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The advent of the postpandemic era has brought renewed attention to the development of business tourism. However, the existing research on business tourism is fragmented, and there are gaps in the current understanding of the subject. This study aims to identify the intellectual structures, current hotspots and research directions in the business tourism field from 1994 to 2023.

A bibliometric visual analysis using VOSviewer was conducted to assimilate the available knowledge from 136 business tourism-related articles collected in the WoS Core Collection database between 1994 and 2023.

The results indicate that the number of business tourism-related research fluctuates, but the overall trend is gradually increasing. It synthesizes four main research themes, which are tourist behavior, destination marketing, information technology and work-family interferences of business travelers.

To the best of the authors’ knowledge, this study is among the first to systematically review business tourism publications over the past 29 years. It assists scholars in gaining a more comprehensive understanding of business tourism research and informs future research directions.

后疫情时代的到来使得商务旅游的发展再次受到关注。然而, 目前的商务旅游研究是零散的, 我们对与这一主题的理解存在空白。本研究旨在确定1994至2023年商务旅游相关领域的知识结构、当前热点和研究方向。

本研究使用VOSviewer进行了文献计量学可视化分析, 以吸收从Web of Science（WOS）核心合集数据库中收集的136 篇1994年至2023年间的商务旅游相关文章中的可用知识。

结果表明, 与商务旅游相关的研究数量呈现出波动但总体逐渐增加的趋势。其中包括四个主要的研究主题, 分别是游客行为、目的地营销、信息技术和商务旅行者的工作与家庭。

本研究系统回顾了过去29年间与商务旅游相关的出版物。从而有助于学者们对商务旅游研究有更全面地了解, 并获得未来的研究方向。

El advenimiento de la era postpandemia ha renovado la atención prestada al desarrollo del turismo de negocios. Sin embargo, la investigación existente sobre el turismo de negocios está fragmentada y existen lagunas en la comprensión actual del tema. El presente estudio tiene como objetivo identificar las estructuras intelectuales, los focos actuales y las direcciones de investigación en el ámbito del turismo de negocios desde 1994 hasta 2023.

Se realizó un análisis visual bibliométrico utilizando VOSviewer para asimilar el conocimiento disponible en 136 artículos relacionados con el turismo de negocios recogidos en la base de datos WoS Core Collection entre 1994 y 2023.

Los resultados indican que el número de investigaciones relacionadas con el turismo de negocios fluctúa, pero la tendencia general es a aumentar gradualmente. Se sintetizan cuatro temas principales de investigación: el comportamiento del turista, el marketing de destinos, la tecnología de la información y las interferencias trabajo-familia de los viajeros de negocios.

Este estudio es uno de los primeros que revisa sistemáticamente las publicaciones sobre turismo de negocios de los últimos 29 años. Ayuda a los investigadores a obtener una comprensión más completa de la investigación sobre el turismo de negocios y sugiere futuras líneas de investigación.

Under the rural revitalization, the effect of China's implementation of rural prefabricated housing is not obvious. Cost has become the biggest obstacle to its development. Therefore, it is necessary to study the factors influencing the cost of prefabricated buildings in villages and clarify the focus of cost control.

This paper focuses on the whole process of prefabricated housing construction in villages in China and uses grounded theory to identify and screen out 27 related factors that affect the construction cost of prefabricated buildings. A system dynamics model is used to dynamically analyze the influencing factors. The engineering examples in rural areas of southern Shaanxi are simulated. Finally, five key factors that influence cost are obtained. Based on this, cost control countermeasures are proposed for rural prefabricated housing in southern Shaanxi.

The results show that: the key factors affecting the cost of prefabricated buildings in villages include the selection of production methods, the degree of design standardization, the quality of construction personnel, the level of construction technology and the circulation cycle of molds. The cost of prefabricated housing in villages can be controlled through five aspects: mass production of components, design exchange and feasibility analysis, improvement of employee professionalism, strict selection of construction schemes and technologies and improvement of mold turnover rate.

The system dynamics model applied in this paper is based on the idealized state. The system boundary is narrow and has a certain subjectivity. It needs further detailed research to make it closer to the engineering practice. In addition, this paper applies the rural engineering example in southern Shaanxi to carry out a single case study, and the universality of the research results needs to be further tested. There are many village construction projects and building types, so the research results can be further enriched through large sample research.

Rural construction is an important step in the implementation of rural revitalization. Exploring the factors that affect the key costs of prefabricated buildings in villages and towns in view of the particularity of rural areas will help provide a reference for their cost control and help the rural development of prefabricated houses.

The research results of this paper can provide a reference for the development of prefabricated buildings in other rural revitalization areas.

Different from the traditional research on urban prefabricated buildings, this paper focuses on rural areas and explores the core factors affecting the cost of prefabricated buildings from the micro level. This study establishes a system dynamics model suitable for the cost control of prefabricated housing at the village level and provides methods for its cost control. Based on the identified key factors affecting costs, cost control measures were proposed for prefabricated housing tailored to the unique characteristics of villages.

