Uncovering the dual influence processes for click-through intention in the mobile social platform: An elaboration likelihood model perspective

The burgeoning growth of mobile social platforms has gained the attention of advertisers, and the way how to stimulate users’ click-through intention in the virtual social community is becoming a research focus of scholars. Drawing on the elaboration likelihood model (ELM), this study develops a theoretical framework to examine the mediation effects of cognitive vs. affective trust on the relationship between dual routes (central route vs. peripheral route) and users’ click-through intention in the context of mobile social platforms. Particularly, users’ prior product experience is incorporated in the research model to examine its moderating effect on the influence processes. A scenario-based survey is conducted in China, and the partial least square method is used to analyze the data. Empirical results suggest that perceived content personalization is positively associated with cognitive trust, and its influence is stronger for experienced users with the recommended product. Social recommendation is positively associated with affective trust, and its impact is higher for users without prior product experience. Advertiser reputation is positively associated with both cognitive trust and affective trust, and its effect on cognitive trust is stronger for users without prior product experience, while its impact on affective trust is higher for users with prior product experience. Moreover, cognitive trust is positively associated with affective trust, and the two trust mechanisms have positive influences on click-through intention toward the recommended product. We discuss theoretical and practical implications in the final section.     

Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator

The Internet is a communication channel that allows individuals to share information and knowledge. However, it is not obvious why individuals share knowledge with strangers for no apparent benefit. What are the critical factors influencing such behavior? To attempt to understand this paradox, we combined the theories of social capital and individual motivation to investigate the factors influencing knowledge sharing behavior in a virtual community, applying a participant involvement concept to analyze the moderating effects of individual motivation on knowledge sharing behavior. By analyzing the results of a survey using a questionnaire, we found that altruism, identification, reciprocity, and shared language had a significant and positive effect on knowledge sharing. Reputation, social interaction, and trust had positive effects on the quality, but not the quantity, of shared knowledge. Participant involvement had a moderating effect on the relationship of altruism and the quantity of shared knowledge. Theoretical and managerial implications are discussed.     

A model for consumer knowledge contribution behavior: the roles of host firm management practices,technology effectiveness,and social capital

This study examines the effects of host firm management practices, social capital, and technological factors on consumer knowledge contribution behavior. Results from 403 respondents in a large firm-hosted virtual community showed that two host firm practices, promoting interaction and organizing offline activities, have positive effects on knowledge contribution behavior through the mediating role of social capital, while the practice of providing incentives has a negative effect. In addition, the impacts of social interaction ties and shared language on knowledge contribution behavior are stronger when the perceived effectiveness of the reputation system is high. The perceived effectiveness of media richness moderates the relationship between trust and knowledge contribution behavior by strengthening the relationship when the perceived effectiveness of media richness is high. Our study suggests that host firms can play an important role in promoting knowledge contributions in virtual communities; and they need to understand the consequences of different management practices, hence able to actively influence and encourage knowledge contribution in virtual communities.     

Building trust networks in the absence of trust relations

User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platforms. These issues pose a great challenge for predicting trust relations and further building trust networks. In this study, we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework, bTrust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviorsand homophily effect in building trust networks.     

Solvers’ participation in crowdsourcing platforms: Examining the impacts of trust,and benefit and cost factors

Organizations are increasingly crowdsourcing their tasks to unknown individual workers, i.e., solvers. Solvers' participation is critical to the success of crowdsourcing activities. However, challenges exist in attracting solvers to participate in crowdsourcing. In this regard, prior research has mainly investigated the influences of benefit factors on solvers’ intention to participate in crowdsourcing. Thus, there is a lack of understanding of the cost factors that influence actual participation behavior, in conjunction with the benefits. Additionally, the role of trust in the cost-benefit analysis remains to be explored. Motivated thus, based on social exchange theory and context-related literature, we develop a model to explain the impacts of benefit and cost factors as well as trust on solver participation behavior in crowdsourcing. The model was tested using survey and archival data from 156 solvers on a large crowdsourcing platform. As hypothesized, monetary reward, skill enhancement, work autonomy, enjoyment, and trust were found to positively affect solvers’ participation in crowdsourcing, while cognitive effort negatively affects their participation. In addition, it was found that monetary reward positively affects trust (trust partially mediates its effect on participation behavior), while loss of knowledge power negatively affects trust. The theoretical contributions and practical implications of the study are discussed.     

