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European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Drawing on the work as calling theory, the purpose of this study is to explore how and when career calling promotes taking charge by focusing on the mediating effects of work meaningfulness and felt obligation and the moderating role of family-friendly human resource practice (FF-HRP).

The authors collected data from 293 supervisor–employee dyads at three time points in southeastern China. Path analysis and bootstrap method were used for hypothesis testing.

Employees' perceived career calling positively affected taking charge through work meaningfulness and felt obligation. The positive effects of career calling on work meaningfulness and felt obligation as well as the indirect effect of career calling on taking charge are stronger when employees perceive high levels of FF-HRP.

Organizational interventions should be designed to enhance employees' sense of calling, and the organization should inspire employees to take charge by awakening their perception of work meaningfulness and obligation. Moreover, FF-HRP should be implemented as a form of organizational support.

This research identifies work meaningfulness and felt obligation as mediators that link career calling to taking charge and reveals the role of FF-HRP in amplifying the positive impact of career calling.

The person who works in an office starts his or her day with a choice of attire. The way they look in the office depends on the decisions they make on their clothes. This study aims to identify the challenges faced by employees in the management positions in the Sri Lankan apparel industry who regularly come into contact with customers when they have to decide upon the most appropriate work attire for the position they are occupying in their organization.

Collection of data was primarily achieved through a well-structured questionnaire containing a mixture of open- and closed-ended questions. Targeted employees were managers, designers and merchandisers belonging to Generation Y whose total number was sufficient to obtain 50 feedbacks. Six more interviews were conducted with the intention of finding out more about this matter.

The majority of employees in the management positions in the Sri Lankan apparel industry who have regular contact with customers prefer to dress in “smart casual attire”, which means semi-formal clothes. Lack of availability of certain varieties of business attire in Sri Lanka proved to be a major challenge for some employees. Overpriced clothing, less comfortable clothing and lack of the right fabrics and designs were also challenges. These findings highlight the importance of manufacturing a wider variety of business attire using moderately priced but comfortable fabrics to make affordable and good quality products. There is a need to have a persuasive merchandising method to achieve good sales and provide a pleasant shopping experience to the customers.

Sri Lankan workwear retailers as well as apparel designers can benefit from the findings of this research as there is no evidence of any other studies on this subject. Therefore, this will help them to fill the market gap for business attire by addressing these challenges.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

Companies avoid taxes in a variety of ways and use different methods to do that, one of the most common being earnings management. The purpose of this paper is to investigate whether companies manipulate their financial data in order to reduce taxes paid.

We considered a sample of 63 listed Romanian companies for the period 2016–2021. The Beneish model was used for estimating earnings management, and the effective tax rate was used to measure tax avoidance. The analysis was carried out using regression analysis in Stata13 software.

The findings of the research indicate a negative and statistically significant association between effective tax rate and earnings management, implying that one of the main reasons why companies manipulate their earnings to reduce tax burden and avoid taxes. Moreover, our results show that return on assets (ROA) has a statistically significant negative influence on the effective tax rate. Furthermore, our analysis reveals that firm size, growth, and Big4 audit have no effect on effective tax rate.

Because it analyzes concrete cases using financial data and provides some recommendations for addressing the issue of tax avoidance, this work is useful in advancing both quantitative and qualitative research on this topic. This research is relevant for businesses, governments, regulators, audit professionals and investors.

The study, by analyzing concrete cases using reported financial data, contributes in filling the gap within the literature that results from a lack of scientific research on the relationship between tax avoidance and earnings management, and then it clarifies the nature of the causal connection between them. Moreover, it considers a combination of firm related variables including performance, size and also audit quality.