Knowledge collaboration among physicians in online health communities: A transactive memory perspective

Knowledge teams have emerged in online health communities (OHCs) where physicians collaborate spontaneously with others through the Internet to gather knowledge. Knowledge collaboration (KC) facilitates physicians’ communication and the provision of better services to patients in today's medical environment. However, the underlying mechanism through which KC improves team performance in OHCs is not clear. This study aims to advance understanding of the KC process by exploring the role of the transactive memory system (TMS). Real operation data from 1071 teams in a leading OHC in China used to understand both the antecedent and consequences of the TMS and the interaction effects among different dimensions of TMS. The findings have demonstrated that leader's capital was a critical factor in KC by promoting the effective TMS development and further affect both team's process and outcome performance. Positive moderating effects of coordination on the relationship between credibility and performance are also found. This study reveals for the first time the role of KC in improving performance in online health markets from the TMS perspective. The findings provide theoretical guidance to physician–physician collaborative teams with guidelines on boosting chances for higher performance.     

Examining branding co-creation in brand communities on social media: Applying the paradigm of Stimulus-Organism-Response

This paper examines the antecedents of branding co-creation that include social networking sites’(SNSs) participation motivations,customer participation, brand trust and brand loyalty in social media brand communities by applying the “Stimulus-Organism-Response paradigm”. The survey method was used to gather data from 407 social media users. Data were analysed using structural equation modeling techniques. The findings reveal that SNSs’ participation motivations positively influence customer participation, which in turn significantly affects brand trust and brand loyalty. Consequently, both brand trust and brand loyalty positively influence branding co-creation in brand communities on social media. Furthermore, brand trust contributes as a mediator between customer participation and brand loyalty on social media brand communities. Although studies on relationships examined through the lens of the Stimulus-Organism-Response paradigm are popular, to the authors’ surprise there is scant literature examining the relationships between SNSs’ participation motivations, customer participation in social media brand communities, brand trust, brand loyalty and branding co-creation.     

Antecedents of privacy calculus components in virtual health communities

Over the past decade, social media technologies have become effective tools not only for entertainment, but also for online health communications. In virtual health communities (VHCs), the members often share their personal health information (PHI) with other members. These information exchanges provide benefits to both the information providers as well as the recipients. The PHI disclosure, however, may entail privacy concerns. Our study used the privacy calculus model to examine the trade-off between individuals’ expected benefits and privacy concerns when disclosing PHI in social media environments. Our results showed that age, health status, and affective commitment influence the balance between the information disclosure drivers and barriers in the privacy calculus model. More specifically, we found that among members of VHCs, healthier people expect to receive fewer personal benefits of communicating PHI in social media environments. Moreover, individuals who are emotionally attached to online communities expect to both receive and provide more benefits while communicating PHI in those communities. We also observed that individuals who are familiar with but not members of VHCs, especially those who are young and healthy, are more concerned about their PHI privacy in online communities.     

Mental disorders on online social media through the lens of language and behaviour: Analysis and visualisation

Due to the worldwide accessibility to the Internet along with the continuous advances in mobile technologies, physical and digital worlds have become completely blended, and the proliferation of social media platforms has taken a leading role over this evolution. In this paper, we undertake a thorough analysis towards better visualising and understanding the factors that characterise and differentiate social media users affected by mental disorders. We perform different experiments studying multiple dimensions of language, including vocabulary uniqueness, word usage, linguistic style, psychometric attributes, emotions’ co-occurrence patterns, and online behavioural traits, including social engagement and posting trends.Our findings reveal significant differences on the use of function words, such as adverbs and verb tense, and topic-specific vocabulary, such as biological processes. As for emotional expression, we observe that affected users tend to share emotions more regularly than control individuals on average. Overall, the monthly posting variance of the affected groups is higher than the control groups. Moreover, we found evidence suggesting that language use on micro-blogging platforms is less distinguishable for users who have a mental disorder than other less restrictive platforms. In particular, we observe on Twitter less quantifiable differences between affected and control groups compared to Reddit.     

