it could replace their work. The bigger question is whether it could be deduced that machines can behave ethically (as human beings) or whether ethics should be disregarded in totality when using artificial intelligence.

Frude and Jandrić (2015) delve further into this argument by questioning whether a machine can have a mind that is similar to a human being. They also ask whether it could be conscious of the challenges or opportunities that exist in the real-world professional environment (Frude & Jandrić 2015). Questions regarding whether a machine can be treated with the same rights accorded to a human being also arise in such debates and it is still unclear how they can be solved in a world where people are still demanding for their rights in different social and political fields.

Purpose of the Study

There has been a lot of scepticism about AI in different fields of business management (Frude & Jandrić 2015). The same reservations have been noted in the accounting field because many professionals are still unclear about its effects on the practice and its relevance to the field (Dahlin 2019). Consequently, some researchers have developed negative attitudes towards it (Dahlin 2019). Nonetheless, the rate of development in artificial intelligence is increasing and it is worrying many professionals who believe that it would replace their jobs or make them irrelevant to the field (Dahlin 2019).

Although the development and integration of AI in the accounting field are still in their infancy stages, there is a need to properly understand the challenge and benefits of artificial intelligence in the accounting field, especially from an education and training point of view. The focus on training and education is pursued in this study because the integration of human and artificial intelligence depends on the effective understanding of AI through similar processes (Frude & Jandrić 2015). This need is supported by the fact that many accounting professionals are unfamiliar with AI and unaware of how it would impact their practice (Dahlin 2019).

Regardless of their lack of misinformation, the human element of accounting cannot be ignored even in the wake of increased developments in the AI field. Therefore, while developments in AI continue to improve through rapid technological changes, human intelligence needs also be improved through education and training. This is why this paper adopts a similar focus in understanding the challenges and benefits of artificial intelligence in the accounting field.

Aim

To investigate the effects of AI on the accounting profession and providing possible solutions to its negative effects from the prisms of education and training

Research Questions

1. How does AI affect the performance of accounting professionals?
2. Have there been any changes in employee attitudes toward AI in the past five years?
3. What factors could influence changes in the attitudes of accounting professionals towards AI?
4. How can the attitudes of accounting professionals towards AI be improved?

Overview of the Research

This study will be a qualitative investigation aimed at understanding the challenges and benefits of AI in the accounting field. The implications of the technology on the discipline will be based on its implications on education and training. The phenomenology research design will form the research design for the review because AI is considered a phenomenon in the business world (Fox 2018). The research informants will be professionals who worked in accounting firms that have used AI.

They were based in China and contacted by the researcher using the WeChat platform – a popular social media tool in China. Data will be analysed using the thematic and coding method, which is generally associated with the review of qualitative data. However, before delving into these discussions, it is important to conduct a review of past findings. The literature review section outlines the findings.

Literature Review

Introduction

This chapter provides an overview of what other scholars have written about the study topic. Key sections of the chapter explain attitudes towards AI, its advantages, application across different industries and its connection with the accounting field. At the end of the chapter, the research gap, which necessitates this study, will be provided. The subsection below highlights the advantages of AI.

Advantages of AI

Several researchers take a positive view of AI by saying that its benefits cut across different industries and functions (Cortese & Walton 2018; Ferri et al. 2018). However, most of them suggest that its early stages of adoption and implementation were confined in consumer application systems (Felzmann et al. 2019). Over time, the trend has changed and businesses are increasingly adopting it in different aspects of their operations with striking results (McKinsey & Company 2019).

Their findings have shown that AI can improve business applications in different areas of operation, including predictive maintenance and the detection of anomalies in factor line assemblies (McKinsey & Company 2019). These advantages are ordinarily observed when high volumes of high-dimensional data are used in AI (McKinsey & Company 2019).

The aviation sector provides an example of an industry that has effectively integrated AI applications in its operations (Kaartemo & Helkkula 2018). For example, aircraft manufacturers have used the system to improve the detection of engine problems (McKinsey & Company 2019). In some cases, technology has been used to autocorrect operational functions (Fenwick & Edwards 2016). The transportation sector has also had a similarly successful application of AI technologies in delivery route maintenance, optimisation of service orders and improvements in fuel efficiency (McKinsey & Company 2019).

The service industry is also another group of businesses that have effectively integrated AI in the internal business processes and are reaping its benefits through improved customer service and sales (through improved integration of customers’ demographic details with service-centred processes to provide individualised services) (McKinsey & Company 2019).

The increased productivity and efficiency generated from AI mostly come from improvements in traditional analytical techniques (Kaartemo & Helkkula 2018). Statistics also support this finding because they show that AI improved productivity and efficiency in about 60% of the times used (McKinsey & Company 2019). Figure 1 below shows that AI can improve the incremental value of products and services offered in different industries by varying percentages.

According to figure 1 above, the travel industry stands to benefit the most from AI services. The transport and logistics industry has also shown potential in improving process outcomes using AI. However, the fields of advanced electronics and aerospace engineering have benefited the least from AI. Nonetheless, the technology remains one of the best catalysts for improving business process outcomes.

Adoption of AI across Companies and Sectors

Researchers have pointed out varying scopes and extents of AI adoption in different countries (Cortese & Walton 2018). The pace of adoption and the extent of AI integration have particularly defined this variation across multiple sectors (Kaartemo & Helkkula 2018). Several studies suggest that most companies have adopted AI in at least one of their business processes (Cortese & Walton 2018; Kaartemo & Helkkula 2018; McKinsey & Company 2019).

About a third of companies are also considering using AI in their internal systems but their efforts are still confined to pilot phases of review. The adoption of AI across multiple business processes is estimated at 20% but the highest percentage of integration has been observed in giant multinational firms, which are projected to adopt AI in at least 97% of their business processes (McKinsey & Company 2019).

