A literature review on the impact of digitalisation on management control

MC serves as a sub-system of the management (Guenther, 2013). Per definition, management accountants are responsible for supporting the management in planning, control and decision-making activities during the execution of strategic and operational activities (Oesterreich et al., 2019). The support can be interpreted in terms of designing and maintaining MC systems and distributing information to relevant stakeholders (Oesterreich et al., 2019). Thus, the tasks of management accountants are considered broader in scope and not only related to accounting issues (Oesterreich et al., 2019).

Guenther (2013) summarises major MC tasks in his analysis and emphasises that a central task of MC is to assess the information needs of information recipients and achieve user-friendly processing and transferal of the offered information (Guenther, 2013, p. 276). Furthermore, MC focuses on supporting the management within the management decision process. “Consequently [MC is] responsible for reaching the performance targets of the firm and systematically aligning the organisation with the targets set previously” (Guenther, 2013, p. 277). However, Guenther (2013) describes the core of MC tasks with the coordination of the three sub-systems planning, monitoring and information support by creating adequate organisation structures and assuring rationality within the whole company.

Different MC roles are derived based on the tasks that are covered by management accountants (Oesterreich et al., 2019). Yazdifar and Tsamenyi (2005) elaborates that the ‘business partner’ role can be derived from MC tasks such as business performance evaluation, implementation of business strategy or strategic planning. Further MC role models were defined on the basis of all activities management accountants perform such as the role of the ‘bean counter’ or the role of the ‘number cruncher’ (Oesterreich et al., 2019; Yazdifar & Tsamenyi, 2005).

The analysis of several studies resulted in following list of tasks that are assigned to MC (in alphabetical order): budgeting; business performance evaluation; coordination of functional activities; cost/financial control; data management; implementation of business strategy; information of management/stakeholder; interpretation of operational information; investment control; management of IT systems; operational planning and decision-making; productivity improvement; profit improvement; project control; reporting; risk control; strategic planning and forecasting; working capital and short-term finance management (Bhimani & Willcocks, 2014; Guenther, 2013; Malmi, 2016; Oesterreich et al., 2019; Yazdifar & Tsamenyi, 2005).

Within the first publication wave from 2001 to 2009, the focus of scientific research was to discuss the effects of digitalisation on the perception of MC and the definition of MC tasks (Burns & Vaivio, 2001; Byrne & Pierce, 2007; Malmi & Brown, 2008; Ribeiro & Scapens, 2006; Yazdifar & Tsamenyi, 2005). Malmi and Brown (2008) describe that the realisation of MC tasks is influenced by the combination of devices and systems that management accountants use. New information technology is driving routine accounting tasks into centralised or outsourced positions while simultaneously decentralise MC (Burns & Vaivio, 2001). Burns and Vaivio (2001) mention budgeting as an example and explain that the widespread distribution and use of decentral business managers are devising and managing their own budgets rather than being given the budget numbers by a central MC position.

The decentralisation of MC tasks led to a transformation of the MC role model. The former reactive realisation of tasks such as business performance evaluation based on cost/financial control can now be extended to a proactive business support role using new technology and data (Malmi & Brown, 2008). This leads to a broadening of the range of tasks of a management accountant. Burns and Vaivio (2001) elaborate that management accountants are increasingly involved in areas such as strategy, information system implementation and change management activities. The integration of MC into strategic activities requires an awareness of the management accountant of an organisational objectives (Malmi & Brown, 2008).

Further, management accountants increasingly play an active role in addressing (digital) opportunities and corresponding changes in business models and organisational strategies (Yazdifar & Tsamenyi, 2005). Considering these tasks, management accountants not only develop and adapt new performance indicators, but also create flexible steering approaches and new portfolio techniques.

The adaption and change of MC tasks also has implications for the required MC competencies. Järvenpää (2007) states that management accountants need to become more and more business orientated, thus develop the mentality of a business partner. Management accountants need to have expertise in the organisation’s underlying business. They need a detailed understanding of the business model and value drivers involved together with sufficient industrial knowledge and an overview of the success factors required to function as business partners in management (Yazdifar & Tsamenyi, 2005). The ability to think strategically empowers management accountants to support the decision-making process both from both strategic and operational angles (Järvenpää, 2007). Although this competency has not only become important since digitalisation influences MC, it will also become increasingly vital to comprehend both traditional and digital business models (Burns & Vaivio, 2001). It is not enough to have basic knowledge about the business model in question. Rather than that, management accountants need to put business models and their value drivers into a meaningful context (Järvenpää, 2007).

