Employees’ proactiveness on enterprise social media and social consequences: An integrated perspective of social network and social exchange theories

This study delves into how employee proactiveness on enterprise social media can influence social outcomes at work, both positively and negatively. Drawing on social network and social exchange theories, we develop a model to understand how virtual proactiveness is perceived by leaders and coworkers. Using a round-robin design, we collected data from 281 respondents in 56 teams across three waves. The results show that affiliative and challenging proactiveness positively relate to leader-member and team-member exchanges. However, challenging proactiveness negatively affects team-member exchange, potentially leading to coworker envy. These findings shed light on the intricate social dynamics in remote working environments.     

Affective social network—happiness inducing social media platform

We propose a human emotion regarding social network, namely, the Affective Social Network. Our suggestion firstly builds a user’s emotion profile in terms of the personality, mood and emotion by analysing the user’s activities in social network. This subsequently builds an Emotional Relationship Matrix (ERM) which represents the depth of the emotional relationship based on the emotion profile. From our proposal, the more elaborate services based on the current user’s emotional state can be provided to users. By considering emotional aspects in a social network, we can effectively answer which users or media contents will show the best results for inducing users’ emotional states. From the experiments, we verified that message containing emotional words and users’ relationship in a social network has significant correlations.     

Detecting and tagging users’ social circles in social media

As a media and communication platform, microblog becomes more popular around the world. Most users follow a large number of celebrities and public medias on microblog; however, these celebrities do not necessarily follow all their fans. Such one-way relationship abounds in ego network and is displayed by the forms of users’ followees and followers, which make it difficult to identify users’ real friends who are contained in merged lists of followees and followers. The aim of this paper is to propose a general algorithm for detecting users’ real friends in social media and dividing them into different social circles automatically according to the closeness of their relationships. Then we analyze these social circles and detect social attributes of these social circles. To verify the effectiveness of the proposed algorithm, we build a microblog application which displays algorithm results of social circles for users and enables users to adjust proposed results according to her/his real social circles. We demonstrate that our algorithm is superior to the traditional clustering method in terms of F value and mean average precision. Furthermore, our method of tagging social attributes of social circles gets high performance by NDCG (normalized discounted cumulative gain).     

Understanding the determinants of users’ subjective well-being in social networking sites: an integration of social capital theory and social presence theory

In order to understand the factors affecting users’ well-being perception derived from social networking sites usage, this study integrates the perspectives of social presence theory and social capital theory to develop a research model. Data were collected from 305 users of Facebook in Taiwan to test the model. The findings reveal that structural capital, relational capital, and cognitive capital exert significant influences on subjective well-being. The results also reveal that structural capital and cognitive capital have positive influence on relational capital, while structural capital influences cognitive capital significantly. The results, on the other hand, show that awareness and affective social presence are the antecedents of structural capital, whereas cognitive social presence and affective social presence influence cognitive capital significantly. Finally, affective social presence is associated with relational capital positively. Theoretical and practical implications are discussed.     

Dynamics of opinion formation,social power evolution,and naïve learning in social networks

The past few decades have witnessed a prevalence of applying dynamical models to the study of social networks. This paper reviews recent advances in the investigation of social networks with a predominant focus on agent-based models. Starting from classical models of opinion dynamics, we survey several recently developed models on opinion formation and social power evolution. These models extend the classical models’ cognitive assumption that individuals’ opinions evolve on a single issue by incorporating various sociological or psychological hypotheses to account for the evolution of opinions over multiple or a sequence of interdependent issues. We summarize basic results on the asymptotic behaviors of these models and discuss their sociological interpretations. In addition, we show how these models play a role in the emergence of collective intelligence by applying them to a naïve learning setting. Novel results that reveal how individuals successfully learn an unknown truth over issue sequences are presented. Finally, we conclude the paper and discuss potential directions for future research.     

Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection

With the rise of social web, there has also been a great concern about the quality of user-generated content on social media sites (SMSs). Deceptive comments harm users’ trust in online social media and cause financial loss to firms. Previous studies use various features and classification algorithms to detect and filter social spam on several social media platforms. However, to the best of our knowledge, previous studies have not exploited both probabilistic topic modeling and incremental learning to detect social spam on SMSs. Thus, the main contribution of this paper is design of a novel detection methodology that combines topic- and user-based features to improve the effectiveness of social spam detection. The proposed methodology exploits a probabilistic generative model, namely the labeled latent Dirichlet allocation (L-LDA), for mining the latent semantics from user-generated comments, and an incremental learning approach for tackling the changing feature space. An experiment based on a large dataset extracted from YouTube demonstrates the effectiveness of our proposed methodology, which achieves an average accuracy of 91.17 % in social spam detection. Our statistical analysis reveals that topic-based features significantly improve social spam detection, which has significant implications for business practice.     

