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Following the hierarchy-of-effects model, this study aims to propose a two-step process framework to investigate social media post efficacy via attraction and likes.

The study analyzes 113,785 social media posts from 126 WeChat official accounts to explore how external (headline features and account type) and internal (content features and media type) features impact social media post attractions and likes, respectively.

The antecedents of post attraction differ from those of post likes. First, headline features (punctuation, length, sentiment and lexical density) and account type significantly influence social media post attraction. Second, content features (depth, tone, domain specificity, lexical density and readability) and media type affect social media post likes.

First, this study considers online user engagement as a two-step process regarding social media posts and explores different influencing factors. Second, the study constructs new variables (account type and domain specificity) in each stage of the two-step process model.

The use of social networking sites (SNSs) can promote life satisfaction mainly because of their social relationship benefits. Although prior studies examined the roles of different types of social capital (SC), the association between online and offline SC is ignored. This research addresses this gap by uncovering a mechanism of transformation between online and offline SC in terms of bonding and bridging types when linking SNSs usage and life satisfaction.

Categorizing the concept of SC into four sub-types from bonding/bridging and online/offline dimensions, the paper establishes a theoretical framework based on the transformation mechanism among these four kinds of SC. A component-based approach, partial least square method, is chosen for hypothesis testing with a survey-based sample collected from WeChat users.

First, SNSs usage is positively related to life satisfaction and four types of SC (i.e. online/offline and bonding/bridging SC). Second, both online bonding SC and offline bridging SC are positively related to life satisfaction and can mediate the relationship between SNSs usage and life satisfaction. Third, offline bonding SC is positively related to online bonding SC and can mediate the relationship between SNSs usage and online bonding SC; on the contrary, online bridging SC is positively related to offline bridging SC and can mediate the relationship between SNSs usage and offline bridging SC.

In the environment of SNSs, users can take vigorous strategies to better balance online and offline spaces and improve life satisfaction by adapting to the characteristics of SNSs in developing different types of SC. Specifically, it is encouraged for users to transfer online bridging SC into offline space and offline bonding SC into online space.

To the best of the authors’ knowledge, this is the first study exploring the association between online and offline SC when linking SNSs usage and life satisfaction. Instead of the single transformation direction from online to offline in prior Internet research, this research has revealed different transformation directions between online and offline SC in terms of bonding and bridging types in the context of SNSs.

Drawing upon the theory of planned behavior (TPB) and the self-regulation framework, the purpose of this paper is to investigate whether and how factors for social media continuance behaviors work differently between social networking sites and microblogging.

A survey method was used to collect two samples of 557 social networking sites users and 568 microblogging users. The proposed research model was tested with the structural equation modeling technique.

The empirical results demonstrate that the impacts of influencing factors on users’ continuance behaviors vary by types of social media services. Information sharing has a stronger impact on microblog users’ satisfaction than social network users while social interaction has a stronger impact on satisfaction for social network users than microblog users. In addition, interpersonal influence is more effective in shaping satisfaction for the social network users while media influence is more effective in shaping satisfaction for the microblog users.

This is one of the first studies that integrate TPB with Bagozzi’s self-regulation framework to understand the behavioral model of social networking and microblogging continuance. The findings show that the impacts of attitudinal beliefs regarding information sharing and social interaction on social media users’ satisfaction are different across social networking and microblogging contexts. Moreover, this study also reveals different effects of two specific subjective norms – interpersonal and media influence – on continued use of social networking and microblogging.

Users' knowledge contribution behaviors are critical for online Q&A communities to thrive. Well-organized question threads in online Q&A communities enable users to clearly read existing answers and their evaluations before contributing. Based on the social comparison and peer influence literature, the authors examine peer influence on the informativeness of knowledge contributions in competitive settings. The authors also consider three levels of moderating factors concerning individuals' perception of competitiveness: question level, thread level and contributor level.

The authors collected data from one of the largest online Q&A communities in China. The hypotheses were validated using hierarchical linear models with cross-classified random effects. The generalized propensity score weighting method was employed for the robustness check.

The authors demonstrate the peer influence due to social comparison concerns among knowledge contribution behaviors in the same question thread. If more prior knowledge contributors choose to contribute long answers in the question thread, the subsequent contributions are more informative. This peer influence is stronger for factual questions and questions with higher popularity of answering but weaker in recommendation-type and well-answered questions and for contributors with higher social status.

This research provides a new cue of peer influence on online UGC contributions in competitive settings initiated by social comparison concerns. Additionally, the authors identify three levels of moderating factors (question level, thread level and contributor level) that are specific to online Q&A settings and are related to a contributor's perception of competitiveness, which affect the direct effect of peer influence on knowledge contributions. Rather than focus on motivation and quality evaluation, the authors concentrate on the specific content of online knowledge contributions. Peer influence here is not based on an actual acquaintance or a following relationship but on answering the same question. The authors also illustrate the competitive peer influence in subjective and personalized behaviors in online UGC communities.

The current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&A communities and whether the expertise-required question influences the role of these factors on the feedback.

