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Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

Building on social media and destination brand-related literature, this study aims to explore World Heritage Sites’ (WHSs) brand diffusion and formation process from long-term and short-term perspectives, which includes brand diffusion, user-generated content (UGC), opinion leaders and brand events’ impact.

This study uses a mixed-method including text mining, keyword analysis and social network analysis to explore the brand formation process of four popular WHSs in Beijing, namely, the Palace Museum, Great Wall, Summer Palace and Temple of Heaven and more than 10,000,000 users’ data on Sina Weibo has been implemented to uncover the underlying social media branding mechanism.

The results show that the number of postings keeps in a stable range in most months, but, in general, there are no common rules for changing trends among the four WHSs; long-term high-frequency keywords related to history and culture account for a higher percentage; different kinds of accounts have varying impacts on information diffusion, in which media accounts lead to a bigger influence. However, more followers do not necessarily mean more interactions and most of the interaction ratio is much lower than 0.01000; brand events facilitate brand dissemination and have an impact on the creation of UGC.

This study is valuable for destination marketers to deeper understand brand diffusion and formation and provides valuable insights for developing effective destination marketing strategies.

Unlike previous studies that only concern a few parts of destination brand formation via social media (e.g. brand diffusion, brand events or opinion leaders’ impact), this study takes a more comprehensive perspective by systematically analyzing the brand formation process of WHSs on social media. By considering both long-term diffusion and short-term representative events, this study provides a more holistic understanding of the branding mechanism.

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

As the application of crowdsourcing contests grows, leveraging the participation of superstars (i.e. solvers who have outstanding performance records in a crowdsourcing platform) becomes an emergent approach for managers to solve crowdsourced problems. Although much is known about superstars’ performance implications, it remains unclear whether and how their participation affects the size of a contest crowd for a crowdsourcing contest. Based on social contagion theory, this paper aims to examine the impact of superstars’ participation on the crowd size and studies how this impact varies across solvers with different heterogeneity in terms of skills, exposure and cultural proximity with superstars in crowdsourcing contests.

This paper uses secondary data from one crowdsourcing platform that includes 6,587 innovation contests to examine superstars’ main and contextual effects on the crowd size of a contest.

Our results reveal that superstars’ participation positively affects the crowd size of a contest in general. This finding suggests that social contagion is a fundamental mechanism underlying crowd formation in crowdsourcing contests. Our results also indicate that in contests that involve multiple superstars, superstars’ effect on crowd size becomes negative when we simultaneously consider other solvers’ heterogeneity in terms of skills, exposure and cultural background, and this negative effect will be intensified by increases in the skill gap, extent of exposure and cultural proximity between superstars and other solvers in the same contest.

Our research enhances the understanding of the influence of superstars and the mechanism underlying the emergence of contest crowds in crowdsourcing contests and contributes knowledge to better understand social contagion in a competitive setting. The results are meaningful for sourcing managers and platform supervisors to design contests and supervise crowd size in crowdsourcing contests.

The COVID-19 global pandemic reframed the argument for the importance of learning management systems (LMS) in unprecedented ways. Meanwhile, LMS users were forced to use these technologies despite the heightened anxiety compounded by the pandemic, very little technical preparation and the postpandemic learning environment that cannot depend on these pandemic-imposed regulations and adoptions. This study aims to examine the impacts of behavioral control and anxiety emotions on adoption decisions.

This quantitative research uses structural equation modeling to test the hypothesized relationships.

Results showed that perceived behavioral control is very important in boosting ease of use perceptions while computer anxiety exhibited a negative impact on perceived ease of use.

This research used cross-sectional data from a medium-sized university. Results must, therefore, be interpreted with this understanding in mind. Nonetheless, this research demonstrates the critical roles of control and emotions in technology adoption decisions among students in an online learning environment.

This research highlights the importance of behavioral control and anxiety emotions in technology adoption decisions. It reveals that to drive adoption, students’ control perceptions must be increased while simultaneously keeping anxiety emotions at bay. This understanding is key to communicating buy-in of LMS to students.

The purpose of this study was to understand factors affecting market mavens' use of social media for fashion-related information provision. The study examined market mavens' motivations to share fashion-related information. Specifically, this study investigates the impact of their motivations (i.e. pleasure from helping, a sense of obligation) and technology acceptance model variables (i.e. beliefs about and attitude toward using social media) on intention to use social media for sharing fashion-related information.

