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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.

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.

Integrating the uses and gratifications (U&G) theory, the notion of information richness and personal epistemology framework, the purpose of this research is to propose and empirically validate a framework which specifies Internet users' urge to click clickbaits.

The hypotheses in the proposed framework were tested using a between-participants experimental design (N = 204) that manipulated information richness (text-only vs. thumbnail clickbaits).

Curiosity, perceived enjoyment and surveillance were significant predictors of the urge to click. In terms of information richness, the urge to click was higher for thumbnail vis-à-vis text-only clickbaits. IEB (IEB) moderated the relation between the gratification of passing time and the urge to click.

This paper represents one of the earliest attempts to investigate Internet users' urge to click clickbaits. Apart from extending the boundary conditions of the U&G theory, it integrates two other theoretical lenses, namely, the notion of information richness and personal epistemology framework, to develop and empirically validate a theoretical framework.

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.

The purpose of this paper is to explore the use of community question answering sites (CQAs) on the topic of terrorism. Three research questions are investigated: what are the dominant themes reflected in terrorism-related questions? How do answer characteristics vary with question themes? How does users’ anonymity relate to question themes and answer characteristics?

Data include 300 questions that attracted 2,194 answers on the community question answering Yahoo! Answers. Content analysis was employed.

The questions reflected the community’s information needs ranging from the life of extremists to counter-terrorism policies. Answers were laden with negative emotions reflecting hate speech and Islamophobia, making claims that were rarely verifiable. Users who posted sensitive content generally remained anonymous.

This paper raises awareness of how CQAs are used to exchange information about sensitive topics such as terrorism. It calls for governments and law enforcement agencies to collaborate with major social media companies to develop a process for cross-platform blacklisting of users and content, as well as identifying those who are vulnerable.

Theoretically, it contributes to the academic discourse on terrorism in CQAs by exploring the type of questions asked, and the sort of answers they attract. Methodologically, the paper serves to enrich the literature around terrorism and social media that has hitherto mostly drawn data from Facebook and Twitter.

This paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what extent does user feedback affect influential users in changing from one archetype to another?

Based on a sample of 13,840 influential users drawn from the Covid-19 community on Zhihu, the archetypes of influential users were derived from their ongoing participation behavior in the community using the Gaussian mixture model. Additionally, user feedback characteristics such as relevance and volume from 222,965 commenters who contributed 546,344 comments were analyzed using the multinomial logistic regression model to investigate the archetype change of influential users.

Findings suggest that influential users could be clustered into three distinctive archetypes: touch-and-go influential users, proactive influential users and super influential users. Moreover, feedback variables have various impacts on the influential user archetype change, including a shift toward creating higher-quality content and fostering increased interaction, a shift toward generating lower-quality content and decreased interaction but improved speed and having mixed effects due to differences in information processing among these archetypes.

This study expands the existing knowledge of influential users and proposes practical approaches to cultivate them further.

This paper seeks to propose and empirically validate a conceptual model on the antecedents of review helpfulness comprising three constructs, namely, valence dissimilarity, lexical dissimilarity and review order.

A panel dataset of customer reviews was collected from Amazon. Using deep learning and text processing techniques, 650,995 reviews on 13,612 products from 570,870 reviewers were analyzed. Using negative binomial regression, four hypotheses were tested.

The results indicate that new reviews with high valence dissimilarity and lexical dissimilarity compared to existing reviews are less helpful. However, over the sequence of reviews, the negative effect of review dissimilarity on review helpfulness can be moderated. This moderation differs for valence and lexical dissimilarity.

This study explains review dissimilarity in the context of online review helpfulness. It draws on the elaboration likelihood model and explains how the impacts of peripheral and central cues are moderated over the sequence of reviews.

The findings of this study provide benefits to online retailers planning to implement online reviews to improve user experience.

This paper highlights the importance of review dissimilarity in identifying user perception of online review helpfulness and understanding the dynamics of this perception over the sequence of reviews, which can lead to improved marketing strategies.

In the wake of a rumor outbreak, individuals exchange three types of messages: rumor messages, counter-rumor messages, and uncertainty-expressing messages. However, the properties of the three types of messages are relatively unknown particularly in the social media context. Hence, the purpose of this paper is to examine these three types of messages posted on social media in the wake of a rumor outbreak.

Data included tweets posted after the outbreak of a rumor that wrongly accused the fast food chain Kentucky Fried Chicken (KFC) for selling rats instead of chicken. Using a deductive approach, codes were derived via content analysis on the tweets. Volume and exposure of tweets were also examined.

Counter-rumor tweets (52 percent) outnumbered rumors tweets (32 percent) and uncertainty-expressing tweets (16 percent). Emotions and personal involvement were abundant in rumor tweets. Expressions of credence and references to URLs were high in counter-rumor tweets. Social ties were found widely in uncertainty-expressing tweets. The high volume and exposure of counter-rumor tweets compared with those of either rumor tweets or uncertainty-expressing tweets highlight the potential of counter-rumors to mitigate rumors.

This research ventures into a relatively unexplored territory by concurrently examining rumor messages, counter-rumor messages and uncertainty-expressing messages in the wake of a rumor outbreak. It reveals that counter-rumor messages have the potential to mitigate rumors on social media.

The purpose of this paper is twofold: to build a theoretical model that identifies textual cues to distinguish between authentic and fictitious reviews, and to empirically validate the theoretical model by examining reviews of positive, negative as well as moderate polarities.

Synthesizing major theories on deceptive communication, the theoretical model identifies four constructs – comprehensibility, specificity, exaggeration and negligence – to predict review authenticity. The predictor constructs were operationalized as holistically as possible. To validate the theoretical model, 1,800 reviews (900 authentic + 900 fictitious) evenly spread across positive, negative and moderate polarities were analyzed using logistic regression.

The performance of the proposed theoretical model was generally promising. However, it could better discern authenticity for positive and negative reviews compared with moderate entries.

The paper advances the extant literature by theorizing the textual differences between authentic and fictitious reviews. It also represents one of the earliest attempts to examine nuances in the textual differences between authentic and fictitious reviews across positive, negative as well as moderate polarities.

This study investigates how autonomous vehicle (AV)-related electronic word-of-mouth (eWOM) of different polarities affects attitude and perceived risk from the perspectives of both passengers and pedestrians and whether any gender differences exist. It also seeks to identify AV-adoption user archetypes.

An online experiment was conducted, manipulating eWOM polarity (positive, negative or mixed) as a between-participants factor.

While eWOM polarity did not affect attitude, perceived risk was the highest in the mixed eWOM condition. Males and females differed from each other in terms of attitude toward AVs from a passenger perspective, attitude toward AVs from a pedestrian perspective and perceived risk for passengers in AVs. Four AV-adoption user archetypes were identified: AV watchfuls, AV optimists, AV nonchalants and AV skeptics.

The paper contributes to the AV adoption literature by adding the effects of eWOM. It not only sheds light on how AV-related eWOM polarity affects attitude and perceived risk but also teases out nuances from the perspectives of passengers and pedestrians as a function of gender.