An empirical study of the determinants of the intention to participate in user-created contents (UCC) services

Up to now UCC services have been dominating the majority of the internet traffic, yet the answers to what motivates people to participate in the UCC services still remain vague and unclear. It is the motivation to find these answers that lead to this study. We adopted technology acceptance model (TAM) to our model and examined the effects of external variables—social identity, telepresence, altruism, perceived playfulness and social trust. Data was collected from undergraduate students in Jeonju University, South Korea, who had experience in UCC. The findings showed that social trust and perceived playfulness play a pivotal role in explaining the individual’s behavioral intention to participate in UCC services. Also, perceived encouragement was found to have significant influence on social trust instead of its direct effect on the intention to participate in UCC services. Additionally, social identity and telepresence were the most important factors of perceived encouragement. This implication can help both researchers and Web practitioners to better understand user behavior in UCC context.     

The influence of disposition and social ties on trust in new virtual teammates

With the increased presence of social media tools such as LinkedIn and Facebook, social network information is now commonplace. Social media websites prominently display the social distance or so-called “degrees of separation” among users, effectively allowing people to view their shared social ties with others, including prospective teammates they have not met. Through the presentation and manipulation of social network information, this longitudinal experiment investigated whether dispositional and relational variables contribute to “swift trust” among new virtual teammates. Data from 74 participants were collected to test a path analytic model predicting that social ties and propensity to trust influence perceptions of a new teammate’s trustworthiness (ability, benevolence, and integrity) as well as the willingness to trust that new teammate when given the opportunity to do so. Path analysis indicated good model fit, but showed no significant evidence that social ties or propensity to trust affect perceived trustworthiness at the initial point of team engagement. Additionally, only one component of perceived trustworthiness (perceived ability) and propensity to trust were found to predict trusting behavior towards a new, unknown, teammate.     

Trust and risk in consumer acceptance of e-services

Consumers’ risk perception and trust are considered among the most important psychological states that influence online behavior. Despite the number of empirical studies that have explored the effects of trust and risk perceptions on consumer acceptance of e-services, the field remains fragmented and the posited research models are contradictory. To address this problem, we examined how trust and risk influence consumer acceptance of e-services through a meta-analysis of 67 studies, followed by tests of competing causal models. The findings confirm that trust and risk are important to e-services acceptance but that trust has a stronger effect size. We found that certain effect sizes were moderated by factors such as the consumer population under study, the type of e-service, and the object of trust under consideration. The data from the meta-analysis best supports the causal logic that positions trust as antecedent to risk perceptions. Risk partially mediates the effects of trust on acceptance.     

Exploring and mitigating social loafing in online communities

The motivation to share members’ knowledge is critical to an online community’s survival and success. Previous research has established that knowledge sharing intentions are based on group cohesion. Several studies also suggested that social loafing behavior will seriously corrode group cohesion. Therefore, social loafing is a key obstacle to fostering online community development. Although substantial studies have been performed on the critical factors that affect social loafing in the learning group, those on online communities are still lacking. By integrating two perspectives, social capital and perceived risk, a richer understanding of social loafing behavior can be gained. In the research model, social ties and perceived risk have been driven by anonymity, offline activities, knowledge quality, and media richness. Social ties and perceived risk are hypothesized to affect social loafing in the online community, which, in turn, is hypothesized as negatively affecting group cohesion. Data collected from 323 online users in online communities provide support for the proposed model. The study shows that social loafing is a significant negative predictor of the users’ group cohesion. The study also shows that social ties and perceived risk are important components of social loafing. Anonymity, offline activities, knowledge quality, and media richness all have strong effects on social ties and perceived risk in the online community. Implications for theory and practice and suggestions for future research are discussed.     

An information security knowledge sharing model in organizations

Knowledge sharing plays an important role in the domain of information security, due to its positive effect on employees' information security awareness. It is acknowledged that security awareness is the most important factor that mitigates the risk of information security breaches in organizations. In this research, a model has been presented that shows how information security knowledge sharing (ISKS) forms and decreases the risk of information security incidents. The Motivation Theory and Theory of Planned Behavior besides Triandis model were applied as the theoretical backbone of the conceptual framework. The results of the data analysis showed that earning a reputation, and gaining promotion as an extrinsic motivation and curiosity satisfaction as an intrinsic motivation have positive effects on employees' attitude toward ISKS. However, self-worth satisfaction does not influence ISKS attitude. In addition, the findings revealed that attitude, perceived behavioral control, and subjective norms have positive effects on ISKS intention and ISKS intention affects ISKS behavior. The outcomes also showed that organizational support influences ISKS behavior more than trust. The results of this research should be of interest to academics and practitioners in the domain of information security.     

