Manage performance with data

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Manage performance with data

How the design and use of performance management systems is affected by business analytics

By

Luc van der Wolde S2508486

MSc BA Management Accounting and Control Supervised by: Dr. A. Bellisario

January 19th₂₀₂₀

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Abstract

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Introduction

The current times of digitisation and rapid technological developments enabled organizations to collect and store great amounts of data. The enormous datasets of so-called ‘Big Data’ cannot be processed by the use of traditional information systems (IS) (Warren, Moffit & Byrnes, 2015) and has been characterized by high volume, variety, velocity, veracity and value (Gupta & Gupta, 2015). The application and use of Big Data have the potential to bring several advantages to organizations (Davenport & Harris, 2007; Griffin & Wright, 2015). Generally, the data is divided in internal enterprise resource planning (ERP) systems, transaction systems, data warehouses or obtained from external parties (Nielsen, 2018). However, challenges emerge when trying to transform the unstructured data into valuable insights, in order to fully exploit the potential advantages of Big Data. Organizations need to develop capabilities and data strategies to extract valuable insights from the datasets (Wamba, Gunasekaran, Akter, Ren, Dubey, & Childe, 2017; Davenport & Bean, 2018). To generate valuable information from the large datasets, organizations have been making significant investments in business analytics (BA) (Constantiou & Kallinikos, 2015). BA is the technique that brings the data together and is able to convert unstructured data into valuable information that can be used for decision-making (Davenport & Harris, 2007). When an organization is able to implement and use BA successfully, it creates an advantage over their competitors (McAfee & Brynjolfsson, 2012). Therefore, using BA in organizational processes might be beneficial for organizations and investments in BA has been one of the largest IT investments in the last couple of years (Kappelman, McLean, Johnson, Torres, Nguyen, Maurer & Snyder, 2017).

As BA has the potential to transform entire processes and can be applied to multiple avenues (Davenport, 2014), it is acknowledged that BA can be used for management control (MC) and performance management (PM) (Elbashir, Collier & Sutton, 2011; Schläfke, Silvi & Möller, 2013). Systems used to support MC and PM are called performance management systems1_{(PMS). PMS are} used to implement business strategy (Bititci, Garengo, Ates, & Nudurupati, 2015), facilitate decision-making (Berrah, Mauris & Vernadat, 2004) and support organizations to achieve organizational objectives and increase performance (Neely, 2005; Ferreira & Otley, 2009). Subsequently, BA is able enhance features of PMS by improving strategy development, target communication and performance measurement (Mello, Leite & Martins, 2014; Warren et al., 2015), and linking non-financial with

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financial performance indicators (Davenport & Bean, 2018). In the design and use of PMS, information technology (IT) plays a crucial role (Chenhall & Langfield-Smith, 2007) and, to use the PMS effectively for decision-making, the data as well as its quality is considered an important success factor (Ittner & Larcker, 2003). Under the influence of BA, decision-making is improved and becomes more data-driven (Klatt, Schläfke & Möller, 2011; McAfee & Brynjolfsson, 2012; Wamba et al., 2017) and can be executed faster (Tien, 2013).

Several authors studied the relationship between BA and PM. For BA to be effective and to exploit it fully for decision-making, it is important to develop analytical skills (Schläfke et al., 2013; Vidgen, Shaw & Grant, 2017). Besides analytical skills, organizations need a culture concentrated on using data in their processes (McAfee & Brynjolfsson, 2012). However, the aforementioned managerial challenges do not explain how BA is a prospect for the design and use of PMS (Schläfke et al., 2013; Chenhall & Moers, 2015; Ask, Magnusson & Bredmar, 2016). Multiple frameworks have been developed to offer guidance to implement BA in PMS, but they show inconsistency in the used concepts of PMS and are not distinct in their explanations of how BA affects the design and use of PMS (Schläfke et al., 2013; Appelbaum, Kogan, Vaserhelyi & Yan, 2017; Nielsen, 2017). An encompassing framework is the Business Performance Analytics framework of Raffoni, Visani, Bartolini & Silvi (2018). They developed a complete framework based on the PMS of Ferreira and Otley (2009) and tested it in a case organization. The collection and analysis of data was found to be crucial to the success a PMS supported by BA (Raffoni et al., 2018). Also, diagnostic and interactive control (Simons, 1995), that refer to monitoring of performance and awareness of performance by facilitating dialogues, were hypothesised and proven to be boosted by BA when implemented for PM purposes (Raffoni et al., 2018). However, the framework was developed within a single case organization and Raffoni et al. (2018) have suggested more empirical work further in other business environments.

Existing literature acknowledges that BA can be used for PM purposes, affecting PMS and highlights several issues emerging from the implementation of BA. However, the effect of BA on PMS has received small attention. Several frameworks were developed to implement BA in PMS but they lack to provide an understanding how PMS in general, and the organizational processes around PMS specifically, are affected. Therefore, a better empirical understanding is required regarding BA and the design and use of PMS (Schläfke et al., 2012; Warren et al., 2015; Appelbaum et al., 2017; Raffoni et al., 2018). This study is relevant since it fills that gap. This study aims to provide insights on how BA affects PMS practically, how processes are affected, what activities are performed to deal with challenges to use BA, and answers the following research question: “How does business analytics

affects the design and operation of performance management systems?”. To answer the research

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data-driven approach. Also, this study shows that the focus to steer the organization is changed to bottom-up. And with discovering the potential to use predictive and prescriptive analytics, the organization faces several technological, organizational and contextual challenges.

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Theoretical Background

The design and use of performance management systems

The foundation of PMS lies in the MC literature and management control systems (MCS) developed earlier (Ferreira & Otley, 2009). The purpose of MC can be two-fold, i.e. to track and manage performance and make decisions based on information (Ferreira & Otley, 2009). In that way MC directs employee activity and regulates employee behaviour (Merchant & van der Stede, 2007). MC is incorporated in management control systems (MCS), which are instruments to support decision-making (Chenhall, 2003), and are used to support managers in implementing the business strategy and controlling performance (Simons, 1995). The design of a MCS depends on the situation in which it has to function, and relates to the environment, technology, size, strategy and culture of the organization (Chenhall, 2003). Examples of MCS that have been developed are; administrative and interpersonal controls (Bruns & Waterhouse, 1975), action, results and personnel/cultural controls (Merchant & Van der Stede, 2007), levers of control framework (Simons, 1995) and Otley’s (1999) performance management framework. However, these MCSs do have a specific focus with respect to managing and controlling performance and their relevance might be too narrow for this study. Therefore, to manage and control organizational performance, this study refers to the PMS of Ferreira & Otley (2009). This framework is developed on the basis of the levers of control- (Simons, 1995) and the performance management framework (Otley, 1999) and includes eight aspects to design a whole PMS: (1) vision and mission, (2) key success factors, (3) organizational structure, (4) strategies and plans, (5) key performance measures, (6), setting targets, (7) performance evaluation, (8) reward systems. Also, the aspects (9) information flows, systems and networks, (10) PMSs use, (11) PMSs change and (12)

strength and coherence are included in the framework (Ferreira & Otley, 2009). As Chenhall (2003)

indicates, when an organization succeeds in serving all twelve aspects, it should be able to tackle issues of a designing and using a PMS.

