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The purpose of this study is to present the evidence of the association between financial statement comparability and corporate financial distress.

This is an empirical study, and this study uses multiple regression analysis to evaluate hypothesis.

The authors find a significant decrease in the probability of financial distress as accounting comparability increases. Findings of this study suggest that distressed firms tend to produce financial statements that compare poorly to those of peer firms; the effectiveness of predicting financial distress with accounting ratios may be conditional on comparability with peers; and financial statement comparability may be predictive of financial distress.

First, this study only used publicly available financial data, which may not be representative of all countries and could differ because of differences in accounting practices. Second, although this study found a connection between accounting comparability and financial distress, it cannot prove a causal relationship, as other factors that were not controlled for may also have an impact. Third, this study used various measures of financial distress, but other measures could lead to different results. Finally, this study did not include all relevant variables, such as industry-specific factors and macroeconomic conditions, which could influence the relationship between accounting comparability and financial distress.

For investors and financial analysts, the results imply that accounting comparability can serve as a useful signal for identifying companies that are more likely to remain financially stable in the long run. Thus, they may prefer to invest in or recommend highly comparable firms over their less comparable counterparts. For auditors, this study underscores the importance of promoting and enforcing accounting standards that improve comparability, as this can help mitigate the risk of financial distress among their clients. Regulators may also consider the implications of the study’s findings when designing policies and guidelines related to financial reporting and disclosure.

To the best of the authors’ knowledge, this is the first study investigating the association between financial statement comparability and corporate financial distress of the US firms. This study uses large, comprehensive and multi-year data. Furthermore, this is the only study that presents the evidence of negative association between comparability and firm financial distress.

The purpose of this paper is to investigate the spillover effects of a bright-line disclosure regulation that required Chinese listed firms to provide earnings forecasts if they anticipated specified, large earnings changes.

The paper examines the discontinuity of the earnings change distribution of firms listed on the Shenzhen Stock Market between 2010 and 2014. The paper finds that firms no longer subject to the bright-line test still exhibited discontinuity in earnings change distribution. The discontinuity lasted for at least three years with magnitude comparable to that of the firms still subject to the bright-line test. In addition, newly listed firms that had never experienced the bright-line test showed similar tendency to avoid the same threshold. There is some evidence that these firms’ avoidance of the −50 per cent changes was partly because of market pressure.

Research on bright-line tests has to date focused on their immediate and direct effects on firms currently subject to such tests. This study finds that a bright-line disclosure regulation’s influence is not limited to the firms directly governed by the regulation. It could lead to widespread and long lasting distortions in financial reporting behaviors of firms not currently subject to such tests.

The paper has implications for regulators who study the economic consequences of bright-line regulations in general and analysts of the Chinese capital market in particular.

This is the first empirical report that bright-line disclosure regulations affected the financial reporting behavior of firms that were not directly subject to the bright-line tests.

This paper aims to investigate whether the Section 404 of Sarbanes–Oxley Act (SOX 404) changed the way banks use accounting information to price corporate loans.

The study uses a sample of 1,173 US-listed firms that issued syndicated loans both before and after their compliance with SOX 404 to analyze the changes in loan spread’s sensitivity to some key accounting metrics such as ROA, interest coverage, leverage and net worth.

The study finds that the interest spread’s sensitivity to key accounting metrics, most noticeably for ROA, declined following the borrower’s compliance with the requirements of SOX 404. The decline was not explainable by borrowers that disclosed internal control weaknesses but concentrated among borrowers suspected of real earnings management (REM).

By examining the effects of SOX 404 on banks’ pricing process, this study augments the literature on SOX’s economic consequences. The findings suggest that lenders perceive little new information from SOX 404 disclosures of internal control deficiencies and are cautious about the accounting information provided by REM borrowers. It also extends the research on the use of accounting information in debt contracting. By examining loan interest’s sensitivity to accounting metrics, it broadens the concept of debt contracting value of accounting information to include accounting’s usefulness for assessing credit risk at loan inception.

Sensor arrays and pattern recognition-based electronic nose (E-nose) is a typical detection and recognition instrument for indoor air quality (IAQ). The E-nose is able to monitor several pollutants in the air by mimicking the human olfactory system. Formaldehyde concentration prediction is one of the major functionalities of the E-nose, and three typical machine learning (ML) algorithms are most frequently used, including back propagation (BP) neural network, radial basis function (RBF) neural network and support vector regression (SVR).

