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The purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality.

Social interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data.

The results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. “Reposting,” “being reposted,” “commenting” and “being commented on” were found to be the key interaction features that reflected Weibo users' personalities, whereas “liking” was not found to be a key feature.

The findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China.

The purpose of this paper is to examine social media adoption models and social media implementation models being used by South African banks when adopting social media marketing. Challenges and opportunities faced are addressed in the paper.

In‐depth interviews were conducted with key informants, which were comprised of five South African social media experts and 28 managements within the banking sector.

Social media is still at its infancy level in South Africa. The ACCESS model and the OASIS model are the most commonly used implementation models in South African banks. Further to that, findings indicate that Facebook and Twitter are the main tools used by banks and they are used for reactive customer service and advertising. Legal and regulatory issues were identified as obstacles to the adoption of social media. All respondents agreed on the need to integrate social media with traditional media. This might be because South African customers are consumers of both the new and traditional media.

The research was limited to South African banks and focused on Facebook, Twitter, MXit, YouTube and Blogs.

The research adds theoretical knowledge on social media adoption models, thus giving a foundation on how other industries can effectively implement social media marketing.

The purpose of this paper is to create an organized picture of the current understanding of social media-based value creation and business models.

Following the process model presented by Fink (2005), a systematic literature review of academic journal articles published between 2005 and 2014 was conducted. The research was grounded on the theoretical foundations of service-dominant logic.

This study offers detailed descriptions and analyses of the major social media mechanisms affecting how value is created in social media-based value networks and the kinds of impact social media can have on present and future business models.

The study is limited to academic research literature on business organizations, excluding all studies related to public and non-profit organizations.

Attention is given to developing an in-depth understanding of the functions and concrete value creation mechanisms of social media-based co-creation within the different organizational processes (e.g. in product and service development and customer services) and to updating the related practices and knowledge.

This study provides new insight into the challenges related to research models and frameworks commonly used for observing value creation, thus highlighting the need for further studies and updates.

The advent of new technologies and change of patients’ behavioral patterns have triggered the provision of medical services through social media. Although the intersection between social media and health has received considerable research attention, there is little research on how health institutions implement social media strategy; thus a roadmap is required to navigate these technological initiatives. So, the purpose of this paper is to overcome this challenge by developing the Health 2.0 maturity model in the healthcare field.

To obtain this aim, the mixed method was applied in this research. In the first step, qualitative research method was used. In this step, along with comprehensive literature review, semi-structured interviews were conducted with the healthcare professionals to find the practices and capabilities of Health 2.0. In the second step, the proposed key dimensions (KD) were assessed and prioritized based on the views of the healthcare professionals using the quantitative survey method. Finally, by considering the architecture of Health 2.0 maturity model, the KDs were assigned to maturity levels based on their priority of implementation using a focus group.

The proposed maturity model is composed of six KDs and five maturity levels based on the Capability Maturity Model Integration architecture. The KDs, as well as their implementation order and weights in the proposed maturity model are presented as a roadmap for applying Health 2.0 effectively.

Employing the Health 2.0 maturity model enables health institutions to assess the current social media capabilities and guide them to select appropriate strategies for progress. Due to the descriptive nature of the proposed model, it allows managers to conduct process-based assessments regarding health 2.0 implementation.

Health 2.0 has been a recurring theme on the agenda of healthcare institutions, but no sensitive tool is available to measure its growth processes. This paper explores the much ignored but critically important subject of Health 2.0 maturity model and its implementation roadmap. The main contribution of this paper is to introduce an integrated roadmap containing the most important capabilities of Health 2.0. The proposed model is both descriptive and prescriptive in nature, and has a significant theoretical contribution to healthcare studies. This paper provides a mechanism to benchmark Health 2.0 efforts and to develop a progressive strategy that would improve its activities.

Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance.

Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies aimed at the prediction of future or current events from social media data are provided.

A taxonomy of prediction models is introduced, along with their relative advantages and the particular scenarios where they have been applied to. The main areas of prediction that have attracted research so far are described, and the main contributions made by the papers in this special issue are summarized. Finally, it is argued that statistical models seem to be the most fruitful approach to apply to make predictions from social media data.

This special issue raises important questions to be addressed in the field of social media-based prediction and forecasting, fills some gaps in current research, and outlines future lines of work.

This study aims to discover the factors which influence user satisfaction levels and their continuous use intention (CUI) of academic library social media, and then considers how to promote and improve further work on library social media to reduce user churn and increase user satisfaction.

