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This paper aims to determine whether Islamic banks (IBs) and conventional banks (CBs) in Tunisia are distinguishable from one another based on financial characteristics during the 2005–2014 period covering the 2008 global financial crisis (GFC) and the 2011 Tunisian revolution.

For the comparison between IBs and CBs, 11 hypotheses are formulated to distinguish between the two types of banks. The authors use a univariate analysis based on the multi-dimension figures investigation and a multivariate one based on the robust OLS technique for panel linear regression with mixed effects.

Bank-specific factors, dummy and dummy interacting variables indicate that there are differences between Islamic and conventional bank behavior. Both methods show that IBs are more liquid, more profitable and riskier than CBs. Post-2011 Tunisian revolution, small IBs (small CBs) are more (less) solvent, large IBs are more stable and both types of banks are more liquid, which explain why Tunisian governments have relay on bank system to cover budget deficits post-2011 revolution.

In investigating the feature of IBs and CBs from the Tunisian context, the authors take into account the effect of two abnormal events (2008 GFC and 2011 Tunisian revolution) on IBs through interaction variables.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

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.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

The authors explore the hypothesis that some movements in commodity prices are anticipated (news shocks) and can trigger aggregate fluctuations in small open emerging economies. This paper aims to discuss the aforementioned objective.

The authors build a multi-sector dynamic stochastic general equilibrium model with endogenous commodity production. There are five exogenous processes: a country-specific interest rate shock that responds to commodity price fluctuations, a productivity (TFP) shock for each sector and a commodity price shock. Both TFP and commodity price shocks are composed of unanticipated and anticipated components.

The authors show that news shocks to commodity prices lead to higher output, investment and consumption, and a countercyclical movement in the trade-balance-to-output ratio. The authors also show that commodity price news shocks explain about 24% of output aggregate fluctuations in the small open economy.

Given the importance of both anticipated and unanticipated commodity price shocks, policymakers should pay attention to developments in commodity markets when designing policies to attenuate the business cycles. Future research should investigate the design of optimal fiscal and monetary policies in SOE subject to news shocks in commodity prices.

This paper contributes to the knowledge of the sources of fluctuations in emerging economies highlighting the importance of a new source: news shocks in commodity prices.

The present study examines the initial working capital policy (WCP) and its evolution for newly established manufacturing firms.

Using panel data of 162 firms over a period of 10 years, the study analyses the persistence-cum-convergence in WCP over the subsequent years through descriptive analysis and difference of means test. Further, the prevalence of ß – convergence, and σ-convergence has been examined using standard least squares regression, dynamic panel analysis and the Wald test.

The results indicate that sample firms continue to follow the initial WCP in the subsequent years with a gradual convergence in the WCP. Alternatively, the firms with aggressive (conservative) WCP at the time of incorporation will continue following it. Further, the firms with aggressive initial WCP have witnessed higher growth than those with conservative initial WCP.

Findings will assist managers and practitioners to understand the dynamics of WCP over the life cycle of the firm and select appropriate WCP as certain policies lead to certain growth paths.

Though working capital management has been recognized as a critical managerial decision, limited research is available on its evolution, especially for newly established manufacturing companies in an emerging economy. Current research attempts to fill this gap and provide valuable insights for the effective management of liquidity.

The emerging business partner role of management accountants (MAs) results in an increased requirement of MAs to make business decisions. Frame dependence cognitive biases regularly influence decisions made in conditions of uncertainty, as is the case in business decision-making. Consequently, this study aims to examine susceptibility of MAs to frame dependence bias.

A survey was conducted among an international sample of practising MAs. The proportion of MAs influenced by framing bias was analysed and compared to findings in other populations. Logistic regression was then used to determine whether MAs who exhibit a higher preference for evidence-based (as opposed to intuitive) decision-making are more susceptible to framing bias.

Despite a comparatively high preference for evidence-based decision-making, the prevalence of framing bias among MAs is comparable to that of other populations. A higher preference for evidence-based decision-making was found to only be associated with higher susceptibility to endowment effect bias.

To the best of the authors’ knowledge, this is the first study to comprehensively examine framing bias for MAs as a group of decision-makers. Additionally, this study’s sample consists of practising MAs, and not only students.

The objective of the present study is to examine the value relevance of accounting information presented by Brazilian banks.

The studied sample derived from Brazil’s Stock Exchange, B3, under the banking segment, resulting in a group of 24 publicly listed companies, whose data ranged from 2017 to 2019. The study was conducted using the disclosure index, made with the intent of evaluating the disclosure adherence of a company to the reporting standard. In this case, Comitê de Pronunciamentos Contábeis (CPC) 40, financial instruments: recognition, evaluation and disclosure, Instrumentos Financeiros: Evidenciação, Brazil’s interpretation of the International Financial Reporting Standards (IFRS) 7.

The results show that for the sample and period, the disclosure index cannot be used as an explanatory variable for the market evaluation of financial institutions.

While other studies have presented a similar approach to the value-relevance theme, the present work is original as it develops the methodology on financial institutions, and even more so on the financial institutions of a developing country.

Population ageing will bring economic challenges in the future. The purpose of this paper is to examine whether increased educational level could mitigate the consequences of population ageing on economic sustainability, measured as the gap between labour income and consumption.

Using the National Transfer Accounts (NTA) methodology, the authors decompose labour income and consumption by age and educational level (low, medium and high) and compare obtained age profiles with those calculated conventionally. In addition, using the population projections by age and educational level, the authors project both profiles to 2060 for selected EU countries and assess future economic sustainability.

The results show that the highly educated have a significantly higher surplus for a longer period then those with lower and medium education. Therefore, the improved educational level of individuals will have a substantially positive impact on labour income in the future—on average by about 32% by 2060 for all EU countries included. However, as the better educated also consume more, higher production does not fully translate into improved economic sustainability, but the resulting net effect is still positive at about 19%.

The authors present for the first time an NTA by education for 15 EU countries and show the importance of including education in the analysis of the economic life cycle. The authors also show that increased educational level will mitigate the consequences of population ageing on economic sustainability in the future.