Search results

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.

This study aims to empirically investigate the impact of political instability on the banking stability of the dual banking system in the Middle East and North African (MENA) countries.

The study measures banking stability with probability of default (PD) and Zscore by employing the generalised method of moment (GMM) between 2007 and 2021 on the dual banking system in the region. The authors further estimate short-long-run situations coupled with a robustness test using a generalised least square (GLS) model.

The authors' findings indicate that institutional factors of political stability, crisis period, high-crisis countries, law and order and macroeconomic indicators influence the two types of banking stability in the region. The authors found the consistency of the factors explaining stability in the region in both short-and long-run situations. Consequently, the study also reveals the adverse effects of crisis periods and high-crisis countries on banking stability.

The results of this study explicitly identify the critical need for sustaining political stability and abiding by laws and order to achieve dual banking stability in the region. Therefore, policymakers may consider allowing the region's banks to operate beyond retail banking since diversification enhances banking stability.

The authors' study balances by employing dual stability measurement in predicting the impact of political instability, law and order and other indicators on the MENA region's two banking models. This study uncovers the effect of the global crisis period on banking stability and high-crisis countries in the region and verifies the models' robustness.

Despite the widespread prevalence of share pledging by Indian promoters, this area remains out of the researchers’ purview. This study aims to bridge this research gap by delineating the impact of promoter share pledging on future stock price crash risk and financial performance in India.

A sample of 257 companies listed on the Standard and Poor’s Bombay Stock Exchange 500 (S&P BSE 500) Index has been analysed using panel (fixed-effects) data regression methodology over 2011–2020. Further, alternative proxies for crash risk and financial performance are adopted to ensure that the study’s initial findings are robust. Finally, the instrumental variable with the two-stage least squares (IV-2SLS) method has also been employed to alleviate endogeneity concerns.

The results suggest a significantly positive relationship between promoter share pledging and future stock price crash risk in India. Conversely, this association is significantly negative for future financial performance. Moreover, the results hold, even after including alternative proxies of stock price crash risk and financial performance and addressing endogeneity concerns.

Owing to the sizeable equity shareholdings of the promoters, share pledging has remained a lucrative source of finance in India. Despite the popularity, the findings of this study question the relevance of share pledging by Indian promoters considering its impact on aggravating future stock price crash risk and deteriorating future financial performance.

This study examines how bilateral international trade among mandatory International Financial Reporting Standards (IFRS) adopter countries moderates the relation between IFRS adoption and firms’ financial reporting quality.

The authors use data from 2007 to 2015 and focus on publicly listed firms from non-European Union countries that adopted IFRS on a mandatory basis.

The authors find that the interaction between mandatory IFRS adoption and a country’s bilateral trade with other countries using IFRS is negatively and significantly related to accruals-based earnings management, which is an inverse measure of financial reporting quality. This result is driven by firms in less developed countries. The improvement in accounting quality is for firms located in countries that both fully and partially adopt IFRS. The authors also find a significant and negative coefficient for the relation between real earnings management and the interaction between mandatory IFRS adoption and a country’s bilateral trade with other IFRS countries in the post-global financial crisis period.

Overall, the authors’ results are consistent with the notion that the mandatory adoption of IFRS creates a positive externality where firms improve their accounting quality because increased financial statement comparability means that foreign customers and suppliers can monitor the quality of earnings more easily.

This research aims to determine the influence of environmental, social and governance (ESG) factors on market performance. The study shows the perspective of ESG on market performance. The study attempted to test the relationship between ESG and Tobin’s Q and the effect of control variables.

The study used panel data from a sample covering 720 firms and ran a fixed-effects model regression during the 2007–2019 period for eight European countries’ listed companies.

The findings reveal that ESG positively impacts Tobin’s Q. According to the findings, high company ESG performance boosts market performance via the moderator effect of competitive advantage. The results indicate that all control variables are significant. The firm’s leverage has a negative relationship with ESG. The size of the firm impacts ESG positively. Also, the results prove that the firm’s size and industry positively affect Tobin’s Q.

The findings of this study suggest that managers, practitioners and authorities interested in learning about ESG scores (ESGSs), market performance and competitive advantage might draw intriguing conclusions from the data. Managers can identify the appropriate levels of competitive advantage that improve market performance. Practitioners must determine whether fit, size, growth, leverage and industry could enhance market performance. The findings also give authorities and the board of directors information on future growth opportunities for the company and the country.

