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This paper aims to systematically visualize the structure and trends from 2005 to 2021, which will help scholars gain a deeper appreciation for existing studies and grasp future research possibilities and directions.

The approach is bibliometric, using VOSviewer and CiteSpace to analyze 765 journal articles and reviews from the Web of Science (WoS) and Scopus databases over the past 16 years.

There is considerable interest in urban tourism destination image (U-TDI), partly because of the role of image in promoting the economic development of urban tourism and the associated benefits to stakeholders. Most research output concerns China, the USA, Spain and the United Kingdom (UK); research in the USA context has had a particularly wide range of influence. Highly cited journals play a crucial role, while subject structure, key articles and high-frequency keywords indicate popular topics, sub-themes and development trends. Drawing on these findings, the authors identify four topics that deserve further study.

This systematic review will enhance understanding of U-TDI research and inform future research directions as well as highlighting the need to explore destination image in greater depth, it guides policymakers in the tourism industry seeking to develop city image.

This study aims to explore the spatial impact of an increase in the minimum wage on the labor productivity of star-rated hotels in China.

The impact is analyzed by using the dynamic spatial Durbin model.

The authors find a U-shaped link between the increase in minimum wage and labor productivity of star-rated hotels. The long-term impact of a minimum wage increase has a greater influence on labor productivity than its short-term effects. While there is no notable spatial spillover impact observed in the sample of 31 provinces in China, the authors do identify a spatial spillover effect of the minimum wage rises on the labor productivity of star-rated hotels in the central area. Furthermore, they observe heterogeneity across China. The eastern and western regions exhibit a U-shaped relationship, whereas the central region exhibits an inverted U-shaped relationship.

The findings of this study allow government agencies to get a more comprehensive comprehension of the actual consequences of minimum wage hikes on the tourism and hospitality sector, thereby establishing a solid basis for them to develop appropriate policies. Moreover, it offers a variety of suggestions aimed at enhancing the quality and efficiency of hotel management.

Research on the effects of minimum wage standards is scant in the hospitality industry. Based on human capital investment theory, this study examines the effect of the minimum wage standard hikes on labor productivity of star-rated hotels from the spatial perspective, filling the existing research gap.

Eco-innovation products, which means achieving more efficient and responsible use of resources and reducing the detrimental impact on the environment, can win a competitive advantage for the enterprises. But it is not easy to implement due to the high cost of eco-innovative technologies development, the uncertainty of market needs and return risk of investment. Many enterprises seek collaborations from their upstream suppliers to jointly carry out eco-innovation, such as Apple, IBM and Nike. A unique feature of collaboration is that efforts by one party enhance the marginal value of the other party's efforts. However, the collaboration will make the partner know the eco-innovation technology and prompt the partner to encroach the market to sell competitive products by herself. Motivated by this observation, this paper considers the optimal collaboration strategy on eco-innovation between upstream and downstream supply chain member and the optimal encroachment strategy of upstream supplier in a supply chain.

This paper models a supply chain wherein a supplier provides products or materials for her manufacturer and cooperates with her manufacturer in eco-innovation. Also, the supplier could encroach on the market to sell similar products by herself. Then this paper uses game theory and mathematical modeling to do relative analysis.

The analysis reveals several interesting insights. First, eco-innovation collaboration makes supplier encroachment no longer only rely on the encroachment cost. The delayed realized eco-innovation efficiency information also plays a vital role. Second, different from previous research, the authors find the manufacturer's preference for supplier encroachment depends on the uncertainty of eco-innovation efficiency and potential market demand. Third, both partial and full encroachment strategies of the supplier can effectively improve the eco-innovation level.

The paper is the first to take the interplay between collaboration and encroachment into account in a supply chain. The results caution enterprises and policymakers to take vertical collaboration and delayed realized information into account in the competitive supply chain before making any operational decisions. Furthermore, the authors propose that governmental intervention aimed at stimulating supplier encroachment in appropriate circumstances can contribute to the improved environmental performance of products.

To further promote application of BIM technology in construction of prefabricated buildings, influencing factors and evolution laws of willingness to apply BIM technology are explored from the perspective of willingness of participants.

In this paper, a tripartite game model involving the design firm, component manufacturer and construction firm is constructed and a system dynamics method is used to explore the influencing factors and game evolution path of three parties' application of BIM technology, from three perspectives, cost, benefit and risk.

The government should formulate measures for promoting the application of BIM according to different BIM application willingness of the parties. When pursuing deeper BIM application, the design firm should pay attention to reducing the speculative benefits of the component manufacturer and the construction firm. The design firm and the component manufacturer should pay attention to balancing the cost and benefit of the design firm while enhancing collaborative efforts. When the component manufacturer and the construction firm cooperate closely, it is necessary to pay attention to balanced distribution of interests of both parties and lower the risk of BIM application.

This study fills a research gap by comprehensively investigating the influencing factors and game evolution paths of willingness of the three parties to apply BIM technology to prefabricated buildings. The research helps to effectively improve the building quality and construction efficiency, and is expected to contribute to the sustainability of built environment in the context of circular economy in China.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.

