Search results

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.

Flow force is one of the crucial factors affecting the performance of conical throttle valves. The purpose of this paper is to determine the relationship between the flow force and operating parameters of the conical throttle valve.

The flow force of the throttle valve can be obtained by the difference between the axial force and static pressure on the valve spool. In this paper, the internal fluid is divided into two regions and the axial force and static pressure are obtained, respectively. In addition, a two-dimensional axisymmetric simulation model and experimental test are carried out to validate the results of the flow force.

It can be seen that the theoretical, simulation and experimental results exhibit high agreement with each other and the error between them decreases with the increase in the size of the opening. The curves of pressure distribution reveal that the pressure on the spool first decreases then increases when it reaches the minimum pressure at the orifice. Additionally, the minimum pressure decreases with the increase of opening and pressure difference. The results also indicate that the increase in the size of the opening and inlet pressure has a positive effect on the flow force. However, the increase in outlet pressure has a negative effect on the flow force.

In this paper, the flow force calculation model of conical throttle valve is established and the influence of operating parameters on the flow force of conical throttle valve is studied.

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 takes the seven overcapacity industries such as the textile industry, electricity and heat, steel, coal, automobile manufacturing, nonferrous metals and petrochemical industry as research objects and proposes a TOPSIS grey relational projection group decision method with mixed multiattributes, which is used for the ranking of the seven industries with overcapacity and provided relevant departments with a basis for decision-making.

First, an evaluation index system from four aspects is established. Secondly, the attributes of linguistic information are converted into two-dimensional interval numbers and triangular fuzzy numbers, and an evaluation matrix is constructed and normalized. This paper uses the AHP method to determine the subjective weights and uses the coefficient of variation method to determine the objective weights. Moreover, this paper sets up the optimization model with the largest comprehensive evaluation value to determine the combined weights. Finally, the TOPSIS grey relational projection method is proposed to calculate the closeness of grey relational projections and to rank them.

This paper analyzes the problem of overcapacity in seven industries with the TOPSIS grey relational projection method. The results show that the four industries of automobile manufacturing, textile, coal and petrochemical are all in serious overcapacity levels, while the three industries of steel, nonferrous metals and electric power are relatively in weak overcapacity level in the three years of 2016–2018. TOPSIS grey relational projection method ranks the overcapacity degree of the seven major overcapacity industries, making the relative overcapacity degree of each industry more clear and providing a reference for the government to formulate targeted policies and measures for each industry.

By using TOPSIS grey relational projection method to evaluate the overcapacity of the seven major overcapacity industries, on the one hand, it makes the relative overcapacity degree of each industry more clear, on the other hand, it can provides the basis for the government and decision-making departments. This helps them promote better the healthy and orderly economic development of the seven major industries and avoid resource waste caused by overcapacity.

This article solves the single evaluation method caused by the limited indicators in the past, combines TOPSIS and the grey relational projection method and applies it to the overcapacity evaluation of the industry, not only applies it to the evaluation of overcapacity for the first time but also involves novel problems and methods, which expands the scope of application of the model.

This study aims to explore the role of equity investment in knowledge-driven business model innovation (BMI) in context of open modes according to the evidence from China’s primary market.

Based on the database of China’s private market and data set of news clouds, the statistic approach is applied to explore and explain whether equity investment promotes knowledge-driven BMI. Machine learning method is also used to prove and predict the performance of such open innovation.

The results of logistic regression show that explanatory variables are significant, providing evidence that knowledge management (KM) promotes BMI through equity investment. By further using back propagation neural network, the classification learning algorithm estimates the possibility of BMI, which can be regarded as a score to quantify the performance of knowledge-driven BMI

The quality of secondhand big data is not very ideal, and future empirical studies should use first-hand survey data.

This study provides new insights into the link between KM and BMI by highlighting the important roles of external investments in open modes.

From the perspective of investment, the findings of this study suggest the importance for stakeholders to share knowledge and strategies for entrepreneurs to manage innovation.

The concepts and indicators related to business models are difficult to quantify currently, while this study provides feasible and practical methods to estimate knowledge-driven BMI with secondhand data from the primary market. The mechanism of knowledge and innovation bridged by the experience from investors is introduced and analyzed.

In the paradox of personalized services and privacy risks, what factors influence users’ decisions is considered an interesting issue worth exploring. The current study aims to empirically explore privacy behavior of social media users by developing a theoretical model based on privacy calculus theory.

Privacy risks, conceptualized as natural risks and integrated risks, were proposed to affect the intention of privacy disclosure and protection. The model was validated through a hybrid approach of structural equation modeling (SEM)-artificial neural network (ANN) to analyze the data collected from 527 effective responses.

The results from the SEM analysis indicated that social interaction and perceived enjoyment were strong determinants of perceived benefits, which in turn played a dominant role in the intention to disclose the privacy in social media. Similarly, trust and privacy invasion experience were significantly related to perceived risks that had the most considerable effect on users’ privacy protection intention. And the following ANN models revealed consistent relationships and rankings with the SEM results.

This study broadened the application perspective of privacy calculus theory to identify both linear and non-linear effects of privacy risks and privacy benefits on users’ intention to disclose or protect their privacy by using a state-of-the-art methodological approach combining SEM and ANN.

This paper aims to build a theory model to examine the influencing mechanism of entrepreneurial environment on new firm performance based on network view and resource‐based view, and then carry out empirical research.

Data were obtained from 112 new firms, through the use of questionnaires, in Changchun city, China.

Evidence indicates that the entrepreneurial environment has an impact on new firm performance through entrepreneurial network and resource acquisition. The results show that eight out of ten hypotheses are supported.

In a hostile, dynamic and complex environment, the new firm should enhance its entrepreneurial network to ensure resource acquisition and then promote performance.

The paper shows definitely the significance of entrepreneurial network as a bridge between external environment and resource acquisition and new firm performance. Entrepreneurial network and resource acquisition are identified as important intermediary variables, and resource combination ability as a moderating variable. This paper examines the influence of external environment on new firm performance. The research has some theoretical and managerial implications for new firms' survival and obtaining growth in highly uncertain and turbulent environments.

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.

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.