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This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

The purpose of this study is to examine the impact of environmental disclosures (END) on the corporate financial performance (CFP) of listed companies in Ghana before and during the Banking crisis (BKC) and the COVID-19 pandemic (COV).

This study used data from 16 companies listed on the Ghana Stock Exchange between 2012 and 2021. The END Index was used, which uses percentile ranking and is guided by Global Reporting Initiative guidelines. A diverse set of empirical tests were used to examine whether ENDs affect CFP during crises.

The study offered support for the stakeholder and signaling theories generally applied to the study of END. The results confirmed that ENDs have a significant positive effect on CFP measures, return on equity and earnings per share, before and during the crises. The BKC and COV had no impact on the CFP.

As Ghana is still recovering from the 2017 to 2020 BKC and COV, the findings of this study highlight the need for managers to embrace END reporting and engagement strategies to improve CFP and firm reputation.

To the best of the authors’ knowledge, this study is the first to examine the effect of END on CFP in the context of before and considering the Ghanaian BKC and COV. In addition, it is one of the few studies that investigates how ENDs affect the CFP of Ghanaian-listed firms.

The aim of this study is to critically examine the transparency and reporting practice of Environmental, Social, and Governance (ESG) controversies within the pharmaceutical and textile industry. Based on the four core dimensions of transparency, we explore which reporting medium is most frequently chosen for the disclosure of negative ESG contributions, the nature and information content of the disclosed incidents and how voluntary adherence to sustainability reporting standards and independent assurances affect the reporting.

We use conceptual content analysis and employ a counter-accounting approach to analyse the disclosure of 190 ESG controversies in 104 corporate reports from the pharmaceutical and textile industries, covering a three-year period from 2018–2020.

The very large majority of controversies are reported only once in the legal proceedings section of the annual report, but not again in the sustainability report, where it would be necessary to provide a balanced picture. Moreover, companies tend to disclose only those controversies that are either associated with high media attention or are expected to be related to litigation, resulting in 26 per cent of controversies not being disclosed at all. The overall quality of disclosure is unsatisfactory and in need of improvement, but comparably higher in the pharmaceutical industry than in the textile industry. Interestingly, neither the application of sustainability reporting standards nor independent assurance seems to positively impact the disclosure behaviour.

Our paper provides new insights into the shortcomings of current ESG controversy disclosures by revealing patterns of selective reporting practices and the strategic framing of issues. In addition, it contributes to the debates on corporate cherry-picking in the adoption of sustainability reporting guidelines and on the effectiveness of external assurance of sustainability reports. Based on the findings, it offers important implications for practitioners, in particular management, policy makers, rating agencies and assurance providers.

This study aims to find the key drivers of green innovation in family firms by examining firm characteristics and geographical factors. It seeks to develop a conceptual framework that explains how internal resources and external environments influence environmental innovation practices in these businesses.

Using machine learning (ML) methods, this study develops a predictive model for green innovation in family firms, drawing on data from 3,289 family businesses across 27 EU Member States and 12 additional countries. The study integrates the Resource-Based View (RBV) and Location Theory to analyze the impact of firm-level resources and geographical contexts on green innovation outcomes.

The results show that both firm-specific resources, such as size, digital capabilities, years of operation and geographical factors, like country location, significantly influence the likelihood of family firms engaging in environmental innovation. Larger, technologically advanced firms are more likely to adopt sustainable practices, and geographic location is crucial due to different regulatory environments and market conditions.

The findings reinforce the RBV by showing the importance of firm-specific resources in driving green innovation and extend Location Theory by emphasizing the role of geographic factors. The study enriches the theoretical understanding of family businesses by showing how noneconomic goals, such as socioemotional wealth and legacy preservation, influence environmental innovation strategies.

Family firms can leverage these findings to enhance their green innovation efforts by investing in technology, fostering sustainability and recognizing the impact of geographic factors. Aligning innovation strategies with both economic and noneconomic goals can help family businesses improve market positioning, comply with regulations and maintain a strong family legacy.

This research contributes a new perspective by integrating the RBV and Location Theory to explore green innovation in family firms, highlighting the interplay between internal resources and external environments. It also shows the effectiveness of machine learning methods in predicting environmental innovation, providing deeper insights than traditional statistical techniques.

In recent years patents have become a very popular data source for forecasting technological changes. However, since a vast amount of patents are “worthless” (Moore, 2005), there is a need to identify the promising ones. For this purpose, previous approaches have mainly used bibliographic data, thus neglecting the benefits of textual data, such as instant accessibility at patent disclosure. To leverage these benefits, this study aims to develop an approach that uses textual patent data for predicting promising patents.

