Abstract

Self-disclosure as influenced by perceived risks and benefits plays an important role within the context of social media use and the associated privacy risk. Some social media platforms, like Facebook (now part of Meta Platforms Inc.), provide users with elaborate means to control privacy risk. Conversely, Instagram (also part of Meta) provides users with fewer such mechanisms as a function of self-disclosure. Therefore, self-disclosure as a product of risk and benefit assessment may differ considerably as a function of the technological affordances that control such disclosure. This is particularly the case considering that such a benefit and risk assessment is further influenced by a user's trust in that provider, not to mention their proclivity for disclosing without any rational risk and benefit assessments, as is the case when disclosing as a function of fear of missing out (FoMO). Given the influence that provider trust and FoMO might have when assessing risks and benefits, this study evaluated the extent to which perceived risks and benefits mediate self-disclosure on Facebook and Instagram, in particular within the context of provider trust and FoMO. Based on an adapted version of privacy calculus, we evaluated our research model by analyzing 720 survey responses using partial least squares path modeling. Our results indicate that perceived benefits mediate the relationship between FoMO and intention to self-disclose when using Instagram, but not when using Facebook. Additionally, we found perceived benefits and perceived risks to mediate the relationship between trust in provider and intention to self-disclose for Facebook and Instagram. Surprisingly, we found no evidence to suggest that the relationship between FoMO and intention to self-disclose is mediated by perceived risks when using Facebook, with the converse being true when using Instagram. We conclude that the transitory (ephemeral) nature of some methods of self-disclosure on Instagram are used as a means to mitigate privacy risks.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call