The Privacy Calculus under Positive and Negative Mood States

The privacy calculus has gained wide acceptance in the privacy literature. It is a parsimonious economic model that explains and predicts privacy-related decisions based on users' subjective calculus of the perceived privacy risks and perceived benefits. In this study, we focus on affect (i.e., positive and negative mood states) and its role in interrupting the privacy calculus. Based on a scenario-based experiment conducted among social media users, the results show that users' privacy calculus is biased depending on the mood state they exhibited. The positive effect of perceived benefits on disclosure likelihood was amplified (trivial) under a positive (negative) mood state, whereas the negative effect of perceived privacy risks on disclosure likelihood was trivial (amplified) under a positive (negative) mood state. Also, the construct of privacy concerns was incapable of predicting disclosure likelihood under both positive and negative mood states. We discuss the theoretical and practical implications of these findings.