Self-Disclosure on Professional Social Networking Sites: A Privacy Calculus Perspective

The prevalence of social networking sites (SNS) raises questions about what information is private and what is not. Some users willingly share information on the site expecting some benefits, but others may be reluctant to do so due to fear of losing control of the shared information. To better understand the delicate relationship between privacy, perceived benefits, and self-disclosure, this study examines the antecedents of self-disclosure behavior on professional SNS (i.e., LinkedIn). A model contextualizing privacy calculus theory combined with the trust factor was developed and evaluated using 661 quantitative data collected through a questionnaire. Then, the data was analyzed using covariance-based structural equation modeling method. The results show that perceived benefit (e.g., self-presentation, career advancement, professional network development, learning, and information exchange), privacy concerns, and perceived control are the factors that directly influence LinkedIn users to disclose personal information. These factors become significant predictors of self-disclosure behavior. Meanwhile, trust in LinkedIn members, perceived severity, and perceived likelihood indirectly influence self-disclosure through privacy concerns. Finally, perceived control directly influences trust in LinkedIn members and trust in the LinkedIn provider. The findings of this study help to understand SNS users' behavior, particularly self-disclosure behavior. SNS users can become more aware of the benefits and risks of their disclosure behavior, allowing them to make more informed decisions. These findings can also be helpful for SNS providers to improve product experience and strategy by effectively encouraging and facilitating self-disclosure practices.