Trolling in online communities: A practice-based theoretical perspective

The literature on trolling has viewed trolling as discrete instances of transgression undertaken by antagonistic individuals. We identify three main issues with current theorizations: diffuse definitions of “trolling,” blurred boundaries between trolling and other online anti-social behaviors, and the context dependency of trolling. To address these unresolved issues, we adopt a practice-based theoretical approach. Informed by this approach, we analyze trolling behaviors not as products of individuals' attitudes, values, and decisions, but rather as behaviors embedded within and occurring as part of social practices. Specifically, we conduct a practice-based theoretical analysis in a multi-site exploratory study involving online archival research and in-depth interviews with online community members. Based on this analysis, we propose that trolling be conceived as a constellation of three social practices: learning, assimilating, and transgressing. Also, we find that practices of trolling transgression can have a dual pro-social and anti-social impact in online communities.     

Web and social media usage by museums: Online value creation

This paper contributes to the understanding of online strategies in the context of museums as examples of cultural organisations, an underrepresented sector in the information management literature. It presents a theoretical framework for understanding the online strategies of museums’ use of Web and social media, their sources of online value (efficiency, novelty, lock-in, complementarities) and some measurements of Internet performance, such as the Alexa Internet ranking and the number of followers of museums in social media. This type of analysis has not been conducted before and the findings will help museum curators and managers of other cultural institutions to appreciate the impact of these technologies and to make better informed decisions regarding online strategies and resource allocation. In addition, the results of this research are applicable to similar organisations, such as archives and cultural exhibitions, as well as to other service organisations related to information, education and entertainment activities.     

A recommender mechanism for social knowledge navigation in an online encyclopedia

In today’s world, knowledge is important for constructing core competitive advantages for individuals and organizations. Recently, Web 2.0 applications and social media have provided a convenient medium for people to share knowledge over the Internet. However, the huge amount of created knowledge can also leads to the problem of information overload. This research proposes a social knowledge navigation mechanism that utilizes the techniques of relevant knowledge network construction, knowledge importance analysis, and knowledge concept ontology construction to generate a visualized recommendation of a knowledge map of sub-concept and knowledge of an article reading sequence for supporting learning activities related to a free online encyclopedia. The results of experiments conducted on Wikipedia show that the proposed mechanism can effectively recommend useful articles and improve a knowledge seeker’s learning effectiveness.     

How social media live streams affect online buyers: A uses and gratifications perspective

Social media live streams (SMLSs) are becoming mainstream e-commerce platforms, especially for direct selling, offering online retailers (e-tailers) immense consumer interaction and engagement levels. Despite their growing prevalence, e-tailers face difficulties capitalising on this platform to enhance retail operations. This paper explores how SMLSs affect consumers' purchase intentions by exploring their motivations for participating in e-commerce SMLSs. To this end, we used the uses and gratifications (U&G) theory to explain how using SMLSs to satisfy consumers' needs could lead to high or low purchase intentions. It was done by collecting cross-sectional data from 795 consumers who have participated in e-commerce SMLSs. The data were analysed using partial least squares-structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). Our findings revealed that people use SMLSs to satisfy their need for value, social identification, and the medium's attributes. Based on these findings, we show six configurations that lead to high or low purchase intentions and make corresponding theoretical propositions. Thus, our study contributes to the research and practice of using SMLSs for online shopping.     

A group recommendation system for online communities

Online communities are virtual spaces over the Internet in which a group of people with similar interests or purposes interact with others and share information. To support group activities in online communities, a group recommendation procedure is needed. Though there have been attempts to establish group recommendation, they focus on off-line environments. Further, aggregating individuals’ preferences into a group preference or merging individual recommendations into group recommendations—an essential component of group recommendation—often results in dissatisfaction of a small number of group members while satisfying the majority. To support group activities in online communities, this paper proposes an improved group recommendation procedure that improves not only the group recommendation effectiveness but also the satisfaction of individual group members. It consists of two phases. The first phase was to generate a recommendation set for a group using the typical collaborative filtering method that most existing group recommendation systems utilize. The second phase was to remove irrelevant items from the recommendation set in order to improve satisfaction of individual members’ preferences. We built a prototype system and performed experiments. Our experiment results showed that the proposed system has consistently higher precision and individual members are more satisfied.     