To understand the antecedents and consequences that influence the feedback received on answers to online community questions, the elaboration likelihood model (ELM) is applied in this study. The authors use web data crawling methods and a combination of quantitative analyses. The data for this study came from Zhihu; in total, 353,775 responses were obtained to 1,531 questions, ranging from 49 to 23,681 responses per question. Each answer received 0 to 113,892 likes and 0 to 6,250 comments.

The answers' cognitive and emotional components and the answerer's influence positively affect user feedback behavior. In addition, the expertise-required question moderates the effects of the answer's cognitive component and emotional component on the user feedback, moderating the effects of the answerer's influence on the user approval feedback.

This study builds upon a limited yet growing body of literature on a theme of great relevance to scholars, practitioners and social media users concerning the effects of the connotation of answers (i.e. their cognitive and emotional components) and the answerer's influence on user feedback (i.e. approval and collaborative feedback) in social Q&A communities. The authors further consider the moderating role of the domain expertise required by the question (expertise-required question). The ELM model is applied to explore the relationships between questions, answers and feedback. The findings of this study add a new perspective to the research on user feedback and have implications for the management of social Q&A communities.

The purpose of this paper is to explore the nature of a competitive action and its impact on the response of rivals in the digital market. Specifically, this paper introduces the concept of action complexity and action variation to delineate the configuration characteristics of each digital competitive action and empirically investigates how these action characteristics further affect rivals’ response speed.

This paper uses structural content analysis methods to code competitive actions based on the news of Chinese online travel agencies (OTAs) from 2010 to 2015. The cox proportional hazards regression models are employed to test the hypotheses.

The results indicate that action complexity of the focal firm is negatively associated with rivals’ response speed as it constrains their interpretation (awareness), motivation and capability to respond, while action variation of the focal firm is positively associated with rivals’ response speed as it enhances their attention (awareness) and motivation to respond. Furthermore, the negative relationship between action complexity and response speed is weaker when action variation is high.

Further to advancing competitive dynamics theory, this paper proposes an action-configuration perspective to explore the particular content and quality of each digital competitive action. The discussion of competitive rivalry between OTAs also enriches the application of competitive dynamics in the digital market. Meanwhile, this paper further clarifies the decision-making process of rivalry drawing on the awareness–motivation–capability (AMC) framework.

Social question and answer (Q&A) systems have been rapidly developed on many e-commerce websites. The purpose of this paper is to explore how social Q&A systems influence consumers' information processing and purchase intention.

The authors design this research based on the information adoption model (IAM). First, the auhors consider the impacts of the central route (information factor) and peripheral route (social factor) on consumers' perception of information usefulness in Q&A systems. Then, the authors verify the influence of information and social aspects on purchase intention and empirically test the model with structural equation modelling (SEM) using 428 effective data samples.

On the whole, the authors prove that purchase intention is influenced by information and social aspects, which are two paths in Q&A systems. Specifically, both answer quality and social presence positively influence information usefulness. Interestingly, respondent credibility and answer consistency do not significantly impact information usefulness. Moreover, information usefulness positively affects information adoption, which positively affects consumer purchase intention.

This paper provides insights on social Q&A system mechanism design.

First, this paper is a useful complement to the research on social Q&A systems on e-commerce websites. Second, the authors provide a new theoretical lens through which the impacts of social Q&A systems on e-commerce websites are understood by extending the IAM. Third, the authors add answer consistency into original information process routes, which obtains a finding that is different from those of prior research.

Getting high-quality data by removing the noisy data from the user-generated content (UGC) is the first step toward data mining and effective decision-making based on ubiquitous and unstructured social media data. This paper aims to design a framework for revoking noisy data from UGC.

In this paper, the authors consider a classification-based framework to remove the noise from the unstructured UGC in social media community. They treat the noise as the concerned topic non-relevant messages and apply a text classification-based approach to remove the noise. They introduce a domain lexicon to help identify the concerned topic from noise and compare the performance of several classification algorithms combined with different feature selection methods.

Experimental results based on a Chinese stock forum show that 84.9 per cent of all the noise data from the UGC could be removed with little valuable information loss. The support vector machines classifier combined with information gain feature extraction model is the best choice for this system. With longer messages getting better classification performance, it has been found that the length of messages affects the system performance.

The proposed method could be used for preprocessing in text mining and new knowledge discovery from the big data.

The purpose of this paper is automatic classification of TV series reviews based on generic categories.

What the authors mainly applied is using surrogate instead of specific roles or actors’ name in reviews to make reviews more generic. Besides, feature selection techniques and different kinds of classifiers are incorporated.

With roles’ and actors’ names replaced by generic tags, the experimental result showed that it can generalize well to agnostic TV series as compared with reviews keeping the original names.

The model presented in this paper must be built on top of an already existed knowledge base like Baidu Encyclopedia. Such database takes lots of work.

Like in digital information supply chain, if reviews are part of the information to be transported or exchanged, then the model presented in this paper can help automatically identify individual review according to different requirements and help the information sharing.

One originality is that the authors proposed the surrogate-based approach to make reviews more generic. Besides, they also built a review data set of hot Chinese TV series, which includes eight generic category labels for each review.