In the second publication wave, the effects of digitalisation on existing MC tasks were described in a focused manner. In this wave, increasingly novel IT tools such as business intelligence (BI) or even artificial intelligence (AI) become more and more relevant. Möller et al. (2020) explain the expansion of MC tasks in recent years as a result of digitalisation. MC has increasingly developed in the sense of a value-added-oriented management function and serves management by reliably aligning entrepreneurial action with the corporate purpose of creating value, thus ensuring a company’s long-term existence. Oesterreich et al. (2019) and Andreassen (2020) state that digitalisation changes MC and the activities of management accountants in the long term. Technological innovations such as high-performance computers and broadband internet will promote the emergence of new business models and a faster, more efficient value chain process. The use of business analytics promises the automation of MC processes and time savings. As a result, MC becomes more efficient. Due to time savings, the available MC resources can perform more activities (Bergmann et al., 2020). Thus, the information support as one of the major tasks of MC (Guenther, 2013) can also be performed more efficiently (Oesterreich et al., 2019).

The impact of digitalisation differs in intensity, extent and affected working steps for different MC tasks (Al-Htaybat & Alberti-Alhtaybat, 2017; Malmi, 2016). Digitalisation has a strong impact on operational activities such as operational planning, budgeting, forecasting, reporting and cost accounting (Al-Htaybat & Alberti-Alhtaybat, 2017; Bergmann et al., 2020; Liu & Vasarhelyi, 2014; Rowbottom et al., 2021). This is based on the resource-intensive characteristic of the MC tasks (Bergmann et al., 2020). New technologies can take over some of the resource-intensive work and lead to an increase of process efficiency (Bergmann et al., 2020). To further explain the impact of digitalisation on MC tasks, the following two paragraphs contain a detailed analysis of digitalisation on reporting and budgeting.

Reporting as one of the core MC activities (Rowbottom et al., 2021) can be divided into further working steps (Al-Htaybat & Alberti-Alhtaybat, 2017; Rowbottom et al., 2021; Troshani & Rowbottom, 2021). Data collection and preparation are followed by report preparation and plausibility checks. After this, the management accountant proceeds to analyse, comment and discuss the reports. According to Rowbottom et al. (2021) the first steps are very resource-intensive. Thus, the majority of the effort in the reporting process lies in non-value-adding activities such as data preparation and plausibilisation (Rowbottom et al., 2021). Digitalisation helps to improve the reporting process by enabling a higher degree of automation and standardisation (Rowbottom et al., 2021). Management accountants get more time for value-creating activities such as analysing, commenting and deriving measures (Rowbottom et al., 2021). In addition, digitalisation is leading to a further increase in the use of external data, especially big data, in corporate management and reporting (Al-Htaybat & Alberti-Alhtaybat, 2017). Big data technologies such as sentiment analyses enable a quantification of unstructured data such as chats, blogs or tweets in social networks (Al-Htaybat & Alberti-Alhtaybat, 2017). Big data technologies can be used to make unstructured data useful for reporting (Al-Htaybat & Alberti-Alhtaybat, 2017). Many reporting recipients such as management or shareholders receive additional information in the form of meaningful interpretations and key recommendations for further company activities (Al-Htaybat & Alberti-Alhtaybat, 2017). Quantified data can be put into a cause-and-effect relationship (Awan et al., 2021). Data forms the basis for the implementation of driver-based management that can be evaluated on a data-based and statistical-quantitative basis rather than qualitative-logical relationships (Awan et al., 2021).

The impact of digitalisation on budgeting is evaluated as the second example. Comparable to the impact on reporting, digitalisation impacts budgeting in several aspects (Bergmann et al., 2020; Henttu-Aho, 2016). The integration of external data such as big data is capable to significantly increase the accuracy and timeliness of budgeting activities (Bergmann et al., 2020). Big data technologies are able to quantify most ‘soft’ value drivers such as consumer behaviour, discussions in professional media and opinions in social media and make them available in a usable form (Bergmann et al., 2020). In combination with statistical analysis methods and machine learning analytics, MC is able to put these value drivers into a logical relationship and transfer them into a evaluable model (Bergmann et al., 2020). Using further analysis methods such as regression analyses, neural networks or causal analyses can help to determine the strength and duration of value drivers (Bergmann et al., 2020).

Kokina et al. (2019) further evaluate significant trends in digital transformation in the financial sector and relates effects of this trend on MC tasks. On the one hand, the pressure for efficiency in the finance function will increase enormously. Routine processes will be radically streamlined and prospectively based on clear decision-making rules. This trend will be enabled using robotics process automation (RPA), digital services, integrated services and agile organisations. Due to highly integrated data, corporate management will shift from reactive-analytical to proactive forecasting (Al-Htaybat & Alberti-Alhtaybat, 2017). This trend is driven by real time data, advanced analytics, scenario modelling and mobile reporting. Trends such as big data, integrated enterprise architecture and blockchain technology will also lead to increased data availability and data linkage (Kokina & Blanchette, 2019).

Furthermore, Möller et al. (2020) describe the paradigm shift in MC that is taking place as a result of digitalisation. Hence, MC and associated planning, reporting and budgeting systems will be developed proactively and prognostically rather than reactive-analytically. Tools such as predictive analytics models will be used to generate forecasts from granular data, some of which will be highly automated and provided with a higher degree of accuracy than traditionally generated forecasts. Evaluations from previous periods are becoming less important and forecasting will become the essential starting point for analyses. Possible evaluations and associated business control are increasingly automated. Additionally, digital MC roles have cross-functional connections and dependencies.

