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The purpose of this study is to examine the state of the literature related to the psychophysiological effects of travel, to identify gaps in the literature and to propose a vision to guide research between now and 2050.

The study was guided by a systematic literature review which started with more than 2,100 papers. The extant review and its findings are written in a conceptual nature with the concluding propositions, for the year 2050, guided by the systematic review and the authors’ personal knowledge.

It was found that research related to the psychophysiological effects of travel has greatly progressed over the past two decades. Findings include evidence that travel reduces both perceived and actual stresses and that taking more vacations has the ability to make people healthier. The study also revealed that travel can have negative effects on physiological health. The overall results led to a call for research on the psychophysiological benefits of travel between now and 2050.

The systematic review of literature was limited to studies conducted in English and to studies that included the words “travel” or “tourism” and “benefits”, stress, “health” and “wellness”. Results provide a discussion of theories that should guide the research agenda.

The review and proposed vision for research provide a detailed guide for researchers to follow. Should the proposed research come to fruition, tourism practitioners globally will have results to aid in engineering tourism experiences that could provide health benefits to visitors.

The proposed vision could have profound social implications, as the understanding of the positive and negative effects of travel should make travel experiences healthier for all. The study also proposes a call for research to determine ways to make the benefits of travel available to both the “haves” and “have nots” so that the benefits of travel can be more inclusive.

The main contribution of this manuscript is that it proposes 11 propositions for research between now and 2050 based on a thorough review of gaps related to the psychophysiological benefits of travel. The resultant propositions provide a clear and unique call for research in this area of inquiry.

本研究的主要目标是检查与旅行的心理生理益处相关的文献状况, 找出文献中的空白, 并提出从现在到2050年指导研究的愿景。

这项研究以一项系统的文献综述为指导, 该综述从2100多篇论文开始。现有的综述及其研究结果是在系统综述和作者个人知识的指导下, 以概念性的形式撰写的, 并附有2050年的结论性命题。

研究发现, 在过去的二十年里, 与旅行的心理生理益处相关的研究取得了长足的进步。研究结果包括有证据表明, 旅行可以减轻感知和实际的压力, 多度假可以使人们更健康。该研究还表明, 旅行会对生理健康产生负面影响。总体结果促使人们呼吁研究从现在到2050年旅行的心理生理益处。

本文的主要贡献是, 它根据对旅行的心理生理益处相关差距的全面回顾, 提出了从现在到2050年的11个研究主题。由此产生的主题为这一研究领域的研究提供了明确而独特的呼吁。

Los objetivos principales del presente estudio eran examinar el estado de la literatura relacionada con los beneficios psicofisiológicos de los viajes, identificar lagunas en la bibliografía y proponer una visión que guíe la investigación de aquí a 2050.

El estudio se guió por una revisión bibliográfica sistemática que partió de más de 2.100 artículos. La revisión existente y sus hallazgos están redactados de forma conceptual con las proposiciones finales para el año 2050, guiadas por la revisión sistemática y el conocimiento personal de los autores.

Se encontró que la investigación relacionada con los beneficios psicofisiológicos de los viajes ha progresado mucho en las dos últimas décadas. Resultados incluyen pruebas de que viajar reduce tanto el estrés percibido como el real y de que tomar más vacaciones tiene la capacidad de hacer que las personas estén más sanas. El estudio también reveló que viajar puede tener efectos negativos sobre la salud fisiológica. Los resultados globales condujeron a una llamada a la investigación sobre los beneficios psicofisiológicos de los viajes de aquí a 2050.

La principal aportación de este manuscrito es que propone once proposiciones para la investigación de aquí a 2050 basadas en una revisión exhaustiva de las lagunas relacionadas con los beneficios psicofisiológicos de los viajes. Las proposiciones resultantes constituyen un llamamiento claro y único a la investigación en este ámbito de estudio.

Corporate social responsibility (CSR) practices frequently result in increased costs for Chinese international contractors (CICs), where profitability is the primary objective; therefore, internal corporate drivers and external pressures play a crucial role in encouraging them to engage in sustainable CSR practices. This study systematically examines the dynamic impact of internal and external stakeholders on the CSR practices of CICs.

This study adopted a structural equation model (SEM) to identify and validate a correlation between stakeholders and CSR practices. Standardized causal coefficients estimated in SEM were used to construct a fuzzy cognitive map (FCM) model to illustrate the effect of stakeholders on CSR practices with linkage direction and weights. Predictive, diagnostic, and hybrid analyses were performed to dynamically model the variation in stakeholders on the evolution of CSR practices.

The empirical results demonstrate that (1) employee participation in CSR has the greatest impact on CSR practices, followed by CSR strategies, partner and customer expectations, and finally government regulations. (2) In the early stage of CSR fulfillment, CSR strategies have the greatest influence on CSR practices; in the later stage of CSR fulfillment, employee participation in CSR has the greatest influence on CSR practices. (3) In the long run, the most effective and economical integrated interventions are those that address employee participation in CSR, partner expectations and customer expectations, and intervention in CSR strategies is needed if the level of CSR practice needs to be improved in the short term.

This study contributes to the research on the influence mechanisms of CSR practices of CICs and systematically analyzes their dynamic influence on CSR practices of CICs from the perspective of stakeholders.

3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.

A business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.

First, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.

The new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.

The new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.

