Search results

Health-related online fake news (HOFN) has become a major social problem. HOFN can lead to the spread of ineffective and even harmful remedies. The study aims to understand Internet users' responses to HOFN during the coronavirus (COVID-19) pandemic using the protective action decision model (PADM).

The authors collected pandemic severity data (regional number of confirmed cases) from government websites of the USA and China (Studies 1 and 2), search behavior from Google and Baidu search engines (Studies 1 and 2) and data regarding trust in two online fake news stories from two national surveys (Studies 2 and 3). All data were analyzed using a multi-level linear model.

The research detected negative time-lagged relationships between pandemic severity and regional HOFN search behavior by three actual fake news stories from the USA and China (Study 1). Importantly, trust in HOFN served as a mediator in the time-lagged relationship between pandemic severity and search behavior (Study 2). Additionally, the relationship between pandemic severity and trust in HOFN varied according to individuals' perceived control (Study 3).

The authors' results underscore the important role of PADM in understanding Internet users' trust in and search for HOFN. When people trust HOFN, they may seek more information to implement further protective actions. Importantly, it appears that trust in HOFN varies with environmental cues (regional pandemic severity) and with individuals' perceived control, providing insight into developing coping strategies during a pandemic.

The purpose of this paper is to provide organizations in the Chinese cultural context with a conceptual model for an integrated adoption of existing knowledge management (KM) methods and to improve the effectiveness of their KM activities.

A comparative analysis is conducted between China and the western world based on a comprehensive document analysis from key databases available. In total, four critical dimensions, which are identified through the review of the related literature including observation, methodology, systems and applications, are used in the analysis for addressing the questions of why, what, how, and where in KM.

The paper rationalizes the need for this study in conformity with the emerging trend of an integrated use of diverse KM methods and approaches for effective KM in organizations. Further, a reference model for the integrated adoption of existing KM methods is developed with respect to the characteristics of the Chinese culture for improving the effectiveness of KM activities in the organizations. Such a model can adequately address the questions of why, what, how, and where in KM in Chinese organizations to the best of Chinese collective interest, which provides more comprehensive and unified KM views, activities, processes and technologies for collaborative innovation in Chinese organizations.

This paper is the first of its kind to systematically review existing KM methods and approaches with respect to the perspectives of observations, methodologies, systems and applications for effective KM in Chinese organizations.

The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data.

A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail.

MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance.

The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

The purpose of this paper is to identify funders’ motivations for investing in crowdfunding. It applies trust theory to propose a research model including three subject measures – fundraiser-related, project-related and platform-related factors. Trust has been categorized into cognitive and affective dimensions to specifically analyze the influential factors.

Bootstrapping is employed to analyze data collected from respondents with investment experience on equity crowdfunding projects. Structural equation modeling techniques are adopted to examine the factors that influence trust between funders and crowdfunding as well as the outcomes of this trust.

The results indicate that calculus trust and relationship trust collectively or separately transmit the effect of some antecedents to investment intention. However, there is no evidence indicating the mediating effects of calculus trust and relationship trust on the relationship of structural assurance and value congruence to investment intention.

This paper provides insights for crowdfunding fundraisers on how to build a strong relationship with funders, and it also gives crowdfunding designers advice on how to improve and perfect the platform functions.

This study contributes to a better understanding of the driving forces of calculus and relationship trust and their influence on investment intention. It is also the first to address a funder’s trust using a theoretical model describing the investor intention in crowdfunding and thereby extending the knowledge base of trust theory.

This study aims to using Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and scanning electron microscope/energy dispersive Xray spectrometer to identify different automotive coatings for forensic purpose.

Two four-layered samples in a hit-and-run case were compared layer by layer with three different methods. FTIR spectroscopy was used to primarily identify the organic and inorganic compositions. Raman spectrum and scanning electron microscope/energy dispersive Xray spectrometer (SEM-EDS) were further used to complement the FTIR results.

Two weak and tiny peaks in one layer found between two samples by FTIR, Raman microscope and SEM-EDS verified the result of differences. The study used the three instruments in combination and found it’s effective in sensing coatings, especially in the inorganic additives.

Using these three instruments in combination is more accurate than individually in multilayered coating analysis for forensic purpose.

