Data disclosure strategy: Navigating the balance between privacy and profit in a dynamic system

Abstract

Digital platforms play a crucial role in our interconnected society, relying on user-disclosed data to enhance advertising revenue and user experiences and provide free services. While data accumulation benefits both platforms and users, it raises privacy concerns. This study explores the interaction between user data disclosure strategies and profitability for a platform and a developer, considering three strategies: mandatory data disclosure with free-to-use, mandatory disclosure with pay-to-use, and user-selective disclosure, allowing payment without data sharing. We formulate a dynamic optimization problem to capture how user data accumulation evolves and influences firm decisions. This framework also degenerates into a static setting for comparison, allowing us to assess the impact of dynamic evolution. Our findings reveal that while static models favor payment-based strategies, dynamic models entail a transition from a free-to-use model, facilitating early-stage data accumulation, to a selective disclosure model that balances privacy concerns and profitability. These findings offer guidance for managers in developing adaptive data disclosure strategies that optimize profitability while addressing user privacy concerns.