A new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

Construction safety resilience is gradually gaining attention in the field of engineering construction as a new management concept and way to improve safety performance. However, how to cope with the dilemma of the unclear relationship of construction safety resilience elements at the practice level and promote the harmonization of construction safety goals and resilience enhancement paths has become an urgent challenge for safe construction.

This study analyzes the components of construction safety resilience elements. A relationship network model of construction safety resilience elements is developed by using the social network analysis method. The location and influence of each element in the network and the interrelationships among the elements are explored in depth.

The findings reveal a robust interconnection among the elements of safety resilience in the construction industry. Key components such as safety behavior, risk prevention and control mechanisms, disaster prevention and mitigation technologies as well as information technology, are positioned at the core of the network. Notably, safety behavior exerts the most significant influence over the other elements, serving as the linchpin of safety management in the construction industry. Moreover, the interplay among safety resilience elements in the construction sector can alter the structure of the relationship network.

This study adopts the social network approach to solve the problem that it is difficult to quantitatively analyze the elements of construction safety resilience and their interrelationships and to clarify the interactions among the core elements, which can help to further assist the construction project manager to continuously optimize safety resilience and improve construction safety.

This paper studies the optimal decision-making and coordination problem of a dual-channel fresh agricultural product (FAP) supply chain. The purpose is to analyze the impact of information sharing on optimal decisions and propose a coordination mechanism to encourage supply chain members to share information.

The two-echelon dual-channel FAP supply chain includes a manufacturer and a retailer. By using the Stackelberg game theory and the backward induction method, the optimal decisions are obtained under information symmetry and asymmetry and the coordination contract is designed.

The results show that supply chain members should comprehensively evaluate the specific situation of product attributes, coefficient of freshness-keeping cost and network operating costs to make decisions. Asymmetric information can exacerbate the deviation of optimal decisions among supply chain members and information sharing is always beneficial to manufacturers but not to retailers. The improved revenue-sharing and cost-sharing contract is an effective coordination mechanism.

The conclusions can provide theoretical guidance for supply chain managers to deal with information asymmetry and improve the competitiveness of the supply chain.

This paper combines the three characteristics that are most closely related to the reality of supply chains, including horizontal and vertical competition of different channels, the perishable characteristics of FAPs and the uncertainty generated by asymmetric demand information.

This study aims to determine how and through what mechanisms the outward foreign direct investment (OFDI) promotion effect of the Belt and Road initiative (BRI-OFDI) affects domestic investment. It is motivated by the context that China is fostering a new development pattern, as well as by the impetus from the Belt and Road initiative for the new pattern.

Drawing on data of Chinese-listed companies, this study uses a difference-in-difference method to explore the effect of the BRI-OFDI on domestic investment and a mediation model to illustrate the mechanisms.

The BRI-OFDI has a significantly positive effect on domestic investment, meaning that the Belt and Road initiative's OFDI promotion effect crowds in domestic investment. The results are heterogeneous: the crowding-in effect mainly exists in non-state-owned and technology-intensive enterprises, while a crowding-out effect is seen in state-owned and labor-intensive enterprises. The easing of corporate financing constraints and the expansion of market demand are two important mechanisms.

This study uses the Belt and Road initiative as an exogenous shock to investigate the impact of the initiative-induced OFDI promotion effect on domestic investment. It addresses the potential endogeneity issue confronting the studies on the relationship between OFDI and domestic investment in the literature. The authors focus on the possible spillover effects of the Belt and Road initiative discussing the impact of the BRI-OFDI on domestic investment from the micro-firm perspective. It offers a new perspective to objectively assess the initiative's policy effect.

Based on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.

This study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.

Based on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.

In terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.

This study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

In the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.

The study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.

In the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.

Previous research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

Social shopping platforms have flourished by using multiple social shopping features, yet little is known about how the combination of these features affects purchase intention, particularly in terms of the product itself. The purpose of the paper is to draw on the concept of social shopping feature richness, adopting a formative approach on the survey used, and endeavors to reveal the concept's impact on consumers' buying intention from a product perspective.

Building on mental accounting and signaling theories, a theoretical model is proposed and empirically evaluated with 356 samples collected using a questionnaire survey.

The results suggest that social shopping feature richness promotes consumers' consumption by providing information signals to satisfy acquisition utility and transaction utility. Specifically, social shopping feature richness enhances perceived product quality, while decreasing negative perceptions regarding price. Moreover, perceived product quality and perceived price significantly influence buying intention through the mechanism of perceived value.

The authors' study highlights the role of the combination of functionally diverse social shopping features on product sales for social shopping platforms.

The mega railway infrastructure projects are faced with complex environments and multi-level management challenges. Thus, the mega railway infrastructure project management system not only needs to focus on its composition, but also needs to consider changes and impacts of internal and external environment.

This study attempts to introduce the concept of dissipative structure from the perspective of complexity theory and constructs a positive entropy and negentropy flow index system for mega railway infrastructure project management system in order to analyze the factors of management system more deeply. The Brusselator model is used to construct the structure of the mega railway infrastructure project management system, and the entropy method is used to calculate the positive entropy and negentropy values to verify whether the management system is a dissipative structure.

A plateau railway project in China was used as an example for an empirical study, not only its own characteristics are analyzed, but also the role of constraints and facilitation of the internal and external environment. Based on the research results, several effective suggestions are put forward to improve the stability and work efficiency of mega railway infrastructure project management system.

This study demonstrates that mega railway infrastructure project management system has the characteristics of dissipative structure. It can provide theoretical support for the development of mega railway infrastructure project management system from disorderly state to orderly state.