Oesterreich et al. (2019) illustrate the close connection between tasks and role concepts in MC. According to Andreassen (2020), MC roles have been categorised into the stereotypes of ‘bean-counters’ and ‘business partners’. Where bean-counters are focused on practical tasks such as measuring and keeping accounts, business partners are involved in decision making and strategic tasks. The tasks underlying these roles are complemented by the roles of the data scientist and the central governance. As a data scientist, the management accountant has to cope with new digital requirements and transfer them into a reportable system with central specifications. Repetitive tasks are automatised, enabling management accountants to spend more time providing assistance to decision-makers (Andreassen, 2020). The governance function ensures that uniform rules are created and maintained during the performance of MC activities.

To exploit the previously explained paradigm shift and fulfil the coordination task, management accountants should possess an extended skillset. The first key competency of a management accountant is the in-depth expert knowledge of central MC processes, concepts and frameworks (Möller et al., 2020). Further, management accountants need to have a comprehensive understanding of internal and external accounting and a profound knowledge of key financial and non-financial figures (Oesterreich & Teuteberg, 2019). Digitalisation will not disrupt this competency in the future as fundamental new knowledge will not be added to this field (Demartini & Taticchi, 2021). However, basic MC processes will tend to adapt to digitalised technologies. The forecasting process for example has favourable preconditions to become largely automated, self-service concepts can replace traditional reporting and planning processes can often be standardised and simplified (Möller et al., 2020). Targeted education in universities and other educational institutions on changes of MC instruments and the use of new ones is required (Daff, 2021).

This leads to the second competency field—technical and methodological knowledge—which describes expertise in the areas of analytical skills, dealing with complexity and problem solving (Oesterreich & Teuteberg, 2019). The ability to analyse data and information in a limited time span and to elaborate measures to improve the performance of a function or the overall company is required for management accountants who are involved in management decision processes as business partners (Oesterreich & Teuteberg, 2019). Data science and IT skills have gained importance for management accountants (Oesterreich & Teuteberg, 2019). A future combination of strategical business partner tasks with selected operational data scientist tasks is thus proposed by Oesterreich et al. (2019). To fulfil these tasks, management accountants need a basic understanding of data architecture, the organisation’s IT system landscape, data extraction, processing and visualisation. It can be noted that the increase in technological possibilities has expanded the required competencies step by step over time (Oesterreich & Teuteberg, 2019). Möller et al. (2020) mention the ability to develop and interpret statistical models as well as expertise in the use of BI tools and knowledge of data protection and data security as examples for the future digital core competencies of management accountants.

Another competency field entails communicating and cooperating with one’s colleagues and supervisors in an adequate, target-oriented manner (Möller et al., 2020). Appropriate stakeholder management includes cooperating with management as well as with functional experts, team members, and other internal and external customers of MC (Möller et al., 2020). Regarding digital trends, management accountants need to be able to respond to developments of communication channels such as social media, and to operate them properly (Möller et al., 2020). This competency was also required in earlier years. However, digital communication and cooperation are becoming increasingly critical in the context of agile organisational forms and new control models (Oesterreich et al., 2019). The competency of communication is followed by the final field of personal skills, which include so-called soft skills (e.g., the ability to solve problems, to think in a solution-oriented manner and to show a certain degree of stamina and perseverance) (Möller et al., 2020). Digitalisation has not completely disrupted this area of expertise. However, the digitalisation of society in general has ensured that management accountants consciously or subconsciously adapt themselves and develop these soft skills (Greve et al., 2017).

Digitalisation changes MC tasks (Al-Htaybat & Alberti-Alhtaybat, 2017; Greve et al., 2017; Möller et al., 2020; Oesterreich et al., 2019). MC is initially relieved by the additional support of new technologies (Oesterreich & Teuteberg, 2019). The relief enables MC to focus more on value-creating activities and get integrated into decision-making processes (Möller et al., 2020). However, management accountant need to adapt to the new situation with an increased set of MC tasks and increase their competencies (Oesterreich & Teuteberg, 2019). Beside functional expertise and the understanding of the business model, analytical and social skills complement the profile of a management accountant (Oesterreich et al., 2019).

To perform MC tasks that were analysed in the previous sub-chapter, management accountants use a set of MC instruments (Guenther, 2013). The design and functionalities of the instruments are oriented to the requirements of each MC task (Guenther, 2013; Henttu-Aho, 2016; Malmi, 2016). MC instruments help management accountants to fulfil a MC task (Malmi, 2016). Thus, MC tasks (‘what’ needs to be done) have a close relation to MC instruments (‘how’ is it done). The digitalisation of MC instruments is directly linked to the digitalisation of MC tasks (Guenther, 2013). Therefore, it is necessary to analyse the influence of digitalisation on MC instruments (Guenther, 2013; Malmi, 2016).