Current research suggests that companies or firms that adopt AI tend to think of the technology as having vast effects on their mid-term and long-term goals (Liu et al. 2018). The potential to expand market share is the main motivation for using AI among most of these businesses sampled but slow adopters are more focused on cost reduction (McKinsey & Company 2019). Researchers have also noted that corporations or enterprises that have devolved their functions on the digital platform are the best adopters of AI (Lazzini et al. 2018). They are also considered the most probable companies to get the most value from the technology (Dudhwala & Larsen 2019).

At a sectoral level, there is an increasing gap between companies that base their operations on virtual production platforms and those that do not (McKinsey & Company 2019). For example, technology companies and financial entities are the leading adopters of AI. The gap between early and late adopters of AI in this field is deemed impactful on the competitive advantage of a business because early adopters gain a lot of experience with AI and use the same capability to leverage their operations, thereby making it difficult for the competitors to “catch up” (McKinsey & Company 2019). Figure 2 below shows that different industries and sectors of the global economy have varying adoption levels of AI.

As mentioned above, the financial and technology sectors are the leading adopters of AI. The rate of technological adoption in the tourism industry, the transport sector and healthcare are also high compared to other fields, such as construction, education and consumer packaging businesses (McKinsey & Company 2019). These varying levels of adoption are not only linked to the nature of businesses in these sectors but also the attitudes of professionals in the industries (Frude & Jandrić 2015).

For example, the information technology (IT) sector is often comprised of professionals who are versant with current developments in AI, thereby allowing them to understand the technology better than employees from another sector, like the healthcare industry, will. Therefore, IT professionals are naturally going to have a stronger inclination towards the adoption of AI compared to their counterparts in the healthcare sector. Alternatively, it could be assumed that professionals in the technology industry have a more positive attitude towards AI compared to their healthcare counterparts. Nonetheless, several challenges to the adoption of AI persists and the burden of implementation has been left mostly to business leaders and managers who are expected to be committed to its adoption.

Attitudes towards Artificial Intelligence

The role of accountants in presenting the true financial performance of a company cannot be overemphasised in a fast-paced world where multiple variables affect corporate performance. This group of professionals not only maintain various systems of recording but also verify data contained in financial books of accounts. In this regard, the purpose of an accountant, in business management, is to present a monetary snapshot of a business or company’s financial health.

By relying on professional insight, they could also predict what financial outcomes could occur in the short-term or near-term (Frude & Jandrić 2015). Indeed, by understanding a company’s financial performance, it is easy to comprehend its strengths and weaknesses and their influence on corporate success. The introduction of AI threatens to change this traditional view of accounting by isolating key processes from human control.

Many researchers have pointed out that developments in AI could influence different aspects of professionals and personal lives (Cortese & Walton 2018). Evidence has been given of its potential effects on the labour market, transport industry, healthcare, education and other fields (McKinsey & Company 2019).

Although pundits and policymakers have started to discuss the ramifications of AI, the opinions of professional groups have yet to be understood. Particularly, the views of accounting professionals towards AI need to be framed within the context of public opinions towards the same phenomenon. For example, Zhang and Dafoe (2019) say that most people (44%) in America express support for AI, while only a small percentage (about 22%) have expressed strong reservations about it (McKinsey & Company 2019).

Evidence suggests that demographic differences could influence people’s support for AI (McKinsey & Company 2019). For example, people with high levels of education express more support for the practice compared to those who have lower qualifications (McKinsey & Company 2019). To explain this position, Zhang and Dafoe (2019) noted that college graduates expressed higher support (51%) for AI compared to those who had a high-school education (29%). Income disparities within households have also been linked with the support for AI because there was higher support for the concept (59%) among people who had an income of more than $100,000 annually compared to those who earned less than $30,000 annually (33%) (Zhang & Dafoe 2019).

People’s professions have also been associated with support or disapproval for AI because employees who hail from a technology background, such as computer programmers, have expressed higher support for AI (58%) compared to those who do not come from such a background (McKinsey & Company 2019). If this assertion were to be applied to the context of this study, it could be assumed that accountants would have lower support for AI compared to computer programmers or software developers.

Gender differences have also been associated with the support for AI because researchers have noted that men express a higher approval rating for AI (44%) compared to their female counterparts, who only have a 35% support for AI (McKinsey & Company 2019). Broadly, although demographic differences have been used to explain support or disapproval of AI, many researchers suggest that a majority of people believe that AI needs to be managed (Zhang & Dafoe 2019). The same view is also held by researchers who have explored people’s views of the use of robots to carry out human functions (Kaartemo & Helkkula 2018).

How Accountants are Using AI

To understand the future of AI in the accounting field, it is important to review how professionals in the same discipline are using AI today. Stated differently, it is vital to understand how AI is being used to solve accounting and business problems relating to today’s globalised environment. However, to gain a proper understanding of this role, it is pivotal to note that many accountants are commonly motivated to use their skills and expertise to help business managers and stakeholders make better decisions (Persson, Radcliffe & Stein 2018).

To do so, they rely on high-quality financial and non-financial instruments of accounting to make their technical analyses (Napitupulu 2018). This role is reflected in their work through several tasks, responsibilities and areas of specialisation that strive to enhance the quality of data available for review (Kaartemo & Helkkula 2018).

Technology has been used to improve the above-mentioned decision-making systems and has been deployed to solve three main types of problems: providing high quality and cheaper data for better decision-making, generating new insights from data analysis processes and freeing up the time to solve other pressing business challenges (Fenwick & Edwards 2016). Indeed, the nature of machine learning itself is predicated on the ability to improve accounting roles and equipping professionals with new skills and competencies to undertake their tasks (Sterne & Razlogova 2019).

Therefore, it is essential to identify accounting problems that could easily be solved using machine learning and those that may not benefit from the same process (or require human intelligence). This approach to problem-solving will make sure that changes are primarily driven by the fulfilment of business objectives and not technological requirements.

