The antecedents and moderators of online privacy disclosure perceptions and behaviours

This thesis explores antecedents, contextual factors and mechanisms of individual privacy disclosure perceptions, intentions and behaviours in digital environments. The research examines the role of time pressure, engagement format, as well as the impact of sequential privacy disclosure, where individuals disclose information at multiple and connected decision stages. This thesis also aims to explore the role of personality traits (BIG5) in privacy disclosure behaviours. First, results from the large-scale online randomised controlled trial (RCT) experiments (n=2776) indicate that under time pressure, individuals decrease the disclosure of sensitive information, suggesting the presence of a trade-off between speed and privacy. Second, individuals’ willingness to disclose sensitive information is also reduced when engaging with service agents (e.g. Artificial Intelligence (AI)-based, Human or hybrid Human AI teams) compared to non-agent-based engagement formats such as traditional web forms, as demonstrated by an online behavioural experiment (n=3176). This effect remains consistent for one-off and multiple engagement scenarios where sensitive information disclosure occurs. Finally, using the structural equation modelling (SEM) approach, I build a theoretical construct integrating personality traits, privacy concerns, and disclosure behaviours, showing that conscientiousness, agreeableness, and openness elevate privacy concerns that drive privacy behaviours. Taken together, these findings extend the Antecedents-Privacy Concerns-Outcome (APCO) theoretical framework (Smith et al., 2011) by incorporating personality traits as another antecedent and demonstrating new boundary conditions under which individuals disclose sensitive information. They also highlight the importance of evidence-based and informed policymaking in the context of privacy regulation that should focus on the long-term aspects of privacy disclosure behaviours, especially with the steadfast advancement of AI. Furthermore, when building privacy-related user-centric experiences, organisations should focus on building a better understanding of individual characteristics to drive personalisation and strive to find the right balance between solutions that foster innovation, boost trust and reduce perceived risks of data sharing.