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The escalating instances of financial distress (FD) in corporate houses across the globe, call for immediate attention from policymakers, practitioners and academics equally. This study aims to examine how board gender diversity (GD) and information disclosures (ID) interact with each other to drive FD.

The authors apply dynamic panel data analysis on a sample of 255 Indian-listed firms from 2016 to 2023 to arrive at the econometric results.

The main findings indicate that while ID exacerbates distress, GD reduces it. In addition, GD also interacts with ID to curtail the adverse effects of disclosures on FD. Therefore, GD acts like a stone that kills two birds simultaneously, first by reducing the distress directly and second by limiting the negative effects of disclosures on distress.

This study extends the understanding of the implications of GD and complements existing research by investigating its direct and indirect impact on FD. It builds on the analysis to propose that GD can foster resilience against adverse FD situations. The findings should apply to other emerging nations after careful consideration of country-specific factors.

This bibliometric analysis aims to comprehensively explore the intersection of artificial intelligence (AI) and high-to-room-temperature superconductors. Focusing on scientific literature, the study investigates trends, collaboration patterns and impactful publications in this interdisciplinary field.

The research employs an advanced search query in the Scopus database, targeting articles on the development of superconductors using artificial intelligence. Data collection involves executing the query, saving the results as a CSV file and analyzing it using R-Studio and VOSviewer. Statistical tools, T-tests, regression analysis and Python coding are utilized to enhance the depth of analysis.

The analysis spans various dimensions, including the overview of bibliometric characteristics, annual scientific production, average citations per year, sources of publications and source production over time. Noteworthy findings include a sustained growth in annual scientific production, a peak in average citations in specific years and the identification of influential journals shaping the field.

While the analysis provides valuable insights, limitations include the potential influence of research biases and the exclusion of non-English articles. Further exploration is encouraged to address these limitations and gain a more nuanced understanding of the field.

Practically, this study aids researchers, practitioners and stakeholders in staying informed, identifying collaboration opportunities and contributing meaningfully to the ongoing growth and impact of high-to-room-temperature superconductors using artificial intelligence.

Socially, the study underscores the collaborative and global nature of research in this field, emphasizing the shared endeavor worldwide to advance the understanding and application of superconductors through artificial intelligence.

This research contributes to the originality of the scientific landscape by offering a comprehensive analysis of the development of high-to-room-temperature superconductors with artificial intelligence. The utilization of advanced bibliometric techniques and the identification of key trends and sources enhance the understanding of this emerging and interdisciplinary research domain.

This study aims to explore the dynamic interplay of key resources (i.e. research and development (R&D), advertising and exports) in affecting the performance of USA manufacturing firms. Specifically, the authors examine the dynamic impact of joint resources and predict differential effect scales contingent on firm capabilities.

This study presents a combined multiple regression analysis (MRA)-multilayer perceptron (MLP) neural network modeling and investigates the complex interlinkage of capabilities, resources and performance. As an innovative approach, the MRA-MLP model investigates the effect of capabilities under the combinatory deployment of joint resources.

This study finds that the impact of joint resources and synergistic rents is not uniform but rather distinctive according to the combinatory conditions and that the pattern is further shaped by firm capabilities. Accordingly, besides signifying the contingent aspect of capabilities across a range of resource combinations, the result also shows that managerial sophistication in adaptive resource control is more than a managerial ethos.

The proposed analytic process provides scientific decision support tools with control mechanisms with respect to deploying multiple resources and setting actionable goals, thereby presenting pragmatic benchmarking options to industry managers.

Using the theoretical underpinnings of the resource-based view (RBV) and resource orchestration, this study advances knowledge about the complex interaction of key resources by presenting a salient analytic process. The empirical design, which portrays holistic interaction patterns, adds to the uniqueness of this study of the complex interlinkages between capabilities, resources and shareholder value.

This paper aims to investigate the influence of four motivational elements (i.e. utilitarian, hedonic, social and escapism motivations) on the propensity of customers to utilize a metaverse hotel, as well as whether age, gender and mobility disability play substantial moderating roles in these relationships.

Data was gathered from 843 US residents who had experienced a hotel stay within the past two years. We tested the hypotheses using structural equation modeling and multigroup analysis.

The findings indicated that, in both age and gender groups, hedonic, social and escapism motivations had significant effects on intentions to use a metaverse hotel, whereas utilitarian motivation did not. The influence of escapism motivation on customers’ usage intentions was significantly more pronounced for males than females, suggesting the moderating role of gender in this relationship. Hedonic and social motivations exerted significant effects on usage intentions in both mobility disability and non-disability groups. The relationship between escapism motivation and intentions to use was significant for the non-disability group only, suggesting the moderating role of disability in this association.

This research provides recommendations for hotel managers and technology providers aiming to enhance the adoption of metaverse hotels by customers and to augment the worth of this technology.