Studies suggest that there is limited use of AI in the accounting field but some common areas of operation where implementation has been relatively well-received are in the use of machine language to code accounting entries and improve the accuracy of rule-based approaches to accounting (Nijam & Jahfer 2018). AI has also been partially adopted in fraud detection and the prediction of possible areas of fraud activities (Napitupulu 2018). Machine-based predictive models of learning have also been developed using AI. Moreover, deep learning models for analysing unstructured data have also been developed from machine-based learning (Nijam & Jahfer 2018).

Practical Challenges Associated with the Adoption of AI

Although studies have shown the potential of AI in improving professional practice (Bechmann & Bowker 2019), many challenges still stand in the way of its full implementation. One of them that relates to training is the need for immense human effort in labelling data. This problem is challenging because supervised learning requires the proper labelling of data (Clarke et al. 2017). Furthermore, many organisations today have a problem of developing large amounts of data that are sufficient for training (Bechmann & Bowker 2019).

The complexity of machine learning is also problematic in data analysis and implementation because it makes it difficult to understand decision-making processes that lead to the formulation of potential solutions (Napitupulu 2018). This challenge is notably increased by the use of multiple variables in decision-making but it is impactful to the accounting field because the relationship between clients and professionals is often underpinned by trust. Since trust is a human attribute, it becomes difficult to account for it in the AI-aided decision-making process. Nonetheless, current research on AI is designed to address this problem by increasing transparency in model development (Sterne & Razlogova 2019).

Another challenge associated with the implementation of AI is the difficulty in generalising applications (Birtchnell 2018). The problem arises from the challenge of transferring experience from an AI model to another one. Stated differently, machine learning is often programmed to suit specific business contexts and it becomes increasingly difficult to exchange applications across different learning groups spread across various corporate sectors (Birtchnell 2018). This problem creates a governance issue.

Different researchers have highlighted the use of AI in the accounting field because the management of human resources has been the norm for many administrators in the sector (Lazzini, Iacoviello & Ferraris 2018). However, when there is a threat to the traditional role of human resources in the field, issues of governance emerge because it is difficult to ascertain how robots or machines can be managed, devoid of human control. Consequently, issues of governance have emerged as a key area of research in the integration of AI in the accounting space. More importantly, professionals and observers alike have raised concerns regarding how to prevent AI-aided technology from contravening existing liberties and privacy concerns in the business sector (Lazzini et al. 2018).

Similarly, the use of AI in propagating false information or misinformation through virtual platforms have also been explored in the same way as the potential threat of using AI to carry out cyberattacks on governments, companies and institutions have been investigated (Bechmann & Bowker 2019). Therefore, the main issue emerging in the investigation of governance issues relating to AI are commonly reviewed through a need to protect the privacy of data held by people and organisations.

Importance of Education and Training in Artificial Intelligence

Different scholars have highlighted the importance of education in accounting (Nijam & Jahfer 2018). Their goal is usually to equip professionals with skills and competencies relating to major developments in the sector (Stevenson, Power, Ferguson & Collison 2018). This goal is necessitated by the fact that the accounting field is linked with multiple changes in the operating environment, including technology, law and ethics (Nijam & Jahfer 2018).

AI falls within the context of technological development and education has been emphasised as a way of sensitising professionals about its implications on the sector. The current emphasis on research has been on how to use some of the skills and competencies offered by AI in the accounting field and how professionals or managers could harness them to solve existing problems, including employee retention and other human resource issues.

The importance of training is not only confined to improving practice but also reinvigorating traditional accounting practices by making them better and more efficient. Rapid changes in globalisation and the need to standardise accounting standards and procedures have further emphasised the importance of effective training in the accounting field (Gammie, Allison & Matson 2018). AI-related education processes are even more central to the growth of the discipline, relative to the impact that they have on the field. However, some researchers have noted that some organisations are hesitant to plan for education seminars because of the perceived lack of adequate teachers or personnel to oversee the process (Gammie et al. 2018).

Moreover, some managers and CEOs still deem some of the developments made in AI to be insufficient in necessitating radical changes in an organisation (McKinsey & Company 2019). This development is informed by the fact that there are still many major developments going on in AI that may deem some of the information currently available obsolete.

Summary

Anecdotal evidence gathered in this paper show that most of the accounting journals and works of literature that have investigated the link between AI and accounting do not sufficiently emphasise the role of education and training. In other words, the existing evidence is summarily explained without a keen understanding of the role of education and training in explaining what the concept is about and its influence on accounting. This study aims to fill this research gap. The techniques used by the researcher in fulfilling this goal are explained in chapter three below.

Methodology

Introduction (Project Summary)

This chapter will highlight the strategies adopted to answer the research questions. To recap, this study is designed to investigate the effects of AI on the accounting profession from the prisms of education and training. The study was guided by five questions, which focused on evaluating whether there have been changes in employee attitudes toward AI in the past five years, examining factors that could influence the attitudes of accounting professionals toward AI and investigating how the attitudes of accounting professionals towards AI could be improved. The approach taken by the researcher in answering the above-mentioned questions is discussed below.

Research Approach

The qualitative and quantitative research approaches are the main techniques used in academic research (Steen, DeFillippi, Sydow, Pryke & Michelfelder 2018). The latter approach is often used in investigations that measure quantifiable variables, while the qualitative technique is applicable when measuring subjective variables (Archibald, Radil, Zhang & Hanson 2015). The researcher used the qualitative research method in this study because it focused on the attitudes of accounting professionals regarding AI.

Attitude is a subjective variable because it is individualistic. In other words, two people may have completely different experiences when subjected to the same stimuli (AI). Similarly, they may have different perspectives on the technological phenomenon. These qualities of the research variable made it difficult to use the quantitative technique to undertake this analysis because it was not possible to measure the variables numerically.