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organizations to learn from experience, generate new ideas and to review strategies and plans (Widener, 2007; Ferreira & Otley, 2009).

It is well acknowledged in the literature that innovations within the infrastructure of information systems and information technology result in changes in managerial processes (Sutton, 1999). For example, the use of ERP-systems in organizations contributed to new ways of executing MC and PM, and ever since the introduction of ERP-systems those IT systems have developed strongly (Chapman & Chua, 2003). Due to these developments in IT, PM cannot be separated from technology (Chapman, 2005; Dechow & Mouritsen, 2005). ERP-systems play a role by integrating the operational systems of organizations (Maguire, Ojiako & Said, 2010). Therefore, their main functionality consists of providing quick and easy access to great amounts of operational and financial data (Davenport, 1997), which supports organizations using their resources more effectively (Noudoostbeni, Ismail, Jenatabadi, & Yasin, 2010). IT also enables organizations to save time in PM, providing future-oriented information and expanding the scope of PM (Scapens & Jazayeri, 2003). IT offers opportunities for PMS and is responsible for the design and use of a PMS as well (Chenhall & Moers, 2015). However, implementation is considered difficult, because there is no standard set of norms and practices for the functioning of PMS (Caglio, 2003; Jack & Kholeif, 2008). Every organization has its own PMS that are adapted to organization specific IS, IT, ideas and execution (Beaubien, 2013).

Business analytics and performance management systems Business analytics

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In order to better understand BA and its scope, Holsapple et al. (2014) introduced three dimensions related to its concept: domain, orientation and techniques. Domain is the situation in which BA is used and can be on a general or specific level. For example, BA can be applied to human resource management, sales, operations, marketing, finance and supply chain management. However, BA can also be used within these disciplines specifically, e.g. customer and marketing analytics (Davenport, 2007; Hauser, 2007). Orientation concerns itself with the direction of BA, which can be descriptive, predictive or prescriptive. The dimension techniques refers to the application of BA. The BA techniques used is not only dependent on the former two dimensions, but also on what data kind of data is used (Appelbaum et al., 2017). In order to better understand how BA plays a role in the domain of MC, the three levels of the orientation dimension will have to be explained. Descriptive analytics describe what happened and are supported by visualizations as Key performance Indicators (KPI) and dashboards (Dilla, Janvrin & Raschke, 2010), and can support decision-making by identifying patterns from historical data (Sivarajah, Kamal, Irani & Weerakkody, 2017). Since the information from descriptive analytics forms the basis for other analytics, descriptive analytics is the first step of the process (Raffoni et al., 2018). Predictive analytics describe what could happen and are supported by statistical techniques in order to report causalities between data inputs and outcomes (IBM; 2013; Appelbaum et al., 2017; Nielsen, 2018). Prescriptive analytics specify what actions should be executed according to the outcomes of descriptive and predictive analytics (Holsapple et al., 2014; IBM, 2013). Prescriptive analytics are different from descriptive, and especially predictive analytics, because it provides organizations with solutions to problems or situations (Appelbaum et al., 2017). Therefore, effective use of prescriptive analytics can be seen was ways to optimize efficiency and effectiveness (Appelbaum et al., 2017).

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Business analytics and performance management systems

It is well known that BA has the potential to be used for PM purposes (Elbashir et al., 2011; Chapman & Kihn, 2009). By successfully implementing BA, decision time will be shorter (Rouhani, Ashrafi, Ravasan, & Afshari, 2016; Tien, 2013) and the PMS will be more effective, but also more sophisticated (Silvi, Bartolini, Raffoni & Visani, 2012). Another advantage is that performance standards can be measured more clearly because BA improves the understanding of cause and effect relationships between variables (Cokins, 2014). With respect to the design of PMS, Warren et al. (2015) state that BA supports and improves strategy development, target communication and performance evaluation. Today, more and more organizations are investing resources to exploit these reputed benefits, which will result in them basing their decision-making on (real-time) information generated from BA (Constantiou & Kallinikos, 2015). However, without having a strategic purpose of implementing BA this is likely to fail (Biesdorf, Court & Willmott, 2013).

To exploit the aforementioned benefits of BA regarding PMS, organizations need to develop a detailed data strategy to use and improve data quality (Davenport & Bean, 2018). The process of making data valuable for use for PM purposes consists of four stages: (1) receiving data from both internal and external sources, (2) access to data via ISs, (3) analyzes of data via business analytics and (4) use analyzes in the decision-making (Raffoni et al., 2018). To receive data and have access to data, organizations made significant investments in ERP systems, databases, inter-organizational sharing mechanisms and web-based software packages (Raffoni et al, 2018). Since the great availability of unstructured and structured data in organization’s databases, it might find difficulties in collecting and selecting data that is relevant (Raffoni et al., 2018; Zhang, Yang & Appelbaum, 2015). Big datasets can create an information overload and thus impede managers in using relevant data for PM purposes (Klatt et al., 2011). For effective BA implementation organizations need to have clear procedures about how data is incorporated (Zhang et al., 2015) and how the data is made complete and reliable, and therefore relevant for PMS (Bhimani & Willcocks, 2014). As a consequence the way how information is visualized as well as reporting capabilities are improved (He, Tian, Li, Akula, Yan & Tao, 2015). Thus, the integration of internal databases and the availability of rich information improves the decision-making processes and PM (Chapman & Kihn, 2009; Hyvönen, 2007). Furthermore, the quality of the used ISs was found to be a critical success factor in implementing BA (Isik, Jones & Sidorova, 2013). Accessibility, attractiveness, ease of use and flexibility are important elements for users to be committed to a system and for letting them use a system (Peters, Işik, Tona, & Popovič, 2016).