This paper comparatively evaluates and analyzes those three ML algorithms under controllable environment, which is built on a marketable sensor arrays E-nose platform. Variable temperature (T), relative humidity (RH) and pollutant concentrations (C) conditions were measured during experiments to support the investigation.

Regression models have been built using the above-mentioned three typical algorithms, and in-depth analysis demonstrates that the model of the BP neural network results in a better prediction performance than others.

Finally, the empirical results prove that ML algorithms, combined with low-cost sensors, can make high-precision contaminant concentration detection indoor.

This study aims to examine whether and how corporate sustainability capability influences energy efficiency through competitive intensity and slack resource availability.

The authors applied a two-wave research design and administered a survey questionnaire to senior-level managers of 78 ISO-14001 and ISO-50001 certified manufacturing companies. The authors use a multi-method approach for data analysis. AMOS 23 software was applied for covariance-based structural equation modeling. In addition, SPSS 25 software was applied for hierarchical regression analysis to examine the causal relationships in the model.

The finding reveals that corporate sustainability capabilities, which include energy-saving opportunities, seizing energy-saving opportunities and resource reconfiguration, significantly improve firms’ energy efficiency. In addition, competitive intensity and slack resource availability positively moderated the relationship between corporate sustainability capability and energy efficiency.

To the best of the authors’ knowledge, this study is the first to examine the link between corporate sustainability capability and energy efficiency in developing countries such as Pakistan. Although the influence of various corporate sustainability capabilities on sustainable performance has been widely examined in the literature, the role of corporate sustainability capability has been limitedly explored with energy efficiency. This study extends the literature by adding to the knowledge of corporate sustainability capability that enhances boundary conditions in developing countries.

This paper aims to discuss flow pattern transition (FPT) as an important factor in multiple-phase flow measurements. Several methods have been proposed to control FPT, but those methods fail to address the many issues in complex flow conditions that can affect flow patterns.

In this paper, a non-intrusive sensor instrumentation is applied to extract measurable data under different flow conditions. Using these data, a simple theoretical–mathematical method along with an orthogonal design is applied to FPT optimization. Orthogonal experiments are designed and carried out according to theoretical guidelines. Three selected process parameters – phase fraction, gas pressure in the initial independent process and liquid speed – are optimized for FPT results to produce a minimum FPT time.

The following results are obtained: the phase fraction in the initial independent process can lead to significant reductions in FPT time, gas pressure plays an important role and liquid speed has no apparent effect on FPT results. Under optimized conditions, FPT time can be shortened to 0.3-0.6 times by controlling the above three parameters compared with normal conditions.

The proposed method is simple, rapid and efficient for evaluating an FPT process and lays the foundation for further FPT applications.

For many optimization problems such as optimal techniques, compositions, producing process, the optimizing objectives in systems are complex relations with respect to a great deal of parameters. Generally, the objective function is hardly obtained, even the searching objective is unquantifiable. So it is difficult to model and optimize the complex systems to some extent.

To the above purpose, a novel approach is presented in this paper. It firstly utilizes the excellent fitting performance of neural network (NN) combined with expert knowledge (EK) to obtain the objective function, and secondly searches the optimal influential parameters with genetic algorithm (GA).

Peaks function inside Matlab and the acural application of comprehensive performance optimization in analog PID control system are studied, respectively. The results of simulation and the actual experiment both show that the modeling and optimizing method presented in the paper are effective.

Expert knowledge is needed for the fuzzy/unquantifiable objective.

The paper presents a useful way to model and optimize complex systems.

The combined approach based on NN, EK and GA is firstly presented and is effectively used in modeling and optimizing complex systems.

This study aims to review the current literature on the positive and negative effects of digitalisation in preventing corruption. It analyses existing research patterns and provides recommendations for future studies.

This paper employed bibliometric analysis and systematic review to scrutinise 190 papers from the Web of Science database from 2000 to 2023. Biblioshiny on R Studio was used for advanced bibliometric analysis to determine publication dynamics, influential journals, publications and impactful authors and a three-field plot to analyse relationships among countries, keywords and journals.

This study provides a bibliometric analysis of the past and actual developments in the field related to the effects of digitalisation on corruption. Based on the systematic literature review on a sample of the 50 most influential articles, this study identified background theories employed, the primary research methodologies adopted and valuable insights into both the positive and negative aspects of the impact of digitalisation on corruption.

This study provides an extended overview of the effects of digitalisation on corruption and advances new avenues for further research related to this field. The white and dark sides of the effects of digitalisation on corruption are highlighted. Furthermore, the study identifies the need for further research in this field to gain a more in-depth understanding of the nexus between digitalisation and corruption.