The justification for using the qualitative research approach is rooted in the fact that it provided the researcher with an opportunity to get in-depth details regarding the questions asked (Allana & Clark 2018). In other words, it allowed the researcher to go beyond the superficial elements of the investigation and probe the respondents’ reasons for making specific statements. This advantage is highlighted by several research studies, which have investigated the merits of the qualitative research approach (Walker and Baxter 2019; de Block and Vis 2018; Wagner, Kawulich & Garner 2019).

Research Design

According to UMSL (2019), there are five main research designs associated with the qualitative research approach. They include phenomenology, grounded theory, ethnography, historical methods, and case studies (UMSL 2019). The characteristics of each of these designs are provided below.

Grounded Theory

The purpose of the grounded theory design is to develop a theory (Chun Tie, Birks & Francis 2019). Typically, many researchers use it to assess social problems and examine how people address them (Ralph, Birks & Chapman 2014). Before findings are developed, scholars often use prepositions to explain specific research phenomena after formulating, testing and redeveloping them (Collins & Stockton 2018). These characteristics of the grounded approach made it inappropriate for use in the study because its focus was not on theory development but rather on understanding the impact that AI would have in the accounting field. The second research design considered for review was the case study approach and it is discussed below.

Case Studies

Researchers often use the case study research design to gain an in-depth understanding of the workings of a specific social group or institution (Ebneyamini & Sadeghi 2018; Rule & John 2015). The main method of data collection is observation because scholars often immerse themselves in a group’s social structure and observe patterns of behaviour (Rashid, Rashid, Warraich, Sabir & Waseem 2019; Herdlein & Zurner 2015). Although these characteristics of analysis make this research design useful in understanding the inner workings of a professional group, it did not apply to this study because its focus was not on a specific social group or institution but a wide profession – accounting.

Ethnography

Researchers who want to describe the characteristics of a cultural group have typically used the ethnography approach to undertake their studies (Lubet 2019; Jerolmack & Khan 2017). They do so by gaining access to culture and gathering data by immersing themselves in it to observe behaviour (Rashid, Caine & Goez 2015; Newmahr & Hannem 2018). The focus on the cultural aspects of scientific dogma made it difficult to use this research design in the investigation. Instead, the study was focused on examining the effects of AI on accounting professionals. The next approach considered for review was the historical technique. Its characteristics are described below.

Historical Methods

The use of historical methods in qualitative research approaches is informed by the need to examine past events to make sense of current findings or anticipate future outcomes (Stutz & Sachs 2018; Chong 2014). This methodological approach is systematic in the manner research variables are assessed and by reconciling conflicting evidence (Crossen-White 2015; Kelly 2019). This dissertation did not follow a systemic approach to data analysis because the questions posed to the respondents were semi-structured. Furthermore, the focus of the study was not on the use of historical records to make sense of the research phenomenon. Consequently, this research design was not selected for use in the study. However, the phenomenology research approach was reviewed and its findings highlighted below.

Phenomenology

The main goal of the phenomenology research design is to describe people’s lived experiences (van Manen 2017). As its name insinuates, this research design is often associated with the examination of different phenomena that affect societies or professions (Ignatow 2018). From this analysis, the uniqueness of people’s lived experiences is observed because the main premise of analysis is understanding that people have different perceptions of reality.

According to Strandmark (2015) and Bovin (2019), scholars who use this research design are often preoccupied with the need to understand what people’s lived experiences mean for a research phenomenon under investigation. These characteristics of the research design made it appropriate for use in the study because AI was the phenomenon under investigation and the researcher intended to understand its impact on accounting professionals – a process, which is akin to understanding their lived experiences.

Data Collection

Data was collected using semi-structured interviews. The questions posed to the respondents were professionally developed because the informants were accounting professionals in different fields of business. Interviews were selected because they allowed the researcher to explore the research questions in-depth compared to alternative data collection methods, such as surveys, which only require a “yes” or “no” response. The interviews were conducted online on the WeChat platform. The researcher selectively used this technique because of the logistical difficulties of conducting the interviews face-to-face because the respondents were in China.

Research Participants

The target population was comprised of five professionals who have advanced experience with the use of artificial intelligence in the accounting profession. They were based in China and worked as teachers or accountants. The respondents were made up of two accounting teachers from a Chinese education institution and three professionals who worked as accountants in a Beijing-based financial institution.

The accountants held junior, semi-senior and senior-level positions in the workplace. The exact positions held in the organisation were that of a chief financial officer (CFO), technology manager, chief executive officer (CEO) and a human resource (HR) manager. Since all the informants were Chinese, the researcher spoke to them in the same language. However, for purposes of data analysis, findings were transcribed into the English language and the final report presented in the same manner. The interview questions posed to them are provided in appendix 1.

Sampling Procedure

The researcher used the purposive sampling method to contact the respondents. This non-probability sampling method is selective in the manner respondents are chosen because there is a bias to only recruit informants who are knowledgeable about the study area (Ignatow 2018). In other words, the selection of the informants depends on the interviewer’s judgement. The researcher employed the purposive sampling method because some of the respondents were known through family members. Therefore, it was easy to contact them and schedule an interview. This technique was appropriately used for this study because it is commonly applied when a small sample is desired (Brayda & Boyce 2014).

The small number of participants was chosen for the study based on the recommendations of Brayda and Boyce (2014), which suggest that a sample of fewer than 12 participants is appropriate for a comprehensive qualitative study. This figure is targeted because a high number of participants could make it difficult for the researcher to establish a good relationship with all of them. This concern is raised because qualitative investigations require the researcher to develop a good rapport with the respondents.