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only be valuable to an organization when it succeeds in attracting competent employees or lets employees develop analytical capabilities (Ransbotham, Kiron & Prentice, 2016). Lastly, IS success also lies in how employees use the information (Davenport, 1997). Therefore, an analytical culture where decision-making is evidence-based instead of intuitive decision-making is crucial (McAfee & Brynjolfsson, 2012; Popovič, Hackney, Tassabehji, & Castelli, 2016). Several problems that could emerge when BA is implemented into PMS are addressed in the literature extensively. However, it is still unclear how these problems are solved practically.

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aware of the performance (Raffoni et al., 2018). Besides this, BA could also be used in forecasting the impact of external factors on performance (Raffoni et al., 2018).

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Methodology

This research tries to develop a novel understanding of how PMS have developed under the influence of BA. Accordingly, research questions that contribute to inductive theory development are appropriate (Edmondson & McManus, 2007). Since little is known in the current field about this topic, and it is unknown what results may arise from the data, the research question is “open-ended” and specific hypotheses or propositions about the relation between BA and PM were left out (Edmondson & McManus, 2007). To be able to generate insights, the data should be rich, exhaustive and suggestive (Edmondson & McManus, 2007). Because exposure from practice is needed to acquire key insights about essential variables, exploratory interviews will be applicable (Edmondson & McManus, 2007).

Research design

The goal of this study is to explore how BA affects PM within organizations. Previous research pointed out that BA influences how PM is carried out, but is insufficient of empirical evidence. A more in-depth understanding of how organizations use BA in PM is needed, making a qualitative research suitable (Eisenhardt, 1989), because such a research approach is able to provide detailed insights in people’s experiences (Ritchie & Lewis, 2003). When limited research regarding a phenomenon is conducted, a qualitative research is appropriate to studies that aim to explore and describe complicated conditions (Saunders, Lewis & Thornhill, 2012). A qualitative research approach is useful because it helps answering ‘how’ questions, which help to explore and explain difficult processes and problems from a natural context (Eisenhardt, 1989; Ritchie & Lewis, 2003). However, a downside is that, by using a qualitative approach, the generalizability of the results is not as high as with quantitative study approaches (Abernethy, Chua, Luckett & Selto, 1999). To explore this study object, a case study design was used specifically. The case study design is best suited when there is a need of knowledge about a relationship between two concepts in real-life context (Yin, 2017). In that case, the research approach enables one to observe the effect of BA on PMS in a real organizational environment. Besides, for theory building in a development phase of a phenomenon, a case study research approach is appropriate (Gunasekaran, Yusuf, Adeleye & Papadopoulos, 2018). By applying the case study approach to a single case organization, it allows to explore contextual, organizational and managerial issues associated with the design and use of a PMS as, whilst also provides more in-depth insights. By performing a case study with semi-structured interviews, other enablers of the design and use of PMS could be discovered as well (Mikalef, Boura, Lekakos & Krogstie, 2019).

Case description

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serves as a global distributor to various channels. Its core businesses are divided in trading goods internationally and serving wholesalers. In selecting the case organization, it was essential that it made use of BA in PMS specifically. The case organization was selected due to its growth strategy and attention points. Specifically, one attention point focusses on digitization by making investments in IT that concentrates on both internal efficiency and client services, which should enable the organization for future growth. Among other things, with smart ICT solutions the case organization innovates and invests in its infrastructure. An example is their BiT-ERP system which enables it the organization to be more efficient in their core activities. The case study mainly concentrates on the finance department, because it is responsible for the financial processes and monitors the financial position, risks and results on a daily basis. It uses several IT systems that deal with vast amounts of data. This contributes to the expectation that this department carries out well established PMS that is affected by BA. Also the director of the sales department was interviewed. Interviewing multiple roles of the organization, helps to explore the phenomenon from different perspectives (van Aken, Berends & van der Bij, 2012).

Data collection

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The second source of data consists of observations and archival records of the ISs used by the organization. These observations and archival records enabled the researcher to better link all information that has been provided in the interviews to how PM is performed within the organization. The researcher’s understanding of the primary data improved and enabled him to give meaning to it.

Table 1. Overview of case interviewees

Data analysis

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Findings

In this chapter a holistic overview of the role of BA in PM is created. Specifically, this chapter clarifies how BA affects the design of PMS and how PMS is used when BA is implemented for PM purposes, based on the interviewee’s experiences and explanations. The chapter will be structured according three aggregate dimensions that emerged from the data, which be found in the figure 1 below:

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Changing routines to be aligned with data-driven approach Implication of renewed data-driven PMS

According to the interviewees, the main elements of the PMS described by Ferreira and Otley (2009), with the introduction of BA into organizational processes, were not subject to major changes. Multiple factors that enable the operation of a PMS were affected by BA and increased the effectiveness of the PMS. However, several challenges emerge. In this digital era, it has become a challenge for an organization that operates world-wide and is expanding continuously to process data and generate valuable information without IS. The backbone of the PMS is the data, and over the years the organization stored a great amount of data in the database of their operational IS, the BiT-ERP system. Every year, month and day the BiT-ERP system will be filled and because of the unending stream of data, the organization reached a point where the available data was too much to be processed manually. So, one of the main reasons to use BA for PMS, according to the interviewees, is because of data overload:

‘We realized that the BiT-ERP was filled with too much data. Therefore, to make sure we still

would be able to process the data and attract information from it we had to think of alternative methods. We do that with BA.’ – B.

However, before BA can be applied to generate valuable information, the unstructured data in the BiT-ERP system needs to be transformed into structured data, because a lot of the unstructured data is not suitable for use. The unstructured data in the database consists of data that is not relevant, duplicate, does not have a meaning and/or is erroneous. The findings show that before an organization starts to work with data and is going to use the data in PMS it needs to have a strategy on which data is going to be used, how the data is turned into valuable information and how the information is going to be used:

‘The fact that it contains a great amount of data is the problem. […] It is useless to transfer data

with the exact same meaning twice or more. The BiT-ERP contains a lot of duplicate data that is not significant or valuable. Therefore, you need to be critical on the data you are going to use.’

– C.

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of data from the BiT-ERP and thus acts as an intermediary between BiT-ERP and BA. The data warehouse bundles the data and puts it into predefined dimensions, which are stored in the data vault that is linked to BA. The data warehouse team is responsible for the data and decides whether a component is proper, significant, free of error and/or valuable and can be included into a dimension. Finally, when the dimensions are developed and filled, they are transferred to the BA tool.

‘A couple of years ago we started to structure the data we have in our BiT-ERP, that is expanding

continuously and becoming unworkable for traditional systems like Windows, in a data warehouse. This enabled us to spread information throughout the organization, i.e. to finance, sales and logistics. We do that with the use of Cognos Analytics.’ – C.