The research presents a vision of how ESG factors affect market performance. This study aims to identify the positive link between ESGSs and European market performance.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

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.

Considering the emergence of e-commerce platforms and their integration into marketing channels, this paper aims to investigate how artificial intelligence (AI) resources – both technological and human – possessed by e-commerce platforms can enhance their channel power by acquiring market-based assets (relational and intellectual).

Based on resource-based theory and resource orchestration theory, the authors developed a framework tested using survey data gathered from the sellers, which incorporated six key variables: the e-commerce platform’s AI technology resources and human resources, rational and intellectual market-based assets, intraplatform competition and channel power. The analyses are performed using the regression analysis technique.

The empirical findings indicate that both technological and human AI resources are crucial in building channel power. In addition, market-based assets serve as a mediator in this relationship, while intraplatform competition moderates the effect of intellectual market-based assets on channel power negatively.

This study contributes to the existing literature by exploring how e-commerce platforms’ AI resources affect their channel power. The results offer valuable guidance to managers and researchers on optimizing AI resources to improve channel power.

Based on the attention-based view (ABV), this study examines the mechanism of external pressure and internal managerial interpretation affecting the promotion of green entrepreneurial orientation (GEO) of agricultural enterprises.

Based on data collected from 208 agricultural enterprises in China, the conceptual model was tested by using hierarchical regression.

The results show that managerial interpretation can affect the promotion of GEO. Command and control regulation, market-based regulation and green market pressure are important external pressures that affect the promotion of GEO. In addition, managerial interpretation mediates the relationship between command and control regulation and GEO, market-based regulation and GEO, as well as green market pressure and GEO.

This study proposes a key path for promoting the adoption and implementation of GEO by agricultural enterprises. The research results provide experience for emerging and developing countries to promote the GEO of agricultural enterprises, which is helpful to alleviate the environmental problems caused by the development of agricultural enterprises.

For the first time, this study introduced the ABV into the research of GEO. The research results enrich the theoretical perspective of GEO and expand the research field of the ABV. In addition, this study fills the research gap that existing research has not paid enough attention to the internal driving factors of GEO and opens the black box between the external pressure and GEO.

The purpose of this study is to analyze the effect of foreign direct investment (FDI) on pollution emissions and how environmental regulation affects this relationship.

In the empirical research, the authors selected panel data for 30 provinces in China from 2005 to 2019 as samples. First, the authors used the instrumental variable method to verify the existence of the above hypotheses in China. Then, the authors analyzed the moderating effect of different types of environmental regulations on the environmental effects of FDI. Next, in further discussion, the authors analyzed the difference between the environmental effect and the moderating effect in different time periods and regions, respectively. Finally, the authors discussed whether the different intensities of environmental regulations lead to the transfer effect of FDI in choosing investment destinations.

The result shows that FDI can help reduce pollution emissions and create a “pollution halo” effect, which is enhanced by command-and-control regulation but suppressed by market-based incentives. The heterogeneity analysis reveals that the 18th National Congress of the Communist Party has weakened the pollution halo effect of FDI, while the environmental effect of FDI in the eastern region is not significant, but in the middle and western regions, there is a significant pollution halo effect and a positive moderating effect of environmental regulations. Finally, further analysis reveals that FDI has a transfer effect under command-and-control environmental regulations.

First, the main purpose of this paper is to study the relationship between FDI and pollution emissions from the perspective of heterogeneous environmental regulation. Therefore, there is no detailed discussion on their effect mechanism of them. Second, limited by data, the authors adopt the single index to measure the stringency index of command-and-control and market-based incentive environmental regulations in China. The single index may not be able to fully reflect the intensity of regional environmental regulation, so the construction of a composite indicator is necessary. These shortcomings are the focus of the authors' future research.

Under the guidance of high-quality development, the conclusions above can provide reference for adjusting FDI policies and improving environmental regulation policies.

The innovations in this paper can be summarized as the following four dimensions: First, the authors use the instrumental variable (IV) method to address endogeneity in the relationship between FDI and pollution emission, which can further ensure the robustness of the research results and increases the credibility of the paper. Second, the authors distinguish between two types of environmental regulations to investigate their moderating effect on the environmental impact of FDI. Third, the authors consider the temporal and spatial heterogeneity of both the environmental effects of FDI and the moderating effect of regulation. Last, the authors analyze the spatial spillover of environmental regulation through the study of the transfer effect.