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The authors investigate how managers adapt their financial reporting and disclosure practices in response to the COVID-19 pandemic through changes in accounting estimates (CAEs).

The authors define the pandemic period as starting on March 1, 2020. The sample consists of 9,575 CAEs disclosed in quarterly (10-Qs) and annual (10-Ks) financial reports by US firms between January 1, 2004 and May 31, 2022. The authors perform multivariate analyses of the impact of the COVID-19 pandemic on the incidence of CAEs and on whether the impact of CAEs on firms' financial performance and reporting quality changes during the pandemic.

In the examination of the CAE footnote disclosures in the quarterly (10-Qs) and annual (10-Ks) reports of US companies, the authors find no evidence that the incidence of CAEs in 10-Ks or the number of firms reporting CAEs are significantly different in the pre-pandemic and pandemic periods, but the incidence of CAEs in 10-Qs is significantly higher in the pandemic period than in the pre-pandemic period. The authors also find that the number of CAEs related to revenue recognition increase significantly in the pandemic period, but CAEs in other categories decrease, with the sharpest drop seen in the liabilities category. Further investigation suggests that although the dollar impact of 10-K CAEs on current financial statements is higher during the pandemic period, firms with CAEs, especially positive CAEs, in either 10-Ks or 10-Qs are less likely to use CAEs to boost earnings in the pandemic period. However, the authors find evidence that firms tend to use CAEs to “big bath” current earnings and create reserve for future period. The authors have not observed any significant differences in how the various phases of the pandemic affect the reporting of CAEs. Additionally, there is no evidence to suggest that financially distressed firms report more or fewer CAEs during the pandemic.

The results are consistent with the notion that, during the pandemic, firms exercise greater caution in their CAE disclosures, refraining from using CAEs as a means of boosting earnings but as a strategy to create reserve for future period. The paper highlights the challenges that various stakeholders face when assessing a company's current and future financial performance based on management's accounting estimates.

This study captures the impact of the COVID-19 pandemic on the incidence of CAEs and CAEs' impact on the financial performance and financial reporting quality of firms during the pandemic.

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.

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 study hypothesized that the impact of board diversity on financial distress (FD) is not direct but rather mediated by the firm’s corporate social responsibility (CSR) activities. Consequently, the purpose of this study is to examine the impact of CSR as a mediator in the board diversity–FD relationship.

The study examined six board diversity dimensions – age, gender, nationality, education and tenure in 81 nonfinancial Pakistan Stock Exchange (PSX)-listed firms from 2010 to 2021. The CSR engagement of the sample firms is evaluated using a multidimensional financial approach and the likelihood of FD is computed using Altman’s Z-score. The system-generalized method of moments estimator is used to meet the study objectives. In addition, several tests are run to determine the robustness of the study’s findings.

Based on the procedure for mediation analysis outlined by Baron and Kenny (1986), the authors found that CSR is significantly inversely associated with the likelihood of FD. Second, board diversity variables age, gender and national diversity were positively associated with CSR. Third, board age, gender and national diversity are significantly inversely related to FD. Finally, it was found that there is partial mediation between board age diversity and FD, whereas full mediation is shown between board age diversity and FD and between board nationality diversity and FD.

This study provides practical insights into PSX’s board diversity for companies, regulators and policymakers.

This research studies the connection between board diversity and FD. In addition, the current study extended the analysis by testing for the first time the mediating role of CSR in the diversity–distress relationship, particularly in the context of an emerging economy.

This study examines whether investors value the timing and/or information of mandatory disclosures in a unique research setting of listed companies in Thailand.

The authors adopt an event-study based approach. Abnormal stock returns are calculated using an OLS market model to measure market reactions to three types of mandatory reports issued by listed Thai firms: financial statements, Form 56-1 and Form 56-2. These reports are released sequentially but contain overlapping information content. Multivariate regression models are employed to examine the market reactions to these regulatory reports and explore the characteristics of firms that affect the market response.

The stock market reacts differentially to these reports. The financial statements, which are filed the earliest and are the most concise, prompt the strongest reaction. Investors similarly react significantly to Form 56-1 and Form 56-2, although Form 56-2 provides additional information beyond Form 56-1. The market reactions to small firms are stronger. Collectively, equity investors focus on the timeliness of disclosures rather than the information disclosed in the mandatory reports.

The evidence provides support for ongoing regulatory initiatives aimed at improving the timeliness of mandatory disclosures in emerging economies.

Prior studies on disclosure regulation investigate either the effect of information content or the timing of mandatory disclosures in isolation. The authors differentiate the effect of information content from disclosure timing and extend the literature by suggesting that investors incrementally value timeliness of disclosures. Investors perceive the benefit of the timely release of quantitative information compared to subsequent narrative disclosures. Between Form 56-1 and Form 56-2, the earlier release of the narrative non-financial information is incrementally traded into share prices.

This study aims to guide firms in emerging markets on whether corporate social responsibility (CSR) engagement facilitates their access to debt with the moderation of asset structure and firm performance. Considering the moderating effect analysis, this study explores the substitutive or complementary effect of these two contingencies on CSR-oriented firms in accessing debt financing.

Drawing on data collected for 16 emerging markets between 2008 and 2019, this study runs country–industry–year fixed-effects regression.

This study finds that CSR performance and reporting facilitate access to debt in emerging markets. However, CSR performance does not have an inverted U-shaped influence on firms’ access to debt financing. The moderation analysis of this study shows that asset tangibility has a negative moderating effect on the link between CSR engagements (i.e. both CSR performance and reporting) and access to debt, confirming a substitutive relationship between asset tangibility and CSR engagements in accessing debt. In contrast, firm performance is positively moderating the nexus between CSR engagement proxies and access to debt, which confirms a complementary type of relationship between firm performance and CSR engagements in accessing debt.