An updated DeLone and McLean information systems success (D&M ISS) model and the expectation confirmation model for information systems continuance (ECM-ISC) with new variables of emotions are used to examine the factors which influence user satisfaction levels and CUI of academic library social media through 445 questionnaires. Partial least squares structural equation modelling was used to analyse the data and presented in tables.

The results show that information quality, system quality and emotions affect user satisfaction and CUI, and reveal that emotions can affect that most obviously.

The WeChat public platform is mainly used in China, so the study only focuses on Chinese academic libraries. There are still limitations on the settings of observed variables which cannot cover all the causes of users’ positive and negative emotions. In addition, although the respondents of this questionnaire can represent academic library users, 445 samples are still fairly low in contrast to the great number of academic library WeChat public platform users.

This study integrates ECM-ISC and D&M ISS models, adds positive and negative emotions as new variables, to broaden the application scope of these models, and demonstrates the applicability of these two models in the fields of researching academic library social media, expanding and deepening related theories above. This also provides practical reference for academic libraries on how to improve user satisfaction and CUI of academic library social media and academic library WeChat public platforms, promoting the development of academic library social media.

This study aims to develop a process model to facilitate enterprises’ co-creating value with consumers through social media.

Drawing on the concepts of internet-based co-creation and collective action theory, this study outlines a five-stage model (Interact-Engage-Propose-Act-Realize, IEPAR) of utilizing social media to co-create with consumers, enriches the model through in-depth interviews with industry experts and briefly illustrates how it can be applied in practice using a service firm case.

This study clarifies the co-creation process in the social media environment. For each of the process’s five stages, the objectives to be accomplished by the social media operator and the means to complete the objectives are illustrated.

This study illustrated the proposed model with a representative service firm. Future study may refine the model by gathering additional data from real implementations to improve its effectiveness in practice.

This study suggests how an enterprise can construct a consumer co-creation platform from a managerial perspective. The proposed model can serve as a reference that enterprises can implement to increase customer value through co-creation using social media.

Enterprises have begun to notice the power of serving as a platform for co-creating value with consumers. However, it is seldom related to literature. The proposed model of the co-creation process in the social media environment can supplement past research.

The aim of this study is to investigate how social media users' experience of seeking emergency information affects their engagement intention toward emergency information with a reciprocity framework integrated with information adoption model.

Drawing on reciprocity theory, indebtedness theory, and information adoption model, an integrative research model is developed. This study employs a questionnaire survey to collect data of 325 social media users in China. Structural equation modeling analyses are conducted to test the proposed theoretical model.

Social media users' experience of seeking emergency information has a strong effect on their perceived information usefulness and indebtedness, while perceived information usefulness further influences community norm, indebtedness, and engagement intention. The authors also found that perceived information usefulness mediates the relationships between experience of seeking emergency information and community norm/indebtedness.

This study offers a new perspective to explain social media users' engagement intention in the diffusion of emergency information. This study contributes to the literature by extending the theoretical framework of reciprocity and applying it to the context of emergency information diffusion. The findings of this study could benefit the practitioners who wish to leverage social media tools for emergency response purposes.

Given the widespread popularity of social media, such as Twitter, Facebook, Google+, and LinkedIn, theorizing and understanding the user attitude and usage behavior of social media site is fundamental in developing future understandings and deployment of these new technologies. One approach to such studies on drivers of social media usage behavior would be to revisit the technology acceptance model (TAM). The purpose of this paper is to discuss these issues.

Decades of extensive research have focussed on validating the TAM, proposed by Davis (1986), for various types of information systems and communication technologies. TAM forecasts individual adoption and voluntary use of technology. This study examines individual adoption behavior of the most popular social networking site Facebook. The influences on the intention of using social networking based on individual's perceived ease of use (EU), the user's critical mass (CM), social networking site capability (CP), perceived playfulness (PP), trustworthiness (TW), and perceived usefulness (PU) is empirically examined with a primary data set of 398 users of Facebook gathered from a web-based questionnaire survey.

The results demonstrate that the revised social media TAM model proposed in this study supports all the hypotheses of social media usage behavior. The results of this study provide evidence for the importance of additional key variables to TAM in considering user engagement on social media sites and other social-media-related business strategies.

Based on our review of existing scientific literature on social media, few empirical studies have been conducted to scientifically evaluate and explain the usage behavior of social media using Facebook. A validated instrument of usage behavior of social media can provide usability experts and practitioners with a validated tool to assess social media acceptance and usage behavior. This can help us gain a better understanding of “who is and who is not using these sites, why and for what purposes” (Boyd and Ellison, 2007).