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To contribute to achieving the Sustainable Development Goals, this study aims to explore how to encourage innovative green behaviors among college students and the mechanisms behind the formation of green innovation behavior. Specifically, this study examines the influences of schools, mentors and college students themselves.

A multilevel, multisource study involving 261 students from 51 groups generally supported this study’s predictions.

Proenvironmental and responsible mentors significantly predicted innovative green behavior among college students. In addition, creative motivation mediated the logical chain among green intellectual capital, emotional intelligence and green innovation behavior.

The study findings offer new insights into the conditions required for college students to engage in green innovation. In addition, they provide practical implications for cultivating green innovation among college students.

The authors proposed and tested a multilevel theory based on the ability–motivation–opportunity framework. In this model, proenvironmental and responsible mentors, green intellectual capital and emotional intelligence triggered innovative green behavior among college students through creative motivation.

This paper models the deterioration process of a multi-component system. Each deterioration process is modelled by the Wiener process. The purposes of this paper are to address these issues and consider the cost process based on the multi-component system.

Condition-based Maintenance is a method for reducing the probability of system failures as well as the operating cost. Nowadays, a system is composed of multiple components. If the deteriorating process of each component can be monitored and then modelled by a stochastic process, the deteriorating process of the system is a stochastic process. The cost of repairing failures of the components in the system forms a stochastic process as well and is known as a cost process.

When a linear combination of the processes, which can be the deterioration processes and the cost processes, exceeds a pre-specified threshold, a replacement policy will be carried out to preventively maintain the system.

Under this setting, this paper investigates maintenance policies based on the deterioration process and the cost process. Numerical examples are given to illustrate the optimisation process.

The performance of corporations in sustainable development is not only a concern of investors, but has also captured ever-increasing attention from consumers. However, the evidence on how these good practices would ultimately benefit brands economically remains insufficient. This study tests the causal effect between corporate Environmental, Social, and Governance (ESG) performance, media coverage, and brand value to reveal the underlying mechanisms of how consumers would react to high ESG performance.

This study uses panel data regression analysis with a sample of Chinese A-share non-financial listed companies from 2010 to 2021. ESG performance, brand value, and media coverage are assessed with Huazheng ESG Rating, the rankings from the China’s 500 Most Valuable Brands' list published by the World Brand Lab, and media index compiled by the Chinese Research Data Services Platform (CNRDS) respectively.

This research confirmed that ESG performance positively impacted brand value in terms of profitability, and that media coverage played a role as a megaphone in this relationship. Large-scale corporates, compared to small ones, benefited more from good ESG ratings due to increased media coverage.

The findings provide evidence of the megaphone effect of media coverage on the relationship between firms’ ESG engagements and brand value in the product market, which has extended the knowledge of media’s monitoring role in the financial market. And this megaphone effect is strengthened by firm size in which larger firms have spotlight effect in draw public attention due to higher expectations in terms of social responsibility.

When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity. Therefore, the purpose of this paper is to propose an accurate and effective model to predict users’ ratings of products for the accurate recommendation of products to users.

First, we introduce an attention mechanism that dynamically assigns weights to user preferences, highlighting key interaction information and enhancing the model’s understanding of user behavior. Second, a fold embedding strategy is employed to segment user interaction data, increasing the information density of each subset while reducing the complexity of the attention mechanism. Finally, a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions, thereby improving the model’s generalization ability.

The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets. On average, the evaluation metrics root mean square error (RMSE) and mean absolute error (MAE) are reduced by 9.11 and 13.3%, respectively. Additionally, the Friedman test results confirm that these improvements are statistically significant. Consequently, the proposed model more accurately captures the intrinsic correlation between users and products, leading to a substantial reduction in prediction error.

We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively. Additionally, we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism. Finally, we implement a masking strategy to encourage the model to focus on key features and patterns, thereby mitigating overfitting.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.

The rapid development of information technology, epitomized by AIGC and the metaverse, presents unprecedented challenges to techno-ethics, exposing society to significant risks and uncertainties. A systematic investigation and discussion of the construction of techno-ethical order become crucial under the paradigm of risk society theory. The selection of conditions and pathways for constructing a techno-ethical order under the risk society theory paradigm becomes an unavoidable and vital issue.