For the identification of promising patents, the authors propose a novel approach which combines link prediction with textual patent data. Thereby the authors are able to predict the emergence of hitherto unmentioned bigrams. By mapping these future bigrams to recent patents, the authors are able to distinguish between promising and nonpromising patents. To validate this approach, the authors apply the methodology to the case example of camera technology.

The authors identify stochastic gradient descent as a suitable algorithm with both a receiver operating characteristic area under curve score and a positive predictive value of 78%, which outperforms chance by a factor of two. In addition, the authors present promising camera patents for diverse application fields, such as cameras for surgical systems, cameras for rearview vision systems in vehicles or light amplification by stimulated emission of radiation detection and ranging cameras for three-dimensional imaging.

This study contributes in at least three directions to scholarship. First, the authors introduce a novel approach by combining link prediction with textual patent analysis and, in this way, leverage the benefits of both worlds. Second, the authors add to all theories that regard novel technologies as a recombination of existing technologies in presenting word combinations from textual data as a suitable instrument for revealing recombination in patents. And third, the approach can be used by scholars as a complementary or even integrative tool with conventional forecasting methods like the Delphi technique or Scenario planning.

At least three practical implications arise from the study. First, incumbent firms of a technology branch can use this approach as an early-warning system to identify technological change and to identify opportunities related to their company’s technological competence and provide inspiration for new ideas. Second, companies seeking to tap into new markets may also be interested in the approach as managers could anticipate whether their company’s technological competences are in line with upcoming trends. Third, the approach may be used as a supportive tool for various purposes, such as investment decisions or technology life cycle analysis.

The approach introduces textual patent data as suitable means for forecasting activities. As the statistical validation reveals, the promising patents identified by the approach are cited significantly more often than patents with less promising prospects.

Corporate social responsibility (CSR) has gained tremendous importance after several corporate scandals, financial crises and the rise of the hyper-competitive world. Firms must address multiple stakeholders’ interests to increase firm value. This study aims to investigate the effect of CSR on firm value. This study also examines the mediating role of enterprise risk management (ERM) and the moderating influence of corporate governance (CG) in this CSR-firm value relationship.

The sample of the study comprises 119 Pakistan Stock Exchange (PSX) listed firms and the study covers the period from 2010 to 2021. The corporate social responsibility performance has been quantified across five dimensions. These aspects are product, environment, employee relations, diversity and community. Four proxies i.e. strategy, operation, reporting and compliance, have been used to measure ERM. The governance quality of the sample companies was evaluated using the governance index, which included 29 governance provisions. The authors used the dynamic panel data technique (system-GMM) is used to achieve the objectives of the study. Furthermore, a firm’s engagement in CSR activities can also be measured through a multinational financial approach to check the robustness of the result.

Based on the regression analysis, the authors discovered that CSR was positively connected with firm value, validating the stakeholder view of CSR. Furthermore, following Baron and Kenny’s (1986) mediation technique, the findings confirm that ERM mediates this association. These results are robust by using the bootstrapping tests by Preacher and Hayes (2004). Furthermore, the result shows that corporate governance (CG) is positively connected with firm performance, and this relationship is strengthened in the presence of an effective governance system in the organization.

This study provides useful insights to regulators, investors and policymakers to consider CSR as a value-enhancing factor and encourage the development of enterprise risk management and compliance with CG mechanisms to improve firm value.

The presented analysis strengthens the existing CSR–firm value relationship by analyzing the mediating and moderating roles of ERM and CG, which have not yet been tested, particularly in the context of Pakistan.

Powder bed additive manufacturing has recently seen substantial growth, yet consistently producing high-quality parts remains challenging. Recoating streaking is a common anomaly that impairs print quality. Several data-driven models for automatically detecting this anomaly have been proposed, each with varying effectiveness. However, comprehensive comparisons among them are lacking. Additionally, these models are often tailored to specific data sets. This research addresses this gap by implementing and comparing these anomaly detection models for recoating streaking in a reproducible way. This study aims to offer a clearer, more objective evaluation of their performance, strengths and weaknesses. Furthermore, this study proposes an improvement to the Line Profiles detection model to broaden its applicability, and a novel preprocessing step was introduced to enhance the models’ performances.

All found anomaly detection models have been implemented along with several preprocessing steps. Additionally, a new universal benchmarking data set has been constructed. Finally, all implemented models have been evaluated on this benchmarking data set and the effect of the different preprocessing steps was studied.

This comparison shows that the improved Line Profiles model established it as the most efficient detection approach in this study’s benchmark data set. Furthermore, while most state-of-the-art neural networks perform very well off the shelf, this comparison shows that specialised detection models outperform all others with the correct preprocessing.

This comparison gives new insights into different recoater streaking (RCS) detection models, showcasing each one with its strengths and weaknesses. Furthermore, the improved Line Profiles model delivers compelling performance in detecting RCS.

