Modeling the adoption and use of social media by nonprofit organizations

This study examines what drives organizational adoption and use of social media through a model built around four key factors - strategy, capacity, governance, and environment. Using Twitter, Facebook, and other data on 100 large US nonprofit organizations, the model is employed to examine the determinants of three key facets of social media utilization: 1) adoption, 2) frequency of use, and 3) dialogue. We find that organizational strategies, capacities, governance features, and external pressures all play a part in these social media adoption and utilization outcomes. Through its integrated, multi-disciplinary theoretical perspective, this study thus helps foster understanding of which types of organizations are able and willing to adopt and juggle multiple social media accounts, to use those accounts to communicate more frequently with their external publics, and to build relationships with those publics through the sending of dialogic messages.Comment: Seungahn Nah and Gregory D. Saxton. (in press). Modeling the adoption and use of social media by nonprofit organizations. New Media & Society, forthcomin

Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

Searching for superspreaders of information in real-world social media

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure

Government 2.5: The Impact of Social Media on Public Sector Accessibility

Innovative approaches to communicating with the masses continue to evolve in the private sector, while accessibility of goods, services, and public information within federal, state, and local government organizations has been declining for decades. This situation has resulted in a lack of trust and sense of isolation from communities. At the same time, the implementation and use of social media have increased exponentially. Despite the simultaneous occurrence of these events, limited research has explored the connection between them. Specifically, the purpose of this case study was to address the central research question of whether the adoption of social media platforms results in increased accessibility of goods and services within the public sector. Rogers\u27s diffusion of innovations theory founded the framework for this study. Data were collected within a local government organization through semistructured interviews with 15 employees and 15 clients, observations of daily operations, and analyses of postings made on selected social media platforms. Inductive coding and a comparative method of analysis generated emerging themes and patterns. Key findings of this study indicated significant increases in public accessibility of goods and services as the result of the implementation and use of social media. Relative to diffusion of innovations theory, findings illustrated the spread of new technology through certain channels among employees and clients. Recommendations focus on establishing strategies to ensure widespread diffusion of social media and to address socioeconomic disparities. Government agencies can use this research as a means to advance social change through open communication, an engaged workforce, and increased transparency

From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In "real" economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods' circulation can be tested and confirmed before their introduction to "real life" and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.Comment: 28 page

Using Consumer-Generated Social Media Posts to Improve Forecasts of Television Premiere Viewership: Extending Diffusion of Innovation Theory

Billions of US dollars in transactions occur each year between media companies and advertisers purchasing commercials on television shows to reach target demographics. This study investigates how consumer enthusiasm can be quantified (via social media posts) as an input to improve forecast models of television series premiere viewership beyond inputs that are typically used in the entertainment industry. Results support that Twitter activity (volume of tweets and retweets) is a driver of consumer viewership of unscripted programs (i.e., reality or competition shows). As such, incorporating electronic word of mouth (eWOM) into forecasting models improves accuracy for predictions of unscripted shows. Furthermore, trend analysis suggests it is possible to calculate a forecast as early as 14 days prior to the premiere date. This research also extends the Diffusion of Innovation theory and diffusion modeling by applying them in the television entertainment environment. Evidence was found supporting Rogers’s (2003) heterophilous communication, also referred to by Granovetter (1973) as “weak ties.” Further, despite a diffusion pattern that differs from other categories, entertainment consumption demonstrates evidence of a mass media (external) channel and an interpersonal eWOM (internal) channel

Disentangling Twitter’s Adoption and Use (Dis)Continuance: A Theoretical and Empirical Amalgamation of Uses and Gratifications and Diffusion of Innovations

Drawing on Uses and Gratifications (UG) Theory and Diffusion of Innovation Theory (DIT), this study aimed to augment an exploration of individual user needs based on UG constructs with an analysis of the material characteristics of the innovation based on DIT constructs to provide a comprehensive explanation of people‘s motivations underlying various Twitter usage levels and frequencies. Whereas previous literature on Social Network Sites (SNS) have explored individuals‘ motivations underlying initial adoption, the equally interesting and relevant question of use (dis-) continuance has so far been largely overlooked. To fill this void in the literature, this study compares active users that have continued to use Twitter and inactive users that initially adopted, yet discontinued usage of Twitter. This study provides insights into different usage levels and frequencies through an investigation of 1) users‘ perceptions of the medium, 2) users‘ expected outcomes associated with the medium‘s use, and 3) the role and effect of mobile access. An analysis of 130 surveys with Partial Least Squares (PLS) and R2 partitioning revealed that an understanding of adoption and use (dis-) continuance of Twitter requires us to account for both user-related motivations (UG) and perceived characteristics of the medium (DIT), as combining UG and DIT increased explanatory power (R2) for the overall sample. Furthermore, our findings showed that inactive users‘ initial adoption and subsequent discontinuance was solely impacted by user-related needs, (i.e. UG constructs), whereas active users‘ continued use was largely motivated by technology characteristics, (i.e. DIT constructs). Finally, our study revealed significant differences between active and inactive users in terms of the devices and platform used for accessing Twitter, with active users reporting a significantly higher use of mobile devices. Based on these findings, we discuss contributions and implications for future research and practice

Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure