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This paper aims to examine recent trends and China's role in the emerging global green system of innovation (GGSI) and present the major achievement in China's R&D sectors and major challenges faced by the country. The authors use China's role in the clean development mechanism (CDM) as a case to demonstrate the country's willingness to adopt new technology and green innovation.

In order to understand China's transformation towards the GGSI, the approach used in this study is a review of innovation systems literature combined with analysis of statistical data from various sources. The authors also build an innovation model for the emerging global green system of innovation to demonstrate the building blocks which allow for transformational system failures to be avoided. The clean development mechanism (CDM) is used as a case example as to how GGSI works.

This paper puts into perspective some recent developments in innovation and argues that there is enough evidence to claim that the world is re‐orienting towards a global green system of innovation in which China is already one of the most significant players.

Through building a new innovation model, this study demonstrates the complexity and the development of innovation in the context of China's transformation towards the GGSI.

The strength of ties between individuals influences the speed and spread of crisis information dissemination (CID). By constructing networks of strong and weak ties, this paper aims to innovatively explore the impacts of strong and weak ties on the CID at the macro level.

To better understand the rules of CID in different kinds of social networks, this paper constructs a CID model based on the strength of ties in social networks and cellular automation, using simulations of CID speed and spread in an entire network, strong tie network and weak tie network generated by MATLAB.

As the article's major theoretical contribution, the results demonstrate that CID is more efficient in a network of weak ties than in a network of strong ties, and that the spread of CID has a positive correlation with the believability of the information disseminated and the dissemination tendency coefficient. Furthermore, the difference of dissemination speeds between strong and weak tie networks varies regularly with information believability and the dissemination tendency coefficient.

This study provides more effective public mechanisms for rapidly evaluating the believability of crisis information and responding to crises in real time. The findings also have some valuable implications for government agencies to improve the efficiency and effect of CID.

Social media provides a convenient way to popularise first aid knowledge amongst the general public. So far, little is known about the factors influencing individuals’ adoption of first aid knowledge on social media. Drawing on the information adoption model (IAM), this study investigates the joint effects of cognitive factors (e.g. perceived information usefulness (PIU)), affective factors (e.g. arousal (AR)) and social factors (e.g. descriptive norms (DN)) on first aid knowledge adoption (KA) and examines their antecedent cues from the perspective of information characteristics.

The data were collected from 375 social media users, and the structural equation model was adopted to analyse the results.

The results indicate that PIU, AR and DN all have positive direct effects on first aid KA. Additionally, the study highlights the positive synergistic effect of AR and PIU. Furthermore, the study suggests that AR is determined by message vividness (MV) and emotional tone (ET), whilst DN are determined by peer endorsement (PEE) and expert endorsement (EXE).

Our research is groundbreaking as it delves into the adoption of first aid knowledge through social media, thus pushing the boundaries of existing information adoption literature. Additionally, our study enhances the IAM by incorporating emotional and social elements and provides valuable insights for promoting the spread of first aid knowledge via social media.

Multi-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.

The authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.

The authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.

Most of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.

This paper aims to explore the influence of founder shareholders’ resources on the allocation of control rights from the perspective of incomplete contract theory and resource-based theory.

This paper analyzes newspaper materials with NVivo11on a case of battle for corporate control in Chinese top-listed company-Vanke Group.

The research shows that human capital is the key resource and the holding proportion of financial resources directly affects the allocation of control rights. At the same time, social capital is unstable and easily broken. At last, institutional environment also affects the degree between the relationship of founder shareholders’ resources and the allocation of control rights. The influence of founder-shareholder resources on the allocation of control rights follows the path of “crisis – founder-shareholder’s resources – founder’s ability - allocation of control rights.”

This study only selects the financial capital, human capital and social capital of Shi Wang, the founder of Vanke, as the analysis object. The study can expand the types of founder shareholder resources to verify and enrich the conclusions.

The current theoretical research in the literature focuses on the necessity of equity and shareholder’s resources versus the control rights. Some key factors and mechanism on the relationship have not been fully clarified. The results of this paper not only extend the combination research of social network and corporate governance, but also provide enterprise founders with references for making reasonable decisions during control battle.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.