Resistance to change

The BA tool used by the organization is Cognos Analytics (CA). On the one hand CA is a consolidation package that is linked to and communicates with several IS of the organization. On the other hand, CA is a tool that visualizes information in the form of standardized reports and dashboards. In the reports and dashboards, CA is able to show deviations between results and budgets, forecasts or results of prior years as well as the source of the deviation e.g. business unit, country or account manager. The implementation of CA changed the way in which employees perform their daily working routines. Routines are defined as activities to perform work and as standard working procedures used by employees (Burns & Scapens, 2000). Hence, the organization has to deal with resistance to change (Ford, Ford & D’Amelio, 2008). To steer employees into the new data-driven environment of CA and support them in using it, it was considered important that the system’s functionalities and visualizations work well, are user-friendly and provide information that is valid. The organization tries to facilitate its employees as much as possible to steer them towards the new data-driven environment and to establish new data-driven working routines. Only then the employees who resist the new way of working could be convinced to transform to the new environment, which will result in the phasing out of resistance to change:

‘We focus on the design and functionality of CA so that we are sure that the reports we offer can be obtained quickly and are user-friendly. Consequently, we are able to guarantee that the systems we build are used. If the performance of the systems is not as it should be, people conclude that is not operating well and consequently will not use it.’ – C.

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meetings to introduce working with CA, covering what the implications and goals are. However, it appeared that employees tend to fall back in old routines when they face change, therefore:

‘Continuously, you have to remind people how we work within this company and what the procedures are. Some people do find this really difficult because change always results in some level of resistance. They have performed their work the same way for years, and we have to tell them that is not the right way of working anymore. They are very critical about it and in general, the change process is very challenging.’ – E.

Employees are not only steered into a new environment of IS, information itself changes because the reports present performance results uniform to the whole group. The provision of information is done differently as well, which impacts the way of working. Nowadays, however, the employees of the operating departments are steered into new routines and as a result they need to search in CA for the information themselves instead:

‘It was hard to get people on the train in the early phase. Prior to the use of BA, they only needed to open their email and click the report, because the information was sent by the finance department. Now people are steered into a new environment in which they have to look for the information themselves.’ – C.

‘People have to get used to the fact that they get the information they need in a different way.’

– A.

To mitigate the problem of resistance there is another action the organization undertakes. Before the actual implementation of a new IS, the organization runs a series of tests in order to test that particular system. In that way it provides itself with a certain level of assurance regarding the design and performance of the system. This is done in order to make sure the system works appropriately and is user-friendly, which will ease employees using the new system. Furthermore, since organizational processes rely on the performance of IS, the organization wants to make sure to be in control of the performance of the IS, and covers risks of malfunctions or failure as much as possible:

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a test-environment, you cannot simply accept or develop the system in an operational-environment. […] The systems in these two environments are separated and distinct. That is the only way to be in control and to be sure about what you do is right.’ – E.

The organization not only tests the entire system, it also tests specific aspects of a system. For example, the reports produced by CA. Since PMS relies heavily on CA and is dependent on the reports it produces, the organization needs to guarantee the output’s credibility. This is not only the case when the data source is the BiT-ERP system, but also when the information depends on data that is collected from multiple internal and/or some external sources:

‘Then we try to merge the data with various mathematical techniques to determine the accuracy of a working method and the completeness of the dataset. That is how we make sure that the advice based on those datasets is valid.’ – A.

Another activity in the transformation to a data-driven approach in PMS is the development of analytical skills. Without these skills employees are not able to understand the data and to derive value from it. For that reason, the organization steers on developing analytical skills by providing resources for training:

‘Regularly we follow (internal and external) trainings. A small group of key users is specialized in

the BA tool and follow extra training to further extend their analytical skills. We do this to expand the knowledge of the organization and to make sure that multiple people have the same knowledge.’ – A.

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Bottom-up steering of the organization Saving time and effort

A recurrent topic mentioned by the interviewees is how the use of big data through BA positively influences the use of PMS. An important feature of BA is that it gives employees the possibility to access information anytime they want and therefore facilitates them to continuously track and manage performance:

‘We can easily acquire reports representing sales results divided per country, customer, account manager and product and match the information with results from former years. […]. This information is always updated and accessible anytime by pressing just one button.’ – D.

Therefore, under the influence of BA, the PMS as well as the organization itself has become more effective and efficient. Greater detail emerges from the data, decision-making is improved and information is standardized for the whole organization, validated, presented unambiguously and always accessible. Besides this, another advantage emerges as a result of implementing BA. Processes of PM are less time exhaustive. As a result, employees do not have to search for relevant data and perform the calculations and/or analyses themselves:

‘Prior to BA, for every business unit separate results needed to be produced manually. […] Now the information is available immediately. As a result, the quality of the reports has increased because it does not contain inaccuracies. Further, the efficiency of the whole process of getting performance information increased.’ – D.

Consequently, with the aforementioned benefits, those features enable the organization to steer from the lowest specific levels to highest general organizational objectives. BA not only replaces a lot of tasks and therefore saves time, it also enables comparability among performance measures. It can give detailed overviews of results and is able to reflect on prior periods or years and compare it to business units, countries and account managers:

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Enhanced organizational responsiveness

The interviewees highlight that BA improves the quality of decision-making. Fundamental for this finding is structured data used to generate valuable information. The data warehouse team is responsible for collecting, structuring and linking data to calculate information that eventually comes together in standardized reports. Since the reports are developed centrally, with the aim to be uniform and valid, it is nearly certain that the information is always accurate and could be followed blindly:

‘Something is made available in the data warehouse through BA only when it is validated by the finance department. Everyone has to work with data that is valid. That is a prerequisite to evaluate KPIs (turnover, margin and working capital) and to steer on that information.’ – C.

The PMS is not improved only because BA facilitates continuous availability of information, another consequence of the implementation and use of BA is information being used consistently throughout the organization. One way the consistency is enhanced, is because of the development of reports that are standardized for the group as a whole. In standardizing the reports, the data warehouse is responsible for the validity of the information, and if so, it authorizes the organization to use the reports:

‘The goal is to ease the sales department. They don’t have to collect, structure and link the data from BiT-ERP themselves anymore. That old process is risky because they might calculate with different data and find other results then the finance department. It is our job to make sure the data is valid and there is uniformity throughout the organization. BA plays a major role in that process.’ – D.

