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To explore how the blockchain affects the pricing and financing decisions in a low-carbon platform supply chain.

Considering the dual roles of the e-commerce platform as a seller and an initiator, a typical game-theoretical method is applied to analyze the behavior of supply chain decision-makers and the impact of key variables on equilibriums.

When loan interest rates are symmetric, whether blockchain is used or not, the e-commerce platform financing mode will always produce higher wholesale price and unit carbon emission reduction, while the retail price is the opposite. Higher unit additional income brought by the blockchain can bring higher economic and environmental performances, and the e-commerce platform financing mode is superior to bank financing mode. The application of blockchain may cause the manufacturer to change his/her financing choice. For bank financing, with the increase of loan interest rates, the advantages brought by blockchain will gradually disappear, but this situation will not occur under e-commerce platform financing.

Blockchain is known for its information transparency properties and its ability to enhance user trust. However, the impacts of applying blockchain in a low-carbon platform supply chain with different financing options are not clear. The authors examine the manufacturer's strategic choices for platform financing and bank financing, whether to adopt blockchain, and the impact of these decisions on carbon emissions reduction, consumer surplus and social welfare. The research conclusion can provide reference for the operation and financing decisions of platform supply chain under the carbon reduction target in the digital economy era.

Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones).

The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling.

By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective.

This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification.

Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

By adopting retrospective evaluation theories, this study aims to explain how innovations provided by separate suppliers in the tourism value chain influence tourist’s perceived value of the overall experience and further uncover which innovative product attributes are more effective in improving tourist perceptions of the overall value.

A survey yielded 584 valid responses from tourists who had experienced specific tourist product innovations during their travels. Structural equation modelling was used to test the proposed theoretical model.

The results reveal that tourists evaluate overall travelling experience value either by recalling an intense, impressive moment (i.e. a heuristic approach) or through an evaluation of the overall utility gained from the whole trip (i.e. a normative approach). Furthermore, innovations that are perceived as increasing convenience and enabling learning contribute to tourists’ overall value perception through both normative and heuristic approaches, while immersion resulting from innovation only contributes to overall perceived value through the heuristic approach.

Given the complex service ecosystem of tourism destinations, each tourism service provider should consider how innovations contribute to the experience of the whole trip and which attributes of innovations increase tourists’ overall perceived experience value.

This study complements existing knowledge by revealing the relationship between product innovation in tourism sectors and tourists’ perceived value of the whole trip. Moreover, it offers a theoretical framework for further investigation into service product innovation in hospitality and tourism industry.

This study aims at developing a better understanding of the relationship between network embeddedness and incremental innovation capability and further examines the moderating effect of open innovation.

This paper adopts hierarchical regressions to validate the theoretical model and collect the patent data of the top 54 firm patentees in the smartphone industry as empirical sample. Using patent citation network data, this paper estimates the relationship between open innovation, network embeddedness and incremental innovation capability.

This paper empirically shows that structural embeddedness exerts a negative effect on incremental innovation capability, while relational embeddedness is positively related to incremental innovation capability. And open innovation strengthens the relationship between network embeddedness and incremental innovation capability.

This paper shifts the focus of the determinants of incremental innovation capability from internal factors to the external network features by exploring the linkage between network embeddedness and incremental innovation capability. A counterintuitive conclusion is that structural embeddedness shows a negative effect on firm's incremental innovation capability. Furthermore, in contrast to most previous studies, which only focus on the direct effect of open innovation on the firm's incremental innovation capability, our study examines the moderating effect of open innovation on the relationships between network embeddedness and incremental innovation capability. At last, the results provide practical guidance for firms to occupy the beneficial network positions and adopt appropriate open innovation strategies to improve their incremental innovation capability.

Drawing on the technology-organization-environment (TOE) framework, this study aims to investigate determinants of performance of artificial intelligence (AI) adoption in hospitality industry during COVID-19 and identifies the relative importance of each determinant.