There has been a burgeoning interest in exploring the impact of uncertainty factors on share returns. However, studies on the influence of global financial uncertainties on emerging market sectoral indices are scarce. Thus, there is a need to have a thorough investigation of the connection between global financial uncertainties and emerging market sectoral indices. To fill this gap, using the theoretical framework of international portfolio diversification (IPD) and utilizing data from 2008 to 2021, this study examines the spillover connection between global uncertainty indices (GUIs) and leading sectoral indices of 28 emerging markets.

The authors employ the quantile spillover-based connectedness approach and minimum connectedness portfolio approach to explore the dynamic connectedness among sectoral indices and global uncertainty indices (GUIs) as well as portfolio implication.

The study found high connectedness among all indices, especially at higher and lower quantiles. Among GUIs, the authors find that stock market volatility (VIX) and oil volatility index (OVX) are strongly interconnected with all leading emerging markets' sectoral indices. Among sectoral indices, the linkage between the financial (F-Index), information technology (IT-Index), and consumer discretionary (CD-Index) sectors shows moderate interconnectedness. In contrast, the communication services (CS-Index) sector has low interconnectedness with the system. In terms of spillover effects, the authors find EVZ, OVX, and the IT sectors to be net recipients for the entire period. The authors also explored portfolio diversification benefits by employing a minimum connectedness portfolio approach. The cumulative returns' findings show a slight decline in the portfolio's value after 2010; during 2012, the pattern remained stable; from 2014 to 2020, the portfolio performed negatively, that is, underperformance due to different events in that period, including COVID-19. The Consumer Discretionary sector is found to be significant because of having the largest weight, 51%, in the portfolio during the study period.

The study suggests that investors should invest in the communication services sector as it is the least connected. However, the connectedness increases during COVID-19, which implies that it may be difficult for investors to benefit from IPD in a crisis period. Hence, to obtain the benefits from IPD, the evidence suggests that investors need to consider Consumer Discretionary sector while considering assets for investment.

The study's uniqueness is that the authors have investigated spillover between GUIs and 28 emerging markets sectoral indices by employing a quantile spillover-based connectedness approach and minimum connectedness portfolio approach with a special focus on portfolio implication.

This study aims to examine the degree of consciousness of forensic accounting (FA) in Jordan. This study surveys practitioners and academicians about their views and thoughts toward the expected role of using FA techniques to detecting and preventing fraud practices and shedding more light on advantages and obstacles of using the FA techniques.

To collect the data, a questionnaire was constructed and distributed to the study population which consists of accounting academics, students and accounting practitioners.

The results of this study show evidence that both students and professionals have a lower level of awareness on the FA concept and its importance. The results also confirm there is a significant correlation between, fraud prevention and detection, advantages of the application of FA, the training courses toward the application of FA and the application of FA in the context of Jordan. It has also been confirmed that there is a number of significant factors hinders this implementation in Jordan.

The findings of this study offer many policy implications for regulators and policymakers on the needed relevant information to address and implement FA in education and practice, thereby activating the FA concept in Jordan.

The primary motivation of this study is driven by the limited and inconclusive research on the FA as a monitoring tool, notably there is a high possibility of fraud and misstatement practices due to the agency conflict. This study is the first of its kind to discuss this topic in the context of Jordan. The need to integrating the accounting education within accounting profession regarding FA becomes an urgent need to develop the awareness level of practitioners when it comes to practice of FA.

The abnormality of the month-of-the-year and Ramadan effects has extensively existed in the stock and other markets. The commercial strategy pattern and the computation of such predictable patterns in the market allow investors to make money. By using anomalies such as the month-of-the-year and the Ramadan effects on earnings management (EM), it is possible to achieve such a goal. This study aims to investigate the month-of-the-year effect and the Ramadan effect on the relationship between accrual earnings management and real earnings management (AEM and REM, respectively) and liquidity in the Iranian capital market.

This empirical analysis comprises a panel data set of 80 listed firms (400 observations) on the Tehran Stock Exchange from 2016 to 2020.

The findings exhibit that when AEM and REM increase, information asymmetry also increases. The simultaneous increase of these variables leads to a decrease in stock liquidity. Furthermore, the results indicate that the month-of-the-year and Ramadan effects intensify the negative relationship between AEM and REM with stock liquidity. Therefore, EM is affected by the investor’s behavior in specific months.

Anomalies caused by the Ramadan effect and the month-of-the-year effect on reducing liquidity in the Iranian stock market were confirmed. Investors can use these anomalies to identify predictable patterns, exchange securities according to those patterns and earn abnormal returns.

To the best of the authors’ knowledge, this is the first study that empirically examined the simultaneous effect of Gregorian and Islamic calendar anomalies on the relationship between EM and liquidity, and while helping managers and other readers, it can be the basis for future research.