The following list of MC instruments is created based on the analysis of different studies (in alphabetical order): activity-based costing; balanced scorecard; benchmarking; conjoint-analysis; cost-earning comparison; critical path method; customer-benefit analysis; incentive systems; life-cycle costing; milestone planning; portfolio analysis; project assessment; risk simulation; scenario modelling; sensitivity analysis; SWOT-analysis; target costing; trend analysis (Bergmann et al., 2020; Guenther, 2013; Henttu-Aho, 2016; Knudsen, 2020; Maiga et al., 2014; Malmi, 2016; Rikhardsson & Yigitbasioglu, 2018; Rom & Rohde, 2007; Taipaleenmäki & Ikäheimo, 2013; Troshani & Rowbottom, 2021; Vitale et al., 2020).

In the first publication wave, the effect of new technologies and systems, e.g., ERP systems on the use of MC instruments has been elaborated. Traditional instruments remain popular (Burns & Vaivio, 2001). However, they are used alongside new and so-called advanced MC instruments such as rolling forecasts, activity-based costing and the balanced scorecard (Burns & Vaivio, 2001, p. 390).

Caglio (2003) discusses possible effects of digitalisation on the finance function and states that the traditional view of accounting is being questioned by the diffusion of large, integrated information systems (i.e. ERP systems). On the other hand, Caglio (2003) provide evidence that accountants’ traditional roles within organizations is declining, since accounting literacy, through ERPs, has become easily transferable to non-accountants, such as information system people and line managers. Following this argumentation, the traditional view of accounting as the core of an organization’s information system is being challenged by digitalisation (Caglio, 2003, pp. 123–124). Thus, a lot of companies started to convert their old finance and MC systems into new ERP systems (Dechow & Mouritsen, 2005). Nevertheless, the change of MC instruments is not a ‘non-risk operation’. New ERP systems such as the new SAP technology now integrates all parts of a business operations. It becomes vital to harmonise existing MC instruments such as reporting to enable efficient MC processes (Dechow & Mouritsen, 2005). “Even if ERP systems may make financial accounting stronger, they do not automatically make management accounting stronger. This perhaps may be a paradox since ERP systems often are presented as technologies for management control” (Dechow & Mouritsen, 2005, p. 730). Dechow and Mouritsen (2005) further explain that ERP systems leads to an increased connectivity within different functions and thus creates the requirement of a mutual control. Dechow and Mouritsen (2005) claim that management control becomes a collective affair as most of the control mechanisms previously sustained within the company in different functions now become transparent and visible by the use of a collective ERP system (Dechow & Mouritsen, 2005).

ANDREASSEN (2020) describes the exponential growth of digital information and the associated rapid spread and further development of digital enabler. Over the next few years, traditional application systems will increasingly become supplemented by digital enablers such as messenger services, cloud computing solutions, mobile applications, sensor technologies and social networks. In addition to internal company data, these applications can be used to collect external data on relevant market developments in great detail, as well as for use in corporate management (Moll & Yigitbasioglu, 2019). According to Andreassen, new ERP-systems and a digitalised IT enable the decentralisation of decisions. Increasingly, local decision-makers will relate to MC to provide structured data, thus reinforcing the connection between decision-makers and MC (Andreassen, 2020, p. 214). Carlsson-Wall et al. (2021) discuss the use of ERP systems and specifically their system-as-a-service (SaaS) delivery method as a new phenomenon for planning processes. They state that digitalisation takes the notion of commodified systems new level. These technologies involve a greater degree of standardization and provide fewer possibilities for localization. Therefore, organizations need to adapt to these systems and not vice versa, which contributes to a loss of authority with respect to system design (Carlsson-Wall et al., 2021, p. 21). Carlsson-Wall et al. (2021) refer to the public sector in their study and explain the relatively large influence of system providers in this field. However, the results of Bergmann et al. (2020) for budgeting processes and Troshani and Rowbottom (2021) for reporting display a similar dependency of companies towards ERP system providers for non-public sectors such as manufacturing industry or trade.