How social is social responses to computers? The function of the degree of anthropomorphism in computer representations

Testing the assumption that more anthropomorphic (human-like) computer representations elicit more social responses from people, a between-participants experiment (N = 168) manipulated 12 computer agents to represent four levels of anthropomorphism: low, medium, high, and real human images. Social responses were assessed with users’ social judgment and homophily perception of the agents, conformity in a choice dilemma task, and competency and trustworthiness ratings of the agents. Linear polynomial trend analyses revealed significant linear trends for almost all the measures. As the agent became more anthropomorphic to being human, it received more social responses from users.     

What motivates health information exchange in social media? The roles of the social cognitive theory and perceived interactivity

Social media has become an attractive platform for users to exchange health information with others. However, little research has been done to identify the determinants of health information exchange. By integrating aspects of the social cognitive theory and perceived interactivity, this study proposes a research model to investigate the antecedents of health information exchange in social media. Data collected from Facebook users with health information exchange experience were used to examine the proposed model. The results demonstrate that human-to-human interaction, human-to-information interaction, outcome expectation of health self-management competence, and outcome expectation of social relationships have a significant impact on health information exchange behavior. The results also reveal that human-to-human interaction exerts a significant influence on the outcome expectation of health self-management competence and the outcome expectation of social relationships, while human-to-information interaction has a positive effect on the outcome expectation of health self-management competence. The implications for the theory and practice and future research directions are discussed.     

My social networking profile: copy,resemblance, or simulacrum? A poststructuralist interpretation of social information systems

This paper offers an introduction to poststructuralist interpretivist research in information systems, through a poststructuralist theoretical reading of the phenomenon and experience of social networking websites, such as Facebook. This is undertaken through an exploration of how loyally a social networking profile can represent the essence of an individual, and whether Platonic notions of essence, and loyalty of copy, are disturbed by the nature of a social networking profile, in ways described by poststructuralist thinker Deleuze's notions of the reversal of Platonism. In bringing a poststructuralist critique to such hugely successful and popular social information systems, the paper attempts to further open up the black box of the computer ‘user’, extend interpretive approaches to information systems research to embrace poststructuralism, and explore how notions of the Self might be reflected through engagement with information system (IS), and how an IS appreciation of the phenomenon of global social networking may benefit from embracing such a poststructuralist approach.     

How social distance shapes human–robot interaction

This paper investigates how social distance can serve as a lens through which we can understand human–robot relationships and develop guidelines for robot design. In two studies, we examine the effects of distance based on physical proximity (proxemic distance), organizational status (power distance), and task structure (task distance) on people׳s experiences with and perceptions of a humanlike robot. In Study 1, participants (n=32) played a card-matching game with a humanlike robot. We manipulated the power distance (supervisor vs. subordinate) and proxemic distance (close vs. distant) between participants and the robot. Participants who interacted with the supervisor robot reported a more positive user experience when the robot was close than when the robot was distant, while interactions with the subordinate robot resulted in a more positive experience when the robot was distant than when the robot was close. In Study 2, participants (n=32) played the game in two different task distances (cooperation vs. competition) and proxemic distances (close vs. distant). Participants who cooperated with the robot reported a more positive experience when the robot was distant than when it was close. In contrast, competing with the robot resulted in a more positive experience when it was close than when the robot was distant. The findings from the two studies highlight the importance of consistency between the status and proxemic behaviors of the robot and of task interdependency in fostering cooperation between the robot and its users. This work also demonstrates how social distance may guide efforts toward a better understanding of human–robot interaction and the development of effective design guidelines.     

Is social capital effective for online learning?

The popularity of online social networking has heightened academic interest in social capital. However, few studies have investigated the role of social capital in online learning. Using exploratory and confirmatory factor analyses of data from online MIS classes, we find that online learning facilitates social capital formation mostly in terms of the dimensions of community, trust, collective action and cooperation, communication, and sociability and inclusion, depending on the media-based human interaction forms of online learning employed. Structural equation modeling confirms a causal effect of social capital on student satisfaction. Social capital is also found to positively affect learning outcomes as measured by students’ group project scores but not class scores. The study's contribution to the literature and practice are discussed.     

Skewing users’ rational risk considerations in social commerce: An empirical examination of the role of social identification

Social commerce has emerged as a new platform that enables users to conduct shopping assisted by inputs from other members and to publicly comment on transactions or products. It therefore adds a social aspect to traditional online commerce environments. Nevertheless, the role of the social facet embedded in such transactions in influencing user behaviors is not fully understood. In this study, we rely on theories of risk deterrence in decision-making and the “risky/choice shift” logic to suggest that the social identification of users regarding their community members skews the way they consider risks in decision-making on these sites. Using data from 175 users of etsy.com, we show that perceived commerce risk reduces intentions to buy from the website and that perceived participation risk curtails intentions to post comments on social commerce forums. The findings further show that the influence of these risk assessments is reduced when the degree of social identification with the website community increases; these risk considerations become negligible in decision-making processes when ’social identification is one standard deviation above the mean. Hence, users’ social identification with the social commerce website community skews their rational decision-making.     

Capturing the participation and social dimensions of computer-supported collaborative learning through social network analysis: which method and measures matter?

