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Online shopping around the world is growing exponentially, especially during the COVID-19 pandemic. This study aims to examine how an online customer's purchasing experience influences his/her buying intention and willingness to believe in fraud news, as well as the ripple impact of satisfaction and trust, with gender as a moderator in an emerging economy during COVID-19.

Based on the underpinning of the stimulus-organism-behavior-consequence (SOBC) theory, the research model was developed, and collected data from 259 respondents using convenience samples technique. Next, the data were analyzed using partial least squares-based structural equation modeling (PLS-SEM), SPSS (Statistical Package for the Social Sciences) and Hayes Process Macro.

The study results confirmed that the online shopping experience (OSE) has positive impact on customers' satisfaction (CS), purchase intention (PI) and customer trust (CT); CS has positive effects on trust toward online shopping and their future product PI; future product PI significantly affects customers' propensity to believe and act on fraud news (PBAFN). The finding also states that gender moderates the relationships of CS to PI, OSE to PI and PI to PBAFN, but doesn't moderate the CT to PI relationship.

The study findings will assist policymakers and online vendors to win customers' hearts and minds' through confirming satisfaction, trust and a negative attitude toward fake news, which will lead to customer loyalty and the sustainable development of the industry. Finally, the limitations and future research directions are discussed.

Leadership is an essential contributor to employee creative self-efficacy, and past research suggests a positive relationship between servant leadership and creative self-efficacy. However, the relationship is complex and contingent upon moderating variables, and this research examines the moderating effect of role clarity by drawing on social exchange theory and social cognitive theory.

Data collected from a survey among 116 emergency room employees is used to test the research model using moderated ordinary least squares regression.

The results confirm a positive relationship between servant leadership and creative self-efficacy and suggest a U-shaped relationship between role clarity and creative self-efficacy. Furthermore, role clarity positively moderates the relationship between servant leadership and creative self-efficacy.

The sample used for this research mainly consisted of highly educated employees within a specific setting. Future research is needed to study if the relationships found in this research can be generalized to other organizational settings.

This research suggests that leaders can support employees' creative self-efficacy through servant leadership, particularly when coupled with high role clarity.

Rapidly changing work environments are characterized by decreased role clarity, so attention is needed to its moderating role on the relationship between servant leadership and creative self-efficacy.

This study aims to explore the evolving field of physics-informed neural networks (PINNs) through an analysis of 996 records retrieved from the Web of Science (WoS) database from 2019 to 2022.

WoS database was analyzed for PINNs using an inhouse python code. The author’s collaborations, most contributing institutes, countries and journals were identified. The trends and application categories were also analyzed.

The papers were classified into seven key domains: Fluid Dynamics and computational fluid dynamics (CFD); Mechanics and Material Science; Electromagnetism and Wave Propagation; Biomedical Engineering and Biophysics; Quantum Mechanics and Physics; Renewable Energy and Power Systems; and Astrophysics and Cosmology. Fluid Dynamics and CFD emerged as the primary focus, accounting for 69.3% of total publications and witnessing exponential growth from 22 papers in 2019 to 366 in 2022. Mechanics and Material Science followed, with an impressive growth trajectory from 3 to 65 papers within the same period. The study also underscored the rising interest in PINNs across diverse fields such as Biomedical Engineering and Biophysics, and Renewable Energy and Power Systems. Furthermore, the focus of the most active countries within each application category was examined, revealing, for instance, the USA’s significant contribution to Fluid Dynamics and CFD with 319 papers and to Mechanics and Material Science with 66 papers.

This analysis illuminates the rapidly expanding role of PINNs in tackling complex scientific problems and highlights its potential for future research across diverse domains.

This study investigated the impact of octomodal mental imagery (OMI) on brand experience and authenticity in advocating sustainable development and responding to the lack of brand experience and customers’ growing demand for authentic brands.

Based on quantitative research and convenience sampling, data for the study were collected from 480 marketing students in Tehran, Iran. The research model is tested using partial least squares structural equation modeling (PLS-SEM).

The results showed that all the sensory attributes of OMI positively influenced consumers’ brand experience. Among the structural attributes of OMI, only spatial mental imagery positively influenced consumers’ brand experience, while autonomy and kinesthetic mental imagery did not have a significant effect. This study also found that consumers’ brand experience positively influenced brand authenticity, while social presence positively moderated the relationship.

This study provides branding managers and scholars with a new reference point and scientific data support for companies to implement brand strategies and marketing models, which helps brands maintain sustainable development in a competitive business environment.

Due to the financial disturbances created by the COVID-19 pandemic and the burden on the government exchequer, it is expected to see a rise in the knowledge base of the research corpus so far as the government's fiscal sustainability is concerned. Therefore, the present research examines a systematic quantitative analysis of public debt sustainability research by applying a bibliometric approach. Research also analyzes journals, institutions, countries and authors contributing to public debt sustainability.

This paper scrutinizes the published scientific research on public debt sustainability based on the dataset of 535 articles from 1991 to 2021 obtained from the Scopus database. Biblioshiny (R-based application) and VoSviewer software were used to perform bibliometric analysis through Performance analysis and science mapping techniques. The authors combined co-citation analysis (CCA), bibliometric analysis, keyword co-occurrence analysis (KCA) and a conceptual thematic map of the most cited articles to find the intellectual structure.

