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Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

Environmental degradation resulting from human activities may adversely affect human health in multiple ways. Until now, policies aimed at mitigating environmental problems such as climate change, environmental pollution and damage to biodiversity have failed to clearly identify and drive the potential benefits of these policies on health. The conducted study assesses and demonstrates how specific environmental policies and instruments influence perceived human health in order to ensure input for a data-driven decision process.

The study was conducted for the 2004–2020 period in European Union (EU) countries with the use of dynamic panel data modeling. Verification of specific policies' impact on dependent variables allows to indicate this their effectiveness and importance. As a result of the computed dynamic panel data models, it has been confirmed that a number of significant and meaningful relationships between the self-perceived health index and environmental variables can be identified.

There is a strong positive impact of environmental taxation on the health index, and the strength of this relationship causes effects to be observed in the very short term, even the following year. In addition, the development of renewable energy sources (RES) and the elimination of fossil fuels from the energy mix exert positive, although milder, effects on health. The reduction of ammonia emissions from agriculture and reducing noise pollution are other health-supporting factors that have been shown to be statistically valid. Results allow to identify the most efficient policies in the analyzed area in order to introduce those with the best results or a mix of such measures.

The results of the authors' research clearly indicate the health benefits of measures primarily aimed at improving environmental factors, such as environmental taxes in general. The authors have also discovered an unexpected negative impact of an increase in the share of energy taxes in total taxes on the health index. The presented study opens several possibilities for further investigation, especially in the context of the rapidly changing geopolitical environment and global efforts to respond to environmental and health challenges. The authors believe that the outcome of the authors' study may provide new arguments to policymakers pursuing solutions that are not always easily acceptable by the public.

This study investigates the effect of total quality management (TQM) on customer satisfaction, personnel satisfaction and company reputation.

The study results rely on a structured survey conducted among an extensive sample of Finnish SMEs. In addition to the examination of the relationship between TQM and company performance in terms of customer satisfaction, personnel satisfaction and company reputation, the study takes a view on the possible effects of the industry, the company size and the certified quality system.

The results reveal that two TQM dimensions, namely Customer Focus and Product Management, were related to companies' customer satisfaction, whereas four TQM dimensions, namely Management/leadership, Customer Focus, Personnel Management and Risk Management, were related to personnel satisfaction. None of the TQM dimensions were related to company reputation. The control variables – the industry, the company size and the certified quality system – were not found to affect customer satisfaction, personnel satisfaction or company reputation.

Most previous studies have been based on traditional TQM classification and have not shown the effects of the latest TQM-related dimensions. Compared to previous studies, this work integrates risk management, digitization, system deployment efficiency and stakeholder management into TQM, which has not been implemented in any previous study. The roles of hard and soft TQM factors have been carefully considered in this study; thus, the study does not place too much emphasis on either direction but provides a balanced picture of the performance of the management systems studied. Although there are studies on the effects of TQM on personnel satisfaction, customer satisfaction and reputation, they are based on a much narrower definition of TQM than that in this study. The business environment is constantly changing, but only a few studies have been conducted to extend the TQM approach. This has led to duplication of studies, and the effects of performance-relevant procedures have not been extensively studied in the past as part of TQM. Therefore, the concept of this study brings significant added value to TQM research and returns the TQM concept to the overall level while considering the requirements of the ISO 9001: 2015 and EFQM 2019 quality standards. The study also considers the effects of ISO 9001 certification and EFQM requirements.