“Their privacy matters too!” A self-other calculus perspective on disclosure of others’ information on social media: The influence of relationship closeness and affect

Abstract

This study addresses the often-overlooked behavioral mechanisms underlying interdependent privacy violations on social media, where individuals (i.e., data senders) post content about other people (i.e., data subjects). In response, we introduce a self-other calculus framework to consider both personal and others' benefits and risks in such sharing decisions. It hypothesizes that data senders weigh these factors, influenced by affect (intrapersonal influence) and their relationship closeness with data subjects (interpersonal influence). Using 2 × 2 between-subjects design, we performed a scenario-based experiment with 1007 participants. Data analysis through multiple linear regression and Hayes's PROCESS macro confirmed our main hypotheses. We identified two important conditions under which people tend to disclose others' information: (1) when data senders and data subjects have a closer interpersonal relationship and (2) when data senders feel emotionally positive. This study shifts extant behavioral research focus from personal privacy to the interpersonal context, as well as examines the context-dependent and bounded-rationality nature of privacy decisions involving multiple stakeholders.