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The Belt and Road Initiative (BRI) is the most comprehensive and substantial international cooperation platform, creating a new market influenced by economic and political factors. In this paper, the authors aim to examine whether and how the BRI impacts the Chinese enterprises' corporate environmental responsibility (CER).

Based on China's listed firms' database from 2011 to 2018, the authors use the PSM-DID method, an econometrics method combined with propensity score matching (PSM) and difference-in-differences (DID), to conduct causal inference between the BRI and Chinese enterprises' CER and conduct a series of robustness analyses. Moreover, the authors explore the mechanisms underlying the main effect from both market and non-market perspectives.

The results suggest that the BRI significantly increases Chinese enterprises' CER. Further analyses show that market competition and government support are two possible mechanisms through which the BRI has an effect on the enterprises' CER.

The research study supplements existing work on the environmental effects of the BRI at a microlevel and adds to the literature on the drivers of CER. The findings offer valuable insights into governments and scholars by demonstrating that CER is a crucial tool for Chinese enterprises to gain a competitive advantage in the increasingly competitive markets along the BRI.

Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain.

The prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment.

The prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information.

The prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises.

The prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

This study aims to clarify the psychological mechanism of international contractors' fraud by linking performance pressure to fraudulent intention through the displacement of responsibility and addressing the moderating role of moral intensity.

Based on moral disengagement theory, performance pressure was hypothesized to be positively associated with fraudulent intention by mediating the displacement of responsibility. Drawing on the issue-contingent theory, moral intensity was hypothesized to inhibit the relationship between performance pressure and displacement of responsibility in three aspects: magnitude of consequences (MC), probability of effect (PE) and social consensus (SC). The scenario-based questionnaire was conducted to collect information from contractors spread across 50 countries. The partial least squares structural equation modeling was employed to assess the proposed model.

The results demonstrated that performance pressure was positively associated with the fraudulent intention, and displacement of responsibility exerted a positive partial mediating impact between performance pressure and fraudulent intention. Regarding moral intensity in the moderating analysis, the negative moderating role of MC and PE was significant, while that of SC was insignificant.

This study provides international construction practitioners with a deep understanding of the formation mechanism of fraud at the psychological level.

It clarifies the psychological mechanism from performance pressure to fraudulent intention by integrating a mediation impact from the displacement of responsibility and a moderation effect from MC and PE. It contributes to the sparse research on how situational factors shape individuals' fraudulent intentions in the international context. It provides a fresh perspective on fraud by constructing a formation model from moral psychological theories.

In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.

The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.

The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.

The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.

The purpose of this paper is to embark on evaluating the role of Chat Generative-Trained Transformer (ChatGPT) in personal knowledge management (PKM) practices of individual knowledge workers across varied disciplines.

The methodology involves four steps, i.e. literature search, screening and selection of relevant data, data analysis and data synthesis related to KM, PKM and generative artificial intelligence (AI) with a focus on ChatGPT. The findings are then synthesized to develop a viewpoint on the challenges and opportunities brought by ChatGPT for individual knowledge workers in enhancing their PKM capability.

This work highlights the prevailing challenges and opportunities experienced by knowledge workers while leveraging PKM through implying ChatGPT. It also encapsulates how some management theories back the cruciality of generative AI (specifically ChatGPT) for PKM.

This study identifies the challenges and opportunities. from existing studies and does not imply empirical data/result. The authors believe that findings can be adjusted to diverse domains regarding knowledge workers’ PKM endeavors. This paper draws some conclusions and calls for further empirical research.

ChatGPT’s capability to accelerate organizational performance compelled scholars to focus in this domain. The linkage of ChatGPT to Knowledge Management is an under-explored area specifically the role of ChatGPT on PKM hasn't been given attention in the existing work. This is one of the earliest studies to explore this context.

The purpose of this paper is to have a deeper understanding of the nonlinear relationship between the impact of climate change on tourism development. Current studies on the effects of climate change on tourism development primarily rely on linear correlation assumptions.

Based on the New Institutional Economics theory, the institutional setting inherently motivates and ensures the growth of the tourism industry. For a precise evaluation of the nonlinear consequences of climate change on tourism, this paper concentrates on Chinese cities between 2011 and 2021, methodically analyzing the influence of climate change on tourism.

The study findings suggest that there is an “inverse U”-shaped nonlinear relationship between climate change and tourism development, initially strengthening and subsequently weakening. Based on these findings, the research further delves into how institutional contexts shape the nonlinear association between climate change and tourism growth. It was found that in a higher institutional backdrop, the “inverse U” curve tends to flatten and surpass the curve adjusted for a lesser institutional context. Upon deeper mechanism analysis, it was observed that cities with more advanced marketization, improved industrial restructuring and enhanced educational growth exhibit a more evident “inverse U”-shaped nonlinear connection between climate change and tourism evolution.