A two-stage approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) is used to analyze survey data from 290 managers in the hospitality industry.

The empirical results reveal that perceived AI risk, management support, innovativeness, competitive pressure and regulatory support significantly influence the performance of AI adoption. Additionally, the ANN results show that competitive pressure and management support are two of the strongest determinants.

This research offers guidelines for hospitality managers to enhance the performance of AI adoption and presents policy-making insights to promote and support organizations to benefit from the adoption of AI technology.

This study conceptualizes the performance of AI adoption from both process and firm levels and examines its determinants based on the TOE framework. By adopting an innovative approach combining PLS-SEM and ANN, the authors not only identify the essential performance determinants of AI adoption but also determine their relative importance.

This study aims to build a global environmental quality protection convention to jointly address the problems of environmental pollution governance worldwide.

From the perspective of environmental pollution of the air, ocean, forest, water and solid waste, the authors summarize the main important measures and mechanisms of environmental pollution governance in various countries.

The results indicate that management research on biodiversity and natural resources must be strengthened, the relationship between economic development and environmental quality management needs to be balanced, the comparative study of domestic and international environmental governance theories and practices should be strengthened, empirical and applied research on environmental governance needs to be focused on, and complete system research on environmental governance and management should be explored. In the future, further strengthening environmental awareness, addressing environmental pollution and managing environmental quality are necessary.

The environment is the foundation of human survival and development. With the development of economy, contradictions between human and natural environment (e.g. air, ocean, forest and water) have become prominent. Environmental pollution governance cannot only help address existing environmental problems but also solve economic problems of various countries. The prerequisite for sustainable development is to lay a solid foundation for the coordinated development of economic growth and pollution management.

The purpose of this paper is to synthesize a microemulsion and investigate its tribological properties as lubricant. Magnesium alloy warm rolling experiments were conducted. Surface morphology was observed and wear form was summarized. The composition of surface residues was analyzed, which sheds light on the lubrication mechanism of microemulsion.

A microemulsion was prepared with a proper amount of oil, surfactant, cosurfactant, water and other additives for magnesium alloy strip warm rolling. Tribological properties, such as maximum non-seizure load (P_B), friction coefficient (μ) and wear scar diameter (D) of the microemulsion were measured and compared with those of emulsion and rolling oil on an MR-10A four-ball tribotester. The extreme pressure anti-wear coefficients (O) were calculated and compared. Warm rolling experiments were carried out on a Ф 170/400 × 300 mm four-high rolling mill at 240°C to compare the finish rolling thickness and surface quality of rolled AZ31B magnesium alloy strip under four lubrication states, namely, no lubrication, rolling oil, microemulsion and emulsion. The surface morphology after warm rolling was observed with confocal laser scanning microscope and scanning electron microscope, respectively. The composition of surface residues was analyzed with energy dispersive spectrometry and X-Ray photoelectron spectroscopy.

Surface morphology indicated that pitting wear, adhesive wear and ploughing wear were three main forms of wear in magnesium alloy warm rolling. Microemulsion had excellent lubrication properties with less residual oil remaining. Two types of adsorption layers formed on magnesium alloy strip surface were responsible for lubrication properties. MgSO₄ and magnesium stearate in the reaction layer played a key role in anti-wear and friction-reduction in warm rolling.

The study is original and gives valuable information on lubrication mechanism of microemulsion in warm rolling of magnesium alloy strips.

The purpose of this study is to discuss the principles and factors that influence the site selection of emergency medical facilities for public health emergencies. This paper discusses the selection of the best facilities from the available facilities, proposes the capacity of new facilities, presents a logistic regression model and establishes a site selection model for emergency medical facilities for public health emergencies in megacities.

Using Guangzhou City as the research object, seven alternative facility points and the points' capacities were preset. Nine demand points were determined, and two facility locations were selected using genetic algorithms (GAs) in MATLAB for programing simulation and operational analysis.

