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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.

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.

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.

Consider the mutual coupling between loads, the purpose of this paper is to study the total transmission efficiency based on different load coil positions relative to the charging platform, to provide the theoretical basis for the design and parameter optimization of one-to-multiple wireless charging platform.

Based on the dual-load series-resonant wireless power transfer system, the expression of system efficiency and its calculation model is achieved using the equivalent circuit theory. Finally, a 96 kHz magnetic resonance wireless power transmission test platform is built up to verify the theoretical analysis given in this paper.

For the completely resonant circuit, the transmission efficiency can be improved by increasing the transmitter-receiver coupling and reducing the coupling between receivers. The total transmission efficiency achieves its lowest value when two loads are with equal competitive capability.

Through the simulation analysis of efficiency formula, the selection principle of impact factors can be applied to the optimization analysis of the transmission efficiency.

The design method of high-resolution capacitor arrays was proposed to improve the precision of successive approximation register (SAR) analog-to-digital converters (ADCs) without calibration and optimize the circuit area.

According to calculation of equivalent series capacitors and change of voltage at the comparator input node, two three-stage structures of capacitor arrays and a general design flow of the multi-stage capacitor arrays were presented. Non-ideal factors on the capacitor arrays were analyzed, and the applications of the two structures were explained based on the capacitor mismatch.

A multi-stage capacitor array for 16-bit SAR ADCs was implemented. The simulation result shows that its nonlinear error was less than 0.3LSB with no gain error and the sampling capacitance accounted for 92.42% of the total capacitance. Effects of capacitive parasitic and mismatch on capacitor arrays were confirmed.

The proposed method focused on capacitor arrays design of high-resolution SAR ADCs. It effectively reduced nonlinear errors, improved SNR and optimized the area of SAR ADCs. The design method was suitable for SAR ADCs with different resolutions to improve their precision.

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.

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.

Smart cities show a “booming” trend both in the academia and the industry in recent years. Scholars across the world have been investigating how new technologies are applied to develop new services to the inhabitants and cities all over the world also address the “smart cities” challenges by promoting policymaking and governance. This paper aims to conduct in-depth research on smart cities by combining the study of governance policy study and information technology study.

This paper empirically mapped the trends of smart city development, outstanding scholars and hot topics about smart cities by analyzing important references using CiteSpace. The authors visualized references and topics to analyze smart city research, based on empirical data from Web of Science. Furthermore, two most important research branches – topics from smart city governance research and those from information systems (IS) research were studied, respectively.

First, the authors mapped the development of research and divided the development into three different stages. Second, the authors explored important, influential and instructive publications and publications’ attributes including authors, institutions, journals and topics. Third, the authors found there are different characteristics between the IS group and the governance group in publication situations, influential institutions, journals and authors, although the research points of the two branches are overlapping and fragmented. Finally, the authors proposed important topics, which include “internet of things (IoT)”, “big data”, “smart city systems” and “smart city management” and the authors predicted that “IoT” and “smart city challenge” would be future trends in recent years.

This study is an innovative research of its category because it visualized the development of smart city research, analyzed both governance and technology branches of smart city research synthetically using CiteSpace and forecasted future trends of smart city research by topics analysis and visualization of evolution.

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.