Especially budgeting as a data-driven MC task is suitable for the application of (digitalised) analytical methods. Business analytics may overcome problems and limitations and lead to an increased satisfaction with the instrument itself (Bergmann et al., 2020). Bergmann et al. (2020) further discuss factors that determine the use of business analytics in the budgeting process and evaluate that a sophisticated data infrastructure is positively associated with the use of analytical methods. Further, the importance of the planning function is positively associated with the use of business analytics in the budgeting processes (Bergmann et al., 2020). Thus, digitalisation enables MC to increase the importance of planning, forecasting and budgeting by leveraging the potentials of digitalisation (Bergmann et al., 2020). Rikhardsson and Yigitbasioglu (2018) also well point out that digitalisation functions as an enabler for business analytics. Conversely, business analytics creates further innovation potential through its use in supporting operational decision-making processes (Rikhardsson & Yigitbasioglu, 2018). One challenge of digitalisation is the full use of the potential regarding the enforcement of data-driven business decisions (Rikhardsson & Yigitbasioglu, 2018). Big data with its potential long-term impact on organisational decision-making has the potential to radically alter the organization of the MC function (Rikhardsson & Yigitbasioglu, 2018, p. 45). Digitalisation projects in recent years have primarily focused on the digital transformation of business processes with the primary aim of improving process efficiency (Rikhardsson and Yigitbasioglu). Rikhardsson and Yigitbasioglu (2018) give an example for this where data on buying behaviour shows stability over time. The company could use this stability and plan with base sales activities on this pattern without having to examine the underlying cause of the behaviour (Rikhardsson & Yigitbasioglu, 2018, p. 45). However, the potential for further business analytics applications is often not recognised and therefore not fully exploited when planning a digitalisation project (Rikhardsson & Yigitbasioglu, 2018). Companies with a high degree of digitalisation in MC use ERP applications more than companies with a lower degree of digitalisation (Youssef & Mahama, 2021). By using BI, data can then be extracted from ERP systems, evaluated and presented. BI applications focus on descriptive analyses and answering diagnostic questions. BI enables MC to retrieve data from an ERP system and to create reports (Rikhardsson & Yigitbasioglu, 2018).

Other MC instruments that are influenced by digitalisation are reports for a cost-earnings comparison or customer-benefit analysis as additional part of cybernetic control. Troshani and Rowbottom (2021) elaborate that digital reporting calls for signification changes to the reporting infrastructure. This implies major impact on MC as the information supply chain as well as regulations and reporting standards need to be adapted to maintain or improve the accessibility, transparency, accuracy and comparability of information reporting in corporate reports (Troshani & Rowbottom, 2021). Digital reporting that is structured and uses reusable formats facilitates monitoring, surveillance and risk assessment within companies. However, Troshani and Rowbottom (2021) also state that the use alone of a digitalised reporting does not necessarily result in positive effects.

Digitalisation leads to an increased use, an improved performance and extended functionalities of company-wide ERP-systems (Carlsson-Wall et al., 2021). Multidimensional analyses and reporting evaluations with large volumes of data can be carried out in real time without having to replicate data in separate data warehouses (Carlsson-Wall et al., 2021). Furthermore, business intelligence systems and reporting front-end tools are accessible to MC as they become more and more cost-effective and customisable (Youssef & Mahama, 2021).

MC instruments that require very resource-intensive handling are strongly affected (Bergmann et al., 2020). Such instruments are in the areas of reporting, operational planning, cost and performance accounting, and forecast activities (Bergmann et al., 2020). Technologies such as big data, RPA, predictive analytics or machine learning can be used to automate instruments completely or partially (Bergmann et al., 2020; Dechow & Mouritsen, 2005; Sutton et al., 2016). In addition to enhancing the quality of the instruments, the efficiency and speed of activities can be increased by digitalisation (Bhimani & Willcocks, 2014). Rather than serving as answering machines for the construction of accurate knowledge leading to rational choices, the targeted use of MC instruments can offer and sustain platforms to achieve wise mediation among the different parties involved. Ultimately, institutions and organisations are looking for reasonable—not rational—choices. Digitalisation enables MC to question decisions and to develop new scenarios rather than solve all problems (Arnaboldi et al., 2017, p. 16; Chenhall & Moers, 2015; Quattrone, 2016).

The first two sub-chapters elaborate the extent to which digitalisation influences tasks and associated instruments of MC. In his framework, Guenther (2013) explains that the activities of MC also have an impact on the entire organisation of the company and influences sub-systems such as HR. MC is not limited to tasks such as coordination, monitoring or information that affect the (internal) structure of MC as a sub-system of management, but rather is included or even responsible for the creation of adequate organisation structures and the design of incentive systems and target agreements (Guenther, 2013, p. 278). This expands the MC conceptualisation to having holistic management controls for the socio-technical system of a firm (Guenther, 2013, p. 282). Digitalisation also has an impact on this relationship (Järvenpää, 2007; Knudsen, 2020; Möller et al., 2020; Peters et al., 2018). In the following sub-chapter, the impact of digitalisation on MC organisation and the connection to sub-systems such as HR organisation will be explained.

MALMI and Brown (2008) elaborate within the first publication wave how the elements of MC work in relation to each other within an organisation and how they are affected by external influences. According to them, MC research has provided much information about the operation of many of these subsystems individually, but not as a package across the organisation. A key finding of their research was that the elements of an MC system are related in a system of multiple unidirectional and bidirectional links (Malmi & Brown, 2008, p. 297). Especially on a transactional level, where MC is carried out by non-management accountants, these links must be organised in a harmonized manner. According to Ribeiro and Scapens (2006), digitalisation leads to an institutional pressure that is exerted on organisations. The level of complexity which is typical of all social systems is influenced by external effects such as digitalisation (Ribeiro & Scapens, 2006, p. 107). Organisational circumstances as the ‘circuits of power’ change (Ribeiro & Scapens, 2006). MC as a binding function with its unidirectional and bidirectional links becomes a key position within the organisational change brought by digitalisation (Malmi & Brown, 2008). During the interaction with functions having a higher degree of digitalisation and functions having a lower degree of digitalisation, MC acts as a central point for organisational exchange (Malmi & Brown, 2008).

