Exploring Mobile Social Networking Sites Continuance Intention from the Perspective of Network Externalities and Mobile Value

Key shifts in the nature and characteristics of technology have led to evolutions in social media usage. For the first time in history, the internet is being accessed from more mobile devices than desktop computers. This revolution is especially felt among social networking sites, many of whom have presciently developed and released mobile applications of their platforms. Drawing from the Perceived Value Theory, Network Externalities and the Expectation Confirmation Theory of IS Continuance, we tested a model that sought to explain the effect of Network Externalities on Perceived Value and in turn Continuance Intention. Data was collected from 452 students from a university in Ghana and analyzed using the Partial Least Square approach to Structural Equation Modeling. Results from the current study showed that Referent Network Size and Perceived Complementarity had significant effects on both Utilitarian and Hedonic Value as well as Satisfaction. Hedonic Value, Utilitarian Value and Satisfaction in turn were found to be significant predictors of Continuance Intention. In all, the model accounted for 58.0% of the variance in continuance intention. The implications and limitations of the current study are discussed, and directions for future research proposed

Examining Self-Disclosure on Social Networking Sites: A Flow Theory and Privacy Perspective

Social media and other web 2.0 tools have provided users with the platform to interact with and also disclose personal information to not only their friends and acquaintances but also relative strangers with unprecedented ease. This has enhanced the ability of people to share more about themselves, their families, and their friends through a variety of media including text, photo, and video, thus developing and sustaining social and business relationships. The purpose of the paper is to identify the factors that predict self-disclosure on social networking sites from the perspective of privacy and flow. Data was collected from 452 students in three leading universities in Ghana and analyzed with Partial Least Square-Structural Equation Modeling. Results from the study revealed that privacy risk was the most significant predictor. We also found privacy awareness, privacy concerns, and privacy invasion experience to be significant predictors of self-disclosure. Interaction and perceived control were found to have significant effect on self-disclosure. In all, the model accounted for 54.6 percent of the variance in self-disclosure. The implications and limitations of the current study are discussed, and directions for future research proposed