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Taking the COVID-19 as the background, this study aims to investigate the direct influencing factors regarding knowledge sharing behavior (KSB) on new media platforms and discuss how the characteristics of the users could enhance the KSB through moderation effect, and provide empirical evidences.

Based on the social exchange theory and after the text analysis of the data collected from the Tiktok platform in 2020, this paper uses the quantitative method to evaluate the factors influence KSB on short video social platform during the COVID-19 outbreak.

KSB on new media platform could be enhanced by richer knowledge content of the video posted and the attribute of the platform users directly. Platform users could affect the trustworthiness of the knowledge shared, thus influence the knowledge sharing. On the early stage of the COVID-19, the richer content of the knowledge released by users could effectively enhance the KSB. On the early stage of the emergency events, the official users could play a significant role on KS. During the mitigation stage of COVID-19, the KSB of the knowledge shared by unofficial users with richer content could be enhanced and the moderation effect is relatively stronger.

The research extends the social exchange theory to a disaster management context. The authors provide an effective reference for future governments to effectively cope with the epidemic and spread public knowledge in an emergency response context. By analyzing the influence of knowledge content and influencer characteristics, it could help the social media platform to improve content management and optimize resource allocation.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

The purpose of this paper is to investigate the impacts of different gray markets’ structures on both supply chain decisions and associated profits.

Within the context of gray markets, using game theory approach in this paper, supply chains have been considered as assets of manufacturers, distributors and speculators, within which manufacturers sell products to distinctive markets either directly or through authorized distributors, while speculators buy products from a lower price market and then sell them in a higher price market. Our study has examined different decision variables within such a framework.

Considering a situation where one manufacturer sells its products either directly in one market (Market 1) or through its authorized distributor (Market 2), due to different products prices in both markets, results have shown that, when market elasticity is less than its critical value, a speculator can sell a gray market product arbitrage in market 2, whereas when the market elasticity is greater than its critical value, a speculator can sell a gray market product arbitrage in market 1. In addition, manufacturers—as leaders of Stackelberg game—are always the most profitable stakeholders within a gray market supply chain.

In this study, equilibrium results for each market have been obtained, optimal results have been compared, and accordingly, valuable insights have been developed. Such results would help managers to take better managerial decisions, as well as strategizing policies in gray markets.

In this paper, we have considered a gray market where both distributors and speculators exist and act as parallel channels. To the best of our knowledge, the extant literature focuses either on distributors or speculators, but never concurrently on both. In fact, the coexistence of one distributor and one speculator in a gray market will impact their own decisions, as well as both decisions and profits of other stakeholders, and hence, will exert an impact on the manufacturer side.

Drawing on the pleasure-arousal-dominance (PAD) emotion model, the emotional states of consumers embedded in online reviews can be described through three dimensions, that is, pleasure, arousal and dominance, rather than only the one-dimensional positive and negative polarity, as in previous studies. Therefore, this study aims to explore the effect of online review emotion on perceived review helpfulness based on these three basic emotional dimensions.

A lexicon-based method is developed to analyze PAD emotions of online reviews from JD.com. The zero-inflated negative binomial regression is utilized to empirically validate the study hypothesis. The authors examine the influence of pleasure, arousal, dominance, emotion diversity and emotion deviation on review helpfulness, as well as the moderating effect of product type on the relationship between all independent variables and online review helpfulness.

The study results show that the pleasure emotion impairs the helpfulness of online reviews, while the arousal and dominance emotions have a positive impact. Moreover, the authors find that compared with search products, the effects of pleasure, arousal and dominance on perceived helpfulness are strengthened for experience products. However, the emotional diversity and emotional deviation have opposite effects on the helpfulness of search products and experience products. Additionally, the results show that dominance emotion plays a more important role in the interaction effect.

The empirical findings confirm the applicability of PAD in the online review context and extend the existing knowledge of the influence of review emotion on helpfulness. A feasible scheme for extracting PAD variables from Chinese text is developed. The study findings also have significant implications for reviewers, merchants and platform managers of e-commerce websites.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

Retail investors are prone to be affected by information dissemination in social media with the rapid development of Web 2.0. The purpose of this study is to recognize the factors that may impact users' retweet behavior, namely information dissemination in the online financial community, through machine learning techniques.

This paper crawled data from the Chinese online financial community (Xueqiu.com) and extracted author-related, content-related, situation-related, stock-related and stock market-related features from the dataset. The best information dissemination prediction model based on these features was determined by evaluating five classifiers with various performance metrics, and the predictability of different feature groups was tested.

Five prevalent classifiers were evaluated with various performance metrics and the random forest classifier was proven to be the best retweet prediction model in the authors’ experiments. Moreover, the predictability of author-related, content-related and market-related features was illustrated to be relatively better than that of the other two feature groups. Several particularly important features, such as the author's followers and the rise and fall of the stock index, were recognized in this paper at last.

This study contributes to in-depth research on information dissemination in the financial domain. The findings of this study have important practical implications for government regulators to supervise public opinion in the financial market.