To develop an automated system for identifying and repairing cracks in asphalt pavements, addressing the urgent need for efficient pavement maintenance solutions amidst increasing workloads and decreasing budgets.

The research was conducted in two main stages: Crack identification: Utilizing the U-Net deep learning model for pixel-level segmentation to identify pavement cracks, followed by morphological operations such as thinning and spur removal to refine the crack trajectories. Automated crack repair path planning: Developing an enhanced hybrid ant colony greedy algorithm (EAC-GA), which integrates the ant colony (AC) algorithm, greedy algorithm (GA) and three local enhancement strategies – PointsExchange, Cracks2OPT and Nearby Cracks 2OPT – to plan the most efficient repair paths with minimal redundant distance.

The EAC-GA demonstrated significant advantages in solution quality compared to the GA, the traditional AC and the AC-GA. Experimental validation on repair areas with varying numbers of cracks (16, 26 and 36) confirmed the effectiveness and scalability of the proposed method.

The originality of this research lies in the application of advanced deep learning and optimization algorithms to the specific problem of pavement crack repair. The value is twofold: Technological innovation in the field of pavement maintenance, offering a more efficient and automated approach to a common and costly issue. The potential for significant economic and operational benefits, particularly in the context of reduced maintenance budgets and increasing maintenance demands.

This study aims to explore the role of urban streets as transitional edges in coordinating socio-spatial interactions within the urban environment. It will focus on how streets can revitalize their surrounding environments and shape sustainable urban living through their characteristics.

Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this study systematically reviewed 67 international research articles published from 2013 to 2023. It delved into the functionalities of urban streets as transitional edges under various social and physical interactions, covering the multidimensional impacts of streets at the social, spatial and individual levels.

The results demonstrate that the physical and social dimensions of streets, through their characteristics as transitional edges, not only complement each other but also effectively promote social space interactions and sustainable urban development. As a key public space at the interface of social and physical realms, streets influence residents' daily lives and balance the socio-spatial environment.

Although this study deepens the understanding of urban streets as transitional edges, it faces limitations due to the scarcity of literature related to transitional edges, which may affect the depth and breadth of the research. Future studies are required to further verify theoretical findings through field research and case studies and to explore practical applications of street design to enhance data comprehensiveness and availability.

The originality of this article lies in defining urban streets as dynamic transitional edge spaces, redefining their dual role in urban design to connect physical forms and social functions. Through a comprehensive literature review, this study provides theoretical support for urban planning and design practices, emphasizing their application value in promoting urban social interaction and sustainable development.

The desire of international retail brands to implement sustainable supply chain practices in the fashion value chain and improve suppliers' sustainability efforts; this research paper elucidates the relationship between blockchain technology and sustainability to impact apparel firms' triple bottom line.

For studying the impact of sustainable supply chain practices on the triple bottom line, a survey questionnaire was chosen and sent out to 500 garment companies simultaneously, of which 371 responded. The data collected is cross-sectional. The questionnaire survey was developed keeping in mind a few demographic elements such as experience, age and qualification to generalize the findings. For analysis, SmartPLS is used to run model structuring and regression analysis.

Test runs on model structure confirm the instrument's validity and reliability. Bootstrapping on the theoretical model to test developed hypotheses suggests that supply chain sustainability practices positively affect social, environmental and economic performance in a direct relationship. Further, indirect relation testing conducted to test blockchain technology's moderation influences only the constructs' relations.

The clubbing of sustainable supply chain practices and blockchain technology is a novel idea in the apparel industry; however, there are more constructs in the context of practice-based theory and supply chain which impact firm performance. Also, the research limits itself from discussing IT infrastructure and smart contract types that impact the technology's performance.

The study provides a framework for interpreting the synergetic influence of SSCP on firm social, environmental and economic performances, which is demanded both by consumers and regulators in an industry. The results suggest that managers sustainably design the production ecosystem, thus eliminating any discrepancy or slackness in the complete chain. Usually, suppliers are ignored, which are precursors in implementing SSCP.

The paper studies sustainability problems through ecological modernization theory and practical-based theory giving a unique perspective on the issue faced by the apparel industry and combining sustainable supply chain practices and blockchain.

An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

The continuous upgrading of new technologies and rapid changes in their external environment have made organizations more dependent than ever on the ability of their employees to quickly identify problems and make timely course corrections. This dependency is not limited to individual employee voice but extends to the collective voice of the team. In the Chinese context, collective silence prevails. Following social identity theory, this study aims to explore the mechanisms and conditional processes underlying the relationship between team faultlines and team voice behavior and examine whether there are differences between the effects of objective and perceived faultlines.

