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We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

This paper aims to study the impact of consumers’ corporate social responsibility (CSR) associations on corporate financial performance and the moderating role of market competition.

The panel data set is analyzed using a random effects regression model. The analyzed data is based on the unique RepZ Responsibility scores published by the global research agency Kantar Millward Brown and contains information about consumer CSR associations.

This study reveals CSR associations' positive, lagged, direct impact on firms’ market value. Market competition moderates this relationship in the way that a company’s market value benefits more from consumers' CSR associations when facing high rather than low market competition.

Consumers' CSR perceptions increase the market value of a company. This effect is intensified when brands are exposed to intense competition, which allows conclusions about CSR as a differentiation strategy to be drawn: To stand out in a competitive market, brands should prioritize improving their CSR associations among consumers to differentiate themselves and increase their market value.

To the best of the authors’ knowledge, this study is the first to test the effect of consumers’ CSR associations on forward-looking financial performance measures. Moreover, by analyzing the moderating effect of market competition on the relationship between CSR associations and firms' market value, this study provides information about the differentiating power of CSR from a brand perspective using a panel-data analysis.

Business schools are vital in promoting responsible management (RM) – a management grounded in ethics and values beneficial to a wide array of stakeholders and overall society. Nevertheless, due to deeply embedded institutional modernistic dynamics and paradigms, RM is, despite its importance, repeatedly marginalised in business school curricula. If students are to engage with RM thinking, then its occlusion represents a pressing issue. Drawing on the United Kingdom (UK) business school context, this paper aims to examine this issue through a framework of institutional theory and consider the role played by (modernistic) institutional accreditation and research assessment processes in marginalisation of RM.

This study used an exploratory qualitative research method. Data were collected from 17 RM expert participants from 15 UK business schools that were signatories to the United Nations Principles for Responsible Management Education through semi-structured in-depth interviews and analysed using the six phases of Braun and Clarke’s thematic analysis.

The study identifies a potent institutional isomorphic amalgam resulting in conservative impacts for RM. This dynamic is termed multiple institutional isomorphic marginalisation (MIIM) – whereby a given domain is occluded and displaced by hegemonic institutional pressures. In RM’s case, MIIM operates through accreditation-driven modernistic-style curricula. This leads business schools to a predilection towards “mainstream” representations of subject areas and a focus on mechanistic research exercises. Consequently, this privileges certain activities over RM development with a range of potential negative effects, including social impacts.

This study fills an important gap concerning the need for a critical, in-depth exploration of the role that international accreditation frameworks and national institutional academic research assessment processes such as the Research Excellence Framework in the UK play in affecting the possible growth and influence of RM. In addition, it uses heterotopia as a conceptual lens to reveal the institutional “mask” of responsibility predominantly at play in the UK business school context, and offers alternative pathways for RM careers.

Magnetic polarizability tensors (MPTs) provide an economical characterisation of conducting magnetic metallic objects and their spectral signature can aid in the solution of metal detection inverse problems, such as scrap metal sorting, searching for unexploded ordnance in areas of former conflict and security screening at event venues and transport hubs. In this work, the authors aim to discuss methods for efficiently building large dictionaries for classification approaches.

Previous work has established explicit formulae for MPT coefficients, underpinned by a rigorous mathematical theory. To assist with the efficient computation of MPTs at differing parameters and objects of interest, this work applies new observations about the way the MPT coefficients can be computed. Furthermore, the authors discuss discretisation strategies for hp-finite elements on meshes of unstructured tetrahedra combined with prismatic boundary layer elements for resolving thin skin depths and using an adaptive proper orthogonal decomposition (POD) reduced-order modelling methodology to accelerate computations for varying parameters.

The success of the proposed methodologies is demonstrated using a series of examples. A significant reduction in computational effort is observed across all examples. The authors identify and recommend a simple discretisation strategy and improved accuracy is obtained using adaptive POD.

The authors present novel computations, timings and error certificates of MPT characterisations of realistic objects made of magnetic materials. A novel postprocessing implementation is introduced and an adaptive POD algorithm is demonstrated.

This article explores how firms' unresponsiveness to Black customer feedback influences Black (vs. White) customers' perceived firm-based discrimination and brand engagement.

Two experimental studies (Study 1(N1) = 254) and Study 1(N2) = 484) are conducted to test the modeled relationships. The data are analyzed using ANOVA, PROCESS Model 4 and PROCESS Model 7.

The findings suggest that though perceived discrimination remains modest in all conditions, Black (vs. White) respondents report higher perceived discrimination when the firm fails to respond to a Black customer's negative or neutral (but not positive) brand-related feedback on social media. The results also indicate that Black (vs. White) customers exhibit lower engagement through perceived discrimination in the case of the firm's unresponsiveness to a Black customer's negative and neutral (but not positive) brand-related feedback regardless of the manager's race.

Prior research on intercultural service encounters and ethnic differences in consumer engagement on social media are combined to examine the relationship between customer race and perceived discrimination based on the firm's unresponsiveness to customers' social media posts.

Manipulations were created based on a fictitious e-tailer. Thus, it is recommend that future researchers examine the extent to which the findings hold for existing (r)etailers. In addition, future studies using secondary data could provide additional evidence for the findings.