The real-time availability of performance information enables interactive use. Standardized information alleviates employees in monitoring performance and reduces discussions about deviated numbers, it also creates better understanding among other departments. Since departments are not debating who is right about the numbers, they can focus on what is really important, i.e. discussing the cause of the performance information and how that implicates organizational performance. Therefore, it has an effect on the ability to react, since dialogues between employees and departments increase awareness and improve organizational coherence. This consequently enhances the identification of causalities for certain occurrences:

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sales department, we are able to see how finance works to generate insights. On the other side, there always has to be an explanation with the numbers because there is always a reason why things turn out as they did. And if you do not know these reasons, you have to ask someone. This led to a more open and transparent communication between the sales and finance department. […] Now there is more collaboration.’ – F.

As employees are better aware of performance, the organization is able to notice irregularities sooner. Because the information provided by BA is validated, somebody will know something happened immediately when information in a report shows a deviation from the standard. The BA tool shows those connections. As the organization monitors performance once per day at minimum, the organization notices deviations much quicker and is therefore able to steer more actively to prevent the organization from making mistakes:

‘When I start my computer, I can see right away why we did not meet our budget. We do not have to find out ourselves because BA shows instantly what business unit caused the deviation from the budget.’ - E.

For the organization it is very easy to ask for a report and to generate insights in the progress of projects and performance on a daily basis. As a consequence, the PMS is used more frequently, with decision-making based on validated data rather than on thoughts and experience of employees:

‘For two decades the reports already contained information regarding turnover, margin, working capital and stock. However, today, the reports are nicer and generate more insights because the quality of the data is improved. For example, we could monitor the age of the stock. If we do not sell this stock and it is becoming too old, then we have to steer the business units so that they sell the old stock.’ – E.

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‘Details become more and more important. Earlier, we steered only on yearly results of turnover

and margin and checked it every now and then during the year. Now 95 percent of the information flow is automated, it is performed in systems and therefore we do not see what happens. So, you need a BA tool to subtract information out of these systems and produce proper reports. Therefore, we can steer from the lowest level, i.e. customer or segment, to the highest level, i.e. business unit. Before we steered top-down and much more on a generic level. We cannot afford that way of working anymore and need tools, because the business has become fine-grained.’ – F.

Automatically, with the organization being focused on details and steering bottom-up, the role of a controller shifts from controlling to analysing and/or advising to serve this change of strategic focus. In this way a controller can better exploit his knowledge and experience and thus be of greater value to the organization:

‘My function is shifting to a more advisory role. Since I do not have to compose the numbers myself, I have more time to use the information and figure out what the numbers imply to the organization. I am better able to analyse the information.’ – D.

Under BA, several factors improved the PMS. Data plays a bigger role in the organization, since it is quick and easier to acquire and always validated. PM can be executed anytime, faster and with less errors, which in turn reduced time and effort in the performance of PM. Furthermore, there is more time for debate about the implications of the information available to the organization, enabling the organization to react quicker to certain events. In short, it enables the organization to focus more on details and short-term targets. Consequently, the main contribution here is that the direction of PMS has been changed since the organization’s focus to reach objectives has shifted from top-down to bottom-up.

Challenges to successfully exploit predictive and prescriptive analytics

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be derived from inadequate listing of raw data, which can be considered human errors, but can also come from systematic errors, such as faulty connections between two or more systems or complete failures. Incidents enable learning about the rear end of organizational processes and are one of the main forces of improving the PMS:

‘Every morning I consult CA for reports, it supports me in understanding the numbers. […] Once, there was a row in BiT-ERP that did not refer to the right cost price. Those things are really important and start to let me think whether that is a human error or a systematic flaw. It is important to be in control of those things and make sure that it does not happen again.’ – B.

Errors in the foundation, i.e. how data is collected, structured and processed, of using BA are incentives for the organization to improve its processes. Furthermore, the innovative culture of the organization also provides freedom to employees regarding the potential improvements for PMS. The data warehouse team is looking into new things that can be standardized into reports, the data science team is looking how external and internal data can be merged to generate value for the organization. The process of innovating the organization with new reports, systems and improved processes lead to more organizational challenges. As soon as one challenge is solved, the next one emerges. Therefore, changing the whole organization towards the use of BA is an iterative process:

‘If something catches our attention, something that does not work, then we make mention of it and report it to the data warehouse team. The data warehouse team discusses the input on a weekly basis and decide if they need to act or if a process or report needs to be adjusted. That is a continuous and iterative process.’ – A.

In addition to the data science team exploring the potentials of BA, the data warehouse team also develops reports that reflects performance measures. The direction of this process is two-fold: the data warehouse team works on its own initiatives, but also has to meet employees’ demands for information:

‘Sometimes we work on application. If the data is there or they want it expanded, then I have to fulfil that request.’ – C.

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that registers working hours, are brought together by BA. It is nearly impossible to process this data from multiple sources by doing it the old way, e.g. via windows applications. Therefore, the organization had to restructure its processes:

‘The information source mainly comes from multiple systems and is hard to consolidate in a consistent way. Accordingly, we reached a point that we had to organize how the data is treated, how we generate reports on the basis of data and how we can present our KPIs more clearly.’ – F.

Employees of the organization do not have to follow strict procedures regarding the implementation or adaptation of BA. In contrast, a lot of trial and error is done regarding the implementation and use of BA. This allows the employees to experiment, but also to be flexible regarding the changes. Employees have the possibility to individually learn the systems and processes and make it their own:

‘Learning to work with the system is more learning on the job. Learning yourself how to program and how to work with the systems. It is both overarching as doing it yourself.’ – A.

Yet, until the organization is able to fully exploit the advantages of predictive BA, it still has to overcome several challenges. First of all, the elementary processes of doing business will be changed due to aforementioned implications of BA, but also, to a certain extent, BA has the potential to replace human intelligence of knowing the alignment between what is going to be sold and what needs to be purchased and vice versa. Therefore, the role of employees regarding the process of purchasing and selling changes and requires employees to think and act differently. With predictive information the numbers are a given and need to be accepted and used by the employees. If they do not want to use that information because it is not trusted, all invested resources are wasted:

’If we move to predictive than it could be elusive for my colleagues. Maybe they are going to doubt the outputs of BA. It is important to realize that a mathematical model forms the foundation to the prediction that could be too complex to humans to understand. […]. They have to trust it.’ – E.

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of parties are involved and departments are dependent of each other. Changing a process in one department will affect other departments as well:

‘That way you keep doing new things which result in new events. The process of digitalizing is change management, it transforms the whole organization. For example, when you implement something, you realize that also the logistics department has to be involved. It requires more efficiency and coherence between the departments.’ – F.

With respect to the outcome of predictive information, there is uncertainty whether BA is able to produce the right outcome at all. To be able to predict organizational performance, the organization needs sufficient data points to run the models. Since it does not use BiT-ERP that long, a precondition is still needed to collect sufficient data:

‘It takes time before we have enough information. Now we are at a point to use BA. Still, a condition is that we have sufficient data, a lot of data points.’ – S.