When trust and distrust collide online: The engenderment and role of consumer ambivalence in online consumer behavior

Trust and distrust are both considered to be crucial in online truster–trustee relationships. Although some research has proposed that trust and distrust are distinct, other research continues to hold that they are merely opposite ends of the same continuum. Given this debate, it is important to consider how distrust is distinguished from trust. To that end, this paper extends the nomological network of distrust and introduces two novel antecedents never introduced in online behavior literature: situational abnormalities and suspicion. For this nomological network, we also propose that trust and distrust coexist in online e-commerce relationships and can result in ambivalence when they both have high attitudinal values (represented in emotions, beliefs, or behaviors).Using an empirical study of online consumer behavior with 521 experienced online consumers, we found strong empirical validation for our newly proposed model. We provide evidence that suspicion and situational abnormalities are separate, important antecedents to distrust. We also examine the effect of ambivalence on the truster’s intentions toward the website and find a small positive effect that increases the user’s intentions toward the website. Finally, we empirically demonstrate the coexistence of trust and distrust as separate constructs and emphasize that distrust has a much larger impact on the truster’s intentions than does trust. We conclude with implications for theory and practice, along with a discussion of the limitations of and future opportunities revealed by this study.     

Liking Obama and Romney (on Facebook): An experimental evaluation of political engagement and efficacy during the 2012 general election

Existing research on the effect of social media use on political behavior has yielded mixed results to date, demonstrating the importance of research examining the effects of varying types of social media communication on political behavior. The experiment reported in this study provides valuable insights into the role of social media in elections. A longitudinal experiment was conducted to assess the effect Facebook use in the 2012 Presidential election had on political information efficacy, external efficacy, and political engagement. Results suggested that while political information efficacy and engagement increased over time, it was not related to following candidates on Facebook.     

Consumer responses to promoted tweets sent by brands and political parties

This study aims to understand how consumers respond to social media advertising (SMA) by focusing on promoted tweets sent by brands and political parties, and examines persuasion knowledge as underlying mechanism of these responses. Two online experiments with between-subjects designs, comparing the effects of SMA (promoted vs. non-promoted tweet) and the source of the tweet (political party vs. brand), were conducted. Study 1 showed that consumers rarely notice it when a tweet is promoted. Study 2 demonstrated that when a promoted tweet was sent by a political party, the recipient's recognition that the tweet was a form of advertisement (i.e., activated persuasion knowledge) reduced online behavioral intention, increased skepticism, and negatively affected source trustworthiness and attitudes. This effect was not present for brands. Although research has shown that social media can be an important platform to engage audiences, this study is the first to study the mechanisms underlying the effects of SMA, and whether there are any boundary conditions to these effects. These findings suggest that political parties should be cautious in their use of social media advertising as it can evoke negative responses.     

Multi-modal microblog classification via multi-task learning

Recent years have witnessed the flourishing of social media platforms (SMPs), such as Twitter, Facebook, and Sina Weibo. The rapid development of these SMPs has resulted in increasingly large scale multimedia data, which has been proved with remarkable marketing values. It is in an urgent need to classify these social media data into a specified list of concerned entities, such as brands, products, and events, to analyze their sales, popularity or influences. But this is a rather challenging task due to the shortness, conversationality, the incompatibility between images and text, and the data diversity of microblogs. In this paper, we present a multi-modal microblog classification method in a multi-task learning framework. Firstly features of different modalities are extracted for each microblog. Specifically, we extract TF-IDF features for each microblog text and low-level visual features and high-level semantic features for each microblog image. Then multiple related classification tasks are learned simultaneously for each feature to increase the sample size for each task and improve the prediction performance. Finally the outputs of each feature are integrated by a Support Vector Machine that learns how to optimally combine and weight each feature. We evaluate the proposed method on Brand-Social-Net to classify the contained 100 brands. Experimental results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.     