Comparing the results of the improved GA, the results show that the improved model has fewer evolutionary generations and a faster operation speed, and that the model outperforms the traditional P-center model. The GA provides a theoretical foundation for determining the construction location of emergency medical facilities in megacities in the event of a public health emergency.

First, in this case study, there is no scientific assessment of the establishment of the capacity of the facility point, but that is a subjective method based on the assumption of the capacity of the surrounding existing hospitals. Second, because this is a theoretical analysis, the model developed in this study does not consider the actual driving speed and driving distance, but the speed of the unified average driving distance and the driving distance to take the average of multiple distances.

The results show that the method increases the selection space of decision-makers, provides them with stable technical support, helps them quickly determine the location of emergency medical facilities to respond to disaster relief work and provides better action plans for decision makers.

The results show that the algorithm performs well, which verifies the applicability of this model. When the solution results of the improved GA are compared, the results show that the improved model has fewer evolutionary generations, faster operation speed and better model than the intermediate model GA. This model can more successfully find the optimal location decision scheme, making that more suitable for the location problem of megacities in the case of public health emergencies.

The research findings provide a theoretical and decision-making basis for the location of government emergency medical facilities, as well as guidance for enterprises constructing emergency medical facilities.

The purpose of this paper is to verify the relationship between the features of social media and knowledge sharing, and to examine how ambient awareness mediates this relationship.

An experiment is designed to stimulate the knowledge work in a famous Chinese business college and 156 valid samples were obtained. AMOS was used in this paper to examine the theoretical model.

There is a correlation among features of social media, ambient awareness and knowledge sharing. Surprisingly, network translucence, which indicates individuals’ meta-knowledge of others’ connections, has no influence on knowledge sharing. Although this is inconsistent with conjecture of the existing literature, it can be well explained by the phenomenon in real life, such as privacy setting in social media.

For employees who use social media to promote knowledge sharing within organizations, this study reminds them of the importance of ambient awareness. For managers, this study can give them some suggestions to make employees take full advantage of social media to achieve optimal benefits of knowledge sharing, thus improving organizational performance and innovation. For social media designers, they can make social media more useful in knowledge work by improving its specific features.

This paper proposes that ambient awareness is the mediator of the effect path between communication and knowledge sharing. And the status perception of coworkers’ exchanging information is closely related to knowledge sharing.

Under the pressure of environment degradation and energy consumption rises, solar photovoltaic power generation (SPPG) has been seen as a strategic emerging industry in China. However, the SPPG projects have many uncertain factors in the process of the life cycle. The purpose of this paper is to evaluate the investment risk comprehensively.

Specifically, this paper firstly identified the risk factors and consequences of SPPG project investment and constructed the investment risk factor evaluation index system. Secondly, the factor analysis method was used to reveal the key investment risk factors and risk consequences. Then, the research hypotheses and conceptual model were proposed, and the empirical analysis was conducted based on the structural equation model (SEM).

The results showed that the six key risk factors (political, economic, technological, management, market and natural) have a significant impact on the risks during the whole period. The influence degree of risk factors in the operational period is such that market factor > political factor > economic factor > management factor > natural factor > technical factor. In addition, the risk of constructional period has a significant impact during the risk of operational period. Except for natural factors, all risk factors are correlated among the mutual influence relationship of risk factors. Finally, this paper puts forward management implications based on the above conclusions: effectively identify investment risk factors and comprehensively improve risk management ability; (refine the risk factors of SPPG project investment and develop targeted risk prevention and control strategies; define risk management objectives and make relevant strategic plans.

Because of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed propositions further.

It can also help stakeholders propose risk prevention strategies throughout the entire process, to promote the sustainable development of SPPG projects.

This study focuses on the investment risk of SPPG projects, by building an evaluation index system to identify key risk factors, and then establishes a SEM covering constructional period risks and operational period risks to better explore SPPG project risks, in an effort to reveal the mutual influence relationship and influence path of investment risk factors of the projects.