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With the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining economic benefits. AI-powered information and Big Data (simply data henceforth) have quickly become some of the most important strategic resources in the global economy. However, their value is not (yet) formally recognized in financial statements, which leads to a growing gap between book and market values and thus limited decision usefulness of the underlying financial statements. The objective of this paper is to identify ways in which the value of data can be reported to improve decision usefulness.

Based on the authors' experience as both long-term practitioners and theoretical accounting scholars, the authors conceptualize and draw up a potential data value chain and show the transformation from raw Big Data to business-relevant AI-powered information during its process.

Analyzing current International Financial Reporting Standards (IFRS) regulations and their applicability, the authors show that current regulations are insufficient to provide useful information on the value of data. Following this, the authors propose a Framework for AI-powered Information and Big Data (FAIIBD) Reporting. This framework also provides insights on the (good) governance of data with the purpose of increasing decision usefulness and connecting to existing frameworks even further. In the conclusion, the authors raise questions concerning this framework that may be worthy of discussion in the scholarly community.

Scholars and practitioners alike are invited to follow up on the conceptual framework from many perspectives.

The framework can serve as a guide towards a better understanding of how to recognize and report AI-powered information and by that (a) limit the valuation gap between book and market value and (b) enhance decision usefulness of financial reporting.

This article proposes a conceptual framework in IFRS to regulators to better deal with the value of AI-powered information and improve the good governance of (Big)data.

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.

This article ties in with current debates on the digital transformation of society and the consequent work changes. Using an artificial intelligence (AI)-based accounting context, the focus of this paper is on actors, roles and tasks and related skills on an individual level. The authors look at the effect of AI-based “smart” technology on the workforce in the broader accounting profession taking an intrafirm perspective, yet acknowledging that the digital transformation encompasses a much larger field in the financial sector.

The authors conduct a Delphi study to identify the new roles and tasks in future accounting. In addition, the authors use expert workshops to clarify the related tasks and skills and determine whether either humans or AI-based technologies perform the roles or collaborate in professional accounting occupations.

The results show that tasks and skills for existing professional occupations in the broader acounting context will be subject to major changes in the next 10 years due to (AI based) digital technologies, while “core” roles and tasks will continue to exist in the future, some will not be performed by humans but by AI-based technology. For other “new” roles, humans will need to make informed use of digital technologies and, to some extent, collaborate with AI-based technology.

The authors look at the effect of AI-based “smart” technology on the workforce in the broader accounting profession, taking an intrafirm perspective.

This article ties in with current debates on the digital transformation of society and the consequent work changes. Using an AI-based accounting context, the focus of this paper is on the new and adapted roles and tasks.

The comprehensive analysis based on the Delphi study and expert workshops provide ample innovative ground for future research on the impact of AI on organisations and society.

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.

This paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's four-component model of antecedents for ethical decision-making. This study derives implications for accounting and auditing scholars and practitioners.

This research is rooted in the hermeneutics tradition of interpretative accounting research, in which the reader and the texts engage in a form of dialogue. To substantiate this dialogue, the authors conduct a theoretically informed, narrative (semi-systematic) literature review spanning the years 2015–2020. This review's narrative is driven by the depicted contexts and the accounting/auditing practices found in selected articles are used as sample instead of the research or methods.

In the thematic coding of the selected papers the authors identify five major ethical challenges of AI-based decision-making in accounting: objectivity, privacy, transparency, accountability and trustworthiness. Using Rest's component model of antecedents for ethical decision-making as a stable framework for our structure, the authors critically discuss the challenges and their relevance for a future human–machine collaboration within varying agency between humans and AI.

This paper contributes to the literature on accounting as a subjectivising as well as mediating practice in a socio-material context. It does so by providing a solid base of arguments that AI alone, despite its enabling and mediating role in accounting, cannot make ethical accounting decisions because it lacks the necessary preconditions in terms of Rest's model of antecedents. What is more, as AI is bound to pre-set goals and subjected to human made conditions despite its autonomous learning and adaptive practices, it lacks true agency. As a consequence, accountability needs to be shared between humans and AI. The authors suggest that related governance as well as internal and external auditing processes need to be adapted in terms of skills and awareness to ensure an ethical AI-based decision-making.

Current accounting practice tends to split environmental complexities into quantifiable, codified elements, producing codified simplifications of the “complex” in pursuit of environmental externalities. This has led to standardization, but has done little to motivate organizations to engage in more environmentally-aware behavior that transcends the coercive dimensions of codification. The work of Alexander von Humboldt (1769–1859) can bring new insights and perspectives to social and environmental accounting (SEA). In discussing Humboldt's philosophy of understanding the interconnectedness between people, their contexts (cultures) and their environment, the authors contribute to the emerging SEA literature on notions of interconnectedness and the web of accountabilities. The authors also explore how a Humboldtian approach may help break through the current epistemological boundaries of SEA by combining accurate measurement with imagery to make the “complex” manageable whilst embracing interconnectedness and hermeneutics.

In this conceptual paper, the authors humbly draw on Humboldt's legacy and explore the underlying philosophical assumptions of Humboldtian science. The authors then contrast these with current SEA approaches in the literature and derive new insights into their intentionality and practical use.

Re-examining Humboldt's pioneering work enables us to pinpoint what might be missing from current SEA approaches and debates. Humboldt upheld an “ethics of precision,” which included both measurement accuracy and qualitative relevance, and combined hands-on scientific fieldwork with the aesthetic ideals and interconnectedness of the age of Romanticism. Drawing on Humboldtian science, the authors propose focusing on the interconnectedness of nature and humanity, embracing the qualitative and hermeneutical and including aesthetics and emotion in environmental visualizations.

The paper elucidates why and how Humboldtian science might inform, guide and enhance the emancipatory potential of SEA in the 21st century. Specifically, the authors discuss Humboldt's approach of linking accurate measurement with imagery to convey a sense of interconnectedness.

This study examines how the strong emphasis placed on the purposes of budgeting, referring to a comprehensive focus on budgeting, is related to top managers' education and tenure while controlling for their functional positions in their respective firms and ages, as well as several company-specific predictors (information quality, firm size, information technology, importance of profit and strategy).

Survey data were collected from senior managers of large manufacturing firms in Finland and Sweden.

The results suggest that academic business education is positively associated with a comprehensive focus on budgeting, but tenure as well as functional position in the company (Chief Financial Officer (CFO) or not) and age are not. Overall, the company-specific control variables in general and information quality in particular are shown to have greater explanatory power than the top management characteristics analyzed.

This study identifies several empirically supported factors that seem to contribute to a comprehensive focus on budgeting. The effects of information quality, business education, the importance of profit and firm size could be considered in future research.

Academic business education matters more than the other top management characteristics analyzed. If organizations want to make comprehensive use of budgets, they should employ business graduates and be mindful of company-specific variables.

This study is the first to address a comprehensive focus on budgeting and some of its determinants. Future research could investigate a broader set of such determinants in different contexts.