First, previous studies on climate change and tourism development largely rely on questionnaire data (Hu et al., 2022). In contrast to these studies, this paper uses dynamic panel data, which to some extent overcomes the subjectivity and difficulty of causality identification in questionnaire data, making our research conclusions more accurate and reliable. Second, this study breaks through the linear relationship hypothesis of previous literature regarding climate change and tourism development. By evaluating the nonlinear relationship of climate change to tourism development from the institutional pressure perspective, it more intricately delineates their interplay mechanism, expanding and supplementing the research literature on the relationship mechanism between climate change and tourism development. Thirdly, the conclusions of this study are beneficial for policymakers to better understand and assess the scope of climate change impacts. It also aids relevant departments in clarifying the direction of institutional environment optimization to elevate the level of tourism development when faced with adverse impacts brought about by climate change.

This paper aims to analyze the research highlights, evolutionary process and future research directions in the field of tourism forecasting.

This study used CiteSpace to conduct a bibliometric analysis of 1,213 tourism forecasting articles.

The results show that tourism forecasting research has experienced three stages. The institutional collaboration includes transnational collaboration and domestic institutional collaboration. Collaboration between countries still needs to be strengthened. The authors’ collaboration is mainly based on on-campus collaboration. Articles with high co-citation are primarily published in core tourism journals and other relevant publications. The research content mainly pertains to tourism demand, revenue management, hotel demand and tourist volumes. Ex ante forecasting during the COVID-19 pandemic has broadened existing tourism forecasting research. The future forecasting research focuses on the rational use of big data, improving the accuracy of models and enhancing the credibility of forecasting results.

This paper uses CiteSpace to analyze tourism forecasting articles to obtain future research trends, which supplements existing research and provides directions for future research.

本文旨在分析旅游预测领域的研究重点、演化过程和未来的研究方向。

本研究使用 CiteSpace 软件对 1213 篇旅游预测文章进行了文 献计量学分析。

结果表明, 旅游预测研究经历三个阶段。机构合作包含国际机构合作和 国内机构合作, 需要持续加强国家之间的合作, 作者之间的合作多以校内合作为 主。高引用文章不仅发表在旅游领域的核心期刊还发表在其他专业的核心期刊上。 旅游预测研究的主要内容为旅游需求、收入管理、酒店需求和游客量。新冠疫情 期间的事前预测拓宽了现有的旅游预测研究。未来预测的研究重点在于合理利用 大数据, 提高模型的准确定以及提高预测结果的可信度。

本文使用 CiteSpace 分析旅游预测文章得到未来研究趋势, 既是对 现有研究的补充, 又为今后的研究提供方向。

Este artículo pretende analizar los aspectos más destacados de la investigación, el proceso evolutivo y las futuras orientaciones de la investigación en el campo de la previsión turística.

Este estudio utilizó CiteSpace para realizar un análisis bibliométrico de 1213 artículos sobre previsión turística.

Los resultados muestran que la investigación sobre previsión turística ha experimentado tres etapas. La colaboración institucional incluye la colaboración transnacional y la colaboración institucional nacional. La colaboración entre países aún debe reforzarse. La colaboración entre autores se basa principalmente en la colaboración dentro del campus. Los artículos con una alta cocitación se publican principalmente en las principales revistas de turismo y en otras publicaciones relevantes. El contenido de la investigación se refiere principalmente a la demanda turística, el revenue management, la demanda hotelera y los volúmenes turísticos. La previsión previa y durante la pandemia de la COVID-19 ha ampliado la investigación existente sobre previsión turística. La futura investigación sobre previsiones se centra en el uso racional de los big data, la mejora de la precisión de los modelos y el aumento de la credibilidad de los resultados de las previsiones.

Este artículo utiliza CiteSpace para analizar artículos de previsión turística con el fin de obtener futuras tendencias de investigación, lo que complementa la investigación existente y proporciona orientaciones para futuras investigaciones.

This study aims to expand tourism knowledge by analysing literature review articles published in English Web of Science (WOS) and Chinese China National Knowledge Infrastructure (CNKI) language journals and reviewing their influence, interconnection and trends.

A three-stage method was designed to understand the tourism research progress. Performance analysis identified the publication timeline, high-yielding journals and authors that published tourism literature reviews and frequently cited papers. Science mapping visualisation examined the intrinsic connections between co-authorship and co-institution. Finally, emerging trend analysis explored the topic modelling and evolution through Latent Dirichlet allocation (LDA) and regression.

