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The study examines the relationship between news intensity, media sentiment and market microstructure invariance-implied measures of trading activity and liquidity of Chinese property developer stocks during the 2020–2022 Chinese property sector crisis.

The authors adopt the extension of the news article invariance hypothesis, which is a generalization of the market microstructure invariance conjecture, from January 2020 to January 2022 to test specific quantitative relationships between the arrival rate of public information, trading activity and a nonlinear function of a proxy for the probability of informed trading. Empirical tests are based on a dataset of 22,412 firm-day observations and two count-data models to correct for overdispersion and the excess number of zeros. Seventy-five stocks of Chinese companies from the property development industry (including the China Evergrande Group) were included in the sample.

The authors reject the news article invariance hypothesis but document a positive and significant relationship between the flow of public information and risk liquidity. Additionally, the authors find that the proxy for informed trading activity is positively related to the arrival rates of public information from October 2021 to January 2022.

The findings support the hypothesis that negative (positive) media sentiment induces significant deterioration (insignificant improvement) in stock liquidity. The authors find that an increase in the number of news articles about a company corresponds to a higher liquidity of Chinese property developers' stocks after controlling for media sentiment.

Drawing on the pleasure-arousal-dominance (PAD) emotion model, the emotional states of consumers embedded in online reviews can be described through three dimensions, that is, pleasure, arousal and dominance, rather than only the one-dimensional positive and negative polarity, as in previous studies. Therefore, this study aims to explore the effect of online review emotion on perceived review helpfulness based on these three basic emotional dimensions.

A lexicon-based method is developed to analyze PAD emotions of online reviews from JD.com. The zero-inflated negative binomial regression is utilized to empirically validate the study hypothesis. The authors examine the influence of pleasure, arousal, dominance, emotion diversity and emotion deviation on review helpfulness, as well as the moderating effect of product type on the relationship between all independent variables and online review helpfulness.

The study results show that the pleasure emotion impairs the helpfulness of online reviews, while the arousal and dominance emotions have a positive impact. Moreover, the authors find that compared with search products, the effects of pleasure, arousal and dominance on perceived helpfulness are strengthened for experience products. However, the emotional diversity and emotional deviation have opposite effects on the helpfulness of search products and experience products. Additionally, the results show that dominance emotion plays a more important role in the interaction effect.

The empirical findings confirm the applicability of PAD in the online review context and extend the existing knowledge of the influence of review emotion on helpfulness. A feasible scheme for extracting PAD variables from Chinese text is developed. The study findings also have significant implications for reviewers, merchants and platform managers of e-commerce websites.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

Hair loss is often overlooked but psychologically challenging. However, the emergence of online health communities provides opportunities for hair loss patients to seek social support through self-disclosure. Nevertheless, not all disclosures receive the desired support. This research explores what patients disclose within the community and how their health narrative (content, form and linguistic style) regarding self-disclosure influences the social support they receive.

This study investigated a 13-year-old online support group for Chinese hair loss patients with nearly 240,000 members. Using structural topic modeling, Linguistic Inquiry and Word Count, and a negative binomial model, the research analyzed the content of self-disclosure and the interrelationships between social support and three narrative dimensions of self-disclosure.

Self-disclosures are classified into 14 topics, grouped under analytical, informative and emotional categories. Emotion-related self-disclosures, whether in content or effective word use, receive deeper social support. Longer and image-rich posts attract more support in quantity, but not necessarily in quality, while cognitive words have a limited impact.

This study addresses the previously overlooked population of hair loss patients within online health communities. It employs a more comprehensive health narrative framework to explore the relationship between self-disclosure and social support, utilizing unsupervised structural topic modeling methods to mine text. The research offers practical implications for how patients seek support and for healthcare professionals in developing doctor-patient communication strategies.

Sharing and disseminating debunking information are critical to correcting rumours and controlling disease when dealing with public health crises. This study investigates the factors that influence social media users' debunking information sharing behaviour from the perspective of persuasion. The authors examined the effects of argument adequacy, emotional polarity, and debunker's identity on debunking information sharing behaviour and investigated the moderating effects of rumour content and target.

The model was tested using 150 COVID-19-related rumours and 2,349 original debunking posts on Sina Weibo.

First, debunking information that contains adequate arguments is more likely to be reposted only when the uncertainty of the rumour content is high. Second, using neutral sentiment as a reference, debunking information containing negative sentiment is shared more often regardless of whether the government is the rumour target, and information containing positive sentiment is more likely to be shared only when the rumour target is the government. Finally, debunking information published by government-type accounts is reposted more often and is enhanced when the rumour target is the government.

The study provides a systematic framework for analysing the behaviour of sharing debunking information among social media users. Specifically, it expands the understanding of the factors that influence debunking information sharing behaviour by examining the effects of persuasive cues on debunking information sharing behaviour and the heterogeneity of these effects across various rumour contexts.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.