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Discussion

The findings bring forward three main concepts that represent how BA affects the design and use of PMS. The use of BA for PM purposes makes organizational processes more complex in terms of data usage and issues as resistance to change emerge. Several practices were used to guide employees towards new data-driven working routines. When BA is used in PMS, the findings show that it leads to the proposition that the focus of steering shifts to bottom-up. Finally, it was found there is potential for using predictive and prescriptive analytics for PMS. However, the advantages are not yet fully exploited. In this section these findings will be discussed.

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achieved, and how the organizational routines could be aligned with the new data-driven PMS. These activities are: forcing to use new procedures, continuously reminding employees of new ways of working, train employees to develop analytics skills, ensuring the quality of information and developing well-functioning and user-friendly IS environment. These problem-solving activities were found to be critical in being able to implement BA in PMS processes.

In addition to the design, BA also affects how the PMS is used by the organization. BA has improved PMSs information quality and consistency (Davenport & Bean, 2018; Redman, 2008), consistent use of information (Ramakrishnan, Jones & Sidorova, 2012) and decision-making (McAfee & Brynjolfsson, 2012), enables to zoom in on details and enables knowledge creation, insight generation and problem-solving (Holsapple et al., 2014). As evidence shows, BA improves the diagnostic control of PMS by facilitating to continuously track performance (Raffoni et al., 2018). Descriptive analytics are able to provide detailed performance information through standardized reports (Raffoni et al., 2018) and enables the comparison of performance with budget and prior years. In line of that finding, BA can reduce time and effort needed to complete tasks, and efficiency is increased (Schläfke et al., 2013). This gives an employee more time to analyse the information and focus on objectives (Davenport & Tay, 2016). In addition, BA enhances interactive use of performance information by providing continuous updated real-time information about performance measures, which consequently can be used further for dialogues (Raffoni et al., 2018). As such, this study finds support for the fact that target communication and performance evaluation (Warren et al., 2015) and the PMSs capability to distribute information (Bourne, Mills, Wilcox, Neely & Platts, 2000) are improved by BA. In that way, it was found that a better understanding regarding performance was created and responsiveness increased. In line with that reasoning, another interesting contribution is proposed. The implementation of BA shifts the focus of steering from top-down to bottom-up. This means that, in order to reach the targets which are being set by management, they are cut into more detailed short-term targets. For example, more details about age of stock are available. Older stock carries more risks and is less profitable when being sold. As soon as one stock gets too old, there will be pressure to sell it as quickly as possible. Also, when particular customers hardly bring in any profits, then the focus to sell will be shifted to more profitable customers. Subsequently, the organization steers bottom-up to reach the organizational targets. By steering bottom-up the organization is better able to manage and be in control of its performance.

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Conclusion

In the last couple of years, BA has been one of the largest IT investments (Kappelman et al., 2017). BA is able to generate valuable information out of unstructured data (Davenport & Harris, 2007) and can be used to increase organizational efficiency and performance (Schläfke et al., 2013; McAfee & Brynjolfson, 2012). In addition, BA can be applied to PMS, which can improve strategy development, target communication and performance evaluation (Warren et al., 2015). However, existing research fails to provide insights in how the design and use of PMS are affected by BA. This study shows the implications of BA and fills that gap.

As the findings indicate, with the implementation of BA in PMS, the complexity and dependency of the PMS increases. In particular the shift of working routines and the quality, reliability and validity of data, information and IS appeared to be crucial for the use of BA. The implementation of BA requires new data-driven working routines, and initially, employees showed resistance towards that change. Here, an important contribution is that when BA is implemented in PMS, an organization has to steer employees towards new working routines and a set of actions was applied for this purpose. In terms of generalizability of existing literature in the field of BA, several improvements to the PMS were found. BA enhances real-time availability of performance measures, because standardized reports that visualize performance are updated continuously and are always accessible. BA increases the ability to spread out the information in a consistent way and reduces the time and effort needed to track and manage performance. In addition, BA enables the organization to zoom in on KPIs and provides more details. As a result, awareness of performance and the organization’s ability to respond to events increased. It is proposed that the organization, in that way, can generate more value. Now, and that is an important contribution, under the influence of BA there is a shift of focus since, to reach organisational objectives, the PMS is used to steer bottom-up instead of top-down. With respect to the implementation of BA, an innovative culture was found to important because in that way the organization’s members are given opportunity to search for improvements or new innovations. In addition to this, several specific challenges were highlighted that impede the exploitation of predictive and prescriptive analytics.

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topic over a longer time-period, investigate a case organization that is in the middle of the transition as well, or investigate a case organization that has already fully implemented BA in PMS. In addition, decision-making was said to be improved but that finding is based exclusively on potentially biased experiences of the interviewees. Therefore, to validate this finding, future research could explore the relationship between the use of BA and decision-making quality statistically. Lastly, mainly employees of the finance department were interviewed about the relationship between BA and PM. As BA might influence other disciplines as well, future research could explore the effect of BA on PMS in human resource management, sales, operations and logistics.

This study provides theoretical contributions with respect to the topic of BA and PMS. Contributing the existing literature, two new concepts emerged from the findings regarding the effect of BA on PMS. Firstly, it is critical in the implementation of BA in PMS that working routines need to be aligned with the new data-driven approach. Several activities were used to guide this process and to establish new working routines. In that way the organisation was able to work out resistance to the renewed data-driven working routines. Future research can further investigate how this occurs, explore the best practices to create new routines and determine whether it is dependent on context. Secondly, it was indicated that BA changes the strategic focus of the organization. As time and effort are reduced due to the detailed standardized information BA generates, as well as increased responsiveness, it enables the organization to steer bottom-up to reach organizational objectives. Furthermore, this concept is relevant to investigate as it might be a determinant contributor to increased organizational performance. As the case organization cannot implement predictive and prescriptive analytics as naturally as descriptive analytics, several challenges were raised. Future research could shed light on the challenges that emerge related to predictive and prescriptive analytics specifically and focus on context, information infrastructure as well as on organizational challenges. Not in light of the purpose of this study, but appearing repeatedly in the findings, is that the role of a financial changes due to the implementation of BA (Brands & Holtzblatt, 2015; Warren et al., 2015). The findings show it to be changing from a controlling role to a more advising and analysing role. Consequently, this topic might also be of relevance for future research.