Exploring adverse drug reactions of diabetes medicine using social media analytics and interactive visualizations

The aim of this study is to propose an automatic and real-time social media analytics framework with interactive data visualizations to support effective exploration of knowledge about adverse drug reaction (ADR) surveillance. This proposed framework has been prototypically implemented on the basis of social media data. A longitudinal diabetes patient online community data (AskaPatient.com) as well as FDA Adverse Event Reporting Systems (FAERS) data as a benchmark were used to evaluate our proposed approach’s performance. Based on the results, our approach significantly increases the precision and accuracy for ADR extraction. The number of ADR cases, the time when the ADRs occurred, and the rating of Glucophage have been visualized that resulted by mining a collection of 870 ADRs posted in Askapatents.com over a certain time period (from 2001 to 2015). The results have important implications for pharmaceutical companies and hospitals wishing to monitor ADRs of medicines.     

Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision

The prevalence of social media has provided consumers with many opportunities to post online reviews on a wide range of products on the Internet. In this study, we attempt to investigate the moderating effect of inconsistent reviews (i.e., a mix of positive and negative reviews) on consumers’ purchase decision. We further examine whether the effect will differ from female to male consumers.     

Why users keep answering questions in online question answering communities: A theoretical and empirical investigation

This study theorized and validated a model of knowledge sharing continuance in a special type of online community, the online question answering (Q&A) community, in which knowledge exchange is reflected mainly by asking and answering specific questions. We created a model that integrated knowledge sharing factors and knowledge self-efficacy into the expectation confirmation theory. The hypotheses derived from this model were empirically validated using an online survey conducted among users of a famous online Q&A community in China, “Yahoo! Answers China”. The results suggested that users’ intention to continue sharing knowledge (i.e., answering questions) was directly influenced by users’ ex-post feelings as consisting of two dimensions: satisfaction, and knowledge self-efficacy. Based on the obtained results, we also found that knowledge self-efficacy and confirmation mediated the relationship between benefits and satisfaction.     

Exploring the complexity of the individualistic culture through social exchange in online reviews

Online reviews have reshaped visitor economies. However, there is a lack of research regarding the motivations of visitors to write online reviews as most existing research disregards the cultural differences between individuals. This research investigates the motivations of museum visitors from individualistic cultures to write online reviews with the critique of Hofstede (1980) through the lens of Social Exchange Theory (SET). This study utilises the netnography methodology. One hundred and eleven detailed TripAdvisor reviews and ten email interviews were collected. Theoretically, we contribute to SET by developing a two-dimensional framework which indicates 1) that the complexity and heterogeneity of individualist culture were identified in the dimension of social exchange between the service provider and the visitors through online reviews; and 2) American and British visitors share similar individualist culture in the second dimension of social exchange between peers in online reviews. Practically, this research can benefit the online reputation management and expectation management for visitor attractions, with the goal of improving their visitor offerings and to minimise negative reviews.     

The digital marketing skills gap: Developing a Digital Marketer Model for the communication industries

Scholarly literature suggests digital marketing skills gaps in industry, but these skills gaps are not clearly identified. The research aims to specify any digital marketing skills gaps encountered by professionals working in communication industries. In-depth interviews were undertaken with 20 communication industry professionals. A focus group followed, testing the rigour of the data.     

Estimating Reputation Polarity on Microblog Posts

In reputation management, knowing what impact a tweet has on the reputation of a brand or company is crucial. The reputation polarity of a tweet is a measure of how the tweet influences the reputation of a brand or company. We consider the task of automatically determining the reputation polarity of a tweet. For this classification task, we propose a feature-based model based on three dimensions: the source of the tweet, the contents of the tweet and the reception of the tweet, i.e., how the tweet is being perceived. For evaluation purposes, we make use of the RepLab 2012 and 2013 datasets. We study and contrast three training scenarios. The first is independent of the entity whose reputation is being managed, the second depends on the entity at stake, but has over 90% fewer training samples per model, on average. The third is dependent on the domain of the entities. We find that reputation polarity is different from sentiment and that having less but entity-dependent training data is significantly more effective for predicting the reputation polarity of a tweet than an entity-independent training scenario. Features related to the reception of a tweet perform significantly better than most other features.     