The purpose of this study is to investigate the application of non-destructive testing methods in measuring bearing oil film thickness to ensure that bearings are in a normal lubrication state. The oil film thickness is a crucial parameter reflecting the lubrication status of bearings, directly influencing the operational state of bearing transmission systems. However, it is challenging to accurately measure the oil film thickness under traditional disassembly conditions due to factors such as bearing structure and working conditions. Therefore, there is an urgent need for a nondestructive testing method to measure the oil film thickness and its status.

This paper introduces methods for optically, electrically and acoustically measuring the oil film thickness and status of bearings. It discusses the adaptability and measurement accuracy of different bearing oil film measurement methods and the impact of varying measurement conditions on accuracy. In addition, it compares the application scenarios of other techniques and the influence of the environment on detection results.

Ultrasonic measurement stands out due to its widespread adaptability, making it suitable for oil film thickness detection in various states and monitoring continuous changes in oil film thickness. Different methods can be selected depending on the measurement environment to compensate for measurement accuracy and enhance detection effectiveness.

This paper reviews the basic principles and latest applications of optical, electrical and acoustic measurement of oil film thickness and status. It analyzes applicable measurement methods for oil film under different conditions. It discusses the future trends of detection methods, providing possible solutions for bearing oil film thickness detection in complex engineering environments.

The Zr-40Ti-4.5Al-4.2V (ZT40) alloy is one of new developed Zirconium alloys with high mechanical properties and great potential for application. The investigation about effects of plastic deformation on microstructure and mechanical properties can promote practical applications of the new high performance ZrTi based alloys. The microstructural evolution and mechanical properties of the ZT40 alloy suffered hot rolling with thickness reduction from 30% to 60% at 775 °C are investigated in this work. Results show that the phase constitution changes from (α + β) to (β + fcc) while the original specimen underwent hot rolling and subsequent water quenching. The β phase in hot rolled specimen adopts preferred orientation form (200) and (211) planes to only (200) plane while the rolling reduction increases from 30% to 60%. Furthermore, no obvious preferred orientation can be detected in specimen with reductions of 60%. Micrographs analysis shows that the dynamic recrystallization occurs in hot rolled specimens. Volume fraction of the DRX grains is approximately 8% in 30% reduction specimen and increases with the increasing of rolling reduction. Nearly full recrystallization is observed in the specimen with reductions of 60%. Hardness test shows that the HV of hot rolled specimen decreases from 384 HV to 329 HV as the increasing of reduction from 30% to 60%. The mechanisms of microstructural evolution and variation of hardness are also discussed. The finding should contribute to understand microstructural evolution, to adjust mechanical properties and to promote practical applications of Zirconium alloys.

The purpose of this paper is to investigate how WeChat addiction influences users’ physical, mental, and social health.

A national survey was conducted in China. A total of 1,058 responses were collected from 31 regions of China.

The regression results show that WeChat addiction is negatively associated with users’ physical, mental, and social health. The negative effects are significant even after adjusting for the effects of the Big Five personality traits, years of using WeChat, and demographic variables such as age, gender, education level, and monthly income. Years of using WeChat is not significantly related to users’ health. It is also found that the influence of WeChat addiction on health outcomes is sensitive to years of WeChat use. The influence is dormant when users have less than three years of WeChat usage, but starts to exhibit itself after three years.

Addictive use of WeChat is associated with declining overall health among Chinese users. Given the cross-sectional nature of this study, definite causal relationship between WeChat addiction and health deterioration cannot be established. Controlled experiments are needed to further examine the causal effects of WeChat addiction.

WeChat is the most popular mobile social network service (SNS) in China, but its comprehensive impact on users’ health is rarely studied. This paper extends the extant research on SNS addiction by providing a deepened understanding of how mobile SNS addiction affects personal health in the unique context of WeChat, which provides an important contribution to the interdisciplinary research in public health, psychology, and information systems.

The purpose of this study is to explore the evolutionary path and stable strategy for the competitive dissemination between disinformation and knowledge on social media to provide effective solutions to curb the dissemination of disinformation and promote the spread of knowledge.

Based on the social capital (SC) theory, the benefit matrix is constructed and an evolutional game model is established in this paper. Through model solving and Matrix Laboratory (MATLAB) simulation, the factors that influence disinformation-believing users (DUs) and knowledge-believing users (KUs) to choose different strategies are analyzed.

The initial dissemination willingness, the disinformation infection probability, the knowledge infection probability and the knowledge penetration probability are proved to be crucial factors influencing the game equilibrium in the competitive dissemination process of disinformation and knowledge. Moreover, some countermeasures and recommendations for the governance of disinformation are proposed.

Currently most research interest lies in the disinformation dissemination model but ignores the interaction between disinformation and knowledge in the diffusion process. This study reveals the dynamic mechanism of social media users disseminating disinformation and knowledge and is expected to promote the formation of cleaner cyberspace.