The proposed model was tested through questionnaires with 377 team members from 71 teams, which were conducted through team leader–member pairing survey. The correlation and hierarchical stepwise regression analyses were used to test the hypotheses rigorously, and the questionnaire data was analyzed using SPSS 26.0, AMOS 25.0 and R 3.6.1.

The results show that both objective and perceived faultlines have a negative impact on team voice behavior and that the latter has a stronger negative effect. Team psychological safety mediates the relationship between team faultlines and team voice behavior. In addition, benevolent leadership, moral leadership and Zhongyong thinking positively moderate the negative effect of objective faultlines on team voice behavior; Zhongyong thinking also moderates the mediating effect of team psychological safety on the relationship between objective faultlines and team voice behavior.

The results of this study provide a deeper understanding of team faultlines and team voice behavior, and practical implications are provided for managers and future researchers to improve voice behavior in organizations.

The purpose of this study is to address the limitations of traditional methods for managing intellectual property rights (IPRs) by proposing a blockchain-based solution. By leveraging blockchain technology and smart contracts, the aim is to create a comprehensive ecosystem that offers advantages such as reduced transaction costs, improved transparency, enhanced security and increased liquidity levels for IP assets.

This paper proposes using blockchain technology to manage intellectual property rights (IPRs) through a smart contract-based ecosystem. It outlines the use of non-fungible tokens (NFTs) on the blockchain to represent IPRs, with smart contracts automating interactions and encoding rules for various processes such as applications, licensing, transfers and royalty distribution. Governance mechanisms, such as decentralized autonomous organizations (DAOs), are employed to allow stakeholders to propose and vote on contract changes, ensuring adaptability. This approach aims to streamline IPR workflows, reduce transaction costs, improve transparency and enhance security.

The findings of this study suggest that implementing a blockchain-based ecosystem for managing intellectual property rights (IPRs) can lead to various benefits. These include reduced transaction costs, improved transparency, enhanced security, increased liquidity levels for IP assets and streamlined automated processes. The use of non-fungible tokens (NFTs) on the blockchain allows for detailed management, valuation and trading of IPRs. Furthermore, simulation results demonstrate the robustness and efficiency of our proposed ecosystem, outperforming traditional IP management systems in terms of transaction speed and cost-effectiveness. These simulations highlight the practical viability of integrating blockchain technology into IP management workflows.

The practical implications of adopting this blockchain-based ecosystem for managing intellectual property rights (IPRs) are significant. By streamlining processes, reducing transaction costs and improving transparency and security, organizations can expedite the protection and commercialization of their IP assets. Additionally, the increased liquidity levels and accessibility of IP assets to investors and financiers can spur innovation and economic growth.

This paper contributes to the field by proposing a novel approach to managing intellectual property rights (IPRs) using blockchain technology and smart contracts. By leveraging non-fungible tokens (NFTs) on the blockchain, the proposed ecosystem offers a more efficient and transparent way of managing IPRs, reducing reliance on costly and opaque traditional methods. The potential benefits include improved efficiency, transparency, security and collaboration in the management and commercialization of IPRs.

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 sensitivity and fragility of the construction industry’s economic system make the economic resilience of the construction industry (ERCI) a key concern for stakeholders and decision-makers. This study aims to measure the ERCI, identify the heterogeneity and spatial differences in ERCI, and provide scientific guidance and improvement paths for the industry. It provides a foundation for the implementation of resilience policies in the construction industry of developing countries in the future.

The comprehensive index method, Theil index method, standard deviation ellipse method and geographic detector model are used to investigate the spatial differences, spatiotemporal evolution characteristics and the influencing factors of the ERCI from 2005 to 2020 in China.

The ERCI was “high in the east and low in the west”, and Jiangsu has the highest value with 0.64. The Theil index of ERCI shows a wave downward pattern, with significant spatial heterogeneity. The overall difference in ERCI is mainly caused by regional differences, with the contribution rates being higher by more than 70%. Besides, the difference between different regions is increasing. The ERCI was centered in Henan Province, showing a clustering trend in the “northeast-southwest” direction, with weakened spatial polarization and a shrinking distribution range. The market size, input level of construction industry factors, industrial scale and economic scale are the main factors influencing economic resilience. The interaction between each influencing factor exhibits an enhanced relationship, including non-linear enhancement and dual-factor enhancement, with no weakening or independent relationship.

Exploring the spatial differences and driving factors of the ERCI in China, which can provide crucial insights and references for stakeholders, authorities and decision-makers in similar construction economic growth leading to the economic growth of the national economy context areas and countries.

The construction industry development is the main engine for the national economy growth of most developing countries. This study establishes a comprehensive evaluation index on the resilience measurement and analyzes the spatial effects, regional heterogeneity and driving factors on ERCI in the largest developing country from a dynamic perspective. Moreover, it explores the multi-factor interaction mechanism in the formation process of ERCI, provides a theoretical basis and empirical support for promoting the healthy development of the construction industry economy and optimizes ways to enhance and improve the level of ERCI.