The empirical evidence of this study implies that creditors critically consider CSR engagements of firms in the loan-granting decision process. Similarly, the inverted U-shaped relationship between CSR and access to debt implies that there is an optimal level of CSR engagement creditors might consider in their decision. Likewise, the moderating effects analysis highlights that asset tangibility and firm performance are two conditions under which CSR performance and reporting are linked to access to debt.

Emerging countries are a different set of countries than developed ones; they have high growth rates and hence need financing, have a weaker institutional environment and have weaker stakeholder power. These particularities motivated the authors to conduct a separate study focusing on CSR and debt financing links drawing on a wide range of emerging countries. Thus, this study adds to the ongoing debate by examining the conditions under which CSR-oriented firms can access debt financing in emerging economies.

This study, rooted in affordance-actualization theory and communication theory, aims to critically examine how ChatGPT influences users’ transition from new adopters to loyal advocates within the context of travel decision-making. It incorporates constructs including communication quality, personalization, anthropomorphism, cognitive and emotional trust (ET), loyalty and intention to adopt into a comprehensive model.

This study used quantitative methods to analyze data from 477 respondents, collected online through a self-administered questionnaire by Embrain, a leading market research company in South Korea. Lavaan package within R studio was used for evaluating the measurement model through confirmatory factor analysis and using structural equation modeling to examine the proposed hypotheses.

The findings reveal a pivotal need for enhancing ChatGPT’s communication quality, particularly in terms of accuracy, currency and understandability. Personalization emerges as a key driver for cognitive trust, while anthropomorphism significantly impacts ET. Interestingly, the study unveils that in the context of travel recommendations, users’ trust in ChatGPT predominantly operates at the cognitive level, significantly impacting loyalty and subsequent adoption intentions.

The findings of this research provide valuable insights for improving Generative AI (GenAI) technology and management practices in travel recommendations.

As one of the few empirical research papers in the burgeoning field of GenAI, this study proposes a highly explanatory model for the process from affordance to actualization in the context of using ChatGPT for travel recommendations.

This study aims to contribute to the debate on goodwill accounting by examining the information content of impairment losses recognized in half-yearly reports. Half-yearly reports provide a suitable context to examine the effectiveness of the impairment process. Due to IFRIC 10 requirements, indeed, managers may have incentives to avoid recognizing impairment losses at the interim reporting date.

The study adopts an archival approach. Based on the traditional Ohlson’s model (1995), it explores the information content of half-yearly impairment losses in the European context over the period 2007–2017.

Findings confirm the relevance of half-yearly reports and suggest that half-yearly impairment losses are significantly associated with stock prices. In particular, investors positively value companies that recognized goodwill impairment losses at the interim reporting date.

The study contributes to the academic debate on goodwill and the effectiveness of the impairment procedure. In particular, it provides empirical evidence on the recognition of goodwill write-offs when it is possible to avoid the impairment test in the absence of indications of impairment.

Findings of this study can support the current debate on accounting for goodwill also in the light of the recent proposals of the IASB on the need to improve the effectiveness of the impairment test.

This study provides original empirical evidence on the goodwill impairment test in half-yearly reports, extending previous research that typically examines this issue in annual reports.

The purpose of this study is to determine the relationship between accounting fraud and corporate sustainability.

Companies listed on the Shenzhen Stock Exchange in 2019 are used to estimate a pooled ordinary least square regression model using panel data. Accounting fraud is represented by accounting disclosure, which is measured by its quality and timeliness, while corporate sustainability is measured by earnings management and corporate social responsibility.

Empirical findings support the hypothesis that the quality and timeliness of accounting disclosure have a statistically favorable impact on the management of company earnings and corporate social responsibility, respectively. Accounting fraud also has an impact on the sustainable development of the company.

Although the inferences of this study are limited to Chinese listed companies, this study may interest other scholars to explore similar topics.

This study aims to investigate the influence of ChatGPT, an AI-based chatbot, on the digital learning experience of students at Mzumbe University.

This study adopted a qualitative research design to gather in-depth insights from participants. Semi-structured interviews and an analysis of previous chat content were used as primary sources of data. Thematic analysis was used to analyze the qualitative data, allowing for the exploration of participants’ perspectives, experiences and opinions regarding the integration of ChatGPT into the learning process.

The results of the study demonstrated that ChatGPT is widely used in educational contexts and has a positive influence on students’ study habits, academic performance, and understanding of course material. Students appreciated the system’s simplicity, tailored instructions, and the promptness and accuracy of the responses. Despite the possibility of isolated mistakes.

It is important to recognize the limitations of this study. First, the sample size was small, limiting the broad application of the results. Second, this study’s narrow emphasis on students at Mzumbe University limits its applicability in other situations. Furthermore, depending on self-reported experiences, biases, such as individual interpretation or recollection bias, can occur.

Educators can maximize ChatGPT in the classroom by using study insights. Its advantages, such as effectiveness and enhanced performance, highlight the possibility for student-centered learning. Practitioners are guided by their awareness of problems, such as probable errors. Constant updates guarantee ChatGPT’s applicability and provide educators with useful advice.

Peer impact is highlighted in this study concerning social factors on the adoption of AI in education. Resolving issues preserves public confidence. Views influence public opinion and direct policymakers in discussions about safe AI use. It influences public attitudes while navigating the ethical integration of AI.

This study offers insightful information about the impact of ChatGPT on digital learning in Tanzania’s higher education. It makes innovative research contributions that enhance educational practices and emphasizes the advantages, difficulties and demands of responsible usage in the context of AI-based chatbots.