Data Analysis

The researcher used the thematic and coding method to analyse data. Armborst (2017), Aveling, Gillespie and Cornish (2015) say that many researchers have successfully used the technique to analyse qualitative data. Today, it is among the leading models of information analysis for the qualitative research method (Armborst 2017). Its proven reliability made it attractive for this study (Elo, Kääriäinen, Kanste, Pölkki, Utriainen & Kyngäs 2014; Ando, Cousins & Young 2014; Nowell, Norris, White & Moules 2017). Consequently, important themes were identified from the interview responses provided by the informants. The process was done by recognising patterns of assertions that would help to answer the research questions. The identified patterns were later correlated with different research nodes, as suggested by Hilton and Azzam (2019).

Developing the nodes later paved the way for the coding process and the development of the research findings. Overall, the researcher undertook the thematic and coding method by following the steps highlighted in figure 3 below.

Ethical Considerations

It is important to review the ethical considerations of this paper because human subjects were used. According to Colnerud (2014), Saunders, Kitzinger and Kitzinger (2015), the use of human subjects emphasises the need to conduct a study ethically to protect the rights of the respondents and the researcher. The main ethical issues the characterised the study are as follows:

Anonymity and Confidentiality

Sugiura, Wiles and Pope (2017) say that anonymity and confidentiality in qualitative research are closely associated with the respect for human dignity and fidelity to the research objectives. Relative to this assertion, the views of the respondents were presented in the study anonymously. In other words, their identities will not be revealed unless expressly stated by the informant in writing. The aim of doing so is to protect them from any consequence that may arise through a publication of the research findings (Lowman & Palys 2014). As suggested by Hannes and Parylo (2014), doing so will make sure that the views presented in the investigation are solely aimed at answering the research questions and not to probe an informant’s personal life. This ethical provision also allowed the participants to give or withhold as much information as they wanted (Scarth 2016).

Voluntary Participation

Brewer (2016), Dixon and Quirke (2018) say that informants should voluntarily be allowed to participate in research studies. Relative to this provision, all the respondents who took part in the study did so voluntarily. Stated differently, they were not coerced or paid to give their views. Furthermore, they were informed of their rights, details of the study and its purpose before participating in the investigation. The purpose was to equip them with all relevant information that would help them to make an informed choice about their involvement in the research. Before volunteering to participate in it, they had to sign an informed consent form. Lastly, the informants were also informed of their right to withdraw from the study without any repercussions. Therefore, they did not feel like they were indirectly forced to participate in it.

Reliability and Validity of Data

It was important to understand the reliability and validity of the interview data because researchers note the difficulty in replicating these types of investigations (Doll 2018; Jackson, Kay & Frank 2015). Stated differently, the flexible nature of qualitative inquiry makes it difficult to replicate the findings, thereby making it easy to question the reliability of information generated. To address these issues, the researcher used the member-check technique to improve the reliability and validity of information obtained from the respondents. This method works by relaying the study’s findings back to the respondents so that they assess whether they represent their actual views, or not (Oranye 2016).

Therefore, the researcher furnished the informants with a statement of the final findings before they were included in the edited report. The respondents had an opportunity to state whether the information presented represented their views, or not and adjustments were made appropriately. Broadly, the member-check technique is an appropriate tool for improving the reliability of qualitative findings. Its merits have been highlighted by several researchers, including Jackson, Kay and Frank (2015).

Findings

The findings generated from this study show that the successful implementation of AI in accounting procedures requires both dedication and commitment to the processes, as well as the realisation of the need to acquire commensurate skills of operation. The biggest challenge that many organisations face is leaving long-held traditions and procedures in accounting, thereby making the change process more difficult than it should be (Miranti 2014). This is particularly true because many accountants practising today did not acquire AI skills. Therefore, they have to participate in organisational processes and seminars that would update them on developments in the field.

One of the respondents said that understanding how AI is changing accounting processes requires an examination of how some giant multinational firms are using the technology. He said,

Look at how EY and Deloitte are using AI in their daily accounting processes. Their document review processes are being updated using AI every day. Initially, these organisations had to rely on extensive and protracted labour-intensive processes to undertake the same tasks and to transfer ownership of client assets. This has fundamentally changed. Just based on these examples alone, I would say that AI is doing the job that most accountants would do when examining convoluted contracts. I should add that this is just scratching the surface (laughs).

One of the respondents also said that people should consider AI a tool for improving the efficiency of undertaking accounting procedures and refrain from looking at it as a negative tool of development that would take away their jobs. Relative to this assertion, one of the respondents said the best way to encourage accounting firms to appreciate AI is to make them look for areas of redundancy or bureaucracy that could be solved using the technology. By doing so, they would be looking at AI as a solutions provider as opposed to a trend that is meant to create problems for employees.

Another respondent said that people should stop fighting the AI trend because it is a “lost war.” Instead, he proposed that they should adapt to the new times and re-evaluate how it would enhance their roles in the accounting subsector of business operations. When asked to clarify how such an evaluation should occur, the respondent said that accounting professionals should consider looking at their positions as that of an adviser and not a doer. In other words, the machines would carry out conventional accounting processes with higher accuracy and efficiency than a human being would do.

Furthermore, few employers would choose an employee, as opposed to an AI-aided tool because people require a salary at the end of the month and a machine does not have such demands. Consequently, he considered comparing AI to human beings as a fruitless exercise because human beings cannot match the efficiency of a machine. Furthermore, employees come with their challenges, such as the need to keep them motivated or happy in the organisation, while machines do not have this problem. Therefore, the respondent said, from a suitability perspective, accounting professionals need to rethink their role in the field.

Discussions

The examination of contracts using AI in the accounting field is one area of interest that emerged in the study to show the extent that AI has been integrated into the discipline. This issue was mentioned by the respondents and highlighted in excerpts of studies done by Amnuai (2019), Naujokaitiene, Tereseviciene and Zydziunaite (2015). Both sets of professionals notably mentioned how giant multinational, accounting firms such as Deloitte have used AI to examine contracts and client agreements.

It is important to understand that this duty was mainly a preserve of the professionals because it is a qualitative process, subject to the ability of human beings to understand varied implications of contractual terms. However, as demonstrated by the informants sampled, AI tools have already been equipped with this capability and are carrying out tasks that would otherwise be deemed impossible for a machine to do.