International Journal of Computer-Supported Collaborative Learning - The increasing use of digital learning tools and platforms in formal and informal learning settings has provided broad access to...     

SocialSearch + : enriching social network with web evidences

This paper introduces the problem of searching for social network accounts, e.g., Twitter accounts, with the rich information available on the Web, e.g., people names, attributes, and relationships to other people. For this purpose, we need to map Twitter accounts with Web entities. However, existing solutions building upon naive textual matching inevitably suffer low precision due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nicknames). To overcome these limitations, we leverage “relational” evidences extracted from the Web corpus. We consider two types of evidence resources—First, web-scale entity relationship graphs, extracted from name co-occurrences crawled from the Web. This co-occurrence relationship can be interpreted as an “implicit” counterpart of Twitter follower relationships. Second, web-scale relational repositories, such as Freebase with complementary strength. Using both textual and relational features obtained from these resources, we learn a ranking function aggregating these features for the accurate ordering of candidate matches. Another key contribution of this paper is to formulate confidence scoring as a separate problem from relevance ranking. A baseline approach is to use the relevance of the top match itself as the confidence score. In contrast, we train a separate classifier, using not only the top relevance score but also various statistical features extracted from the relevance scores of all candidates, and empirically validate that our approach outperforms the baseline approach. We evaluate our proposed system using real-life internet-scale entity-relationship and social network graphs.     

Designing social commerce platforms based on consumers’ intentions

Social commerce has been gaining momentum over the last few years as a novel form of e-commerce, creating substantial changes for both businesses and consumers. However, little is known about how consumer behaviour is influenced by characteristics on social commerce platforms. The purpose of this research is to elucidate how user intentions to purchase and to spread word-of-mouth (WOM) are influenced by characteristics present on social commerce platforms. More specifically, we adopt a uses-and-gratifications perspective and examine the influence of socialising, personal recommendation agents, product selection, and information availability. Partial least squares structural equation modelling analysis is performed on a sample of 165 social commerce users. Outcomes of the analysis indicate that socialising and personal recommendation agents positively influence purchase and WOM intentions, while product selection is found to only enhance purchase intentions. Interestingly, our findings reveal that information availability has no significant effect on purchase and WOM intentions. Finally, we find that when purchase intentions are triggered, they will tend increase consumers’ intentions to WOM.     

Is the social use of media for seeking connectedness or for avoiding social isolation? Mechanisms underlying media use and subjective well-being

This study provides a resolution for two contrasting hypotheses around media use, the augmentation and the displacement hypotheses. To do this, we conducted an online survey of 300 Korean adults examining the relationships among the social use of media, face-to-face communication, social isolation, connectedness, and subjective well-being. The results indicate that connectedness, not avoiding social isolation, mediates the effects of the social use of media on subjective well-being. On the other hand, both connectedness and avoiding social isolation mediate the effects of face-to-face communication on subjective well-being. These results suggest that the social use of media is limited to seeking connectedness to others, whereas face-to-face communication can facilitate avoiding social isolation as well as seeking connectedness, which can explain why the two contrasting hypothesis, the augmentation and the displacement hypotheses, can be right. In the domain of seeking connectedness, media can augment face-to-face communication. On the other hand, in the domain of avoiding social isolation, media may displace face-to-face communication.     

Managing peer-to-peer networks with human tactics in social interactions

Small-world phenomena have been observed in existing peer-to-peer (P2P) networks which has proved useful in the design of P2P file-sharing systems. Most studies of constructing small world behaviours on P2P are based on the concept of clustering peer nodes into groups, communities or clusters. However, managing additional multilayer topology increases maintenance overhead, especially in highly dynamic environments. In this paper, we present Social-like P2P systems (Social-P2Ps) for object discovery by self-managing P2P topology with human tactics in social networks. In Social-P2Ps, queries are routed intelligently even with limited cached knowledge and node connections. Unlike community-based P2P file-sharing systems, we do not intend to create and maintain peer groups or communities consciously. In contrast, each node connects to other peer nodes with the same interests spontaneously by the result of daily searches.     

The impact of technology on older adults’ social isolation

Research indicates that social isolation and loneliness have a negative effect on health and wellbeing among older people. Various technology-based interventions have been offered to reduce social isolation; however, research demonstrating the role of various types of technologies and their effectiveness in dealing with social isolation among seniors is scarce. This study undertakes a systematic literature review of empirical studies on various types of technologies and their effectiveness in alleviating social isolation among seniors. Relevant electronic databases were searched and through 6886 initial set of studies published from 2000 to 2015 we have found eight different technologies that have been applied to alleviate social isolation, namely, general ICT, video game, robotics, personal reminder information and social management system, asynchronous peer support chat room, social network sites, Telecare and 3D virtual environment. We further evaluated the effectiveness of the technologies with social isolation among seniors. Findings show that technologies can be used to reduce social isolation among seniors. However, more studies are needed to evaluate the effectiveness of new technologies.