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This study examines the phenomenon of “knowledge influencers,” individuals who convey expert information to non-expert audiences and attract users to subscribe to their self-created knowledge products. It seeks to address two research questions: (1) What are the antecedents that promote perceived attractiveness of knowledge influencers? and (2) How does perceived attractiveness of knowledge influencers affect users’ willingness to subscribe to knowledge products?

Guided by self-branding theory, which suggests that individuals strategically shape user perceptions and interactions to create an appealing image, the study employed a sequential mixed-methods approach. Qualitative interviews were conducted with knowledge influencers and their subscribers, followed by a quantitative survey of users with knowledge subscription experience to validate the findings.

Results suggested that knowledge influencers could enhance their attractiveness to users by promoting perceived professionalism, perceived familiarity, and perceived connectedness. Perceived attractiveness of knowledge influencers could directly affect users’ willingness to subscribe or indirectly through the role of users’ attachment to knowledge influencers.

By understanding the factors driving users’ subscription intentions, platform operators and influencers can refine their strategies to enhance user attachment and optimize monetization opportunities through personalized interactions and tailored content offerings.

This study contributes to the literature by elucidating the relationship between perceived attractiveness and users’ subscription intentions, offering new insights into the dynamics of online knowledge consumption.

This study aims to investigate motivators, mediator and moderator of users' sustained information seeking on academic social networking sites (ASNSs).

Drawing upon the expectancy–value theory and related information-seeking literature, the study developed a theoretical model to explain why and how users intend to continue seeking information on ASNSs. Thereafter, a field survey with 385 participants was conducted to test the model. Finally, a content analysis of participants' post-survey feedback was performed to complement the model test results by showing more fine-grained findings.

Results suggest that information usefulness and information adoption (IA) are significant to users' sustained information seeking on ASNSs, while users' satisfaction with ASNSs may play a mediating role in the relationship between information usefulness and sustained information seeking. Additionally, self-efficacy for critical thinking (SCT) weakens the impact of IA on users' satisfaction with ASNSs. The post-survey feedback analysis indicates that information usefulness is more critical to sustained information seeking for users with high SCT, whereas IA becomes more crucial to users' satisfaction with ASNSs and sustained information seeking for users with low SCT.

Although the extant literature has distinguished between information seeking and sustained information seeking, empirical research into users' sustained information seeking on ASNSs is limited. The study fills this gap by proposing and validating relevant factors and the boundary condition of users' sustained information seeking.

This study explores identity signaling used by an emerging class of knowledge celebrities in China – Knowledge Wanghong – who sell knowledge products on online platforms. Because identity signaling may involve constructing unique online identities and controlling over product-related and seller-related characteristics, the purpose of this study is two-fold: (1) to uncover different online identities of knowledge celebrities; and (2) to examine the extent to which the online identity type is associated with their product-related characteristics, seller-related characteristics and sales performance.

A unique data set was collected from a Chinese leading pay-for-knowledge platform – Zhihu – which featured the online profiles of tens of thousands of knowledge celebrities. Online identity types were derived from their self-edited content using Latent Dirichlet Allocation (LDA) topic modeling. Thereafter, their product-related characteristics, seller-related characteristics and respective sales performance were analyzed across different identity types using analysis of variance (ANOVA) and multiple-group linear regression.

Knowledge celebrities are clustered into four distinctive online identities: Mentor, Broker, Storyteller and Geek. Product-related characteristics, sell-related characteristics and sales performance varied across four different identities. Additionally, the online identity type moderated the relationships among their product-related characteristics, sell-related characteristics and sales performance.

As emerging-phenomenon-based research, this study extends related literature by using the notion of identity signaling to analyze a peculiar group of online celebrities who are setting an important trend in the pay-for-knowledge model in China.

As an increasing number of users have acquired information across the web and mobile platforms for social question and answering (Q&A), it is of interest to explore whether there are differences in social Q&A usages between the two platforms. The purpose of this paper is to compare web and mobile platforms of a social Q&A service from the user’s perspective in terms of three dimensions, namely, demographics, individual-based constructs, and information-based constructs.

Because Zhihu.com is one of the most popular social Q&A sites in China, the authors used online questionnaires to investigate its users’ perceptions of these three dimensions. From January to March 2016, the authors obtained 278 valid responses in total through snowball and convenient sampling. Collected data are analyzed through descriptive statistics and inferential statistics.

The results indicate that there exist significant differences between web users and mobile users on Zhihu.com in terms of gender, affinity, and information seeking. More specifically, compared to the male users, more female users rely on the mobile platform to access the information service; mobile users perceive higher affinity with Zhihu.com than web users; and mobile users perceive higher information-seeking intention than web users do.

Regarding the theoretical aspect, this study proposes a conceptual framework for comparison between the web and mobile platforms of social Q&A from the user’s perspective. Regarding the practical aspect, the comparative results of this study could give social Q&A service providers useful information about users’ differences between web and mobile platforms of social Q&A services.

The existing Nonlinear Dynamic Vibration Absorbers (NLDVAs) have the disadvantages of complex structure, high cost, high installation space requirements and difficulty in miniaturization. And most of the NLDVAs have not been applied to reality. To address the above issues, a novel Triple-magnet Magnetic Dynamic Vibration Absorber (TMDVA) with tunable stiffness, only composed of triple cylindrical permanent magnets and an acrylic tube, is designed, modeled and tested in this paper.