Rom and Rohde (2007) illustrate on the example of shop floor workers that initiate postings to the stock and payables accounts when they key in a goods returns notice that MC has become a dispersed activity. The increased implementation of company-wide ERP-systems promotes a company-wide integration and decentralisation of control (Rom & Rohde, 2007). However, according to Rom and Rohde (2007), a full integration of MC into other corporate functions like HR is a time consuming process. Furthermore, the relationship from performance measurement to HR can be illustrated via a bidirectional relationship (Rom & Rohde, 2007). The performance indicators measured by MC can only be interpreted and translated into further improvement measures in an exchange with the HR function (Harney & Jordan, 2008). Harney and Jordan (2008) illustrated in their study that cooperation between the HR function and MC can serve to identify factors that influence employee performance. However, particular attention should be paid due to sensitive data and employee rights in the process of performance evaluation (Harney & Jordan, 2008). The additional instruments brought up by digitalisation should be thoroughly evaluated for their feasibility and acceptance by the employees (Harney & Jordan, 2008).

The close connection between digitalisation of the MC organisation to other corporate functions underlines the increasing integration of MC into the overall corporate landscape. MC is no longer perceived as a silo-function but serves as additional support for functional analyses.

To better understand the effects of digitalisation on MC organisation, it is necessary to analyse the framework conditions under which MC operates. Especially within the second publication wave after the financial crisis in 2009, the MC organisation and the entire finance function have been under increasing pressure to improve cost and process efficiency (Greve et al., 2017). In detail, there is a demand for the further automation and standardisation of MC processes and for a leaner MC organisation (Korhonen et al., 2020). In response to increasing efficiency pressure, selected MC activities have been centralised in recent years to achieve efficiency gains through economies of scale. According to Taipaleenmäki and Ikäheimo (2013), the finance department of the future will be consistently organised with respect to transactional and analytical processes. Analytical tasks are bundled and provided centrally. For this purpose, a clear task split is defined for each MC process (Peters et al., 2018). The tasks are then assigned to the corresponding individuals to subsequently bundle similar tasks organisationally. However, not all MC tasks are equally suitable for centralised bundling. The focus is on highly standardised processes that are frequently repeated, highly structured and follow clear guidance. Examples for process bundling are the provision of standardised reports or the creation of cost centres (Kokina & Blanchette, 2019).

Chenhall et al. (2011) state that companies have tried to bundle these types of transactional activities in past years into a central unit such as a shared service centre (SSC), to leverage efficiency gains. By focusing on innovative business processes, a company can use its resources more efficiently and focus on strategic initiatives such as the implementation of new business models or the digitalisation of further business processes. However, the approach to outsource transactional activities into an SSC only makes sense if there are sufficiently large volumes of transactions. In the meantime, SSCs from external third-party providers are also being used in some cases (Quinn, 2014).

According to Korhonen et al. (2020), the next step to achieve further efficiency gains is to digitalise SSCs and the corresponding activities to match with their performance. Korhonen et al. (2020) explains a possible use-case of RPA for SSC activities. In this case of usage, a software robot performs financial accounting activities by extracting data from ERP systems on the basis of a standard report, prepares the data in Excel and generates a report of results that the robot uses to fulfil a reporting tool (Korhonen et al., 2020).

Under the pressure to further improve the cost and processual efficiency of the MC organisation, organisations are creating new positions or entire organisational units grounded in the new role of the data scientist (Davenport & Patil, 2012). When building up new competencies in the area of data science, the question of an overall fitting organisational design and integration into the company arises. In addition to external consulting services, Korhonen et al. (2020) distinguishes between three different approaches for building up data science know-how. In the simplest form, companies build up their own data science know-how by training selected management accountants or employees from departments such as IT or BI. Numerous seminar providers have already reacted to this trend and offered specific training courses. Another way to build up data science expertise is to create new positions for data scientists within a company. The new positions are located either in MC or other departments that have certain proximity to data and data analysis. The third option is to create entire organisational units for the new role. The new units in which data scientists work centrally have names such as ‘Data Lab’ or ‘Digital Lab’ as well as ‘Data & Analytics’ or ‘Data Science’. Kokina and Blanchette (2019) refers to numerous practical examples for the implementation of such organisational functions, such as the original equipment manufacturer (OEM) Volkswagen with its Data Lab or the insurance company ERGO with its Digital Lab. There are various alternatives for the hierarchical integration of new business units into a company. The key question that arises is whether the new business units report to the Chief Financial Officer (CFO) or the Chief Digital Officer (CDO) (Korhonen et al., 2020).

