We study two privacy protection mechanisms motivated by emerging privacy regulations, limited retention and self-protection, in the context of data-driven (personalized) pricing. Limited retention refers to the removal of private data from a firm's database, and self-protection refers to a...

This paper describes the impression data and corresponding user, creator, and music content card data from NetEase Cloud Music. This data set is collectively supplied by the Revenue Management and Pricing (RMP) Section of INFORMS and NetEase Cloud Music to support data-driven research in...

Problem definition: We study customer-centric privacy management in service systems and explore the consequences of extended control over personal information by customers in such systems.Methodology: We adopt a stylized queueing model to capture a service environment that features a service...