Building investor trust in the P2P lending platform with a focus on Chinese P2P lending platforms

Despite the rapid development of peer-to-peer (P2P) lending in the world, massive Chinese P2P platforms have failed due to moral risk and liquidity risk, which decreased investors’ confidence in P2P lending. A major question facing the future growth and development of P2P platforms is how to best build investors’ trust. This study was based on the elaboration likelihood model and examines how to persuade investors to develop initial trust in P2P platform. The empirical analysis data were collected from 70 Chinese P2P platforms. The multiple linear regression results support central route variables: the financial and credit status of P2P platforms are key elements in building the trust of investors and impacting their decisions. The subordinate route variables including social capital, risk management, and operating duration provide the necessary support to increase the number of platform investors. The relationship between the average interest rate and number of investors is not linear but exhibits an inverted U-shaped curve. The disclosure of information by the borrowers does not significantly affect the number of platform investors. These findings are an important complement to existing research and will facilitate future development efforts for P2P lending and the rational investments of investors.     

Understanding knowledge sharing between IT professionals – an integration of social cognitive and social exchange theory

The research includes various constructs based on social exchange theory and social cognitive theory. This study mainly explored the relationships among organisational justice, trust, commitment and knowledge-sharing cognition and verified their mediating effects through two variables of trust and commitment. A survey utilising a questionnaire was used with 252 IT professionals from IT companies and departments in Taiwan. Structural equation modelling was used to analyse the data and to evaluate the research model. The results showed that the research model fitted the data well, and the main determinant of knowledge-sharing intention was the building of organisational justice between knowledge intensive workers, confirming our hypotheses that trust and commitment foster organisational commitment. In turn, organisational commitment will help to build individual knowledge-sharing self-efficacy and both directly and indirectly affect intentions to share knowledge.     

The effects of social media based brand communities on brand community markers,value creation practices,brand trust and brand loyalty

Social media based brand communities are communities initiated on the platform of social media. In this article, we explore whether brand communities based on social media (a special type of online brand communities) have positive effects on the main community elements and value creation practices in the communities as well as on brand trust and brand loyalty. A survey based empirical study with 441 respondents was conducted. The results of structural equation modeling show that brand communities established on social media have positive effects on community markers (i.e., shared consciousness, shared rituals and traditions, and obligations to society), which have positive effects on value creation practices (i.e., social networking, community engagement, impressions management, and brand use). Such communities could enhance brand loyalty through brand use and impression management practices. We show that brand trust has a full mediating role in converting value creation practices into brand loyalty. Implications for practice and future research opportunities are discussed.     

社会网络环境下基于公众行为大数据属性挖掘的大群体应急决策方法及应用

针对社会网络环境下复杂大群体应急决策中决策属性信息难以获得问题,提出社会网络环境下公众行为大数据驱动的大群体应急决策方法.首先,通过挖掘社交平台上的公众行为大数据,利用TF-IDF、Word2vec技术进行关键词提取、聚类及其影响力分析,从大量行为数据中挖掘大群体决策属性信息以辅助专家决策,使决策结果具有更高的科学性和有效性;其次,构建决策者间基于信任关系和观点相似度的社会网络,采用同时考虑信任和相似度的聚类方法对决策者进行聚类,并基于社会网络分析获得决策者权重;然后,提出基于决策者间信任关系的共识调整方法进行共识调整以获得最终群体决策矩阵和方案排序,通过引入决策者客观自信度避免个别决策者过分自信行为的影响;最后,通过一个新冠疫情案例分析说明方法的可行性和有效性.     

Proposing the online community self-disclosure model: the case of working professionals in France and the U.K. who use online communities

The global use of online communities has exploded to involve hundreds of millions of users. Despite the tremendous social impact and business opportunities afforded by these communities, little information systems (IS) research has addressed them – especially in a cross-cultural context. Our research proposes an online community self-disclosure model, tested in a cross-cultural setting using data provided by French and British working professionals. Our model is based on social exchange theory (SET) and social penetration theory (SPT), as well as on cross-cultural theory related to individualism-collectivism. SET explains that individuals engage in relationships when the perceived costs associated with the relationship are less than the expected benefits. SPT extends SET to explain that individuals participate in self-disclosure to foster relationships – reciprocation is the primary benefit of self-disclosure, whereas risk is the foundational cost of self-disclosure. Our study established several important findings: positive social influence to use an online community increases online community self-disclosure; reciprocity increases self-disclosure; online community trust increases self-disclosure; and privacy risk beliefs decrease self-disclosure. Meanwhile, a tendency toward collectivism increases self-disclosure. We further found that French participants had higher scores on horizontal individualism than British participants. Several other findings and their implications for practice are also discussed.