Another key aspect of the organisational change caused by digitalisation is the new significance of data management within the organisation (Szukits, 2022). Data management refers to the handling of master and transaction data relevant for a company’s operating model and is a core element of the digitalisation of MC (Szukits, 2022). The increased demand for data analyses requires error-free, accessible and harmonised data within the company (Reinking et al., 2020a). Digitalisation leads to the standardisation and unification of data sources (Reinking et al., 2020a). This requires a common understanding of the data model for master and transaction data within all IT-systems in all corporate functions (Reinking et al., 2020a). The integration of IT-systems from the MC function with other corporate functions such as procurement, production and HR can be achieved by the combination of data warehouses and master data management systems (Reinking et al., 2020a). While the data warehouse brings together transactional data from heterogeneous systems into a single data source, the master data management system provides the necessary processes and functions to unify, maintain and keep master data consistent across all internal systems (Reinking et al., 2020a). The organisation and governance, i.e., the company-wide regulation of data quality, have been professionalised by digitalisation and centralised in the corporate function ‘data management’ (Reinking et al., 2020a). The availability and efficiency of fast broadband connections and the range of powerful cloud-based applications have increased in recent years (Andreassen, 2020). Cloud-based applications are available as software-as-a-service model, meaning that maintenance and implementation is covered by the software provider (Andreassen, 2020). Thus, they can be customised to fit the requirements of the MC function and enable the integration of the MC function into the corporate organisation (Szukits, 2022).

Building on the findings of the first and second publication wave, the influence of digitalisation on MC organisation can be summarised with a decentralisation of decisions. Digitalisation leads to the fact that subjective and relative figures and images become objective numbers and facts. Management accountants can store this information from different functions and use the data for detailed functional performance analyses. Digitalisation made this objective feature of accounting numbers even more pervasive and visible. The former separation between different functions and MC is dissolved step-by-step. Access to a larger amount of data will incidentally allow multiple loci of controls and therefore the diffusion of power (Quattrone, 2016, p. 119). In other words, the production and consumption of MC numbers is separated in different functions. Quattrone (2016) continues to describe the effects of digitalisation on the overall organisation as he states that data are now delivered to decision makers who are formally and substantially excluded from their manufacture (Quattrone, 2016, p. 120). For this reason, it is important to expand MC competencies so that management accountants become business partners who can interpret analyses (Quattrone, 2016). Only with a deep processual and entrepreneurial mindset, information passed on between different functions can be properly analysed, processed and passed on (Davenport & Patil, 2012).

Quattrone (2016) summarises the effects of digitalisation on MC organisation with a paradox. Digitalisation “will increase the belief into the possibility to give better visibility to organizational activities. […] On the other hand, it will augment uncertainty. Spurious correlations and the need to select from among the ocean of data and options by which one will be flooded will augment, not diminish” (Quattrone, 2016, p. 120). In this context, it is important to emphasise one of the main functions of MC; to establish and continue dialogues between different functions and the management (Quattrone, 2016).

In addition to the elaboration of the impact of digitalisation on the design of the MC function, social aspects of digitalisation on the behaviour of MC are also considered within the framework of Guenther (2013). MC is necessary to guard against the possibilities that employees act in a way the organisation does not want them to do or fail to do something employees should do (Malmi & Brown, 2008, p. 289). Thus, behavioural aspects of MC can be defined as the attempt to increase the probability that individuals or groups behave in ways that lead to the attainment of organisational goals (Guenther, 2013; Malmi & Brown, 2008). Guenther (2013) refers to the control theory, the principal agent theory and the behavioural accounting theory that describe the influence of social aspects on the design of the MC function.

According to Malmi and Brown (2008), MC is one core element regarding the management of employees within a company. Managing employees requires management accountants to deal with behavioural aspects of employees as well as behavioural aspects within MC itself (Malmi & Brown, 2008). MC controls in the interaction with the management of an organisation (Malmi & Brown, 2008). This dialogue is based on performance analyses conducted by management accountants (Malmi & Brown, 2008). However, based on the principal-agent theory, the principal (in this case MC) has an information deficit in comparison to the agents (co-workers) (Malmi & Brown, 2008). A distinction can be made between asymmetrically distributed information before contracts are concluded (adverse-selection problem) and after contracts are concluded (moral-hazard problem) (Malmi & Brown, 2008). The exploitation of digital technologies can help to reduce both problems (Revellino & Mouritsen, 2009). MC can utilise digital performance measurement instruments to minimise the information deficit by an increased provision of information (Fullerton & McWatters, 2002). Simultaneously, co-workers can use those performance measurement instruments and send out positive signals (Fullerton & McWatters, 2002).

Another possibility to reduce hidden information, hidden intention or hidden action of agents within a company is the use of motivation and incentive systems (Fullerton & McWatters, 2002). Although such systems have been discussed and implemented in companies beforehand, digitalisation with the increase in data availability resulted in an increased set of objectively measurable performance indicator (Fullerton & McWatters, 2002). Thus, in addition to an incentive system based on internal developments, a market and competitor performance analysis can be used to expand the incentive system (Fullerton & McWatters, 2002). Performance-based variable salary components can further motivate employees to perform better (Fullerton & McWatters, 2002). Beside the implementation however, MC has to communicate and promote the use of objective data within performance measurement and incentive systems (Malmi & Brown, 2008). This impacts the behavioural aspects of MC as the cooperation and communication between MC, employees and management changes (Malmi & Brown, 2008).