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Reference List

Abernethy, M. A., Chua, W. F., Luckett, P. F., & Selto, F. H. (1999). Research in managerial accounting: Learning from other’s experiences. Accounting and Finance, 39, 1-27

Aken, J., Berends, J., & Bij, J. (2012). Problem solving in organizations: A methodological handbook for

business and management students (2nd edition ed.). Cambridge: Cambridge University Press.

Appelbaum, D., Kogan, A., Vaserhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29-44.

Ask, U., Magnusson, J., & Bredmar, K. (2016). Big data use in performance measurement and management: A call for action. Journal of Business and Economics, 7(3), 402-417.

Beaubien, L. (2013). Technology, change, and management control: a temporal perspective.

Accounting, Auditing, Accountability Journal, 26(1), 48-74.

Becker, M. C. (2004). Organizational routines: a review of the literature. Industrial and Corporate

Change, 13(4), 643-677.

Berrah, L., Mauris, G., & Vernadat, F. (2004). Information aggregation in industrial performance measurement: rationales, issues and definitions. International Journal of Production Research

42(20), 4271–4293.

Bhimani, A., & Willcocks, L. (2014). Digitisation, ‘Big Data’ and the transformation of accounting information. Accounting and Business Research, 44(4), 469–490.

Biesdorf, S., Court, D., & Willmott, P. (2013). Big data: What’s your plan? McKinsey Quarterly, 2, 40-51. Bititci, U. S., Garengo, P., Ates, A., & Nudurupati, S. S. (2015). Value of maturity models in performance

measurement. International Journal of Production Research 53(10), 3062–3085.

Blumberg, B., Cooper, D. R., & Schindler, P. S. (2014). Business Research Methods (4th edition). Berkshire: McGraw-Hill Education.

Bourne, M., Mills, J., Wilcox, M., Neely, A., & Platts, K. (2000). Designing, implementing and updating performance measurement systems. International Journal of Operations & Production

Management, 20(7), 754–771.

Brands, K., & Holtzblatt, M. (2015). Business analytics: transforming the role of management accountants. Management Accounting Quarterly, 16(3), 1-12.

Bruns Jr., W. J., & Waterhouse, J. H. (1975). Budgetary control and organization structure. Journal of

Accounting Research, 13(2), 177-203.

(35)

35

Bughin, J., Livingston, J., & Marwaha, S. (2011). Seizing the potential of “Big Data”. McKinsey Quarterly

(October), 103–109.

Burns, J. E., & Scapens, R. W. (2000). Conceptualising management accounting change: an institutional framework. Management Accounting Research, 11, 3-25.

Caglio, A. (2003). Enterprise resource planning systems and accountants: towards hybridization?

European Accounting Review, 12(1), 55-74.

Chapman, C. S. (2005). Not because they are new: Developing the contribution of enterprise resource planning systems to management control research. Accounting, Organizations and Society,

30(7/8), 685–689.

Chapman, C. S., & Kihn, L.-A. (2009). Information systems integration, enabling control and performance. Accounting, Organizations and Society 34(2), 151–169.

Chapman, C., & Chua, W. F. (2003). Technology-driven integration, automation, and standardization of

business processes: implications for accounting, in Bhimani, A. (Ed.), Management Accounting in

the Digital Economy, Oxford: Oxford University Press

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.

Chenhall, R. H. (2003). Management control systems design within its organizational context: findings from contingency-based research and directions for the future. Accounting, Organizations and

Society, 28(2/3), 127-168.

Chenhall, R. H., & Langfield-Smith, K. (2007). Multiple perspectives of performance measures.

European Management Journal, 25(4), 266-282.

Chenhall, R. H., & Moers, F. (2015). The role of innovation in the evolution of management accounting and its integration into management control. Accounting, Organizations and Society, 47, 1-13. CIMA (Chartered Institute of Management Accountants). 2014. Big Data. Readying Business for the

Big Data Revolution. Retrieved on 10-03-2019October 3, 2019

http://www.cgma.org/Resources/Reports/DownloadableDocuments/CGMA-briefing-big-data.pdf.

Cokins, G. (2014). Mining the past to see the future. Strategic Finance, 96(11), 23–30.

Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology, 30(1), 4–57.

Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017). Assessing business value of big data analytics in European firms. Journal of Business Research, 70, 379–390.

Davenport, T. H. (1997). Information ecology: Mastering the information and knowledge environment. New York: Oxford University Press.

(36)

36

Davenport, T. H. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy & Leadership, 42, 45–50.

Davenport, T. H., & Bean, R. (2018). Big companies are embracing analytics, but most still don’t have a data-driven culture. Harvard Business Review, 2-4.

Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics. The new science of winning. Boston: Harvard Business Press.

Davenport, T. H., & Patil, D. J. (2012). Data Scientist. Harvard Business Review, 90(10), 70–76.

Davenport, T. H., & Tay, A. (2016). Finance must ramp up role as analytics leader. Retrieved on 12-29-2019: https://www.cfo.com/analytics/2016/08/finance-must-ramp-role-analytics-leader. Dechow, N., & Mouritsen, J. (2005). Enterprise resource planning systems, management control and

the quest for integration. Accounting, Organizations and Society 30(7/8), 691–733.

Dechow, N., Granlund, M., & Mouritsen, J. (2006). Management control of the complex organization: Relationships between management accounting and information technology. Handbooks of

Management Accounting Research, 2, 625-640.

Dilla, W., Janvrin, D.J., & Raschke, R. (2010). Interactive data visualization: new directions for accounting information systems research. Journal of Information Systems, 24(2), 1-37.

Edmondson, A. C., & McManus, S. E. (2007). Methodological fit in management field research.

Academy of Management Review, 32(4), 1155-1179.

Edwards, R., & Holland, J. (2013). What is qualitative interviewing? London: Bloomsbury.

Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review,

14(4), 532-550.

Elbashir, M. Z., Collier, P. A., & Sutton, S. G. (2011). The role of organizational absorptive capacity in strategic use of business intelligence to support integrated management control systems. The

Accounting Review, 86(1), 155–184.

Ferreira, A., & Otley, D. (2009). The design and use of performance management systems: An extended framework for analysis. Management Accounting Research, 20(4), 263-282.

Ford, J. D., Ford, L. W., & D’Amelio, A. (2008). Resistance to change: The rest of the story. Academy of

Management Review, 33(2), 362-377.

Freeman, K., & Spenner, P. (2012). To Keep Your Customers, Keep It Simple. Harvard Business Review,

90(5), 108-114.

Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2012). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organization Research Methods, 16(1), 15-31.

(37)

37

Gupta, G. U., & Gupta, A. (2015). Vision: A missing key dimension in the 5v big data framework. Journal

of International Business Research and Marketing, 1(3), 40–47

Hauser, W. J. (2007). Marketing analytics: the evolution of marketing research in the twenty-first century. Direct Marketing: An International Journal, 1(1), 38-54.

