Visualisation of data in management accounting reports

Abstract

Purpose – Managers are confronted with increasing information overload and growing pressure for effective and efficient decision making. The visualisation of data represents a way to overcome this dilemma and to improve management decision quality. The purpose of this paper is to transfer insights from visualisation research to the managerial accounting context and clarify the impact of visualisation on management accounting reports and decision making. The authors deduce implications for behavioural management accounting research, teaching, and business practice from previous findings and the results. Design/methodology/approach – The authors conducted an experiment with students and experienced managers. Participants had to evaluate eight different business units based on four accounts (sales, EBIT, FPY, and delivery reliability). The information the authors provided to the participants was either presented as tables only, or in tables and graphs. Findings – The empirical results show that supplementary graphs improve decision quality, especially within the manager sample but do not affect decision confidence in a performance evaluation task. The authors furthermore find that managers perform poorly when only provided with tables, and they achieve the overall best score when provided with both tables and graphs, whereas students perform similarly in both conditions. The authors additionally show that proficiency affects not only decision quality but also decision confidence. Research limitations/implications – The results differ from predictions based solely on the cognitive fit model, as the authors found differences in decision quality to be stronger within the group of managers. The cognitive fit model proposes that decision making performance will improve when the problem representation and the decision making task match. Applying the model to a management context, it is obviously insufficient to explain the differences the authors obtained in the experiment. The authors observed that proficiency plays a role in such performance evaluation tasks. Practical implications – Based on the results, management accountants should analyse the task that needs to be solved with the reported data. By analysing the type of task, accountants can derive the information processing strategy that will most likely be used by executives for problem solving and determine the suitable visualisation format based on the cognitive fit model. Moderate or complex monitoring tasks will presumably be accessed with perceptual information processing. Data should thus be visualised with graphs. Originality/value – The authors provide empirical evidence that supplementary graphs in management reports improve decision quality but not decision confidence. The authors furthermore illustrate the limits of the explaining power of the cognitive fit model in a management report context. In an extension of cognitive fit theory, the authors argue that proficiency plays a crucial role in performance evaluation tasks. The authors propose a process for visualisation of management reports based on their findings and previous findings.

Does design matter when visualizing Big Data? An empirical study to investigate the effect of visualization type and interaction use

“…Overall, we can state that the visualization results in better performance, when it fits to the task-identify asks rather for visualization types that show data in disaggregated form (parallel and polar coordinates) while the task type summarize asks rather for visualizations that show data in an aggregated form. This is very much in line with previous research asking for a cognitive fit (Ohlert and Weißenberger 2015;Hirsch et al 2015;Vessey and Galletta 1991).…”

“…Visualizing Big Data proves to be of great importance when problems (or tasks) are high in complexity (Hirsch et al 2015;Perkhofer 2019), or not sufficiently welldefined for computers to handle algorithmically, meaning human involvement and transparency is required (e.g. in fraud detection) (Munzner 2014;Kehrer and Hauser 2013;Dilla and Raschke 2015;Keim et al 2008).…”

Interactive visualization of big data in the field of accounting

“…Already identified and very promising areas for interactive type II visualizations in managerial accounting are fraud detection, records and risk management (Dilla and Raschke, 2015;Lemieux et al, 2014). Further, conventional reporting practice (internal and external) could benefit a great deal, as these visualization types are also task and data optimized for semi-structured and unstructured data sets which are increasingly being used (Hirsch et al, 2015;Yigitbasioglu and Velcu, 2012). In conclusion, the mentioned gap between research and practice remains predominant, possibly negatively affecting decisionmaking in a Big Data related context.…”

Accounting for economic sustainability: environmental, social and governance perspectives

“…Especially, the level of aggregation and the cognitive load the processing of the data causes is in focus, when the authors research and discuss how future management reports need to provide a certain level of interaction between the data and the diverse audience. With their research, they follow and contribute to important streams in the visualization (Hirsch et al, 2015), accounting information systems (Dilla et al, 2010) and accounting education ( Janvrin et al, 2014) communities. They find that the lack of knowledge and experience regarding new visualization types and interaction techniques, and the often sole focus on Excel as a visualization tool can be identified as the main barriers, while the use of multiple data sources and the gradual implementation of further software tools can be seen as main drivers for the adoption of interactive visualizations.…”