Accounting firms that are unfamiliar with AI need to rethink how they undertake their organisational processes because they will lose the competitive advantage they should have over other industry players. However, integrating AI into existing organisational processes is difficult because of the need to make several adjustments concerning organisational processes (Dudhwala & Larsen 2019). As Murphy and Quinn (2018) point out, these adjustments may involve fulfilling their roles as trusted professionals and even devising new ways of meeting client expectations.

Conclusion and Recommendations

Conclusion

To comprehend the challenges and potential ramifications of AI in the accounting field, the first chapter of this paper has outlined what the concept is about first. To recap, it has been demonstrated that AI is the ability to equip machines with the ability to think. In other words, AI centres on the development of intelligent machines. While there are reports and criticism regarding the role of this concept in improving or ending human existence, this paper focused on a scientific perspective of its integration with human intelligence in the accounting field.

The findings of this study will be instrumental in building a positive image of the future, especially between the integration of AI and human intelligence. The goal is to minimise the scepticism associated with AI and focus more on the probability or potential of the technology to solve perennial accounting problems or enhance existing professional skills to address current challenges in the field.

Recommendations

Compulsory Training

Based on the insights provided in this paper; there is a strong need for compulsory training of accounting professionals about AI. The training and education requirements need to be made compulsory through a decree or legislative amendment because the future of professionals in the field is pegged on their understanding of it. Indeed, it would be difficult to effectively advance the practice if accountants remain ignorant about its implications and yet it is a trend that would not only change the sector but also others related to it (such as auditing). For example, the need for compulsory training is supported by the double-faceted nature of the accounting field because it is often pegged with auditing. Training and education would help to master both conceptual and practical aspects of AI and its relationship with the discipline.

Reference List

Allana, S. and Clark, A (2018) Applying Meta-Theory to Qualitative and Mixed-Methods Research: A Discussion of Critical Realism and Heart Failure Disease Management Interventions Research. International Journal of Qualitative Methods 17.1 pp 1-10.

Amnuai, W (2019) Analyses of Rhetorical Moves and Linguistic Realizations in Accounting Research Article Abstracts Published in International and Thai-based Journals. SAGE Open 9.1 pp 1-10.

Ando, H., Cousins, R. and Young, C (2014) Achieving Saturation in Thematic Analysis: Development and Refinement of a Codebook. Comprehensive Psychology 3.1 pp 1-10.

Archibald, M. M., Radil, A. I., Zhang, X. and Hanson, W. E (2015) Current Mixed Methods Practices in Qualitative Research: A Content Analysis of Leading Journals. International Journal of Qualitative Methods 14.2 pp 5-33.

Armborst, A (2017) Thematic Proximity in Content Analysis. SAGE Open 7.2 pp. 1-10.

Aveling, E. L., Gillespie, A. and Cornish, F (2015) A Qualitative Method For Analysing Multivoicedness. Qualitative Research 15.6 pp. 670-687.

Bechmann, A. and Bowker, G. C (2019) Unsupervised by any Other Name: Hidden Layers Of Knowledge Production In Artificial Intelligence On Social Media. Big Data & Society 6.1 pp. 1-10.

Birtchnell, T (2018) Listening Without Ears: Artificial Intelligence in Audio Mastering. Big Data & Society 5.2 pp 1-10.

Bovin, A. J (2019) Breaking the Silence: The Phenomenology of the Female High School Band Director. Update: Applications of Research in Music Education 4.1 pp. 1-10.

Brayda, W. C. and Boyce, T. D (2014) So you Really Want to Interview Me?: Navigating “Sensitive” Qualitative Research Interviewing. International Journal of Qualitative Methods 13.1 pp. 318-334.

Brewer, J. D (2016) The Ethics of Ethical Debates in Peace and Conflict Research: Notes towards the Development of a Research Covenant. Methodological Innovations 9.1 pp. 1-10.

Carriço, G (2018) The EU and Artificial Intelligence: A Human-Centered Perspective. European View 17.1 pp. 29-36.

Chong, J. I (2014) Popular Narratives versus Chinese History: Implications for Understanding an Emergent China. European Journal of International Relations 20.4 pp. 939-964.

Chun Tie, Y., Birks, M. and Francis, K (2019) Grounded Theory Research: A Design Framework for Novice Researchers. SAGE Open Medicine 7.1 pp 1-10.

Clarke, A. M., Chambers, D. and Barry, M. M (2017) Bridging the Digital Disconnect: Exploring the Views of Professionals on Using Technology to Promote Young People’s Mental Health. School Psychology International 38.4 pp. 380-397.

Collins, C. S. and Stockton, C. M (2018) The Central Role of Theory in Qualitative Research. International Journal of Qualitative Methods 17.1 pp. 1-10.

Colnerud, G (2014) Ethical Dilemmas in Research In Relation To Ethical Review: An Empirical Study. Research Ethics 10.4 pp. 238-253.

Cortese, C and Walton, P (2018) Histories of Accounting Standard-Setting – An Introduction. Accounting History 23.3 pp. 261-264.

Crossen-White, H. L (2015) Using Digital Archives in Historical Research: What Are the Ethical Concerns for A ‘Forgotten’ Individual? Research Ethics 11.2 pp. 108-119.

Dahbi, S., Ezzine, L. and Moussami, H (2017) Modeling of Cutting Performances In Turning Process Using Artificial Neural Networks. International Journal of Engineering Business Management 9.1 pp. 1-10.

Dahlin, E (2019) Are Robots Stealing Our Jobs? Socius 5.1 pp. 1-10.

de Block, D. and Vis, B (2018) Addressing the Challenges Related to Transforming Qualitative Into Quantitative Data in Qualitative Comparative Analysis. Journal of Mixed Methods Research 4.1 pp. 1-10.