According to Chenhall and Moers (2015), MC systems are likely to be used if employees found them to be useful and if they provide satisfaction to individuals. The satisfaction depends on the quality of information individuals receive by MC (Chenhall & Moers, 2015). MC uses digital instruments to process performance data and provide the data in a consumable way (Chenhall & Moers, 2015). If MC succeeds in this, digitalisation helps individuals to approach their tasks with enhanced information and exploit the information to improve decision-making-processes and individual performance (Chenhall & Moers, 2015). Thus, the improvement of performance measures can help to increase individuals job satisfaction.

However, Chenhall and Moers (2015) also elaborate that the implementation of new technologies effects the behaviour within MC systems. The implementation of technology has an influence on complexity, task uncertainty and interdependence within an organisation (Chenhall & Moers, 2015). Organisations that produce highly specialised, non-standard, differentiated products are likely to employ complex technologies to produce single units or batches of a product (Chenhall & Moers, 2015). The production involves processes that have a low degree of analysability and many sub-processes with exceptions in the process flow (Chenhall & Moers, 2015). The imperfect processual and technical knowledge of MC results in a low ability to measure outputs and performances (Chenhall & Moers, 2015). As a potential result, performance measurements may have a negative association within the organisation (Chenhall & Moers, 2015). In contrast, organisations that produce standard, undifferentiated products using automated processes can be analysed exploiting new digital performance measurement systems (Chenhall & Moers, 2015).

To define the influence of digitalisation on behavioural aspects of MC, it is therefore necessary to analyse the business and operating model of a company (Abernethy et al., 2013; Liew, 2015). Digitalisation enables the implementation of new methods and technologies that reduces the information asymmetry (Abernethy et al., 2013). The use of standardised databases leads to an integration of MC to other corporate functions and increases the number of possible performance management analyses (Abernethy et al., 2013). Furthermore, Liew (2015) evaluated that information technology enforces automated MC and has an impact on the decision-making behaviour of individuals. The increased use of information technology helped corporate management to learn more about internal operations and issues faced within the company (Liew, 2015).

However, the increase in analyses does not necessarily lead to an improvement of the MC function (Abernethy et al., 2013; Liew, 2015). Abernethy et al. (2013) examine how the use of traditional and new performance measures leads to dysfunctional behaviour by causing managers to pay attention to the wrong things (Abernethy et al., 2013, p. 950). Problems of myopia in intertemporal decisions dominate the rationale for including non-financial actions in the design of performance measurement systems. Abernethy et al. (2013) indicate that the choice of the performance management system has a positive impact on the long-term perspective of MC. In the course of digitalisation, it will become increasingly important to select the ‘right’ instrument from among many different instruments, and to tailor it specifically to the company’s needs.

To leverage big data, algorithms or the automation of processes, it is important that research also take a non-positivistic stance and elaborate on how the reduction of human judgement influences the validity of decisions made based on new digital methods (Quattrone, 2016). According to Arnaboldi et al. (2017), the performative role of MC and big data creates and sustains a paradox in practice. If digital methods such as big data deepen an organisation’s belief in the possibility of improving rational decision-making through better measurement and representation, full transparency within management can emerge. Databases and statistical models are relied upon to enhance transparency, predict individuals’ wishes and steer future actions. Arnaboldi et al. (2017) thus underscore the risks and challenges of big data. They state that management accountants are used to work with traditional instruments. It is necessary to note that more data will not automatically lead to better results or a better performance. There is often uncertainty; as such, new (digital) instruments first have to be adequately used to leverage their advantages. The most efficient way in which that can be done in practice is still unexplored and deserves a stream of its own research (Arnaboldi et al., 2017, p. 13). Big data augments uncertainty through spurious correlations and incomplete connections that may emerge from the large amount of data that organisations are collecting, storing and confronting (Arnaboldi et al., 2017; McAfee et al., 2012; Quattrone, 2016).

Digitalisation has an impact on the intraorganisational cooperation and the cooperation within the MC system. Information asymmetries can be minimised using digital technologies (Abernethy et al., 2013). However, individual and social factors influence the business and operating model (Chenhall & Moers, 2015). The reduction of information gaps and the redesign of MC processes can also be associated with negative emotions by management accountants or other employees (Abernethy et al., 2013). Previously unknown conflict potentials can arise that can be addressed choosing suitable MC instruments and change measures (Chenhall & Moers, 2015).

Positive emotions that arise within the digitalisation process are reflected in an increased participation of management accountants in cross-functional activities (Fullerton & McWatters, 2002). MC utilises the advantages of new technologies such as increased data availability and transparency to improve MC processes and workflows (Fullerton & McWatters, 2002).