Drawing on risk society theory, this study employs scientometrics and qualitative comparative analysis (QCA) to empirically analyze the key factors and conditional pathways in the construction of techno-ethical order. Initially, a quantitative analysis is conducted on 1,490 thematic literature retrieved from CNKI and WoS to identify the hot topics and core concepts in techno-ethical research. Subsequently, QCA configuration analysis is applied to calculate eight evaluation indicators and their weights from the perspectives of government, society and individuals. Finally, the study explores the mechanisms of the impact of these indicators’ weights on the construction of techno-ethical order.

The analysis of factor weights and pathways indicates that the selection of pathways for constructing techno-ethical order is influenced both by the inherent development patterns of technology and societal systems and cultural values. Literature metrics analysis reveals an overall trend of sustained growth in techno-ethical research, indicating an unprecedented prosperity in this field. Alongside technological advancements, keywords related to “artificial intelligence” play a crucial role in current techno-ethical research. Configuration analysis demonstrates that conditional variables from the dimensions of government, society and individuals form a configuration pathway, influencing and synergistically impacting the overall level of techno-ethical order construction. Attention should be given to the mutual constraints and synergistic effects of factors related to technological development, societal systems and cultural values.

This study, grounded in the risk society theory paradigm, quantitatively explores the key factors and pathways of techno-ethical order construction in academic texts, expanding new perspectives, providing novel insights, establishing innovative methodologies and extending new boundaries in the field. Further enrichment of the dataset and in-depth discussions are warranted for continued advancement.

This study explores the spatial and temporal relationship between tourism activities and transportation-related carbon dioxide (CO₂) emissions in the United States (US) from 2003 to 2022 using advanced geospatial modeling techniques.

The research integrated geographic information systems (GIS) to map tourist attractions against high-resolution annual emissions data. The analysis covered 3,108 US counties, focusing on county-level attraction densities and annual on-road CO₂ emission patterns. Advanced spatial analysis techniques, including bivariate mapping and local bivariate relationship testing, were employed to assess potential correlations.

The findings reveal limited evidence of significant associations between tourism activities and transportation-based CO₂ emissions around major urban centers, with decreases observed in Eastern states and the Midwest, particularly in non-coastal areas, from 2003 to 2022. Most counties (86.03%) show no statistically significant relationship between changes in tourism density and on-road CO₂ emissions. However, 1.90% of counties show a positive linear relationship, 2.64% a negative linear relationship, 0.29% a concave relationship, 1.61% a convex relationship and 7.63% a complex, undefined relationship. Despite this, the 110% national growth in tourism output and resource consumption from 2003–2022 raises potential sustainability concerns.

To tackle sustainability issues in tourism, policymakers and stakeholders can integrate emissions accounting, climate modeling and sustainability governance. Effective interventions are vital for balancing tourism demands with climate resilience efforts promoting social equity and environmental justice.

This study’s innovative application of geospatial modeling and comprehensive spatial analysis provides new insights into the complex relationship between tourism activities and CO₂ emissions. The research highlights the challenges in isolating tourism’s specific impacts on emissions and underscores the need for more granular geographic assessments or comprehensive emission inventories to fully understand tourism’s environmental footprint.

Homophily, a prominent phenomenon in social networking, profoundly shapes user behaviors on social media but has not been well studied in the livestream commerce context. This study aims to investigate its moderation role in leveraging the effects of key livestream commerce factors – perceived expertise of live streamers and perceived interaction during live streaming – on audience trust, a critical determinant of purchase intentions.

A survey was conducted among livestream shoppers on Taobao. A sample of 313 responses was analyzed. SPSS (version 29) was used for general statistical analysis. The partial least squares structural equation modeling approach with SmartPLS 4.1 software was used to assess the research model and hypotheses.

The results reveal noteworthy differential effects of homophily: it negatively moderates the expertise–trust association but positively moderates the interaction–trust relationship. When the audience perceives strong homophily with live streamers, their trust in these live streamers becomes increasingly contingent on the level of interaction, whereas the effect of perceived expertise diminishes.

The insights on the differential effects of homophily are novel to the literature. These findings extend theoretical understanding of the homophily effect and provide valuable guidance for live streamers, marketers and platforms seeking to reinforce audience trust and drive purchase intentions in livestream commerce.