Dixon, S. and Quirke, L (2018) What’s the Harm? The Coverage of Ethics and Harm Avoidance in Research Methods Textbooks. Teaching Sociology 46.1 pp. 12-24.

Doll, J. L (2018) Structured Interviews: Developing Interviewing Skills in Human Resource Management Courses. Management Teaching Review 3.1 pp. 46-61.

Dudhwala, F. and Larsen, L. B (2019) Recalibration in Counting and Accounting Practices: Dealing with Algorithmic Output in Public and Private. Big Data & Society 6.2 pp. 1-10.

Ebneyamini, S. and Sadeghi, M. R (2018) Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods 17.1 pp. 1-10.

Elo, S., Kääriäinen, M., Kanste, O., Pölkki, T., Utriainen, K. and Kyngäs, H (2014) Qualitative Content Analysis: A Focus on Trustworthiness. SAGE Open 4.1 pp. 1-10.

Felzmann, H., Villaronga, E. F., Lutz, C. and Tamò-Larrieux, A (2019) Transparency You Can Trust: Transparency Requirements For Artificial Intelligence Between Legal Norms and Contextual Concerns. Big Data & Society 6.1 pp. 1-10.

Fenwick, T. and Edwards, R (2016) Exploring the Impact of Digital Technologies on Professional Responsibilities and Education. European Educational Research Journal 15.1 pp. 117-131.

Ferri, P., Lusiani, M. and Pareschi, L (2018) Accounting for Accounting History: A topic modeling approach (1996–2015). Accounting History 23.2 pp. 173-205.

Fox, S (2018) Domesticating Artificial Intelligence: Expanding Human Self-Expression through Applications of Artificial Intelligence in Prosumption. Journal of Consumer Culture 18.1 pp. 169-183.

Frude, N. and Jandrić, P (2015) The Intimate Machine – 30 years on. E-Learning and Digital Media, 12.3 pp. 410-424.

Gammie, E., Allison, M. and Matson, M (2018) Entry routes into the Institute of Chartered Accountants of Scotland training: Status versus sustainability. Accounting History 23.1 pp. 14-43.

Geiger, R. S (2017) Beyond Opening up The Black Box: Investigating the Role of Algorithmic Systems in Wikipedian Organizational Culture. Big Data & Society 4.2 pp. 1-10.

Hannes, K. and Parylo, O (2014) Let’s Play it Safe: Ethical Considerations from Participants in a Photovoice Research Project. International Journal of Qualitative Methods 13.1 pp. 255-274.

Herdlein, R. and Zurner, E (2015) Student Satisfaction, Needs, and Learning Outcomes: A Case Study Approach at a European University. SAGE Open 5.2 pp. 1-10.

Hilton, L. G. and Azzam, T (2019) Crowdsourcing Qualitative Thematic Analysis. American Journal of Evaluation 4.9 pp. 1-10.

Ignatow, G (2018) Beyond Bourdieu: From Genetic Structuralism to Relational Phenomenology. Contemporary Sociology 47.1 pp. 42-43.

Jackson, J. L., Kay, C. and Frank, M (2015) The Validity And Reliability Of Attending Evaluations Of Medicine Residents. SAGE Open Medicine 3.1 pp. 1-10.

Jerolmack, C. and Khan, S (2017) The Analytic Lenses of Ethnography. Socius 3.1 pp. 1-10.

Kaartemo, V. and Helkkula, A (2018) A Systematic Review of Artificial Intelligence and Robots in Value Co-creation: Current Status and Future Research Avenues. Journal of Creating Value 4.2 pp. 211-228.

Kelly, M. G (2019) A Map Is More Than Just a Graph: Geospatial Educational Research and the Importance of Historical Context. AERA Open 5.1 pp. 1-10.

Kirschner, P. A. and Stoyanov, S (2018) Educating Youth for Nonexistent/Not Yet Existing Professions. Educational Policy 2.6 pp. 1-10

Lazzini, A., Iacoviello, G. and Ferraris F. R (2018) Evolution of Accounting Education in Italy, 1890–1935. Accounting History 23.1 pp. 44-70.

Levine, D (2019) Automation as Part of the Solution. Journal of Management Inquiry 28.3 pp. 316-318.

Liu, X., Keane, P. A. and Denniston, A. K (2018) Time to Regenerate: The Doctor in the Age of Artificial Intelligence. Journal of the Royal Society of Medicine 111.4 pp. 113-116.

Lowman, J. and Palys, T (2014) The Betrayal Of Research Confidentiality In British Sociology. Research Ethics 10.2 pp. 97-118.

Lubet, S (2019) Accuracy in Ethnography: Narratives, Documents, and Circumstances. Contexts 18.1 pp. 42-46.

Mann, S (2017) Big Data is a Big Lie without Little Data: Humanistic Intelligence as a Human Right. Big Data & Society 5.2 pp. 1-10.

McKinsey & Company (2019) The Promise and Challenge of the Age of Artificial Intelligence. Online. Web.

Miranti, P (2014) The Emergence Of Accounting As A Global Profession – An Introduction. Accounting History 19.1 pp. 3-11.

Murphy, B. and Quinn, M (2018) The Emergence Of Mandatory Continuing Professional Education At The Institute of Certified Public Accountants in Ireland. Accounting History 23.1 pp. 93-116.

Napitupulu, I. H (2018) Organizational Culture in Management Accounting Information System: Survey on State-owned Enterprises (SOEs) Indonesia. Global Business Review 19.3 pp. 556-571.

Naujokaitiene, J., Tereseviciene, M. and Zydziunaite, V (2015) Organizational Support for Employee Engagement in Technology-Enhanced Learning. SAGE Open 5.4 pp. 1-10.

Newmahr, S. and Hannem, S (2018) Surrogate Ethnography: Fieldwork, the Academy, and Resisting the IRB. Journal of Contemporary Ethnography 47.1 pp. 3-27.