The three different instruments all present unique information on the composition, and provided similar and mutually verifiable results on the two samples.

With this method, scientists could identify and discriminate important coating evidences with tiny but characteristic differences.

The primary objective of this research is to explore the underlying mechanisms of social ties influencing lurking behaviour in Microblog by combing role theory and social identity theory.

An online survey of 312 Microblog users in China was conducted to test the proposed research model and hypotheses. The partial least squares (PLS) approach was used to analyse the data.

The results show that social ties affect lurking behaviour on Microblog in two ways, directly and indirectly through role stress. Social ties have a negative direct impact on lurking behaviour. Based on the multiple mediation analysis, social ties positively affect lurking through role overload and negatively affect lurking via role ambiguity. Perceived heterogeneity strengthens the positive effects of social ties on role overload and conflict, and weakens the negative impact of social ties on role ambiguity. Social identification weakens the positive relationship between social ties and role overload and strengthens the negative relationship between social ties and role ambiguity.

This study extends the literature on the social interaction reasons behind lurking behaviour in Microblog by revealing the effects of social ties in Microblog. The theoretical mechanisms of social ties on lurking behaviour in Microblog are unveiled by discovering the direct effect of social ties, the mediating effects of multidimensional role stress, and the moderating effects of different kinds of social identity.

Prior studies fail to reach a consensus on the effects of extrinsic motivation (EM) on knowledge contribution in virtual communities. To fill this research gap, this research proposes two mechanisms of EM – direct effect versus indirect effect via intrinsic motivation (IM) – and introduces prosocial motivation (PSM) as the moderator to define the valence of these two mechanisms.

This research adopts structural equation modeling to validate hypotheses based on 448 responses from XiaoMi online community users.

The results of the moderated mediation analysis show that the direct effect, indirect effect via IM and total effect of EM on knowledge contribution are contingent on the level of PSM. Specifically, as the level of PSM increases, the direct, indirect and total effects of EM on knowledge contribution and the effects of EM on IM change from positive to insignificant and then to negative. Although IM constantly plays an important role across levels of PSM, its effect is stronger when the degree of PSM is high.

This paper advances knowledge of an integrated framework of EM by simultaneously describing its direct and indirect effects, describing its positive and negative effects and untangling the boundary conditions under which each effect of EM applies.

The purpose of this paper is to examine the direct effects of Internet use on rural residents' income growth and the indirect effects of increasing their income by promoting rural residents' entrepreneurial and non-agricultural employment.

Regarding the implementation of the rural revitalization strategy, based on the 2016CFPS data, multiple linear regression analysis and mediation effect analysis are used. To decrease the potential endogeneity of the model, we used the instrumental variable in the model.

The results show that: (1) Internet use has a direct effect on rural residents' income growth; (2) rural residents' entrepreneurial or non-agricultural employment affects the mechanism of Internet use and their income growth, so that can perform an indirect promotion effect; (3) the direct promotion effect of Internet use is stronger than the indirect promotion effect of entrepreneurship and non-agricultural employment.

The effect of using Internet for the income growth of Chinese farmers has been confirmed by some scholars, but the specific mechanism is still relatively vague. The originality is to consider the intermediary transmission effect of entrepreneurship and non-agricultural employment in the study of the impact of Internet use on Chinese farmers' income growth, and use the mediation effect model for empirical analysis. The empirical research results further reveal this mechanism.

This paper aims to anticipate the possible development direction of WAAM. For large-scale and complex components, the material loss and cycle time of wire arc additive manufacturing (WAAM) are lower than those of conventional manufacturing. However, the high-precision WAAM currently requires longer cycle times for correcting dimensional errors. Therefore, new technologies need to be developed to achieve high-precision and high-efficiency WAAM.

This paper analyses the innovations in high-precision WAAM in the past five years from a mechanistic point of view.

Controlling heat to improve precision is an effective method. Methods of heat control include reducing the amount of heat entering the deposited interlayer or transferring the accumulated heat out of the interlayer in time. Based on this, an effective and highly precise WAAM is achievable in combination with multi-scale sensors and a complete expert system.

Therefore, a development direction for intelligent WAAM is proposed. Using the optimised process parameters based on machine learning, adjusting the parameters according to the sensors’ in-process feedback, achieving heat control and high precision manufacturing.