This research fills the voids in the current literature by formulating and empirically evaluating a research framework to gain deeper insights into the motivations that drive the acceptance of a metaverse hotel.

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.

This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.

(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.

The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

Professional social networking sites (SNS) are widely employed by business individuals to build formal relationships, career opportunities, and professional development. While the characteristics of professional SNS are generally different from other SNS, there is limited understanding of the determinants of users’ continued usage on this platform. The study addresses this research gap by developing a conceptual framework that relates perceived values perspective (utilitarian value, hedonic value) and sociability dimensions (social presence, social benefit, social support, and self-presentation) to continuance intention to use professional SNS. Data were gathered from a questionnaire distributed on LinkedIn and analyzed using PLS-SEM. The findings contribute to the emerging literature on the IS continuance domain, particularly in the area of professional SNS. Furthermore, the study can help professional SNS providers properly manage to retain existing users for sustainable business performance.

This study aims to investigate the relationship between melt pool dimensions and acoustic emission (AE) signal magnitudes obtained during laser powder bed fusion (L-PBF) process of austenitic stainless steel. Specifically, by varying laser power and scan speed, the widths, depths and aspect ratios of melt pools were compared with AE signal magnitudes across a frequency range of 100–200 kHz.

Experiments were conducted under different laser powers at a fixed scan speed and scan speeds at a fixed laser power. Melt pool dimensions were measured from cross-sectional optical images, and AE signals were obtained using a piezoelectric AE sensor installed beneath the build plate. Short-Time Fourier Transform (STFT) was applied to AE signals, and the magnitudes of frequency components were obtained.

A strong correlation between melt pool dimensions and STFT magnitude was obtained. Pearson correlation coefficients between melt pool dimensions and STFT magnitudes were above 0.9 and the p-values were below 0.05. Increasing the laser energy enlarged the volume of melt pool and intensified the oscillation of melt pool. When scan speed exceeded 1,100 mm/s, STFT magnitude showed a slight increase owing to the increase in the vapor pressure.

Previous studies used AE signals to detect defects, but this study found a correlation between STFT magnitude and melt pool dimensions in L-PBF process. It was also found that STFT magnitude was more affected by vapor pressure at higher scan speeds. Monitoring STFT magnitude can help to understand melt pool dynamics, maintain process consistency and identify irregularities in real time.

The purpose of this study is to enhance the safety and comfort of tourists in scenic areas undergoing renovation and transformation by developing a comprehensive safety assessment model that takes into account both internal and external factors affecting tourist and construction safety.

The research employs a multi-level tourist-construction interaction safety assessment index system, which is constructed through a deep analysis of factors such as the construction environment, tourist behavior and safety signs. The study utilizes game theory in conjunction with three main objective and subjective weight distribution methods to determine the weights of the index system, ensuring the objectivity and effectiveness of the assessment results. The cloud model and cloud generator are applied for the language transformation of the indicators, leading to a comprehensive assessment of construction safety.

The survey results indicate that the safety risks of the case project are relatively high, with limited impact of time segments on safety risks, and the risk level during weekends is slightly higher than on weekdays, but the difference is not significant. Among the reviewed influencing factors, compliance with safety signs and the proportion of people crossing construction areas are the factors with the highest risk level, representing a large number of tourists ignoring safety guidance and forcibly crossing construction areas, facing construction dangers, posing a great challenge to safety management.

This study offers a novel methodological approach to safety risk assessment in similar environments, contributing to the field by improving the systematicness and scientific nature of safety management. It provides a scientific assessment tool for the safety management of tourists in scenic area renovation projects, aiming to achieve the dual objectives of tourist safety and construction efficiency.

Public scepticism is becoming more prevalent surrounding organizations' corporate social responsibility (CSR) implementation and communication. Management research has tended to over-fixate on the strategic paybacks of CSR, and less attention has been devoted to examining why CSR is under siege due to the pervasive issue of scepticism coming from a plethora of stakeholders. This study provides a scoping review to appraise the status quo of CSR vis-à-vis scepticism scholarship and examine how the two concepts have been contextualized in relation to one another in the extant literature in question. The findings illustrate that the process nature of CSR scepticism is complex, given that CSR can yield both a buffering and boomerang effect on CSR practitioners. The qualitative content analysis performed on 58 studies, published from 2007 to 2022, elaborates on CSR scepticism as a multidimensional construct with three distinctive typologies of research approaches identified, explicating how the CSR and scepticism topicalities have been cross-examined in relation to one another: “Typology 1: Dispositional Scepticism and the Buffering Effect of CSR on Scepticism”; “Typology 2: Situational Scepticism and the Boomerang Effect of CSR on Scepticism”; “Typology 3: Centrality of CSR Scepticism and CSR Scepticism Mitigation.” This study offers a conceptual insight into the prevalent issue of scepticism in the CSR context, while also informing marketing, communication, and public relations professionals about the necessity of mitigating CSR scepticism, which poses a barrier to effective CSR implementation and communication processes.