An online survey yielded 862 responses from US female respondents. A subset (N = 307) representing those high in market mavenism was used for the study. Structural equation modeling was employed for the analysis.

The results confirmed that market mavens were driven by pleasure from helping and a sense of obligation to share fashion-related information. These motivations and attitude toward using social media to disseminate fashion-related information positively influenced market mavens' intention to use it to disseminate fashion-related information. Furthermore, belief variables (i.e. perceived usefulness, ease of use and enjoyment associated with social media) indirectly impacted this intention.

The study adds to the scant research examining market mavens' motivations for sharing fashion-related information with others and their intention to use social media. It provides valuable insights for fashion retailers looking to enhance the impact of social media marketing through the deployment of market mavens – very knowledgeable, motivated and trusted consumers.

The sport industry, due to the great importance of intangible assets, represents a field of particular interest for the analysis of intellectual capital disclosure (ICD). However, this sector is still underexplored in the academic literature. This study aims to fill this gap by analysing the level of intellectual capital (IC) information disclosed by the most important basketball clubs on their website and the factors capable of influencing the dissemination of such information. Specifically, it examines the impact of social media visibility – in terms of number of social networks, number of posts, number of followers and internet visibility – on the ICD level.

Firstly, this study performs a manual content analysis of the websites of the top 80 European and US basketball clubs aimed at analysing the ICD level. Secondly, it provides for a regression analysis to test the impact of social media visibility on the amount of IC information disclosed.

Empirical results show a low level of ICD among the basketball clubs examined. They also demonstrate the positive impact of number of posts, number of followers and internet visibility on the amount of IC information disclosed online.

This study extends the analysis of the ICD to the sport industry, still little examined by the academic literature. In this regards, to the best of the authors’ knowledge, this is the first study to explore the ICD in the basketball industry.

Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can help us understand the development pattern of the public attention.

This study proposes the prediction model for the attention transfer behavior of social media users in the context of multitopic competition and reveals the important influencing factors of users' attention transfer. Microblogging features are selected from the dimensions of users, time, topics and competitiveness. The microblogging posts on eight topic categories from Sina Weibo, the most popular microblogging platform in China, are used for empirical analysis. A novel indicator named transfer tendency of a feature value is proposed to identify the important factors for attention transfer.

The accuracy of the prediction model based on Light GBM reaches 91%. It is found that user features are the most important for the attention transfer of microblogging users among all the features. The conditions of attention transfer in all aspects are also revealed.

The findings can help governments and enterprises understand the competition mechanism among multiple topics and improve their ability to cope with public opinions in the complex environment.

This chapter demonstrates a parallel between the presidential campaigns of Jair Bolsonaro (Brazil, 2018) and Donald Trump (United States, 2016) regarding their use of social media. Specifically, tweets from the former candidates on the social network sites were analyzed during a one-month timeframe before their respective presidential elections. Tweets were selected for analysis based on the fact that Twitter was the main platform used by both former presidential candidates. The analysis of the data reveals considerable similarities between the communication strategies of the two candidates. This research enlists McCombs and Shaw (1972) agenda setting theory based on their study of media during North American presidential campaigns in 1968 and Lippmann’s (2008) concept of public opinion. The methodology employed is based on Bardin (2011).

Nowadays, online platforms which provide products or services try to implement their homegrown communities to facilitate users' social interactions. Reviewers' activities in these communities can reflect their interests. Based on the theory of homophily, the authors aim to explore the impacts of the reviewer preference similarity and opinion similarity on the rate of product diffusion.

First, the authors construct reviewer similarity network based on their common interests and propose typical network metrics to measure reviewer preference similarity. Second, the authors measure reviewer opinion similarity with natural language processing. Finally, based on a panel data from an online video platform in China, both the fixed-effect and random-effect panel data models are constructed.

The authors find that reviewer preference similarity has a positive effect on the product diffusion, whereas reviewer opinion similarity has a negative effect on the diffusion. Furthermore, temporal distance moderates the relationship between reviewer similarity and the product diffusion. As a double-edged sword, review preference similarity hinders product diffusion in the initial phase, whereas benefits it in the later phase. Reviewer opinion similarity is always detrimental to product diffusion, especially in the initial phase.

This paper extends the understanding of homophily from the micro peer level to the group level by constructing reviewers' similarity network and highlights the important role of reviewer preference similarity and opinion similarity in product diffusion. The results also provide important insights for managers to design and implement diversity strategies for better product adoption in the community context.