The research identified three dominating clusters, e.g. fiscal sustainability and policy rules, empirical sustainability testing and debt and growth dynamics. Another finding was that most articles were analytical and empirical and few descriptive articles were found. Owing to the empirical nature of the domain, the issues concerning public debt sustainability have continued to change over the past decades for different economies, reflecting the complexity and diversity of economic structures of different economies at different times.

The insight of this article provides academicians and researchers with a more refined comprehension of the conceptual and intellectual structure of the research corpus. The present research complements the existing literature review studies by pushing the research towards emerging or less developed issues such as financial and debt crises.

This paper explores the association between directors’ and officers’ liability insurance (D&O insurance) and management tone manipulation.

This study uses data from A-share listed non-financial companies from 2009 to 2021 as its sample for empirical tests. In addition, the study relies on text analysis and the construction of models to investigate the relationship between D&O insurance and management tone manipulation.

The authors find that the purchase of D&O insurance will lead to management tone manipulation in the “management discussion and analysis” part of companies’ annual reports, and operating risk and agent cost are the two paths for the effect. Further analysis shows that having a male CEO and employing high-quality auditors can weaken the positive impact of D&O insurance on tone manipulation.

This paper provides a new approach for studying the literature related to D&O insurance and management behavior, and the findings enrich our understanding of the influencing factors and the mechanism of management tone manipulation, thus revealing policy implications for further standardization of the terms and system of D&O insurance in China.

This qualitative work aims to explore the university students’ attitude toward advantages, drawbacks and prospects of ChatGPT.

This paper applies well accepted Colaizzi’s phenomenological descriptive method of enquiry and content analysis method to reveal the ChatGPT user experience of students in the higher education level.

The study’s findings indicate that ChatGPT enhances the quality of learning and facilitates faster learning among university students. However, despite numerous positive outcomes, it is noted that ChatGPT may diminish students' creativity by swiftly addressing their critical queries. Over time, students may experience a decline in patience and critical thinking skills as they excessively rely on ChatGPT, potentially leading to ethical misconduct.

This paper primarily explores the advantages and drawbacks of using ChatGPT in the university context of Bangladesh. The present study creates a platform for future research in this domain with comprehensive study design. The study results alert the policy makers to improve upcoming version of ChatGPT with convenient user experience and academicians as this paper unleash several positive as well as negative consequences of using this AI-enabled chatbot.

This study aims to integrate the theory of planned behavior (TPB), norm activation model (NAM) and stimulus–organism–behavior–consequences theory (SOBC) with the moderators to investigate the main determinants of energy-saving behaviors as well as how group-level factors and media publicity significantly facilitate the energy-saving intention-behavior linkage among dormitory students in higher education institutions (HEIs).

A valid sample of 325 dormitory students resided in universities of Vietnam and a three-step analysis approach via SPSS 28.0 and AMOS 25.0 were used to test the hypothesized model.

This study yields that external stimuluses (subjective norms and perceived behavioral control) positively and strongly stimulate energy–energy attitude, while internal stimuluses (ascription of responsibility, awareness of consequences) arouse personal norms. Both internal and external stimuluses were found to have serially mediation effects on energy-saving behaviors via attitude toward energy saving, personal norms and energy-saving intention. Remarkably, group interaction and media publicity exert prominent positive moderation effects on the energy-saving intention–behavior relationship.

The findings of this research can be valuable for HEIs and policymakers to inspire university students’ energy conservation behavior for sustainable development goals.

This study contributed to the proenvironmental literature by adopting the SOBC paradigm that strengthens the integration of TPB and NAM models to explore the main determinants of dormitory students’ energy-saving behaviors, explain the underlying mediation mechanisms of organisms and behavioral responses and illustrate the moderation role of group-level factors and media publicity.

This study aims to address these challenges by enhancing the resistance of Ag-based pastes to corrosion and sulfurization, thereby improving their performance and weatherability in high-power and high-frequency electronic applications.

This study investigates the influence of Sn doping in W-doped Ag paste to enhance resistance against electrochemical corrosion and sulfurization. A systematic examination was conducted using transient liquid phase sintering and solid–liquid inter-diffusion techniques to understand the microstructural and electrochemical properties.

This study found that Sn addition in W-doped Ag paste significantly improves its resistance to electrochemical corrosion and sulfurization. The sintering process at 600°C led to the formation of an Ag₂WO₄ phase at the grain boundaries, which, along with the presence of Sn, effectively inhibited the growth of Ag₂WO₄ grains. The 0.5% Sn-doped samples exhibited optimal anti-corrosion properties, demonstrating a longer grain boundary length and a passivation effect that significantly reduced the corrosion rate. No Ag₂S phase was detected in the weatherability tests, confirming the enhanced durability of the doped samples.

The findings of this study highlight the potential of Sn-doped Ag-W composites as a promising material for electronic components, particularly in environments prone to sulfurization and corrosion. By improving the anti-corrosion properties and reducing the grain size, this study offers a new approach to extending the lifespan and reliability of electronic devices, making a significant contribution to the development of advanced materials for high-power and high-frequency applications.

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.