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Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

This paper aims to investigate materiality judgement providing insights, critiques and future research paths in light of the open debate on the role of materiality in corporate financial disclosure, highlighting potential connections and implications with sustainability and intellectual capital (IC) reporting.

The research presents an overview of the analysis of financial materiality, including new stimuli from recent studies and regulatory requirements for financial and non-financial reporting. Accordingly, this study used a systematic literature review (SLR) based on a combination of content, text and bibliometric analysis of materiality in accounting research studies, collecting data from the Scopus database as one of the most relevant repositories.

The SLR identified four relevant research trends, concerning: (1) the relevance of materiality principles in corporate disclosure; (2) financial reporting practices and materiality; (3) theories and approaches in defining financial materiality and (4) the existence of quantitative and qualitative thresholds in the materiality judgement.

The results provide theoretical and practical implications when comprehending the development of the concept of financial materiality in financial statements and whether they can be appropriate in reporting IC as well. We identified future research paths.

From a practical perspective, this study is useful for companies implementing financial materiality based on stakeholder engagement and improving their transparency in financial and non-financial reporting practices.

The research investigates if the process for assessing materiality is in line with the expectations of all stakeholders involved in financial and non-financial reporting.

This research is the first to investigate the scientific basis and applicability of the concept of financial materiality to sustainability and IC reporting.

Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed.

Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference.

The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized.

This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

Health technologies are advancing rapidly and becoming more expensive, posing a challenge for financing healthcare systems. Health technology assessment (HTA) improves the efficiency of resource allocation by facilitating evidence-informed decisions on the value of health technologies. Our study aims to create a customized HTA roadmap for Oman based on a gap analysis between the current and future status of HTA implementation.

We surveyed participants of an advanced HTA training program to assess the current state of HTA implementation in Oman and explore long-term goals. A list of draft recommendations was developed in areas with room for improvement. The list was then validated for its feasibility in a round table discussion with senior health policy experts to conclude on specific actions for HTA implementation.

Survey results aligned well with expert discussions. The round table discussion concluded with a phasic action plan for HTA implementation. In the short term (1–2 years), efforts will focus on building capacity through training programs. For medium-term actions (3–5 years), plans include expanding the HTA unit and introducing multiple cost-effectiveness thresholds while from 6–10 years, publishing of HTA recommendations, critical appraisal reports, and timelines is recommended.

Although the HTA system in Oman is still in its early stages, strong initiatives are being taken for its advancement. This structured approach ensures a comprehensive integration of HTA into the healthcare system, enhancing decision-making and promoting a sustainable, evidence-based system addressing the population’s needs.

The purpose of this study is to examine how the interplay of stressor (e.g. fear of missing out, FoMO) and strains (e.g. perceived social overload, communication overload, information overload and system feature overload) in social networking sites (SNS) use can contribute to users’ SNS fatigue from a configurational view.

Data were collected among 363 SNS users in China via an online survey, and fuzzy-set qualitative comparative analysis (fsQCA) was applied in this study to scrutinize the different combinations of FoMO and overload that contribute to the same outcome of SNS fatigue.

Six combinations of casual conditions were identified to underlie SNS fatigue. The results showed that FoMO, perceived information overload and system feature overload are the core conditions that contribute to SNS fatigue when combined with other types of overloads.

The current work supplements the research findings on SNS fatigue by identifying the configurations contributing to SNS fatigue from the joint effects of stressor (FoMO) and strain (perceived social overload, communication overload, information overload and system feature overload) and by providing explanations for SNS fatigue from the configurational perspective.

The purpose of this paper is to investigate the relationship between board gender equality and environmental, social and governance (ESG) performance in the European banking sector. The study examines whether and how the presence of women on the board of directors (BoD) influences ESG dimensions.

The authors analyzed a sample of 72 European Union banks for the period 2015–2021 and developed an econometric model applying unbalanced panel data regression with firm fixed effects and controls per year. To test the research hypotheses, the authors considered gender equality in terms of female participation on the BoD and measured ESG dimensions by using the ESG score provided by Refinitiv.

The findings suggest a significant positive relationship between the number of women on BoD and the ESG performance of European banks only up to a certain threshold of female directors (at least three women). The study also explores how the proportion of women on BoD influences the individual ESG pillars. The results show that the percentage of female directors has a positive and statistically significant impact on the social dimension of the ESG framework.