Managerial attention is accentuated among customer feedback responsiveness, engagement and perceived firm discrimination. Managers are encouraged to adopt communication strategies that complement the firm's strategy and social media presence.

This research investigates the effect of audit report lag on the cost of equity capital. We argue that an extended audit report lag reduces the value of information and raises concerns for investors, resulting in an increased cost of equity capital.

We hypothesize that audit report lag increases the firm cost of equity capital. We conduct ordinary least squares (OLS) regression analyses to examine our hypothesis. Finally, we also perform a range of sensitivity tests to examine the hypothesis and robustness of findings.

Using a sample of the listed US firms from 2003 to 2018, we find that firms with higher audit report lag have a higher cost of equity capital. Our findings are economically significant as one standard deviation increase in audit report lag raises 3.82 basis points of cost of equity capital. Furthermore, our results remain robust to endogeneity concerns and alternative proxies for the cost of equity capital measures. Finally, we confirm that audit report lag increases the firm cost of equity capital through increasing information asymmetry and future financial restatement as a mediating channel.

We contribute to the theoretical discussion about the role of audit report lag and investors' perceptions. Overall, our results suggest that audit report lag affects a firm cost of equity capital.

Going beyond the traditional approach of formal and informal institutions as antecedents of entrepreneurship (directly) and development (indirectly), this paper seeks to explore knowledge institutions as a necessary input for entrepreneurship and the development of societies.

Institutional economics lenses are utilized to observe other factors (e.g. the number of R&D staff and researchers from the public sector) that involve laws and socialization processes, which at the same time create knowledge useful for entrepreneurs and society. These ideas are tested through a sample of 281 observations from 17 autonomous communities and two autonomous cities in Spain. The information coming from the Global Entrepreneurship Monitor (GEM), Ministry of Economics, Industry, and Competitiveness, and INE (Instituto Nacional de Estadística), was analyzed through 3SLS, which is useful for a simultaneous equation strategy.

Knowledge institutions such as the number of R&D staff and researchers from the public sector are found positively associated with entrepreneurship, which is a factor directly and positively linked to economic development across Spanish regions.

The findings help the operationalization of other institutions considered in institutional economics theory and its application to entrepreneurship research. Moreover, the results bring new insights into the knowledge spillover theory of entrepreneurship in the public sector, in which the institutional analysis is implicit.

Safety research has focused on drivers, pedestrians and vehicles, with scarce attention given to highway traffic officers (HTOs). This paper develops a robust prediction model which enables highway safety authorities to predict exclusive incidents occurring on the highway such as incursions and environmental hazards, respond effectively to diverse safety risk incident scenarios and aid in timely safety precautions to minimise HTO incidents.

Using data from a highway incident database, a supervised machine learning method that employs three algorithms [namely Support Vector Machine (SVM), Random Forests (RF) and Naïve Bayes (NB)] was applied, and their performances were comparatively analysed. Three data balancing algorithms were also applied to handle the class imbalance challenge. A five-phase sequential method, which includes (1) data collection, (2) data pre-processing, (3) model selection, (4) data balancing and (5) model evaluation, was implemented.

The findings indicate that SVM with a polynomial kernel combined with the Synthetic Minority Over-sampling Technique (SMOTE) algorithm is the best model to predict the various incidents, and the Random Under-sampling (RU) algorithm was the most inefficient in improving model accuracy. Weather/visibility, age range and location were the most significant factors in predicting highway incidents.

This is the first study to develop a prediction model for HTOs and utilise an incident database solely dedicated to HTOs to forecast various incident outcomes in highway operations. The prediction model will provide evidence-based information to safety officers to train HTOs on impending risks predicted by the model thereby equipping workers with resilient shocks such as awareness, anticipation and flexibility.

This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms.

The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model.

The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners.

This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies.

In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

This study aims to comprehensively investigate the electron beam powder bed fusion (EB-PBF) process for copper, offering validated estimations of melt pool temperature and morphology through numerical and analytical approaches. This work also assesses how process parameters influence the temperature fluctuations and the morphological changes of the melt pool.

Two distinct methods, an analytical model and a numerical simulation, were used to assess temperature profiles, melt pool morphology and associated heat transfer mechanisms, including conduction and keyhole mode. The analytical model considers conduction as the dominant heat transfer mechanism; the numerical model also includes convection and radiation, incorporating specific parameters such as beam power, scan speed, thermophysical material properties and powder interactions.

Both the analytical model and numerical simulations are highly correlated. Results indicated that the analytical model, emphasising material conduction, exhibited exceptional precision, although at substantially reduced cost. Statistical analysis of numerical outcomes underscored the substantial impact of beam power and scan speed on melt pool morphology and temperature in EB-PBF of copper.

This numerical simulation of copper in EB-PBF is the first high-fidelity model to consider the interaction between powder and substrate comprehensively. It accurately captures material properties, powder size distribution, thermal dynamics (including heat transfer between powder and substrate), phase changes and fluid dynamics. The model also integrates advanced computational methods such as computational fluid dynamics and discrete element method. The proposed model and simulation offer a valuable predictive tool for melt pool temperature, heat transfer processes and morphology. These insights are critical for ensuring the bonding quality of subsequent layers and, consequently, influencing the overall quality of the printed parts.