The key statistics and collaborations relationships of tourism literature reviews were traced. LDA identified 45 and 22 topics, which narrowed the barriers in tourism studies. The regression analysis divided these topics into “hot”, “fresh”, “bell-shaped” and “stable” patterns. These modes represent the progress of tourism studies. The topic “new emerging technologies and the internet” is the focus of tourism literature reviews published in both databases. Future research could pay more attention to the topics in the “hot” and “fresh” patterns. The results enrich the progress of tourism literature reviews and provide a direction for future research.

To the best of the authors’ knowledge, this study is the first literature analysis for tourism literature reviews published in WOS versus CNKI journals. The proposed three-stage systematic method is used for the first time for the literature review and can guide future research.

本研究旨在通过分析英文WOS和中文CNKI语言期刊上发表的文献综述文章, 回顾其影响、相互联系和趋势, 来扩大旅游知识体系。

本研究设计了一个三阶段方法来了解旅游研究进展。绩效分析确定了出版时间线、发表的旅游文献综述的高产期刊和作者以及经常被引用的文章。科学地图可视化审视了合作作者和合作机构之间的内在联系。最后, 新兴趋势分析通过潜在狄利克雷分配和回归探讨了主题建模和演变。

本文追踪了旅游文献综述的关键统计数据和合作情况。潜在狄利克雷分配确定了45个和22个主题, 这缩小了旅游研究中的研究缺口。回归分析将这些主题分为“热门”、“新鲜”、“钟形”和“稳定”模式。这些模式代表了旅游研究的进展。主题“新兴技术和互联网”是不同数据库中发表的旅游文献综述的焦点。未来的研究可以更多地关注“热门”和“新鲜”模式中的主题。研究结果丰富了旅游文献综述的进展, 为今后的研究提供了方向。

这项研究是首次对WOS与CNKI期刊上发表的旅游文献评论进行文献分析。所提出的三阶段系统方法首次用于文献综述, 可以指导未来的研究。

El objetivo de este estudio es ampliar el conocimiento turístico analizando los artículos de revisión documental publicados en revistas, tanto en la versión WOS en inglés cómo en CNKI China, y revisando sus efectos, interconexiones y tendencias.

Se ha diseñado el método de tres etapas para comprender el progreso de la investigación turística. El análisis del desempeño determinó la línea de tiempo de publicación, las revistas de alto rendimiento y los comentarios de la literatura turística publicados por los autores, así como los artículos citados con frecuencia. La visualización de los mapas científicos, examina los vínculos intrínsecos entre los autores colaboradores y las instituciones colaboradoras. Finalmente, el análisis de tendencias emergentes explora el modelado temático y la evolución a través de posibles asignaciones y regresiones de dilick-ray.

Se han analizado las estadísticas clave y las relaciones de cooperación de la revisión de la literatura turística. La asignación potencial de dilich-ray identifica 45 y 22 temas, lo que reduce las barreras en la investigación turística. El análisis de regresión divide estos temas en patrones “populares”, “novedosos”, “en forma de campana” y “estables”. Estos modelos representan el avance de la investigación turística. El tema “tecnologías emergentes e internet” es el foco de la revisión de la literatura turística publicada en diferentes bases de datos. La investigación futura puede centrarse más en temas en modelos “populares” y “novedosos”. Los resultados de la investigación enriquecen el progreso de la revisión de la literatura turística y proporcionan una dirección para futuras investigaciones.

El estudio es el primer análisis documental de los comentarios de la literatura turística publicados en las revistas WOS y CNKI. El método sistemático de tres etapas propuesto se utiliza por primera vez en la revisión documental y puede guiar futuras investigaciones.

As a high-performance engineering plastic, polyimide (PI) is widely used in the aerospace, electronics and automotive industries. This paper aims to review the latest progress in the tribological properties of PI-based composites, especially the effects of nanofiller selection, composite structure design and material modification on the tribological and mechanical properties of PI-matrix composites.

The preparation technology of PI and its composites is introduced and the effects of carbon nanotubes (CNTs), carbon fibers (CFs), graphene and its derivatives on the mechanical and tribological properties of PI-based composites are discussed. The effects of different nanofillers on tensile strength, tensile modulus, coefficient of friction and wear rate of PI-based composites are compared.

CNTs can serve as the strengthening and lubricating phase of PI, whereas CFs can significantly enhance the mechanical properties of the matrix. Two-dimensional graphene and its derivatives have a high modulus of elasticity and self-lubricating properties, making them ideal nanofillers to improve the lubrication performance of PI. In addition, copolymerization can improve the fracture toughness and impact resistance of PI, thereby enhancing its mechanical properties.

The mechanical and tribological properties of PI matrix composites vary depending on the nanofiller. Compared with nanofibers and nanoparticles, layered reinforcements can better improve the friction properties of PI composites. The synergistic effect of different composite fillers will become an important research system in the field of tribology in the future.