(1) A novel TMDVA is designed. (2) Theoretical and experimental methods. (3) Equivalent dynamics model.

It is found that adjusting the magnet distance can effectively optimize the vibration reduction effect of the TMDVA under different resonance conditions. When the resonance frequency of the cantilever changes, the magnet distance of the TMDVA with a high vibration reduction effect shows an approximately linear relationship with the resonance frequency of the cantilever which is convenient for the design optimization of the TMDVA.

Both the simulation and experimental results prove that the TMDVA can effectively reduce the vibration of the cantilever even if the resonance frequency of the cantilever changes, which shows the strong robustness of the TMDVA. Given all that, the TMDVA has potential application value in the passive vibration reduction of engineering structures.

An appropriate equivalent model is the key to the effective analysis of the system and structure in which permanent magnet takes part. At present, there are several equivalent models for calculating the interacting magnetic force between permanent magnets including magnetizing current, magnetic charge and magnetic dipole–dipole model. How to choose the most appropriate and efficient model still needs further discussion.

This paper chooses cuboid, cylindrical and spherical permanent magnets as calculating objects to investigate the detailed calculation procedures based on three equivalent models, magnetizing current, magnetic charge and magnetic dipole–dipole model. By comparing the accuracies of those models with experiment measurement, the applicability of three equivalent models for describing permanent magnets with different shapes is analyzed.

Similar calculation accuracies of the equivalent magnetizing current model and magnetic charge model are verified by comparison between simulation and experiment results. However, the magnetic dipole–dipole model can only accurately calculate for spherical magnet instead of other nonellipsoid magnets, because dipole model cannot describe the specific characteristics of magnet's shape, only sphere can be treated as the topological form of a dipole, namely a filled dot.

This work provides reference basis for choosing a proper model to calculate magnetic force in the design of electromechanical structures with permanent magnets. The applicability of different equivalent models describing permanent magnets with different shapes is discussed and the equivalence between the models is also analyzed.

This study presents evidence on the relations of stock market performance and industrial production growth for a group of 20 industrial markets. Evidence supports the notion that an increase in stock returns or a rise in the market value of stocks contributes positively to industrial production growth. Evidence suggests that stock market risk has a significantly negative effect on production growth for advanced markets. The Granger test finds a unidirectional causality running from stock returns or stock volatility to industrial growth. However, the United States shows a bilateral causality between stock volatility and industrial production growth.

The purpose of this study is to seek to quantify how unique service resources and consumer habit affect e-service loyalty (e-loyalty) in a highly competitive market. This study is grounded on Chinese social networking service (SNS) industry. A resource-based view is introduced as an alternative perspective to understand building consumer loyalty in e-service contexts.

A research framework is developed by reviewing prior literature. An online survey is conducted to collect research data. Based on 221 valid responses, the research model is tested by using partial least squares path modeling technique.

The features of market environments affects the loyalty of consumers to e-service providers. Consumers become mercenary in highly competitive and low differentiation e-service markets like Chinese SNS industry. The interaction of satisfaction and switching cost affects loyalty. Satisfied consumers can be either loyal or not loyal to a service provider depending on their level of switching cost, but unsatisfied consumers will have no loyalty. In addition, users are loyal to a SNS partly because it is their habit to use the service. Our study suggests that relying on consumer satisfaction to build e-loyalty may be problematic and risky.

The study represents an attempt to introduce the resource-based view to e-loyalty research. The research highlights the importance of habit in building consumers’ e-service satisfaction and loyalty and contributes to new insights on the importance of industry environment in determining e-service satisfaction–loyalty relationship based on studying consumers in a highly competitive market.

This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A) community represented by Zhihu.

A random sample of issue tag data generated by topics in the Zhihu network environment is selected. By defining user quality and selecting the top 20% and bottom 20% of users to focus on, i.e. top users and bot users, the authors apply time slicing for both types of data to construct label knowledge networks, use Q-Q diagrams and ARIMA models to analyze network indicators and introduce the theory and methods of network motif.

This study shows that when the power index of degree distribution is less than or equal to 3.1, the ARIMA model with rank index of label network has a higher fitting degree. With the development of the community, the correlation between tags in the tagging knowledge network is very weak.

It is not comprehensive and sufficient to classify users only according to their activity levels. And traditional statistical analysis is not applicable to large data sets. In the follow-up work, the authors will further explore the characteristics of the network at a larger scale and longer timescale and consider adding more node features, including some edge features. Then, users are statistically classified according to the attributes of nodes and edges to construct complex networks, and algorithms such as machine learning and deep learning are used to calculate large-scale data sets to deeply study the evolution of knowledge networks.

This paper uses the real data of the Zhihu community to divide users according to user activity and combines the theoretical methods of statistical testing, time series and network motifs to carry out the time series evolution of the knowledge network of the Q&A community. And these research methods provide other network problems with some new ideas. Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates.

Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. For the community, understanding the formation mechanism of its network structure and key nodes in the network is conducive to improving the knowledge system of the content, finding user behavior preferences and improving user experience. Future research work will focus on identifying outbreak points from a large number of topics, predicting topical trends and conducting timely public opinion guidance and control.

In terms of data selection, the user quality is defined; the Zhihu tags are divided into two categories for time slicing; and network indicators and network motifs are compared and analyzed. In addition, statistical tests, time series analysis and network modality theory are used to analyze the tags.