He, W., Tian X., Li Y., Akula V., Yan G., & Tao, R. (2015). Gaining competitive intelligence from social media data. Industrial Management & Data Systems, 115(9), 1622–1636.

Henri, J. (2006). Management control systems and strategy: A resource-based perspective. Accounting,

Organizations and Society, 31(6), 529-558.

Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision

Support Systems, 64, 130-141.

Hyvönen, J. (2007). Strategy, performance measurement techniques and information technology of the firm and their links to organizational performance. Management Accounting Research 18(3), 343–366.

IBM, (2013). Descriptive, predictive, prescriptive: transforming asset and facilities management with analytics. In: Thought Leadership White Paper, (October 2013).

Isik, O., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13–23

Ittner, C. D., & Larcker, D. F. (2003). Coming up short on nonfinancial performance measures. Harvard

Business Review, 81(11), 88-98.

Jack, L., & Kholeif, A. (2008). Enterprise resource planning and a contest to limit the role of management accountants: a strong structuration perspective. Accounting Forum, 32(1), 30-45. Kaplan, R. S., & Norton, P. D. (1992). The balanced scoreboard – measures that drive performance.

Harvard Business Review, 70(1), 71–79.

Kappelman, L., McLean, E. R., Johnson, V., Torres, R. R., Nguyen, Q., Maurer, C., & Snyder, M. (2017). The 2016 SIM IT key issues and trends study. MIS Quarterly Executive, 16(1), 47–80.

Klatt, T., Schläfke, M., & Möller, K. (2011). Integrating Business Analytics into Strategic Planning for Better Performance. Journal of Business Strategy, 32(6), 30–39.

Koufteros, X., Verghese, A., & Lucianetti, L. (2014). The effect of performance measurement system on firm performance: a cross-sectional and a longitudinal study. Journal of Operations

Management, 32(6), 313-336.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.

Maguire, S., Ojiako, U., & Said, A. (2010). ERP implementation in Omantel: a case study. Industrial

(38)

38

McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review,

90(10), 60–68.

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., & Barton, D. (2012). Big Data. The Management Revolution. Harvard Business Review, 90(10), 61–67.

Mello, R., Leite, L. R., & Martins, R. A. (2014). Is big data the next big thing in performance measurement systems. Proceedings of the Industrial and Systems Engineering Research

Conference, 1837–1846.

Merchant, K., & Van der Stede, W. A. (2007). Management Control Systems (2nd edition). Harlow, Financial Times/Prentice Hall.

Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261–276.

Neely, A. (2005). The evolution of performance measurement research: developments in the last decade and research agenda for the next. International Journal of Operations & Production

Management, 25(12), 1264–1277.

Nielsen, S. (2017). Business analytics and performance management: a small data example combining td-abc and bsc for simulation and optimization. SSRN Electronic Journal, 1–23.

Nielsen, S. (2018). Reflections on the applicability of business analytics for management accounting – and future perspectives for the accountant. Journal of Accounting & Organizational Change,

14(2), 167-187.

Noudoostbeni, A., Ismail, N.A., Jenatabadi, H.S., & Yasin, N.M. (2010). An effective end-user knowledge concern training method in enterprise resource planning (ERP) based on critical factors (CFs) in Malaysian SMEs. International Journal of Business and Management, 5(7), 167-94.

Nudurupati, S. S., & Bititci, U. S. (2005). Implementation and impact of IT supported performance measurement systems. Production Planning & Control, 16(2), 152–162.

Nudurupati, S. S., Bititci, U. S., Kumar, V., & Chan, F. T. S. (2011). State of the art literature review on performance measurement. Computers and Industrial Engineering, 60(2), 279-290.

Otley, D., (1999). Performance management: a framework for management control systems research.

Management Accounting Research 10(4), 363–382.

Peters, T., Işik, Ö., Tona, O., & Popovič, A. (2016). How system quality influences mobile BI use: The mediating role of engagement. International Journal of Information Management, 36(5), 773– 783.

Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2016). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20, 1–14.

(39)

39

Raffoni, A., Visani, F., Bartolini, M., & Silvi, R. (2018). Business performance analytics: Exploring the potential for performance management systems. Production Planning & Control, 29(1), 51-67. Raguseo, E. (2018). Big data technologies: An empirical investigation on their adoption, benefits and

risks for companies. International Journal of Information Management, 38(1), 187–195.

Ramakrishnan, T., Jones, M.C., & Sidorova, A. (2012). Factors influencing business intelligence (BI) data collection strategies: an empirical investigation. Decision Support Systems, 52(2), 486–496. Ransbotham, S., Kiron, D., & Prentice, P.K. (2016). Beyond the hype: The hard work behind analytics

success. MIT Sloan Management Review, march.

Redman, T. C. (2008). Data driven: profiting from your most important business asset. Boston: Harvard Business Press.

Ritchie J., & Lewis, J. (2003). Qualitative Research Practice. London: SAGE.

Rouhani, S., Ashrafi, A., Ravasan, A. Z., & Afshari, S. (2016). The impact model of business intelligence on decision support and organizational benefits. Journal of Enterprise Information Management,

29 (1), 19-50.

Saunders, M. N. K., Lewis, P., & Thornhill, A. (2012). Research methods for business students. Harlow, Essex, U.K.: Pearson Education Limited.

Scapens, B., & Jazayeri, M. (2003). ERP systems and management accounting change: opportunities of impacts. European Accounting Review, 12(1), 201-33.

Schläfke, M., Silvi, R., & Möller, K. (2013). A framework for business analytics in performance management. International Journal of Productivity and Performance Management, 62(1), 110– 122.

Silvi, R., Bartolini, M., Raffoni, A., & Visani, F. (2012). Business Performance Analytics: Level of adoption and support provided to performance measurement systems. Management Control, 3(Special Issue), 117-142.

Simons, R. (1995). Levers of Control. How Managers Use Innovative Control Systems to Drive Strategic

Renewal. Boston: Harvard Business School Press.

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.

Sutton, S. G., (1999). The changing face of accounting and the driving force of advanced information technologies. International Journal of Accounting Information Systems, Sampler paper, 2–6. Teittinen, H., Pellinen, J., & Järvenpää, M. (2013). ERP in action — Challenges and benefits for

management control in SME context. International Journal of Accounting Information Systems,

14(4), 278-296.

Tien, J. M. (2013). Big Data: Unleashing information. Journal of Systems Science and Systems

Manage performance with data