虚拟化技术处理数字媒体内容

随着虚拟化和仿真技术的不断成熟,通过引入虚拟化技术可以解决一些传统模式下比较繁杂的问题.本文提出了把服务器和客户机系统进行虚拟化转换,并在虚拟化环境中对媒体文件进行存储、传输和访问的处理.通过实验验证该模式是否可行,以及系统运行是否稳定和可靠.     

Enhancing team creative performance through social media and transactive memory system

Effective teamwork, knowledge coordination, and knowledge creation are recognized as essential sources of team effectiveness and creativity in modern organizations. Nowadays, social media is significantly modifying the patterns in the use of technology to support knowledge management practices in teams. At the same time, the literature shows that transactive memory system (TMS), which refers to how team members share their distributed knowledge and expertise, is an important factor affecting team performance. However, minimal effort has been made to elaborate on the precise role of social media in supporting TMS for enhancing absorptive capacity (ACAP) and knowledge creation capability (KCC) of the team, which in turn might influence team creative performance (TCP). Therefore, to address this gap in the literature, a theoretical model is developed and validated. Survey data collected from 334 members of 68 knowledge work teams indicated that social media use at work has a positive relationship with TMS and both social media use at work and TMS are positively related to ACAP and KCC of the team. Results further indicated that ACAP positively influenced KCC, and both have a direct relationship with TCP. This study shows that careful investment in social media by an organization can enhance meta-knowledge of “who knows what” within teams. Finally, exploring external knowledge alone is not enough. Instead, organizations must ensure external knowledge is utilized to create new knowledge to improve TCP.     

Impact of avatar identification on online gamer loyalty: Perspectives of social identity and social capital theories

Online games have created significant opportunities for electronic commerce managers. The degree to which online gamers regard their avatars—their gaming representations— as themselves is known to be influential to gamers’ behavior but little is known about how such identification impacts online gamer loyalty (i.e., gamers’ continued intention to play). This study filled this gap by developing its research framework from the perspective of the social identity theory and the social capital theory. Responses from 1384 online gamers were collected, and structural equation modeling was used to test the hypotheses. The analytical results indicate that avatar identification (the degree to which users regard avatars as themselves) is positively related to participation in gaming communities and social presence (the degree of awareness of other persons and interpersonal relationships). These were further positively related to online gamer loyalty. This study is the first using the two theories, i.e., the social identity and social capital theoretical perspectives, to clarify the mechanism underlying the impact of avatar identification on online gamer loyalty, assisting electronic commerce managers to create a loyal user base.     

Fairness and devotion go far: Integrating online justice and value co-creation in virtual communities

Although building long-term, successful virtual communities is important, rare studies have examined both in- and extra-role value co-creation behaviors from the perspective of social exchange theory and equity theory. Specially, we incorporate five different online justice perceptions into our model and examine the mediating role of “sense of virtual community” between these perceived online justice antecedents and both in- and extra-role online value co-creation behavior (reflected by knowledge contribution and online community citizenship behaviors, respectively). We empirically examine the model using data from 278 members of virtual communities. The results reveal that perceived online justice leads to value co-creation behavior through sense of virtual community. The findings elicit several implications for theory and practice.     

Identifying emotional causes of mental disorders from social media for effective intervention

Identifying the emotional causes of mental illnesses is key to effective intervention. Existing emotion-cause analysis approaches can effectively detect simple emotion-cause expressions where only one cause and one emotion exist. However, emotions may often result from multiple causes, implicitly or explicitly, with complex interactions among these causes. Moreover, the same causes may result in multiple emotions. How to model the complex interactions between multiple emotion spans and cause spans remains under-explored. To tackle this problem, a contrastive learning-based framework is presented to detect the complex emotion-cause pairs with the introduction of negative samples and positive samples. Additionally, we developed a large-scale emotion-cause dataset with complex emotion-cause instances based on subreddits associated with mental health. Our proposed approach was compared to prevailing CNN-based, LSTM-based, Transformer-based and GNN-based methods. Extensive experiments have been conducted and the quantifiable outcomes indicate that our proposed solution achieves competitive performance on simple emotion-cause pairs and significantly outperformed baseline methods in extracting complex emotion-cause pairs. Empirical studies further demonstrated that our proposed approach can be used to reveal the emotional causes of mental disorders for effective intervention.