Nijam, H. M. and Jahfer, A (2018) IFRS Adoption and Value Relevance of Accounting Information: Evidence from a Developing Country. Global Business Review 19.6 pp. 1416-1435.

Nowell, L. S., Norris, J. M., White, D. E. and Moules, N. J (2017) Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods 16.1 pp. 1-10.

Oleinik, A (2019) What are Neural Networks Not Good At? On Artificial Creativity. Big Data & Society 6.1 pp. 1-10.

Oranye, N. O (2016) The Validity of Standardized Interviews Used for University Admission into Health Professional Programs: A Rasch Analysis. SAGE Open 6.3 pp. 1-10.

Pakdemirli, E (2019a) Artificial Intelligence In Radiology: Friend Or Foe? Where Are We Now And Where Are We Heading? Acta Radiologica Open 8.2 pp. 1-10.

Pakdemirli, E (2019b) New Subspecialty or Close Ally Emerging On the Radiology Horizon: Artificial Intelligence. Acta Radiologica Open 8.3 p.p 1-10.

Persson, M. E., Radcliffe, V. S. and Stein, M (2018) The Pursuit of A Cognitive Standard for the Accounting Profession. Accounting History 23.1 pp. 71-92.

Ralph, N., Birks, M. and Chapman, Y (2014) Contextual Positioning: Using Documents as Extant Data in Grounded Theory Research. SAGE Open 4.3 pp. 1-10.

Rashid, Y., Rashid, A., Warraich, M. A., Sabir, S. S. and Waseem, A (2019) Case Study Method: A Step-by-Step Guide for Business Researchers. International Journal of Qualitative Methods 18.1 pp. 1-10.

Rana, R (2018) How Artificial Intelligence Will Impact the Accounting Industry? Online. Web.

Rashid, M., Caine, V. and Goez, H (2015) The Encounters and Challenges of Ethnography as a Methodology in Health Research. International Journal of Qualitative Methods 14.5 pp. 1-10.

Rule, P. and John, V. M (2015) A Necessary Dialogue: Theory in Case Study Research. International Journal of Qualitative Methods 14.4 pp. 1-10

Saunders, B., Kitzinger, J. and Kitzinger, C (2015) Anonymising Interview Data: Challenges and Compromise in Practice. Qualitative Research 15.5 pp. 616-632.

Scarth, B. J (2016) Bereaved Participants’ Reasons For Wanting Their Real Names Used In Thanatology Research. Research Ethics 12.2 pp. 80-96.

Stano, P., Kuruma, Y. and Damiano, L (2018) Synthetic Biology and (Embodied) Artificial Intelligence: Opportunities and Challenges. Adaptive Behavior 26.1 pp. 41-44.

Strandmark, M (2015) Method Development at Nordic School of Public Health NHV: Phenomenology and Grounded Theory. Scandinavian Journal of Public Health 43.16 pp. 61-65.

Steen, J., DeFillippi, R., Sydow, J., Pryke, S. and Michelfelder, I (2018) Projects and Networks: Understanding Resource Flows and Governance of Temporary Organizations with Quantitative and Qualitative Research Methods. Project Management Journal 49.2 pp. 3-17.

Sterne, J. and Razlogova, E (2019) Machine Learning in Context, or Learning from LANDR: Artificial Intelligence and the Platformization of Music Mastering. Social Media and Society 5.2 pp. 1-10.

Stevenson, L., Power, D., Ferguson, J. and Collison, D (2018) The Development Of Accounting In UK Universities: An Oral History. Accounting History 23.1 pp. 117-137.

Stutz, C. and Sachs, S (2018) Facing the Normative Challenges: The Potential of Reflexive Historical Research. Business & Society 57.1 pp. 98-130.

Sugiura, L., Wiles, R. and Pope, C (2017) Ethical Challenges In Online Research: Public/Private Perceptions. Research Ethics 13.3 pp. 184-199.

UMSL (2019) Qualitative Research Designs. Online. Web.

van Manen, M (2017) But Is It Phenomenology? Qualitative Health Research 27.6 pp. 775-779.

Wagner, C., Kawulich, B. and Garner, M (2019) A Mixed Research Synthesis of Literature on Teaching Qualitative Research Methods. SAGE Open 9.3 pp. 1-10.

Walker, C. and Baxter, J (2019) Method Sequence and Dominance in Mixed Methods Research: A Case Study of the Social Acceptance of Wind Energy Literature. International Journal of Qualitative Methods 18.1 pp. 1-10.

Zhang, B. and Dafoe, A (2019) Artificial Intelligence: American Attitudes and Trends. Online. Web.

Appendix 1

Interview questions

• What role do you hold in your organization? What does it involve?
• Have you been doing this job since you graduated? What was your previous work experience like? (the reason for changing; effects of AI on different periods, how to adapt to the changing business landscape)
• There are no coincidences, only events we cannot understand
• There are no secrets, only mysteries
• What do you understand by artificial intelligence?
• With the development of artificial intelligence, what do you think it will affect the industry based on your professional field or working environment? Prompt for challenge and benefits.
• Do you think this will affect recruitment processes in your organization?
• As we know, many jobs have been replaced by artificial intelligence robots. What skills do you think employees need to have to keep their jobs even as companies embrace AI? How do you think these skills should be acquired or nurtured?
• Do you think artificial intelligence could replace professional personnel to some extent? What kind of work can be finished by AI and what cannot be accomplished?
• Will development in AI require higher education?
• Will developments in AI change the organization structure?
• What kind of changes or revolutions should be made in the next 2, 5 and 10 years?

  • Accounting course curriculums; accounting cert6ificatrion (teachers)
  • Financial system; accounting certification; recruitment; professional training (accountants)
  • Professional training; management system; recruitment; (CEO, CFO, AI technology manager and HR manager
• Is there anything else about AI, professional education and training that you think is important?