This study aims to prepare an imprinted composite membrane with grafted temperature-sensitive blocks for the efficient adsorption and separation of rhenium(Re) from aqueous solutions.

PVDF resin membrane was used as the substrate, dopamine and chitosan (CS) were used to modify the membrane surface and temperature-sensitive block PDEA was grafted on the membrane surface. Then acrylic acid (AA) and N-methylol acrylamide (N-MAM) were used as the functional monomers, ethyleneglycol dimethacrylate (EGDMA) as the cross-linker and ascorbic acid-hydrogen peroxide (Vc-H₂O₂) as the initiator to obtain the temperature-sensitive ReO4⁻ imprinted composite membranes.

The effect of the preparation process on the performance of CS–Re–TIICM was investigated in detail, and the optimal preparation conditions were as follows: the molar ratios of AA–NH₄ReO₄, N-MAM and EGDMA were 0.13, 0.60 and 1.00, respectively. The optimal temperature and time of the reaction were 40 °C and 24 h. The maximum adsorption capacity of CS–Re–TIICM prepared under optimal conditions was 0.1071 mmol/g, and the separation was 3.90 when MnO4⁻ was used as the interfering ion. The quasi first-order kinetics model and Langmuir model were more suitable to describe the adsorption process.

With the increasing demand for Re, the recovery of Re from Re-containing secondary resources becomes important. This study demonstrated a new material that could be separated and recovered Re in a complex environment, which could effectively alleviate the conflict between the supply and demand of Re.

This contribution provided a new material for the selective separation and purification of ReO4⁻, and the adsorption capacity and separation of CS–Re–TIICM were increased with 1.673 times and 1.219 time compared with other Re adsorbents, respectively. In addition, when it was used for the purification of NH₄ReO₄ crude, the purity was increased from 91.950% to 99.999%.

This paper aims to explore the underlying mechanism whereby information-induced stress, resulting from the burdensome nature, questionable information reliability, misleading content and diffuse characteristics of infodemic pressure, impacts individuals’ online information-related behavior and health-related preventive behavior.

We conducted a cross-sectional survey organized with social media users during the post-pandemic period. Based on the 342 valid responses, structural equation modeling was employed to validate the research model.

The results substantiate our multidimensional view of infodemic stress, which encompasses dimensions including information overload, uncertainty, diffusivity and insecurity. We found that the infodemic stress contributes to pandemic fatigue, then engenders both negative information behavior and reduced engagement in preventive behavior. Furthermore, infodemic stress has also been found to have a direct positive effect on individuals’ engagement in preventive behavior.

This study introduces the concept of “infodemic stress” and presents a comprehensive framework to capture its various dimensions. This study offers novel insights into the psychological and behavioral repercussions of the infodemic stress transmitted through social media.

The purpose of the study is to investigate the financing channels and carbon emission abatement preferences of supply chain members, and further examine the optimal contract design of the retailer.

This paper develops a low-carbon supply chain composed of one retailer and one manufacturer, in which the retailer provides trade credit to the manufacturer. Considering the cap-and-trade regulation, the manufacturer with uncertain yield makes decision on whether to invest in emission abatement. There are bank loan and trade credit to finance production for the manufacturer and green credit to finance emission abatement investment. Meanwhile, the retailer may provide the manufacturer with three kinds of contracts to improve emission abatement efficiency, namely, revenue sharing, cost sharing or both sharing.

The results show that the retailer prefers to offer financing service at lower interest rate, but trade (and green) credit financing is always optimal for manufacturer and supply chain. The investment in emission abatement is value-added to all players. The sharing contracts offered by the retailer at lower sharing ratios can realize Pareto improvement of the system regardless of the financing scheme. However, comparing with the revenue or cost sharing contract, the existence of optimal sharing ratios makes the both sharing contract more favorable to the retailer.

The findings provide guidance for the emission-dependent manufacturer in financing and emission abatement decisions, as well as recommendations for the retailer to offer loan service and sharing contract.

This paper integrates green credit into bank loan or trade credit to analyze the financing decision of the manufacturer with uncertain yield and further considers the influence of three kinds of sharing contracts introduced by the retailer on improving operational performance.

Currently, many studies have shown an increasing interest in owner-dynamic capabilities (ODCs). Existing studies mainly focus on the dynamic capability basis and capability development within the owner organization, whereas they rarely analyze the capability mobilization within the network of participants in megaprojects. Therefore, this study aims to explain the interaction and evolution of the mobilization strategies of ODCs and the cooperative strategies of other participants.

This study develops a tripartite evolutionary game model to analyze the evolutionarily stable strategy of the owner, the reciprocal participants and the general participants. Results are numerically simulated with a validation case. The asymptotic stability of multiple group strategies is discussed under the replicator dynamic system.

This study suggests that resource complementarity significantly reduces the difficulty of mobilization. Moreover, these strategies are only effective with sufficient ODCs. The results indicate that reciprocal participants are more sensitive to the change in resource complementarity.

This study provides strategic guidance for mobilizing ODCs in megaprojects to better embrace uncertainty and stress, contributing to the dynamic capability literature with an evolutionary game approach. And new insight for the study of reciprocity preference in megaprojects is also provided.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.