This study examines why privacy concerns do not consistently deter online information disclosure by focusing on internal evaluative dynamics underlying privacy decisions. Drawing on theories of attitudinal ambivalence and cognitive–affective inconsistency, it investigates how internal tensions shape the translation of privacy concerns into disclosure behavior. Using two-phase data comprising a survey, the research distinguishes between threat-based and coping-based evaluative conflicts by operationalizing ambivalence and cognitive–affective inconsistency across privacy risks, perceived benefits, self-efficacy, and response efficacy. Results from Phase 1, based on 540 Amazon Mechanical Turk participants, indicate that while privacy concerns generally reduce disclosure intentions, this effect is significantly weakened when individuals experience higher levels of cognitive–affective inconsistency and ambivalence. Although ambivalence significantly reduces the magnitude of inconsistency, it has a limited influence on the moderating role of inconsistency. Phase 2 findings further show that under conditions of high ambivalence, cognitive–affective inconsistency related to self-efficacy exerts a significant effect in situation-specific disclosure contexts. By elucidating the dynamic interplay of the internal tensions, this study clarifies when and why privacy concerns fail to predict disclosure behavior and highlights the importance of incorporating internal evaluative dynamics into models of digital privacy decision-making.
Jongtae Yu (Wed,) studied this question.