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Despite the burgeoning popularity of virtual gifting in live streaming, research lacks an in-depth understanding of the drivers behind this behavior. Using para-social relationship (PSR), this study aims to capture viewers’ lively social feelings toward the streamer as the key factor leading to the purchase behavior of virtual gifts. It also aims to establish a theoretical link between PSR and viewers’ holistic experience in live streaming as captured by cognitive absorption and aims to investigates the role of technological features (i.e. viewer–streamer and viewer–viewer interactivity, streamer-level and viewer-level deep profiling and design aesthetics) in shaping viewers’ experience.

Based on 433 survey responses, this study employs a combination of structural equation modeling and neural networks to offer valuable insights into the relationships between the technological environment, viewer experience and viewer behavior.

Our results highlight the salience of PSR in promoting the purchase of virtual gifts through cognitive absorption and the importance of the technological environment in eliciting the viewer experience. This study sheds light on the development of PSR in a technological environment and its relationship with cognitive absorption.

By applying PSR to conceptualize viewers’ perceived connection with the streamer, this study extends the research on purchase behavior in the non-shopping context by providing an enlightened understanding of virtual gift purchase behavior in live streaming. Moreover, by theoretically linking PSR with cognitive absorption, virtual gift purchase and technological features of live streaming, it enriches the theory of PSR and bridges the gap between the design practice of supporting the IT infrastructure of live streaming and research.

Social media (SM) platforms have become major sources for generating, sharing and gathering political and election news. Although there appears to be an assumption that reliance on SM for political news consumption will continue to gain in popularity, there are reasons to believe that many Americans are retreating from using SM for political news. The purpose of this study is to examine if Americans are reducing reliance on SM for political news and to analyze why retreat may be happening.

Using longitudinal panel data from Pew’s American Trends Panel study, the authors tracked 993 respondents from February of 2016 to November of 2019 to monitor their reliance on SM for political news leading up to the 2020 US presidential election.

The results of this study indicate that a sizeable percentage of people (about a third) are retreating from SM platforms for political news consumption and some are abandoning it altogether – people we refer to as new SM “nones.” The authors find that retreat from SM is associated with increased distrust of the information found on the platforms. Concerns about fake news, incivility on SM and information overload were unrelated to retreat from use of SM for political news consumption.

The findings of this study are novel and suggest that reliance on SM for political news by the public may have waxed, seen its zenith and may now be waning largely because of distrust in the information found on SM platforms.

Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.

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.

By building and examining an integral model, the principal objectives of this research are to systematically explore how indirect and direct network externalities lead to user loyalty toward WeChat through the mediating effect of perceived gratifications.

The data were collected through an online survey of 688 young people in Mainland China. To empirically assess the conceptual model, zero-order correlation analyses and structural equation modeling were carried out utilizing web-based data.

Path analysis results demonstrate that indirect network externalities and direct network externalities exert a significant impact on users' hedonic gratifications and utilitarian gratifications. Moreover, the study discovers the significant mediating influences of utilitarian gratifications on the association between indirect network externalities and user loyalty.

Theoretically, this article may extend the scope of diverse studies on the association between network externalities and perceived gratifications and offer fresh insights into how mobile social media could actually improve user loyalty through enhancing perceived values among younger generation. Practically, this research assists mobile social media practitioners in retaining users and gaining competitive advantages over rival applications.

Although the extraordinary growth of WeChat has successfully become the dominant media by which individuals develop interpersonal network and contact with others, the roles of perceived gratifications between network externalities and user loyalty toward WeChat have not yet been investigated in depth. These obtained outcomes not only enrich the existing literature regarding the relationship between network externalities and affective response, but also offer fresh insights to mobile social media designers, marketers and users.

The Internet is used as a tool to seek health information by individuals. Mental health concerns are the high prevalence of the novel coronavirus disease 2019 (COVID-19) and preventive steps are required to curb the illness. Therefore, to gain more insight into health concerns, it is now a common practice to seek health information on the Internet. This study propose an integrated theoretical model to explore the relationship between COVID-19 protocols and perceived online trust with online health information seeking intention (OHISI) and a moderating effect of perceived severity and perceived urgency.

Data are collected from 325 athletes in the category of individual and team sports through an online survey in a Likert-scale questionnaire. The analysis is performed with a quantitative methodology.

The study reveals the bright side of online health information (OHI), which brings athletes together and has played out with virtual happy hours, meetings and events. The bright side of OHI reflects social, cultural, technological and economic benefits. An OHI chatbot offers bright personalised side information to the individual seeker, which is more convenient and efficient than human capabilities.

The pivotal contribution is the integrated theoretical framework that is derived from multidisciplinary literature to capture the complexity of OHI. Also, it conceptualises the constructs in the context of OHI and COVID-19.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

This study examines a controversial issue (biotechnology) and how news disputes about misinformation related to the issue impacts individuals' attitudes toward a biotechnology company and their trust in the media source.

This study conducts a 2 (risk: low vs. high) x 2 (pre-existing attitude: anti gene-editing technology vs. pro gene-editing technology) x 2 (dispute message: absent vs. present) x 2 (media source: Buzzfeed vs NYT) factorial online experiment using a Qualtrics panel (N = 1,080) to examine the impact on individuals' attitudes toward a biotechnology company and trust in the media source.

Results indicate that dispute messages enhance attitudes toward the company but decrease trust in media sources. Interaction effects between pre-existing attitude and the dispute message, along with perceived risk and the dispute message, illustrate stark differences in how individuals with favorable vs. unfavorable pre-existing attitudes assessed the company after viewing the dispute message.

This study applies arguments from extant literature about prebunking and debunking misinformation. Specifically, this study investigates how dispute messages, a form of debunking through source derogation, actually impact individuals' perceptions of media credibility and/or their attitudes about the content they are reading. The study findings also reveal new insights regarding the interaction between pre-existing attitudes and perceived risk, as well as how dispute messages interact with each of the aforementioned factors.

Technology addiction is an increasingly severe problem. TikTok has become increasingly popular recently, and its addiction is also a major concern. This study aims to examine the antecedents and outcomes of TikTok addiction.

The authors collect 579 data from Chinese users using an online survey. The authors use structural equation modeling with partial least squares (PLS-SEM) to analyze data and test hypotheses.

The results illustrate that perceived enjoyment, social relationship, utilitarian need and social influence positively affect TikTok addiction. Both social anxiety and loneliness have positive effects on TikTok addiction. Moreover, parasocial relationships positively moderate the association between the antecedents of self-determination theory (SDT) (perceived enjoyment, social relationship, utilitarian needs, social influence, social anxiety and loneliness) and TikTok addiction. Meanwhile, TikTok addiction intensifies conflicts, including technology-family conflict, technology-person conflict and technology-work conflict. These conflicts reduce life satisfaction.

It offers practical implications for preventing and avoiding TikTok addiction to create a healthy environment.

This study is one of the few to provide a complete process of TikTok addiction. It systematically investigates the antecedents and outcomes of TikTok addiction.