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This paper examines whether ownership type has a moderating influence on dividend payouts during the COVID-19 pandemic crisis with respect to changes in profits. Future uncertainties because of the pandemic will result in a perceived need for liquidity within the company, but retaining cash may be risky for shareholders who could look for less risky alternatives. The dividend payout strategy is thus even more closely related to the overall type concentration and strategy of the owners during the crisis.

The effects are explored and tested on early data from 2019 to 2020 of Finnish companies using ANCOVA while controlling for profitability and sector variables.

A significant effect on dividend payout during the COVID crisis was found when the companies are dominantly held by individual owners validating early suggestions on such an influence. Therefore, this study contributes further to the academic debates on the influence of ownership concentration in times of crises. This study lists certain sectors which experience diminished profits during such a crisis which pinpoints sector separation in future discussions.

This study explores early data from a specific context in the Nordic countries. However, it does so out of purpose as explained in the paper.

Ownership type and concentration matters when it comes to dividend payout decisions under uncertainty with regard to changes in profit. Investors need to accept these behavioural insights into their decisions.

This study examines the signalling effect of dividends by analysing how actual or anticipated change in profitability due to a crisis is reflected by owners and leads to dividend payout decisions under uncertainty.

Looking at the limits of artificial intelligence (AI) and controlling based on complexity and system-theoretical deliberations, the authors aimed to derive a future outlook of the possible applications and provide insights into a future complementary of human–machine information processing. Derived from these examples, the authors propose a research agenda in five areas to further the field.

This article is conceptual in its nature, yet a theoretically informed semi-systematic literature review from various disciplines together with empirically validated future research questions provides the background of the overall narration.

AI is found to be severely limited in its application to controlling and is discussed from the perspectives of complexity and cybernetics. A total of three such limits, namely the Bremermann limit, the problems with a partial detectability and controllability of complex systems and the inherent biases in the complementarity of human and machine information processing, are presented as salient and representative examples. The authors then go on and carefully illustrate how a human–machine collaboration could look like depending on the specifics of the task and the environment. With this, the authors propose different angles on future research that could revolutionise the application of AI in accounting leadership.

Future research on the value promises of AI in controlling needs to take into account physical and computational effects and may embrace a complexity lens.

AI may have severe limits in its application for accounting and controlling because of the vast amount of information in complex systems.

The research agenda consists of five areas that are derived from the previous discussion. These areas are as follows: organisational transformation, human–machine collaboration, regulation, technological innovation and ethical considerations. For each of these areas, the research questions, potential theoretical underpinnings as well as methodological considerations are provided.

Graphs are powerful tools which affect a reader’s impression and decision making. However, graphs in annual reports have a long tradition of being designed in order to give a more favourable impression of the company’s performance. The purpose of this paper is to add to the understanding of how large listed companies in Europe choose to use and misuse graphical representation.

This comprehensive study investigates annual reports of the top 50 European companies listed in the fortune 500 index. Company reports are analysed over a period of seven years resulting in 4,683 graphs. The authors investigate the development of the three major areas of impression management – selectivity, graphical measurement distortion and presentational enhancement – individually by company as well as collectively for the entire sample.

The main findings are that topics displayed, and how they are presented, significantly change over time and that graphs are much more likely to exaggerate positive trends than to understate them. Additionally, it can be found that longer time sequences (greater than five years) almost exclusively depict favourable trends (86 per cent) and graphical measurement distortions are applied on purpose for both key financial variables (KFV) as well as for non-KFV (around 30 per cent in all years).

The sample for this study are the biggest 50 companies in Europe. It is not clear, if these companies are a representative sample for all publicly traded companies in Europe. Further research is needed regarding small and medium size companies.

The findings show that companies primarily produce graphs in order to influence the perception of their stakeholders rather than to display the topics in accordance with the “true and fair view” principle that is requested by the IASB. However, standard setters like the IASB or the FASB have not yet released any particular information on how to use graphs correctly and avoid misleading information. This study should provide a solid base for further discussions in this regard as companies still use graphs to give a favourable impression of the company and deliberately misuse them in order to achieve this aim.

This study contributes to the research field of impression management by answering the quest for more longitudinal studies and offers an extended focus while examining not only KFV but all variables depicted in annual reports.