The investigation is highly relevant to investors considering ESG issues in their decision-making process. The overall findings support policymakers and regulators on how to improve ESG performance through the design and the application of corporate governance (CG) mechanisms. From a managerial perspective, the study suggests that managers and CEOs should focus their efforts on establishing the right gender combination of directors on bank BoDs.

This paper offers an in-depth examination of the CG practices of banks, and it attempts to bridge the gap in prior literature on the determinants of ESG issues in the European banking industry. To the best of the authors’ knowledge, this study is the first that investigates the relationship between the representation of women on BoDs and the ESG dimensions measured by the Refinitiv Eikon score. The use of critical mass theory adds a fresh perspective to the literature on ESG in Europe since the influence of board gender diversity on ESG performance of the European banks is still unaccounted for. This study addresses this pressing research issue drawing on resource dependence, agency and legitimacy theories.

Research serves to elucidate and tackle real-world issues (e.g. capitalizing opportunities and solving problems). Critical to research is the concept of validity, which gauges the extent to which research is adequate and appropriate in representing what it intends to measure and test. In this vein, this article aims to present a typology of validity to aid researchers in this endeavor.

Employing a synthesis approach informed by the 3Es of expertise, experience, and exposure, this article maintains a sharp focus on delineating the concept of validity and presenting its typology.

This article emphasizes the importance of validity and explains how and when different types of validity can be established. First and foremost, content validity and face validity are prerequisites assessed before data collection, whereas convergent validity and discriminant validity come into play during the evaluation of the measurement model post-data collection, while nomological validity and predictive validity are crucial in the evaluation of the structural model following the evaluation of the measurement model. Additionally, content, face, convergent and discriminant validity contribute to construct validity as they pertain to concept(s), while nomological and predictive validity contribute to criterion validity as they relate to relationship(s). Last but not least, content and face validity are established by humans, thereby contributing to the assessment of substantive significance, whereas convergent, discriminant, nomological and predictive validity are established by statistics, thereby contributing to the assessment of statistical significance.

This article contributes to a deeper understanding of validity’s multifaceted nature in research, providing a practical guide for its application across various research stages.

This study aims to investigate which configurations of organizational-level and group-level management controls support an identity fit for management accountants in the management accounting profession. It aims to complement recent qualitative management accounting research. This stream just begun to use role and identity theory to investigate role expectations, conflicts and coping strategies of management accountants when they struggle with their work identity.

Based on configuration theory, this study uses a fuzzy-set qualitative comparative analysis to indicate all possible configurations of formal and informal management controls that improve management accountants’ sense of their identity in an organization. The analyses are based on the results of a cross-sectional survey of 277 management accountants from Germany, Austria, Switzerland and Liechtenstein.

The results show that a strong group culture and high psychological safety at the group level are relevant conditions for a high identity fit. Further, the configurations differ regarding the career stages of management accountants.

This study contributes to work identity research of management accountants and to research on formal and informal control configurations as a control package. It is of particular importance for various professions that are affected by role change, as from the findings on management accountants’ identity fit, implications can also be made for other organizational functions that need to engage in identity work.

This study aims to investigate the effect of total external debt stock on economic growth of the East African Community (EAC) bloc.

The study applies balanced panel data for seven of the eight EAC member states, spanning the period from 2013 to 2022, and uses panel data models, i.e. pooled ordinary least squares, random and fixed effects models.

The findings reveal a significant positive correlation between total external debt stock and economic growth, supporting the economic theory that reasonable levels of borrowing can stimulate economic growth, particularly when funds are channeled into productive activities. However, the relationship between foreign direct investment and economic growth lacks statistical significance, indicating challenges in attracting sufficient investment for substantial growth within the EAC bloc. Trade openness shows a negative and statistically insignificant correlation with economic growth. Additionally, the study finds a positive and significant correlation between the unemployment rate and economic growth, while the inflation rate demonstrates a positive but statistically insignificant relationship with economic growth.

The study recommends improvements in debt management practices, enhancements in the business environment, infrastructure investments, a reassessment of trade policies and initiatives to stimulate job creation and SME development. More importantly, governments should focus on expanding the tax base in ways that stimulate growth, thereby reducing reliance on external debt.

This study is unique as it revisits the effect of external debt stock on economic growth following Somalia’s recent membership in EAC bloc.